372 lines
13 KiB
Python
372 lines
13 KiB
Python
# coding=utf-8
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import asyncio
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import logging
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import uuid
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import os
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import sys
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import json
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import time
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import hashlib
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from PIL import Image
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# 将项目根目录添加到 sys.path
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project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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if project_root not in sys.path:
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sys.path.append(project_root)
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import uiautomator2 as u2
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from Core.BaseCrawler import BaseCrawler
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from Apps.XinDianTu import Kit
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from Apps.XinDianTu.Kit import take_screenshot
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from Util.ObsUtil import ObsUploader
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from Util.RedisKit import RedisKit
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import cv2
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from Apps.XinDianTu.Service import XinDianTuService
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from Apps.XinDianTu.ReadImageKit import ReadImageKit
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from Config.Config import (
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OBS_TMP_PREFIX, CDN_DOMAIN, TEMP_IMAGE_DIR
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)
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from Apps.XinDianTu.Config.Setting import (
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SCROLL_DISTANCE_RATIO,
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MAX_SCROLLS, REDIS_STATION_EXPIRE,
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WAIT_DETAIL_PAGE_LOAD, WAIT_BACK_TO_LIST, WAIT_AFTER_SCROLL,
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MAX_CRAWL_DISTANCE
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)
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# --- 用户配置区域 ---
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# 是否保留截图文件（True=保留备查，False=随用随删）
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KEEP_SCREENSHOTS = True
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# [Testing] 是否在启动时清除 Redis 中的场站处理记录
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# True: 每次运行前清除记录，方便反复测试同一个场站
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# False: 生产模式，保留记录以避免重复爬取
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TEST_CLEAR_REDIS = True
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# 配置说明：
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# SCROLL_DISTANCE_RATIO 控制翻页时的滑动距离（在 Config.py 中修改）。
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# 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，
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# 请尝试减小 SCROLL_DISTANCE_RATIO（例如设置为 0.4 或 0.3）。
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# 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。
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# 配置日志输出
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.StreamHandler(sys.stdout)
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]
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)
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logger = logging.getLogger("StationList")
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# 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
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logging.getLogger("OpenXinDianTu").setLevel(logging.INFO)
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logging.getLogger("FullProcess").setLevel(logging.INFO)
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class XinDianTuCrawler(BaseCrawler):
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"""
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新电途小程序爬虫实现
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"""
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def __init__(self, service=None):
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super().__init__(service)
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# 初始化配置参数
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self.max_scrolls = MAX_SCROLLS
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self.uploader = ObsUploader()
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self.redis_kit = RedisKit()
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async def start(self):
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"""
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实现 BaseCrawler 的启动入口
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"""
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# 兼容旧逻辑，直接调用 main
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await main(self.service)
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async def open_app(self):
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# 实际逻辑在 Opener.py，此处可作为封装层
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pass
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async def crawl_list(self):
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# 实际逻辑在 get_station_list，此处可作为封装层
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pass
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async def crawl_detail(self, station_info):
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pass
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async def is_already_crawled(redis_kit, station_name):
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"""
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检查场站是否已经爬取过，仅按名称去重
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"""
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cleaned_name = Kit.clean_station_name(station_name)
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if not cleaned_name:
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return False
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redis_key = f"crawled:xdt:{cleaned_name}"
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if await redis_kit.get_data(redis_key):
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return True
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return False
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async def analyze_prices_background(service, station_name, image_paths):
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"""
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后台异步处理价格图片分析
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"""
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try:
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# 使用多图解析并去重
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all_rows = await ReadImageKit.parse_price_schedule_multi(station_name, image_paths)
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if all_rows:
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# 24小时铺平
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hourly_schedule = ReadImageKit.hourly_full_day(all_rows)
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await service.process_price_detail_data(station_name, hourly_schedule)
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logger.info(f"后台处理完成: {station_name} 的分时价格已保存。")
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else:
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logger.warning(f"后台处理失败: {station_name} 未能解析出价格时段。")
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# 处理完成后清理图片
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for path in image_paths:
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try:
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if os.path.exists(path):
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os.remove(path)
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except:
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pass
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except Exception as e:
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logger.error(f"分析价格后台任务异常 ({station_name}): {e}")
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return True
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async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
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"""
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获取场站列表并处理翻页 (异步优化版)
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"""
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redis_kit = RedisKit()
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window_size = d.window_size()
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w, h = window_size[0], window_size[1]
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device_info = d.info
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device_info['width'] = w
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device_info['height'] = h
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logger.info(f"开始爬取列表，设备: {device_info.get('productName')} | 分辨率: {w}x{h}")
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background_tasks = []
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for i in range(max_scrolls + 1):
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logger.info(f"正在处理第 {i + 1} 页...")
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# 1. 拍摄截图
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image_uuid = str(uuid.uuid4())
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screenshot_path = take_screenshot(d, image_uuid, save_dir=TEMP_IMAGE_DIR)
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logger.info(f"列表页截图已完成: {screenshot_path}")
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# 2. 执行本地图形学分析，生成 _vl.jpg 和 .json
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logger.info("正在执行图形学切片分析...")
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Kit.crop_cards_from_image(screenshot_path)
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json_path = screenshot_path.replace(".jpg", ".json")
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vl_img_path = screenshot_path.replace(".jpg", "_vl.jpg")
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if not os.path.exists(json_path) or not os.path.exists(vl_img_path):
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logger.warning("未识别到任何卡片，跳过当前页")
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continue
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with open(json_path, 'r', encoding='utf-8') as f:
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json_metadata = json.load(f)
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# 3. 【优化】后台异步上传 _vl.jpg 供以后参考，不再等待上传
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vl_object_key = f"{OBS_TMP_PREFIX}/{image_uuid}_vl.jpg"
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loop = asyncio.get_running_loop()
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loop.run_in_executor(None, uploader.upload_file, vl_object_key, vl_img_path)
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# 4. 【优化】直接使用本地路径调用 VL 模型识别，避免等待上传
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logger.info("正在调用 VL 模型识别场站信息...")
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stations = await service.process_station_list_vl(vl_img_path, json_metadata, device_info=device_info)
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logger.info(f"本页识别到 {len(stations)} 个场站")
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# 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)
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# 这里的 stations 列表长度应该与 json_metadata["cards"] 对应
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if json_metadata.get("cards") and stations:
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for idx, card in enumerate(json_metadata["cards"]):
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# 检查索引是否越界 (VL 模型可能返回的数组长度不一致)
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if idx < len(stations) and stations[idx]:
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st = stations[idx]
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station_name = st.get("station_name")
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if not station_name: continue
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# 检查 Redis 去重
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if await is_already_crawled(redis_kit, station_name):
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logger.info(f"场站 {station_name} 匹配到已处理记录，跳过。")
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continue
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click_x, click_y = card["click_point"]
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logger.info(f"准备处理第 {idx + 1} 个场站: {station_name}, 点击坐标: ({click_x}, {click_y})")
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d.click(int(click_x), int(click_y))
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# 等待二级页面加载
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await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD)
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# 截取二级页面图
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detail_uuid = str(uuid.uuid4())
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detail_path = take_screenshot(d, detail_uuid, save_dir=TEMP_IMAGE_DIR)
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# 【优化】后台解析详情页地址，直接传本地路径，避免等待上传
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logger.info(f"已启动后台分析详情页: {station_name}")
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task_addr = asyncio.create_task(service.process_station_address(station_name, detail_path, device_info=device_info))
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background_tasks.append(task_addr)
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# 【优化】后台异步上传详情页截图供以后参考
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detail_object_key = f"{OBS_TMP_PREFIX}/{detail_uuid}.jpg"
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loop = asyncio.get_running_loop()
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loop.run_in_executor(None, uploader.upload_file, detail_object_key, detail_path)
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# --- 抓取价格时段信息 ---
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# 识别 "全部时段" 按钮
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pos = Kit.find_expand_button_position(detail_path, debug_dir=TEMP_IMAGE_DIR, debug_filename_prefix=station_name)
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if pos:
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ex, ey = pos
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logger.info(f"找到 '全部时段' 按钮: ({ex}, {ey})")
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d.click(ex, ey)
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await asyncio.sleep(1.5)
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price_screenshots = []
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for p_idx in range(1, 4):
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p_uuid = f"{hashlib.md5(station_name.encode('utf-8')).hexdigest()}_p_{p_idx}"
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p_path = take_screenshot(d, p_uuid, save_dir=TEMP_IMAGE_DIR)
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price_screenshots.append(p_path)
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if p_idx < 3:
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d.swipe_ext("up", scale=0.7)
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await asyncio.sleep(1.0)
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# 【异步优化】后台处理价格
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logger.info(f"已启动后台分析价格页: {station_name}")
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task_price = asyncio.create_task(analyze_prices_background(service, station_name, price_screenshots))
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background_tasks.append(task_price)
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# 从三级页面返回
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d.press("back")
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await asyncio.sleep(0.5)
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# 从二级页面返回
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d.press("back")
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await asyncio.sleep(WAIT_BACK_TO_LIST)
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# 记录 Redis 去重 (仅按名称去重)
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cleaned = Kit.clean_station_name(station_name)
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await redis_kit.set_data(f"crawled:xdt:{cleaned}", "1", expire=REDIS_STATION_EXPIRE)
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# 清理已完成的后台任务
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done_tasks = [t for t in background_tasks if t.done()]
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for t in done_tasks:
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if t.exception():
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logger.error(f"后台任务异常: {t.exception()}")
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background_tasks.remove(t)
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# 6. 翻页
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logger.info("执行翻页滑动...")
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d.swipe_ext("up", scale=SCROLL_DISTANCE_RATIO)
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await asyncio.sleep(WAIT_AFTER_SCROLL)
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# 等待所有后台任务完成
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if background_tasks:
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logger.info(f"正在等待剩余 {len(background_tasks)} 个后台任务完成...")
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await asyncio.gather(*background_tasks, return_exceptions=True)
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logger.info("采集任务完成")
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return True
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def clean_images_dir():
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"""清理 Images 目录下除保留文件外的所有文件"""
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base_dir = os.path.dirname(os.path.abspath(__file__))
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images_dir = os.path.join(base_dir, "Images")
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if not os.path.exists(images_dir):
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return
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keep_files = {'1.jpg', '2.jpg', '3.jpg', '4.jpg'}
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logger.info(f"正在清理 Images 目录 (保留: {keep_files})...")
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deleted_count = 0
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for filename in os.listdir(images_dir):
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if filename in keep_files:
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continue
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file_path = os.path.join(images_dir, filename)
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try:
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if os.path.isfile(file_path):
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os.remove(file_path)
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deleted_count += 1
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except Exception as e:
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logger.warning(f"删除文件失败 {filename}: {e}")
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logger.info(f"清理完成，共删除 {deleted_count} 个文件。")
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async def clean_redis_data(redis_kit):
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"""清除 Redis 中的场站处理记录"""
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if TEST_CLEAR_REDIS:
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logger.info("测试模式开启：正在清除 Redis 中的场站处理记录 (processed_station:*)...")
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keys = await redis_kit.keys("processed_station:*")
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if keys:
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count = await redis_kit.delete(*keys)
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logger.info(f"已清除 {count} 条场站处理记录")
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else:
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logger.info("未发现旧的场站处理记录")
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async def main(service=None, do_cleanup=True):
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# 连接设备
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d = u2.connect()
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# 初始化服务
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should_close_service = False
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if service is None:
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service = XinDianTuService()
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should_close_service = True
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# 初始化数据库连接（XinDianTuService 需要）
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await service.init_db()
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uploader = ObsUploader()
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redis_kit = RedisKit()
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try:
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if do_cleanup:
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# 清理图片目录
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clean_images_dir()
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# [Testing] 如果配置为测试模式，启动时清除 Redis 记录
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await clean_redis_data(redis_kit)
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# 清理过期数据
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# await service.cleanup_old_data()
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# 获取场站列表
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stations = await get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS)
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if stations:
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logger.info("场站列表采集完成。")
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else:
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logger.warning("未采集到任何场站信息。")
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return True
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except Exception as e:
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logger.exception(f"运行过程中出现异常: {e}")
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return False
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finally:
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# 关闭数据库连接
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if should_close_service:
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await service.close_db()
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if __name__ == "__main__":
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try:
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asyncio.run(main())
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except KeyboardInterrupt:
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logger.info("程序被用户中断.")
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except Exception as e:
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logger.exception(f"程序崩溃: {e}")
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