diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 961fef2..6ffb249 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -463,11 +463,11 @@ def model_losss(sqlitedb,end_time):
     # 准确率基准与绘图上下界逻辑一致
     # predict_y[['min_price','max_price']] = predict_y[['min_within_quantile','max_within_quantile']]
     # 模型评估前五均值      
-    predict_y['min_price'] = predict_y[modelnames].mean(axis=1) -1
-    predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1
+    # predict_y['min_price'] = predict_y[modelnames].mean(axis=1) -1
+    # predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1
     # 模型评估前十均值      
-    # predict_y['min_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) -1
-    # predict_y['max_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) +1
+    predict_y['min_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) -1.5
+    predict_y['max_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) +1.5
     # 模型评估前十最大最小
     # allmodelnames 和 predict_y 列 重复的
     # allmodelnames = [col for col in allmodelnames if col in predict_y.columns]
@@ -523,26 +523,26 @@ def model_losss(sqlitedb,end_time):
         endtime = end_time
         endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')
         up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)
-        up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)][4:-2]
-        up_week_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates]
-        return up_week_dates
-    up_week_dates = get_week_date(end_time)
+        up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)]
+        create_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates[4:-3]]
+        ds_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates[-7:-2]]
+        return create_dates,ds_dates
     
+    create_dates,ds_dates = get_week_date(end_time)
     # 计算准确率并保存结果
-    def _get_accuracy_rate(df,up_week_dates,endtime):
+    def _get_accuracy_rate(df,create_dates,ds_dates):
         df3 = df.copy()
-        df3 = df3[df3['CREAT_DATE'].isin(up_week_dates)]
-        df3 = df3[df3['ds'].isin(up_week_dates)]
+        df3 = df3[df3['CREAT_DATE'].isin(create_dates)]
+        df3 = df3[df3['ds'].isin(ds_dates)]
         accuracy_rote = 0
-        for i,group in df3.groupby('ds'):
-            print('权重：',weight_dict[len(group)-1])
-            print('准确率：',(group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1])
+        for i,group in df3.groupby('CREAT_DATE'):
             accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1]
-        df3.to_csv(os.path.join(dataset,f'accuracy_{endtime}.csv'),index=False)
         df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率'])
-        df4.loc[len(df4)] = {'开始日期':up_week_dates[0],'结束日期':up_week_dates[-1],'准确率':accuracy_rote}
+        df4.loc[len(df4)] = {'开始日期':ds_dates[0],'结束日期':ds_dates[-1],'准确率':accuracy_rote}
         df4.to_sql("accuracy_rote",  con=sqlitedb.connection, if_exists='append', index=False)
-    _get_accuracy_rate(df,up_week_dates,end_time)
+        # return df4
+    
+    _get_accuracy_rate(df,create_dates,ds_dates)
     
     def _add_abs_error_rate():
         # 计算每个预测值与真实值之间的偏差率