finance_data_process/house/house-fang-jian/house_fang_jian.py

"""不动产房间数据处理
"""

import re
from datetime import datetime
from dateutil.relativedelta import relativedelta
from loguru import logger
import pandas as pd
import psycopg
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/room/'
# 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():
    org_map = {}  # 用于存储组织机构信息
    third_org_list_map = {}  # 用于存储三级组织机构列表
    area_map = {}  # 用于存储区域信息
    districts_list_map = {}  # 用于存储区县信息

    # 连接到 PostgreSQL 数据库
    with psycopg.connect(
            conninfo=conn_info,
            row_factory=psycopg.rows.dict_row  # 设置返回结果为字典格式
    ) as conn:
        with conn.cursor() as curs:
            # 查询所有二级组织机构（grade=1）
            sql = """
            select * from common.organization where grade = 1
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            second_orgs = curs.fetchall()
            # 初始化 third_org_list_map，以每个二级组织的 ID 为键，值为空列表
            for x in second_orgs:
                third_org_list_map[x['id']] = []

            # 查询所有组织机构信息
            sql = """
            select * from common.organization
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            orgs = curs.fetchall()
            # 构建 org_map 和 third_org_list_map
            for x in orgs:
                if x['parent_id'] in third_org_list_map:
                    third_org_list_map[x['parent_id']].append(x)
                org_map[x['id']] = x

            # 查询所有一级行政区划（area_grade=1），并按 area_id 排序
            sql = """
            select * from common.area where area_grade = 1 order by area_id
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            cities = curs.fetchall()
            # 初始化 districts_list_map，以每个城市的 area_id 为键，值为空列表
            for x in cities:
                districts_list_map[x['area_id']] = []

            # 查询所有区域信息
            sql = """
            select * from common.area
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            areas = curs.fetchall()
            # 构建 area_map 和 districts_list_map
            for x in areas:
                if x['parent_id'] in districts_list_map:
                    districts_list_map[x['parent_id']].append(x)
                area_map[x['area_id']] = x

    # 读取 Excel 文件中的数据
    df = pd.read_excel(io=input_path)
    # 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)', '建筑名称', '标准地址',
                        '所在楼层', '楼层建筑总面积（㎡）', '楼层使用总面积（㎡）', '房间名称', '房间状态', '租赁类别',
                        '房间类别(一级)', '房间类别(二级)', '工位总数', '是否临街', '建筑面积（㎡）', '建筑面积-自用（㎡）',
                        '建筑面积-闲置（㎡）', '建筑面积-出租（㎡）', '建筑面积-不可使用（㎡）', '使用面积（㎡）',
                        '使用面积-自用（㎡）', '使用面积-闲置（㎡）', '使用面积-出租（㎡）', '使用面积-不可使用（㎡）',
                        '闲置开始时间', '不可使用原因', '层高（m）', '承重（kg/㎡）']
    # 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}")
    # 清理 DataFrame 中的空白字符（排除特定列）
    columns_to_clean = list(filter(lambda x: x not in ('房间名称'), df.columns))
    df[columns_to_clean] = df[columns_to_clean].map(
        lambda x: re.sub(r'\s+', '', x) if type(x) is str else x
    )

    # 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}')

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

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

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

    # 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'

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

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

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

    # 定义函数：根据地址和资产所属单位获取城市 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 or area_name in third_unit or area_name in address:
                return area_id
        return ''

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

    # 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 ''

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

    # 定义函数：根据地址和城市信息获取区县 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 ''

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

    # 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 ''

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

    def get_int(x):
        try:
            return int(x)
        except Exception:
            logger.warning(f"转换失败: {x}")
            return ""

    df['工位总数'] = df['工位总数'].apply(get_int)
    # 在 DataFrame 中插入年月列
    df.insert(0, 'year_month', year_month)
    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',
                 '建筑名称': 'building_name', '标准地址': 'address', '所在楼层': 'floor',
                 '楼层建筑总面积（㎡）': 'floor_building_area',
                 '楼层使用总面积（㎡）': 'floor_usable_area', '房间名称': 'room_name', '房间状态': 'room_status',
                 '租赁类别': 'rent_type', '房间类别(一级)': 'first_room_type', '房间类别(二级)': 'second_room_type',
                 '工位总数': 'seat_num', '是否临街': 'frontage', '建筑面积（㎡）': 'building_area',
                 '建筑面积-自用（㎡）': 'building_area_self_use', '建筑面积-闲置（㎡）': 'building_area_idle',
                 '建筑面积-出租（㎡）': 'building_area_rent', '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-闲置（㎡）': 'usable_area_idle', '使用面积-出租（㎡）': 'usable_area_rent',
                 '使用面积-不可使用（㎡）': 'usable_area_unusable', '闲置开始时间': 'idle_start_date',
                 '不可使用原因': 'unusable_reason', '层高（m）': 'floor_height', '承重（kg/㎡）': 'load_bearing'},
        inplace=True)
    df = df[['year_month', 'first_unit', 'second_unit', 'third_unit', 'building_name', 'address', 'floor',
             'floor_building_area', 'floor_usable_area', 'room_name', 'room_status', 'rent_type',
             'first_room_type', 'second_room_type', 'seat_num', 'frontage', 'building_area',
             'building_area_self_use', 'building_area_idle', 'building_area_rent', 'building_area_unusable',
             'usable_area', 'usable_area_self_use', 'usable_area_idle', 'usable_area_rent', 'usable_area_unusable',
             'idle_start_date', 'unusable_reason', 'floor_height', 'load_bearing', 'area_no', 'area_name',
             'city_no', 'city_name', 'city_id', 'city', 'district_id', 'district']]
    # 打印 DataFrame 的信息
    df.info()

    # 将处理后的数据保存为 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.room_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,address,floor,floor_building_area,floor_usable_area,room_name,room_status,rent_type,first_room_type,second_room_type,seat_num,frontage,building_area,building_area_self_use,building_area_idle,building_area_rent,building_area_unusable,usable_area,usable_area_self_use,usable_area_idle,usable_area_rent,usable_area_unusable,idle_start_date,unusable_reason,floor_height,load_bearing,area_no,area_name,city_no,city_name,city_id,city,district_id,district"
    # 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:
            # 更新人均办公面积
            sql = f"""
            with
            t100 as (
            select
                id as area_no,
                name as area_name,
                order_num as area_order
            from
                common.organization
            where
                id in ('-11', '-12')
            ),
            t101 as (
            select
                area_no,
                sum(building_area_self_use) as building_area_self_use_sum
            from
                house.room_month
            where
                second_room_type = '办公用房'
                and year_month = {year_month}
                and area_no in ('-11', '-12')
            group by
                area_no
            ),
            t102 as (
            select
                *
            from
                house.staff_second_unit
            where
                year_month = (
                select
                    max(year_month)
                from
                    house.staff_second_unit)
                and area_no in ('-11', '-12') 
            ),
            t103 as (
            select
                t100.area_no,
                t100.area_name,
                '' as city_no,
                '' as city_name,
                t101.building_area_self_use_sum,
                t102.total,
                t100.area_order,
                0 as city_order
            from
                t100
            left join t101 on
                t100.area_no = t101.area_no
            left join t102 on
                t100.area_no = t102.area_no
            ),
            t200 as (
            select
                b.id as area_no,
                b.name as area_name,
                a.id as city_no,
                a.name as city_name,
                b.order_num as area_order,
                a.order_num as city_order
            from
                common.organization a
            left join common.organization b on
                a.parent_id = b.id
            where
                a.unhide = 1
                and a.grade = 2
                and a.parent_id not in ('-11', '-12')
            order by
                b.id,
                a.id
            ),
            t201 as (
            select
                area_no,
                city_no,
                sum(building_area_self_use) as building_area_self_use_sum
            from
                house.room_month
            where
                second_room_type = '办公用房'
                and area_no not in ('-11', '-12')
                    and year_month = {year_month}
                group by
                    area_no,
                    city_no
            ),
            t202 as (
            select
                *
            from
                house.staff_third_unit
            where
                year_month = (
                select
                    max(year_month)
                from
                    house.staff_third_unit)
                and area_no not in ('-11', '-12')
            ),
            t203 as (
            select
                t200.area_no,
                t200.area_name,
                t200.city_no,
                t200.city_name,
                t201.building_area_self_use_sum,
                t202.total,
                t200.area_order,
                t200.city_order
            from
                t200
            left join t201 on
                t200.area_no = t201.area_no
                and t200.city_no = t201.city_no
            left join t202 on
                t200.area_no = t202.area_no
                and t200.city_no = t202.city_no
            ),
            t301 as (
            select
                *
            from
                t103
            union all
            select
                *
            from
                t203
            )
            insert
                into
                house.building_office_area_stat
            (
            year_month,
                area_no,
                area_name,
                city_no,
                city_name,
                building_area_self_use_sum,
                total,
                area_avg,
                area_order,
                city_order
            )
            select
                {year_month} as year_month,
                area_no,
                area_name,
                city_no,
                city_name,
                coalesce(building_area_self_use_sum, 0) as building_area_self_use_sum,
                coalesce(total, 0) as total,
                case
                    when total = 0 then null
                    else round(coalesce(building_area_self_use_sum, 0) / total, 2)
                end as area_avg,
                area_order,
                city_order
            from
                t301
            order by
                area_order,
                city_order
            """
            logger.info(f"sql: {sql}")
            curs.execute(sql)
            logger.info(f"update {curs.rowcount}")


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