# -*- coding: utf-8 -*-
"""``DistrictPredictor`` — 地区匹配规则预测。

Phase 5 从 ``interface/predictor.py``（约 6770-8032 行）迁出。
原 ``from common.Utils import *`` / ``from common.nerUtils import *``
已替换为显式 import；``os.path.dirname(__file__)`` 路径引用替换为
``predictors._common.INTERFACE_DIR``。使用 district_tuple.pkl、
area_variance_dic.pkl 等地区字典。
"""
from __future__ import absolute_import

import os
import re
import pickle
from collections import Counter

from BiddingKG.dl.common.logging import log
from BiddingKG.dl.common.nerUtils import getTokens, getNers
from BiddingKG.dl.entityLink.entityLink import get_business_data
from BiddingKG.dl.predictors._common import INTERFACE_DIR

__all__ = ["DistrictPredictor"]


class DistrictPredictor():
    def __init__(self):
        # with open(os.path.dirname(__file__)+'/district_dic.pkl', 'rb') as f:
        #     dist_dic = pickle.load(f)
        #     short_name = '|'.join(sorted(set([v['简称'] for v in dist_dic.values()]), key=lambda x: len(x), reverse=True))
        #     full_name = '|'.join(sorted(set([v['全称'] for v in dist_dic.values()]), key=lambda x: len(x), reverse=True))
        #     short2id = {}
        #     full2id = {}
        #     for k, v in dist_dic.items():
        #         if v['简称'] not in short2id:
        #             short2id[v['简称']] = [k]
        #         else:
        #             short2id[v['简称']].append(k)
        #         if v['全称'] not in full2id:
        #             full2id[v['全称']] = [k]
        #         else:
        #             full2id[v['全称']].append(k)
        #     self.dist_dic = dist_dic
        #     self.short_name = short_name
        #     self.full_name = full_name
        #     self.short2id = short2id
        #     self.full2id = full2id
        # # self.f = open(os.path.dirname(__file__)+'/../test/data/district_predict.txt', 'w', encoding='utf-8')
        with open(os.path.join(INTERFACE_DIR, 'district_tuple.pkl'), 'rb') as f:
            district_tuple = pickle.load(f)
            self.p_pro, self.p_city, self.p_dis, self.idx_dic, self.full_dic, self.short_dic = district_tuple
            # self.pettern = "((?P<prov>%s)(?P<city>%s)?(?P<dist>%s)?)|((?P<city1>%s)(?P<dist1>%s)?)|(?P<dist2>%s)" % (
            #     self.p_pro, self.p_city, self.p_dis, self.p_city, self.p_dis, self.p_dis)
            short_pro = '黑龙江|内蒙古|青海|陕西|辽宁|贵州|西藏|福建|甘肃|湖南|湖北|海南|浙江|河南|河北|江西|江苏|新疆|广西|广东|山西|山东|安徽|宁夏|四川|吉林|云南'
            self.pettern = "(?P<prov>%s)##(?P<city>%s)##(?P<dist>%s)" % (
                self.p_pro, self.p_city, self.p_dis)
            self.pettern_pro = re.compile("(?P<prov>%s)"%self.p_pro)
            self.pettern_city = re.compile("(%s)?(?P<city>%s)"%(short_pro, self.p_city)) # 20250925补充省简称，解决 海南昌江 匹配为南昌问题
            self.pettern_dist = re.compile("(%s)?(?P<dist>%s)"%(short_pro, self.p_dis))

        with open(os.path.join(INTERFACE_DIR, "area_variance_dic.pkl"), 'rb') as f: # 20241113 地区变更新旧名称对照字典
            self.area_variance_dic = pickle.load(f)

        self.multi_dist = ['向阳区', '宝山区', '南沙区', '和平区', '新城区', '鼓楼区', '南山区', '白云区', '朝阳区',
                                     '江北区', '城关区', '永定区', '普陀区', '长安区', '市中区', '西安区', '通州区', '西湖区',
                                     '龙华区', '城中区', '河东区', '桥西区', '青山区', '新华区', '铁西区', '铁东区', '海州区', '滨海新区']

    def find_whole_areas(self, text, pettern, area_variance_dic, full_dic, weight=1):
        '''
        通过正则匹配字符串返回地址
        :param pettern: 地址正则 广东省|广西省|...
        :param text: 待匹配文本
        :return:
        '''
        province_l, city_l, district_l = [], [], []
        citys = []

        text = str(text).replace('(', '（').replace(')', '）')
        text = re.sub('\d{2,4}年度?|[\d/-]{1,5}[月日]|\d+|[a-zA-Z0-9]+', ' ', text)
        text = re.sub(
            '复合肥|海南岛|兴业银行|双河口|阳光|杭州湾|新城区|中粮屯河|老城(区|改造|更新|升级|翻新)|沙县小吃|北京时间|福田汽车|中山(大学|公园|纪念堂)|孙中山|海天水泥|阳光采购|示范县|珠江城?|西九龙站|广州路北|安阳山村|电信|联通|北京现代|祁连山|锡铁山|大黄山(?!市)|红旗汽车', # 570445994 广州路北侧 预测为 广州 路北
            ' ', text)  # 544151395 赤壁市老城区燃气管道老化更新改造
        text = re.sub('珠海城市', '珠海', text)  # 修复 426624023 珠海城市 预测为海城市
        text = re.sub('怒江州', '怒江傈僳族自治州', text)  # 修复 423589589  所属地域：怒江州 识别为广西 - 崇左 - 江州
        text = re.sub('茂名滨海新区', '茂名市', text)
        text = re.sub('中山([东南西][部区环]|黄圃|南头|东凤|小榄|石岐|翠亨|南朗)', '中山市', text)
        text = re.sub('横州市', '横县', text)  # 例：547363890 修复广西南宁横州 不在地区表问题
        text = re.sub('广东中山', '广东中山市', text)
        text = re.sub('朝阳柳城经济开发区', '朝阳市', text)
        text = re.sub('安徽徽运城市', '安徽', text) # 653054198 安徽徽运城市运营管理有限公司
        text = re.sub('西安丰镇', '宝应', text) # 修复 681946122 宝应县西安丰镇
        text = re.sub('西城区', '西城', text) # 修复 653130377 标题 董家口电厂至新区西城区长输热力管线工程目 预测错北京西城

        # 处理地区+大学不在该地区问题
        text = re.sub('(河北工业大学)', '天津市', text)
        text = re.sub('(西藏民族大学)', '咸阳市', text)
        text = re.sub('(四川外国语大学|四川美术学院)', '重庆市', text)
        text = re.sub('(中山大学)', '广州市', text)
        text = re.sub('(滨州医学院)', '烟台市', text)

        ser = re.search('海南(昌江|白沙|乐东|陵水|保亭|琼中)(黎族)?', text)
        if ser and '黎族' not in ser.group(0):
            text = text.replace(ser.group(0), ser.group(0) + '黎族')
        for k, v in area_variance_dic.items():  # 20241113 根据地区变更信息替换文本
            text = text.replace(k, v)
        text = re.sub('\s+', ' ', text)

        if re.search('[\u4e00-\u9fa5]', text) == None:
            return province_l, city_l, district_l

        name_set = set() # 提取到的所有地址集合
        tokens = getTokens([text], useselffool=True)[0]
        # print('句子：', text)
        # print('分词：', tokens)

        for pettern in [self.pettern_pro, self.pettern_city, self.pettern_dist]: # pettern.split('##')
            for it in re.finditer(pettern, text):
                if it.group(0) == '站前':  # 20240314 修复类似 中铁二局新建沪苏湖铁路工程站前VI标项目 错识别为 省份：辽宁， 城市：营口，区县：站前
                    continue
                for k, v in it.groupdict().items():
                    if v != None:
                        if it.end() == it.end(k) and re.search('[省市区县州旗盟]$', v) == None and re.search(
                                '^([东南西北中一二三四五六七八九十大小]?(村|镇|街|路|道|社区|巷|坊)|酒店|宾馆|经济开发区|开发区|新区|公园|广场|公馆|小区|幼儿园)', # |医院|[大中小]学 # 20250917取消地区+医院过滤，大部分是在该地区
                                # 城市不匹配为区的地址 修复 滨州北海经济开发区 北海新区 等提取为北海
                                text[it.end(k):]) != None and not re.match('路桥', text[it.end(k):]): # 修复 746181930 甘肃路桥 被路去掉
                            continue
                        if k in ['prov']:
                            if v in full_dic['province']:
                                score = 2
                            else:
                                score = 1
                                if re.search('^(\w{,2}[分支](公司|局|行|院|干?线)|(机务|车辆|车务|工务|电务|供电|动车)?段|地铁|(火车|高铁)?站|港|地区|区域|基地)'
                                        , text[it.end(k):]) or re.search('^(（%s）|\-%s)' % (v, v),
                                                                         text[max(0, it.start(k) - 1):]):
                                    score += 1
                                elif re.search('大学|学院', text[:it.start(k)]) and re.search('分校|校区', text[it.end(k):]): # 长春市第八十七中学南阳校区 不在南阳市
                                    score += 1
                            if len(v) < 3 and v not in tokens: # 不在分词里面概率降低
                                score /= 2
                            else:
                                score += it.end(k) / len(text) / 10
                            province_l.append((v, score * weight))
                        elif k in ['city', 'city1']:
                            if v in full_dic['city']:
                                score = 2
                            else:
                                score = 1
                                if re.search('^(\w{,2}[分支](公司|局|行|院|干?线)|(机务|车辆|车务|工务|电务|供电|动车)?段|地铁|(火车|高铁)?站|港|地区|区域|基地)'
                                        , text[it.end(k):]) or re.search('^(（%s）|\-%s)' % (v, v),
                                                                         text[max(0, it.start(k) - 1):]):
                                    score += 1
                                elif re.search('大学|学院', text[:it.start(k)]) and re.search('分校|校区', text[it.end(k):]):
                                    score += 1
                            if len(v) < 3 and v not in tokens:  # 不在分词里面概率降低 优化 653058471 山东恒通化工股份有限公司 错分 通化
                                score /= 2
                            else:
                                score += it.end(k) / len(text) / 10  # 优化 572840045 上海铁路公安局合肥公安处 这种表达
                            city_l.append((v, score * weight))
                            citys.append(v)
                        elif k in ['dist', 'dist1', 'dist2']:
                            if v in ['东区', '西区', '城区', '郊区', '矿区', '东至']:
                                continue
                            elif v.endswith('城市') and text[it.start(k)-1:it.start(k)+1] in citys: # 修复 上海城市 珠海城市 等 预测为 海城市
                                continue
                            if v in self.multi_dist or re.search('\w城区$', v): # 多个城市有的区概率降低
                                score = 0.5
                            elif v in full_dic['district'] and (len(v) > 2 or v.endswith('县')): # 20250709 修复 萧县 等概率过低
                                score = 2
                            else:
                                score = 0.5
                                if re.search('^(\w{,2}[分支](公司|局|行|院|干?线)|(机务|车辆|车务|工务|电务|供电|动车)?段|地铁|(火车|高铁)?站|港|地区|区域|基地)'
                                        , text[it.end(k):]) or (
                                        re.match('\s*%s' % v, text) and it.start(k) < 2) or re.search(
                                    '^(（%s）|\-%s)' % (v, v), text[max(0, it.start(k) - 1):]):
                                    score += 0.5
                                elif re.search('大学|学院', text[:it.start(k)]) and re.search('分校|校区', text[it.end(k):]):
                                    score += 0.5
                            if len(v) < 3 and v not in tokens:  # 不在分词里面概率降低，三字以上不受限制 ，避免类似 德令哈工务段 分词不对
                                score /= 2
                            # score += it.end(k) / len(text) / 10
                            district_l.append((v, score * weight))
                        name_set.add(v)
        if len(name_set) > 1: # 解决 类似 山西宁武 匹配出 山西 西宁 宁武 问题 修复 653057648 把峨眉山市作为眉山市
            names = re.findall('|'.join(sorted(name_set, key=lambda x: len(x), reverse=True)), text)
            province_l = [it for it in province_l if it[0] in names]
            city_l = [it for it in city_l if it[0] in names]
            district_l = [it for it in district_l if it[0] in names]

        return province_l, city_l, district_l

    def merge_score(self, province_l, city_l, district_l, full_dic, short_dic, idx_dic, filter_short_dist=True):
        '''
        合并分数，下级地区分数加到上级
        :param province_l: 提取到的省份列表 [(name, score)]
        :param city_l: 提取到的城市列表 [(name, score)]
        :param district_l: 提取到的区县列表 [(name, score)]
        :param filter_short_dist: 是否过滤不在省份下的区县简称权重
        :return:
        '''
        pro_ids = dict()
        city_ids = dict()
        dis_ids = dict()
        for pro in province_l:
            name, score = pro
            idx = full_dic['province'][name] if name in full_dic['province'] else short_dic['province'][name]
            if idx not in pro_ids:
                pro_ids[idx] = 0
            pro_ids[idx] += score

        tmp_pro = {}
        for city in city_l:
            name, score = city
            if name in full_dic['city']:
                for idx in full_dic['city'][name]:
                    if idx not in city_ids:
                        city_ids[idx] = 0
                    city_ids[idx] += score
                    pro_idx = idx_dic[idx]['省']
                    if pro_idx in tmp_pro:
                        tmp_pro[pro_idx] += score
                    else:
                        tmp_pro[pro_idx] = score
            elif name in short_dic['city']:
                for idx in short_dic['city'][name]:
                    if idx not in city_ids:
                        city_ids[idx] = 0
                    city_ids[idx] += score
                    pro_idx = idx_dic[idx]['省']
                    if pro_ids != {} and pro_idx not in pro_ids: # 如果省份不为空且简称不在省份分值降低 优化 653150317 海南农垦阳江农场有限公司 错分 阳江
                        score -= 0.1
                    if pro_idx in tmp_pro:
                        tmp_pro[pro_idx] += score
                    else:
                        tmp_pro[pro_idx] = score
        if set(tmp_pro) & set(pro_ids) != set():
            for k, v in tmp_pro.items():
                if k in pro_ids:
                    pro_ids[k] += v
                else:
                    pro_ids[k] = v
        else:
            pro_ids.update(tmp_pro)
        tmp_pro = {}
        tmp_city = {}
        for dis in district_l:
            name, score = dis
            if name in full_dic['district']:
                for idx in full_dic['district'][name]:
                    if idx not in dis_ids:
                        dis_ids[idx] = 0
                    dis_ids[idx] += score
                    pro_idx = idx_dic[idx]['省']
                    if pro_idx in tmp_pro:
                        tmp_pro[pro_idx] += score
                    else:
                        if name in self.multi_dist:  # 多个城市重复名称，需过滤
                            continue
                        tmp_pro[pro_idx] = score
                    city_idx = idx_dic[idx]['市']
                    if city_idx in tmp_city:
                        tmp_city[city_idx] += score
                    else:
                        if name in self.multi_dist:  # 多个城市重复名称，需过滤
                            continue
                        tmp_city[city_idx] = score
            elif name in short_dic['district']:
                for idx in short_dic['district'][name]:
                    if idx not in dis_ids:
                        dis_ids[idx] = 0
                    dis_ids[idx] += score
                    pro_idx = idx_dic[idx]['省']
                    if pro_ids != {} and pro_idx not in pro_ids: # 如果省份不为空且简称不在省份分值降低
                        score -= 0.1
                    if filter_short_dist and score < 1:  # pro_idx not in pro_ids
                        continue
                    if pro_idx in tmp_pro:
                        tmp_pro[pro_idx] += score
                    else:
                        tmp_pro[pro_idx] = score
                    city_idx = idx_dic[idx]['市']
                    if city_idx in tmp_city:
                        tmp_city[city_idx] += score
                    else:
                        tmp_city[city_idx] = score
        if set(tmp_pro) & set(pro_ids) != set():
            for k, v in tmp_pro.items():
                if k in pro_ids:
                    pro_ids[k] += v
        else:
            pro_ids.update(tmp_pro)
        if set(tmp_city) & set(city_ids) != set():
            for k, v in tmp_city.items():
                if k in city_ids:
                    city_ids[k] += v
        else:
            city_ids.update(tmp_city)
        return pro_ids, city_ids, dis_ids
    @staticmethod
    def get_final_addr(pro_ids, city_ids, dis_ids, idx_dic):
        '''
        先把所有匹配的全称、简称转为id,如果省份不为空，城市不为空且有城市属于省份的取该城市
        :param province_l: 匹配到的所有省份
        :param city_l: 匹配到的所有城市
        :param district_l: 匹配到的所有区县
        :return:
        '''
        big_area = ""
        pred_pro = ""
        pred_city = ""
        pred_dis = ""

        final_pro = ""
        final_city = ""
        prob = 0
        max_score = 0
        code_dic = {
            'province_code': '',
            'city_code': '',
            'district_code': ''
        }
        if len(pro_ids) >= 1:
            pro_l = sorted([(k, v) for k, v in pro_ids.items()], key=lambda x: x[1], reverse=True)
            scores = [it[1] for it in pro_l]
            prob = max(scores) / sum(scores)
            max_score = max(scores)
            final_pro, score = pro_l[0]
            if score >= 0.01:
                pred_pro = idx_dic[final_pro]['返回名称']
                big_area = idx_dic[final_pro]['大区']
                code_dic['province_code'] = idx_dic[final_pro]['编码']
        if pred_pro != "" and len(city_ids) >= 1:
            city_l = sorted([(k, v) for k, v in city_ids.items()], key=lambda x: x[1], reverse=True)
            for it in city_l:
                if idx_dic[it[0]]['省'] == final_pro:
                    final_city = it[0]
                    pred_city = idx_dic[final_city]['返回名称']
                    code_dic['city_code'] = idx_dic[final_city]['编码']
                    break
        if final_city != "" and len(set(dis_ids)) >= 1:
            dis_l = sorted([(k, v) for k, v in dis_ids.items()], key=lambda x: x[1], reverse=True)
            for it in dis_l:
                if idx_dic[it[0]]['市'] == final_city:
                    pred_dis = idx_dic[it[0]]['返回名称']
                    code_dic['district_code'] = idx_dic[it[0]]['编码']
        elif pred_pro != "" and pred_city == "" and len(set(dis_ids)) >= 1:  # 20241111 省份不为空，市为空，如果区县在省份下，补充对应的市县
            dis_l = sorted([(k, v) for k, v in dis_ids.items()], key=lambda x: x[1], reverse=True)
            for it in dis_l:
                if idx_dic[it[0]]['省'] == final_pro:
                    pred_city = idx_dic[idx_dic[it[0]]['市']]['返回名称']
                    pred_dis = idx_dic[it[0]]['返回名称']
                    code_dic['city_code'] = idx_dic[idx_dic[it[0]]['市']]['编码']
                    code_dic['district_code'] = idx_dic[it[0]]['编码']
        return big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic
    @staticmethod
    def get_ree_addr(prem):
        tenderee = ""
        tenderee_address = ""
        try:
            for v in prem.values():
                for link in v['roleList']:
                    if link['role_name'] == 'tenderee' and tenderee == "":
                        tenderee = link['role_text']
                        tenderee_address = link['address']
        except Exception as e:
            print('解析prem 获取招标人、及地址出错')
        return tenderee, tenderee_address
    @staticmethod
    def get_role_address(text):
        '''正则匹配获取招标人地址
           3：地址直接在招标人后面 招标人：xxx,地址：xxx
           4：招标、代理一起，两个地址一起 招标人：xxx， 代理人：xxx, 地址：xxx， 地址：xxx.
        '''
        p3 = '(招标|采购|甲)(人|方|单位)(信息：|（甲方）)?(名称)?：[\w（）]{4,15}，(联系)?地址：(?P<addr>(\w{1,13}(自治[区州县旗]|地区|[省市区县旗盟])[^\w]*)+|\w{2,15}[，。])'
        p4 = '(招标|采购|甲)(人|方|单位)(信息：|（甲方）)?(名称)?：[\w（）]{4,15}，(招标|采购)?代理(人|机构)(名称)?：[\w（）]{4,15}，(联系)?地址：(?P<addr>(\w{1,13}(自治[区州县旗]|地区|[省市区县旗盟])[^\w]*)+|\w{2,35})，(联系)?地址：'
        p5 = '(采购|招标)(人|单位)(联系)?地址：(?P<addr>(\w{1,13}(自治[区州县旗]|地区|[省市区县旗盟])[^\w]*)+|\w{2,15}[，。])'
        if re.search(p3, text):
            return re.search(p3, text).group('addr')
        elif re.search(p4, text):
            return re.search(p4, text).group('addr')
        elif re.search(p5, text):
            return re.search(p5, text).group('addr')
        else:
            return ''
    @staticmethod
    def get_all_addr(list_entity, filter_attach=False):
        '''
        获取所有招标或代理人名称及所有地址
        :param list_entity: 实体列表
        :param filter_attach: 是否过滤附件实体
        :return:
        '''
        tenderee_l = []
        addr_l = []
        for ent in list_entity:
            if ent.entity_type not in ['org', 'company', 'location'] or (filter_attach and ent.in_attachment):
                continue
            if ent.entity_type == 'location' and len(ent.entity_text) > 2:
                addr_l.append(ent.entity_text)
            elif ent.entity_type in ['org', 'company']:
                if ent.label in [0, 1]:  # 加招标或代理
                    tenderee_l.append(ent.entity_text)
                elif re.search('[局委]$', ent.entity_text):
                    tenderee_l.append(ent.entity_text)
            if len(addr_l) > 10: # 只取前10个地址
                break
        return ' '.join(set(addr_l)), ' '.join(set(tenderee_l))

    def addr_process(self, addr_text):
        addr_text = addr_text.replace('(', '（').replace(')', '）')
        if re.search('[省市县]', addr_text) == None:
            ser = re.search('\w{2,}区', addr_text)
            ser2 = re.match('\w{2,5}镇', addr_text)
            if ser:
                addr_text = addr_text[:ser.end()]
            elif ser2:
                addr_text = addr_text[:ser2.end()]
        return addr_text

    def predict_area(self,docid, title, content, web_source_name, prem={}, addr_dic={}, list_entity=[]):
        if re.match('([^，]{3,50}网络竞价会)，', content): # 修复 701397187 标题只有拍卖的东西，第一句有地址
            title_auction = re.match('([^，]{3,50}网络竞价会)，', content).group(1)
            if title.find(title_auction[:3]) == -1:
                title += ' ' + title_auction
        filter_attach = False # 是否过滤附件内容
        if '##attachment##' in content:
            main, att = content.split('##attachment##')
            if 2000 < len(main) < len(att):  # 正文超过500字且附件比正文长过滤附件内容
                filter_attach = True
                # print('正文超过500字过滤附件内容')
        ree, addr_ree = self.get_ree_addr(prem)
        addr_ree = self.addr_process(addr_ree)
        addr_bus = ''
        if len(addr_ree) < 3 and ree != '':
            have_bus, bus_dic = get_business_data(ree)
            if have_bus:
                addr_bus = '%s %s %s' % (bus_dic.get('province', ''), bus_dic.get('city', ''), bus_dic.get('district', ''))
        all_addr, tenderees = self.get_all_addr(list_entity, filter_attach)

        return self.predict_distrist(docid, title, web_source_name, ree, addr_ree, addr_bus, addr_dic, tenderees, all_addr)


    def predict_distrist(self,docid, title, web_source_name, ree, addr_ree, addr_bus,location_dic={}, tenderees='', all_addr=''):
        '''
        优先顺序项目地址、收货地址、招标人地址 / 工商地址、开标地址 / 联系地址、站源名称
        '''
        area_dic = {'area': '全国', 'province': '全国', 'city': '未知', 'district': '未知', "is_in_text": False}

        in_content = False
        not_sure = True # 是否不确定地区
        msc = "" # 日志信息
        addr_dic = {}
        prov_name_l = [] # 保存预测到的省份名称
        city_name_l = [] # 保存预测到的城市名称
        final_key = ''
        company_title = ''
        title_raw = title
        if len(title.strip()) > 4:
            ner_title = getNers([title], True)[0]
            company_title = []
            for ner in ner_title:
                if ner[2] in ['org', 'company']:
                    company_title.append(ner[3])
                    title = title.replace(ner[3], '#')
            company_title = "#".join(company_title)

        if re.search('拍卖|法院', ree):
            company_title += ' ' + ree
            ree = ''

        if web_source_name in ('中原云商', '中原云商电子招投标平台'): # 修复某些公告没地区
            web_source_name = '河南中原云商'

        for key in ['addr_project', 'addr_delivery', 'addr_bidopen', 'addr_bidsend', 'addr_contact']:
            addr = location_dic.get(key, '')
            addr = self.addr_process(addr)
            if len(addr) < 2:
                continue
            province_l, city_l, district_l = self.find_whole_areas('%s'%addr, self.pettern, self.area_variance_dic, self.full_dic)
            if len(province_l+city_l+district_l)>0:
                pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic,self.short_dic, self.idx_dic)
                big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = self.get_final_addr(pro_ids, city_ids, dis_ids, self.idx_dic)
                if max_score < 2 and len(addr) > len(''.join([it[0] for it in province_l+city_l+district_l]))*2: # 修复 712151540 北京银行大厦6楼厨房及22楼 提取为北京
                    log('地区匹配非正常地址：%s, 预测为：%s %s %s, docid：%s'%(addr, pred_pro, pred_city, pred_dis, docid))
                    continue
                if pred_pro != '':
                    addr_dic[key] = {
                        'keyword': (province_l, city_l, district_l),
                        'result': (big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic)
                    }
                    msc += "使用%s信息：%s， 预测为：%s %s %s；" % (key, addr, pred_pro, pred_city, pred_dis)
                    prov_name_l.append(pred_pro)
                    if pred_city != '':
                        city_name_l.append(pred_city)
                    if key == 'addr_project' and addr_dic['addr_project']['result'][2] != '' and addr_dic['addr_project']['result'][4] > 0.6 and (addr_dic['addr_project']['result'][5] >= 2 or addr_dic['addr_project']['keyword'][2]==[]):
                        big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic['addr_project']['result']
                        not_sure = False
                        final_key = key
                        msc += "最终使用：%s，预测为：%s %s %s；"%(final_key, pred_pro, pred_city, pred_dis)
                        if addr.endswith('#在附件'):
                            log('地区匹配使用附件中的项目地址：%s；预测为：%s %s；docid：%s'%(addr, pred_pro, pred_city, docid))
                        break
        if not_sure:
            for key, text in zip(['tenderee', 'company_title', 'web_source_name'], [ree, company_title, web_source_name]):
                if len(text) < 4:
                    continue
                weight = 0.48 if key == 'web_source_name' else 1
                province_l, city_l, district_l = self.find_whole_areas('%s'%text, self.pettern, self.area_variance_dic, self.full_dic, weight=weight)
                if len(province_l+city_l+district_l)>0:
                    pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic,self.short_dic, self.idx_dic)
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = self.get_final_addr(pro_ids, city_ids, dis_ids, self.idx_dic)
                    if pred_pro != '':
                        addr_dic[key] = {
                            'keyword': (province_l, city_l, district_l),
                            'result': (big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic)
                        }
                        msc += "使用%s信息：%s， 预测为：%s %s %s；" % (key, text, pred_pro, pred_city, pred_dis)
                        prov_name_l.append(pred_pro)
                        if pred_city != '':
                            city_name_l.append(pred_city)
                elif key == 'company_title' and re.search('\w{1,}[省市县]', text): # 修复 2025年可克达拉市政府部门独立办公楼聘用保安保洁采购服务 实体提取为 克达拉市政府 造成市漏提
                    title = title_raw
            # 提取招标地址
            if len(addr_ree) >= 3:
                province_l, city_l, district_l = self.find_whole_areas('%s' % addr_ree, self.pettern, self.area_variance_dic,self.full_dic)
                if len(province_l + city_l + district_l) > 0:
                    pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic,self.short_dic, self.idx_dic)
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = self.get_final_addr(pro_ids,city_ids,dis_ids,self.idx_dic)
                    if pred_pro != '':
                        addr_dic['addr_tenderee'] = {
                            'keyword': (province_l, city_l, district_l),
                            'result': (big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic)
                        }
                        msc += "使用招标人地址：%s， 预测为：%s %s %s；" % (addr_ree, pred_pro, pred_city, pred_dis)
                        prov_name_l.append(pred_pro)
                        if pred_city != '':
                            city_name_l.append(pred_city)
            # 提取招标人工商登记地址
            if len(addr_bus) >= 3:
                province_l, city_l, district_l = self.find_whole_areas('%s' % addr_bus, self.pettern, self.area_variance_dic,self.full_dic)
                if len(province_l + city_l + district_l) > 0:
                    pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic,self.short_dic, self.idx_dic)
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = self.get_final_addr(pro_ids,city_ids,dis_ids,self.idx_dic)
                    if pred_pro != '':
                        addr_dic['addr_bus'] = {
                            'keyword': (province_l, city_l, district_l),
                            'result': (big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic)
                        }
                        msc += "使用招标人工商登记地址：%s， 预测为：%s %s %s；" % (addr_bus, pred_pro, pred_city, pred_dis)
                        prov_name_l.append(pred_pro)
                        if pred_city != '':
                            city_name_l.append(pred_city)
            # 提取标题地址
            if len(title) > 3:
                province_l, city_l, district_l = self.find_whole_areas('%s' % title, self.pettern, self.area_variance_dic,self.full_dic)
                if len(province_l + city_l + district_l) > 0:
                    pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic,self.short_dic, self.idx_dic)
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = self.get_final_addr(pro_ids,city_ids,dis_ids,self.idx_dic)
                    if pred_pro != '':
                        addr_dic['addr_title'] = {
                            'keyword': (province_l, city_l, district_l),
                            'result': (big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic)
                        }
                        msc += "使用标题地址：%s， 预测为：%s %s %s；" % (title, pred_pro, pred_city, pred_dis)
                        prov_name_l.append(pred_pro)
                        if pred_city != '':
                            city_name_l.append(pred_city)
            if len(addr_dic) == 1:
                for key in addr_dic:
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic[key]['result']
                    final_key = key
                    msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)
            elif len(addr_dic) > 1:
                prov_count = Counter(prov_name_l)
                city_count = Counter(city_name_l)
                if len(prov_count) == 1 and len(city_count) == 1:
                    score_max = 0
                    for key in ['addr_project', 'addr_delivery', 'addr_tenderee', 'addr_title', 'tenderee', 'company_title',
                                'addr_bidopen', 'addr_bidsend', 'addr_contact', 'addr_bus', 'web_source_name']:
                        if key in addr_dic and addr_dic[key]['result'][1] == prov_name_l[0] and addr_dic[key]['result'][2] == city_name_l[0] and addr_dic[key]['result'][5] > score_max:
                            score_max = addr_dic[key]['result'][5]
                            final_key = key
                            if addr_dic[final_key]['result'][3] != '': # 2026/2/2 省市匹配，且有区县停止循环
                                break
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic[final_key]['result']
                    msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)

                        # if key in addr_dic and addr_dic[key]['result'][1] == prov_name_l[0] and addr_dic[key]['result'][2] == city_name_l[0]:
                        #     # big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic[key]['result']
                        #     final_key = key
                        #     msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)
                        #     break
                elif 'addr_project' in addr_dic and addr_dic['addr_project']['result'][2] == '': # 只有省份
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic['addr_project']['result']
                    final_key = 'addr_project'
                    score_max = 0
                    tmp_key = ''
                    for key in ['addr_delivery', 'addr_tenderee', 'addr_title', 'tenderee', 'company_title',
                                'addr_bidopen', 'addr_bidsend', 'addr_contact', 'addr_bus', 'web_source_name']:
                        if key in addr_dic and addr_dic[key]['result'][1] == pred_pro and addr_dic[key]['result'][2] != '' and addr_dic[key]['result'][5] > score_max:
                            score_max = addr_dic[key]['result'][5]
                            tmp_key = key
                            if addr_dic[tmp_key]['result'][3] != '':  # 2026/2/2 省市匹配，且有区县停止循环
                                break
                    if tmp_key != '':
                        final_key += ',%s' % tmp_key
                        big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic[tmp_key]['result']
                    msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)
                elif 'addr_delivery' in addr_dic and addr_dic['addr_delivery']['result'][2] != '' and addr_dic['addr_delivery']['result'][4] > 0.7 and addr_dic['addr_delivery']['result'][5] >= 2:
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic['addr_delivery']['result']
                    final_key = 'addr_delivery'
                    msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)
                elif 'addr_title' in addr_dic and addr_dic['addr_title']['result'][2] != '' and addr_dic['addr_title']['result'][4] > 0.7 and addr_dic['addr_title']['result'][5] >= 2:
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic['addr_title']['result']
                    final_key = 'addr_title'
                    msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)
                elif 'tenderee' in addr_dic and addr_dic['tenderee']['result'][2] != '' and addr_dic['tenderee']['result'][4] > 0.7 and addr_dic['tenderee']['result'][5] >= 2: # 补概率 653492303 广州市妇女儿童医疗中心柳州医院 预测错广州
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic['tenderee']['result']
                    final_key = 'tenderee'
                    msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)
                elif 'addr_tenderee' in addr_dic and addr_dic['addr_tenderee']['result'][2] != '' and addr_dic['addr_tenderee']['result'][5] >= 2:
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = addr_dic['addr_tenderee']['result']
                    final_key = 'addr_tenderee'
                    msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)
                else:
                    province_l, city_l, district_l = [], [], []
                    for key in addr_dic:
                        if key in ['addr_delivery', 'addr_bidopen', 'addr_bidsend', 'addr_contact', 'addr_bus']: # 地址类型只加一项，因为一般都有三项造成单个地址总分过大
                            if addr_dic[key]['keyword'][2]:
                                district_l += addr_dic[key]['keyword'][2]
                            elif addr_dic[key]['keyword'][1]:
                                city_l += addr_dic[key]['keyword'][1]
                            else:
                                province_l += addr_dic[key]['keyword'][0]
                        else:
                            province_l += addr_dic[key]['keyword'][0]
                            city_l += addr_dic[key]['keyword'][1]
                            district_l += addr_dic[key]['keyword'][2]
                        if key in ['addr_project', 'addr_title']: # 加重项目、标题地址权重
                            province_l += addr_dic[key]['keyword'][0]
                            city_l += addr_dic[key]['keyword'][1]
                            district_l += addr_dic[key]['keyword'][2]
                    pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic, self.short_dic, self.idx_dic)
                    big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = self.get_final_addr(pro_ids, city_ids, dis_ids, self.idx_dic)
                    if pred_city != '':
                        score_max = 0
                        for key in ['addr_delivery', 'addr_tenderee', 'addr_title', 'tenderee', 'company_title',
                                    'addr_bidopen', 'addr_bidsend', 'addr_contact', 'addr_bus', 'web_source_name']:
                            if key in addr_dic and addr_dic[key]['result'][2] == pred_city and addr_dic[key]['result'][5] > score_max:
                                final_key = key
                                score_max = addr_dic[key]['result'][5]
                                if addr_dic[final_key]['result'][3] == pred_dis:  # 2026/2/2 省市匹配，且有区县停止循环
                                    break
                    if pred_pro != '' and final_key == "":
                        for key in ['addr_delivery', 'addr_tenderee', 'addr_title', 'tenderee', 'company_title',
                                    'addr_bidopen', 'addr_bidsend', 'addr_contact', 'addr_bus', 'web_source_name']:
                            if key in addr_dic and addr_dic[key]['result'][1] == pred_pro:
                                final_key = key
                                break
                    msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)
            else:
                # 取所有的地址
                province_l, city_l, district_l = self.find_whole_areas('%s %s %s' % (title_raw, tenderees, all_addr), self.pettern, self.area_variance_dic,self.full_dic, weight=0.5)
                pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic, self.short_dic, self.idx_dic)
                big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = self.get_final_addr(pro_ids, city_ids, dis_ids, self.idx_dic)
                final_key = 'all_addr'
                in_content = True
                msc += "最终使用：%s，预测为：%s %s %s；" % (final_key, pred_pro, pred_city, pred_dis)
        if len(addr_dic) > 0 and pred_city == '': # 如果非正文提取缺少城市,补充
            province_l, city_l, district_l = self.find_whole_areas('%s %s %s' % (title_raw, tenderees, all_addr), self.pettern, self.area_variance_dic,self.full_dic, weight=0.5)
            pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic, self.short_dic, self.idx_dic)
            rs_tmp = self.get_final_addr(pro_ids, city_ids, dis_ids, self.idx_dic)
            if rs_tmp[1] == pred_pro and rs_tmp[4] > 0.5 and rs_tmp[2] != '':
                big_area, pred_pro, pred_city, pred_dis, prob, max_score, code_dic = rs_tmp
                msc += "只有省份，所有地址补充市县：%s %s ；"%(pred_city, pred_dis)
                final_key += ',all_addr'

        dist_source = {
            'addr_project': '项目地址',
            'addr_delivery': '收货地址',
            'addr_title': '标题',
            'tenderee': "招标人",
            'company_title': '标题公司',
            'addr_tenderee': '招标人地址',
            'addr_bus': '招标人工商地址',
            'web_source_name': '站源',
            'addr_bidopen': '开标地址',
            'addr_bidsend': '邮件地址',
            'addr_contact': '联系地址',
            'all_addr': '文中所有地址'
        }

        if big_area != "":
            area_dic['area'] = big_area
        if pred_pro != "":
            area_dic['province'] = pred_pro
            source_l = [dist_source.get(k, '') for k in final_key.split(',')]
            area_dic['dist_source'] = ';'.join(source_l)
        if pred_city != "":
            area_dic['city'] = pred_city
        if pred_dis != "":
            area_dic['district'] = pred_dis
        for k, v in code_dic.items():
            if v != '':
                area_dic[k] = v

        if pred_city in ['北京', '天津', '上海', '重庆']:  # 直辖市调整县级
            area_dic['city'] = area_dic['district']
            area_dic['district'] = '未知'
            if 'district_code' in area_dic:
                area_dic['city_code'] = area_dic['district_code']
                area_dic.pop('district_code')
            if area_dic['city'] == '未知' and 'city_code' in area_dic:
                area_dic.pop('city_code')

        area_dic['is_in_text'] = in_content
        # area_dic['prob'] = prob
        # area_dic['max_score'] = max_score
        # print('最终地址：', pred_pro, pred_city, pred_dis)
        if prob < 0.6 or max_score < 2:
            log('地区匹配预测，最终结果：%s %s %s， 预测：%s， docid：%s' % (pred_pro, pred_city, pred_dis, msc, docid))
        return {'district': area_dic}

    def predict_area_backup(self,docid, title, content, web_source_name, prem={}, addr_dic={}, list_entity=[]):
        area_dic = {'area': '全国', 'province': '全国', 'city': '未知', 'district': '未知', "is_in_text": False}
        addr_project = addr_dic.get('addr_project', '').replace('(', '（').replace(')', '）')
        addr_delivery = addr_dic.get('addr_delivery', '').replace('(', '（').replace(')', '）')
        addr_bidopen = addr_dic.get('addr_bidopen', '').replace('(', '（').replace(')', '）')
        addr_bidsend = addr_dic.get('addr_bidsend', '').replace('(', '（').replace(')', '）')
        addr_contact = addr_dic.get('addr_contact', '').replace('(', '（').replace(')', '）')
        in_content = False
        not_sure = True # 是否不确定地区
        filter_attach = False # 是否过滤附件内容
        msc = "" # 日志信息

        if '##attachment##' in content:
            main, att = content.split('##attachment##')
            if 500 < len(main) < len(att):  # 正文超过500字且附件比正文长过滤附件内容
                filter_attach = True
                # print('正文超过500字过滤附件内容')

        if web_source_name in ('中原云商', '中原云商电子招投标平台'): # 修复某些公告没地区
            web_source_name = '河南中原云商'

        province_l, city_l, district_l = self.find_whole_areas('%s %s'%(title, addr_project), self.pettern, self.area_variance_dic, self.full_dic)
        pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic, self.short_dic, self.idx_dic)
        big_area_1, pred_pro_1, pred_city_1, pred_dis_1, prob_1, max_score, code_dic_1 = self.get_final_addr(pro_ids, city_ids, dis_ids, self.idx_dic)
        big_area, pred_pro, pred_city, pred_dis, prob, code_dic = big_area_1, pred_pro_1, pred_city_1, pred_dis_1, prob_1, code_dic_1
        # print('关键词1：', province_l, city_l, district_l)
        # print('输入：', '标题：%s； 项目地址：%s'%(title, addr_project))
        # print('分数：', pro_ids, city_ids, dis_ids, prob, max_score)
        msc += '第一次预测，标题：%s； 项目地址：%s； 预测为：%s %s ##'%(title, addr_project, pred_pro, pred_city)
        if pred_city_1 == "" or prob < 0.7 or max_score<2:
            ree, addr = self.get_ree_addr(prem)
            have_bus, bus_dic = get_business_data(ree) if ree != '' else False, {}
            if ree in title:
                ree = '##'
            rule_ree_addr = self.get_role_address(content)
            if rule_ree_addr:
                addr = rule_ree_addr

            # addr = content
            # ree = ''
            province_l2, city_l2, district_l2 = self.find_whole_areas('%s %s %s' % (ree, addr, addr_delivery), self.pettern, self.area_variance_dic, self.full_dic, weight=1)
            province_l.extend(province_l2)
            city_l.extend(city_l2)
            district_l.extend(district_l2)
            pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic, self.short_dic, self.idx_dic)
            big_area_2, pred_pro_2, pred_city_2, pred_dis_2, prob_2, max_score, code_dic_2 = self.get_final_addr(pro_ids, city_ids, dis_ids, self.idx_dic)
            big_area, pred_pro, pred_city, pred_dis, prob, code_dic = big_area_2, pred_pro_2, pred_city_2, pred_dis_2, prob_2, code_dic_2
            # print('关键词2：', province_l, city_l, district_l)
            # print('输入：', '招标人：%s； 招标人地址：%s； 收货地址：%s' % (ree, addr, addr_delivery))
            # print('分数：', pro_ids, city_ids, dis_ids, prob, max_score)
            msc += '第二次预测，招标人：%s； 招标人地址：%s； 收货地址：%s； 预测为：%s %s ##' % (ree, addr, addr_delivery, pred_pro, pred_city)
            if re.search('省|市|县|自治', addr_project) and prob_1 !=0.5 and pred_pro_1 != '' and pred_pro_1 != pred_pro_2: # 如果有项目地址使用项目地址  要有省市县等 275127622 工程地点为狮山镇颜峰综合区岐山至人和段道路， 提错 岐山
                not_sure = False
                big_area, pred_pro, pred_city, pred_dis, code_dic = big_area_1, pred_pro_1, pred_city_1, pred_dis_1, code_dic_1
                msc += "有项目地址，一二次预测省份不同，改为第一次结果。"
            if not_sure and (pred_city_2 == "" or prob < 0.7 or max_score<2):
                addr_bus = '%s %s'%(bus_dic.get('province', ''), bus_dic.get('city', ''))
                province_l3, city_l3, district_l3 = self.find_whole_areas('%s %s； %s； %s'%(addr_bus, addr_contact, addr_bidopen, addr_bidsend), self.pettern, self.area_variance_dic, self.full_dic, weight=0.6)
                province_l.extend(province_l3)
                city_l.extend(city_l3)
                district_l.extend(district_l3)
                pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic, self.short_dic, self.idx_dic)
                big_area_3, pred_pro_3, pred_city_3, pred_dis_3, prob_3, max_score, code_dic_3 = self.get_final_addr(pro_ids, city_ids, dis_ids, self.idx_dic)
                big_area, pred_pro, pred_city, pred_dis, prob, code_dic = big_area_3, pred_pro_3, pred_city_3, pred_dis_3, prob_3, code_dic_3
                # print('关键词3：', province_l, city_l, district_l)
                # print('输入：', '联系：%s， 开标：%s， 邮寄：%s'%(addr_contact, addr_bidopen, addr_bidsend))
                # print('分数：', pro_ids, city_ids, dis_ids, prob, max_score)
                msc += '第三次预测，工商地址：%s；联系：%s； 开标：%s； 邮寄：%s； 预测为：%s %s ##' % (addr_bus, addr_contact, addr_bidopen, addr_bidsend, pred_pro, pred_city)
                if pred_city_2 != "" and prob_2 !=0.5 and pred_city_2 != pred_city_3:
                    not_sure = False
                    big_area, pred_pro, pred_city, pred_dis, code_dic = big_area_2, pred_pro_2, pred_city_2, pred_dis_2, code_dic_2 # 如果招标人、招标人地址、收货地址与开标地址、联系地址等不一致，取招标人地址
                    msc += "二三次预测城市不一致，改为第二次结果。"
                if not_sure and (pred_city_3 == "" or prob < 0.6 or max_score < 2):
                    all_addr, tenderees = self.get_all_addr(list_entity, filter_attach)
                    province_l4, city_l4, district_l4 = self.find_whole_areas('%s %s %s' % (web_source_name, tenderees, all_addr), self.pettern, self.area_variance_dic, self.full_dic, weight=0.3)
                    province_l.extend(province_l4)
                    city_l.extend(city_l4)
                    district_l.extend(district_l4)
                    pro_ids, city_ids, dis_ids = self.merge_score(province_l, city_l, district_l, self.full_dic, self.short_dic, self.idx_dic)
                    big_area_4, pred_pro_4, pred_city_4, pred_dis_4, prob_4, max_score, code_dic_4 = self.get_final_addr(pro_ids, city_ids,dis_ids, self.idx_dic)
                    big_area, pred_pro, pred_city, pred_dis, prob, code_dic = big_area_4, pred_pro_4, pred_city_4, pred_dis_4, prob_4, code_dic_4
                    if pred_city_3 != "" and prob_3 !=0.5 and pred_city_3 != pred_city_4:
                        not_sure = False
                        big_area, pred_pro, pred_city, pred_dis, code_dic = big_area_3, pred_pro_3, pred_city_3, pred_dis_3, code_dic_3  # 如果开标地址等提取的城市与所有地址提取的城市不一致，取开标地址等
                        msc += "三四次预测城市不一致，改为第三次结果。"
                    if pred_pro_3 != pred_pro_4 and (prob < 0.6 or max_score < 2) or max_score < 1:
                        in_content = True
                    # print('关键词4：', province_l, city_l, district_l)
                    # print('输入：', '站源：%s， 角色：%s， 地址：%s' % (web_source_name, tenderees, all_addr))
                    # print('分数：', pro_ids, city_ids, dis_ids, prob, max_score)
                    msc += '第四次预测，站源：%s； 招标、代理角色：%s； 所有地址：%s； 预测为：%s %s ##' % (web_source_name, tenderees, all_addr, pred_pro, pred_city)

        if big_area != "":
            area_dic['area'] = big_area
        if pred_pro != "":
            area_dic['province'] = pred_pro
        if pred_city != "":
            area_dic['city'] = pred_city
        if pred_dis != "":
            area_dic['district'] = pred_dis
        for k, v in code_dic.items():
            if v != '':
                area_dic[k] = v

        if pred_city in ['北京', '天津', '上海', '重庆']: # 直辖市调整县级
            area_dic['city'] = area_dic['district']
            area_dic['district'] = '未知'
            if 'district_code' in area_dic:
                area_dic['city_code'] = area_dic['district_code']
                area_dic.pop('district_code')
            if area_dic['city'] == '未知' and 'city_code' in area_dic:
                area_dic.pop('city_code')

        area_dic['is_in_text'] = in_content
        # area_dic['prob'] = prob
        # area_dic['max_score'] = max_score
        # print('最终地址：', pred_pro, pred_city, pred_dis)
        if prob < 0.6 or max_score < 2:
            log('地区匹配预测，最终结果：%s %s %s， 预测：%s， docid：%s'%(pred_pro, pred_city, pred_dis, msc, docid))
        return {'district': area_dic}

    def get_area(self, text, web_name, in_content=False):
        p_pro, p_city, p_dis, idx_dic, full_dic, short_dic = self.p_pro, self.p_city, self.p_dis, self.idx_dic, self.full_dic, self.short_dic

        def get_final_addr(pro_ids, city_ids, dis_ids):
            '''
            先把所有匹配的全称、简称转为id,如果省份不为空，城市不为空且有城市属于省份的取该城市
            :param province_l: 匹配到的所有省份
            :param city_l: 匹配到的所有城市
            :param district_l: 匹配到的所有区县
            :return:
            '''
            big_area = ""
            pred_pro = ""
            pred_city = ""
            pred_dis = ""

            final_pro = ""
            final_city = ""
            pro_prob = 0
            city_prob = 0
            if len(pro_ids) >= 1:
                pro_l = sorted([(k, v) for k, v in pro_ids.items()], key=lambda x: x[1], reverse=True)
                scores = [it[1] for it in pro_l]
                pro_prob = max(scores)/sum(scores)
                final_pro, score = pro_l[0]
                if score >= 0.01:
                    pred_pro = idx_dic[final_pro]['返回名称']
                    big_area = idx_dic[final_pro]['大区']
                # else:
                #     print("得分过低，过滤掉", idx_dic[final_pro]['返回名称'], score)

            if pred_pro != "" and len(city_ids) >= 1:
                city_l = sorted([(k, v) for k, v in city_ids.items()], key=lambda x: x[1], reverse=True)
                scores = [it[1] for it in city_l]
                city_prob = max(scores) / sum(scores)
                for it in city_l:
                    if idx_dic[it[0]]['省'] == final_pro:
                        final_city = it[0]
                        pred_city = idx_dic[final_city]['返回名称']
                        break
            if final_city != "" and len(set(dis_ids)) >= 1:
                dis_l = sorted([(k, v) for k, v in dis_ids.items()], key=lambda x: x[1], reverse=True)
                for it in dis_l:
                    if idx_dic[it[0]]['市'] == final_city:
                        pred_dis = idx_dic[it[0]]['返回名称']
            elif pred_pro != "" and pred_city == "" and len(set(dis_ids)) >= 1: # 20241111 省份不为空，市为空，如果区县在省份下，补充对应的市县
                dis_l = sorted([(k, v) for k, v in dis_ids.items()], key=lambda x: x[1], reverse=True)
                for it in dis_l:
                    if idx_dic[it[0]]['省'] == final_pro:
                        pred_city = idx_dic[idx_dic[it[0]]['市']]['返回名称']
                        pred_dis = idx_dic[it[0]]['返回名称']
                        # print('20241111 省份不为空，市为空，如果区县在省份下，补充对应的市县: ', pred_city, pred_dis)

            if pred_city in ['北京', '天津', '上海', '重庆']:
                pred_city = pred_dis
                pred_dis = ""
            return big_area, pred_pro, pred_city, pred_dis

        def find_areas(pettern, text):
            '''
            通过正则匹配字符串返回地址
            :param pettern: 地址正则 广东省|广西省|...
            :param text: 待匹配文本
            :return:
            '''
            addr = []
            for it in re.finditer(pettern, text):
                if re.search('[省市区县旗盟]$', it.group(0)) == None and re.search(
                        '^([东南西北中一二三四五六七八九十大小]?(村|镇|街|路|道|社区)|酒店|宾馆)', text[it.end():]):
                    continue
                if it.group(0) == '站前': # 20240314 修复类似 中铁二局新建沪苏湖铁路工程站前VI标项目 错识别为 省份：辽宁， 城市：营口，区县：站前
                    continue
                if re.search('^(经济开发区|开发区|新区)', text[it.end():]) and re.search('广州市', pettern): # 城市不匹配为区的地址 修复 滨州北海经济开发区 北海新区 等提取为北海
                    continue
                addr.append((it.group(0), it.start(), it.end()))
                if re.search('^([分支](公司|局|行|校|院|干?线)|\w{,3}段|地铁|(火车|高铁)?站|\w{,3}项目)', text[it.end():]):
                    addr.append((it.group(0), it.start(), it.end()))
            return addr


        def chage_area2score(group_list, max_len):
            '''
            把匹配的的地址转为分数
            :param group_list: [('name', b, e)]
            :return:
            '''
            area_list = []
            if group_list != []:
                for it in group_list:
                    name, b, e = it
                    area_list.append((name, (e - b + e) / max_len / 2))
            return area_list

        def find_whole_areas(text):
            '''
            通过正则匹配字符串返回地址
            :param pettern: 地址正则 广东省|广西省|...
            :param text: 待匹配文本
            :return:
            '''
            pettern = "((?P<prov>%s)(?P<city>%s)?(?P<dist>%s)?)|((?P<city1>%s)(?P<dist1>%s)?)|(?P<dist2>%s)" % (
            p_pro, p_city, p_dis, p_city, p_dis, p_dis)
            province_l, city_l, district_l = [], [], []
            for it in re.finditer(pettern, text):
                if re.search('[省市区县旗盟]', it.group(0)) == None and re.search(
                        '^([东南西北中一二三四五六七八九十大小]?(村|镇|街|路|道|社区)|酒店|宾馆)', text[it.end():]):
                    continue
                if it.group(0) == '站前':  # 20240314 修复类似 中铁二局新建沪苏湖铁路工程站前VI标项目 错识别为 省份：辽宁， 城市：营口，区县：站前
                    continue
                for k, v in it.groupdict().items():
                    if v != None:
                        if k in ['prov']:
                            province_l.append((it.group(k), it.start(k), it.end(k)))
                        elif k in ['city', 'city1']:
                            if re.search('^(经济开发区|开发区|新区)', text[it.end(k):]):  # 城市不匹配为区的地址 修复 滨州北海经济开发区 北海新区 等提取为北海
                                continue
                            city_l.append((it.group(k), it.start(k), it.end(k)))
                            if re.search('^([分支](公司|局|行|校|院|干?线)|\w{,3}段|地铁|(火车|高铁)?站|\w{,3}项目)', text[it.end(k):]):
                                city_l.append((it.group(k), it.start(k), it.end(k)))
                        elif k in ['dist', 'dist1', 'dist2']:
                            if it.group(k)=='昌江' and '景德镇' not in it.group(0):
                                district_l.append(('昌江黎族', it.start(k), it.end(k)))
                            else:
                                district_l.append((it.group(k), it.start(k), it.end(k)))
            return province_l, city_l, district_l

        def get_pro_city_dis_score(text, text_weight=1):
            text = re.sub('复合肥|海南岛|兴业银行|双河口|阳光|杭州湾|新城区|中粮屯河|老城(区|改造|更新|升级|翻新)|沙县小吃|北京时间', ' ', text) # 544151395 赤壁市老城区燃气管道老化更新改造
            text = re.sub('珠海城市', '珠海', text)  # 修复 426624023 珠海城市 预测为海城市
            text = re.sub('怒江州', '怒江傈僳族自治州', text)  # 修复 423589589  所属地域：怒江州 识别为广西 - 崇左 - 江州
            text = re.sub('茂名滨海新区', '茂名市', text)
            text = re.sub('中山([东南西][部区环]|黄圃|南头|东凤|小榄|石岐|翠亨|南朗)', '中山市', text)
            text = re.sub('横州市', '横县', text) # 例：547363890 修复广西南宁横州 不在地区表问题
            ser = re.search('海南(昌江|白沙|乐东|陵水|保亭|琼中)(黎族)?', text)
            if ser and '黎族' not in ser.group(0):
                text = text.replace(ser.group(0), ser.group(0)+'黎族')
            for k, v in self.area_variance_dic.items(): # 20241113 根据地区变更信息替换文本
                text = text.replace(k, v)
            # province_l = find_areas(p_pro, text)
            # city_l = find_areas(p_city, text)
            # district_l = find_areas(p_dis, text)

            province_l, city_l, district_l = find_whole_areas(text) # 20240703 优化地址提取，解决类似 海南昌江 得到 海南 南昌 结果

            # if len(province_l) == len(city_l) == 0:
            #     district_l = [it for it in district_l if
            #                   re.search('[市县旗区]$', it[0])]  # 20240428去掉只有区县地址且不是全称的匹配，避免错误 例 凌云工业股份有限公司 提取地区为广西白色凌云

            province_l = chage_area2score(province_l, max_len=len(text))
            city_l = chage_area2score(city_l, max_len=len(text))
            district_l = chage_area2score(district_l, max_len=len(text))

            pro_ids = dict()
            city_ids = dict()
            dis_ids = dict()
            for pro in province_l:
                name, score = pro
                assert (name in full_dic['province'] or name in short_dic['province'])
                if name in full_dic['province']:
                    idx = full_dic['province'][name]
                    if idx not in pro_ids:
                        pro_ids[idx] = 0
                    pro_ids[idx] += (score + 1)
                else:
                    idx = short_dic['province'][name]
                    if idx not in pro_ids:
                        pro_ids[idx] = 0
                    pro_ids[idx] += (score + 0)

            for city in city_l:
                name, score = city
                if name in full_dic['city']:
                    w = 0.1 if len(full_dic['city'][name]) > 1 else 1
                    for idx in full_dic['city'][name]:
                        if idx not in city_ids:
                            city_ids[idx] = 0
                        # weight = idx_dic[idx]['权重']
                        city_ids[idx] += (score + 2) * w

                        pro_idx = idx_dic[idx]['省']
                        if pro_idx in pro_ids:
                            pro_ids[pro_idx] += (score + 2) * w
                        else:
                            pro_ids[pro_idx] = (score + 2) * w * 0.5
                elif name in short_dic['city']:
                    w = 0.1 if len(short_dic['city'][name]) > 1 else 1
                    for idx in short_dic['city'][name]:
                        if idx not in city_ids:
                            city_ids[idx] = 0
                        weight = idx_dic[idx]['权重']
                        city_ids[idx] += (score + 1) * w * weight

                        pro_idx = idx_dic[idx]['省']
                        if pro_idx in pro_ids:
                            pro_ids[pro_idx] += (score + 1) * w * weight
                        else:
                            pro_ids[pro_idx] = (score + 1) * w * weight * 0.5

            for dis in district_l:
                name, score = dis
                if name in full_dic['district']:
                    w = 0.1 if len(full_dic['district'][name]) > 1 else 1
                    for idx in full_dic['district'][name]:
                        if idx not in dis_ids:
                            dis_ids[idx] = 0
                        # weight = idx_dic[idx]['权重']
                        dis_ids[idx] += (score + 1) * w

                        pro_idx = idx_dic[idx]['省']
                        if pro_idx in pro_ids:
                            pro_ids[pro_idx] += (score + 1) * w
                        else:
                            pro_ids[pro_idx] = (score + 1) * w * 0.5
                        city_idx = idx_dic[idx]['市']
                        if city_idx in city_ids:
                            city_ids[city_idx] += (score + 1) * w
                        else:
                            city_ids[city_idx] = (score + 1) * w * 0.5
                elif name in short_dic['district']:
                    w = 0.1 if len(short_dic['district'][name]) > 1 else 1
                    for idx in short_dic['district'][name]:
                        if idx not in dis_ids:
                            dis_ids[idx] = 0
                        weight = idx_dic[idx]['权重']
                        dis_ids[idx] += (score + 0) * w
                        if idx_dic[idx]['市'] not in city_ids and idx_dic[idx]['省'] not in pro_ids: # 20241111 区县简称不在获取到的省、市范围内的过滤掉
                            continue
                        pro_idx = idx_dic[idx]['省']
                        if pro_idx in pro_ids:
                            pro_ids[pro_idx] += (score + 0) * w * weight
                        # else: # 20241015 注销 区县简称且不在提取的省市下面，不加分，避免提取错误 例：536550843
                        #     pro_ids[pro_idx] = (score + 0) * w * weight * 0.5
                        city_idx = idx_dic[idx]['市']
                        if city_idx in city_ids:
                            city_ids[city_idx] += (score + 0) * w * weight
                        # else: # 20241015 注销 区县简称且不在提取的省市下面，不加分，避免提取错误 例：536550843
                        #     city_ids[city_idx] = (score + 0) * w * weight * 0.1
                        elif pro_idx in pro_ids:
                            city_ids[city_idx] = (score + 0) * w * weight * 0.1

            for k, v in pro_ids.items():
                pro_ids[k] = v * text_weight
            for k, v in city_ids.items():
                city_ids[k] = v * text_weight
            for k, v in dis_ids.items():
                dis_ids[k] = v * text_weight
            return pro_ids, city_ids, dis_ids

        area_dic = {'area': '全国', 'province': '全国', 'city': '未知', 'district': '未知', "is_in_text": False}

        pro_ids, city_ids, dis_ids = get_pro_city_dis_score(text)
        pro_ids1, city_ids1, dis_ids1 = get_pro_city_dis_score(web_name, text_weight=0.01) # 20240422 修改为站源名称只取前三字，避免类似 459056219 中金岭南阳光采购平台 错提取阳光
        for k in pro_ids1:
            if k in pro_ids:
                pro_ids[k] += pro_ids1[k]
            else:
                pro_ids[k] = pro_ids1[k]
        for k in city_ids1:
            if k in city_ids:
                city_ids[k] += city_ids1[k]
            else:
                city_ids[k] = city_ids1[k]
        for k in dis_ids1:
            if k in dis_ids:
                dis_ids[k] += dis_ids1[k]
            else:
                dis_ids[k] = dis_ids1[k]

        big_area, pred_pro, pred_city, pred_dis = get_final_addr(pro_ids, city_ids, dis_ids)
        if big_area != "":
            area_dic['area'] = big_area
        if pred_pro != "":
            area_dic['province'] = pred_pro
        if pred_city !=  "":
            area_dic['city'] = pred_city
        if pred_dis != "":
            area_dic['district'] = pred_dis
        if in_content:
            area_dic['is_in_text'] = True

        return {'district': area_dic}

    def predict(self, project_name, prem, title, list_articles, web_source_name = "", list_entitys=""):
        '''
        先匹配 project_name+tenderee+tenderee_address， 如果缺少省或市 再匹配 title+content
        :param project_name:
        :param prem:
        :param title:
        :param list_articles:
        :param web_source_name:
        :return:
        '''

        def get_ree_addr(prem):
            tenderee = ""
            tenderee_address = ""
            try:
                for v in prem[0]['prem'].values():
                    for link in v['roleList']:
                        if link['role_name'] == 'tenderee' and tenderee == "":
                            tenderee = link['role_text']
                            tenderee_address = link['address']
            except Exception as e:
                print('解析prem 获取招标人、及地址出错')
            return tenderee, tenderee_address

        def get_role_address(text):
            '''正则匹配获取招标人地址
               3：地址直接在招标人后面 招标人：xxx,地址：xxx
               4：招标、代理一起，两个地址一起 招标人：xxx， 代理人：xxx, 地址：xxx， 地址：xxx.
            '''
            p3 = '(招标|采购|甲)(人|方|单位)(信息：|（甲方）)?(名称)?：[\w（）]{4,15}，(联系)?地址：(?P<addr>(\w{1,13}(自治[区州县旗]|地区|[省市区县旗盟])[^\w]*)+|\w{2,15}[，。])'
            p4 = '(招标|采购|甲)(人|方|单位)(信息：|（甲方）)?(名称)?：[\w（）]{4,15}，(招标|采购)?代理(人|机构)(名称)?：[\w（）]{4,15}，(联系)?地址：(?P<addr>(\w{1,13}(自治[区州县旗]|地区|[省市区县旗盟])[^\w]*)+|\w{2,15}[，。])'
            p5 = '(采购|招标)(人|单位)(联系)?地址：(?P<addr>(\w{1,13}(自治[区州县旗]|地区|[省市区县旗盟])[^\w]*)+|\w{2,15}[，。])'
            if re.search(p3, text):
                return re.search(p3, text).group('addr')
            elif re.search(p4, text):
                return re.search(p4, text).group('addr')
            elif re.search(p5, text):
                return re.search(p5, text).group('addr')
            else:
                return ''

        def get_project_addr(text):
            p1 = '(项目|施工|实施|建设|工程|服务|交货|送货|收货|展示|看样|拍卖)(地址|地点|位置|所在地区?)(位于)?：(?P<addr>(\w{1,13}(自治[区州县旗]|地区|[省市区县旗盟])[^\w]*)+([\w（）]{,20}[，。])?|\w{2,15}[，。])'
            p2 = '项目位于(?P<addr>\w{2}市\w{2,4}区)'
            if re.search(p1, text):
                return re.search(p1, text).group('addr')
            elif re.search(p2, text):
                return re.search(p2, text).group('addr')
            else:
                return ''

        def get_bid_addr(text):
            p2 = '(磋商|谈判|开标|投标|评标|报名|递交|评审|发售|所属)(地址|地点|所在地区?|地域)：(?P<addr>(\w{1,13}(自治[区州县旗]|地区|[省市区县旗盟])[^\w]*)+|\w{2,15}[，。])'
            if re.search(p2, text):
                return re.search(p2, text).group('addr')
            else:
                return ''

        def get_all_addr(list_entitys):
            tenderee_l = []
            addr_l = []
            for ent in list_entitys[0]:
                if ent.entity_type == 'location' and len(ent.entity_text) > 2:
                    addr_l.append(ent.entity_text)
                elif ent.entity_type in ['org', 'company']:
                    if ent.label in [0, 1]:  # 加招标或代理
                        tenderee_l.append(ent.entity_text)
            return ' '.join(addr_l), ' '.join(tenderee_l)

        def get_title_addr(text):
            p1 = '(?P<addr>(\w{1,13}(自治[区州县旗]|地区|[省市区县旗盟])[^\w]*)+|\w{2,15}[，。])'
            if re.search(p1, text):
                return re.search(p1, text).group('addr')
            else:
                return ''

        if '##attachment##' in list_articles[0].content:
            content, attachment = list_articles[0].content.split('##attachment##')
            if len(content) < 200:
                content += attachment
        else:
            content = list_articles[0].content

        tenderee, tenderee_address = get_ree_addr(prem)
        msc = ""
        pro_addr = get_project_addr(content)
        if pro_addr != "" and re.search('(采购人|招标人)?指定地点', pro_addr)==None: # 排除错误项目地址 例：554024168 1.5服务地点：采购人指定地点。
            msc += '使用规则提取的项目地址；'
            tenderee_address = pro_addr
        else:
            role_addr = get_role_address(content)
            if role_addr != "" and re.search('(采购人|招标人)?指定地点', role_addr)==None:
                msc += '使用规则提取的联系人地址；'
                tenderee_address = role_addr

        if tenderee_address == "":
            title_addr = get_title_addr(title)
            if title_addr != "":
                msc += '使用规则提取的标题地址；'
                tenderee_address = title_addr
            else:
                bid_addr = get_bid_addr(content)
                if bid_addr != "":
                    msc += '使用规则提取的开标地址；'
                    tenderee_address = bid_addr

        project_name = str(project_name)
        tenderee = str(tenderee)

        # print('招标人地址',role_addr, tenderee_address)

        project_name = project_name + title if project_name not in title else title
        # project_name = project_name.replace(tenderee, '')
        if len(project_name)>3:
            entity_list = getNers([project_name],useselffool=False) # 2024/4/26 修改为去重项目名称中所有公司名称
            for tup in entity_list[0]:
                if tup[2] in ['org', 'company']:
                    project_name = project_name.replace(tup[3], '')

        text1 = "{0} {1} {2}".format(tenderee, tenderee_address, project_name)

        web_source_name = str(web_source_name)  # 修复某些不是字符串类型造成报错
        text1 = re.sub('复合肥|铁路|公路|新会计', ' ', text1)  # 预防提取错 合肥 路南 新会 等地区

        if pro_addr and re.search('\w{2,}([市县旗盟]|自治[区州县旗])', pro_addr):
            if re.search('[市县旗盟]', pro_addr)==None: # 修复 486623506 项目地址不完整
                pro_addr = text1 + ' '+ pro_addr
            msc += '## 使用项目地址输入：%s ##；' % pro_addr
            rs = self.get_area(pro_addr, '')
            msc += '预测结果：省份：%s， 城市：%s，区县：%s；' % (
                rs['district']['province'], rs['district']['city'], rs['district']['district'])
            if rs['district']['province'] != '全国' and rs['district']['city'] != '未知':
                # print('地区匹配：', msc)
                return rs

        # print('text1:', text1)
        msc += '## 第一次预测输入：%s ##；' % text1
        rs = self.get_area(text1, '')  # 2024/4/22 调整第一次输入不带站源名称，避免出错
        msc += '预测结果：省份：%s， 城市：%s，区县：%s；' % (
            rs['district']['province'], rs['district']['city'], rs['district']['district'])
        # self.f.write('%s %s \n' % (list_articles[0].id, msc))
        # print('地区匹配：', msc)
        if rs['district']['province'] == '全国' or rs['district']['city'] == '未知':
            # msc = ""
            all_addr, tenderees = get_all_addr(list_entitys)
            text2 = tenderees + " " + all_addr + ' ' + title
            msc += '使用实体列表所有招标人+所有地址；'
            # text2 += title + content if len(content)<2000 else title + content[:1000] + content[-1000:]
            text2 = re.sub('复合肥|铁路|公路|新会计', ' ', text2)
            # print('text2:', text2)
            msc += '## 第二次预测输入：%s %s##' % (text2,web_source_name)
            rs2 = self.get_area(text2, web_source_name, in_content=True)
            # rs2['district']['is_in_text'] = True
            if rs['district']['province'] == '全国' and rs2['district']['province'] != '全国':
                rs = rs2
            elif rs['district']['province'] == rs2['district']['province'] and rs2['district']['city'] != '未知':
                rs = rs2
            msc += '预测结果：省份：%s， 城市：%s，区县：%s' % (
                rs['district']['province'], rs['district']['city'], rs['district']['district'])
        # self.f.write('%s %s \n'%(list_articles[0].id, msc))
        # print('地区匹配：', msc)
        return rs