'commit'

Apps/TeLaiDian/Crawler.py

@@ -84,10 +84,13 @@ class TeLaiDianCrawler(BaseCrawler):
         await self.clear_ads(d)
         
         # [优化] 向下滚动以刷新/校准地理位置
-        logger.info("执行下拉操作以刷新校准地理位置...")
-        d.swipe_ext("down", scale=0.6)
-        logger.info("等待 2 秒让位置校准生效...")
-        await asyncio.sleep(2.0)
+        # 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

Apps/TeLaiDian/ReadImageKit.py

@@ -267,9 +267,19 @@ class ReadImageKit:
             
             final_stations = []
             if cv_bboxes and isinstance(vlm_results, list):
+                img = read_image(image_path)
+                h, w = img.shape[:2] if img is not None else (2400, 1080)
+                y_threshold = h * 0.58
+                
                 for i, res in enumerate(vlm_results):
                     if i < len(cv_bboxes):
                         bbox = cv_bboxes[i]
+                        # 物理坐标硬过滤：即使 CV 识别到了，y 坐标在 58% 以上的也统统干掉
+                        center_y = (bbox[1] + bbox[3]) // 2
+                        if center_y < y_threshold:
+                            logger.warning(f"CV 误报过滤 (y={center_y} < {y_threshold}): 可能是顶部营销卡片")
+                            continue
+                            
                         if res and (res.get("is_valid") is True or (res.get("name") and res.get("is_valid") is not False)):
                             final_stations.append({
                                 "name": res.get("name"),