diff --git a/config_jingbo.py b/config_jingbo.py
index c47bfac..1d2ae36 100644
--- a/config_jingbo.py
+++ b/config_jingbo.py
@@ -250,9 +250,9 @@ is_corr = False # 特征是否参与滞后领先提升相关系数
 add_kdj = False # 是否添加kdj指标
 if add_kdj and is_edbnamelist:
     edbnamelist = edbnamelist+['K','D','J']
+
 ### 模型参数  
 y = 'Brent连1合约价格' # 原油指标数据的目标变量  Brent连1合约价格   Brent活跃合约
-# y = '期货结算价(连续):布伦特原油:前一个观测值' # ineoil的目标变量  
 horizon =5 # 预测的步长
 input_size = 40  # 输入序列长度
 train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
@@ -267,10 +267,12 @@ k = 100 # 特征筛选数量，如果是0或者值比特征数量大，代表全
 corr_threshold = 0.6 # 相关性大于0.6的特征
 rote = 0.06 # 绘图上下界阈值
 
+### 计算准确率
+weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
+
+
 ### 文件
 data_set = '原油指标数据.xlsx' # 数据集文件  
-# data_set = 'INE_OIL(1).csv'
-### 文件夹
 dataset = 'yuanyoudataset' # 数据集文件夹
 
 # 数据库名称
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 44e4c2a..66945d9 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -495,29 +495,31 @@ def model_losss(sqlitedb,end_time):
     df['ACCURACY'] = df.apply(calculate_accuracy, axis=1)
     # df['ACCURACY'] = df.apply(is_within_range, axis=1)
     # 取结束日期上一周的日期
-    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]
-    # 
-    df3 = df.copy()
-    df3 = df3[df3['CREAT_DATE'].isin(up_week_dates)]
-    df3 = df3[df3['ds'].isin(up_week_dates)]
-    # df3.to_csv(os.path.join(dataset,f'accuracy_{endtime}.csv'),index=False)
-    total = len(df3)
-    accuracy_rote = 0
-    # 设置权重字典
-    weight_dict = [0.4,0.15,0.1,0.1,0.25]
-    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])
-        accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1]
-    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}
-    print(df4)
-    df4.to_sql("accuracy_rote",  con=sqlitedb.connection, if_exists='append', index=False)
-
+    def get_week_date(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)
+    
+    # 计算准确率并保存结果
+    def _get_accuracy_rate(df,up_week_dates,endtime):
+        df3 = df.copy()
+        df3 = df3[df3['CREAT_DATE'].isin(up_week_dates)]
+        df3 = df3[df3['ds'].isin(up_week_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])
+            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.to_sql("accuracy_rote",  con=sqlitedb.connection, if_exists='append', index=False)
+    _get_accuracy_rate(df,up_week_dates,end_time)
+    
     def _add_abs_error_rate():
         # 计算每个预测值与真实值之间的偏差率
         for model in allmodelnames:
@@ -534,7 +536,6 @@ def model_losss(sqlitedb,end_time):
         # 将最小偏差率对应的模型的预测值添加到DataFrame中
         df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions
         df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name
-
     # _add_abs_error_rate()
 
     # 判断 df 的数值列转为float