# coding=utf-8 import asyncio import logging import uuid import os import sys import time 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 Apps.AiTeJiYiChong import Kit from Apps.AiTeJiYiChong.Kit import take_screenshot from Apps.AiTeJiYiChong.ReadImageKit import ReadImageKit from Util.RedisKit import RedisKit from Apps.AiTeJiYiChong.Service import AiTeJiYiChongService from Config.Config import TEMP_IMAGE_DIR from Apps.AiTeJiYiChong.Config.Setting import ( SCROLL_DISTANCE_RATIO, MAX_SCROLLS, REDIS_STATION_EXPIRE, WAIT_AFTER_SCROLL, MAX_CRAWL_DISTANCE ) logger = logging.getLogger("AiTeJiYiChongCrawler") async def get_station_list(d, service, max_scrolls=MAX_SCROLLS): """ 获取场站列表并处理翻页 """ redis_kit = RedisKit() window_size = d.window_size() w, h = window_size[0], window_size[1] device_info = d.info device_info['width'] = w device_info['height'] = h logger.info(f"开始爬取列表，设备: {device_info.get('productName')} | 分辨率: {w}x{h}") for i in range(max_scrolls + 1): logger.info(f"正在处理第 {i + 1} 页...") # 1. 拍摄截图 image_uuid = str(uuid.uuid4()) 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}") # 2. 执行图形学分析，生成 _flag.jpg, _vl.jpg 和 .json logger.info("正在执行图形学切片分析...") json_data = Kit.crop_cards_from_image(screenshot_path) # 3. 调用 VL 模型识别并保存数据 stations = await service.process_station_list_vl(screenshot_path, device_info=device_info) logger.info(f"本页识别到 {len(stations)} 个场站") # 4. 匹配几何卡片与 VL 识别结果 if json_data and json_data.get("cards") and stations: for card in json_data["cards"]: card_rect = card["rect"] # [x1, y1, x2, y2] for st in stations: st_bounds = st.get("bounds") # [x1, y1, x2, y2] (0-1000) if not st_bounds: continue # 转换 VL 坐标到像素坐标 st_y1_px = st_bounds[1] * h / 1000 st_y2_px = st_bounds[3] * h / 1000 # 计算 y 轴重叠 overlap = min(card_rect[3], st_y2_px) - max(card_rect[1], st_y1_px) if overlap > 50: # 有显著重叠 card["station_name"] = st.get("station_name") logger.info(f"匹配成功: 几何卡片 {card_rect} -> 场站 {card['station_name']}") break # 5. 遍历处理本页所有场站 if json_data and json_data.get("cards"): for card_idx, card in enumerate(json_data["cards"]): station_name = card.get("station_name") if not station_name: logger.warning(f"第 {card_idx + 1} 个几何卡片未匹配到场站名称，跳过。") continue # 检查 Redis 去重 redis_key = f"crawled:aite:{station_name}" if await redis_kit.get_data(redis_key): logger.info(f"场站 {station_name} 已处理，跳过。") continue click_x, click_y = card["click_point"] logger.info(f"准备处理第 {card_idx + 1} 个场站: {station_name}, 点击坐标: ({click_x}, {click_y})") d.click(click_x, click_y) # 等待二级页面加载 await asyncio.sleep(3) # 截取二级页面图 detail_uuid = f"detail_{station_name}_{image_uuid}" detail_path = take_screenshot(d, detail_uuid, save_dir=TEMP_IMAGE_DIR) # 5. 调用详情页处理逻辑 (二级页面：提取地址) logger.info(f"正在解析详情页基础数据: {detail_path}") detail_data = await service.process_station_detail(detail_path, station_name=station_name) # 6. 寻找 timePrice.jpg 图标并进入三级页面 (分时价格页) time_price_template = os.path.join(os.path.dirname(__file__), "BiaoShi", "timePrice.jpg") coords = Kit.find_template_coords(detail_path, time_price_template) if coords: cx, cy, conf = coords logger.info(f"找到分时价格图标，进入三级页面...") d.click(cx, cy) await asyncio.sleep(3) # 截取三级页面并处理滑动价格 price_detail_uuid = f"price_detail_{station_name}_{image_uuid}" price_detail_path = take_screenshot(d, price_detail_uuid, save_dir=TEMP_IMAGE_DIR) all_prices = [] # 初始识别 prices = await ReadImageKit.get_price_detail_from_image(price_detail_path) if prices: all_prices.extend(prices) last_md5 = Kit.get_file_md5(price_detail_path) # 滑动逻辑 for scroll_idx in range(3): logger.info(f"执行第 {scroll_idx + 1} 次滑动以抓取更多价格...") d.swipe(w // 2, int(h * 0.7), w // 2, int(h * 0.3), duration=0.5) await asyncio.sleep(2) scroll_path = take_screenshot(d, f"price_scroll_{scroll_idx}_{station_name}", save_dir=TEMP_IMAGE_DIR) current_md5 = Kit.get_file_md5(scroll_path) if current_md5 == last_md5: logger.info("检测到屏幕未发生变化（已滑到底部），停止滑动识别。") break last_md5 = current_md5 new_prices = await ReadImageKit.get_price_detail_from_image(scroll_path) if new_prices: for np_item in new_prices: if not any(np_i.get("start") == np_item.get("start") for np_i in all_prices): all_prices.append(np_item) else: break if all_prices: all_prices.sort(key=lambda x: x.get("start", "00:00")) hourly_schedule = ReadImageKit.expand_schedule_to_24h(all_prices) await service.process_price_detail_data(station_name, hourly_schedule) logger.info(f"场站 {station_name} 三级页面价格处理完成。") # 从三级页面返回二级页面 logger.info("从三级页面返回二级页面...") d.press("back") await asyncio.sleep(1.5) else: logger.warning(f"场站 {station_name} 未找到分时价格入口。") # 从二级页面返回列表页 logger.info("从二级页面返回列表页...") d.press("back") await asyncio.sleep(1.5) # 记录 Redis await redis_kit.set_data(redis_key, "1", expire=REDIS_STATION_EXPIRE) # 5. 翻页滑动 (如果没进入二级页面) logger.info("执行翻页滑动...") start_x, start_y = w // 2, int(h * 0.8) end_x, end_y = w // 2, int(h * (0.8 - SCROLL_DISTANCE_RATIO)) d.swipe(start_x, start_y, end_x, end_y, duration=0.5) await asyncio.sleep(WAIT_AFTER_SCROLL) logger.info("达到最大翻页次数，爬取结束。") return True async def main(service=None, do_cleanup=True): """ 爬虫主入口 """ if do_cleanup: Kit.clear_temp_dir() if service is None: service = AiTeJiYiChongService() await service.init_db() d = u2.connect() try: await get_station_list(d, service) return True except Exception as e: logger.error(f"爬取过程中出现异常: {e}") return False finally: # 如果是内部初始化的 service，则在此关闭 pass if __name__ == "__main__": asyncio.run(main())