'commit'

Controller/YltAnalyticsController.py

@@ -86,7 +86,7 @@ async def get_operators_price_trends(days: int = 7):
     model = YltAnalyticsModel()
     rows = await model.get_operators_price_trends(days)
     
-    # 数据结构: { operator: { date_str: [sums_of_24h, counts_of_24h] } }
+    # 数据结构: { operator: { datetime_str: [sums_of_price, counts_of_station] } }
     trend_data = {}
     for op in operators:
         trend_data[op] = {}
@@ -95,47 +95,65 @@ async def get_operators_price_trends(days: int = 7):
         op = row.get("operator")
         if op not in trend_data:
             continue
-        d_str = str(row.get("date_str"))
-        schedule_json = row.get("schedule_json")
         
-        if d_str not in trend_data[op]:
-            trend_data[op][d_str] = {"sums": [0.0] * 24, "counts": [0] * 24}
+        # 将日期和 schedule_json 展开为 24 小时的数据点
+        d_str = str(row.get("date_str"))
+        # 将 2026-01-21 转换为 01/21
+        try:
+            date_parts = d_str.split('-')
+            display_date = date_parts[1] + '/' + date_parts[2]
+        except:
+            display_date = d_str
             
+        schedule_json = row.get("schedule_json")
         series = extract_hourly_prices_from_schedule(schedule_json)
-        for i in range(24):
-            v = series[i]
+        
+        for hour in range(24):
+            v = series[hour]
             if v is not None:
-                trend_data[op][d_str]["sums"][i] += float(v)
-                trend_data[op][d_str]["counts"][i] += 1
+                # 使用原始日期 YYYY-MM-DD 用于排序，显示时由前端或后端格式化
+                # 这里我们构造一个带补全的时间字符串，方便自然排序
+                dt_key = f"{d_str} {hour:02d}:00"
+                if dt_key not in trend_data[op]:
+                    trend_data[op][dt_key] = {"sum": 0.0, "count": 0, "display": f"{display_date} {hour:02d}:00"}
+                trend_data[op][dt_key]["sum"] += float(v)
+                trend_data[op][dt_key]["count"] += 1
                 
     # 转换为 ECharts 友好格式
-    # 1. 获取所有日期并排序
-    all_dates = sorted(list(set(str(row.get("date_str")) for row in rows)))
+    # 1. 获取所有时间点并排序
+    all_time_keys = set()
+    for op in operators:
+        all_time_keys.update(trend_data[op].keys())
+    sorted_keys = sorted(list(all_time_keys))
     
-    # 2. 为每个运营商计算每天的平均价格（24小时的平均值）
+    # 2. 提取显示用的标签
+    display_dates = []
+    if sorted_keys:
+        # 从任意一个存在的运营商数据中获取 display 标签
+        first_op = operators[0]
+        for key in sorted_keys:
+            # 找到包含该 key 的 display 标签
+            label = key # fallback
+            for op in operators:
+                if key in trend_data[op]:
+                    label = trend_data[op][key]["display"]
+                    break
+            display_dates.append(label)
+    
+    # 3. 为每个运营商构建完整的时间序列数据
     series_result = []
     for op in operators:
-        op_trend = []
-        for d in all_dates:
-            if d in trend_data[op]:
-                day_stats = trend_data[op][d]
-                day_avg_sum = 0.0
-                day_hour_count = 0
-                for i in range(24):
-                    if day_stats["counts"][i] > 0:
-                        day_avg_sum += (day_stats["sums"][i] / day_stats["counts"][i])
-                        day_hour_count += 1
-                
-                if day_hour_count > 0:
-                    op_trend.append(round(day_avg_sum / day_hour_count, 4))
-                else:
-                    op_trend.append(None)
+        op_data = []
+        for key in sorted_keys:
+            if key in trend_data[op]:
+                stats = trend_data[op][key]
+                op_data.append(round(stats["sum"] / stats["count"], 4))
             else:
-                op_trend.append(None)
-        series_result.append({"name": op, "data": op_trend})
+                op_data.append(None)
+        series_result.append({"name": op, "data": op_data})
         
     return {
-        "dates": all_dates,
+        "dates": display_dates,
         "series": series_result
     }