1

.cursor/rules/detectionlogic.mdc

+---
+alwaysApply: true
+---

database/config/channel_config.yaml

@@ -18,13 +18,13 @@ channels:
 channel2:
   general:
     task_id: '1'
-    task_name: 海信现场测试
-    save_liquid_data_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_海信现场测试
+    task_name: test
+    save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_test
 channel3:
   general:
     task_id: '1'
-    task_name: 海信现场测试
-    save_liquid_data_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_海信现场测试
+    task_name: test
+    save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_test
 channel4:
   general:
     task_id: '1'
@@ -33,7 +33,7 @@ channel4:
 channel1:
   general:
     task_id: '1'
-    task_name: 历史回放功能展示
+    task_name: 曲线
     area_count: 0
     safe_low: 2.0mm
     safe_high: 10.0mm
@@ -41,7 +41,7 @@ channel1:
     video_format: AVI
     push_address: ''
     video_path: ''
-    save_liquid_data_path: d:\restructure\liquid_level_line_detection_system\database\mission_result\1_历史回放功能展示
+    save_liquid_data_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_曲线
     areas:
       area_1: 通道1_区域1
     area_heights:

database/config/mission/1_曲线.yaml

+task_id: '1'
+task_id: '1'
+task_name: 曲线
+status: 未启动
+selected_channels:
+- 通道1
+created_time: '2025-12-02 11:19:50'
+mission_result_folder_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_曲线

database/mission_result/1_曲线/1_曲线.yaml

+task_id: '1'
+task_id: '1'
+task_name: 曲线
+status: 未启动
+selected_channels:
+- 通道1
+created_time: '2025-12-02 11:19:50'
+mission_result_folder_path: D:\restructure\liquid_level_line_detection_system\database\mission_result\1_曲线

handlers/videopage/curvepanel_handler.py

@@ -364,37 +364,17 @@ class CurvePanelHandler:
         print(f"   - 实际添加数据点数: {added_count}")
         print(f"   - 更新后数据点数: {after_add_count}")
         
-        # 🔥 同步布局模式：根据时间轴范围过滤数据
-        is_sync_layout = hasattr(self, '_video_layout_mode') and self._video_layout_mode == 1
-        if is_sync_layout and self.curve_panel:
-            start_time, end_time = self.curve_panel.getTimeAxisRange()
-            if start_time is not None and end_time is not None:
-                print(f"   - 同步布局模式，过滤时间范围: {start_time:.3f} 到 {end_time:.3f}")
-                # 过滤数据点，只保留在时间范围内的点
-                filtered_time = []
-                filtered_value = []
-                for t, v in zip(channel['time'], channel['value']):
-                    if start_time <= t <= end_time:
-                        filtered_time.append(t)
-                        filtered_value.append(v)
-                
-                before_filter = len(channel['time'])
-                channel['time'] = filtered_time
-                channel['value'] = filtered_value
-                after_filter = len(channel['time'])
-                print(f"   - 时间过滤: {before_filter} -> {after_filter} 个数据点")
-        
-        # 🔥 限制数据点数量
-        # - 'realtime' 同步布局模式：限制为3000个点（滚动窗口）
-        # - 'history' 历史回放模式：显示完整数据（不采样）
-        
-        if self.curve_load_mode == 'realtime':
-            # 同步布局模式：保留最新3000个点
-            if len(channel['time']) > self.max_points:
-                before_limit = len(channel['time'])
-                channel['time'] = channel['time'][-self.max_points:]
-                channel['value'] = channel['value'][-self.max_points:]
-                print(f"   - 限制数据点: {before_limit} -> {len(channel['time'])}")
+        # 🔥 数据点数量限制（已禁用）
+        # - 原逻辑：'realtime' 同步布局模式限制为3000个点（滚动窗口）
+        # - 已禁用：不再限制数据点数量，所有模式都显示完整数据
+        
+        # if self.curve_load_mode == 'realtime':
+        #     # 同步布局模式：保留最新3000个点
+        #     if len(channel['time']) > self.max_points:
+        #         before_limit = len(channel['time'])
+        #         channel['time'] = channel['time'][-self.max_points:]
+        #         channel['value'] = channel['value'][-self.max_points:]
+        #         print(f"   - 限制数据点: {before_limit} -> {len(channel['time'])}")
         
         # 🔥 处理时间间隔断点：超过2分钟的数据点之间插入NaN断开连接
         processed_time, processed_value = self._processTimeGaps(
@@ -404,9 +384,43 @@ class CurvePanelHandler:
         )
         print(f"   - 处理后数据点数: {len(processed_time)}")
         
+        # 🔥 数据验证：确保有数据才更新UI
+        if not processed_time or not processed_value:
+            print(f"   - ⚠️ 处理后数据为空，跳过UI更新")
+            return
+        
+        if len(processed_time) != len(processed_value):
+            print(f"   - ⚠️ 处理后数据长度不匹配: time={len(processed_time)}, value={len(processed_value)}")
+            return
+        
+        # 🔥 检查是否有有效数据点（至少有一个非NaN值）
+        has_valid_data = False
+        for i in range(len(processed_value)):
+            if np.isfinite(processed_value[i]) and np.isfinite(processed_time[i]):
+                has_valid_data = True
+                break
+        
+        if not has_valid_data:
+            print(f"   - ⚠️ 没有有效数据点（全是NaN/Inf），跳过UI更新")
+            return
+        
         #  更新UI显示（只更新一次）
         if self.curve_panel:
             print(f"   - 开始更新UI显示...")
+            # 🔥 安全地计算数据范围（避免NaN值导致错误）
+            try:
+                valid_values = [v for v in processed_value if np.isfinite(v)]
+                valid_times = [t for t in processed_time if np.isfinite(t)]
+                if valid_values and valid_times:
+                    value_range_str = f"[{min(valid_values):.2f}, {max(valid_values):.2f}]"
+                    time_range_str = f"[{min(valid_times):.2f}, {max(valid_times):.2f}]"
+                else:
+                    value_range_str = "无有效值"
+                    time_range_str = "无有效值"
+                print(f"   - 数据详情: time范围={time_range_str}, value范围={value_range_str}")
+            except Exception as e:
+                print(f"   - 数据详情: 计算范围时出错: {e}")
+            
             self.curve_panel.updateCurveDisplay(
                 channel_id,
                 processed_time,
@@ -414,13 +428,26 @@ class CurvePanelHandler:
             )
             print(f"   - UI显示更新完成")
             
-            # 更新X轴范围
+            # 🔥 根据加载模式决定X轴范围设置
             if channel['time']:
+                min_time = min(channel['time'])
                 max_time = max(channel['time'])
-                self.curve_panel.setXRangeAuto(max_time)
+                
+                if self.curve_load_mode == 'history':
+                    # 历史模式：显示全部数据范围
+                    if channel['value']:
+                        max_value = max(channel['value'])
+                    else:
+                        max_value = 23
+                    self.curve_panel.setViewAll(min_time, max_time, max_value)
+                    print(f"   - 历史模式：显示全部数据范围 [{min_time:.2f}, {max_time:.2f}]")
+                else:
+                    # 实时模式：只显示最后几分钟（自动跟随）
+                    self.curve_panel.setXRangeAuto(max_time)
+                    print(f"   - 实时模式：显示最后几分钟数据")
             
-            # 更新Y轴范围
-            if channel['value']:
+            # 更新Y轴范围（仅在实时模式下）
+            if self.curve_load_mode == 'realtime' and channel['value']:
                 max_value = max(channel['value'])
                 self.curve_panel.setYRangeAuto(max_value)
         else:
@@ -439,6 +466,14 @@ class CurvePanelHandler:
         Returns:
             tuple: (处理后的时间列表, 处理后的数值列表)
         """
+        # 🔥 处理空数据情况
+        if not time_data or not value_data:
+            return [], []
+        
+        if len(time_data) != len(value_data):
+            print(f"⚠️ [时间间隔处理] 数据长度不匹配: time={len(time_data)}, value={len(value_data)}")
+            return time_data, value_data
+        
         if len(time_data) <= 1:
             return time_data, value_data
         
@@ -707,7 +742,15 @@ class CurvePanelHandler:
             tuple: (starttime, endtime) - 时间轴起始时刻和结束时刻（Unix时间戳）
         """
         if self.curve_panel:
-            return self.curve_panel.getTimeAxisRange()
+            result = self.curve_panel.getTimeAxisRange()
+            # 🔥 调试信息：输出handler获取的结果
+            import datetime
+            start_time, end_time = result
+            start_str = datetime.datetime.fromtimestamp(start_time).strftime('%Y-%m-%d %H:%M:%S') if start_time else 'None'
+            end_str = datetime.datetime.fromtimestamp(end_time).strftime('%Y-%m-%d %H:%M:%S') if end_time else 'None'
+            print(f"🎯 [Handler获取时间轴范围] starttime={start_time} ({start_str}) -> endtime={end_time} ({end_str})")
+            return result
+        print(f"⚠️ [Handler获取时间轴范围] curve_panel不存在，返回(None, None)")
         return (None, None)
     
     # ========== 数据管理方法 ==========
@@ -1027,6 +1070,33 @@ class CurvePanelHandler:
             print(f"✅ [曲线数据加载] 成功加载 {count} 个文件")
             # 🔥 设置历史数据已加载标志
             self._history_data_loaded = True
+            
+            # 🔥 历史数据加载完成后，确保显示全部数据范围
+            if self.curve_load_mode == 'history' and self.curve_panel:
+                # 找出所有通道的最小和最大时间值、最大数值
+                min_time = None
+                max_time = None
+                max_value = 0
+                
+                for channel_id, channel in self.channel_data.items():
+                    if len(channel['time']) > 0:
+                        channel_min_time = min(channel['time'])
+                        channel_max_time = max(channel['time'])
+                        if min_time is None or channel_min_time < min_time:
+                            min_time = channel_min_time
+                        if max_time is None or channel_max_time > max_time:
+                            max_time = channel_max_time
+                    
+                    if len(channel['value']) > 0:
+                        channel_max_value = max(channel['value'])
+                        max_value = max(max_value, channel_max_value)
+                
+                # 如果有数据，设置视图显示全部范围
+                if min_time is not None and max_time is not None:
+                    print(f"🔄 [历史数据加载完成] 设置视图显示全部数据范围")
+                    print(f"   - 时间范围: [{min_time:.2f}, {max_time:.2f}]")
+                    print(f"   - 数值范围: [0, {max_value:.2f}]")
+                    self.curve_panel.setViewAll(min_time, max_time, max_value)
         else:
             print(f"⚠️ [曲线数据加载] 加载失败")
     

handlers/videopage/detection.py

@@ -11,6 +11,13 @@ from pathlib import Path
 # 导入动态路径获取函数
 from database.config import get_temp_models_dir
 
+# 导入检测逻辑模块
+from handlers.videopage.detection_logic import (
+    parse_yolo_result,
+    calculate_liquid_height,
+    process_yolo_result_to_liquid_height
+)
+
 
 
 # ==================== 辅助函数 ====================
@@ -25,84 +32,6 @@ def get_class_color(class_name):
     return color_map.get(class_name, (128, 128, 128))  # 默认灰色
 
 
-def calculate_foam_boundary_lines(mask):
-    """计算foam mask的顶部和底部边界线"""
-    if np.sum(mask) == 0:
-        return None, None
-    
-    y_coords = np.where(mask)[0]
-    
-    if len(y_coords) == 0:
-        return None, None
-    
-    top_y = np.min(y_coords)
-    bottom_y = np.max(y_coords)
-    
-    # 计算顶部边界线的平均位置
-    top_region_height = max(1, int((bottom_y - top_y) * 0.1))
-    top_region_mask = mask[top_y:top_y + top_region_height, :]
-    if np.sum(top_region_mask) > 0:
-        top_y_coords = np.where(top_region_mask)[0] + top_y
-        top_line_y = np.mean(top_y_coords)
-    else:
-        top_line_y = top_y
-    
-    # 计算底部边界线的平均位置
-    bottom_region_height = max(1, int((bottom_y - top_y) * 0.1))
-    bottom_region_mask = mask[bottom_y - bottom_region_height:bottom_y + 1, :]
-    if np.sum(bottom_region_mask) > 0:
-        bottom_y_coords = np.where(bottom_region_mask)[0] + (bottom_y - bottom_region_height)
-        bottom_line_y = np.mean(bottom_y_coords)
-    else:
-        bottom_line_y = bottom_y
-    
-    return top_line_y, bottom_line_y
-
-
-def analyze_multiple_foams(foam_masks, container_pixel_height):
-    """分析多个foam，找到可能的液位边界"""
-    if len(foam_masks) < 2:
-        return None
-    
-    foam_boundaries = []
-    
-    # 计算每个foam的边界信息
-    for i, mask in enumerate(foam_masks):
-        top_y, bottom_y = calculate_foam_boundary_lines(mask)
-        if top_y is not None and bottom_y is not None:
-            center_y = (top_y + bottom_y) / 2
-            foam_boundaries.append({
-                'index': i,
-                'top_y': top_y,
-                'bottom_y': bottom_y,
-                'center_y': center_y
-            })
-    
-    if len(foam_boundaries) < 2:
-        return None
-    
-    # 按垂直位置排序
-    foam_boundaries.sort(key=lambda x: x['center_y'])
-    
-    error_threshold_px = container_pixel_height * 0.1
-    
-    # 检查相邻foam之间的边界
-    for i in range(len(foam_boundaries) - 1):
-        upper_foam = foam_boundaries[i]
-        lower_foam = foam_boundaries[i + 1]
-        
-        upper_bottom = upper_foam['bottom_y']
-        lower_top = lower_foam['top_y']
-        
-        boundary_distance = abs(upper_bottom - lower_top)
-        
-        if boundary_distance <= error_threshold_px:
-            liquid_level_y = (upper_bottom + lower_top) / 2
-            return liquid_level_y
-    
-    return None
-
-
 def stable_median(data, max_std=1.0):
     """稳健地计算中位数"""
     if len(data) == 0:
@@ -606,26 +535,13 @@ class LiquidDetectionEngine:
             
             liquid_height = None
             
-            # 处理检测结果
-            if mission_result.masks is not None:
-                masks = mission_result.masks.data.cpu().numpy() > 0.5
-                classes = mission_result.boxes.cls.cpu().numpy().astype(int)
-                confidences = mission_result.boxes.conf.cpu().numpy()
-            else:
-                return None
-            
-            # 收集所有mask信息
-            all_masks_info = []
-            for i in range(len(masks)):
-                class_name = self.model.names[classes[i]]
-                conf = confidences[i]
-                
-                if confidences[i] >= 0.5:
-                    resized_mask = cv2.resize(
-                        masks[i].astype(np.uint8),
-                        (cropped.shape[1], cropped.shape[0])
-                    ) > 0.5
-                    all_masks_info.append((resized_mask, class_name, confidences[i]))
+            # 解析YOLO推理结果
+            all_masks_info = parse_yolo_result(
+                mission_result=mission_result,
+                model_names=self.model.names,
+                image_shape=cropped.shape[:2],
+                confidence_threshold=0.5
+            )
             
             if len(all_masks_info) == 0:
                 return None
@@ -642,12 +558,12 @@ class LiquidDetectionEngine:
             # print(f"   - 裁剪图像中容器高度: {container_bottom_in_crop - container_top_in_crop}px（应等于{container_pixel_height}px）")
             
             # 分析mask获取液位高度（使用裁剪图像坐标）
-            liquid_height = self._enhanced_liquid_detection(
-                all_masks_info,
-                container_bottom_in_crop,  #  使用裁剪图像坐标
-                container_pixel_height,
-                container_height_mm,
-                idx
+            liquid_height = calculate_liquid_height(
+                all_masks_info=all_masks_info,
+                container_bottom=container_bottom_in_crop,  # 使用裁剪图像坐标
+                container_pixel_height=container_pixel_height,
+                container_height_mm=container_height_mm,
+                no_liquid_count=self.no_liquid_count[idx] if idx < len(self.no_liquid_count) else 0
             )
             
             return liquid_height
@@ -656,85 +572,6 @@ class LiquidDetectionEngine:
             print(f"[检测-目标{idx}] ❌ 检测异常: {e}")
             return None
     
-    def _enhanced_liquid_detection(self, all_masks_info, container_bottom, 
-                                   container_pixel_height, container_height_mm, idx):
-        """
-        增强的液位检测，结合连续帧逻辑和foam分析
-        
-        Args:
-            all_masks_info: mask信息列表 [(mask, class_name, confidence), ...]
-            container_bottom: 容器底部y坐标
-            container_pixel_height: 容器像素高度
-            container_height_mm: 容器实际高度（毫米）
-            idx: 目标索引
-        
-        Returns:
-            float: 液位高度（毫米），失败返回 None
-        """
-        pixel_per_mm = container_pixel_height / container_height_mm
-        
-        # 分离不同类别的mask
-        liquid_masks = []
-        foam_masks = []
-        air_masks = []
-        
-        for mask, class_name, confidence in all_masks_info:
-            if class_name == 'liquid':
-                liquid_masks.append(mask)
-            elif class_name == 'foam':
-                foam_masks.append(mask)
-            elif class_name == 'air':
-                air_masks.append(mask)
-        
-        # 方法1：直接liquid检测（优先）
-        if liquid_masks:
-            # 找到最上层的液体mask
-            topmost_y = float('inf')
-            for i, mask in enumerate(liquid_masks):
-                y_indices = np.where(mask)[0]
-                if len(y_indices) > 0:
-                    mask_top_y = np.min(y_indices)
-                    # print(f"   - liquid mask {i+1}: 顶部y={mask_top_y}px")
-                    if mask_top_y < topmost_y:
-                        topmost_y = mask_top_y
-            
-            if topmost_y != float('inf'):
-                liquid_height_px = container_bottom - topmost_y
-                liquid_height_mm = liquid_height_px / pixel_per_mm
-                
-                return max(0, min(liquid_height_mm, container_height_mm))
-        
-        # 方法2：foam边界分析（备选）- 连续3帧未检测到liquid时启用
-        
-        if self.no_liquid_count[idx] >= 3:
-            if len(foam_masks) >= 2:
-                # 多个foam，寻找液位边界
-                liquid_y = analyze_multiple_foams(foam_masks, container_pixel_height)
-                if liquid_y is not None:
-                    liquid_height_px = container_bottom - liquid_y
-                    liquid_height_mm = liquid_height_px / pixel_per_mm
-                    return max(0, min(liquid_height_mm, container_height_mm))
-            
-            elif len(foam_masks) == 1:
-                # 单个foam，使用下边界
-                foam_mask = foam_masks[0]
-                top_y, bottom_y = calculate_foam_boundary_lines(foam_mask)
-                if bottom_y is not None:
-                    liquid_height_px = container_bottom - bottom_y
-                    liquid_height_mm = liquid_height_px / pixel_per_mm
-                    return max(0, min(liquid_height_mm, container_height_mm))
-            
-            elif len(air_masks) == 1:
-                # 单个air，使用下边界
-                air_mask = air_masks[0]
-                y_coords = np.where(air_mask)[0]
-                if len(y_coords) > 0:
-                    bottom_y = np.max(y_coords)
-                    liquid_height_px = container_bottom - bottom_y
-                    liquid_height_mm = liquid_height_px / pixel_per_mm
-                    return max(0, min(liquid_height_mm, container_height_mm))
-        
-        return None
     
     def _apply_kalman_filter(self, observation, idx, container_height_mm):
         """

rules/曲线绘制逻辑.md

-时间轴变量starttime，endtime
+时间轴变量starttime，endtime
@@ -2,4 +2,4 @@
 采样逻辑，根据数据点个数设置不同采样率
 @curvepanel_handler.py#L371-378 修改self._video_layout_mode = 1同步布局，绘制曲线的数据范围
 实验最长时间三天，strattime到endtime，缩放限制
-曲线面板所有都历史模式下，
+曲线面板历史模式下，，
\ No newline at end of file

widgets/videopage/curvepanel.py

@@ -624,11 +624,45 @@ class CurvePanel(QtWidgets.QWidget):
         注意：此方法只负责UI更新，数据处理由handler完成
         """
         if channel_id not in self.curve_items:
+            print(f"⚠️ [UI更新] 通道不存在: {channel_id}")
             return
         
+        # 🔥 数据验证和调试
+        if not time_data or not value_data:
+            print(f"⚠️ [UI更新] 数据为空: channel_id={channel_id}, time_data长度={len(time_data) if time_data else 0}, value_data长度={len(value_data) if value_data else 0}")
+            # 即使数据为空，也调用setData清空显示
+            curve = self.curve_items[channel_id]
+            curve.setData([], [])
+            return
+        
+        if len(time_data) != len(value_data):
+            print(f"⚠️ [UI更新] 数据长度不匹配: channel_id={channel_id}, time_data长度={len(time_data)}, value_data长度={len(value_data)}")
+            return
+        
+        # 🔥 转换为numpy数组（PyQtGraph需要numpy数组）
+        try:
+            time_array = np.array(time_data, dtype=np.float64)
+            value_array = np.array(value_data, dtype=np.float64)
+        except Exception as e:
+            print(f"⚠️ [UI更新] 数据转换失败: channel_id={channel_id}, 错误={e}")
+            return
+        
+        # 🔥 检查有效数据点数量（排除NaN和Inf）
+        valid_mask = np.isfinite(time_array) & np.isfinite(value_array)
+        valid_count = np.sum(valid_mask)
+        
+        if valid_count == 0:
+            print(f"⚠️ [UI更新] 没有有效数据点: channel_id={channel_id}, 总数据点={len(time_array)}, 有效数据点=0")
+            # 即使没有有效数据，也调用setData清空显示
+            curve = self.curve_items[channel_id]
+            curve.setData([], [])
+            return
+        
+        print(f"✅ [UI更新] 更新曲线: channel_id={channel_id}, 总数据点={len(time_array)}, 有效数据点={valid_count}")
+        
         #  只更新UI显示
         curve = self.curve_items[channel_id]
-        curve.setData(time_data, value_data)
+        curve.setData(time_array, value_array)
         
         # 动态调整X轴范围
         self._updateXRange()
@@ -729,6 +763,12 @@ class CurvePanel(QtWidgets.QWidget):
         Returns:
             tuple: (starttime, endtime) - 时间轴起始时刻和结束时刻（Unix时间戳）
         """
+        # 🔥 调试信息：输出获取的时间轴范围
+        import datetime
+        start_str = datetime.datetime.fromtimestamp(self.starttime).strftime('%Y-%m-%d %H:%M:%S') if self.starttime else 'None'
+        end_str = datetime.datetime.fromtimestamp(self.endtime).strftime('%Y-%m-%d %H:%M:%S') if self.endtime else 'None'
+        print(f"📊 [获取时间轴范围] starttime={self.starttime} ({start_str}) -> endtime={self.endtime} ({end_str})")
+        
         return (self.starttime, self.endtime)
     
     
@@ -793,12 +833,35 @@ class CurvePanel(QtWidgets.QWidget):
         
         Args:
             max_time: 最大时间戳
-            view_width: 显示宽度（秒），默认60秒
+            view_width: 显示宽度（秒），默认120秒（2分钟）
         """
         if self.chk_auto_follow.isChecked():
             min_time = max_time - view_width
             self.plot_widget.setXRange(min_time, max_time, padding=0)
     
+    def setViewAll(self, min_time, max_time, max_value):
+        """
+        显示全部数据范围（用于历史数据加载）
+        
+        Args:
+            min_time: 最小时间戳
+            max_time: 最大时间戳
+            max_value: 最大数值
+        """
+        # 设置X轴显示全部时间范围（留出5%的边距）
+        time_span = max_time - min_time
+        if time_span > 0:
+            padding = time_span * 0.05  # 5%边距
+            self.plot_widget.setXRange(min_time - padding, max_time + padding, padding=0)
+        
+        # 设置Y轴范围（留出10%的边距）
+        if max_value > 0:
+            y_max = min(23, max_value * 1.1)  # 不超过23
+            self.plot_widget.setYRange(0, y_max, padding=0)
+        
+        # 固定X轴位置
+        self._fixXAxisPosition()
+    
     def setYRangeAuto(self, max_value):
         """
         自动设置Y轴范围（由handler调用）
@@ -829,6 +892,26 @@ class CurvePanel(QtWidgets.QWidget):
         self.starttime = x_min  # 时间轴起始时刻（Unix时间戳）
         self.endtime = x_max    # 时间轴结束时刻（Unix时间戳）
         
+        # 🔥 调试信息：输出时间轴范围变化（安全处理时间戳转换）
+        import datetime
+        try:
+            if x_min and np.isfinite(x_min):
+                start_str = datetime.datetime.fromtimestamp(x_min).strftime('%Y-%m-%d %H:%M:%S')
+            else:
+                start_str = 'None'
+        except (OSError, ValueError, OverflowError):
+            start_str = f'Invalid({x_min})'
+        
+        try:
+            if x_max and np.isfinite(x_max):
+                end_str = datetime.datetime.fromtimestamp(x_max).strftime('%Y-%m-%d %H:%M:%S')
+            else:
+                end_str = 'None'
+        except (OSError, ValueError, OverflowError):
+            end_str = f'Invalid({x_max})'
+        
+        print(f"🔄 [时间轴范围] starttime={x_min:.3f} ({start_str}) -> endtime={x_max:.3f} ({end_str})")
+        
         # 确保Y轴范围在0-23之间
         if y_min < 0 or y_max > 23:
             # 调整到合法范围