再次修改训练与预测判定过程

data_preprocess.py

@@ -906,28 +906,28 @@ def preprocess_data_simple(df_input, is_train=False):
     )
     
     # 第三步：段级余票变化（上一段终点余票 -> 当前段起点余票），首段为 n->n
-    _seg_keys = ['gid', 'baggage', 'price_change_segment']
-    _seg_seats = (
-        df_input.groupby(_seg_keys, as_index=False)['seats_remaining']
-        .agg(_seg_first_seats='first', _seg_last_seats='last')
-    )
-    _seg_seats['_prev_seg_last_seats'] = (
-        _seg_seats.groupby(['gid', 'baggage'], group_keys=False)['_seg_last_seats']
-        .shift(1)
-        .fillna(_seg_seats['_seg_first_seats'])
-    )
+    # _seg_keys = ['gid', 'baggage', 'price_change_segment']
+    # _seg_seats = (
+    #     df_input.groupby(_seg_keys, as_index=False)['seats_remaining']
+    #     .agg(_seg_first_seats='first', _seg_last_seats='last')
+    # )
+    # _seg_seats['_prev_seg_last_seats'] = (
+    #     _seg_seats.groupby(['gid', 'baggage'], group_keys=False)['_seg_last_seats']
+    #     .shift(1)
+    #     .fillna(_seg_seats['_seg_first_seats'])
+    # )
     
-    _seg_seats['_seg_first_seats'] = pd.to_numeric(_seg_seats['_seg_first_seats'], errors='coerce').round().astype('Int64')
-    _seg_seats['_prev_seg_last_seats'] = pd.to_numeric(_seg_seats['_prev_seg_last_seats'], errors='coerce').round().astype('Int64')
-    _seg_seats['seats_remaining_transition'] = (
-        _seg_seats['_prev_seg_last_seats'].astype(str) + '->' + _seg_seats['_seg_first_seats'].astype(str)
-    )
+    # _seg_seats['_seg_first_seats'] = pd.to_numeric(_seg_seats['_seg_first_seats'], errors='coerce').round().astype('Int64')
+    # _seg_seats['_prev_seg_last_seats'] = pd.to_numeric(_seg_seats['_prev_seg_last_seats'], errors='coerce').round().astype('Int64')
+    # _seg_seats['seats_remaining_transition'] = (
+    #     _seg_seats['_prev_seg_last_seats'].astype(str) + '->' + _seg_seats['_seg_first_seats'].astype(str)
+    # )
 
-    df_input = df_input.merge(
-        _seg_seats[_seg_keys + ['seats_remaining_transition']],
-        on=_seg_keys,
-        how='left'
-    )
+    # df_input = df_input.merge(
+    #     _seg_seats[_seg_keys + ['seats_remaining_transition']],
+    #     on=_seg_keys,
+    #     how='left'
+    # )
 
     # 可选：删除临时列
     # df_input = df_input.drop(columns=['price_change_segment'])
@@ -964,30 +964,31 @@ def preprocess_data_simple(df_input, is_train=False):
         prev_amo = df_target.groupby('gid', group_keys=False)['price_change_amount'].shift(1)
         prev_dur = df_target.groupby('gid', group_keys=False)['price_duration_hours'].shift(1)
         prev_price = df_target.groupby('gid', group_keys=False)['adult_total_price'].shift(1)
-        prev_seats_trans = df_target.groupby('gid', group_keys=False)['seats_remaining_transition'].shift(1)
+        prev_seats = df_target.groupby('gid', group_keys=False)['seats_remaining'].shift(1)
 
         # 对于先升后降（先降后降）的分析
         seg_start_mask = df_target['price_duration_hours'].eq(1)   # 开始变价节点
-        # 正例库仅保留24小时内发生的降价：上一价格段持续时长需<=24h
+        # 正例库
         prev_pct_num = pd.to_numeric(prev_pct, errors='coerce')
         drop_mask = (
             seg_start_mask
             & prev_pct_num.notna()
             & (df_target['price_change_percent'] < 0)
-            & prev_dur.le(24)
+            # & prev_dur.le(24)  # 仅保留24小时内发生的降价：上一价格段持续时长需<=24h
         )
         
-        df_drop_nodes = df_target.loc[drop_mask, ['gid', 'hours_until_departure', 'days_to_departure', 'update_hour']].copy()
+        df_drop_nodes = df_target.loc[drop_mask, ['gid', 'hours_until_departure', 'days_to_departure', 'update_hour', 'seats_remaining']].copy()
         df_drop_nodes.rename(columns={'hours_until_departure': 'drop_hours_until_departure'}, inplace=True)
         df_drop_nodes.rename(columns={'days_to_departure': 'drop_days_to_departure'}, inplace=True)
         df_drop_nodes.rename(columns={'update_hour': 'drop_update_hour'}, inplace=True)
+        df_drop_nodes.rename(columns={'seats_remaining': 'drop_seats_remaining'}, inplace=True)
         df_drop_nodes['drop_price_change_percent'] = df_target.loc[drop_mask, 'price_change_percent'].astype(float).round(4).to_numpy()
         df_drop_nodes['drop_price_change_amount'] = df_target.loc[drop_mask, 'price_change_amount'].astype(float).round(2).to_numpy()
         df_drop_nodes['high_price_duration_hours'] = prev_dur.loc[drop_mask].astype(float).to_numpy()
         df_drop_nodes['high_price_change_percent'] = prev_pct.loc[drop_mask].astype(float).round(4).to_numpy()
         df_drop_nodes['high_price_change_amount'] = prev_amo.loc[drop_mask].astype(float).round(2).to_numpy()
         df_drop_nodes['high_price_amount'] = prev_price.loc[drop_mask].astype(float).round(2).to_numpy()
-        df_drop_nodes['high_price_seats_remaining_transition'] = prev_seats_trans.loc[drop_mask].astype(str)
+        df_drop_nodes['high_price_seats_remaining'] = prev_seats.loc[drop_mask].astype(int)
         df_drop_nodes = df_drop_nodes.reset_index(drop=True)
 
         flight_info_cols = [
@@ -1003,9 +1004,9 @@ def preprocess_data_simple(df_input, is_train=False):
         df_drop_nodes = df_drop_nodes.merge(df_gid_info, on='gid', how='left')
 
         drop_info_cols = ['drop_update_hour', 'drop_days_to_departure',
-                          'drop_hours_until_departure', 'drop_price_change_percent', 'drop_price_change_amount',
+                          'drop_hours_until_departure', 'drop_price_change_percent', 'drop_price_change_amount', 'drop_seats_remaining',
                           'high_price_duration_hours', 'high_price_change_percent', 'high_price_change_amount',
-                          'high_price_amount', 'high_price_seats_remaining_transition',
+                          'high_price_amount', 'high_price_seats_remaining',
         ]
         # 按顺序排列 保留gid
         df_drop_nodes = df_drop_nodes[flight_info_cols + drop_info_cols]
@@ -1013,7 +1014,7 @@ def preprocess_data_simple(df_input, is_train=False):
         df_drop_nodes = df_drop_nodes[df_drop_nodes['drop_hours_until_departure'] <= 360]
         df_drop_nodes = df_drop_nodes[df_drop_nodes['start_hours_until_departure'] >= 72]        
         df_drop_nodes = df_drop_nodes[df_drop_nodes['high_price_duration_hours'] > 1.0]       # 维持时间太短的不计  
-        df_drop_nodes = df_drop_nodes[df_drop_nodes['drop_price_change_amount'].abs() >= 1]   # 1$之内的降价不计
+        df_drop_nodes = df_drop_nodes[df_drop_nodes['drop_price_change_amount'].abs() > 1]   # 1$之内的降价不计
 
         # 反例库：所有有效节点（不限升价）中，未来24小时内未发生降价
         # seg_start_mask = df_target['price_duration_hours'].eq(1)
@@ -1022,51 +1023,40 @@ def preprocess_data_simple(df_input, is_train=False):
         valid_mask = seg_start_mask & prev_pct_num.notna()
         
         curr_pct = pd.to_numeric(df_target['price_change_percent'], errors='coerce')
-        prev_dur_num = pd.to_numeric(prev_dur, errors='coerce')
+        # prev_dur_num = pd.to_numeric(prev_dur, errors='coerce')
         pos_case_mask = curr_pct.ge(0)
-        neg_case_mask = curr_pct.lt(0) & prev_dur_num.gt(24)
+        # neg_case_mask = curr_pct.lt(0) & prev_dur_num.gt(24)
 
-        # next_seg_hours = pd.Series(index=df_target.index, dtype='float64')
-        # next_seg_pct = pd.Series(index=df_target.index, dtype='float64')
-        # next_seg_hours.loc[seg_start_mask] = (
-        #     df_target.loc[seg_start_mask].groupby('gid')['hours_until_departure'].shift(-1).to_numpy()
-        # )
-        # next_seg_pct.loc[seg_start_mask] = (
-        #     df_target.loc[seg_start_mask].groupby('gid')['price_change_percent'].shift(-1).to_numpy()
-        # )
-
-        # hours_to_next_seg = df_target['hours_until_departure'] - next_seg_hours
-        # drop_within_24h = next_seg_pct.lt(0) & hours_to_next_seg.ge(0) & hours_to_next_seg.le(24)
-
-        rise_mask = valid_mask & (pos_case_mask | neg_case_mask)
+        rise_mask = valid_mask & pos_case_mask   # (pos_case_mask | neg_case_mask)
         # rise_mask = seg_start_mask & ((prev_pct > 0) | (prev_pct < 0)) & (df_target['price_change_percent'] > 0)
 
-        df_rise_nodes = df_target.loc[rise_mask, ['gid', 'hours_until_departure', 'days_to_departure', 'update_hour']].copy()
+        df_rise_nodes = df_target.loc[rise_mask, ['gid', 'hours_until_departure', 'days_to_departure', 'update_hour', 'seats_remaining']].copy()
         df_rise_nodes.rename(columns={'hours_until_departure': 'rise_hours_until_departure'}, inplace=True)
         df_rise_nodes.rename(columns={'days_to_departure': 'rise_days_to_departure'}, inplace=True)
         df_rise_nodes.rename(columns={'update_hour': 'rise_update_hour'}, inplace=True)
+        df_rise_nodes.rename(columns={'seats_remaining': 'rise_seats_remaining'}, inplace=True)
         df_rise_nodes['rise_price_change_percent'] = df_target.loc[rise_mask, 'price_change_percent'].astype(float).round(4).to_numpy()
         df_rise_nodes['rise_price_change_amount'] = df_target.loc[rise_mask, 'price_change_amount'].astype(float).round(2).to_numpy()
         df_rise_nodes['prev_rise_duration_hours'] = prev_dur.loc[rise_mask].astype(float).to_numpy()
         df_rise_nodes['prev_rise_change_percent'] = prev_pct.loc[rise_mask].astype(float).round(4).to_numpy()
         df_rise_nodes['prev_rise_change_amount'] = prev_amo.loc[rise_mask].astype(float).round(2).to_numpy()
         df_rise_nodes['prev_rise_amount'] = prev_price.loc[rise_mask].astype(float).round(2).to_numpy()
-        df_rise_nodes['prev_rise_seats_remaining_transition'] = prev_seats_trans.loc[rise_mask].astype(str)
+        df_rise_nodes['prev_rise_seats_remaining'] = prev_seats.loc[rise_mask].astype(int)
         df_rise_nodes = df_rise_nodes.reset_index(drop=True)
 
         df_rise_nodes = df_rise_nodes.merge(df_gid_info, on='gid', how='left')
         rise_info_cols = [
             'rise_update_hour', 'rise_days_to_departure',
-            'rise_hours_until_departure', 'rise_price_change_percent', 'rise_price_change_amount',
+            'rise_hours_until_departure', 'rise_price_change_percent', 'rise_price_change_amount', 'rise_seats_remaining',
             'prev_rise_duration_hours', 'prev_rise_change_percent', 'prev_rise_change_amount',
-            'prev_rise_amount', 'prev_rise_seats_remaining_transition',
+            'prev_rise_amount', 'prev_rise_seats_remaining',
         ]
         df_rise_nodes = df_rise_nodes[flight_info_cols + rise_info_cols]
         df_rise_nodes['start_hours_until_departure'] = (df_rise_nodes['rise_hours_until_departure'] + df_rise_nodes['prev_rise_duration_hours']).round().astype('Int64')
         df_rise_nodes = df_rise_nodes[df_rise_nodes['rise_hours_until_departure'] <= 360]
         df_rise_nodes = df_rise_nodes[df_rise_nodes['start_hours_until_departure'] >= 72]
         df_rise_nodes = df_rise_nodes[df_rise_nodes['prev_rise_duration_hours'] > 1.0]        # 维持时间太短的不计
-        df_rise_nodes = df_rise_nodes[df_rise_nodes['rise_price_change_amount'].abs() >= 1]   # 1$之内的改变不计
+        df_rise_nodes = df_rise_nodes[df_rise_nodes['rise_price_change_amount'].abs() > 1]   # 1$之内的改变不计
 
         # 制作历史包络线
         envelope_group = ['city_pair', 'flight_number_1', 'flight_number_2', 'flight_day']
@@ -1325,7 +1315,7 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
     # del df_hist, df_future, df_envelope_all, df_envelope_agg, df_top   # df_drop_freq, df_rise_freq
     
     total_cnt_before = len(df_min_hours)   # 记录下过滤前的总数
-    df_min_hours = df_min_hours[(df_min_hours['is_good_target'] == 1) & (df_min_hours['seats_remaining'] >= 1)].reset_index(drop=True)   # 保留值得投放的 
+    df_min_hours = df_min_hours[(df_min_hours['is_good_target'] == 1) & (df_min_hours['seats_remaining'] >= 2)].reset_index(drop=True)   # 保留值得投放的 
     total_cnt_after = len(df_min_hours)    # 记录下过滤后的总数
 
     # 余票为1的样本去掉
@@ -1349,9 +1339,9 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
     df_min_hours['rise_price_sample_size'] = 0
 
     # 这个阈值取多少?
-    pct_threshold = 0.1
+    pct_threshold = 0.3
     # pct_threshold = 2
-    pct_threshold_1 = 0.1
+    pct_threshold_1 = 0.3
     # pct_threshold_c = 0.001
 
     for idx, row in df_min_hours.iterrows(): 
@@ -1369,7 +1359,7 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
         # seats_remaining_change_amount = row['seats_remaining_change_amount']
         price_amount = row['adult_total_price']
         seats_remaining = row['seats_remaining']
-        seats_remaining_transition = row['seats_remaining_transition']
+        # seats_remaining_transition = row['seats_remaining_transition']
         # envelope_position = row['envelope_position']
 
         length_drop = 0
@@ -1402,9 +1392,10 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
                 pct_vals = pd.to_numeric(df_drop_nodes_part['high_price_change_percent'], errors='coerce')
                 df_drop_gap = df_drop_nodes_part.loc[
                     pct_vals.notna(),
-                    ['drop_days_to_departure', 'drop_hours_until_departure', 'drop_price_change_percent', 'drop_price_change_amount',
+                    ['flight_day',
+                     'drop_days_to_departure', 'drop_hours_until_departure', 'drop_price_change_percent', 'drop_price_change_amount',
                      'high_price_duration_hours', 'high_price_change_percent', 
-                     'high_price_change_amount', 'high_price_amount', 'high_price_seats_remaining_transition']
+                     'high_price_change_amount', 'high_price_amount', 'high_price_seats_remaining', 'start_hours_until_departure']
                 ].copy()
                 df_drop_gap['pct_gap'] = (pct_vals.loc[pct_vals.notna()] - pct_base)
                 df_drop_gap['pct_abs_gap'] = df_drop_gap['pct_gap'].abs()
@@ -1423,7 +1414,8 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
                     (df_drop_gap['pct_abs_gap'] <= pct_threshold)
                     & (df_drop_gap['price_abs_gap'] <= 0.001)
                     & same_sign_mask
-                    & (df_drop_gap['high_price_seats_remaining_transition'] == seats_remaining_transition)
+                    # & (df_drop_gap['high_price_seats_remaining'] == seats_remaining)
+                    & (df_drop_gap['high_price_duration_hours'] <= 48)
                 ].copy()
                 # df_match = df_drop_gap[(df_drop_gap['pct_abs_gap'] <= pct_threshold) & (df_drop_gap['price_abs_gap'] <= 1.0)].copy()
                 # df_drop_gap = df_drop_gap.sort_values(['price_abs_gap'], ascending=[True])
@@ -1441,16 +1433,21 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
                         # df_match_chk = df_match_chk.loc[dur_vals.notna()].copy()
                         # df_match_chk = df_match_chk.loc[(dur_vals.loc[dur_vals.notna()] - float(dur_base)).abs() <= 36].copy()
 
-                        # drop_hud_vals = pd.to_numeric(df_match_chk['drop_hours_until_departure'], errors='coerce')
-                        # df_match_chk = df_match_chk.loc[drop_hud_vals.notna()].copy()
-                        # df_match_chk = df_match_chk.loc[(float(hud_base) - drop_hud_vals.loc[drop_hud_vals.notna()]) >= -24].copy()
+                        # 正例收紧 (距离起飞的小时数)
+                        drop_hud_vals = pd.to_numeric(df_match_chk['drop_hours_until_departure'], errors='coerce')
+                        df_match_chk = df_match_chk.loc[drop_hud_vals.notna()].copy()
+                        df_match_chk = df_match_chk.loc[(float(hud_base) - drop_hud_vals.loc[drop_hud_vals.notna()]) >= 0].copy()
+                        
+                        start_hud_vals = pd.to_numeric(df_match_chk['start_hours_until_departure'], errors='coerce')
+                        df_match_chk = df_match_chk.loc[start_hud_vals.notna()].copy()
+                        df_match_chk = df_match_chk.loc[(float(hud_base) - start_hud_vals.loc[start_hud_vals.notna()]) <= 0].copy()
 
-                        # 正例收紧
+                        # 正例收紧 (持续小时数)
                         dur_num_chk = pd.to_numeric(df_match_chk['high_price_duration_hours'], errors='coerce')
                         dur_delta = dur_num_chk - float(dur_base)
                         df_match_chk = df_match_chk.assign(dur_delta=dur_delta)
                         df_match_chk = df_match_chk.loc[df_match_chk['dur_delta'].notna()].copy()
-                        df_match_chk = df_match_chk.loc[df_match_chk['dur_delta'].abs() <= 72].copy()
+                        # df_match_chk = df_match_chk.loc[df_match_chk['dur_delta'].abs() <= 24].copy()
                         
                         # seats_vals = pd.to_numeric(df_match_chk['high_price_seats_remaining_change_amount'], errors='coerce')
                         # df_match_chk = df_match_chk.loc[seats_vals.notna()].copy()
@@ -1521,9 +1518,10 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
                 pct_vals_1 = pd.to_numeric(df_rise_nodes_part['prev_rise_change_percent'], errors='coerce')
                 df_rise_gap_1 = df_rise_nodes_part.loc[
                     pct_vals_1.notna(),
-                    ['rise_days_to_departure', 'rise_hours_until_departure', 'rise_price_change_percent', 'rise_price_change_amount',
+                    ['flight_day',
+                     'rise_days_to_departure', 'rise_hours_until_departure', 'rise_price_change_percent', 'rise_price_change_amount',
                      'prev_rise_duration_hours', 'prev_rise_change_percent', 
-                     'prev_rise_change_amount', 'prev_rise_amount', 'prev_rise_seats_remaining_transition']
+                     'prev_rise_change_amount', 'prev_rise_amount', 'prev_rise_seats_remaining', 'start_hours_until_departure']
                 ].copy()
                 df_rise_gap_1['pct_gap'] = (pct_vals_1.loc[pct_vals_1.notna()] - pct_base_1)
                 df_rise_gap_1['pct_abs_gap'] = df_rise_gap_1['pct_gap'].abs()
@@ -1542,7 +1540,7 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
                     (df_rise_gap_1['pct_abs_gap'] <= pct_threshold_1)
                     & (df_rise_gap_1['price_abs_gap'] <= 0.001)
                     & same_sign_mask_1
-                    & (df_rise_gap_1['prev_rise_seats_remaining_transition'] == seats_remaining_transition)
+                    # & (df_rise_gap_1['prev_rise_seats_remaining'] == seats_remaining)
                 ].copy()
                 # df_match_1 = df_rise_gap_1.loc[(df_rise_gap_1['pct_abs_gap'] <= pct_threshold_1) & (df_rise_gap_1['price_abs_gap'] <= 1.0)].copy()
                 # df_rise_gap_1 = df_rise_gap_1.sort_values(['price_abs_gap'], ascending=[True])
@@ -1562,18 +1560,23 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
                         df_match_chk_1 = df_match_1.copy()
 
                         # 反例收紧：48小时内发生降价的不算显著反例
-                        _rise_pct_chk = pd.to_numeric(df_match_chk_1['rise_price_change_percent'], errors='coerce')
-                        _prev_dur_chk = pd.to_numeric(df_match_chk_1['prev_rise_duration_hours'], errors='coerce')
-                        _exclude_mask = _rise_pct_chk.lt(0) & _prev_dur_chk.lt(48)
-                        df_match_chk_1 = df_match_chk_1.loc[~_exclude_mask.fillna(False)].copy()
-
+                        # _rise_pct_chk = pd.to_numeric(df_match_chk_1['rise_price_change_percent'], errors='coerce')
+                        # _prev_dur_chk = pd.to_numeric(df_match_chk_1['prev_rise_duration_hours'], errors='coerce')
+                        # _exclude_mask = _rise_pct_chk.lt(0) & _prev_dur_chk.lt(48)
+                        # df_match_chk_1 = df_match_chk_1.loc[~_exclude_mask.fillna(False)].copy()
+                         
                         # dur_vals_1 = pd.to_numeric(df_match_chk_1['modify_rise_price_duration_hours'], errors='coerce')
                         # df_match_chk_1 = df_match_chk_1.loc[dur_vals_1.notna()].copy()
                         # df_match_chk_1 = df_match_chk_1.loc[(dur_vals_1.loc[dur_vals_1.notna()] - float(dur_base_1)).abs() <= 24].copy()
 
-                        # rise_hud_vals_1 = pd.to_numeric(df_match_chk_1['rise_hours_until_departure'], errors='coerce')
-                        # df_match_chk_1 = df_match_chk_1.loc[rise_hud_vals_1.notna()].copy()
-                        # df_match_chk_1 = df_match_chk_1.loc[(float(hud_base_1) - rise_hud_vals_1.loc[rise_hud_vals_1.notna()]) >= -24].copy()
+                        # 反例收紧 (距离起飞的小时数)
+                        rise_hud_vals_1 = pd.to_numeric(df_match_chk_1['rise_hours_until_departure'], errors='coerce')
+                        df_match_chk_1 = df_match_chk_1.loc[rise_hud_vals_1.notna()].copy()
+                        df_match_chk_1 = df_match_chk_1.loc[(float(hud_base_1) - rise_hud_vals_1.loc[rise_hud_vals_1.notna()]) >= 0].copy()
+
+                        start_hud_vals_1 = pd.to_numeric(df_match_chk_1['start_hours_until_departure'], errors='coerce')
+                        df_match_chk_1 = df_match_chk_1.loc[start_hud_vals_1.notna()].copy()
+                        df_match_chk_1 = df_match_chk_1.loc[(float(hud_base_1) - start_hud_vals_1.loc[start_hud_vals_1.notna()]) <= 0].copy()
 
                         # seats_vals_1 = pd.to_numeric(df_match_chk_1['rise_seats_remaining_change_amount'], errors='coerce')
                         # df_match_chk_1 = df_match_chk_1.loc[seats_vals_1.notna()].copy()

main_tr_0.py

@@ -50,7 +50,7 @@ def start_train():
     # date_end = datetime.today().strftime("%Y-%m-%d")
     date_end = (datetime.today() + timedelta(days=1)).strftime("%Y-%m-%d")   # 截止到明天
     date_begin = date_end
-    # date_begin = "2026-05-07"   # 2026-03-01  2026-04-28 2026-05-07
+    # date_begin = "2026-04-05"   # 2026-03-01 2026-04-05
 
     print(f"训练时间范围: {date_begin} 到 {date_end}")
 

result_keep_verify.py

@@ -394,4 +394,4 @@ def verify_process_2(min_batch_time_str, max_batch_time_str):
 if __name__ == "__main__":
     # verify_process("202604071700", "202604090900")
     # verify_process_2("202604211700", "202604220900")
-    verify_process_2("202604281500", "202604291300")
+    verify_process_2("202605121000", "202605121000")