# coding=utf-8 import asyncio import os import sys import time import json import cv2 from Apps.TeLaiDian.Kit import ( take_screenshot, get_image_content_md5, clean_station_name, setup_logger, detect_price_click_point_cv, read_image, save_image ) from Apps.TeLaiDian.ReadImageKit import ReadImageKit from Apps.TeLaiDian.Service import TeLaiDianService from Apps.TeLaiDian.Config.Setting import ( SCROLL_DISTANCE_RATIO, WAIT_AFTER_SCROLL, MAX_STATIONS_COUNT, SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO, WAIT_DETAIL_PAGE_LOAD, WAIT_BACK_TO_LIST, DETAIL_SCROLL_DISTANCE_RATIO ) from Core.BaseCrawler import BaseCrawler import uiautomator2 as u2 # 项目根目录处理 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) # 初始化日志 logger = setup_logger("TeLaiDianCrawler") class TeLaiDianCrawler(BaseCrawler): def __init__(self, service=None): super().__init__(service or TeLaiDianService()) self.read_image_kit = ReadImageKit() async def start(self): """ 实现 BaseCrawler 的启动入口 注意：在 T4 等全流程脚本中，小程序由 Opener 提前打开， 此处直接开始爬取逻辑。 """ d = u2.connect() await self.crawl_list_logic(d) async def open_app(self): """ 打开特来电小程序 """ from Apps.TeLaiDian import Opener return await Opener.open_mini_program() async def clear_ads(self, d, max_rounds=3): """ 清理页面上的广告弹窗 """ logger.info(f"开始清理广告弹窗，最多尝试 {max_rounds} 轮...") for i in range(max_rounds): ad_screen = take_screenshot(d, f"tld_ad_check_{int(time.time())}.jpg") res = await self.read_image_kit.find_close_button_vlm(ad_screen) if res.get("has_ad") and res.get("close_point"): close_point = res.get("close_point") w, h = d.window_size() target_x = int(close_point[0] * w / 1000) target_y = int(close_point[1] * h / 1000) # 安全校验：绝对不能点击微信小程序的胶囊按钮区 (右上角) # 胶囊按钮通常在 x > 80% 宽度 且 y < 100 像素(或 8% 高度) 的区域 if target_x > w * 0.75 and target_y < 150: logger.warning(f"⚠️ 拒绝点击疑似微信胶囊按钮的区域: ({target_x}, {target_y})") continue logger.info(f"第 {i+1} 轮发现广告: {res.get('reason')}，点击关闭: ({target_x}, {target_y})") d.click(target_x, target_y) await asyncio.sleep(1.5) else: logger.info(f"第 {i+1} 轮未发现明显广告，清理结束。") if os.path.exists(ad_screen): os.remove(ad_screen) break if os.path.exists(ad_screen): os.remove(ad_screen) async def crawl_list_logic(self, d): # [临时禁用] 广告清理逻辑误触严重，先解决主流程问题 # await self.clear_ads(d) # [优化] 向下滚动以刷新/校准地理位置 # 使用更加显式的 swipe 方式：从屏幕 30% 划到 80% w, h = d.window_size() logger.info(f"执行显式下拉刷新操作: (x={w//2}, y1={int(h*0.3)} -> y2={int(h*0.8)})") d.swipe(w // 2, int(h * 0.3), w // 2, int(h * 0.8), duration=0.5) logger.info("等待 2.5 秒确保位置校准和列表刷新完成...") await asyncio.sleep(2.5) processed_count = 0 last_md5 = None processed_station_names = set() # 用于列表级去重 while processed_count < MAX_STATIONS_COUNT: # 1. 截图并分析 screenshot_path = take_screenshot(d, f"tld_list_{int(time.time())}.jpg") # 检测是否滚动到底部 curr_md5 = get_image_content_md5(screenshot_path, top_ratio=SAFE_EXCLUDE_RATIO, bottom_ratio=BOTTOM_SAFE_EXCLUDE_RATIO) if last_md5 == curr_md5: logger.info("内容无变化，判定已到底部") if os.path.exists(screenshot_path): os.remove(screenshot_path) break last_md5 = curr_md5 stations = await self.read_image_kit.analyze_station_list(screenshot_path) if not stations: # 检查是否意外退出了小程序 is_wrong_page = await self.check_wrong_page(d, screenshot_path) if is_wrong_page: logger.error("检测到已退出详情列表页（可能回到了搜索页），尝试重新进入...") await self.open_app() await asyncio.sleep(5) continue logger.info("本页未检测到场站，尝试滑动...") d.swipe_ext("up", scale=SCROLL_DISTANCE_RATIO) await asyncio.sleep(WAIT_AFTER_SCROLL) continue for station in stations: if processed_count >= MAX_STATIONS_COUNT: break name = station.get("name") point = station.get("point") if not name or not point: continue if name in processed_station_names: logger.info(f"跳过已处理场站: {name}") continue logger.info(f"处理场站: {name} (坐标: {point})") # 点击进入详情 d.click(point[0], point[1]) logger.info(f"已点击场站 '{name}'，等待 {WAIT_DETAIL_PAGE_LOAD}s 加载详情页...") await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD) # 截图验证是否进入详情页 detail_check_path = take_screenshot(d, f"tld_detail_check_{int(time.time())}.jpg") logger.info(f"详情页快照已保存: {detail_check_path}") # 简单验证：如果标题包含 "我的卡券"、"优惠券"、"新人福利" 等，说明点错了 is_wrong_page = await self.check_wrong_page(d, detail_check_path) if is_wrong_page: logger.warning(f"检测到进入了错误页面，尝试返回列表...") # 尝试点击左上角的返回箭头，如果没有，则执行系统 back d.click(40, 70) # 特来电通常左上角有返回箭头 await asyncio.sleep(1.0) d.press("back") await asyncio.sleep(WAIT_BACK_TO_LIST) if os.path.exists(detail_check_path): os.remove(detail_check_path) continue # 爬取详情 await self.crawl_detail_logic(d, station) processed_station_names.add(name) # 返回列表 d.press("back") await asyncio.sleep(WAIT_BACK_TO_LIST) # 返回列表后也检查一下是否有新广告弹出 await self.clear_ads(d, max_rounds=1) processed_count += 1 # 滑动到下一页 d.swipe_ext("up", scale=SCROLL_DISTANCE_RATIO) await asyncio.sleep(WAIT_AFTER_SCROLL) if os.path.exists(screenshot_path): os.remove(screenshot_path) async def check_wrong_page(self, d, image_path): """ 检查是否误触进入了错误的页面（如：我的卡券、活动页等） """ data = await self.read_image_kit.check_wrong_page_vlm(image_path) is_detail = data.get("is_detail_page", True) if not is_detail: logger.warning(f"⚠️ 确认进入错误页面: {data.get('page_type')} ({data.get('reason')})") return not is_detail async def crawl_detail_logic(self, d, station_info): """ 在详情页提取价格和状态信息 """ # 1. 识别第一屏的基础信息 (名称、精确地址) first_screen_path = take_screenshot(d, f"tld_detail_basic_{int(time.time())}.jpg") basic_info = await self.read_image_kit.analyze_detail_basic_info(first_screen_path) station_name = basic_info.get("name") or station_info.get("name") address = basic_info.get("address") or station_info.get("address") logger.info(f"详情页基础信息识别完成: {station_name} | {address}") # 2. 小步快跑寻找价格入口 (结合 CV 和 VLM) found_entrance = False entrance_point = None max_search_steps = 4 logger.info(f"开始“小步快跑”策略寻找价格入口，最多尝试 {max_search_steps} 次小幅度滑动...") for step in range(max_search_steps): current_screen = take_screenshot(d, f"tld_search_price_step_{step}.jpg") logger.info(f"--- 寻找入口 第 {step+1} 步 ---") # 优先使用 CV 快速识别橘红色价格 P0 logger.info("尝试 CV 识别价格卡片 (P0)...") cv_point = detect_price_click_point_cv(current_screen) if cv_point: logger.info(f"✅ CV 在第 {step+1} 步成功定位入口: {cv_point}") entrance_point = cv_point found_entrance = True else: # CV 没找到，使用 VLM 进行深度语义检查 logger.info("CV 未找到，启动 VLM 深度语义识别...") vlm_res = await self.read_image_kit.find_price_entrance_vlm(current_screen) if vlm_res.get("found"): norm_point = vlm_res.get("point") # [x, y] in 0-1000 if norm_point and len(norm_point) == 2: w, h = d.window_size() entrance_point = [int(norm_point[0] * w / 1000), int(norm_point[1] * h / 1000)] logger.info(f"✅ VLM 在第 {step+1} 步成功定位入口: {entrance_point} ({vlm_res.get('reason')})") found_entrance = True else: logger.info(f"第 {step+1} 步未发现入口: {vlm_res.get('reason', '未知原因')}") # 如果找到入口，进行标注并点击 if found_entrance and entrance_point: debug_flag_path = current_screen.replace(".jpg", "_entrance_found.jpg") img_debug = read_image(current_screen) if img_debug is not None: cv2.circle(img_debug, (entrance_point[0], entrance_point[1]), 25, (0, 255, 0), 5) # 绿色大圆圈 save_image(debug_flag_path, img_debug) logger.info(f"入口位置标注图已保存: {debug_flag_path}") logger.info(f"正在点击价格入口: {entrance_point}") d.click(entrance_point[0], entrance_point[1]) await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD) # 清理临时截图 if os.path.exists(current_screen): os.remove(current_screen) break # 没找到，小步向上滚动 if step < max_search_steps - 1: scroll_scale = 0.35 logger.info(f"未发现入口，执行小幅度向上滑动 (scale={scroll_scale})...") d.swipe_ext("up", scale=scroll_scale) await asyncio.sleep(1.2) # 清理临时截图 if os.path.exists(current_screen): os.remove(current_screen) if not found_entrance: logger.warning("“小步快跑”策略未能找到价格入口，尝试坐标兜底...") w, h = d.window_size() d.click(w // 2, int(h * 0.45)) await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD) # 3. 进入分时电价页面后的处理 try: # 1. 延长等待时间，等待小程序自动定位到当前时段的滚动完成 logger.info("已点击进入价格详情，等待小程序自动滚动定位完成 (4秒)...") await asyncio.sleep(4.0) # 2. 回到 00:00 原点：要看到上面的内容，需要“向下拉动”页面（即向上滚动列表） logger.info("执行向下拉动，尝试回到 00:00 时段顶部...") for i in range(3): # swipe_ext("down") 是手指从上往下划，动作是“向下”，结果是页面“向上”滚动 d.swipe_ext("down", scale=0.8) await asyncio.sleep(0.5) except Exception as e: logger.error(f"处理分时电价页面初始状态失败: {e}") # 4. 循环滑动抓取完整分时电价 all_prices = [] last_price_md5 = None price_page_count = 0 max_price_pages = 4 # 增加到4页确保覆盖 00:00-24:00 screenshot_tasks = [] # 用于异步分析图片的任务列表 temp_screenshots = [] # 记录临时文件以便后续清理 logger.info("开始循环截图（UI操作优先，后台并行分析）...") try: while price_page_count < max_price_pages: price_screen_path = take_screenshot(d, f"tld_detail_price_{int(time.time())}_{price_page_count}.jpg") # 校验页面是否发生滚动变化 curr_md5 = get_image_content_md5(price_screen_path, top_ratio=0.2, bottom_ratio=0.2) if curr_md5 == last_price_md5: logger.info("价格页面内容无变化，判定已触底") if os.path.exists(price_screen_path): os.remove(price_screen_path) break last_price_md5 = curr_md5 temp_screenshots.append(price_screen_path) logger.info(f"已截取价格详情页第 {price_page_count + 1} 页: {price_screen_path}，启动后台异步分析") # 使用 asyncio.create_task 立即在后台开始执行分析 task = asyncio.create_task(self.read_image_kit.analyze_detail_price(price_screen_path)) screenshot_tasks.append(task) # 向上滚动列表（手指向上划），看后面的时段 logger.info("向上滚动列表，准备截取下一屏价格...") d.swipe_ext("up", scale=0.8) await asyncio.sleep(1.0) price_page_count += 1 # 等待所有后台分析任务完成 if screenshot_tasks: logger.info(f"UI 操作已完成，等待 {len(screenshot_tasks)} 个后台分析任务结束...") results = await asyncio.gather(*screenshot_tasks, return_exceptions=True) for res in results: if isinstance(res, Exception): logger.error(f"后台分析任务出错: {res}") continue if res: # 深度去重：根据时段 (start, end) 合并 for p in res: is_duplicate = False for existing in all_prices: if p.get('start') == existing.get('start') and p.get('end') == existing.get('end'): is_duplicate = True # 字段补全逻辑 for key in ['price', 'plus_price', 'market_price', 'elec_price', 'service_price']: if p.get(key) is not None and (existing.get(key) is None or existing.get(key) == 0): existing[key] = p[key] break if not is_duplicate: all_prices.append(p) except Exception as e: logger.error(f"抓取价格详情过程中发生异常: {e}") finally: # 无论是否异常，都要确保清理未完成的任务，避免 "never awaited" 警告 for task in screenshot_tasks: if not task.done(): task.cancel() # 清理所有临时截图 for path in temp_screenshots: if os.path.exists(path): try: os.remove(path) except: pass # 5. 保存数据 if all_prices: station_name_clean = clean_station_name(station_name) # 对价格按时间排序 try: all_prices.sort(key=lambda x: x.get('start', '00:00')) except: pass logger.info(f"✅ 场站 {station_name_clean} 共提取到 {len(all_prices)} 条价格信息，准备保存...") await self.service.save_station_data(station_name_clean, address, all_prices) else: logger.warning(f"❌ 未能提取到任何价格信息，请检查页面识别逻辑") # 清理临时截图 for p in [first_screen_path, price_screen_path]: if os.path.exists(p): os.remove(p) async def crawl_list(self): """ 实现 BaseCrawler 的抽象方法 """ d = u2.connect() await self.crawl_list_logic(d) async def crawl_detail(self, station_info): """ 实现 BaseCrawler 的抽象方法 """ # 逻辑已在 crawl_list_logic 中通过 crawl_detail_logic 调用 pass async def main(service=None): crawler = TeLaiDianCrawler(service=service) await crawler.start() if __name__ == "__main__": asyncio.run(main())