diff --git a/Apps/TeLaiDian/Crawler.py b/Apps/TeLaiDian/Crawler.py
index 723863b..793a49e 100644
--- a/Apps/TeLaiDian/Crawler.py
+++ b/Apps/TeLaiDian/Crawler.py
@@ -120,6 +120,15 @@ class TeLaiDianCrawler(BaseCrawler):
                 detail_check_path = take_screenshot(d, f"tld_detail_check_{int(time.time())}.jpg")
                 logger.info(f"详情页快照已保存: {detail_check_path}")
 
+                # 简单验证：如果标题包含 "我的卡券"、"优惠券" 等，说明点错了
+                is_wrong_page = await self.check_wrong_page(d, detail_check_path)
+                if is_wrong_page:
+                    logger.warning(f"检测到进入了错误页面，尝试返回列表...")
+                    d.press("back")
+                    await asyncio.sleep(WAIT_BACK_TO_LIST)
+                    if os.path.exists(detail_check_path): os.remove(detail_check_path)
+                    continue
+
                 # 爬取详情
                 await self.crawl_detail_logic(d, station)
                 
@@ -136,6 +145,31 @@ class TeLaiDianCrawler(BaseCrawler):
             
             if os.path.exists(screenshot_path): os.remove(screenshot_path)
 
+    async def check_wrong_page(self, d, image_path):
+        """
+        检查是否误触进入了错误的页面（如：我的卡券、活动页等）
+        """
+        prompt = """
+        分析这张截图，判断这是否是一个“充电站详情页”。
+        特征：详情页通常包含场站名称、电价列表、电桩状态等。
+        如果页面显示的是“我的卡券”、“优惠券”、“个人中心”或“活动广告”，则不是详情页。
+        
+        输出格式为 JSON：
+        {
+            "is_detail_page": true/false,
+            "page_type": "detail" / "coupons" / "ad" / "other",
+            "reason": "判断依据"
+        }
+        """
+        try:
+            res_text = await self.read_image_kit.vlm.analyze_image(image_path, prompt)
+            json_str = self.read_image_kit.vlm.extract_json(res_text)
+            data = json.loads(json_str)
+            return not data.get("is_detail_page", True)
+        except Exception as e:
+            logger.error(f"检查页面类型失败: {e}")
+            return False
+
     async def crawl_detail_logic(self, d, station_info):
         """
         在详情页提取价格和状态信息
diff --git a/Apps/TeLaiDian/Kit.py b/Apps/TeLaiDian/Kit.py
index 6377f22..908fcca 100644
--- a/Apps/TeLaiDian/Kit.py
+++ b/Apps/TeLaiDian/Kit.py
@@ -169,19 +169,22 @@ def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
     # 限制检测范围
     top_limit = int(h * top_ratio)
     bottom_limit = int(h * (1 - bottom_ratio))
+    logger.info(f"CV检测卡片: h={h}, w={w}, top_limit={top_limit}, bottom_limit={bottom_limit}")
     
     # 使用自适应阈值
     thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
     
     # 闭运算：连接断开的边缘
-    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (w // 2, 1))
+    # 减小水平核宽度，增加一点垂直连接，以适应卡片边缘不连续的情况
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (w // 4, 3))
     closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
     
     # 寻找轮廓
     contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     
     cards = []
-    min_card_width = int(w * 0.7)
+    # 降低最小宽度要求，有些卡片边缘可能没完全闭合
+    min_card_width = int(w * 0.6)
     
     temp_boxes = []
     for cnt in contours:
@@ -189,11 +192,18 @@ def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
         
         # 增加最小高度验证 MIN_CARD_HEIGHT
         if (cw > min_card_width and 
-            ch > MIN_CARD_HEIGHT and
+            ch > MIN_CARD_HEIGHT * 0.8 and # 稍微放宽高度限制
             y > top_limit and 
             y + ch < bottom_limit):
             
+            logger.info(f"✅ 找到候选卡片: y={y}, h={ch}, w={cw}")
             temp_boxes.append((x, y, cw, ch))
+        else:
+            if cw > min_card_width:
+                logger.debug(f"❌ 排除候选(不符条件): y={y}, h={ch}, w={cw} (limit: {top_limit}-{bottom_limit}, min_h: {MIN_CARD_HEIGHT*0.8})")
+    
+    if not temp_boxes:
+        logger.warning(f"⚠️ CV 未能在指定范围 ({top_limit}-{bottom_limit}) 内检测到任何场站卡片")
             
     # 按 Y 轴排序
     temp_boxes.sort(key=lambda b: b[1])
diff --git a/Apps/TeLaiDian/ReadImageKit.py b/Apps/TeLaiDian/ReadImageKit.py
index 957f86d..fe96caa 100644
--- a/Apps/TeLaiDian/ReadImageKit.py
+++ b/Apps/TeLaiDian/ReadImageKit.py
@@ -189,7 +189,11 @@ class ReadImageKit:
         else:
             prompt = """
             分析这张充电站列表截图，提取所有充电站卡片信息。
-            忽略顶部的筛选栏，仅提取下方重复出现的场站卡片。
+            
+            **重要限制**：
+            1. 忽略页面上半部分（坐标 y < 500）的所有内容，包括顶部的搜索框、广告 Banner 和“PLUS会员/我的卡券”等图标。
+            2. 仅提取下方重复出现的、包含“充电站”名称的矩形卡片。
+            
             输出格式为 JSON 数组，每个对象包含：
             - "name": 场站名称
             - "address": 场站地址
@@ -215,7 +219,22 @@ class ReadImageKit:
                                 "bbox": bbox
                             })
             elif not cv_bboxes:
-                final_stations = vlm_results if isinstance(vlm_results, list) else []
+                vlm_list = vlm_results if isinstance(vlm_results, list) else []
+                w, h = 0, 0
+                for res in vlm_list:
+                    p = res.get("point")
+                    if p and len(p) == 2:
+                        # 如果坐标小于 1000，认为是归一化坐标，需要转换
+                        if p[0] <= 1000 and p[1] <= 1000:
+                            if w == 0:
+                                import cv2
+                                img = cv2.imread(image_path)
+                                if img is not None:
+                                    h, w = img.shape[:2]
+                                else:
+                                    w, h = 1080, 2400 # 兜底
+                            res["point"] = [int(p[0] * w / 1000), int(p[1] * h / 1000)]
+                    final_stations.append(res)
                 
             return final_stations
         except Exception as e: