"""Phase 6 migration from interface/getAttributes.py. This module hosts the role/money/KM-matching functions migrated verbatim from ``BiddingKG.dl.interface.getAttributes`` as part of the Phase 6 assembly re-organization. It contains the legal role-combination search, expectation scoring, role-list extraction, KM-based attribute dispatch, and the large ``findAttributeAfterEntity`` routine that links money / service-time / ratio / contacts attributes to role entities, plus the package-role-money entry points ``initPackageAttr`` and ``getPackageRoleMoney``. The relation-extraction model is lazily loaded on first use to avoid triggering a model load at import time. """ from __future__ import absolute_import import re import copy import math import uuid from decimal import Decimal from scipy.optimize import linear_sum_assignment import numpy as np from BiddingKG.dl.common.Utils import getUnifyMoney from BiddingKG.dl.common.attr_utils import extract_serviceTime from BiddingKG.dl.interface.Entitys import PREM, Role, Entity, Match from BiddingKG.dl.assembly.package_scope import ( getPackage, getPackagesFromArticle, dict_role_id, role2id_dict, ) _relationExtraction_model = None def _get_relationExtraction_model(): global _relationExtraction_model if _relationExtraction_model is None: from BiddingKG.dl.interface.modelFactory import Model_relation_extraction _relationExtraction_model = Model_relation_extraction() return _relationExtraction_model __all__ = [ "get_legal_comba", "get_dict_entity_prob", "getSumExpectation", "getRoleList", "dispatch", "findAttributeAfterEntity", "initPackageAttr", "getPackageRoleMoney", ] def get_legal_comba(list_entity,dict_role_combination): #拿到一个包中所有合法的组合 def circle_package(_dict_legal_combination): list_dict_role_first = [] for _role in _dict_legal_combination: if len(list_dict_role_first)==0: for _entity in _dict_legal_combination[_role]: if _entity !="": list_dict_role_first.append({_role:_entity}) else: list_dict_role_after = [] _find_count = 0 for _entity in _dict_legal_combination[_role]: if _entity !="": for _dict in list_dict_role_first: _flag = True for _key1 in _dict: if _entity==_dict[_key1]: #修改为招标人和代理人可以为同一个 if str(_key1) in ["0","1"] and str(_role) in ["0","1"]: _flag = True else: _flag = False if _flag: _find_count += 1 _new_dict = copy.copy(_dict) _new_dict[_role] = _entity if len(list_dict_role_after)>100000: break list_dict_role_after.append(_new_dict) else: # 2021/5/25 update,同一实体（entity_text）不同角色 if len(list_dict_role_after) > 100000: break for _dict in list_dict_role_first: for _key1 in _dict: if _entity == _dict[_key1]: _new_dict = copy.copy(_dict) _new_dict.pop(_key1) _new_dict[_role] = _entity list_dict_role_after.append({_role:_entity}) if len(list_dict_role_after)==0: pass else: list_dict_role_first.extend(list_dict_role_after) return list_dict_role_first def recursive_package(_dict_legal_combination,set_legal_entity,dict_one_selution,list_all_selution): last_layer = False #若是空组合则放回空 if len(_dict_legal_combination.keys())==0: return [] #递归到最后一层则修改状态 if len(_dict_legal_combination.keys())==1: last_layer = True #取一个角色开始进行遍历 _key_role = list(_dict_legal_combination.keys())[0] for item in _dict_legal_combination[_key_role]: copy_dict_one_selution = copy.copy(dict_one_selution) copy_dict_legal_combination = {} copy_set_legal_entity = copy.copy(set_legal_entity) #复制余下的所有角色，进行下一轮递归 for _key in _dict_legal_combination.keys(): if _key!=_key_role: copy_dict_legal_combination[_key] = _dict_legal_combination[_key] #修改为招标人和代理人可以为同一个 if item !="": _flag = True if str(_key_role) in ["0","1"]: for _key_flag in copy_dict_one_selution: if _key_flag not in ["0","1"] and copy_dict_one_selution[_key_flag]==item: _flag = False else: for _key_flag in copy_dict_one_selution: if copy_dict_one_selution[_key_flag]==item: _flag = False if _flag: copy_dict_one_selution[_key_role] = item ''' if item not in copy_set_legal_entity: if item !="": copy_dict_one_selution[_key_role] = item ''' copy_set_legal_entity.add(item) if last_layer: list_all_selution.append(copy_dict_one_selution) else: recursive_package(copy_dict_legal_combination,copy_set_legal_entity,copy_dict_one_selution,list_all_selution) #递归匹配各个包的结果 def recursive_packages(_dict_legal_combination,dict_one_selution,list_all_selution): last_layer = False if len(_dict_legal_combination.keys())==0: return [] if len(_dict_legal_combination.keys())==1: last_layer = True _key_pack = list(_dict_legal_combination.keys())[0] for item in _dict_legal_combination[_key_pack]: copy_dict_one_selution = copy.copy(dict_one_selution) copy_dict_legal_combination = {} for _key in _dict_legal_combination.keys(): if _key!=_key_pack: copy_dict_legal_combination[_key] = _dict_legal_combination[_key] for _key_role in item.keys(): copy_dict_one_selution[_key_pack+"$$"+_key_role] = item[_key_role] if last_layer: list_all_selution.append(copy_dict_one_selution) else: recursive_packages(copy_dict_legal_combination,copy_dict_one_selution,list_all_selution) return list_all_selution #循环获取所有包组合 def circle_pageages(_dict_legal_combination): list_all_selution = [] for _key_pack in _dict_legal_combination.keys(): list_key_selution = [] for item in _dict_legal_combination[_key_pack]: _dict = dict() for _key_role in item.keys(): _dict[_key_pack+"$$"+_key_role] = item[_key_role] list_key_selution.append(_dict) if len(list_all_selution)==0: list_all_selution = list_key_selution else: _list_all_selution = [] for item_1 in list_all_selution: for item_2 in list_key_selution: _list_all_selution.append(dict(item_1,**item_2)) list_all_selution = _list_all_selution return list_all_selution #拿到各个包解析之后的结果 _dict_legal_combination = {} for packageName in dict_role_combination.keys(): _list_all_selution = [] # recursive_package(dict_role_combination[packageName], set(), {}, _list_all_selution) _list_all_selution = circle_package(dict_role_combination[packageName]) ''' # print("===1") # print(packageName) for item in _list_all_selution: # print(item) # print("===2") ''' #去除包含子集 list_all_selution_simple = [] _list_set_all_selution = [] for item_selution in _list_all_selution: item_set_selution = set() for _key in item_selution.keys(): item_set_selution.add((_key,item_selution[_key])) _list_set_all_selution.append(item_set_selution) if len(_list_set_all_selution)>1000: _dict_legal_combination[packageName] = _list_all_selution continue for i in range(len(_list_set_all_selution)): be_included = False for j in range(len(_list_set_all_selution)): if i!=j: if len(set(_list_set_all_selution[i])&set(_list_set_all_selution[j]))==len(_list_set_all_selution[i]) and len(_list_set_all_selution[i])!=len(_list_set_all_selution[j]): be_included = True if not be_included: list_all_selution_simple.append(_list_all_selution[i]) _dict_legal_combination[packageName] = list_all_selution_simple _list_final_comba = [] #对各个包的结果进行排列组合 _comba_count = 1 for _key in _dict_legal_combination.keys(): _comba_count *= len(_dict_legal_combination[_key]) #如果过大，则每个包只取概率最大的那个 dict_pack_entity_prob = get_dict_entity_prob(list_entity) if _comba_count>250: new_dict_legal_combination = dict() for _key_pack in _dict_legal_combination.keys(): MAX_PROB = -1000 _MAX_PROB_COMBA = None for item in _dict_legal_combination[_key_pack]: # print(_key_pack,item) _dict = dict() for _key in item.keys(): _dict[str(_key_pack)+"$$"+str(_key)] = item[_key] _prob = getSumExpectation(dict_pack_entity_prob, _dict) if _prob>MAX_PROB: MAX_PROB = _prob _MAX_PROB_COMBA = [item] if _MAX_PROB_COMBA is not None: new_dict_legal_combination[_key_pack] = _MAX_PROB_COMBA _dict_legal_combination = new_dict_legal_combination #recursive_packages(_dict_legal_combination, {}, _list_final_comba) _list_final_comba = circle_pageages(_dict_legal_combination) #除了Project包(招标人和代理人)，其他包是不会有冲突的 #查看是否有一个实体出现在了Project包和其他包中，如有，要进行裁剪 _list_real_comba = [] for dict_item in _list_final_comba: set_project = set() set_other = set() for _key in list(dict_item.keys()): if _key.split("$$")[0]=="Project": set_project.add(dict_item[_key]) else: set_other.add(dict_item[_key]) set_common = set_project&set_other if len(set_common)>0: dict_project = {} dict_not_project = {} for _key in list(dict_item.keys()): if dict_item[_key] in set_common: if str(_key.split("$$")[0])=="Project": dict_project[_key] = dict_item[_key] else: dict_not_project[_key] = dict_item[_key] else: dict_project[_key] = dict_item[_key] dict_not_project[_key] = dict_item[_key] _list_real_comba.append(dict_project) _list_real_comba.append(dict_not_project) else: _list_real_comba.append(dict_item) return _list_real_comba def get_dict_entity_prob(list_entity,on_value=0.5): dict_pack_entity_prob = {} for in_attachment in [False,True]: identified_role = [] if in_attachment==True: identified_role = [value[0] for value in dict_pack_entity_prob.values()] for entity in list_entity: if entity.entity_type in ['org','company'] and entity.in_attachment==in_attachment: values = entity.values role_prob = float(values[int(entity.label)]) _key = entity.packageName+"$$"+str(entity.label) if role_prob>=on_value and str(entity.label)!="5": _key_prob = _key+"$text$"+entity.entity_text if in_attachment == True: role_prob = 0.8 if role_prob>0.8 else role_prob #附件的概率修改低点 # if entity.entity_text in identified_role: # 2023/7/3 注释掉，选取概率最大的作为连接概率 # continue if _key_prob in dict_pack_entity_prob: # new_prob = role_prob+dict_pack_entity_prob[_key_prob][1] if role_prob>0.9 else max(role_prob, dict_pack_entity_prob[_key_prob][1]) # dict_pack_entity_prob[_key_prob] = [entity.entity_text, new_prob] #公司同角色多次出现概率累计 if role_prob>dict_pack_entity_prob[_key_prob][1]: dict_pack_entity_prob[_key_prob] = [entity.entity_text,role_prob] else: dict_pack_entity_prob[_key_prob] = [entity.entity_text,role_prob] return dict_pack_entity_prob def getSumExpectation(dict_pack_entity_prob,combination,on_value=0.5): ''' expect = 0 for entity in list_entity: if entity.entity_type in ['org','company']: values = entity.values role_prob = float(values[int(entity.label)]) _key = entity.packageName+"$$"+str(entity.label) if role_prob>on_value and str(entity.label)!="5": if _key in combination.keys() and combination[_key]==entity.entity_text: expect += math.pow(role_prob,4) else: expect -= math.pow(role_prob,4) ''' #修改为同一个实体只取对应包-角色的最大的概率值 expect = 0 dict_entity_prob = {} for _key_pack_entity in dict_pack_entity_prob: _key_pack = _key_pack_entity.split("$text$")[0] role_prob = dict_pack_entity_prob[_key_pack_entity][1] if _key_pack in combination.keys() and combination[_key_pack]==dict_pack_entity_prob[_key_pack_entity][0]: if _key_pack_entity in dict_entity_prob.keys(): if dict_entity_prob[_key_pack_entity]-role_prob: dict_entity_prob[_key_pack_entity] = -role_prob else: dict_entity_prob[_key_pack_entity] = -role_prob # for entity in list_entity: # if entity.entity_type in ['org','company']: # values = entity.values # role_prob = float(values[int(entity.label)]) # _key = entity.packageName+"$$"+str(entity.label) # if role_prob>=on_value and str(entity.label)!="5": # if _key in combination.keys() and combination[_key]==entity.entity_text: # _key_prob = _key+entity.entity_text # if _key_prob in dict_entity_prob.keys(): # if dict_entity_prob[_key_prob]-role_prob: # dict_entity_prob[_key_prob] = -role_prob # else: # dict_entity_prob[_key_prob] = -role_prob for _key in dict_entity_prob.keys(): symbol = 1 if dict_entity_prob[_key]>0 else -1 expect += symbol*math.pow(dict_entity_prob[_key],2) return expect def getRoleList(list_sentence,list_entity,on_value = 0.5): ''' @summary: 搜索树，得到所有不矛盾的角色组合，取合计期望值最大的作为结果返回 @param: list_sentence:文章所有的sentence list_entity:文章所有的实体 on_value:概率阈值 @return:文章的角色list ''' pack = getPackagesFromArticle(list_sentence,list_entity) if pack is None: return None # PackageList,PackageSet,dict_PackageCode = pack PackageList,PackageSet,dict_PackageCode,main_body_pack = pack #拿到所有可能的情况 dict_role_combination = {} tenderee_or_agency_set = set() # 记录所有预测为招标或代理的实体集合 win_tenderer_set = set() # 记录所有预测为中标的实体集合 # print(PackageList) #拿到各个实体的packageName,packageCode main_contain_winner = False # 2024/10/11 判断正文是否包含中标人 for entity in list_entity: if entity.entity_type in ['org','company'] and entity.label==2 and entity.values[entity.label]>0.7 and entity.in_attachment==False: main_contain_winner = True break for entity in list_entity: if entity.entity_type in ['org','company']: #限制附件里角色values[label]最大概率prob max_prob = 0.85 if str(entity.label)!="5" and entity.in_attachment: if entity.values[entity.label]>max_prob: entity.values[entity.label] = max_prob #过滤掉字数小于3个的实体 if len(entity.entity_text)<=3: continue values = entity.values role_prob = float(values[int(entity.label)]) if role_prob>=on_value and str(entity.label)!="5": if main_contain_winner and entity.in_attachment and entity.label in [2,3,4]: # 2024/10/11 正文包含中标人，不再提取附件中标人 避免 例：504046747 附件角色OCR错字变两个标段 continue if str(entity.label) in ["0","1"]: packageName = "Project" else: if len(PackageSet)>0: packagePointer,_ = getPackage(PackageList,entity.sentence_index,entity.begin_index,"role-"+str(entity.label)) if packagePointer is None: #continue packageName = "Project" # print(entity.entity_text, packageName,entity.sentence_index,entity.begin_index) else: #add pointer_pack entity.pointer_pack = packagePointer packageName = packagePointer.entity_text # print(entity.entity_text, packageName) else: packageName = "Project" find_flag = False if packageName in dict_PackageCode.keys(): packageCode = dict_PackageCode[packageName] else: packageCode = "" entity.packageCode = packageCode role_name = dict_role_id.get(str(entity.label)) entity.roleName = role_name entity.packageName = packageName if entity.packageName in dict_role_combination.keys(): if str(entity.label) in dict_role_combination[entity.packageName].keys(): dict_role_combination[entity.packageName][str(entity.label)].add(entity.entity_text) else: dict_role_combination[entity.packageName][str(entity.label)] = set([entity.entity_text]) else: dict_role_combination[entity.packageName] = {} #初始化空值 roleIds = [0,1,2,3,4] for _roleId in roleIds: dict_role_combination[entity.packageName][str(_roleId)] = set([""]) dict_role_combination[entity.packageName][str(entity.label)].add(entity.entity_text) list_real_comba = get_legal_comba(list_entity,dict_role_combination) # print("===role_combination",dict_role_combination) # print("== real_comba",list_real_comba) #拿到最大期望值的组合 max_index = 0 max_expect = -100 _index = 0 dict_pack_entity_prob = get_dict_entity_prob(list_entity) for item_combination in list_real_comba: expect = getSumExpectation(dict_pack_entity_prob, item_combination) if expect>max_expect: max_index = _index max_expect = expect _index += 1 RoleList = [] RoleSet = set() if len(list_real_comba)>0: for _key in list_real_comba[max_index].keys(): packageName = _key.split("$$")[0] label = _key.split("$$")[1] role_name = dict_role_id.get(str(label)) entity_text = list_real_comba[max_index][_key] entity_prob = dict_pack_entity_prob.get(_key+'$text$'+entity_text, ['',0])[1] # entity_text = list_real_comba[max_index][_key][0] # entity_prob = list_real_comba[max_index][_key][1] if packageName in dict_PackageCode.keys(): packagecode = dict_PackageCode.get(packageName) else: packagecode = "" RoleList.append(PREM(packageName,packagecode,role_name,entity_text,entity_prob,0,0.0,[])) if str(label) in ["0", "1"]: tenderee_or_agency_set.add(entity_text) elif str(label) in ["2"] and entity_prob > 0.8: win_tenderer_set.add(entity_text) # if len(list_real_comba) > 1 and label == '2': # 20240809 由于包号对应不上注销 # multi_winner = [] # for comba in list_real_comba: # tmp_ent = comba.get(_key, '') # tmp_prob = dict_pack_entity_prob.get(_key+'$text$'+tmp_ent, ['',0])[1] # if tmp_ent !='' and tmp_prob>0.8: # multi_winner.append(comba[_key]) # if len(set(multi_winner)) > 1: # RoleList[-1].multi_winner = multi_winner # print('RoleList: ', RoleList) RoleSet.add(entity_text) #根据最优树来修正list_entity中角色对包的连接 for _entity in list_entity: if _entity.pointer_pack is not None: _pack_name = _entity.pointer_pack.entity_text _find_flag = False for _prem in RoleList: if _prem.packageName==_pack_name and _prem.entity_text==_entity.entity_text: _find_flag = True if not _find_flag: _entity.pointer_pack = None return RoleList,RoleSet,PackageList,PackageSet,win_tenderer_set,tenderee_or_agency_set,main_body_pack def dispatch(match_list): main_roles = list(set([match.main_role for match in match_list])) # print('main_roles',[i.entity_text for i in main_roles]) attributes = list(set([match.attribute for match in match_list])) # try: # print('attributes',[i.entity_text for i in attributes]) # except: # pass label = np.zeros(shape=(len(main_roles), len(attributes))) for match in match_list: main_role = match.main_role attribute = match.attribute value = match.value label[main_roles.index(main_role), attributes.index(attribute)] = value + 10000 # print(label) gragh = -label # km算法 row, col = linear_sum_assignment(gragh) max_dispatch = [(i, j) for i, j, value in zip(row, col, gragh[row, col]) if value] # return [Match(main_roles[row], attributes[col]) for row, col in max_dispatch] return [(main_roles[row], attributes[col]) for row, col in max_dispatch] def findAttributeAfterEntity(PackDict,roleSet,PackageList,PackageSet,list_sentence,list_entity,list_outline,winter_scope,on_value = 0.5,on_value_person=0.5,sentence_len=4): ''' @param: PackDict:文章包dict roleSet:文章所有角色的公司名称 PackageList:文章的包信息 PackageSet:文章所有包的名称 list_entity:文章所有经过模型处理的实体 on_value:金额模型的阈值 on_value_person:联系人模型的阈值 sentence_len:公司和属性间隔句子的最大长度 @return:添加了属性信息的角色list ''' #根据roleid添加金额到rolelist中 def addMoneyByRoleid(packDict,packageName,roleid,money,money_prob): for i in range(len(packDict[packageName]["roleList"])): if packDict[packageName]["roleList"][i].role_name==dict_role_id.get(str(roleid)): if money_prob>packDict[packageName]["roleList"][i].money_prob: packDict[packageName]["roleList"][i].money = money packDict[packageName]["roleList"][i].money_prob = money_prob return packDict #根据实体名称添加金额到rolelist中 def addMoneyByEntity(packDict,packageName,entity,money,money_prob): for i in range(len(packDict[packageName]["roleList"])): if packDict[packageName]["roleList"][i].entity_text==entity: # if money_prob>packDict[packageName]["roleList"][i].money_prob: # packDict[packageName]["roleList"][i].money = money # packDict[packageName]["roleList"][i].money_prob = money_prob if packDict[packageName]["roleList"][i].money_prob==0 : # 2021/7/20第一次更新金额 if money.notes == '单价': packDict[packageName]["roleList"][i].unit_price = money.entity_text else: packDict[packageName]["roleList"][i].money = money.entity_text packDict[packageName]["roleList"][i].money_prob = money_prob packDict[packageName]["roleList"][i].money_unit = money.money_unit elif money_prob>packDict[packageName]["roleList"][i].money_prob+0.2 or (money.notes in ['大写'] and money.in_attachment==False): # 2021/7/20改为优先选择大写金额, # print('已连接金额概率：money_prob:',packDict[packageName]["roleList"][i].money_prob) # print('链接金额备注 ',money.notes, money.entity_text, money.values) if money.notes == '单价': packDict[packageName]["roleList"][i].unit_price = money.entity_text else: packDict[packageName]["roleList"][i].money = money.entity_text packDict[packageName]["roleList"][i].money_prob = money_prob packDict[packageName]["roleList"][i].money_unit = money.money_unit # print('连接中的金额：{0}, 单位：{1}'.format(money.entity_text, money.money_unit)) return packDict def addRatioByEntity(packDict,packageName,entity,ratio): for i in range(len(packDict[packageName]["roleList"])): if packDict[packageName]["roleList"][i].entity_text==entity: packDict[packageName]["roleList"][i].ratio = ratio.ratio_value def addServiceTimeByEntity(packDict,packageName,entity,serviceTime): for i in range(len(packDict[packageName]["roleList"])): if packDict[packageName]["roleList"][i].entity_text==entity and not packDict[packageName]["roleList"][i].serviceTime: # packDict[packageName]["roleList"][i].serviceTime = serviceTime.entity_text packDict[packageName]["roleList"][i].serviceTime = extract_serviceTime(serviceTime.entity_text,"") #根据实体名称得到角色 def getRoleWithText(packDict,entity_text): for pack in packDict.keys(): for i in range(len(packDict[pack]["roleList"])): if packDict[pack]["roleList"][i].entity_text==entity_text: return packDict[pack]["roleList"][i].role_name def doesEntityOrLinkedEntity_inRoleSet(entity,RoleSet): _list_entitys = [entity]+entity.linked_entitys for _entity in _list_entitys: if _entity.entity_text in RoleSet: return True p_entity = 0 # 2021/7/19 顺序比较金额，前面是后面的一万倍则把前面金额/10000 # money_list = [it for it in list_entity if it.entity_type=="money"] # for i in range(len(money_list)-1): # for j in range(1, len(money_list)): # if (float(money_list[i].entity_text) > 5000000000 or money_list[j].notes=='大写') and \ # Decimal(money_list[i].entity_text)/Decimal(money_list[j].entity_text)==10000: # money_list[i].entity_text = str(Decimal(money_list[i].entity_text)/10000) # # print('连接前修改大于50亿金额：前面是后面的一万倍则把前面金额/10000') '''同样金额同时有元及万元单位的，把万元的金额改为元''' wanyuan = [] yuan = [] for it in list_entity: if it.entity_type == "money" and float(it.entity_text)>1000000: # 20240523 修改为百万以上金额才对比万倍关系，其他又行业限额纠正避免有些万元单位提取不到从而被除一万 例：52435607 最高限价（万元）：22679.32 蜀冈招标控制价22679.32工程地点南路西侧（万元） if it.money_unit == '万元' or float(it.entity_text)>5000000000: wanyuan.append(it) if it.money_unit == '元' or float(it.entity_text)<5000000: yuan.append(it) if wanyuan != [] and yuan != []: for m1 in wanyuan: for m2 in yuan: if Decimal(m1.entity_text)/Decimal(m2.entity_text) == 10000: m1.entity_text = m2.entity_text #遍历所有实体 # while(p_entity=on_value: if str(entity.label)=="0": packagePointer,_ = getPackage(PackageList,entity.sentence_index,entity.begin_index,"money-"+str(entity.label)) if packagePointer is None: packageName = "Project" else: packageName = packagePointer.entity_text addMoneyByRoleid(PackDict, packageName, "0", entity.entity_text, entity.values[entity.label]) ''' ''' # 2020/11/25 与下面的联系人连接步骤重复，取消 if entity.entity_type=="person": if entity.values[entity.label]>=on_value_person: if str(entity.label)=="1": for i in range(len(PackDict["Project"]["roleList"])): if PackDict["Project"]["roleList"][i].role_name=="tenderee": PackDict["Project"]["roleList"][i].linklist.append((entity.entity_text,entity.person_phone)) # add pointer_person for _entity in list_entity: if dict_role_id.get(str(_entity.label))=="tenderee": for i in range(len(PackDict["Project"]["roleList"])): if PackDict["Project"]["roleList"][i].entity_text==_entity.entity_text and PackDict["Project"]["roleList"][i].role_name=="tenderee": _entity.pointer_person = entity elif str(entity.label)=="2": for i in range(len(PackDict["Project"]["roleList"])): if PackDict["Project"]["roleList"][i].role_name=="agency": PackDict["Project"]["roleList"][i].linklist.append((entity.entity_text,entity.person_phone)) # add pointer_person for _entity in list_entity: if dict_role_id.get(str(_entity.label))=="agency": for i in range(len(PackDict["Project"]["roleList"])): if PackDict["Project"]["roleList"][i].entity_text==_entity.entity_text and PackDict["Project"]["roleList"][i].role_name=="agency": _entity.pointer_person = entity ''' # #金额往前找实体 # if entity.entity_type=="money": # if entity.values[entity.label]>=on_value: # p_entity_money= p_entity # entity_money = list_entity[p_entity_money] # if len(PackageSet)>0: # packagePointer,_ = getPackage(PackageList,entity_money.sentence_index,entity_money.begin_index,"money-"+str(entity_money.entity_text)+"-"+str(entity_money.label)) # if packagePointer is None: # packageName_entity = "Project" # else: # packageName_entity = packagePointer.entity_text # else: # packageName_entity = "Project" # while(p_entity_money>0): # entity_before = list_entity[p_entity_money] # if entity_before.entity_type in ['org','company']: # if str(entity_before.label)=="1": # addMoneyByEntity(PackDict, packageName_entity, entity_before.entity_text, entity_money.entity_text, entity_money.values[entity_money.label]) # #add pointer_money # entity_before.pointer_money = entity_money # break # p_entity_money -= 1 #如果实体属于角色集合，则往后找属性 # if doesEntityOrLinkedEntity_inRoleSet(entity, roleSet): # # p_entity += 1 # #循环查找符合的属性 # while(p_entity=sentence_len: # p_entity -= 1 # break # #若是遇到公司实体，则跳出循环 # if entity_after.entity_type in ['org','company']: # p_entity -= 1 # break # if entity_after.values is not None: # if entity_after.entity_type=="money": # if entity_after.values[entity_after.label]>=on_value: # ''' # #招标金额从后往前找 # if str(entity_after.label)=="0": # packagePointer,_ = getPackage(PackageList,entity.sentence_index,entity.begin_index,"money-"+str(entity.label)) # if packagePointer is None: # packageName = "Project" # else: # packageName = packagePointer.entity_text # addMoneyByRoleid(PackDict, packageName, "0", entity_after.entity_text, entity_after.values[entity_after.label]) # ''' # if str(entity_after.label)=="1": # #print(entity_after.entity_text,entity.entity_text) # _list_entitys = [entity]+entity.linked_entitys # if len(PackageSet)>0: # packagePointer,_ = getPackage(PackageList,entity_after.sentence_index,entity_after.begin_index,"money-"+str(entity_after.entity_text)+"-"+str(entity_after.label)) # if packagePointer is None: # packageName_entity = "Project" # else: # packageName_entity = packagePointer.entity_text # else: # packageName_entity = "Project" # if str(entity.label) in ["2","3","4"]: # # addMoneyByEntity(PackDict, packageName_entity, entity.entity_text, entity_after.entity_text, entity_after.values[entity_after.label]) # if entity_after.notes == '单价' or float(entity_after.entity_text)<5000: #2021/12/17 调整小金额阈值，避免203608823.html 两次金额一次万元没提取到的情况 # addMoneyByEntity(PackDict, packageName_entity, entity.entity_text, entity_after, # 0.5) # entity.pointer_money = entity_after # # print('role zhao money', entity.entity_text, '中标金额：', entity_after.entity_text) # else: # addMoneyByEntity(PackDict, packageName_entity, entity.entity_text, entity_after, # entity_after.values[entity_after.label]) # entity.pointer_money = entity_after # # print('role zhao money', entity.entity_text, '中标金额：', entity_after.entity_text) # if entity_after.values[entity_after.label]>0.6: # break # 2021/7/16 新增，找到中标金额，非单价即停止，不再往后找金额 # #add pointer_money # # entity.pointer_money = entity_after # # print('role zhao money', entity.entity_text, '中标金额：', entity_after.entity_text) # # if entity_after.notes!='单价': # # break # 2021/7/16 新增，找到中标金额即停止，不再往后找金额 # ''' # if entity_after.entity_type=="person": # if entity_after.values[entity_after.label]>=on_value_person: # if str(entity_after.label)=="1": # for i in range(len(roleList)): # if roleList[i].role_name=="tenderee": # roleList[i].linklist.append((entity_after.entity_text,entity_after.person_phone)) # elif str(entity_after.label)=="2": # for i in range(len(roleList)): # if roleList[i].role_name=="agency": # roleList[i].linklist.append((entity_after.entity_text,entity_after.person_phone)) # elif str(entity_after.label)=="3": # _list_entitys = [entity]+entity.linked_entitys # for _entity in _list_entitys: # for i in range(len(roleList)): # if roleList[i].entity_text==_entity.entity_text: # if entity_after.sentence_index-_entity.sentence_index>1 and len(roleList[i].linklist)>0: # break # roleList[i].linklist.append((entity_after.entity_text,entity_after.person_phone)) # ''' # # p_entity += 1 # # p_entity += 1 # 记录每句的分词数量 tokens_num_dict = dict() last_tokens_num = 0 for sentence in list_sentence: _index = sentence.sentence_index if _index == 0: tokens_num_dict[_index] = 0 else: tokens_num_dict[_index] = tokens_num_dict[_index - 1] + last_tokens_num last_tokens_num = len(sentence.tokens) attribute_type = ['money','serviceTime','ratio']# 'money'仅指“中投标金额” for link_attribute in attribute_type: temp_entity_list = [] if link_attribute=="money": temp_entity_list = [ent for ent in list_entity if (ent.entity_type in ['org','company'] and ent.label in [2,3,4]) or (ent.entity_type=='money' and ent.label==1 and ent.values[ent.label]>=0.5)] # 删除重复的‘中投标金额’，一般为大小写两种样式 drop_tendererMoney = [] for ent_idx in range(len(temp_entity_list)-1): entity = temp_entity_list[ent_idx] if entity.entity_type=='money': next_entity = temp_entity_list[ent_idx+1] if next_entity.entity_type=='money': if getUnifyMoney(entity.entity_text)==getUnifyMoney(next_entity.entity_text): if (tokens_num_dict[next_entity.sentence_index] + next_entity.begin_index) - ( tokens_num_dict[entity.sentence_index] + entity.end_index) < 10: drop_tendererMoney.append(next_entity) for _drop in drop_tendererMoney: temp_entity_list.remove(_drop) elif link_attribute=="serviceTime": temp_entity_list = [ent for ent in list_entity if (ent.entity_type in ['org','company'] and ent.label in [2,3,4]) or ent.entity_type=='serviceTime'] elif link_attribute=="ratio": temp_entity_list = [ent for ent in list_entity if (ent.entity_type in ['org','company'] and ent.label in [2,3,4]) or ent.entity_type=='ratio'] temp_entity_list = sorted(temp_entity_list,key=lambda x: (x.sentence_index, x.begin_index)) temp_match_list = [] for ent_idx in range(len(temp_entity_list)): entity = temp_entity_list[ent_idx] if entity.entity_type in ['org','company']: match_nums = 0 tenderer_nums = 0 #经过其他中投标人的数量 byNotTenderer_match_nums = 0 #跟在中投标人后面的属性 for after_index in range(ent_idx + 1, min(len(temp_entity_list), ent_idx + 4)): after_entity = temp_entity_list[after_index] if entity.in_attachment != after_entity.in_attachment: # 正文与附件的不能相连 break if after_entity.entity_type == link_attribute: distance = (tokens_num_dict[after_entity.sentence_index] + after_entity.begin_index) - ( tokens_num_dict[entity.sentence_index] + entity.end_index) sentence_distance = after_entity.sentence_index - entity.sentence_index value = (-1 / 2 * (distance ** 2)) / 10000 if link_attribute == "money": if after_entity.notes == '单价': value = value * 100 elif after_entity.values[after_entity.label] <= 0.6: value *= 20 if sentence_distance == 0: if distance < 100: # value = (-1 / 2 * (distance ** 2)) / 10000 temp_match_list.append(Match(entity, after_entity, value)) match_nums += 1 if not tenderer_nums: byNotTenderer_match_nums += 1 else: break else: if distance < 60: # value = (-1 / 2 * (distance ** 2)) / 10000 temp_match_list.append(Match(entity, after_entity, value)) match_nums += 1 if not tenderer_nums: byNotTenderer_match_nums += 1 else: break else: tenderer_nums += 1 #前向查找属性 if ent_idx!=0 and (not match_nums or not byNotTenderer_match_nums): previous_entity = temp_entity_list[ent_idx - 1] if previous_entity.entity_type == link_attribute: # if previous_entity.sentence_index == entity.sentence_index: distance = (tokens_num_dict[entity.sentence_index] + entity.begin_index) - ( tokens_num_dict[previous_entity.sentence_index] + previous_entity.end_index) if distance < 40: # 前向 没有 /10000 value = (-1 / 2 * (distance ** 2)) temp_match_list.append(Match(entity, previous_entity, value)) # km算法分配求解 dispatch_result = dispatch(temp_match_list) dispatch_result = sorted(dispatch_result, key=lambda x: (x[0].sentence_index,x[0].begin_index)) for match in dispatch_result: _entity = match[0] _attribute = match[1] if link_attribute=='money': _entity.pointer_money = _attribute packagePointer, _ = getPackage(PackageList, _attribute.sentence_index, _attribute.begin_index, "money-" + str(_attribute.entity_text) + "-" + str(_attribute.label)) # print(_entity.entity_text,_attribute.entity_text) if packagePointer is None: packageName_entity = "Project" else: packageName_entity = packagePointer.entity_text if _attribute.notes == '单价' or float(_attribute.entity_text) < 5000 or _attribute.values[_attribute.label] <= 0.6: # 2021/12/17 调整小金额阈值，避免203608823.html 两次金额一次万元没提取到的情况 # print(packageName_entity,_attribute.entity_text, _attribute.values[_attribute.label]) addMoneyByEntity(PackDict, packageName_entity, _entity.entity_text, _attribute,0.5) else: # print(packageName_entity,_attribute.entity_text, _attribute.values[_attribute.label]) addMoneyByEntity(PackDict, packageName_entity, _entity.entity_text, _attribute, _attribute.values[_attribute.label]) elif link_attribute=='serviceTime': _entity.pointer_serviceTime = _attribute packagePointer, _ = getPackage(PackageList, _attribute.sentence_index, _attribute.begin_index, "serviceTime-" + str(_attribute.entity_text) + "-" + str(_attribute.label)) if packagePointer is None: packageName_entity = "Project" else: packageName_entity = packagePointer.entity_text addServiceTimeByEntity(PackDict, packageName_entity, _entity.entity_text, _attribute) elif link_attribute=='ratio': _entity.pointer_ratio = _attribute packagePointer, _ = getPackage(PackageList, _attribute.sentence_index, _attribute.begin_index, "ratio-" + str(_attribute.entity_text) + "-" + str(_attribute.label)) if packagePointer is None: packageName_entity = "Project" else: packageName_entity = packagePointer.entity_text addRatioByEntity(PackDict, packageName_entity, _entity.entity_text, _attribute) '''''' # 通过模型分类的招标/代理联系人 list_sentence = sorted(list_sentence, key=lambda x: x.sentence_index) person_list = [entity for entity in list_entity if entity.entity_type == 'person' and entity.label in [1, 2]] tenderee_contact = set() tenderee_phone = set() agency_contact = set() agency_phone = set() winter_contact = set() rule_winter_phone = set() tenderee_entity_set = set() agency_entity_set = set() for _person in person_list: if _person.label == 1: tenderee_contact.add(_person.entity_text) if _person.label == 2: agency_contact.add(_person.entity_text) for _entity in [entity for entity in list_entity if entity.entity_type in ['company','org']]: if _entity.label==0: tenderee_entity_set.add(_entity.entity_text) elif _entity.label==1: agency_entity_set.add(_entity.entity_text) # 正则匹配无 '主体/联系人' 的电话 # 例："采购人联系方式：0833-5226788，" phone_pattern = '(1[3-9][0-9][-—－―]?\d{4}[-—－―]?\d{4}|' \ '\+86.?1[3-9]\d{9}|' \ '0[1-9]\d{1,2}[-—－―][2-9]\d{6,7}/[1-9]\d{6,10}|' \ '0[1-9]\d{1,2}[-—－―][2-9]\d{6}\d?.?转\d{1,4}|' \ '0[1-9]\d{1,2}[-—－―][2-9]\d{6}\d?[-—－―]\d{1,4}|' \ '0[1-9]\d{1,2}[-—－―]?[2-9]\d{6}\d?(?=1[3-9]\d{9})|' \ '0[1-9]\d{1,2}[-—－―]?[2-9]\d{6}\d?(?=0[1-9]\d{1,2}[-—－―]?[2-9]\d{6}\d?)|' \ '0[1-9]\d{1,2}[-—－―]?[2-9]\d{6}\d?(?=[2-9]\d{6,7})|' \ '0[1-9]\d{1,2}[-—－―]?[2-9]\d{6}\d?|' \ '[\（|\(]0[1-9]\d{1,2}[\）|\)]-?[2-9]\d{6}\d?-?\d{,4}|' \ '[2-9]\d{6,7})' re_tenderee_phone = re.compile( # "(?:(?:(?:采购|招标|议价|议标|比选|业主|委托)(?:人|公司|单位|组织|部门)|建设(?:单位|业主)|(?:采购|招标|甲)方|询价单位|项目业主|业主)(?:单位)?[^。代理]{0,5}(?:电话|联系方式|联系人|联系电话)[:：]?[^。]{0,7}?)" "(?:(?:(?:遴选|寻源|采购|招标|竞价|议价|比选|(?:[^受被]|^)委托|询比?价|比价|评选|谈判|邀标|邀请|洽谈|约谈|选取|抽取|抽选|项目|需求?|甲方?|转让|招租|议标|合同主体|挂牌|出租|出让|出售|标卖|处置|发包|最终|建设|业主|竞卖|申购|公选)" "(?:人|方|商|单位|组织|用户|业主|主体|部门|公司|企业))(?:单位)?[^。代理]{0,5}(?:电话|联系方式|联系人|联系电话)[:：]?[^。]{0,7}?)" # 电话号码 + phone_pattern) # 例："采购人地址和联系方式：峨边彝族自治县教育局，0833-5226788，" re_tenderee_phone2 = re.compile( # "(?:(?:(?:采购|招标|议价|议标|比选|业主)(?:人|公司|单位|组织|部门)|建设(?:单位|业主)|(?:采购|招标|甲)方|询价单位|项目业主|业主)(?:单位)?[^。代理]{0,3}(?:地址)[^。]{0,3}(?:电话|联系方式|联系人|联系电话)[:：]?[^。]{0,20}?)" "(?:(?:(?:遴选|寻源|采购|招标|竞价|议价|比选|(?:[^受被]|^)委托|询比?价|比价|评选|谈判|邀标|邀请|洽谈|约谈|选取|抽取|抽选|项目|需求?|甲方?|转让|招租|议标|合同主体|挂牌|出租|出让|出售|标卖|处置|发包|最终|建设|业主|竞卖|申购|公选)" "(?:人|方|商|单位|组织|用户|业主|主体|部门|公司|企业))(?:单位)?[^。代理]{0,3}(?:地址)[^。]{0,3}(?:电话|联系方式|联系人|联系电话|联系人和联系方式)[:：]?[^。]{0,20}?)" # 电话号码 + phone_pattern) re_agent_phone = re.compile( "(?:(?:(?:代理|[受被]委托)(?:人|方|商|机构|公司|单位|组织|企业)|采购机构|集中采购机构|集采机构|招标机构)[^。]{0,5}(?:电话|联系方式|联系人|联系电话|联系人和联系方式)[:：]?[^。]{0,7}?)" # 电话号码 + phone_pattern) re_agent_phone2 = re.compile( "(?:(?:(?:代理|[受被]委托)(?:人|方|商|机构|公司|单位|组织|企业)|采购机构|集中采购机构|集采机构|招标机构)[^。]{0,3}(?:地址)[^。]{0,3}(?:电话|联系方式|联系人|联系电话|联系人和联系方式)[:：]?[^。]{0,20}?)" # 电话号码 + phone_pattern) re_win_tenderer_phone = re.compile( "(?:(?:(?:乙|竞得|受让|买受|签约|供货|供应?|合作|承做|承包|承建|承销|承保|承接|承制|承担|承修|承租(?:（包）)?|入围|入选|竞买|中标|中选|中价|中签|成交|候选)" "(?:候选|投标)?(?:人|单位|(?:中介)?(?:服务)?机构|供应商|客户|方|公司|企业|厂商|商|社会资本方?)|选定单位|中[标选]银行|成交对象)[^。审核]{0,5}(?:负责人|联系人|项目)?(?:经理|电话|联系方式|联系人|负责人|联系电话|联系人和联系方式)[:：]?[^。]{0,7}?)" + phone_pattern) re_win_tenderer_phone2 = re.compile( "(?:(?:(?:乙|竞得|受让|买受|签约|供货|供应?|合作|承做|承包|承建|承销|承保|承接|承制|承担|承修|承租(?:（包）)?|入围|入选|竞买|中标|中选|中价|中签|成交|候选)" "(?:候选|投标)?(?:人|单位|(?:中介)?(?:服务)?机构|供应商|客户|方|公司|企业|厂商|商|社会资本方?)|选定单位|中[标选]银行|成交对象)[^。]{0,3}(?:地址)[^。审核]{0,3}(?:负责人|联系人|项目)?(?:经理|电话|联系方式|联系人|负责人|联系电话|联系人和联系方式)[:：]?[^。]{0,20}?)" + phone_pattern) not_win_tenderer_contact = re.compile("纪检|监察|质疑|投诉|监督|受理|请.{0,4}(联系|与)" "|(遴选|寻源|采购|招标|竞价|议价|比选|委托|询比?价|比价|评选|谈判|邀标|邀请|洽谈|约谈|选取|抽取|抽选|项目|需求?|甲方?|转让|招租|议标|合同主体|挂牌|出租|出让|出售|标卖|处置|发包|最终|建设|业主|竞卖|申购|公选|发布|代理|拍卖|转出){1,2}" "(人|方|商|单位|组织|用户|业主|主体|部门|公司|企业|工厂|银行|机构){0,2}" "[\u4e00-\u9fa5]{0,4}(联系|咨询|电话)(人|电话|方式)?") content = "" for _sentence in list_sentence: content += "".join(_sentence.tokens) _content = copy.deepcopy(content) while re.search("(.)(，)([^0-9])|([^0-9])(，)(.)", content): content_words = list(content) for i in re.finditer("(.)(，)([^0-9])", content): content_words[i.span(2)[0]] = "" for i in re.finditer("([^0-9])(，)(.)", content): content_words[i.span(2)[0]] = "" content = "".join(content_words) content = re.sub("[:：]|[\(（]|[\)）]", "", content) _tenderee_phone = re.findall(re_tenderee_phone, content) # 更新正则确定的角色属性 for i in range(len(PackDict["Project"]["roleList"])): if PackDict["Project"]["roleList"][i].role_name == "tenderee": _tenderee_phone = re.findall(re_tenderee_phone, content) if _tenderee_phone: for _phone in _tenderee_phone: _phone = _phone.split("/") # 分割多个号码 for one_phone in _phone: PackDict["Project"]["roleList"][i].linklist.append(("", one_phone)) tenderee_phone.add(one_phone) _tenderee_phone2 = re.findall(re_tenderee_phone2, content) if _tenderee_phone2: for _phone in _tenderee_phone2: _phone = _phone.split("/") for one_phone in _phone: PackDict["Project"]["roleList"][i].linklist.append(("", one_phone)) tenderee_phone.add(one_phone) if PackDict["Project"]["roleList"][i].role_name == "agency": _agent_phone = re.findall(re_agent_phone, content) if _agent_phone: for _phone in _agent_phone: _phone = _phone.split("/") for one_phone in _phone: PackDict["Project"]["roleList"][i].linklist.append(("", one_phone)) agency_phone.add(one_phone) _agent_phone2 = re.findall(re_agent_phone2, content) if _agent_phone2: for _phone in _agent_phone2: _phone = _phone.split("/") for one_phone in _phone: PackDict["Project"]["roleList"][i].linklist.append(("", one_phone)) agency_phone.add(one_phone) # 中标人联系方式规则筛选 _winter_phone = re.findall(re_win_tenderer_phone, content) if _winter_phone: for _phone in _winter_phone: _phone = _phone.split("/") for one_phone in _phone: rule_winter_phone.add(one_phone) _winter_phone2 = re.findall(re_win_tenderer_phone2, content) if _winter_phone2: for _phone in _winter_phone2: _phone = _phone.split("/") for one_phone in _phone: rule_winter_phone.add(one_phone) # 正则提取电话号码实体 # key_word = re.compile('((?:电话|联系方式|联系人).{0,4}?)([0-1]\d{6,11})') phone = re.compile('1[3-9][0-9][-—－―]?\d{4}[-—－―]?\d{4}|' '\+86.?1[3-9]\d{9}|' # '0[^0]\d{1,2}[-—－―][1-9]\d{6,7}/[1-9]\d{6,10}|' '0[1-9]\d{1,2}[-—－―][2-9]\d{6}\d?[-—－―]\d{1,4}|' '0[1-9]\d{1,2}[-—－―]{0,2}[2-9]\d{6}\d?(?=1[3-9]\d{9})|' '0[1-9]\d{1,2}[-—－―]{0,2}[2-9]\d{6}\d?(?=0[1-9]\d{1,2}[-—－―]?[2-9]\d{6}\d?)|' '0[1-9]\d{1,2}[-—－―]{0,2}[2-9]\d{6}\d?(?=[2-9]\d{6,7})|' '0[1-9]\d{1,2}[-—－―]{0,2}[2-9]\d{6}\d?|' '[\（|\(]0[1-9]\d{1,2}[\）|\)]-?[2-9]\d{6}\d?-?\d{,4}|' '400\d{7}转\d{1,4}|' '[2-9]\d{6,7}') url_pattern = re.compile("http[s]?://(?:[a-zA-Z]|[0-9]|[#$\-_@.&+=\?:/]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+") email_pattern = re.compile("[a-zA-Z0-9][a-zA-Z0-9_-]+(?:\.[a-zA-Z0-9_-]+)*@" "[a-zA-Z0-9_-]+(?:\.[a-zA-Z0-9_-]+)*(?:\.[a-zA-Z]{2,})") phone_entitys = [] code_entitys = [ent for ent in list_entity if ent.entity_type=='code'] for _sentence in list_sentence: sentence_text = _sentence.sentence_text # 过长数字串直接过滤替换 for _re in re.findall("\d{50,}",sentence_text): sentence_text = sentence_text.replace(_re,"#"*len(_re)) in_attachment = _sentence.in_attachment list_tokenbegin = [] begin = 0 for i in range(0, len(_sentence.tokens)): list_tokenbegin.append(begin) begin += len(str(_sentence.tokens[i])) list_tokenbegin.append(begin + 1) # 排除网址、邮箱、项目编号实体 error_list = [] for i in re.finditer(url_pattern, sentence_text): error_list.append((i.start(), i.end())) for i in re.finditer(email_pattern, sentence_text): error_list.append((i.start(), i.end())) for code_ent in [ent for ent in code_entitys if ent.sentence_index==_sentence.sentence_index]: error_list.append((code_ent.wordOffset_begin,code_ent.wordOffset_end)) res_set = set() for i in re.finditer(phone, sentence_text): is_continue = False for error_ent in error_list: if i.start()>=error_ent[0] and i.end()<=error_ent[1]: is_continue = True break if is_continue: continue res_set.add((i.group(), i.start(), i.end())) res_set = sorted(list(res_set),key=lambda x:x[1]) # 限制数量，防止异常数据处理时间过长 res_set = res_set[:200] last_phone_mask = True error_numStr_index = [] sentence_phone_list = [] for item_idx in range(len(res_set)): item = res_set[item_idx] phone_left = sentence_text[max(0, item[1] - 10):item[1]] phone_right = sentence_text[item[2]:item[2] + 10] phone_left_num = re.search("[\da-zA-Z\-—－―]+$",phone_left) numStr_left = item[1] if phone_left_num: numStr_left -= len(phone_left_num.group()) phone_right_num = re.search("^[\da-zA-Z\-—－―]+",phone_right) numStr_right = item[2] if phone_right_num: numStr_right += len(phone_right_num.group()) numStr_index = (numStr_left,numStr_right) if re.search("电话|手机|联系[人方]|联系方式",re.sub("，","",phone_left)): pass else: # 排除“传真号”和其它错误项 if re.search("传，?真|信，?箱|邮，?[编箱件]|QQ|qq", phone_left): if not re.search("电，?话", phone_left): error_numStr_index.append(numStr_index) last_phone_mask = False continue if re.search("身份证号?码?|注册[证号]|帐号|编[号码]|报价|费率|标价|证号|证书|资格证|资质|价格|金额|型号|附件|代码|列号|行号|税号|[\(\（]万?元[\)\）]|[a-zA-Z]+\d*$", re.sub("，","",phone_left)): error_numStr_index.append(numStr_index) last_phone_mask = False continue if re.search("^\d{0,4}[.,]\d{2,}|^[0-9a-zA-Z\.]*@|^\d*[a-zA-Z]+|元", phone_right): error_numStr_index.append(numStr_index) last_phone_mask = False continue # 号码含有0过多，不符合规则 if re.search("0{6,}",item[0]): error_numStr_index.append(numStr_index) last_phone_mask = False continue # 前后跟着字母 if re.search("[a-zA-Z/]+$", phone_left) or re.search("^[a-zA-Z/]+", phone_right): error_numStr_index.append(numStr_index) last_phone_mask = False continue # 时间日期类排除 if re.search("时间|日期", phone_left): error_numStr_index.append(numStr_index) last_phone_mask = False continue # 排除号码实体为时间格式 ，例如：20150515 if re.search("^20(1[0-9]|2[0-5])(0[1-9]|1[012])(0[1-9]|[1-2][0-9]|3[01])$",item[0]): error_numStr_index.append(numStr_index) last_phone_mask = False continue # 前后跟着长度小于一定值数字的正则排除 if re.search("\d+[-—－―]?\d*$",phone_left) or re.search("^\d+[-—－―]?\d*",phone_right): phone_left_number = re.search("\d+[-—－―]?\d*$",phone_left) phone_right_number = re.search("^\d+[-—－―]?\d+",phone_right) if phone_left_number: if len(phone_left_number.group())<7: error_numStr_index.append(numStr_index) last_phone_mask = False continue if phone_right_number: if len(phone_right_number.group())<7: error_numStr_index.append(numStr_index) last_phone_mask = False continue left_context = re.search("[\da-zA-Z\-—－―]+$",sentence_text[:item[1]]) if left_context: if len(left_context.group()) != len("".join(re.findall(phone, left_context.group()))): # if not re.search("(" + phone.pattern + ")$", left_context.group()): error_numStr_index.append(numStr_index) last_phone_mask = False continue right_context = re.search("^[\da-zA-Z\-—－―]+", sentence_text[item[2]:]) if right_context: if len(right_context.group()) != len("".join(re.findall(phone, right_context.group()))): # if not re.search("^(" + phone.pattern + ")", right_context.group()): error_numStr_index.append(numStr_index) last_phone_mask = False continue # if:上一个phone实体不符合条件 if not last_phone_mask: item_start = item[1] last_item_end = res_set[item_idx-1][2] if item_start - last_item_end<=1 or re.search("^[\da-zA-Z\-—－―、]+$",sentence_text[last_item_end:item_start]): error_numStr_index.append(numStr_index) last_phone_mask = False continue sentence_phone_list.append(item) last_phone_mask = True if error_numStr_index: drop_list = [] for item in sentence_phone_list: for err_index in error_numStr_index: if (item[1]>=err_index[0] and item[1]<=err_index[1]) or (item[2]>=err_index[0] and item[2]<=err_index[1]) or (item[1]<=err_index[0] and item[2]>=err_index[1]): drop_list.append(item) break for _drop_item in drop_list: sentence_phone_list.remove(_drop_item) for item in sentence_phone_list: for j in range(len(list_tokenbegin)): if list_tokenbegin[j] == item[1]: begin_index = j break elif list_tokenbegin[j] > item[1]: begin_index = j - 1 break for j in range(begin_index, len(list_tokenbegin)): if list_tokenbegin[j] >= item[2]: end_index = j - 1 break phone_text = re.sub("[-—－―]+","-",item[0]).replace("(","（").replace(")","）") _entity = Entity(_sentence.doc_id, None, phone_text, "phone", _sentence.sentence_index, begin_index, end_index, item[1], item[2],in_attachment=in_attachment) phone_entitys.append(_entity) # print('phone_set:',set([ent.entity_text for ent in phone_entitys])) def is_company(entity,text): # 判断"公司"实体是否为地址地点 if entity.label!=5 and entity.values[entity.label]>0.5: return True if ent.is_tail==True: return False entity_left = text[max(0,entity.wordOffset_begin-30):entity.wordOffset_begin] entity_left1 = re.sub("，（）\(\)","",entity_left) entity_left1 = entity_left1[-5:] entity_left2 = [i for i in entity_left.split("，") if i] if entity_left2: entity_left2 = entity_left2[-1] else: entity_left2 = "" if re.search("地址|地点|银行[：:]",entity_left1) or re.search("地址|地点|银行[：:]",entity_left2): return False else: return True pre_entity = [] for ent in list_entity: if (ent.entity_type in ['company','org','phone'] and is_company(ent,list_sentence[ent.sentence_index].sentence_text)) or (ent.entity_type=='person' and ent.label in [1,2,3]) \ or (ent.entity_type=='location' and len(ent.entity_text)>5): pre_entity.append(ent) # text_data,pre_data = relationExtraction_model.encode(pre_entity + phone_entitys, list_sentence) text_data,pre_data = _get_relationExtraction_model().encode([ent for ent in pre_entity+phone_entitys if ent.in_attachment==False], list_sentence) # print(pre_data) maxlen = 512 relation_list = [] if 04: break for _text_data, _pre_data in temp_data: relation_list.extend(_get_relationExtraction_model().predict(_text_data,_pre_data)) temp_data = [] start = start + maxlen - 120 if temp_data: deal_data += len(temp_data) if deal_data <= 4: for _text_data, _pre_data in temp_data: relation_list.extend(_get_relationExtraction_model().predict(_text_data, _pre_data)) # print("预测数据：",len(temp_data)) # 去重结果 relation_list = list(set(relation_list)) # print([(rel[0].entity_text,rel[2].entity_text) for rel in relation_list]) # relation_list = [] # 放弃原来的模型连接，结果不好控制 right_combination = [('org','person'),('company','person'),('company','location'),('org','location'),('person','phone')] linked_company = set() linked_person = set() linked_connetPerson = set() linked_phone = set() for predicate in ["rel_address","rel_phone","rel_person"]: _match_list = [] _match_combo = [] for relation in relation_list: _subject = relation[0] _object = relation[2] if isinstance(_subject,Entity) and isinstance(_object,Entity) and (_subject.entity_type,_object.entity_type) in right_combination: if _subject.in_attachment != _object.in_attachment: continue if relation[1]==predicate: distance = (tokens_num_dict[_object.sentence_index] + _object.begin_index) - ( tokens_num_dict[_subject.sentence_index] + _subject.end_index) if predicate=="rel_person": # print(predicate, _subject.entity_text, _object.entity_text) if (_subject.label==0 and _object.entity_text in agency_contact ) or (_subject.label==1 and _object.entity_text in tenderee_contact): continue # 角色为中标候选人，排除"质疑|投诉|监督|受理"相关的联系人 # if _subject.label in [2,3,4] and re.search("纪检|监察|质疑|投诉|监督|受理|项目(单位)?联系|^联系人|请.{0,4}联系",list_sentence[_object.sentence_index].sentence_text[max(0,_object.wordOffset_begin-10):_object.wordOffset_begin]): if _subject.label in [2,3,4] and re.search(not_win_tenderer_contact,list_sentence[_object.sentence_index].sentence_text[max(0,_object.wordOffset_begin-15):_object.wordOffset_begin]): # print('not_win_tenderer_contact1') continue # 角色为招标/代理人，排除"纪检|监察"相关的联系人 if (_subject.label in [0,1] or _subject.entity_text in tenderee_entity_set|agency_entity_set) and re.search("纪检|监察|投诉|监督|乙方|中标",list_sentence[_object.sentence_index].sentence_text[max(0,_object.wordOffset_begin - 10):_object.wordOffset_begin]): # if _subject.label in [0,1] and re.search("纪检|监察|乙方|中标",list_sentence[_object.sentence_index].sentence_text[_subject.end_index:_object.wordOffset_begin]): continue if _object.sentence_index!=0 and _object.wordOffset_begin<=10: if _subject.label in [2, 3, 4] and re.search("请.{0,4}联系", list_sentence[_object.sentence_index-1].sentence_text[-10:]+ list_sentence[_object.sentence_index].sentence_text[0:_object.wordOffset_begin]): continue # 角色为中标候选人，排除距离过远的联系人 if _subject.label in [2, 3, 4] and distance>=40: continue if distance>0: value = (-1 / 2 * (distance ** 2))/10000 else: distance = abs(distance) value = (-1 / 2 * (distance ** 2)) _match_list.append(Match(_subject,_object,value)) _match_combo.append((_subject,_object)) match_result = dispatch(_match_list) error_list = [] for mat in list(set(_match_combo)-set(match_result)): for temp in match_result: if mat[1]==temp[1] and mat[0]!=temp[0]: error_list.append(mat) break result = list(set(_match_combo)-set(error_list)) if predicate=='rel_person': # 从后往前更新状态，已近后向链接的属性不在前向链接（解决错误链接） result = sorted(result,key=lambda x:x[1].begin_index,reverse=True) for combo in result: is_continue = False if not combo[0].pointer_person: combo[0].pointer_person = [] if combo[1].begin_indexcombo[0].begin_index: is_continue = True break if is_continue: continue combo[0].pointer_person.append(combo[1]) linked_company.add(combo[0]) linked_person.add(combo[1]) # print(1,combo[0].entity_text,combo[1].entity_text) if predicate=='rel_address': result = sorted(result,key=lambda x:x[1].begin_index,reverse=True) for combo in result: if combo[0].pointer_address: continue combo[0].pointer_address = combo[1] # print(2,combo[0].entity_text,combo[1].entity_text) if predicate=='rel_phone': result = sorted(result,key=lambda x:x[1].begin_index,reverse=True) for combo in result: is_continue = False if not combo[0].person_phone: combo[0].person_phone = [] if combo[1].begin_indexcombo[0].begin_index: is_continue = True break if is_continue: continue combo[0].person_phone.append(combo[1]) linked_connetPerson.add(combo[0]) linked_phone.add(combo[1]) if combo[0].label in [1,2]: if PackDict.get("Project"): for i in range(len(PackDict["Project"]["roleList"])): if (combo[0].label==1 and PackDict["Project"]["roleList"][i].role_name=='tenderee') \ or (combo[0].label==2 and PackDict["Project"]["roleList"][i].role_name=='agency'): PackDict["Project"]["roleList"][i].linklist.append((combo[0].entity_text,combo[1].entity_text)) break # print(3,combo[0].entity_text,combo[1].entity_text) # "公司——地址" 链接规则补充 company_lacation_EntityList = [ent for ent in pre_entity if ent.entity_type in ['company', 'org', 'location']] # company_lacation_EntityList = [ent for ent in pre_entity if (ent.entity_type in ['company', 'org'] and ent.label!=5) or ent.entity_type=="location"] company_lacation_EntityList = sorted(company_lacation_EntityList, key=lambda x: (x.sentence_index, x.begin_index)) t_match_list = [] for ent_idx in range(len(company_lacation_EntityList)): entity = company_lacation_EntityList[ent_idx] if entity.entity_type in ['company', 'org'] and entity.label!=5: match_nums = 0 company_nums = 0 # 经过其他公司的数量 location_nums = 0 # 经过住址的数量 for after_index in range(ent_idx + 1, min(len(company_lacation_EntityList), ent_idx + 5)): after_entity = company_lacation_EntityList[after_index] if after_entity.entity_type == "location": distance = (tokens_num_dict[after_entity.sentence_index] + after_entity.begin_index) - ( tokens_num_dict[entity.sentence_index] + entity.end_index) location_nums += 1 if distance > 100 or location_nums >= 3: break sentence_distance = after_entity.sentence_index - entity.sentence_index value = (-1 / 2 * (distance ** 2)) / 10000 if sentence_distance == 0: if distance < 60: t_match_list.append(Match(entity, after_entity, value)) match_nums += 1 if company_nums: break else: if distance < 50: t_match_list.append(Match(entity, after_entity, value)) match_nums += 1 if company_nums: break else: # type:company/org company_nums += 1 if entity.label in [2, 3, 4] and after_entity.label in [0, 1]: break if entity.label in [0, 1] and after_entity.label in [2, 3, 4]: break if entity.label in [0, 1] and after_entity.label not in [0, 1]: break # km算法分配求解 # for item in t_match_list: # print("loc_rela",item.main_role.entity_text,item.attribute.entity_text) relate_location_result = dispatch(t_match_list) relate_location_result = sorted(relate_location_result, key=lambda x: (x[0].sentence_index, x[0].begin_index)) for match in relate_location_result: _company = match[0] _relation = match[1] # print("loc_relation1", _company.entity_text, _relation.entity_text, ) if not _company.pointer_address: # print('loc_relation2',_company.entity_text,_relation.entity_text) _company.pointer_address = _relation # "联系人——联系电话" 链接规则补充 # person_phone_EntityList = [ent for ent in pre_entity+ phone_entitys if ent.entity_type not in ['company','org','location']] person_phone_EntityList = [ent for ent in pre_entity+ phone_entitys if ent.entity_type not in ['location']] person_phone_EntityList = sorted(person_phone_EntityList, key=lambda x: (x.sentence_index, x.begin_index)) t_match_list = [] for ent_idx in range(len(person_phone_EntityList)): entity = person_phone_EntityList[ent_idx] if entity.entity_type=="person": match_nums = 0 person_nums = 0 # 经过其他中联系人的数量 byNotPerson_match_nums = 0 # 跟在联系人后面的属性 phone_nums = 0 # 经过电话的数量 for after_index in range(ent_idx + 1, min(len(person_phone_EntityList), ent_idx + 8)): after_entity = person_phone_EntityList[after_index] if after_entity.entity_type == "phone": distance = (tokens_num_dict[after_entity.sentence_index] + after_entity.begin_index) - ( tokens_num_dict[entity.sentence_index] + entity.end_index) phone_nums += 1 if distance>100 or phone_nums>=4: break sentence_distance = after_entity.sentence_index - entity.sentence_index value = (-1 / 2 * (distance ** 2)) / 10000 if sentence_distance == 0: if distance < 70: # value = (-1 / 2 * (distance ** 2)) / 10000 t_match_list.append(Match(entity, after_entity, value)) match_nums += 1 if not person_nums: byNotPerson_match_nums += 1 else: break else: if distance < 30: # value = (-1 / 2 * (distance ** 2)) / 10000 t_match_list.append(Match(entity, after_entity, value)) match_nums += 1 if not person_nums: byNotPerson_match_nums += 1 else: break elif after_entity.entity_type == "person": person_nums += 1 elif after_entity.entity_type in ["company","org"]: break # 前向查找属性 if ent_idx != 0 and (not match_nums or not byNotPerson_match_nums): previous_entity = person_phone_EntityList[ent_idx - 1] if previous_entity.entity_type == 'phone': # if previous_entity.sentence_index == entity.sentence_index: distance = (tokens_num_dict[entity.sentence_index] + entity.begin_index) - ( tokens_num_dict[previous_entity.sentence_index] + previous_entity.end_index) if distance < 30: # 前向 没有 /10000 value = (-1 / 2 * (distance ** 2)) t_match_list.append(Match(entity, previous_entity, value)) # km算法分配求解(person-phone) t_match_list = [mat for mat in t_match_list if mat.main_role not in linked_connetPerson and mat.attribute not in linked_phone] # print([(mat.main_role.entity_text,mat.attribute.entity_text) for mat in t_match_list]) personphone_result = dispatch(t_match_list) personphone_result = sorted(personphone_result, key=lambda x: (x[0].sentence_index, x[0].begin_index)) for match in personphone_result: _person = match[0] _phone = match[1] if not _person.person_phone: _person.person_phone = [] _person.person_phone.append(_phone) # 多个招标人/代理人或者别称 for idx in range(1,len(pre_entity)): _pre_entity = pre_entity[idx] if _pre_entity in linked_company and _pre_entity.label==5: last_ent = pre_entity[idx-1] if last_ent.entity_type in ['company','org'] and last_ent.label in [0,1]: if last_ent.sentence_index==_pre_entity.sentence_index: mid_text = list_sentence[_pre_entity.sentence_index].sentence_text[last_ent.wordOffset_end:_pre_entity.wordOffset_begin] if len(mid_text)<=20 and "，" not in mid_text and re.search("[、\(（]",mid_text): _pre_entity.label = last_ent.label _pre_entity.values[last_ent.label] = 0.6 # 2022/01/25 固定电话可连多个联系人 temp_person_entitys = [entity for entity in pre_entity if entity.entity_type == 'person'] temp_person_entitys2 = [] #和固定电话相连的联系人 for entity in temp_person_entitys: if entity.person_phone: for _phone in entity.person_phone: if not re.search("^1[3-9]\d{9}$", _phone.entity_text): temp_person_entitys2.append(entity) break for index in range(len(temp_person_entitys)): entity = temp_person_entitys[index] if entity in temp_person_entitys2: last_person = entity for after_index in range(index + 1, min(len(temp_person_entitys), index + 5)): after_entity = temp_person_entitys[after_index] if after_entity.sentence_index == last_person.sentence_index and after_entity.begin_index - last_person.end_index < 3: for _phone in entity.person_phone: if not re.search("^1[3-9]\d{9}$", _phone.entity_text): if _phone not in after_entity.person_phone: after_entity.person_phone.append(_phone) last_person = after_entity else: break if index==0: continue last_person = entity for before_index in range(index-1, max(-1,index-5), -1): before_entity = temp_person_entitys[before_index] if before_entity.sentence_index == last_person.sentence_index and last_person.begin_index - before_entity.end_index < 3: for _phone in entity.person_phone: if not re.search("^1[3-9]\d{9}$", _phone.entity_text): if _phone not in before_entity.person_phone: before_entity.person_phone.append(_phone) last_person = before_entity else: break # 更新person为招标/代理联系人的联系方式 for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): if PackDict[k]["roleList"][i].role_name == "tenderee": for _person in person_list: if _person.label==1:#招标联系人 person_phone = [phone for phone in _person.person_phone] if _person.person_phone else [] for _p in person_phone: PackDict[k]["roleList"][i].linklist.append((_person.entity_text, _p.entity_text)) if not person_phone: PackDict[k]["roleList"][i].linklist.append((_person.entity_text,"")) if PackDict[k]["roleList"][i].role_name == "agency": for _person in person_list: if _person.label==2:#代理联系人 person_phone = [phone for phone in _person.person_phone] if _person.person_phone else [] for _p in person_phone: PackDict[k]["roleList"][i].linklist.append((_person.entity_text, _p.entity_text)) if not person_phone: PackDict[k]["roleList"][i].linklist.append((_person.entity_text,"")) # 更新 PackDict not_sure_linked = [] for link_p in list(linked_company): for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): if PackDict[k]["roleList"][i].role_name == "tenderee": if PackDict[k]["roleList"][i].entity_text != link_p.entity_text and link_p.label == 0: not_sure_linked.append(link_p) continue if PackDict[k]["roleList"][i].entity_text == link_p.entity_text: for per in link_p.pointer_person: person_phone = [phone for phone in per.person_phone] if per.person_phone else [] if not person_phone: if per.entity_text not in agency_contact: PackDict[k]["roleList"][i].linklist.append((per.entity_text, "")) continue for _p in person_phone: if per.entity_text not in agency_contact and _p.entity_text not in agency_phone: PackDict[k]["roleList"][i].linklist.append((per.entity_text, _p.entity_text)) elif PackDict[k]["roleList"][i].role_name == "agency": if PackDict[k]["roleList"][i].entity_text != link_p.entity_text and link_p.label == 1: not_sure_linked.append(link_p) continue if PackDict[k]["roleList"][i].entity_text == link_p.entity_text: for per in link_p.pointer_person: person_phone = [phone for phone in per.person_phone] if per.person_phone else [] if not person_phone: if per.entity_text not in tenderee_contact: PackDict[k]["roleList"][i].linklist.append((per.entity_text, "")) continue for _p in person_phone: if per.entity_text not in tenderee_contact and _p.entity_text not in tenderee_phone: PackDict[k]["roleList"][i].linklist.append((per.entity_text, _p.entity_text)) else: if PackDict[k]["roleList"][i].entity_text == link_p.entity_text: for per in link_p.pointer_person: person_phone = [phone for phone in per.person_phone] if per.person_phone else [] if not person_phone: if per.entity_text not in tenderee_contact and per.entity_text not in agency_contact: # 角色为中标候选人，联系人无号码且上文没有联系关键词时排除 if re.search("联系人|联系方式|电话|负责人|经理|法人|法定代表人",list_sentence[per.sentence_index].sentence_text[max(0, per.wordOffset_begin - 10):per.wordOffset_begin]): PackDict[k]["roleList"][i].linklist.append((per.entity_text, "")) winter_contact.add(per.entity_text) continue for _p in person_phone: if per.entity_text not in tenderee_contact and _p.entity_text not in tenderee_phone and \ per.entity_text not in agency_contact and _p.entity_text not in agency_phone: PackDict[k]["roleList"][i].linklist.append((per.entity_text, _p.entity_text)) winter_contact.add(per.entity_text) # 更新org/company实体label为0，1的链接 for link_p in not_sure_linked: for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): if PackDict[k]["roleList"][i].role_name == "tenderee": if link_p.label == 0: for per in link_p.pointer_person: person_phone = [phone for phone in per.person_phone] if per.person_phone else [] if not person_phone: if per.entity_text not in agency_contact and per.entity_text not in winter_contact: PackDict[k]["roleList"][i].linklist.append((per.entity_text, "")) continue for _p in person_phone: if per.entity_text not in agency_contact and _p.entity_text not in agency_phone and per.entity_text not in winter_contact: PackDict[k]["roleList"][i].linklist.append((per.entity_text, _p.entity_text)) elif PackDict[k]["roleList"][i].role_name == "agency": if link_p.label == 1: for per in link_p.pointer_person: person_phone = [phone for phone in per.person_phone] if per.person_phone else [] if not person_phone: if per.entity_text not in tenderee_contact and per.entity_text not in winter_contact: PackDict[k]["roleList"][i].linklist.append((per.entity_text, "")) continue for _p in person_phone: if per.entity_text not in tenderee_contact and _p.entity_text not in tenderee_phone and per.entity_text not in winter_contact: PackDict[k]["roleList"][i].linklist.append((per.entity_text, _p.entity_text)) # 使用中标信息大纲提取联系人 winter_scope_group = [] if winter_scope: winter_scope_begin = winter_scope[0] winter_scope_end = winter_scope[1] # print(list_sentence[winter_scope_begin[0]].sentence_text[winter_scope_begin[1]:winter_scope_end[1]]) winter_temporary_list = [] for entity in list_entity: if entity.entity_type in ['org', 'company', 'person']: winter_temporary_list.append(entity) winter_temporary_list = sorted(winter_temporary_list, key=lambda x: (x.sentence_index, x.begin_index)) winter_temporary_list2 = [] for _entity in winter_temporary_list: if _entity.sentence_index>=winter_scope_begin[0] and _entity.sentence_index<=winter_scope_end[0]: if (_entity.sentence_index==winter_scope_begin[0] and _entity.wordOffset_begin>=winter_scope_begin[1]) or \ _entity.sentence_index>winter_scope_begin[0]: if (_entity.sentence_index == winter_scope_end[0] and _entity.wordOffset_end<=winter_scope_end[1]) or \ _entity.sentence_index 2: break if after_entity.entity_type == 'person': distance = (tokens_num_dict[after_entity.sentence_index] + after_entity.begin_index) - ( tokens_num_dict[entity.sentence_index] + entity.end_index) # 实体为中标人/候选人，联系人已确定类别【1，2】 if entity.label in [2, 3, 4] and after_entity.label in [1, 2]: break if entity.label in [2, 3, 4] and distance >= 30: break # 角色为中标候选人，排除"质疑|投诉|监督|受理"相关的联系人 if entity.label in [2, 3, 4] and re.search(not_win_tenderer_contact, list_sentence[after_entity.sentence_index].sentence_text[max(0,after_entity.wordOffset_begin - 15):after_entity.wordOffset_begin]): break # 角色为中标候选人，联系人无号码且上文没有联系关键词时排除 if entity.label in [2, 3, 4] and not after_entity.person_phone and not re.search( "联系人|联系方式|电话|负责人|经理|法人|法定代表人", list_sentence[after_entity.sentence_index].sentence_text[max(0,after_entity.wordOffset_begin - 10):after_entity.wordOffset_begin]): continue # 角色为招标/代理人，排除"纪检|监察"相关的联系人 if (entity.label in [0,1] or entity.entity_text in tenderee_entity_set|agency_entity_set) and re.search("纪检|监察|投诉|监督|乙方|中标", list_sentence[after_entity.sentence_index].sentence_text[max(0,after_entity.wordOffset_begin - 10):after_entity.wordOffset_begin]): break if after_entity.sentence_index != 0 and after_entity.wordOffset_begin <= 10: if entity.label in [2, 3, 4] and re.search("请.{0,5}联系",list_sentence[after_entity.sentence_index - 1].sentence_text[-10:] + list_sentence[after_entity.sentence_index].sentence_text[0:after_entity.wordOffset_begin]): continue if distance < 80: if (entity.label == 0 and after_entity.label == 1) or ( entity.label == 1 and after_entity.label == 2): distance = distance / 100 value = (-1 / 2 * (distance ** 2)) / 10000 match_list_winter.append(Match(entity, after_entity, value)) match_nums += 1 # 前向查找匹配 if index != 0: previous_entity = winter_scope_group[index - 1] if previous_entity.entity_type == 'person' and previous_entity.label in [1,2,3]: if entity.label in [2, 3, 4] and previous_entity.label in [1, 2]: continue # 角色为中标候选人，排除"质疑|投诉|监督|受理"相关的联系人 if entity.label in [2, 3, 4] and re.search(not_win_tenderer_contact, list_sentence[previous_entity.sentence_index].sentence_text[ max(0,previous_entity.wordOffset_begin - 15):previous_entity.wordOffset_begin]): break # 角色为中标候选人，联系人无号码且上文没有联系关键词时排除 if entity.label in [2, 3, 4] and not previous_entity.person_phone and not re.search( "联系人|联系方式|电话|负责人|经理|法人|法定代表人",list_sentence[previous_entity.sentence_index].sentence_text[ max(0, previous_entity.wordOffset_begin - 10):previous_entity.wordOffset_begin]): continue # 角色为招标/代理人，排除"纪检|监察"相关的联系人 if (entity.label in [0,1] or entity.entity_text in tenderee_entity_set|agency_entity_set) and re.search("纪检|监察|投诉|监督|乙方|中标", list_sentence[previous_entity.sentence_index].sentence_text[ max(0,previous_entity.wordOffset_begin - 10):previous_entity.wordOffset_begin]): break if previous_entity.sentence_index == entity.sentence_index: distance = (tokens_num_dict[entity.sentence_index] + entity.begin_index) - ( tokens_num_dict[ previous_entity.sentence_index] + previous_entity.end_index) if distance < 30: # 距离相等时，前向添加处罚值 # distance += 1 # 前向 没有 /10000 value = (-1 / 2 * (distance ** 2)) match_list_winter.append(Match(entity, previous_entity, value)) # test # match_list_winter = company_contact_link([winter_scope_group]) # km算法分配求解 result_winter = dispatch(match_list_winter) for match in result_winter: _company = match[0] _person = match[1] _person = _person.entity_text # 更新中标人联系方式 if _company.label==2: phone_ = [i.entity_text for i in match[1].person_phone] if match[1].person_phone else [] for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): if PackDict[k]["roleList"][i].role_name == "win_tenderer": if PackDict[k]["roleList"][i].entity_text == _company.entity_text: if _person not in tenderee_contact and len(set(phone_) & set(tenderee_phone)) == 0 and \ _person not in agency_contact and len(set(phone_) & set(agency_phone)) == 0: if not phone_: PackDict[k]["roleList"][i].linklist.append((_person, "")) for p in phone_: PackDict[k]["roleList"][i].linklist.append((_person, p)) if phone_: for p in phone_: rule_winter_phone.add(p) # print('rule_winter_phone',rule_winter_phone) re_split = re.compile("[^\u4e00-\u9fa5、](十一|十二|十三|十四|十五|一|二|三|四|五|六|七|八|九|十)、") split_list = [0] * 16 split_dict = { "一、": 1, "二、": 2, "三、": 3, "四、": 4, "五、": 5, "六、": 6, "七、": 7, "八、": 8, "九、": 9, "十、": 10, "十一、": 11, "十二、": 12, "十三、": 13, "十四、": 14, "十五、": 15 } for item in re.finditer(re_split, _content): _index = split_dict.get(item.group()[1:]) if not split_list[_index]: split_list[_index] = item.span()[0] + 1 split_list = [i for i in split_list if i != 0] start = 0 new_split_list = [] for idx in split_list: new_split_list.append((start, idx)) start = idx new_split_list.append((start, len(_content))) # 实体列表按照“公告分段”分组 words_num_dict = dict() last_words_num = 0 for sentence in list_sentence: _index = sentence.sentence_index if _index == 0: words_num_dict[_index] = 0 else: words_num_dict[_index] = words_num_dict[_index - 1] + last_words_num last_words_num = len(sentence.sentence_text) # 公司-联系人连接（km算法） re_phone = re.compile('1[3-9][0-9][-—－―]?\d{4}[-—－―]?\d{4}|' '\+86.?1[3-9]\d{9}|' # '0[1-9]\d{1,2}[-—－―][1-9]\d{6,7}/[1-9]\d{6,10}|' '0[1-9]\d{1,2}[-—－―][2-9]\d{6,7}[^\d]?转\d{1,4}|' '0[1-9]\d{1,2}[-—－―][2-9]\d{6}\d?[-—－―]\d{1,4}|' '0[1-9]\d{1,2}[-—－―]{0,2}[2-9]\d{6}\d?(?=1[3-9]\d{9})|' '0[1-9]\d{1,2}[-—－―]{0,2}[2-9]\d{6}\d?(?=0[1-9]\d{1,2}[-—－―]?[2-9]\d{6}\d?)|' '0[1-9]\d{1,2}[-—－―]{0,2}[2-9]\d{6}\d?(?=[2-9]\d{6,7})|' '0[1-9]\d{1,2}[-—－―]{0,2}[2-9]\d{6}\d?|' '[\（|\(]0[1-9]\d{1,2}[\）|\)]-?[2-9]\d{6,7}-?\d{,4}|' '400\d{7}转\d{1,4}|' '[2-9]\d{6,7}') key_phone = re.compile("联系方式|电话|联系人|负责人") temporary_list2 = [] for entity in list_entity: # if entity.entity_type in ['org', 'company', 'person'] and entity.is_tail==False: if entity.entity_type in ['org', 'company', 'person']: temporary_list2.append(entity) temporary_list2 = sorted(temporary_list2, key=lambda x: (x.sentence_index, x.begin_index)) new_temporary_list2 = [] for _split in new_split_list: temp_list = [] for _entity in temporary_list2: if words_num_dict[_entity.sentence_index] + _entity.wordOffset_begin >= _split[0] and words_num_dict[ _entity.sentence_index] + _entity.wordOffset_end < _split[1]: temp_list.append(_entity) elif words_num_dict[_entity.sentence_index] + _entity.wordOffset_begin >= _split[1]: break new_temporary_list2.append(temp_list) # print(new_temporary_list2) match_list2 = [] for split_index in range(len(new_temporary_list2)): split_entitys = new_temporary_list2[split_index] if len(split_entitys)<=1: continue is_skip = False for index in range(len(split_entitys)): entity = split_entitys[index] if is_skip: is_skip = False continue else: if entity.entity_type in ['org', 'company']: if entity.label != 5 or entity.entity_text in roleSet: match_nums = 0 for after_index in range(index + 1, min(len(split_entitys), index + 4)): after_entity = split_entitys[after_index] if entity.in_attachment != after_entity.in_attachment: break if after_entity.entity_type in ['person']: distance = (tokens_num_dict[after_entity.sentence_index] + after_entity.begin_index) - ( tokens_num_dict[entity.sentence_index] + entity.end_index) # 实体为中标人/候选人，联系人已确定类别【1，2】 if entity.label in [2, 3, 4] and after_entity.label in [1, 2]: break if entity.label in [2, 3, 4] and distance>=30: break # 角色为中标候选人，排除"质疑|投诉|监督|受理"相关的联系人 # if entity.label in [2, 3, 4] and re.search("纪检|监察|质疑|投诉|监督|受理|项目(单位|业主)?联系|(采购|招标)人?联系|请.{0,4}联系", list_sentence[after_entity.sentence_index].sentence_text[max(0,after_entity.wordOffset_begin - 10):after_entity.wordOffset_begin]): if entity.label in [2, 3, 4] and re.search(not_win_tenderer_contact, list_sentence[after_entity.sentence_index].sentence_text[max(0,after_entity.wordOffset_begin - 15):after_entity.wordOffset_begin]): # print('not_win_tenderer_contact2') break # 角色为中标候选人，联系人无号码且上文没有联系关键词时排除 # print('test',after_entity.entity_text,after_entity.person_phone,list_sentence[after_entity.sentence_index].sentence_text[max(0,after_entity.wordOffset_begin - 10):after_entity.wordOffset_begin]) if entity.label in [2, 3, 4] and not after_entity.person_phone and not re.search("联系人|联系方式|电话|负责人|经理|法人|法定代表人",list_sentence[after_entity.sentence_index].sentence_text[max(0,after_entity.wordOffset_begin - 10):after_entity.wordOffset_begin]): continue # 角色为招标/代理人，排除"纪检|监察"相关的联系人 if (entity.label in [0,1] or entity.entity_text in tenderee_entity_set|agency_entity_set) and re.search("纪检|监察|投诉|监督|乙方|中标",list_sentence[after_entity.sentence_index].sentence_text[max(0,after_entity.wordOffset_begin - 10):after_entity.wordOffset_begin]): break if after_entity.sentence_index != 0 and after_entity.wordOffset_begin <= 10: if entity.label in [2, 3, 4] and re.search("请.{0,5}联系", list_sentence[after_entity.sentence_index - 1].sentence_text[-10:] + list_sentence[after_entity.sentence_index].sentence_text[0:after_entity.wordOffset_begin]): continue if after_entity.label in [1, 2, 3]: # distance = (tokens_num_dict[ # after_entity.sentence_index] + after_entity.begin_index) - ( # tokens_num_dict[entity.sentence_index] + entity.end_index) sentence_distance = after_entity.sentence_index - entity.sentence_index if sentence_distance == 0: if distance < 100: if entity.label in [2, 3, 4] and distance>40: break if (entity.label == 0 and after_entity.label == 1) or ( entity.label == 1 and after_entity.label == 2): distance = distance / 100 value = (-1 / 2 * (distance ** 2)) / 10000 match_list2.append(Match(entity, after_entity, value)) match_nums += 1 else: if distance < 60: if entity.label in [2, 3, 4] and distance>20: break if (entity.label == 0 and after_entity.label == 1) or ( entity.label == 1 and after_entity.label == 2): distance = distance / 100 value = (-1 / 2 * (distance ** 2)) / 10000 match_list2.append(Match(entity, after_entity, value)) match_nums += 1 if after_entity.entity_type in ['org', 'company']: if entity.label in [2, 3, 4] and after_entity.label in [0, 1]: break # 解决在‘地址’中识别出org/company的问题 # if entity.label in [0,1] and after_index==index+1 and after_entity.label not in [0,1]: if entity.label != 5 and after_index == index + 1 and ( after_entity.label == entity.label or after_entity.label == 5): distance = (tokens_num_dict[ after_entity.sentence_index] + after_entity.begin_index) - ( tokens_num_dict[entity.sentence_index] + entity.end_index) if distance < 20: after_entity_left = list_sentence[after_entity.sentence_index].tokens[max(0, after_entity.begin_index - 10):after_entity.begin_index] after_entity_right = list_sentence[after_entity.sentence_index].tokens[ after_entity.end_index + 1:after_entity.end_index + 6] after_entity_left = "".join(after_entity_left) if len(after_entity_left) > 20: after_entity_left = after_entity_left[-20:] after_entity_right = "".join(after_entity_right)[:10] if re.search("地，?址", after_entity_left): is_skip = True continue if re.search("\(|（", after_entity_left) and re.search("\)|）",after_entity_right): is_skip = True continue if entity.label in [0, 1] and after_entity.label in [0, 1] and entity.label == after_entity.label: break if entity.label in [0, 1] and after_entity.label in [0, 1] and split_entitys[ index + 1].entity_type == "person": break if entity.label in [0, 1 ,5] and after_entity.label in [2, 3, 4]: break if entity.label in [2, 3, 4] and after_entity.label in [0, 1]: break # 搜索没有联系人的电话 mid_tokens = [] is_same_sentence = False if index == len(split_entitys) - 1: for i in range(entity.sentence_index, len(list_sentence)): mid_tokens += list_sentence[i].tokens mid_tokens = mid_tokens[entity.end_index + 1:] mid_sentence = "".join(mid_tokens) have_phone = re.findall(re_phone, mid_sentence) if have_phone: if re.findall(re_phone, mid_sentence.split("。")[0]): is_same_sentence = True _phone = have_phone[0] if _phone in [ent.entity_text for ent in phone_entitys]: phone_begin = mid_sentence.find(_phone) if words_num_dict[entity.sentence_index] + entity.wordOffset_begin + phone_begin < \ new_split_list[split_index][1]: mid_sentence = mid_sentence[max(0, phone_begin - 15):phone_begin].replace("，", "") if re.search(key_phone, mid_sentence): # if entity.label in [2, 3, 4] and re.search("纪检|监察|质疑|投诉|监督|受理|项目(单位|业主)?联系|(采购|招标)人?联系|请.{0,4}联系",mid_sentence[-10:]): if entity.label in [2, 3, 4] and re.search(not_win_tenderer_contact,mid_sentence[-15:]): # print('not_win_tenderer_contact3') pass else: distance = 1 if is_same_sentence: if phone_begin <= 200: if entity.label in [2,3,4] and phone_begin>80: break value = (-1 / 2 * (distance ** 2)) / 10000 match_list2.append(Match(entity, (entity, _phone), value)) match_nums += 1 else: if phone_begin <= 60: if entity.label in [2,3,4] and phone_begin>40: break value = (-1 / 2 * (distance ** 2)) / 10000 match_list2.append(Match(entity, (entity, _phone), value)) match_nums += 1 else: next_entity = split_entitys[index + 1] if next_entity.entity_type in ["org","company"]: _entity_left = list_sentence[next_entity.sentence_index].sentence_text[entity.wordOffset_end:next_entity.wordOffset_begin] _entity_left2 = re.sub("，（）\(\):：", "", _entity_left) _entity_left2 = _entity_left2[-5:] if re.search("(地，?址|地，?点)[:：][^，。]*$", _entity_left) or re.search("地址|地点", _entity_left2): if index + 2<= len(split_entitys) - 1: next_entity = split_entitys[index + 2] if len(_entity_left)<=2 and re.search("[、（\(]",_entity_left): if index + 2 <= len(split_entitys) - 1: next_entity = split_entitys[index + 2] if entity.sentence_index == next_entity.sentence_index: mid_tokens += list_sentence[entity.sentence_index].tokens[ entity.end_index + 1:next_entity.begin_index] else: sentence_index = entity.sentence_index while sentence_index <= next_entity.sentence_index: mid_tokens += list_sentence[sentence_index].tokens sentence_index += 1 mid_tokens = mid_tokens[entity.end_index + 1:-(len( list_sentence[next_entity.sentence_index].tokens) - next_entity.begin_index) + 1] mid_sentence = "".join(mid_tokens) have_phone = re.findall(re_phone, mid_sentence) if have_phone: if re.findall(re_phone, mid_sentence.split("。")[0]): is_same_sentence = True _phone = have_phone[0] if _phone in [ent.entity_text for ent in phone_entitys]: phone_begin = mid_sentence.find(_phone) mid_sentence = mid_sentence[max(0, phone_begin - 15):phone_begin].replace("，", "") if re.search(key_phone, mid_sentence): p_phone = [p.entity_text for p in next_entity.person_phone] if next_entity.person_phone else [] if next_entity.entity_type == 'person' and _phone in p_phone: pass # elif entity.label in [2, 3, 4] and re.search("纪检|监察|质疑|投诉|监督|受理|项目(单位|业主)?联系|(采购|招标)人?联系|请.{0,4}联系", mid_sentence[-10:]): elif entity.label in [2, 3, 4] and re.search(not_win_tenderer_contact, mid_sentence[-15:]): # print('not_win_tenderer_contact4') pass else: distance = (tokens_num_dict[ next_entity.sentence_index] + next_entity.begin_index) - ( tokens_num_dict[entity.sentence_index] + entity.end_index) distance = distance / 2 if is_same_sentence: if phone_begin <= 200: value = (-1 / 2 * (distance ** 2)) / 10000 match_list2.append(Match(entity, (entity, _phone), value)) match_nums += 1 else: if phone_begin <= 60: value = (-1 / 2 * (distance ** 2)) / 10000 match_list2.append(Match(entity, (entity, _phone), value)) match_nums += 1 # 实体无匹配时，尝试前向查找匹配 if not match_nums: if (entity.label != 5 or entity.entity_text in roleSet) and entity.values[entity.label] >= 0.5 and index != 0: previous_entity = split_entitys[index - 1] if previous_entity.entity_type == 'person' and previous_entity.label in [1, 2, 3]: if entity.label in [2, 3, 4] and previous_entity.label in [1, 2]: continue # 角色为中标候选人，排除"质疑|投诉|监督|受理"相关的联系人 if entity.label in [2, 3, 4] and re.search(not_win_tenderer_contact,list_sentence[previous_entity.sentence_index].sentence_text[ max(0,previous_entity.wordOffset_begin - 15):previous_entity.wordOffset_begin]): # print('not_win_tenderer_contact2') break # 角色为中标候选人，联系人无号码且上文没有联系关键词时排除 if entity.label in [2, 3,4] and not previous_entity.person_phone and not re.search("联系人|联系方式|电话|负责人|经理|法人|法定代表人", list_sentence[previous_entity.sentence_index].sentence_text[max(0,previous_entity.wordOffset_begin - 10):previous_entity.wordOffset_begin]): continue # 角色为招标/代理人，排除"纪检|监察"相关的联系人 if (entity.label in [0,1] or entity.entity_text in tenderee_entity_set|agency_entity_set) and re.search("纪检|监察|投诉|监督|乙方|中标", list_sentence[previous_entity.sentence_index].sentence_text[ max(0,previous_entity.wordOffset_begin - 10):previous_entity.wordOffset_begin]): break if previous_entity.sentence_index == entity.sentence_index: distance = (tokens_num_dict[entity.sentence_index] + entity.begin_index) - ( tokens_num_dict[ previous_entity.sentence_index] + previous_entity.end_index) if distance < 20: # 距离相等时，前向添加处罚值 # distance += 1 # 前向 没有 /10000 value = (-1 / 2 * (distance ** 2)) match_list2.append(Match(entity, previous_entity, value)) # print(match_list2) # print([(mat.main_role.entity_text,mat.attribute.entity_text if not isinstance(mat.attribute, tuple) else mat.attribute[1]) for mat in match_list2]) match_list2 = [mat for mat in match_list2 if mat.main_role not in linked_company and mat.attribute not in linked_person] # print(match_list2) # print([(mat.main_role.entity_text,mat.attribute.entity_text if not isinstance(mat.attribute, tuple) else mat.attribute[1]) for mat in match_list2]) # km算法分配求解 result2 = dispatch(match_list2) result2.sort(key=lambda x: (x[0].sentence_index, x[0].begin_index)) # print(result2) for match in result2: entity = match[0] # print(entity.entity_text) # print(entity.label) # print(match[1]) entity_index = list_entity.index(entity) is_update = False if isinstance(match[1], tuple): person_ = '' phone_ = match[1][1].split("/") # 分割多个号码 # print(person_,phone_) else: person_ = match[1].entity_text phone_ = [i.entity_text for i in match[1].person_phone] if match[1].person_phone else [] for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): if PackDict[k]["roleList"][i].role_name == "tenderee": # if not PackDict[k]["roleList"][i].linklist: if PackDict[k]["roleList"][i].entity_text == entity.entity_text or entity.label == 0: if person_ not in agency_contact and len(set(phone_)&set(agency_phone))==0 and person_ not in winter_contact: if not phone_: PackDict[k]["roleList"][i].linklist.append((person_, "")) for p in phone_: # if not person_ and len() PackDict[k]["roleList"][i].linklist.append((person_, p)) is_update = True elif PackDict[k]["roleList"][i].role_name == "agency": # if not PackDict[k]["roleList"][i].linklist: if PackDict[k]["roleList"][i].entity_text == entity.entity_text or entity.label == 1 and person_ not in winter_contact: if person_ not in tenderee_contact and len(set(phone_)&set(tenderee_phone))==0: if not phone_: PackDict[k]["roleList"][i].linklist.append((person_, "")) for p in phone_: PackDict[k]["roleList"][i].linklist.append((person_, p)) is_update = True else: if PackDict[k]["roleList"][i].entity_text == entity.entity_text: # if not PackDict[k]["roleList"][i].linklist: if len([item for item in PackDict[k]["roleList"][i].linklist if item[1]])==0: # 有联系人但无联系方式（号码） if person_ not in tenderee_contact and len(set(phone_)&set(tenderee_phone))==0 and \ person_ not in agency_contact and len(set(phone_)&set(agency_phone))==0: if PackDict[k]["roleList"][i].linklist and entity.in_attachment: # 中标联系人已有值时，跳过附件提取的 # print('win test', person_, phone_) continue if not phone_: PackDict[k]["roleList"][i].linklist.append((person_, "")) for p in phone_: # print('win test',person_, p) PackDict[k]["roleList"][i].linklist.append((person_, p)) is_update = True if not person_: is_update = False if is_update: # 更新 list_entity if not list_entity[entity_index].pointer_person: list_entity[entity_index].pointer_person = [] list_entity[entity_index].pointer_person.append(match[1]) # print('tenderee_contact',tenderee_contact) # print('tenderee_phone',tenderee_phone) # print('agency_contact',agency_contact) # print('agency_phone',agency_phone) # print('PackDict') # for k in PackDict.keys(): # for i in range(len(PackDict[k]["roleList"])): # print(PackDict[k]["roleList"][i].role_name) # print(PackDict[k]["roleList"][i].entity_text) # print(PackDict[k]["roleList"][i].linklist) linked_person = [] linked_persons_with = [] for company_entity in [entity for entity in list_entity if entity.entity_type in ['company','org']]: if company_entity.pointer_person: for _person in company_entity.pointer_person: linked_person.append(_person) linked_persons_with.append(company_entity) # 一个公司对应多个联系人的补充 person_entitys = [entity for entity in list_entity if entity.entity_type=='person'] person_entitys = person_entitys[::-1] for index in range(len(person_entitys)): entity = person_entitys[index] prepare_link = [] if entity not in linked_person: prepare_link.append(entity) last_person = entity for after_index in range(index + 1, min(len(person_entitys), index + 5)): after_entity = person_entitys[after_index] if after_entity.sentence_index==last_person.sentence_index and last_person.begin_index-after_entity.end_index<5: if after_entity in linked_person: _index = linked_person.index(after_entity) with_company = linked_persons_with[_index] for i in range(len(PackDict["Project"]["roleList"])): if PackDict["Project"]["roleList"][i].role_name == "tenderee": if PackDict["Project"]["roleList"][i].entity_text == with_company.entity_text or with_company.label == 0: for item in prepare_link: person_phone = [p.entity_text for p in item.person_phone] if item.person_phone else [] for _p in person_phone: PackDict["Project"]["roleList"][i].linklist.append((item.entity_text, _p)) with_company.pointer_person.append(item) linked_person.append(item) elif PackDict["Project"]["roleList"][i].role_name == "agency": if PackDict["Project"]["roleList"][i].entity_text == with_company.entity_text or with_company.label == 1: for item in prepare_link: person_phone = [p.entity_text for p in item.person_phone] if item.person_phone else [] for _p in person_phone: PackDict["Project"]["roleList"][i].linklist.append((item.entity_text, _p)) with_company.pointer_person.append(item) linked_person.append(item) else: if PackDict["Project"]["roleList"][i].entity_text == with_company.entity_text: for item in prepare_link: person_phone = [p.entity_text for p in item.person_phone] if item.person_phone else [] for _p in person_phone: PackDict["Project"]["roleList"][i].linklist.append((item.entity_text, _p)) with_company.pointer_person.append(item) linked_person.append(item) break else: prepare_link.append(after_entity) last_person = after_entity continue # 统一同类角色的属性 for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): for _entity in list_entity: if _entity.entity_type in ['org','company']: is_same = False is_similar = False # entity_text相同 if _entity.entity_text==PackDict[k]["roleList"][i].entity_text: is_same = True # entity.label为【0，1】 if _entity.label in [0,1] and dict_role_id[str(_entity.label)]==PackDict[k]["roleList"][i].role_name: is_similar = True if is_same: linked_entitys = _entity.linked_entitys if linked_entitys: for linked_entity in linked_entitys: pointer_person = linked_entity.pointer_person if linked_entity.pointer_person else [] for _pointer_person in pointer_person: _phone = [p.entity_text for p in _pointer_person.person_phone] if _pointer_person.person_phone else [] for _p in _phone: if (_pointer_person.entity_text,_p) not in PackDict[k]["roleList"][i].linklist: PackDict[k]["roleList"][i].linklist.append((_pointer_person.entity_text,_p)) elif is_similar: pointer_person = _entity.pointer_person if _entity.pointer_person else [] for _pointer_person in pointer_person: _phone = [p.entity_text for p in _pointer_person.person_phone] if _pointer_person.person_phone else [] for _p in _phone: if (_pointer_person.entity_text, _p) not in PackDict[k]["roleList"][i].linklist: PackDict[k]["roleList"][i].linklist.append( (_pointer_person.entity_text, _p)) # "roleList"中联系人电话去重 tenderee_agency_phone = [] tenderee_agency_contact = [] for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): if PackDict[k]["roleList"][i].role_name in ['agency','tenderee']: tenderee_agency_phone.extend([person_phone[1] for person_phone in PackDict[k]["roleList"][i].linklist if person_phone[1]]) tenderee_agency_contact.extend([person_phone[0]+'-'+person_phone[1] for person_phone in PackDict[k]["roleList"][i].linklist]) # 带有联系人的电话 with_person = [person_phone[1] for person_phone in PackDict[k]["roleList"][i].linklist if person_phone[0]] # 带有电话的联系人 with_phone = [person_phone[0] for person_phone in PackDict[k]["roleList"][i].linklist if person_phone[1]] remove_list = [] for item in PackDict[k]["roleList"][i].linklist: if not item[0]: if item[1] in with_person: # 删除重复的无联系人电话 remove_list.append(item) elif not item[1]: if item[0] in with_phone: remove_list.append(item) for _item in remove_list: PackDict[k]["roleList"][i].linklist.remove(_item) # 中标候选人联系方式异常排除 for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): if PackDict[k]["roleList"][i].role_name in ['win_tenderer', 'second_tenderer','third_tenderer']: if tenderee_agency_phone or tenderee_agency_contact: remove_list = [] for item in PackDict[k]["roleList"][i].linklist: if item[1] and item[1] in tenderee_agency_phone: if item[1] not in rule_winter_phone: # print('remove win phone',item) remove_list.append(item) elif item[0]+'-'+item[1] in tenderee_agency_contact: if item[1] not in rule_winter_phone: # print('remove win phone', item) remove_list.append(item) for _item in remove_list: PackDict[k]["roleList"][i].linklist.remove(_item) elif not tenderee_agency_phone: # 公告中无招标代理联系方式时，可排除中标联系方式 remove_list = [] for _item in PackDict[k]["roleList"][i].linklist: # 排除非正则规则识别的联系方式 if _item[1] not in rule_winter_phone: remove_list.append(_item) # print('remove_list',remove_list) for _item in remove_list: PackDict[k]["roleList"][i].linklist.remove(_item) # PackDict更新company/org地址 last_role_prob = {} for ent in pre_entity: if ent.entity_type in ['company','org']: if ent.pointer_address: for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): if PackDict[k]["roleList"][i].entity_text == ent.entity_text: if not PackDict[k]["roleList"][i].address: PackDict[k]["roleList"][i].address = ent.pointer_address.entity_text last_role_prob[PackDict[k]["roleList"][i].role_name] = ent.values[role2id_dict[PackDict[k]["roleList"][i].role_name]] else: if PackDict[k]["roleList"][i].role_name in ['tenderee','agency']: # 角色为招标/代理人时，取其实体概率高的链接地址作为角色address if ent.values[role2id_dict[PackDict[k]["roleList"][i].role_name]] > last_role_prob[PackDict[k]["roleList"][i].role_name]: PackDict[k]["roleList"][i].address = ent.pointer_address.entity_text last_role_prob[PackDict[k]["roleList"][i].role_name] = ent.values[role2id_dict[PackDict[k]["roleList"][i].role_name]] else: if len(ent.pointer_address.entity_text) > len(PackDict[k]["roleList"][i].address): PackDict[k]["roleList"][i].address = ent.pointer_address.entity_text # 联系人——电子邮箱链接 temporary_list3 = [entity for entity in list_entity if entity.entity_type=='email' or (entity.entity_type=='person' and entity.label in [1,2,3])] temporary_list3 = sorted(temporary_list3, key=lambda x: (x.sentence_index, x.begin_index)) new_temporary_list3 = [] for _split in new_split_list: temp_list = [] for _entity in temporary_list3: if words_num_dict[_entity.sentence_index] + _entity.wordOffset_begin >= _split[0] and words_num_dict[ _entity.sentence_index] + _entity.wordOffset_end < _split[1]: temp_list.append(_entity) elif words_num_dict[_entity.sentence_index] + _entity.wordOffset_begin >= _split[1]: break new_temporary_list3.append(temp_list) # print(new_temporary_list3) match_list3 = [] for split_index in range(len(new_temporary_list3)): split_entitys = new_temporary_list3[split_index] for index in range(len(split_entitys)): entity = split_entitys[index] if entity.entity_type == 'person': match_nums = 0 for after_index in range(index + 1, min(len(split_entitys), index + 4)): after_entity = split_entitys[after_index] if match_nums > 2: break if after_entity.entity_type == 'email': distance = (tokens_num_dict[after_entity.sentence_index] + after_entity.begin_index) - ( tokens_num_dict[entity.sentence_index] + entity.end_index) sentence_distance = after_entity.sentence_index - entity.sentence_index if sentence_distance == 0: if distance < 100: if (entity.label == 0 and after_entity.label == 1) or ( entity.label == 1 and after_entity.label == 2): distance = distance / 100 value = (-1 / 2 * (distance ** 2)) / 10000 match_list3.append(Match(entity, after_entity, value)) match_nums += 1 else: if distance < 60: if (entity.label == 0 and after_entity.label == 1) or ( entity.label == 1 and after_entity.label == 2): distance = distance / 100 value = (-1 / 2 * (distance ** 2)) / 10000 match_list3.append(Match(entity, after_entity, value)) match_nums += 1 # 前向查找匹配 # if not match_nums: if index != 0: previous_entity = split_entitys[index - 1] if previous_entity.entity_type == 'email': if previous_entity.sentence_index == entity.sentence_index: distance = (tokens_num_dict[entity.sentence_index] + entity.begin_index) - ( tokens_num_dict[ previous_entity.sentence_index] + previous_entity.end_index) if distance < 30: # 距离相等时，前向添加处罚值 # distance += 1 # 前向 没有 /10000 value = (-1 / 2 * (distance ** 2)) match_list3.append(Match(entity, previous_entity, value)) # print(match_list3) # km算法分配求解 result3 = dispatch(match_list3) for match in result3: match_person = match[0] match_email = match[1] match_person.pointer_email = match_email # # 1）第一个公司实体的招标人，则看看下一个实体是否为代理人，如果是则联系人错位连接 。2）在同一句中往后找联系人。3）连接不上在整个文章找联系人。 # temp_ent_list = [] # 临时列表，记录0,1角色及3联系人 # other_person = [] # 阈值以上的联系人列表 # link_person = [] # 有电话没联系上角色的person列表 # other_ent = [] # link_ent = [] # found_person = False # ent_list = [] # for entity in list_entity: # if entity.entity_type in ['org','company','person']: # ent_list.append(entity) # # ent_list = [entity for entity in list_entity if entity.entity_type in ['org','company','person']] # #for list_index in range(len(ent_list)): # #if ent_list[list_index].entity_type in ['org','company'] and ent_list[list_index].label == 0 and list_index+2on_value_person: # if str(entity.label)=="1": # for i in range(len(PackDict["Project"]["roleList"])): # if PackDict["Project"]["roleList"][i].role_name=="tenderee": # PackDict["Project"]["roleList"][i].linklist.append((entity.entity_text,entity.person_phone)) # link_person.append(entity.entity_text) # link_ent.append(PackDict["Project"]["roleList"][i].entity_text) # # add pointer_person # for _entity in list_entity: # if dict_role_id.get(str(_entity.label))=="tenderee": # for i in range(len(PackDict["Project"]["roleList"])): # if PackDict["Project"]["roleList"][i].entity_text==_entity.entity_text and PackDict["Project"]["roleList"][i].role_name=="tenderee": # _entity.pointer_person = entity # elif str(entity.label)=="2": # for i in range(len(PackDict["Project"]["roleList"])): # if PackDict["Project"]["roleList"][i].role_name=="agency": # PackDict["Project"]["roleList"][i].linklist.append((entity.entity_text,entity.person_phone)) # link_person.append(entity.entity_text) # link_ent.append(PackDict["Project"]["roleList"][i].entity_text) # # add pointer_person # for _entity in list_entity: # if dict_role_id.get(str(_entity.label))=="agency": # for i in range(len(PackDict["Project"]["roleList"])): # if PackDict["Project"]["roleList"][i].entity_text==_entity.entity_text and PackDict["Project"]["roleList"][i].role_name=="agency": # _entity.pointer_person = entity # elif str(entity.label)=="3": # if entity.entity_text in sure_person_set: # 2020/11/25 排除已经确定角色的联系人 # continue # #not_link_person.append((entity_after.entity_text,entity_after.person_phone)) # other_person.append(entity.entity_text) # temp_ent_list.append((entity.entity_text,entity.person_phone,entity)) # # #if entity.entity_text in roleSet: # if entity.entity_text in roleSet: # if entity.label in [0,1]: # other_ent.append(entity.entity_text) # temp_ent_list.append((entity.entity_text, entity.label,entity)) # for behind_index in range(index+1, len(ent_list)): # entity_after = ent_list[behind_index] # if entity_after.sentence_index-entity.sentence_index>=1 or entity_after.entity_type in ['org','company']: # 只在本句中找联系人 # break # if entity_after.values is not None: # if entity_after.entity_type=="person": # if str(entity_after.label) == "0": # 2020/11/25角色后面为非联系人 停止继续往后找 # break # if entity_after.values[entity_after.label]>on_value_person: # if str(entity_after.label)=="1": # for i in range(len(PackDict["Project"]["roleList"])): # if PackDict["Project"]["roleList"][i].role_name=="tenderee": # PackDict["Project"]["roleList"][i].linklist.append((entity_after.entity_text,entity_after.person_phone)) # link_person.append(entity_after.entity_text) # link_ent.append(PackDict["Project"]["roleList"][i].entity_text) # elif str(entity_after.label)=="2": # for i in range(len(PackDict["Project"]["roleList"])): # if PackDict["Project"]["roleList"][i].role_name=="agency": # PackDict["Project"]["roleList"][i].linklist.append((entity_after.entity_text,entity_after.person_phone)) # link_person.append(entity_after.entity_text) # link_ent.append(PackDict["Project"]["roleList"][i].entity_text) # elif str(entity_after.label)=="3": # if entity_after.entity_text in sure_person_set: # 2020/11/25 如果姓名已经出现在确定角色联系人中则停止往后找 # break # elif entity_after.begin_index - entity.end_index > 30:#2020/10/25 如果角色实体与联系人实体间隔大于阈值停止 # break # for pack in PackDict.keys(): # for i in range(len(PackDict[pack]["roleList"])): # if PackDict[pack]["roleList"][i].entity_text==entity.entity_text: # #if entity_after.sentence_index-entity.sentence_index>1 and len(roleList[i].linklist)>0: # #break # PackDict[pack]["roleList"][i].linklist.append((entity_after.entity_text,entity_after.person_phone)) # link_person.append(entity_after.entity_text) # #add pointer_person # entity.pointer_person = entity_after # # not_link_person = [person for person in other_person if person not in link_person] # not_link_ent = [ent for ent in other_ent if ent not in link_ent] # if len(not_link_person) > 0 and len(not_link_ent) > 0 : # item = temp_ent_list # for i in range(len(item)): # if item[i][0] in not_link_ent and item[i][1] == 0 and i+3 < len(item): # if item[i+1][0] in other_ent and item[i+1][1] == 1 and item[i+2][0] in other_person and item[i+3][0] in other_person: # item[i+1], item[i+2] = item[i+2], item[i+1] # for i in range(len(item)-1, -1, -1): # if item[i][0] in not_link_ent: # for pack in PackDict.keys(): # for role in PackDict[pack]["roleList"]: # if role.entity_text == item[i][0] and len(role.linklist) < 1: # for j in range(i+1, len(item)): # if item[j][0] in not_link_person: # role.linklist.append(item[j][:2]) # #add pointer_person # item[i][2].pointer_person = item[j][2] # break # else: # break # # 电话没有联系人的处理 # role_with_no_phone = [] # for i in range(len(PackDict["Project"]["roleList"])): # if PackDict["Project"]["roleList"][i].role_name in ["tenderee","agency"]: # if len(PackDict["Project"]["roleList"][i].linklist)==0: # 找出没有联系人的招标/代理人 # role_with_no_phone.append(PackDict["Project"]["roleList"][i].entity_text) # else: # phone_nums = 0 # for link in PackDict["Project"]["roleList"][i].linklist: # if link[1]: # phone_nums += 1 # break # if not phone_nums: # role_with_no_phone.append(PackDict["Project"]["roleList"][i].entity_text) # if role_with_no_phone: # phone_with_person = [entity.person_phone for entity in list_entity if entity.entity_type == "person"] # # phone_with_person = [phone for phone in phone_with_person if phone] # # dict_index_sentence = {} # for _sentence in list_sentence: # dict_index_sentence[_sentence.sentence_index] = _sentence # new_entity_list = [entity for entity in list_entity if entity.entity_type in ['org','company','person']] # for index in range(len(new_entity_list)): # entity = new_entity_list[index] # if entity.entity_text in role_with_no_phone: # e_sentence = dict_index_sentence[entity.sentence_index] # entity_right = e_sentence.tokens[entity.end_index:entity.end_index+40] # entity_right = "".join(entity_right) # if index+1-1: # entity_right = entity_right[:entity_right.find(new_entity_list[index+1].entity_text)] # have_phone = re.findall(phone,entity_right) # if have_phone: # _phone = have_phone[0] # phone_begin = entity_right.find(_phone) # if _phone not in phone_with_person and re.search(key_phone,entity_right[:phone_begin]): # # entity.person_phone = _phone # for i in range(len(PackDict["Project"]["roleList"])): # if PackDict["Project"]["roleList"][i].entity_text == entity.entity_text: # PackDict["Project"]["roleList"][i].linklist.append(('', _phone)) #寻找多标段招标金额 p_entity = len(list_entity)-1 set_tenderer_money = set() list_tenderer_money = [] #2021/7/16 新增列表，倒序保存所有中标金额 unit_list = [] #2021/8/17 新增，保存金额单位 #遍历所有实体 max_prob = 0 # 保存招标金额最大概率 while(p_entity>=0): entity = list_entity[p_entity] if entity.entity_type=="money": # 2021/12/03 添加成本警戒线、保证金 if entity.notes in ['保证金', '成本警戒线']: packagePointer, _flag = getPackage(PackageList, entity.sentence_index, entity.begin_index, "money-" + str(entity.label), MAX_DIS=2, DIRECT="L") if packagePointer is None: packageName = "Project" else: packageName = packagePointer.entity_text if packageName == "Project": # if PackDict["Project"]["tendereeMoney"]=on_value: if str(entity.label)=="1" and entity.notes != '单价': if entity.in_attachment == False: # 20251009 修复 659780102 正文中标人与附件错误金额链接， 某服务采购代理业务成交金额或者暂定价为150万元， set_tenderer_money.add(float(entity.entity_text)) list_tenderer_money.append(float(entity.entity_text)) # 2021/7/16 新增列表，倒序保存所有中标金额 unit_list.append(entity.money_unit) # if str(entity.label)=="0": if str(entity.label)=="0" and (entity.notes!='总投资' or float(entity.entity_text)<100000000): ''' if p_entity>0: p_before = list_entity[p_entity-1] if p_before.entity_type=="money" and p_before.label==entity.label and p_before.entity_text==entity.entity_text and abs(entity.begin_index-p_before.end_index)<=2: p_entity -= 1 continue ''' packagePointer,_flag = getPackage(PackageList,entity.sentence_index,entity.begin_index,"money-"+str(entity.label),MAX_DIS=2,DIRECT="L") if packagePointer is None: packageName = "Project" else: packageName = packagePointer.entity_text if packageName=="Project": # if PackDict["Project"]["tendereeMoney"]on_value: if entity.values[entity.label]>max_prob-0.005: # 选择最大概率招标金额 2024/05/23 相差0.005尽量选前面的 if entity.notes == '单价': PackDict["Project"]["unit_tendereeMoney"] = str(Decimal(entity.entity_text)) else: PackDict["Project"]["tendereeMoney"] = str(Decimal(entity.entity_text)) PackDict["Project"]["tendereeMoneyUnit"] = entity.money_unit max_prob = entity.values[entity.label] else: if entity.notes == '单价': PackDict[packageName]["unit_tendereeMoney"] = str(Decimal(entity.entity_text)) else: PackDict[packageName]["tendereeMoney"] = str(Decimal(entity.entity_text)) PackDict[packageName]["tendereeMoneyUnit"] = entity.money_unit #add pointer_tendereeMoney packagePointer.pointer_tendereeMoney = entity p_entity -= 1 '''标段链接包名包号''' pk_name_l = [] pk_code_l = [] count_dic = { 'package': set(), 'name': set(), 'code': set() } def get_sort_dist(l, max_sent_dist=2): ''' 计算标段与其他要素距离，并按距离排序返回字典 :param l: [(entity.entity_type, entity.entity_text, entity.sentence_index, entity.wordOffset_begin, entity.wordOffset_end)] :param max_sent_dist: 最大句子距离 :return: ''' l.sort(key=lambda x: [x[2],x[3],x[4]]) # 20241204 多个字段排序 修复 561998414 第一标段西铭矿清水泵采购 标段和包名开始位置一样的情况 link_dic = {} i = 1 while i < len(l): ty1, ent1, s1, b1, e1, in_att1 = l[i - 1] ty2, ent2, s2, b2, e2, in_att2 = l[i] if ty1 != ty2 and in_att1 == in_att2 and s2 - s1 <= max_sent_dist: if ty1 == 'package': if ent1 not in link_dic: link_dic[ent1] = [] if s1 == s2: dist = abs(b2 - e1) if b2 > e1 else 0 else: dist = len(list_sentence[s1].sentence_text) - e1 for id in range(s1+1, s2): dist += len(list_sentence[id].sentence_text) dist += b2 if in_att1: dist += 100 # 附件的距离加100 link_dic[ent1].append((s2 - s1, dist, ent2)) elif ty2 == 'package': if ent2 not in link_dic: link_dic[ent2] = [] if s1 == s2: dist = abs(b2 - e1) if b2 > e1 else 0 else: dist = len(list_sentence[s1].sentence_text) - e1 for id in range(s1+1, s2): dist += len(list_sentence[id].sentence_text) dist += b2 if in_att1: dist += 100 # 附件的距离加100 if s1!=s2 or e1!=e2: dist += 30 # 包号在实体后面距离再加30 link_dic[ent2].append((s2 - s1, dist, ent1)) i += 1 return link_dic for entity in list_entity: if entity.entity_type == 'package': pk_name_l.append((entity.entity_type, entity.entity_text, entity.sentence_index, entity.wordOffset_begin, entity.wordOffset_end, entity.in_attachment)) pk_code_l.append((entity.entity_type, entity.entity_text, entity.sentence_index, entity.wordOffset_begin, entity.wordOffset_end, entity.in_attachment)) count_dic['package'].add(entity.entity_text) elif entity.entity_type == 'name': pk_name_l.append((entity.entity_type, entity.entity_text, entity.sentence_index, entity.wordOffset_begin, entity.wordOffset_end, entity.in_attachment)) count_dic['name'].add(entity.entity_text) elif entity.entity_type == 'code': pk_code_l.append((entity.entity_type, entity.entity_text, entity.sentence_index, entity.wordOffset_begin, entity.wordOffset_end, entity.in_attachment)) count_dic['code'].add(entity.entity_text) if len(count_dic['package']) > 0: if len(count_dic['name'])>0: link_dic = get_sort_dist(pk_name_l) for k, v in link_dic.items(): v.sort(key=lambda x: [x[0], x[1]]) if v[0][0] < 2 and v[0][1] < 200: # 标段号与包名句子数小于2，字距离小于200的才添加 PackDict[k]["name"] = v[0][2] if len(count_dic['code'])>0: link_dic = get_sort_dist(pk_code_l) for k, v in link_dic.items(): v.sort(key=lambda x: [x[0], x[1]]) if v[0][0] < 2 and v[0][1] < 200: PackDict[k]["code"] = v[0][2] #删除一个机构有多个角色的数据 #删除重复人、概率不回传 final_roleList = [] list_pop = [] set_tenderer_role = set() dict_pack_tenderer_money = dict() for pack in PackDict.keys(): #删除无效包 if float(PackDict[pack].get("unit_tendereeMoney", 0)) > 10000 and PackDict[pack]["tendereeMoney"]==0: # 单价补充招标金额 PackDict[pack]["tendereeMoney"] = PackDict[pack].get("unit_tendereeMoney", 0) if PackDict[pack]["code"]=="" and PackDict[pack]["tendereeMoney"]==0 and len(PackDict[pack]["roleList"])==0: list_pop.append(pack) for i in range(len(PackDict[pack]["roleList"])): if PackDict[pack]["roleList"][i].role_name=="win_tenderer": if PackDict[pack]["roleList"][i].money==0: set_tenderer_role.add(PackDict[pack]["roleList"][i]) dict_pack_tenderer_money[pack] = [PackDict[pack]["roleList"][i],set()] #找到包的中投标金额 for _index in range(len(PackageList)): if "hit" in PackageList[_index]: for _hit in list(PackageList[_index]["hit"]): if len(_hit.split("-"))==3: _money = float(_hit.split("-")[1]) if _hit.split("-")[0]=="money" else None # 补充金额前新增负号‘-’导致错误的规则 elif len(_hit.split("-"))==4: _money = float(_hit.split("-")[2]) if _hit.split("-")[0] == "money" else None else: _money = None if PackageList[_index]["name"] in dict_pack_tenderer_money and _money is not None: dict_pack_tenderer_money[PackageList[_index]["name"]][1].add(_money) #只找到一个中标人和中标金额 if len(set_tenderer_money)==1 and len(set_tenderer_role)==1: list(set_tenderer_role)[0].money = list(set_tenderer_money)[0] list(set_tenderer_role)[0].money_unit = unit_list[0] # print('一个中标人一个金额：', list(set_tenderer_money)[0]) #找到一个中标人和多个招标金额 if len(set_tenderer_money)>1 and len(set_tenderer_role)==1: _maxMoney = list(set_tenderer_money)[0] _sumMoney = 0 for _m in list(set_tenderer_money): _sumMoney += _m if _m>_maxMoney: _maxMoney = _m if _sumMoney/_maxMoney==2: list(set_tenderer_role)[0].money = _maxMoney # print('一人多金额分项合计 取最大金额：', _maxMoney) else: # list(set_tenderer_role)[0].money = _maxMoney if min(list_tenderer_money)>200000 and list_tenderer_money[-1]/min(list_tenderer_money)>9000: list(set_tenderer_role)[0].money = min(list_tenderer_money) list(set_tenderer_role)[0].money_unit = unit_list[list_tenderer_money.index(min(list_tenderer_money))] # print('一人多金额 且最小的大于20万第一个金额比最小金额大几千倍的最小中标金额：', min(list_tenderer_money)) else: list(set_tenderer_role)[0].money = list_tenderer_money[-1] # 2021/7/16 修改 不是单价合计方式取第一个中标金额 list(set_tenderer_role)[0].money_unit = unit_list[-1] # 金额单位 # print('一人多金额 取第一个中标金额：', list_tenderer_money[-1]) #每个包都只找到一个金额 _flag_pack_money = True for k,v in dict_pack_tenderer_money.items(): if len(v[1])!=1: _flag_pack_money = False if _flag_pack_money and len(PackageSet)==len(dict_pack_tenderer_money.keys()): for k,v in dict_pack_tenderer_money.items(): if float(v[0].unit_price) < float(list(v[1])[0]): # 20241128 金额大于单价时才作链接金额 v[0].money = list(v[1])[0] # 2021/7/16 #增加判断中标金额是否远大于招标金额逻辑 for pack in PackDict.keys(): for i in range(len(PackDict[pack]["roleList"])): if float(PackDict[pack]["tendereeMoney"]) > 0: # print('金额数据类型：',type(PackDict[pack]["roleList"][i].money)) if float(PackDict[pack]["roleList"][i].money) >10000000 and \ float(PackDict[pack]["roleList"][i].money)/float(PackDict[pack]["tendereeMoney"])>=1000: PackDict[pack]["roleList"][i].money = float(PackDict[pack]["roleList"][i].money) / 10000 # print('招标金额校正中标金额') # 2022/04/01 #增加判断中标金额是否远小于招标金额逻辑，比例相差10000倍左右（中标金额“万”单位丢失或未识别） for pack in PackDict.keys(): for i in range(len(PackDict[pack]["roleList"])): if float(PackDict[pack]["tendereeMoney"]) > 0 and float(PackDict[pack]["roleList"][i].money) > 0.: if float(PackDict[pack]["roleList"][i].money) < 1000 and \ float(PackDict[pack]["tendereeMoney"])/float(PackDict[pack]["roleList"][i].money)>=9995 and \ float(PackDict[pack]["tendereeMoney"])/float(PackDict[pack]["roleList"][i].money)<11000: PackDict[pack]["roleList"][i].money = float(PackDict[pack]["roleList"][i].money) * 10000 # 2021/7/19 #增加判断中标金额是否远大于第二三中标金额 for pack in PackDict.keys(): tmp_moneys = [] for i in range(len(PackDict[pack]["roleList"])): if float(PackDict[pack]["roleList"][i].money) >100000: tmp_moneys.append(float(PackDict[pack]["roleList"][i].money)) if len(tmp_moneys)>2 and max(tmp_moneys)/min(tmp_moneys)>1000: for i in range(len(PackDict[pack]["roleList"])): if float(PackDict[pack]["roleList"][i].money)/min(tmp_moneys)>1000: PackDict[pack]["roleList"][i].money = float(PackDict[pack]["roleList"][i].money) / 10000 # print('通过其他中标人投标金额校正中标金额') for item in list_pop: PackDict.pop(item) # 公告中只有"招标人"且无"联系人"链接时 if len(PackDict)==1: k = list(PackDict.keys())[0] tenderee_agency_role = [role for role in PackDict[k]["roleList"] if role.role_name in ['tenderee','agency','win_tenderer']] if len(tenderee_agency_role)==1: exist_person = [] exist_phone = [] for role in PackDict[k]["roleList"]: for group in role.linklist: if group[0]: exist_person.append(group[0]) if group[1]: exist_phone.append(group[1]) if tenderee_agency_role[0].role_name == "tenderee": if not tenderee_agency_role[0].linklist: get_contacts = False if not get_contacts: # 根据大纲Outline类召回联系人 for outline in list_outline: if re.search("联系人|联系方|联系方式|联系电话|电话|负责人|与.{2,4}联系",outline.outline_summary) and \ not re.search("代理|乙方|竞得|受让|买受|签约|供货|供应|承做|承包|承建|承销|承保|承接|承制|承担|承修|承租(?:（包）)?|入围|入选|竞买|中标|中选|中价|中签|成交|候选",outline.outline_summary): for t_person in [p for p in temporary_list2 if p.entity_type=='person' and p.label==3]: if words_num_dict[t_person.sentence_index] + t_person.wordOffset_begin >= words_num_dict[outline.sentence_begin_index] + outline.wordOffset_begin and words_num_dict[ t_person.sentence_index] + t_person.wordOffset_end < words_num_dict[outline.sentence_end_index] + outline.wordOffset_end: if t_person.person_phone: _phone = [p.entity_text for p in t_person.person_phone] for _p in _phone: if t_person.entity_text not in exist_person and _p not in ",".join(exist_phone): tenderee_agency_role[0].linklist.append((t_person.entity_text, _p)) get_contacts = True break elif words_num_dict[t_person.sentence_index] + t_person.wordOffset_begin >= \ words_num_dict[outline.sentence_end_index] + outline.wordOffset_end: break if not get_contacts: sentence_phone = phone.findall(outline.outline_text) if sentence_phone: if sentence_phone[0] not in ",".join(exist_phone): tenderee_agency_role[0].linklist.append(("", sentence_phone[0])) get_contacts = True break # if not get_contacts: # 会召回错误数据，不启用规则 # # 直接取文中倒数第一个联系人 # for _entity in temporary_list2[::-1]: # if _entity.entity_type=='person' and _entity.label==3: # if _entity.person_phone: # _phone = [p.entity_text for p in _entity.person_phone] # for _p in _phone: # if _entity.entity_text not in exist_person and _p not in ",".join(exist_phone): # tenderee_agency_role[0].linklist.append((_entity.entity_text, _p)) # get_contacts = True # break # if not get_contacts: # 会召回错误数据，不启用规则 # # 如果文中只有一个“phone”实体，则直接取为联系人电话 # if len(phone_entitys) == 1: # if phone_entitys[0].entity_text not in ",".join(exist_phone): # tenderee_agency_role[0].linklist.append(("", phone_entitys[0].entity_text)) # get_contacts = True if not get_contacts: # 通过大纲Outline类直接取电话 if len(new_split_list) > 1: for _start, _end in new_split_list: temp_sentence = _content[_start:_end] sentence_outline = temp_sentence.split("，:：")[0] if re.search("联系人|联系方|联系方式|联系电话|电话|负责人|与.{2,4}联系", sentence_outline) and \ not re.search("代理|乙方|竞得|受让|买受|签约|供货|供应|承做|承包|承建|承销|承保|承接|承制|承担|承修|承租(?:（包）)?|入围|入选|竞买|中标|中选|中价|中签|成交|候选",sentence_outline): sentence_phone = phone.findall(temp_sentence) if sentence_phone: if sentence_phone[0] in [ent.entity_text for ent in phone_entitys] and sentence_phone[0] not in ",".join(exist_phone): tenderee_agency_role[0].linklist.append(("", sentence_phone[0])) get_contacts = True break if not get_contacts: # 通过正则提取句子段落进行提取电话 contacts_person = "(?:联系人|联系方|联系方式|负责人|电话|联系电话)[:：]?" tenderee_pattern = "(?:(?:采购|招标|议价|议标|比选)(?:人|公司|单位|组织|部门)|建设(?:单位|业主)|(?:采购|招标|甲)方|询价单位|项目业主|业主|业主单位)[^。]{0,5}" contact_pattern_list = [tenderee_pattern + contacts_person, "(?:采购[^。，]{0,2}项目|采购事项|招标)[^。，]{0,4}" + contacts_person, "(?:项目|采购)[^。，]{0,4}" + contacts_person, "(?:报名|报价|业务咨询|业务|投标咨询)[^。，]{0,4}" + contacts_person, ] for _pattern in contact_pattern_list: get_tenderee_contacts = False for regular_match in re.finditer(_pattern, _content): match_text = _content[regular_match.end():regular_match.end() + 50] match_text = match_text.split("。")[0] sentence_phone = phone.findall(match_text) if sentence_phone: if sentence_phone[0] not in ",".join(exist_phone): tenderee_agency_role[0].linklist.append(("", sentence_phone[0])) get_tenderee_contacts = True break if get_tenderee_contacts: break # 如果同一个电话连到了不同的单位就直接去掉(2024-09-03 新增) get_phone_dict = dict() for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): for item in PackDict[k]["roleList"][i].linklist: if item[1]: if item[1] not in get_phone_dict: get_phone_dict[item[1]] = set() get_phone_dict[item[1]].add(PackDict[k]["roleList"][i].entity_text) # print(get_phone_dict) remove_phone = [] for phone,role_list in get_phone_dict.items(): if len(role_list)>1: remove_phone.append(phone) for k in PackDict.keys(): for i in range(len(PackDict[k]["roleList"])): remove_list = [] for item in PackDict[k]["roleList"][i].linklist: if item[1] and item[1] in remove_phone: remove_list.append(item) for _item in remove_list: PackDict[k]["roleList"][i].linklist.remove(_item) for pack in PackDict.keys(): for i in range(len(PackDict[pack]["roleList"])): PackDict[pack]["roleList"][i] = PackDict[pack]["roleList"][i].getString() return PackDict def initPackageAttr(RoleList,PackageSet,win_tenderer_set,tenderee_or_agency_set, main_body_pack): ''' @summary: 根据拿到的roleList和packageSet初始化接口返回的数据 ''' packDict = dict() packDict["Project"] = {"code":"","tendereeMoney":0,"roleList":[], 'tendereeMoneyUnit':''} for item in list(PackageSet): packDict[item] = {"code":"","tendereeMoney":0,"roleList":[], 'tendereeMoneyUnit':''} packDict[item]['in_attachment'] = False if item in main_body_pack else True for item in RoleList: if packDict[item.packageName]["code"] =="": packDict[item.packageName]["code"] = item.packageCode # packDict[item.packageName]["roleList"].append(Role(item.role_name,item.entity_text,0,0,0.0,[])) # packDict[item.packageName]["roleList"].append(Role(item.role_name,item.entity_text,0,0,0.0,[])) #Role(角色名称，实体名称，角色阈值，金额，金额阈值，连接列表，金额单位) packDict[item.packageName]["roleList"].append(Role(item.role_name,item.entity_text,item.role_prob,0,0.0,[],set(item.multi_winner)-win_tenderer_set-tenderee_or_agency_set)) #Role(角色名称，实体名称，角色阈值，金额，金额阈值，连接列表，多中标人) return packDict def getPackageRoleMoney(list_sentence,list_entity,list_outline,winter_scope): ''' @param: list_sentence:文章的句子list list_entity:文章的实体list @return: 拿到文章的包-标段号-角色-实体名称-金额-联系人-联系电话 ''' # print("=1") theRole = getRoleList(list_sentence,list_entity) if not theRole: return [] # RoleList,RoleSet,PackageList,PackageSet = theRole RoleList,RoleSet,PackageList,PackageSet,win_tenderer_set,tenderee_or_agency_set,main_body_pack = theRole ''' for item in PackageList: # print(item) ''' # PackDict = initPackageAttr(RoleList, PackageSet) PackDict = initPackageAttr(RoleList, PackageSet, win_tenderer_set,tenderee_or_agency_set,main_body_pack) PackDict = findAttributeAfterEntity(PackDict, RoleSet, PackageList, PackageSet, list_sentence, list_entity, list_outline, winter_scope) return PackDict