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HuangHai
2026-01-12 16:53:43 +08:00
parent 4cef36dc96
commit f263c133b8
7 changed files with 441 additions and 758 deletions
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25
Apps/AiTeJiYiChong/Crawler.py
View File

@@ -30,29 +30,15 @@ logger = logging.getLogger("AiTeJiYiChongCrawler")
async def is_already_crawled(redis_kit, station_name):
"""
检查场站是否已经爬取过，支持前缀模糊匹配处理截断名称
检查场站是否已经爬取过，仅按名称去重
"""
cleaned_name = Kit.clean_station_name(station_name)
if not cleaned_name:
return False
# 1. 检查精确匹配
redis_key = f"crawled:aite:{cleaned_name}"
if await redis_kit.get_data(redis_key):
return True
# 2. 如果名称被截断（原名包含 . ），尝试前缀匹配
# 逻辑：在 Redis 中查找是否有以当前清理后的名称开头的已完成场站
# 为了效率，我们配合 Redis 的 keys 模式（小规模数据下可用）
if ".." in station_name or station_name.endswith("."):
# 获取所有已爬取的 key 进行内存前缀比对，或者利用 Redis 模糊查询
# 这里采用一种更高效的方式：在详情页保存时，额外保存一个前缀索引
prefix_key = f"crawled:aite:prefix:{cleaned_name[:6]}" # 取前6位作为索引前缀
stored_names = await redis_kit.get_data(prefix_key)
if stored_names and isinstance(stored_names, list):
for full_name in stored_names:
if full_name.startswith(cleaned_name):
return True
return False
async def get_station_list(d, service, max_scrolls=MAX_SCROLLS):
@@ -190,16 +176,9 @@ async def get_station_list(d, service, max_scrolls=MAX_SCROLLS):
d.press("back")
await asyncio.sleep(0.8)
# 记录 Redis (保存全名并建立前缀索引)
# 记录 Redis (仅按名称去重，不再使用前缀索引)
full_name_key = f"crawled:aite:{Kit.clean_station_name(station_name)}"
await redis_kit.set_data(full_name_key, "1", expire=REDIS_STATION_EXPIRE)
# 建立前缀索引 (取前6位，方便模糊查找)
prefix_idx_key = f"crawled:aite:prefix:{Kit.clean_station_name(station_name)[:6]}"
existing_names = await redis_kit.get_data(prefix_idx_key) or []
if station_name not in existing_names:
existing_names.append(station_name)
await redis_kit.set_data(prefix_idx_key, existing_names, expire=REDIS_STATION_EXPIRE)
# 在每一页结束时，清理已完成的任务，防止内存堆积
done_tasks = [t for t in background_tasks if t.done()]

11
Apps/AiTeJiYiChong/Doc/流程说明.md
View File

@@ -44,9 +44,14 @@
2. **用户体验**: UI 操作（点击、滑动、返回）是连续的，不会因为网络请求波动而卡顿。
- **任务追踪**: 在 `Crawler.py` 中维护 `background_tasks` 列表，在每页结束时清理已完成任务，并在整个爬取结束时使用 `asyncio.gather` 等待残余任务。
### 3.2 智能去重机制
- **Redis 模糊匹配**: 针对小程序中场站名称可能被省略号截断（如“万达茂...”）的情况，不仅存储全名，还存储场站名称的前 6 位作为前缀索引。
- **逻辑**: 先查全名，再查前缀索引。如果前缀匹配成功，则视为已爬取，跳过重复工作。
### 3.2 场站去重策略
- **按名称去重**: 仅依据列表显示的场站名称进行去重，不再使用前缀或模糊匹配。
- **处理规则**:
- 名称先经 `clean_station_name` 去掉尾部省略号与空格，例如 `AAABBB...` → `AAABBB`；
- 首次遇到该名称时处理并写入 Redis 键 `crawled:aite:{name}`；
- 后续再遇到同名（包括省略号截断的同名）直接跳过，不再点击进入详情页。
- **设计原因**: 逻辑直白、符合业务预期，降低模糊匹配带来的误判风险。
- **一致性说明**: 新电途项目采用同样的按名称去重策略以保持跨项目行为一致。
### 3.3 坐标系转换
- **VL 坐标**: 0-1000 的归一化坐标。

485
Apps/XinDianTu/Crawler.py
View File

@@ -23,6 +23,7 @@ from Util.RedisKit import RedisKit
from Util.PaddleOCRKit import get_ocr_kit
import cv2
from Apps.XinDianTu.Service import XinDianTuService
from Apps.XinDianTu.ReadImageKit import ReadImageKit
from Config.Config import (
OBS_TMP_PREFIX, CDN_DOMAIN, TEMP_IMAGE_DIR
)
@@ -95,416 +96,188 @@ class XinDianTuCrawler(BaseCrawler):
pass
async def is_already_crawled(redis_kit, station_name):
"""
检查场站是否已经爬取过，仅按名称去重
"""
cleaned_name = Kit.clean_station_name(station_name)
if not cleaned_name:
return False
redis_key = f"crawled:xdt:{cleaned_name}"
if await redis_kit.get_data(redis_key):
return True
return False
async def analyze_prices_background(service, station_name, image_paths):
"""
后台异步处理价格图片分析
"""
try:
# 使用多图解析并去重
all_rows = await ReadImageKit.parse_price_schedule_multi(station_name, image_paths)
if all_rows:
# 24小时铺平
hourly_schedule = ReadImageKit.hourly_full_day(all_rows)
await service.process_price_detail_data(station_name, hourly_schedule)
logger.info(f"后台处理完成: {station_name} 的分时价格已保存。")
else:
logger.warning(f"后台处理失败: {station_name} 未能解析出价格时段。")
# 处理完成后清理图片
for path in image_paths:
try:
if os.path.exists(path):
os.remove(path)
except:
pass
except Exception as e:
logger.error(f"分析价格后台任务异常 ({station_name}): {e}")
return True
async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
"""
获取场站列表并处理翻页
获取场站列表并处理翻页 (异步优化版)
"""
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}")
logger.info(f"开始爬取列表，设备: {device_info.get('productName')} | 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 = TEMP_IMAGE_DIR
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)")
screenshot_path = take_screenshot(d, image_uuid, save_dir=TEMP_IMAGE_DIR)
logger.info(f"列表页截图已完成: {screenshot_path}")
# 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
# 2. 执行本地图形学分析，生成 _vl.jpg 和 .json
logger.info("正在执行图形学切片分析...")
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")
logger.warning("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()
# 3. 【优化】后台异步上传 _vl.jpg 供以后参考，不再等待上传
vl_object_key = f"{OBS_TMP_PREFIX}/{image_uuid}_vl.jpg"
loop = asyncio.get_running_loop()
loop.run_in_executor(None, uploader.upload_file, vl_object_key, vl_img_path)
# 4. 【优化】直接使用本地路径调用 VL 模型识别，避免等待上传
logger.info("正在调用 VL 模型识别场站信息...")
stations = await service.process_station_list_vl(vl_img_path, json_metadata, device_info=device_info)
logger.info(f"本页识别到 {len(stations)} 个场站")
stations = []
try:
ocr_kit = get_ocr_kit()
# 5. 匹配几何卡片与 VL 识别结果 (XinDianTu 的 parse_vl_image 已经按顺序返回了)
# 这里的 stations 列表长度应该与 json_metadata["cards"] 对应
if json_metadata.get("cards") and stations:
for idx, card in enumerate(json_metadata["cards"]):
# 检查索引是否越界 (VL 模型可能返回的数组长度不一致)
if idx < len(stations) and stations[idx]:
st = stations[idx]
station_name = st.get("station_name")
if not station_name: continue
# 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)
# 检查 Redis 去重
if await is_already_crawled(redis_kit, station_name):
logger.info(f"场站 {station_name} 匹配到已处理记录，跳过。")
continue
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...")
click_x, click_y = card["click_point"]
logger.info(f"准备处理第 {idx + 1} 个场站: {station_name}, 点击坐标: ({click_x}, {click_y})")
d.click(int(click_x), int(click_y))
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
# 等待二级页面加载
await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD)
# 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()
# 截取二级页面图
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")
detail_path = take_screenshot(d, detail_uuid, save_dir=TEMP_IMAGE_DIR)
# 2. 上传至 OBS
t_d_up = time.time()
# 【优化】后台解析详情页地址，直接传本地路径，避免等待上传
logger.info(f"已启动后台分析详情页: {station_name}")
task_addr = asyncio.create_task(service.process_station_address(station_name, detail_path, device_info=device_info))
background_tasks.append(task_addr)
# 【优化】后台异步上传详情页截图供以后参考
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_IMAGE_DIR)
# 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
loop = asyncio.get_running_loop()
loop.run_in_executor(None, uploader.upload_file, detail_object_key, detail_path)
# --- 抓取价格时段信息 ---
# 识别 "全部时段" 按钮
pos = Kit.find_expand_button_position(detail_path, debug_dir=TEMP_IMAGE_DIR, debug_filename_prefix=station_name)
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)
ex, ey = pos
logger.info(f"找到 '全部时段' 按钮: ({ex}, {ey})")
d.click(ex, ey)
await asyncio.sleep(1.5)
# 抓取3屏截图
price_image_urls = []
price_screenshots = []
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")
p_uuid = f"{hashlib.md5(station_name.encode('utf-8')).hexdigest()}_p_{p_idx}"
p_path = take_screenshot(d, p_uuid, save_dir=TEMP_IMAGE_DIR)
price_screenshots.append(p_path)
if p_idx < 3:
d.swipe_ext("up", scale=0.7)
await asyncio.sleep(1.0)
# 【异步优化】后台处理价格
logger.info(f"已启动后台分析价格页: {station_name}")
task_price = asyncio.create_task(analyze_prices_background(service, station_name, price_screenshots))
background_tasks.append(task_price)
# 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...")
# 从三级页面返回
d.press("back")
await asyncio.sleep(0.5)
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}")
# 从二级页面返回
d.press("back")
await asyncio.sleep(WAIT_BACK_TO_LIST)
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")
# 记录 Redis 去重 (仅按名称去重)
cleaned = Kit.clean_station_name(station_name)
await redis_kit.set_data(f"crawled:xdt:{cleaned}", "1", expire=REDIS_STATION_EXPIRE)
except Exception as e:
logger.warning(f"抓取价格时段信息时发生异常: {e}")
# 清理已完成的后台任务
done_tasks = [t for t in background_tasks if t.done()]
for t in done_tasks:
if t.exception():
logger.error(f"后台任务异常: {t.exception()}")
background_tasks.remove(t)
# 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
# 6. 翻页
logger.info("执行翻页滑动...")
d.swipe_ext("up", scale=SCROLL_DISTANCE_RATIO)
await asyncio.sleep(WAIT_AFTER_SCROLL)
# 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...")
# 等待所有后台任务完成
if background_tasks:
await asyncio.gather(*background_tasks)
logger.info("所有后台任务已完成。")
logger.info(f"正在等待剩余 {len(background_tasks)} 个后台任务完成...")
await asyncio.gather(*background_tasks, return_exceptions=True)
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
logger.info("采集任务完成")
return True
def clean_images_dir():

56
Apps/XinDianTu/Kit.py
View File

@@ -2,7 +2,8 @@ import logging
import os
import cv2
import numpy as np
import time
import time
import hashlib
from Apps.XinDianTu.Config.Setting import BOTTOM_SAFE_EXCLUDE_RATIO
from Config.Config import TEMP_IMAGE_DIR
@@ -11,24 +12,40 @@ logger = logging.getLogger(__name__)
def read_image(path):
"""读取图片，支持中文路径"""
"""读取图片，支持中文路径及鲁棒性检查"""
try:
return cv2.imdecode(np.fromfile(path, dtype=np.uint8), -1)
if not path or not os.path.exists(path):
return None
if os.path.getsize(path) == 0:
return None
return cv2.imdecode(np.fromfile(path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
except Exception as e:
logger.info(f"Error reading image {path}: {e}")
logger.error(f"读取图片失败 {path}: {e}")
return None
def save_image(path, img):
"""保存图片，支持中文路径"""
"""保存图片，支持中文路径及鲁棒性检查"""
try:
if img is None:
return False
# 确保目录存在
dir_name = os.path.dirname(path)
if dir_name and not os.path.exists(dir_name):
os.makedirs(dir_name, exist_ok=True)
ext = os.path.splitext(path)[1]
if not ext:
ext = ".jpg"
cv2.imencode(ext, img)[1].tofile(path)
return True
success, encoded_img = cv2.imencode(ext, img)
if success:
encoded_img.tofile(path)
return True
return False
except Exception as e:
logger.error(f"Error saving image {path}: {e}")
logger.error(f"保存图片失败 {path}: {e}")
return False
@@ -672,6 +689,29 @@ def get_row_stats(gray):
return row_means, row_stds, edge_means
def clean_station_name(name):
"""
清理场站名称，移除结尾的省略号及多余空格，方便比对
"""
if not name: return ""
name = name.strip()
# 移除结尾的 . (通常是截断标识)
while name.endswith("."):
name = name[:-1]
return name.strip()
def get_file_md5(path):
"""计算文件的 MD5 值"""
if not os.path.exists(path):
return ""
hash_md5 = hashlib.md5()
with open(path, "rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
hash_md5.update(chunk)
return hash_md5.hexdigest()
def crop_cards_from_image(img_path, output_dir=None, save_debug=True):
"""
从图片中裁剪场站卡片

24
Apps/XinDianTu/Opener.py
View File

@@ -9,6 +9,7 @@ import uiautomator2 as u2
from Apps.XinDianTu.Kit import take_screenshot, detect_black_agree_button, detect_any_ad_close, \
detect_bottom_close_circle, is_background_dimmed, click_image_template
from Apps.XinDianTu.ReadImageKit import ReadImageKit
from Config.Config import TEMP_IMAGE_DIR
# pip install adbutils
@@ -87,7 +88,28 @@ async def check_and_close_ad(d):
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。")
# 3. 视觉大模型检测方案 (VL Model) - 作为兜底
logger.info("本地图形学检测未发现广告，尝试调用视觉大模型检测...")
try:
window_size = d.window_size()
device_info = {
"width": window_size[0],
"height": window_size[1],
"productName": d.info.get('productName', 'unknown')
}
ad_result = await ReadImageKit.detect_ad(screenshot_path, device_info=device_info)
if ad_result.get("has_ad") and ad_result.get("uia_center_x") is not None:
x, y = ad_result["uia_center_x"], ad_result["uia_center_y"]
logger.info(f"通过视觉大模型检测到广告关闭按钮: ({x}, {y})")
d.click(x, y)
if os.path.exists(screenshot_path):
os.remove(screenshot_path)
return True
except Exception as e:
logger.error(f"视觉大模型广告检测异常: {e}")
logger.info(f"本地图形学和 VL 检测完成，未发现已知广告。")
# 清理本地截图
if os.path.exists(screenshot_path):

572
Apps/XinDianTu/ReadImageKit.py
View File

@@ -7,7 +7,7 @@ import json
import aiohttp
import logging
import base64
from openai import OpenAI, BadRequestError
from openai import AsyncOpenAI, BadRequestError
from Config.Config import (
ALY_LLM_API_KEY, VL_MODEL_NAME, VL_MODEL_NAME_AD
)
@@ -20,7 +20,7 @@ logger = logging.getLogger(__name__)
class ReadImageKit:
_client = OpenAI(
_client = AsyncOpenAI(
api_key=ALY_LLM_API_KEY,
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)
@@ -207,11 +207,18 @@ class ReadImageKit:
return obj
@staticmethod
async def find_all_time_button_coordinate(image_url: str, device_info: dict = None) -> dict:
async def find_all_time_button_coordinate(image_url_or_path: 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 = ReadImageKit._FALLBACK_DEVICE_INFO
if os.path.exists(image_url_or_path):
with open(image_url_or_path, "rb") as f:
encoded = base64.b64encode(f.read()).decode("utf-8")
img_input = {"url": f"data:image/jpeg;base64,{encoded}"}
else:
img_input = {"url": image_url_or_path}
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"
@@ -223,27 +230,21 @@ class ReadImageKit:
"如果未找到，返回空JSON {}。"
)
loop = asyncio.get_event_loop()
resp = await loop.run_in_executor(
None,
lambda: ReadImageKit._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},
],
},
],
)
resp = await ReadImageKit._client.chat.completions.create(
model=VL_MODEL_NAME,
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": img_input,
},
{"type": "text", "text": json.dumps(device_info, ensure_ascii=False)},
{"type": "text", "text": prompt},
],
},
],
)
content = resp.choices[0].message.content or ""
@@ -265,11 +266,18 @@ class ReadImageKit:
return {}
@staticmethod
async def find_station_coordinate_first_page(image_url: str, station_name: str, device_info: dict = None) -> dict:
async def find_station_coordinate_first_page(image_url_or_path: 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 = ReadImageKit._FALLBACK_DEVICE_INFO
if os.path.exists(image_url_or_path):
with open(image_url_or_path, "rb") as f:
encoded = base64.b64encode(f.read()).decode("utf-8")
img_input = {"url": f"data:image/jpeg;base64,{encoded}"}
else:
img_input = {"url": image_url_or_path}
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，"
@@ -280,22 +288,18 @@ class ReadImageKit:
"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: ReadImageKit._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},
],
},
],
)
resp = await ReadImageKit._client.chat.completions.create(
model=VL_MODEL_NAME,
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": img_input},
{"type": "text", "text": json.dumps(device_info, ensure_ascii=False)},
{"type": "text", "text": prompt},
],
},
],
)
content = resp.choices[0].message.content or ""
@@ -316,32 +320,33 @@ class ReadImageKit:
return {}
@staticmethod
async def parse_first_level_image_url(image_url: str, device_info: dict = None) -> list:
async def parse_first_level_image_url(image_url_or_path: 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 = ReadImageKit._FALLBACK_DEVICE_INFO
loop = asyncio.get_event_loop()
resp = await loop.run_in_executor(
None,
lambda: ReadImageKit._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": ReadImageKit._prompt},
],
},
],
)
if os.path.exists(image_url_or_path):
with open(image_url_or_path, "rb") as f:
encoded = base64.b64encode(f.read()).decode("utf-8")
img_input = {"url": f"data:image/jpeg;base64,{encoded}"}
else:
img_input = {"url": image_url_or_path}
resp = await ReadImageKit._client.chat.completions.create(
model=VL_MODEL_NAME,
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": img_input,
},
{"type": "text", "text": json.dumps(device_info, ensure_ascii=False)},
{"type": "text", "text": ReadImageKit._prompt},
],
},
],
)
content = resp.choices[0].message.content or ""
@@ -397,7 +402,7 @@ class ReadImageKit:
if name:
idxs.append(i)
tasks.append(asyncio.create_task(
ReadImageKit.find_station_coordinate_first_page(image_url, name, device_info)
ReadImageKit.find_station_coordinate_first_page(image_url_or_path, name, device_info)
))
if tasks:
results = await asyncio.gather(*tasks, return_exceptions=True)
@@ -421,13 +426,20 @@ class ReadImageKit:
return await ReadImageKit.get_stations_hybrid(image_path, uploader, cdn_domain)
@staticmethod
async def parse_vl_image(vl_image_url, json_metadata, device_info=None):
async def parse_vl_image(image_url_or_path: str, 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 []
if os.path.exists(image_url_or_path):
with open(image_url_or_path, "rb") as f:
encoded = base64.b64encode(f.read()).decode("utf-8")
img_input = {"url": f"data:image/jpeg;base64,{encoded}"}
else:
img_input = {"url": image_url_or_path}
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])
@@ -450,21 +462,17 @@ class ReadImageKit:
"严格返回纯JSON格式。"
)
loop = asyncio.get_event_loop()
resp = await loop.run_in_executor(
None,
lambda: ReadImageKit._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},
],
},
],
)
resp = await ReadImageKit._client.chat.completions.create(
model=VL_MODEL_NAME,
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": img_input},
{"type": "text", "text": prompt},
],
},
],
)
content = resp.choices[0].message.content or ""
@@ -769,25 +777,21 @@ class ReadImageKit:
"""
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: ReadImageKit._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
)
resp = await ReadImageKit._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
@@ -803,12 +807,12 @@ class ReadImageKit:
return {"station_name": "未知"}
@staticmethod
async def detect_ad(image_url: str, device_info: dict = None) -> dict:
async def detect_ad(image_url_or_path: 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
image_url_or_path: 图片地址或本地路径
device_info: 设备信息
返回：
@@ -822,34 +826,37 @@ class ReadImageKit:
logger.warning("未提供动态设备信息，使用通用回退配置。")
device_info = ReadImageKit._FALLBACK_DEVICE_INFO
if os.path.exists(image_url_or_path):
with open(image_url_or_path, "rb") as f:
encoded = base64.b64encode(f.read()).decode("utf-8")
img_input = {"url": f"data:image/jpeg;base64,{encoded}"}
else:
img_input = {"url": image_url_or_path}
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）。"
"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 or 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: ReadImageKit._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},
],
},
],
)
resp = await ReadImageKit._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": img_input,
},
{"type": "text", "text": json.dumps(device_info, ensure_ascii=False)},
{"type": "text", "text": prompt},
],
},
],
)
content = resp.choices[0].message.content or ""
@@ -889,32 +896,48 @@ class ReadImageKit:
return None
@staticmethod
async def parse_price_schedule(station_name: str, image_url: str, device_info: dict = None) -> list:
async def get_image_md5(image_url_or_path: str) -> str:
"""
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）
计算图片的 MD5（用于内容去重），支持 URL 或 本地路径
"""
if not image_url_or_path:
return ""
# 如果是本地文件
if os.path.exists(image_url_or_path):
from Apps.XinDianTu.Kit import get_file_md5
return get_file_md5(image_url_or_path)
# 如果是 URL
try:
async with aiohttp.ClientSession() as session:
async with session.get(image_url_or_path, timeout=10) as resp:
if resp.status != 200:
return ""
content = await resp.read()
return hashlib.md5(content).hexdigest()
except Exception as e:
logger.error(f"MD5 获取失败 ({image_url_or_path}): {e}")
return ""
@staticmethod
async def parse_price_schedule(station_name: str, image_url_or_path: str, device_info: dict = None) -> list:
"""
解析价格时段表，支持 URL 或 本地路径
"""
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 = ReadImageKit._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
# 处理图片输入 (URL or Local Path)
if os.path.exists(image_url_or_path):
with open(image_url_or_path, "rb") as f:
encoded = base64.b64encode(f.read()).decode("utf-8")
img_input = {"url": f"data:image/jpeg;base64,{encoded}"}
else:
img_input = {"url": image_url_or_path}
# 视觉解析提示词
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），"
@@ -925,205 +948,46 @@ class ReadImageKit:
"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 ReadImageKit._client.chat.completions.create(
try:
resp = await ReadImageKit._client.chat.completions.create(
model=VL_MODEL_NAME,
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url": url_val},
},
{"type": "image_url", "image_url": img_input},
{"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 ReadImageKit._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 []
content = resp.choices[0].message.content or ""
raw = ReadImageKit._extract_json(content)
return json.loads(raw)
except Exception as e:
logger.error(f"API Unexpected Error: {e}")
return []
content = resp.choices[0].message.content or ""
raw = ReadImageKit._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}")
logger.error(f"解析价格失败 ({station_name}): {e}")
return []
@staticmethod
def _to_minutes(t: str) -> int:
async def parse_address(station_name: str, image_url_or_path: str, device_info: dict = None) -> dict:
"""
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 = ReadImageKit._to_minutes(r.get("start"))
e = ReadImageKit._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": ReadImageKit._fmt(cur),
"end": ReadImageKit._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)
解析地址信息，支持 URL 或 本地路径
"""
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 = ReadImageKit._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(ReadImageKit.find_all_time_button_coordinate(image_url, device_info))
# 处理图片输入 (URL or Local Path)
if os.path.exists(image_url_or_path):
with open(image_url_or_path, "rb") as f:
encoded = base64.b64encode(f.read()).decode("utf-8")
img_input = {"url": f"data:image/jpeg;base64,{encoded}"}
else:
img_input = {"url": image_url_or_path}
# 寻找按钮坐标任务
button_task = asyncio.create_task(ReadImageKit.find_all_time_button_coordinate(image_url_or_path, device_info))
prompt = (
"仅输出JSON对象（不含任何说明文字）。"
@@ -1136,78 +1000,56 @@ class ReadImageKit:
"严格返回纯JSON格式。"
)
loop = asyncio.get_event_loop()
resp = await loop.run_in_executor(
None,
lambda: ReadImageKit._client.chat.completions.create(
result = {}
try:
resp = await ReadImageKit._client.chat.completions.create(
model=VL_MODEL_NAME,
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url": image_url},
},
{"type": "image_url", "image_url": img_input},
{"type": "text", "text": json.dumps(device_info, ensure_ascii=False)},
{"type": "text", "text": prompt},
],
},
],
)
)
content = resp.choices[0].message.content or ""
raw = ReadImageKit._extract_json(content)
result = {}
try:
data = json.loads(raw)
if isinstance(data, dict):
result = data
content = resp.choices[0].message.content or ""
raw = ReadImageKit._extract_json(content)
result = json.loads(raw)
except Exception as e:
logger.error(f"Error parsing address JSON: {e}")
logger.error(f"解析地址失败 ({station_name}): {e}")
# 等待并合并按钮坐标结果
# 合并按钮坐标
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}")
logger.error(f"按钮坐标获取失败: {e}")
return result
@staticmethod
async def parse_price_schedule_multi(station_name: str, image_urls: list, device_info: dict = None) -> list:
async def parse_price_schedule_multi(station_name: str, image_urls_or_paths: 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）
多图解析并去重合并
"""
if not image_urls:
if not image_urls_or_paths:
return []
# MD5 -> URL 的映射，用于去重
md5_to_url = {}
for u in image_urls:
m = await ReadImageKit._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
# MD5 去重
md5_to_img = {}
for img in image_urls_or_paths:
m = await ReadImageKit.get_image_md5(img)
if m and m not in md5_to_img:
md5_to_img[m] = img
unique_imgs = list(md5_to_img.values())
combined = []
for u in unique_urls:
rows = await ReadImageKit.parse_price_schedule(station_name, u, device_info=device_info)
for img in unique_imgs:
rows = await ReadImageKit.parse_price_schedule(station_name, img, device_info=device_info)
if rows:
combined.extend(rows)
return combined

26
Apps/XinDianTu/Service.py
View File

@@ -42,11 +42,11 @@ class XinDianTuService:
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:
async def process_station_list_vl(self, vl_image_url_or_path, json_metadata, device_info=None) -> list:
"""
基于 VL 模式处理场站列表
"""
station_list = await ReadImageKit.parse_vl_image(vl_image_url, json_metadata, device_info=device_info)
station_list = await ReadImageKit.parse_vl_image(vl_image_url_or_path, json_metadata, device_info=device_info)
if not station_list:
return []
@@ -490,6 +490,28 @@ class XinDianTuService:
await session.commit()
logger.info(f" - Station status recorded for: {name} (Piles: {free}/{total}, Price: {current_price})")
async def process_price_detail_data(self, station_name, hourly_schedule) -> bool:
"""
直接保存已处理好的小时段价格数据
"""
if not station_name or not hourly_schedule:
return False
station_hash = self.get_hash(station_name)
now = datetime.now()
async with await self.db.get_session() as session:
schedule_id = self.generate_id()
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()
return True
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}'")