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alg_recsys_recall_4h_region_trend.py

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+# -*- coding: utf-8 -*-
+import multiprocessing
+import traceback
+import gevent
+import datetime
+import pandas as pd
+import math
+from functools import reduce
+from odps import ODPS
+from threading import Timer, Thread
+from utils import MysqlHelper, RedisHelper, get_data_from_odps, filter_video_status, filter_shield_video, \
+    check_table_partition_exits, filter_video_status_app, send_msg_to_feishu, filter_political_videos
+from config import set_config
+from log import Log
+from check_video_limit_distribute import update_limit_video_score
+
+
+config_, _ = set_config()
+log_ = Log()
+
+region_code = config_.REGION_CODE
+
+def get_region_code(region):
+    """获取省份对应的code"""
+    mysql_helper = MysqlHelper(mysql_info=config_.MYSQL_INFO)
+    sql = f"SELECT ad_code FROM region_adcode WHERE parent_id = 0 AND region LIKE '{region}%';"
+    ad_code = mysql_helper.get_data(sql=sql)
+    return ad_code[0][0]
+
+
+
+
+def get_rov_redis_key(now_date):
+    """获取rov模型结果存放key"""
+    redis_helper = RedisHelper()
+    now_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
+    key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{now_dt}'
+    if not redis_helper.key_exists(key_name=key_name):
+        pre_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
+        key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{pre_dt}'
+    return key_name
+
+
+def get_day_30day_videos(now_date, data_key, rule_key):
+    """获取天级更新相对30天的视频id"""
+    redis_helper = RedisHelper()
+    day_30day_recall_key_prefix = config_.RECALL_KEY_NAME_PREFIX_30DAY
+    now_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
+    day_30day_recall_key_name = f"{day_30day_recall_key_prefix}{data_key}:{rule_key}:{now_dt}"
+    if not redis_helper.key_exists(key_name=day_30day_recall_key_name):
+        redis_dt = datetime.datetime.strftime((now_date - datetime.timedelta(days=1)), '%Y%m%d')
+        day_30day_recall_key_name = f"{day_30day_recall_key_prefix}{data_key}:{rule_key}:{redis_dt}"
+    data = redis_helper.get_all_data_from_zset(key_name=day_30day_recall_key_name, with_scores=True)
+    if data is None:
+        return None
+    video_ids = [int(video_id) for video_id, _ in data]
+    return video_ids
+
+
+def get_feature_data(project, table, now_date):
+    """获取特征数据"""
+    dt = datetime.datetime.strftime(now_date, '%Y%m%d%H')
+    # dt = '2022041310'
+    records = get_data_from_odps(date=dt, project=project, table=table)
+    feature_data = []
+    for record in records:
+        item = {}
+        for feature_name in features:
+            item[feature_name] = record[feature_name]
+        feature_data.append(item)
+    feature_df = pd.DataFrame(feature_data)
+    return feature_df
+
+
+def cal_score_initial(df, param):
+    """
+    计算score
+    :param df: 特征数据
+    :param param: 规则参数
+    :return:
+    """
+    # score计算公式: sharerate*backrate*logback*ctr
+    # sharerate = lastonehour_share/(lastonehour_play+1000)
+    # backrate = lastonehour_return/(lastonehour_share+10)
+    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+    # score = sharerate * backrate * LOG(lastonehour_return+1) * K2
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+    if param.get('view_type', None) == 'video-show':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
+    elif param.get('view_type', None) == 'video-show-region':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 1000)
+    else:
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+
+    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
+
+    df['score1'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2']
+
+    click_score_rate = param.get('click_score_rate', None)
+    back_score_rate = param.get('click_score_rate', None)
+    if click_score_rate is not None:
+        df['score'] = (1 - click_score_rate) * df['score1'] + click_score_rate * df['K2']
+    elif back_score_rate is not None:
+        df['score'] = (1 - back_score_rate) * df['score1'] + back_score_rate * df['back_rate']
+    else:
+        df['score'] = df['score1']
+
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def cal_score_add_return(df, param):
+    # score计算公式: sharerate*(backrate*logback + backrate2*logback_now2 + backrate3*logback_now3)*ctr
+    # sharerate = lastonehour_share/(lastonehour_play+1000)
+    # backrate = lastonehour_return/(lastonehour_share+10)
+    # backrate2 = lasttwohour_return_now/(lasttwohour_share+10)
+    # backrate3 = lastthreehour_return_now/(lastthreehour_share+10)
+    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+    # score = k2 * sharerate * (backrate * LOG(lastonehour_return+1) + backrate_2 * LOG(lasttwohour_return_now+1) + backrate_3 * LOG(lastthreehour_return_now+1))
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+    df['back_rate2'] = df['lasttwohour_return_now'] / (df['lasttwohour_share'] + 10)
+    df['log_back2'] = (df['lasttwohour_return_now'] + 1).apply(math.log)
+    df['back_rate3'] = df['lastthreehour_return_now'] / (df['lastthreehour_share'] + 10)
+    df['log_back3'] = (df['lastthreehour_return_now'] + 1).apply(math.log)
+
+    if param.get('view_type', None) == 'video-show':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
+    elif param.get('view_type', None) == 'video-show-region':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 1000)
+    else:
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
+
+    df['score'] = df['K2'] * df['share_rate'] * (
+            df['back_rate'] * df['log_back'] +
+            df['back_rate2'] * df['log_back2'] +
+            df['back_rate3'] * df['log_back3']
+    )
+
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def cal_score_multiply_return_retention(df, param):
+    # score计算公式: k2 * sharerate * backrate * LOG(lastonehour_return+1) * 前两小时回流留存
+    # sharerate = lastonehour_share/(lastonehour_play+1000)
+    # backrate = lastonehour_return/(lastonehour_share+10)
+    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+    # 前两小时回流留存 return_retention_initial = (lasttwohour_return_now + lastthreehour_return_now)/(lasttwohour_return + lastthreehour_return + 1)
+    # return_retention = 0.5 if return_retention_initial == 0 else return_retention_initial
+    # score = k2 * sharerate * backrate * LOG(lastonehour_return+1) * return_retention
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+
+    if param.get('view_type', None) == 'video-show':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
+    elif param.get('view_type', None) == 'video-show-region':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 1000)
+    else:
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+
+    df['return_retention_initial'] = (df['lasttwohour_return_now'] + df['lastthreehour_return_now']) / \
+                                     (df['lasttwohour_return'] + df['lastthreehour_return'] + 1)
+    df['return_retention'] = df['return_retention_initial'].apply(lambda x: 0.5 if x == 0 else x)
+
+    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
+
+    df['score'] = df['K2'] * df['share_rate'] * df['back_rate'] * df['log_back'] * df['return_retention']
+
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def cal_score_update_backrate(df, param):
+    # score计算公式: k2 * sharerate * (backrate + backrate * backrate_2 * backrate_3) * LOG(lastonehour_return+1)
+    # sharerate = lastonehour_share/(lastonehour_play+1000)
+    # backrate = lastonehour_return/(lastonehour_share+10)
+    # backrate2 = lasttwohour_return_now/(lasttwohour_share+10)
+    # backrate3 = lastthreehour_return_now/(lastthreehour_share+10)
+    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+    # backrate1_3_initial = backrate * backrate_2 * backrate_3
+    # backrate1_3 = 0.02 if backrate1_3_initial == 0 else backrate1_3_initial
+    # score = k2 * sharerate * (backrate + backrate1_3) * LOG(lastonehour_return+1)
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
+    df['back_rate2'] = df['lasttwohour_return_now'] / (df['lasttwohour_share'] + 10)
+    df['back_rate3'] = df['lastthreehour_return_now'] / (df['lastthreehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+
+    if param.get('view_type', None) == 'video-show':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
+    elif param.get('view_type', None) == 'video-show-region':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 1000)
+    else:
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+
+    df['backrate1_3_initial'] = df['back_rate'] * df['back_rate2'] * df['back_rate3']
+    df['backrate1_3'] = df['backrate1_3_initial'].apply(lambda x: 0.02 if x == 0 else x)
+    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
+
+    df['score'] = df['K2'] * df['share_rate'] * (df['back_rate'] + df['backrate1_3']) * df['log_back']
+
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def cal_score_with_new_return(df, param):
+    # 回流数据使用 分享限制地域，回流不限制地域 统计数据
+    # score计算公式: sharerate*backrate*logback*ctr
+    # sharerate = lastonehour_share/(lastonehour_play+1000)
+    # backrate = lastonehour_return_new/(lastonehour_share+10)
+    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+    # score = sharerate * backrate * LOG(lastonehour_return_new+1) * K2
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return_new'] / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return_new'] + 1).apply(math.log)
+    if param.get('view_type', None) == 'video-show':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
+    elif param.get('view_type', None) == 'video-show-region':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 1000)
+    else:
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+    df['platform_return_rate'] = df['platform_return_new'] / df['lastonehour_return_new']
+
+    df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2']
+
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def cal_score_multiply_return_retention_with_new_return(df, param):
+    # 回流数据使用 分享限制地域，回流不限制地域 统计数据
+    # score计算公式: k2 * sharerate * backrate * LOG(lastonehour_return_new+1) * 前两小时回流留存
+    # sharerate = lastonehour_share/(lastonehour_play+1000)
+    # backrate = lastonehour_return_new/(lastonehour_share+10)
+    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+    # 前两小时回流留存 return_retention_initial = (lasttwohour_return_now_new + lastthreehour_return_now_new)/(lasttwohour_return_new + lastthreehour_return_new + 1)
+    # return_retention = 0.5 if return_retention_initial == 0 else return_retention_initial
+    # score = k2 * sharerate * backrate * LOG(lastonehour_return_new+1) * return_retention
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return_new'] / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return_new'] + 1).apply(math.log)
+
+    if param.get('view_type', None) == 'video-show':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
+    elif param.get('view_type', None) == 'video-show-region':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 1000)
+    else:
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+
+    df['return_retention_initial'] = (df['lasttwohour_return_now_new'] + df['lastthreehour_return_now_new']) / \
+                                     (df['lasttwohour_return_new'] + df['lastthreehour_return_new'] + 1)
+    df['return_retention'] = df['return_retention_initial'].apply(lambda x: 0.5 if x == 0 else x)
+
+    df['platform_return_rate'] = df['platform_return_new'] / df['lastonehour_return_new']
+
+    df['score'] = df['K2'] * df['share_rate'] * df['back_rate'] * df['log_back'] * df['return_retention']
+
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def cal_score_with_back_view0(df, param):
+    # score = sharerate*backrate*log(return+1)*CTR,
+    # sharerate=(lastonehour_share+1)/(lastonehour_play+1000)
+    # backrate=(lastonehour_return+1)/(lastonehour_share+10)
+    # CTR=(lastonehour_play+1)/(lastonehour_view+100), ctr不进行校正
+    df = df.fillna(0)
+    df['share_rate'] = (df['lastonehour_share'] + 1) / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = (df['lastonehour_return'] + 1) / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+    df['ctr'] = (df['lastonehour_play'] + 1) / (df['lastonehour_view'] + 100)
+    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
+    df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['ctr']
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def cal_score_with_back_view1(df, param):
+    # score = back_play_rate*log(return+1)*CTR,
+    # back_play_rate=(lastonehour_return+1)/(lastonehour_play+1000)
+    # CTR=(lastonehour_play+1)/(lastonehour_view+100), ctr不进行校正
+    df = df.fillna(0)
+    df['back_play_rate'] = (df['lastonehour_return'] + 1) / (df['lastonehour_play'] + 1000)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+    df['ctr'] = (df['lastonehour_play'] + 1) / (df['lastonehour_view'] + 100)
+    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
+    df['score'] = df['back_play_rate'] * df['log_back'] * df['ctr']
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def cal_score_with_back_rate_exponential_weighting1(df, param):
+    """
+    计算score
+    :param df: 特征数据
+    :param param: 规则参数
+    :return:
+    """
+    # score计算公式: score = sharerate * backrate ^ 2 * LOG(lastonehour_return + 1) * K2
+    # sharerate = lastonehour_share / (lastonehour_play + 1000)
+    # backrate = lastonehour_return / (lastonehour_share + 10)
+    # ctr = lastonehour_play / (lastonehour_show + 1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+    if param.get('view_type', None) == 'video-show':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
+    elif param.get('view_type', None) == 'video-show-region':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 1000)
+    else:
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+
+    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
+
+    df['score'] = df['share_rate'] * df['back_rate'] ** 2 * df['log_back'] * df['K2']
+
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def cal_score_with_back_rate_exponential_weighting2(df, param):
+    """
+    计算score
+    :param df: 特征数据
+    :param param: 规则参数
+    :return:
+    """
+    # score计算公式: score = sharerate ^ 0.5 * backrate ^ 2 * LOG(lastonehour_return + 1) * K2 ^ 0.5
+    # sharerate = lastonehour_share / (lastonehour_play + 1000)
+    # backrate = lastonehour_return / (lastonehour_share + 10)
+    # ctr = lastonehour_play / (lastonehour_show + 1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+    if param.get('view_type', None) == 'video-show':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
+    elif param.get('view_type', None) == 'video-show-region':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 1000)
+    else:
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+
+    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
+
+    df['score'] = df['share_rate'] ** 0.5 * df['back_rate'] ** 2 * df['log_back'] * df['K2'] ** 0.5
+
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+def cal_score_with_back_rate_by_rank_weighting(df, param):
+    """
+    add by sunmingze 20231123
+    计算score
+    :param df: 特征数据
+    :param param: 规则参数
+    :return:
+    """
+    # score计算公式: score =  1 / sharerate(rank)^0.5 + 5 / backrate(rank)^0.5 + 10 / LOG(lastonehour_return +1)(rank) ^0.5
+    #   +  1 / K2(rank)^0.5
+    # sharerate = lastonehour_share / (lastonehour_play + 1000)
+    # backrate = lastonehour_return / (lastonehour_share + 10)
+    # ctr = lastonehour_play / (lastonehour_show + 1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+    if param.get('view_type', None) == 'video-show':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
+    elif param.get('view_type', None) == 'video-show-region':
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 1000)
+    else:
+        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+
+    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
+
+    # 分别的得到sharerate、backrate、K值、return人数的序关系
+    df['rank_by_sharerate'] = df['share_rate'].rank(ascending=0, method='dense')
+    df['rank_by_backrate'] = df['back_rate'].rank(ascending=0, method='dense')
+    df['rank_by_K2'] = df['K2'].rank(ascending=0, method='dense')
+    df['rank_by_logback'] = df['log_back'].rank(ascending=0, method='dense')
+
+    # 计算基于序的加法关系函数
+    df['score'] = 1/(df['rank_by_sharerate'] + 10) + 5/(df['rank_by_backrate'] + 10)
+    df['score'] = df['score'] + 5/(df['rank_by_logback'] + 10) + 1/(df['rank_by_K2'] + 10)
+
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+
+
+def cal_score(df, param):
+    if param.get('return_data', None) == 'share_region_return':
+        if param.get('score_func', None) == 'multiply_return_retention':
+            df = cal_score_multiply_return_retention_with_new_return(df=df, param=param)
+        else:
+            df = cal_score_with_new_return(df=df, param=param)
+    else:
+        if param.get('score_func', None) == 'add_backrate*log(return+1)':
+            df = cal_score_add_return(df=df, param=param)
+        elif param.get('score_func', None) == 'multiply_return_retention':
+            df = cal_score_multiply_return_retention(df=df, param=param)
+        elif param.get('score_func', None) == 'update_backrate':
+            df = cal_score_update_backrate(df=df, param=param)
+        elif param.get('score_func', None) == 'back_view0':
+            df = cal_score_with_back_view0(df=df, param=param)
+        elif param.get('score_func', None) == 'back_view1':
+            df = cal_score_with_back_view1(df=df, param=param)
+        elif param.get('score_func', None) == 'back_rate_exponential_weighting1':
+            df = cal_score_with_back_rate_exponential_weighting1(df=df, param=param)
+        elif param.get('score_func', None) == 'back_rate_exponential_weighting2':
+            df = cal_score_with_back_rate_exponential_weighting2(df=df, param=param)
+        elif param.get('score_func', None) == 'back_rate_rank_weighting':
+            df = cal_score_with_back_rate_by_rank_weighting(df=df, param=param)
+        else:
+            df = cal_score_initial(df=df, param=param)
+    return df
+
+
+def add_func1(initial_df, pre_h_df):
+    """当前小时级数据与前几个小时数据合并"""
+    score_list = initial_df['score'].to_list()
+    if len(score_list) > 0:
+        min_score = min(score_list)
+    else:
+        min_score = 0
+    pre_h_df = pre_h_df[pre_h_df['score'] > min_score]
+    df = pd.concat([initial_df, pre_h_df], ignore_index=True)
+    # videoid去重，保留分值高
+    df['videoid'] = df['videoid'].astype(int)
+    df = df.sort_values(by=['score'], ascending=False)
+    df = df.drop_duplicates(subset=['videoid'], keep="first")
+    return df
+
+
+def add_func2(initial_df, pre_h_df):
+    """当前小时级数据与前几个小时数据合并: 当前小时存在的视频以当前小时为准，否则以高分为主"""
+    score_list = initial_df['score'].to_list()
+    if len(score_list) > 0:
+        min_score = min(score_list)
+    else:
+        min_score = 0
+    initial_video_id_list = initial_df['videoid'].to_list()
+    pre_h_df = pre_h_df[pre_h_df['score'] > min_score]
+    pre_h_df = pre_h_df[~pre_h_df['videoid'].isin(initial_video_id_list)]
+
+    df = pd.concat([initial_df, pre_h_df], ignore_index=True)
+    # videoid去重，保留分值高
+    df['videoid'] = df['videoid'].astype(int)
+    df = df.sort_values(by=['score'], ascending=False)
+    df = df.drop_duplicates(subset=['videoid'], keep="first")
+    return df
+
+
+def add_videos(initial_df, now_date, rule_key, region, data_key, hour_count, top, add_func):
+    """
+    地域小时级数据列表中增加前6h优质视频
+    :param initial_df: 地域小时级筛选结果
+    :param now_date:
+    :param data_key:
+    :param region:
+    :param rule_key:
+    :param hour_count: 前几个小时, type-int
+    :param top: type-int
+    :return: df
+    """
+    redis_helper = RedisHelper()
+    pre_h_data = []
+    for i in range(1, hour_count+1):
+        pre_date = now_date - datetime.timedelta(hours=i)
+        pre_h = pre_date.hour
+        pre_h_recall_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H}{region}:{data_key}:{rule_key}:" \
+                                f"{datetime.datetime.strftime(pre_date, '%Y%m%d')}:{pre_h}"
+        pre_h_top_data = redis_helper.get_data_zset_with_index(key_name=pre_h_recall_key_name,
+                                                               start=0, end=top-1,
+                                                               desc=True, with_scores=True)
+        if pre_h_top_data is None:
+            continue
+        pre_h_data.extend(pre_h_top_data)
+    pre_h_df = pd.DataFrame(data=pre_h_data, columns=['videoid', 'score'])
+    if add_func == 'func2':
+        df = add_func2(initial_df=initial_df, pre_h_df=pre_h_df)
+    else:
+        df = add_func1(initial_df=initial_df, pre_h_df=pre_h_df)
+    return df
+
+
+def video_rank(df, now_date, now_h, rule_key, param, region, data_key, rule_rank_h_flag,
+               add_videos_with_pre_h=False, hour_count=0):
+    """
+    获取符合进入召回源条件的视频，与每日更新的rov模型结果视频列表进行合并
+    :param df:
+    :param now_date:
+    :param now_h:
+    :param rule_key: 小时级数据进入条件
+    :param param: 小时级数据进入条件参数
+    :param region: 所属地域
+    :return:
+    """
+    redis_helper = RedisHelper()
+
+    # 获取符合进入召回源条件的视频，进入条件：小时级回流>=20 && score>=0.005
+    return_count = param.get('return_count', 1)
+    score_value = param.get('score_rule', 0)
+    platform_return_rate = param.get('platform_return_rate', 0)
+    h_recall_df = df[(df['lastonehour_return'] >= return_count) & (df['score'] >= score_value)
+                     & (df['platform_return_rate'] >= platform_return_rate)]
+
+    # videoid重复时，保留分值高
+    h_recall_df = h_recall_df.sort_values(by=['score'], ascending=False)
+    h_recall_df = h_recall_df.drop_duplicates(subset=['videoid'], keep='first')
+    h_recall_df['videoid'] = h_recall_df['videoid'].astype(int)
+
+    log_.info(f"各种规则过滤后，一共有多少个视频 = {len(h_recall_df)}")
+    # 增加打捞的优质视频
+    if add_videos_with_pre_h is True:
+        add_func = param.get('add_func', None)
+        h_recall_df = add_videos(initial_df=h_recall_df, now_date=now_date, rule_key=rule_key,
+                                 region=region, data_key=data_key, hour_count=hour_count, top=10, add_func=add_func)
+        log_.info(f"打捞优质视频完成")
+    h_recall_videos = h_recall_df['videoid'].to_list()
+    log_.info(f"各种规则增加后，一共有多少个视频 = {len(h_recall_videos)}")
+    # 视频状态过滤
+    if data_key in ['data7', ]:
+        filtered_videos = filter_video_status_app(h_recall_videos)
+    else:
+        filtered_videos = filter_video_status(h_recall_videos)
+
+    # 屏蔽视频过滤
+    shield_config = param.get('shield_config', config_.SHIELD_CONFIG)
+    shield_key_name_list = shield_config.get(region, None)
+    if shield_key_name_list is not None:
+        filtered_videos = filter_shield_video(video_ids=filtered_videos, shield_key_name_list=shield_key_name_list)
+
+    # 涉政视频过滤
+    political_filter = param.get('political_filter', None)
+    if political_filter is True:
+        filtered_videos = filter_political_videos(video_ids=filtered_videos)
+    log_.info(f"视频状态-涉政等-过滤后，一共有多少个视频 = {len(filtered_videos)}")
+
+
+    h_video_ids = []
+    by_30day_rule_key = param.get('30day_rule_key', None)
+    if by_30day_rule_key is not None:
+        # 与相对30天列表去重
+        h_video_ids = get_day_30day_videos(now_date=now_date, data_key=data_key, rule_key=by_30day_rule_key)
+        if h_video_ids is not None:
+            filtered_videos = [video_id for video_id in filtered_videos if int(video_id) not in h_video_ids]
+
+    # 写入对应的redis
+    h_recall_result = {}
+    for video_id in filtered_videos:
+        score = h_recall_df[h_recall_df['videoid'] == video_id]['score']
+        h_recall_result[int(video_id)] = float(score)
+        h_video_ids.append(int(video_id))
+    h_recall_key_name = \
+        f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H}{region}:{data_key}:{rule_key}:" \
+        f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+    log_.info("打印地域1小时的某个地域{},redis key:{}".format(region, h_recall_key_name))
+    if len(h_recall_result) > 0:
+        log_.info(f"开始写入头部数据：count = {len(h_recall_result)}, key = {h_recall_key_name}")
+        redis_helper.add_data_with_zset(key_name=h_recall_key_name, data=h_recall_result, expire_time=2 * 24 * 3600)
+        # 限流视频score调整
+        tmp = update_limit_video_score(initial_videos=h_recall_result, key_name=h_recall_key_name)
+        if tmp:
+            log_.info(f"走了限流逻辑后：count = {len(h_recall_result)}, key = {h_recall_key_name}")
+        else:
+            log_.info("走了限流逻辑，但没更改redis，未生效。")
+        # 清空线上过滤应用列表
+        # redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER}{region}.{app_type}.{data_key}.{rule_key}")
+    else:
+        log_.info(f"无数据，不写入。")
+
+    # h_rule_key = param.get('h_rule_key', None)
+    # region_24h_rule_key = param.get('region_24h_rule_key', 'rule1')
+    # by_24h_rule_key = param.get('24h_rule_key', None)
+    # by_48h_rule_key = param.get('48h_rule_key', None)
+    # dup_remove = param.get('dup_remove', True)
+    # # 与其他召回视频池去重，存入对应的redis
+    # dup_to_redis(h_video_ids=h_video_ids, now_date=now_date, now_h=now_h, rule_key=rule_key, h_rule_key=h_rule_key,
+    #              region_24h_rule_key=region_24h_rule_key, by_24h_rule_key=by_24h_rule_key,
+    #              by_48h_rule_key=by_48h_rule_key, region=region, data_key=data_key,
+    #              rule_rank_h_flag=rule_rank_h_flag, political_filter=political_filter,
+    #              shield_config=shield_config, dup_remove=dup_remove)
+
+
+def dup_data(h_video_ids, initial_key_name, dup_key_name, region, political_filter, shield_config, dup_remove):
+    redis_helper = RedisHelper()
+    if redis_helper.key_exists(key_name=initial_key_name):
+        initial_data = redis_helper.get_all_data_from_zset(key_name=initial_key_name, with_scores=True)
+        # 屏蔽视频过滤
+        initial_video_ids = [int(video_id) for video_id, _ in initial_data]
+        shield_key_name_list = shield_config.get(region, None)
+        if shield_key_name_list is not None:
+            initial_video_ids = filter_shield_video(video_ids=initial_video_ids,
+                                                    shield_key_name_list=shield_key_name_list)
+        # 涉政视频过滤
+        if political_filter is True:
+            initial_video_ids = filter_political_videos(video_ids=initial_video_ids)
+
+        dup_data = {}
+        # 视频去重逻辑
+        if dup_remove is True:
+            for video_id, score in initial_data:
+                if int(video_id) not in h_video_ids and int(video_id) in initial_video_ids:
+                    dup_data[int(video_id)] = score
+                    h_video_ids.append(int(video_id))
+        else:
+            for video_id, score in initial_data:
+                if int(video_id) in initial_video_ids:
+                    dup_data[int(video_id)] = score
+
+        if len(dup_data) > 0:
+            redis_helper.add_data_with_zset(key_name=dup_key_name, data=dup_data, expire_time=2 * 24 * 3600)
+            # 限流视频score调整
+            update_limit_video_score(initial_videos=dup_data, key_name=dup_key_name)
+    return h_video_ids
+
+
+def dup_to_redis(h_video_ids, now_date, now_h, rule_key, h_rule_key, region_24h_rule_key, by_24h_rule_key, by_48h_rule_key,
+                 region, data_key, rule_rank_h_flag, political_filter, shield_config, dup_remove):
+    """将地域分组小时级数据与其他召回视频池去重，存入对应的redis"""
+    # ##### 去重更新不区分地域小时级列表，并另存为redis中
+    if h_rule_key is not None:
+        h_key_name = \
+            f"{config_.RECALL_KEY_NAME_PREFIX_BY_H_H}{data_key}:{h_rule_key}:" \
+            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        h_dup_key_name = \
+            f"{config_.RECALL_KEY_NAME_PREFIX_DUP_H_H}{region}:{data_key}:{rule_key}:" \
+            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=h_key_name,
+                               dup_key_name=h_dup_key_name, region=region, political_filter=political_filter,
+                               shield_config=shield_config, dup_remove=dup_remove)
+
+    # ##### 去重更新地域分组小时级24h列表，并另存为redis中
+    region_24h_key_name = \
+        f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_24H}{region}:{data_key}:{region_24h_rule_key}:" \
+        f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+    region_24h_dup_key_name = \
+        f"{config_.RECALL_KEY_NAME_PREFIX_DUP1_REGION_24H_H}{region}:{data_key}:{rule_key}:" \
+        f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+    h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=region_24h_key_name,
+                           dup_key_name=region_24h_dup_key_name, region=region, political_filter=political_filter,
+                           shield_config=shield_config, dup_remove=dup_remove)
+
+    if rule_rank_h_flag == '48h':
+
+        # ##### 去重小程序相对48h更新结果，并另存为redis中
+        h_48h_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_48H}{data_key}:{by_48h_rule_key}:" \
+                         f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        h_48h_dup_key_name = \
+            f"{config_.RECALL_KEY_NAME_PREFIX_DUP2_REGION_48H_H}{region}:{data_key}:{rule_key}:" \
+            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=h_48h_key_name,
+                               dup_key_name=h_48h_dup_key_name, region=region, political_filter=political_filter,
+                               shield_config=shield_config, dup_remove=dup_remove)
+
+        # ##### 去重小程序相对48h 筛选后剩余数据 更新结果，并另存为redis中
+        if by_48h_rule_key == 'rule1':
+            other_h_48h_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_48H_OTHER}{data_key}:" \
+                                   f"{by_48h_rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+            other_h_48h_dup_key_name = \
+                f"{config_.RECALL_KEY_NAME_PREFIX_DUP3_REGION_48H_H}{region}:{data_key}:{rule_key}:" \
+                f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+            h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=other_h_48h_key_name,
+                                   dup_key_name=other_h_48h_dup_key_name, region=region,
+                                   political_filter=political_filter, shield_config=shield_config,
+                                   dup_remove=dup_remove)
+
+    else:
+        # ##### 去重小程序相对24h更新结果，并另存为redis中
+        h_24h_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H}{data_key}:{by_24h_rule_key}:" \
+                         f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        h_24h_dup_key_name = \
+            f"{config_.RECALL_KEY_NAME_PREFIX_DUP2_REGION_24H_H}{region}:{data_key}:{rule_key}:" \
+            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=h_24h_key_name,
+                               dup_key_name=h_24h_dup_key_name, region=region, political_filter=political_filter,
+                               shield_config=shield_config, dup_remove=dup_remove)
+
+        # ##### 去重小程序相对24h 筛选后剩余数据 更新结果，并另存为redis中
+        # if by_24h_rule_key in ['rule3', 'rule4']:
+        other_h_24h_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H_OTHER}{data_key}:" \
+                               f"{by_24h_rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        other_h_24h_dup_key_name = \
+            f"{config_.RECALL_KEY_NAME_PREFIX_DUP3_REGION_24H_H}{region}:{data_key}:{rule_key}:" \
+            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=other_h_24h_key_name,
+                               dup_key_name=other_h_24h_dup_key_name, region=region, political_filter=political_filter,
+                               shield_config=shield_config, dup_remove=dup_remove)
+
+    # ##### 去重小程序模型更新结果，并另存为redis中
+    # model_key_name = get_rov_redis_key(now_date=now_date)
+    # model_data_dup_key_name = \
+    #     f"{config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H}{region}:{data_key}:{rule_key}:" \
+    #     f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+    # h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=model_key_name,
+    #                        dup_key_name=model_data_dup_key_name, region=region)
+
+
+def merge_df(df_left, df_right):
+    """
+    df按照videoid, code 合并，对应特征求和
+    :param df_left:
+    :param df_right:
+    :return:
+    """
+    df_merged = pd.merge(df_left, df_right, on=['videoid', 'code'], how='outer', suffixes=['_x', '_y'])
+    df_merged.fillna(0, inplace=True)
+    feature_list = ['videoid', 'code']
+    for feature in features:
+        if feature in ['apptype', 'videoid', 'code']:
+            continue
+        df_merged[feature] = df_merged[f'{feature}_x'] + df_merged[f'{feature}_y']
+        feature_list.append(feature)
+    return df_merged[feature_list]
+
+
+def merge_df_with_score(df_left, df_right):
+    """
+    df 按照[videoid, code]合并，平台回流人数、回流人数、分数 分别求和
+    :param df_left:
+    :param df_right:
+    :return:
+    """
+    df_merged = pd.merge(df_left, df_right, on=['videoid', 'code'], how='outer', suffixes=['_x', '_y'])
+    df_merged.fillna(0, inplace=True)
+    feature_list = ['videoid', 'code', 'lastonehour_return', 'platform_return', 'score']
+    for feature in feature_list[2:]:
+        df_merged[feature] = df_merged[f'{feature}_x'] + df_merged[f'{feature}_y']
+    return df_merged[feature_list]
+
+
+def process_with_region(region, df_merged, data_key, rule_key, rule_param, now_date, now_h,
+                        rule_rank_h_flag, add_videos_with_pre_h, hour_count):
+    log_.info(f"多协程的region = {region} 开始执行")
+    region_df = df_merged[df_merged['code'] == region]
+    log_.info(f'该区域region = {region}, 下有多少数据量 = {len(region_df)}')
+    score_df = cal_score(df=region_df, param=rule_param)
+    video_rank(df=score_df, now_date=now_date, now_h=now_h, rule_key=rule_key, param=rule_param,
+               region=region, data_key=data_key, rule_rank_h_flag=rule_rank_h_flag,
+               add_videos_with_pre_h=add_videos_with_pre_h, hour_count=hour_count)
+    log_.info(f"多协程的region = {region} 完成执行")
+
+
+def process_with_region2(region, df_merged, data_key, rule_key, rule_param, now_date, now_h,
+                         rule_rank_h_flag, add_videos_with_pre_h, hour_count):
+    log_.info(f"region = {region} start...")
+    region_score_df = df_merged[df_merged['code'] == region]
+    log_.info(f'region = {region}, region_score_df count = {len(region_score_df)}')
+    video_rank(df=region_score_df, now_date=now_date, now_h=now_h, region=region,
+               rule_key=rule_key, param=rule_param, data_key=data_key, rule_rank_h_flag=rule_rank_h_flag,
+               add_videos_with_pre_h=add_videos_with_pre_h, hour_count=hour_count)
+    log_.info(f"region = {region} end!")
+
+
+def process_with_app_type(app_type, params, region_code_list, feature_df, now_date, now_h, rule_rank_h_flag):
+    log_.info(f"app_type = {app_type} start...")
+    data_params_item = params.get('data_params')
+    rule_params_item = params.get('rule_params')
+    task_list = []
+    for param in params.get('params_list'):
+        data_key = param.get('data')
+        data_param = data_params_item.get(data_key)
+        log_.info(f"data_key = {data_key}, data_param = {data_param}")
+        df_list = [feature_df[feature_df['apptype'] == apptype] for apptype in data_param]
+        df_merged = reduce(merge_df, df_list)
+
+        rule_key = param.get('rule')
+        rule_param = rule_params_item.get(rule_key)
+        log_.info(f"rule_key = {rule_key}, rule_param = {rule_param}")
+        task_list.extend(
+            [
+                gevent.spawn(process_with_region, region, df_merged, app_type, data_key, rule_key, rule_param,
+                             now_date, now_h, rule_rank_h_flag)
+                for region in region_code_list
+            ]
+        )
+    gevent.joinall(task_list)
+    log_.info(f"app_type = {app_type} end!")
+
+
+    # log_.info(f"app_type = {app_type}")
+    # task_list = []
+    # for data_key, data_param in params['data_params'].items():
+    #     log_.info(f"data_key = {data_key}, data_param = {data_param}")
+    #     df_list = [feature_df[feature_df['apptype'] == apptype] for apptype in data_param]
+    #     df_merged = reduce(merge_df, df_list)
+    #     for rule_key, rule_param in params['rule_params'].items():
+    #         log_.info(f"rule_key = {rule_key}, rule_param = {rule_param}")
+    #         task_list.extend(
+    #             [
+    #                 gevent.spawn(process_with_region, region, df_merged, app_type, data_key, rule_key, rule_param,
+    #                              now_date, now_h)
+    #                 for region in region_code_list
+    #             ]
+    #         )
+    # gevent.joinall(task_list)
+
+
+def copy_data_for_city(region, city_code, data_key, rule_key, now_date, now_h, shield_config):
+    """copy 对应数据到城市对应redis，并做相应屏蔽视频过滤"""
+    log_.info(f"city_code = {city_code} start ...")
+    redis_helper = RedisHelper()
+    key_prefix_list = [
+        config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H,  # 地域小时级
+        config_.RECALL_KEY_NAME_PREFIX_DUP1_REGION_24H_H,  # 地域相对24h
+        config_.RECALL_KEY_NAME_PREFIX_DUP2_REGION_24H_H,  # 不区分地域相对24h
+        config_.RECALL_KEY_NAME_PREFIX_DUP3_REGION_24H_H,  # 不区分地域相对24h筛选后
+        config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H,  # rov大列表
+    ]
+    for key_prefix in key_prefix_list:
+        region_key = f"{key_prefix}{region}:{data_key}:{rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        city_key = f"{key_prefix}{city_code}:{data_key}:{rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        if not redis_helper.key_exists(key_name=region_key):
+            continue
+        region_data = redis_helper.get_all_data_from_zset(key_name=region_key, with_scores=True)
+        if not region_data:
+            continue
+        # 屏蔽视频过滤
+        region_video_ids = [int(video_id) for video_id, _ in region_data]
+        shield_key_name_list = shield_config.get(city_code, None)
+        # shield_key_name_list = config_.SHIELD_CONFIG.get(city_code, None)
+        if shield_key_name_list is not None:
+            filtered_video_ids = filter_shield_video(video_ids=region_video_ids,
+                                                     shield_key_name_list=shield_key_name_list)
+        else:
+            filtered_video_ids = region_video_ids
+        city_data = {}
+        for video_id, score in region_data:
+            if int(video_id) in filtered_video_ids:
+                city_data[int(video_id)] = score
+
+        if len(city_data) > 0:
+            redis_helper.add_data_with_zset(key_name=city_key, data=city_data, expire_time=2 * 24 * 3600)
+
+    log_.info(f"city_code = {city_code} end!")
+
+
+def process_with_param(param, data_params_item, rule_params_item, region_code_list, feature_df, now_date, now_h, rule_rank_h_flag):
+    data_key = param.get('data')
+    data_param = data_params_item.get(data_key)
+    rule_key = param.get('rule')
+    rule_param = rule_params_item.get(rule_key)
+    merge_func = rule_param.get('merge_func', None)
+    log_.info("数据采用:{},统计采用{}.".format(data_key, rule_key))
+    log_.info("具体的规则是:{}.".format(rule_param))
+    # 是否在地域小时级数据中增加打捞的优质视频
+    add_videos_with_pre_h = rule_param.get('add_videos_with_pre_h', False)
+    hour_count = rule_param.get('hour_count', 0)
+
+    if merge_func == 2:
+        score_df_list = []
+        for apptype, weight in data_param.items():
+            df = feature_df[feature_df['apptype'] == apptype]
+            # 计算score
+            score_df = cal_score(df=df, param=rule_param)
+            score_df['score'] = score_df['score'] * weight
+            score_df_list.append(score_df)
+        # 分数合并
+        df_merged = reduce(merge_df_with_score, score_df_list)
+        # 更新平台回流比
+        df_merged['platform_return_rate'] = df_merged['platform_return'] / df_merged['lastonehour_return']
+        task_list = [
+            gevent.spawn(process_with_region2,
+                         region, df_merged, data_key, rule_key, rule_param, now_date, now_h, rule_rank_h_flag,
+                         add_videos_with_pre_h, hour_count)
+            for region in region_code_list
+        ]
+    else:
+        df_list = [feature_df[feature_df['apptype'] == apptype] for apptype in data_param]
+        df_merged = reduce(merge_df, df_list)
+        task_list = [
+            gevent.spawn(process_with_region,
+                         region, df_merged, data_key, rule_key, rule_param, now_date, now_h, rule_rank_h_flag,
+                         add_videos_with_pre_h, hour_count)
+            for region in region_code_list
+        ]
+
+    gevent.joinall(task_list)
+
+    # 特殊城市视频数据准备
+    # 屏蔽视频过滤
+    # shield_config = rule_param.get('shield_config', config_.SHIELD_CONFIG)
+    # for region, city_list in config_.REGION_CITY_MAPPING.items():
+    #     t = [
+    #         gevent.spawn(
+    #             copy_data_for_city,
+    #             region, city_code, data_key, rule_key, now_date, now_h, shield_config
+    #         )
+    #         for city_code in city_list
+    #     ]
+    #     gevent.joinall(t)
+    log_.info(f"多进程的 param = {param} 完成执行!")
+
+
+def rank_by_h(project, table, now_date, now_h, rule_params, region_code_list, rule_rank_h_flag):
+    # 获取特征数据
+    feature_df = get_feature_data(project=project, table=table, now_date=now_date)
+    feature_df['apptype'] = feature_df['apptype'].astype(int)
+    data_params_item = rule_params.get('data_params')
+    rule_params_item = rule_params.get('rule_params')
+    params_list = rule_params.get('params_list')
+    pool = multiprocessing.Pool(processes=len(params_list))
+    for param in params_list:
+        pool.apply_async(
+            func=process_with_param,
+            args=(param, data_params_item, rule_params_item, region_code_list, feature_df, now_date, now_h, rule_rank_h_flag)
+        )
+    pool.close()
+    pool.join()
+
+
+
+def h_bottom_process(param, rule_params_item, region_code_list, key_prefix, redis_dt, redis_h,
+                     now_date, now_h, rule_rank_h_flag):
+    redis_helper = RedisHelper()
+    data_key = param.get('data')
+    rule_key = param.get('rule')
+    rule_param = rule_params_item.get(rule_key)
+    log_.info(f"data_key = {data_key}, rule_key = {rule_key}, rule_param = {rule_param}")
+    h_rule_key = rule_param.get('h_rule_key', None)
+    region_24h_rule_key = rule_param.get('region_24h_rule_key', 'rule1')
+    by_24h_rule_key = rule_param.get('24h_rule_key', None)
+    by_48h_rule_key = rule_param.get('48h_rule_key', None)
+    # 涉政视频过滤
+    political_filter = param.get('political_filter', None)
+    # 屏蔽视频过滤
+    shield_config = param.get('shield_config', config_.SHIELD_CONFIG)
+    dup_remove = param.get('dup_remove', True)
+    for region in region_code_list:
+        log_.info(f"region = {region}")
+        key_name = f"{key_prefix}{region}:{data_key}:{rule_key}:{redis_dt}:{redis_h}"
+        initial_data = redis_helper.get_all_data_from_zset(key_name=key_name, with_scores=True)
+        if initial_data is None:
+            initial_data = []
+        final_data = dict()
+        h_video_ids = []
+        for video_id, score in initial_data:
+            final_data[video_id] = score
+            h_video_ids.append(int(video_id))
+        # 存入对应的redis
+        final_key_name = \
+            f"{key_prefix}{region}:{data_key}:{rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+        if len(final_data) > 0:
+            redis_helper.add_data_with_zset(key_name=final_key_name, data=final_data, expire_time=2 * 24 * 3600)
+        # 与其他召回视频池去重，存入对应的redis
+        dup_to_redis(h_video_ids=h_video_ids, now_date=now_date, now_h=now_h, rule_key=rule_key, h_rule_key=h_rule_key,
+                     region_24h_rule_key=region_24h_rule_key, region=region,
+                     data_key=data_key, by_24h_rule_key=by_24h_rule_key,
+                     by_48h_rule_key=by_48h_rule_key, rule_rank_h_flag=rule_rank_h_flag,
+                     political_filter=political_filter, shield_config=shield_config, dup_remove=dup_remove)
+    # 特殊城市视频数据准备
+    for region, city_list in config_.REGION_CITY_MAPPING.items():
+        t = [
+            gevent.spawn(
+                copy_data_for_city,
+                region, city_code, data_key, rule_key, now_date, now_h, shield_config
+            )
+            for city_code in city_list
+        ]
+        gevent.joinall(t)
+
+
+def h_rank_bottom(now_date, now_h, rule_params, region_code_list, rule_rank_h_flag):
+    """未按时更新数据，用上一小时结果作为当前小时的数据"""
+    # 获取rov模型结果
+    # redis_helper = RedisHelper()
+    if now_h == 0:
+        redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
+        redis_h = 23
+    else:
+        redis_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
+        redis_h = now_h - 1
+
+    # 以上一小时的地域分组数据作为当前小时的数据
+    key_prefix = config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H
+    rule_params_item = rule_params.get('rule_params')
+    params_list = rule_params.get('params_list')
+    pool = multiprocessing.Pool(processes=len(params_list))
+    for param in params_list:
+        pool.apply_async(
+            func=h_bottom_process,
+            args=(param, rule_params_item, region_code_list, key_prefix, redis_dt, redis_h, now_date, now_h, rule_rank_h_flag)
+        )
+    pool.close()
+    pool.join()
+
+def check_data(project, table, partition) -> int:
+    """检查数据是否准备好,输出数据条数"""
+    odps = ODPS(
+        access_id=config_.ODPS_CONFIG['ACCESSID'],
+        secret_access_key=config_.ODPS_CONFIG['ACCESSKEY'],
+        project=project,
+        endpoint=config_.ODPS_CONFIG['ENDPOINT'],
+        connect_timeout=3000,
+        read_timeout=500000,
+        pool_maxsize=1000,
+        pool_connections=1000
+    )
+    try:
+        t = odps.get_table(name=table)
+        check_res = t.exist_partition(partition_spec=f'dt={partition}')
+        if check_res:
+            sql = f'select * from {project}.{table} where dt = {partition}'
+            with odps.execute_sql(sql=sql).open_reader() as reader:
+                data_count = reader.count
+        else:
+            data_count = 0
+    except Exception as e:
+        log_.error("table:{},partition:{} no data. return data_count=0".format(table, partition))
+        data_count = 0
+    return data_count
+
+def get_table_data(project, table, partition) -> list[dict]:
+    """获取全部分区数据"""
+    records = get_data_from_odps(date=partition, project=project, table=table)
+    data = []
+    for record in records:
+        tmp = {}
+        for col_name in ["region", "videoid_array_sum", "videoid_array_avg"]:
+            tmp[col_name] = record[col_name]
+        data.append(tmp)
+    return data
+"""
+    数据表链接：https://dmc-cn-hangzhou.data.aliyun.com/dm/odps-table/odps.loghubods.alg_recsys_recall_strategy_trend/
+"""
+def h_timer_check():
+    try:
+        log_.info(f"开始执行: {datetime.datetime.strftime(datetime.datetime.today(), '%Y%m%d%H')}")
+        #1 判断数据表是否生产完成
+        project = "loghubods"
+        table = "alg_recsys_recall_strategy_trend"
+        partition = "dt=2023122019"
+        table_data_cnt = check_data(project, table, partition)
+        if table_data_cnt == 0:
+            log_.info("上游数据{}未就绪{}，等待...".format(table, partition))
+            Timer(60, h_timer_check).start()
+        else:
+            #2 读取数据表
+            data = get_table_data(project, table, partition)
+
+            #3 写入redis
+            redis_helper = RedisHelper()
+            redis_helper.add_data_with_zset(key_name=h_recall_key_name, data=h_recall_result, expire_time=2 * 24 * 3600)
+
+        region_code_list = [code for region, code in region_code.items()]
+        now_date = datetime.datetime.today()
+        now_h = datetime.datetime.now().hour
+        now_min = datetime.datetime.now().minute
+        if now_h == 0:
+            log_.info("当前时间{}小时，使用bottom的data，开始。".format(now_h))
+            h_rank_bottom(now_date=now_date, now_h=now_h, rule_params=rule_params, region_code_list=region_code_list,
+                          rule_rank_h_flag=rule_rank_h_flag)
+            log_.info("----------当前时间{}小时，使用bottom的data，完成----------".format(now_h))
+            return
+        # 查看当前小时更新的数据是否已准备好
+        h_data_count = h_data_check(project=project, table=table, now_date=now_date)
+        if h_data_count > 0:
+            log_.info('上游数据表查询数据条数 h_data_count = {}，开始计算。'.format(h_data_count))
+            # 数据准备好，进行更新
+            rank_by_h(now_date=now_date, now_h=now_h, rule_params=rule_params,
+                      project=project, table=table, region_code_list=region_code_list, rule_rank_h_flag=rule_rank_h_flag)
+            log_.info("数据1----------正常完成----------")
+        elif now_min > 40:
+            log_.info('当前分钟超过40，预计执行无法完成，使用 bottom data!')
+            h_rank_bottom(now_date=now_date, now_h=now_h, rule_params=rule_params, region_code_list=region_code_list,
+                          rule_rank_h_flag=rule_rank_h_flag)
+            log_.info('----------当前分钟超过40，使用bottom的data，完成----------')
+        else:
+            # 数据没准备好，1分钟后重新检查
+            log_.info("上游数据未就绪，等待...")
+            Timer(60, h_timer_check).start()
+
+    except Exception as e:
+        log_.error(f"4小时地域-趋势统计数据更新失败, exception: {e}, traceback: {traceback.format_exc()}")
+        send_msg_to_feishu(
+            webhook=config_.FEISHU_ROBOT['server_robot'].get('webhook'),
+            key_word=config_.FEISHU_ROBOT['server_robot'].get('key_word'),
+            msg_text=f"rov-offline{config_.ENV_TEXT} - 4小时地域-趋势统计数据更新失败\n"
+                     f"exception: {e}\n"
+                     f"traceback: {traceback.format_exc()}"
+        )
+
+
+if __name__ == '__main__':
+    log_.info("文件alg_recsys_recall_4h_region_trend.py:「4小时地域-趋势统计」 开始执行")
+    h_timer_check()

config.py

@@ -18,6 +18,7 @@ class BaseConfig(object):
         'PIAO_QUAN_VIDEO_PLUS': 21,  # 票圈视频+
         'JOURNEY': 22,  # 票圈足迹
         'BLESSING_YEAR': 3,  # 票圈福年
+        'PIAO_QUAN_BLESSING': 2,  # 票圈 | 祝福
     }
     # 数据存放路径
     DATA_DIR_PATH = './data'
@@ -207,14 +208,16 @@ class BaseConfig(object):
             APP_TYPE['VLOG']: 0.3,
             APP_TYPE['LOVE_LIVE']: 0.2,
             APP_TYPE['LONG_VIDEO']: 0.2,
-            APP_TYPE['SHORT_VIDEO']: 0.05,
-            APP_TYPE['WAN_NENG_VIDEO']: 0.05,
+            APP_TYPE['SHORT_VIDEO']: 0.1,
+            # APP_TYPE['WAN_NENG_VIDEO']: 1,
             # APP_TYPE['LAO_HAO_KAN_VIDEO']: 1,
             # APP_TYPE['ZUI_JING_QI']: 1,
             APP_TYPE['APP']: 0.05,
             APP_TYPE['PIAO_QUAN_VIDEO_PLUS']: 0.05,
             APP_TYPE['JOURNEY']: 0.05,
-            APP_TYPE['BLESSING_YEAR']: 0.05
+            APP_TYPE['BLESSING_YEAR']: 0.04,
+            APP_TYPE['PIAO_QUAN_BLESSING']: 0.01
+
         },
 
     }
@@ -879,6 +882,10 @@ class BaseConfig(object):
             'project': 'loghubods',
             'table': 'alg_recsys_user_info'
         },
+        'ad_out_v1_item': {
+            'project': 'loghubods',
+            'table': 'alg_recsys_video_info'
+        },
         'user_group': {
             'project': 'loghubods',
             'table': 'user_share_return_admodel'
@@ -2487,15 +2494,7 @@ class DevelopmentConfig(BaseConfig):
 
     # 测试环境 过滤用mysql地址
     FILTER_MYSQL_INFO = {
-        # 'host': 'am-bp1g3ys9u00u483uc131930.ads.aliyuncs.com',
-        # 'port': 3306,
-        # 'user': 'lv_manager',
-        # 'password': 'lv_manager@2020',
-        # 'db': 'longvideo',
-        # 'charset': 'utf8'
-
-        ##### test环境的filter mysql会过滤掉所有数据，测试时先使用pro的filter mysql。 注意测试结束后切换注释。
-        'host': 'am-bp15tqt957i3b3sgi131950.ads.aliyuncs.com',
+        'host': 'am-bp1g3ys9u00u483uc131930.ads.aliyuncs.com',
         'port': 3306,
         'user': 'lv_manager',
         'password': 'lv_manager@2020',
@@ -2579,20 +2578,18 @@ class TestConfig(BaseConfig):
 
     # 测试环境 过滤用mysql地址
     FILTER_MYSQL_INFO = {
-        # 'host': 'am-bp1g3ys9u00u483uc131930.ads.aliyuncs.com',
-        # 'port': 3306,
-        # 'user': 'lv_manager',
-        # 'password': 'lv_manager@2020',
-        # 'db': 'longvideo',
-        # 'charset': 'utf8'
-
-        ##### test环境的filter mysql会过滤掉所有数据，测试时先使用pro的filter mysql。 注意测试结束后切换注释。
-        'host': 'am-bp15tqt957i3b3sgi131950.ads.aliyuncs.com',
+        'host': 'am-bp1g3ys9u00u483uc131930.ads.aliyuncs.com',
         'port': 3306,
         'user': 'lv_manager',
         'password': 'lv_manager@2020',
         'db': 'longvideo',
         'charset': 'utf8'
+        # 'host': 'am-bp15tqt957i3b3sgi131950.ads.aliyuncs.com',
+        # 'port': 3306,
+        # 'user': 'lv_manager',
+        # 'password': 'lv_manager@2020',
+        # 'db': 'longvideo',
+        # 'charset': 'utf8'
     }
 
     # 日志服务配置