146 lines
5.2 KiB
Python
146 lines
5.2 KiB
Python
import os
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import re
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import time
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import json
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import pandas as pd
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from Board.board_db import FIND_DATA_COUNT, FIND_DATA, UPSERT_DATA, FIND_DATA_FILTER, FIND_DATA_FILTER_SORT_LIMIT, \
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REMOVE_COLLECTION, INSERT_DATA_MANY, INSERT_DATA
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from DBHelper.MongoHelper import MongoHelper
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def get_monitor_data_impl():
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db = MongoHelper("tfse_v0.21")
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monitor_data = dict()
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def log_data_impl():
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monitor_data['异常日志'] = 0
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def feedback_data_impl():
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monitor_data['留言反馈'] = 0
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def services_data_impl():
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monitor_data['服务次数'] = db.find_all_data_with_count(
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"企业数据",
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"评价记录",
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{"进行状态": "完成"}
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)
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def verified_company_impl():
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monitor_data['认证企业'] = db.find_all_data_with_count(
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"应用端",
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"企业用户",
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{"已认证": "是"}
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)
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log_data_impl()
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feedback_data_impl()
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services_data_impl()
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verified_company_impl()
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return monitor_data
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def get_rating_static_impl():
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db = MongoHelper("tfse_v0.21")
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# 日期正则表达式 20xx-xx-xx格式
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# data_regex = "((((19|20)\d{2})-(0?(1|[3-9])|1[012])-(0?[1-9]|[12]\d|30))|(((19|20)\d{2})-(0?[13578]|1[02])-31)|(((19|20)\d{2})-0?2-(0?[1-9]|1\d|2[0-8]))|((((19|20)([13579][26]|[2468][048]|0[48]))|(2000))-0?2-29))$"
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# 日期匹配结果
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# match_result = re.match(data_regex, start)
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# 若没有匹配到需求格式日期，则默认查询起始日期是30天以前
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# if not match_result:
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# start = time.strftime("%Y-%m-%d", time.localtime(time.time() - 60*60*24*30))
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# 默认查询起始日期是30天以前
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start = time.strftime("%Y-%m-%d", time.localtime(time.time() - 60*60*24*30))
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records = db.find_data_with_aggregate(
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"企业数据",
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"评价记录",
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[
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{"$match": {"进行状态": "完成", "评价完成日期": {"$gte": start}}},
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{"$group": {"_id": "$评价完成日期", "count": {"$sum": 1}}},
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{"$sort": {"_id": 1}}
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]
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)
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# 聚类统计用的_id字段更名为date
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for data in records:
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data['date'] = data.pop('_id')
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return records
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def static_dashboard_data_impl():
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def monitor_data():
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"""
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监测数据
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"""
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data = dict()
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data['认证企业'] = FIND_DATA_COUNT("TFSE", "用户", "用户信息", {"已认证": "是"})
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data['服务次数'] = FIND_DATA_COUNT("TFSE", "评价", "评价记录", {"进行状态": "完成"})
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data['留言反馈'] = 0
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data['异常日志'] = 0
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REMOVE_COLLECTION('ADMIN', '看板', '监测数据')
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INSERT_DATA("ADMIN", "看板", "监测数据", data)
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def services_static():
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"""
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服务次数
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delta_seconds 统计服务次数的时间范围
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start_date_time_stamp 统计起始时间的时间戳
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start_date 统计起始日期
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"""
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delta_seconds = 60*60*24*365
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start_date_time_stamp = time.time() - delta_seconds
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start_date = time.strftime("%Y-%m-%d", time.localtime(start_date_time_stamp))
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records = FIND_DATA("TFSE", "评价", "评价记录", {"进行状态": "完成", "评价时间": {"$gt": start_date}})
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df_rating_times = pd.DataFrame(pd.DataFrame(records)[['评价时间']].value_counts()).sort_index()
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times_statics_by_date = dict(zip([tuple_date[0] for tuple_date in df_rating_times.index.values.tolist()], df_rating_times[0].tolist()))
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for item in times_statics_by_date.items():
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data = dict()
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data['日期'] = item[0]
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data['服务次数'] = item[1]
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UPSERT_DATA("ADMIN", "看板", "服务统计", {"日期": item[0]}, data)
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def industry_distribute():
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"""
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行业分布
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"""
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with open(os.path.abspath(os.path.dirname(__file__)+'/static/tyc_industry_II_to_I.json'), encoding='utf8') as f:
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industry_refers = json.load(f)
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records = FIND_DATA_FILTER("TFSE", "企业", "公司基本信息", {}, {"工商信息.行业": 1})
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df_industry = pd.DataFrame([record['工商信息'] for record in records])
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df_industry['行业'] = df_industry['行业'].apply(lambda x: industry_refers[x])
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df_industry_count = df_industry['行业'].value_counts()
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dict_industry = dict(zip(df_industry_count.index.tolist(), df_industry_count.values.tolist()))
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for item in dict_industry.items():
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data = dict()
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data['行业名称'] = item[0]
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data['公司数量'] = item[1]
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UPSERT_DATA("ADMIN", "看板", "行业分布", {"行业名称": item[0]}, data)
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def new_company_info():
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"""
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新增企业信息
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"""
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records = FIND_DATA_FILTER_SORT_LIMIT("TFSE", "用户", "用户信息", {"已认证": "是"}, {"企业ID": 1, "企业名称": 1, "注册时间": 1}, [("注册时间", -1)], 10)
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REMOVE_COLLECTION('ADMIN', '看板', '新增企业信息')
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INSERT_DATA_MANY('ADMIN', '看板', '新增企业信息', records)
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def main_process():
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monitor_data()
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services_static()
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industry_distribute()
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new_company_info()
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return 0
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return main_process()
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