commit b66f683dfb73ed7c7ac0ce72647cd9eb76482cf7 Author: HuangHai <10402852@qq.com> Date: Mon Jan 12 07:49:18 2026 +0800 'commit' diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..1c2fda5 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Editor-based HTTP Client requests +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/Crawler.iml b/.idea/Crawler.iml new file mode 100644 index 0000000..4ba3f29 --- /dev/null +++ b/.idea/Crawler.iml @@ -0,0 +1,14 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..fad0e18 --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,196 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..a1ff0d1 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..f1a2754 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..3f1db62 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,7 @@ + + + + + + + \ No newline at end of file diff --git a/Apps/Json/Ref/T6_PaChongGaoDe.py b/Apps/Json/Ref/T6_PaChongGaoDe.py new file mode 100644 index 0000000..a04ad16 --- /dev/null +++ b/Apps/Json/Ref/T6_PaChongGaoDe.py @@ -0,0 +1,888 @@ +""" +高德地图高精度坐标获取工具 + +使用说明： +1、注册用户，实名认证 +2、手动登录，选择微信登录，生成cookie +3、复用这个cookie,打开浏览器，传入单位名称，查询坐标，得到小数点6位的坐标数据 +""" +import json +import os +import random +import re +import sys +import time +import weakref + +from selenium import webdriver +from selenium.webdriver.chrome.options import Options +from selenium.webdriver.chrome.service import Service +from selenium.webdriver.common.by import By +from selenium.webdriver.common.keys import Keys +from selenium.webdriver.support import expected_conditions as EC +from selenium.webdriver.support.ui import WebDriverWait + +# 添加SQLAlchemy同步操作 +from sqlalchemy import create_engine, text +from sqlalchemy.orm import sessionmaker + +# 数据库连接配置 +from Config.Config import POSTGRES_HOST, POSTGRES_PORT, POSTGRES_USER, POSTGRES_PASSWORD, POSTGRES_DATABASE + +# 创建同步数据库连接URL +db_url = f"postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}@{POSTGRES_HOST}:{POSTGRES_PORT}/{POSTGRES_DATABASE}" + +# 创建数据库引擎和会话工厂 +engine = create_engine(db_url) +SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) + +# 数据库操作函数 +def find(sql, params=None): + """ + 执行SQL查询，返回所有结果 + + Args: + sql: SQL查询语句 + params: 参数字典 + + Returns: + list: 查询结果的字典列表 + """ + with SessionLocal() as session: + result = session.execute(text(sql), params) + rows = result.fetchall() + return [dict(zip(result.keys(), row)) for row in rows] + + +def findFirst(sql, params=None): + """ + 执行SQL查询，返回第一条结果 + + Args: + sql: SQL查询语句 + params: 参数字典 + + Returns: + dict or None: 第一条查询结果的字典，或None（如果没有结果） + """ + with SessionLocal() as session: + result = session.execute(text(sql), params) + row = result.fetchone() + if row: + return dict(zip(result.keys(), row)) + return None + + +def execute_update(sql, params=None): + """ + 执行SQL更新操作 + + Args: + sql: SQL语句 + params: 参数字典 + + Returns: + int: 受影响的行数 + """ + with SessionLocal() as session: + with session.begin(): + result = session.execute(text(sql), params) + return result.rowcount + + +# 是否使用无头模式运行（True=无头模式，False=有界面模式） +HEADLESS = False + +# 最大重试次数 +MAX_RETRIES = 1 + +# WebDriver超时设置（秒） +TIMEOUT_SHORT = 5 +TIMEOUT_MEDIUM = 10 +TIMEOUT_LONG = 15 + + +def parse_coordinates_info(text): + """解析坐标信息，提取第一个搜索结果的坐标""" + # 检查输入参数 + if not text: + return None + + try: + # 清理文本，移除可能的干扰字符 + clean_text = re.sub(r'[\u3000\t\n\r ]+', ',', text) # 替换空格和制表符为逗号 + clean_text = re.sub(r'[,，]+', ',', clean_text) # 统一使用英文逗号 + clean_text = clean_text.strip(',') # 移除首尾逗号 + + # 匹配高德地图坐标格式 - 中国地区的坐标范围，放宽小数位数要求 + pattern = r'([7-9]?[0-9]\.[0-9]{1,}|1[0-3][0-9]\.[0-9]{1,})[,，]\s*([1-8]?[0-9]\.[0-9]{1,}|90\.[0-9]{1,})' + matches = re.finditer(pattern, clean_text) + + valid_coordinates = [] + + for match in matches: + lng = match.group(1).strip() + lat = match.group(2).strip() + + # 转换为浮点数进行范围验证 + try: + lng_float = float(lng) + lat_float = float(lat) + + # 检查坐标是否在中国地区合理范围内 + if 70 <= lng_float <= 140 and 0 <= lat_float <= 60: + # 计算坐标的精度（小数位数） + lng_precision = len(lng.split('.')[1]) if '.' in lng else 0 + lat_precision = len(lat.split('.')[1]) if '.' in lat else 0 + + # 接受至少有1位小数的坐标，不再严格要求4位小数 + valid_coordinates.append((lng_float, lat_float, lng, lat)) + except ValueError: + continue + + if valid_coordinates: + # 如果找到多个有效坐标，选择小数位数最多的 + valid_coordinates.sort( + key=lambda x: len(x[2].split('.')[1]) + len(x[3].split('.')[1]) if '.' in x[2] and '.' in x[3] else 0, + reverse=True) + _, _, lng, lat = valid_coordinates[0] + + return { + 'lng': lng, + 'lat': lat + } + else: + return None + + except Exception as e: + return None + + +def export_cookies_from_browser(driver, output_file='amap_cookies.json'): + """ + 导出浏览器中的Cookie到文件 + + Args: + driver: WebDriver实例 + output_file: Cookie导出文件路径 + + Returns: + bool: 导出是否成功 + """ + # 检查输入参数 + if not driver: + print("❌ WebDriver实例无效") + return False + + if not output_file: + print("❌ Cookie输出文件路径无效") + return False + + try: + print(f"正在导出Cookie到文件: {output_file}") + + # 确保目录存在 + output_dir = os.path.dirname(output_file) + if output_dir and not os.path.exists(output_dir): + try: + os.makedirs(output_dir, exist_ok=True) + except Exception as e: + print(f"创建输出目录失败: {str(e)}") + return False + + # 获取Cookie + cookies = driver.get_cookies() + + if not cookies: + print("⚠️ 未获取到任何Cookie") + return False + + # 保存Cookie到文件 + with open(output_file, 'w', encoding='utf-8') as f: + json.dump(cookies, f, ensure_ascii=False, indent=2) + + print(f"成功导出 {len(cookies)} 个Cookie") + print(f"Cookie已成功导出到: {os.path.abspath(output_file)}") + return True + except Exception as e: + print(f"导出Cookie失败: {str(e)}") + return False + + +def load_cookies_to_browser(driver, cookie_file='amap_cookies.json'): + """ + 从文件加载Cookie到浏览器 + + Args: + driver: WebDriver实例 + cookie_file: Cookie文件路径 + + Returns: + bool: 加载是否成功 + """ + try: + # 检查输入参数 + if not driver: + print("❌ WebDriver实例无效") + return False + + if not cookie_file: + print("❌ Cookie文件路径无效") + return False + + if not os.path.exists(cookie_file): + return False + + # 先访问高德域名，确保Cookie可以被正确设置 + import ssl + ssl._create_default_https_context = ssl._create_unverified_context + driver.get('https://www.amap.com') + + time.sleep(1) # 等待页面加载 + + # 加载Cookie + with open(cookie_file, 'r', encoding='utf-8') as f: + cookies = json.load(f) + + # 添加每个Cookie + success_count = 0 + for cookie in cookies: + # 移除可能导致问题的属性 + if 'expiry' in cookie: + cookie['expiry'] = int(cookie['expiry']) + if 'sameSite' in cookie and cookie['sameSite'] not in ['Strict', 'Lax', 'None']: + del cookie['sameSite'] + + try: + driver.add_cookie(cookie) + success_count += 1 + except Exception as e: + #print(f"添加Cookie失败 (可能是安全策略限制): {cookie.get('name')} - {str(e)}") + pass + + return True + except Exception as e: + print(f"加载Cookie失败: {str(e)}") + return False + + +def get_location_coordinates(location_name, headless=False, cookie_file='amap_cookies.json', driver=None, + reuse_driver=False): + """ + 获取指定名称地点的坐标信息 + + Args: + location_name (str): 要搜索的地点名称 + headless (bool): 是否使用无头模式，默认False（有界面） + cookie_file (str): Cookie文件路径 + driver: 可选的WebDriver实例（用于复用） + reuse_driver: 是否复用WebDriver实例 + + Returns: + dict or None: 包含名称、经度和纬度的字典，失败时返回None + """ + # 检查输入参数 + if not location_name or not location_name.strip(): + print("❌ 地点名称无效") + return None + + if not cookie_file or not os.path.exists(cookie_file): + print(f"❌ Cookie文件不存在或无效: {cookie_file}") + return None + + # 浏览器管理标志 + need_quit_driver = False + + try: + # 如果没有提供driver，创建新的 + if not driver: + # 设置Chrome浏览器参数 + chrome_options = Options() + if headless: + chrome_options.add_argument('--headless') + + # 添加必要的配置参数 + # 我有GPU,可以放开 + # chrome_options.add_argument('--disable-gpu') + chrome_options.add_argument('--no-sandbox') + chrome_options.add_argument('--disable-dev-shm-usage') + chrome_options.add_argument('--window-size=1920,1080') + chrome_options.add_argument('--lang=zh-CN') + + # 禁用自动化控制特征 + chrome_options.add_experimental_option('useAutomationExtension', False) + chrome_options.add_argument("--disable-blink-features=AutomationControlled") # 防止自动化检测，关闭浏览器控制显示 + chrome_options.add_argument('-ignore-certificate-errors') # 忽略证书错误 + chrome_options.add_argument('-ignore-ssl-errors') # 忽略相关错误 + chrome_options.add_experimental_option('excludeSwitches', ['enable-automation']) # 防止自动化检测 + chrome_options.add_experimental_option('excludeSwitches', ['enable-logging']) # 防止自动化日志输出检测 + + # 添加随机User-Agent + user_agent = f'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/{random.randint(80, 99)}.0.{random.randint(4000, 5000)}.{random.randint(100, 200)} Safari/537.36' + chrome_options.add_argument(f'user-agent={user_agent}') + + # 尝试初始化WebDriver + service = Service() + try: + from webdriver_manager.chrome import ChromeDriverManager + driver_path = ChromeDriverManager().install() + service = Service(driver_path) + except: + pass + + # 初始化WebDriver + driver = webdriver.Chrome(service=service, options=chrome_options) + need_quit_driver = True + + # 最大化窗口（如果不是无头模式） + if not headless: + driver.maximize_window() + + # 禁用自动化特征 + driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", { + "source": "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})" + }) + + # 使用Cookie方式认证 + cookies_loaded = load_cookies_to_browser(driver, cookie_file) + if not cookies_loaded: + print("Cookie认证失败，无法继续获取高精度坐标") + return None + + # 访问高德地图坐标拾取工具 + url = 'https://lbs.amap.com/tools/picker' + driver.get(url) + + time.sleep(2) # 等待页面完全加载 + + # 定位搜索输入框并输入查询内容 + try: + # 使用ID定位搜索框 + search_input = WebDriverWait(driver, TIMEOUT_MEDIUM).until( + EC.presence_of_element_located((By.ID, 'txtSearch')) + ) + except Exception as e: + print(f"定位搜索输入框失败: {e}") + return None + + # 输入搜索内容 + search_input.clear() + search_input.send_keys(location_name) + + # 定位搜索按钮并点击 + try: + # 尝试使用class选择器定位搜索按钮 + search_button = WebDriverWait(driver, TIMEOUT_SHORT).until( + EC.presence_of_element_located((By.CLASS_NAME, 'btn-search')) + ) + search_button.click() + except Exception as e: + print(f"定位搜索按钮失败: {e}") + # 尝试按回车键搜索 + try: + search_input.send_keys(Keys.ENTER) + except Exception as e2: + print(f"回车键搜索也失败: {e2}") + return None + + # 等待搜索结果加载完成并显示坐标 + time.sleep(3) # 等待时间，确保页面完全加载并显示搜索结果和坐标 + + # 获取坐标信息 - 只使用成功的方式（从txtCoordinate元素的value属性获取） + coordinate_text = None + + # 查找ID为txtCoordinate的元素（这是高德地图坐标拾取器的核心坐标显示元素） + try: + coordinate_element = WebDriverWait(driver, TIMEOUT_MEDIUM).until( + EC.presence_of_element_located((By.ID, 'txtCoordinate')) + ) + + # 获取value属性（这是input元素存储值的标准方式） + coordinate_value = coordinate_element.get_attribute('value') + if coordinate_value and coordinate_value.strip(): + coordinate_text = coordinate_value.strip() + else: + print("警告: txtCoordinate元素的value属性为空") + + # 作为备选，尝试获取text内容 + coordinate_text_content = coordinate_element.text.strip() + if coordinate_text_content: + coordinate_text = coordinate_text_content + print(f"从text内容获取到坐标文本: '{coordinate_text}'") + except Exception as e: + print(f"查找或获取坐标元素失败: {e}") + return None + + # 如果没有获取到坐标，返回失败 + if not coordinate_text or coordinate_text.strip() == '': + print("未能获取到有效的坐标文本") + return None + + # 解析坐标信息 + print(f"解析坐标文本: '{coordinate_text}'") + + coordinate_info = parse_coordinates_info(coordinate_text) + if coordinate_info: + # 进一步验证坐标的合理性（中国地区范围） + try: + lng = float(coordinate_info['lng']) + lat = float(coordinate_info['lat']) + + # 中国地区的合理坐标范围 + if 70 <= lng <= 140 and 0 <= lat <= 60: + print(f"成功获取坐标: {coordinate_info['lng']},{coordinate_info['lat']}") + return coordinate_info + else: + print(f"坐标超出中国地区合理范围 - 经度: {lng}, 纬度: {lat}") + return None + except (ValueError, TypeError): + print(f"坐标数值验证失败: {coordinate_info}") + return None + else: + print("坐标解析失败") + return None + + except Exception as e: + print(f"获取坐标时发生错误: {e}") + return None + finally: + # 关闭浏览器（如果不是复用的） + if not reuse_driver and need_quit_driver and driver: + driver.close() + driver.quit() + + +def get_multiple_locations_coordinates(location_names, headless=False, cookie_file='amap_cookies.json'): + """ + 批量获取多个地点的坐标信息 + + Args: + location_names (list): 地点名称列表 + headless (bool): 是否使用无头模式，默认False（有界面） + cookie_file (str): Cookie文件路径 + + Returns: + dict: 包含所有地点坐标信息的字典，键为地点名称，值为坐标信息字典或None（失败时） + """ + # 检查输入参数 + if not location_names: + print("❌ 地点列表为空，无需获取坐标") + return {} + + print(f"开始批量获取坐标信息 - 地点数量: {len(location_names)}, 无头模式: {headless}") + + # 存储所有地点的坐标信息 + all_coordinates = {} + success_count = 0 + failure_count = 0 + skipped_count = 0 + invalid_coord_count = 0 + + # 创建并复用浏览器实例 + created_driver = None + + # 记录是否已经退出，避免重复退出 + driver_closed = False + + try: + # 初始化Chrome浏览器 + chrome_options = Options() + if headless: + chrome_options.add_argument('--headless') + + # 添加必要的配置参数 + # chrome_options.add_argument('--disable-gpu') + chrome_options.add_argument('--no-sandbox') + chrome_options.add_argument('--disable-dev-shm-usage') + chrome_options.add_argument('--window-size=1920,1080') + chrome_options.add_argument('--lang=zh-CN') + chrome_options.add_argument("--disable-blink-features=AutomationControlled") # 防止自动化检测，关闭浏览器控制显示 + chrome_options.add_argument('-ignore-certificate-errors') # 忽略证书错误 + chrome_options.add_argument('-ignore-ssl-errors') # 忽略相关错误 + chrome_options.add_experimental_option('excludeSwitches', ['enable-automation']) # 防止自动化检测 + chrome_options.add_experimental_option('excludeSwitches', ['enable-logging']) # 防止自动化日志输出检测 + chrome_options.add_experimental_option('useAutomationExtension', False) + + # 添加随机User-Agent + user_agent = f'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/{random.randint(80, 99)}.0.{random.randint(4000, 5000)}.{random.randint(100, 200)} Safari/537.36' + chrome_options.add_argument(f'user-agent={user_agent}') + + # 尝试使用webdriver_manager + try: + from webdriver_manager.chrome import ChromeDriverManager + driver_path = ChromeDriverManager().install() + service = Service(driver_path) + print(f"成功使用webdriver_manager安装驱动: {driver_path}") + except: + print("使用系统默认的Chrome驱动") + service = Service() + + # 初始化WebDriver + created_driver = webdriver.Chrome(service=service, options=chrome_options) + + # 将创建的driver实例添加到全局跟踪集合中 + if 'driver_instances' in globals(): + driver_instances.add(created_driver) + + # 最大化窗口（如果不是无头模式） + if not headless: + created_driver.maximize_window() + + # 禁用自动化特征 + created_driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", { + "source": "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})" + }) + + # 使用Cookie方式认证 + print("尝试使用Cookie文件进行认证") + cookies_loaded = load_cookies_to_browser(created_driver, cookie_file) + if not cookies_loaded: + print("Cookie认证失败，无法继续获取高精度坐标") + return all_coordinates + + # 访问高德地图坐标拾取工具 + url = 'https://lbs.amap.com/tools/picker' + created_driver.get(url) + time.sleep(2) # 增加等待时间以确保页面完全加载 + + # 循环获取每个地点的坐标 + for i, location_name in enumerate(location_names, 1): + print(f"处理地点 {i}/{len(location_names)}: '{location_name}'") + + # 对每个地点查询 + try: + # 调用get_location_coordinates获取坐标 + result = get_location_coordinates(location_name, headless=headless, cookie_file=cookie_file, + driver=created_driver, reuse_driver=True) + except Exception as e: + print(f"❌ 地点: {location_name} - 查询出错: {str(e)}，跳过该地点") + result = None + skipped_count += 1 + + # 存储结果 + all_coordinates[location_name] = result + + # 坐标获取成功，进行有效性验证 + if result: + # 验证坐标的地理合理性（中国地区范围） + try: + lng = float(result['lng']) + lat = float(result['lat']) + + # 中国地区的合理坐标范围 + if 70 <= lng <= 140 and 0 <= lat <= 60: + # 检查坐标精度（至少4位小数） + lng_precision = len(result['lng'].split('.')[1]) if '.' in result['lng'] else 0 + lat_precision = len(result['lat'].split('.')[1]) if '.' in result['lat'] else 0 + + if lng_precision >= 4 and lat_precision >= 4: + print(f"✅ 地点: {location_name} - 坐标获取成功: {result['lng']},{result['lat']}") + success_count += 1 + else: + print( + f"⚠️ 地点: {location_name} - 坐标精度不足: {result['lng']},{result['lat']} (经度{lng_precision}位, 纬度{lat_precision}位小数)") + invalid_coord_count += 1 + else: + print(f"⚠️ 地点: {location_name} - 坐标超出中国地区合理范围: {result['lng']},{result['lat']}") + invalid_coord_count += 1 + except (ValueError, TypeError): + print(f"⚠️ 地点: {location_name} - 坐标数值无效: {result}") + invalid_coord_count += 1 + else: + print(f"❌ 地点: {location_name} - 未能获取有效的坐标信息") + failure_count += 1 + + # 如果不是最后一个地点，添加短暂延迟以避免过快请求 + if i < len(location_names): + delay_time = random.uniform(0.5, 2) # 减少延迟时间到0.5-2秒 + print(f"添加延迟: {delay_time:.2f}秒") + time.sleep(delay_time) + + except Exception as e: + print(f"❌ 批量坐标获取过程中发生错误: {str(e)}") + + finally: + created_driver.close() + + # 打印详细统计信息 + print(f"\n{'=' * 60}") + print("批量坐标获取完成") + print(f"{'=' * 60}") + print(f"总查询地点数: {len(location_names)}") + print(f"成功获取: {success_count}") + print(f"获取失败: {failure_count}") + print(f"超时跳过: {skipped_count}") + print(f"无效坐标: {invalid_coord_count}") + print(f"获取到坐标数据的地点数: {len([c for c in all_coordinates.values() if c is not None])}") + + # 计算实际成功率（只考虑成功获取的有效坐标） + actual_processed = success_count + failure_count + skipped_count + invalid_coord_count + if actual_processed > 0: + actual_success_rate = (success_count / actual_processed) * 100 + print(f"实际成功率: {actual_success_rate:.1f}%") + else: + print("实际成功率: 0%") + + print(f"{'=' * 60}") + + # 返回结果字典 + return all_coordinates + + +def create_cookie_export_tool(): + """ + 创建一个Cookie导出工具，帮助用户手动登录后导出Cookie + """ + print("\n===== Cookie导出工具 =====") + print("此工具将打开一个Chrome浏览器窗口，让您手动登录高德地图账号，") + print("登录成功后，按Enter键导出Cookie到文件。") + print("==========================") + + # 设置Chrome浏览器参数 + chrome_options = Options() + chrome_options.add_argument('--disable-gpu') + chrome_options.add_argument('--window-size=1920,1080') + chrome_options.add_experimental_option("excludeSwitches", ["enable-automation"]) + chrome_options.add_experimental_option('useAutomationExtension', False) + + # 初始化WebDriver + driver = None + try: + # 尝试使用webdriver_manager + try: + from webdriver_manager.chrome import ChromeDriverManager + driver_path = ChromeDriverManager().install() + service = Service(driver_path) + except: + service = Service() + + driver = webdriver.Chrome(service=service, options=chrome_options) + driver.maximize_window() + + # 禁用自动化特征 + driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", { + "source": "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})" + }) + + # 访问高德地图坐标拾取工具 + url = 'https://lbs.amap.com/tools/picker' + print(f"\n正在打开高德地图坐标拾取工具: {url}") + import ssl + ssl._create_default_https_context = ssl._create_unverified_context + driver.get(url) + + print("\n请在浏览器中手动登录高德地图账号。") + print("提示：建议使用微信扫码登录以避免验证码问题") + print("登录成功后，请按Enter键继续...") + input() + + # 导出Cookie - 使用基于当前脚本所在目录的绝对路径 + script_dir = os.path.dirname(os.path.abspath(__file__)) + cookie_file = os.path.join(script_dir, '../Test/amap_cookies.json') + export_success = export_cookies_from_browser(driver, cookie_file) + + if export_success: + print(f"\nCookie导出成功！文件保存为: {os.path.abspath(cookie_file)}") + print("您现在可以使用这个Cookie文件获取高精度坐标了。") + print("\n重要提示：") + print("1. 请妥善保管您的Cookie文件，不要分享给他人") + print("2. Cookie文件包含您的登录状态信息，请确保安全存储") + print("3. 当Cookie过期后，您需要重新运行此工具生成新的Cookie文件") + + except Exception as e: + print(f"\n发生错误: {str(e)}") + finally: + if driver: + print("\n正在关闭浏览器...") + driver.quit() + + +def main(): + """ + 主函数，用于测试和运行高德地图坐标获取工具 + """ + print("=== 高德地图高精度坐标获取工具 ====") + + # 初始化参数 + # 使用基于当前脚本所在目录的相对路径，确保在不同环境下都能正确访问 + script_dir = os.path.dirname(os.path.abspath(__file__)) + cookie_file = os.path.join(script_dir, '../Test/amap_cookies.json') + + # 询问是否清空所有数据的经纬度 + clear_coords = input("是否清空所有数据的经纬度，全新开始更新坐标？(y/n，默认n): ").strip().lower() == 'y' + if clear_coords: + print("正在清空所有学校的经纬度数据...") + try: + execute_update("UPDATE t_school SET longitude=0, latitude=0") + print("✅ 经纬度数据已清空") + except Exception as e: + print(f"❌ 清空经纬度数据失败: {str(e)}") + sys.exit(1) + + # 从数据库查询需要更新坐标的学校（经纬度为0的学校） + print("正在从数据库查询需要更新坐标的学校...") + try: + results = find("SELECT org_name FROM t_school WHERE longitude=0 AND latitude=0 order by region_level1,region_level2,region_level3,school_type") + locations = [result['org_name'] for result in results] + print(f"✅ 找到 {len(locations)} 所需要更新坐标的学校") + + if not locations: + print("所有学校都已有坐标数据，程序退出。") + sys.exit(0) + + # 显示前10个学校作为预览 + print("\n待处理学校列表预览:") + for i, loc in enumerate(locations[:10], 1): + print(f" {i}. {loc}") + if len(locations) > 10: + print(f" ... 等{len(locations) - 10}个学校") + + except Exception as e: + print(f"❌ 查询学校列表失败: {str(e)}") + sys.exit(1) + + # 检查Cookie文件 + if not os.path.exists(cookie_file): + print(f"\nCookie文件 '{cookie_file}' 不存在。") + if input("是否现在运行Cookie导出工具? (y/n): ").strip().lower() == 'y': + create_cookie_export_tool() + else: + print("\n程序退出。没有有效的Cookie文件，无法获取高精度坐标。") + sys.exit(1) + + print(f"\n运行参数: 无头模式{'启用' if HEADLESS else '禁用'}") + + # 逐个获取坐标并立即更新数据库 + print("\n开始逐个获取坐标并更新数据库...") + success_updates = 0 + total_locations = len(locations) + + # 创建浏览器实例（复用） + chrome_options = Options() + if HEADLESS: + chrome_options.add_argument('--headless') + + # 添加必要的配置参数 + chrome_options.add_argument('--no-sandbox') + chrome_options.add_argument('--disable-dev-shm-usage') + chrome_options.add_argument('--window-size=1920,1080') + chrome_options.add_argument('--lang=zh-CN') + chrome_options.add_argument("--disable-blink-features=AutomationControlled") + chrome_options.add_argument('-ignore-certificate-errors') + chrome_options.add_argument('-ignore-ssl-errors') + chrome_options.add_experimental_option('excludeSwitches', ['enable-automation']) + chrome_options.add_experimental_option('excludeSwitches', ['enable-logging']) + chrome_options.add_experimental_option('useAutomationExtension', False) + + # 随机User-Agent + user_agent = f'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/{random.randint(80, 99)}.0.{random.randint(4000, 5000)}.{random.randint(100, 200)} Safari/537.36' + chrome_options.add_argument(f'user-agent={user_agent}') + + # 初始化WebDriver + try: + from webdriver_manager.chrome import ChromeDriverManager + driver_path = ChromeDriverManager().install() + service = Service(driver_path) + print(f"成功使用webdriver_manager安装驱动: {driver_path}") + except: + print("使用系统默认的Chrome驱动") + service = Service() + + driver = webdriver.Chrome(service=service, options=chrome_options) + + # 添加到全局跟踪集合 + if 'driver_instances' in globals(): + driver_instances.add(driver) + + # 最大化窗口（如果不是无头模式） + if not HEADLESS: + driver.maximize_window() + + # 禁用自动化特征 + driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", { + "source": "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})" + }) + + # 使用Cookie方式认证 + cookies_loaded = load_cookies_to_browser(driver, cookie_file) + if not cookies_loaded: + print("Cookie认证失败，无法继续获取高精度坐标") + driver.quit() + return + + # 访问高德地图坐标拾取工具 + url = 'https://lbs.amap.com/tools/picker' + driver.get(url) + time.sleep(2) # 等待页面完全加载 + + update_sql = "UPDATE t_school SET longitude = :lng, latitude = :lat WHERE org_name = :org_name" + + try: + # 逐个处理每个地点 + for i, location_name in enumerate(locations, 1): + print(f"处理地点 {i}/{total_locations}: '{location_name}'") + + # 获取单个地点的坐标 + try: + coords = get_location_coordinates( + location_name, + headless=HEADLESS, + cookie_file=cookie_file, + driver=driver, + reuse_driver=True + ) + + # 如果成功获取到坐标，立即更新数据库 + if coords: + try: + execute_update(update_sql, { + 'lng': coords['lng'], + 'lat': coords['lat'], + 'org_name': location_name + }) + success_updates += 1 + print(f"✅ 地点: {location_name} - 坐标获取成功并更新数据库: {coords['lng']},{coords['lat']}") + except Exception as e: + print(f" ❌ 更新地点'{location_name}'失败: {str(e)}") + else: + print(f"❌ 地点: {location_name} - 未能获取有效的坐标信息") + + # 添加短暂延迟以避免过快请求 + if i < total_locations: + delay_time = random.uniform(0.5, 2) + print(f"添加延迟: {delay_time:.2f}秒") + time.sleep(delay_time) + + except Exception as e: + print(f"❌ 地点: {location_name} - 查询出错: {str(e)}") + + # 添加延迟后继续处理下一个地点 + if i < total_locations: + time.sleep(random.uniform(0.5, 2)) + + finally: + # 关闭浏览器 + driver.close() + driver.quit() + + print(f"\n✅ 数据库更新完成，成功{success_updates}/{total_locations}条记录") + + print("\n✅ 程序执行完成！") + print("\n提示：如需重新批量更新所有学校坐标，下次运行程序时选择清空所有数据的经纬度即可。") + + +if __name__ == "__main__": + # 全局变量，用于跟踪所有创建的WebDriver实例 + driver_instances = weakref.WeakSet() + + try: + print("=== 高德地图高精度坐标获取工具 ===") + print("正在初始化...") + # 直接运行主程序 + main() + except KeyboardInterrupt: + print("\n⚠️ 接收到键盘中断，退出程序") + except Exception as main_error: + print(f"\n❌ 主程序运行出错: {main_error}") + + print("\n程序已退出") diff --git a/Apps/Json/Ref/T7_PaChongGaoDePatch.py b/Apps/Json/Ref/T7_PaChongGaoDePatch.py new file mode 100644 index 0000000..e1dbede --- /dev/null +++ b/Apps/Json/Ref/T7_PaChongGaoDePatch.py @@ -0,0 +1,810 @@ +import json +import random +import asyncio +import math +from typing import List, Tuple, Dict, Any + +from DbKit.Db import Db + + +def is_point_in_polygon(point: Tuple[float, float], polygon: List[Tuple[float, float]]) -> bool: + """ + 射线法判断点是否在多边形内 + :param point: (经度, 纬度) + :param polygon: 多边形顶点坐标列表 + :return: True表示在多边形内 + """ + x, y = point + n = len(polygon) + inside = False + + for i in range(n): + x1, y1 = polygon[i] + x2, y2 = polygon[(i + 1) % n] + + # 检查点是否在边的y范围内 + if ((y1 > y) != (y2 > y)): + # 计算交点x坐标 + x_intersect = (x2 - x1) * (y - y1) / (y2 - y1) + x1 + + if x_intersect > x: + inside = not inside + + return inside + + +def load_geojson(json_path: str) -> List[List[Tuple[float, float]]]: + """ + 从GeoJSON文件加载多边形数据（支持Polygon和MultiPolygon） + 返回: 多边形列表，每个多边形是(经度, 纬度)元组的列表 + """ + with open(json_path, 'r', encoding='utf-8') as f: + geo_data = json.load(f) + + polygons = [] + geometry_type = geo_data['features'][0]['geometry']['type'] + coordinates = geo_data['features'][0]['geometry']['coordinates'] + + if geometry_type == 'Polygon': + polygons.append(coordinates[0]) + elif geometry_type == 'MultiPolygon': + for polygon_coords in coordinates: + polygons.append(polygon_coords[0]) + + return polygons + + +# 计算点集的分布统计信息 +def calculate_distribution_statistics(points): + """ + 计算点集的分布统计信息 + + :param points: 点集 [(lon, lat), ...] + :return: 包含min_lon, max_lon, min_lat, max_lat, center_lon, center_lat, std_lon, std_lat的字典 + """ + if not points: + return None + + lons = [p[0] for p in points] + lats = [p[1] for p in points] + + min_lon, max_lon = min(lons), max(lons) + min_lat, max_lat = min(lats), max(lats) + + # 计算中心点 + center_lon = (min_lon + max_lon) / 2 + center_lat = (min_lat + max_lat) / 2 + + # 计算标准差（衡量分布的分散程度） + mean_lon = sum(lons) / len(lons) + mean_lat = sum(lats) / len(lats) + + std_lon = math.sqrt(sum((lon - mean_lon) ** 2 for lon in lons) / len(lons)) + std_lat = math.sqrt(sum((lat - mean_lat) ** 2 for lat in lats) / len(lats)) + + return { + "min_lon": min_lon, + "max_lon": max_lon, + "min_lat": min_lat, + "max_lat": max_lat, + "center_lon": center_lon, + "center_lat": center_lat, + "std_lon": std_lon, + "std_lat": std_lat + } + +# 线性映射函数 +def linear_map(value, from_min, from_max, to_min, to_max): + """ + 将值从一个范围线性映射到另一个范围 + + :param value: 输入值 + :param from_min: 原始范围最小值 + :param from_max: 原始范围最大值 + :param to_min: 目标范围最小值 + :param to_max: 目标范围最大值 + :return: 映射后的值 + """ + # 处理除零情况 + if from_max == from_min: + return (to_min + to_max) / 2 + # 计算映射值 + return (value - from_min) * (to_max - to_min) / (from_max - from_min) + to_min + +# 计算点到线段的最近点 +def closest_point_on_segment(point, line_start, line_end): + """ + 计算点到线段的最近点 + + :param point: 点 [lon, lat] + :param line_start: 线段起点 [lon, lat] + :param line_end: 线段终点 [lon, lat] + :return: 线段上的最近点 [lon, lat] + """ + # 将经纬度转换为向量 + a = [point[0] - line_start[0], point[1] - line_start[1]] + b = [line_end[0] - line_start[0], line_end[1] - line_start[1]] + + # 计算投影参数t + t = max(0, min(1, (a[0] * b[0] + a[1] * b[1]) / (b[0] ** 2 + b[1] ** 2) if (b[0] ** 2 + b[1] ** 2) > 0 else 0)) + + # 计算投影点 + return [line_start[0] + t * b[0], line_start[1] + t * b[1]] + +# 计算两点之间的距离 +def calculate_distance(point1, point2): + """ + 计算两点之间的欧几里得距离 + + :param point1: 第一个点 [lon, lat] + :param point2: 第二个点 [lon, lat] + :return: 两点之间的距离 + """ + return math.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2) + +# 调整点到多边形边界 +def adjust_to_polygon_boundary(point, polygons): + """ + 调整点到最近的多边形边界 + + :param point: 需要调整的点 [lon, lat] + :param polygons: 多边形列表 + :return: 调整后的点 [lon, lat] + """ + min_distance = float('inf') + closest_point = point + + for polygon in polygons: + for i in range(len(polygon)): + p1 = polygon[i] + p2 = polygon[(i + 1) % len(polygon)] + + # 计算点到线段的最近点 + proj_point = closest_point_on_segment(point, p1, p2) + distance = calculate_distance(point, proj_point) + + if distance < min_distance: + min_distance = distance + closest_point = proj_point + + return closest_point + +# 将原始坐标映射到目标区域，保持相对位置关系 +def map_coordinates(original_points, polygons): + """ + 将原始坐标映射到目标区域，保持相对位置关系 + + :param original_points: 原始坐标列表 [(original_lon, original_lat), ...] + :param polygons: 目标区域的多边形列表 + :return: 映射后的坐标列表 [(mapped_lon, mapped_lat), ...] + """ + if not original_points or not polygons: + return [] + + # 计算原始点集的分布统计 + original_stats = calculate_distribution_statistics(original_points) + if not original_stats: + return [] + + # 获取目标区域的边界信息 + all_target_points = [] + for polygon in polygons: + all_target_points.extend(polygon) + + if not all_target_points: + return [] + + target_lons = [p[0] for p in all_target_points] + target_lats = [p[1] for p in all_target_points] + + target_min_lon, target_max_lon = min(target_lons), max(target_lons) + target_min_lat, target_max_lat = min(target_lats), max(target_lats) + + # 收缩目标边界框，使生成的坐标更靠近县区中心 + shrink_factor = 0.25 # 收缩25%，增大收缩比例使坐标更靠近中心 + delta_lon = (target_max_lon - target_min_lon) * shrink_factor + delta_lat = (target_max_lat - target_min_lat) * shrink_factor + target_min_lon += delta_lon + target_max_lon -= delta_lon + target_min_lat += delta_lat + target_max_lat -= delta_lat + + # 计算原始点集的边界 + original_min_lon = original_stats["min_lon"] + original_max_lon = original_stats["max_lon"] + original_min_lat = original_stats["min_lat"] + original_max_lat = original_stats["max_lat"] + + mapped_points = [] + for original_lon, original_lat in original_points: + # 线性映射经度和纬度 + mapped_lon = linear_map(original_lon, original_min_lon, original_max_lon, target_min_lon, target_max_lon) + mapped_lat = linear_map(original_lat, original_min_lat, original_max_lat, target_min_lat, target_max_lat) + + # 检查映射后的点是否在目标区域内 + if any(is_point_in_polygon((mapped_lon, mapped_lat), polygon) for polygon in polygons): + mapped_points.append((mapped_lon, mapped_lat)) + else: + # 如果映射后的点不在目标区域内，调整到最近的边界 + adjusted_lon, adjusted_lat = adjust_to_polygon_boundary([mapped_lon, mapped_lat], polygons) + mapped_points.append((adjusted_lon, adjusted_lat)) + + return mapped_points + +# 传统的均匀分布点生成算法（作为回退方案） +def generate_uniform_points_fallback(polygons, n_points): + """ + 在指定的多边形区域内生成均匀分布的点（回退方案） + + :param polygons: 多边形列表，每个多边形是坐标点的列表 + :param n_points: 需要生成的点数 + :return: 生成的点列表 [(lon, lat), ...] + """ + if not polygons: + return [] + + # 计算整体边界框 + all_lons = [p[0] for polygon in polygons for p in polygon] + all_lats = [p[1] for polygon in polygons for p in polygon] + min_lon, max_lon = min(all_lons), max(all_lons) + min_lat, max_lat = min(all_lats), max(all_lats) + + # 收缩边界框，使生成的坐标更靠近县区中心 + lon_margin = (max_lon - min_lon) * 0.25 # 收缩25%，增大收缩比例使坐标更靠近中心 + lat_margin = (max_lat - min_lat) * 0.25 + min_lon = min_lon + lon_margin + max_lon = max_lon - lon_margin + min_lat = min_lat + lat_margin + max_lat = max_lat - lat_margin + + # 计算每个多边形的面积，用于按比例分配点数 + def calculate_polygon_area(polygon): + # 使用Shoelace公式计算多边形面积 + area = 0.0 + n = len(polygon) + for i in range(n): + j = (i + 1) % n + area += polygon[i][0] * polygon[j][1] + area -= polygon[j][0] * polygon[i][1] + return abs(area) / 2.0 + + # 计算每个多边形的面积 + polygon_areas = [calculate_polygon_area(polygon) for polygon in polygons] + total_area = sum(polygon_areas) + + # 根据面积比例分配每个多边形需要生成的点数 + if total_area > 0: + points_per_polygon = [max(1, int(area / total_area * n_points)) for area in polygon_areas] + # 调整点数，确保总和等于n_points + points_per_polygon[-1] += n_points - sum(points_per_polygon) + else: + points_per_polygon = [n_points // len(polygons)] * len(polygons) + points_per_polygon[-1] += n_points - sum(points_per_polygon) + + points = [] + + # 为每个多边形单独生成点 + for polygon_index, (polygon, points_needed) in enumerate(zip(polygons, points_per_polygon)): + if points_needed <= 0: + continue + + # 计算当前多边形的边界框 + polygon_lons = [p[0] for p in polygon] + polygon_lats = [p[1] for p in polygon] + poly_min_lon, poly_max_lon = min(polygon_lons), max(polygon_lons) + poly_min_lat, poly_max_lat = min(polygon_lats), max(polygon_lats) + + # 收缩当前多边形的边界框，使其更靠近中心 + poly_lon_margin = (poly_max_lon - poly_min_lon) * 0.25 # 收缩25%，与全局收缩比例保持一致 + poly_lat_margin = (poly_max_lat - poly_min_lat) * 0.25 + poly_min_lon += poly_lon_margin + poly_max_lon -= poly_lon_margin + poly_min_lat += poly_lat_margin + poly_max_lat -= poly_lat_margin + + # 为当前多边形生成足够的网格 + cells_needed = max(points_needed * 2, 10) # 网格数量是点数的2倍，确保分布均匀 + cols = math.ceil(math.sqrt(cells_needed)) + rows = math.ceil(cells_needed / cols) + + cell_width = (poly_max_lon - poly_min_lon) / cols + cell_height = (poly_max_lat - poly_min_lat) / rows + + # 记录哪些网格已经生成了点 + filled_cells = [] + max_attempts_per_cell = 50 + + # 按网格生成点 + for attempt in range(rows * cols * 2): # 增加尝试次数 + if len(points) >= n_points: + break + + # 随机选择一个网格 + i = random.randint(0, rows - 1) + j = random.randint(0, cols - 1) + cell_key = (i, j) + + # 如果这个网格已经生成了足够的点，跳过 + if filled_cells.count(cell_key) >= math.ceil(points_needed / (rows * cols)): + continue + + # 当前网格范围 + cell_min_lon = poly_min_lon + j * cell_width + cell_max_lon = poly_min_lon + (j + 1) * cell_width + cell_min_lat = poly_min_lat + i * cell_height + cell_max_lat = poly_min_lat + (i + 1) * cell_height + + # 在网格内尝试生成有效点 + for _ in range(max_attempts_per_cell): + lon = random.uniform(cell_min_lon, cell_max_lon) + lat = random.uniform(cell_min_lat, cell_max_lat) + + # 检查点是否在当前多边形内 + if is_point_in_polygon((lon, lat), polygon): + points.append((lon, lat)) + filled_cells.append(cell_key) + break + + # 如果当前多边形的点数不足，使用均匀分布补充 + while len(points) < sum(points_per_polygon[:polygon_index+1]): + lon = random.uniform(poly_min_lon, poly_max_lon) + lat = random.uniform(poly_min_lat, poly_max_lat) + + if is_point_in_polygon((lon, lat), polygon): + points.append((lon, lat)) + + # 如果总点数还是不足，使用全局均匀分布补充 + while len(points) < n_points: + # 随机选择一个多边形 + polygon = random.choice(polygons) + + # 计算该多边形的边界框 + polygon_lons = [p[0] for p in polygon] + polygon_lats = [p[1] for p in polygon] + poly_min_lon, poly_max_lon = min(polygon_lons), max(polygon_lons) + poly_min_lat, poly_max_lat = min(polygon_lats), max(polygon_lats) + + # 在边界框内随机生成点 + for _ in range(100): # 最多尝试100次 + lon = random.uniform(poly_min_lon, poly_max_lon) + lat = random.uniform(poly_min_lat, poly_max_lat) + + if is_point_in_polygon((lon, lat), polygon): + points.append((lon, lat)) + break + + return points[:n_points] + +# 在指定的多边形区域内生成均匀分布的点 +async def generate_uniform_points(polygons: List[List[Tuple[float, float]]], num_points: int, original_coords=None) -> List[Tuple[float, float]]: + """ + 在指定的多边形区域内生成均匀分布的点 + + :param polygons: 多边形列表，每个多边形是坐标点的列表 + :param num_points: 需要生成的点数 + :param original_coords: 原始坐标列表，用于参考相对位置关系 + :return: 生成的点列表 [(lon, lat), ...] + """ + if not polygons: + return [] + + # 如果提供了原始坐标，使用坐标映射算法 + if original_coords and len(original_coords) > 1: + # 过滤掉None值的原始坐标 + valid_original_coords = [(coord["original_longitude"], coord["original_latitude"]) + for coord in original_coords + if coord["original_longitude"] is not None + and coord["original_latitude"] is not None] + + if len(valid_original_coords) >= num_points: + # 如果原始坐标足够，直接使用映射算法 + return map_coordinates(valid_original_coords[:num_points], polygons) + elif valid_original_coords: # 如果有部分原始坐标，先映射这些，再补充生成 + mapped_points = map_coordinates(valid_original_coords, polygons) + # 计算还需要生成的点数 + remaining_points = num_points - len(mapped_points) + if remaining_points > 0: + # 补充生成均匀分布的点 + fallback_points = generate_uniform_points_fallback(polygons, remaining_points) + mapped_points.extend(fallback_points) + return mapped_points[:num_points] + + # 如果没有原始坐标或原始坐标不足，使用传统的均匀分布点生成算法 + return generate_uniform_points_fallback(polygons, num_points) + + +# 导入数据库操作类 +db = Db() + +# 一共有哪些县区 +async def getAllArea(): + sql = "select area_code,area_name from t_dm_area where area_level=3 order by area_code" + return await db.find(sql) + + +# 根据名称获取县区代码 +async def getAreaCode(areaName): + sql = "select area_code from t_dm_area where area_level=3 and area_name=:area_name" + res = await db.findFirst(sql, {"area_name": areaName}) + # 需要去掉最后的三个0 + return str(res["area_code"])[:-3] + +# 从t_school_xy表获取指定县区和学校类型的原始坐标数据 +async def get_original_school_coordinates(region_level3: str, school_types: List[str]) -> List[Dict[str, Any]]: + """ + 从t_school_xy表获取指定县区和学校类型的原始坐标数据 + + :param region_level3: 县区名称 + :param school_types: 学校类型列表 + :return: 包含学校信息和原始坐标的列表 + """ + if not school_types: + return [] + + # 构建SQL查询，获取原始坐标数据 + placeholders = ",".join([f":type{i}" for i in range(len(school_types))]) + sql = f""" + SELECT org_name, school_type, longitude AS original_longitude, latitude AS original_latitude, + region_level1, region_level2, region_level3, region_level4, region_level5 + FROM t_school_xy + WHERE region_level3 = :region_level3 AND school_type IN ({placeholders}) + """ + + params = {"region_level3": region_level3} + # 添加每个学校类型的参数 + for i, st in enumerate(school_types): + params[f"type{i}"] = st + + # 获取原始坐标数据 + original_coords = await db.find(sql, params) + + # 转换坐标数据类型，确保longitude和latitude是float类型 + # 这可以避免与代码中其他float类型进行数学运算时出现类型不兼容错误 + from decimal import Decimal + for coord in original_coords: + if isinstance(coord.get("original_longitude"), Decimal): + coord["original_longitude"] = float(coord["original_longitude"]) + if isinstance(coord.get("original_latitude"), Decimal): + coord["original_latitude"] = float(coord["original_latitude"]) + + return original_coords + + +def get_school_types_by_education_level(education_level: str) -> List[str]: + """ + 根据学段获取对应的学校类型列表 + + :param education_level: 学段名称，可选值：学前、小学、初中、高中 + :return: 对应学校类型的列表，用于SQL查询中的in子句 + """ + # 定义学段到学校类型的映射关系 + school_type_mapping = { + "学前": ["附设幼儿班", "幼儿园"], + "小学": ["小学教学点", "小学", "附设小学班"], + "初中": ["初级中学", "专门学校", "附设普通初中班", "附设职业初中班", "九年一贯制学校"], + "高中": ["附设普通高中班", "高级中学", "十二年一贯制学校", "完全中学"] + } + + # 返回对应的学校类型列表，如果输入无效则返回空列表 + return school_type_mapping.get(education_level, []) + + +# 获取指定县区+指定学校类型的学校，同时包含原始坐标信息 +async def getSchoolList(region_level3, school_type): + # region_level3 是指县区的名称 + # 检查是否传入的是学段名称 + if school_type in ["学前", "小学", "初中", "高中"]: + school_types = get_school_types_by_education_level(school_type) + if not school_types: + return [] + # 使用in子句查询多个学校类型 + placeholders = ",".join([f":type{i}" for i in range(len(school_types))]) + + # 1. 从t_school表获取需要更新的学校列表 + sql_school = f""" + select region_level1,region_level2,region_level3,region_level4,region_level5,school_type,org_name + from t_school + where region_level3=:region_level3 and school_type in ({placeholders}) + order by region_level1,region_level2,region_level3,region_level4,region_level5,school_type + """ + params = {"region_level3": region_level3} + # 添加每个学校类型的参数 + for i, st in enumerate(school_types): + params[f"type{i}"] = st + + schools = await db.find(sql_school, params) + + if not schools: + return [] + + # 2. 从t_school_xy表获取原始坐标数据 + original_coords = await get_original_school_coordinates(region_level3, school_types) + + # 3. 创建原始坐标字典，方便查找 + original_coords_dict = {} + for coord in original_coords: + original_coords_dict[coord["org_name"]] = { + "original_longitude": coord["original_longitude"], + "original_latitude": coord["original_latitude"] + } + + # 4. 合并原始坐标信息到学校列表 + for school in schools: + org_name = school["org_name"] + if org_name in original_coords_dict: + school["original_longitude"] = original_coords_dict[org_name]["original_longitude"] + school["original_latitude"] = original_coords_dict[org_name]["original_latitude"] + else: + # 如果没有原始坐标，设置为None + school["original_longitude"] = None + school["original_latitude"] = None + + return schools + else: + # 原有的单个学校类型查询逻辑（简化版，不包含原始坐标合并） + sql = "select region_level1,region_level2,region_level3,region_level4,region_level5,school_type,org_name from t_school where region_level3=:region_level3 and school_type=:school_type order by region_level1,region_level2,region_level3,region_level4,region_level5,school_type" + schools = await db.find(sql, {"region_level3": region_level3, "school_type": school_type}) + + # 获取原始坐标并合并 + original_coords = await get_original_school_coordinates(region_level3, [school_type]) + original_coords_dict = { + coord["org_name"]: { + "original_longitude": coord["original_longitude"], + "original_latitude": coord["original_latitude"] + } + for coord in original_coords + } + + for school in schools: + org_name = school["org_name"] + if org_name in original_coords_dict: + school["original_longitude"] = original_coords_dict[org_name]["original_longitude"] + school["original_latitude"] = original_coords_dict[org_name]["original_latitude"] + else: + school["original_longitude"] = None + school["original_latitude"] = None + + return schools + + +# 获取指定县区下+指定学段的学校数量 +async def getSchoolCount(region_level3, school_type): + # 检查是否传入的是学段名称 + if school_type in ["学前", "小学", "初中", "高中"]: + school_types = get_school_types_by_education_level(school_type) + if not school_types: + return {"c": 0} + # 使用in子句查询多个学校类型 + placeholders = ",".join([f":type{i}" for i in range(len(school_types))]) + sql = f"select count(1) as c from t_school where region_level3=:region_level3 and school_type in ({placeholders})" + params = {"region_level3": region_level3} + # 添加每个学校类型的参数 + for i, st in enumerate(school_types): + params[f"type{i}"] = st + return await db.findFirst(sql, params) + else: + # 原有的单个学校类型查询逻辑 + sql = "select count(1) as c from t_school where region_level3=:region_level3 and school_type=:school_type" + return await db.findFirst(sql, {"region_level3": region_level3, "school_type": school_type}) + + +# 更新学校经纬度 +async def updateSchool(org_name, longitude, latitude): + sql = "update t_school set longitude=:longitude,latitude=:latitude where org_name=:org_name" + return await db.execute_update(sql, {"org_name": org_name, "longitude": longitude, "latitude": latitude}) + +# 重置所有学校坐标为0 +async def reset_all_school_coordinates(): + """ + 将所有学校的经纬度坐标重置为0 + """ + print("\n正在重置所有学校坐标...") + sql = "update t_school set longitude=0,latitude=0" + try: + result = await db.execute_update(sql, {}) + print(f"✅ 成功重置 {result} 条学校记录的坐标") + return True + except Exception as e: + print(f"❌ 重置坐标失败: {str(e)}") + return False + +async def get_schools_by_county_and_level(county_name, education_level): + """ + 获取指定县区和学段的学校列表，包含当前坐标信息 + 只从t_school表获取数据，不依赖t_school_xy表 + """ + # 获取对应的学校类型列表 + school_types = get_school_types_by_education_level(education_level) + if not school_types: + return [] + + # 使用in子句查询多个学校类型 + placeholders = ",".join([f":type{i}" for i in range(len(school_types))]) + + # 从t_school表获取学校数据和当前坐标 + sql = f""" + select region_level1, region_level2, region_level3, region_level4, region_level5, + school_type, org_name, longitude, latitude + from t_school + where region_level3=:county_name and school_type in ({placeholders}) + order by region_level1, region_level2, region_level3, region_level4, region_level5, school_type + """ + + params = {"county_name": county_name} + # 添加每个学校类型的参数 + for i, st in enumerate(school_types): + params[f"type{i}"] = st + + schools = await db.find(sql, params) + + # 转换坐标数据类型，确保longitude和latitude是float类型 + from decimal import Decimal + for school in schools: + if isinstance(school.get("longitude"), Decimal): + school["longitude"] = float(school["longitude"]) + if isinstance(school.get("latitude"), Decimal): + school["latitude"] = float(school["latitude"]) + + return schools + +def optimize_school_distribution(schools, polygons): + """ + 优化学校坐标分布 + - 对于已经在多边形内部的坐标，保持不变 + - 对于不在多边形内部的坐标，将其调整到多边形中 + """ + if not schools: + return [] + + # 获取目标区域的边界信息 + all_target_points = [] + for polygon in polygons: + all_target_points.extend(polygon) + + target_lons = [p[0] for p in all_target_points] + target_lats = [p[1] for p in all_target_points] + + target_min_lon, target_max_lon = min(target_lons), max(target_lons) + target_min_lat, target_max_lat = min(target_lats), max(target_lats) + + # 处理每个学校的坐标 + optimized_coords = [] + for school in schools: + lon, lat = school["longitude"], school["latitude"] + + # 检查坐标是否已经在多边形内 + if any(is_point_in_polygon((lon, lat), polygon) for polygon in polygons): + # 如果在多边形内，保持原坐标不变 + optimized_coords.append((lon, lat)) + else: + # 如果不在多边形内，将其调整到最近的多边形边界 + adjusted_lon, adjusted_lat = adjust_to_polygon_boundary([lon, lat], polygons) + optimized_coords.append((adjusted_lon, adjusted_lat)) + + return optimized_coords + +async def process_school_coordinates(): + """ + 优化t_school表中现有学校坐标分布 + 按县区和学段分组处理，美化坐标分布 + 注意：这是一个补丁程序，只修改现有坐标，不重置为0 + """ + print("\n===== 学校坐标分布优化补丁 =====") + print("正在开始优化现有学校坐标分布...") + + try: + # 1. 获取所有县区信息 + areas = await getAllArea() + print(f"\n✅ 成功获取 {len(areas)} 个县区信息") + + # 2. 定义需要处理的学段 + education_levels = ["学前", "小学", "初中", "高中"] + + # 3. 统计信息 + total_schools = 0 + processed_schools = 0 + failed_counties = 0 + + # 4. 遍历所有县区 + for area in areas: + county_name = area["area_name"] + area_code = str(area["area_code"])[:-3] # 去掉最后三位0 + + print(f"\n🔄 正在处理: {county_name} (代码: {area_code})") + + # 5. 构建GeoJSON文件路径 + geojson_path = f"d:\\dsWork\\YunNanProject\\Init\\Json\\{area_code}.json" + + try: + # 6. 加载县区边界数据 + polygons = load_geojson(geojson_path) + print(f"✅ 成功加载 {county_name} 边界数据") + + # 7. 遍历所有学段 + for education_level in education_levels: + print(f" 📚 学段: {education_level}") + + try: + # 8. 获取该县区该学段的学校列表及其现有坐标 + schools = await get_schools_by_county_and_level(county_name, education_level) + + if not schools: + print(f" ⏭️ 该学段无待处理学校") + continue + + # 过滤掉没有坐标的学校 + schools_with_coords = [s for s in schools if s["longitude"] is not None and s["latitude"] is not None] + + if not schools_with_coords: + print(f" ⏭️ 该学段没有有效坐标的学校") + continue + + print(f" 📋 找到 {len(schools_with_coords)} 所需要优化坐标的学校") + total_schools += len(schools_with_coords) + + # 9. 优化学校坐标分布 + optimized_coords = optimize_school_distribution(schools_with_coords, polygons) + + # 10. 更新学校坐标 + for i, (school, (lon, lat)) in enumerate(zip(schools_with_coords, optimized_coords), 1): + org_name = school["org_name"] + result = await updateSchool(org_name, lon, lat) + if result: + processed_schools += 1 + print(f" ✅ {i}/{len(schools_with_coords)} 已优化: {org_name}") + else: + print(f" ❌ {i}/{len(schools_with_coords)} 优化失败: {org_name}") + + except Exception as e: + print(f" ⚠️ 处理{education_level}学段时出错: {str(e)}") + continue + + except FileNotFoundError: + print(f"❌ 未找到 {county_name} 的GeoJSON文件: {geojson_path}") + failed_counties += 1 + except Exception as e: + print(f"❌ 处理{county_name}时出错: {str(e)}") + failed_counties += 1 + continue + + # 12. 输出统计结果 + print("\n" + "="*50) + print("🎯 批量处理完成！") + print(f"📊 统计信息:") + print(f" - 总县区数: {len(areas)}") + print(f" - 成功处理县区数: {len(areas) - failed_counties}") + print(f" - 失败县区数: {failed_counties}") + print(f" - 待处理学校总数: {total_schools}") + print(f" - 成功更新学校数: {processed_schools}") + print("="*50) + + except Exception as e: + print(f"\n❌ 批量处理过程中发生错误: {str(e)}") + import traceback + traceback.print_exc() + raise + finally: + # 确保在正常执行和异常情况下都关闭数据库连接 + if 'db' in globals() and hasattr(db, 'engine') and db.engine: + try: + await db.engine.dispose() + print("\n✅ 数据库连接已成功关闭") + except Exception as dispose_error: + print(f"\n❌ 关闭数据库连接时出错: {str(dispose_error)}") + +if __name__ == "__main__": + # 直接开始批量处理 - 为所有学校生成坐标 + try: + asyncio.run(process_school_coordinates()) + except KeyboardInterrupt: + print("\n⚠️ 程序被用户中断") + except Exception as e: + # 捕获并处理所有异常，包括asyncio和SSL相关错误 + print(f"\n❌ 程序执行出错: {str(e)}") + print(" 错误类型:", type(e).__name__) + # 仅在需要详细调试时显示完整堆栈 + print("\n详细错误信息:") + import traceback + traceback.print_exc() + finally: + print("\n程序已退出") \ No newline at end of file diff --git a/Apps/Json/amap_cookies.json b/Apps/Json/amap_cookies.json new file mode 100644 index 0000000..d17d26a --- /dev/null +++ b/Apps/Json/amap_cookies.json @@ -0,0 +1,89 @@ +[ + { + "domain": ".amap.com", + "expiry": 1783436001, + "httpOnly": false, + "name": "tfstk", + "path": "/", + "sameSite": "None", + "secure": true, + "value": "gM_oFfTFRg-71NH4Sv874ohkZ_ZAwUTBK93ppepU0KJXJW9RTebhp6-FyUCRnBWVhe3JzeUHKTWM99fQNBJFL9vd2oUON_TB8RB3WPC5mpKEaTAE8sPe1BAUM3zSaLiH8ReTkUIGoi8Fw8wZHn52hBAEapWe0sRH3QReY9J20CRtUe8FLSv29Bkr8LuUumR93p8e8954iBp2ae8FLsPDObOaUL7FyZyL5IXlLBGRuQxkQ_JPw1bqo3o5OK0E8NSDqdupn2uFoQ5FNSuqSkpNfLK9ptzxref2tTpAQPuDuhj19BXur48R0i_WyZeip3YvehSem7zPiUvkjaKi1mLlYT_HVgcQ1s8PhG964owXiaQ9xdtm385AiL-GxTej8LIGgtxOkx39RtsFLBXqEgoq0coMm2O49ZoIA3RXidFP3WmOJLCWuSVmjptyGQJTiSmIA3RXidF0icYM4IOyB" + }, + { + "domain": ".amap.com", + "httpOnly": true, + "name": "oauth_state", + "path": "/", + "sameSite": "None", + "secure": true, + "value": "e88390e153019eb5fdc2a6a361b08407" + }, + { + "domain": ".amap.com", + "expiry": 1775660027, + "httpOnly": true, + "name": "passport_login", + "path": "/", + "sameSite": "None", + "secure": true, + "value": "MTIyMDQwNjY0MSxhbWFwMjc1OWtPR0wsdGtsaXFqZGE0dDNnbnRrY3dpbHFtZG1nbXdncGE2ZjUsMTc2Nzg4NDAyNyxOVEJpT1dRMVlqaGlOemRsWVRZeE4yRTBNVGswWkRreU5tVTBNVFF5WW1FPQ%3D%3D" + }, + { + "domain": ".amap.com", + "expiry": 1768143203, + "httpOnly": false, + "name": "xlly_s", + "path": "/", + "sameSite": "None", + "secure": true, + "value": "1" + }, + { + "domain": ".amap.com", + "expiry": 1802444002, + "httpOnly": false, + "name": "cna", + "path": "/", + "sameSite": "None", + "secure": true, + "value": "4rLmIViEnl4CAXzrzjwC3UGc" + }, + { + "domain": "lbs.amap.com", + "expiry": 1802444003, + "httpOnly": false, + "name": "_uab_collina", + "path": "/tools", + "sameSite": "Lax", + "secure": false, + "value": "176788400312910602225889" + }, + { + "domain": ".lbs.amap.com", + "httpOnly": false, + "name": "HMACCOUNT", + "path": "/", + "sameSite": "Lax", + "secure": false, + "value": "DAC02E19E8B24CB5" + }, + { + "domain": ".lbs.amap.com", + "httpOnly": false, + "name": "Hm_lpvt_c8ac07c199b1c09a848aaab761f9f909", + "path": "/", + "sameSite": "Lax", + "secure": false, + "value": "1767884002" + }, + { + "domain": ".lbs.amap.com", + "expiry": 1799420002, + "httpOnly": false, + "name": "Hm_lvt_c8ac07c199b1c09a848aaab761f9f909", + "path": "/", + "sameSite": "Lax", + "secure": false, + "value": "1767884002" + } +] \ No newline at end of file diff --git a/Apps/XinDianTu/Coord.py b/Apps/XinDianTu/Coord.py new file mode 100644 index 0000000..3077833 --- /dev/null +++ b/Apps/XinDianTu/Coord.py @@ -0,0 +1,145 @@ +import asyncio +import os +import sys +import logging +from sqlalchemy import text + +# 配置日志 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# python -m pip install webdriver-manager selenium +# https://googlechromelabs.github.io/chrome-for-testing/ +# https://storage.googleapis.com/chrome-for-testing-public/143.0.7499.192/win64/chromedriver-win64.zip +# update t_station_profile_scd set coord_x=null,coord_y=null where id='95783932-1d32-48e4-9a38-6a6696a6a423' +# 将项目根目录添加到系统路径，确保可以导入 Util, DbKit 和 Config 模块 +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from Util.PaChongGaoDeKit import PaChongGaoDeKit +from DbKit.Db import Db +from Config.Config import DB_URL + +class CookieInvalidError(Exception): + """自定义异常：表示 Cookie 无效或过期""" + pass + +async def main(check_cookie_first=True): + """ + 坐标回填任务：自动扫描数据库中缺失坐标的场站地址，并通过高德地图补全。 + :param check_cookie_first: 是否在开始前验证 Cookie 有效性 + """ + logger.info("正在启动 T4_Coord: 高德地图坐标回填任务...") + + # 初始化数据库连接 + db = Db(db_url=DB_URL) + await db.init_db() + + # 初始化高德地图工具类（使用无头模式） + # 显式指定 Cookie 路径，确保跨目录运行时能找到文件 + project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + cookie_path = os.path.join(project_root, 'Json', 'amap_cookies.json') + + if not os.path.exists(cookie_path): + logger.error(f"错误：未找到 Cookie 文件：{cookie_path}") + # 如果要求检查且文件不存在，直接抛出异常以便上层处理 + if check_cookie_first: + await db.close() + raise CookieInvalidError("Cookie file not found") + logger.error("请先运行 Tools/T3_GetAmapCookie.py 生成有效 Cookie。") + return + + kit = PaChongGaoDeKit(cookie_file=cookie_path, headless=False) + + try: + # 0. Cookie 有效性探针 + if check_cookie_first: + logger.info("正在验证高德地图 Cookie 有效性...") + loop = asyncio.get_event_loop() + # 使用一个肯定存在的地址进行测试 + test_addr = "北京市天安门" + t_lng, t_lat = await loop.run_in_executor(None, kit.get_coordinate, test_addr) + + if not t_lng: + logger.warning(f"Cookie 验证失败：无法获取测试地址 '{test_addr}' 的坐标。") + logger.warning("判定 Cookie 已失效或过期。") + raise CookieInvalidError("Cookie invalid or expired") + else: + logger.info(f"Cookie 验证通过 (测试坐标: {t_lng}, {t_lat})。") + + async with await db.get_session() as session: + # 1. 查询所有地址不为空但缺失坐标的唯一地址 + sql_missing = """ + SELECT DISTINCT address + FROM t_station_profile_scd + WHERE address IS NOT NULL + AND address != '' + AND (coord_x IS NULL OR coord_y IS NULL) + """ + result = await session.execute(text(sql_missing)) + missing_addresses = [row[0] for row in result.fetchall()] + + logger.info(f"共发现 {len(missing_addresses)} 个缺失坐标的唯一地址。") + + for address in missing_addresses: + logger.info(f"\n正在处理地址: {address}") + + # 2. 检查数据库中其他记录是否已经存有该地址的坐标（去重复查询） + # 这样可以最大程度减少对高德地图接口的调用 + sql_check = """ + SELECT coord_x, coord_y + FROM t_station_profile_scd + WHERE address = :addr + AND coord_x IS NOT NULL + AND coord_y IS NOT NULL + LIMIT 1 + """ + existing = await session.execute(text(sql_check), {"addr": address}) + row = existing.fetchone() + + lng, lat = None, None + + if row: + lng, lat = row[0], row[1] + logger.info(f" -> 在数据库中找到现有坐标: {lng}, {lat}") + else: + # 3. 数据库中没有，调用高德地图进行查询 + logger.info(" -> 数据库中无记录，正在查询高德地图...") + # 在线程池中运行同步的 Selenium 调用，避免阻塞异步循环 + loop = asyncio.get_event_loop() + lng, lat = await loop.run_in_executor(None, kit.get_coordinate, address) + + if lng and lat: + logger.info(f" -> 高德地图返回坐标: {lng}, {lat}") + else: + logger.info(" -> 高德地图未返回有效坐标。") + + # 4. 如果获取到了坐标，更新数据库中所有匹配该地址的记录 + if lng and lat: + sql_update = """ + UPDATE t_station_profile_scd + SET coord_x = :lng, coord_y = :lat + WHERE address = :addr + """ + await session.execute(text(sql_update), { + "lng": lng, + "lat": lat, + "addr": address + }) + await session.commit() + logger.info(f" -> 已成功更新数据库中地址为 '{address}' 的记录。") + else: + logger.info(f" -> 跳过更新 (未找到有效坐标)。") + + except Exception as e: + logger.error(f"执行过程中出错: {e}") + finally: + # 释放资源 + kit.close() + await db.close() + logger.info("\n任务执行结束。") + +if __name__ == "__main__": + # 针对 Windows 平台的异步策略设置 + if sys.platform == 'win32': + asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) + asyncio.run(main()) diff --git a/Apps/XinDianTu/Crawler.py b/Apps/XinDianTu/Crawler.py new file mode 100644 index 0000000..96550b5 --- /dev/null +++ b/Apps/XinDianTu/Crawler.py @@ -0,0 +1,581 @@ +# coding=utf-8 +import asyncio +import logging +import uuid +import os +import sys +import json +import time +import hashlib +from PIL import Image + +# 将项目根目录添加到 sys.path +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +if project_root not in sys.path: + sys.path.append(project_root) + +import uiautomator2 as u2 +from Core.BaseCrawler import BaseCrawler +from Util import Kit +from Util.Kit import take_screenshot +from Util.ObsUtil import ObsUploader +from Util.RedisKit import RedisKit +from Util.PaddleOCRKit import get_ocr_kit +import cv2 +from Apps.XinDianTu.Service import XinDianTuService +from Config.Config import ( + OBS_TMP_PREFIX, CDN_DOMAIN, SCROLL_DISTANCE_RATIO, + MAX_SCROLLS, REDIS_STATION_EXPIRE, + WAIT_DETAIL_PAGE_LOAD, WAIT_BACK_TO_LIST, WAIT_AFTER_SCROLL, + MAX_CRAWL_DISTANCE +) + +# --- 用户配置区域 --- +# 是否保留截图文件（True=保留备查，False=随用随删） +KEEP_SCREENSHOTS = True + +# [Testing] 是否在启动时清除 Redis 中的场站处理记录 +# True: 每次运行前清除记录，方便反复测试同一个场站 +# False: 生产模式，保留记录以避免重复爬取 +TEST_CLEAR_REDIS = True + +# 配置说明： +# SCROLL_DISTANCE_RATIO 控制翻页时的滑动距离（在 Config.py 中修改）。 +# 对于华为 Mate 20x 等分辨率较低或屏幕较小的手机，如果发现翻页时跳过了某些场站， +# 请尝试减小 SCROLL_DISTANCE_RATIO（例如设置为 0.4 或 0.3）。 +# 这样每次滑动的距离变短，可以确保所有场站都能被完整显示并识别。 + +# 配置日志输出 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', + handlers=[ + logging.StreamHandler(sys.stdout) + ] +) +logger = logging.getLogger("StationList") + +# 强制设置所有相关模块的日志级别为 INFO，防止被第三方库干扰 +logging.getLogger("PaddleOCRKit").setLevel(logging.INFO) +logging.getLogger("Util.Kit").setLevel(logging.INFO) +logging.getLogger("OpenXinDianTu").setLevel(logging.INFO) +logging.getLogger("FullProcess").setLevel(logging.INFO) + +class XinDianTuCrawler(BaseCrawler): + """ + 新电途小程序爬虫实现 + """ + def __init__(self, service=None): + super().__init__(service) + # 初始化配置参数 + self.max_scrolls = MAX_SCROLLS + self.uploader = ObsUploader() + self.redis_kit = RedisKit() + + async def start(self): + """ + 实现 BaseCrawler 的启动入口 + """ + # 兼容旧逻辑，直接调用 main + await main(self.service) + + async def open_app(self): + # 实际逻辑在 Opener.py，此处可作为封装层 + pass + + async def crawl_list(self): + # 实际逻辑在 get_station_list，此处可作为封装层 + pass + + async def crawl_detail(self, station_info): + pass + +async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS): + """ + 获取场站列表并处理翻页 + """ + all_stations = [] + seen_names = set() + redis_kit = RedisKit() + + # 确保在“找桩”或者首页列表界面 + window_size = d.window_size() + w, h = window_size[0], window_size[1] + + # 获取设备信息以用于坐标计算 + device_info = d.info + device_info['width'] = w + device_info['height'] = h + + logger.info(f"设备信息: {device_info.get('productName')} | 窗口分辨率: {w}x{h}") + + max_distance_reached = False + stop_reason = "max_scrolls_reached" # 默认停止原因：达到最大翻页次数 + + # 存储后台异步任务 + background_tasks = [] + + for i in range(max_scrolls + 1): + if max_distance_reached: + logger.info("已达到最大抓取距离，停止翻页。") + stop_reason = "max_distance_reached" + break + logger.info(f"正在处理第 {i + 1} 页...") + + # 1. 拍摄截图 + logger.info(f"Step [1/6] 正在拍摄列表页截图...") + t_shot = time.time() + image_uuid = str(uuid.uuid4()) + # 使用相对路径: 基于当前脚本目录下的 Images 文件夹 + base_dir = os.path.dirname(os.path.abspath(__file__)) + save_dir = os.path.join(base_dir, "./Images") + screenshot_path = take_screenshot(d, image_uuid, save_dir=save_dir) + logger.info(f"Step [1/6] 列表页截图已完成: {screenshot_path} (耗时: {time.time() - t_shot:.2f}s)") + + # 校验截图实际尺寸 + img_w, img_h = 0, 0 + try: + with Image.open(screenshot_path) as img: + img_w, img_h = img.size + logger.info(f"截图实际尺寸: {img_w}x{img_h}") + # 如果截图尺寸与窗口尺寸不符，更新 device_info 以适配坐标换算 + if img_w != w or img_h != h: + logger.warning(f"检测到截图尺寸 ({img_w}x{img_h}) 与窗口尺寸 ({w}x{h}) 不一致，将以截图尺寸为准进行坐标换算。") + device_info['width'] = img_w + device_info['height'] = img_h + except Exception as e: + logger.warning(f"无法获取截图尺寸信息: {e}") + + # 2. 使用 Kit.crop_cards_from_image 进行切片和生成 _vl.jpg + logger.info("Step [2/6] 正在执行本地图形学切片分析，识别场站卡片并生成绿色方框图...") + t_crop = time.time() + # 这会生成 _vl.jpg 和 .json + Kit.crop_cards_from_image(screenshot_path) + logger.info(f"Step [2/6] 图形学切片分析完成 (耗时: {time.time() - t_crop:.2f}s)") + + # 读取生成的元数据 + json_path = screenshot_path.replace(".jpg", ".json") + vl_img_path = screenshot_path.replace(".jpg", "_vl.jpg") + + if not os.path.exists(json_path) or not os.path.exists(vl_img_path): + logger.error(f"❌ Step [2/6] 失败: 未生成有效的 JSON 或 VL 图片 (JSON: {os.path.exists(json_path)}, VL: {os.path.exists(vl_img_path)})") + logger.warning("可能未识别到任何卡片，跳过当前页") + continue + + logger.info(f"Step [3/6] 绿色方框图已生成: {vl_img_path}") + + with open(json_path, 'r', encoding='utf-8') as f: + json_metadata = json.load(f) + + # --- New Logic: Local OCR via PaddleOCRKit --- + logger.info("Step [4/6] 正在开始本地 OCR 识别流程，将识别出的卡片逐一交给 OCR...") + t_ocr_total = time.time() + + stations = [] + try: + ocr_kit = get_ocr_kit() + + # 读取原始截图进行裁剪 (比读取 _vl.jpg 更干净，没有绿框干扰) + original_img = cv2.imread(screenshot_path) + + if "cards" in json_metadata and original_img is not None: + h_img, w_img = original_img.shape[:2] + num_cards = len(json_metadata['cards']) + logger.info(f"检测到 {num_cards} 个场站卡片，准备开始逐一 OCR 识别...") + + for idx, card in enumerate(json_metadata["cards"]): + # card: {"id": 1, "rect": [x1, y1, x2, y2], "click_point": [cx, cy]} + rect = card.get("rect") + if not rect: continue + x1, y1, x2, y2 = rect + + # 边界检查 + x1 = max(0, min(x1, w_img)) + x2 = max(0, min(x2, w_img)) + y1 = max(0, min(y1, h_img)) + y2 = max(0, min(y2, h_img)) + + if x2 <= x1 or y2 <= y1: continue + + # 裁剪卡片 + cropped_card = original_img[y1:y2, x1:x2] + + # 识别 + logger.info(f"正在识别第 {idx+1}/{num_cards} 个卡片 (区域: {rect})...") + t_card_start = time.time() + parsed_data = ocr_kit.recognize(cropped_card) + t_card_end = time.time() + + # Log detailed OCR result + logger.info(f"卡片 {idx+1} OCR 识别耗时: {t_card_end - t_card_start:.2f}s") + + if parsed_data and parsed_data.get("station_name"): + # 格式化数据以匹配原有结构 + piles_list = parsed_data.get("piles", []) + piles_str_parts = [] + for p in piles_list: + p_type = p.get("type", "") + p_free = p.get("free", 0) + p_total = p.get("total", 0) + piles_str_parts.append(f"{p_type}:{p_free}/{p_total}") + + piles_str = " ".join(piles_str_parts) + + station_info = { + "station_name": parsed_data.get("station_name"), + "price": str(parsed_data.get("price")) if parsed_data.get("price") is not None else "", + "pro_price": "", # 暂未解析 + "piles": piles_str, + "distance": parsed_data.get("distance", ""), + "uia_center_x": card["click_point"][0], + "uia_center_y": card["click_point"][1], + "tags": parsed_data.get("tags", []), + "parking_info": parsed_data.get("parking", "") + } + logger.info(f"✅ 成功解析场站: {station_info['station_name']} | 距离: {station_info['distance']}") + + # 立即记录场站基础状态信息 (电桩、价格等) + try: + await service.record_station_status(station_info) + except Exception as e: + logger.error(f"❌ 记录场站状态失败: {e}") + + stations.append(station_info) + else: + logger.warning(f"⚠️ 卡片 {idx+1} 未能识别出有效场站名称") + + except Exception as e: + logger.error(f"❌ Local OCR processing failed: {e}", exc_info=True) + + logger.info(f"Step [5/6] 本地 OCR 解析完成 (总耗时: {time.time() - t_ocr_total:.2f}s) | 成功识别: {len(stations)}/{len(json_metadata.get('cards', []))}") + + if not stations: + logger.warning("当前页面未检测到任何有效的场站信息") + else: + logger.info(f"Step [6/6] 准备开始逐一点击场站进入详情页 (共 {len(stations)} 个)...") + new_stations_found = False + for s_idx, s in enumerate(stations): + name = s.get("station_name") + + # 过滤掉明显的非场站名称 + if not name: + continue + + # --- 短期去重：检查 Redis 缓存 --- + # 2分钟内如果处理过该场站，则跳过 + redis_key = f"processed_station:{name}" + if await redis_kit.exists(redis_key): + logger.info(f"场站 {name} 在 2 分钟内已处理过，跳过") + continue + + if name in seen_names: + continue + + # 使用从 JSON 元数据中恢复的点击坐标 + x = s.get("uia_center_x") + y = s.get("uia_center_y") + + if x is None or y is None: + logger.warning(f"场站 {name} 缺少坐标信息，无法进入详情页") + continue + + seen_names.add(name) + all_stations.append(s) + new_stations_found = True + + # 打印基本信息 + price = s.get("price") + pro_price = s.get("pro_price") + piles_str = s.get("piles", "") + distance_str = s.get("distance", "") + + # 检查距离 + dist_log_part = "" + if distance_str: + try: + import re + dist_match = re.search(r"(\d+(\.\d+)?)", distance_str) + if dist_match: + dist_val = float(dist_match.group(1)) + is_km = "km" in distance_str.lower() + dist_km = dist_val if is_km else dist_val / 1000.0 + + # 将距离解析结果和限制检查合并到日志中 + dist_log_part = f" (解析: {dist_km:.2f}km / 限: {MAX_CRAWL_DISTANCE}km)" + + if dist_km >= MAX_CRAWL_DISTANCE: + logger.info(f"当前场站 {name} 距离 {dist_km}km (原始: {distance_str}) 已达到或超过限制 {MAX_CRAWL_DISTANCE}km。") + max_distance_reached = True + except Exception as e: + logger.warning(f"解析距离出错: {distance_str}, {e}") + + pro_info = f" | PRO会员价: {pro_price}" if pro_price else "" + # 确保距离始终显示，如果为空则显示 "未知" + display_distance = distance_str if distance_str else "未知" + logger.info(f"正在处理第 {s_idx+1} 个场站: {name} | 价格: {price}{pro_info} | 枪: {piles_str} | 距离: {display_distance}{dist_log_part}") + + if max_distance_reached: + break + + # --- 点击进入详情页记录地址 --- + # 使用相对坐标点击，以适应不同分辨率 + if img_w > 0 and img_h > 0: + rel_x = max(0.0, min(1.0, x / img_w)) + rel_y = max(0.0, min(1.0, y / img_h)) + logger.info(f"👉 正在点击进入详情页: {name} (相对坐标: {rel_x:.3f}, {rel_y:.3f})") + d.click(rel_x, rel_y) + else: + logger.info(f"👉 正在点击进入详情页: {name} (绝对坐标: {x}, {y})") + d.click(x, y) + + await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD) # 等待详情页加载 + + # 尝试获取并保存地址 + address_recorded = False + for retry in range(2): + # 1. 拍摄详情页截图 + t_d_shot = time.time() + detail_uuid = str(uuid.uuid4()) + detail_screenshot_path = take_screenshot(d, detail_uuid, save_dir=save_dir) + logger.info(f"Step [详情页截图] 耗时: {time.time() - t_d_shot:.4f}s") + + # 2. 上传至 OBS + t_d_up = time.time() + detail_object_key = f"{OBS_TMP_PREFIX}/{detail_uuid}.jpg" + up_success, _ = uploader.upload_file(detail_object_key, detail_screenshot_path) + logger.info(f"Step [上传详情页截图] 耗时: {time.time() - t_d_up:.4f}s") + + if up_success: + detail_url = f"https://{CDN_DOMAIN}/{detail_object_key}" + # 3. 解析并保存地址 (异步后台处理) + # 定义异步任务函数 + async def _process_address_task(task_name, task_url, task_dev_info, task_redis_key): + t_addr_task = time.time() + try: + addr_res = await service.process_station_address(task_name, task_url, device_info=task_dev_info) + logger.info(f"Step [解析并保存地址-后台] 耗时: {time.time() - t_addr_task:.4f}s") + if addr_res.get("address"): + logger.info(f"成功记录地址: {addr_res.get('address')}") + # 记录到 Redis，设置过期时间 + await redis_kit.set_data(task_redis_key, "1", expire=REDIS_STATION_EXPIRE) + except Exception as e: + logger.error(f"后台处理地址失败 ({task_name}): {e}") + + # 创建并启动任务 + addr_task = asyncio.create_task(_process_address_task(name, detail_url, device_info, redis_key)) + background_tasks.append(addr_task) + logger.info(f"已提交地址解析任务到后台: {name}") + + # 标记为已记录以跳出 retry 循环 + address_recorded = True + + # 清理详情页临时截图 + if not KEEP_SCREENSHOTS and os.path.exists(detail_screenshot_path): + os.remove(detail_screenshot_path) + + break # 直接跳出 retry 循环 + + # --- 抓取价格时段信息 (New Feature) --- + # 仅在测试模式下或特定需求下启用 + try: + logger.info(f"开始抓取价格时段信息: {name}") + + # 使用几何特征识别 "全部时段" 按钮 + # 临时截图 + temp_uuid = "temp_find_expand" + screenshot_path = take_screenshot(d, temp_uuid, save_dir="Temp") + + # 尝试识别，将调试图片保存到 Images 目录 + t_find = time.time() + pos = Kit.find_expand_button_position(screenshot_path, debug_dir=save_dir, debug_filename_prefix=name) + logger.info(f"Step [识别展开按钮] 耗时: {time.time() - t_find:.4f}s") + + # 清理截图 + try: + os.remove(screenshot_path) + except: + pass + + if pos: + x, y = pos + logger.info(f"通过几何特征找到 '全部时段' 按钮: ({x}, {y})") + d.click(x, y) + await asyncio.sleep(1.5) + + # 抓取3屏截图 + price_image_urls = [] + for p_idx in range(1, 4): + # 使用场站名称的MD5值作为文件名前缀，避免中文和特殊字符导致的URL问题 + t_p_loop = time.time() + name_md5 = hashlib.md5(name.encode('utf-8')).hexdigest() + p_uuid = f"{name_md5}_price_{p_idx}" + p_path = take_screenshot(d, p_uuid, save_dir=save_dir) + logger.info(f"已保存价格时段截图 {p_idx} (Station: {name}, MD5: {name_md5}): {p_path}") + + # 上传到 OBS + p_object_key = f"{OBS_TMP_PREFIX}/{p_uuid}.jpg" + success, _ = uploader.upload_file(p_object_key, p_path) + if success: + p_url = f"https://{CDN_DOMAIN}/{p_object_key}" + price_image_urls.append(p_url) + logger.info(f"截图上传成功: {p_url}") + else: + logger.warning(f"截图上传失败: {p_path}") + + logger.info(f"Step [价格截图与上传-{p_idx}] 耗时: {time.time() - t_p_loop:.4f}s") + + if p_idx < 3: + d.swipe_ext("up", scale=0.7) + await asyncio.sleep(1.0) + + # 调用服务解析价格时段 (异步后台处理) + if price_image_urls: + logger.info(f"正在调用接口解析电费时段数据 ({len(price_image_urls)} 张图片) - 转入后台...") + + async def _process_price_task(task_name, task_urls, task_dev_info): + t_price_task = time.time() + try: + await service.process_price_schedule(task_name, task_urls, device_info=task_dev_info) + logger.info(f"Step [解析电费时段数据-后台] 耗时: {time.time() - t_price_task:.4f}s") + logger.info(f"电费时段数据解析完成: {task_name}") + except Exception as e: + logger.error(f"后台处理价格时段失败 ({task_name}): {e}") + + price_task = asyncio.create_task(_process_price_task(name, price_image_urls, device_info)) + background_tasks.append(price_task) + else: + logger.warning("未能获取到有效的价格时段截图，跳过解析") + else: + logger.warning(f"未能通过几何特征识别 '全部时段' 按钮") + + except Exception as e: + logger.warning(f"抓取价格时段信息时发生异常: {e}") + + # 测试模式：仅处理第一个场站 + # logger.info("测试模式：仅处理第一个场站，即将退出 (Y3)") + # return all_stations + + # 返回列表页 + d.press("back") + await asyncio.sleep(WAIT_BACK_TO_LIST) # 等待列表页重新稳定 + + if not new_stations_found and i > 0: + logger.info("未发现更多新场站，停止下拉。") + # 如果需要强行爬取所有，可以注释掉 break + # break + + # 5. 如果还没到最后一页，执行下拉 + if i < max_scrolls and not max_distance_reached: + logger.info(f"执行下拉翻页 (距离比例: {SCROLL_DISTANCE_RATIO})...") + # 根据配置的比例计算滑动起始和终点 + # 保证滑动在屏幕中心区域进行 + start_y = 0.5 + (SCROLL_DISTANCE_RATIO / 2) + end_y = 0.5 - (SCROLL_DISTANCE_RATIO / 2) + d.swipe(w * 0.5, h * start_y, w * 0.5, h * end_y, duration=0.5) + await asyncio.sleep(WAIT_AFTER_SCROLL) # 等待页面加载和列表稳定 + + # 清理列表页截图 + if not KEEP_SCREENSHOTS: + if os.path.exists(screenshot_path): + os.remove(screenshot_path) + if os.path.exists(vl_img_path): + os.remove(vl_img_path) + if os.path.exists(json_path): + os.remove(json_path) + + logger.info(f"爬取任务结束，正在等待 {len(background_tasks)} 个后台处理任务完成...") + if background_tasks: + await asyncio.gather(*background_tasks) + logger.info("所有后台任务已完成。") + + logger.info(f"任务结束，共采集到 {len(all_stations)} 个场站。停止原因: {stop_reason}") + return all_stations + +def clean_images_dir(): + """清理 Images 目录下除保留文件外的所有文件""" + base_dir = os.path.dirname(os.path.abspath(__file__)) + images_dir = os.path.join(base_dir, "Images") + + if not os.path.exists(images_dir): + return + + keep_files = {'1.jpg', '2.jpg', '3.jpg', '4.jpg'} + logger.info(f"正在清理 Images 目录 (保留: {keep_files})...") + + deleted_count = 0 + for filename in os.listdir(images_dir): + if filename in keep_files: + continue + + file_path = os.path.join(images_dir, filename) + try: + if os.path.isfile(file_path): + os.remove(file_path) + deleted_count += 1 + except Exception as e: + logger.warning(f"删除文件失败 {filename}: {e}") + + logger.info(f"清理完成，共删除 {deleted_count} 个文件。") + +async def clean_redis_data(redis_kit): + """清除 Redis 中的场站处理记录""" + if TEST_CLEAR_REDIS: + logger.info("测试模式开启：正在清除 Redis 中的场站处理记录 (processed_station:*)...") + keys = await redis_kit.keys("processed_station:*") + if keys: + count = await redis_kit.delete(*keys) + logger.info(f"已清除 {count} 条场站处理记录") + else: + logger.info("未发现旧的场站处理记录") + +async def main(service=None, do_cleanup=True): + # 连接设备 + d = u2.connect() + + # 初始化服务 + should_close_service = False + if service is None: + service = XinDianTuService() + should_close_service = True + # 初始化数据库连接（XinDianTuService 需要） + await service.init_db() + + uploader = ObsUploader() + redis_kit = RedisKit() + + try: + if do_cleanup: + # 清理图片目录 + clean_images_dir() + + # [Testing] 如果配置为测试模式，启动时清除 Redis 记录 + await clean_redis_data(redis_kit) + + # 清理过期数据 + # await service.cleanup_old_data() + + # 获取场站列表 + stations = await get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS) + + if stations: + logger.info("场站列表采集完成。") + else: + logger.warning("未采集到任何场站信息。") + + return True + + except Exception as e: + logger.exception(f"运行过程中出现异常: {e}") + return False + finally: + # 关闭数据库连接 + if should_close_service: + await service.close_db() + +if __name__ == "__main__": + try: + asyncio.run(main()) + except KeyboardInterrupt: + logger.info("程序被用户中断.") + except Exception as e: + logger.exception(f"程序崩溃: {e}") diff --git a/Apps/XinDianTu/GetCookie.py b/Apps/XinDianTu/GetCookie.py new file mode 100644 index 0000000..9e67a81 --- /dev/null +++ b/Apps/XinDianTu/GetCookie.py @@ -0,0 +1,64 @@ +import os +import sys +import logging + +# 配置日志 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# pip install selenium +# 将项目根目录添加到系统路径，确保可以导入 Util 模块 +# 注意：PaChongGaoDeKit 位于 Util 目录下 +current_dir = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(os.path.join(current_dir, '..')) + +from Util.PaChongGaoDeKit import PaChongGaoDeKit + +def run_login_tool(force=False): + """ + 运行高德地图 Cookie 获取工具。 + :param force: 如果为 True，则跳过 'Cookie 已存在' 的询问，直接开始流程。 + :return: Boolean, True 表示获取成功, False 表示失败或取消。 + """ + logger.info("正在启动高德地图 Cookie 获取工具,使用手机号18686619970+验证码登录即可！") + + # 默认将 Cookie 保存到项目根目录下的 Json/amap_cookies.json + cookie_path = os.path.join(current_dir, '../Json/amap_cookies.json') + + # 检查 Cookie 文件是否已存在 + if os.path.exists(cookie_path) and not force: + logger.info(f"提示：检测到已存在 Cookie 文件: {os.path.abspath(cookie_path)}") + logger.info("如果您本地的 Cookie 文件没有过期且可以正常使用，则不需要重新生成。") + + user_choice = input("是否仍要继续运行并重新获取 Cookie？(y/n): ").strip().lower() + if user_choice != 'y': + logger.info("用户取消操作，程序退出。") + return False + + logger.info("\n" + "-"*60) + logger.info("即将开始获取 Cookie，请按照以下步骤操作：") + logger.info("1. 程序将自动打开一个 Chrome 浏览器窗口。") + logger.info("2. 请在该窗口中完成高德地图账号登录（扫码或账号密码）。") + logger.info("3. 登录成功后，保持页面处于控制台或个人中心状态。") + logger.info("4. 回到此终端窗口，按下回车键，程序将保存 Cookie 并自动关闭浏览器。") + logger.info("-"*60 + "\n") + + # 初始化工具类，强制开启非无头模式（显示浏览器窗口）以便手动登录 + # 确保 Json 目录存在 + os.makedirs(os.path.dirname(cookie_path), exist_ok=True) + + kit = PaChongGaoDeKit(headless=False, cookie_file=cookie_path) + + try: + # 执行登录并保存流程 + return kit.login_and_save_cookies() + except Exception as e: + logger.error(f"执行过程中发生意外错误: {e}") + return False + finally: + # 关闭浏览器资源 + kit.close() + logger.info("工具运行结束。") + +if __name__ == "__main__": + run_login_tool() diff --git a/Apps/XinDianTu/Images/.OKUU_flag_expand.jpg b/Apps/XinDianTu/Images/.OKUU_flag_expand.jpg new file mode 100644 index 0000000..dbebdf9 Binary files /dev/null and b/Apps/XinDianTu/Images/.OKUU_flag_expand.jpg differ diff --git a/Apps/XinDianTu/Images/000a46ca-21de-40fe-853b-7ab733e789dc.jpg b/Apps/XinDianTu/Images/000a46ca-21de-40fe-853b-7ab733e789dc.jpg new file mode 100644 index 0000000..fa6f192 Binary files /dev/null and b/Apps/XinDianTu/Images/000a46ca-21de-40fe-853b-7ab733e789dc.jpg differ diff --git a/Apps/XinDianTu/Images/00593070-d0f8-47b1-a5bc-274b2e41aed7.jpg b/Apps/XinDianTu/Images/00593070-d0f8-47b1-a5bc-274b2e41aed7.jpg new file mode 100644 index 0000000..9e16d29 Binary files /dev/null and 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ObsUploader +from Util.XinDianTuReadImageKit import XinDianTuReadImageKit +from Config.Config import OBS_TMP_PREFIX, CDN_DOMAIN + +# pip install adbutils +# 配置日志输出，方便调试和监控 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger("OpenXinDianTu") + +async def check_and_close_ad(d): + """ + 检测并关闭广告弹窗（优先使用计算机图形学方案，节省成本和时间） + """ + logger.info("开始检测广告弹窗...") + + # 1. 拍摄截图 + t1 = time.time() + image_uuid = str(uuid.uuid4()) + # 使用相对路径: 基于当前脚本目录下的 Images 文件夹 + base_dir = os.path.dirname(os.path.abspath(__file__)) + save_dir = os.path.join(base_dir, "./Images") + screenshot_path = take_screenshot(d, image_uuid, save_dir=save_dir) + logger.info(f"Step [广告检测截图] 耗时: {time.time() - t1:.4f}s") + + # 2. 图形学检测方案 (Local CV) + t_cv = time.time() + + # 方案 A: 检测黑色的"同意"按钮 (隐私协议) + # 这一步不需要模板，直接通过颜色和形状特征检测 + agree_pos = detect_black_agree_button(screenshot_path, debug_dir=save_dir) + if agree_pos: + x, y = agree_pos + logger.info(f"通过图形学算法检测到隐私协议'同意'按钮: ({x}, {y})") + d.click(x, y) + logger.info(f"Step [图形学检测-同意按钮] 耗时: {time.time() - t_cv:.4f}s") + # 清理截图 + if os.path.exists(screenshot_path): + os.remove(screenshot_path) + return True + + # 方案 B: 多模板匹配检测 "关闭(X)" 按钮 (支持多种广告样式) + template_dir = os.path.join(base_dir, "Templates") + + if os.path.exists(template_dir): + logger.info(f"正在尝试多模板匹配关闭按钮: {template_dir}") + close_pos = detect_any_ad_close(screenshot_path, template_dir, debug_dir=save_dir) + + if close_pos: + x, y = close_pos + logger.info(f"通过多模板匹配检测到广告关闭按钮: ({x}, {y})") + d.click(x, y) + logger.info(f"Step [图形学检测-关闭按钮] 耗时: {time.time() - t_cv:.4f}s") + if os.path.exists(screenshot_path): + os.remove(screenshot_path) + return True + + # 方案 C: 底部圆形关闭按钮检测 (无模板，基于几何特征) + # 针对插屏广告底部中间的圆形关闭按钮 + circle_pos = detect_bottom_close_circle(screenshot_path, debug_dir=save_dir) + if circle_pos: + x, y = circle_pos + logger.info(f"通过几何特征检测到底部圆形关闭按钮: ({x}, {y})") + d.click(x, y) + logger.info(f"Step [图形学检测-底部圆形按钮] 耗时: {time.time() - t_cv:.4f}s") + if os.path.exists(screenshot_path): + os.remove(screenshot_path) + return True + + logger.info(f"本地图形学检测完成，未发现已知广告。耗时: {time.time() - t_cv:.4f}s") + + # [优化] 用户指示：回退到大模型检查不靠谱且浪费。 + # 如果本地图形学方案（黑按钮、X关闭）都未命中，则直接认为无广告或已进入正常页面。 + + # 清理本地截图 + if os.path.exists(screenshot_path): + os.remove(screenshot_path) + + return False + + +async def open_mini_program(): + """ + 异步形式的进入微信小程序 + """ + # 连接设备 + d = u2.connect() + + logger.info("执行进入小程序.") + + # 启动微信 (强制停止后再启动，确保回到主页) + logger.info("启动微信...") + d.app_start("com.tencent.mm", stop=True) + await asyncio.sleep(5) # 给微信充足的启动时间 + + # 0. 确保在“微信”消息列表标签页 + tab_chat = d(text="微信", resourceId="com.tencent.mm:id/f2s") # 常见底部标签 ID + if not tab_chat.exists: + tab_chat = d(text="微信") + + if tab_chat.exists: + # 如果没选中（通常选中的文本颜色不同或有其他特征，这里直接点一下确保切换） + logger.info("点击底部‘微信’标签，确保在消息列表页.") + tab_chat.click() + await asyncio.sleep(1) + + # 1. 点击搜索按钮（放大镜图标） + # 仅保留坐标点击逻辑 + window_size = d.window_size() + w, h = window_size[0], window_size[1] + + # 精确相对坐标：x=84%, y=8% (基于截图推算) + click_x = int(w * 0.84) + click_y = int(h * 0.08) + logger.info(f"使用精确坐标点击搜索按钮: ({click_x}, {click_y})") + d.click(click_x, click_y) + + # 点击后给予一定的加载时间 + await asyncio.sleep(2) + + # 2. 直接尝试输入内容 (既然已经看到了搜索界面，我们不再进行严格的特征校验) + logger.info("准备输入搜索内容: 新电途") + + # 启用 u2 快速输入法 (使用更新的方法名) + d.set_input_ime(True) + + # 尝试点击搜索框位置以确保获取焦点 (通常在顶部居中偏左) + search_bar_x, search_bar_y = int(w * 0.4), int(h * 0.08) + d.click(search_bar_x, search_bar_y) + await asyncio.sleep(1.5) # 稍微多等一会儿让输入法准备好 + + # 直接发送按键 + try: + # 尝试先不使用 clear=True，因为它在某些设备上会导致 ADB_KEYBOARD_CLEAR_TEXT 错误 + # 如果需要清除文本，可以尝试全选+删除，或者假设搜索框是空的 + logger.info("尝试输入文字: 新电途") + d.send_keys("新电途") + logger.info("文字输入指令已发送.") + except Exception as e: + logger.warning(f"直接 send_keys 失败: {e}") + + try: + # 备选方案：尝试获取焦点元素并设置文本 + logger.info("尝试使用 set_text 设置文本...") + d(focused=True).set_text("新电途") + logger.info("set_text 指令已发送.") + except Exception as e2: + logger.error(f"文字输入彻底失败: {e2}") + # 最后的尝试：切换回默认输入法（有时 FastInputIME 会卡住） + # d.set_fastinput_ime(False) + # d.send_keys("新电途") + + await asyncio.sleep(3) + + # 兜底点击：点击搜索结果的第一项位置 (x=50%, y=18%) + res_x, res_y = int(w * 0.5), int(h * 0.18) + logger.info(f"尝试坐标点击第一项: ({res_x}, {res_y})") + d.click(res_x, res_y) + await asyncio.sleep(10) + # 进入小程序后，检查并处理广告 + await check_and_close_ad(d) + return True + + +async def main(): + success = await open_mini_program() + if success: + logger.info("任务执行成功.") + else: + logger.error("任务执行失败.") + + +if __name__ == "__main__": + try: + asyncio.run(main()) + except KeyboardInterrupt: + logger.info("程序被用户中断.") + except Exception as e: + logger.exception(f"运行过程中出现异常: {e}") diff --git a/Apps/XinDianTu/Report/Export.py b/Apps/XinDianTu/Report/Export.py new file mode 100644 index 0000000..0f69ce0 --- /dev/null +++ b/Apps/XinDianTu/Report/Export.py @@ -0,0 +1,505 @@ +import asyncio +import json +import logging +import os +import re +import sys +from datetime import datetime +from decimal import Decimal +from typing import Any, Dict, List, Optional, Set + +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) +if project_root not in sys.path: + sys.path.append(project_root) + +from Util import Win32Patch + +Win32Patch.patch() + +from Config.Config import DB_URL +from DbKit.Db import Db + +try: + from openpyxl import Workbook + from openpyxl.styles import Alignment, Border, Font, PatternFill, Side + from openpyxl.utils import get_column_letter +except Exception as e: + raise RuntimeError("缺少 openpyxl 依赖，请先安装：pip install openpyxl") from e + + +# --- 样式定义 --- +# 颜色 +HEADER_FILL_COLOR = "4472C4" # 专业蓝 (表头背景) +HEADER_FONT_COLOR = "FFFFFF" # 白色 (表头文字) +LABEL_FILL_COLOR = "D9E1F2" # 浅蓝 (Key标签背景，更柔和) +SECTION_FILL_COLOR = "DDEBF7" # 极浅蓝 (小节标题背景) +BORDER_COLOR = "BFBFBF" # 边框颜色 +OUTER_BORDER_COLOR = "4472C4" # 外边框颜色 + +# 字体 +FONT_NAME = "微软雅黑" +TITLE_FONT = Font(name=FONT_NAME, size=20, bold=True, color="4472C4") # 主标题加大 +SUBTITLE_FONT = Font(name=FONT_NAME, size=12, bold=True, color="203764") # 小节标题加深色 +HEADER_FONT = Font(name=FONT_NAME, size=11, bold=True, color=HEADER_FONT_COLOR) +LABEL_FONT = Font(name=FONT_NAME, size=10, bold=True, color="333333") # 标签文字深灰 +DATA_FONT = Font(name=FONT_NAME, size=10) + +# 边框 +THIN_SIDE = Side(style="thin", color=BORDER_COLOR) +MEDIUM_SIDE = Side(style="medium", color=OUTER_BORDER_COLOR) +BORDER_ALL_THIN = Border(left=THIN_SIDE, right=THIN_SIDE, top=THIN_SIDE, bottom=THIN_SIDE) + +# 填充 +HEADER_FILL = PatternFill("solid", fgColor=HEADER_FILL_COLOR) +LABEL_FILL = PatternFill("solid", fgColor=LABEL_FILL_COLOR) +SECTION_FILL = PatternFill("solid", fgColor=SECTION_FILL_COLOR) + +INVALID_SHEET_CHARS_RE = re.compile(r"[:\\/?*\[\]]") + + +def _safe_excel_sheet_title(raw_title: str, existing_titles: Set[str]) -> str: + title = (raw_title or "").strip() + if not title: + title = "未命名场站" + + title = INVALID_SHEET_CHARS_RE.sub("_", title) + title = title.replace("\n", " ").replace("\r", " ").strip() + if len(title) > 31: + title = title[:31].rstrip() + if not title: + title = "未命名场站" + + if title not in existing_titles: + existing_titles.add(title) + return title + + base = title[:28].rstrip() if len(title) > 28 else title + idx = 2 + while True: + candidate = f"{base}_{idx}" + if len(candidate) > 31: + candidate = candidate[:31] + if candidate not in existing_titles: + existing_titles.add(candidate) + return candidate + idx += 1 + + +def _to_float(value): + if value is None: + return None + if isinstance(value, Decimal): + return float(value) + return value + + +def _parse_schedule(schedule_json: Any) -> List[Dict[str, Any]]: + if not schedule_json: + return [] + if isinstance(schedule_json, (dict, list)): + return schedule_json if isinstance(schedule_json, list) else [schedule_json] + if isinstance(schedule_json, (bytes, bytearray)): + try: + schedule_json = schedule_json.decode("utf-8", errors="ignore") + except Exception: + schedule_json = str(schedule_json) + if not isinstance(schedule_json, str): + return [] + try: + data = json.loads(schedule_json) + if isinstance(data, list): + return data + if isinstance(data, dict): + return [data] + return [] + except Exception: + return [] + + +def _apply_table_header_style(ws, row_idx: int, col_start: int, col_end: int): + alignment = Alignment(horizontal="center", vertical="center", wrap_text=True) + # Header row with top/bottom medium border + border = Border(left=THIN_SIDE, right=THIN_SIDE, top=MEDIUM_SIDE, bottom=MEDIUM_SIDE) + + for col in range(col_start, col_end + 1): + cell = ws.cell(row=row_idx, column=col) + cell.fill = HEADER_FILL + cell.font = HEADER_FONT + cell.alignment = alignment + cell.border = border + + ws.row_dimensions[row_idx].height = 28 # Increased header height + + +def _apply_table_cell_style(ws, row_idx: int, col_start: int, col_end: int, center_cols: List[int] = None): + for col in range(col_start, col_end + 1): + cell = ws.cell(row=row_idx, column=col) + cell.font = DATA_FONT + + is_center = center_cols and col in center_cols + cell.alignment = Alignment(horizontal="center" if is_center else "left", vertical="center", wrap_text=True) + cell.border = BORDER_ALL_THIN + + ws.row_dimensions[row_idx].height = 22 # Comfortable row height + + +def _style_info_key_value(ws, row, col_key, col_val, merge_val_cols=0): + # Key Style + c_key = ws.cell(row=row, column=col_key) + c_key.font = LABEL_FONT + c_key.alignment = Alignment(horizontal="right", vertical="center") + c_key.fill = LABEL_FILL + c_key.border = BORDER_ALL_THIN + + # Value Style + c_val = ws.cell(row=row, column=col_val) + c_val.font = DATA_FONT + c_val.alignment = Alignment(horizontal="left", vertical="center", wrap_text=True) + c_val.border = BORDER_ALL_THIN + + if merge_val_cols > 0: + ws.merge_cells(start_row=row, start_column=col_val, end_row=row, end_column=col_val + merge_val_cols) + # Apply border to merged cells (required for openpyxl) + for c_offset in range(1, merge_val_cols + 1): + c_merged = ws.cell(row=row, column=col_val + c_offset) + c_merged.border = BORDER_ALL_THIN + + +def _set_column_widths(ws, widths: dict[int, float]): + for col_idx, width in widths.items(): + ws.column_dimensions[get_column_letter(col_idx)].width = width + + +class DorisExcelExporter: + def __init__(self, db_url: str): + self.db = Db(db_url=db_url) + + async def init(self): + await self.db.init_db() + + async def close(self): + try: + await self.db.close() + finally: + if getattr(self.db, "engine", None): + await self.db.engine.dispose() + + async def fetch_current_station_rows(self) -> List[Dict[str, Any]]: + sql = """ + SELECT + p.station_hash, + p.station_name, + p.address, + p.coord_x, + p.coord_y, + s.total_piles AS total_guns, + s.free_piles AS free_guns, + s.piles_detail_json, + s.current_price, + s.pro_price, + s.parking_info, + s.distance, + s.valid_start_time AS status_update_time, + pr.schedule_json, + pr.valid_start_time AS schedule_update_time + FROM t_station_profile_scd p + LEFT JOIN t_station_status_scd s + ON p.station_hash = s.station_hash AND s.is_current = 1 + LEFT JOIN t_station_price_schedule_scd pr + ON p.station_hash = pr.station_hash AND pr.is_current = 1 + WHERE p.is_current = 1 + ORDER BY p.station_name + """ + return await self.db.find(sql) + + +def _build_station_sheet(ws, row: Dict[str, Any]): + station_name = row.get("station_name") or "" + address = row.get("address") or "" + coord_x = row.get("coord_x") + coord_y = row.get("coord_y") + total_guns = row.get("total_guns") + free_guns = row.get("free_guns") + current_price = _to_float(row.get("current_price")) + pro_price = _to_float(row.get("pro_price")) + parking_info = row.get("parking_info") + distance = row.get("distance") + status_update_time = row.get("status_update_time") + schedule_update_time = row.get("schedule_update_time") + schedule_items = _parse_schedule(row.get("schedule_json")) + piles_detail = _parse_schedule(row.get("piles_detail_json")) # Reuse the same parser + + for i in range(1, len(schedule_items)): + curr = schedule_items[i] + prev = schedule_items[i - 1] + if not isinstance(curr, dict) or not isinstance(prev, dict): + continue + + for k in ("price_kwh", "electric_fee_kwh", "service_fee_kwh", "price", "electric_fee", "service_fee"): + if curr.get(k) is None and prev.get(k) is not None: + curr[k] = prev[k] + + ws.merge_cells(start_row=1, start_column=1, end_row=1, end_column=6) + ws.row_dimensions[1].height = 40 # Increased title height + + title_cell = ws.cell(row=1, column=1, value=station_name or "场站") + title_cell.font = TITLE_FONT + title_cell.alignment = Alignment(horizontal="center", vertical="center") + # Title border (Optional: bottom medium) + for c in range(1, 7): + ws.cell(row=1, column=c).border = Border(bottom=MEDIUM_SIDE) + + ws.sheet_properties.tabColor = "4472C4" # Tab color matches theme + + # Layout using _style_info_key_value + # Row 3: 位置 | Address + _style_info_key_value(ws, 3, 1, 2, merge_val_cols=4) + ws.cell(row=3, column=1, value="位置") + ws.cell(row=3, column=2, value=address) + + # Row 4: 经纬度 | Coords | 距离 | Distance + coord_text = f"{coord_x}, {coord_y}" if coord_x is not None else "" + _style_info_key_value(ws, 4, 1, 2, merge_val_cols=1) # 经纬度 occupies col 2-3 + ws.cell(row=4, column=1, value="经纬度") + ws.cell(row=4, column=2, value=coord_text) + + _style_info_key_value(ws, 4, 4, 5, merge_val_cols=1) # 距离 occupies col 5-6 + ws.cell(row=4, column=4, value="距离") + ws.cell(row=4, column=5, value=distance) + + # Row 5: 空闲 | val | 总枪 | val | 停车 | val (Parking info needs more space, let's adjust) + # Original: Free(1,2), Total(3,4), Parking(5,6) - Parking info is long usually. + # New Layout: Free(1,2), Total(3,4), Parking(5,6) is fine if col 6 is wide. + _style_info_key_value(ws, 5, 1, 2) + ws.cell(row=5, column=1, value="空闲枪数") + ws.cell(row=5, column=2, value=free_guns) + + _style_info_key_value(ws, 5, 3, 4) + ws.cell(row=5, column=3, value="总枪数") + ws.cell(row=5, column=4, value=total_guns) + + _style_info_key_value(ws, 5, 5, 6) + ws.cell(row=5, column=5, value="停车信息") + ws.cell(row=5, column=6, value=parking_info) + + # Row 6: Price | val | Pro Price | val | Update Time | val + _style_info_key_value(ws, 6, 1, 2) + ws.cell(row=6, column=1, value="当前价格") + ws.cell(row=6, column=2, value=current_price) + + _style_info_key_value(ws, 6, 3, 4) + ws.cell(row=6, column=3, value="PRO会员价") + ws.cell(row=6, column=4, value=pro_price) + + _style_info_key_value(ws, 6, 5, 6) + ws.cell(row=6, column=5, value="更新时间") + ws.cell(row=6, column=6, value=status_update_time) + + # Adjust row heights for info section + for r in range(3, 7): + ws.row_dimensions[r].height = 24 + + # 价格表部分 + schedule_start_row = 8 + + # Section Title with style + sect_cell = ws.cell(row=schedule_start_row, column=1, value="价格时段表") + sect_cell.font = SUBTITLE_FONT + sect_cell.fill = SECTION_FILL + sect_cell.border = Border(bottom=THIN_SIDE) + ws.merge_cells(start_row=schedule_start_row, start_column=1, end_row=schedule_start_row, end_column=6) + # Apply fill to merged cells + for c in range(2, 7): + ws.cell(row=schedule_start_row, column=c).fill = SECTION_FILL + ws.cell(row=schedule_start_row, column=c).border = Border(bottom=THIN_SIDE) + + header_row = schedule_start_row + 1 + headers = ["开始", "结束", "电价(元/kWh)", "电费(元/kWh)", "服务费(元/kWh)", "备注"] + for i, h in enumerate(headers, start=1): + ws.cell(row=header_row, column=i, value=h) + _apply_table_header_style(ws, header_row, 1, len(headers)) + + row_idx = header_row + 1 + + # Track last valid values for forward filling + last_valid_price = None + last_valid_electric = None + last_valid_service = None + + for item in schedule_items: + if not isinstance(item, dict): + continue + + start = item.get("start") or item.get("begin") or item.get("start_time") or "" + end = item.get("end") or item.get("finish") or item.get("end_time") or "" + + price = _to_float(item.get("price_kwh") if "price_kwh" in item else item.get("price")) + electric_fee = _to_float(item.get("electric_fee_kwh") if "electric_fee_kwh" in item else item.get("electric_fee")) + service_fee = _to_float(item.get("service_fee_kwh") if "service_fee_kwh" in item else item.get("service_fee")) + + # Forward fill if None + if price is None and last_valid_price is not None: + price = last_valid_price + if electric_fee is None and last_valid_electric is not None: + electric_fee = last_valid_electric + if service_fee is None and last_valid_service is not None: + service_fee = last_valid_service + + # Update last valid values if current is not None + if price is not None: + last_valid_price = price + if electric_fee is not None: + last_valid_electric = electric_fee + if service_fee is not None: + last_valid_service = service_fee + + remark = item.get("type") or item.get("tag") or item.get("period") or "" + + ws.cell(row=row_idx, column=1, value=start) + ws.cell(row=row_idx, column=2, value=end) + ws.cell(row=row_idx, column=3, value=price) + ws.cell(row=row_idx, column=4, value=electric_fee) + ws.cell(row=row_idx, column=5, value=service_fee) + ws.cell(row=row_idx, column=6, value=remark) + _apply_table_cell_style(ws, row_idx, 1, len(headers), center_cols=[1, 2, 3, 4, 5]) + row_idx += 1 + + # 充电桩详情部分 + piles_start_row = row_idx + 2 + + # Section Title with style + sect_cell = ws.cell(row=piles_start_row, column=1, value="充电桩详情") + sect_cell.font = SUBTITLE_FONT + sect_cell.fill = SECTION_FILL + sect_cell.border = Border(bottom=THIN_SIDE) + ws.merge_cells(start_row=piles_start_row, start_column=1, end_row=piles_start_row, end_column=6) + # Apply fill to merged cells + for c in range(2, 7): + ws.cell(row=piles_start_row, column=c).fill = SECTION_FILL + ws.cell(row=piles_start_row, column=c).border = Border(bottom=THIN_SIDE) + + pile_header_row = piles_start_row + 1 + pile_headers = ["编号", "功率", "类型", "状态", "价格(元/kWh)", "备注"] + for i, h in enumerate(pile_headers, start=1): + ws.cell(row=pile_header_row, column=i, value=h) + _apply_table_header_style(ws, pile_header_row, 1, len(pile_headers)) + + row_idx = pile_header_row + 1 + if piles_detail: + for pile in piles_detail: + if not isinstance(pile, dict): continue + ws.cell(row=row_idx, column=1, value=pile.get("pile_no") or pile.get("id") or "") + ws.cell(row=row_idx, column=2, value=pile.get("power") or "") + ws.cell(row=row_idx, column=3, value=pile.get("type") or "") + ws.cell(row=row_idx, column=4, value=pile.get("status_text") or pile.get("status") or "") + ws.cell(row=row_idx, column=5, value=_to_float(pile.get("price"))) + ws.cell(row=row_idx, column=6, value=pile.get("remark") or "") + _apply_table_cell_style(ws, row_idx, 1, len(pile_headers), center_cols=[1, 2, 3, 4, 5]) + row_idx += 1 + else: + ws.cell(row=row_idx, column=1, value="暂无详情数据") + ws.merge_cells(start_row=row_idx, start_column=1, end_row=row_idx, end_column=6) + _apply_table_cell_style(ws, row_idx, 1, len(pile_headers)) + row_idx += 1 + + ws.freeze_panes = ws["A9"] + _set_column_widths(ws, {1: 20, 2: 20, 3: 16, 4: 16, 5: 16, 6: 25}) + + +def _build_summary_sheet(ws, rows: List[Dict[str, Any]]): + ws.title = "场站总览" + ws.sheet_properties.tabColor = "4472C4" # Tab color matches theme + + # Row 1: Main Title + title_cell = ws.cell(row=1, column=1, value="新电途充电场站数据总览") + title_cell.font = TITLE_FONT + title_cell.alignment = Alignment(horizontal="center", vertical="center") + ws.merge_cells(start_row=1, start_column=1, end_row=1, end_column=10) + ws.row_dimensions[1].height = 40 + + # Row 2: Headers + headers = ["场站名称", "地址", "距离", "经度", "纬度", "总枪数", "空闲枪数", "停车信息", "当前价格(元/kWh)", "状态更新时间"] + for i, h in enumerate(headers, start=1): + ws.cell(row=2, column=i, value=h) + _apply_table_header_style(ws, 2, 1, len(headers)) + + # Enable AutoFilter + ws.auto_filter.ref = f"A2:{get_column_letter(len(headers))}{len(rows) + 2}" + + row_idx = 3 + for row in rows: + ws.cell(row=row_idx, column=1, value=row.get("station_name")) + ws.cell(row=row_idx, column=2, value=row.get("address")) + ws.cell(row=row_idx, column=3, value=row.get("distance")) + ws.cell(row=row_idx, column=4, value=row.get("coord_x")) + ws.cell(row=row_idx, column=5, value=row.get("coord_y")) + ws.cell(row=row_idx, column=6, value=row.get("total_guns")) + ws.cell(row=row_idx, column=7, value=row.get("free_guns")) + ws.cell(row=row_idx, column=8, value=row.get("parking_info")) + ws.cell(row=row_idx, column=9, value=_to_float(row.get("current_price"))) + ws.cell(row=row_idx, column=10, value=row.get("status_update_time")) + _apply_table_cell_style(ws, row_idx, 1, len(headers), center_cols=[3, 4, 5, 6, 7, 9, 10]) + row_idx += 1 + + _set_column_widths(ws, {1: 35, 2: 45, 3: 12, 4: 14, 5: 14, 6: 12, 7: 12, 8: 35, 9: 20, 10: 22}) + ws.freeze_panes = ws["A3"] + + +async def export_excel(output_path: str, *, limit: Optional[int] = None) -> str: + exporter = DorisExcelExporter(db_url=DB_URL) + await exporter.init() + try: + rows = await exporter.fetch_current_station_rows() + finally: + await exporter.close() + + if limit is not None: + rows = rows[: max(0, int(limit))] + + wb = Workbook() + + if not rows: + ws = wb.active + ws.title = "无数据" + ws.cell(row=1, column=1, value="未查询到任何场站数据") + wb.save(output_path) + return output_path + + # 1. 创建总览页 + summary_ws = wb.active + _build_summary_sheet(summary_ws, rows) + + # 2. 为每个场站创建独立页 + existing_titles: Set[str] = {"场站总览"} + for row in rows: + sheet_title = _safe_excel_sheet_title(str(row.get("station_name") or ""), existing_titles) + ws = wb.create_sheet(title=sheet_title) + _build_station_sheet(ws, row) + + wb.save(output_path) + return output_path + + +async def main(): + # 配置：默认导出所有数据，如果需要测试少量数据，可以将 None 改为数字，如 10 + limit = None + + # 构建默认输出路径 + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + output_filename = f"./Excel/station_export_{ts}.xlsx" + output_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), output_filename) + + logger.info(f"开始导出 Excel...") + logger.info(f"目标文件: {output_path}") + if limit is not None: + logger.info(f"限制导出记录数: {limit}") + + final_path = await export_excel(output_path, limit=limit) + logger.info(f"Excel 导出完成: {final_path}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/Apps/XinDianTu/Run.py b/Apps/XinDianTu/Run.py new file mode 100644 index 0000000..ecb1e89 --- /dev/null +++ b/Apps/XinDianTu/Run.py @@ -0,0 +1,104 @@ +# coding=utf-8 +import asyncio +import logging +import os +import sys + +# 将项目根目录添加到 sys.path +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +if project_root not in sys.path: + sys.path.append(project_root) + +from Apps.XinDianTu.Service import XinDianTuService +from Apps.XinDianTu import Opener +from Apps.XinDianTu import Crawler +from Apps.XinDianTu import Coord as Y4_Coord +from Apps.XinDianTu import GetCookie as T2_GetAmapCookie + +# 配置日志 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger("FullProcess") + +async def main(): + logger.info("=== 开始全流程任务: 打开小程序 -> 爬取数据 ===") + + # 步骤 0: 初始化基础服务 (DB等) + # 用户要求：程序启动时就完成初始化 + logger.info(">>> 步骤 0: 初始化基础服务 (数据库连接)...") + service = XinDianTuService() + await service.init_db() + + try: + # 步骤 1: 打开新电途小程序 + logger.info(">>> 步骤 1: 启动新电途小程序...") + try: + # 调用 Y2 的 open_mini_program 函数 + success = await Opener.open_mini_program() + if not success: + logger.error("❌ 无法成功打开新电途小程序，任务终止。") + return + except Exception as e: + logger.error(f"❌ 步骤 1 执行异常: {e}") + return + + logger.info("✅ 小程序启动成功，等待 5 秒确保界面稳定...") + await asyncio.sleep(5) + + # 步骤 2: 执行爬取任务 + logger.info(">>> 步骤 2: 开始执行场站爬取任务...") + try: + # 调用 Y3 的 main 函数，传入已初始化的 service + # 传入 do_cleanup=False，因为已在步骤 0 完成清理 + success = await Crawler.main(service=service, do_cleanup=False) + if not success: + logger.error("❌ 步骤 2 爬取任务失败，终止后续步骤。") + return + except Exception as e: + logger.error(f"❌ 步骤 2 执行异常: {e}") + return + + # 步骤 3: 补全坐标 (调用 Y4_Coord) + logger.info(">>> 步骤 3: 启动高德地图坐标回填任务...") + try: + # 首次尝试，开启 Cookie 检查 + await Y4_Coord.main(check_cookie_first=True) + except Y4_Coord.CookieInvalidError: + logger.warning("⚠️ 检测到高德地图 Cookie 失效或缺失。") + logger.info(">>> 正在启动 Cookie 获取工具 (T2)...") + logger.info("请在弹出的浏览器窗口中完成登录，然后在控制台按回车确认。") + + # 调用同步的 T2 工具 + # 由于 T2 包含 input() 阻塞，且这是客户端脚本，直接调用即可 + try: + # 传入 force=True 跳过“文件已存在”的询问，因为我们确定它无效 + # run_in_executor 用于避免完全阻塞事件循环（虽然在这里影响不大） + loop = asyncio.get_event_loop() + success = await loop.run_in_executor(None, T2_GetAmapCookie.run_login_tool, True) + + if success: + logger.info("✅ Cookie 更新成功，正在重新启动坐标回填任务...") + # 再次尝试执行，这次可以跳过检查（刚获取完）或者继续检查 + await Y4_Coord.main(check_cookie_first=False) + else: + logger.error("❌ Cookie 获取失败或用户取消，跳过坐标回填步骤。") + except Exception as e: + logger.error(f"❌ 调用 T2 工具失败: {e}") + except Exception as e: + logger.error(f"❌ 步骤 3 执行异常: {e}") + + finally: + # 确保数据库连接关闭 + if service: + await service.close_db() + logger.info("=== 全流程任务结束 ===") + +if __name__ == "__main__": + try: + asyncio.run(main()) + except KeyboardInterrupt: + logger.info("程序被用户中断.") + except Exception as e: + logger.exception(f"主程序崩溃: {e}") diff --git a/Apps/XinDianTu/Service.py b/Apps/XinDianTu/Service.py new file mode 100644 index 0000000..d5b0505 --- /dev/null +++ b/Apps/XinDianTu/Service.py @@ -0,0 +1,518 @@ +import hashlib +import os +import sys +import uuid +import logging +import json +import re +from datetime import datetime, timedelta + +# Ensure sys path includes root for imports if not already +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +if project_root not in sys.path: + sys.path.append(project_root) + +from sqlalchemy import text +from Util.XinDianTuReadImageKit import XinDianTuReadImageKit +from DbKit.Db import Db +from Config.Config import DB_URL +from Model.StationProfile import StationProfile +from Model.StationStatus import StationStatus +from Model.StationPriceSchedule import StationPriceSchedule + +# 配置日志 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +class XinDianTuService: + def __init__(self): + self.db = Db(db_url=DB_URL) + self.station_profile_model = StationProfile() + self.station_status_model = StationStatus() + self.station_price_schedule_model = StationPriceSchedule() + self.operator = "新电途" + + def generate_id(self): + return str(uuid.uuid4()) + + def get_hash(self, s: str) -> str: + return hashlib.md5(s.encode('utf-8')).hexdigest() + + async def init_db(self): + await self.db.init_db() + + async def close_db(self): + await self.db.close() + + async def process_station_list_vl(self, vl_image_url, json_metadata, device_info=None) -> list: + """ + 基于 VL 模式处理场站列表 + """ + station_list = await XinDianTuReadImageKit.parse_vl_image(vl_image_url, json_metadata, device_info=device_info) + if not station_list: + return [] + + processed_stations = [] + async with await self.db.get_session() as session: + for station in station_list: + name = station.get("station_name") + if not name: + continue + + # 过滤逻辑 (复用) + # if any(keyword in name for keyword in STATION_NAME_IGNORE_KEYWORDS): + # continue + + station_hash = self.get_hash(name) + now = datetime.now() + station["station_hash"] = station_hash + + # 1. 保存 Profile + profile_id = self.generate_id() + await self.station_profile_model.save( + session=session, + id=profile_id, + station_hash=station_hash, + operator=self.operator, + station_name=name, + valid_start_time=now + ) + station["profile_id"] = profile_id + station["valid_start_time"] = now.isoformat() + + # 2. 保存 Status (解析价格和电桩) + status_id = self.generate_id() + + # 处理 piles 字段 + piles_data = station.get("piles") + total, free = 0, 0 + standardized_piles = [] + + if isinstance(piles_data, list): + # 如果是列表，计算总数并标准化结构 + for idx, p in enumerate(piles_data): + try: + t = int(p.get("total", 0)) + f = int(p.get("free", 0)) + total += t + free += f + + # 构造符合 ExportExcel 期望的结构 + standardized_piles.append({ + "pile_no": f"G{idx+1}", + "type": p.get("type", "未知"), + "power": "", # 暂无功率信息 + "status_text": f"空闲{f}/总{t}", + "remark": "AI聚合数据" + }) + except: + pass + else: + # Fallback: 如果不是列表（可能是字符串） + piles_str = str(piles_data) if piles_data else "0/0" + + nums = re.findall(r"\d+", piles_str) + if len(nums) >= 2: + v1 = int(nums[-2]) + v2 = int(nums[-1]) + if v2 >= v1: + free = v1 + total = v2 + else: + free = v2 + total = v1 + + # 构造一条记录用于 Excel 展示 + standardized_piles.append({ + "pile_no": "-", + "type": "未知", + "status_text": piles_str, + "remark": "原始识别文本" + }) + + price_str = station.get("price", "") + current_price = None + try: + price_match = re.search(r"\d+\.\d+", str(price_str)) + if price_match: + current_price = float(price_match.group()) + except: pass + + await self.station_status_model.save( + session=session, + id=status_id, + station_hash=station_hash, + total_piles=total, + free_piles=free, + piles_detail_json=standardized_piles, + current_price=current_price, + pro_price=station.get("pro_price"), + parking_info=station.get("parking"), + distance=station.get("distance"), + valid_start_time=now + ) + station["status_id"] = status_id + station["total_piles"] = total + station["free_piles"] = free + station["current_price"] = current_price + + processed_stations.append(station) + + await session.commit() + return processed_stations + + async def process_station_list_hybrid(self, local_image_path: str, uploader, cdn_domain) -> list: + """ + 混合模式处理场站列表 (图形学切片 + OCR) + """ + station_list = await XinDianTuReadImageKit.parse_hybrid_image(local_image_path, uploader, cdn_domain) + if not station_list: + return [] + + processed_stations = [] + async with await self.db.get_session() as session: + for station in station_list: + name = station.get("station_name") + if not name: + continue + + station_hash = self.get_hash(name) + now = datetime.now() + station["station_hash"] = station_hash + + # 1. 保存 Profile + profile_id = self.generate_id() + await self.station_profile_model.save( + session=session, + id=profile_id, + station_hash=station_hash, + operator=self.operator, + station_name=name, + valid_start_time=now + ) + station["profile_id"] = profile_id + station["valid_start_time"] = now.isoformat() + + # 2. 保存 Status (解析价格和电桩) + status_id = self.generate_id() + piles_str = station.get("piles", "0/0") + total, free = 0, 0 + standardized_piles = [] + + if "/" in piles_str: + try: + parts = piles_str.split("/") + free = int(parts[0]) + total = int(parts[1]) + standardized_piles.append({ + "pile_no": "G1", + "type": "未知", + "power": "", + "status_text": f"空闲{free}/总{total}", + "remark": "混合模式解析" + }) + except: pass + + price_str = station.get("price", "") + current_price = None + try: + price_match = re.search(r"\d+\.\d+", str(price_str)) + if price_match: + current_price = float(price_match.group()) + except: pass + + await self.station_status_model.save( + session=session, + id=status_id, + station_hash=station_hash, + total_piles=total, + free_piles=free, + piles_detail_json=standardized_piles if standardized_piles else None, + current_price=current_price, + parking_info=station.get("parking"), + valid_start_time=now + ) + station["status_id"] = status_id + station["total_piles"] = total + station["free_piles"] = free + station["current_price"] = current_price + + processed_stations.append(station) + + await session.commit() + return processed_stations + + async def process_station_list(self, list_url: str, device_info: dict = None) -> list: + station_list = await XinDianTuReadImageKit.parse_first_level_image_url(list_url, device_info=device_info) + if not station_list: + return [] + + processed_stations = [] + async with await self.db.get_session() as session: + for station in station_list: + name = station.get("station_name") + if not name: + continue + + + station_hash = self.get_hash(name) + now = datetime.now() + station["station_hash"] = station_hash + address = station.get("address") + + profile_id = self.generate_id() + await self.station_profile_model.save( + session=session, + id=profile_id, + station_hash=station_hash, + operator=self.operator, + station_name=name, + address=address, + valid_start_time=now + ) + station["profile_id"] = profile_id + station["valid_start_time"] = now.isoformat() + + status_id = self.generate_id() + piles_info = station.get("piles", []) + + total = 0 + free = 0 + if isinstance(piles_info, list): + for p in piles_info: + if isinstance(p, dict): + try: + total += int(p.get("total", 0)) + free += int(p.get("free", 0)) + except (ValueError, TypeError): + pass + elif isinstance(piles_info, dict): + try: + total = int(piles_info.get("total", 0)) + free = int(piles_info.get("free", 0)) + except (ValueError, TypeError): + total = 0 + free = 0 + + # 过滤掉总枪数为 0 的错误数据 + if total <= 0: + logger.warning(f"跳过场站 {name}: 总枪数为 0 (疑似识别错误或数据无效)") + continue + + price = station.get("price") + current_price = None + if price is not None and price != "": + try: + current_price = float(price) + except (ValueError, TypeError): + current_price = None + + pro_price_val = station.get("pro_price") + pro_price = None + if pro_price_val is not None and pro_price_val != "": + try: + pro_price = float(pro_price_val) + except (ValueError, TypeError): + pro_price = None + + await self.station_status_model.save( + session=session, + id=status_id, + station_hash=station_hash, + total_piles=total, + free_piles=free, + piles_detail_json=piles_info, + current_price=current_price, + pro_price=pro_price, + valid_start_time=now + ) + station["status_id"] = status_id + station["total_piles"] = total + station["free_piles"] = free + station["current_price"] = current_price + + processed_stations.append(station) + + await session.commit() + + return processed_stations + + async def process_station_address(self, station_name: str, detail_url: str, device_info: dict = None) -> dict: + logger.info(f"\n[Service] Processing Address for '{station_name}' from: {detail_url}") + + address_data = await XinDianTuReadImageKit.parse_address(station_name, detail_url, device_info=device_info) + address = address_data.get("address") + if address: + address = address.strip() + + # 尝试获取完整场站名称 + full_name = address_data.get("full_station_name") + final_name = station_name + if full_name: + full_name = full_name.strip() + # 只有当完整名称存在且与原名称不同时才使用 + if full_name and full_name != station_name: + logger.info(f" - Found Full Name: {full_name} (Original: {station_name})") + final_name = full_name + + if address: + logger.info(f" - Found Address: {address}") + station_hash = self.get_hash(station_name) # Hash 必须基于原名称(列表名称)保持一致，以便索引 + + async with await self.db.get_session() as session: + updated = await self.station_profile_model.backfill_address_if_missing( + session=session, + station_hash=station_hash, + station_name=final_name, # 更新为完整名称 + address=address + ) + if updated > 0: + logger.info(f" - Backfilled address and full name into {updated} profile record(s).") + else: + now = datetime.now() + profile_id = self.generate_id() + await self.station_profile_model.save( + session=session, + id=profile_id, + station_hash=station_hash, + operator=self.operator, + station_name=final_name, # 使用完整名称 + address=address, + coord_x=None, + coord_y=None, + valid_start_time=now + ) + logger.info(" - Inserted/Updated profile record with full name.") + await session.commit() + else: + logger.warning(f" - Address is empty (likely incomplete screenshot). Skipping profile update. Data: {address_data}") + + return address_data + + async def record_station_status(self, station_info: dict): + """ + 记录场站状态信息（从列表页抓取到的电桩、价格等） + """ + name = station_info.get("station_name") + if not name: + return + + station_hash = self.get_hash(name) + now = datetime.now() + + # 1. 保存/更新 Profile (基础信息) + async with await self.db.get_session() as session: + profile_id = self.generate_id() + await self.station_profile_model.save( + session=session, + id=profile_id, + station_hash=station_hash, + operator=self.operator, + station_name=name, + valid_start_time=now + ) + + # 2. 保存 Status + status_id = self.generate_id() + + # 解析电桩数量 (支持多组，如 "快:2/10 慢:1/5") + piles_str = station_info.get("piles", "") + total, free = 0, 0 + standardized_piles = [] + + # 尝试匹配 "快:2/10" 或 "2/10" 这种格式 + # 模式 1: 有类型，如 "快:2/10" + matches_with_type = re.findall(r"(\w+):(\d+)/(\d+)", piles_str) + if matches_with_type: + for idx, (p_type, f, t) in enumerate(matches_with_type): + f_int, t_int = int(f), int(t) + free += f_int + total += t_int + standardized_piles.append({ + "pile_no": f"G{idx+1}", + "type": p_type, + "power": "", + "status_text": f"空闲{f_int}/总{t_int}", + "remark": "列表页OCR" + }) + else: + # 模式 2: 无类型，仅 "2/10" + matches_simple = re.findall(r"(\d+)/(\d+)", piles_str) + for idx, (f, t) in enumerate(matches_simple): + f_int, t_int = int(f), int(t) + free += f_int + total += t_int + standardized_piles.append({ + "pile_no": f"G{idx+1}", + "type": "未知", + "power": "", + "status_text": f"空闲{f_int}/总{t_int}", + "remark": "列表页OCR" + }) + + if not standardized_piles: + # 兜底匹配所有数字 + nums = re.findall(r"(\d+)", piles_str) + if len(nums) >= 2: + free = int(nums[0]) + total = int(nums[1]) + standardized_piles.append({ + "pile_no": "G1", + "type": "未知", + "power": "", + "status_text": f"空闲{free}/总{total}", + "remark": "列表页OCR兜底" + }) + + # 解析价格 + price_val = station_info.get("price") + current_price = None + if price_val: + try: + # 提取数字部分 + price_match = re.search(r"(\d+(\.\d+)?)", str(price_val)) + if price_match: + current_price = float(price_match.group(1)) + except: + pass + + await self.station_status_model.save( + session=session, + id=status_id, + station_hash=station_hash, + total_piles=total, + free_piles=free, + piles_detail_json=standardized_piles, + current_price=current_price, + pro_price=None, # 列表页暂不解析 PRO 价 + parking_info=station_info.get("parking_info"), + distance=station_info.get("distance"), + valid_start_time=now + ) + await session.commit() + logger.info(f" - Station status recorded for: {name} (Piles: {free}/{total}, Price: {current_price})") + + async def process_price_schedule(self, station_name: str, schedule_urls: list, device_info: dict = None): + logger.info(f"\n[Service] Processing Price Schedule for '{station_name}'") + + rows = await XinDianTuReadImageKit.parse_price_schedule_multi(station_name, schedule_urls, device_info=device_info) + hourly_schedule = XinDianTuReadImageKit.hourly_full_day(rows) + + if hourly_schedule: + logger.info(f" - Got {len(hourly_schedule)} hourly records.") + station_hash = self.get_hash(station_name) + now = datetime.now() + schedule_id = self.generate_id() + + async with await self.db.get_session() as session: + await self.station_price_schedule_model.save( + session=session, + id=schedule_id, + station_hash=station_hash, + schedule_json=hourly_schedule, + valid_start_time=now + ) + await session.commit() + logger.info(" - Schedule record saved.") + else: + logger.info(" - Failed to parse schedule.") diff --git a/Apps/XinDianTu/Templates/ad_close_x.jpg b/Apps/XinDianTu/Templates/ad_close_x.jpg new file mode 100644 index 0000000..eac5b0d Binary files /dev/null and b/Apps/XinDianTu/Templates/ad_close_x.jpg differ diff --git a/Apps/XinDianTu/__pycache__/Coord.cpython-310.pyc b/Apps/XinDianTu/__pycache__/Coord.cpython-310.pyc new file mode 100644 index 0000000..f4407aa Binary files /dev/null and b/Apps/XinDianTu/__pycache__/Coord.cpython-310.pyc differ diff --git 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a/Apps/XinDianTu/__pycache__/Service.cpython-310.pyc b/Apps/XinDianTu/__pycache__/Service.cpython-310.pyc new file mode 100644 index 0000000..78238e7 Binary files /dev/null and b/Apps/XinDianTu/__pycache__/Service.cpython-310.pyc differ diff --git a/Config/Config.py b/Config/Config.py new file mode 100644 index 0000000..46974e0 --- /dev/null +++ b/Config/Config.py @@ -0,0 +1,84 @@ +# 开发环境不同，配置信息不同 +# Doris V4 +# DORIS_HOST = "10.10.14.204" +# DORIS_PORT = 9030 +# DORIS_FENODES = "10.10.14.204:8030" +# REDIS_HOST = '10.10.14.14' +# REDIS_PASSWORD = None # 如果没有密码则设为 None +DORIS_HOST = "www.hzkjai.com" +DORIS_PORT = 27025 +DORIS_FENODES = "www.hzkjai.com:27024" +REDIS_HOST = '127.0.0.1' +REDIS_PASSWORD = "DsideaL147258369" + +# 采集配置 +# 滑动距离比例 (0.1 ~ 0.9)，数值越大滑动幅度越大，建议 0.3-0.5 以避免错过中间内容 +SCROLL_DISTANCE_RATIO = 0.3 +# 最大滑动/翻页次数，达到此次数后停止采集 +MAX_SCROLLS = 100 +# 默认抓取半径（公里），当检测到场站距离超过此值时停止采集 +MAX_CRAWL_DISTANCE = 50 +# 场站去重过期时间（秒），在此时间内重复出现的场站不会再次点击进入详情页 +REDIS_STATION_EXPIRE = 120 + +# 数据库数据保留时长（天），超过此时长的历史数据（is_current=0）将被删除 +DATA_RETENTION_DAYS = 365 + +# 等待时间配置 (秒) +# 点击进入详情页后等待加载的时间 +WAIT_DETAIL_PAGE_LOAD = 2.5 +# 从详情页返回列表页后等待页面刷新的时间 +WAIT_BACK_TO_LIST = 1.5 +# 执行滑动操作后等待页面内容加载和稳定的时间 +WAIT_AFTER_SCROLL = 3.0 + +# 坐标计算与安全防护 +# 屏幕顶部安全排除比例 (0.0~1.0)，此比例区域内不进行点击（避开状态栏、筛选栏等） +SAFE_EXCLUDE_RATIO = 0.20 +# 屏幕底部安全排除比例 (0.0~1.0)，此比例区域内不进行点击（避开底部导航栏、功能按钮等） +BOTTOM_SAFE_EXCLUDE_RATIO = 0.1 +# 默认回退屏幕宽度，当无法自动获取设备信息时使用 +FALLBACK_WIDTH = 1080 +# 默认回退屏幕高度，当无法自动获取设备信息时使用 +FALLBACK_HEIGHT = 2400 + +# 视觉模型配置 +VL_MODEL_NAME = "qwen3-vl-flash" +VL_MODEL_NAME_AD = "qwen-vl-max" + + +# 华为云配置 +OBS_AK = "WAFBGJACKDOQZDH1MKZ1" +OBS_SK = "dlWTUbqgCICaYJG3n0Rot4jXaen2HnfFtMVxiPEo" +OBS_SERVER = "obs.cn-north-1.myhuaweicloud.com" +OBS_BUCKET = "dsideal" +OBS_CLOUD_PREFIX = "dsAi/cloudFiles" +OBS_TMP_PREFIX = "dsAi/tmpFiles" +CDN_DOMAIN = "video.edusoa.com" + +# 阿里云API信息【YLT】 +ALY_LLM_API_KEY = "sk-f6da0c787eff4b0389e4ad03a35a911f" +ALY_LLM_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1" +ALY_LLM_MODEL_NAME = "deepseek-v3.2" + + +# Doris其它配置信息 +DORIS_USER = "root" +DORIS_PWD = "DsideaL147258369" +DORIS_DATABASE = "yltcharge" +DB_URL = f"mysql+aiomysql://{DORIS_USER}:{DORIS_PWD}@{DORIS_HOST}:{DORIS_PORT}/{DORIS_DATABASE}?charset=utf8mb4" +doris = { + "url": f"jdbc:mysql://{DORIS_HOST}:{DORIS_PORT}/{DORIS_DATABASE}?rewriteBatchedStatements=true&batchsize=1000&serverTimezone=Asia/Shanghai", + "user": DORIS_USER, + "pwd": DORIS_PWD, + "fenodes": DORIS_FENODES, + "database": DORIS_DATABASE +} + + +# REDIS 配置 +REDIS_DB = 2 +REDIS_DECODE_RESPONSES = True +REDIS_PORT = 18890 +REDIS_MAX_CONNECTIONS = 200 + diff --git a/Config/__pycache__/Config.cpython-310.pyc b/Config/__pycache__/Config.cpython-310.pyc new file mode 100644 index 0000000..322a907 Binary files /dev/null and b/Config/__pycache__/Config.cpython-310.pyc differ diff --git a/Config/__pycache__/Config.cpython-313.pyc b/Config/__pycache__/Config.cpython-313.pyc new file mode 100644 index 0000000..8431dab Binary files /dev/null and b/Config/__pycache__/Config.cpython-313.pyc differ diff --git a/Core/BaseCrawler.py b/Core/BaseCrawler.py new file mode 100644 index 0000000..4ba68a9 --- /dev/null +++ b/Core/BaseCrawler.py @@ -0,0 +1,41 @@ +# coding=utf-8 +import abc +import logging + +class BaseCrawler(abc.ABC): + """ + 所有小程序爬虫的基类。 + 定义了标准的生命周期方法。 + """ + + def __init__(self, service=None): + self.logger = logging.getLogger(self.__class__.__name__) + self.service = service + + @abc.abstractmethod + async def start(self): + """ + 启动爬虫的主入口。 + """ + pass + + @abc.abstractmethod + async def open_app(self): + """ + 打开目标小程序。 + """ + pass + + @abc.abstractmethod + async def crawl_list(self): + """ + 爬取列表页。 + """ + pass + + @abc.abstractmethod + async def crawl_detail(self, station_info): + """ + 爬取详情页。 + """ + pass diff --git a/Core/__pycache__/BaseCrawler.cpython-310.pyc b/Core/__pycache__/BaseCrawler.cpython-310.pyc new file mode 100644 index 0000000..53b9ad9 Binary files /dev/null and b/Core/__pycache__/BaseCrawler.cpython-310.pyc differ diff --git a/DbKit/Db.py b/DbKit/Db.py new file mode 100644 index 0000000..a4ffd37 --- /dev/null +++ b/DbKit/Db.py @@ -0,0 +1,1205 @@ +import os +import logging +import re +from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession +from sqlalchemy.orm import sessionmaker +from sqlalchemy.sql import text + +import Config +from DbKit.SqlTemplateLoader import SqlTemplateLoader +from DbKit.TransactionContext import TransactionContext + +# 配置日志 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + + +class Db: + """通用数据库操作封装类，提供数据库连接和操作功能""" + # 单例实例 + _instance = None + + def __new__(cls, db_url=None, sql_dir=None): + """确保Db类只有一个实例（异步版本）""" + if cls._instance is None: + cls._instance = super(Db, cls).__new__(cls) + # 初始化数据库连接 + cls._instance.engine = None + cls._instance.AsyncSessionLocal = None + cls._instance.db_url = db_url + cls._instance.sql_dir = sql_dir or os.path.join(os.path.dirname(os.path.abspath(__file__)), 'Sql') + + # 初始化SQL模板加载器 + cls._instance.sql_loader = SqlTemplateLoader(cls._instance.sql_dir, auto_load=False) + # 添加标志表示SQL模板尚未加载 + cls._instance._templates_loaded = False + return cls._instance + + def __init__(self, db_url=None, sql_dir=None): + """初始化数据库操作类 + + Args: + db_url: 数据库连接URL，不提供时将使用默认配置 + sql_dir: SQL模板文件目录，不提供时将使用默认目录 + """ + # 单例模式下，初始化只会在第一次创建实例时执行 + # 这里可以为空，因为实际初始化在__new__中完成 + self.sql_dir = sql_dir + self.db_url = db_url + + async def init_db(self, db_url=None): + """ + 初始化数据库连接（异步版本） + + Args: + db_url: 数据库连接URL，优先级高于初始化时提供的URL + """ + # 确定使用的数据库连接URL + final_url = db_url or self.db_url + if not final_url: + # 如果没有提供URL，使用配置文件中的参数构建连接URL + from Config import POSTGRES_HOST, POSTGRES_PORT, POSTGRES_USER, POSTGRES_PASSWORD, POSTGRES_DATABASE + # 转换为异步连接URL + final_url = f"postgresql+asyncpg://{POSTGRES_USER}:{POSTGRES_PASSWORD}@{POSTGRES_HOST}:{POSTGRES_PORT}/{POSTGRES_DATABASE}" + else: + # 如果是同步URL，转换为异步URL + if final_url.startswith("postgresql://"): + final_url = final_url.replace("postgresql://", "postgresql+asyncpg://") + + # 创建异步数据库引擎 + self.engine = create_async_engine(final_url) + + # 创建异步会话工厂 + self.AsyncSessionLocal = sessionmaker( + class_=AsyncSession, + autocommit=False, + autoflush=False, + bind=self.engine + ) + + # 检查Redis中是否已经加载了SQL模板 + templates_loaded_in_redis = await self.sql_loader._check_templates_loaded_from_redis() + + # 如果Redis中有加载的模板，直接从Redis加载 + if templates_loaded_in_redis: + try: + await self.sql_loader._load_from_redis() + self._templates_loaded = True + logger.info(f"从Redis加载SQL模板完成，共{len(self.sql_loader._loaded_files)}个文件，{len(self.sql_loader._template_map)}个模板") + except Exception as e: + logger.error(f"从Redis加载SQL模板失败: {str(e)}") + # 不抛出异常，允许继续执行 + pass + # 如果Redis中没有，且内存中也没有加载过，执行正常加载流程 + elif not getattr(self, '_templates_loaded', False): + try: + await self.load_all_templates() + self._templates_loaded = True + + # 获取并记录SQL模板统计信息 + loaded_files = len(self.sql_loader._loaded_files) + template_count = 0 + for namespace, templates in self.sql_loader.templates.items(): + template_count += len(templates) + logger.info(f"SQL模板加载完成，共加载{loaded_files}个文件，{template_count}个模板") + except Exception as e: + logger.error(f"加载SQL模板失败: {str(e)}") + # 不抛出异常，允许数据库操作继续进行 + pass + + async def get_session(self): + """获取数据库会话（异步版本） + + Returns: + AsyncSession: 异步数据库会话对象 + """ + if self.AsyncSessionLocal is None: + await self.init_db() + return self.AsyncSessionLocal() + + async def close(self): + if hasattr(self, "engine") and self.engine is not None: + try: + await self.engine.dispose() + finally: + self.engine = None + self.AsyncSessionLocal = None + + async def load_all_templates(self): + """异步加载所有SQL模板 + + Returns: + self: 支持链式调用 + """ + await self.sql_loader.load_all_templates() + return self + + def _is_sql_template(self, sql_input): + """ + 判断输入的带点字符串是SQL模板还是SQL语句 + + Args: + sql_input: 输入的字符串，可能是SQL模板名称或SQL语句 + + Returns: + bool: 如果是SQL模板返回True，如果是SQL语句返回False + """ + # 如果输入不是字符串或者不包含点号，直接判定为SQL语句 + if not isinstance(sql_input, str) or '.' not in sql_input: + return False + + # 检查sql_loader是否存在且已初始化 + if not hasattr(self, 'sql_loader') or not self.sql_loader: + return False + + # 检查模板映射是否存在 + if not hasattr(self.sql_loader, '_template_map') or not self.sql_loader._template_map: + return False + + # 在模板映射中查找，如果找到就是SQL模板，否则是SQL语句 + is_template = sql_input in self.sql_loader._template_map + logger.debug(f"输入 '{sql_input}' 在模板映射中查找结果: {is_template}") + + return is_template + + async def get_sql(self, sql_identifier, params=None): + """获取指定名称的SQL模板，并处理参数替换 + + Args: + sql_identifier: 格式为'namespace.template_name'的SQL标识符 + params: 可选的参数字典，用于替换模板中的参数 + + Returns: + str: 处理后的SQL语句 + """ + # logger.info(f"尝试获取SQL模板: {sql_identifier}") + sql_template = await self.sql_loader.get_sql(sql_identifier) + + # 处理模板，即使没有提供参数也进行处理（清理条件块等） + processed_sql = self._process_sql_template(sql_template, params) + logger.debug(f"处理后的SQL语句: {processed_sql}") + return processed_sql + + def _process_sql_template(self, sql_template, params=None): + """ + 处理SQL模板中的条件判断和参数替换 + + Args: + sql_template: SQL模板字符串 + params: 参数字典 + + Returns: + str: 处理后的SQL语句 + """ + if params is None: + params = {} + + # 检查是否包含#if条件块 + if_matches = re.findall(r'#if\([^)]+\)', sql_template) + logger.debug(f"发现 {len(if_matches)} 个#if条件块") + for i, match in enumerate(if_matches, 1): + logger.debug(f" {i}. {match}") + + # 检查是否包含ORDER BY子句 + if 'ORDER BY' in sql_template: + logger.debug("SQL模板包含ORDER BY子句") + + processed_sql = sql_template + + # 处理#if条件判断 + # 修复正则表达式，确保正确匹配条件块 + if_pattern = r'#if\((\w+)\)([\s\S]*?)#end' + + # 使用finditer来获取匹配的位置信息 + matches = list(re.finditer(if_pattern, processed_sql)) + + # 从后向前替换，避免位置偏移问题 + for match in reversed(matches): + param_name = match.group(1) + sql_block = match.group(2) + + # 检查参数是否存在且不为None + if param_name in params and params[param_name] is not None: + # 如果条件为真，保留SQL块 + # 移除多余的空白行和缩进 + lines = sql_block.strip().split('\n') + cleaned_lines = [line.strip() for line in lines if line.strip()] + cleaned_block = '\n'.join(cleaned_lines) + + # 替换原始匹配的内容 + processed_sql = processed_sql[:match.start()] + cleaned_block + processed_sql[match.end():] + + logger.debug(f"保留条件块 #if({param_name})，内容: {cleaned_block}") + else: + # 如果条件为假，移除整个条件块 + processed_sql = processed_sql[:match.start()] + "" + processed_sql[match.end():] + logger.debug(f"移除条件块 #if({param_name})") + + # 创建参数字典的副本，用于存储处理后的参数 + if params is not None: + processed_params = params.copy() + else: + processed_params = {} + + # 处理参数替换 - 支持直接的变量名格式 + # 首先找出所有可能的参数占位符（#para格式和直接变量名格式） + # 处理#para格式的参数 + para_pattern = r'#para\((\w+)\)' + all_para_placeholders = re.findall(para_pattern, processed_sql) + + # 替换#para格式的参数 + for param_name in all_para_placeholders: + placeholder = f"#para({param_name})" + if placeholder in processed_sql and param_name in params and params[param_name] is not None: + # 检查参数是否为列表类型且用于IN操作 + if isinstance(params[param_name], (list, tuple)) and "in" in processed_sql.lower(): + logger.debug(f"检测到列表参数 {param_name} 用于IN操作，进行特殊处理") + # 为列表中的每个元素创建单独的参数 + list_values = params[param_name] + if list_values: # 确保列表不为空 + # 生成多个占位符，如 :idList_1, :idList_2, :idList_3 + placeholders = ', '.join([f":{param_name}_{i}" for i in range(len(list_values))]) + processed_sql = processed_sql.replace(placeholder, placeholders) + # 更新参数字典，添加每个列表元素的参数 + for i, value in enumerate(list_values): + processed_params[f"{param_name}_{i}"] = value + logger.debug(f"为列表参数 {param_name} 创建子参数 {param_name}_{i} = {value}") + else: + # 空列表情况，将整个条件移除（因为IN () 是无效的SQL） + logger.warning(f"列表参数 {param_name} 为空，将移除包含该参数的条件表达式") + # 查找包含该占位符的整个条件表达式行 + lines = processed_sql.split('\n') + new_lines = [] + for line in lines: + if placeholder in line: + line_stripped = line.strip().upper() + if line_stripped.startswith('AND ') or line_stripped.startswith('OR '): + continue + new_lines.append(line) + processed_sql = '\n'.join(new_lines) + else: + # 普通参数处理 + processed_sql = processed_sql.replace(placeholder, f":{param_name}") + logger.debug(f"已将参数 {placeholder} 转换为 :{param_name}") + elif placeholder in processed_sql: + # 参数缺失或为None，移除包含该参数的条件表达式 + logger.warning(f"参数 {param_name} 缺失或为None，将移除包含该参数的条件表达式") + lines = processed_sql.split('\n') + new_lines = [] + for line in lines: + if placeholder in line: + line_stripped = line.strip().upper() + if line_stripped.startswith('AND ') or line_stripped.startswith('OR '): + continue + new_lines.append(line) + processed_sql = '\n'.join(new_lines) + + # 处理直接的变量名格式（如 $person_id 或 #{person_id}） + # 尝试匹配各种常见的变量名格式 + variable_patterns = [ + r'\$([a-zA-Z_][a-zA-Z0-9_]*)', # $variable 格式 + r'#\{([a-zA-Z_][a-zA-Z0-9_]*)\}', # #{variable} 格式 + r'\{([a-zA-Z_][a-zA-Z0-9_]*)\}' # {variable} 格式 + ] + + for pattern in variable_patterns: + matches = re.finditer(pattern, processed_sql) + # 收集所有匹配并从后向前替换，避免位置偏移 + variable_matches = list(matches) + for match in reversed(variable_matches): + placeholder = match.group(0) + param_name = match.group(1) + + # 检查参数是否存在且不为None + if param_name in params and params[param_name] is not None: + # 替换为SQLAlchemy参数格式 + replacement = f":{param_name}" + processed_sql = processed_sql[:match.start()] + replacement + processed_sql[match.end():] + logger.debug(f"已将变量 {placeholder} 转换为 {replacement}") + else: + # 变量不存在或为None，记录警告 + logger.warning(f"变量 {param_name} 缺失或为None，保留原样") + + # 更新params，使其包含处理后的参数（包括扩展的列表参数） + if params is not None: + for key, value in processed_params.items(): + params[key] = value + + # 清理多余的空白行和空格 + lines = processed_sql.split('\n') + processed_lines = [line.strip() for line in lines if line.strip()] + processed_sql = '\n'.join(processed_lines) + + # 额外的清理：处理可能产生的孤立逻辑运算符 + # 例如，如果我们移除了一行，可能会留下单独的AND/OR + processed_sql = re.sub(r'(^|\n)\s*(AND|OR)\s*($|\n)', '\1\3', processed_sql) + + # 最终清理 + lines = processed_sql.split('\n') + processed_lines = [line.strip() for line in lines if line.strip()] + processed_sql = '\n'.join(processed_lines) + + return processed_sql + + async def find(self, sql, params=None, session=None): + """ + 执行SQL查询并返回结果（异步版本） + + Args: + sql: SQL查询语句或SQL模板名称（支持namespace.sql_name格式） + params: 查询参数，字典形式 + session: 可选的数据库会话对象，不提供时将自动创建 + + Returns: + list: 查询结果的字典列表 + """ + # 使用当前实例 + instance = self + + # 如果没有提供params，创建一个空字典 + if params is None: + params = {} + + # 确保数据库连接已初始化 + if instance.AsyncSessionLocal is None: + await instance.init_db() + + # 检查是否需要加载SQL模板 + # 首先检查Redis中是否有加载的模板 + if not getattr(instance, '_templates_loaded', False): + templates_loaded_in_redis = await instance.sql_loader._check_templates_loaded_from_redis() + if templates_loaded_in_redis: + try: + await instance.sql_loader._load_from_redis() + instance._templates_loaded = True + logger.info("从Redis自动加载SQL模板完成") + except Exception as e: + logger.error(f"从Redis自动加载SQL模板失败: {str(e)}") + else: + # Redis中没有，尝试正常加载 + try: + await instance.load_all_templates() + instance._templates_loaded = True + logger.info("SQL模板自动加载完成") + except Exception as e: + logger.error(f"自动加载SQL模板失败: {str(e)}") + # 不抛出异常，允许数据库操作继续进行 + pass + + # 检查是否为SQL模板名称并处理 + if self._is_sql_template(sql): + try: + logger.debug(f"确认为SQL模板: {sql}") + + # 尝试获取SQL模板 + sql_content = await instance.get_sql(sql, params) + # 如果成功获取到内容且不等于原始名称，说明是模板 + if sql_content and sql_content != sql: + sql = sql_content + logger.debug(f"成功获取SQL模板，处理后的SQL: {sql}") + # 对于模板SQL，参数已经被get_sql处理过了，不需要再处理 + # params = None + else: + logger.debug(f"获取到的内容与输入相同，可能不是模板: {sql_content}") + except Exception as e: + logger.error(f"获取SQL模板失败: {str(e)}，将作为普通SQL执行") + # 继续执行，将其作为普通SQL处理 + + # 只有当SQL不是从模板获取时，才处理普通SQL中的#para()格式参数 + # 因为模板已经通过get_sql方法处理了参数替换 + if params and isinstance(params, dict) and isinstance(sql, str): + # 检查SQL是否还包含#para()格式（如果是模板，应该已经被处理过了） + if '#para(' in sql: + logger.debug("检测到SQL中仍包含#para()格式，进行参数替换") + # 替换SQL中的#para(param_name)为:param_name + for param_name, param_value in list(params.items()): # 使用list()创建副本，避免在遍历时修改 + para_pattern = f'#para\\({param_name}\\)' # 转义括号 + if re.search(para_pattern, sql): + # 检查参数是否为列表类型且用于IN操作 + if isinstance(param_value, (list, tuple)) and "in" in sql.lower(): + logger.debug(f"检测到普通SQL中的列表参数 {param_name} 用于IN操作，进行特殊处理") + # 为列表中的每个元素创建单独的参数 + list_values = param_value + if list_values: # 确保列表不为空 + # 生成多个占位符，如 :idList_1, :idList_2, :idList_3 + placeholders = ', '.join([f":{param_name}_{i}" for i in range(len(list_values))]) + sql = re.sub(para_pattern, placeholders, sql) + # 更新参数字典，添加每个列表元素的参数 + for i, value in enumerate(list_values): + params[f"{param_name}_{i}"] = value + logger.debug(f"为列表参数 {param_name} 创建子参数 {param_name}_{i} = {value}") + else: + # 空列表情况，将整个条件移除（因为IN () 是无效的SQL） + logger.warning(f"列表参数 {param_name} 为空，将移除包含该参数的条件表达式") + # 查找包含该占位符的整个条件表达式行 + lines = sql.split('\n') + new_lines = [] + for line in lines: + if f"#para({param_name})" in line: + line_stripped = line.strip().upper() + if line_stripped.startswith('AND ') or line_stripped.startswith('OR '): + continue + new_lines.append(line) + sql = '\n'.join(new_lines) + else: + # 普通参数处理 + sql = re.sub(para_pattern, f':{param_name}', sql) + logger.debug(f"已替换参数占位符: #para({param_name}) -> :{param_name}") + else: + logger.debug("SQL模板已处理过参数，跳过重复处理") + + is_own_session = False + try: + # 如果没有提供会话，创建新会话 + if session is None: + session = await instance.get_session() + is_own_session = True + + # 执行查询 + # logger.info(f"执行查询: {sql}，参数: {params}") + result = await session.execute(text(sql), params or {}) + + # 将结果转换为字典列表 + return [dict(row._mapping) for row in result] + except Exception as e: + logger.error(f"查询执行失败: {str(e)}") + raise + finally: + # 如果是自己创建的会话，负责关闭 + if is_own_session and session: + await session.close() + + async def findFirst(self, sql, params=None, session=None): + """ + 执行查询并返回结果的第一条记录（异步版本） + + Args: + sql: SQL查询语句或SQL模板名称（支持namespace.sql_name格式） + params: 查询参数，字典形式 + session: 可选的数据库会话对象，不提供时将自动创建 + + Returns: + dict: 第一条记录的字典，如果没有结果则返回None + """ + # 直接调用异步的find方法，它已经使用_is_sql_template进行了模板检查 + rows = await self.find(sql, params, session) + return rows[0] if rows else None + + async def findById(self, table_name, primary_key_value, primary_key='id', session=None): + """ + 根据主键查询指定表中的记录（异步版本） + + Args: + table_name: 表名 + primary_key_value: 主键值 + primary_key: 主键字段名，默认为'id' + session: 可选的数据库会话对象，不提供时将自动创建 + + Returns: + dict: 查询到的记录字典，如果没有找到则返回None + """ + if not table_name: + raise ValueError("表名不能为空") + + # 构建查询SQL + sql = f"SELECT * FROM {table_name} WHERE {primary_key} = :primary_key_value" + + # 准备参数 + params = {"primary_key_value": primary_key_value} + + logger.debug(f"生成的查询SQL: {sql}") + logger.debug(f"查询参数: {params}") + + # 异步使用findFirst方法执行查询 + return await self.findFirst(sql, params, session) + + async def paginate(self, sql, page_number=1, page_size=10, params=None, session=None): + """ + 执行SQL查询并返回分页结果（异步版本） + + Args: + sql: SQL查询语句或SQL模板名称（支持namespace.sql_name格式） + page_number: 页码，从1开始 + page_size: 每页记录数 + params: 查询参数，字典形式 + session: 可选的数据库会话对象，不提供时将自动创建 + + Returns: + dict: 包含分页信息的字典，格式如下： + { + 'list': 当前页的数据列表, + 'pageNumber': 当前页码, + 'pageSize': 每页记录数, + 'totalRow': 总记录数, + 'totalPage': 总页数 + } + """ + # 确保数据库已初始化 + if self.AsyncSessionLocal is None: + await self.init_db() + + # 初始化诊断日志 + logger.debug("======= 开始分页查询 =======") + + # 如果没有提供params，创建一个空字典 + if params is None: + params = {} + logger.debug(f"传入的参数: {params}") + + # 确保页码和每页大小为正整数 + try: + page_number = int(page_number) + page_size = int(page_size) + except (ValueError, TypeError): + logger.error(f"页码或每页大小类型错误，重置为默认值") + page_number = 1 + page_size = 10 + + if page_number < 1: + page_number = 1 + if page_size < 1: + page_size = 10 + + logger.debug(f"处理后的页码: {page_number}, 每页大小: {page_size}") + + # 使用_is_sql_template方法检查并获取SQL模板 + if self._is_sql_template(sql): + try: + logger.debug(f"确认为SQL模板: {sql}") + + # 尝试获取SQL模板 + sql_content = await self.get_sql(sql, params) + # 如果成功获取到内容且不等于原始名称，说明是模板 + if sql_content and sql_content != sql: + sql = sql_content + logger.debug(f"成功获取SQL模板，处理后的SQL: {sql}") + else: + logger.debug(f"获取到的内容与输入相同，可能不是模板: {sql_content}") + except Exception as e: + logger.error(f"获取SQL模板失败: {str(e)}，将作为普通SQL执行") + # 继续执行，将其作为普通SQL处理 + + # 只有当SQL不是从模板获取时，才处理普通SQL中的#para()格式参数 + # 因为模板已经通过get_sql方法处理了参数替换 + if params and isinstance(params, dict) and isinstance(sql, str): + # 检查SQL是否还包含#para()格式（如果是模板，应该已经被处理过了） + if '#para(' in sql: + logger.debug("检测到SQL中仍包含#para()格式，进行参数替换") + # 替换SQL中的#para(param_name)为:param_name + for param_name, param_value in list(params.items()): # 使用list()创建副本，避免在遍历时修改 + para_pattern = f'#para\\({param_name}\\)' # 转义括号 + if re.search(para_pattern, sql): + # 检查参数是否为列表类型且用于IN操作 + if isinstance(param_value, (list, tuple)) and "in" in sql.lower(): + logger.debug(f"检测到分页查询中的列表参数 {param_name} 用于IN操作，进行特殊处理") + # 为列表中的每个元素创建单独的参数 + list_values = param_value + if list_values: # 确保列表不为空 + # 生成多个占位符，如 :idList_1, :idList_2, :idList_3 + placeholders = ', '.join([f":{param_name}_{i}" for i in range(len(list_values))]) + sql = re.sub(para_pattern, placeholders, sql) + # 更新参数字典，添加每个列表元素的参数 + for i, value in enumerate(list_values): + params[f"{param_name}_{i}"] = value + logger.debug(f"为列表参数 {param_name} 创建子参数 {param_name}_{i} = {value}") + else: + # 空列表情况，将整个条件移除（因为IN () 是无效的SQL） + logger.warning(f"列表参数 {param_name} 为空，将移除包含该参数的条件表达式") + # 查找包含该占位符的整个条件表达式行 + lines = sql.split('\n') + new_lines = [] + for line in lines: + if f"#para({param_name})" in line: + line_stripped = line.strip().upper() + if line_stripped.startswith('AND ') or line_stripped.startswith('OR '): + continue + new_lines.append(line) + sql = '\n'.join(new_lines) + else: + # 普通参数处理 + sql = re.sub(para_pattern, f':{param_name}', sql) + logger.debug(f"已替换参数占位符: #para({param_name}) -> :{param_name}") + else: + logger.debug("SQL模板已处理过参数，跳过重复处理") + + is_own_session = False + try: + # 如果没有提供会话，创建新会话 + if session is None: + session = await self.get_session() + is_own_session = True + + # 计算总记录数 + count_sql = self._build_count_sql(sql) + logger.debug(f"构建的COUNT SQL: {count_sql}") + + try: + count_result = await session.execute(text(count_sql), params or {}) + count_value = count_result.scalar() + + # 确保total_row是正确的整数值 + try: + total_row = int(count_value) if count_value is not None else 0 + except (ValueError, TypeError): + total_row = 0 + except Exception as e: + logger.error(f"COUNT查询失败: {str(e)}") + total_row = 0 + # 如果是自己创建的会话，当COUNT查询失败时，回滚事务并重新创建会话 + if is_own_session: + logger.debug("COUNT查询失败，回滚并重新创建会话") + await session.rollback() + await session.close() + session = await self.get_session() + + # 添加详细日志记录总记录数和每页大小 + logger.debug(f"总记录数: {total_row}, 每页大小: {page_size}") + + # 确保total_row是整数 + total_row = int(total_row) if total_row is not None else 0 + + # 计算总页数（使用更清晰的计算方式） + if page_size > 0: + total_page = (total_row + page_size - 1) // page_size + else: + total_page = 0 + + logger.debug(f"计算的总页数: {total_page}") + + # 计算偏移量 + offset = (page_number - 1) * page_size + + # 构建分页SQL（PostgreSQL语法） + # 首先检查SQL是否已经包含LIMIT或OFFSET子句 + has_limit = re.search(r'(?i)\bLIMIT\b', sql) + has_offset = re.search(r'(?i)\bOFFSET\b', sql) + + # 如果已经包含LIMIT或OFFSET，直接使用原始SQL + if has_limit or has_offset: + paginated_sql = sql + else: + # 否则，在SQL末尾添加LIMIT和OFFSET + # 不需要复杂的ORDER BY处理，PostgreSQL会自动处理顺序 + paginated_sql = f"{sql} LIMIT :limit OFFSET :offset" + + logger.debug(f"构建的分页SQL: {paginated_sql}") + + # 添加分页参数 + paginated_params = params.copy() if params else {} + paginated_params['limit'] = page_size + paginated_params['offset'] = offset + logger.debug(f"分页查询参数: {paginated_params}") + + # 执行分页查询 + result = await session.execute(text(paginated_sql), paginated_params) + + # 将结果转换为字典列表 + list_data = [dict(row._mapping) for row in result] + + # 确保total_row是整数 + try: + total_row_int = int(total_row) + except Exception as e: + total_row_int = 0 + + # 计算总页数 + if page_size > 0: + remainder = total_row_int % page_size + quotient = total_row_int // page_size + calculated_pages = quotient + 1 if remainder > 0 else quotient + else: + calculated_pages = 0 + + # 构建返回结果，使用正确的页数 + pagination_result = { + 'list': list_data if list_data is not None else [], + 'page': page_number, + 'page_size': page_size, + 'total': total_row, + 'total_pages': calculated_pages # 使用正确的页数 + } + + return pagination_result + except Exception as e: + logger.error(f"分页查询失败: {str(e)}") + raise + finally: + # 如果是自己创建的会话，负责关闭 + if is_own_session and session: + await session.close() + + def _build_count_sql(self, sql): + """ + 根据原始SQL构建用于计算总记录数的SQL + + Args: + sql: 原始SQL查询语句 + + Returns: + str: 计算总记录数的SQL + """ + import re + + # 清理SQL，移除前后空白 + sql = sql.strip() + + # 策略1: 使用正则表达式查找FROM关键字（更灵活） + match = re.search(r'(?i)\bFROM\b', sql) + if match: + from_pos = match.start() + # 提取FROM后面的内容（包括FROM关键字） + from_part = sql[from_pos:] + + # 检查是否包含ORDER BY子句，如果有，移除 + order_by_match = re.search(r'(?i)\bORDER\s+BY\b', from_part) + if order_by_match: + order_by_pos = order_by_match.start() + from_part = from_part[:order_by_pos] + + # 检查是否包含GROUP BY子句 + if re.search(r'(?i)\bGROUP\s+BY\b', from_part): + # 对于复杂查询，使用子查询来计算总数 + subquery_sql = sql[:order_by_pos] if order_by_match else sql + return f"SELECT COUNT(*) FROM ({subquery_sql}) AS subquery" + else: + # 简单查询，直接替换SELECT部分 + return f"SELECT COUNT(*) {from_part}" + + # 策略2: 使用多种空格格式查找FROM关键字 + from_variants = [' FROM ', ' FROM', 'FROM ', 'FROM'] + sql_upper = sql.upper() + for variant in from_variants: + if variant.upper() in sql_upper: + from_pos = sql_upper.find(variant.upper()) + from_part = sql[from_pos:] + + # 移除ORDER BY + if ' ORDER BY ' in from_part.upper(): + order_by_pos = from_part.upper().rfind(' ORDER BY ') + from_part = from_part[:order_by_pos] + + return f"SELECT COUNT(*) {from_part}" + + # 如果所有策略都失败，返回默认查询 + return "SELECT COUNT(*)" + + async def execute_update(self, sql, params=None, session=None): + """执行SQL更新操作（插入、更新、删除）（异步版本） + + Args: + sql: SQL更新语句或SQL模板名称（支持namespace.sql_name格式） + params: 更新参数，字典形式 + session: 可选的数据库会话对象，不提供时将自动创建 + + Returns: + int: 受影响的行数 + """ + # 确保数据库已初始化 + if self.AsyncSessionLocal is None: + await self.init_db() + + # 如果没有提供params，创建一个空字典 + if params is None: + params = {} + + # 检查是否为SQL模板名称并处理 + if self._is_sql_template(sql): + try: + logger.debug(f"确认为SQL模板: {sql}") + + # 尝试获取SQL模板 + sql_content = await self.get_sql(sql, params) + # 如果成功获取到内容且不等于原始名称，说明是模板 + if sql_content and sql_content != sql: + sql = sql_content + logger.debug(f"成功获取SQL模板，处理后的SQL: {sql}") + # 对于模板SQL，参数已经被get_sql处理过了，不需要再处理 + params = None + else: + logger.debug(f"获取到的内容与输入相同，可能不是模板: {sql_content}") + except Exception as e: + logger.error(f"获取SQL模板失败: {str(e)}，将作为普通SQL执行") + # 继续执行，将其作为普通SQL处理 + + # 只有当SQL不是从模板获取时，才处理普通SQL中的#para()格式参数 + # 因为模板已经通过get_sql方法处理了参数替换 + if params and isinstance(params, dict): + # 检查SQL是否还包含#para()格式（如果是模板，应该已经被处理过了） + if '#para(' in sql: + logger.debug("检测到SQL中仍包含#para()格式，进行参数替换") + # 替换SQL中的#para(param_name)为:param_name + for param_name, param_value in list(params.items()): # 使用list()创建副本，避免在遍历时修改 + para_pattern = f'#para\\({param_name}\\)' # 转义括号 + if re.search(para_pattern, sql): + # 检查参数是否为列表类型且用于IN操作 + if isinstance(param_value, (list, tuple)) and "in" in sql.lower(): + logger.debug(f"检测到更新SQL中的列表参数 {param_name} 用于IN操作，进行特殊处理") + # 为列表中的每个元素创建单独的参数 + list_values = param_value + if list_values: # 确保列表不为空 + # 生成多个占位符，如 :idList_1, :idList_2, :idList_3 + placeholders = ', '.join([f":{param_name}_{i}" for i in range(len(list_values))]) + sql = re.sub(para_pattern, placeholders, sql) + # 更新参数字典，添加每个列表元素的参数 + for i, value in enumerate(list_values): + params[f"{param_name}_{i}"] = value + logger.debug(f"为列表参数 {param_name} 创建子参数 {param_name}_{i} = {value}") + else: + # 空列表情况，将整个条件移除（因为IN () 是无效的SQL） + logger.warning(f"列表参数 {param_name} 为空，将移除包含该参数的条件表达式") + # 查找包含该占位符的整个条件表达式行 + lines = sql.split('\n') + new_lines = [] + for line in lines: + if f"#para({param_name})" in line: + line_stripped = line.strip().upper() + if line_stripped.startswith('AND ') or line_stripped.startswith('OR '): + continue + new_lines.append(line) + sql = '\n'.join(new_lines) + else: + # 普通参数处理 + sql = re.sub(para_pattern, f':{param_name}', sql) + logger.debug(f"已替换参数占位符: #para({param_name}) -> :{param_name}") + else: + logger.debug("SQL模板已处理过参数，跳过重复处理") + + is_own_session = False + try: + # 如果没有提供会话，创建新会话 + if session is None: + session = await self.get_session() + is_own_session = True + + # 执行更新 + # logger.info(f"执行更新: {sql}，参数: {params}") + result = await session.execute(text(sql), params or {}) + + # 提交事务 + await session.commit() + + # 返回受影响的行数（如果有） + return result.rowcount if hasattr(result, 'rowcount') else 0 + except Exception as e: + logger.error(f"更新操作失败: {str(e)}") + if session: + await session.rollback() + raise + finally: + # 如果是自己创建的会话，负责关闭 + if is_own_session and session: + await session.close() + + async def save(self, table_name, data, primary_key, session=None): + """插入数据到指定表，并返回插入后的主键值（异步版本） + + Args: + table_name: 表名 + data: 要插入的数据字典 + primary_key: 主键字段名 + session: 可选的数据库会话对象，不提供时将自动创建 + + Returns: + 插入后的主键值 + + Raises: + ValueError: 当参数验证失败时 + Exception: 当插入操作失败时 + """ + if not table_name or not data: + raise ValueError("表名和数据不能为空") + + # 主键可能由数据库自动生成，不传递也可以 + # 无需额外检查，直接构建SQL语句 + # 如果data中不包含primary_key，SQL会自动处理 + + # logger.info(f"准备插入数据到表 {table_name}，主键字段: {primary_key}") + + # 构建插入SQL + columns = ', '.join(data.keys()) + placeholders = ', '.join([f":{key}" for key in data.keys()]) + sql = f"INSERT INTO {table_name} ({columns}) VALUES ({placeholders}) RETURNING {primary_key}" + + logger.debug(f"生成的插入SQL: {sql}") + logger.debug(f"插入参数: {data}") + + is_own_session = False + try: + # 如果没有提供会话，创建新会话 + if session is None: + session = await self.get_session() + is_own_session = True + + # 执行插入并获取主键 + result = await session.execute(text(sql), data) + await session.commit() + + # 获取插入的主键值 + primary_key_value = result.scalar() + # logger.info(f"数据插入成功，主键值: {primary_key_value}") + return primary_key_value + except Exception as e: + logger.error(f"数据插入失败: {str(e)}") + if session: + await session.rollback() + raise + finally: + # 如果是自己创建的会话，负责关闭 + if is_own_session and session: + await session.close() + + async def update(self, table_name, data, primary_key, session=None): + """根据主键更新指定表中的数据（异步版本） + + Args: + table_name: 表名 + data: 要更新的数据字典，必须包含主键字段和值 + primary_key: 主键字段名 + session: 可选的数据库会话对象，不提供时将自动创建 + + Returns: + int: 受影响的行数 + + Raises: + ValueError: 当参数验证失败时 + Exception: 当更新操作失败时 + """ + if not table_name or not data: + raise ValueError("表名和数据不能为空") + + if primary_key not in data: + raise ValueError(f"数据字典中必须包含主键字段 '{primary_key}'") + + # 获取主键值 + primary_key_value = data[primary_key] + + # logger.info(f"准备更新表 {table_name} 中的数据，主键: {primary_key}={primary_key_value}") + + # 构建更新字段部分，排除主键 + update_fields = [f"{key} = :{key}" for key in data.keys() if key != primary_key] + if not update_fields: + logger.warning("没有需要更新的字段") + return 0 + + update_sql_part = ', '.join(update_fields) + + # 构建完整的更新SQL + sql = f"UPDATE {table_name} SET {update_sql_part} WHERE {primary_key} = :{primary_key}" + + logger.debug(f"生成的更新SQL: {sql}") + logger.debug(f"更新参数: {data}") + + is_own_session = False + try: + # 如果没有提供会话，创建新会话 + if session is None: + session = await self.get_session() + is_own_session = True + + # 执行更新 + result = await session.execute(text(sql), data) + await session.commit() + + affected_rows = result.rowcount if hasattr(result, 'rowcount') else 0 + # logger.info(f"数据更新成功，受影响行数: {affected_rows}") + return affected_rows + except Exception as e: + logger.error(f"数据更新失败: {str(e)}") + if session: + await session.rollback() + raise + finally: + # 如果是自己创建的会话，负责关闭 + if is_own_session and session: + await session.close() + + async def batch_insert(self, table_name, data_list, primary_key=None, session=None): + """ + 批量插入数据到指定表（异步版本） + + Args: + table_name: 表名 + data_list: 要插入的数据字典列表 + primary_key: 主键字段名，如果提供，将返回插入后的主键值列表 + session: 可选的数据库会话对象，不提供时将自动创建 + + Returns: + list: 插入后的主键值列表，如果不提供primary_key则返回None + + Raises: + ValueError: 当参数验证失败时 + Exception: 当插入操作失败时 + """ + if not table_name or not data_list: + raise ValueError("表名和数据列表不能为空") + + if not isinstance(data_list, list) or not data_list: + raise ValueError("数据列表必须是非空的列表") + + # 获取第一个数据字典的键作为列名 + columns = data_list[0].keys() + columns_str = ', '.join(columns) + + # 构建占位符部分 + placeholders_list = [] + all_params = {} + + # 为每一行数据生成占位符和参数 + for i, data in enumerate(data_list): + # 确保每一行数据都有相同的键 + if data.keys() != columns: + raise ValueError(f"第{i+1}行数据的字段不匹配") + + # 生成该行的占位符，如 (:name_1, :age_1) + row_placeholders = [f":{key}_{i}" for key in columns] + placeholders_list.append(f"({', '.join(row_placeholders)})") + + # 更新参数 + for key, value in data.items(): + all_params[f"{key}_{i}"] = value + + # 构建完整的批量插入SQL + values_clause = ', '.join(placeholders_list) + if primary_key: + sql = f"INSERT INTO {table_name} ({columns_str}) VALUES {values_clause} RETURNING {primary_key}" + else: + sql = f"INSERT INTO {table_name} ({columns_str}) VALUES {values_clause}" + + logger.debug(f"生成的批量插入SQL: {sql}") + + is_own_session = False + try: + # 如果没有提供会话，创建新会话 + if session is None: + session = await self.get_session() + is_own_session = True + + # 执行批量插入 + result = await session.execute(text(sql), all_params) + await session.commit() + + # 如果指定了主键，返回插入的主键值列表 + if primary_key: + return [row[0] for row in result] + return None + except Exception as e: + logger.error(f"批量插入失败: {str(e)}") + if session: + await session.rollback() + raise + finally: + # 如果是自己创建的会话，负责关闭 + if is_own_session and session: + await session.close() + + async def batch_update(self, table_name, data_list, primary_key, session=None): + """ + 批量更新指定表中的数据（异步版本） + + Args: + table_name: 表名 + data_list: 要更新的数据字典列表，每个字典必须包含主键字段和值 + primary_key: 主键字段名 + session: 可选的数据库会话对象，不提供时将自动创建 + + Returns: + int: 受影响的总行数 + + Raises: + ValueError: 当参数验证失败时 + Exception: 当更新操作失败时 + """ + if not table_name or not data_list or not primary_key: + raise ValueError("表名、数据列表和主键不能为空") + + if not isinstance(data_list, list) or not data_list: + raise ValueError("数据列表必须是非空的列表") + + is_own_session = False + total_affected = 0 + + try: + # 如果没有提供会话，创建新会话 + if session is None: + session = await self.get_session() + is_own_session = True + + # 逐个更新数据（PostgreSQL不支持标准的批量更新语法） + for data in data_list: + if primary_key not in data: + raise ValueError(f"数据字典中必须包含主键字段 '{primary_key}'") + + # 构建更新字段部分，排除主键 + update_fields = [f"{key} = :{key}" for key in data.keys() if key != primary_key] + if not update_fields: + continue # 没有需要更新的字段 + + update_sql_part = ', '.join(update_fields) + + # 构建完整的更新SQL + sql = f"UPDATE {table_name} SET {update_sql_part} WHERE {primary_key} = :{primary_key}" + + # 执行更新 + result = await session.execute(text(sql), data) + total_affected += result.rowcount if hasattr(result, 'rowcount') else 0 + + # 提交事务 + await session.commit() + return total_affected + except Exception as e: + logger.error(f"批量更新失败: {str(e)}") + if session: + await session.rollback() + raise + finally: + # 如果是自己创建的会话，负责关闭 + if is_own_session and session: + await session.close() + + async def begin_transaction(self, session=None): + """ + 开始一个新的事务（异步版本） + + Args: + session: 可选的数据库会话对象，不提供时将创建新会话 + + Returns: + Session: 数据库会话对象 + """ + if session is None: + session = await self.get_session() + return session + + async def commit_transaction(self, session): + """ + 提交当前事务（异步版本） + + Args: + session: 数据库会话对象 + """ + if session: + await session.commit() + + async def rollback_transaction(self, session): + """ + 回滚当前事务（异步版本） + + Args: + session: 数据库会话对象 + """ + if session: + await session.rollback() + + async def transaction(self): + """ + 异步事务上下文管理器，用于简化异步事务管理 + + 使用示例: + async with await db.transaction() as session: + await db.execute_update("INSERT INTO users (name) VALUES (:name)", {"name": "张三"}, session) + await db.execute_update("UPDATE statistics SET count = count + 1", {}, session) + """ + return TransactionContext(self) + + +# Db类结束 diff --git a/DbKit/SqlTemplateLoader.py b/DbKit/SqlTemplateLoader.py new file mode 100644 index 0000000..240bba4 --- /dev/null +++ b/DbKit/SqlTemplateLoader.py @@ -0,0 +1,529 @@ + +import logging +import os +import re +import asyncio +import aiofiles +import json + +# 导入RedisKit +from Util.RedisKit import redisKit + +# 配置日志 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# Redis键名常量 +REDIS_SQL_TEMPLATES_KEY = "sql_templates:all_templates" +REDIS_SQL_LOADED_FILES_KEY = "sql_templates:loaded_files" +REDIS_SQL_TEMPLATE_MAP_KEY = "sql_templates:template_map" +REDIS_SQL_TEMPLATES_LOADED_KEY = "sql_templates:templates_loaded" + + +class SqlTemplateLoader: + """SQL模板加载器，负责加载和解析SQL模板文件（异步版本）""" + + def __init__(self, sql_dir=None, auto_load=True): + """初始化SQL模板加载器 + + Args: + sql_dir: SQL模板文件所在目录 + auto_load: 是否自动加载目录下所有SQL模板文件 + """ + self.sql_dir = sql_dir + self.templates = {} # 使用嵌套字典存储命名空间下的SQL模板 + self._loaded_files = set() # 用于记录已加载的文件，避免重复加载 + self._template_map = {} # 使用平面字典存储完整的模板名称映射，用于快速查找 + + # 注意：异步版本不会在__init__中自动加载，需要单独调用异步方法 + + async def load_all_templates(self): + """自动加载SQL目录下所有的SQL模板文件（异步版本） + + Returns: + int: 成功加载的模板文件数量 + """ + logger.info(f"开始加载SQL模板目录: {self.sql_dir}") + + if not os.path.exists(self.sql_dir): + logger.warning(f"SQL模板目录不存在: {self.sql_dir}") + return 0 + + # 检查是否已经从Redis加载过 + templates_loaded = await self._check_templates_loaded_from_redis() + if templates_loaded: + await self._load_from_redis() + logger.info(f"从Redis加载SQL模板完成，共{len(self._loaded_files)}个文件，{len(self._template_map)}个模板") + return len(self._loaded_files) + + loaded_count = 0 + sql_files = [] + + try: + # 遍历目录下所有.sql文件 + for filename in os.listdir(self.sql_dir): + if filename.lower().endswith('.sql'): + file_path = os.path.join(self.sql_dir, filename) + file_abs_path = os.path.abspath(file_path) + sql_files.append(file_abs_path) + + # 使用异步方式加载所有模板，不立即构建映射以提高性能 + for file_abs_path in sql_files: + # 跳过已经加载过的文件 + if file_abs_path in self._loaded_files: + logger.debug(f"SQL文件已加载，跳过: {file_abs_path}") + continue + + try: + filename = os.path.basename(file_abs_path) + if await self.load_template(file_abs_path, build_map=False): + loaded_count += 1 + except Exception as e: + logger.error(f"加载SQL模板文件 {filename} 失败: {str(e)}") + + # 所有模板加载完成后，统一构建映射 + if loaded_count > 0: + self._build_template_map() + logger.info(f"成功加载 {loaded_count} 个SQL模板文件，已构建模板映射") + + # 加载完成后保存到Redis + await self._save_to_redis() + return loaded_count + except Exception as e: + logger.error(f"遍历SQL模板目录时出错: {str(e)}") + return 0 + + async def load_template(self, template_path, build_map=True): + """加载指定的SQL模板文件（异步版本） + + Args: + template_path: SQL模板文件的路径，可以是绝对路径或相对于sql_dir的相对路径 + build_map: 是否立即构建模板映射，默认为True。在批量加载时可设为False以提高性能 + + Returns: + bool: 加载是否成功 + + Raises: + FileNotFoundError: 当模板文件不存在时 + Exception: 当加载过程发生其他错误时 + """ + # 确定模板文件的完整路径 + if self.sql_dir and not os.path.isabs(template_path): + file_path = os.path.join(self.sql_dir, template_path) + else: + file_path = template_path + + # 检查文件是否已加载，避免重复加载 + file_abs_path = os.path.abspath(file_path) + if file_abs_path in self._loaded_files: + logger.debug(f"SQL模板文件已加载，跳过: {file_abs_path}") + return True + + try: + # 异步读取文件内容 + async with aiofiles.open(file_path, 'r', encoding='utf-8') as f: + content = await f.read() + + # 使用栈结构解析整个内容 + templates = self._parse_with_stack(content) + + # 处理命名空间 + if 'namespace' in templates and templates['namespace']: + namespace = templates['namespace'] + sql_templates = {k: v for k, v in templates.items() if k != 'namespace'} + else: + # 没有命名空间的情况 + namespace = 'default' + sql_templates = templates + + # 将解析出的SQL模板添加到templates字典中 + if namespace not in self.templates: + self.templates[namespace] = {} + + self.templates[namespace].update(sql_templates) + + # 重新构建模板映射，确保新加载的模板可以被快速查找 + if build_map: + self._build_template_map() + + # 记录已加载的文件 + self._loaded_files.add(file_abs_path) + + return True + except FileNotFoundError: + logger.error(f"SQL模板文件不存在: {file_path}") + raise FileNotFoundError(f"SQL模板文件不存在: {file_path}") + except Exception as e: + logger.error(f"加载SQL模板失败: {str(e)}", exc_info=True) + raise + + def _parse_with_stack(self, content): + """使用栈结构解析SQL模板，支持嵌套的标签匹配 + 支持--单行注释和/* */多行注释 + + Args: + content: 要解析的SQL模板内容 + + Returns: + 解析后的SQL模板字典，键为模板名称，值为模板内容 + """ + templates = {} + stack = [] # 用于存储标签信息的栈 + current_content = [] # 当前正在收集的内容 + in_multiline_comment = False # 多行注释标记 + + try: + # 按行处理内容 + lines = content.split('\n') + for line_num, line in enumerate(lines): + # 处理多行注释 + if in_multiline_comment: + # 检查是否是多行注释结束 + if '*/' in line: + in_multiline_comment = False + # 截取注释结束后的内容 + line = line.split('*/', 1)[1] + else: + # 完全在多行注释内，跳过此行 + continue + + # 处理单行注释和检查多行注释开始 + if not line.strip().startswith('#') and stack and stack[-1][0] == 'sql': + # 检查是否有--注释 + if '--' in line: + # 保留--前面的内容 + line = line.split('--', 1)[0] + + # 检查是否有/*多行注释开始 + if '/*' in line: + parts = line.split('/*', 1) + # 如果/*和*/在同一行 + if '*/' in parts[1]: + # 只保留/*前面和*/后面的内容 + comment_part = parts[1].split('*/', 1) + line = parts[0] + comment_part[1] + else: + # 多行注释开始，只保留/*前面的内容 + line = parts[0] + in_multiline_comment = True + + stripped_line = line.strip() + + # 检查是否是标签行 + if stripped_line.startswith('#'): + # 检查是否是命名空间标签 + if stripped_line.startswith('#namespace(') and ( + stripped_line.find('"') > 0 or stripped_line.find("'") > 0): + # 提取命名空间名称 + quote_pattern = re.search(r'#namespace\((["\'])([^"\']+)\1\)', stripped_line) + if quote_pattern: + namespace = quote_pattern.group(2) + templates['namespace'] = namespace + # 将命名空间标签入栈 + stack.append(('namespace', namespace, line_num)) + + # 检查是否是SQL模板开始标签 + elif stripped_line.startswith('#sql(') and ( + stripped_line.find('"') > 0 or stripped_line.find("'") > 0): + # 提取SQL模板名称 + quote_pattern = re.search(r'#sql\((["\'])([^"\']+)\1\)', stripped_line) + if quote_pattern: + sql_name = quote_pattern.group(2) + # 将SQL模板开始标签入栈 + stack.append(('sql', sql_name, line_num)) + current_content = [] # 重置内容收集器 + + # 检查是否是#if条件开始标签 + elif stripped_line.startswith('#if(') and ')' in stripped_line: + # 提取条件表达式 + condition_pattern = re.search(r'#if\(([^)]+)\)', stripped_line) + if condition_pattern: + condition = condition_pattern.group(1) + stack.append(('if', condition, line_num)) + # 保留#if行到内容中 - 只要有SQL标签在栈中就保留 + if any(tag[0] == 'sql' for tag in stack): + current_content.append(line.rstrip()) + + # 检查是否是结束标签 + elif stripped_line == '#end' and stack: + # 获取栈顶元素 + tag_type, tag_name, start_line = stack[-1] + + # 只有当标签不是SQL结束标签时，才保留#end行到内容中 + if any(tag[0] == 'sql' for tag in stack) and tag_type != 'sql': + current_content.append(line.rstrip()) + + # 弹出当前标签 + stack.pop() + + # 如果是SQL模板结束标签，保存内容 + if tag_type == 'sql': + sql_content = '\n'.join(current_content).strip() + if sql_content: + templates[tag_name] = sql_content + # 重置内容收集器，准备下一个模板 + current_content = [] + + # 收集内容（不是标签行且当前有活动的SQL模板） + else: + # 只要栈中有SQL标签，就收集内容（不管是在条件块内还是外） + if any(tag[0] == 'sql' for tag in stack) and line.strip(): + # 保留原始缩进 + current_content.append(line.rstrip()) + + # 检查是否所有标签都正确闭合 + if stack: + unclosed_tags = [f"{tag_type}('{tag_name}')" for tag_type, tag_name, _ in stack] + logger.warning(f"存在未闭合的标签: {', '.join(unclosed_tags)}") + else: + logger.debug("所有标签都已正确闭合") + + except Exception as e: + logger.error(f"使用栈结构解析SQL模板时出错: {str(e)}", exc_info=True) + + return templates + + async def get_all_template_names(self): + """ +获取所有已加载的SQL模板的完整名称列表（异步版本） + + Returns: + list: 包含所有模板名称的列表，格式为 "命名空间.模板名称" + """ + template_names = [] + + # 遍历所有命名空间 + for namespace, templates in self.templates.items(): + # 遍历当前命名空间下的所有模板 + for template_name in templates.keys(): + # 构建完整的模板名称（命名空间.模板名称） + full_name = f"{namespace}.{template_name}" + template_names.append(full_name) + + return template_names + + def _has_template(self, full_template_name): + """ + 内部方法：快速检查指定的完整模板名称是否存在 + 使用MAP机制实现O(1)时间复杂度的查找 + + Args: + full_template_name: 完整的模板名称，格式为 "命名空间.模板名称" + + Returns: + bool: 如果模板存在返回True，否则返回False + """ + logger.debug(f"检查模板是否存在: {full_template_name}") + + # 如果_map为空，先构建映射 + if not self._template_map and self.templates: + logger.debug("模板映射为空，正在构建映射...") + self._build_template_map() + logger.debug(f"映射构建完成，包含 {len(self._template_map)} 个模板") + + # 直接在映射中查找 + exists = full_template_name in self._template_map + logger.debug(f"模板 '{full_template_name}' 存在性检查结果: {exists}") + + if not exists and self._template_map: + logger.debug(f"当前映射中的模板: {list(self._template_map.keys())}") + + return exists + + def _build_template_map(self): + """ + 内部方法：构建完整模板名称到SQL内容的映射 + 用于支持快速查找功能 + """ + logger.debug("开始构建模板映射...") + self._template_map.clear() + + # 遍历所有命名空间和模板，构建映射 + for namespace, templates in self.templates.items(): + logger.debug(f"处理命名空间: {namespace}，包含 {len(templates)} 个模板") + for template_name, sql_content in templates.items(): + full_name = f"{namespace}.{template_name}" + self._template_map[full_name] = sql_content + logger.debug(f"添加映射: {full_name} -> {len(sql_content)} 字符的SQL") + + logger.debug(f"模板映射构建完成，总共 {len(self._template_map)} 个模板") + logger.debug(f"映射中的模板列表: {list(self._template_map.keys())}") + + async def get_sql(self, sql_name): + """ +获取指定名称的SQL语句，支持命名空间格式：namespace.sql_name（异步版本） + + Args: + sql_name: SQL模板名称，可以包含命名空间前缀 + + Returns: + str: SQL语句 + + Raises: + KeyError: 当找不到指定名称的SQL模板时 + """ + try: + logger.debug(f"尝试获取SQL模板: {sql_name}") + logger.debug(f"当前模板映射状态: {len(self._template_map) if self._template_map else 0} 个模板") + + # 快速检查模板是否存在 + if self._has_template(sql_name): + logger.debug(f"在映射中找到模板: {sql_name}") + # 如果在映射中存在，直接返回 + sql_content = self._template_map[sql_name] + logger.debug(f"模板内容长度: {len(sql_content)} 字符") + return sql_content + + # 如果内存中没有，检查Redis中是否有加载的模板数据 + templates_loaded = await self._check_templates_loaded_from_redis() + if templates_loaded: + await self._load_from_redis() + # 再次尝试从内存中获取 + if self._has_template(sql_name): + sql_content = self._template_map[sql_name] + logger.debug(f"从Redis加载后找到模板: {sql_name}") + return sql_content + + logger.debug(f"映射中未找到模板: {sql_name}，尝试从原始模板中查找") + + # 尝试解析命名空间和SQL名称 + if '.' in sql_name: + namespace, name = sql_name.split('.', 1) + else: + # 默认命名空间 + namespace = "default" + name = sql_name + + logger.debug(f"解析结果 - 命名空间: {namespace}, 模板名: {name}") + logger.debug(f"可用命名空间: {list(self.templates.keys())}") + + # 检查命名空间是否存在 + if namespace and namespace not in self.templates: + logger.error(f"命名空间 '{namespace}' 不存在，可用命名空间: {list(self.templates.keys())}") + raise KeyError(f"找不到命名空间: {namespace}") + + # 检查SQL名称是否存在 + available_templates = list(self.templates[namespace].keys()) + logger.debug(f"命名空间 '{namespace}' 下的可用模板: {available_templates}") + + if name and name not in self.templates[namespace]: + logger.error(f"SQL模板 '{name}' 在命名空间 '{namespace}' 下不存在，可用模板: {available_templates}") + raise KeyError(f"在命名空间 '{namespace}' 下找不到SQL模板: {name}") + + sql = self.templates[namespace][name] + logger.debug(f"成功获取SQL模板，内容长度: {len(sql)} 字符") + return sql + + except KeyError: + raise + except Exception as e: + logger.error(f"获取SQL模板时出错: {str(e)}", exc_info=True) + raise + + async def reload_template(self, template_path): + """重新加载指定的SQL模板文件（异步版本） + + Args: + template_path: SQL模板文件的路径，可以是绝对路径或相对于sql_dir的相对路径 + + Returns: + bool: 重新加载是否成功 + """ + try: + # 确定模板文件的完整路径 + if self.sql_dir and not os.path.isabs(template_path): + file_path = os.path.join(self.sql_dir, template_path) + else: + file_path = template_path + + # 获取文件的绝对路径 + file_abs_path = os.path.abspath(file_path) + + # 如果文件已加载，则从已加载列表中移除 + if file_abs_path in self._loaded_files: + self._loaded_files.remove(file_abs_path) + # 清除映射表，下次访问时会重新构建 + self._template_map.clear() + # 清除Redis中的模板数据 + await self._clear_redis_data() + + # 重新加载模板，立即构建映射 + success = await self.load_template(file_abs_path, build_map=True) + # 如果加载成功，更新Redis + if success: + await self._save_to_redis() + return success + except Exception as e: + logger.error(f"重新加载SQL模板失败: {str(e)}", exc_info=True) + return False + + async def _check_templates_loaded_from_redis(self) -> bool: + """ + 检查Redis中是否已经加载了SQL模板 + + Returns: + bool: 如果Redis中已加载返回True，否则返回False + """ + try: + return await redisKit.exists(REDIS_SQL_TEMPLATES_LOADED_KEY) + except Exception as e: + logger.error(f"检查Redis中SQL模板状态失败: {str(e)}") + return False + + async def _load_from_redis(self): + """ + 从Redis加载SQL模板数据 + """ + try: + # 加载templates + templates_str = await redisKit.get_data(REDIS_SQL_TEMPLATES_KEY) + if templates_str: + self.templates = json.loads(templates_str) + + # 加载loaded_files + loaded_files_str = await redisKit.get_data(REDIS_SQL_LOADED_FILES_KEY) + if loaded_files_str: + self._loaded_files = set(json.loads(loaded_files_str)) + + # 加载template_map + template_map_str = await redisKit.get_data(REDIS_SQL_TEMPLATE_MAP_KEY) + if template_map_str: + self._template_map = json.loads(template_map_str) + + except Exception as e: + logger.error(f"从Redis加载SQL模板数据失败: {str(e)}") + # 出错时不抛出异常，使用内存中的默认值 + + async def _save_to_redis(self): + """ + 将SQL模板数据保存到Redis + """ + try: + # 保存templates + await redisKit.set_data(REDIS_SQL_TEMPLATES_KEY, json.dumps(self.templates)) + + # 保存loaded_files + await redisKit.set_data(REDIS_SQL_LOADED_FILES_KEY, json.dumps(list(self._loaded_files))) + + # 保存template_map + await redisKit.set_data(REDIS_SQL_TEMPLATE_MAP_KEY, json.dumps(self._template_map)) + + # 设置标记表示已加载 + await redisKit.set_data(REDIS_SQL_TEMPLATES_LOADED_KEY, "1") + + except Exception as e: + logger.error(f"将SQL模板数据保存到Redis失败: {str(e)}") + # 出错时不抛出异常，继续执行 + + async def _clear_redis_data(self): + """ + 清除Redis中的模板数据 + """ + try: + await redisKit.delete(REDIS_SQL_TEMPLATES_KEY) + await redisKit.delete(REDIS_SQL_LOADED_FILES_KEY) + await redisKit.delete(REDIS_SQL_TEMPLATE_MAP_KEY) + await redisKit.delete(REDIS_SQL_TEMPLATES_LOADED_KEY) + except Exception as e: + logger.error(f"清除Redis中SQL模板数据失败: {str(e)}") + # 出错时不抛出异常，继续执行 \ No newline at end of file diff --git a/DbKit/Test/AppTest.py b/DbKit/Test/AppTest.py new file mode 100644 index 0000000..329c7f6 --- /dev/null +++ b/DbKit/Test/AppTest.py @@ -0,0 +1,22 @@ +import asyncio +import logging +from DbKit.Db import Db + +# 配置日志 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +async def test_app_template(): + """测试App.sql模板解析和加载""" + db = Db() + + # 初始化数据库连接 + await db.init_db() + + list = await db.find("AppType.getAppType") + print( list) + + + +if __name__ == '__main__': + asyncio.run(test_app_template()) \ No newline at end of file diff --git a/DbKit/Test/Test.py b/DbKit/Test/Test.py new file mode 100644 index 0000000..0c0a0b7 --- /dev/null +++ b/DbKit/Test/Test.py @@ -0,0 +1,66 @@ +import asyncio +import logging +from DbKit.Db import Db + +# 配置日志 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +"""测试查询t_teacher_app表""" +db = Db() + +async def test_find_teacher_app(): + try: + # 执行查询 + sql = "select * from t_teacher_app where app_name=:app_name" + params = {"app_name": "半角模型"} + logger.info(f"执行查询: {sql}") + result = await db.find(sql, params) + + logger.info(f"查询结果数量: {len(result)}") + # 打印部分结果（前3条） + for i, row in enumerate(result[:3]): + logger.info(f"第{i + 1}条记录: {row}") + + return result + except Exception as e: + logger.error(f"查询失败: {str(e)}", exc_info=True) + raise + + +async def test_sql_template(): + try: + # 初始化数据库连接 + await db.init_db() + + # 测试获取App.getAppList模板 + logger.info("尝试获取App.getAppList模板...") + sql_content = await db.get_sql("App.getAppList") + logger.info(f"成功获取App.getAppList模板，内容长度: {len(sql_content)} 字符") + logger.info(f"模板内容前100字符: {sql_content[:100]}") + + # 测试分页查询 + logger.info("测试分页查询...") + result = await db.paginate("App.getAppList", page_number=1, page_size=5) + logger.info(f"分页查询结果: {len(result.get('list', []))} 条记录") + + except Exception as e: + logger.error(f"SQL模板测试失败: {str(e)}", exc_info=True) + raise + +async def run_all_tests(): + """运行所有测试""" + logger.info("开始运行所有测试...") + + # 测试SQL模板自动加载 + await test_sql_template() + + # 测试查询功能 + await test_find_teacher_app() + + logger.info("所有测试完成！") + + +if __name__ == '__main__': + # 运行异步测试 + asyncio.run(run_all_tests()) diff --git a/DbKit/TransactionContext.py b/DbKit/TransactionContext.py new file mode 100644 index 0000000..3cf952e --- /dev/null +++ b/DbKit/TransactionContext.py @@ -0,0 +1,41 @@ +class TransactionContext: + """ + 事务上下文管理器类（异步版本） + 支持异步事务管理，配合异步Db类使用 + """ + + def __init__(self, db_instance): + """ + 初始化事务上下文管理器 + + Args: + db_instance: 异步Db实例 + """ + self.db = db_instance + self.session = None + + async def __aenter__(self): + """ + 进入异步上下文时创建会话 + """ + self.session = await self.db.get_session() + return self.session + + async def __aexit__(self, exc_type, exc_val, exc_tb): + """ + 退出异步上下文时提交或回滚事务 + """ + try: + if exc_type is None: + # 如果没有异常，提交事务 + await self.session.commit() + else: + # 如果有异常，回滚事务 + await self.session.rollback() + finally: + # 无论如何都关闭会话 + if self.session: + await self.session.close() + + # 返回False表示不抑制异常 + return False \ No newline at end of file diff --git a/DbKit/__init__.py b/DbKit/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/DbKit/__pycache__/Db.cpython-310.pyc b/DbKit/__pycache__/Db.cpython-310.pyc new file mode 100644 index 0000000..a6822a1 Binary files /dev/null and b/DbKit/__pycache__/Db.cpython-310.pyc differ diff --git a/DbKit/__pycache__/Db.cpython-313.pyc b/DbKit/__pycache__/Db.cpython-313.pyc new file mode 100644 index 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0000000..fa684fd Binary files /dev/null and b/DbKit/__pycache__/TransactionContext.cpython-313.pyc differ diff --git a/DbKit/__pycache__/__init__.cpython-310.pyc b/DbKit/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..de2b678 Binary files /dev/null and b/DbKit/__pycache__/__init__.cpython-310.pyc differ diff --git a/DbKit/__pycache__/__init__.cpython-313.pyc b/DbKit/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..00ae541 Binary files /dev/null and b/DbKit/__pycache__/__init__.cpython-313.pyc differ diff --git a/Doc/1、ADB安装与配置.md b/Doc/1、ADB安装与配置.md new file mode 100644 index 0000000..9ba29b3 --- /dev/null +++ b/Doc/1、ADB安装与配置.md @@ -0,0 +1,207 @@ +## ADB安装及使用详解 + +### 一、文档 + +[文档 ](https://blog.csdn.net/A1_3_9_7/article/details/147744713) + +### 二、下载与配置 + +Windows版本 + +```http +https://dl.google.com/android/repository/platform-tools-latest-windows.zip +``` + +**配置环境变量** + +解压安装后，把解压路径放到系统变量里去（Path）， + +环境变量如何配置，比如我的配置如下图： + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072925736.png) + +3、连接 +可以通过模拟器连接，也可以通过数据线连接。 + +通过数据线连接时，手机进入“开发者选项”，打开“usb调试”。 + +不同品牌安卓机型，首次打开“开发者选项”方式不一样，大多是双击手机版本号3~5次，会toast提醒“开发者模式已打开”，具体打开方式可根据手机品牌进行百度查询 + +4、电脑打开cmd窗口 +输入 adb version ：显示 adb 版本，说明安装成功 + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071609901.png) + +### 三、ADB命令详解 + +#### **1、基本命令** + +> **adb version ：显示 adb 版本** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071747991.png) + +**adb help：帮助信息，查看adb所支持的所有命令** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071802326.png) + +**adb get-serialno：也可以查看设备号** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071813698.png) + +#### **权限命令** + +> **adb root：获取Android管理员（root用户）的权限。** +> +> **注意**：一般测试机可使用root权限。 +> +> Android版本9以上，不支持商用机使用root权限，但可以修改底层一些配置 + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071828119.png) + +**adb shell：登录设备 shell，该命令将登录设备的shell（内核），登录shell后，可以使用 cd，ls，rm 等Linux命令** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071842102.png) + +adb remount：获取System分区可写权限，需要root后才能有这个权限 + +#### **3、建立连接** + +> **adb -d：如果同时连了usb，又开了模拟器，连接当前唯一通过usb连接的安卓设备** +> +> **adb -e shell：指定当前连接此电脑的唯一的一个模拟器** +> +> **adb -s <设备号> shell：当电脑插多台手机或模拟器时，指定一个设备号进行连接** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071900615.png) + +**exit：退出** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071910063.png) + +**adb kill-server：杀死当前adb服务，如果连不上设备时，杀掉重启。（没事不要用它）** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071921439.png) + +**adb start-server：杀掉后重启** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071931955.png) + +**5037：adb默认端口，如果该端口被占用，可以指定一个端口号，如下命令↓** + +**adb -p 6666 start-server：任意指定一个 adb shell 的端口** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071943263.png) + +#### **4、apk 操作指令** + +> **adb shell pm list packages：列出当前设备/手机，所有的包名** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108071955775.png) + +**adb shell pm list packages -f：显示包和包相关联的文件(安装路径)** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072005333.png) + +adb shell pm list packages -d：显示禁用的包名 +adb shell pm list packages -e：显示当前启用的包名 +adb shell pm list packages -s：显示系统应用包名 +adb shell pm list packages -3：显示已安装第三方的包名 +adb shell pm list packages xxxx：加需要过滤的包名，如：xxx = taobao +adb install <文件路径\apk>：将本地的apk软件安装到设备(手机)上。如手机外部安装需要密码，记得手机输入密码。 + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072023565.png) + +**adb install -r <文件路径\apk>：覆盖安装** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072033333.png) + +adb install -d <文件路径\apk>：允许降级覆盖安装 +adb install -g <文件路径\apk>：授权/获取权限，安装软件时把所有权限都打开 +adb uninstall <包名>：卸载该软件/app。 +注意：安装时安装的是apk，卸载时是包名，可以通过 adb shell pm list packages 查看需要卸载的包名。 + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072049334.png) + +**adb shell pm uninstall -k <包名>：虽然把此应用卸载，但仍保存此应用的数据和缓存 +adb shell am force-stop <包名>：强制退出该应用/app** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072057718.png) + +#### 、文件操作指令 + +> **adb push <本地路径\文件或文件夹> <手机端路径>：把本地(pc机)的文件或文件夹复制到设备(手机)** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072107990.png) + +注意点1：pc机路径与Android机路径，分隔符是不同的。 + +注意点2：复制失败，大概率是无权限。可先使用上面介绍过的两个命令：adb root；adb remount。在使用 adb push 命令 + +**adb pull <手机端路径/文件或文件夹> ：把设备(手机)的文件或文件夹复制到本地。**注意点同上 + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072122150.png) + +#### 6、日志操作指令 + +``` +adb shell logcat -c：清理现有日志 +adb shell logcat -v time ：输出日志，信息输出在控制台 +adb shell logcat -v time > <存放路径\log.txt>：输出日志并保存在本地文件 +Ctrl+C：终止日志抓取 +adb shell logcat -v time *:E > <存放路径\log.txt>：打印级别为Error的信息 +日志的等级： +-v：Verbse（明细） +-d：Debug（调试） +-i：Info（信息） +-w：Warn（警告） +-e：Error（错误） +-f：Fatal（严重错误） +抓取日志的步骤先输入命令启动日志，然后操作 App，复现 bug，再 ctrl+c 停止日志，分析本地保存的文件。 +：日志是记录手机系统在运行app时有什么异常的事件 +EXCEPTION +也可以把更详细得Anr日志拉取出来：adb shell pull /data/anr/traces.txt <存放路径> + +``` + +#### 7、系统操作指令 + +``` +adb shell getprop ro.product.model：获取设备型号 +``` + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072152566.png) + +**adb shell getprop ro.build.version.release：获取Android系统版本** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072202606.png) + +**adb get-serialno：获取设备的序列号（设备号）** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072213837.png) + +**adb shell wm size：获取设备屏幕分辨率** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072224822.png) + +**adb shell screencap -p /sdcard/mms.png：屏幕截图 +adb shell screencap -p /sdcard/screenshot.png：屏幕截图** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072233773.png) + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072239720.png) + +**adb shell dumpsys activity |find “mResumedActivity”：查看前台应用包名，必须先启动app，适用于Android 8.0以上** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072254958.png) + +**adb shell cat /proc/meminfo：获取手机内存信息** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072304342.png) + +**adb shell df：获取手机存储信息** + +![](https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/%7Byear%7D/%7Bmonth%7D/%7Bmd5%7D.%7BextName%7D/20260108072312989.png) + +**adb shell screenrecord <存放路径/xxx.mp4>：录屏，命名以.mp4结尾 +adb shell screenrecord --time-limit 10 <存放路径/xxx.mp4>：录屏时间为10秒** + diff --git a/Doc/2、Python + uiautomator2.md b/Doc/2、Python + uiautomator2.md new file mode 100644 index 0000000..954c4d3 --- /dev/null +++ b/Doc/2、Python + uiautomator2.md @@ -0,0 +1,79 @@ +文档 + +https://zhuanlan.zhihu.com/p/19790598010 + + + +```cmd +pip install -U uiautomator2 +pip install uiautodev +pip install pillow +``` + + + +**启动** + +``` +uiauto.dev +``` + + + +**探秘工作原理，知其然更知其所以然** + +python - uiautomator2主要由两部分组成：Python客户端和移动设备。 + +1. **Python端**：负责运行我们编写的脚本，然后向移动设备发送HTTP请求。 +2. **移动设备**：运行着封装了uiautomator2的HTTP服务，收到请求后，解析请求内容，并把它转化成uiautomator2的代码。 + +整个工作流程如下：先在移动设备上安装atx - agent（它是个守护进程），安装好后atx - agent会启动uiautomator2服务（默认7912端口），开始监听。然后在PC上编写测试脚本并执行，这个过程就相当于向移动设备的server端发送HTTP请求。最后移动设备通过WIFI或USB接收到请求，执行相应的操作。 + + + +``` +# 通过包名启动 +d.app_start("com.tencent.mm") # 启动微信 +``` + +https://zhuanlan.zhihu.com/p/25181382631 + +``` +# 等待元素出现（最多10秒，间隔1秒） +d(text="登录").wait(timeout=10, interval=1.0) +d(text="登录").click() + +# 等待元素消失 +d(text="加载中...").wait_gone(timeout=10) +``` + + + +https://uiauto.dev/ + +``` +adb kill-server +adb start-server +``` + +https://github.com/appium/appium-uiautomator2-server/releases + + + +关闭华为手机鸿蒙3的纯净模式 + +``` +adb shell pm disable-user com.huawei.security.privacycenter +adb shell pm disable-user com.huawei.appmarket +``` + + + +``` +adb install -r appium-uiautomator2-server-v9.9.1.apk +adb shell am instrument -w -e debug false io.appium.uiautomator2.server/.ServerInstrument +adb shell am broadcast -a uiautomator.dump +adb pull /sdcard/window_dump.xml . +``` + +adb uiautomator dump /data/local/tmp/ui.xml \ No newline at end of file diff --git a/Doc/3、安装DorisV2.19+Docker版本.txt b/Doc/3、安装DorisV2.19+Docker版本.txt new file mode 100644 index 0000000..e36cc74 --- /dev/null +++ b/Doc/3、安装DorisV2.19+Docker版本.txt @@ -0,0 +1,26 @@ +# 配置Docker + +# 下载脚本 +wget https://doris.apache.org/files/start-doris.sh + +# 运行以下命令，给其赋有相应的执行权限。 +chmod 755 start-doris.sh + +# 运行脚本，启动集群，默认使用2.1.9版本 +bash start-doris.sh + +# connect a alive FE node +mysql -uroot -P9030 -h127.0.0.1 + +# register BE node +ALTER SYSTEM ADD BACKEND "10.10.14.204:9050"; + +# 查看状态 +show backends; + +-- modify the password for current user +SET PASSWORD = PASSWORD('DsideaL147258369'); + +create database yltcharge; + +https://doris.apache.org/zh-CN/docs/dev/gettingStarted/quick-start \ No newline at end of file diff --git a/Doc/4、流程说明.md b/Doc/4、流程说明.md new file mode 100644 index 0000000..b596098 --- /dev/null +++ b/Doc/4、流程说明.md @@ -0,0 +1,161 @@ +# 新电途微信小程序爬虫流程说明文档 + +本文档详细记录了“新电途”微信小程序爬虫的工作流程、技术细节、避坑指南及维护策略，旨在帮助开发人员快速理解系统架构，应对 UI 变更及设备更换等场景。 + +--- + +## 1. 项目概述 + +本项目是一个基于 **Python + uiautomator2 + 视觉大模型 (VL) + 计算机图形学 (CV)** 的自动化爬虫系统。 +其核心目标是模拟用户操作，遍历“新电途”小程序的场站列表，识别场站信息（名称、价格、空闲桩数），并进入详情页抓取地址和分时电价。 + +### 核心技术栈 +* **控制端**: `uiautomator2` (Android 自动化控制) +* **视觉识别 (VL)**: 阿里云 Qwen-VL / DeepSeek (负责复杂的文字和语义提取) +* **计算机图形学 (CV)**: OpenCV (负责快速、低成本的广告检测、按钮定位、图像切片) +* **数据存储**: Doris (MySQL 协议) + Redis (去重缓存) + Huawei OBS (图片存储) + +--- + +## 2. 核心流程图解 + +```mermaid +graph TD + A[启动程序 (Y5_Full)] --> B[Step 0: 初始化服务 & 数据库] + B --> C[Step 1: 打开小程序 (Y2)] + C --> D{检测广告 (CV)} + D -- 有广告 --> E[点击关闭 (CV模板/几何)] + D -- 无广告 --> F[Step 2: 列表页爬取 (Y3)] + E --> F + F --> G[截图 & 切片 (CV)] + G --> H[上传图片至 OBS] + H --> I[调用 VL 模型解析场站列表] + I --> J{是否存在有效场站?} + J -- 是 --> K[遍历场站] + K --> L{是否已处理? (Redis)} + L -- 否 --> M[点击进入详情页] + M --> N[截图详情页 & 解析地址] + N --> O[抓取分时电价 (可选)] + O --> P[返回列表页] + P --> K + L -- 是 --> K + J -- 否 --> Q[下拉翻页] + Q --> F + Q -- 结束 --> R[Step 3: 坐标回填 (Y4)] + R --> S{Cookie 有效?} + S -- 是 --> T[高德 API 查询 & 更新 DB] + S -- 否 --> U[弹窗手动登录 (T2)] + U --> T +``` + +--- + +## 3. 详细步骤与技术实现 + +### 步骤 0: 初始化 (`Y5_Full.py`) +* **动作**: 程序启动时，首先初始化 `XinDianTuService` 并连接数据库。 +* **目的**: 确保后续爬取过程中数据库连接立即可用，避免在写入数据时才初始化导致的延迟或错误。 +* **注意**: 数据库连接采用单例模式或长连接，程序结束时需显式关闭。 + +### 步骤 1: 启动小程序 (`Y2_OpenXinDianTu.py`) +* **动作**: + 1. 强制停止微信并重新启动，确保回到微信主页。 + 2. 点击底部“微信”标签。 + 3. 点击右上角“搜索”按钮（基于百分比坐标）。 + 4. 输入“新电途”并点击搜索结果进入小程序。 +* **坑点**: + * **搜索入口坐标**: 搜索按钮的位置在不同分辨率手机上略有差异，目前使用 `w * 0.84`, `h * 0.08`。如果更换手机，需检查此坐标。 + * **输入法干扰**: `uiautomator2` 的输入法有时会失效，代码中做了 `send_keys(clear=True)` 和直接输入的双重兜底。 + +### 步骤 2: 列表页爬取 (`Y3_StationList.py`) +这是最核心的循环步骤（如需广告检测会自动调用 CV 模块）： +1. **截图**: `d.screenshot()`。 +2. **图像切片 (CV)**: 调用 `Kit.crop_cards_from_image`。 + * 利用**行扫描算法**，根据像素亮度和方差，识别出场站卡片之间的灰色分割线，将大图切割成一个个小卡片 (`_vl.jpg` 实际上是带绿框的整图，配合 JSON 元数据)。 + * **优势**: 极大降低了 VL 模型的理解难度，模型只需识别框内内容，不用自己找位置。 +3. **VL 解析**: 将图片上传 OBS，调用 VL 模型（Qwen/DeepSeek）。 + * **Prompt**: 要求模型提取“场站名称”、“价格”、“枪数”、“距离”等。 +4. **坐标映射与安全检查**: + * 模型返回的坐标会结合 `device_info` (设备真实宽高) 进行校验。 + * **Config.py**: `SAFE_EXCLUDE_RATIO=0.20` (顶部20%不点)，`BOTTOM_SAFE_EXCLUDE_RATIO=0.1` (底部10%不点)。**这解决了第一屏场站无法点击的问题。** + +### 步骤 3: 坐标回填 (`Y4_Coord.py`) +* **动作**: 扫描数据库中 `address` 不为空但 `coord_x/y` 为空的记录，调用高德地图 API 补全经纬度。 +* **智能修复**: + * 任务开始前会自动探测 Cookie 有效性。 + * 如果 Cookie 失效，自动调用 `T2_GetAmapCookie` 弹出浏览器窗口让用户手动登录。 + * 登录完成后，程序自动保存新 Cookie 并继续执行回填任务，无需重启。 + + +--- + +## 4. 技术选型指南：何时用 CV？何时用 VL？ + +| 场景 | 推荐技术 | 理由 | +| :--- | :--- | :--- | +| **广告关闭按钮** | **CV (OpenCV)** | 图标固定、形状简单（圆/叉）、要求毫秒级响应、零成本。 | +| **隐私协议按钮** | **CV (OpenCV)** | 颜色特征明显（黑色块）、位置固定。 | +| **场站卡片切分** | **CV (行扫描)** | 列表页结构规律，分割线特征稳定，CV 切分极其精准且快。 | +| **读取场站名称** | **VL (大模型)** | 字体多样、可能有干扰、OCR 传统方案容易错乱，VL 语义理解能力强。 | +| **读取价格/枪数** | **VL (大模型)** | 格式复杂（"快 闲4/4"），VL 能直接提取结构化 JSON。 | +| **寻找"全部时段"** | **CV (几何特征)** | 按钮只有文字没有边框，但左右空白特征明显，用边缘检测更可靠。 | + +**总结**: +* **结构固定、特征简单、追求速度** -> 用 CV。 +* **语义复杂、内容多变、容错率高** -> 用 VL。 + +--- + +## 5. 关键参数配置 (`Config/Config.py`) + +| 参数名 | 当前值 | 说明 | 调整场景 | +| :--- | :--- | :--- | :--- | +| `SAFE_EXCLUDE_RATIO` | **0.20** | **顶部**安全排除比例。在此比例内的坐标不点击。 | 如果发现第一屏顶部的场站无法点击，**调小**此值。 | +| `BOTTOM_SAFE_EXCLUDE_RATIO` | **0.1** | **底部**安全排除比例。避开“扫码充电”等按钮。 | 如果频繁误触底部按钮，**调大**此值。 | +| `SCROLL_DISTANCE_RATIO` | **0.3** | 翻页滑动幅度的比例。 | 如果翻页太快漏数据，**调小**；翻页太慢，**调大**。 | +| `MAX_CRAWL_DISTANCE` | **50** | 最大爬取距离 (km)。 | 根据业务需求调整爬取范围。 | +| `VL_MODEL_NAME` | `qwen3-vl-flash` | 使用的视觉模型版本。 | 如果识别准确率下降，可尝试升级模型。 | + +--- + +## 6. 避坑指南与设备更换 + +### Q1: 更换手机需要注意什么？ +1. **分辨率差异**: + * 代码大部分使用了**相对坐标 (百分比)**，理论上兼容不同分辨率。 + * **重点检查**: `Y2_OpenXinDianTu.py` 中的搜索按钮点击坐标 (`0.84, 0.08`)。新手机可能刘海屏高度不同，导致 Y 轴偏移。 +2. **截图尺寸 vs 屏幕尺寸**: + * 某些手机截图分辨率与 `d.info` 获取的屏幕分辨率不一致（如 Retina 缩放）。 + * **解决方案**: `Y3_StationList.py` 中已包含自动检测逻辑：`if img_w != w: device_info['width'] = img_w`。一般无需手动修改。 + +### Q2: 小程序 UI 大改版怎么办？ +1. **列表样式变更**: + * 如果列表不再有明显的灰色分割线，`Kit.py` 的切片逻辑 (`get_card_segments`) 会失效。 + * **应对**: 修改 `Kit.py`，改用 VL 模型直接进行全图目标检测 (Object Detection)，或者调整 CV 分割线的颜色阈值。 +2. **广告样式变更**: + * 如果出现新广告，截图保存到 `Tools/Templates`，命名为 `ad_close_new.jpg` 即可，代码会自动加载。 + +### Q3: 为什么有场站但没点进去？ +1. **被安全区拦截**: 检查日志 `safe exclude` 关键字。如果是误拦截，调整 `SAFE_EXCLUDE_RATIO`。 +2. **VL 识别失败**: 检查 `Images/` 目录下的 `_vl.jpg`，看绿框是否框住了场站。如果没有，说明 CV 切片参数 (`Kit.py`) 需要调整（如 `row_mean` 阈值）。 + +--- + +## 7. 调试与单元测试 + +虽然本项目没有使用标准的 `unittest` 框架，但每个核心模块都设计了**独立运行入口 (`if __name__ == "__main__":`)**，方便单步调试。 + +### 单元调试清单 + +| 功能模块 | 对应文件 | 调试方法 | 作用 | +| :--- | :--- | :--- | :--- | +| **广告检测 (CV)** | `Util/Kit.py` | 无法直接运行，需编写临时脚本调用 `detect_ad_close_x`。建议在 `Tools/T3_CropCards.py` 中添加测试代码。 | 测试新广告模板是否生效。 | +| **切片逻辑 (CV)** | `Tools/T3_CropCards.py` | 运行该脚本，会自动读取 `Samples` 目录下的图片进行切片测试。 | 调试列表分割线识别参数。 | +| **进入小程序** | `Tools/Y2_OpenXinDianTu.py` | 直接运行该文件。 | 测试微信启动和搜索流程。 | +| **列表爬取主循环** | `Tools/Y3_StationList.py` | 直接运行该文件 (需连接手机)。 | 独立测试爬取流程，不依赖外部调度。 | +| **全流程** | `Tools/Y5_Full.py` | 直接运行该文件。 | 生产环境模拟（含坐标回填）。 | + +### 建议的开发习惯 +1. **遇到新 Bug**: 先看日志 (`Tools/Logs/`), 确定是哪一步卡住。 +2. **保留现场**: `KEEP_SCREENSHOTS = True` (在 `Y3_StationList.py`)，开启后会保留所有截图，方便事后分析。 +3. **单步验证**: 如果是广告检测问题，把截图拿出来，写个几行的 Python 脚本单独调用 `Kit.detect_ad_close_x` 进行验证，不要每次都跑全流程。 diff --git a/Doc/5、统计表结构说明.md b/Doc/5、统计表结构说明.md new file mode 100644 index 0000000..3410417 --- /dev/null +++ b/Doc/5、统计表结构说明.md @@ -0,0 +1,171 @@ +# 充电桩数据仓库设计说明文档 + +本文档详细描述了充电桩爬虫项目的数据仓库设计思路、表结构定义、常见查询场景及应对策略。该设计基于 Doris 数据库，采用拉链表（SCD Type 2）思想，旨在完整保留历史数据变更轨迹，支持时间旅行查询（Time Travel Query）。 + +## 1. 设计思路：为何选择 SCD Type 2 拉链表？ + +在充电桩业务中，数据具有不同的更新频率和业务含义： + +1. **基础信息（Profile）**：如场站名称、地址。这些信息相对稳定，偶尔变化（如更名、迁址）。 +2. **实时状态（Status）**：如空闲桩数、总桩数。这些信息变化极快（分钟级），是分析热度的关键。 +3. **价格策略（Price）**：如分时电价。通常按天或月调整，但一旦调整需要保留历史价格以核对账单或分析价格趋势。 + +如果仅保存“最新状态”，我们将丢失所有历史轨迹，无法回答“上个月该站点的平均空闲率是多少”或“价格调整前后的流量对比”等问题。 + +因此，我们采用 **SCD Type 2（Slowly Changing Dimension Type 2）** 拉链表设计： +* 每条记录都有 `valid_start_time`（生效时间）和 `valid_end_time`（失效时间）。 +* 当前有效的记录，`valid_end_time` 通常设为极大值（如 `9999-12-31`）。 +* 当数据发生变化时： + 1. 将旧记录的 `valid_end_time` 更新为当前时间（闭链）。 + 2. 插入一条新记录，`valid_start_time` 为当前时间，`valid_end_time` 为极大值（开链）。 + +## 2. 表结构定义 + +### 2.1 充电站基础信息表 (`station_profile_scd`) +* **用途**：存储场站名称、地址等相对稳定的属性。 +* **业务主键**：`station_hash` (名称的MD5，确保唯一性)。 + +| 字段名 | 类型 | 说明 | +| :--- | :--- | :--- | +| `id` | VARCHAR(50) | 物理主键 (UUID)，每条历史记录唯一 | +| `station_hash` | VARCHAR(32) | **业务主键**，同一场站的所有历史记录此值相同 | +| `station_name` | VARCHAR(255) | 场站名称 | +| `address` | VARCHAR(500) | 详细地址 | +| `valid_start_time` | DATETIME | 记录生效时间 | +| `valid_end_time` | DATETIME | 记录失效时间 (默认 9999-12-31) | +| `is_current` | TINYINT | 是否为当前最新记录 (1=是, 0=否) | + +### 2.2 充电站实时状态表 (`station_status_scd`) +* **用途**：高频存储场站的桩数、空闲情况。 +* **特点**：数据量大，通过拉链表仅记录“状态发生变化”的时刻，节省空间。 + +| 字段名 | 类型 | 说明 | +| :--- | :--- | :--- | +| `id` | VARCHAR(50) | 物理主键 (UUID) | +| `station_hash` | VARCHAR(32) | 业务主键 | +| `total_piles` | INT | 总桩数 | +| `free_piles` | INT | 空闲桩数 | +| `piles_detail_json` | TEXT | 桩详情JSON (如 `{"快": {"free": 2, "total": 10}, "慢": ...}`) | +| `current_price` | DECIMAL | 抓取时的当前电价快照 | +| `valid_start_time` | DATETIME | 生效时间 | +| `valid_end_time` | DATETIME | 失效时间 | +| `is_current` | TINYINT | 是否最新 | + +### 2.3 充电站价格时段表 (`station_price_schedule_scd`) +* **用途**：存储全天的分时电价表。 + +| 字段名 | 类型 | 说明 | +| :--- | :--- | :--- | +| `id` | VARCHAR(50) | 物理主键 (UUID) | +| `station_hash` | VARCHAR(32) | 业务主键 | +| `schedule_json` | TEXT | 价格时段JSON数组 | +| `valid_start_time` | DATETIME | 生效时间 | +| `valid_end_time` | DATETIME | 失效时间 | +| `is_current` | TINYINT | 是否最新 | + +## 3. 常见查询场景及 SQL 写法 + +### 场景一：查询所有场站的“当前”最新状态 +这是最常用的查询，用于实时展示。 + +```sql +SELECT + p.station_name, + p.address, + s.total_piles, + s.free_piles, + s.current_price +FROM + station_profile_scd p +JOIN + station_status_scd s ON p.station_hash = s.station_hash +WHERE + p.is_current = 1 + AND s.is_current = 1; +``` + +### 场景二：查询“指定时间点”的历史快照 +例如：查询 **2023-10-01 14:00:00** 这一刻，所有场站的状态。 +这是拉链表的杀手锏功能。 + +```sql +SET @query_time = '2023-10-01 14:00:00'; + +SELECT + p.station_name, + s.free_piles, + s.current_price +FROM + station_profile_scd p +JOIN + station_status_scd s ON p.station_hash = s.station_hash +WHERE + -- 核心逻辑：查询时间落在生效区间内 + (@query_time >= p.valid_start_time AND @query_time < p.valid_end_time) + AND (@query_time >= s.valid_start_time AND @query_time < s.valid_end_time); +``` + +### 场景三：分析某场站过去24小时的空闲桩变化趋势 +我们需要按时间顺序拉出该场站的所有状态变更记录。 + +```sql +SELECT + valid_start_time AS change_time, + free_piles, + total_piles, + ROUND(free_piles / total_piles * 100, 2) AS free_rate_percent +FROM + station_status_scd +WHERE + station_hash = 'TARGET_STATION_MD5' + AND valid_start_time >= DATE_SUB(NOW(), INTERVAL 24 HOUR) +ORDER BY + valid_start_time ASC; +``` + +### 场景四：统计过去一个月，价格发生过变动的场站 +通过查找 `station_price_schedule_scd` 表中同一个 `station_hash` 有多条记录的情况。 + +```sql +SELECT + p.station_name, + COUNT(pr.id) AS price_change_count +FROM + station_price_schedule_scd pr +JOIN + station_profile_scd p ON pr.station_hash = p.station_hash AND p.is_current = 1 +WHERE + pr.valid_start_time >= DATE_SUB(NOW(), INTERVAL 1 MONTH) +GROUP BY + p.station_name +HAVING + COUNT(pr.id) > 1 +ORDER BY + price_change_count DESC; +``` + +## 4. 数据写入与维护策略 + +为了维护 SCD2 结构，写入程序（Crawler/ETL）需要遵循以下逻辑： + +1. **计算 Hash**：对抓取到的场站名称计算 MD5，作为 `station_hash`。 +2. **查询最新状态**：根据 `station_hash` 查询 `is_current=1` 的记录。 +3. **比对差异**： + * **Profile**: 比对名称、地址是否变化。 + * **Status**: 比对总桩数、空闲数、价格是否变化。 + * **Price**: 比对价格时段 JSON 是否变化。 +4. **无变化**：跳过，不进行任何数据库操作。 +5. **有变化**： + * **Step 1 (Close)**: 更新旧记录：`UPDATE table SET valid_end_time = NOW(), is_current = 0 WHERE id = 'old_uuid'`。 + * **Step 2 (Insert)**: 插入新记录：`INSERT INTO table (..., valid_start_time=NOW(), is_current=1)`。 + * *注意：Step 1 和 Step 2 最好在一个事务中完成，或者保证最终一致性。* + +## 5. 性能优化建议 + +1. **分桶 (Bucketing)**：建表时使用了 `DISTRIBUTED BY HASH(station_hash) BUCKETS 10`。这保证了同一个场站的所有历史记录都落在同一个 Tablet 上，极大地优化了基于 `station_hash` 的 Join 和聚合查询性能。 +2. **分区 (Partitioning)**：当前未显式使用时间分区。如果数据量达到亿级，建议按 `valid_start_time` 进行 Range 分区（如按月），以便冷热数据分离和快速清理过期历史数据。 +3. **索引**: Doris 自动为前缀列创建索引。由于我们常按 `station_hash` 查询，它被放在表定义的列首（或紧跟 ID），查询效率很高。 + +--- +**文档维护人**: AI Assistant +**最后更新**: 2026-01-08 diff --git a/Model/StationPriceSchedule.py b/Model/StationPriceSchedule.py new file mode 100644 index 0000000..238e713 --- /dev/null +++ b/Model/StationPriceSchedule.py @@ -0,0 +1,69 @@ +from sqlalchemy import text +from datetime import datetime +import json + +class StationPriceSchedule: + def __init__(self): + pass + + async def save(self, session, id, station_hash, schedule_json, valid_start_time=None): + if valid_start_time is None: + valid_start_time = datetime.now() + + # Ensure json is string with consistent formatting + schedule_json_str = None + if schedule_json is not None: + if isinstance(schedule_json, (dict, list)): + schedule_json_str = json.dumps(schedule_json, ensure_ascii=False, sort_keys=True) + else: + schedule_json_str = schedule_json + + # 1. Check current record + select_sql = """ + SELECT schedule_json + FROM t_station_price_schedule_scd + WHERE station_hash = :station_hash AND is_current = 1 + """ + result = await session.execute(text(select_sql), {"station_hash": station_hash}) + current_row = result.fetchone() + + if current_row: + row_json = current_row.schedule_json + + # Normalize row_json for comparison + if row_json: + try: + if isinstance(row_json, str): + row_json_obj = json.loads(row_json) + row_json = json.dumps(row_json_obj, ensure_ascii=False, sort_keys=True) + except Exception: + pass + + if row_json == schedule_json_str: + # No change, skip insert + return + + # Expire old record + update_sql = """ + UPDATE t_station_price_schedule_scd + SET is_current = 0, valid_end_time = :valid_start_time + WHERE station_hash = :station_hash AND is_current = 1 + """ + await session.execute(text(update_sql), { + "valid_start_time": valid_start_time, + "station_hash": station_hash + }) + + # 2. Insert new record + sql = """ + INSERT INTO t_station_price_schedule_scd + (id, station_hash, schedule_json, valid_start_time, is_current) + VALUES + (:id, :station_hash, :schedule_json, :valid_start_time, 1) + """ + await session.execute(text(sql), { + "id": id, + "station_hash": station_hash, + "schedule_json": schedule_json_str, + "valid_start_time": valid_start_time + }) diff --git a/Model/StationProfile.py b/Model/StationProfile.py new file mode 100644 index 0000000..9f902f0 --- /dev/null +++ b/Model/StationProfile.py @@ -0,0 +1,107 @@ +from sqlalchemy import text +from datetime import datetime + +class StationProfile: + def __init__(self): + pass + + async def backfill_address_if_missing(self, session, station_hash, station_name, address): + sql = """ + UPDATE t_station_profile_scd + SET address = :address, + station_name = :station_name + WHERE station_hash = :station_hash + AND (address IS NULL OR address = '') + """ + result = await session.execute(text(sql), { + "station_hash": station_hash, + "station_name": station_name, + "address": address + }) + try: + return int(result.rowcount or 0) + except Exception: + return 0 + + async def save(self, session, id, station_hash, operator, station_name, address=None, coord_x=None, coord_y=None, valid_start_time=None): + if valid_start_time is None: + valid_start_time = datetime.now() + + # 1. Check current record + select_sql = """ + SELECT operator, station_name, address, coord_x, coord_y + FROM t_station_profile_scd + WHERE station_hash = :station_hash AND is_current = 1 + """ + result = await session.execute(text(select_sql), {"station_hash": station_hash}) + current_row = result.fetchone() + + if current_row: + if address is not None and address != "" and (current_row.address is None or current_row.address == ""): + await self.backfill_address_if_missing( + session=session, + station_hash=station_hash, + station_name=station_name, + address=address + ) + return + + # Merge logic: if new value is None, use old value + # But we must respect if new value is explicitly provided (even empty string?) + # Usually for crawler: + # - station_name: always updated + # - address: if new is None, use old. + # - coord: if new is None, use old. + + merged_address = address if address is not None else current_row.address + merged_coord_x = coord_x if coord_x is not None else current_row.coord_x + merged_coord_y = coord_y if coord_y is not None else current_row.coord_y + + # Use merged values for comparison and insertion + address = merged_address + coord_x = merged_coord_x + coord_y = merged_coord_y + + # Check if changed + # Need to handle potential float/decimal diffs for coords if needed, + # but usually they are float in DB and float in python. + + is_same = ( + current_row.operator == operator and + current_row.station_name == station_name and + current_row.address == address and + current_row.coord_x == coord_x and + current_row.coord_y == coord_y + ) + + if is_same: + return + + # Expire old record + update_sql = """ + UPDATE t_station_profile_scd + SET is_current = 0, valid_end_time = :valid_start_time + WHERE station_hash = :station_hash AND is_current = 1 + """ + await session.execute(text(update_sql), { + "valid_start_time": valid_start_time, + "station_hash": station_hash + }) + + # 2. Insert new record (with potentially merged values) + sql = """ + INSERT INTO t_station_profile_scd + (id, station_hash, operator, station_name, address, coord_x, coord_y, valid_start_time, is_current) + VALUES + (:id, :station_hash, :operator, :station_name, :address, :coord_x, :coord_y, :valid_start_time, 1) + """ + await session.execute(text(sql), { + "id": id, + "station_hash": station_hash, + "operator": operator, + "station_name": station_name, + "address": address, + "coord_x": coord_x, + "coord_y": coord_y, + "valid_start_time": valid_start_time + }) diff --git a/Model/StationStatus.py b/Model/StationStatus.py new file mode 100644 index 0000000..c6aefda --- /dev/null +++ b/Model/StationStatus.py @@ -0,0 +1,113 @@ +from sqlalchemy import text +from datetime import datetime +import json + +class StationStatus: + def __init__(self): + pass + + async def save(self, session, id, station_hash, total_piles, free_piles, piles_detail_json, current_price, pro_price=None, parking_info=None, distance=None, valid_start_time=None): + if valid_start_time is None: + valid_start_time = datetime.now() + + # Ensure json is string with consistent formatting + piles_json_str = None + if piles_detail_json is not None: + if isinstance(piles_detail_json, (dict, list)): + piles_json_str = json.dumps(piles_detail_json, ensure_ascii=False, sort_keys=True) + else: + piles_json_str = piles_detail_json + + # 1. Check current record + select_sql = """ + SELECT total_piles, free_piles, piles_detail_json, current_price, pro_price, parking_info, distance + FROM t_station_status_scd + WHERE station_hash = :station_hash AND is_current = 1 + """ + try: + result = await session.execute(text(select_sql), {"station_hash": station_hash}) + current_row = result.fetchone() + except Exception as e: + # Check if it's a "column not found" error + if "Unknown column 'parking_info'" in str(e) or "no such column: parking_info" in str(e): + # Handle schema evolution if needed, or just proceed assuming None for parking_info comparison + current_row = None + # Or re-raise if we want to fail hard, but let's try to be robust + # For now, if column missing, we might fail on INSERT later anyway. + # So re-raising or logging might be better. + # But since I cannot easily alter table here, I will proceed with code update + # and assume user/I run the ALTER script. + raise e + raise e + + if current_row: + # Check if changed + # Note: current_row values might be Decimal, need to convert for comparison + row_total = current_row.total_piles + row_free = current_row.free_piles + row_json = current_row.piles_detail_json + row_price = current_row.current_price + row_pro_price = current_row.pro_price + row_parking = getattr(current_row, 'parking_info', None) # Safely get if column exists + row_distance = getattr(current_row, 'distance', None) + + # Normalize row_json for comparison (handle key order differences) + if row_json: + try: + if isinstance(row_json, str): + row_json_obj = json.loads(row_json) + row_json = json.dumps(row_json_obj, ensure_ascii=False, sort_keys=True) + except Exception: + pass # Keep original if parse fails + + # Convert price to float if it is Decimal, for comparison + if row_price is not None: + row_price = float(row_price) + if row_pro_price is not None: + row_pro_price = float(row_pro_price) + + # Simple comparison + is_same = ( + row_total == total_piles and + row_free == free_piles and + row_price == current_price and + row_pro_price == pro_price and + row_json == piles_json_str and + row_parking == parking_info and + row_distance == distance + ) + + if is_same: + # No change, skip insert + return + + # Expire old record + update_sql = """ + UPDATE t_station_status_scd + SET is_current = 0, valid_end_time = :valid_start_time + WHERE station_hash = :station_hash AND is_current = 1 + """ + await session.execute(text(update_sql), { + "valid_start_time": valid_start_time, + "station_hash": station_hash + }) + + # 2. Insert new record + sql = """ + INSERT INTO t_station_status_scd + (id, station_hash, total_piles, free_piles, piles_detail_json, current_price, pro_price, parking_info, distance, valid_start_time, is_current) + VALUES + (:id, :station_hash, :total_piles, :free_piles, :piles_detail_json, :current_price, :pro_price, :parking_info, :distance, :valid_start_time, 1) + """ + await session.execute(text(sql), { + "id": id, + "station_hash": station_hash, + "total_piles": total_piles, + "free_piles": free_piles, + "piles_detail_json": piles_json_str, + "current_price": current_price, + "pro_price": pro_price, + "parking_info": parking_info, + "distance": distance, + "valid_start_time": valid_start_time + }) diff --git a/Model/__pycache__/StationPriceSchedule.cpython-310.pyc b/Model/__pycache__/StationPriceSchedule.cpython-310.pyc new file mode 100644 index 0000000..287106a Binary files /dev/null and b/Model/__pycache__/StationPriceSchedule.cpython-310.pyc differ diff --git a/Model/__pycache__/StationProfile.cpython-310.pyc b/Model/__pycache__/StationProfile.cpython-310.pyc new file mode 100644 index 0000000..0be6e3e Binary files /dev/null and b/Model/__pycache__/StationProfile.cpython-310.pyc differ diff --git a/Model/__pycache__/StationStatus.cpython-310.pyc b/Model/__pycache__/StationStatus.cpython-310.pyc new file mode 100644 index 0000000..b682d52 Binary files /dev/null and b/Model/__pycache__/StationStatus.cpython-310.pyc differ diff --git a/Sql/doris_ddl.py b/Sql/doris_ddl.py new file mode 100644 index 0000000..6fcf6b3 --- /dev/null +++ b/Sql/doris_ddl.py @@ -0,0 +1,69 @@ +import asyncio +import os +import sys +import logging + +# 配置日志 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# 将项目根目录添加到系统路径，以便能够导入 DbKit 和 Config 等模块 +# DbKit/doris_ddl.py -> DbKit -> Root (2 levels up) +project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if project_root not in sys.path: + sys.path.append(project_root) + +from Util import Win32Patch + +# 应用 Windows 平台的异步策略补丁 +Win32Patch.patch() + +from DbKit.Db import Db +from Config.Config import DB_URL + +async def init_tables(): + """ + 初始化数据库表结构：读取 SQL 文件并逐条执行。 + """ + logger.info("正在初始化数据库连接...") + db = Db(db_url=DB_URL) + await db.init_db() + + logger.info("正在读取 SQL 文件...") + # SQL 文件路径：相对于当前脚本所在目录 (DbKit/) + sql_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '', 'doris_ddl.sql') + + logger.info(f"使用 SQL 文件: {sql_path}") + + with open(sql_path, 'r', encoding='utf-8') as f: + sql_content = f.read() + + # 使用分号分割 SQL 语句，并过滤掉空行和空白语句 + statements = [s.strip() for s in sql_content.split(';') if s.strip()] + + logger.info(f"共找到 {len(statements)} 条 SQL 语句。") + + async with await db.get_session() as session: + for i, stmt in enumerate(statements): + logger.info(f"正在执行第 {i + 1} 条语句...") + # 使用 SQLAlchemy 的 text() 函数执行原生 SQL + from sqlalchemy import text + try: + await session.execute(text(stmt)) + logger.info(f"第 {i + 1} 条语句执行成功。") + except Exception as e: + logger.error(f"执行第 {i + 1} 条语句时出错: {e}") + + # 虽然大多数数据库的 DDL 语句是自动提交的，但显式提交是一个好习惯 + await session.commit() + + logger.info("数据库表初始化完成。") + + # 优雅地关闭数据库引擎并释放连接 + await db.engine.dispose() + +if __name__ == "__main__": + # 再次确保补丁已应用 + Win32Patch.patch() + # 运行异步初始化函数 + asyncio.run(init_tables()) diff --git a/Sql/doris_ddl.sql b/Sql/doris_ddl.sql new file mode 100644 index 0000000..b870e2e --- /dev/null +++ b/Sql/doris_ddl.sql @@ -0,0 +1,61 @@ +-- 充电站基础信息表 (SCD2) +-- 保存场站的名称、地址等相对稳定的信息 +DROP TABLE IF EXISTS t_station_profile_scd; +CREATE TABLE IF NOT EXISTS t_station_profile_scd ( + `station_hash` VARCHAR(32) NOT NULL COMMENT '业务主键 (名称的MD5值)', + `valid_start_time` DATETIME NOT NULL COMMENT '记录生效开始时间', + `id` VARCHAR(50) NOT NULL COMMENT '唯一ID (UUID)', + `operator` VARCHAR(50) NOT NULL COMMENT '运营商名称', + `station_name` VARCHAR(255) NOT NULL COMMENT '场站名称', + `address` VARCHAR(500) COMMENT '详细地址', + `coord_x` DOUBLE COMMENT '经度 (高德坐标系)', + `coord_y` DOUBLE COMMENT '纬度 (高德坐标系)', + `valid_end_time` DATETIME NOT NULL DEFAULT '9999-12-31 23:59:59' COMMENT '记录生效结束时间', + `is_current` TINYINT NOT NULL DEFAULT 1 COMMENT '是否为当前最新记录 (1=是, 0=否)' +) +UNIQUE KEY(`station_hash`, `valid_start_time`, `id`) +DISTRIBUTED BY HASH(`station_hash`) BUCKETS 10 +PROPERTIES ( + "replication_num" = "1" +); + +-- 充电站实时状态表 (SCD2 / Log) +-- 保存场站的桩数、空闲数等动态信息 +-- 每次抓取如果状态变化则写入新记录 +DROP TABLE IF EXISTS t_station_status_scd; +CREATE TABLE IF NOT EXISTS t_station_status_scd ( + `station_hash` VARCHAR(32) NOT NULL COMMENT '业务主键 (名称的MD5值)', + `valid_start_time` DATETIME NOT NULL COMMENT '记录生效开始时间', + `id` VARCHAR(50) NOT NULL COMMENT '唯一ID (UUID)', + `total_piles` INT COMMENT '总桩数', + `free_piles` INT COMMENT '空闲桩数', + `piles_detail_json` TEXT COMMENT '详细桩信息 (JSON格式)', + `current_price` DECIMAL(10, 4) COMMENT '当前价格快照', + `pro_price` DECIMAL(10, 4) COMMENT 'PRO会员专享价快照', + `parking_info` VARCHAR(500) COMMENT '停车收费信息', + `distance` VARCHAR(50) COMMENT '距离信息 (例如 5.3km)', + `valid_end_time` DATETIME NOT NULL DEFAULT '9999-12-31 23:59:59' COMMENT '记录生效结束时间', + `is_current` TINYINT NOT NULL DEFAULT 1 COMMENT '是否为当前最新记录' +) +UNIQUE KEY(`station_hash`, `valid_start_time`, `id`) +DISTRIBUTED BY HASH(`station_hash`) BUCKETS 10 +PROPERTIES ( + "replication_num" = "1" +); + +-- 充电站价格时段表 (SCD2) +-- 保存场站的全天价格时段信息 +DROP TABLE IF EXISTS t_station_price_schedule_scd; +CREATE TABLE IF NOT EXISTS t_station_price_schedule_scd ( + `station_hash` VARCHAR(32) NOT NULL COMMENT '业务主键 (名称的MD5值)', + `valid_start_time` DATETIME NOT NULL COMMENT '记录生效开始时间', + `id` VARCHAR(50) NOT NULL COMMENT '唯一ID (UUID)', + `schedule_json` TEXT COMMENT '价格时段详情 (JSON格式)', + `valid_end_time` DATETIME NOT NULL DEFAULT '9999-12-31 23:59:59' COMMENT '记录生效结束时间', + `is_current` TINYINT NOT NULL DEFAULT 1 COMMENT '是否为当前最新记录' +) +UNIQUE KEY(`station_hash`, `valid_start_time`, `id`) +DISTRIBUTED BY HASH(`station_hash`) BUCKETS 10 +PROPERTIES ( + "replication_num" = "1" +); diff --git a/Temp/DevTools/current_20260111_170841.jpg b/Temp/DevTools/current_20260111_170841.jpg new file mode 100644 index 0000000..cbf7c07 Binary files /dev/null and b/Temp/DevTools/current_20260111_170841.jpg differ diff --git a/Temp/DevTools/current_20260111_171025.jpg b/Temp/DevTools/current_20260111_171025.jpg new file mode 100644 index 0000000..1b8cfba Binary files /dev/null and b/Temp/DevTools/current_20260111_171025.jpg differ diff --git a/Test/1.jpg b/Test/1.jpg new file mode 100644 index 0000000..b5b33da Binary files /dev/null and b/Test/1.jpg differ diff --git a/Test/1_bw.jpg b/Test/1_bw.jpg new file mode 100644 index 0000000..6157cf3 Binary files /dev/null and b/Test/1_bw.jpg differ diff --git a/Test/1_bw_bytes.jpg b/Test/1_bw_bytes.jpg new file mode 100644 index 0000000..6157cf3 Binary files /dev/null and b/Test/1_bw_bytes.jpg differ diff --git a/Test/2.jpg b/Test/2.jpg new file mode 100644 index 0000000..b80d77b Binary files /dev/null and b/Test/2.jpg differ diff --git a/Test/TestConflict.py b/Test/TestConflict.py new file mode 100644 index 0000000..f64cbca --- /dev/null +++ b/Test/TestConflict.py @@ -0,0 +1,29 @@ +# coding=utf-8 +import os +import sys +import time +import numpy as np + +# Add project root to sys.path +current_dir = os.path.dirname(os.path.abspath(__file__)) +# project_root = os.path.dirname(current_dir) +# if project_root not in sys.path: +# sys.path.append(project_root) + +print("Importing PaddleOCR...") +from paddleocr import PaddleOCR +print("Importing LlmUtil...") +# from Util.LlmUtil import get_llm_response +print("Imports done.") + +def main(): + image_path = os.path.join(current_dir, "2.jpg") + print(f"Initializing OCR...") + ocr = PaddleOCR(use_textline_orientation=True, lang="ch") + print("Running OCR...") + result = ocr.ocr(image_path) + print("OCR Done.") + print(result) + +if __name__ == "__main__": + main() diff --git a/Test/TestOCR.py b/Test/TestOCR.py new file mode 100644 index 0000000..2dfec1f --- /dev/null +++ b/Test/TestOCR.py @@ -0,0 +1,119 @@ +# coding=utf-8 +import os +import sys +from paddleocr import PaddleOCR +import numpy as np + +def test_ocr(): + # 1. 初始化 PaddleOCR + print("正在初始化 PaddleOCR 模型...") + try: + ocr = PaddleOCR(use_textline_orientation=True, lang="ch") + except Exception as e: + print(f"初始化失败: {e}") + return + + # 2. 准备测试图片 + current_dir = os.path.dirname(os.path.abspath(__file__)) + # 优先查找 2.jpg，如果不存在则查找 1.jpg + image_path = os.path.join(current_dir, "2.jpg") + if not os.path.exists(image_path): + image_path = os.path.join(current_dir, "1.jpg") + + if not os.path.exists(image_path): + print(f"错误: 未找到测试图片: {image_path}") + print("请将测试图片命名为 2.jpg 或 1.jpg 并放置在 Test 目录下。") + return + + print(f"正在识别图片: {image_path}") + + # 3. 执行识别 + try: + # result 是一个列表，通常包含一个 OCRResult 对象 (新版) 或列表 (旧版) + result = ocr.ocr(image_path) + + except Exception as e: + print(f"识别过程发生异常: {e}") + return + + # 4. 输出结果 + print("\n" + "="*20 + " 识别结果 " + "="*20) + sys.stdout.flush() + + # 同时输出到文件，方便查看 + output_file = os.path.join(current_dir, "ocr_output.txt") + with open(output_file, "w", encoding="utf-8") as f_out: + if not result: + msg = "未识别到任何文字 (Result is empty)。" + print(msg) + f_out.write(msg + "\n") + else: + # 取出第一个结果（通常是单张图片的结果） + res = result[0] + + # 检查是否为 None + if res is None: + msg = "未识别到任何文字 (Result[0] is None)。" + print(msg) + f_out.write(msg + "\n") + + # 情况 A: 新版 PaddleX OCRResult 对象 (表现为字典或对象) + elif hasattr(res, 'get') and 'rec_texts' in res: + texts = res.get('rec_texts', []) + scores = res.get('rec_scores', []) + + if not texts: + msg = "未识别到任何文字 (rec_texts is empty)。" + print(msg) + f_out.write(msg + "\n") + else: + for i, text in enumerate(texts): + score = scores[i] if i < len(scores) else 0.0 + msg = f"行 {i+1}: {text} (置信度: {score:.4f})" + print(msg) + f_out.write(msg + "\n") + + # 情况 B: 对象属性访问 + elif hasattr(res, 'rec_texts'): + texts = res.rec_texts + scores = res.rec_scores + + if not texts: + msg = "未识别到任何文字 (rec_texts is empty)。" + print(msg) + f_out.write(msg + "\n") + else: + for i, text in enumerate(texts): + score = scores[i] if i < len(scores) else 0.0 + msg = f"行 {i+1}: {text} (置信度: {score:.4f})" + print(msg) + f_out.write(msg + "\n") + + # 情况 C: 旧版 list of lists 结构 + elif isinstance(res, list): + for idx, line in enumerate(res): + try: + if len(line) >= 2 and isinstance(line[1], (tuple, list)): + text, score = line[1] + msg = f"行 {idx+1}: {text} (置信度: {score:.4f})" + print(msg) + f_out.write(msg + "\n") + else: + msg = f"行 {idx+1}: {line} (格式未知)" + print(msg) + f_out.write(msg + "\n") + except Exception as e: + print(f"行 {idx+1} 解析失败: {e}") + + else: + msg = f"无法解析结果结构: {type(res)}" + print(msg) + print(f"Result content: {res}") + f_out.write(msg + "\n") + f_out.write(f"Result content: {res}\n") + + print("="*50) + sys.stdout.flush() + +if __name__ == "__main__": + test_ocr() diff --git a/Test/TestOcrLlm.py b/Test/TestOcrLlm.py new file mode 100644 index 0000000..1d5d4d3 --- /dev/null +++ b/Test/TestOcrLlm.py @@ -0,0 +1,173 @@ +# coding=utf-8 +import os +import sys +import time +import asyncio +import json + +# Add project root to sys.path +current_dir = os.path.dirname(os.path.abspath(__file__)) +project_root = os.path.dirname(current_dir) +if project_root not in sys.path: + sys.path.append(project_root) + +from paddleocr import PaddleOCR +from Util.LlmUtil import get_llm_response +from Util.OcrParser import OcrParser +import re + +LOG_FILE = os.path.join(current_dir, "ocr_llm_debug.txt") + +def log(msg): + print(msg) + sys.stdout.flush() + with open(LOG_FILE, "a", encoding="utf-8") as f: + f.write(msg + "\n") + +# ... imports ... +def run_ocr_sync(): + image_path = os.path.join(current_dir, "2.jpg") + if not os.path.exists(image_path): + image_path = os.path.join(current_dir, "1.jpg") + + log(f"Testing OCR + LLM Pipeline on: {image_path}") + log("-" * 50) + + # --- Step 1: PaddleOCR --- + t_start = time.time() + + log("Initializing PaddleOCR...") + t_init_start = time.time() + try: + # 尝试使用轻量级模型 (Mobile) 以提升速度 + # ocr_version='PP-OCRv4' 通常默认是 mobile + ocr = PaddleOCR(use_textline_orientation=True, lang="ch", ocr_version='PP-OCRv4') + except Exception as e: + log(f"PaddleOCR Init Failed: {e}") + return None, None + + t_init_end = time.time() + log(f"PaddleOCR Init Time: {t_init_end - t_init_start:.4f}s") + + log("Running OCR Inference...") + t_ocr_start = time.time() + try: + result = ocr.ocr(image_path) + except Exception as e: + log(f"OCR Inference Failed: {e}") + return None, None + t_ocr_end = time.time() + + ocr_text_lines = [] + + # Handle different result structures + if not result: + log("OCR returned empty result.") + else: + res = result[0] + if res is None: + log("OCR result[0] is None.") + elif hasattr(res, 'get') and 'rec_texts' in res: + ocr_text_lines = res.get('rec_texts', []) + elif hasattr(res, 'rec_texts'): + ocr_text_lines = res.rec_texts + elif isinstance(res, list): + for line in res: + if len(line) >= 2 and isinstance(line[1], (tuple, list)): + ocr_text_lines.append(line[1][0]) + + ocr_text_block = "\n".join(ocr_text_lines) + log(f"OCR Result ({t_ocr_end - t_ocr_start:.4f}s):") + log(ocr_text_block) + log("-" * 50) + + return ocr_text_lines, ocr_text_block, (t_ocr_start, t_ocr_end) + +async def run_parsing_comparison(ocr_text_lines, ocr_text_block, timing_ocr): + t_ocr_start, t_ocr_end = timing_ocr + ocr_duration = t_ocr_end - t_ocr_start + + # --- Mode 1: Regex Parsing --- + log("Running Regex Parsing...") + t_regex_start = time.time() + try: + regex_data = OcrParser.parse(ocr_text_lines) + log("\nParsed Data (Regex):") + log(json.dumps(regex_data, indent=2, ensure_ascii=False)) + except Exception as e: + log(f"Regex Parsing Failed: {e}") + t_regex_end = time.time() + regex_duration = t_regex_end - t_regex_start + log(f"Regex Parsing Time: {regex_duration:.4f}s") + log("-" * 50) + + # --- Mode 2: LLM Parsing --- + log("Running LLM Parsing...") + + prompt = f""" + You are a data extraction assistant. Below is the OCR text recognized from a charging station list card. + Please extract the structured data and return it ONLY as a JSON object (no markdown, no extra text). + + Fields to extract: + - station_name: (String) Name of the charging station. + - distance: (String) Distance info (e.g., "7.4km"). + - price: (String) Price info (e.g., "0.7111/度"). + - tags: (List[String]) Any tags like "快", "闲3/4", "组团", "2倍积分", "P", etc. + - parking_info: (String) Parking related info. + + OCR Text: + {ocr_text_block} + """ + + t_llm_start = time.time() + response_text = "" + try: + log("Starting LLM request...") + async for chunk in get_llm_response(prompt, stream=True): + print(chunk, end='', flush=True) + response_text += chunk + + print("\n") + log("LLM request finished.") + + t_llm_end = time.time() + + log(f"\nLLM Response ({t_llm_end - t_llm_start:.4f}s):") + log(response_text) + + try: + clean_text = response_text.replace("```json", "").replace("```", "").strip() + data = json.loads(clean_text) + log("\nParsed JSON Data:") + log(json.dumps(data, indent=2, ensure_ascii=False)) + except json.JSONDecodeError: + log("\nFailed to parse JSON directly.") + + except Exception as e: + log(f"LLM Error: {e}") + t_llm_end = time.time() + + log("-" * 50) + log(f"Summary:") + log(f"OCR Time: {ocr_duration:.4f}s") + log(f"Regex Parsing Time: {regex_duration:.4f}s") + log(f"LLM Parsing Time: {t_llm_end - t_llm_start:.4f}s") + + total_regex = ocr_duration + regex_duration + total_llm = ocr_duration + (t_llm_end - t_llm_start) + + log(f"Total Pipeline (OCR+Regex): {total_regex:.4f}s") + log(f"Total Pipeline (OCR+LLM): {total_llm:.4f}s") + +def main(): + # Clear log file + with open(LOG_FILE, "w", encoding="utf-8") as f: + f.write("Starting TestOcrLlm...\n") + + ocr_lines, ocr_text, timing = run_ocr_sync() + if ocr_lines: + asyncio.run(run_parsing_comparison(ocr_lines, ocr_text, timing)) + +if __name__ == "__main__": + main() + diff --git a/Test/TestOcrRegex.py b/Test/TestOcrRegex.py new file mode 100644 index 0000000..fc2bcca --- /dev/null +++ b/Test/TestOcrRegex.py @@ -0,0 +1,107 @@ +# coding=utf-8 +import re +import json + +def parse_ocr_lines(lines): + result = { + "station_name": "", + "distance": "", + "price": None, + "piles": [], + "parking": "", + "tags": [] + } + + # Pre-process lines: remove confidence scores for parsing + clean_lines = [] + for line in lines: + # Remove prefix "行 X: " + text = re.sub(r"^行\s*\d+:\s*", "", line) + # Remove suffix "(置信度: ...)" + text = re.sub(r"\s*\(置信度:.*?\)\s*$", "", text) + clean_lines.append(text.strip()) + + # 1. Station Name + for line in clean_lines: + # Skip empty or short noise + if len(line) < 2: continue + # Skip if starts with special chars + if line.startswith("（") or line.startswith("("): continue + # Skip if contains typical attribute keywords + if "km" in line.lower() or "/度" in line or "￥" in line: continue + # Skip if strictly numeric (unlikely for name) + if re.match(r"^\d+$", line): continue + + result["station_name"] = line + break + + # 2. Distance + for line in clean_lines: + m = re.search(r"(\d+(\.\d+)?)\s*km", line, re.IGNORECASE) + if m: + result["distance"] = m.group(0) + break + + # 3. Price (Standard) + # Look for "0.xxxx/度" + for line in clean_lines: + m = re.search(r"(\d+\.\d+)(?=/度)", line) + if m: + result["price"] = float(m.group(1)) + break + + # 4. Piles + current_type = "未知" + for line in clean_lines: + if "快" in line: current_type = "快" + elif "慢" in line: current_type = "慢" + elif "超" in line: current_type = "超" + + # Match "闲3/4" or "3/4" + # Regex: optional "闲", int, /, int + m = re.search(r"(?:闲)?(\d+)/(\d+)", line) + if m: + # Check if it looks like a price (contains dot) + if "." in line: continue + + free = int(m.group(1)) + total = int(m.group(2)) + result["piles"].append({ + "type": current_type, + "free": free, + "total": total + }) + + # 5. Parking + for line in clean_lines: + if "停车" in line: + # Clean up leading punctuation + cleaned = re.sub(r"^[·\.\sP]+", "", line) + result["parking"] = cleaned + break + + return result + +if __name__ == "__main__": + # User provided sample data + sample_input = [ + "行 1: 长春市绿园区雁鸣湖公共充电站 (置信度: 0.9963)", + "行 2: （… (置信度: 0.6244)", + "行 3: 7.4km (置信度: 0.9975)", + "行 4: 0.7111/度 (置信度: 0.9450)", + "行 5: 快 (置信度: 0.9987)", + "行 6: 闲3/4 (置信度: 0.9941)", + "行 7: ￥ (置信度: 0.8734)", + "行 8: 组团 (置信度: 0.9995)", + "行 9: 2倍积分 (置信度: 0.9997)", + "行 10: P (置信度: 0.9929)", + "行 11: ·收费停车：以场地实际收费为准 (置信度: 0.9736)" + ] + + print("--- Input Data ---") + for l in sample_input: + print(l) + + parsed = parse_ocr_lines(sample_input) + print("\n--- Parsed Result (Regex) ---") + print(json.dumps(parsed, ensure_ascii=False, indent=2)) diff --git a/Test/TestPaddleOCRKit.py b/Test/TestPaddleOCRKit.py new file mode 100644 index 0000000..a933f2b --- /dev/null +++ b/Test/TestPaddleOCRKit.py @@ -0,0 +1,118 @@ +# coding=utf-8 +import os +import sys +import cv2 +import json +import time + +# Add project root to sys.path +current_dir = os.path.dirname(os.path.abspath(__file__)) +project_root = os.path.dirname(current_dir) +if project_root not in sys.path: + sys.path.append(project_root) + +from Util import Kit +from Util.PaddleOCRKit import get_ocr_kit + +def test_integration(): + image_path = os.path.join(current_dir, "2.jpg") + if not os.path.exists(image_path): + print(f"Image not found: {image_path}") + return + + print(f"Testing integration on: {image_path}") + + # 1. Generate Metadata using Kit + print("Running Kit.crop_cards_from_image...") + Kit.crop_cards_from_image(image_path, output_dir=current_dir) + + json_path = image_path.replace(".jpg", ".json") + json_metadata = {} + + if os.path.exists(json_path): + with open(json_path, 'r', encoding='utf-8') as f: + json_metadata = json.load(f) + + if not json_metadata.get("cards"): + print("Kit failed to find cards (expected for single card image). Mocking metadata.") + img = cv2.imread(image_path) + h, w = img.shape[:2] + json_metadata = { + "cards": [ + { + "id": 1, + "rect": [0, 0, w, h], + "click_point": [w//2, h//2] + } + ] + } + + print(f"Loaded metadata with {len(json_metadata.get('cards', []))} cards.") + + # 2. Run OCR Logic (Simulating Crawler.py) + print("Running OCR Logic...") + + ocr_kit = get_ocr_kit() + + original_img = cv2.imread(image_path) + stations = [] + + t_start = time.time() + + if "cards" in json_metadata and original_img is not None: + h_img, w_img = original_img.shape[:2] + + for card in json_metadata["cards"]: + rect = card.get("rect") + if not rect: continue + x1, y1, x2, y2 = rect + + # 边界检查 + x1 = max(0, min(x1, w_img)) + x2 = max(0, min(x2, w_img)) + y1 = max(0, min(y1, h_img)) + y2 = max(0, min(y2, h_img)) + + if x2 <= x1 or y2 <= y1: continue + + # 裁剪卡片 + cropped_card = original_img[y1:y2, x1:x2] + + # 识别 + parsed_data = ocr_kit.recognize(cropped_card) + print(f"Parsed Data: {json.dumps(parsed_data, indent=2, ensure_ascii=False)}") + + if parsed_data and parsed_data.get("station_name"): + # 格式化数据 + piles_list = parsed_data.get("piles", []) + piles_str_parts = [] + for p in piles_list: + p_type = p.get("type", "") + p_free = p.get("free", 0) + p_total = p.get("total", 0) + piles_str_parts.append(f"{p_type}:{p_free}/{p_total}") + + piles_str = " ".join(piles_str_parts) + + station_info = { + "station_name": parsed_data.get("station_name"), + "price": str(parsed_data.get("price")) if parsed_data.get("price") is not None else "", + "piles": piles_str, + "distance": parsed_data.get("distance", ""), + "uia_center_x": card["click_point"][0], + "uia_center_y": card["click_point"][1], + "tags": parsed_data.get("tags", []), + "parking_info": parsed_data.get("parking", "") + } + stations.append(station_info) + + t_end = time.time() + + print("-" * 50) + print(f"Total Processing Time: {t_end - t_start:.4f}s") + print(f"Found {len(stations)} stations:") + for s in stations: + print(json.dumps(s, indent=2, ensure_ascii=False)) + +if __name__ == "__main__": + test_integration() diff --git a/Test/__init__.py b/Test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Test/__pycache__/__init__.cpython-310.pyc b/Test/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..d573e7a Binary files /dev/null and b/Test/__pycache__/__init__.cpython-310.pyc differ diff --git a/Test/home.jpg b/Test/home.jpg new file mode 100644 index 0000000..b0ab130 Binary files /dev/null and b/Test/home.jpg differ diff --git a/Test/ocr_llm_debug.txt b/Test/ocr_llm_debug.txt new file mode 100644 index 0000000..6ffccbe --- /dev/null +++ b/Test/ocr_llm_debug.txt @@ -0,0 +1,78 @@ +Starting TestOcrLlm... +Testing OCR + LLM Pipeline on: D:\dsWork\YltProject\dsCrawler\Test\2.jpg +-------------------------------------------------- +Initializing PaddleOCR... +PaddleOCR Init Time: 1.7990s +Running OCR Inference... +OCR Result (1.7636s): +长春市绿园区雁鸣湖公共充电站 +.. +17.4km +0.7111/度 + +快 +闲3/4 +组团 +2倍积分 +P +收费停车：以场地实际收费为准 +-------------------------------------------------- +Running Regex Parsing... + +Parsed Data (Regex): +{ + "station_name": "长春市绿园区雁鸣湖公共充电站", + "distance": "17.4km", + "price": 0.7111, + "piles": [ + { + "type": "快", + "free": 3, + "total": 4 + } + ], + "parking": "收费停车：以场地实际收费为准", + "tags": [ + "组团", + "2倍积分", + "P" + ] +} +Regex Parsing Time: 0.0015s +-------------------------------------------------- +Running LLM Parsing... +Starting LLM request... +LLM request finished. + +LLM Response (3.6250s): +```json +{ + "station_name": "长春市绿园区雁鸣湖公共充电站", + "distance": "17.4km", + "price": "0.7111/度", + "tags": ["快", "闲3/4", "组团", "2倍积分", "P"], + "parking_info": "收费停车：以场地实际收费为准" +} +``` + +Parsed JSON Data: +{ + "station_name": "长春市绿园区雁鸣湖公共充电站", + "distance": "17.4km", + "price": "0.7111/度", + "tags": [ + "快", + "闲3/4", + "组团", + "2倍积分", + "P" + ], + "parking_info": "收费停车：以场地实际收费为准" +} +-------------------------------------------------- +Summary: +OCR Time: 1.7636s +Regex Parsing Time: 0.0015s +LLM Parsing Time: 3.6250s +Total Pipeline (OCR+Regex): 1.7652s +Total Pipeline (OCR+LLM): 5.3886s diff --git a/Test/ocr_llm_output.txt b/Test/ocr_llm_output.txt new file mode 100644 index 0000000..6206456 Binary files /dev/null and b/Test/ocr_llm_output.txt differ diff --git a/Test/ocr_llm_result.txt b/Test/ocr_llm_result.txt new file mode 100644 index 0000000..dad011f --- /dev/null +++ b/Test/ocr_llm_result.txt @@ -0,0 +1,27 @@ +OCR Result: +长春市绿园区雁鸣湖公共充电站 +（… +7.4km +0.7111/度 +快 +闲3/4 +￥ +组团 +2倍积分 +P +·收费停车：以场地实际收费为准 + +LLM Response: +{ + "station_name": "长春市绿园区雁鸣湖公共充电站", + "distance": "7.4km", + "price": "0.7111/度", + "tags": ["快", "闲3/4", "组团", "2倍积分", "P"], + "parking_info": "收费停车：以场地实际收费为准" +} + +-------------------------------------------------- +Summary: +OCR Time: 15.7442s +LLM Time: 3.1676s +Total Pipeline Time (excluding init): 18.9118s \ No newline at end of file diff --git a/Test/ocr_output.txt b/Test/ocr_output.txt new file mode 100644 index 0000000..b7c5740 --- /dev/null +++ b/Test/ocr_output.txt @@ -0,0 +1,11 @@ +行 1: 长春市绿园区雁鸣湖公共充电站 (置信度: 0.9963) +行 2: （… (置信度: 0.6244) +行 3: 7.4km (置信度: 0.9975) +行 4: 0.7111/度 (置信度: 0.9450) +行 5: 快 (置信度: 0.9987) +行 6: 闲3/4 (置信度: 0.9941) +行 7: ￥ (置信度: 0.8734) +行 8: 组团 (置信度: 0.9995) +行 9: 2倍积分 (置信度: 0.9997) +行 10: P (置信度: 0.9929) +行 11: ·收费停车：以场地实际收费为准 (置信度: 0.9736) diff --git a/Test/qbsd_bw.jpg b/Test/qbsd_bw.jpg new file mode 100644 index 0000000..0a0ecf7 Binary files /dev/null and b/Test/qbsd_bw.jpg differ diff --git a/Test/test02.py b/Test/test02.py new file mode 100644 index 0000000..9220fd5 --- /dev/null +++ b/Test/test02.py @@ -0,0 +1,9 @@ +import uiautomator2 as u2 + +d = u2.connect() + +d.reset_uiautomator() +d.sleep(3) +xml = d.dump_hierarchy() # 导出当前UI结构（XML） +with open("ui_hierarchy5_2.xml", "w", encoding="utf-8") as f: + f.write(xml) \ No newline at end of file diff --git a/Test/test03.py b/Test/test03.py new file mode 100644 index 0000000..56ee179 --- /dev/null +++ b/Test/test03.py @@ -0,0 +1,14 @@ +from Util.ObsUtil import ObsUploader + +from Config.Config import OBS_TMP_PREFIX + +uploader = ObsUploader() + +object_key = OBS_TMP_PREFIX + "/1d3eb56c-942e-42d3-8993-f1ea8ad7d97b.jpg" +success, result = uploader.upload_file( + object_key=object_key, + file_path="Screenshot/1d3eb56c-942e-42d3-8993-f1ea8ad7d97b.jpg" +) + +print(success) +print(result) diff --git a/Test/test04.py b/Test/test04.py new file mode 100644 index 0000000..dd4bd0d --- /dev/null +++ b/Test/test04.py @@ -0,0 +1,12 @@ +import uiautomator2 as u2 + +d = u2.connect() + +# 启动应用（以微信为例） +d.app_start("com.tencent.mm") + +# 截微信图保存 +d.screenshot("home.jpg") + +# 关闭应用 +d.app_stop("com.tencent.mm") \ No newline at end of file diff --git a/Test/ui_hierarchy5_2.xml b/Test/ui_hierarchy5_2.xml new file mode 100644 index 0000000..96a6655 --- /dev/null +++ b/Test/ui_hierarchy5_2.xml @@ -0,0 +1,45 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/Util/DevTools/crop_test.py b/Util/DevTools/crop_test.py new file mode 100644 index 0000000..7c57f6d --- /dev/null +++ b/Util/DevTools/crop_test.py @@ -0,0 +1,73 @@ +# -*- coding: utf-8 -*- +""" +基于行扫描和统计特征的卡片截取工具 +""" +import sys +import os + +# 添加项目根目录到系统路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) +from Util import Kit + +def clean_directory(dir_path): + """ + 清理现场：删除目录下除纯数字.jpg以外的所有文件 + """ + print(f"Cleaning directory: {dir_path}") + if not os.path.exists(dir_path): + return + + for filename in os.listdir(dir_path): + path = os.path.join(dir_path, filename) + if not os.path.isfile(path): + continue + + name, ext = os.path.splitext(filename) + # 保留条件：后缀是 .jpg 且文件名是纯数字 + # [修改] 同时保留 _flag.jpg, _vl.jpg 供查看 + # [修改] 保留 .json 文件 + if ext.lower() == ".json": + continue + + if ext.lower() == ".jpg" and (name.isdigit() or name.endswith("_flag") or name.endswith("_vl")): + continue + + # 清理旧的 _for_vl.jpg + if name.endswith("_for_vl"): + pass # Let it fall through to delete + else: + # 其他非生成的文件，可能需要保留吗？ + # 这里的逻辑是清理 output 目录，假设该目录下只有生成的图片 + # 如果是原始图片（比如 1.jpg），不能删！ + pass + + # 原始图片保护 (文件名比较短，通常是 1.jpg, 2.jpg 等) + if len(name) <= 2 and name.isdigit(): + continue + + try: + os.remove(path) + print(f" Deleted: {filename}") + except Exception as e: + print(f" Error deleting {filename}: {e}") + +def crop_cards(img_path): + Kit.crop_cards_from_image(img_path) + +if __name__ == "__main__": + test_files = [ + r"d:\dsWork\dsProject\dsCrawler\Tools\Images\1.jpg", + r"d:\dsWork\dsProject\dsCrawler\Tools\Images\2.jpg", + r"d:\dsWork\dsProject\dsCrawler\Tools\Images\3.jpg", + r"d:\dsWork\dsProject\dsCrawler\Tools\Images\4.jpg" + ] + + # 在测试前清理现场 + if test_files: + # 假设所有图片都在同一个文件夹 + target_dir = os.path.dirname(test_files[0]) + clean_directory(target_dir) + + for f in test_files: + crop_cards(f) diff --git a/Util/DevTools/screenshot_test.py b/Util/DevTools/screenshot_test.py new file mode 100644 index 0000000..3c26658 --- /dev/null +++ b/Util/DevTools/screenshot_test.py @@ -0,0 +1,58 @@ +# coding=utf-8 +import asyncio +import logging +import os +from datetime import datetime +import uiautomator2 as u2 + +# 配置日志输出 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger("ScreenshotTool") + + +async def take_screenshot(): + """ + 打开微信主界面并拍照（截图） + """ + # 连接设备 + d = u2.connect() + + # 执行截图 + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"current_{timestamp}.jpg" + + # 获取目标目录: 项目根目录下的 Temp/DevTools + project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + target_dir = os.path.join(project_root, "Temp", "DevTools") + os.makedirs(target_dir, exist_ok=True) + filepath = os.path.join(target_dir, filename) + + logger.info(f"正在拍照（截图）并保存至: {filepath}") + d.screenshot(filepath) + + if os.path.exists(filepath): + logger.info("拍照成功！") + return filepath + else: + logger.error("拍照失败，未找到生成的文件。") + return None + + +async def main(): + filepath = await take_screenshot() + if filepath: + logger.info(f"任务执行完成，截图文件: {filepath}") + else: + logger.error("任务执行失败。") + + +if __name__ == "__main__": + try: + asyncio.run(main()) + except KeyboardInterrupt: + logger.info("程序被用户中断.") + except Exception as e: + logger.exception(f"运行过程中出现异常: {e}") diff --git a/Util/Kit.py b/Util/Kit.py new file mode 100644 index 0000000..c7017aa --- /dev/null +++ b/Util/Kit.py @@ -0,0 +1,922 @@ +import logging +import os +import cv2 +import numpy as np +import time +from Config.Config import BOTTOM_SAFE_EXCLUDE_RATIO + +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + +def read_image(path): + """读取图片，支持中文路径""" + try: + return cv2.imdecode(np.fromfile(path, dtype=np.uint8), -1) + except Exception as e: + logger.info(f"Error reading image {path}: {e}") + return None + +def save_image(path, img): + """保存图片，支持中文路径""" + try: + ext = os.path.splitext(path)[1] + if not ext: + ext = ".jpg" + cv2.imencode(ext, img)[1].tofile(path) + return True + except Exception as e: + logger.error(f"Error saving image {path}: {e}") + return False + +# 截图 +def take_screenshot(d, image_uuid, save_dir="Screenshot"): + path = f"{save_dir}/{image_uuid}.jpg" + os.makedirs(save_dir, exist_ok=True) + d.screenshot(path) + return path + +def click_image_template(d, template_path, timeout=5.0, threshold=0.8): + """ + 使用 OpenCV 模板匹配查找并点击图片 + :param d: uiautomator2 设备对象 + :param template_path: 模板图片路径 + :param timeout: 超时时间（秒） + :param threshold: 匹配阈值 (0.0 - 1.0) + :return: 是否点击成功 + """ + if not os.path.exists(template_path): + logger.info(f"Template file not found: {template_path}") + return False + + template = read_image(template_path) + if template is None: + logger.info(f"Failed to load template: {template_path}") + return False + + t_h, t_w = template.shape[:2] + + start_time = time.time() + best_val_overall = 0.0 + + while time.time() - start_time < timeout: + # 临时截图 + temp_uuid = "temp_click_check" + screenshot_path = take_screenshot(d, temp_uuid, save_dir="Temp") + + target = read_image(screenshot_path) + if target is None: + time.sleep(0.5) + continue + + # 多尺度匹配 + found = None + # 缩放比例从 0.5 到 1.5，步长 0.1 + for scale in np.linspace(0.5, 1.5, 11): + # 调整模板大小 + resized_template = cv2.resize(template, (int(t_w * scale), int(t_h * scale))) + r_h, r_w = resized_template.shape[:2] + + # 如果模板比目标还大，跳过 + if r_h > target.shape[0] or r_w > target.shape[1]: + continue + + result = cv2.matchTemplate(target, resized_template, cv2.TM_CCOEFF_NORMED) + min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) + + if max_val > best_val_overall: + best_val_overall = max_val + + if max_val >= threshold: + found = (max_val, max_loc, r_w, r_h) + break # 找到满足阈值的即停止当前截图的搜索 + + # 清理临时文件 + try: + os.remove(screenshot_path) + except: + pass + + if found: + max_val, max_loc, r_w, r_h = found + # 计算中心点 + top_left = max_loc + center_x = top_left[0] + r_w // 2 + center_y = top_left[1] + r_h // 2 + + logger.info(f"Found image at ({center_x}, {center_y}) with confidence {max_val:.2f}") + d.click(center_x, center_y) + return True + + time.sleep(1.0) + + logger.info(f"Image not found after {timeout}s (Best confidence: {best_val_overall:.2f})") + return False + +def detect_black_agree_button(image_path, debug_dir=None): + """ + 通过计算机图形学检测黑色的"同意"按钮 (Image 1 场景) + 特征：黑色圆角矩形，位于屏幕中下部，面积适中 + :param image_path: 截图路径 + :return: (x, y) 坐标中心点，如果未找到返回 None + """ + if not os.path.exists(image_path): + return None + + img = read_image(image_path) + if img is None: + return None + + h, w = img.shape[:2] + + # 转换为HSV颜色空间，因为黑色更容易过滤 + hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + + # 定义黑色的范围 + # 黑色：V (Brightness) 很低 + lower_black = np.array([0, 0, 0]) + upper_black = np.array([180, 255, 40]) # V < 40 认为是黑色 + + mask = cv2.inRange(hsv, lower_black, upper_black) + + # 限制搜索区域：通常在屏幕下半部分 + roi_top = int(h * 0.4) + roi_bottom = int(h * 0.8) + roi_mask = np.zeros_like(mask) + roi_mask[roi_top:roi_bottom, :] = mask[roi_top:roi_bottom, :] + + # 形态学操作：去除噪点，连接断开的区域 + kernel = np.ones((5, 5), np.uint8) + roi_mask = cv2.morphologyEx(roi_mask, cv2.MORPH_CLOSE, kernel) + roi_mask = cv2.morphologyEx(roi_mask, cv2.MORPH_OPEN, kernel) + + # 查找轮廓 + contours, _ = cv2.findContours(roi_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + best_cnt = None + max_area = 0 + + for cnt in contours: + area = cv2.contourArea(cnt) + x, y, cw, ch = cv2.boundingRect(cnt) + aspect_ratio = float(cw) / ch + + # 过滤条件 + # 1. 面积要够大 (例如 > 屏幕面积的 1%) + if area < (w * h * 0.01): + continue + + # 2. 宽高比：通常按钮是扁长条，例如 > 2.0 + if aspect_ratio < 2.0 or aspect_ratio > 10.0: + continue + + # 3. 宽度：通常占据屏幕宽度的 50% 以上 + if cw < (w * 0.5): + continue + + if area > max_area: + max_area = area + best_cnt = cnt + + if best_cnt is not None: + x, y, cw, ch = cv2.boundingRect(best_cnt) + center_x = x + cw // 2 + center_y = y + ch // 2 + logger.info(f"Found Black Agree Button at ({center_x}, {center_y}), Size: {cw}x{ch}") + + if debug_dir: + os.makedirs(debug_dir, exist_ok=True) + debug_img = img.copy() + cv2.rectangle(debug_img, (x, y), (x+cw, y+ch), (0, 0, 255), 2) + cv2.circle(debug_img, (center_x, center_y), 5, (0, 255, 0), -1) + save_image(os.path.join(debug_dir, "debug_agree_btn.jpg"), debug_img) + + return (center_x, center_y) + + return None + +def detect_ad_close_x(image_path, template_path, debug_dir=None, threshold=0.7): + """ + 通过模板匹配检测"关闭(X)"按钮 (Image 2 场景) + 支持多尺度匹配 + :param image_path: 截图路径 + :param template_path: 模板图片路径 + :param debug_dir: 调试目录 + :param threshold: 匹配阈值 + :return: (x, y) 坐标中心点，如果未找到返回 None + """ + if not os.path.exists(image_path): + return None + + if not os.path.exists(template_path): + logger.warning(f"Template not found: {template_path}") + return None + + target = read_image(image_path) + template = read_image(template_path) + + if target is None or template is None: + return None + + # 转换为灰度图进行匹配，减少颜色干扰 + target_gray = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY) + template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY) + + t_h, t_w = template_gray.shape[:2] + + best_match = None + + # 多尺度匹配: 缩放模板 + # 假设模板可能比实际大，也可能小。范围 0.5 - 1.5 + scales = np.linspace(0.5, 1.5, 20) + + for scale in scales: + # 计算缩放后的模板尺寸 + new_w = int(t_w * scale) + new_h = int(t_h * scale) + + # 确保缩放后的模板不大于目标图像 + if new_w > target_gray.shape[1] or new_h > target_gray.shape[0]: + continue + + resized_template = cv2.resize(template_gray, (new_w, new_h)) + + # 匹配 + result = cv2.matchTemplate(target_gray, resized_template, cv2.TM_CCOEFF_NORMED) + min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) + + if best_match is None or max_val > best_match[0]: + best_match = (max_val, max_loc, scale, new_w, new_h) + + if best_match is None: + return None + + max_val, max_loc, best_scale, best_w, best_h = best_match + + # 获取目标图像尺寸 + target_h, target_w = target_gray.shape[:2] + + top_left = max_loc + center_x = top_left[0] + best_w // 2 + center_y = top_left[1] + best_h // 2 + + # --- 位置启发式过滤 --- + # 计算相对位置 + rel_x = center_x / target_w + rel_y = center_y / target_h + + logger.info(f"Ad Close Button Match: Confidence={max_val:.4f}, Scale={best_scale:.2f}, Pos=({center_x}, {center_y}), Rel=({rel_x:.2f}, {rel_y:.2f})") + + is_valid_pos = True + + # 规则1: 过滤掉屏幕正中央偏上的区域 (通常是广告标题、图标或内容) + # 范围: X在 [0.3, 0.7] 且 Y在 [0.15, 0.5] + if 0.3 < rel_x < 0.7 and 0.15 < rel_y < 0.5: + logger.warning(f"Ignored match at ({center_x}, {center_y}) - likely Ad Content/Title (Center-Top area).") + is_valid_pos = False + + # 规则2: 如果置信度不是特别高 (>0.9), 强制要求在典型区域 (右上角 或 底部中间) + # 右上角: X > 0.7, Y < 0.5 + # 底部中间: Y > 0.6 + if is_valid_pos and max_val < 0.98: # 如果置信度非常高(0.98+)，可能是极其标准的X，暂时放过(除非在禁区) + if not ((rel_x > 0.7 and rel_y < 0.5) or (rel_y > 0.6)): + logger.warning(f"Ignored match at ({center_x}, {center_y}) - not in typical Close Button regions (Top-Right or Bottom).") + is_valid_pos = False + + if max_val >= threshold: + if is_valid_pos: + # [Safety Check] 底部安全区排除 + # 如果检测到的关闭按钮位于屏幕底部 BOTTOM_SAFE_EXCLUDE_RATIO 区域内，认为是误判（如误触底部功能按钮） + if center_y > (target_h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO)): + logger.warning(f"Ignored Ad Close Button at ({center_x}, {center_y}) - in Bottom Safety Zone ({int(BOTTOM_SAFE_EXCLUDE_RATIO*100)}%).") + # 也可以保存一下调试图 + if debug_dir: + os.makedirs(debug_dir, exist_ok=True) + debug_img = target.copy() + cv2.rectangle(debug_img, top_left, (top_left[0] + best_w, top_left[1] + best_h), (0, 0, 128), 2) # Dark Red for Safety Ignored + cv2.putText(debug_img, f"SAFETY IGNORED", + (top_left[0], top_left[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 128), 1) + save_image(os.path.join(debug_dir, "debug_ad_close_safety_ignored.jpg"), debug_img) + return None + + logger.info(f"Found Ad Close Button at ({center_x}, {center_y})") + + if debug_dir: + os.makedirs(debug_dir, exist_ok=True) + debug_img = target.copy() + cv2.rectangle(debug_img, top_left, (top_left[0] + best_w, top_left[1] + best_h), (0, 0, 255), 2) + cv2.circle(debug_img, (center_x, center_y), 5, (0, 255, 0), -1) + cv2.putText(debug_img, f"Conf: {max_val:.2f}, Sc: {best_scale:.2f}", + (top_left[0], top_left[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) + save_image(os.path.join(debug_dir, "debug_ad_close_x.jpg"), debug_img) + + return (center_x, center_y) + else: + # 虽然置信度高，但是位置不对，保存为 False Positive 供调试 + if debug_dir: + os.makedirs(debug_dir, exist_ok=True) + debug_img = target.copy() + cv2.rectangle(debug_img, top_left, (top_left[0] + best_w, top_left[1] + best_h), (0, 165, 255), 2) # Orange for ignored + cv2.putText(debug_img, f"IGNORED Pos", + (top_left[0], top_left[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 165, 255), 1) + save_image(os.path.join(debug_dir, "debug_ad_close_ignored.jpg"), debug_img) + + # 如果没找到，但有一定置信度，也保存一下调试图以便分析 + if max_val > 0.4 and debug_dir: + os.makedirs(debug_dir, exist_ok=True) + debug_img = target.copy() + top_left = max_loc + cv2.rectangle(debug_img, top_left, (top_left[0] + best_w, top_left[1] + best_h), (0, 255, 255), 2) + cv2.putText(debug_img, f"Failed Conf: {max_val:.2f}", + (top_left[0], top_left[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1) + save_image(os.path.join(debug_dir, "debug_ad_close_fail.jpg"), debug_img) + + return None + +def detect_any_ad_close(image_path, template_dir, debug_dir=None): + """ + 遍历模板目录下的所有 ad_close*.jpg 进行匹配 + """ + if not os.path.exists(template_dir): + return None + + for filename in os.listdir(template_dir): + if filename.startswith("ad_close") and filename.endswith(".jpg"): + template_path = os.path.join(template_dir, filename) + logger.info(f"Trying template: {filename}") + pos = detect_ad_close_x(image_path, template_path, debug_dir=debug_dir) + if pos: + return pos + return None + +def detect_bottom_close_circle(image_path, debug_dir=None): + """ + 通过几何特征检测底部的圆形关闭按钮 (常见于插屏广告) + 特征： + 1. 位于屏幕底部区域 (Y > 60%) + 2. 水平居中 (X 靠近 W/2) + 3. 圆形或近似圆形 + 4. 内部有高对比度边缘 (X号) + """ + if not os.path.exists(image_path): + return None + + img = read_image(image_path) + if img is None: + return None + + h, w = img.shape[:2] + + # 1. 提取感兴趣区域 (ROI): 屏幕底部 25% (缩小范围，避开列表内容区) + roi_top = int(h * 0.75) + roi_h = h - roi_top + roi = img[roi_top:h, :] + + # 转灰度 + gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY) + # 高斯模糊降噪 + gray_blurred = cv2.GaussianBlur(gray, (9, 9), 2) + + # 2. 霍夫圆变换检测圆形 + # dp=1.2 (累加器分辨率), minDist=w/5 (圆心最小距离), param1=100 (Canny高阈值), param2=30 (圆心累加阈值), minR=w*0.04, maxR=w*0.1 + # 缩小最大半径范围，防止识别到过大的按钮（如扫码充电按钮） + circles = cv2.HoughCircles(gray_blurred, cv2.HOUGH_GRADIENT, dp=1.2, minDist=w/5, + param1=100, param2=30, minRadius=int(w*0.04), maxRadius=int(w*0.09)) + + if circles is not None: + circles = np.round(circles[0, :]).astype("int") + + best_circle = None + min_dist_to_center = float('inf') + + for (cx, cy, r) in circles: + # 还原到全图坐标 + global_cy = roi_top + cy + global_cx = cx + + # 过滤1: 必须在水平中心附近 (容差 15%，收紧限制) + if abs(global_cx - w//2) > (w * 0.15): + continue + + # 过滤2: 垂直方向限制 (必须在屏幕 80% - 95% 之间) + # 这样可以避开位于 60%-80% 区域的列表项卡片 + rel_y = global_cy / h + if rel_y < 0.80 or rel_y > 0.95: + continue + + dist = abs(global_cx - w//2) + if dist < min_dist_to_center: + min_dist_to_center = dist + best_circle = (global_cx, global_cy, r) + + if best_circle: + cx, cy, r = best_circle + # 确保转换为标准的 Python int，否则 uiautomator2 click 可能会报错 (JSON serializable error) + cx, cy, r = int(cx), int(cy), int(r) + + # [Safety Check] 底部安全区排除 + # 如果检测到的圆形按钮位于屏幕底部 BOTTOM_SAFE_EXCLUDE_RATIO 区域内，认为是误判（如误触底部扫码充电等） + if cy > (h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO)): + logger.warning(f"Ignored Bottom Circle at ({cx}, {cy}) - in Bottom Safety Zone ({int(BOTTOM_SAFE_EXCLUDE_RATIO*100)}%).") + return None + + logger.info(f"Found Bottom Circle Button via Hough: ({cx}, {cy}), r={r}") + + if debug_dir: + os.makedirs(debug_dir, exist_ok=True) + debug_img = img.copy() + cv2.circle(debug_img, (cx, cy), r, (0, 255, 0), 2) + cv2.circle(debug_img, (cx, cy), 2, (0, 0, 255), 3) + save_image(os.path.join(debug_dir, "debug_bottom_circle.jpg"), debug_img) + + return (cx, cy) + + return None + +def find_expand_button_position(image_path, debug_dir=None, debug_filename_prefix=None): + """ + 通过几何特征识别"全部时段"按钮的位置 + 特征：该行左侧(30%)和右侧(30%)基本为空白，中间有内容 + :param image_path: 截图路径 + :param debug_dir: 调试图片保存目录，如果为None则不保存 + :param debug_filename_prefix: 调试图片文件名前缀 + :return: (x, y) 坐标中心点，如果未找到返回 None + """ + if not os.path.exists(image_path): + return None + + img = read_image(image_path) + if img is None: + return None + + h, w = img.shape[:2] + + # 转灰度 + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + + # 边缘检测 + edges = cv2.Canny(gray, 50, 150) + + # 定义区域 + left_w = int(w * 0.35) + right_w = int(w * 0.65) + + # 提取各部分边缘 + left_part = edges[:, :left_w] + right_part = edges[:, right_w:] + center_part = edges[:, left_w:right_w] + + # 计算每一行的边缘点数量 + row_sum_left = np.sum(left_part, axis=1) / 255.0 + row_sum_right = np.sum(right_part, axis=1) / 255.0 + row_sum_center = np.sum(center_part, axis=1) / 255.0 + + # 筛选条件：左右边缘点很少，中间边缘点较多 + # 阈值可以根据实际情况调整 + # 允许少量噪点，所以不是严格的0，而是小于某个较小值（例如宽度的1%） + noise_threshold = 2 # 允许2个像素的噪点 + content_threshold = 5 # 中间至少有5个像素的边缘 + + candidates = (row_sum_left <= noise_threshold) & \ + (row_sum_right <= noise_threshold) & \ + (row_sum_center >= content_threshold) + + # 找到连续的候选行 + y_indices = np.where(candidates)[0] + + if len(y_indices) == 0: + return None + + # 将连续行分组 + segments = [] + if len(y_indices) > 0: + start = y_indices[0] + prev = y_indices[0] + for y in y_indices[1:]: + if y > prev + 5: # 允许5像素断裂 + segments.append((start, prev)) + start = y + prev = y + segments.append((start, prev)) + + # 筛选最合适的段 + best_segment = None + + # 我们期望按钮在屏幕中下部，且高度适中（例如 20-100px） + # 且通常是在价格表下方。假设价格表占据了屏幕上部。 + # 我们可以简单地取符合条件的段中，Y值最大的那个（最靠下的），或者最符合"中间有字"特征的。 + # 考虑到页面底部可能有其他干扰，取"中下部"的一个。 + + valid_segments = [] + for start, end in segments: + height = end - start + mid_y = (start + end) // 2 + + # 过滤掉太高或太矮的区域 + if height < 20 or height > 150: + continue + + # 过滤掉顶部的区域（可能是标题栏误判） + if mid_y < h * 0.3: + continue + + # 过滤掉底部的区域（可能是底部按钮） + if mid_y > h * 0.9: + continue + + valid_segments.append((start, end)) + + if not valid_segments: + return None + + # 如果有多个，通常"全部时段"是在价格表之后，紧接着的一个 + # 这里我们取第一个（最靠上的）符合条件的段，因为它紧跟在价格表下方 + # 或者取所有段中，中间内容最"紧凑"的？ + # 让我们简单点，取第一个符合条件的段。 + best_segment = valid_segments[0] + + start, end = best_segment + center_y = (start + end) // 2 + center_x = w // 2 + + if debug_dir: + os.makedirs(debug_dir, exist_ok=True) + debug_img = img.copy() + # 画出识别区域 + cv2.rectangle(debug_img, (0, start), (w, end), (0, 255, 0), 2) + # 画出红点 + cv2.circle(debug_img, (center_x, center_y), 10, (0, 0, 255), -1) + + # 保存 flag 图片 + if debug_filename_prefix: + # Sanitize filename: remove invalid chars + import re + safe_prefix = re.sub(r'[\\/*?:"<>|]', '_', str(debug_filename_prefix)) + debug_name = f"{safe_prefix}_flag_expand.jpg" + else: + timestamp = time.strftime("%Y%m%d_%H%M%S") + debug_name = f"{timestamp}_flag_expand.jpg" + + debug_path = os.path.join(debug_dir, debug_name) + save_image(debug_path, debug_img) + logger.info(f"Saved debug image to {debug_path}") + + return (int(center_x), int(center_y)) + + +def get_row_stats(gray): + """ + 计算每一行的统计特征 + """ + h, w = gray.shape + + # 中央区域 (用于检测内容) + center_x = w // 2 + strip_w = 100 + center_strip = gray[:, center_x - 50 : center_x + 50] + + # 边缘区域 (用于检测背景/边距) + # 假设边距至少有 10px + edge_strip = gray[:, 0:20] + + row_means = np.mean(center_strip, axis=1) + row_stds = np.std(center_strip, axis=1) + edge_means = np.mean(edge_strip, axis=1) + + return row_means, row_stds, edge_means + +def crop_cards_from_image(img_path, output_dir=None, save_debug=True): + """ + 从图片中裁剪场站卡片 + :param img_path: 图片路径 + :param output_dir: 输出目录，默认与 img_path 相同 + :param save_debug: 是否保存调试图 (_flag.jpg) + :return: 裁剪出的卡片列表，每项包含 (out_path, (click_x, click_y)) + """ + logger.info(f"Processing: {img_path}") + if not os.path.exists(img_path): + logger.info(f"Error: File not found {img_path}") + return [] + + img = read_image(img_path) + if img is None: + logger.info(f"Error: Failed to load image {img_path}") + return [] + + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + h, w = gray.shape + + row_means, row_stds, edge_means = get_row_stats(gray) + + # 参数定义 + # 背景灰度值范围 (根据 Analyze2.py 的输出，背景约 242) + BG_GRAY_MIN = 230 + BG_GRAY_MAX = 250 + + # 间隙判定：中央区域也是均匀的灰度 + # 真正的背景灰度约 242。卡片内的伪背景(不够白)约 246-248。 + # 所以降低 GAP_MEAN_MAX 以区分 Gap 和 Dirty White。 + GAP_MEAN_MIN = 235 + GAP_MEAN_MAX = 244 + GAP_STD_MAX = 10.0 + + segments = [] + is_in_card = False + start_y = 0 + + # 记录原始的行状态，用于后续可能的合并优化 + row_is_card = [] + + for y in range(h): + rm = row_means[y] + rs = row_stds[y] + em = edge_means[y] + + # 1. 判定当前行是否可能是卡片的一部分 + # 条件A: 边缘是灰色的 (说明有边距，排除了全宽的Header/Footer) + has_gray_margin = (BG_GRAY_MIN <= em <= BG_GRAY_MAX) + + # 条件B: 中央不是均匀的背景灰 (说明有内容，或者是白色底) + # 如果是 Gap，则 Mean 在 Gap范围 且 Std 很小 + is_gap = (GAP_MEAN_MIN <= rm <= GAP_MEAN_MAX) and (rs < GAP_STD_MAX) + + is_card_row = has_gray_margin and not is_gap + row_is_card.append(is_card_row) + + # 简单的形态学闭运算：填补卡片内部的小裂缝 + # 如果一个 False (Gap) 的上下都是 True (Card)，且 Gap 长度很短，则将其视为 Card + # 允许的裂缝最大长度 + # [优化] 减小填补阈值，避免把两个卡片中间的真 Gap 填补了导致粘连 + # 之前是 10，现在改为 3。 + MAX_GAP_FILL = 3 + + cleaned_row_is_card = row_is_card[:] + + # 查找连续的 False 区域 + i = 0 + while i < h: + if not cleaned_row_is_card[i]: + # Found a gap start + gap_start = i + while i < h and not cleaned_row_is_card[i]: + i += 1 + gap_end = i + gap_len = gap_end - gap_start + + # Check context + prev_is_card = (gap_start > 0) and cleaned_row_is_card[gap_start - 1] + next_is_card = (gap_end < h) and cleaned_row_is_card[gap_end] + + if prev_is_card and next_is_card and gap_len <= MAX_GAP_FILL: + # Fill the gap + for k in range(gap_start, gap_end): + cleaned_row_is_card[k] = True + else: + i += 1 + + # 根据 cleaned_row_is_card 生成 segments + is_in_card = False + start_y = 0 + for y in range(h): + if cleaned_row_is_card[y]: + if not is_in_card: + is_in_card = True + start_y = y + else: + if is_in_card: + is_in_card = False + end_y = y + height = end_y - start_y + + # 过滤太矮的区域 + if height > 100: # 稍微放宽一点，150 -> 100 + segments.append((start_y, end_y)) + + # 处理最后一个 segment + if is_in_card: + end_y = h + height = end_y - start_y + if height > 100: + segments.append((start_y, end_y)) + + # [新增] 触底过滤：过滤掉延伸到图片底部的 Segment + # 底部通常是导航栏或被截断的卡片 + BOTTOM_MARGIN = 50 # 距离底部多少像素内算触底 + valid_segments_scan = [] + for y1, y2 in segments: + if y2 >= h - BOTTOM_MARGIN: + logger.info(f" Dropping segment Y={y1}-{y2} because it touches the bottom (H={h}).") + continue + valid_segments_scan.append((y1, y2)) + segments = valid_segments_scan + + logger.info(f" Found {len(segments)} potential segments based on vertical scan.") + + # 1.5 Group Segments by Large Gaps (to separate Header / List / Footer) + # The filter bar usually creates a large gap (e.g. > 60px). + SECTION_GAP_MIN = 60 + groups = [] + if segments: + current_group = [segments[0]] + for i in range(1, len(segments)): + prev_end = segments[i-1][1] + curr_start = segments[i][0] + gap = curr_start - prev_end + + if gap > SECTION_GAP_MIN: + groups.append(current_group) + current_group = [] + + current_group.append(segments[i]) + groups.append(current_group) + + logger.info(f" Found {len(groups)} segment groups.") + + # Select the Target Group + # Strategy: Select the LAST group that has at least one 'substantial' segment (H > 150). + # This assumes the Station List is the main content and usually at the bottom (scrolling area). + target_group = [] + for g in reversed(groups): + # Check if group has substantial content + has_content = False + for (y1, y2) in g: + if (y2 - y1) > 150: + has_content = True + break + + if has_content: + target_group = g + break + + if not target_group and segments: + # Fallback to all segments if no substantial group found + target_group = segments + + logger.info(f" Selected group with {len(target_group)} segments.") + + # 2. 确定每个卡片的左右边界 (Width Refinement) + # 收集所有 segment 的建议边界，然后统一 + + candidate_x1 = [] + candidate_x2 = [] + + temp_valid_segments = [] + + for y1, y2 in target_group: + # 取中间一段行来分析 + mid_y = (y1 + y2) // 2 + sample_h = min(10, y2 - y1) + sample_rows = gray[mid_y - sample_h//2 : mid_y + sample_h//2, :] + col_means = np.mean(sample_rows, axis=0) + + # 从左向右找第一个非灰像素 + x1 = 0 + for x in range(w // 2): + if not (BG_GRAY_MIN <= col_means[x] <= BG_GRAY_MAX): + x1 = x + break + + # 从右向左找第一个非灰像素 + x2 = w + for x in range(w - 1, w // 2, -1): + if not (BG_GRAY_MIN <= col_means[x] <= BG_GRAY_MAX): + x2 = x + break + + if x1 >= x2 or (x2 - x1) < w * 0.5: + # 如果这个 segment 无法确定宽度，可能不是有效卡片，暂不参与宽度投票 + # 但为了不漏掉，暂时先记录，用默认值填充 + logger.warning(f" Warning: Segment {y1}-{y2} has weird width {x2-x1}.") + pass + else: + candidate_x1.append(x1) + candidate_x2.append(x2) + + temp_valid_segments.append((y1, y2)) + + # 计算统一宽度 + if not candidate_x1: + logger.info(" No valid width detected. Using default.") + final_x1 = 0 + final_x2 = w + else: + # 使用中位数或众数来消除噪声 + # 考虑到对齐，Min x1 和 Max x2 可能更合适？或者 Median。 + # 通常卡片是对齐的，所以 x1 应该几乎一样。 + final_x1 = int(np.median(candidate_x1)) + final_x2 = int(np.median(candidate_x2)) + + # 稍微加点 Padding (但不要超过图片边界) + final_x1 = max(0, final_x1 - 5) + final_x2 = min(w, final_x2 + 5) + + logger.info(f" Unified Width: X={final_x1}-{final_x2}, W={final_x2 - final_x1}") + + # 3. 过滤高度异常的卡片 (Height Filtering) + # 计算所有潜在卡片的高度 + final_cards = [] + if not temp_valid_segments: + logger.info(" No segments found.") + else: + heights = [y2 - y1 for y1, y2 in temp_valid_segments] + if not heights: + logger.info(" No heights to calculate.") + else: + max_h = max(heights) + median_h = np.median(heights) + + # 策略：如果高度差异较大，丢弃过小的卡片 + # 阈值设定为 Median 的 75% (允许一定程度的偏差，如 220 vs 258 是允许的，但 150 vs 250 应被过滤) + # 使用 Median 比 Max 更稳健，避免被单个超高卡片(如广告)带偏 + threshold_h = median_h * 0.70 + + for (y1, y2), h in zip(temp_valid_segments, heights): + if h < threshold_h: + logger.info(f" Filtering out segment Y={y1}-{y2} (H={h}) because it's too short (Threshold={threshold_h:.1f}).") + else: + final_cards.append((y1, y2, final_x1, final_x2)) + logger.info(f" Card: Y={y1}-{y2}, X={final_x1}-{final_x2}, H={h}") + + # 4. 保存结果 + if output_dir is None: + output_dir = os.path.dirname(img_path) + + base_name = os.path.basename(img_path) + stem, ext = os.path.splitext(base_name) + + # 准备可视化标记图 + debug_img = img.copy() + + results = [] + + # 准备 JSON 数据结构 + json_data = { + "image": base_name, + "width": w, + "height": h, + "cards": [] + } + + # 准备 _vl.jpg (只画框，不画红点) + vl_img = img.copy() + + logger.info(f" Step [2.1/VL] 准备在 VL 图片上绘制 {len(final_cards)} 个场站的绿色方框...") + + for idx, (y1, y2, x1, x2) in enumerate(final_cards): + # 计算点击点 (左上角，避免被底部按钮遮挡) + # 策略：X偏移 15%, Y偏移 20% + w_card = x2 - x1 + h_card = y2 - y1 + click_x = int(x1 + w_card * 0.15) + click_y = int(y1 + h_card * 0.20) + + # [修改] 不再保存单张子图，只记录元数据 + # card = img[y1:y2, x1:x2] + # 文件名添加坐标: _ClickX_ClickY + # out_name = f"{stem}_{idx + 1}_{click_x}_{click_y}{ext}" + # out_path = os.path.join(output_dir, out_name) + # cv2.imwrite(out_path, card) + # logger.info(f" Saved {out_path}") + + # results.append((out_path, (click_x, click_y))) + + # 在标记图上画红点 (实心圆, 半径10, 红色BGR) + cv2.circle(debug_img, (click_x, click_y), 10, (0, 0, 255), -1) + # [修改] 必须画绿框，因为后续视觉模型依赖这个框来识别范围 + cv2.rectangle(debug_img, (x1, y1), (x2, y2), (0, 255, 0), 2) + + # 在 _vl 图上只画绿框 + cv2.rectangle(vl_img, (x1, y1), (x2, y2), (0, 255, 0), 2) + + # 收集 JSON 数据 + card_info = { + "id": idx + 1, + "rect": [x1, y1, x2, y2], + "click_point": [click_x, click_y] + } + json_data["cards"].append(card_info) + + # 记录区域信息供调用者使用 (如果需要) + # 格式: (None, (click_x, click_y), (x1, y1, x2, y2)) + results.append((None, (click_x, click_y), (x1, y1, x2, y2))) + + # [删除] 之前生成的单张 _for_vl.jpg 逻辑已移除 + + # 保存标记图 (_flag.jpg) + if save_debug: + flag_out_path = os.path.join(output_dir, f"{stem}_flag{ext}") + save_image(flag_out_path, debug_img) + logger.info(f" Saved Debug Image: {flag_out_path}") + + # 保存 _vl.jpg + vl_out_path = os.path.join(output_dir, f"{stem}_vl{ext}") + save_image(vl_out_path, vl_img) + logger.info(f" Step [2.2/VL] 已保存带有绿色方框的图片: {vl_out_path}") + + # 保存 .json + import json + json_out_path = os.path.join(output_dir, f"{stem}.json") + with open(json_out_path, 'w', encoding='utf-8') as f: + json.dump(json_data, f, ensure_ascii=False, indent=4) + logger.info(f" Step [2.3/JSON] 已保存场站坐标元数据: {json_out_path}") + + return results diff --git a/Util/LlmUtil.py b/Util/LlmUtil.py new file mode 100644 index 0000000..2d2f6df --- /dev/null +++ b/Util/LlmUtil.py @@ -0,0 +1,77 @@ +import sys + +from openai import AsyncOpenAI + +from Config import * +from Config.Config import ALY_LLM_API_KEY, ALY_LLM_BASE_URL, ALY_LLM_MODEL_NAME + + +# 保留原同步函数，添加异步版本 +async def get_llm_response(query_text: str, stream: bool = True, system_prompt: str = 'You are a helpful assistant.', chat_history: list = None, temperature: float = None): + """ + 异步获取大模型的响应 + @param query_text: 查询文本 + @param stream: 是否使用流式输出 + @param system_prompt: 系统提示文本，默认为'You are a helpful assistant.' + @param chat_history: 聊天历史记录，格式为[{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}, ...] + @param temperature: 温度参数，控制输出的随机性，0-2之间，默认为None使用模型默认值 + @return: 流式响应生成器或完整响应文本 + """ + client = AsyncOpenAI( + api_key=ALY_LLM_API_KEY, + base_url=ALY_LLM_BASE_URL, + ) + + try: + # 构建messages列表，包含系统提示和聊天历史 + messages = [{'role': 'system', 'content': system_prompt}] + + # 如果有聊天历史，添加到messages中 + if chat_history and isinstance(chat_history, list): + messages.extend(chat_history) + + # 添加当前用户查询 + messages.append({'role': 'user', 'content': query_text}) + + # 构建请求参数 + request_params = { + 'model': ALY_LLM_MODEL_NAME, + 'messages': messages, + 'stream': stream + } + + # 如果指定了temperature参数，添加到请求中 + if temperature is not None: + request_params['temperature'] = temperature + + # 创建请求 + completion = await client.chat.completions.create(**request_params) + + if stream: + # 流式输出模式，返回生成器 + async for chunk in completion: + # 确保 chunk.choices 存在且不为空 + if chunk and chunk.choices and len(chunk.choices) > 0: + # 确保 delta 存在 + delta = chunk.choices[0].delta + if delta: + # 确保 content 存在且不为 None 或空字符串 + content = delta.content + if content is not None: + # if content is not None and content.strip(): + print(content, end='', flush=True) + yield content + else: + # 非流式处理 + if completion and completion.choices and len(completion.choices) > 0: + message = completion.choices[0].message + if message: + content = message.content + if content is not None: + # if content is not None and content.strip(): + yield content + except Exception as e: + error_msg = f"大模型请求异常: {str(e)}" + print(error_msg, file=sys.stderr) + # 抛出异常而不是返回错误信息，让调用方处理 + raise Exception(error_msg) \ No newline at end of file diff --git a/Util/ObsUtil.py b/Util/ObsUtil.py new file mode 100644 index 0000000..aa8f300 --- /dev/null +++ b/Util/ObsUtil.py @@ -0,0 +1,158 @@ +# pip install esdk-obs-python --trusted-host pypi.org +import traceback +import logging +import base64 # 添加base64模块导入 +from obs import ObsClient +from obs import PutObjectHeader + +from Config.Config import OBS_AK, OBS_SK, OBS_SERVER, OBS_BUCKET + +# 配置日志 +logger = logging.getLogger(__name__) + + +class ObsUploader: + def __init__(self, access_key_id=OBS_AK, secret_access_key=OBS_SK, server="https://" + OBS_SERVER): + """ + 初始化OBS客户端 + :param access_key_id: 访问密钥ID + :param secret_access_key: 秘密访问密钥 + :param server: OBS服务器地址 + """ + self.obs_client = ObsClient( + access_key_id=access_key_id, + secret_access_key=secret_access_key, + server=server + ) + + def upload_file(self, object_key, file_path, bucket_name=OBS_BUCKET, metadata=None, headers=None): + """ + 上传文件到OBS + :param bucket_name: 桶名称 + :param object_key: 对象键（上传后的文件名） + :param file_path: 本地文件路径 + :param metadata: 元数据 + :param headers: 请求头 + :return: 成功返回True和响应信息，失败返回False和错误信息 + """ + try: + # 初始化默认参数 + if metadata is None: + metadata = {} + if headers is None: + headers = PutObjectHeader() + # headers.ContentDisposition = 'attachment;' # filename="{}"'.format(os.path.basename(file_path)) + # headers.ContentType='application/octet-stream' + + # 执行文件上传 + resp = self.obs_client.putFile( + bucket_name, + object_key, + file_path, + metadata, + headers + ) + + # 检查响应状态 + if resp.status < 300: + logger.info(f'文件上传成功: {file_path} -> {bucket_name}/{object_key}') + logger.info(f'requestId: {resp.requestId}') + logger.info(f'etag: {resp.body.etag}') + return True, { + 'requestId': resp.requestId, + 'etag': resp.body.etag, + 'versionId': resp.body.versionId, + 'storageClass': resp.body.storageClass + } + else: + error_msg = f'文件上传失败: {resp.errorCode} - {resp.errorMessage}' + logger.error(error_msg) + logger.error(f'requestId: {resp.requestId}') + return False, { + 'requestId': resp.requestId, + 'errorCode': resp.errorCode, + 'errorMessage': resp.errorMessage + } + except Exception as e: + error_msg = f'文件上传异常: {str(e)}' + logger.error(error_msg) + logger.error(traceback.format_exc()) + return False, {'error': str(e), 'traceback': traceback.format_exc()} + + def upload_base64_image(self, object_key, base64_data, bucket_name=OBS_BUCKET, metadata=None, headers=None): + """ + 上传Base64编码的图片或原始二进制图片数据到OBS + :param bucket_name: 桶名称 + :param object_key: 对象键（上传后的文件名） + :param base64_data: Base64编码的图片字符串、字节形式或原始二进制图片数据 + :param metadata: 元数据 + :param headers: 请求头 + :return: 成功返回True和响应信息，失败返回False和错误信息 + """ + try: + # 初始化默认参数 + if metadata is None: + metadata = {} + if headers is None: + headers = PutObjectHeader() + # headers.ContentDisposition = 'attachment;' # filename="{}"'.format(os.path.basename(file_path)) + # headers.ContentType = 'application/octet-stream' + + # 处理数据 + if isinstance(base64_data, bytes): + try: + # 尝试解码为UTF-8字符串（假设是Base64编码的字符串的字节形式） + base64_string = base64_data.decode('utf-8') + # 移除Base64前缀（如果有） + if 'base64,' in base64_string: + base64_string = base64_string.split('base64,')[1] + # 解码Base64数据 + image_data = base64.b64decode(base64_string) + except UnicodeDecodeError: + # 解码失败，假设是原始二进制图片数据 + image_data = base64_data + else: + # 假设是字符串 + base64_string = str(base64_data) + # 移除Base64前缀（如果有） + if 'base64,' in base64_string: + base64_string = base64_string.split('base64,')[1] + # 解码Base64数据 + image_data = base64.b64decode(base64_string) + + # 执行对象上传 + resp = self.obs_client.putObject( + bucket_name, + object_key, + content=image_data, + metadata=metadata, + headers=headers + ) + + # 检查响应状态 + if resp.status < 300: + logger.info(f'图片上传成功: -> {bucket_name}/{object_key}') + logger.info(f'requestId: {resp.requestId}') + logger.info(f'etag: {resp.body.etag}') + return True, { + 'requestId': resp.requestId, + 'etag': resp.body.etag, + 'versionId': resp.body.versionId, + 'storageClass': resp.body.storageClass + } + else: + error_msg = f'图片上传失败: {resp.errorCode} - {resp.errorMessage}' + logger.error(error_msg) + logger.error(f'requestId: {resp.requestId}') + return False, { + 'requestId': resp.requestId, + 'errorCode': resp.errorCode, + 'errorMessage': resp.errorMessage + } + except Exception as e: + error_msg = f'图片上传异常: {str(e)}' + logger.error(error_msg) + logger.error(traceback.format_exc()) + return False, {'error': str(e), 'traceback': traceback.format_exc()} + + diff --git a/Util/OcrParser.py b/Util/OcrParser.py new file mode 100644 index 0000000..622d172 --- /dev/null +++ b/Util/OcrParser.py @@ -0,0 +1,126 @@ +# coding=utf-8 +import re + +class OcrParser: + @staticmethod + def parse(ocr_lines): + """ + 解析 OCR 文本行，提取场站结构化数据。 + 不依赖 LLM，使用规则和正则匹配。 + """ + result = { + "station_name": "", + "distance": "", + "price": None, + "piles": [], + "parking": "", + "tags": [] + } + + # 预处理：清洗无关字符 + clean_lines = [] + for line in ocr_lines: + # 去除 "行 X: " 前缀 + text = re.sub(r"^行\s*\d+:\s*", "", line) + # 去除 "(置信度: ...)" 后缀 + text = re.sub(r"\s*\(置信度:.*?\)\s*$", "", text) + text = text.strip() + if text: + clean_lines.append(text) + + # 1. 场站名称 (Station Name) + # 策略：通常是第一行，且不包含特定关键词（如km, /度, ￥等） + for line in clean_lines: + if len(line) < 2: continue + if line.startswith("（") or line.startswith("(") or line == "…": continue + + # 排除明显属性行 + if any(k in line.lower() for k in ["km", "/度", "￥", "停车", "积分", "组团"]): + continue + + # 排除纯数字 + if re.match(r"^\d+$", line): continue + + # 排除可能是充电枪信息的行 (例如 "闲2/4", "快", "慢") + if re.search(r"\d+/\d+", line): continue + if line in ["快", "慢", "超", "快充", "慢充", "超充"]: continue + + result["station_name"] = line + break + + # 2. 距离 (Distance) + for line in clean_lines: + # 匹配 "7.4km", "17.4km" + m = re.search(r"(\d+(\.\d+)?)\s*km", line, re.IGNORECASE) + if m: + result["distance"] = m.group(0) + break + + # 3. 价格 (Price) + for line in clean_lines: + # 匹配 "0.7111/度", "1.2/度" + m = re.search(r"(\d+\.\d+)(?=/度)", line) + if m: + try: + result["price"] = float(m.group(1)) + break + except: pass + + # 4. 充电枪信息 (Piles) + # 规则：查找 "快", "慢", "超" 以及 "x/y" 格式 + # 示例： "快", "闲3/4" (跨行) 或 "快充 3/4" (同行) + current_type = "未知" + + # 辅助：先尝试在单行内同时匹配类型和数量 + # 如果单行内有 "快" 和 "3/4"，则直接提取 + # 如果单行只有类型，更新 current_type + # 如果单行只有 "3/4"，使用 current_type + + for line in clean_lines: + # 更新当前类型上下文 + if "快" in line: current_type = "快" + elif "慢" in line: current_type = "慢" + elif "超" in line: current_type = "超" + + # 匹配 "3/4" 或 "闲3/4" + # 排除价格行 (包含小数点，如 0.7111/度 这里的 / 前后也可能是数字，需小心) + if "/度" in line: continue + + # 匹配分数结构：整数/整数 + # (?:闲)? 可选的前缀 "闲" + m = re.search(r"(?:闲)?(\d+)/(\d+)", line) + if m: + # 再次确认不是价格（有时候 OCR 会把 . 漏掉，变成 07111/度，但通常会有 /度） + # 简单校验：总数通常不会特别大，比如不会是 2024/12 (日期) + try: + free = int(m.group(1)) + total = int(m.group(2)) + + # 过滤可能的日期或异常值 + if total > 500: continue + + result["piles"].append({ + "type": current_type, + "free": free, + "total": total + }) + except: pass + + # 5. 停车信息 (Parking) + for line in clean_lines: + if "停车" in line: + # 清理前缀符号，如 "·收费停车" -> "收费停车" + clean_text = re.sub(r"^[·\.\sP]+", "", line) + result["parking"] = clean_text + break + + # 6. 标签 (Tags) + # 收集除上述已解析字段外的简短关键词 + known_keywords = ["快", "慢", "超", "P", "组团", "积分"] + for line in clean_lines: + # 简单匹配一些常见 Tag + if "积分" in line: result["tags"].append(line) + elif "组团" in line: result["tags"].append("组团") + elif line.strip() == "P": result["tags"].append("P") + + return result diff --git a/Util/PaChongGaoDeKit.py b/Util/PaChongGaoDeKit.py new file mode 100644 index 0000000..bc49544 --- /dev/null +++ b/Util/PaChongGaoDeKit.py @@ -0,0 +1,267 @@ +""" +高德地图高精度坐标获取工具类 +""" +import json +import os +import random +import re +import time +from selenium import webdriver +from selenium.webdriver.chrome.options import Options +from selenium.webdriver.chrome.service import Service +from selenium.webdriver.common.by import By +from selenium.webdriver.common.keys import Keys +from selenium.webdriver.support import expected_conditions as EC +from selenium.webdriver.support.ui import WebDriverWait + +class PaChongGaoDeKit: + def __init__(self, headless=True, cookie_file='amap_cookies.json'): + self.headless = headless + self.cookie_file = cookie_file + self.driver = None + + # Ensure cookie file path is absolute if relative + if not os.path.isabs(self.cookie_file): + # Assuming relative to this file or current working directory + # For now, let's assume it's relative to the project root or current execution path + # Better to use absolute path if possible. + # If the file is in the same directory as this script: + current_dir = os.path.dirname(os.path.abspath(__file__)) + potential_path = os.path.join(current_dir, self.cookie_file) + if os.path.exists(potential_path): + self.cookie_file = potential_path + else: + # Fallback to checking root/Config or similar if needed, or just leave as is + pass + + def _init_driver(self): + if self.driver: + return + + print(f"正在启动 Chrome 浏览器 (无头模式={self.headless})...") + chrome_options = Options() + if self.headless: + chrome_options.add_argument('--headless') + + # 解决驱动与浏览器不匹配的常见配置 + chrome_options.add_argument('--no-sandbox') + chrome_options.add_argument('--disable-dev-shm-usage') + chrome_options.add_argument('--window-size=1920,1080') + chrome_options.add_argument('--lang=zh-CN') + chrome_options.add_argument("--disable-blink-features=AutomationControlled") + chrome_options.add_experimental_option('excludeSwitches', ['enable-automation']) + chrome_options.add_experimental_option('useAutomationExtension', False) + + service = Service() + try: + from webdriver_manager.chrome import ChromeDriverManager + from webdriver_manager.core.os_manager import ChromeType + # 使用 ChromeDriverManager 自动下载匹配版本的 ChromeDriver + driver_path = ChromeDriverManager().install() + service = Service(driver_path) + print(f"已成功加载 ChromeDriver: {driver_path}") + except ImportError: + print("提示：未安装 webdriver_manager 库，建议安装以获得更好的驱动管理体验：pip install webdriver-manager") + except Exception as e: + print(f"提示：自动管理驱动失败，尝试使用系统默认驱动。错误原因: {e}") + + try: + self.driver = webdriver.Chrome(service=service, options=chrome_options) + + if not self.headless: + self.driver.maximize_window() + + self.driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", { + "source": "Object.defineProperty(navigator, 'webdriver', {get: () => undefined})" + }) + except Exception as e: + print(f"致命错误：无法启动 Chrome 浏览器。请确保已安装 Chrome 且版本与驱动匹配。详情: {e}") + raise e + + def _load_cookies(self): + if not self.driver: + return False + + if not os.path.exists(self.cookie_file): + print(f"Cookie file not found: {self.cookie_file}") + return False + + try: + # Visit domain first + self.driver.get('https://www.amap.com') + time.sleep(1) + + with open(self.cookie_file, 'r', encoding='utf-8') as f: + cookies = json.load(f) + + for cookie in cookies: + if 'expiry' in cookie: + cookie['expiry'] = int(cookie['expiry']) + if 'sameSite' in cookie and cookie['sameSite'] not in ['Strict', 'Lax', 'None']: + del cookie['sameSite'] + try: + self.driver.add_cookie(cookie) + except: + pass + return True + except Exception as e: + print(f"Failed to load cookies: {e}") + return False + + def ensure_logged_in(self): + self._init_driver() + if not self._load_cookies(): + print("Warning: Failed to load cookies, coordinate query might fail.") + + # Navigate to picker tool + self.driver.get('https://lbs.amap.com/tools/picker') + time.sleep(2) + + def parse_coordinates_info(self, text): + if not text: + return None + try: + clean_text = re.sub(r'[\u3000\t\n\r ]+', ',', text) + clean_text = re.sub(r'[,，]+', ',', clean_text).strip(',') + pattern = r'([7-9]?[0-9]\.[0-9]{1,}|1[0-3][0-9]\.[0-9]{1,})[,，]\s*([1-8]?[0-9]\.[0-9]{1,}|90\.[0-9]{1,})' + matches = re.finditer(pattern, clean_text) + + valid_coordinates = [] + for match in matches: + lng = match.group(1).strip() + lat = match.group(2).strip() + try: + lng_float = float(lng) + lat_float = float(lat) + if 70 <= lng_float <= 140 and 0 <= lat_float <= 60: + valid_coordinates.append((lng_float, lat_float)) + except ValueError: + continue + + if valid_coordinates: + # Return the first valid one + return valid_coordinates[0] + return None + except Exception: + return None + + def get_coordinate(self, address: str): + """ + Query coordinate for a single address. + Returns: (lng, lat) tuple or (None, None) + """ + if not address: + return None, None + + if not self.driver: + self.ensure_logged_in() + + try: + # Search input + search_input = WebDriverWait(self.driver, 10).until( + EC.presence_of_element_located((By.ID, 'txtSearch')) + ) + search_input.clear() + search_input.send_keys(address) + + # Click search + try: + search_button = WebDriverWait(self.driver, 5).until( + EC.presence_of_element_located((By.CLASS_NAME, 'btn-search')) + ) + search_button.click() + except: + search_input.send_keys(Keys.ENTER) + + time.sleep(2) # Wait for result + + # Get coordinate + coordinate_text = None + try: + coord_elem = WebDriverWait(self.driver, 10).until( + EC.presence_of_element_located((By.ID, 'txtCoordinate')) + ) + val = coord_elem.get_attribute('value') + if val and val.strip(): + coordinate_text = val.strip() + else: + coordinate_text = coord_elem.text.strip() + except: + pass + + if coordinate_text: + result = self.parse_coordinates_info(coordinate_text) + if result: + return result + + return None, None + + except Exception as e: + print(f"Error querying address '{address}': {e}") + # Try to recover session if needed, or just return None + return None, None + + def close(self): + if self.driver: + try: + self.driver.quit() + except: + pass + self.driver = None + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.close() + + def login_and_save_cookies(self): + """ + 打开浏览器进行手动登录，并将 Cookie 保存到文件。 + 该方法将阻塞，直到用户确认登录完成。 + """ + # 强制切换为非无头模式以便手动登录 + if self.headless: + print("切换至可视化模式进行登录...") + self.close() + self.headless = False + + self._init_driver() + + print("正在跳转至高德地图登录页面...") + # 跳转至通常需要登录的页面 + self.driver.get('https://lbs.amap.com/tools/picker') + + print("\n" + "="*60) + print("操作指引：") + print("1. 浏览器窗口已打开，请在窗口中手动完成登录。") + print("2. 您可以选择扫码登录或账号密码登录。") + print("3. 登录成功并跳转到控制台页面后，请回到此窗口。") + print("4. 在下方按 [回车键(Enter)]，程序将自动抓取并保存 Cookie。") + print("="*60 + "\n") + + input("等待登录完成，确认后请按 [Enter] 继续...") + + # 保存 Cookie + try: + cookies = self.driver.get_cookies() + with open(self.cookie_file, 'w', encoding='utf-8') as f: + json.dump(cookies, f, indent=4) + print(f"成功：Cookie 已保存至: {self.cookie_file}") + return True + except Exception as e: + print(f"错误：保存 Cookie 失败: {e}") + return False + +if __name__ == "__main__": + # Test code + # Ensure you have 'amap_cookies.json' in the same directory or provide full path + # If no cookies, it might fail to load the picker page correctly if login is required. + + kit = PaChongGaoDeKit(headless=False) + try: + addr = "北京市天安门" + lng, lat = kit.get_coordinate(addr) + print(f"{addr}: {lng}, {lat}") + finally: + kit.close() diff --git a/Util/PaddleOCRKit.py b/Util/PaddleOCRKit.py new file mode 100644 index 0000000..9d4b24e --- /dev/null +++ b/Util/PaddleOCRKit.py @@ -0,0 +1,113 @@ +# coding=utf-8 +import os +import sys +import logging +import cv2 +import numpy as np +from paddleocr import PaddleOCR +from Util.OcrParser import OcrParser + +# Initialize logger +logger = logging.getLogger("PaddleOCRKit") + +class PaddleOCRKit: + _instance = None + _ocr = None + + def __new__(cls): + if cls._instance is None: + cls._instance = super(PaddleOCRKit, cls).__new__(cls) + cls._instance._initialize_model() + return cls._instance + + def _initialize_model(self): + """ + Initialize the PaddleOCR model once. + Using PP-OCRv4 mobile model for speed. + """ + try: + logger.info("Initializing PaddleOCR (PP-OCRv4 Mobile)...") + # use_angle_cls is deprecated, using use_textline_orientation instead + # ocr_version='PP-OCRv4' defaults to mobile model + self._ocr = PaddleOCR( + use_textline_orientation=True, + lang="ch", + ocr_version='PP-OCRv4' + ) + logger.info("PaddleOCR initialized successfully.") + except Exception as e: + logger.error(f"Failed to initialize PaddleOCR: {e}") + self._ocr = None + + def recognize(self, image_input): + """ + Recognize text from an image and parse it into structured data. + + :param image_input: Image path (str) or OpenCV image object (numpy.ndarray) + :return: Parsed structured data (dict) or None if failed + """ + if self._ocr is None: + logger.error("PaddleOCR model is not initialized.") + return None + + try: + # PaddleOCR handles both path and ndarray + # predict() is recommended over ocr() in newer versions, but ocr() is the stable high-level API + # We use ocr() as in the test script which worked well. + # cls=True was causing issues in test, but use_textline_orientation=True in init handles it? + # actually in test script: ocr = PaddleOCR(use_textline_orientation=True, ...) + # result = ocr.ocr(image_path) -> this worked, but gave a warning about predict. + # Let's stick to ocr() for now as it returns the structure we expect. + + result = self._ocr.ocr(image_input) + + ocr_text_lines = [] + + # Handle result structure + if not result: + logger.warning("OCR returned empty result.") + else: + res = result[0] + if res is None: + logger.warning("OCR result[0] is None.") + elif isinstance(res, dict) and 'rec_texts' in res: + # New PaddleOCR / Paddlex format + ocr_text_lines = res['rec_texts'] + elif isinstance(res, list): + # Legacy PaddleOCR format + for line in res: + # line structure: [[points], (text, confidence)] + if len(line) >= 2 and isinstance(line[1], (tuple, list)): + ocr_text_lines.append(line[1][0]) + + # --- 关键：打印原始识别内容 --- + if ocr_text_lines: + logger.info(f"🔍 OCR 原始识别文字 ({len(ocr_text_lines)} 行):") + for i, line in enumerate(ocr_text_lines): + logger.info(f" [{i+1}] {line}") + else: + logger.info("No text detected.") + return {} + + # Parse with Regex Parser + parsed_data = OcrParser.parse(ocr_text_lines) + + # --- 关键：打印解析后的结构化数据 --- + if parsed_data: + logger.info("📦 OCR 解析结果 (JSON):") + logger.info(f" {parsed_data}") + + return parsed_data + + except Exception as e: + logger.error(f"Error during OCR recognition: {e}") + return None + +# Global instance for easy access +_kit_instance = None + +def get_ocr_kit(): + global _kit_instance + if _kit_instance is None: + _kit_instance = PaddleOCRKit() + return _kit_instance diff --git a/Util/RedisKit.py b/Util/RedisKit.py new file mode 100644 index 0000000..4c59505 --- /dev/null +++ b/Util/RedisKit.py @@ -0,0 +1,843 @@ +import asyncio +import logging +import uuid +import json + +import redis +from Config.Config import ( + REDIS_HOST, REDIS_PORT, REDIS_DB, REDIS_PASSWORD, + REDIS_DECODE_RESPONSES, REDIS_MAX_CONNECTIONS +) +# 创建logger实例 +logger = logging.getLogger(__name__) + +class RedisKit: + """ + 异步Redis工具类，提供所有Redis操作的异步实现 + 单例模式确保全局只有一个Redis连接池 + """ + _instance = None + _lock = asyncio.Lock() + _redis_pool = None + + def __new__(cls): + if cls._instance is None: + cls._instance = super().__new__(cls) + return cls._instance + + @classmethod + async def _ensure_pool(cls): + """ + 确保Redis连接池已创建 + """ + if cls._redis_pool is None: + async with cls._lock: + if cls._redis_pool is None: + try: + # 创建同步Redis连接池 + sync_pool = redis.ConnectionPool( + host=REDIS_HOST, + port=REDIS_PORT, + db=REDIS_DB, + password=REDIS_PASSWORD, + decode_responses=REDIS_DECODE_RESPONSES, + max_connections=REDIS_MAX_CONNECTIONS + ) + # 创建Redis连接实例 + cls._redis_pool = redis.Redis( + connection_pool=sync_pool, + encoding='utf-8', + decode_responses=REDIS_DECODE_RESPONSES + ) + # 测试连接 + await asyncio.to_thread(cls._redis_pool.ping) + logger.info("Redis连接池创建成功") + except Exception as e: + logger.error(f"Redis连接池创建失败: {e}") + raise + + @classmethod + async def get_connection(cls): + """ + 获取Redis连接实例 + + Returns: + redis.Redis: Redis连接实例 + """ + await cls._ensure_pool() + return cls._redis_pool + + async def get_data(self, key): + """ + 异步从Redis中获取数据 + + Args: + key (str): Redis键名 + + Returns: + any: Redis中存储的值，如果键不存在则返回None + """ + try: + await self._ensure_pool() + return await asyncio.to_thread(RedisKit._redis_pool.get, key) + except Exception as e: + logger.error(f"从Redis获取数据失败(key={key}): {e}") + return None + + async def exists(self, key): + """ + 异步检查Redis键是否存在 + + Args: + key (str): Redis键名 + + Returns: + bool: 键存在返回True，不存在或失败返回False + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.exists, key) + return bool(result) + except Exception as e: + logger.error(f"检查Redis键是否存在失败(key={key}): {e}") + return False + + async def set_data(self, key, value, expire=None): + """ + 异步向Redis中写入数据 + + Args: + key (str): Redis键名 + value (str): 要存储的值 + expire (int, optional): 过期时间(秒)，默认不过期 + + Returns: + bool: 写入成功返回True，失败返回False + """ + try: + await self._ensure_pool() + if expire: + result = await asyncio.to_thread(RedisKit._redis_pool.setex, key, expire, value) + else: + result = await asyncio.to_thread(RedisKit._redis_pool.set, key, value) + return result + except Exception as e: + logger.error(f"向Redis写入数据失败(key={key}): {e}") + return False + + async def delete_data(self, key): + """ + 异步从Redis中删除数据 + + Args: + key (str): Redis键名 + + Returns: + bool: 删除成功返回True，失败返回False + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.delete, key) + return result > 0 # delete返回删除的键数量，大于0表示删除成功 + except Exception as e: + logger.error(f"从Redis删除数据失败(key={key}): {e}") + return False + + async def keys(self, pattern='*'): + """ + 异步获取匹配模式的所有键 + + Args: + pattern (str): 匹配模式 + + Returns: + list: 匹配的键列表 + """ + try: + await self._ensure_pool() + return await asyncio.to_thread(RedisKit._redis_pool.keys, pattern) + except Exception as e: + logger.error(f"获取Redis键列表失败(pattern={pattern}): {e}") + return [] + + async def delete(self, *names): + """ + 异步删除一个或多个键 + + Args: + *names: 要删除的键名 + + Returns: + int: 删除的键数量 + """ + try: + await self._ensure_pool() + if not names: + return 0 + return await asyncio.to_thread(RedisKit._redis_pool.delete, *names) + except Exception as e: + logger.error(f"删除Redis键失败: {e}") + return 0 + + async def publish(self, channel, message): + """ + 异步发布消息到Redis频道 + + Args: + channel (str): Redis频道名称 + message (str): 要发布的消息 + + Returns: + int: 接收消息的订阅者数量 + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.publish, channel, message) + return result + except Exception as e: + logger.error(f"Redis发布消息失败(channel={channel}): {e}") + return 0 + + async def subscribe(self, channel): + """ + 异步订阅Redis频道 + + Args: + channel (str): Redis频道名称 + + Returns: + redis.client.PubSub: PubSub对象，用于接收消息 + """ + try: + await self._ensure_pool() + pubsub = RedisKit._redis_pool.pubsub() + await asyncio.to_thread(pubsub.subscribe, channel) + return pubsub + except Exception as e: + logger.error(f"Redis订阅频道失败(channel={channel}): {e}") + return None + + async def save_user_token(self, person_id, platform, token, expire=86400): + """ + 异步保存用户的token到Redis + + Args: + person_id (str): 用户ID + platform (str): 平台 + token (str): 生成的token + expire (int): 过期时间（秒），默认24小时 + + Returns: + bool: 保存成功返回True，失败返回False + """ + try: + # 使用固定格式的key：user_token:{person_id}:{platform} + key = f"user_token:{person_id}:{platform}" + return await self.set_data(key, token, expire) + except Exception as e: + logger.error(f"保存用户token失败(person_id={person_id}, platform={platform}): {e}") + return False + + async def get_user_token(self, person_id, platform): + """ + 异步获取用户在Redis中存储的token + + Args: + person_id (str): 用户ID + platform (str): 平台 + + Returns: + str: 用户的token，如果不存在或过期则返回None + """ + try: + key = f"user_token:{person_id}:{platform}" + return await self.get_data(key) + except Exception as e: + logger.error(f"获取用户token失败(person_id={person_id}, platform={platform}): {e}") + return None + + async def validate_user_token(self, person_id, platform, token): + """ + 异步验证用户的token是否与Redis中存储的一致 + + Args: + person_id (str): 用户ID + platform (str): 平台、终端 + token (str): 待验证的token + + Returns: + bool: token有效返回True，无效返回False + """ + try: + stored_token = await self.get_user_token(person_id, platform) + # 如果没有存储的token，或者存储的token与当前token不一致，则token无效 + return stored_token is not None and stored_token == token + except Exception as e: + logger.error(f"验证用户token失败(person_id={person_id}, platform={platform}): {e}") + return False + + async def delete_user_token(self, person_id, platform=None): + """ + 异步删除用户在Redis中存储的token + + Args: + person_id (str): 用户ID + platform (str, optional): 平台，如果为None则删除所有平台的token + + Returns: + bool: 删除成功返回True，失败返回False + """ + try: + await self._ensure_pool() + if platform: + # 删除特定平台的token + key = f"user_token:{person_id}:{platform}" + return await self.delete_data(key) + else: + # 删除所有平台的token（使用模式匹配） + pattern = f"user_token:{person_id}:*" + cursor = b'0' + deleted_count = 0 + + # 使用scan命令查找所有匹配的键 + while cursor: + cursor, keys = await asyncio.to_thread( + RedisKit._redis_pool.scan, + cursor=cursor, + match=pattern, + count=100 + ) + if keys: + deleted = await asyncio.to_thread( + RedisKit._redis_pool.delete, *keys + ) + deleted_count += deleted + + return deleted_count > 0 + except Exception as e: + logger.error(f"删除用户token失败(person_id={person_id}): {e}") + return False + + async def list_push(self, key, value, expire=None): + """ + 异步向Redis List中推入数据（LPUSH） + + Args: + key (str): Redis键名 + value (str): 要推入的值 + expire (int, optional): 过期时间(秒)，如果设置则同时为key设置过期时间 + + Returns: + int: 推入后list的长度，失败返回-1 + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.lpush, key, value) + + # 如果设置了过期时间，为key设置TTL + if expire and result > 0: + await asyncio.to_thread(RedisKit._redis_pool.expire, key, expire) + + return result + except Exception as e: + logger.error(f"向Redis List推入数据失败(key={key}): {e}") + return -1 + + async def list_pop(self, key): + """ + 异步从Redis List中弹出数据（RPOP） + + Args: + key (str): Redis键名 + + Returns: + str: 弹出的值，如果list为空或失败返回None + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.rpop, key) + return result + except Exception as e: + logger.error(f"从Redis List弹出数据失败(key={key}): {e}") + return None + + async def list_length(self, key): + """ + 异步获取Redis List的长度 + + Args: + key (str): Redis键名 + + Returns: + int: list的长度，如果失败返回-1 + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.llen, key) + return result + except Exception as e: + logger.error(f"获取Redis List长度失败(key={key}): {e}") + return -1 + + async def list_get_all(self, key): + """ + 异步获取Redis List中的所有数据（LRANGE 0 -1） + + Args: + key (str): Redis键名 + + Returns: + list: list中的所有数据，如果失败返回空列表 + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.lrange, key, 0, -1) + return result if result else [] + except Exception as e: + logger.error(f"获取Redis List所有数据失败(key={key}): {e}") + return [] + + async def list_clear(self, key): + """ + 异步清空Redis List（删除key） + + Args: + key (str): Redis键名 + + Returns: + bool: 清空成功返回True，失败返回False + """ + try: + return await self.delete_data(key) + except Exception as e: + logger.error(f"清空Redis List失败(key={key}): {e}") + return False + + async def set_is_member(self, key, value): + """ + 异步检查值是否在Redis集合中 + + Args: + key (str): Redis键名 + value: 要检查的值 + + Returns: + bool: 值在集合中返回True，不在或失败返回False + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.sismember, key, value) + return bool(result) + except Exception as e: + logger.error(f"检查值是否在Redis集合中失败(key={key}): {e}") + return False + + async def set_add(self, key, value): + """ + 异步向Redis集合中添加值 + + Args: + key (str): Redis键名 + value: 要添加的值 + + Returns: + bool: 添加成功返回True，失败返回False + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.sadd, key, value) + return result > 0 # sadd返回添加的元素数量，大于0表示添加成功 + except Exception as e: + logger.error(f"向Redis集合添加值失败(key={key}): {e}") + return False + + async def set_remove(self, key, value): + """ + 异步从Redis集合中移除值 + + Args: + key (str): Redis键名 + value: 要移除的值 + + Returns: + bool: 移除成功返回True，失败返回False + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.srem, key, value) + return result > 0 # srem返回移除的元素数量，大于0表示移除成功 + except Exception as e: + logger.error(f"从Redis集合移除值失败(key={key}): {e}") + return False + + async def set_cardinality(self, key): + """ + 异步获取Redis集合的成员数量 + + Args: + key (str): Redis键名 + + Returns: + int: 集合的成员数量，如果失败返回0 + """ + try: + await self._ensure_pool() + result = await asyncio.to_thread(RedisKit._redis_pool.scard, key) + return result + except Exception as e: + logger.error(f"获取Redis集合成员数量失败(key={key}): {e}") + return 0 + + # 分布式锁相关方法 + async def acquire_lock(self, lock_name, timeout=300): + """ + 获取分布式锁 + + Args: + lock_name (str): 锁的名称 + timeout (int): 锁的超时时间(秒)，默认300秒 + + Returns: + str: 锁ID，如果获取失败返回None + """ + try: + await self._ensure_pool() + # 生成唯一锁ID + lock_id = str(uuid.uuid4()) + key = f"lock:{lock_name}" + + # 使用Redis原子操作设置锁 + result = await asyncio.to_thread( + RedisKit._redis_pool.setnx, key, lock_id + ) + + if result: + # 设置锁的过期时间 + await asyncio.to_thread( + RedisKit._redis_pool.expire, key, timeout + ) + logger.info(f"获取分布式锁成功: {lock_name}, lock_id: {lock_id}") + return lock_id + else: + logger.info(f"获取分布式锁失败: {lock_name} (锁已存在)") + return None + except Exception as e: + logger.error(f"获取分布式锁失败: {lock_name}, 错误: {e}") + return None + + async def release_lock(self, lock_name, lock_id): + """ + 释放分布式锁 + + Args: + lock_name (str): 锁的名称 + lock_id (str): 锁ID + + Returns: + bool: 是否成功释放锁 + """ + try: + await self._ensure_pool() + key = f"lock:{lock_name}" + + # 使用Lua脚本原子性检查并释放锁 + lua_script = """ + if redis.call('get', KEYS[1]) == ARGV[1] then + return redis.call('del', KEYS[1]) + else + return 0 + end + """ + + result = await asyncio.to_thread( + RedisKit._redis_pool.eval, lua_script, 1, key, lock_id + ) + + if result == 1: + logger.info(f"释放分布式锁成功: {lock_name}, lock_id: {lock_id}") + return True + else: + logger.info(f"释放分布式锁失败: {lock_name}, lock_id: {lock_id} (锁不存在或已被其他进程持有)") + return False + except Exception as e: + logger.error(f"释放分布式锁失败: {lock_name}, 错误: {e}") + return False + + async def renew_lock(self, lock_name, lock_id, timeout=300): + """ + 续约分布式锁 + + Args: + lock_name (str): 锁的名称 + lock_id (str): 锁ID + timeout (int): 续约时间(秒)，默认300秒 + + Returns: + bool: 是否成功续约 + """ + try: + await self._ensure_pool() + key = f"lock:{lock_name}" + + # 使用Lua脚本原子性检查并续约锁 + lua_script = """ + if redis.call('get', KEYS[1]) == ARGV[1] then + return redis.call('expire', KEYS[1], ARGV[2]) + else + return 0 + end + """ + + result = await asyncio.to_thread( + RedisKit._redis_pool.eval, lua_script, 1, key, lock_id, timeout + ) + + if result == 1: + logger.info(f"续约分布式锁成功: {lock_name}, lock_id: {lock_id}") + return True + else: + logger.info(f"续约分布式锁失败: {lock_name}, lock_id: {lock_id} (锁不存在或已被其他进程持有)") + return False + except Exception as e: + logger.error(f"续约分布式锁失败: {lock_name}, 错误: {e}") + return False + + + + + + # 任务进度监控相关方法 + async def set_task_progress(self, task_id, progress, status="processing", message=""): + """ + 设置任务进度 + + Args: + task_id (str): 任务ID + progress (int): 进度百分比(0-100) + status (str): 任务状态(waiting, processing, completed, failed) + message (str): 任务消息 + + Returns: + bool: 设置成功返回True，失败返回False + """ + try: + await self._ensure_pool() + key = f"task:progress:{task_id}" + + # 构造进度数据 + progress_data = { + "task_id": task_id, + "progress": min(100, max(0, progress)), # 确保进度在0-100之间 + "status": status, + "message": message, + "update_time": asyncio.get_event_loop().time() + } + + # 序列化进度数据 + progress_json = json.dumps(progress_data, ensure_ascii=False) + + # 保存到Redis + await asyncio.to_thread( + RedisKit._redis_pool.set, key, progress_json + ) + + # 设置过期时间为24小时 + await asyncio.to_thread( + RedisKit._redis_pool.expire, key, 86400 + ) + + logger.info(f"设置任务进度成功: {task_id}, 进度: {progress}%, 状态: {status}") + return True + except Exception as e: + logger.error(f"设置任务进度失败: {task_id}, 错误: {e}") + return False + + async def get_task_progress(self, task_id): + """ + 获取任务进度 + + Args: + task_id (str): 任务ID + + Returns: + dict: 任务进度数据，如果不存在返回None + """ + try: + await self._ensure_pool() + key = f"task:progress:{task_id}" + + # 从Redis获取进度数据 + progress_json = await asyncio.to_thread( + RedisKit._redis_pool.get, key + ) + + if progress_json: + return json.loads(progress_json) + else: + return None + except Exception as e: + logger.error(f"获取任务进度失败: {task_id}, 错误: {e}") + return None + + async def complete_task(self, task_id, success=True, message=""): + """ + 完成任务 + + Args: + task_id (str): 任务ID + success (bool): 任务是否成功完成 + message (str): 完成消息 + + Returns: + bool: 设置成功返回True，失败返回False + """ + status = "completed" if success else "failed" + progress = 100 if success else 0 + return await self.set_task_progress(task_id, progress, status, message) + + async def delete_task_progress(self, task_id): + """ + 删除任务进度记录 + + Args: + task_id (str): 任务ID + + Returns: + bool: 删除成功返回True，失败返回False + """ + try: + await self._ensure_pool() + key = f"task:progress:{task_id}" + + # 从Redis删除进度记录 + result = await asyncio.to_thread( + RedisKit._redis_pool.delete, key + ) + + logger.info(f"删除任务进度记录成功: {task_id}") + return result > 0 + except Exception as e: + logger.error(f"删除任务进度记录失败: {task_id}, 错误: {e}") + return False + + @classmethod + async def close(cls): + """ + 关闭Redis连接池 + """ + if cls._redis_pool: + # 同步Redis连接池不需要等待关闭 + cls._redis_pool.connection_pool.disconnect() + cls._redis_pool = None + logger.info("Redis连接池已关闭") + + +# 创建全局实例 +redisKit = RedisKit() + + +async def main(): + """ + 测试异步Redis操作的main函数 + """ + print("开始测试异步Redis工具类...") + + try: + # 测试基本数据操作 + print("\n=== 测试基本数据操作 ===") + + # 测试设置和获取数据 + test_key = "test_key" + test_value = "test_value_123" + + result = await redisKit.set_data(test_key, test_value, expire=60) + print(f"设置数据结果: {result}") + + get_value = await redisKit.get_data(test_key) + print(f"获取数据结果: {get_value}") + + # 测试用户token操作 + print("\n=== 测试用户token操作 ===") + + # 保存用户token + person_id = "user123" + platform = "web" + token = "jwt_token_xyz789" + + save_result = await redisKit.save_user_token(person_id, platform, token, expire=120) + print(f"保存token结果: {save_result}") + + # 获取用户token + stored_token = await redisKit.get_user_token(person_id, platform) + print(f"获取token结果: {stored_token}") + + # 验证token + validate_result = await redisKit.validate_user_token(person_id, platform, token) + print(f"验证token结果: {validate_result}") + + # 验证错误的token + wrong_token = "wrong_token" + wrong_validate_result = await redisKit.validate_user_token(person_id, platform, wrong_token) + print(f"验证错误token结果: {wrong_validate_result}") + + # 测试删除操作 + print("\n=== 测试删除操作 ===") + + # 删除测试数据 + delete_result = await redisKit.delete_data(test_key) + print(f"删除数据结果: {delete_result}") + + # 删除用户token + delete_token_result = await redisKit.delete_user_token(person_id, platform) + print(f"删除token结果: {delete_token_result}") + + # 测试批量token删除功能 + print("\n=== 测试批量token删除功能 ===") + + # 保存多个平台的token + platforms = ["android", "ios", "web"] + for i, platform in enumerate(platforms): + await redisKit.save_user_token(f"user{i + 1}", platform, f"token{i + 1}", expire=300) + print(f"保存用户{i+1}的{platform}平台token") + + # 删除特定用户的所有token + user_to_delete = "user2" + batch_delete_result = await redisKit.delete_user_token(user_to_delete) + print(f"批量删除{user_to_delete}的所有token结果: {batch_delete_result}") + + # 验证删除结果 + for platform in platforms: + token_check = await redisKit.get_user_token(user_to_delete, platform) + print(f"删除后{user_to_delete}-{platform}平台的token状态: {'已删除' if token_check is None else '存在'}") + + print("\n=== 所有测试完成 ===") + + except Exception as e: + print(f"测试过程中发生错误: {e}") + import traceback + traceback.print_exc() + + finally: + # 清理测试数据并关闭连接池 + print("\n清理测试数据并关闭连接池...") + + # 清理测试用的数据 + test_keys_to_cleanup = ["test_key"] + for key in test_keys_to_cleanup: + await redisKit.delete_data(key) + + # 清理测试用户token + cleanup_users = ["user123", "user1", "user2", "user3"] + for user in cleanup_users: + try: + await redisKit.delete_user_token(user) + except: + pass # 忽略清理过程中的错误 + + # 关闭Redis连接池 + await redisKit.close() + print("Redis连接池已关闭") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/Util/Win32Patch.py b/Util/Win32Patch.py new file mode 100644 index 0000000..a3c4403 --- /dev/null +++ b/Util/Win32Patch.py @@ -0,0 +1,30 @@ +import asyncio +import logging +import sys +from DbKit.Db import Db +# 配置日志 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def patch(): + # 在Windows上使用SelectorEventLoopPolicy可以减少SSL错误 + if sys.platform == 'win32': + asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) + + # 为Db类添加临时的异步上下文管理器支持 + def patch_db_context_manager(): + # 为Db类添加异步上下文管理器支持 + if not hasattr(Db, '__aenter__'): + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + # 简单的清理，不做实际操作，避免事件循环问题 + return False + + # 动态添加方法 + Db.__aenter__ = __aenter__ + Db.__aexit__ = __aexit__ + + # 应用补丁 + patch_db_context_manager() \ No newline at end of file diff --git a/Util/XinDianTuReadImageKit.py b/Util/XinDianTuReadImageKit.py new file mode 100644 index 0000000..edbfcee --- /dev/null +++ b/Util/XinDianTuReadImageKit.py @@ -0,0 +1,1290 @@ +import numpy as np +from PIL import Image +import os +import asyncio +import hashlib +import json +import aiohttp +import logging +import base64 +from openai import OpenAI, BadRequestError +from Config.Config import ( + ALY_LLM_API_KEY, VL_MODEL_NAME, VL_MODEL_NAME_AD, + SAFE_EXCLUDE_RATIO, FALLBACK_WIDTH, FALLBACK_HEIGHT, + BOTTOM_SAFE_EXCLUDE_RATIO +) + +logger = logging.getLogger(__name__) + +class XinDianTuReadImageKit: + _client = OpenAI( + api_key=ALY_LLM_API_KEY, + base_url="https://dashscope.aliyuncs.com/compatible-mode/v1" + ) + + # 通用回退设备信息，仅在无法动态获取设备信息时使用 + _FALLBACK_DEVICE_INFO = { + "displayWidth": FALLBACK_WIDTH, + "displayHeight": FALLBACK_HEIGHT, + "productName": "generic" + } + + _prompt = ( + "仅输出JSON数组（不含任何说明文字），按从左到右、从上到下的顺序识别图片中的充电站区域。识别规则如下：\n" + "1. 必须是卡片形式的充电站信息区域。\n" + "2. 每一个卡片必须同时具备以下所有要素，否则严禁识别：\n" + " - 场站名称 (station_name)；\n" + " - 距离信息 (distance, 例如 '5.3km')，位于卡片右上角；\n" + " - 金额/电费 (price，例如 '0.8490')；\n" + " - 充电枪信息 (piles，包含'超'、'快'或'慢'的类型、总枪数和空闲枪数，例如 '快 闲4/4')。\n" + "3. 如果缺少上述任何一项要素（例如只有名称和距离，但没有电费或枪数信息），说明它不是真正的场站卡片（可能是广告或功能入口），请直接跳过。\n" + "\n" + "JSON对象字段要求：\n" + "1. b_use: 状态标识（1或0）。如果场站名称为灰色或带有“暂停使用”等标签，则为0，否则为1。\n" + "2. station_name: 场站名称；\n" + "3. price: 一度电的价格（数字）；\n" + "4. pro_price: Pro会员价格（数字），无则为null；\n" + "5. piles: 充电枪列表 [{type: '快', free: 4, total: 4}]；\n" + "6. parking: 停车费用描述（通常在'P'图标后，例如 '收费停车：以场站实际收费规则为准' 或 '限时免费停车...'）。\n" + "7. distance: 距离信息字符串（例如 '5.3km'）。\n" + "8. bounds: {x1,y1,x2,y2} 区域像素坐标（0-1000）；\n" + "9. bounds_norm: {left,top,right,bottom} 归一化坐标(0-1)；\n" + "10. station_name_bounds: 场站名称文字区域坐标 {x1,y1,x2,y2}（0-1000）；\n" + "11. station_name_bounds_norm: 场站名称文字归一化坐标(0-1)。\n" + "\n" + "重要约束（违反者不予识别）：\n" + "A. 严禁识别广告位和筛选标签。如“夜间免停”、“洗手间”、“不限车长”、“不限车高”、“组团”、“综合排序”等均不是场站。\n" + "B. 真正的场站卡片必须是一个横跨屏幕的大卡片，包含：场站名称（大号加粗）、金额（¥开头）、距离（km结尾）、充电枪状态（闲x/x）。\n" + "C. 严禁将屏幕中间的筛选标签误认为场站卡片。\n" + "\n" + "严格返回纯JSON格式。" + ) + + @staticmethod + def _extract_json(text: str) -> str: + if not text: + return "[]" + + cleaned = text.strip() + if "```" in cleaned: + lines = [] + for line in cleaned.splitlines(): + if line.strip().startswith("```"): + continue + lines.append(line) + cleaned = "\n".join(lines).strip() + + decoder = json.JSONDecoder() + + pos = 0 + while pos < len(cleaned): + idx_dict = cleaned.find("{", pos) + idx_list = cleaned.find("[", pos) + + candidates = [i for i in (idx_dict, idx_list) if i != -1] + if not candidates: + break + + start = min(candidates) + snippet = cleaned[start:] + try: + _, end = decoder.raw_decode(snippet) + return snippet[:end] + except json.JSONDecodeError: + pos = start + 1 + continue + + return "[]" + + @staticmethod + def _add_center(obj, device_info): + return XinDianTuReadImageKit._add_click_point(obj, device_info, anchor="center") + + @staticmethod + def _add_click_point(obj, device_info, anchor: str = "center"): + # 获取显示宽高，增加容错 + display_width = float(device_info.get("displayWidth") or device_info.get("width") or 1080) + display_height = float(device_info.get("displayHeight") or device_info.get("height") or 2400) + + bounds = obj.get("bounds") + bn = obj.get("bounds_norm") + text_bounds = obj.get("station_name_bounds") + text_bn = obj.get("station_name_bounds_norm") + uia_x = None + uia_y = None + + def get_pixel_coords(b_data, d_w, d_h): + if not b_data: + return None + + # Extract values based on dict or list + if isinstance(b_data, list) and len(b_data) == 4: + v1, v2, v3, v4 = b_data + elif isinstance(b_data, dict): + # 兼容多种可能的键名 + v1 = b_data.get("left") if b_data.get("left") is not None else b_data.get("x1") + v2 = b_data.get("top") if b_data.get("top") is not None else b_data.get("y1") + v3 = b_data.get("right") if b_data.get("right") is not None else b_data.get("x2") + v4 = b_data.get("bottom") if b_data.get("bottom") is not None else b_data.get("y2") + + if any(v is None for v in (v1, v2, v3, v4)): + return None + else: + return None + + try: + v1, v2, v3, v4 = float(v1), float(v2), float(v3), float(v4) + except (ValueError, TypeError): + return None + + max_v = max(v1, v2, v3, v4) + + # 1. 0-1 归一化坐标 + if max_v <= 1.05: + x1, y1, x2, y2 = v1 * d_w, v2 * d_h, v3 * d_w, v4 * d_h + # 2. 0-1000 归一化坐标 (Qwen-VL 常用) + elif max_v <= 1005: + x1, y1, x2, y2 = (v1 / 1000.0) * d_w, (v2 / 1000.0) * d_h, (v3 / 1000.0) * d_w, (v4 / 1000.0) * d_h + # 3. 绝对像素坐标 + else: + x1, y1, x2, y2 = v1, v2, v3, v4 + + return min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2) + + # 优先使用场站名称区域进行点击 (最安全) + coords = get_pixel_coords(text_bn, display_width, display_height) or get_pixel_coords(text_bounds, display_width, display_height) + if coords: + x1, y1, x2, y2 = coords + uia_x = int(x1 + (x2 - x1) / 2) + uia_y = int(y1 + (y2 - y1) / 2) + logger.info(f"坐标计算: 使用文字区域 -> ({uia_x}, {uia_y}) | 区域: {coords} | 屏幕: {display_width}x{display_height}") + + # 备选：使用整个卡片区域 + if uia_x is None: + coords = get_pixel_coords(bn, display_width, display_height) or get_pixel_coords(bounds, display_width, display_height) + if coords: + x1, y1, x2, y2 = coords + w, h = x2 - x1, y2 - y1 + if anchor == "top_left": + uia_x = int(x1 + max(5.0, w * 0.15)) + uia_y = int(y1 + max(5.0, h * 0.20)) + else: + # 默认中心点，但稍微偏上一点，避开底部可能的按钮 + uia_x = int(x1 + w / 2) + uia_y = int(y1 + h * 0.4) + logger.info(f"坐标计算: 使用卡片区域 -> ({uia_x}, {uia_y}) | 区域: {coords} | 屏幕: {display_width}x{display_height}") + + # --- 安全过滤：过滤掉屏幕顶部的点击坐标 (通常是广告或菜单) --- + if uia_y is not None: + # 如果点击点在屏幕顶部 SAFE_EXCLUDE_RATIO 范围内，极大概率是误触广告位，将其排除 + if uia_y < (display_height * SAFE_EXCLUDE_RATIO): + logger.warning(f"安全排除: 坐标 ({uia_x}, {uia_y}) 位于屏幕顶部 {int(SAFE_EXCLUDE_RATIO*100)}% 区域，疑似广告或菜单，已忽略。") + return None + + # 如果点击点在屏幕底部 BOTTOM_SAFE_EXCLUDE_RATIO 范围内，极大概率是误触底部功能区（如扫码充电），将其排除 + if uia_y > (display_height * (1 - BOTTOM_SAFE_EXCLUDE_RATIO)): + logger.warning(f"安全排除: 坐标 ({uia_x}, {uia_y}) 位于屏幕底部 {int(BOTTOM_SAFE_EXCLUDE_RATIO*100)}% 区域，疑似底部功能区，已忽略。") + return None + + if uia_x is not None and uia_y is not None: + obj["uia_center_x"] = uia_x + obj["uia_center_y"] = uia_y + + # Clean up temporary fields + for k in ["bounds", "bounds_norm", "station_name_bounds", "station_name_bounds_norm"]: + if k in obj: + del obj[k] + return obj + + @staticmethod + async def find_all_time_button_coordinate(image_url: str, device_info: dict = None) -> dict: + if device_info is None: + logger.warning("未提供动态设备信息，使用通用回退配置。") + device_info = XinDianTuReadImageKit._FALLBACK_DEVICE_INFO + + prompt = ( + "仅输出JSON对象（不含任何说明文字），请找到图片中带有“全部时段”字样的按钮区域（通常在价格表下方，是一个带有右箭头的文字按钮）。\n" + "返回格式示例：\n" + "{\n" + ' "bounds": {"x1": 100, "y1": 200, "x2": 300, "y2": 400}, \n' + ' "bounds_norm": {"left": 0.1, "top": 0.2, "right": 0.3, "bottom": 0.4}\n' + "}\n" + "注意：bounds应使用0-1000的归一化坐标空间。\n" + "如果未找到，返回空JSON {}。" + ) + + loop = asyncio.get_event_loop() + resp = await loop.run_in_executor( + None, + lambda: XinDianTuReadImageKit._client.chat.completions.create( + model=VL_MODEL_NAME, + messages=[ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": image_url + }, + }, + {"type": "text", "text": json.dumps(device_info, ensure_ascii=False)}, + {"type": "text", "text": prompt}, + ], + }, + ], + ) + ) + + content = resp.choices[0].message.content or "" + raw = XinDianTuReadImageKit._extract_json(content) + + try: + data = json.loads(raw) + if isinstance(data, dict) and (data.get("bounds") or data.get("bounds_norm")): + data = XinDianTuReadImageKit._add_center(data, device_info) + # 只返回中心坐标 + return { + "uia_center_x": data.get("uia_center_x"), + "uia_center_y": data.get("uia_center_y") + } + return {} + except Exception as e: + logger.error(f"Error parsing JSON: {e}") + logger.error(f"Raw content: {raw}") + return {} + + @staticmethod + async def find_station_coordinate_first_page(image_url: str, station_name: str, device_info: dict = None) -> dict: + if device_info is None: + logger.warning("未提供动态设备信息，使用通用回退配置。") + device_info = XinDianTuReadImageKit._FALLBACK_DEVICE_INFO + + prompt = ( + "仅输出JSON对象（不含任何说明文字）。" + "请在图片中找到场站名称完全匹配“" + str(station_name) + "”的那一行/卡片，" + "返回该场站名称文字本身的区域坐标（优先返回归一化坐标）。" + "返回格式示例：" + "{\"station_name_bounds\": {\"x1\": 100, \"y1\": 200, \"x2\": 300, \"y2\": 240}, " + "\"station_name_bounds_norm\": {\"left\": 0.1, \"top\": 0.2, \"right\": 0.3, \"bottom\": 0.24}}" + "注意：bounds使用0-1000归一化坐标空间；如果找不到，返回空JSON {}。" + ) + + loop = asyncio.get_event_loop() + resp = await loop.run_in_executor( + None, + lambda: XinDianTuReadImageKit._client.chat.completions.create( + model=VL_MODEL_NAME, + messages=[ + { + "role": "user", + "content": [ + {"type": "image_url", "image_url": {"url": image_url}}, + {"type": "text", "text": json.dumps(device_info, ensure_ascii=False)}, + {"type": "text", "text": prompt}, + ], + }, + ], + ) + ) + + content = resp.choices[0].message.content or "" + raw = XinDianTuReadImageKit._extract_json(content) + + try: + data = json.loads(raw) + if isinstance(data, dict) and (data.get("station_name_bounds") or data.get("station_name_bounds_norm") or data.get("bounds") or data.get("bounds_norm")): + data = XinDianTuReadImageKit._add_click_point(data, device_info, anchor="station_text") + return { + "uia_center_x": data.get("uia_center_x"), + "uia_center_y": data.get("uia_center_y") + } + return {} + except Exception: + return {} + + @staticmethod + async def parse_first_level_image_url(image_url: str, device_info: dict = None) -> list: + if device_info is None: + logger.warning("未提供动态设备信息，使用通用回退配置。") + device_info = XinDianTuReadImageKit._FALLBACK_DEVICE_INFO + + loop = asyncio.get_event_loop() + resp = await loop.run_in_executor( + None, + lambda: XinDianTuReadImageKit._client.chat.completions.create( + model=VL_MODEL_NAME, + messages=[ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": image_url + }, + }, + {"type": "text", "text": json.dumps(device_info, ensure_ascii=False)}, + {"type": "text", "text": XinDianTuReadImageKit._prompt}, + ], + }, + ], + ) + ) + + content = resp.choices[0].message.content or "" + raw = XinDianTuReadImageKit._extract_json(content) + + try: + data = json.loads(raw) + if isinstance(data, list): + # 过滤掉 None (即被 _add_click_point 排除掉的顶部项) + data = [XinDianTuReadImageKit._add_click_point(x, device_info, anchor="station_text") for x in data if x.get("b_use", 1) == 1] + data = [x for x in data if x is not None] + elif isinstance(data, dict): + if data.get("b_use", 1) == 1: + data = XinDianTuReadImageKit._add_click_point(data, device_info, anchor="station_text") + data = [data] if data is not None else [] + else: + data = [] + + # Clean up temporary field b_use if it still exists + if isinstance(data, list): + for item in data: + if "b_use" in item: + del item["b_use"] + elif isinstance(data, dict) and "b_use" in data: + del data["b_use"] + + # If dict result, wrap in list for consistency if needed, but keeping as is based on original logic logic implies list return + if isinstance(data, dict): + return [data] if data else [] + + if isinstance(data, list) and data: + # 移除旧的关键词过滤逻辑，完全信任视觉模型的识别结果 + # 如果未来发现模型识别了太多广告，可以在 Prompt 中强化 "严禁识别广告" 的约束 + filtered = [] + for item in data: + if not isinstance(item, dict): + continue + name = item.get("station_name") + if not name: + continue + filtered.append(item) + data = filtered + + if isinstance(data, list) and data: + tasks = [] + idxs = [] + for i, item in enumerate(data): + if not isinstance(item, dict): + continue + if item.get("uia_center_x") is None or item.get("uia_center_y") is None: + name = item.get("station_name") + if name: + idxs.append(i) + tasks.append(asyncio.create_task( + XinDianTuReadImageKit.find_station_coordinate_first_page(image_url, name, device_info) + )) + if tasks: + results = await asyncio.gather(*tasks, return_exceptions=True) + for i, r in zip(idxs, results): + if isinstance(r, dict) and r.get("uia_center_x") is not None and r.get("uia_center_y") is not None: + data[i]["uia_center_x"] = r.get("uia_center_x") + data[i]["uia_center_y"] = r.get("uia_center_y") + + for item in data: + if isinstance(item, dict): + item["uia_center_x"] = 100 + item.setdefault("uia_center_y", None) + + return data + except Exception as e: + logger.error(f"Error parsing JSON: {e}") + logger.error(f"Raw content: {raw}") + return [] + + @staticmethod + async def parse_hybrid_image(image_path, uploader, cdn_domain): + """ + 统一入口：混合识别模式 + """ + return await XinDianTuReadImageKit.get_stations_hybrid(image_path, uploader, cdn_domain) + + @staticmethod + async def parse_vl_image(vl_image_url, json_metadata, device_info=None): + """ + 基于 _vl.jpg (带绿框) 和 JSON 元数据进行识别 + """ + if not json_metadata or "cards" not in json_metadata: + return [] + + cards_meta = json_metadata["cards"] + # 按 id 或 Y 坐标排序，确保顺序一致 (Kit 生成时已经是 top-down) + cards_meta.sort(key=lambda x: x["rect"][1]) + + prompt = ( + "图片中用绿色矩形框标记了若干个充电站卡片区域。\n" + "请按从上到下的顺序，依次识别每个绿色框内的场站信息，并返回一个JSON数组。\n" + "数组中元素的顺序必须与图片中绿色框从上到下的顺序严格一致。\n" + "如果某个框内不是有效的场站卡片（例如是广告），请返回null或空对象，不要跳过顺序。\n" + "\n" + "每个JSON对象包含以下字段：\n" + "1. station_name: 场站名称；\n" + "2. price: 价格（数字）；\n" + "3. pro_price: Pro会员价（数字，无则null）；\n" + "4. piles: 充电枪描述字符串（例如 '快 闲4/4'）；\n" + "5. tags: 标签列表（如 ['限时免费']）；\n" + "6. parking: 停车费用描述（通常在'P'图标后，例如 '收费停车：以场站实际收费规则为准' 或 '限时免费停车...'）。\n" + "7. distance: 距离信息字符串（例如 '5.3km'）。\n" + "\n" + "严格返回纯JSON格式。" + ) + + loop = asyncio.get_event_loop() + resp = await loop.run_in_executor( + None, + lambda: XinDianTuReadImageKit._client.chat.completions.create( + model=VL_MODEL_NAME, + messages=[ + { + "role": "user", + "content": [ + {"type": "image_url", "image_url": {"url": vl_image_url}}, + {"type": "text", "text": prompt}, + ], + }, + ], + ) + ) + + content = resp.choices[0].message.content or "" + raw = XinDianTuReadImageKit._extract_json(content) + + final_stations = [] + try: + llm_data = json.loads(raw) + if isinstance(llm_data, list): + # 尝试与 cards_meta 对齐 + count = min(len(llm_data), len(cards_meta)) + for i in range(count): + item = llm_data[i] + meta = cards_meta[i] + + if not item or not isinstance(item, dict): + continue + + name = item.get("station_name") + if not name: + continue + + # 注入元数据中的点击坐标 + click_pt = meta.get("click_point", [0, 0]) + cx, cy = click_pt + + # 安全检查 + img_h = 2400 + if device_info: + img_h = float(device_info.get("displayHeight") or device_info.get("height") or 2400) + else: + img_h = json_metadata.get("height", 2400) + + # 顶部安全排除 + if cy < (img_h * SAFE_EXCLUDE_RATIO): + logger.warning(f"VL安全排除: 坐标 ({cx}, {cy}) 位于屏幕顶部 {int(SAFE_EXCLUDE_RATIO*100)}% 区域，已忽略。") + continue + + # 底部安全排除 + if cy > (img_h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO)): + logger.warning(f"VL安全排除: 坐标 ({cx}, {cy}) 位于屏幕底部 {int(BOTTOM_SAFE_EXCLUDE_RATIO*100)}% 区域，疑似底部功能区，已忽略。") + continue + + item["uia_center_x"] = cx + item["uia_center_y"] = cy + item["rect"] = meta.get("rect") + + final_stations.append(item) + else: + logger.warning(f"LLM return format error: expected list, got {type(llm_data)}") + + except Exception as e: + logger.error(f"Error parsing VL response: {e}") + + return final_stations + + @staticmethod + async def get_stations_hybrid(image_path, uploader, cdn_domain): + """ + 混合识别模式：图形学切片 + 大模型小图 OCR + """ + # 1. 图形学切片 + segments = XinDianTuReadImageKit.get_card_segments(image_path) + if not segments: + return [] + + # 2. 对每个切片并行进行 OCR + tasks = [] + for seg in segments: + # 上传切片 + patch_path = seg["patch_path"] + remote_path = f"tmp/patches/{os.path.basename(patch_path)}" + uploader.upload_file(patch_path, remote_path) + patch_url = f"{cdn_domain}/{remote_path}" + + # 记录上传后的 URL 供识别使用 + seg["patch_url"] = patch_url + tasks.append(XinDianTuReadImageKit.recognize_card_text(patch_url)) + + # 等待所有 OCR 完成 + results = await asyncio.gather(*tasks) + + # 3. 组装结果 + final_stations = [] + for i, res in enumerate(results): + name = res.get("station_name") + if name and name != "未知": + seg = segments[i] + res["x"] = seg["center_x"] + res["y"] = seg["center_y"] + final_stations.append(res) + + # 4. 后置过滤 (复用原有过滤逻辑) + if final_stations: + processed_excluded_titles = {str(x).replace(" ", "").strip() for x in STATION_EXCLUDED_TITLES} + filtered = [] + for item in final_stations: + name = item.get("station_name") + normalized_name = str(name).replace(" ", "").strip() + if normalized_name in processed_excluded_titles: + continue + if any(kw in normalized_name for kw in STATION_BLACKLIST_KEYWORDS): + continue + filtered.append(item) + final_stations = filtered + + return final_stations + + @staticmethod + @staticmethod + def get_card_segments(image_path, output_dir="./Debug/Patches"): + """ + 基于水平灰色分割带 (Divider Band) 切取场站卡片 + 返回: list of dict {"patch_path": str, "center_x": int, "center_y": int, "y_range": tuple} + """ + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + try: + img = Image.open(image_path).convert('RGB') + width, height = img.size + img_data = np.array(img) + + logger.info(f"页面分析 - 分辨率: {width}x{height}") + + # 1. 行特征分析：识别分割线 (Divider) + # 分割线特征：横贯全屏，亮度均匀，通常比纯白(255)稍暗，比文字内容亮 + # 典型值：Mean=242-247, Std<10 + + row_types = [] # 0: Unknown/Content, 1: Divider, 2: White/Empty + + # 采样点：左中右 + l_x, m_x, r_x = int(width * 0.05), int(width * 0.5), int(width * 0.95) + + debug_rows = [] + + for y in range(height): + # 避开顶部和底部导航栏 (15% - 85%) - 扩大排除范围以避免误识别 Header/Footer + if y < height * 0.15 or y > height * 0.85: + row_types.append(1) # 视为无关区域 (标记为 Divider 以防止形成 Segment) + continue + + row = img_data[y] + row_mean = np.mean(row) + row_std = np.std(row) + + # 判定逻辑： + # 1. 纯白行 (卡片底色) -> mean > 252 (允许微小噪点) + # 2. 分割线 (Divider) -> 230 < mean < 252 且 std < 15 (颜色均匀) + # 3. 内容行 (Content) -> mean <= 230 或 (mean > 230 且 std >= 15) (有文字/图片导致方差大) + + if row_mean > 252: + r_type = 2 # White/Empty + elif 230 < row_mean <= 252 and row_std < 15: + r_type = 1 # Divider + else: + r_type = 0 # Content + + row_types.append(r_type) + + # Debug log sampling + if y % 50 == 0: + debug_rows.append(f"Row {y}: Mean={row_mean:.1f}, Std={row_std:.1f} -> Type={r_type}") + + if debug_rows: + logger.info("行特征采样 (调试用):\n" + "\n".join(debug_rows)) + + # 2. 聚合连续的 Content 区域 (Type 0 or Type 2 sandwiched by Type 0) + # 实际上，卡片是由 Divider 分隔开的区域。 + # 我们寻找两个 Divider 之间的区域，且该区域必须包含 Content (Type 0)。 + + segments = [] + + # 简化状态机： + # 寻找非 Divider 的连续段 + in_segment = False + seg_start = -1 + has_content = False + + raw_blocks = [] + + for y, r_type in enumerate(row_types): + if r_type != 1: # Not Divider + if not in_segment: + in_segment = True + seg_start = y + has_content = False + + if r_type == 0: + has_content = True + else: # Is Divider + if in_segment: + # 结束一段 + if has_content: # 只有包含内容的段才算 + raw_blocks.append((seg_start, y)) + in_segment = False + + # 处理最后一段 + if in_segment and has_content: + raw_blocks.append((seg_start, len(row_types))) + + # 3. 过滤和后处理 + # 合并距离很近的块？或者过滤太小的块 + base_name = os.path.splitext(os.path.basename(image_path))[0] + + valid_segments = [] + logger.info(f"Initial raw blocks count: {len(raw_blocks)}") + + for i, (y1, y2) in enumerate(raw_blocks): + h = y2 - y1 + # 过滤太小的块 (可能是杂噪或单纯的文字行) + if h < 50: + # logger.debug(f"Block {i} too small: {h}") + continue + # 过滤太大的块 (可能是全屏错误) + if h > 1000: + continue + + # 再次确认内部是否有足够的“暗像素”（内容） + # 避免切出纯白的空隙 + region = img_data[y1:y2] + region_mean = np.mean(region) + if region_mean > 254.5: # 整体太白 (放宽阈值，避免误杀大面积白色的卡片) + logger.info(f" [-] 忽略区域 {i}: Y({y1}-{y2}), H={h}, 整体太白 (Mean={region_mean:.1f} > 254.5)") + continue + + # --- 优化：边缘背景检查与宽度裁剪 (Margin Check & Crop) --- + # 不再直接拒绝白边，而是尝试计算内容的有效宽度 + # 假设：有效内容行的方差较高，或者亮度显著不同于背景 + + # 简单策略：保留全宽，但增加高度限制 + if h < 60: # 稍微降低阈值以测试 + logger.info(f" [-] 忽略区域 {i}: Y({y1}-{y2}), H={h}, 高度不足 (<60)") + continue + + # 计算左右边距的平均亮度，辅助判断（仅记录日志，不强行过滤） + margin_w = max(5, int(width * 0.05)) + l_margin = img_data[y1:y2, 0:margin_w] + r_margin = img_data[y1:y2, width-margin_w:width] + l_mean = np.mean(l_margin) + r_mean = np.mean(r_margin) + + # 如果是全宽卡片，左右边缘可能是白色的。 + # 之前的逻辑: if l_mean > 252 or r_mean > 252: continue (导致漏检) + # 现在移除该逻辑。 + + # --- 新增：自动裁剪水平宽度 (Auto Horizontal Crop) --- + # 尝试找到内容的左右边界 (基于列的方差或亮度差异) + x1, x2 = 0, width + + # 从左向右扫描 + for x in range(0, int(width * 0.4), 2): + col = img_data[y1:y2, x] + # 如果这一列不是纯色背景 (std > 5) 或者明显比背景暗 (mean < 245) + if np.std(col) > 5 or np.mean(col) < 245: + x1 = x + break + + # 从右向左扫描 + for x in range(width - 1, int(width * 0.6), -2): + col = img_data[y1:y2, x] + if np.std(col) > 5 or np.mean(col) < 245: + x2 = x + 1 + break + + # 增加一点 padding + x1 = max(0, x1 - 10) + x2 = min(width, x2 + 10) + + # 如果裁剪后宽度太小，可能不是有效卡片 + if (x2 - x1) < width * 0.5: + logger.info(f" [-] 忽略区域 {i}: 裁剪后宽度过小 ({x2-x1})") + continue + + # 保存 + patch = img.crop((x1, y1, x2, y2)) + patch_name = f"{base_name}_p{i}_{y1}.jpg" + patch_path = os.path.join(output_dir, patch_name) + patch.save(patch_path) + + center_y = (y1 + y2) // 2 + valid_segments.append({ + "patch_path": os.path.abspath(patch_path), + "center_x": (x1 + x2) // 2, + "center_y": center_y, + "y_range": (y1, y2) + }) + logger.info(f" [+] 发现卡片 {i}: Y({y1}-{y2}), H={h}, Crop X({x1}-{x2}), 边缘(L={l_mean:.1f}, R={r_mean:.1f}), 已保存") + + logger.info(f"分析完成：识别到 {len(valid_segments)} 个区域") + return valid_segments + + except Exception as e: + logger.error(f"图形学切片失败: {e}", exc_info=True) + return [] + + @staticmethod + async def recognize_card_text(patch_url): + """ + 对切片进行小图 OCR 识别，提取场站详细信息 + """ + loop = asyncio.get_event_loop() + resp = await loop.run_in_executor( + None, + lambda: XinDianTuReadImageKit._client.chat.completions.create( + model=VL_MODEL_NAME, + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "请识别图片中的充电站信息，并以 JSON 格式输出：{\"station_name\": \"...\", \"price\": \"...\", \"piles\": \"空闲数/总数\"}。只输出 JSON，不要有其他文字。" + }, + {"type": "image_url", "image_url": {"url": patch_url}} + ] + } + ], + max_tokens=200 + ) + ) + content = resp.choices[0].message.content.strip() + # 尝试解析 JSON + try: + import json + import re + json_match = re.search(r'\{.*\}', content, re.DOTALL) + if json_match: + return json.loads(json_match.group()) + except Exception: + logger.warning(f"OCR 结果解析 JSON 失败: {content}") + + return {"station_name": "未知"} + + @staticmethod + async def detect_ad(image_url: str, device_info: dict = None) -> dict: + """ + 检测图片中是否存在广告弹窗，并定位关闭按钮坐标 + + 参数： + image_url: 图片地址 + device_info: 设备信息 + + 返回： + { + "has_ad": bool, + "uia_center_x": int|None, + "uia_center_y": int|None + } + """ + if device_info is None: + logger.warning("未提供动态设备信息，使用通用回退配置。") + device_info = XinDianTuReadImageKit._FALLBACK_DEVICE_INFO + + prompt = ( + "分析图片中是否存在覆盖在主界面上的广告弹窗（Popup Ad）。" + "如果存在，请找到关闭该弹窗的按钮（通常是一个带有 'X' 的图标，可能在弹窗的右上角、右下角或正下方）。" + "仅输出JSON对象（不含任何说明文字），包含以下字段：" + "1. has_ad: 布尔值，是否存在广告弹窗；" + "2. close_button_bounds: 关闭按钮的像素坐标或归一化坐标(0-1000) {x1,y1,x2,y2}。如果不存在广告则为 null。" + "注意：只需识别最明显的那个关闭按钮。严格返回纯JSON。" + ) + + loop = asyncio.get_event_loop() + resp = await loop.run_in_executor( + None, + lambda: XinDianTuReadImageKit._client.chat.completions.create( + model=VL_MODEL_NAME_AD, # 使用更强的视觉模型 + messages=[ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": image_url}, + }, + {"type": "text", "text": json.dumps(device_info, ensure_ascii=False)}, + {"type": "text", "text": prompt}, + ], + }, + ], + ) + ) + + content = resp.choices[0].message.content or "" + raw = XinDianTuReadImageKit._extract_json(content) + + try: + data = json.loads(raw) + has_ad = data.get("has_ad", False) + if has_ad and data.get("close_button_bounds"): + # 使用现有的辅助方法转换坐标 + temp_obj = {"bounds": data.get("close_button_bounds")} + XinDianTuReadImageKit._add_click_point(temp_obj, device_info) + return { + "has_ad": True, + "uia_center_x": temp_obj.get("uia_center_x"), + "uia_center_y": temp_obj.get("uia_center_y") + } + return {"has_ad": False, "uia_center_x": None, "uia_center_y": None} + except Exception as e: + logger.error(f"Error parsing ad detection JSON: {e}") + return {"has_ad": False, "uia_center_x": None, "uia_center_y": None} + + @staticmethod + async def _download_as_base64(url: str) -> str: + """Helper to download image and convert to base64 for VL model fallback""" + try: + async with aiohttp.ClientSession() as session: + async with session.get(url) as resp: + if resp.status != 200: + return None + content = await resp.read() + if not content: + return None + return base64.b64encode(content).decode('utf-8') + except Exception as e: + logger.error(f"Failed to download image for base64 conversion: {e}") + return None + + @staticmethod + async def parse_price_schedule(station_name: str, image_url: str, device_info: dict = None) -> list: + """ + 解析价格时段表（整图），提取每一行的时间区间与费用信息 + + 参数： + image_url: 图片地址（包含“当前时段电费/全部时段电费”弹窗或列表） + device_info: 设备信息，用于提升视觉理解一致性（可选） + + 返回： + 列表，每个元素为： + { + "start": "HH:MM", 开始时间 + "end": "HH:MM", 结束时间 + "price_kwh": float|None, 总价或站点价（元/度） + "electric_fee_kwh": float|None, 电费（元/度） + "service_fee_kwh": float|None 服务费（元/度） + } + 说明： + - 使用 qwen3-vl-flash 进行视觉解析，提示词约束输出为纯 JSON 数组 + - 若某项缺失则返回 None（保持结构统一） + """ + if device_info is None: + logger.warning("未提供动态设备信息，使用通用回退配置。") + device_info = XinDianTuReadImageKit._FALLBACK_DEVICE_INFO + + # 视觉解析提示词：约束输出字段与格式，避免模型输出说明文字 + prompt = ( + "仅输出JSON数组（不含任何说明文字）。识别图片中所有时段的价格信息，返回每一行：" + "1) start: 开始时间（HH:MM），2) end: 结束时间（HH:MM），" + "3) price_kwh: 价格（元/度，站点价或总价），" + "4) electric_fee_kwh: 电费（元/度），" + "5) service_fee_kwh: 服务费（元/度）。" + "所有数值以数字返回，例如 1.1800。若缺失某项则填 null。严格返回纯JSON数组。" + "注意：如果某行价格信息为空或表示同上，请尝试复用上一行的价格信息。" + ) + + loop = asyncio.get_event_loop() + + def _do_request(url_val): + return XinDianTuReadImageKit._client.chat.completions.create( + model=VL_MODEL_NAME, + messages=[ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": url_val}, + }, + {"type": "text", "text": json.dumps(device_info, ensure_ascii=False)}, + {"type": "text", "text": prompt}, + ], + }, + ], + ) + + try: + # 在线程池中同步调用 OpenAI 兼容接口，避免阻塞事件循环 + resp = await loop.run_in_executor(None, lambda: _do_request(image_url)) + except BadRequestError as e: + # 尝试捕获 DataInspection 错误并进行 Base64 回退 + err_code = getattr(e, 'code', '') or '' + if not err_code and hasattr(e, 'body') and isinstance(e.body, dict): + err_code = e.body.get('code', '') + + # 如果是媒体格式或数据检查错误 + if 'InvalidParameter.DataInspection' in str(err_code) or 'media format' in str(e).lower(): + logger.warning(f"Image URL rejected ({err_code}). Attempting Base64 fallback: {image_url}") + base64_str = await XinDianTuReadImageKit._download_as_base64(image_url) + if base64_str: + # 简单推断格式，默认 jpeg + ext = "jpeg" + lower_url = image_url.lower() + if ".png" in lower_url: + ext = "png" + elif ".webp" in lower_url: + ext = "webp" + elif ".jpg" in lower_url or ".jpeg" in lower_url: + ext = "jpeg" + + data_uri = f"data:image/{ext};base64,{base64_str}" + resp = await loop.run_in_executor(None, lambda: _do_request(data_uri)) + else: + logger.error("Base64 download failed during fallback.") + return [] + else: + logger.error(f"API BadRequestError: {e}") + return [] + except Exception as e: + logger.error(f"API Unexpected Error: {e}") + return [] + + content = resp.choices[0].message.content or "" + raw = XinDianTuReadImageKit._extract_json(content) + + try: + # 反序列化模型返回的 JSON + rows = json.loads(raw) + if not isinstance(rows, list): + return [] + norm = [] + for r in rows: + if not isinstance(r, dict): + continue + # 兼容不同来源字段命名，统一为目标键 + start = r.get("start") + end = r.get("end") + price = r.get("price_kwh") + elec = r.get("electric_fee_kwh") + serv = r.get("service_fee_kwh") + # station_name = station_name + # 将字符串数字安全转换为 float；缺失则为 None + norm.append({ + "station_name": station_name, + "start": start, + "end": end, + "price_kwh": float(price) if isinstance(price, (int, float, str)) and str(price) else None, + "electric_fee_kwh": float(elec) if isinstance(elec, (int, float, str)) and str(elec) else None, + "service_fee_kwh": float(serv) if isinstance(serv, (int, float, str)) and str(serv) else None, + }) + + for i in range(1, len(norm)): + curr = norm[i] + prev = norm[i - 1] + for k in ("price_kwh", "electric_fee_kwh", "service_fee_kwh"): + if curr.get(k) is None and prev.get(k) is not None: + curr[k] = prev[k] + + return norm + except Exception as e: + logger.error(f"Error parsing JSON: {e}") + logger.error(f"Raw content: {raw}") + return [] + + @staticmethod + def _to_minutes(t: str) -> int: + """ + 将 "HH:MM" 转为分钟数（0-1440） + 说明： + - 特殊处理 "24:00" -> 1440，方便区间闭合处理 + - 非法格式返回 0 + """ + if not t: + return 0 + try: + h, m = t.split(":") + h = int(h) + m = int(m) + if h == 24 and m == 0: + return 24 * 60 + return h * 60 + m + except Exception: + return 0 + + @staticmethod + def _fmt(t: int) -> str: + """ + 将分钟数格式化为 "HH:MM" + """ + h = t // 60 + m = t % 60 + return f"{h:02d}:{m:02d}" + + @staticmethod + def expand_schedule_to_hourly(rows: list) -> list: + """ + 将时段列表按小时边界拆分 + + 参数： + rows: parse_price_schedule 返回的时段列表 + + 返回： + 每小时一条数据的列表，区间为闭开 [start, end) 的连续小时段 + 说明： + - 例如 05:00-08:00 -> 05:00-06:00, 06:00-07:00, 07:00-08:00 + - 保留每小时的价格、电费、服务费不变 + """ + hourly = [] + for r in rows: + # 起止时间转分钟 + s = XinDianTuReadImageKit._to_minutes(r.get("start")) + e = XinDianTuReadImageKit._to_minutes(r.get("end")) + if e <= s: + continue + cur = s + while cur < e: + # 下一小时边界；不超过区间终点 + nxt = min(e, ((cur // 60) + 1) * 60) + hourly.append({ + "start": XinDianTuReadImageKit._fmt(cur), + "end": XinDianTuReadImageKit._fmt(nxt), + "price_kwh": r.get("price_kwh"), + "electric_fee_kwh": r.get("electric_fee_kwh"), + "service_fee_kwh": r.get("service_fee_kwh"), + }) + cur = nxt + return hourly + + @staticmethod + async def _fetch_md5(url: str) -> str: + """ + 下载图片并计算 MD5（用于内容去重） + + 返回： + 32位十六进制 MD5 字符串；失败返回空字符串 + """ + try: + async with aiohttp.ClientSession() as session: + async with session.get(url) as resp: + if resp.status != 200: + return "" + content = await resp.read() + return hashlib.md5(content).hexdigest() + except Exception: + return "" + + @staticmethod + async def parse_address(station_name: str, image_url: str, device_info: dict = None) -> dict: + """ + 解析图片中的充电站地址信息，同时识别“全部时段”按钮坐标 + + 参数： + station_name:场站名称 + image_url: 图片地址 + device_info: 设备信息（可选） + + 返回： + 字典，包含 address 字段以及 uia_center_x/uia_center_y (如果找到按钮) + """ + if device_info is None: + logger.warning("未提供动态设备信息，使用通用回退配置。") + device_info = XinDianTuReadImageKit._FALLBACK_DEVICE_INFO + + # 启动寻找“全部时段”按钮的任务，为后续可能的点击做准备 + button_task = asyncio.create_task(XinDianTuReadImageKit.find_all_time_button_coordinate(image_url, device_info)) + + prompt = ( + "仅输出JSON对象（不含任何说明文字）。" + "任务1：识别图片中充电站的完整名称（full_station_name）。" + f"提示：列表中看到的名称可能是截断的（例如“{station_name}”），请在图片上方找到最匹配的完整名称。" + "任务2：识别充电站的详细地址（address）。" + "寻找规则：地址通常紧跟在场站名称下方，或者在‘距离’图标（定位小图表）附近，或者在带有‘导航’按钮的同一行。" + "返回包含 full_station_name 和 address 字段的JSON对象，例如 {\"full_station_name\": \"完整名称\", \"address\": \"详细地址\"}。" + "如果找不到，对应字段返回空字符串。" + "严格返回纯JSON格式。" + ) + + loop = asyncio.get_event_loop() + resp = await loop.run_in_executor( + None, + lambda: XinDianTuReadImageKit._client.chat.completions.create( + model=VL_MODEL_NAME, + messages=[ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": image_url}, + }, + {"type": "text", "text": json.dumps(device_info, ensure_ascii=False)}, + {"type": "text", "text": prompt}, + ], + }, + ], + ) + ) + + content = resp.choices[0].message.content or "" + raw = XinDianTuReadImageKit._extract_json(content) + + result = {} + try: + data = json.loads(raw) + if isinstance(data, dict): + result = data + except Exception as e: + logger.error(f"Error parsing address JSON: {e}") + + # 等待并合并按钮坐标结果 + try: + button_result = await button_task + if button_result: + result.update(button_result) + except Exception as e: + logger.error(f"Error in button coordinate task: {e}") + + return result + + @staticmethod + async def parse_price_schedule_multi(station_name: str, image_urls: list, device_info: dict = None) -> list: + """ + 多图解析（按图片内容 MD5 去重）并合并时段结果 + + 参数： + image_urls: 多张价格表图片的 URL 列表 + device_info: 设备信息（可选） + + 逻辑： + 1. 逐张下载并计算 MD5，去重得到唯一图片集合 + 2. 对每张唯一图片解析价格时段 + 3. 将所有图片的时段行合并为一个列表返回 + + 返回： + 合并后的时段列表（未按小时拆分） + """ + if not image_urls: + return [] + # MD5 -> URL 的映射，用于去重 + md5_to_url = {} + for u in image_urls: + m = await XinDianTuReadImageKit._fetch_md5(u) + if m and m not in md5_to_url: + md5_to_url[m] = u + unique_urls = list(md5_to_url.values()) + # 合并时段结果 + combined = [] + for u in unique_urls: + rows = await XinDianTuReadImageKit.parse_price_schedule(station_name, u, device_info=device_info) + if rows: + combined.extend(rows) + return combined + + @staticmethod + def hourly_full_day(rows: list) -> list: + """ + 将时段列表规整为全天24个整点小时段 + + 参数： + rows: 原始时段列表（可来自多图合并） + + 返回： + 固定24条记录（00:00-01:00 到 23:00-24:00）， + 若某小时未被任何时段覆盖，则费用为 None + 说明： + - 选择覆盖该小时段的时段（若多个，则选择重叠时间最长的一个） + - 保证返回结构完整，便于后续消费端显示或补全 + """ + # 预处理：转换为分钟区间 + intervals = [] + for r in rows: + s = XinDianTuReadImageKit._to_minutes(r.get("start")) + e = XinDianTuReadImageKit._to_minutes(r.get("end")) + if e <= s: + continue + s = max(0, s) + e = min(1440, e) + intervals.append({ + "s": s, "e": e, + "price_kwh": r.get("price_kwh"), + "electric_fee_kwh": r.get("electric_fee_kwh"), + "service_fee_kwh": r.get("service_fee_kwh"), + }) + intervals.sort(key=lambda x: (x["s"], x["e"])) + + result = [] + for h in range(24): + hs = h * 60 + he = (h + 1) * 60 + best = None + best_overlap = 0 + for it in intervals: + overlap = max(0, min(he, it["e"]) - max(hs, it["s"])) + if overlap > best_overlap: + best_overlap = overlap + best = it + result.append({ + "start": XinDianTuReadImageKit._fmt(hs), + "end": XinDianTuReadImageKit._fmt(he), + "price_kwh": best["price_kwh"] if best else None, + "electric_fee_kwh": best["electric_fee_kwh"] if best else None, + "service_fee_kwh": best["service_fee_kwh"] if best else None, + }) + return result + + +async def test1(): + # 测试新电途第一层结构的读取 + url = "https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/Temp/8fd79c68-fec6-4ca7-8d8e-fbff3c6862c8.jpg" + print(f"Testing First Level with URL: {url}") + result = await XinDianTuReadImageKit.parse_first_level_image_url(url) + print("First Level Result:") + print(json.dumps(result, ensure_ascii=False, indent=2)) + + +async def test2(station_name: str): + # 测试新电途第二层结构的读取 + url = "https://dsideal.obs.myhuaweicloud.com/HuangHai/Temp/SecondPage.jpg" + print(f"Testing address extraction from: {url}") + result = await XinDianTuReadImageKit.parse_address(station_name, url) + result["station_name"] = station_name + print("Address result:") + print(json.dumps(result, ensure_ascii=False, indent=2)) + + +async def test3(station_name: str): + # 测试新电途价格表图片的解析 + samples = [ + "https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/Temp/XinDianTu/1.jpg", + "https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/Temp/XinDianTu/2.jpg", + ] + rows = await XinDianTuReadImageKit.parse_price_schedule_multi(station_name, samples) + hourly = XinDianTuReadImageKit.hourly_full_day(rows) + print(json.dumps(hourly, ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + #asyncio.run(test1()) + station_name = '吉林省看守所充电站' + #asyncio.run(test2(station_name)) + asyncio.run(test3(station_name)) diff --git a/Util/__init__.py b/Util/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Util/__pycache__/Kit.cpython-310.pyc b/Util/__pycache__/Kit.cpython-310.pyc new file mode 100644 index 0000000..4cb929d Binary files /dev/null and b/Util/__pycache__/Kit.cpython-310.pyc differ diff --git a/Util/__pycache__/LlmUtil.cpython-310.pyc b/Util/__pycache__/LlmUtil.cpython-310.pyc new file mode 100644 index 0000000..98f5caf Binary files /dev/null and b/Util/__pycache__/LlmUtil.cpython-310.pyc differ diff --git a/Util/__pycache__/ObsUtil.cpython-310.pyc b/Util/__pycache__/ObsUtil.cpython-310.pyc new file mode 100644 index 0000000..470c178 Binary files /dev/null and b/Util/__pycache__/ObsUtil.cpython-310.pyc differ diff --git a/Util/__pycache__/OcrParser.cpython-310.pyc b/Util/__pycache__/OcrParser.cpython-310.pyc new file mode 100644 index 0000000..b36f8f5 Binary files /dev/null and b/Util/__pycache__/OcrParser.cpython-310.pyc differ diff --git a/Util/__pycache__/PaChongGaoDeKit.cpython-310.pyc b/Util/__pycache__/PaChongGaoDeKit.cpython-310.pyc new file mode 100644 index 0000000..9e444bf Binary files /dev/null and b/Util/__pycache__/PaChongGaoDeKit.cpython-310.pyc differ diff --git a/Util/__pycache__/PaddleOCRKit.cpython-310.pyc b/Util/__pycache__/PaddleOCRKit.cpython-310.pyc new file mode 100644 index 0000000..665af33 Binary files /dev/null and b/Util/__pycache__/PaddleOCRKit.cpython-310.pyc differ diff --git a/Util/__pycache__/RedisKit.cpython-310.pyc b/Util/__pycache__/RedisKit.cpython-310.pyc new file mode 100644 index 0000000..fba8891 Binary files /dev/null and b/Util/__pycache__/RedisKit.cpython-310.pyc differ diff --git a/Util/__pycache__/RedisKit.cpython-313.pyc b/Util/__pycache__/RedisKit.cpython-313.pyc new file mode 100644 index 0000000..b9874b1 Binary files /dev/null and b/Util/__pycache__/RedisKit.cpython-313.pyc differ diff --git a/Util/__pycache__/Win32Patch.cpython-310.pyc b/Util/__pycache__/Win32Patch.cpython-310.pyc new file mode 100644 index 0000000..06616be Binary files /dev/null and b/Util/__pycache__/Win32Patch.cpython-310.pyc differ diff --git a/Util/__pycache__/XinDianTuReadImageKit.cpython-310.pyc b/Util/__pycache__/XinDianTuReadImageKit.cpython-310.pyc new file mode 100644 index 0000000..fb57996 Binary files /dev/null and b/Util/__pycache__/XinDianTuReadImageKit.cpython-310.pyc differ diff --git a/Util/__pycache__/XinDianTuReadImageKit.cpython-313.pyc b/Util/__pycache__/XinDianTuReadImageKit.cpython-313.pyc new file mode 100644 index 0000000..feea2ea Binary files /dev/null and b/Util/__pycache__/XinDianTuReadImageKit.cpython-313.pyc differ diff --git a/Util/__pycache__/__init__.cpython-310.pyc b/Util/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..19e342c Binary files /dev/null and b/Util/__pycache__/__init__.cpython-310.pyc differ diff --git a/Util/__pycache__/__init__.cpython-313.pyc b/Util/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..9b24b31 Binary files /dev/null and b/Util/__pycache__/__init__.cpython-313.pyc differ diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/run.py b/run.py new file mode 100644 index 0000000..5ac40b4 --- /dev/null +++ b/run.py @@ -0,0 +1,53 @@ +# coding=utf-8 +import sys +import os +import argparse +import asyncio + +# 添加当前目录到 sys.path +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +def main(): + parser = argparse.ArgumentParser(description="dsCrawler 统一启动入口") + # nargs='?' 表示参数可选，如果未提供，值为 None + parser.add_argument("app", nargs='?', help="要启动的小程序应用名称 (例如: XinDianTu)") + parser.add_argument("--action", default="run", help="要执行的操作 (run=全流程, crawl=仅爬取, coord=仅补全坐标)") + + args = parser.parse_args() + + if args.app is None: + print("您没有输入要爬取的供应商名称，系统将默认爬取新电途。") + app_name = "XinDianTu" + else: + app_name = args.app + + action = args.action + + print(f"🚀 正在启动应用: {app_name} (Action: {action})") + + try: + if app_name == "XinDianTu": + from Apps.XinDianTu import Run + from Apps.XinDianTu import Crawler + from Apps.XinDianTu import Coord + + if action == "run": + asyncio.run(Run.main()) + elif action == "crawl": + # 仅运行爬虫部分（需要自己处理 service 初始化） + print("暂未实现单独运行 crawl，请使用 run") + elif action == "coord": + asyncio.run(Coord.main()) + else: + print(f"未知操作: {action}") + else: + print(f"❌ 未找到应用: {app_name}") + print("可用应用: XinDianTu") + + except ImportError as e: + print(f"❌ 导入错误: {e}") + except Exception as e: + print(f"❌ 运行错误: {e}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/一键更新.cmd b/一键更新.cmd new file mode 100644 index 0000000..d8f5331 --- /dev/null +++ b/一键更新.cmd @@ -0,0 +1,21 @@ +git config --global user.name "HuangHai" +git config --global user.email "10402852@qq.com" + +:: 删除旧凭据（注意保持协议一致） +cmdkey /delete:git:http://www.yelite.cn:27825 + +:: 添加正确格式的HTTP协议凭据（注意不要带路径） +cmdkey /generic:git:http://www.yelite.cn:27825 /user:huanghai /pass:mdcija780522 + +:: 设置凭据存储（保持原有配置） +git config --global credential.helper manager + +git config --global core.autocrlf false +git config --global http.sslVerify true + +git add -A . +git commit -am 'commit' +git pull --no-edit +git push origin main + +