'commit'

Apps/TeLaiDian/Config/Setting.py

@@ -3,6 +3,7 @@
 # 采集配置
 SCROLL_DISTANCE_RATIO = 0.5
 MAX_STATIONS_COUNT = 100
+FIRST_RUN_ONLY_ONE_STATION = True
 
 # 调试绘图配置
 DRAW_DEBUG_BOXES = True

Apps/TeLaiDian/Crawler.py

@@ -14,7 +14,7 @@ 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
+    WAIT_BACK_TO_LIST, DETAIL_SCROLL_DISTANCE_RATIO, FIRST_RUN_ONLY_ONE_STATION
 )
 from Core.BaseCrawler import BaseCrawler
 import uiautomator2 as u2
@@ -170,6 +170,11 @@ class TeLaiDianCrawler(BaseCrawler):
                 # 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
+                if FIRST_RUN_ONLY_ONE_STATION:
+                    logger.info("已完成首个场站的全流程采集，根据配置退出爬取任务。")
+                    if os.path.exists(screenshot_path):
+                        os.remove(screenshot_path)
+                    return
             
             # 滑动到下一页
             d.swipe_ext("up", scale=SCROLL_DISTANCE_RATIO)
@@ -191,96 +196,80 @@ class TeLaiDianCrawler(BaseCrawler):
         """
         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)
+        # 1. 截取第一屏的基础信息图（名称、精确地址），并异步识别
         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}")
+        station_name = station_info.get("name")
+        address = station_info.get("address")
+        logger.info(f"已截取详情页首屏截图，启动异步基础信息识别任务: {station_name} | {address}")
 
-        # 2. 小步快跑寻找价格入口 (结合 CV 和 VLM)
-        found_entrance = False
+        async def analyze_basic_info_background(image_path, fallback_name, fallback_address):
+            try:
+                basic_info = await self.read_image_kit.analyze_detail_basic_info(image_path)
+                name2 = basic_info.get("name") or fallback_name
+                addr2 = basic_info.get("address") or fallback_address
+                logger.info(f"[异步] 详情页基础信息识别完成: {name2} | {addr2}")
+            except Exception as ex:
+                logger.error(f"[异步] 分析详情页基础信息失败: {ex}")
+            finally:
+                if os.path.exists(image_path):
+                    try:
+                        os.remove(image_path)
+                    except:
+                        pass
+
+        asyncio.create_task(analyze_basic_info_background(first_screen_path, station_name, address))
+
+        # 2. 一次向上滑动，确保当前底部文字完全顶到上沿之外
+        w, h = d.window_size()
+        logger.info("执行一次向上滑动，将当前底部文字完全推至屏幕上沿之外...")
+        d.swipe(w * 0.5, h * 0.75, w * 0.5, h * 0.25, 0.8)
+        await asyncio.sleep(1.5)
+
+        # 3. 使用模板 jgxx.jpg 匹配“价格信息 当前价”入口并点击
+        template_jgxx = os.path.join(os.path.dirname(__file__), "Template", "jgxx.jpg")
         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 os.path.exists(template_jgxx):
+            logger.info(f"使用模板匹配价格入口: {template_jgxx}")
+            try:
+                match_res = d.image.match(template_jgxx)
+            except Exception as e:
+                match_res = None
+                logger.error(f"模板匹配价格入口失败: {e}")
 
-        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()
+            if match_res:
+                if hasattr(match_res, "point") and match_res.point:
+                    entrance_point = match_res.point
+                elif isinstance(match_res, dict):
+                    if "point" in match_res and match_res["point"]:
+                        entrance_point = match_res["point"]
+                    elif "x" in match_res and "y" in match_res:
+                        entrance_point = (match_res["x"], match_res["y"])
+                elif isinstance(match_res, (list, tuple)) and len(match_res) >= 2:
+                    entrance_point = (match_res[0], match_res[1])
+
+        if entrance_point:
+            logger.info(f"通过 jgxx.jpg 成功找到价格入口，点击坐标: {entrance_point}")
+            d.click(int(entrance_point[0]), int(entrance_point[1]))
+            await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD)
+        else:
+            logger.warning("未能通过 jgxx.jpg 找到价格入口，使用坐标兜底点击页面中部偏下位置。")
             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
+        # 4. 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)
+            logger.info("执行两次大幅向下拉动，确保回到 00:00 时段顶部...")
+            for _ in range(2):
+                d.swipe_ext("down", scale=0.9)
+                await asyncio.sleep(1.0)
         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
+        # 5. 循环滑动抓取完整分时电价
         all_prices = []
         last_price_md5 = None
         price_page_count = 0
@@ -353,7 +342,7 @@ class TeLaiDianCrawler(BaseCrawler):
                     except:
                         pass
 
-        # 5. 保存数据
+        # 6. 保存数据
         if all_prices:
             station_name_clean = clean_station_name(station_name)
             # 对价格按时间排序
@@ -365,10 +354,9 @@ class TeLaiDianCrawler(BaseCrawler):
             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)
+        # 清理价格页临时截图（详情页首屏截图已在异步任务中清理）
+        if os.path.exists(price_screen_path):
+            os.remove(price_screen_path)
 
     async def crawl_list(self):
         """