'commit'

Apps/TeLaiDian/Config/Setting.py

@@ -10,8 +10,8 @@ DEBUG_BOX_COLOR = (0, 255, 0)
 DEBUG_BOX_THICKNESS = 3
 
 # 等待时间配置 (秒)
-WAIT_DETAIL_PAGE_LOAD = 2.0
-WAIT_BACK_TO_LIST = 1.0
+WAIT_DETAIL_PAGE_LOAD = 3.5
+WAIT_BACK_TO_LIST = 1.5
 WAIT_AFTER_SCROLL = 2.5
 
 # 坐标计算与安全防护

Apps/TeLaiDian/Crawler.py

@@ -1,10 +1,14 @@
 # coding=utf-8
 import asyncio
-import logging
+import asyncio
 import os
 import sys
 import time
-from Apps.TeLaiDian.Kit import take_screenshot, get_image_content_md5, clean_station_name, setup_logger
+import cv2
+from Apps.TeLaiDian.Kit import (
+    take_screenshot, get_image_content_md5, clean_station_name, 
+    setup_logger, detect_price_click_point_cv, read_image, save_image
+)
 from Apps.TeLaiDian.ReadImageKit import ReadImageKit
 from Apps.TeLaiDian.Service import TeLaiDianService
 from Apps.TeLaiDian.Config.Setting import (
@@ -120,37 +124,92 @@ class TeLaiDianCrawler(BaseCrawler):
         d.swipe_ext("up", scale=DETAIL_SCROLL_DISTANCE_RATIO)
         await asyncio.sleep(1.5)
 
-        # 3. 点击“价格信息”按钮 (jgxx.jpg)
-        template_path = os.path.join(project_root, "Apps", "TeLaiDian", "Template", "jgxx.jpg")
-        logger.info(f"尝试点击价格详情按钮: {template_path}")
+        # 3. 点击“价格信息”区域 (识别橘红色价格 P0)
+        price_button_screen = take_screenshot(d, f"tld_before_price_click_{int(time.time())}.jpg")
+        logger.info("正在通过 CV 寻找橘红色价格区域 (P0)...")
+        
+        click_point = detect_price_click_point_cv(price_button_screen)
+        
+        # 调试：生成点击点标注图
+        if click_point:
+            debug_flag_path = price_button_screen.replace(".jpg", "_click_debug.jpg")
+            img_debug = read_image(price_button_screen)
+            if img_debug is not None:
+                cv2.circle(img_debug, (click_point[0], click_point[1]), 20, (0, 0, 255), -1) # 红色大圆点
+                cv2.line(img_debug, (click_point[0]-40, click_point[1]), (click_point[0]+40, click_point[1]), (255, 255, 255), 3)
+                cv2.line(img_debug, (click_point[0], click_point[1]-40), (click_point[0], click_point[1]+40), (255, 255, 255), 3)
+                save_image(debug_flag_path, img_debug)
+                logger.info(f"点击点调试图已保存: {debug_flag_path}")
         
         try:
-            # 使用 uiautomator2 的图像识别点击
-            match = d.image.match(template_path)
-            if match:
-                logger.info(f"找到价格按钮，坐标: {match['point']}")
-                d.image.click(template_path)
+            if click_point:
+                logger.info(f"CV 成功定位价格区域，点击坐标: {click_point}")
+                d.click(click_point[0], click_point[1])
                 await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD)
             else:
-                logger.warning("未找到价格按钮模板，尝试备选方案：直接点击屏幕下方区域")
-                # 备选方案：如果模板匹配失败，尝试点击屏幕中下方
-                w, h = d.window_size()
-                d.click(w // 2, int(h * 0.8))
+                logger.warning("CV 未能定位价格区域，尝试模板匹配兜底...")
+                template_path = os.path.join(project_root, "Apps", "TeLaiDian", "Template", "jgxx.jpg")
+                match = d.image.match(template_path)
+                if match:
+                    d.image.click(template_path)
+                else:
+                    logger.warning("模板匹配也失败，执行坐标兜底...")
+                    w, h = d.window_size()
+                    d.click(w // 2, int(h * 0.45)) # 滑动后价格通常在屏幕中上部
                 await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD)
         except Exception as e:
-            logger.error(f"点击价格按钮失败: {e}")
+            logger.error(f"点击价格区域失败: {e}")
+        finally:
+            if os.path.exists(price_button_screen): os.remove(price_button_screen)
 
-        # 4. 截图并分析价格表
-        price_screen_path = take_screenshot(d, f"tld_detail_price_{int(time.time())}.jpg")
-        prices = await self.read_image_kit.analyze_detail_price(price_screen_path)
+        # 4. 循环滑动抓取完整分时电价
+        all_prices = []
+        last_price_md5 = None
+        price_page_count = 0
+        max_price_pages = 3 # 分时电价通常不会超过3页
+
+        logger.info("开始循环滑动抓取完整分时电价...")
         
+        while price_page_count < max_price_pages:
+            price_screen_path = take_screenshot(d, f"tld_detail_price_{int(time.time())}.jpg")
+            
+            # 校验页面是否发生滚动变化
+            curr_md5 = get_image_content_md5(price_screen_path, top_ratio=0.2, bottom_ratio=0.2)
+            if curr_md5 == last_price_md5:
+                logger.info("价格页面内容无变化，判定已触底")
+                if os.path.exists(price_screen_path): os.remove(price_screen_path)
+                break
+            last_price_md5 = curr_md5
+
+            logger.info(f"正在分析价格详情页第 {price_page_count + 1} 页: {price_screen_path}")
+            page_prices = await self.read_image_kit.analyze_detail_price(price_screen_path)
+            
+            if page_prices:
+                # 简单去重：根据时段合并
+                for p in page_prices:
+                    if p not in all_prices:
+                        all_prices.append(p)
+            
+            # 向上滑动一点点，继续抓取
+            d.swipe_ext("up", scale=0.6)
+            await asyncio.sleep(1.5)
+            price_page_count += 1
+            
+            # 清理临时截图
+            if os.path.exists(price_screen_path): os.remove(price_screen_path)
+
         # 5. 保存数据
-        if prices:
+        if all_prices:
             station_name_clean = clean_station_name(station_name)
-            logger.info(f"场站 {station_name_clean} 提取到 {len(prices)} 条价格信息，准备保存...")
-            await self.service.save_station_data(station_name_clean, address, prices)
+            # 对价格按时间排序
+            try:
+                all_prices.sort(key=lambda x: x.get('start', '00:00'))
+            except:
+                pass
+            logger.info(f"✅ 场站 {station_name_clean} 共提取到 {len(all_prices)} 条价格信息，准备保存...")
+            await self.service.save_station_data(station_name_clean, address, all_prices)
         else:
-            logger.warning(f"未能从 {price_screen_path} 提取到价格信息")
+            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]:

Apps/TeLaiDian/Kit.py

@@ -93,6 +93,64 @@ def save_image(path, img):
         logger.error(f"Error saving image {path}: {e}")
         return False
 
+def detect_price_click_point_cv(image_path):
+    """
+    使用 HSV 颜色过滤定位详情页的橘红色价格区域，返回最左侧区域的中心点击点
+    """
+    img = read_image(image_path)
+    if img is None:
+        return None
+    
+    h, w = img.shape[:2]
+    # 1. 转换为 HSV 空间
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    
+    # 2. 橘红色的 HSV 范围 (适配特来电价格颜色)
+    lower_orange = np.array([0, 150, 150])
+    upper_orange = np.array([20, 255, 255])
+    mask = cv2.inRange(hsv, lower_orange, upper_orange)
+    
+    # 3. 对掩码进行膨胀，连接数字
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 20))
+    dilated = cv2.dilate(mask, kernel)
+    
+    # 4. 寻找轮廓
+    contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    detected_areas = []
+    for cnt in contours:
+        x, y, cw, ch = cv2.boundingRect(cnt)
+        # 1. 过滤掉宽度过大（可能是横幅广告）或过小（可能是杂点）的区域
+        # 2. 价格区域 P0 通常在屏幕的中部，且宽度约为屏幕的一半
+        if 200 < y < h * 0.8 and 100 < cw < w * 0.6 and ch > 30:
+            detected_areas.append([x, y, x + cw, y + ch])
+
+    if not detected_areas:
+        # 备选：如果 HSV 失败，尝试通过轮廓大小寻找
+        # 1.1556 这种大数字通常会有很明显的轮廓
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
+        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10))
+        closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
+        contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        for cnt in contours:
+            x, y, cw, ch = cv2.boundingRect(cnt)
+            if 200 < y < h * 0.6 and 150 < cw < 300 and 50 < ch < 150:
+                detected_areas.append([x, y, x + cw, y + ch])
+
+    if not detected_areas:
+        return None
+
+    # 5. 按 X 轴排序，取最左边的区域 (即用户确认的 P0)
+    # 但要排除掉可能在最左侧的导航栏返回按钮等小元素，所以前面加了宽度限制
+    detected_areas.sort(key=lambda b: b[0])
+    target = detected_areas[0]
+    
+    center_x = (target[0] + target[2]) // 2
+    center_y = (target[1] + target[3]) // 2
+    
+    return [center_x, center_y]
+
 def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
     """
     使用计算机图形学 (OpenCV) 检测列表中的场站卡片。

Apps/TeLaiDian/ReadImageKit.py

@@ -22,27 +22,43 @@ class ReadImageKit:
 
     async def analyze_detail_price(self, image_path):
         """
-        分析详情页截图，提取电价信息
+        分析详情页截图，提取电价信息，包括优惠价、PLUS价和挂牌价
         """
         prompt = """
-        分析这张充电站详情页截图，提取**电价时段表**。
-        请仔细寻找包含“时段”、“电价”、“服务费”或“总价”的表格或列表。
+        分析这张充电站价格详情页截图，提取**分时电价表**。
+        对于每个时段，请识别并提取以下所有价格信息（如果存在）：
+        1. 优惠价 (通常是红色或加粗的大字，作为默认 price)
+        2. PLUS会员价 (标有 "PLUS" 标签的价格)
+        3. 挂牌价 (标有 "挂牌价" 标签的价格)
+        4. 电费 (Base electricity price)
+        5. 服务费 (Service fee)
         
         请提取每个时段的：
-        1. 开始时间 (HH:MM)
-        2. 结束时间 (HH:MM)
-        3. 总电价 (元/度，包含电费和服务费)
+        - start: 开始时间 (HH:MM)
+        - end: 结束时间 (HH:MM)
+        - price: 优惠价 (元/度)
+        - plus_price: PLUS会员价 (元/度)
+        - market_price: 挂牌价 (元/度)
+        - elec_price: 电费 (元/度)
+        - service_price: 服务费 (元/度)
         
         输出格式为 JSON 数组：
         [
             {
-                "start": "00:00",
-                "end": "08:00",
-                "price": 1.23
+                "start": "16:00",
+                "end": "21:00",
+                "price": 1.3435,
+                "plus_price": 1.3035,
+                "market_price": 1.4435,
+                "elec_price": 0.9435,
+                "service_price": 0.4000
             },
             ...
         ]
-        如果无法识别任何价格信息，请返回空数组 []。
+        注意：
+        - 如果某个字段缺失，请设为 null。
+        - 确保 price 包含电费和服务费的总和。
+        - 如果无法识别任何价格信息，请返回空数组 []。
         """
         try:
             res_text = await self.vlm.analyze_image(image_path, prompt)

Apps/TeLaiDian/Service.py

@@ -52,18 +52,38 @@ class TeLaiDianService:
         now = datetime.now()
 
         # 将价格转换为 24 小时的 schedule 格式 (0-23)
-        hourly_schedule = [0.0] * 24
+        # 每个小时存储一个包含多重价格的字典，以记录优惠价、PLUS价和挂牌价
+        hourly_schedule = [None] * 24
         for p in prices:
             try:
-                start_hour = int(p['start'].split(':')[0])
-                end_hour = int(p['end'].split(':')[0])
-                price = float(p['price'])
+                start_parts = p['start'].split(':')
+                end_parts = p['end'].split(':')
+                start_hour = int(start_parts[0])
+                end_hour = int(end_parts[0])
                 
-                # 处理跨天的情况（如 23:00 - 01:00）
+                # 处理 00:00 作为结束时间的情况 (表示 24:00)
+                if end_hour == 0 and (int(end_parts[1]) == 0 if len(end_parts) > 1 else True):
+                    if start_hour != 0:
+                        end_hour = 24
+                
+                # 提取各项价格
+                price_data = {
+                    "price": float(p.get('price')) if p.get('price') is not None else 0.0,
+                    "plus_price": float(p.get('plus_price')) if p.get('plus_price') is not None else None,
+                    "market_price": float(p.get('market_price')) if p.get('market_price') is not None else None,
+                    "elec_price": float(p.get('elec_price')) if p.get('elec_price') is not None else None,
+                    "service_price": float(p.get('service_price')) if p.get('service_price') is not None else None
+                }
+                
+                # 填充对应的小时槽位
                 curr = start_hour
-                while curr != end_hour:
-                    hourly_schedule[curr] = price
-                    curr = (curr + 1) % 24
+                # 如果是跨天的，比如 23:00 - 01:00
+                if end_hour < start_hour:
+                    end_hour += 24
+                
+                while curr < end_hour:
+                    hourly_schedule[curr % 24] = price_data
+                    curr += 1
             except Exception as e:
                 logger.error(f"解析价格时段失败: {p}, error: {e}")
 
@@ -80,7 +100,7 @@ class TeLaiDianService:
                 valid_start_time=now
             )
 
-            # 2. 保存价格
+            # 2. 保存价格计划
             schedule_id = self.generate_id()
             await self.station_price_schedule_model.save(
                 session=session,
@@ -89,6 +109,24 @@ class TeLaiDianService:
                 schedule_json=hourly_schedule,
                 valid_start_time=now
             )
+
+            # 3. 保存当前状态快照 (包含当前小时的价格)
+            current_hour = now.hour
+            current_price_info = hourly_schedule[current_hour] or {}
+            
+            status_id = self.generate_id()
+            await self.station_status_model.save(
+                session=session,
+                id=status_id,
+                station_hash=station_hash,
+                total_piles=None, # 特来电暂时没抓取总桩数
+                free_piles=None,
+                piles_detail_json=None,
+                current_price=current_price_info.get('price'),
+                pro_price=current_price_info.get('plus_price'),
+                market_price=current_price_info.get('market_price'),
+                valid_start_time=now
+            )
             await session.commit()
         
         logger.info(f"成功保存场站数据: {station_name}")