finance_data_process/house/house-building/house_building.py

"""不动产建筑数据处理
"""

# 导入必要的库
import re  # 正则表达式库，用于字符串处理
from datetime import datetime  # 日期时间库，用于处理日期和时间
from dateutil.relativedelta import relativedelta  # 日期时间相对偏移库，用于计算相对日期
from decimal import Decimal  # 高精度小数库，用于精确的数值计算

from loguru import logger  # 日志库，用于记录日志信息
import pandas as pd  # 数据分析库，用于处理数据框
import psycopg  # PostgreSQL数据库连接库，用于与PostgreSQL交互
import subprocess
import paramiko

# 配置日志记录器，将日志写入文件a.log
logger.add(sink='a.log')
ssh_hostname = '172.16.107.4'  # 定义远程主机地址
ssh_port = 22  # 定义SSH服务的端口号
ssh_username = 'app'  # 定义登录远程主机的用户名
ssh_password = '(l4w0ST_'  # 定义登录远程主机的密码
# 服务器文件夹路径
remote_dir_path = '/data/history/house/building/'
# 数据库连接信息
db_host = "172.16.107.5"  # 数据库主机地址
db_port = 5432         # 数据库端口号
db_username = "finance"  # 数据库用户名
db_password = "Finance@unicom23"  # 数据库密码
dbname = "financialdb"       # 数据库名称
conn_info = f"host='{db_host}' port={db_port} user='{db_username}' password='{db_password}' dbname='{dbname}'"
# 获取当前日期，并计算上个月的第一天
today = datetime.today()
start_date = today - relativedelta(months=1, day=1)
year_month = start_date.strftime('%Y%m')
# 数据文件路径
input_path = 'data.xlsx'
# 输出文件路径
output_path = 'output.csv'


def data_process():
    # 初始化全局变量，用于存储组织、区域等映射关系
    org_map = {}  # 组织ID到组织信息的映射
    third_org_list_map = {}  # 二级组织ID到其下属三级组织列表的映射
    area_map = {}  # 区域ID到区域信息的映射
    districts_list_map = {}  # 城市ID到其下属区县列表的映射

    # 连接到PostgreSQL数据库，并获取组织和区域数据
    with psycopg.connect(
            conninfo=conn_info,
            row_factory=psycopg.rows.dict_row
    ) as conn:
        with conn.cursor() as curs:
            # 查询所有一级组织（grade=1）
            sql = """
            select * from common.organization where grade = 1
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            second_orgs = curs.fetchall()
            for x in second_orgs:
                third_org_list_map[x['id']] = []  # 初始化每个二级组织的三级组织列表为空

            # 查询所有组织
            sql = """
            select * from common.organization
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            orgs = curs.fetchall()
            for x in orgs:
                if x['parent_id'] in third_org_list_map:
                    third_org_list_map[x['parent_id']].append(x)  # 将三级组织添加到对应的二级组织列表中
                org_map[x['id']] = x  # 构建组织ID到组织信息的映射

            # 查询所有省级区域（area_grade=1）
            sql = """
            select * from common.area where area_grade = 1 order by area_id
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            cities = curs.fetchall()
            for x in cities:
                districts_list_map[x['area_id']] = []  # 初始化每个城市的区县列表为空

            # 查询所有区域
            sql = """
            select * from common.area
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            areas = curs.fetchall()
            for x in areas:
                if x['parent_id'] in districts_list_map:
                    districts_list_map[x['parent_id']].append(x)  # 将区县添加到对应的城市列表中
                area_map[x['area_id']] = x  # 构建区域ID到区域信息的映射

    # 读取Excel文件中的数据并进行预处理
    df = pd.read_excel(io=input_path)  # 读取Excel文件
    # 获取当前 DataFrame 的列名列表
    columns = df.columns.tolist()
    # 定义所需的字段列表
    required_columns = ['资产所属单位(一级)', '资产所属单位(二级)', '资产所属单位(三级)', '建筑别名', '盘点状态',
                        '盘点情况', '是否修改', '建筑是否存在待核对信息', '楼层房间是否存在待核对信息', '建筑ID',
                        '得房率', '上级局址名称', '局址ID', '上级土地名称', '房屋来源', '取得日期', '房龄开始年份',
                        '投资主体', '管理层级', '房屋结构', '楼层总数', '是否临街', '是否有院落', '整栋是否独有',
                        '是否有房产证', '无房产证原因', '是否有资产卡片', '资产编号', '资产标签号', '使用状态',
                        '建筑用途', '权属状态', '建筑占地面积(㎡)', '建筑面积(㎡)', '建筑面积-自用(㎡)',
                        '建筑面积-出租(㎡)', '建筑面积-闲置(㎡)', '建筑面积-不可使用(㎡)', '使用面积(㎡)',
                        '使用面积-自用(㎡)', '使用面积-出租(㎡)', '使用面积-闲置(㎡)', '使用面积-不可使用(㎡)', '楼长姓名',
                        '楼长所在单位', '经度', '纬度', '标准地址', '实际产权人', '是否完成清查']
    # 检查是否有缺失的字段
    missing_columns = [col for col in required_columns if col not in columns]
    # 检查是否有多余的字段
    ex_columns = [col for col in columns if col not in required_columns]
    # 如果存在缺失字段，则抛出运行时错误并提示缺少哪些字段
    if missing_columns or ex_columns:
        raise RuntimeError(f"缺少以下字段: {missing_columns}；存在以下多余字段：{ex_columns}")
    df = df.map(lambda x: re.sub(r'\s+', '', x) if type(x) is str else x)  # 去除字符串字段中的多余空格
    df.drop_duplicates(subset=['建筑ID'], keep='last', inplace=True)  # 去重，保留最后一条记录

    # 定义函数：根据资产所属单位获取二级组织机构编码
    def get_area_no(x):
        second_unit = x['资产所属单位(二级)']
        third_unit = x['资产所属单位(三级)']
        if '河北' == second_unit:
            return '-12'
        if '长途通信传输局' == second_unit:
            return '-11'
        if '保定' in second_unit and ('雄县' in third_unit or '容城' in third_unit or '安新' in third_unit):
            return '782'
        for second_org in second_orgs:
            area_name = second_org['name']
            area_no = second_org['id']
            if area_name in second_unit:
                return area_no
        raise RuntimeError(f'二级组织机构编码匹配失败: {second_unit}')

    df['area_no'] = df.apply(get_area_no, axis=1)  # 应用函数，生成二级组织机构编码列

    # 定义函数：根据二级组织机构编码获取二级组织机构名称
    def get_area_name(x):
        area_no = x['area_no']
        second_org = org_map[area_no]
        area_name = second_org['name']
        return area_name

    df['area_name'] = df.apply(get_area_name, axis=1)  # 应用函数，生成二级组织机构名称列

    # 定义函数：根据资产所属单位获取三级组织机构编码
    def get_city_no(x):
        third_unit = x['资产所属单位(三级)']
        area_name = x['area_name']
        area_no = x['area_no']
        if area_name == '石家庄':
            if '矿区' in third_unit:
                return 'D0130185'
            if '井陉' in third_unit:
                return 'D0130121'
        if area_name == '秦皇岛':
            if '北戴河新区' in third_unit:
                return 'D0130185'
            if '北戴河' in third_unit:
                return 'D0130304'
        if area_name == '唐山':
            if '滦县' in third_unit:
                return 'D0130223'
            if '高新技术开发区' in third_unit:
                return 'D0130205'
        if area_name == '邢台':
            if '内丘' in third_unit:
                return 'D0130523'
            if '任泽' in third_unit:
                return 'D0130526'
        if area_name == '邯郸':
            if '峰峰' in third_unit:
                return 'D0130406'
        if area_name == '省机动局':
            if '沧州' in third_unit:
                return 'HECS180'
            if '唐山' in third_unit:
                return 'HECS181'
            if '秦皇岛' in third_unit:
                return 'HECS182'
            if '廊坊' in third_unit:
                return 'HECS183'
            if '张家口' in third_unit:
                return 'HECS184'
            if '邢台' in third_unit:
                return 'HECS185'
            if '邯郸' in third_unit:
                return 'HECS186'
            if '保定' in third_unit:
                return 'HECS187'
            if '石家庄' in third_unit:
                return 'HECS188'
            if '承德' in third_unit:
                return 'HECS189'
            if '衡水' in third_unit:
                return 'HECS720'
            if '雄安' in third_unit:
                return 'HECS728'
            return 'HECS018'
        if '雄安' == area_name:
            third_unit = third_unit.replace('雄安新区', '')
        third_org_list = third_org_list_map[area_no]
        for third_org in third_org_list:
            city_name = third_org['name']
            if city_name in third_unit:
                return third_org['id']
        if '沧州' == area_name:
            return 'D0130911'
        if '唐山' == area_name:
            return 'D0130202'
        if '秦皇岛' == area_name:
            return 'D0130302'
        if '廊坊' == area_name:
            return 'D0131000'
        if '张家口' == area_name:
            return 'D0130701'
        if '邢台' == area_name:
            return 'D0130502'
        if '邯郸' == area_name:
            return 'D0130402'
        if '保定' == area_name:
            return 'D0130601'
        if '石家庄' == area_name:
            return 'D0130186'
        if '承德' == area_name:
            return 'D0130801'
        if '衡水' == area_name:
            return 'D0133001'
        if '雄安' == area_name:
            return 'D0130830'
        return 'HE001'

    df['city_no'] = df.apply(get_city_no, axis=1)  # 应用函数，生成三级组织机构编码列

    # 定义函数：根据三级组织机构编码获取三级组织机构名称
    def get_city_name(x):
        city_no = x['city_no']
        third_org = org_map[city_no]
        city_name = third_org['name']
        return city_name

    df['city_name'] = df.apply(get_city_name, axis=1)  # 应用函数，生成三级组织机构名称列

    # 定义函数：根据标准地址获取城市ID
    def get_city_id(x):
        address = x['标准地址']
        second_unit = x['资产所属单位(二级)']
        third_unit = x['资产所属单位(三级)']
        if '雄安' in address or ('保定' in address and ('雄县' in address or '容城' in address or '安新' in address)):
            return '133100'
        for city in cities:
            area_name = city['short_name']
            area_id = city['area_id']
            if area_name in second_unit:
                return area_id
            if area_name in third_unit:
                return area_id
            if area_name in address:
                return area_id
        return ''

    df['city_id'] = df.apply(get_city_id, axis=1)  # 应用函数，生成城市ID列

    # 定义函数：根据城市ID获取城市名称
    def get_city(x):
        city_id = x['city_id']
        area = area_map.get(city_id)
        if pd.notna(area):
            city = area['area_name']
            return city
        return ''

    df['city'] = df.apply(get_city, axis=1)  # 应用函数，生成城市名称列

    # 定义函数：根据标准地址获取区县ID
    def get_district_id(x):
        address = x['标准地址']
        city = x['city']
        city_id = x['city_id']
        if pd.isna(city) or pd.isna(address):
            return ''
        if city == '石家庄':
            if '矿区' in address:
                return '130107'
            if '井陉' in address:
                return '130121'
        if city == '唐山':
            if '滦县' in address:
                return '130284'
        if city == '邢台':
            if '内邱' in address:
                return '130523'
            if '任县' in address:
                return '130505'
        if city == '雄安':
            address = address.replace('雄安新区', '')
        districts = districts_list_map.get(city_id)
        if not districts:
            return ''
        for district in districts:
            district_name = district['short_name']
            if district_name in address:
                return district['area_id']
        return ''

    df['district_id'] = df.apply(get_district_id, axis=1)  # 应用函数，生成区县ID列

    # 定义函数：根据区县ID获取区县名称
    def get_district(x):
        district_id = x['district_id']
        area = area_map.get(district_id)
        if pd.notna(area):
            district = area['area_name']
            return district
        return ''

    df['district'] = df.apply(get_district, axis=1)  # 应用函数，生成区县名称列

    # 定义函数：将百分比字符串转换为小数
    def convert_percentage_to_number(x):
        if pd.notna(x) and isinstance(x, str) and x.endswith('%'):
            return Decimal(x[:-1]) / Decimal('100')
        return x

    df['得房率'] = df['得房率'].apply(convert_percentage_to_number)  # 应用函数，将得房率转换为小数
    df['year_no'] = start_date.year  # 年份列
    df['month_no'] = start_date.month  # 月份列

    def get_int(x):
        try:
            return int(x)
        except Exception:
            return ""

    df['房龄开始年份'] = df['房龄开始年份'].apply(get_int)

    # 定义函数：计算房龄
    def get_house_age(x):
        house_year_began = x['房龄开始年份']
        if pd.notna(house_year_began) and house_year_began:
            current_year = start_date.year
            return current_year - house_year_began
        return ''

    df['house_age'] = df.apply(get_house_age, axis=1)  # 应用函数，生成房龄列
    df.insert(0, 'year_month', year_month)  # 在数据框第一列插入年月列
    df.rename(
        columns={'资产所属单位(一级)': 'first_unit', '资产所属单位(二级)': 'second_unit', '资产所属单位(三级)': 'third_unit',
                 '建筑别名': 'building_name', '盘点状态': 'inventory_status', '盘点情况': 'inventory_situation',
                 '是否修改': 'modify', '建筑是否存在待核对信息': 'building_to_be_verified',
                 '楼层房间是否存在待核对信息': 'floor_room_to_be_verified', '建筑ID': 'building_id',
                 '得房率': 'housing_acquisition_rate', '上级局址名称': 'site_name', '局址ID': 'site_id',
                 '上级土地名称': 'land_name', '房屋来源': 'housing_source', '取得日期': 'acquisition_date',
                 '房龄开始年份': 'house_year_began', '投资主体': 'investor', '管理层级': 'management_level',
                 '房屋结构': 'building_structure', '楼层总数': 'total_floors', '是否临街': 'frontage',
                 '是否有院落': 'courtyard', '整栋是否独有': 'whole_building',
                 '是否有房产证': 'property_ownership_certificate',
                 '无房产证原因': 'no_property_ownership_certificate_reason', '是否有资产卡片': 'has_asset_card',
                 '资产编号': 'assets_num', '资产标签号': 'assets_tag_num', '使用状态': 'usage_status',
                 '建筑用途': 'building_use', '权属状态': 'ownership_status', '建筑占地面积(㎡)': 'floor_area',
                 '建筑面积(㎡)': 'building_area', '建筑面积-自用(㎡)': 'building_area_self_use',
                 '建筑面积-出租(㎡)': 'building_area_rent', '建筑面积-闲置(㎡)': 'building_area_idle',
                 '建筑面积-不可使用(㎡)': 'building_area_unusable', '使用面积(㎡)': 'usable_area',
                 '使用面积-自用(㎡)': 'usable_area_self_use', '使用面积-出租(㎡)': 'usable_area_rent',
                 '使用面积-闲置(㎡)': 'usable_area_idle', '使用面积-不可使用(㎡)': 'usable_area_unusable',
                 '楼长姓名': 'community_assistant_name', '楼长所在单位': 'community_assistant_unit', '经度': 'lng_jt',
                 '纬度': 'lat_jt', '标准地址': 'address', '实际产权人': 'property_owner', '是否完成清查': 'checked'}, inplace=True)
    df = df[['year_month', 'first_unit', 'second_unit', 'third_unit', 'building_name', 'inventory_status',
             'inventory_situation', 'modify', 'building_to_be_verified', 'floor_room_to_be_verified', 'building_id',
             'housing_acquisition_rate', 'site_name', 'site_id', 'land_name', 'housing_source', 'acquisition_date',
             'house_year_began', 'investor', 'management_level', 'building_structure', 'total_floors', 'frontage',
             'courtyard', 'whole_building', 'property_ownership_certificate',
             'no_property_ownership_certificate_reason', 'has_asset_card', 'assets_num', 'assets_tag_num',
             'usage_status', 'building_use', 'ownership_status', 'floor_area', 'building_area',
             'building_area_self_use', 'building_area_rent', 'building_area_idle', 'building_area_unusable',
             'usable_area', 'usable_area_self_use', 'usable_area_rent', 'usable_area_idle', 'usable_area_unusable',
             'community_assistant_name', 'community_assistant_unit', 'lng_jt', 'lat_jt', 'address',
             'property_owner', 'checked', 'area_no', 'area_name', 'city_no', 'city_name', 'city_id', 'city',
             'district_id', 'district', 'year_no', 'month_no', 'house_age']]
    # 打印数据框信息
    df.info()

    # 将结果保存为CSV文件
    df.to_csv(path_or_buf=output_path, index=False, encoding='utf-8-sig', lineterminator='\n')


def data_import():
    # 定义 PowerShell 脚本的路径
    script_path = r"../../copy.ps1"
    # 目标表和文件信息
    table = "house.building_month"  # 数据库目标表名
    # 表字段列名，用于指定导入数据的列顺序
    columns = "year_month,first_unit,second_unit,third_unit,building_name,inventory_status,inventory_situation,modify,building_to_be_verified,floor_room_to_be_verified,building_id,housing_acquisition_rate,site_name,site_id,land_name,housing_source,acquisition_date,house_year_began,investor,management_level,building_structure,total_floors,frontage,courtyard,whole_building,property_ownership_certificate,no_property_ownership_certificate_reason,has_asset_card,assets_num,assets_tag_num,usage_status,building_use,ownership_status,floor_area,building_area,building_area_self_use,building_area_rent,building_area_idle,building_area_unusable,usable_area,usable_area_self_use,usable_area_rent,usable_area_idle,usable_area_unusable,community_assistant_name,community_assistant_unit,lng_jt,lat_jt,address,property_owner,checked,area_no,area_name,city_no,city_name,city_id,city,district_id,district,year_no,month_no,house_age"
    # 构造执行 PowerShell 脚本的命令
    command = f"powershell -NoProfile -NonInteractive -File {script_path} -db_host {db_host} -db_port {db_port} -db_username {db_username} -db_password {db_password} -dbname {dbname} -table {table} -filename {output_path} -columns {columns}"
    # 打印生成的命令，方便调试和日志记录
    logger.info("command: {}", command)
    # 使用 subprocess 模块运行 PowerShell 命令，并捕获输出
    completed_process = subprocess.run(
        command,  # 执行的命令
        check=False,  # 如果命令执行失败，不抛出异常
        text=True,  # 将输出作为字符串处理
        capture_output=True,  # 捕获标准输出和标准错误
    )
    # 打印命令执行的结果，包括返回码、标准输出和标准错误
    logger.info("导入结果：\n{}\n{}\n{}", completed_process.returncode, completed_process.stdout,
                completed_process.stderr)
    # 定义正则表达式，用于匹配标准输出中的 COPY 结果
    p = re.compile(r"^(COPY) (\d+)$")
    count = None  # 初始化计数变量
    matcher = p.match(completed_process.stdout)  # 匹配标准输出中的 COPY 结果
    if matcher:
        count = int(matcher.group(2))  # 提取导入的数据行数
    # 如果没有成功提取到导入数据的行数，抛出运行时异常
    if count is None:
        raise RuntimeError("导入数据失败")


def upload_file():
    remote_path = f'{remote_dir_path}{year_month}.xlsx'  # 定义远程主机的目标文件路径
    # 使用paramiko.SSHClient创建一个SSH客户端对象，并通过with语句管理其上下文
    with paramiko.SSHClient() as ssh:
        # 设置自动添加主机密钥策略，避免因未知主机密钥导致连接失败
        ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
        # 连接到远程主机，传入主机地址、端口、用户名和密码
        ssh.connect(ssh_hostname, port=ssh_port, username=ssh_username, password=ssh_password)
        # 执行远程命令，创建远程目录（如果不存在）
        ssh.exec_command(f'mkdir -p {remote_dir_path}')
        # 打开SFTP会话，用于文件传输，并通过with语句管理其上下文
        with ssh.open_sftp() as sftp:
            # 记录日志，提示即将上传的本地文件和远程目标路径
            logger.info("upload {} to {}", input_path, remote_path)
            # 使用SFTP的put方法将本地文件上传到远程主机
            sftp.put(input_path, remote_path)
            # 记录日志，提示文件已成功上传
            logger.info("uploaded {}", input_path)


def data_update():
    with psycopg.connect(
            conninfo=conn_info,
    ) as conn:
        with conn.cursor() as curs:
            # 更新局址信息
            sql = f"""
            update
                house.building_month a
            set
                site_num = b.site_num,
                city_level = b.city_level,
                city_region = b.city_region,
                area_sector = b.area_sector,
                has_land = b.has_land
            from
                house.site_month b
            where
                a.site_id = b.site_id
                and a.year_month = b.year_month
                and a.year_month = {year_month}
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            logger.info(f"update {curs.rowcount}")

            # 更新经纬度
            sql = f"""
            with
            t101 as (
            select
                *
            from
                house.building_month
            where
                year_month = 202312
            )
            update
                house.building_month a
            set
                lng_wgs84 = b.lng_wgs84,
                lat_wgs84 = b.lat_wgs84,
                lng_bd09 = b.lng_bd09,
                lat_bd09 = b.lat_bd09,
                building_img = b.building_img
            from
                t101 b
            where
                a.year_month = {year_month}
                and a.building_id = b.building_id
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            logger.info(f"update {curs.rowcount}")

            # 更新闲置建筑面积超过1000平米策略
            sql = f"""
            insert
                into
                house.building_idle_strategy
            (
                year_month,
                building_id,
                first_unit,
                second_unit,
                third_unit,
                site_num,
                site_name,
                address,
                city_level,
                city_region,
                area_sector,
                has_land,
                site_id,
                building_name,
                housing_acquisition_rate,
                housing_source,
                acquisition_date,
                house_year_began,
                investor,
                management_level,
                building_structure,
                total_floors,
                assets_num,
                assets_tag_num,
                usage_status,
                building_use,
                ownership_status,
                floor_area,
                building_area,
                building_area_self_use,
                building_area_rent,
                building_area_idle,
                building_area_unusable,
                usable_area,
                usable_area_self_use,
                usable_area_rent,
                usable_area_idle,
                usable_area_unusable,
                city,
                district,
                lng_wgs84,
                lat_wgs84,
                lng_bd09,
                lat_bd09,
                building_img,
                area_no,
                area_name,
                city_no,
                city_name,
                year_no,
                month_no,
                house_age,
                land_name,
                frontage,
                courtyard,
                whole_building,
                property_ownership_certificate,
                no_property_ownership_certificate_reason,
                unrelated_assets,
                community_assistant_name,
                community_assistant_unit,
                lng_jt,
                lat_jt,
                property_owner,
                checked,
                city_id,
                district_id
            )
            select
                year_month,
                building_id,
                first_unit,
                second_unit,
                third_unit,
                site_num,
                site_name,
                address,
                city_level,
                city_region,
                area_sector,
                has_land,
                site_id,
                building_name,
                housing_acquisition_rate,
                housing_source,
                acquisition_date,
                house_year_began,
                investor,
                management_level,
                building_structure,
                total_floors,
                assets_num,
                assets_tag_num,
                usage_status,
                building_use,
                ownership_status,
                floor_area,
                building_area,
                building_area_self_use,
                building_area_rent,
                building_area_idle,
                building_area_unusable,
                usable_area,
                usable_area_self_use,
                usable_area_rent,
                usable_area_idle,
                usable_area_unusable,
                city,
                district,
                lng_wgs84,
                lat_wgs84,
                lng_bd09,
                lat_bd09,
                building_img,
                area_no,
                area_name,
                city_no,
                city_name,
                year_no,
                month_no,
                house_age,
                land_name,
                frontage,
                courtyard,
                whole_building,
                property_ownership_certificate,
                no_property_ownership_certificate_reason,
                unrelated_assets,
                community_assistant_name,
                community_assistant_unit,
                lng_jt,
                lat_jt,
                property_owner,
                checked,
                city_id,
                district_id
            from
                house.building_month
            where
                building_area_idle > 1000
                and year_month = {year_month}
            order by
                building_area_idle desc
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            logger.info(f"update {curs.rowcount}")

            sql = f"""
            with
            t101 as (
            select
                *,
                row_number() over (
                order by building_area_idle desc) as sort
            from
                house.building_idle_strategy
            where
                year_month = {year_month}
            ),
            t201 as (
            select
                area_no,
                area_name,
                city_no,
                city_name,
                'kpi_301320_155_01' as kpi_code,
                '闲置建筑面积' as kpi_name,
                round(building_area_idle, 2)::varchar as kpi_value,
                '1' as kpi_type,
                building_id as jk_object_no,
                building_name as jk_object,
                sort
            from
                t101
            ),
            t202 as (
            select
                area_no,
                area_name,
                city_no,
                city_name,
                'kpi_301320_155_02' as kpi_code,
                '房产名称' as kpi_name,
                building_name as kpi_value,
                '0' as kpi_type,
                building_id as jk_object_no,
                building_name as jk_object,
                sort
            from
                t101
            ),
            t203 as (
            select
                area_no,
                area_name,
                city_no,
                city_name,
                'kpi_301320_155_03' as kpi_code,
                '房产编号' as kpi_name,
                building_id as kpi_value,
                '0' as kpi_type,
                building_id as jk_object_no,
                building_name as jk_object,
                sort
            from
                t101
            ),
            t204 as (
            select
                area_no,
                area_name,
                city_no,
                city_name,
                'kpi_301320_155_04' as kpi_code,
                '房产总建筑面积' as kpi_name,
                round(building_area, 2)::varchar as kpi_value,
                '0' as kpi_type,
                building_id as jk_object_no,
                building_name as jk_object,
                sort
            from
                t101
            ),
            t301 as (
            select
                *
            from
                t201
            union all
            select
                *
            from
                t202
            union all
            select
                *
            from
                t203
            union all
            select
                *
            from
                t204
            )
            insert
                into
                publish.house_building_idle_strategy
            (
                acct_date,
                dept_code,
                dept_name,
                strategy_code,
                area_no,
                area_name,
                city_no,
                city_name,
                sale_no,
                sale_name,
                jk_object_no,
                jk_object,
                kpi_code,
                kpi_name,
                kpi_value,
                kpi_type,
                sort
            )
            select
                {year_month} as acct_date,
                '301320' as dept_code,
                '河北省分公司纵横运营中心' as dept_name,
                '301320_155' as strategy_code,
                area_no,
                area_name,
                city_no,
                city_name,
                '' as sale_no,
                '' as sale_name,
                jk_object_no,
                jk_object,
                kpi_code,
                kpi_name,
                kpi_value,
                kpi_type,
                sort
            from
                t301
            order by
                sort,
                kpi_code
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            logger.info(f"update {curs.rowcount}")


data_process()
data_import()
upload_file()
data_update()