diff --git a/config_jingbo.py b/config_jingbo.py
index d11207b..ff08ed8 100644
--- a/config_jingbo.py
+++ b/config_jingbo.py
@@ -225,7 +225,7 @@ table_name = 'v_tbl_crude_oil_warning'
 ### 开关
 is_train = True # 是否训练
 is_debug = False # 是否调试
-is_eta = False # 是否使用eta接口
+is_eta = True # 是否使用eta接口
 is_timefurture = True # 是否使用时间特征
 is_fivemodels = False # 是否使用之前保存的最佳的5个模型
 is_edbcode = False # 特征使用edbcoding列表中的
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index f91f215..dd39d1b 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -197,10 +197,29 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
     df_predict=nf.predict(df_test).reset_index()
     df_predict.astype({col: 'float32' for col in df_predict.columns if col not in ['ds'] })
     
+    # 添加预测时间
+    df_predict['created_dt'] = end_time
+    
     # 保存预测值
     df_predict.to_csv(os.path.join(dataset,"predict.csv"),index=False)
-    
-    df_predict2 = df_predict.copy()
+
+    # 将预测结果保存到数据库
+    def save_to_database(df):
+        if not sqlitedb.check_table_exists('predict'):
+            df.to_sql('predict',sqlitedb.connection,index=False)
+        else:
+            for col in df.columns:
+                    sqlitedb.add_column_if_not_exists('predict',col,'TEXT')
+            for row in df.itertuples(index=False):
+                row_dict = row._asdict()
+                columns=row_dict.keys()
+                check_query = sqlitedb.select_data('predict',where_condition = f"ds = '{row.ds} and model = {row.model}'")
+                if len(check_query) > 0:
+                    set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
+                    sqlitedb.update_data('predict',set_clause,where_condition = f"ds = '{row.ds}'")
+                    continue
+                sqlitedb.insert_data('predict',tuple(row_dict.values()),columns=columns)
+    save_to_database(df_predict)
     
     # 把预测值上传到eta
     if is_update_eta:
@@ -307,28 +326,30 @@ def model_losss(sqlitedb,end_time):
     except ValueError :
         df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
 
-    def first_row_to_database(df):
-        # # 取第一行数据存储到数据库中
-        first_row = df.head(1)
-        first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
-        # 将预测结果保存到数据库
-        if not sqlitedb.check_table_exists('trueandpredict'):
-            first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
-        else:
-            for col in first_row.columns:
-                    sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
-            for row in first_row.itertuples(index=False):
-                row_dict = row._asdict()
-                columns=row_dict.keys()
-                check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
-                if len(check_query) > 0:
-                    set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
-                    sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
-                    continue
-                sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
-
-    first_row_to_database(df_predict)
+    # def first_row_to_database(df):
+    #     # # 取第一行数据存储到数据库中
+    #     first_row = df.head(1)
+    #     first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
+    #     # 将预测结果保存到数据库
+    #     if not sqlitedb.check_table_exists('trueandpredict'):
+    #         first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
+    #     else:
+    #         for col in first_row.columns:
+    #                 sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
+    #         for row in first_row.itertuples(index=False):
+    #             row_dict = row._asdict()
+    #             columns=row_dict.keys()
+    #             check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
+    #             if len(check_query) > 0:
+    #                 set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
+    #                 sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
+    #                 continue
+    #             sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
 
+    # first_row_to_database(df_predict)
+    
+    
+    
     df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
 
     # 计算每个模型与最佳模型的绝对误差比例，根据设置的阈值rote筛选预测值显示最大最小值