tijao

.idea/.gitignore

@@ -0,0 +1,8 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/encodings.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding">
+    <file url="file://$PROJECT_DIR$/tools/分数字段结果分析/result_export.py" charset="UTF-8" />
+  </component>
+</project>

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,12 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="model.goods_ner_client.servicerd" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (venv)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/quality_tools.iml" filepath="$PROJECT_DIR$/.idea/quality_tools.iml" />
+    </modules>
+  </component>
+</project>

.idea/quality_tools.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

lib/ids

@@ -0,0 +1,1273 @@
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lib/mongo_tools.py

@@ -3,13 +3,12 @@
 # author : zhaolongyue
 #date : 2023-07-03
 from urllib.parse import quote_plus
-
 from pymongo import MongoClient
 from bson import ObjectId
 
 class MongoUtil:
     @staticmethod
-    def get_coon(host="192.168.3.167:27088", database=None, collection=None, authdb=None, authuser=None, authpass=None):
+    def get_coon(host="192.168.3.167:27088", port=None,database=None, collection=None, authdb=None, authuser=None, authpass=None):
         """
         获取mongo数据库连接
         :param host:
@@ -33,6 +32,32 @@ class MongoUtil:
 
         return conn
 
+    @staticmethod
+    def get_coon_test(host="192.168.3.167", port=27080, database=None, collection=None, authdb=None, authuser=None, authpass=None):
+        """
+        获取mongo数据库连接
+        :param host:
+        :param port:
+        :param database:
+        :param collection:
+        :param authdb:
+        :param authuser:
+        :param authpass:
+        :return:
+        """
+        if database is None:
+            raise RuntimeError('database is None')
+        if collection is None:
+            raise RuntimeError('collection is None')
+        conn = MongoClient(host, port, unicode_decode_error_handler="ignore")
+        print(conn)
+        if authdb is not None:
+            db_auth = conn[authdb]
+            db_auth.authenticate(authuser, authpass)
+        db = conn[database]
+        collection = db[collection]
+        return collection
+
 class CoonUtil:
     @staticmethod
     def get_coon(**kwargs):
@@ -41,8 +66,7 @@ class CoonUtil:
         :param kwargs:
         :return:
         """
-        coon = MongoUtil.get_coon(host=kwargs["mg_host"], port=kwargs["mg_port"],
-                                              database=kwargs["database"], collection=kwargs["collection"])
+        coon = MongoUtil.get_coon_test(host=kwargs["mg_host"], port=kwargs["mg_port"],database=kwargs["database"], collection=kwargs["collection"])
         return coon
 class MongoSentence:
     @staticmethod

lib/monitor_tools.py

@@ -157,6 +157,10 @@ class monitorTools:
             "情报_环境采购",
             "情报_家具招投标"
         ]
+        # tags = [
+        #     "情报_环境采购",
+        #     "情报_家具招投标"
+        # ]
         data={}
         for tag in tags:
             query = {"comeintime": {"$gte": start_date, "$lt": end_date},"tag_topinformation":tag}

tools/mongo断流监控/es_monitor.py

@@ -9,15 +9,15 @@ es_bidding_count=monitor.es_bidding()
 #存库
 monitor.save_to_mongo("es_bidding",es_bidding_count)
 print("es_bidding入库成功")
-#es-bidding_ai
-es_bidding_ai_count=monitor.es_bidding_ai()
-monitor.save_to_mongo("es_bidding_ai",es_bidding_ai_count)
-print("es_bidding_ai入库成功")
+# #es-bidding_ai
+# es_bidding_ai_count=monitor.es_bidding_ai()
+# monitor.save_to_mongo("es_bidding_ai",es_bidding_ai_count)
+# print("es_bidding_ai入库成功")
 #es-bidding_碎片化
 es_bidding_fragment_count=monitor.es_bidding_fragment()
 monitor.save_to_mongo("es_bidding_fragment",es_bidding_fragment_count)
 print("es_bidding_fragment入库成功")
-#es-拟在建
+# #es-拟在建
 es_nzj_count=monitor.es_nzj()
 monitor.save_to_mongo("es_nzj",es_nzj_count)
 print("es_nzj入库成功")

tools/mongo断流监控/mongo_monitor.py

@@ -13,15 +13,15 @@ print("bidding入库成功")
 bidding_ai_count=monitor.bidding_ai()
 monitor.save_to_mongo("bidding_ai",bidding_ai_count)
 print("bidding_ai入库成功")
-#nzj-mysql
+# nzj-mysql
 nzj_count=monitor.nzj()
 monitor.save_to_mongo("nzj",nzj_count)
 print("nzj入库成功")
-#bidding_碎片化
-bidding_fragment_count=monitor.bidding_fragment()
-monitor.save_to_mongo("bidding_fragment",bidding_fragment_count)
-print("bidding_fragment入库成功")
-#人脉clickhouse数据
+# bidding_碎片化
+# bidding_fragment_count=monitor.bidding_fragment()
+# monitor.save_to_mongo("bidding_fragment",bidding_fragment_count)
+# print("bidding_fragment入库成功")
+# 人脉clickhouse数据
 connections_count=monitor.connections()
 monitor.save_to_mongo('connections',connections_count)
 print("connections入库成功")

tools/test.py

@@ -0,0 +1 @@
+main_ids=[]

tools/从es导出数据/es.py

@@ -11,17 +11,17 @@ def ES_bidding(es_query):
     db_config = {
         # es
         'es_host': '127.0.0.1',
-        'es_port': 9800,
-        'es_http_auth': ('test_dataQuality','t9s3Ed0gBBowf5'),  # 重新申请
+        'es_port': 19800,
+        'es_http_auth': ('jianyuGr','we3g8glKfe#'),  # 重新申请
         'timeout': 10000,
         # 'index': "projectset",
         'index': "bidding",
         'size': 1000,
         # mongo存的数据库表
-        'mg_host': '192.168.3.206',
-        'mg_port': 27080,
+        'mg_host': '192.168.3.149',
+        'mg_port': 27180,
         'database': 'data_quality',
-        'collection': 'bidding_20231221'
+        'collection': 'bidding_20250109'
     }
     query = es_query
     # 传入查询语句query 以及配置信息
@@ -30,7 +30,7 @@ def ES_bidding(es_query):
 def run():
     # 根据ES语句查找bidding
     es_query = {"track_total_hits": True,
-                "query": {"bool": {"must": [{"range": {"publishtime": {"from": "1703001600", "to": "1703048399"}}}]}}}
+                "query": {"bool": {"must": [{"range": {"publishtime": {"from": "1736352000", "to": "1736398799"}}}]}}}
     # es_query = {"track_total_hits": True,
     #             "query": {"bool": {"must": [{"range": {"publishtime": {"from": "1691337600", "to": "1691424000"}}},
     #                                         {"terms": {"subtype": ["中标", "合同","成交"]}}]}}}

tools/从mongo导出数据/mongo_into_mongo.py

@@ -39,7 +39,6 @@ with MongoClient('192.168.3.149', 27180) as client:
                 print(row["_id"]+"在大库没找到")
             if result:
                 result["_id"] = ObjectId(row["_id"])
-                insertdb.insert2db("standard_sample_data_all_ai",result)
                 insertdb.insert2db("standard_sample_data_all",result)
 
 # 关闭数据库连接

tools/从mongo导出数据/mongo_into_mongo_ai.py

@@ -0,0 +1,65 @@
+from pymongo import MongoClient
+from bson import ObjectId
+from lib.mogodb_helper import MongoDBInterface
+
+#复制mongo数据源 从一个源1到另一个源3
+# 从源1中导入到源3 ，和源2 一样的数据
+#mongo库 源1
+MongodbConfig = {
+    "ip_port": "127.0.0.1:27088",
+    "user": "viewdata",
+    "password": "viewdata",
+    "db": "qfw_ai",
+}
+
+mdb = MongoDBInterface(MongodbConfig)
+#mongo库 源3
+MongodbConfigInsert = {
+    "ip_port": "192.168.3.149:27180",
+    "db": "data_quality",
+}
+insertdb = MongoDBInterface(MongodbConfigInsert)
+
+max_id = ObjectId("0" * 24)
+# max_id = ObjectId("655ec5609aed6eb2ffa654ca")
+
+# 连接MongoDB数据库
+with MongoClient('192.168.3.149', 27180) as client:
+    #源2
+    db = client.data_quality
+    coll = db.standard_sample_data
+    for row in coll.find().sort("_id", 1):
+    # for item in coll_user.find({"_id": {"$gte": max_id}}).sort("_id", 1):
+        _id = row.get("_id", "")
+        # print(row["_id"])
+        if _id:
+            m_id = ObjectId(_id)
+            result=mdb.find_by_id("bidding",m_id)
+            if result==None:
+                print(str(row["_id"])+"在大库没找到")
+            if result:
+                result["_id"] = ObjectId(row["_id"])
+                insertdb.insert2db("standard_sample_data_all_ai",result)
+
+# 关闭数据库连接
+client.close()
+
+
+"""
+
+http://172.17.162.35:8880/search
+
+# 深圳分公司安检信息系统维护服务 0.0 南方航空物流股份有限公司 广东 深圳市 ['安检']  []
+# 运输事业发展中心信息系统运维-交通委北区机房、视频会议及运输中心终端运维管理技术服务项目 825250.0 北京市运输事业发展中心 北京 北京市 ['运输事业发展中心信息系统运维 交通委北>区机房 视频会议及运输中心终端运维管理技术服务项目', '运输事业发展中心信息系统运维 交通委北区机房 视频会议及运输中心终端运维管理技术服务项目'] 北京航天星桥科技股份有限公司 []
+# 软件开发项目(软件开发-拓信智防) 0.0 指定集成公司与河北拓信智防电子科技有限公司 河北 保定市 ['车辆管理平台']  []
+# 新乡高新技术产业开发区社会治理委员会“雪亮工程”维保项目 3410000.0 新乡高新技术产业开发区社会治理委员会 河南 新乡市 ['维保', '雪亮工程']  []
+# 山西省大同市第三人民医院新建医技、急诊楼配套项目(暂估价)医用箱式中型物流传输系统采购项目 20028500.0 山西省大同市第三人民医院 山西 大同市 ['箱式中型物流']  []
+# 山西省大同市第三人民医院新建医技、急诊楼配套项目(暂估价)医用箱式中型物流传输系统采购项目 20028500.0 山西省大同市第三人民医院 山西 大同市 ['物流系统']  []
+
+
+广州市白云区三元里群英大街13号加装电梯工程 0.0 广州市白云区三元里群英大街13号加装电梯工程 广东 广州市 ['电梯']  []
+软件开发服务(二次)软件开发服务 6500000.0 呼伦贝尔市住房和城乡建设局 内蒙古 呼伦贝尔市 ['软件', '软件开发服务 二次 软件开发服务']  []
+富顺县救灾物资储备库建设工程项目 2346407.0 富顺县应急管理指挥中心 四川 自贡市 ['备库建设', '救灾物资']  []
+中共成都市委党校明志楼多媒体教室LED屏采购项目 650000.0 中共成都市委党校 四川 成都市 ['LED一体机', '触控一体机'] 成都香草山信息技术有限责任公司 []
+吉木萨尔县大有镇村庄规划(2021-2035年)编制项目 150000.0 吉木萨尔县大有镇人民政府 新疆 昌吉回族自治州 ['建材']  []
+"""

tools/从mongo导出数据/pull_mongo.py

@@ -0,0 +1,20 @@
+from pymongo import MongoClient
+from bson import ObjectId
+from lib.mongo_tools import MongoUtil,Data_save,MongoSentence
+#拉取mongo数据
+#复制mongo数据源 从一个源1到源2
+
+#mongo库 源1
+# 连接到源数据库
+collection = MongoClient(f'mongodb://127.0.0.1:27099/',unicode_decode_error_handler="ignore", directConnection=True)['jyqyfw']['usermail']
+query = {"appid": "jyGQ1XQQsEAwNeSENOFR9D", "comeintime": {"$gte": 1735488000, "$lt": 1735574400}}
+source_coll_user = collection.find(query)
+
+# 连接到目标数据库
+db = MongoClient('192.168.3.149', 27180, unicode_decode_error_handler="ignore").data_quality
+target_coll_user = db["liantong"]
+
+for item in source_coll_user:
+    target_coll_user.insert_one(item)
+    print("数据复制完成")
+

tools/从mongo导出数据/test.py

@@ -0,0 +1,63 @@
+from pymongo import MongoClient
+from bson import ObjectId
+from lib.mogodb_helper import MongoDBInterface
+
+#复制mongo数据源 从一个源到另一个源
+
+#mongo库 源1
+# MongodbConfig = {
+#     "ip_port": "127.0.0.1:27092",
+#     "user": "viewdata",
+#     "password": "viewdata",
+#     "db": "qfw",
+# }
+
+MongodbConfig = {
+    "ip_port": "192.168.3.149:27180",
+    "db": "data_quality",
+}
+mdb = MongoDBInterface(MongodbConfig)
+#mongo库 源2
+MongodbConfigInsert = {
+    "ip_port": "192.168.3.149:27180",
+    "db": "data_quality",
+}
+insertdb = MongoDBInterface(MongodbConfigInsert)
+
+# 连接MongoDB数据库
+with MongoClient('192.168.3.149:27180') as client:
+    db = client.data_quality
+    coll = db.bidding_20241107
+    for row in coll.find({ "_id" : { "$gte" : ObjectId("672c5b7e5c97a83c5a116550") }}).sort("_id", 1):
+        title = row.get("title", "")
+        if title:
+            result=mdb.find_rule_by_title("bidding_20241107_ai",title)
+            if result:
+                result_title=result.get("title","")
+                if result_title==title:
+                    print(row["title"])
+                    result_id = result.get("_id", "")
+                    coll.update_one({"_id": row["_id"]}, {"$set": {"flag": 2}})
+                    mdb.update_by_id("bidding_20241107_ai",result_id,{"flag": 2})
+# 关闭数据库连接
+client.close()
+
+
+"""
+
+http://172.17.162.35:8880/search
+
+# 深圳分公司安检信息系统维护服务 0.0 南方航空物流股份有限公司 广东 深圳市 ['安检']  []
+# 运输事业发展中心信息系统运维-交通委北区机房、视频会议及运输中心终端运维管理技术服务项目 825250.0 北京市运输事业发展中心 北京 北京市 ['运输事业发展中心信息系统运维 交通委北>区机房 视频会议及运输中心终端运维管理技术服务项目', '运输事业发展中心信息系统运维 交通委北区机房 视频会议及运输中心终端运维管理技术服务项目'] 北京航天星桥科技股份有限公司 []
+# 软件开发项目(软件开发-拓信智防) 0.0 指定集成公司与河北拓信智防电子科技有限公司 河北 保定市 ['车辆管理平台']  []
+# 新乡高新技术产业开发区社会治理委员会“雪亮工程”维保项目 3410000.0 新乡高新技术产业开发区社会治理委员会 河南 新乡市 ['维保', '雪亮工程']  []
+# 山西省大同市第三人民医院新建医技、急诊楼配套项目(暂估价)医用箱式中型物流传输系统采购项目 20028500.0 山西省大同市第三人民医院 山西 大同市 ['箱式中型物流']  []
+# 山西省大同市第三人民医院新建医技、急诊楼配套项目(暂估价)医用箱式中型物流传输系统采购项目 20028500.0 山西省大同市第三人民医院 山西 大同市 ['物流系统']  []
+
+
+广州市白云区三元里群英大街13号加装电梯工程 0.0 广州市白云区三元里群英大街13号加装电梯工程 广东 广州市 ['电梯']  []
+软件开发服务(二次)软件开发服务 6500000.0 呼伦贝尔市住房和城乡建设局 内蒙古 呼伦贝尔市 ['软件', '软件开发服务 二次 软件开发服务']  []
+富顺县救灾物资储备库建设工程项目 2346407.0 富顺县应急管理指挥中心 四川 自贡市 ['备库建设', '救灾物资']  []
+中共成都市委党校明志楼多媒体教室LED屏采购项目 650000.0 中共成都市委党校 四川 成都市 ['LED一体机', '触控一体机'] 成都香草山信息技术有限责任公司 []
+吉木萨尔县大有镇村庄规划(2021-2035年)编制项目 150000.0 吉木萨尔县大有镇人民政府 新疆 昌吉回族自治州 ['建材']  []
+"""

tools/从mongo库导出数据execl/mongo_to_execl.py

@@ -21,7 +21,7 @@ def export_to_excel(db_name, collection_name, fields, output_file):
     projection = {field: 1 for field in fields}
 
     # 查询数据
-    data = collection.find({"flag":3},projection)
+    data = collection.find({"flag":1},projection).sort("_id", 1)
 
     # 将数据转换为DataFrame
     df = pd.DataFrame(list(data))
@@ -33,11 +33,11 @@ if __name__ == "__main__":
     # 连接到 MongoDB
 
     db_name = 'data_quality'  # 替换为你的数据库名称
-    # collection_name = 'standard_sample_data_all' # 替换为你的集合名称
-    collection_name = 'bidding_20241128_ai'  # 替换为你的集合名称
+    # collection_name = 'standard_sample_data_all_ai' # 替换为你的集合名称
+    collection_name = 'customer_data'  # 替换为你的集合名称
     # 定义参数
 
-    fields = ['_id', 'site','toptype','subtype','area','city','buyer','projectname','projectcode','budget','s_winner','bidamount','multipackage','href','jyhref']  # 替换为你需要导出的字段
+    fields = ['id', 'site','toptype','subtype','area','city','buyer','projectname','projectcode','budget','s_winner','bidamount','multipackage_ai','href','jyhref']  # 替换为你需要导出的字段
     output_file = 'output.xlsx'
 
     # 调用函数导出数据

tools/分数字段结果分析/DataExport_forTesting.py

@@ -2,7 +2,7 @@ from pymongo import MongoClient
 from datetime import datetime, timedelta
 import pandas as pd
 import openpyxl
-
+# 数据入库量及数据监控时效 导出execl
 # MongoDB连接配置
 host = '192.168.3.149'
 port = 27180
@@ -191,10 +191,10 @@ for doc in data_timeliness:
 date_range = f"{start_time.strftime('%Y/%m/%d')}-{end_time.strftime('%Y/%m/%d')}"
 
 # 构建Excel数据
-columns = ['日期', '标准库每周入库数据量', '高质量库每周入库数据量', '人脉管理数据', '拟在建数据量(全国)'] + list(bidding_fragment_data.keys())
+columns = ['日期', '标讯每周入库数据量', '高质量库每周入库数据量', '人脉管理数据', '拟在建数据量(全国)'] + list(bidding_fragment_data.keys())
 data_row_mongo = [date_range, bidding_count, bidding_ai_count, connections_count, nzj_count] + list(bidding_fragment_data.values())
 
-columns_es = ['日期', '标准库每周入库数据量', '高质量库每周数据入库量', '拟在建数据量(全国)'] + list(es_bidding_fragment_data.keys())
+columns_es = ['日期', '标讯每周入库数据量', '高质量库每周数据入库量', '拟在建数据量(全国)'] + list(es_bidding_fragment_data.keys())
 data_row_es = [date_range, es_bidding_count, es_bidding_ai_count, es_nzj_count] + list(es_bidding_fragment_data.values())
 
 columns_timeliness = ['日期'] + list(timeliness_data.keys())

tools/分数字段结果分析/result_export.py

@@ -6,6 +6,7 @@ from pymongo import MongoClient
 from openpyxl import load_workbook
 from sympy.physics.continuum_mechanics.beam import numpy
 
+#标准库字段以及错误原因
 
 # 将这个函数定义放在你的脚本顶部或其他函数定义附近
 def convert_numpy_int(obj):
@@ -22,7 +23,7 @@ def convert_numpy_int(obj):
 host = '192.168.3.149'  # MongoDB主机地址
 port = 27180  # MongoDB端口
 dbname = 'data_quality'  # 数据库名称
-collection_name = 'bidding_20241033'  # 集合名称
+collection_name = 'bidding_20241219'  # 集合名称
 
 # 创建MongoDB连接
 client = MongoClient(host, port)

tools/分数字段结果分析/result_export_ai.py

@@ -0,0 +1,184 @@
+from datetime import datetime
+import pandas as pd
+import numpy as np
+from pymongo import MongoClient
+from openpyxl import load_workbook
+
+#高质量字段以及错误原因
+# 将这个函数定义放在你的脚本顶部或其他函数定义附近
+def convert_numpy_int(obj):
+    if isinstance(obj, np.int64):
+        return int(obj)
+    elif isinstance(obj, dict):
+        return {key: convert_numpy_int(value) for key, value in obj.items()}
+    elif isinstance(obj, list):
+        return [convert_numpy_int(elem) for elem in obj]
+    else:
+        return obj
+
+
+# MongoDB连接配置
+host = '192.168.3.149'  # MongoDB主机地址
+port = 27180  # MongoDB端口
+dbname = 'data_quality'  # 数据库名称
+collection_name = 'bidding_20241219_ai'  # 集合名称
+
+# 创建MongoDB连接
+client = MongoClient(host, port)
+db = client[dbname]
+collection = db[collection_name]
+# 定义字段中英文映射
+column_name_mapping = {
+    "area_qa": "省份",
+    "bidamount_qa": "中标金额",
+    "budget_qa": "预算",
+    "buyer_qa": "采购单位",
+    "multipackage_qa": "分包",
+    "projectcode_qa": "项目编号",
+    "projectname_qa": "项目名称",
+    "title_qa": "标题",
+    "winner_qa": "中标单位",
+    "score": "标讯总分数",
+    "bidopentime_qa": "开标时间",
+    "publishtime_qa": "发布时间",
+    "toptype_qa": "信息一级分类",
+    "subtype_qa": "信息二级分类"
+}
+
+# 从MongoDB读取数据
+data = pd.DataFrame(list(collection.find({},{k:1 for k,v in column_name_mapping.items()})))
+
+
+# 选择字段名以 '_qa' 结尾的列
+qa_columns = [col for col in data.columns if col.endswith('_qa')]
+
+# 仅保留 '_qa' 结尾的字段，并进行列名映射
+data = data[qa_columns]
+data.rename(columns=column_name_mapping, inplace=True)
+
+# 输出当前的数据列名
+print("当前的列名：")
+print(data.columns)
+
+
+# analyze_column 函数，处理 NaN 值
+def analyze_column(dataframe, column_name):
+    if column_name not in dataframe.columns:
+        total = len(dataframe)
+        correct = total
+        error = 0
+    else:
+        total = len(dataframe[column_name])
+        correct = dataframe[column_name].apply(lambda x: pd.isna(x) or x == {}).sum()
+        error = total - correct
+
+    accuracy = correct / total if total > 0 else 0
+    error_rate = error / total if total > 0 else 0
+
+    # 收集错误原因
+    error_reasons = dataframe[column_name].apply(
+        lambda x: x if x != {} and not pd.isna(x) else None).dropna().value_counts()
+
+    return total, correct, error, accuracy, error_rate, error_reasons
+
+
+# 重新格式化错误原因的数据结构
+def reformat_error_reasons_safe(error_reasons_series):
+    reformatted_reasons = {}
+
+    for error_dict, count in error_reasons_series.items():
+        if isinstance(error_dict, dict):  # 如果是字典类型的错误原因
+            for error_code, reason in error_dict.items():
+                if ',' in reason:
+                    parts = reason.split(',')
+                    formatted_reason = parts[1].strip()
+                else:
+                    formatted_reason = reason.strip()
+
+                if formatted_reason:
+                    key = (formatted_reason,)
+                    if key not in reformatted_reasons:
+                        reformatted_reasons[key] = count
+                    else:
+                        reformatted_reasons[key] += count
+        elif isinstance(error_dict, list):  # 如果是列表类型的错误原因
+            key = (tuple(error_dict),) if error_dict else None
+            if key not in reformatted_reasons:
+                reformatted_reasons[key] = count
+            else:
+                reformatted_reasons[key] += count
+        else:  # 其他类型的错误原因
+            key = (error_dict,) if error_dict else None
+            if key not in reformatted_reasons:
+                reformatted_reasons[key] = count
+            else:
+                reformatted_reasons[key] += count
+
+    formatted_results = {
+        str(key[0]): value for key, value in reformatted_reasons.items() if key and key[0] != ''
+    }
+    return formatted_results
+
+
+# 对每个字段进行分析
+fields_to_analyze = data.columns  # 直接使用已选定的 '_qa' 字段
+expanded_analysis_results = []
+
+for col in fields_to_analyze:
+    total, correct, error, accuracy, error_rate, error_reasons = analyze_column(data, col)
+    reformatted_error_reasons = reformat_error_reasons_safe(error_reasons)
+
+    for reason, count in reformatted_error_reasons.items():
+        reason = str(reason).replace('(', '').replace(',)', '').replace("'", '')
+        if error > 0:
+            single_reason_error_rate = count / error
+        else:
+            single_reason_error_rate = 0  # 防止除以零的情况
+
+        expanded_analysis_results.append({
+            '字段': col,
+            '总量': total,
+            '正确数量': correct,
+            '错误数量': error,
+            '正确率': f'{accuracy:.2%}',
+            '错误率': f'{error_rate:.2%}',
+            '错误原因': reason,
+            '错误次数': count,
+            '单个原因错误率': f'{single_reason_error_rate:.2%}'
+        })
+
+# 创建DataFrame并进行写入操作
+expanded_analysis_results_df = pd.DataFrame(expanded_analysis_results)
+
+# 使用 pd.ExcelWriter 进行写入操作
+with pd.ExcelWriter('临时文件.xlsx', engine='openpyxl') as writer:
+    # 将分析结果写入Excel
+    expanded_analysis_results_df.to_excel(writer, sheet_name='字段分析结果', index=False)
+
+# 假设您的分析结果已经保存在一个临时文件中
+temp_analysis_file = '临时文件.xlsx'  # 临时文件的路径
+
+# 加载您想要合并结果到的Excel文件
+modified_file_path = 'pin.xlsx'  # 拼接文件路径
+wb = load_workbook(modified_file_path)
+
+# 加载包含分析结果的临时Excel文件
+temp_wb = load_workbook(temp_analysis_file)
+
+# 将临时文件中的工作表复制到修改过的文件中
+for sheet_name in temp_wb.sheetnames:
+    source = temp_wb[sheet_name]
+    target = wb.create_sheet(sheet_name)
+
+    for row in source.iter_rows(min_row=1, max_col=source.max_column, max_row=source.max_row, values_only=True):
+        target.append(row)
+
+# 保存最终的合并文件
+final_merged_file_path = '质量分析报告.xlsx'  # 最终合并文件的路径
+wb.save(final_merged_file_path)
+
+# 关闭MongoDB连接
+client.close()
+
+
+

tools/分数字段结果分析/score_ai.py

@@ -0,0 +1,49 @@
+import pandas as pd
+from pymongo import MongoClient
+
+# MongoDB连接配置
+host = '192.168.3.149'  # MongoDB主机地址
+port = 27180  # MongoDB端口
+dbname = 'data_quality'  # 数据库名称
+collection_name = 'bidding_20241219_ai'  # 集合名称
+
+# 创建MongoDB连接
+client = MongoClient(host, port)
+db = client[dbname]
+collection = db[collection_name]
+
+# 只查询 "score" 字段，减少不必要的数据传输
+data = pd.DataFrame(list(collection.find({}, {'score': 1}).batch_size(100)))
+
+# 确保将 "score" 字段转换为浮点数
+if "score" in data.columns:
+    data['score'] = data['score'].astype(float)
+
+    # 统计标讯总分数的分布
+    score_counts = data['score'].value_counts().sort_index()
+    total_scores = len(data['score'])
+
+    # 计算百分比
+    score_percentages = (score_counts / total_scores) * 100
+
+    # 创建分布结果的 DataFrame
+    score_distribution_df = pd.DataFrame({
+        '标讯总分数': score_counts.index,
+        '数量': score_counts.values,
+        '百分比': score_percentages.values
+    })
+
+    # 百分比格式化为字符串，并附加百分号
+    score_distribution_df['百分比'] = score_distribution_df['百分比'].apply(lambda x: f'{x:.2f}%')
+
+    # 添加总量列
+    score_distribution_df['总量'] = total_scores
+
+    # 对分数进行倒序排序
+    score_distribution_df = score_distribution_df.sort_values(by='标讯总分数', ascending=False)
+
+    # 输出到 Excel 文件
+    with pd.ExcelWriter('标讯总分数统计.xlsx', engine='openpyxl') as writer:
+        score_distribution_df.to_excel(writer, sheet_name='标讯总分数分布', index=False)
+
+    print("标讯总分数统计已保存到 '标讯总分数统计.xlsx' 文件中。")

tools/基于标准样本数据对比/compare_standard_result.py

@@ -34,7 +34,8 @@ def Compare(collection_name):
     coll = db["standard_sample_data"]
 
     # for item in coll_user.find({"_id": {"$gte": max_id}}).sort("_id", 1):
-    for row in coll.find({ "_id" : ObjectId("659214ad66cf0db42a4a985c") }).sort("_id", 1):
+    # for row in coll.find({ "_id" : ObjectId("6662dce466cf0db42a9954d9") }).sort("_id", 1):
+    for row in coll.find().sort("_id", 1):
         _id = row.get("_id", "")
         toptype =row.get("toptype","")
         subtype = row.get("subtype", "")
@@ -79,37 +80,37 @@ def Compare(collection_name):
 
                 if toptype != toptype_bid:
                     toptype_num += 1
-                    print("标讯一级分类不一致的id为："+str(result.get("_id","")))
+                    # print("标讯一级分类不一致的id为："+str(result.get("_id","")))
                 if subtype != subtype_bid :
                     subtype_num += 1
-                    print("标讯二级分类不一致的id为：" +str(result.get("_id","")))
+                    # print("标讯二级分类不一致的id为：" +str(result.get("_id","")))
                 if area != area_bid :
                     area_num += 1
-                    print("省份不一致的id为：" +str(result.get("_id","")))
+                    # print("省份不一致的id为：" +str(result.get("_id","")))
                 if city != city_bid :
                     city_num += 1
-                    print("城市不一致的id为：" +str(result.get("_id","")))
+                    # print("城市不一致的id为：" +str(result.get("_id","")))
                 if buyer != buyer_bid :
                     buyer_num += 1
-                    print("采购单位不一致的id为：" +str(result.get("_id","")))
+                    # print("采购单位不一致的id为：" +str(result.get("_id","")))
                 if projectname != projectname_bid :
                     projectname_num += 1
-                    print("项目名称不一致的id为：" +str(result.get("_id","")))
+                    # print("项目名称不一致的id为：" +str(result.get("_id","")))
                 if projectcode != projectcode_bid :
                     projectcode_num += 1
-                    print("项目编号不一致的id为：" +str(result.get("_id","")))
+                    # print("项目编号不一致的id为：" +str(result.get("_id","")))
                 if budget != budget_bid :
                     budget_num += 1
-                    print("预算不一致的id为：" +str(result.get("_id","")))
+                    # print("预算不一致的id为：" +str(result.get("_id","")))
                 if s_winner != s_winner_bid :
                     s_winner_num += 1
-                    print("中标单位不一致的id为：" +str(result.get("_id","")))
+                    # print("中标单位不一致的id为：" +str(result.get("_id","")))
                 if bidamount != bidamount_bid :
                     bidamount_num += 1
-                    print("中标金额不一致的id为：" +str(result.get("_id","")))
+                    # print("中标金额不一致的id为：" +str(result.get("_id","")))
                 if multipacket != multipackage_bid :
                     multipackage_num += 1
-                    print("是否分包不一致的id为：" +str(result.get("_id","")))
+                    # print("是否分包不一致的id为：" +str(result.get("_id","")))
     print(f"标讯一级分类错误总数为 {toptype_num}")
     print(f"标讯二级分类错误总数为 {subtype_num}")
     print(f"省份错误总数为 {area_num}")

tools/基于标准样本数据对比/compare_standard_result_sort.py

@@ -0,0 +1,135 @@
+from pymongo import MongoClient
+from bson import ObjectId
+from lib.mogodb_helper import MongoDBInterface
+
+#源1和源2，与源3（标准样本数据)对比，出结果
+#mongo库
+MongodbConfig = {
+    "ip_port": "192.168.3.149:27180",
+    "db": "data_quality",
+}
+mdb = MongoDBInterface(MongodbConfig)
+
+max_id = ObjectId("0" * 24)
+# max_id = ObjectId("655ec5609aed6eb2ffa654ca")
+
+# columns_to_check = ['toptype','subtype','area','city','buyer','projectname','projectcode','budget','s_winner','bidamount','multipackage']
+# 连接MongoDB数据库
+#源1--标准库 和 标准样本库对比
+def Compare(collection_name):
+    toptype_num=0
+    subtype_num =0
+    area_num = 0
+    city_num = 0
+    buyer_num = 0
+    projectname_num = 0
+    projectcode_num = 0
+    budget_num = 0
+    s_winner_num = 0
+    bidamount_num = 0
+    multipackage_num = 0
+
+    #标准样本数据源
+    db = MongoClient('192.168.3.149', 27180, unicode_decode_error_handler="ignore").data_quality
+    coll = db["standard_sample_data"]
+    query = {
+        "subtype": {"$in": ["成交", "单一", "废标", "合同", "结果变更", "流标", "验收", "中标", "其它"]}
+    }
+    # for item in coll_user.find({"_id": {"$gte": max_id}}).sort("_id", 1):
+    # for row in coll.find({ "_id" : ObjectId("6662dce466cf0db42a9954d9") }).sort("_id", 1):
+    for row in coll.find(query).sort("_id", 1):
+        _id = row.get("_id", "")
+        toptype =row.get("toptype","")
+        subtype = row.get("subtype", "")
+        area = row.get("area", "")
+        city = row.get("city", "")
+        buyer = row.get("buyer", "")
+        projectname = row.get("projectname", "")
+        projectcode = row.get("projectcode", "")
+        budget = row.get("budget", "")
+        s_winner = row.get("s_winner", "")
+        bidamount = row.get("bidamount", "")
+        multipacket = row.get("multipackage", "")
+        # print(row["_id"])
+        if _id:
+            m_id = ObjectId(_id)
+            result=mdb.find_by_id(collection_name,m_id)
+            if result==None:
+                print(row["_id"]+"在标准库没找到")
+            if result:
+                toptype_bid = result.get("toptype", "")
+                subtype_bid = result.get("subtype", "")
+                area_bid = result.get("area", "")
+                city_bid = result.get("city", "")
+                buyer_bid = result.get("buyer", "")
+                projectname_bid = result.get("projectname", "")
+                projectcode_bid = result.get("projectcode", "")
+                budget_bid = result.get("budget", "")
+                s_winner_bid = result.get("s_winner", "")
+                bidamount_bid = result.get("bidamount", "")
+                multipackage_bid = result.get("multipackage", "")
+                com_package = row.get("com_package", "")
+                # 大模型抽取的数据，分包是按照 com_package 中 是否为多个值 为依据，来判断多包，所以，需要做一个转换
+                if multipackage_bid:
+                    multipackage_bid = multipackage_bid
+                else:
+                    multipackage_bid = 0
+                if com_package:
+                    if len(com_package) > 1:
+                        multipackage_bid = 1
+                    else:
+                        multipackage_bid  = 0
+
+                if toptype != toptype_bid:
+                    toptype_num += 1
+                    # print("标讯一级分类不一致的id为："+str(result.get("_id","")))
+                if subtype != subtype_bid :
+                    subtype_num += 1
+                    # print("标讯二级分类不一致的id为：" +str(result.get("_id","")))
+                if area != area_bid :
+                    area_num += 1
+                    # print("省份不一致的id为：" +str(result.get("_id","")))
+                if city != city_bid :
+                    city_num += 1
+                    # print("城市不一致的id为：" +str(result.get("_id","")))
+                if buyer != buyer_bid :
+                    buyer_num += 1
+                    # print("采购单位不一致的id为：" +str(result.get("_id","")))
+                if projectname != projectname_bid :
+                    projectname_num += 1
+                    # print("项目名称不一致的id为：" +str(result.get("_id","")))
+                if projectcode != projectcode_bid :
+                    projectcode_num += 1
+                    # print("项目编号不一致的id为：" +str(result.get("_id","")))
+                if budget != budget_bid :
+                    budget_num += 1
+                    # print("预算不一致的id为：" +str(result.get("_id","")))
+                if s_winner != s_winner_bid :
+                    s_winner_num += 1
+                    # print("中标单位不一致的id为：" +str(result.get("_id","")))
+                if bidamount != bidamount_bid :
+                    bidamount_num += 1
+                    # print("中标金额不一致的id为：" +str(result.get("_id","")))
+                if multipacket != multipackage_bid :
+                    multipackage_num += 1
+                    # print("是否分包不一致的id为：" +str(result.get("_id","")))
+    print(f"标讯一级分类错误总数为 {toptype_num}")
+    print(f"标讯二级分类错误总数为 {subtype_num}")
+    print(f"省份错误总数为 {area_num}")
+    print(f"城市错误总数为 {city_num}")
+    print(f"采购单位错误总数为 {buyer_num}")
+    print(f"项目名称错误总数为 {projectname_num}")
+    print(f"项目编号错误总数为 {projectcode_num}")
+    print(f"预算错误总数为 {budget_num}")
+    print(f"中标单位错误总数为 {s_winner_num}")
+    print(f"中标金额错误总数为 {bidamount_num}")
+    print(f"是否分包错误总数为 {multipackage_num}")
+    return toptype_num,
+#源1-规则抽取的数据 和 标准样本库做 对比
+print("规则抽取的数据和标准样本库做对比结果如下：")
+Compare("standard_sample_data_all")
+
+#源2-大模型抽取的数据 和 标准样本库做 对比
+print("-----------------------------------------：")
+print("大模型抽取的数据和标准样本库做对比结果如下：")
+Compare("standard_sample_data_all_ai")

tools/标的物质量分析/purchasing.py

@@ -0,0 +1,70 @@
+from bson import ObjectId
+from pymongo import MongoClient
+
+# 高质量库标的物质量分析
+'''
+0.8--->凡是抽取到的金额不存在于文中，或者大模型抽取的金额数值无法格式化
+0.9-->在金额正确的基础上，如果品牌和规格型号都不存在则为0.9
+0.95-->规格型号或者品牌存在一个
+0.99-->标的物字段全部存在
+服务和工程>=0.9 算质量合格
+货物>=0.95     算质量合格
+'''
+
+
+def purchasing():
+    num = 0
+    high_purchasing_extract=0
+    # 货物
+    cargo_purchasing_extract = 0
+    # 服务和工程
+    service_purchasing_extract = 0
+    other_purchasing_extract = 0
+    miss_extract = 0
+    total_score =0
+    db = MongoClient('192.168.3.149', 27180, unicode_decode_error_handler="ignore").data_quality
+    collection = db["bidding_20241212_ai"]
+    for col in collection.find().sort("_id", 1):
+        basicClass = col.get("basicClass", '')
+        purchasinglist_label = col.get("purchasinglist_label", "")
+        # 如果purchasinglist_label存在，说明是大模型抽的标的物
+        if purchasinglist_label:
+            token_limit_label = purchasinglist_label.get("token_limit_label", "")
+            purchasinglist = col.get("purchasinglist", "")
+            length=len(purchasinglist)
+            for item in purchasinglist:
+                score = item.get("score", "")
+                total_score+=score
+            score=total_score/length
+            if token_limit_label >= 1:
+                # 提示语过长，被截取，漏抽
+                miss_extract += 1
+            else:
+                # if basicClass == "货物" and score >= 0.9:
+                #     cargo_purchasing_extract += 1
+                if score >=0.95:
+                    high_purchasing_extract += 1
+                if 0.95>score >=0.9:
+                    if basicClass == "服务" or basicClass == "工程":
+                        service_purchasing_extract += 1
+                    else:
+                        cargo_purchasing_extract += 1
+                if 0.9 > score >= 0.85:
+                    if basicClass == "服务" or basicClass == "工程":
+                        service_purchasing_extract += 1
+                    else:
+                        cargo_purchasing_extract += 1
+                if score < 0.85:
+                    other_purchasing_extract += 1
+
+            num += 1
+    return num,high_purchasing_extract, cargo_purchasing_extract, service_purchasing_extract, other_purchasing_extract,miss_extract
+
+
+num, high_purchasing_extract,cargo_purchasing_extract, service_purchasing_extract, other_purchasing_extract,miss_extract = purchasing()
+print("大模型抽取的数据总量为：", num)
+print("平均得分>=0.95的数量为",high_purchasing_extract)
+print("平均得分>=0.9的数量为：", cargo_purchasing_extract)
+print("服务类和工程类平均得分>=0.85的数量为：", service_purchasing_extract)
+print("平均得分<0.85的数量为",other_purchasing_extract)
+print("大模型漏抽取数量为：", miss_extract)

tools/标讯数据重复率分析/repeat.py

@@ -0,0 +1,49 @@
+from pymongo import MongoClient
+from bson import ObjectId
+from lib.mongo_tools import MongoUtil,Data_save,MongoSentence
+#判断标讯数据的重复率
+
+def calculate_repeat_rate(str1, str2):
+    import Levenshtein
+
+    # str1 = "茹河、红河河流沟道生态治理与农田整治提升项目(六盘山森林生态保护修复单元)-彭阳县茹河流域乃河水库至店洼水库生态缓冲带修复工程一期"
+    # str2 = "茹河、红河河流沟道生态治理与农田整治提升项目(六盘山森林生态保护修复单元)彭阳县茹河流域乃河水库至店洼水库生态缓冲带修复工程一期"
+
+    distance = Levenshtein.distance(str1, str2)
+    max_length = max(len(str1), len(str2))
+    if max_length != 0:
+        similarity = 1 - distance / max_length
+        return similarity
+    else:
+        return 1.0
+
+# 连接到目标数据库
+db = MongoClient('192.168.3.149', 27180, unicode_decode_error_handler="ignore").data_quality
+coll_user = db["liantong"]
+data_map={}
+documents = list(coll_user.find({ "flag" : { "$ne" : "cfsj" } }).sort("_id",1))
+
+for i in range(len(documents)):
+    for j in range(i+1,len(documents)):
+        i_title=documents[i].get("title", "")
+        j_title = documents[j].get("title", "")
+        i_projectname=documents[i].get("projectname", "")
+        j_projectname = documents[j].get("projectname", "")
+        radio1=calculate_repeat_rate(i_title,j_title)
+        radio2=calculate_repeat_rate(i_projectname,j_projectname)
+
+        if radio1>=0.9 or radio2>=0.9:
+            if documents[i].get("projectcode","")== documents[j].get("projectcode","") and documents[i].get("subtype","")== documents[j].get("subtype","")=='竞谈':
+                data_map.setdefault(documents[i].get("_id",""),[]).append(str(documents[j].get("_id","")))
+# and documents[i].get("subtype","")== documents[j].get("subtype","")=='采购意向'
+# and documents[i].get("subtype","")==documents[j].get("subtype","")
+# for i in range(len(documents)):
+#     for j in range(i+1,len(documents)):
+#         i_id=documents[i].get("id", "")
+#         j_id = documents[j].get("id", "")
+#         if i_id==j_id :
+#                 data_map.setdefault(documents[i].get("_id",""),[]).append(str(documents[j].get("_id","")))
+# 打印结果
+for key, value in data_map.items():
+    print(f"{key} -> {value}")
+

tools/标讯数据重复率分析/test.py

@@ -0,0 +1,8 @@
+import Levenshtein
+
+str1 = "茹河、红河河流沟道生态治理与农田整治提升项目(六盘山森林生态保护修复单元)-彭阳县茹河流域乃河水库至店洼水库生态缓冲带修复工程一期"
+str2 = "茹河、红河河流沟道生态治理与农田整治提升项目(六盘山森林生态保护修复单元)彭阳县茹河流域乃河水库至店洼水库生态缓冲带修复工程一期"
+
+distance = Levenshtein.distance(str1, str2)
+similarity = 1 - distance / max(len(str1), len(str2))
+print(f"Similarity: {similarity}")

tools/样本数据导出/ai_exchange_to_multipacket.py

@@ -0,0 +1,12 @@
+from pymongo import MongoClient
+
+#将高质量库的多包标识 转换成 multipackage_ai 字段表示,便于后续数据导出
+
+db = MongoClient('192.168.3.149', 27180, unicode_decode_error_handler="ignore").data_quality
+collection = db["customer_data"]
+for info in collection.find({"flag":1}).sort("_id", 1):
+        com_package=info.get("com_package", "")
+        if len(com_package) > 1:
+                collection.update_one({"_id": info["_id"]}, {"$set": {"multipackage_ai": 1}})
+        else:
+                collection.update_one({"_id": info["_id"]}, {"$set": {"multipackage_ai": 0}})

tools/样本数据导出/fix_site_data_export.py

@@ -4,9 +4,9 @@ from pymongo import MongoClient
 def sample_data(N,M):
     # 连接MongoDB数据库
     db = MongoClient('192.168.3.149', 27180, unicode_decode_error_handler="ignore").data_quality
-    collection = db["bidding_20241128_ai"]
+    collection = db["bidding_20241205_ai"]
     # 把符合条件的站点名称存起来
-    site_list = {"湖北政务服务网","广东省网上中介服务超市","黑龙江省政府采购电子卖场","湖北省政府采购网","欧贝易购","江西省网上中介服务超市","八戒公采","山西省政府采购电子卖场","政府采购云平台网上服务市场","政府采购频道"}
+    site_list = {"陕西采购与招标网","北京市投资项目在线审批监管平台","浙江省投资项目在线审批监管平台"}
     # 初始化已标记的文档数量
     marked_count = 0
     marked_site_count = 0
@@ -44,7 +44,7 @@ def sample_data(N,M):
                 for info in collection.find({"site": site}).sort("_id", 1).skip(i*jiange).limit(1):
                     print(f"Updating document with _id: {info['_id']}")
                     # 更新文档，设置标记
-                    update_result = collection.update_one({"_id": info["_id"]}, {"$set": {"flag": 3}})
+                    update_result = collection.update_one({"_id": info["_id"]}, {"$set": {"flag": 2}})
                     if update_result.modified_count == 0:
                         print("No document updated for _id:", info["_id"])
                     else:
@@ -57,4 +57,4 @@ def sample_data(N,M):
 
     print(f"Total marked documents: {marked_count}")
 
-sample_data(200,10)
+sample_data(35,10)

tools/样本数据导出/sample_data_export.py

@@ -3,7 +3,8 @@ from pymongo import MongoClient
 def sample_data(N):
     # 连接MongoDB数据库
     db = MongoClient('192.168.3.149', 27180, unicode_decode_error_handler="ignore").data_quality
-    coll_user = db["bidding_20241128_ai"]
+    coll_user = db["customer_data"]
+
 
     # 统计总的数据量
     count_all = coll_user.estimated_document_count()
@@ -50,10 +51,10 @@ def sample_data(N):
             if marked_count >= N:
                 break  # 再次检查是否已达到目标数量
 
-            for info in coll_user.find({"site": key}).sort("title", 1).skip(i*2).limit(1):
+            for info in coll_user.find({"site": key}).sort("_id", 1).skip(i*jiange).limit(1):
                 print(f"Updating document with _id: {info['_id']}")
                 # 更新文档，设置标记
-                update_result = coll_user.update_one({"_id": info["_id"]}, {"$set": {"flag": 1}})
+                update_result = coll_user.update_one({"_id": info["_id"]}, {"$set": {"flag": 2}})
                 if update_result.modified_count == 0:
                     print("No document updated for _id:", info["_id"])
                 else:
@@ -65,4 +66,4 @@ def sample_data(N):
 
     print(f"Total marked documents: {marked_count}")
 
-sample_data(200)
+sample_data(500)

tools/爬虫相关/爬虫代码输出.py

@@ -0,0 +1,31 @@
+from pymongo import MongoClient
+
+# 输出某段时间新上架及修改的爬虫代码
+"""
+查询语句：
+{comeintime:{$gte:1736956800,$lte:1737043200},types:"审核",platform:{$exists:false}}
+{comeintime:{$gte:1736956800,$lte:1737043200},types:"审核上线",platform:"通用平台"}
+"""
+def export():
+    spidercodes=[]
+    n=0
+    m=0
+    collection = MongoClient(f'mongodb://{"127.0.0.1:27089"}/',
+                       unicode_decode_error_handler="ignore", directConnection=True)["editor"]["lua_logs_auditor"]
+
+    query = {"comeintime":{"$gte":1736697600,"$lte":1737129600},"types":"审核","platform":{"$exists":"false"}}
+    query1 = {"comeintime":{"$gte":1736697600,"$lte":1737129600},"types":"审核上线","platform":"通用平台"}
+
+    for info in collection.find(query).sort("_id", 1):
+        spidercodes.append(info.get("code",""))
+        n+=1
+
+    for info in collection.find(query1).sort("_id", 1):
+        spidercodes.append(info.get("code",""))
+        m+=1
+
+    print(n+m)
+    return spidercodes
+
+spidercodes=export()
+print(spidercodes)

tools/读execl数据入mongo库/execl_into_mongo.py

@@ -5,16 +5,20 @@ from bson.objectid import ObjectId
 # 连接到 MongoDB
 client = MongoClient('mongodb://192.168.3.149:27180/')
 db = client['data_quality']  # 替换为你的数据库名称
-collection = db['standard_sample_data']  # 替换为你的集合名称
+collection = db['standard_sample_data_all']  # 替换为你的集合名称
 
-# 读取 Excel 文件
-excel_file_path = 'sample_data.xlsx'  # 替换为你的 Excel 文件路径
 
-# 尝试读取 Excel 文件，并指定 dtype 参数
-df = pd.read_excel(excel_file_path, na_values=['', 'NA', 'N/A'], dtype={'budget': float, 'bidamount': float})
+
+# 读取 Excel 文件中的 '标准样本数据汇总' 工作表
+file_path = '/Users/miaobao/Downloads/标准样本数据汇总.xlsx'
+sheet_name = '标准样本数据汇总'
+
+# 使用 pandas 读取 Excel 文件
+df = pd.read_excel(file_path, sheet_name=sheet_name, na_values=['', 'NA', 'N/A'], dtype={'budget': float, 'bidamount': float})
+
 
 # 指定需要检查的列名
-columns_to_check = ['_id', 'site','toptype','subtype','area','city','buyer','projectname','projectcode','budget','s_winner','bidamount','multipackage','href','jyhref']  # 替换为你需要检查的列名
+columns_to_check = ['_id', 'site','toptype','subtype','area','city','buyer','projectname','projectcode','budget','s_winner','bidamount','multipackage','label','href','jyhref']  # 替换为你需要检查的列名
 df[columns_to_check] = df[columns_to_check].where(pd.notnull(df[columns_to_check]), '')
 
 # 将 _id 列转换为 ObjectId 类型