tijiao

lib/detail_url.py

@@ -0,0 +1,29 @@
+import base64
+
+
+
+class SimpleEncrypt:
+    def __init__(self, key):
+        self.key = key
+
+    def do_encode(self, bs):
+        tmp = bytearray(self.key.encode())
+        i = 0
+        while i < len(bs):
+            for j in range(len(tmp)):
+                if i >= len(bs):
+                    break
+                bs[i] = bs[i] ^ tmp[j]
+                i += 1
+
+    def encode_string(self, s):
+        data = bytearray(s.encode())
+        self.do_encode(data)
+        return base64.b64encode(data).decode()
+
+    def decode_string(self, s):
+        data = bytearray(base64.b64decode(s))
+        self.do_encode(data)
+        return data.decode()
+
+SE3 = SimpleEncrypt(key="entservice")

lib/es_tools.py

@@ -35,6 +35,8 @@ class EsUtil:
             results = []
             mongo_tools.MongoSentence.insert_many(coon, save_bulk)
             print(count, "数据已保存")
+
+    @staticmethod
     def get_es(**kwargs):
         """
 		获取es连接

oss/client.py

@@ -0,0 +1,327 @@
+import grpc
+import pb_pb2 as pb
+import pb_pb2_grpc as pb_grpc
+import time
+import random
+import string
+from concurrent.futures import ThreadPoolExecutor
+import argparse
+import statistics
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+
+
+class ServiceClient:
+    def __init__(self, host='127.0.0.1', port=50051):
+        self.channel = grpc.insecure_channel(
+            f'{host}:{port}',
+            options=[
+                ('grpc.keepalive_time_ms', 10000),  # 10秒心跳
+                ('grpc.keepalive_timeout_ms', 5000),  # 5秒超时
+                ('grpc.keepalive_permit_without_calls', 1),
+                ('grpc.http2.max_pings_without_data', 0),
+                ('grpc.max_reconnect_backoff_ms', 5000)
+            ]
+        )
+        self.stub = pb_grpc.ServiceStub(self.channel)
+
+    def download(self, bucket_id, object_name):
+        """Client-side streaming download"""
+        request = pb.DownloadRequest(bucketID=bucket_id, objectName=object_name)
+        responses = self.stub.Download(request)
+
+        received_data = bytearray()
+        for response in responses:
+            if response.errorCode != 0:
+                print(f"Error: {response.errorMsg}")
+                return None
+            received_data.extend(response.data)
+
+        return bytes(received_data)
+
+    def upload(self, file_data, bucket_id, object_name, gzip=False):
+        """Server-side streaming upload"""
+
+        def request_generator():
+            # Split data into chunks (simulate streaming)
+            chunk_size = 1024 * 1024  # 1MB chunks
+            for i in range(0, len(file_data), chunk_size):
+                yield pb.UploadRequest(
+                    stream=file_data[i:i + chunk_size],
+                    gzip=gzip,
+                    bucketID=bucket_id,
+                    objectName=object_name
+                )
+
+        response = self.stub.Upload(request_generator())
+        if response.errorCode != 0:
+            print(f"Upload error: {response.errorMsg}")
+            return False
+        return True
+
+    def get_bid_detail(self, bucket_id, object_name):
+        """Client-side streaming for bid details"""
+        request = pb.DownloadRequest(bucketID=bucket_id, objectName=object_name)
+        responses = self.stub.GetBidDetail(request)
+
+        details = []
+        for response in responses:
+            if response.errorCode != 0:
+                print(f"Error: {response.errorMsg}")
+                break
+            details.append(response.data.decode('utf-8'))
+
+        return details
+
+    def close(self):
+        self.channel.close()
+
+
+class StressTester:
+    def __init__(self, host, port, num_threads=10):
+        self.host = host
+        self.port = port
+        self.num_threads = num_threads
+        self.results = {
+            'upload': {'times': [], 'success': 0, 'fail': 0},
+            'download': {'times': [], 'success': 0, 'fail': 0},
+            'bid_detail': {'times': [], 'success': 0, 'fail': 0}
+        }
+
+    def _generate_random_data(self, size_kb):
+        """Generate random data of specified size in KB"""
+        return ''.join(random.choices(string.ascii_letters + string.digits, k=size_kb * 1024)).encode('utf-8')
+
+    def _test_upload(self, client, size_kb):
+        """Test upload operation"""
+        start_time = time.time()
+        try:
+            data = self._generate_random_data(size_kb)
+            bucket_id = f"stress_bucket_{random.randint(1, 1000)}"
+            object_name = f"stress_object_{random.randint(1, 1000)}.txt"
+
+            success = client.upload(data, bucket_id, object_name)
+            elapsed = time.time() - start_time
+
+            if success:
+                self.results['upload']['success'] += 1
+                self.results['upload']['times'].append(elapsed)
+            else:
+                self.results['upload']['fail'] += 1
+        except Exception as e:
+            self.results['upload']['fail'] += 1
+            print(f"Upload error: {str(e)}")
+
+    def _test_download(self, client, size_kb):
+        """Test download operation"""
+        start_time = time.time()
+        try:
+            # First upload a file to download
+            data = self._generate_random_data(size_kb)
+            bucket_id = "stress_download_bucket"
+            object_name = "stress_download_object.txt"
+
+            # Upload then immediately download
+            if client.upload(data, bucket_id, object_name):
+                downloaded_data = client.download(bucket_id, object_name)
+                elapsed = time.time() - start_time
+
+                if downloaded_data is not None and len(downloaded_data) == len(data):
+                    self.results['download']['success'] += 1
+                    self.results['download']['times'].append(elapsed)
+                else:
+                    self.results['download']['fail'] += 1
+            else:
+                self.results['download']['fail'] += 1
+        except Exception as e:
+            self.results['download']['fail'] += 1
+            print(f"Download error: {str(e)}")
+
+    def _test_bid_detail(self, client):
+        """Test get_bid_detail operation"""
+        start_time = time.time()
+        try:
+            bucket_id = "detail"
+            object_name = "67c123333309c0998b619793"
+
+            details = client.get_bid_detail(bucket_id, object_name)
+            elapsed = time.time() - start_time
+
+            if details is not None:
+                self.results['bid_detail']['success'] += 1
+                self.results['bid_detail']['times'].append(elapsed)
+            else:
+                self.results['bid_detail']['fail'] += 1
+        except Exception as e:
+            self.results['bid_detail']['fail'] += 1
+            print(f"Bid detail error: {str(e)}")
+
+    def run_test(self, test_type, num_requests=100, size_kb=10):
+        """Run stress test for specified operation"""
+        print(f"Starting {test_type} stress test with {num_requests} requests...")
+
+        with ThreadPoolExecutor(max_workers=self.num_threads) as executor:
+            futures = []
+
+            for _ in tqdm(range(num_requests), desc=f"Running {test_type} tests"):
+                client = ServiceClient(self.host, self.port)
+
+                if test_type == 'upload':
+                    futures.append(executor.submit(self._test_upload, client, size_kb))
+                elif test_type == 'download':
+                    futures.append(executor.submit(self._test_download, client, size_kb))
+                elif test_type == 'bid_detail':
+                    futures.append(executor.submit(self._test_bid_detail, client))
+
+                # Close client after test
+                client.close()
+
+            # Wait for all tests to complete
+            for future in futures:
+                future.result()
+
+        self._report_results(test_type)
+
+    def _report_results(self, test_type):
+        """Generate and display test results"""
+        results = self.results[test_type]
+        total = results['success'] + results['fail']
+
+        if results['success'] > 0:
+            avg_time = statistics.mean(results['times'])
+            min_time = min(results['times'])
+            max_time = max(results['times'])
+            std_dev = statistics.stdev(results['times']) if len(results['times']) > 1 else 0
+        else:
+            avg_time = min_time = max_time = std_dev = 0
+
+        print("\n" + "=" * 50)
+        print(f"Stress Test Results for {test_type}:")
+        print(f"Total requests: {total}")
+        print(f"Successful: {results['success']} ({results['success'] / total * 100:.2f}%)")
+        print(f"Failed: {results['fail']} ({results['fail'] / total * 100:.2f}%)")
+        print(f"Average time: {avg_time:.4f}s")
+        print(f"Min time: {min_time:.4f}s")
+        print(f"Max time: {max_time:.4f}s")
+        print(f"Standard deviation: {std_dev:.4f}s")
+        print("=" * 50 + "\n")
+
+        # Plot histogram of response times
+        if results['success'] > 0:
+            plt.figure(figsize=(10, 6))
+            plt.hist(results['times'], bins=20, edgecolor='black')
+            plt.title(f'Response Time Distribution for {test_type}')
+            plt.xlabel('Time (seconds)')
+            plt.ylabel('Frequency')
+            plt.grid(True)
+            plt.show()
+
+    def run_all_tests(self, num_requests=100, size_kb=10):
+        """Run all available tests"""
+        self.run_test('upload', num_requests, size_kb)
+        self.run_test('download', num_requests, size_kb)
+        self.run_test('bid_detail', num_requests)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='gRPC Client with Stress Testing')
+    parser.add_argument('--host', default='localhost', help='Server host')
+    parser.add_argument('--port', type=int, default=50051, help='Server port')
+    parser.add_argument('--test', choices=['upload', 'download', 'bid_detail', 'all'],
+                        help='Type of test to run')
+    parser.add_argument('--requests', type=int, default=100, help='Number of requests')
+    parser.add_argument('--size', type=int, default=10, help='Data size in KB (for upload/download)')
+    parser.add_argument('--threads', type=int, default=10, help='Number of concurrent threads')
+
+    args = parser.parse_args()
+
+    if args.test:
+        tester = StressTester(args.host, args.port, args.threads)
+
+        if args.test == 'all':
+            tester.run_all_tests(args.requests, args.size)
+        else:
+            tester.run_test(args.test, args.requests, args.size)
+    else:
+        # Interactive mode
+        client = ServiceClient(args.host, args.port)
+
+        try:
+            while True:
+                print("\nOptions:")
+                print("1. Upload file")
+                print("2. Download file")
+                print("3. Get bid details")
+                print("4. Exit")
+
+                choice = input("Enter your choice: ")
+
+                if choice == '1':
+                    file_path = input("Enter file path: ")
+                    bucket_id = input("Enter bucket ID: ")
+                    object_name = input("Enter object name: ")
+                    gzip = input("Use gzip compression? (y/n): ").lower() == 'y'
+
+                    try:
+                        with open(file_path, 'rb') as f:
+                            data = f.read()
+
+                        start_time = time.time()
+                        success = client.upload(data, bucket_id, object_name, gzip)
+                        elapsed = time.time() - start_time
+
+                        if success:
+                            print(f"Upload successful in {elapsed:.2f} seconds")
+                        else:
+                            print("Upload failed")
+                    except Exception as e:
+                        print(f"Error: {str(e)}")
+
+                elif choice == '2':
+                    bucket_id = input("Enter bucket ID: ")
+                    object_name = input("Enter object name: ")
+
+                    start_time = time.time()
+                    data = client.download(bucket_id, object_name)
+                    elapsed = time.time() - start_time
+
+                    if data is not None:
+                        print(f"Downloaded {len(data)} bytes in {elapsed:.2f} seconds")
+                        save = input("Save to file? (y/n): ").lower() == 'y'
+                        if save:
+                            file_path = input("Enter save path: ")
+                            with open(file_path, 'wb') as f:
+                                f.write(data)
+                            print("File saved successfully")
+                    else:
+                        print("Download failed")
+
+                elif choice == '3':
+                    bucket_id = input("Enter bucket ID: ")
+                    object_name = input("Enter object name: ")
+
+                    start_time = time.time()
+                    details = client.get_bid_detail(bucket_id, object_name)
+                    elapsed = time.time() - start_time
+
+                    if details:
+                        print(f"Got {len(details)} bid details in {elapsed:.2f} seconds")
+                        for i, detail in enumerate(details[:5]):  # Show first 5 details
+                            print(f"{i + 1}. {detail}")
+                        if len(details) > 5:
+                            print(f"... and {len(details) - 5} more")
+                    else:
+                        print("Failed to get bid details")
+
+                elif choice == '4':
+                    break
+
+                else:
+                    print("Invalid choice")
+
+        finally:
+            client.close()
+
+
+if __name__ == '__main__':
+    main()

oss/pb_pb2.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# NO CHECKED-IN PROTOBUF GENCODE
+# source: pb.proto
+# Protobuf Python Version: 5.29.0
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import runtime_version as _runtime_version
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+_runtime_version.ValidateProtobufRuntimeVersion(
+    _runtime_version.Domain.PUBLIC,
+    5,
+    29,
+    0,
+    '',
+    'pb.proto'
+)
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x08pb.proto\x12\x02pb\"7\n\x0f\x44ownloadRequest\x12\x10\n\x08\x62ucketID\x18\x01 \x01(\t\x12\x12\n\nobjectName\x18\x02 \x01(\t\"S\n\rUploadRequest\x12\x0e\n\x06stream\x18\x01 \x01(\x0c\x12\x0c\n\x04gzip\x18\x02 \x01(\x08\x12\x10\n\x08\x62ucketID\x18\x03 \x01(\t\x12\x12\n\nobjectName\x18\x04 \x01(\t\"=\n\x08Response\x12\x0c\n\x04\x64\x61ta\x18\x01 \x01(\x0c\x12\x11\n\terrorCode\x18\x02 \x01(\x03\x12\x10\n\x08\x65rrorMsg\x18\x03 \x01(\t2\x9c\x01\n\x07Service\x12/\n\x08\x44ownload\x12\x13.pb.DownloadRequest\x1a\x0c.pb.Response0\x01\x12+\n\x06Upload\x12\x11.pb.UploadRequest\x1a\x0c.pb.Response(\x01\x12\x33\n\x0cGetBidDetail\x12\x13.pb.DownloadRequest\x1a\x0c.pb.Response0\x01\x42\x07Z\x05../pbb\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'pb_pb2', _globals)
+if not _descriptor._USE_C_DESCRIPTORS:
+  _globals['DESCRIPTOR']._loaded_options = None
+  _globals['DESCRIPTOR']._serialized_options = b'Z\005../pb'
+  _globals['_DOWNLOADREQUEST']._serialized_start=16
+  _globals['_DOWNLOADREQUEST']._serialized_end=71
+  _globals['_UPLOADREQUEST']._serialized_start=73
+  _globals['_UPLOADREQUEST']._serialized_end=156
+  _globals['_RESPONSE']._serialized_start=158
+  _globals['_RESPONSE']._serialized_end=219
+  _globals['_SERVICE']._serialized_start=222
+  _globals['_SERVICE']._serialized_end=378
+# @@protoc_insertion_point(module_scope)

oss/pb_pb2_grpc.py

@@ -0,0 +1,183 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+import warnings
+
+import pb_pb2 as pb__pb2
+
+GRPC_GENERATED_VERSION = '1.29.0'
+GRPC_VERSION = grpc.__version__
+_version_not_supported = False
+
+try:
+    from grpc._utilities import first_version_is_lower
+    _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION)
+except ImportError:
+    _version_not_supported = True
+
+if _version_not_supported:
+    raise RuntimeError(
+        f'The grpc package installed is at version {GRPC_VERSION},'
+        + f' but the generated code in pb_pb2_grpc.py depends on'
+        + f' grpcio>={GRPC_GENERATED_VERSION}.'
+        + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}'
+        + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.'
+    )
+
+
+class ServiceStub(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def __init__(self, channel):
+        """Constructor.
+
+        Args:
+            channel: A grpc.Channel.
+        """
+        self.Download = channel.unary_stream(
+                '/pb.Service/Download',
+                request_serializer=pb__pb2.DownloadRequest.SerializeToString,
+                response_deserializer=pb__pb2.Response.FromString,
+                _registered_method=True)
+        self.Upload = channel.stream_unary(
+                '/pb.Service/Upload',
+                request_serializer=pb__pb2.UploadRequest.SerializeToString,
+                response_deserializer=pb__pb2.Response.FromString,
+                _registered_method=True)
+        self.GetBidDetail = channel.unary_stream(
+                '/pb.Service/GetBidDetail',
+                request_serializer=pb__pb2.DownloadRequest.SerializeToString,
+                response_deserializer=pb__pb2.Response.FromString,
+                _registered_method=True)
+
+
+class ServiceServicer(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def Download(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Upload(self, request_iterator, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def GetBidDetail(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+
+def add_ServiceServicer_to_server(servicer, server):
+    rpc_method_handlers = {
+            'Download': grpc.unary_stream_rpc_method_handler(
+                    servicer.Download,
+                    request_deserializer=pb__pb2.DownloadRequest.FromString,
+                    response_serializer=pb__pb2.Response.SerializeToString,
+            ),
+            'Upload': grpc.stream_unary_rpc_method_handler(
+                    servicer.Upload,
+                    request_deserializer=pb__pb2.UploadRequest.FromString,
+                    response_serializer=pb__pb2.Response.SerializeToString,
+            ),
+            'GetBidDetail': grpc.unary_stream_rpc_method_handler(
+                    servicer.GetBidDetail,
+                    request_deserializer=pb__pb2.DownloadRequest.FromString,
+                    response_serializer=pb__pb2.Response.SerializeToString,
+            ),
+    }
+    generic_handler = grpc.method_handlers_generic_handler(
+            'pb.Service', rpc_method_handlers)
+    server.add_generic_rpc_handlers((generic_handler,))
+    server.add_registered_method_handlers('pb.Service', rpc_method_handlers)
+
+
+ # This class is part of an EXPERIMENTAL API.
+class Service(object):
+    """Missing associated documentation comment in .proto file."""
+
+    @staticmethod
+    def Download(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_stream(
+            request,
+            target,
+            '/pb.Service/Download',
+            pb__pb2.DownloadRequest.SerializeToString,
+            pb__pb2.Response.FromString,
+            options,
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)
+
+    @staticmethod
+    def Upload(request_iterator,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.stream_unary(
+            request_iterator,
+            target,
+            '/pb.Service/Upload',
+            pb__pb2.UploadRequest.SerializeToString,
+            pb__pb2.Response.FromString,
+            options,
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)
+
+    @staticmethod
+    def GetBidDetail(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_stream(
+            request,
+            target,
+            '/pb.Service/GetBidDetail',
+            pb__pb2.DownloadRequest.SerializeToString,
+            pb__pb2.Response.FromString,
+            options,
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)

oss/proto/pb.proto

@@ -0,0 +1,29 @@
+syntax = "proto3";
+
+package pb;
+
+option go_package = "../pb";
+
+service Service {
+  rpc Download (DownloadRequest) returns (stream Response);
+  rpc Upload (stream UploadRequest) returns (Response);
+  rpc GetBidDetail (DownloadRequest) returns (stream Response);
+}
+
+message DownloadRequest {
+  string bucketID = 1;
+  string objectName = 2;
+}
+
+message UploadRequest {
+  bytes stream = 1; // 客户端将文件数据传递过来
+  bool gzip = 2; //是否压缩
+  string bucketID = 3; //桶id
+  string objectName = 4; //对象名称
+}
+
+message Response {
+  bytes data = 1;
+  int64 errorCode = 2;
+  string errorMsg = 3;
+}

tools/test.py

@@ -1,21 +1,8 @@
-from pymongo import MongoClient
+from datetime import datetime, timedelta
 
-client = MongoClient('mongodb://172.20.45.129:27002/')
-db = client['data_quality']
-collection = db['bidding_202505']
+# 定义一周的时间范围，转换为Unix时间戳格式
+end_date = datetime(2025, 7, 10, 12, 0, 0)  # 需要创建datetime对象
+start_date = int((end_date - timedelta(days=7)).timestamp())
+end_timestamp = int(end_date.timestamp())
 
-# 删除 comeintime 在 [1747152000, 1747238400] 范围内的文档
-# result = collection.delete_many({
-#     "comeintime": {
-#         "$gte": 1747152000,  # 大于等于 from
-#         "$lte": 1747238400   # 小于等于 to
-#     }
-# })
-count = collection.count_documents({
-    "comeintime": {
-        "$gte": 1747152000,
-        "$lte": 1747238400
-    }
-})
-print(f"符合条件的数据量: {count}")
-# print(f"删除了 {result.deleted_count} 条文档")
+print(f"开始时间：{start_date} -- 结束时间：{end_timestamp}")

tools/从es导出数据/es.py

@@ -21,7 +21,7 @@ def ES_bidding(es_query):
         'mg_host': '172.20.45.129',
         'mg_port': 27002,
         'database': 'data_quality',
-        'collection': 'bidding_20250515_fujian'
+        'collection': 'bidding_20250702'
     }
     query = es_query
     # 传入查询语句query 以及配置信息
@@ -29,50 +29,8 @@ 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": {
-                                    "gte": 1747238400,
-                                    "lte": 1747324800
-                                }
-                            }
-                        }, {
-                            "bool": {
-                                "must": [{
-                                    "multi_match": {
-                                        "query": "详见附件",
-                                        "type": "phrase",
-                                        "fields": ["detail"]
-                                    }
-                                }]
-                            }
-                        }],
-                        "must_not": []
-                    }
-                },
-                "highlight": {
-                    "pre_tags": [""],
-                    "post_tags": [""],
-                    "fields": {
-                        "detail": {
-                            "fragment_size": 115,
-                            "number_of_fragments": 1
-                        }
-                    }
-                },
-                "sort": [{
-                    "dataweight": "desc"
-                }, {
-                    "publishtime": "desc"
-                }],
-                "from": 0
-            }
-    # es_query = {"track_total_hits": True,
-    #             "query": {"bool": {"must": [{"range": {"comeintime": {"from": "1747324800", "to": "1747411200"}}}]}}}
+                "query": {"bool": {"must": [{"range": {"comeintime": {"from": "1751385600", "to": "1751472000"}}}]}}}
     # 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库导出数据execl/_id.csv

@@ -0,0 +1,8183 @@
+_id
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tools/从mongo库导出数据execl/bak.py

@@ -0,0 +1,62 @@
+# 导入必要的库
+from pymongo import MongoClient
+import pandas as pd
+import pytz  # 用于处理时区
+def export_to_excel(db_name, collection_name, fields, output_file):
+    """
+    从MongoDB导出特定字段到Excel文件。
+
+    参数:
+    - db_name: 数据库名称
+    - collection_name: 集合名称
+    - fields: 要导出的字段列表（例如 ['name', 'age']）
+    - output_file: 输出的Excel文件名
+    """
+    # 连接到MongoDB
+    client = MongoClient('mongodb://172.20.45.129:27002/')
+    # client = MongoClient('mongodb://127.0.0.1:27087/', unicode_decode_error_handler="ignore",directConnection=True)  # 清洗库
+
+    db = client[db_name]
+    collection = db[collection_name]
+
+    # 构建查询和投影
+    projection = {field: 1 for field in fields}
+
+    # 查询数据
+    data = collection.find({"flag_liumiao": 1},projection).sort("_id", -1)
+
+    # 将数据转换为DataFrame
+    df = pd.DataFrame(list(data))
+
+    # 转换时间戳字段
+    time_fields = ['bidopentime', 'bidendtime']
+    for field in time_fields:
+        if field in df.columns:
+            # 1. 转换为 datetime 对象（秒级时间戳）
+            df[field] = pd.to_datetime(df[field], unit='s', errors='coerce')
+            # 2. 转换为北京时间（UTC+8）
+            beijing_tz = pytz.timezone('Asia/Shanghai')
+            df[field] = df[field].dt.tz_localize('UTC').dt.tz_convert(beijing_tz)
+            # 3. 去掉时区信息，保留纯时间（否则Excel可能显示异常）
+            df[field] = df[field].dt.tz_localize(None)
+
+    # 导出到Excel文件
+    df.to_excel(output_file, index=False)
+
+if __name__ == "__main__":
+    # 连接到 MongoDB
+
+    db_name = 'wjh'  # 替换为你的数据库名称
+    # db_name = 'jyqyfw'  # 替换为你的数据库名称
+    # collection_name = 'standard_sample_data_all_ai' # 替换为你的集合名称
+    collection_name = 'usermail_Unicom_1_2_pkg'  # 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','label','href','jybxhref','package']  # 替换为你需要导出的字段
+    # fields = ['_id','id','site','href','jybxhref','spidercode','projectinfo']  # 替换为你需要导出的字段
+    output_file = 'output.xlsx'
+
+    # 调用函数导出数据
+    export_to_excel(db_name, collection_name, fields, output_file)
+    print(f"数据已成功导出到 {output_file}")
+

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

@@ -2,6 +2,7 @@
 from pymongo import MongoClient
 import pandas as pd
 import pytz  # 用于处理时区
+
 def export_to_excel(db_name, collection_name, fields, output_file):
     """
     从MongoDB导出特定字段到Excel文件。
@@ -13,7 +14,9 @@ def export_to_excel(db_name, collection_name, fields, output_file):
     - output_file: 输出的Excel文件名
     """
     # 连接到MongoDB
-    client = MongoClient('mongodb://172.20.45.129:27002/')
+    # client = MongoClient('mongodb://172.20.45.129:27002/')
+    client = MongoClient('mongodb://127.0.0.1:27087/', unicode_decode_error_handler="ignore",directConnection=True)  # 修改为你的连接地址
+
     # client = MongoClient('mongodb://127.0.0.1:27087/', unicode_decode_error_handler="ignore",directConnection=True)  # 清洗库
 
     db = client[db_name]
@@ -23,22 +26,22 @@ 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({"mark_1": 1},projection).sort("_id", -1)
+    data = collection.find({"tag": 11},projection).sort("_id", 1)
 
     # 将数据转换为DataFrame
     df = pd.DataFrame(list(data))
 
-    # 转换时间戳字段
-    time_fields = ['bidopentime', 'bidendtime']
-    for field in time_fields:
-        if field in df.columns:
-            # 1. 转换为 datetime 对象（秒级时间戳）
-            df[field] = pd.to_datetime(df[field], unit='s', errors='coerce')
-            # 2. 转换为北京时间（UTC+8）
-            beijing_tz = pytz.timezone('Asia/Shanghai')
-            df[field] = df[field].dt.tz_localize('UTC').dt.tz_convert(beijing_tz)
-            # 3. 去掉时区信息，保留纯时间（否则Excel可能显示异常）
-            df[field] = df[field].dt.tz_localize(None)
+    # # 转换时间戳字段
+    # time_fields = ['bidopentime', 'bidendtime']
+    # for field in time_fields:
+    #     if field in df.columns:
+    #         # 1. 转换为 datetime 对象（秒级时间戳）
+    #         df[field] = pd.to_datetime(df[field], unit='s', errors='coerce')
+    #         # 2. 转换为北京时间（UTC+8）
+    #         beijing_tz = pytz.timezone('Asia/Shanghai')
+    #         df[field] = df[field].dt.tz_localize('UTC').dt.tz_convert(beijing_tz)
+    #         # 3. 去掉时区信息，保留纯时间（否则Excel可能显示异常）
+    #         df[field] = df[field].dt.tz_localize(None)
 
     # 导出到Excel文件
     df.to_excel(output_file, index=False)
@@ -46,14 +49,16 @@ def export_to_excel(db_name, collection_name, fields, output_file):
 if __name__ == "__main__":
     # 连接到 MongoDB
 
-    db_name = 'wjh'  # 替换为你的数据库名称
+    db_name = 'jyqyfw'  # 替换为你的数据库名称
     # db_name = 'jyqyfw'  # 替换为你的数据库名称
     # collection_name = 'standard_sample_data_all_ai' # 替换为你的集合名称
-    collection_name = 'unicom_1_2'  # 替换为你的集合名称
+    # collection_name = 'xzh_20250714'  # 替换为你的集合名称
+    collection_name = 'xzh_20250714_1'  # 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','bidopentime','bidendtime','label','href','jybxhref','spidercode']  # 替换为你需要导出的字段
-    # fields = ['_id','id','site','href','jybxhref','spidercode','projectinfo']  # 替换为你需要导出的字段
+    # fields = ['_id','id','site','toptype','subtype','area','city','buyer','projectname','projectcode','budget','s_winner','bidamount','label','href','jybxhref','package']  # 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','label','href','jybxhref']  # 替换为你需要导出的字段
+    fields = ['id','title','projectname','href','projectcode','subtype','s_winner','bidamount','buyer']  # 替换为你需要导出的字段
     output_file = 'output.xlsx'
 
     # 调用函数导出数据

tools/从mongo库导出数据execl/mongo_to_execl_多包.py

@@ -0,0 +1,85 @@
+# 导入必要的库
+from pymongo import MongoClient
+import pandas as pd
+import pytz  # 用于处理时区
+
+
+def export_to_excel(db_name, collection_name, fields, output_file):
+    """
+    从MongoDB导出特定字段到Excel文件。
+
+    参数:
+    - db_name: 数据库名称
+    - collection_name: 集合名称
+    - fields: 要导出的字段列表（例如 ['name', 'age']）
+    - output_file: 输出的Excel文件名
+    """
+    # 连接到MongoDB
+    client = MongoClient('mongodb://172.20.45.129:27002/')
+    # client = MongoClient('mongodb://127.0.0.1:27087/', unicode_decode_error_handler="ignore",directConnection=True)  # 清洗库
+
+    db = client[db_name]
+    collection = db[collection_name]
+
+    # 构建查询和投影
+    projection = {field: 1 for field in fields}
+
+    # 查询数据
+    data = collection.find({"flag_liumiao": 1}, projection).sort("_id", -1)
+
+    # 将数据转换为DataFrame
+    df = pd.DataFrame(list(data))
+
+    # 处理package字段，展开为多个列
+    if 'package' in df.columns:
+        # 创建一个空的DataFrame来存储展开后的package数据
+        package_df = pd.DataFrame()
+
+        # 遍历每一行的package字段
+        for idx, row in df.iterrows():
+            if isinstance(row['package'], dict):
+                # 遍历每个标包
+                for package_name, package_info in row['package'].items():
+                    # 为每个标包创建列
+                    if isinstance(package_info, dict):
+                        for field in ['winner', 'bidamount']:
+                            col_name = f"{package_name}_{field}"
+                            package_df.at[idx, col_name] = package_info.get(field)
+
+        # 将展开后的package数据合并到原DataFrame
+        df = pd.concat([df, package_df], axis=1)
+
+        # 删除原始的package列
+        df = df.drop(columns=['package'])
+
+    # 转换时间戳字段
+    time_fields = ['bidopentime', 'bidendtime']
+    for field in time_fields:
+        if field in df.columns:
+            # 1. 转换为 datetime 对象（秒级时间戳）
+            df[field] = pd.to_datetime(df[field], unit='s', errors='coerce')
+            # 2. 转换为北京时间（UTC+8）
+            beijing_tz = pytz.timezone('Asia/Shanghai')
+            df[field] = df[field].dt.tz_localize('UTC').dt.tz_convert(beijing_tz)
+            # 3. 去掉时区信息，保留纯时间（否则Excel可能显示异常）
+            df[field] = df[field].dt.tz_localize(None)
+
+    # 导出到Excel文件
+    df.to_excel(output_file, index=False)
+
+
+if __name__ == "__main__":
+    # 连接到 MongoDB
+    db_name = 'wjh'  # 替换为你的数据库名称
+    # db_name = 'jyqyfw'  # 替换为你的数据库名称
+    # collection_name = 'standard_sample_data_all_ai' # 替换为你的集合名称
+    collection_name = 'usermail_Unicom_1_2_pkg'  # 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', 'label', 'href', 'jybxhref', 'package']  # 替换为你需要导出的字段
+    # fields = ['_id','id','site','href','jybxhref','spidercode','projectinfo']  # 替换为你需要导出的字段
+    output_file = 'output.xlsx'
+
+    # 调用函数导出数据
+    export_to_excel(db_name, collection_name, fields, output_file)
+    print(f"数据已成功导出到 {output_file}")

tools/从mongo库导出数据execl/根据mongoid导出数据.py

@@ -0,0 +1,54 @@
+from bson import ObjectId
+from pymongo import MongoClient
+import csv
+import openpyxl  # For Excel file operations
+
+def process_tagged_documents():
+    # 直接在URI中包含用户名和密码
+    username = "viewdata"
+    password = "viewdata"
+    host = "127.0.0.1"  # 例如: localhost 或 192.168.1.100
+    port = "27088"  # 默认MongoDB端口
+
+    # 构建连接URI
+    mongo_uri = f"mongodb://{username}:{password}@{host}:{port}/"
+
+
+    # 连接MongoDB
+    client2 = MongoClient(mongo_uri,unicode_decode_error_handler="ignore", directConnection=True)  #bidding库
+
+    # 定义数据库和集合
+    db2 = client2['qfw']  # 替换为实际的数据库2名称
+
+    collection2 = db2['bidding']  # 替换为实际的bidding集合名称
+    # Create a new Excel workbook and worksheet
+    workbook = openpyxl.Workbook()
+    worksheet = workbook.active
+    worksheet.title = "Bidding Data"
+    # 读取 CSV 文件中的 spidercodes
+    with open('_id.csv', mode='r', encoding='utf-8') as csv_file:
+        csv_reader = csv.reader(csv_file)
+        next(csv_reader)  # 跳过标题行
+        for row in csv_reader:
+            _id = row[0]
+            doc_id = ObjectId(_id)
+
+            # 在库2的bidding集合中查找该id
+            bidding_doc = collection2.find_one({'_id': doc_id})
+            if bidding_doc:
+                # 提取需要的字段
+                # Prepare data for Excel
+                result = [
+                    str(doc_id),  # Convert ObjectId to string
+                    bidding_doc.get('site', ''),
+                    bidding_doc.get('href', '')
+                ]
+                worksheet.append(result)
+            print(result)
+    # Save the Excel file
+    excel_filename = 'bidding_results.xlsx'
+    workbook.save(excel_filename)
+    print(f"Results saved to {excel_filename}")
+
+if __name__ == '__main__':
+    process_tagged_documents()

tools/数据抽样/fix_site_data_export.py

@@ -1,63 +1,57 @@
 from pymongo import MongoClient
 #从已知站点导出N条样本数据,每个站点导出M条数据
 
-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_20250123"]
-    # 把符合条件的站点名称存起来
-    site_list = {"中粮贸易阳光采购平台","航天电子采购平台","青海省政府采购电子卖场","金正大集团电子采购平台",
-"建华建材电子采购平台",
-"淄博市政府采购网上商城",
-"福建省宁德市政府采购网"}
-    # 初始化已标记的文档数量
+def sample_data_random(N, M):
+    client = MongoClient('172.20.45.129', 27002, unicode_decode_error_handler="ignore")
+    db = client.data_quality
+    collection = db.bidding_20250702
+
+    site_list = {
+        "应进必进阳光交易农村三资数字交易平台",
+        "东方希望数字化采购平台",
+        "首创股份电子商务平台",
+        "南充市政府采购网上商城",
+        "中国铁塔在线商务平台",
+        "公采云",
+        "甘肃省阳光招标采购平台",
+        '招采通',
+        '中国邮政电子采购与供应平台',
+        '济南公共资源交易中心'
+    }
+
     marked_count = 0
-    marked_site_count = 0
-    # 选取每个站点数据量
     for site in site_list:
         if marked_count >= N:
-            break  # 如果已经达到或超过目标数量，停止处理
-        marked_site_count = 0
-        # 聚合查询
+            break
+
+        # 检查该站点的数据量是否足够
+        site_count = collection.count_documents({"site": site})
+        if site_count == 0:
+            print(f"站点 {site} 无数据，跳过")
+            continue
+
+        # 计算实际要抽的数量（避免超过 M 或剩余需要的 N - marked_count）
+        sample_size = min(M, N - marked_count, site_count)
+
+        # 使用 $sample 随机抽取
         pipeline = [
-            {
-                '$match': {'site': site}
-            },
-            {
-                '$group': {
-                     '_id': '$site',
-                    'count': {'$sum': 1}
-                }
-            },
-            {
-                '$sort': {'count': -1}  # 根据 count 降序排序，可选
-            }
+            {"$match": {"site": site}},
+            {"$sample": {"size": sample_size}},
+            {"$project": {"_id": 1}}  # 只返回 _id，减少数据传输
         ]
-        result = collection.aggregate(pipeline)
-        for doc in result:
-            print(f"Field Value: {doc['_id']}, Count: {doc['count']}")
-            # 计算每次抽取的间隔
-            jiange = int(doc['count'] / M)
-
-            # 从每个站点等间隔地取数据
-            for i in range(M):
-                if marked_site_count >= M:
-                    break  # 再次检查是否已达到目标数量
-
-                for info in collection.find({"site": site}).sort("_id", 1).skip(i*jiange).limit(1):
-                    print(f"Updating document with _id: {info['_id']}")
-                    # 更新文档，设置标记
-                    update_result = collection.update_one({"_id": info["_id"]}, {"$set": {"flag": 3}})
-                    if update_result.modified_count == 0:
-                        print("No document updated for _id:", info["_id"])
-                    else:
-                        print("Document updated successfully for _id:", info["_id"])
-                    marked_site_count += 1
-                    marked_count += 1
-
-                    if marked_site_count >= M:
-                        break  # 再次检查是否已达到目标数量
-
-    print(f"Total marked documents: {marked_count}")
-
-sample_data(200,10)
+        sampled_docs = list(collection.aggregate(pipeline))
+
+        # 批量更新标记
+        if sampled_docs:
+            sampled_ids = [doc["_id"] for doc in sampled_docs]
+            update_result = collection.update_many(
+                {"_id": {"$in": sampled_ids}},
+                {"$set": {"flag_liumiao": 1}}
+            )
+            marked_count += update_result.modified_count
+            print(f"站点 {site}: 成功标记 {update_result.modified_count} 条数据")
+
+    print(f"总计标记 {marked_count} 条数据")
+
+
+sample_data_random(100, 10)

tools/数据抽样/sample_data_export_new.py

@@ -1,15 +1,15 @@
 from pymongo import MongoClient
 def sample_data(N):
-    db = MongoClient('172.20.45.129', 27002, unicode_decode_error_handler="ignore").wjh
+    db = MongoClient('172.20.45.129', 27002, unicode_decode_error_handler="ignore").data_quality
     # db = MongoClient('mongodb://127.0.0.1:27087/', unicode_decode_error_handler="ignore",directConnection=True).jyqyfw  # 清洗库
 
-    coll_user = db["unicom_1_2"]
+    coll_user = db["missing_fid_attachments"]
 
-    filter_condition = {"tag_1": 1}
+    # filter_condition = {"tag_1": 1}
     # 获取所有站点及其文档数
     site_list = {}
     site_count = coll_user.aggregate([
-        {"$match": filter_condition},
+        # {"$match": filter_condition},
         {"$group": {"_id": "$site", "count": {"$sum": 1}}},
         {"$sort": {"count": -1}}
     ])
@@ -33,7 +33,12 @@ def sample_data(N):
 
         # 使用随机抽样
         pipeline = [
-            {"$match": {"site": site, **filter_condition}},
+            {"$match": {
+                    "site": site,
+                    # **filter_condition
+                    }
+             },
+            {"$match": {"site": site}},
             {"$sample": {"size": num}},
             {"$project": {"_id": 1}}
         ]
@@ -44,10 +49,10 @@ def sample_data(N):
 
         update_result = coll_user.update_many(
             {"_id": {"$in": sampled_ids}},
-            {"$set": {"mark_1": 1}}
+            {"$set": {"flag": 1}}
         )
         marked_count += update_result.modified_count
         remaining -= update_result.modified_count
 
     print(f"Total marked documents: {marked_count}")
-sample_data(500)
+sample_data(8000)

tools/数据抽样/完全随机抽样.py

@@ -0,0 +1,53 @@
+from pymongo import MongoClient
+
+
+def completely_random_sample(N, db_name="data_quality", collection_name="bidding_20250702"):
+    """
+    完全随机抽样（3万数据量优化版）
+    - 严格无偏随机
+    - 精确控制样本量 = N
+    - 自动过滤 tag=1 或 2 的文档
+    """
+    client = MongoClient('172.20.45.129', 27002, unicode_decode_error_handler="ignore",directConnection=True)
+    db = client[db_name]
+    collection = db[collection_name]
+
+    print(f"目标: 从 {collection_name} 随机抽取 {N} 条数据")
+
+    try:
+        # 1. 检查符合条件的文档总数
+        total_eligible = collection.count_documents({ "pkg_tag" : { "$ne" : 0 }, "mark":1} )
+        # total_eligible = collection.count_documents({})
+        if total_eligible < N:
+            raise ValueError(f"错误: 仅 {total_eligible} 条数据符合条件，无法抽取 {N} 条")
+
+        # 2. 使用 $rand + $sort 随机抽样（比 $sample 更均匀）
+        pipeline = [
+            {"$match": { "pkg_tag" : { "$ne" : 0 }, "mark":1}} ,
+            {"$sample": {"size": N}},
+            {"$project": {"_id": 1}}  # 仅返回 _id
+        ]
+
+        # 3. 执行抽样并获取 _id 列表
+        sampled_ids = [doc["_id"] for doc in collection.aggregate(pipeline)]
+        if len(sampled_ids) != N:
+            raise RuntimeError(f"抽样失败: 仅获取 {len(sampled_ids)} 条数据")
+
+        # 4. 批量标记已抽样文档（可选）
+        collection.update_many(
+            {"_id": {"$in": sampled_ids}},
+            {"$set": {"mark_pkg_duobao": 1}}  # 标记已抽样
+        )
+
+        print(f"成功: 随机抽取并标记 {N} 条数据")
+        return sampled_ids  # 返回抽样结果的 _id 列表（可选）
+
+    except Exception as e:
+        print(f"错误: {str(e)}")
+        return None
+    finally:
+        client.close()
+
+
+# 示例调用
+sampled_ids = completely_random_sample(100)

tools/数据质量监控平台/kb-数据问题统计/kb错误原因及占比统计.py

@@ -0,0 +1,25 @@
+import pandas as pd
+
+# 读取CSV文件
+df = pd.read_csv('错误原因.csv')
+
+# 统计每个错误原因的数量
+error_stats = df['错误原因'].value_counts().reset_index()
+error_stats.columns = ['错误类别', '数量']
+
+# 计算占比
+total = error_stats['数量'].sum()
+error_stats['占比'] = (error_stats['数量'] / total * 100).round(2).astype(str) + '%'
+
+# 输出到Excel文件
+output_file = '错误原因统计.xlsx'
+with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
+    error_stats.to_excel(writer, index=False, sheet_name='错误统计')
+
+    # 调整列宽
+    worksheet = writer.sheets['错误统计']
+    worksheet.column_dimensions['A'].width = 30  # 错误类别列宽
+    worksheet.column_dimensions['B'].width = 10  # 数量列宽
+    worksheet.column_dimensions['C'].width = 10  # 占比列宽
+
+print(f"统计结果已保存到 {output_file}")

tools/数据质量监控平台/kb-数据问题统计/task_kb.py

@@ -36,7 +36,7 @@ def test_connection():
             user="kanboard",
             password="K99b3e9qa9d",
             database="kanboard",
-            port=19988
+            port=4001
         )
         cursor_local = db_connection_local.cursor()
 

tools/数据质量监控平台/kb-数据问题统计/task_kb_根据条件导出kb数据.py

@@ -0,0 +1,137 @@
+import pymysql
+from pymysql import MySQLError
+from datetime import datetime, timedelta
+import pandas as pd
+
+def close_connection(cursor, connection):
+    """确保数据库连接被关闭"""
+    if cursor:
+        cursor.close()
+    if connection:
+        connection.close()
+
+
+def test_connection():
+    cursor_local = None
+    db_connection_local = None
+    data_list = []  # 用于存储所有要导出的数据
+    processed_task_ids = set()  # 用于记录已处理的任务ID，避免重复
+    try:
+        print("开始连接本地数据库...")
+        db_connection_local = pymysql.connect(
+            host="127.0.0.1",
+            user="kanboard",
+            password="K99b3e9qa9d",
+            database="kanboard",
+            port=4001
+        )
+        cursor_local = db_connection_local.cursor()
+
+        print("开始连接远程数据库...")
+        db_connection_remote = pymysql.connect(
+            host="172.20.45.129",
+            user="root",
+            password="=PDT49#80Z!RVv52_z",
+            database="quality",
+            port=4000
+        )
+
+        print("开始执行查询...")
+        query = """
+        SELECT DISTINCT t.id AS task_id, 
+               t.date_creation, 
+               t.date_moved, 
+               t.external_uri, 
+               t.title,
+               t.description,          
+               t.owner_id, 
+               t.creator_id, 
+               t.column_id
+        FROM tasks t
+        JOIN task_has_tags tht ON t.id = tht.task_id
+        JOIN tags tg ON tht.tag_id = tg.id
+        WHERE t.project_id = 261
+          AND tg.project_id = 261
+          AND tg.name ='20250620'
+        """
+        cursor_local.execute(query)
+        results = cursor_local.fetchall()
+        if not results:
+            print("没有找到符合条件的数据")
+            return
+
+        print(f"查询到 {len(results)} 条数据")
+
+        # 获取 owner_name 和 creator_name
+        cursor_local.execute("SELECT id, name FROM users")
+        users = cursor_local.fetchall()
+        user_dict = {user[0]: user[1] for user in users}
+
+        # 获取当前日期
+        current_date = datetime.utcnow().strftime('%Y-%m-%d')  # 当前日期格式：YYYY-MM-DD
+
+        print("开始插入数据...")
+        for task in results:
+            task_id, date_started, date_moved, external_uri, title, description,owner_id, creator_id, column_id = task
+
+            # 检查是否已处理过该任务ID
+            if task_id in processed_task_ids:
+                continue
+            processed_task_ids.add(task_id)
+
+            # 获取任务对应的 column_id 和 task_status
+            if column_id == 1057:
+                task_status = '已创建'
+            elif column_id in [1058, 1060, 1065]:
+                task_status = '处理中'
+            elif column_id == 1064:
+                task_status = '已完成'
+            else:
+                task_status = '未定义'  # 如果 column_id 不在预设范围内
+
+            creation_date = datetime.utcfromtimestamp(date_started).strftime('%Y-%m-%d')
+            year = datetime.utcfromtimestamp(date_started).year
+            month = datetime.utcfromtimestamp(date_started).month
+            modification_date = datetime.utcfromtimestamp(date_moved).strftime('%Y-%m-%d') if date_moved else None
+
+            assignee_name = user_dict.get(owner_id, None)
+            creator_name = user_dict.get(creator_id, None)
+            work_order_link = f"https://jykb.jydev.jianyu360.com/task/{task_id}"
+
+
+            # 添加到数据列表
+            data_list.append({
+                "年份": year,
+                "月份": month,
+                "任务ID": task_id,
+                "工单链接": work_order_link,
+                "创建人": creator_name,
+                "指派人": assignee_name,
+                "标题": title,
+                "任务描述":description,
+                "任务创建时间": creation_date,
+                "任务修改时间": modification_date,
+                "kb任务状态": task_status,
+                "导出时间": current_date
+            })
+
+        # 导出到Excel
+        # 将导出操作移到循环外部（只执行一次）
+        if data_list:  # 确保有数据才导出
+            df = pd.DataFrame(data_list)
+            excel_file = "kb_tasks_特定条件_export.xlsx"
+            df.to_excel(excel_file, index=False)
+            print(f"数据已成功导出到 {excel_file}")
+
+        db_connection_remote.commit()
+        print("数据处理完成！")
+
+    except MySQLError as e:
+        print(f"发生错误: {e}")
+
+    finally:
+        close_connection(cursor_local, db_connection_local)
+
+
+if __name__ == "__main__":
+    test_connection()

tools/数据质量监控平台/kb-数据问题统计/task_kb_每周汇报.py

@@ -0,0 +1,188 @@
+import pymysql
+from pymysql import MySQLError
+from datetime import datetime, timedelta
+import pandas as pd
+
+
+def get_last_week_monday_friday():
+    """获取上周一和上周五的 Unix 时间戳"""
+    today = datetime.utcnow()
+    last_monday = today - timedelta(days=today.weekday() + 7)  # 上周一
+    last_friday = last_monday + timedelta(days=4)  # 上周五
+
+    last_monday_timestamp = int(last_monday.replace(hour=0, minute=0, second=0).timestamp())
+    last_friday_timestamp = int(last_friday.replace(hour=23, minute=59, second=59).timestamp())
+
+    return last_monday_timestamp, last_friday_timestamp
+
+def get_current_week_monday_friday():
+    """获取本周一和本周五的 Unix 时间戳"""
+    today = datetime.utcnow()
+    current_monday = today - timedelta(days=today.weekday())  # 本周一
+    current_friday = current_monday + timedelta(days=4)      # 本周五
+
+    current_monday_timestamp = int(current_monday.replace(hour=0, minute=0, second=0).timestamp())
+    current_friday_timestamp = int(current_friday.replace(hour=23, minute=59, second=59).timestamp())
+
+    return current_monday_timestamp, current_friday_timestamp
+
+def close_connection(cursor, connection):
+    """确保数据库连接被关闭"""
+    if cursor:
+        cursor.close()
+    if connection:
+        connection.close()
+
+
+def test_connection():
+    cursor_local = None
+    db_connection_local = None
+    data_list = []  # 用于存储所有要导出的数据
+    processed_task_ids = set()  # 用于记录已处理的任务ID，避免重复
+    try:
+        print("开始连接本地数据库...")
+        db_connection_local = pymysql.connect(
+            host="127.0.0.1",
+            user="kanboard",
+            password="K99b3e9qa9d",
+            database="kanboard",
+            port=4001
+        )
+        cursor_local = db_connection_local.cursor()
+
+        print("开始连接远程数据库...")
+        db_connection_remote = pymysql.connect(
+            host="172.20.45.129",
+            user="root",
+            password="=PDT49#80Z!RVv52_z",
+            database="quality",
+            port=4000
+        )
+
+        # 计算上周一到上周五的时间范围
+        start_timestamp, end_timestamp = get_last_week_monday_friday()
+        print(
+            f"查询时间范围: {datetime.utcfromtimestamp(start_timestamp)} - {datetime.utcfromtimestamp(end_timestamp)}")
+
+        print("开始执行查询...")
+        query = """
+        SELECT DISTINCT t.id AS task_id, 
+               t.date_started, 
+               t.date_moved, 
+               t.external_uri, 
+               t.title,
+               t.description,          
+               t.owner_id, 
+               t.creator_id, 
+               t.column_id
+        FROM tasks t
+        JOIN task_has_tags tht ON t.id = tht.task_id
+        JOIN tags tg ON tht.tag_id = tg.id
+        WHERE t.project_id = 261
+          AND t.date_started >= %s
+          AND t.date_started <= %s
+          AND tg.project_id = 261
+        """
+        cursor_local.execute(query, (start_timestamp, end_timestamp))
+        results = cursor_local.fetchall()
+        if not results:
+            print("没有找到符合条件的数据")
+            return
+
+        print(f"查询到 {len(results)} 条数据")
+
+        # 获取 owner_name 和 creator_name
+        cursor_local.execute("SELECT id, name FROM users")
+        users = cursor_local.fetchall()
+        user_dict = {user[0]: user[1] for user in users}
+
+        # # 获取远程数据库中的 kb_error_dict 表的 category 列
+        # cursor_remote.execute("SELECT category FROM kb_error_dict")
+        # categories = cursor_remote.fetchall()
+        # category_list = [category[0] for category in categories]  # 获取所有 category 值
+
+        # 获取当前日期
+        current_date = datetime.utcnow().strftime('%Y-%m-%d')  # 当前日期格式：YYYY-MM-DD
+
+        print("开始插入数据...")
+        for task in results:
+            task_id, date_started, date_moved, external_uri, title, description,owner_id, creator_id, column_id = task
+
+            # 检查是否已处理过该任务ID
+            if task_id in processed_task_ids:
+                continue
+            processed_task_ids.add(task_id)
+
+            # 获取任务对应的 column_id 和 task_status
+            if column_id == 1057:
+                task_status = '已创建'
+            elif column_id in [1058, 1060, 1065]:
+                task_status = '处理中'
+            elif column_id == 1064:
+                task_status = '已完成'
+            else:
+                task_status = '未定义'  # 如果 column_id 不在预设范围内
+
+            # 获取任务对应的所有标签名称
+            cursor_local.execute(""" 
+                SELECT tg.name 
+                FROM task_has_tags tht 
+                JOIN tags tg ON tht.tag_id = tg.id 
+                WHERE tht.task_id = %s AND tg.project_id = 261 
+            """, (task_id,))
+            tags = cursor_local.fetchall()
+            tags_list = [tag[0] for tag in tags]  # 获取所有标签名称
+            # 提取所有以 "原因_" 开头的元素
+            reason_tags = [tag for tag in tags_list if tag.startswith('原因_')]
+
+            # 用逗号拼接成一个字符串
+            tags_str = ", ".join(reason_tags) if reason_tags else ""
+
+            creation_date = datetime.utcfromtimestamp(date_started).strftime('%Y-%m-%d')
+            year = datetime.utcfromtimestamp(date_started).year
+            month = datetime.utcfromtimestamp(date_started).month
+            modification_date = datetime.utcfromtimestamp(date_moved).strftime('%Y-%m-%d') if date_moved else None
+
+            assignee_name = user_dict.get(owner_id, None)
+            creator_name = user_dict.get(creator_id, None)
+            work_order_link = f"https://jykb.jydev.jianyu360.com/task/{task_id}"
+
+            # 插入数据时，增加 created_time 字段
+            if tags_str:  # 只有当标签不为空时才插入
+                # 添加到数据列表
+                data_list.append({
+                    "年份": year,
+                    "月份": month,
+                    "任务ID": task_id,
+                    "工单链接": work_order_link,
+                    "创建人": creator_name,
+                    "指派人": assignee_name,
+                    "标题": title,
+                    "任务描述":description,
+                    "任务创建时间": creation_date,
+                    "任务修改时间": modification_date,
+                    "错误原因": tags_str,
+                    "kb任务状态": task_status,
+                    "导出时间": current_date
+                })
+
+        # 导出到Excel
+        # 将导出操作移到循环外部（只执行一次）
+        if data_list:  # 确保有数据才导出
+            df = pd.DataFrame(data_list)
+            excel_file = "kb_tasks_export.xlsx"
+            df.to_excel(excel_file, index=False)
+            print(f"数据已成功导出到 {excel_file}")
+
+        db_connection_remote.commit()
+        print("数据处理完成！")
+
+    except MySQLError as e:
+        print(f"发生错误: {e}")
+
+    finally:
+        close_connection(cursor_local, db_connection_local)
+
+
+if __name__ == "__main__":
+    test_connection()

tools/数据质量监控平台/kb-数据问题统计/错误原因.csv

@@ -0,0 +1,195 @@
+错误原因
+原因_漏采
+原因_判重错误
+原因_漏采
+原因_站点未收录
+原因_判重错误
+原因_站点未收录
+原因_城市错误
+原因_站点未收录
+原因_城市错误
+原因_判重错误
+原因_产权数据不生索引
+原因_漏采
+原因_城市错误
+原因_竞品独家数据
+原因_判重错误
+原因_漏采
+原因_数据延迟采集
+原因_数据延迟采集
+原因_数据延迟采集
+原因_漏采
+原因_竞品独家数据
+原因_站点未收录
+原因_爬虫待开发
+原因_站点未收录
+无问题_已收录
+无问题_已收录
+原因_城市错误
+原因_网站问题
+原因_无问题
+原因_网站需登录
+原因_漏采
+原因_站点未收录
+无问题_已收录
+原因_数据延迟采集
+原因_判重错误
+原因_网站问题
+原因_产权数据不生索引
+原因_中标金额错误
+原因_中标金额错误
+原因_漏采
+原因_漏采
+原因_漏采
+原因_漏采
+原因_漏采
+原因_竞品独家数据
+原因_数据延迟采集
+原因_漏采
+原因_产权数据不生索引
+原因_附件问题
+无问题_已收录
+原因_无问题
+原因_漏采
+原因_公众号
+原因_城市错误
+原因_漏采
+无问题_已收录
+原因_漏采
+原因_城市错误
+原因_城市错误
+原因_漏采
+原因_中标金额错误
+原因_漏采
+原因_中标单位错误
+无问题_已收录
+原因_分类标签错误
+原因_漏采
+原因_公众号
+原因_站点未收录
+原因_爬虫待开发
+原因_数据缺失
+原因_网站需登录
+原因_栏目缺失
+原因_漏采
+原因_漏采
+原因_漏采
+原因_预算错误
+原因_网站需登录
+原因_爬虫待开发
+原因_漏采
+原因_漏采
+原因_漏采
+原因_爬虫待开发
+原因_漏采
+原因_爬虫待开发
+原因_站点未收录
+原因_网站需登录
+原因_竞品独家数据
+原因_漏采
+原因_爬虫待开发
+原因_无问题
+原因_站点未收录
+原因_网站需登录
+原因_漏采
+原因_竞品独家数据
+原因_网站需登录
+原因_网站需登录
+原因_判重错误
+原因_爬虫待开发
+无问题_已收录
+原因_网站需登录
+原因_城市错误
+原因_漏采
+原因_栏目缺失
+无问题_已收录
+原因_预算错误
+原因_网站需登录
+原因_站点未收录
+原因_站点未收录
+原因_网站需登录
+原因_判重错误
+原因_网站变更
+原因_网站问题
+原因_网站问题
+原因_竞品独家数据
+原因_栏目缺失
+原因_城市错误
+原因_站点未收录
+原因_无问题
+无问题_已收录
+原因_站点未收录
+原因_漏采
+原因_网站需登录
+原因_漏采
+原因_网站需登录
+原因_漏采
+原因_漏采
+原因_网站需登录
+原因_公众号
+原因_爬虫待开发
+原因_漏采
+原因_网站需登录
+原因_判重错误
+原因_漏采
+原因_漏采
+原因_站点未收录
+原因_漏采
+原因_漏采
+原因_漏采
+原因_漏采
+原因_漏采
+原因_爬虫待开发
+原因_爬虫待开发
+原因_站点未收录
+原因_无问题
+原因_站点未收录
+原因_站点未收录
+原因_中标单位错误
+原因_漏采
+原因_漏采
+原因_中标单位错误
+原因_漏采
+原因_数据延迟采集
+原因_数据延迟采集
+原因_漏采
+原因_网站需登录
+原因_栏目缺失
+原因_数据延迟采集
+原因_漏采
+原因_漏采
+原因_站点未收录
+原因_网站需登录
+原因_栏目缺失
+原因_网站问题
+原因_爬虫待开发
+原因_竞品独家数据
+原因_分类标签错误
+原因_栏目缺失
+原因_网站问题
+原因_漏采
+原因_网站需登录
+原因_站点未收录
+原因_站点未收录
+原因_漏采
+原因_竞品独家数据
+无问题_已收录
+原因_数据缺失
+原因_数据缺失_爬虫待开发
+原因_网站问题
+原因_数据缺失_漏采
+原因_数据缺失_信息采集不全/有误
+原因_数据缺失_信息采集不全/有误
+原因_数据缺失
+原因_数据缺失_栏目缺失
+原因_数据缺失
+原因_中标单位错误
+原因_中标单位错误
+原因_中标金额错误
+原因_城市错误
+原因_开标时间错误
+原因_数据缺失_信息采集不全/有误
+原因_数据缺失_信息采集不全/有误
+原因_无问题
+原因_无问题
+原因_预算错误

tools/数据质量监控平台/标讯基础信息分析结果入库.py

@@ -26,8 +26,8 @@ def get_this_week_thursday():
 # query_create_time = get_this_week_thursday().strftime("%Y-%m-%d")  # ✅ 动态获取
 # query_create_time = '2025-04-02'
 now_date = (datetime.now()).strftime("%Y-%m-%d")
-yesterday_date = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
-batch_id = yesterday_date
+# yesterday_date = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
+batch_id = now_date
 
 
 # 从 MySQL 获取数据

tools/标讯数据重复率分析/repeat.py

@@ -5,10 +5,8 @@ from lib.mongo_tools import MongoUtil,Data_save,MongoSentence
 
 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:
@@ -16,12 +14,19 @@ def calculate_repeat_rate(str1, str2):
         return similarity
     else:
         return 1.0
-
+#判断互为包含关系
+def is_mutually_contained(str1, str2):
+    """判断两个字符串是否互为包含关系"""
+    return str1 in str2 or str2 in str1
 # 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" : { "$ne" : "cfsj" } }).sort("_id",1))
+# db = MongoClient('192.168.3.149', 27180, unicode_decode_error_handler="ignore").data_quality
+# coll_user = db["liantong"]
+client = MongoClient('mongodb://127.0.0.1:27087/',unicode_decode_error_handler="ignore", directConnection=True)  # 修改为你的连接地址
+db = client["jyqyfw"]                       # 替换为你的数据库名
+col = db["xzh_20250714"]                    # 替换为你的集合名
+col1 = db["xzh_20250714_1"]
+
+documents = list(col.find({ "repeat" : { "$ne" : True } , "flag" : { "$exists" : False } }).sort("_id",1))
 
 for i in range(len(documents)):
     for j in range(i+1,len(documents)):
@@ -31,19 +36,36 @@ for i in range(len(documents)):
         j_projectname = documents[j].get("projectname", "")
         radio1=calculate_repeat_rate(i_title,j_title)
         radio2=calculate_repeat_rate(i_projectname,j_projectname)
+        i_projectcode=documents[i].get("projectcode", "")
+        j_projectcode = documents[j].get("projectcode", "")
+        i_buyer=documents[i].get("buyer", "")
+        j_buyer=documents[j].get("buyer", "")
+        i_s_winner = documents[i].get("s_winner", "")
+        j_s_winner = documents[j].get("s_winner", "")
+        i_bidamount = documents[i].get("bidamount", "")
+        j_bidamount = documents[j].get("bidamount", "")
+        i_toptype = documents[i].get("toptype", "")
+        j_toptype = documents[j].get("toptype", "")
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_buyer==j_buyer and i_projectcode==j_projectcode and i_bidamount==j_bidamount:
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_buyer==j_buyer and i_projectcode==j_projectcode :
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_s_winner==j_s_winner and i_projectcode==j_projectcode and i_bidamount==j_bidamount:
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_s_winner==j_s_winner and i_projectcode==j_projectcode :
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_toptype in ("拟建","采购意向")  and j_toptype in ("拟建","采购意向") :
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_projectcode==j_projectcode:
+        if (calculate_repeat_rate(i_title , j_title) >=0.8 or calculate_repeat_rate(j_projectname , j_projectname)>=0.8 ) and i_projectcode==j_projectcode:
+            # 更新文档i
+            col.update_one(
+                {"_id": documents[i]["_id"]},
+                {"$set": {"flag": 13}}
+            )
+            # 更新文档j
+            col.update_one(
+                {"_id": documents[j]["_id"]},
+                {"$set": {"flag": 13}}
+            )
+
+            # 更新内存中的文档状态，避免重复处理
+            documents[i]["flag"] = 13
+            documents[j]["flag"] = 13
 
-        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/repeat_v2.py

@@ -0,0 +1,253 @@
+from elasticsearch import Elasticsearch, helpers
+from pymongo import MongoClient
+from datetime import datetime, timedelta
+import hashlib
+import numpy as np
+from tqdm import tqdm
+from datasketch import MinHash, MinHashLSH
+import multiprocessing
+import os
+import json
+from typing import List, Dict, Any, Iterator
+
+# 配置信息
+ES_HOST = "http://127.0.0.1:19800"
+ES_INDEX = "bidding"
+es_username = "jianyuGr"
+es_password = "we3g8glKfe#"
+MONGO_URI = "mongodb://172.20.45.129:27002/"
+MONGO_DB = "data_quality"
+MONGO_COLLECTION = "duplicate_records"
+BATCH_SIZE = 1000  # 批量处理大小
+THREADS = min(8, os.cpu_count())  # 并发线程数
+LSH_THRESHOLD = 0.5  # 相似度阈值
+
+# 初始化连接
+es = Elasticsearch(ES_HOST, http_auth=(es_username, es_password),timeout=30, max_retries=3)
+mongo_client = MongoClient(MONGO_URI)
+db = mongo_client[MONGO_DB]
+dup_collection = db[MONGO_COLLECTION]
+
+
+class ESDataLoader:
+    """Elasticsearch数据加载器"""
+
+    @staticmethod
+    def generate_date_ranges(start_date: datetime, end_date: datetime) -> Iterator[tuple]:
+        """生成按天划分的时间段"""
+        delta = end_date - start_date
+        for i in range(delta.days + 1):
+            day = start_date + timedelta(days=i)
+            yield (day, day + timedelta(days=1))
+
+    @staticmethod
+    def scroll_query(date_range: tuple, fields: List[str]) -> Iterator[Dict[str, Any]]:
+        """
+        滚动查询ES数据
+        参数:
+            date_range: (start_date, end_date) 时间范围
+            fields: 需要查询的字段列表
+        返回:
+            文档生成器
+        """
+        size =BATCH_SIZE
+        query = {
+            "query": {
+                "bool": {
+                    "must": [
+                        {"range": {"publishtime": {"gte": date_range[0],
+                                                   "lt": date_range[1]}}}
+                    ]
+                }
+            },
+            "_source": fields,
+            "size": size,
+            "sort": {"_id": "asc"}
+        }
+        search_after = None
+        while True:
+            if search_after:
+                query["search_after"] = search_after
+            response = es.search(index="bidding", body=query)
+            hits = response['hits']['hits']
+            if not hits:
+                break
+            for hit in hits:
+                yield {
+                    "_id": hit["_id"],
+                    **hit["_source"],
+                    "es_index": ES_INDEX,
+                    "retrieve_time": datetime.utcnow()
+                }
+            # 获取最后一条的 sort 值作为下一页起点
+            search_after = hits[-1]["sort"]
+
+
+class DeduplicationEngine:
+    """去重引擎核心类"""
+
+    def __init__(self):
+        self.lsh = MinHashLSH(threshold=LSH_THRESHOLD, num_perm=128)
+        self.seen_hashes = set()
+
+    @staticmethod
+    def generate_doc_hash(doc: Dict[str, Any]) -> str:
+        """生成文档唯一哈希"""
+        hash_str = f"{doc.get('projectname', '')}_{doc.get('projectcode', '')}_{doc.get('buyer', '')}_{doc.get('subtype', '')}"
+        return hashlib.md5(hash_str.encode('utf-8')).hexdigest()
+
+    @staticmethod
+    def create_minhash(doc: Dict[str, Any]) -> MinHash:
+        """为文档创建MinHash"""
+        mh = MinHash(num_perm=128)
+        text = f"{doc.get('projectname', '')}_{doc.get('projectcode', '')}_{doc.get('buyer', '')}_{doc.get('toptype', '')}"
+        for word in text.split()[:100]:  # 限制词数防止过长
+            mh.update(word.encode('utf-8'))
+        return mh
+
+    def process_document(self, doc: Dict[str, Any]) -> List[str]:
+        """
+        处理单个文档，返回匹配的重复文档ID列表
+        参数:
+            doc: 文档数据
+        返回:
+            重复文档ID列表
+        """
+        required_fields = ["title", "href", "projectname", "buyer","projectcode","toptype","subtype"]
+        for field in required_fields:
+            if field not in doc:
+                return []  # 或 log.warning(f"缺失字段: {field}")
+
+        doc_hash = self.generate_doc_hash(doc)
+
+        # 精确去重检查
+        if doc_hash in self.seen_hashes:
+            return []
+
+        self.seen_hashes.add(doc_hash)
+
+        # 相似度检查
+        mh = self.create_minhash(doc)
+        duplicate_ids = self.lsh.query(mh)
+
+        # 添加到索引
+        self.lsh.insert(doc["_id"], mh)
+
+        return duplicate_ids
+
+
+class DuplicateStorage:
+    """重复数据存储管理器"""
+
+    @staticmethod
+    def create_indexes():
+        """创建MongoDB索引"""
+        dup_collection.create_index([("base_doc_id", 1)])
+        dup_collection.create_index([("duplicate_ids", 1)])
+        dup_collection.create_index([("detection_time", -1)])
+
+    @staticmethod
+    def save_duplicates(base_doc: Dict[str, Any], duplicate_ids: List[str]):
+        """
+        保存重复记录到MongoDB
+        参数:
+            base_doc: 基准文档
+            duplicate_ids: 重复文档ID列表
+        """
+        if not duplicate_ids:
+            return
+
+        record = {
+            "base_doc_id": base_doc["_id"],
+            "base_doc": {
+                "title": base_doc["title"],
+                "buyer": base_doc["buyer"],
+                "publishtime": base_doc["publishtime"],
+                "href": base_doc["href"]
+            },
+            "duplicate_ids": duplicate_ids,
+            "detection_time": datetime.utcnow(),
+            "source_index": base_doc.get("es_index", ES_INDEX),
+            "hash": DeduplicationEngine.generate_doc_hash(base_doc)
+        }
+
+        dup_collection.insert_one(record)
+
+
+def parallel_deduplicate(chunk: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    并行处理数据块的去重
+    参数:
+        chunk: 文档数据块
+    返回:
+        重复记录列表
+    """
+    engine = DeduplicationEngine()
+    results = []
+
+    for doc in chunk:
+        dup_ids = engine.process_document(doc)
+        if dup_ids:
+            results.append({
+                "base_doc": doc,
+                "duplicate_ids": dup_ids
+            })
+
+    return results
+
+
+def main(days: int = 2):
+    """主处理流程"""
+    # 初始化MongoDB索引
+    DuplicateStorage.create_indexes()
+
+    # 准备日期范围
+    end_date = datetime.utcnow()
+    start_date = end_date - timedelta(days=days)
+
+
+    print(f"开始处理 {start_date} 至 {end_date} 的数据...")
+    total_duplicates = 0
+
+    # 按天处理数据
+    for day_start, day_end in tqdm(ESDataLoader.generate_date_ranges(start_date, end_date),desc="处理每日数据"):
+        # 转换为时间戳（秒级）
+        day_end = int(day_end.timestamp())
+        day_start = int(day_start.timestamp())
+        # 加载数据
+        records = list(ESDataLoader.scroll_query(
+            date_range=(day_start, day_end),
+            fields=["title", "href", "publishtime", "buyer", "projectname"]
+        ))
+
+        if not records:
+            continue
+
+        # 数据分块
+        chunks = np.array_split(records, THREADS)
+
+        # 并行处理
+        with multiprocessing.Pool(THREADS) as pool:
+            results = pool.map(parallel_deduplicate, chunks)
+
+            # 保存结果
+            for batch in results:
+                for item in batch:
+                    DuplicateStorage.save_duplicates(
+                        item["base_doc"],
+                        item["duplicate_ids"]
+                    )
+                    total_duplicates += len(item["duplicate_ids"])
+
+    print(f"处理完成，共发现 {total_duplicates} 条重复记录")
+    print(f"结果已保存到MongoDB: {MONGO_DB}.{MONGO_COLLECTION}")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="ES数据去重工具")
+    parser.add_argument("--days", type=int, default=2, help="检测天数范围")
+    args = parser.parse_args()
+
+    main(days=args.days)

tools/标讯数据重复率分析/repeat_v3.py

@@ -0,0 +1,207 @@
+from elasticsearch import Elasticsearch
+from pymongo import MongoClient
+from datetime import datetime, timedelta
+from lib.detail_url import SE3
+import multiprocessing
+
+from typing import List, Dict, Any, Iterator
+from difflib import SequenceMatcher
+from collections import defaultdict
+from sentence_transformers import SentenceTransformer,util
+import os
+
+
+os.environ['HTTP_PROXY'] = 'http://127.0.0.1:7897'
+os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:7897'
+# 配置信息
+ES_HOST = "http://127.0.0.1:19800"
+ES_INDEX = "bidding"
+es_username = "jianyuGr"
+es_password = "we3g8glKfe#"
+MONGO_URI = "mongodb://172.20.45.129:27002/"
+MONGO_DB = "data_quality"
+MONGO_COLLECTION = "duplicate_records"
+BATCH_SIZE = 1000
+THREADS = min(8, os.cpu_count())
+
+es = Elasticsearch(ES_HOST, http_auth=(es_username, es_password), timeout=30)
+mongo_client = MongoClient(MONGO_URI)
+db = mongo_client[MONGO_DB]
+collection = db[MONGO_COLLECTION]
+
+model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") # ✅ 轻量模型
+
+
+def similarity(a: str, b: str) -> float:
+    return SequenceMatcher(None, a, b).ratio()
+
+def embedding_sim(a: str, b: str) -> float:
+    emb1 = model.encode(a, convert_to_tensor=True)
+    emb2 = model.encode(b, convert_to_tensor=True)
+    return float(util.cos_sim(emb1, emb2)[0][0])
+
+def match_stage(doc1: Dict, doc2: Dict) -> int:
+    title1 = doc1.get("title", "")
+    title2 = doc2.get("title", "")
+    projectname1 = doc1.get("projectname", "")
+    projectname2 = doc2.get("projectname", "")
+    buyer1 = doc1.get("buyer", "")
+    buyer2 = doc2.get("buyer", "")
+    code1 = doc1.get("projectcode", "")
+    code2 = doc2.get("projectcode", "")
+    subtype1 = doc1.get("subtype", "")
+    subtype2 = doc2.get("subtype", "")
+    toptype1 = doc1.get("toptype", "")
+    toptype2 = doc2.get("toptype", "")
+    win1 = doc1.get("s_winner", "")
+    win2 = doc2.get("s_winner", "")
+
+    def same(x, y): return x and y and x == y
+    def include(x, y): return x and y and (x in y or y in x)
+    sim = similarity(title1, title2)
+    emb_sim = embedding_sim(title1, title2)
+
+    if (same(title1, title2) or same(projectname1, projectname2)) and same(buyer1, buyer2) and same(code1, code2) and same(subtype1, subtype2):
+        return 1
+    if (same(title1, title2) or same(projectname1, projectname2)) and same(buyer1, buyer2) and same(code1, code2) and same(toptype1, toptype2):
+        return 2
+    if (same(title1, title2) or same(projectname1, projectname2)) and same(buyer1, buyer2) and same(code1, code2):
+        return 3
+    if same(buyer1, buyer2) and same(code1, code2) and same(subtype1, subtype2):
+        return 4
+    if (include(title1, title2) or include(projectname1, projectname2)) and same(buyer1, buyer2) and same(code1, code2):
+        return 5
+    if emb_sim >= 0.98 and same(buyer1, buyer2) and same(code1, code2):
+        return 6
+    if emb_sim >= 0.98 and same(win1, win2) and same(code1, code2):
+        return 7
+    if emb_sim >= 0.98 and code1 == code2 and (toptype1 in ["拟建", "采购意向"] or toptype2 in ["拟建", "采购意向"]):
+        return 8
+    if emb_sim >= 0.98 and same(code1, code2):
+        return 9
+    if emb_sim >= 0.9 and same(code1, code2):
+        return 10
+    if emb_sim >= 0.8 and same(code1, code2):
+        return 11
+    return 0
+
+def search_documents(start, end, max_docs=100) -> List[Dict[str, Any]]:
+    query = {
+        "query": {
+            "range": {
+                "publishtime": {
+                    "gte": start,
+                    "lt": end
+                }
+            }
+        },
+        "size": BATCH_SIZE,
+        "sort": [{"publishtime": "asc"}, {"_id": "asc"}]
+    }
+    docs = []
+    search_after = None
+    page = 0
+    while True:
+        if search_after:
+            query["search_after"] = search_after
+        res = es.search(index=ES_INDEX, body=query)
+        hits = res["hits"]["hits"]
+        print(f"第 {page + 1} 页，返回 {len(hits)} 条")
+        if not hits:
+            print("已没有更多数据，跳出循环。")
+            break
+
+        for hit in hits:
+            doc = hit["_source"]
+            doc["_id"] = hit["_id"]
+            docs.append(doc
+                        )
+            # ✅ 限制返回条数
+            if len(docs) >= max_docs:
+                print(f"已达到最大限制：{max_docs} 条，提前终止。")
+                return docs
+
+        search_after = hits[-1]["sort"]  # 正确设置 search_after
+        page += 1
+    print(f"共获取 {len(docs)} 条文档")
+    return docs
+
+
+def process_group(group_docs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    results = []
+    n = len(group_docs)
+    matched = set()
+    for i in range(n):
+        d1 = group_docs[i]
+        _id1 = d1["_id"]
+        for j in range(i + 1, n):
+            d2 = group_docs[j]
+            pair = tuple(sorted((d1["_id"], d2["_id"])))
+            if pair in matched:
+                continue
+            _id2 =d2["_id"]
+            flag = match_stage(d1, d2)
+            if flag > 0:
+                matched.add(pair)
+                results.append({
+                    "base_doc_id": _id1,
+                    "base_doc_projectname":d1["projectname"],
+                    "base_doc_href": d1["href"],
+                    'base_doc_jybxhref': f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(_id1)}.html?appid",
+                    "duplicate_doc_id": _id2,
+                    "duplicate_doc_projectname": d2["projectname"],
+                    "duplicate_doc_href": d2["href"],
+                    'duplicate_doc_jybxhref': f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(_id2)}.html?appid",
+                    "flag": flag
+                })
+    return results
+
+def detect_duplicates(docs: List[Dict[str, Any]]):
+    buckets = defaultdict(list)
+    for doc in docs:
+        group_key = f"{doc.get('projectcode', '')}_{doc.get('projectname', '')}_{doc.get('buyer', '')}_{doc.get('toptype', '')}"
+        buckets[group_key].append(doc)
+
+    for k, v in buckets.items():
+        print(f"Group: {k}, Count: {len(v)}")
+        print("样例文档标题：", [doc.get("title", "") for doc in v[:3]])  # 每组最多打印前三条标题
+
+    print(f"共 {len(buckets)} 个分组")
+    all_results = []
+    for group in buckets.values():
+
+        if len(group) < 2:
+            continue  # 组内少于2条，跳过
+        # res = process_group(group)
+        # all_results.extend(res)
+
+
+    with multiprocessing.Pool(THREADS) as pool:
+        all_results = pool.map(process_group, buckets.values())
+        all_records = [r for batch in all_results for r in batch]
+        pool.close()  # 显式关闭
+        pool.join()  # 等待所有子进程结束
+
+    print(f"发现重复记录 {len(all_records)} 对")
+
+    for i in range(0, len(all_records), 1000):
+        batch = all_records[i:i + 1000]
+        collection.insert_many(batch)
+        print(f"已成功插入 {len(batch)} 条记录到 MongoDB")
+
+def main():
+    # 准备日期范围
+
+    end = datetime.utcnow()
+    start = end - timedelta(days=1)
+    day_end = int(end.timestamp())
+    day_start = int(start.timestamp())
+    print(f"处理时间段: {start} - {end}")
+    docs = search_documents(day_start, day_end,max_docs=100)
+    print(f"获取文档数：{len(docs)}")
+    detect_duplicates(docs)
+    print("去重完成")
+
+
+if __name__ == "__main__":
+    main()

tools/标讯数据重复率分析/repeat_v4.py

@@ -0,0 +1,268 @@
+from elasticsearch import Elasticsearch
+from pymongo import MongoClient
+from datetime import  timedelta
+from lib.detail_url import SE3
+import multiprocessing
+from datetime import datetime
+from typing import List, Dict, Any
+from difflib import SequenceMatcher
+from collections import defaultdict
+from sentence_transformers import SentenceTransformer,util
+import os
+
+
+os.environ['HTTP_PROXY'] = 'http://127.0.0.1:7897'
+os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:7897'
+# 配置信息
+ES_HOST = "http://127.0.0.1:19800"
+ES_INDEX = "bidding"
+es_username = "jianyuGr"
+es_password = "we3g8glKfe#"
+MONGO_URI = "mongodb://172.20.45.129:27002/"
+MONGO_DB = "data_quality"
+MONGO_COLLECTION = "duplicate_records"
+BATCH_SIZE = 1000
+THREADS = min(8, os.cpu_count())
+
+MONGO_URI_source = "mongodb://localhost:27017"
+MONGO_DB_source = "your_db"
+MONGO_COLLECTION_source = "your_collection"
+
+es = Elasticsearch(ES_HOST, http_auth=(es_username, es_password), timeout=30)
+mongo_client = MongoClient(MONGO_URI)
+db = mongo_client[MONGO_DB]
+collection = db[MONGO_COLLECTION]
+
+mongo_client_source = MongoClient(MONGO_URI_source)
+collection_source = mongo_client[MONGO_DB_source][MONGO_COLLECTION_source]
+
+model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") # ✅ 轻量模型
+
+
+def similarity(a: str, b: str) -> float:
+    return SequenceMatcher(None, a, b).ratio()
+
+def embedding_sim(a: str, b: str) -> float:
+    emb1 = model.encode(a, convert_to_tensor=True)
+    emb2 = model.encode(b, convert_to_tensor=True)
+    return float(util.cos_sim(emb1, emb2)[0][0])
+
+def get_max_embedding_sim(d1, d2):
+    sim_title = embedding_sim(d1.get("title", ""), d2.get("title", ""))
+    sim_projectname = embedding_sim(d1.get("projectname", ""), d2.get("projectname", ""))
+    return max(sim_title, sim_projectname)
+# 工具函数
+def same(x, y):
+    return x and y and x == y
+
+def include(x, y):
+    return x and y and (x in y or y in x)
+
+def rule_1(d1, d2):
+    return same(d1.get("href"), d2.get("href"))  \
+
+
+def rule_2(d1, d2):
+    return same(d1.get("title"), d2.get("title"))  \
+        and same(d1.get("buyer"), d2.get("buyer")) \
+        and same(d1.get("projectcode"), d2.get("projectcode")) \
+        and same(d1.get("subtype"), d2.get("subtype"))
+
+def rule_3(d1, d2):
+    return (same(d1.get("title"), d2.get("title")) or same(d1.get("projectname"), d2.get("projectname"))) \
+        and same(d1.get("buyer"), d2.get("buyer")) \
+        and same(d1.get("projectcode"), d2.get("projectcode")) \
+        and same(d1.get("toptype"), d2.get("toptype"))
+
+def rule_4(d1, d2):
+    return (same(d1.get("title"), d2.get("title")) or same(d1.get("projectname"), d2.get("projectname"))) \
+        and same(d1.get("buyer"), d2.get("buyer")) \
+        and same(d1.get("projectcode"), d2.get("projectcode"))
+
+def rule_5(d1, d2):
+    return same(d1.get("buyer"), d2.get("buyer")) \
+        and same(d1.get("projectcode"), d2.get("projectcode")) \
+        and same(d1.get("subtype"), d2.get("subtype"))
+
+def rule_6(d1, d2):
+    return (include(d1.get("title"), d2.get("title")) or include(d1.get("projectname"), d2.get("projectname"))) \
+        and same(d1.get("buyer"), d2.get("buyer")) \
+        and same(d1.get("projectcode"), d2.get("projectcode"))
+
+def rule_7(d1, d2):
+    emb_sim = get_max_embedding_sim(d1, d2)
+    return emb_sim >= 0.98 and same(d1.get("buyer"), d2.get("buyer")) and same(d1.get("projectcode"), d2.get("projectcode"))
+
+def rule_8(d1, d2):
+    emb_sim = get_max_embedding_sim(d1, d2)
+    return emb_sim >= 0.98 and same(d1.get("s_winner"), d2.get("s_winner")) and same(d1.get("projectcode"), d2.get("projectcode"))
+
+def rule_9(d1, d2):
+    emb_sim = get_max_embedding_sim(d1, d2)
+    return emb_sim >= 0.98 and same(d1.get("projectcode"), d2.get("projectcode")) and \
+           (d1.get("toptype") in ["拟建", "采购意向"] or d2.get("toptype") in ["拟建", "采购意向"])
+
+def rule_10(d1, d2):
+    emb_sim = get_max_embedding_sim(d1, d2)
+    return emb_sim >= 0.98 and same(d1.get("projectcode"), d2.get("projectcode"))
+
+def rule_11(d1, d2):
+    return d1.get("emb_sim", 0) >= 0.9 and same(d1.get("projectcode"), d2.get("projectcode"))
+
+def rule_12(d1, d2):
+    return d1.get("emb_sim", 0) >= 0.8 and same(d1.get("projectcode"), d2.get("projectcode"))
+
+rules = [
+    (1, rule_1), (2, rule_2), (3, rule_3), (4, rule_4), (5, rule_5),
+    (6, rule_6), (7, rule_7), (8, rule_8), (9, rule_9), (10, rule_10), (11, rule_11), (12, rule_12)
+]
+
+def process_group(docs: List[Dict]) -> List[Dict]:
+    n = len(docs)
+    matched_ids = set()
+    results = []
+
+    for flag, rule in rules:
+        for i in range(n):
+            d1 = docs[i]
+            _id1 = d1["_id"]
+            if _id1 in matched_ids:
+                continue
+            for j in range(i + 1, n):
+                d2 = docs[j]
+                _id2 = d2["_id"]
+                if _id2 in matched_ids:
+                    continue
+                if rule(d1, d2):
+                    results.append({
+                        "base_doc_id": _id1,
+                        "base_doc_projectname": d1["projectname"],
+                        "base_doc_href": d1["href"],
+                        'base_doc_jybxhref': f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(_id1)}.html?appid",
+                        "duplicate_doc_id": _id2,
+                        "duplicate_doc_projectname": d2["projectname"],
+                        "duplicate_doc_href": d2["href"],
+                        'duplicate_doc_jybxhref': f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(_id2)}.html?appid",
+                        "flag": flag
+                    })
+                    matched_ids.add(d1["_id"])
+                    matched_ids.add(d2["_id"])
+        print(f"第 {flag} 批完成，累计匹配 {len(matched_ids)} 条")
+
+    return results
+
+
+def search_documents(start, end, max_docs=100) -> List[Dict[str, Any]]:
+    query = {
+        "query": {
+            "range": {
+                "publishtime": {
+                    "gte": start,
+                    "lt": end
+                }
+            }
+        },
+        "size": BATCH_SIZE,
+        "sort": [{"publishtime": "asc"}, {"_id": "asc"}]
+    }
+    docs = []
+    search_after = None
+    page = 0
+    while True:
+        if search_after:
+            query["search_after"] = search_after
+        res = es.search(index=ES_INDEX, body=query)
+        hits = res["hits"]["hits"]
+        print(f"第 {page + 1} 页，返回 {len(hits)} 条")
+        if not hits:
+            print("已没有更多数据，跳出循环。")
+            break
+
+        for hit in hits:
+            doc = hit["_source"]
+            doc["_id"] = hit["_id"]
+            docs.append(doc)
+            # ✅ 限制返回条数
+            if len(docs) >= max_docs:
+                print(f"已达到最大限制：{max_docs} 条，提前终止。")
+                return docs
+
+        search_after = hits[-1]["sort"]  # 正确设置 search_after
+        page += 1
+    print(f"共获取 {len(docs)} 条文档")
+    return docs
+
+def search_documents_mongo(start, end, max_docs=1000) -> List[Dict[str, Any]]:
+    """
+    从 MongoDB 查询指定时间段内的数据
+    """
+    query = {
+        "publishtime": {
+            "$gte": start,
+            "$lt": end
+        }
+    }
+
+    cursor = collection.find(query).sort([
+        ("publishtime", 1),
+        ("_id", 1)
+    ]).limit(max_docs)
+
+    docs = []
+    for doc in cursor:
+        doc["_id"] = str(doc["_id"])  # 统一处理为字符串 ID
+        docs.append(doc)
+
+    print(f"共获取 {len(docs)} 条文档")
+    return docs
+
+
+def detect_duplicates(docs: List[Dict[str, Any]]):
+    buckets = defaultdict(list)
+    for doc in docs:
+        group_key = f"{doc.get('projectcode', '')}_{doc.get('projectname', '')}_{doc.get('buyer', '')}_{doc.get('toptype', '')}"
+        buckets[group_key].append(doc)
+
+    for k, v in buckets.items():
+        print(f"Group: {k}, Count: {len(v)}")
+        print("样例文档标题：", [doc.get("title", "") for doc in v[:3]])  # 每组最多打印前三条标题
+
+    print(f"共 {len(buckets)} 个分组")
+    all_results = []
+    for group in buckets.values():
+
+        if len(group) < 2:
+            continue  # 组内少于2条，跳过
+        # res = process_group(group)
+        # all_results.extend(res)
+
+
+    with multiprocessing.Pool(THREADS) as pool:
+        all_results = pool.map(process_group, buckets.values())
+        all_records = [r for batch in all_results for r in batch]
+        pool.close()  # 显式关闭
+        pool.join()  # 等待所有子进程结束
+
+    print(f"发现重复记录 {len(all_records)} 对")
+
+    for i in range(0, len(all_records), 1000):
+        batch = all_records[i:i + 1000]
+        collection.insert_many(batch)
+        print(f"已成功插入 {len(batch)} 条记录到 MongoDB")
+
+def main():
+    # 准备日期范围
+
+    end = datetime.utcnow()
+    start = end - timedelta(days=1)
+    day_end = int(end.timestamp())
+    day_start = int(start.timestamp())
+    print(f"处理时间段: {start} - {end}")
+    docs = search_documents(day_start, day_end,max_docs=1000)
+    print(f"获取文档数：{len(docs)}")
+    detect_duplicates(docs)
+    print("重复数据存库完成")
+
+
+if __name__ == "__main__":
+    main()

tools/标讯数据重复率分析/repeat_v5.py

@@ -0,0 +1,75 @@
+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
+#判断互为包含关系
+def is_mutually_contained(str1, str2):
+    """判断两个字符串是否互为包含关系"""
+    return str1 in str2 or str2 in str1
+# 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"]
+client = MongoClient('mongodb://127.0.0.1:27087/',unicode_decode_error_handler="ignore", directConnection=True)  # 修改为你的连接地址
+db = client["jyqyfw"]                       # 替换为你的数据库名
+col = db["xzh_20250714"]                    # 替换为你的集合名
+col1 = db["xzh_20250714_1"]
+
+documents = list(col.find({ "repeat" : { "$ne" : True } , "flag" : { "$exists" : False } , "tag" : { "$exists" : False }}).sort("_id",1))
+documents1=list(col1.find())
+for i in range(len(documents)):
+    for j in range(len(documents1)):
+        i_title=documents[i].get("title", "")
+        j_title = documents1[j].get("title", "")
+        i_projectname=documents[i].get("projectname", "")
+        j_projectname = documents1[j].get("projectname", "")
+        radio1=calculate_repeat_rate(i_title,j_title)
+        radio2=calculate_repeat_rate(i_projectname,j_projectname)
+        i_projectcode=documents[i].get("projectcode", "")
+        j_projectcode = documents1[j].get("projectcode", "")
+        i_buyer=documents[i].get("buyer", "")
+        j_buyer=documents1[j].get("buyer", "")
+        i_s_winner = documents[i].get("s_winner", "")
+        j_s_winner = documents1[j].get("s_winner", "")
+        i_bidamount = documents[i].get("bidamount", "")
+        j_bidamount = documents1[j].get("bidamount", "")
+        i_toptype = documents[i].get("toptype", "")
+        j_toptype = documents1[j].get("toptype", "")
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_buyer==j_buyer and i_projectcode==j_projectcode and i_bidamount==j_bidamount:
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_buyer==j_buyer and i_projectcode==j_projectcode :
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_s_winner==j_s_winner and i_projectcode==j_projectcode and i_bidamount==j_bidamount:
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_s_winner==j_s_winner and i_projectcode==j_projectcode :
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_toptype in ("拟建","采购意向")  and j_toptype in ("拟建","采购意向") :
+        # if (calculate_repeat_rate(i_title , j_title) >=0.98 or calculate_repeat_rate(j_projectname , j_projectname)>=0.98 ) and i_projectcode==j_projectcode:
+        if (calculate_repeat_rate(i_title , j_title) >=0.8 or calculate_repeat_rate(j_projectname , j_projectname)>=0.8 ) and i_projectcode==j_projectcode:
+        # if (i_title ==j_title or i_projectname == j_projectname) and i_projectcode==j_projectcode and i_buyer==j_buyer and i_bidamount==j_bidamount:
+        # if (i_title ==j_title or i_projectname == j_projectname) and i_projectcode==j_projectcode and i_buyer==j_buyer :
+        # if  i_projectcode==j_projectcode and i_buyer==j_buyer and i_bidamount==j_bidamount:
+        # if (is_mutually_contained(i_title , j_title) or is_mutually_contained(j_projectname , j_projectname)) and i_projectcode==j_projectcode and i_buyer==j_buyer and i_bidamount==j_bidamount:
+        # if (is_mutually_contained(i_title , j_title) or is_mutually_contained(j_projectname , j_projectname)) and i_projectcode==j_projectcode and i_buyer==j_buyer :
+            # 更新文档i
+            col.update_one(
+                {"_id": documents[i]["_id"]},
+                {"$set": {"tag": 12}}
+            )
+            # 更新文档j
+            col1.update_one(
+                {"_id": documents1[j]["_id"]},
+                {"$set": {"tag": 12}}
+            )
+
+            # 更新内存中的文档状态，避免重复处理
+            documents[i]["tag"] = 12
+            documents1[j]["tag"] = 12
+

tools/标讯数据重复率分析/test.py

@@ -1,8 +1,302 @@
-import Levenshtein
+import pymongo
+from pymongo import MongoClient
 
-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"
+# 给定的ID列表
+given_ids =[
+    "686b2368d5d8e4081f88f0fd",
+    "686b2fa2d5d8e4081f8929ca",
+    "686b4a70d5d8e4081f89b660",
+    "686cdf8ed5d8e4081f8f18fa",
+    "6870a794d5d8e4081f9db86d",
+    "68706689d5d8e4081f9ca2da",
+    "68707995d5d8e4081f9cfc02",
+    "686c2bf2d5d8e4081f8c8c36",
+    "686b7f02d5d8e4081f8a5923",
+    "686b8d8cd5d8e4081f8aac59",
+    "686b9038d5d8e4081f8abea2",
+    "686b8f3cd5d8e4081f8ab792",
+    "686b8dd5d5d8e4081f8aae4b",
+    "686b8ff8d5d8e4081f8abbbb",
+    "686b8be1d5d8e4081f8aa000",
+    "686bed2fd5d8e4081f8bc6bb",
+    "686b9a8bd5d8e4081f8b0025",
+    "686bed2fd5d8e4081f8bc6ba",
+    "686bb2bdd5d8e4081f8b68f7",
+    "686bed2fd5d8e4081f8bc6b8",
+    "686ba629d5d8e4081f8b3dc5",
+    "68708a52d5d8e4081f9d5056",
+    "686ba731d5d8e4081f8b4113",
+    "686ba707d5d8e4081f8b3ff0",
+    "686d0b5ad5d8e4081f9035fc",
+    "686c867dd5d8e4081f8d66dd",
+    "686df806d5d8e4081f93d9df",
+    "686dee62d5d8e4081f935987",
+    "686c942cd5d8e4081f8db7fb",
+    "686cb548d5d8e4081f8e29c1",
+    "686cbac5d5d8e4081f8e3c03",
+    "686c9ac2d5d8e4081f8dd970",
+    "686cbfd2d5d8e4081f8e4b7f",
+    "686ca31fd5d8e4081f8df918",
+    "686ceeaad5d8e4081f8f95ba",
+    "686ce491d5d8e4081f8f44de",
+    "686cd840d5d8e4081f8ed9e7",
+    "686ceaf2d5d8e4081f8f7c30",
+    "686cef83d5d8e4081f8f9e66",
+    "686d07e5d5d8e4081f9027cb",
+    "686cf6edd5d8e4081f8fd336",
+    "686cfaecd5d8e4081f8fea46",
+    "686cfcb7d5d8e4081f8ff442",
+    "686cfa15d5d8e4081f8fe74f",
+    "686d047ad5d8e4081f901a23",
+    "686d0987d5d8e4081f90305c",
+    "686d1fdad5d8e4081f907818",
+    "686d26fcd5d8e4081f908b6b",
+    "686d415ed5d8e4081f90d2ba",
+    "686cd89dd5d8e4081f8edb67",
+    "686dc7f6d5d8e4081f922660",
+    "686dc258d5d8e4081f92100d",
+    "686dc1e5d5d8e4081f920d80",
+    "686de2f4d5d8e4081f92bee3",
+    "686de1e2d5d8e4081f92b534",
+    "686ecd95d5d8e4081f97be2d",
+    "686e078ad5d8e4081f947919",
+    "686e036dd5d8e4081f945bc2",
+    "686e1d1bd5d8e4081f951376",
+    "686e0840d5d8e4081f948687",
+    "686e1f57d5d8e4081f952002",
+    "686e1873d5d8e4081f94f588",
+    "686df18ed5d8e4081f93817c",
+    "686e1f8cd5d8e4081f9521b3",
+    "686e341cd5d8e4081f95a7de",
+    "686e47ffd5d8e4081f95f170",
+    "686e5f3bd5d8e4081f9679df",
+    "686e603ed5d8e4081f969cc5",
+    "686e5105d5d8e4081f96027a",
+    "686e608dd5d8e4081f96a56e",
+    "686e5e90d5d8e4081f966092",
+    "686e6c21d5d8e4081f96cac0",
+    "686e60e0d5d8e4081f96ae81",
+    "686f2c34d5d8e4081f989355",
+    "686f30b3d5d8e4081f98ad77",
+    "686f487ad5d8e4081f992d29",
+    "686f3c2dd5d8e4081f98eb39",
+    "686f6888d5d8e4081f998a55",
+    "686f6889d5d8e4081f998ad5",
+    "686febf5d5d8e4081f9bdb1f",
+    "686f7217d5d8e4081f99c2b9",
+    "686f84fad5d8e4081f9a2b4f",
+    "686f8d26d5d8e4081f9a5f0a",
+    "686f9063d5d8e4081f9a70dd",
+    "686f9d7fd5d8e4081f9ae5a9",
+    "686f6bbbd5d8e4081f999fc8",
+    "686f78fdd5d8e4081f99e830",
+    "686fb8edd5d8e4081f9b5744",
+    "686b5f02d5d8e4081f89eb37",
+    "686b4f03d5d8e4081f89c6eb",
+    "68707a30d5d8e4081f9cfeb7",
+    "686b6dd5d5d8e4081f8a0f1e",
+    "686b7f2cd5d8e4081f8a5a54",
+    "686b902ed5d8e4081f8abe54",
+    "686b8b29d5d8e4081f8a9d91",
+    "686b8f31d5d8e4081f8ab740",
+    "686b7f89d5d8e4081f8a5bff",
+    "686ba38fd5d8e4081f8b314b",
+    "686e0830d5d8e4081f948573",
+    "686f34e2d5d8e4081f98c244",
+    "686badfcd5d8e4081f8b5b1b",
+    "686cfc19d5d8e4081f8ff02c",
+    "68705a13d5d8e4081f9c83db",
+    "686bf0c7d5d8e4081f8bdbb3",
+    "686bf55bd5d8e4081f8c00e0",
+    "68706844d5d8e4081f9cab24",
+    "687072bcd5d8e4081f9cdabc",
+    "68707166d5d8e4081f9cd41d",
+    "68707cdcd5d8e4081f9d0adc",
+    "68707eafd5d8e4081f9d137e",
+    "6870a8f8d5d8e4081f9dbc07",
+    "6870b983d5d8e4081f9df38c",
+    "686f0aaad5d8e4081f97fd13",
+    "686b3a6dd5d8e4081f8962e1",
+    "686b3971d5d8e4081f895bb0",
+    "686c2bcad5d8e4081f8c8c10",
+    "686caf44d5d8e4081f8e19e3",
+    "686b524cd5d8e4081f89d03c",
+    "686b4be1d5d8e4081f89bb1e",
+    "686ccca9d5d8e4081f8e8790",
+    "686cd92ad5d8e4081f8ee2cd",
+    "686e24d7d5d8e4081f95424e",
+    "686cd96cd5d8e4081f8ee619",
+    "686d0d42d5d8e4081f904010",
+    "686cc915d5d8e4081f8e772c",
+    "686e3388d5d8e4081f95a330",
+    "686cdd72d5d8e4081f8f0588",
+    "686ce7f4d5d8e4081f8f613d",
+    "686ce640d5d8e4081f8f51df",
+    "686ce3ded5d8e4081f8f3ef9",
+    "686d55ecd5d8e4081f9155c1",
+    "686cebfed5d8e4081f8f829b",
+    "686cef3ad5d8e4081f8f9cd8",
+    "686cebd3d5d8e4081f8f80ba",
+    "686cedafd5d8e4081f8f9006",
+    "686cebc8d5d8e4081f8f8067",
+    "686cf6d3d5d8e4081f8fd187",
+    "686d0cf8d5d8e4081f903da0",
+    "686c7d3ed5d8e4081f8d36c8",
+    "686c8b22d5d8e4081f8d8428",
+    "686c8abdd5d8e4081f8d8342",
+    "686ca7dcd5d8e4081f8e0634",
+    "686c91d2d5d8e4081f8daae0",
+    "686c98abd5d8e4081f8dcdcf",
+    "686cc4add5d8e4081f8e630c",
+    "686df3aed5d8e4081f93aa40",
+    "686cc46fd5d8e4081f8e61f3",
+    "686cc432d5d8e4081f8e6189",
+    "686cc077d5d8e4081f8e4f2e",
+    "686dc58fd5d8e4081f921c6c",
+    "686dc63cd5d8e4081f921f32",
+    "686dc651d5d8e4081f921fc4",
+    "686dd0ced5d8e4081f924bb2",
+    "686ddeacd5d8e4081f928b8b",
+    "686dfd29d5d8e4081f941386",
+    "686e057dd5d8e4081f94649b",
+    "686e05f9d5d8e4081f94664a",
+    "686e015ed5d8e4081f943ff3",
+    "686e1834d5d8e4081f94f397",
+    "686e7d5cd5d8e4081f96e990",
+    "686e1ebad5d8e4081f951bd3",
+    "686e1874d5d8e4081f94f5f9",
+    "686e1501d5d8e4081f94de1a",
+    "686e2632d5d8e4081f954bf0",
+    "686e23d4d5d8e4081f953714",
+    "686e3a94d5d8e4081f95d2ae",
+    "686e3ff1d5d8e4081f95dea2",
+    "686e3427d5d8e4081f95a836",
+    "686e5a64d5d8e4081f9608e8",
+    "686e3267d5d8e4081f959681",
+    "686e6e6ed5d8e4081f96cf99",
+    "686e3822d5d8e4081f95c913",
+    "686e5c1fd5d8e4081f960fe2",
+    "686e4bb9d5d8e4081f95f9fe",
+    "686f3d5ad5d8e4081f98f029",
+    "686e5d9bd5d8e4081f963078",
+    "686e5f89d5d8e4081f9684c8",
+    "686e6081d5d8e4081f96a406",
+    "686e6263d5d8e4081f96b7be",
+    "686f8081d5d8e4081f9a1106",
+    "686f8de7d5d8e4081f9a6323",
+    "686f9242d5d8e4081f9a83c7",
+    "686f9c0cd5d8e4081f9adb8e",
+    "686f9024d5d8e4081f9a6f52",
+    "686fa059d5d8e4081f9af625",
+    "686f9f00d5d8e4081f9aee5a",
+    "686f9ab5d5d8e4081f9ad200",
+    "686fdbaed5d8e4081f9b920c",
+    "686f7bb6d5d8e4081f99f61e",
+    "686f852dd5d8e4081f9a2ce0",
+    "686f095cd5d8e4081f97fb32",
+    "686f6457d5d8e4081f997687",
+    "686f1918d5d8e4081f982f6a",
+    "686f33ddd5d8e4081f98bcc4",
+    "686f696ed5d8e4081f998f8a",
+    "686f7bd5d5d8e4081f99f6c1",
+    "68703b1ed5d8e4081f9c643e",
+    "6870624bd5d8e4081f9c92c6",
+    "68707082d5d8e4081f9ccf65",
+    "687087a2d5d8e4081f9d427c",
+    "68707f92d5d8e4081f9d16f6",
+    "687087a1d5d8e4081f9d4266",
+    "68708ce6d5d8e4081f9d5c6c",
+    "6870a64cd5d8e4081f9db464",
+    "6870b517d5d8e4081f9de26f",
+    "6870b135d5d8e4081f9dd362",
+    "6870b616d5d8e4081f9de544",
+    "6870c9f9d5d8e4081f9e5923",
+    "6870b80fd5d8e4081f9ded31",
+    "685a0503c936757aa185ddc8",
+    "6853e7f9c936757aa17116e8",
+    "6848e6775f834436f0930945",
+    "685a77b6c936757aa188ab00",
+    "68625899d5d8e4081f711ab5",
+    "686390afd5d8e4081f757cec",
+    "67d93d663309c0998bbde35e",
+    "685cbe51c936757aa1935f2a",
+    "686e2454d5d8e4081f953b7c",
+    "687095fcd5d8e4081f9d8ecd",
+    "686cbd52d5d8e4081f8e4379",
+    "686ce2f2d5d8e4081f8f354d",
+    "686e0795d5d8e4081f947a2d",
+    "686d0d02d5d8e4081f903db8",
+    "686c7650d5d8e4081f8d14cc",
+    "686c7b31d5d8e4081f8d2d1b",
+    "686b8ef4d5d8e4081f8ab63a",
+    "686b4c5cd5d8e4081f89bcac",
+    "686e602dd5d8e4081f9699f7",
+    "686e1bd3d5d8e4081f950ba7",
+    "686e1f96d5d8e4081f9521f9",
+    "686b8d50d5d8e4081f8aa94f",
+    "686cd415d5d8e4081f8eb79b",
+    "6870b10dd5d8e4081f9dd2e4",
+    "686b6603d5d8e4081f89f97a",
+    "686f6eaed5d8e4081f99b136",
+    "686d44a6d5d8e4081f90fa49",
+    "686b878ed5d8e4081f8a87fb",
+    "686b3b29d5d8e4081f8966d9",
+    "686b8907d5d8e4081f8a8fc8",
+    "686e8ac0d5d8e4081f97020f",
+    "686cdae6d5d8e4081f8ef1c6",
+    "686f958fd5d8e4081f9aa40d",
+    "686b722fd5d8e4081f8a1dcb",
+    "686cdbd9d5d8e4081f8ef923",
+    "686c899fd5d8e4081f8d7a27",
+    "686cc90bd5d8e4081f8e76e8",
+    "686de12fd5d8e4081f92ac9c",
+    "686e5fd8d5d8e4081f968fac",
+    "68703331d5d8e4081f9c5e65",
+    "687083f7d5d8e4081f9d2e4e",
+    "686b2c73d5d8e4081f891c43",
+    "6853f16bc936757aa17150b3",
+    "686e6011d5d8e4081f969787",
+    "686f5effd5d8e4081f996310",
+    "686e1339d5d8e4081f94d6aa",
+    "685b00e6c936757aa18b1c5b",
+    "6863407ed5d8e4081f73d8aa",
+    "686dd38bd5d8e4081f92589a",
+    "68626764d5d8e4081f71983f"
+]
 
-distance = Levenshtein.distance(str1, str2)
-similarity = 1 - distance / max(len(str1), len(str2))
-print(f"Similarity: {similarity}")
+# 连接MongoDB数据库
+client = MongoClient('mongodb://127.0.0.1:27087/',unicode_decode_error_handler="ignore", directConnection=True)  # 修改为你的连接地址
+db = client["jyqyfw"]                       # 替换为你的数据库名
+col = db["xzh_20250714"]                    # 替换为你的集合名
+
+
+
+def find_matching_ids(given_ids):
+    result_ids = []
+
+    # 首先检查给定的ID是否直接存在于"id"字段中
+    for given_id in given_ids:
+        # 检查是否作为主id存在
+        doc = col.find_one({"id": given_id})
+        if doc:
+            result_ids.append(doc["id"])
+            continue
+
+        # 检查是否作为history_repeat_id中的key存在
+        doc = col.find_one({f"history_repeat_id.{given_id}": {"$exists": True}})
+        if doc:
+            result_ids.append(doc["id"])
+
+    return result_ids
+
+
+# 执行查询
+matched_ids = find_matching_ids(given_ids)
+
+# 打印结果，保持原始顺序
+print("匹配到的ID（按原始顺序）：")
+for matched_id in matched_ids:
+    print(matched_id)
+
+# 关闭数据库连接
+client.close()

tools/标讯数据重复率分析/导出重复数据.py

@@ -0,0 +1,44 @@
+from pymongo import MongoClient
+import pandas as pd
+from lib.detail_url import SE3
+
+
+# MongoDB连接配置
+client = MongoClient("mongodb://localhost:27017")  # 修改为实际连接
+db = client["your_database"]                       # 替换为你的数据库名
+col = db["your_collection"]                        # 替换为你的集合名
+
+# 查询主数据（没有 flag 字段）
+main_docs = col.find({"flag": {"$exists": False}}, {"_id": 1, "duplicate_doc_ids": 1})
+
+rows = []
+
+for doc in main_docs:
+    main_id = str(doc["_id"])
+    main_href = f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(main_id)}.html?appid"
+
+    # 第一行：主数据
+    rows.append({
+        "main_id": main_id,
+        "main_jybxhref": main_href,
+        "duplicate_id": "",
+        "duplicate_jybxhref": ""
+    })
+
+    # 后续行：重复数据
+    for dup_id in doc.get("duplicate_doc_ids", []):
+        dup_id_str = str(dup_id)
+        dup_href = f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(dup_id_str)}.html?appid"
+        rows.append({
+            "main_id": "",
+            "main_jybxhref": "",
+            "duplicate_id": dup_id_str,
+            "duplicate_jybxhref": dup_href
+        })
+
+# 转为 DataFrame 并导出 Excel
+df = pd.DataFrame(rows)
+output_file = "main_and_duplicates_expanded.xlsx"
+df.to_excel(output_file, index=False)
+
+print(f"导出完成，共 {len(df)} 行数据，保存至 {output_file}")

tools/标讯数据重复率分析/导出重复数据_v1.py

@@ -0,0 +1,49 @@
+from pymongo import MongoClient
+import pandas as pd
+from bson import ObjectId
+from lib.detail_url import SE3
+# MongoDB连接配置
+client = MongoClient('mongodb://127.0.0.1:27087/',unicode_decode_error_handler="ignore", directConnection=True)  # 修改为你的连接地址
+db = client["jyqyfw"]                       # 替换为你的数据库名
+col = db["xzh_20250714"]                    # 替换为你的集合名
+col1 = db["xzh_20250714_1"]
+
+# 需要导出的字段
+fields = ["id","title", "projectname", "href", "projectcode", "repeattext", "subtype", "s_winner", "bidamount", "buyer"]
+
+rows = []
+
+# 查询所有 repeat=true 的重复数据
+repeat_docs = col.find({"ishistory": True})
+
+for repeat_doc in repeat_docs:
+    repeat_row = {"类型": "主数据"}
+    for field in fields:
+        repeat_row[field] = repeat_doc.get(field, "")
+    rows.append(repeat_row)
+    # 构造 jyhref
+    id_value = repeat_doc.get("id", "")
+    repeat_row["jyhref"] = f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(id_value)}.html?appid" if id_value else ""
+    # 尝试查找原始数据
+    # repeatid = repeat_doc.get("repeatid")
+    for main_id_str in repeat_doc.get("history_repeat_id", {}).keys():
+        main_doc = col1.find_one({"id": main_id_str})
+        main_row = {"类型": "重复数据"}
+        if main_doc:
+            for field in fields:
+                main_row[field] = main_doc.get(field, "")
+            id_main = main_doc.get("id", "")
+            main_row["jyhref"] = f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(id_main)}.html?appid" if id_value else ""
+        else:
+            main_row = {key: "未找到" for key in ["类型"] + fields}
+            main_row["类型"] = "原始数据"
+
+        rows.append(main_row)
+
+
+# 导出为 Excel
+df = pd.DataFrame(rows)
+output_file = "repeated_with_source.xlsx"
+df.to_excel(output_file, index=False)
+
+print(f"已导出共 {len(rows)} 行数据，文件保存为 {output_file}")

tools/标讯数据重复率分析/导出重复数据_v2.py

@@ -0,0 +1,54 @@
+from pymongo import MongoClient
+import pandas as pd
+from bson import ObjectId
+from lib.detail_url import SE3
+# MongoDB连接配置
+client = MongoClient('mongodb://127.0.0.1:27087/',unicode_decode_error_handler="ignore", directConnection=True)  # 修改为你的连接地址
+db = client["jyqyfw"]                       # 替换为你的数据库名
+col = db["xzh_20250714"]                        # 替换为你的集合名
+# 导出字段列表
+fields = ["id","title", "projectname", "href", "projectcode", "repeattext","subtype", "s_winner", "bidamount", "buyer"]
+
+rows = []
+
+# 查询主数据：repeat=False 且 repeatid_ids 不为空
+main_docs = col.find({
+    "repeat": False,
+    "repeatid_ids": {"$exists": True, "$ne": {}}
+})
+
+for main_doc in main_docs:
+    # 第一行：主数据
+    main_row = {"类型": "主数据"}
+    for field in fields:
+        main_row[field] = main_doc.get(field, "")
+    rows.append(main_row)
+    # 构造 jyhref
+    id_value = main_doc.get("id", "")
+    main_row["jyhref"] = f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(id_value)}.html?appid" if id_value else ""
+    # 查找 repeatid_ids 中的所有文档
+    for repeat_id_str in main_doc.get("repeatid_ids", {}).keys():
+        try:
+            repeat_doc = col.find_one({"id": repeat_id_str})
+        except:
+            repeat_doc = None
+
+        repeat_row = {"类型": "重复数据"}
+
+        if repeat_doc:
+            for field in fields:
+                repeat_row[field] = repeat_doc.get(field, "")
+            id_repeat = repeat_doc.get("id", "")
+            repeat_row["jyhref"] = f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(id_repeat)}.html?appid" if id_value else ""
+        else:
+            for field in fields:
+                repeat_row[field] = "未找到"
+
+        rows.append(repeat_row)
+
+# 转为 DataFrame 并导出 Excel
+df = pd.DataFrame(rows)
+output_file = "main_with_repeatids_grouped.xlsx"
+df.to_excel(output_file, index=False)
+
+print(f"导出完成，共 {len(df)} 行，保存至 {output_file}")

tools/标讯数据附件为空数量统计/统计附件缺失数量.py

@@ -0,0 +1,138 @@
+from elasticsearch import Elasticsearch
+import time
+
+
+def count_attachments_status(es_host, es_index, es_username, es_password, batch_size=1000):
+    """
+    仅统计附件状态的脚本
+    统计分类：
+    1. 有org_url且有fid（正确情况）
+    2. 有org_url但无fid（错误情况）
+    3. 无org_url（需检查情况）
+    4. 无attachments字段
+    """
+    # 初始化ES客户端
+    es = Elasticsearch(
+        es_host,
+        http_auth=(es_username, es_password),
+        port=19800,
+        timeout=10000,
+        max_retries=3
+    )
+
+    # 初始化统计
+    stats = {
+        'total_processed': 0,
+        'org_url_with_fid': 0,  # 有org_url且有fid
+        'org_url_no_fid': 0,    # 有org_url但无fid
+        'no_org_url': 0,        # 无org_url
+        'no_attachments': 0,    # 无attachments字段
+        'errors': 0
+    }
+
+    query = {
+        "track_total_hits": True,
+        "query": {
+            "bool": {
+                "must": [{
+                    "range": {
+                        "comeintime": {
+                            "from": "1748534400",
+                            "to": "1751212800"
+                        }
+                    }
+                }]
+            }
+        }
+    }
+
+    try:
+        # 初始查询
+        resp = es.search(
+            index=es_index,
+            scroll='5m',
+            size=batch_size,
+            body=query
+        )
+        scroll_id = resp['_scroll_id']
+        total_docs = resp['hits']['total']['value']
+
+        print(f"开始处理 {total_docs} 条文档...")
+
+        while True:
+            hits = resp['hits']['hits']
+            if not hits:
+                break
+
+            for hit in hits:
+                stats['total_processed'] += 1
+                doc = hit.get('_source', {})
+                attachments = doc.get('projectinfo', {}).get('attachments')
+
+                # 分类统计逻辑
+                if attachments is None:
+                    stats['no_attachments'] += 1
+                elif not attachments:
+                    stats['no_attachments'] += 1
+                else:
+                    first_att = next(iter(attachments.values()))
+                    has_org_url = 'org_url' in first_att
+                    has_fid = 'fid' in first_att
+
+                    if has_org_url:
+                        if has_fid:
+                            stats['org_url_with_fid'] += 1
+                        else:
+                            stats['org_url_no_fid'] += 1
+                    else:
+                        stats['no_org_url'] += 1
+
+            # 进度报告
+            if stats['total_processed'] % 1000 == 0:
+                print_progress(stats, total_docs)
+
+            # 获取下一批
+            resp = es.scroll(scroll_id=scroll_id, scroll='5m')
+            scroll_id = resp['_scroll_id']
+
+    except Exception as e:
+        print(f"处理失败: {str(e)}")
+        stats['errors'] += 1
+    finally:
+        print_final_report(stats, total_docs)
+
+
+def print_progress(stats, total):
+    """打印处理进度"""
+    progress = stats['total_processed'] / total * 100
+    print(f"\n进度: {progress:.1f}%")
+    print(f"已处理: {stats['total_processed']}/{total}")
+    print(f"正确附件(有org_url和fid): {stats['org_url_with_fid']}")
+    print(f"错误附件(有org_url无fid): {stats['org_url_no_fid']}")
+    print(f"无org_url: {stats['no_org_url']}")
+    print(f"无attachments: {stats['no_attachments']}")
+
+
+def print_final_report(stats, total):
+    """打印最终统计"""
+    print("\n" + "=" * 50)
+    print("附件状态统计结果".center(50))
+    print("=" * 50)
+    print(f"总文档数: {total}")
+    print(f"已处理文档: {stats['total_processed']}")
+    print(f"正确附件(有org_url和fid): {stats['org_url_with_fid']} ({(stats['org_url_with_fid']/stats['total_processed'])*100:.1f}%)")
+    print(f"错误附件(有org_url无fid): {stats['org_url_no_fid']} ({(stats['org_url_no_fid']/stats['total_processed'])*100:.1f}%)")
+    print(f"无org_url需检查: {stats['no_org_url']} ({(stats['no_org_url']/stats['total_processed'])*100:.1f}%)")
+    print(f"无attachments: {stats['no_attachments']} ({(stats['no_attachments']/stats['total_processed'])*100:.1f}%)")
+    print(f"错误数量: {stats['errors']}")
+    print("=" * 50)
+
+
+if __name__ == "__main__":
+    count_attachments_status(
+        es_host="http://127.0.0.1:19800",
+        es_index="bidding",
+        es_username="jianyuGr",
+        es_password="we3g8glKfe#",
+        batch_size=1000
+    )

tools/标讯数据附件为空数量统计/附件缺失率第二版.py

@@ -0,0 +1,203 @@
+from pymongo import MongoClient
+from elasticsearch import Elasticsearch,helpers
+from tqdm import tqdm
+import time
+from datetime import datetime
+from lib.detail_url import SE3
+
+# 配置常量
+BATCH_SIZE = 10000
+ES_SCROLL_TIME = '5m'
+MONGO_BATCH_SIZE = 1000
+
+
+# 初始化连接
+def init_connections():
+    # ES 连接配置
+    es_host = "http://127.0.0.1:19800"
+    es_username = "jianyuGr"
+    es_password = "we3g8glKfe#"
+
+    # 初始化 Elasticsearch 客户端
+    es = Elasticsearch(es_host, http_auth=(es_username, es_password), retry_on_timeout=True)  # 使用基本认证
+    # MongoDB连接
+    mongo_client_bid =  MongoClient(f'mongodb://{"viewdata"}:{"viewdata"}@{"127.0.0.1:27088"}/', unicode_decode_error_handler="ignore",directConnection=True)["qfw"]
+    mongo_client = MongoClient('mongodb://172.20.45.129:27002/', unicode_decode_error_handler="ignore")
+
+    return es, mongo_client_bid,mongo_client
+
+
+def get_es_query():
+    """构建正确的ES查询"""
+    return {
+        "track_total_hits": True,
+        "query": {
+            "bool": {
+                "must": [
+                    {
+                        "range": {
+                            "publishtime": {
+                                "gte": 1748534400,
+                                "lte": 1751212800
+                            }
+                        }
+                    },
+                    {
+                        "bool": {
+                            "should": [
+                                {
+                                    "multi_match": {
+                                        "query": "附件",
+                                        "type": "phrase",
+                                        "fields": ["detail"]
+                                    }
+                                },
+                                {
+                                    "multi_match": {
+                                        "query": "详情请访问原网页！",
+                                        "type": "phrase",
+                                        "fields": ["detail"]
+                                    }
+                                }
+                            ]
+                        }
+                    }
+                ]
+            }
+        },
+        "highlight": {
+            "pre_tags": [""],
+            "post_tags": [""],
+            "fields": {
+                "detail": {
+                    "fragment_size": 115,
+                    "number_of_fragments": 1
+                }
+            }
+        },
+        "sort": [
+            {"dataweight": "desc"},
+            {"publishtime": "desc"}
+        ],
+        "size": 1000,
+        "_source": False
+    }
+
+es_query = {
+    "query": {
+        "bool": {
+            "filter": [
+                {
+                    "term": {
+                        "_id": "68627259d5d8e4081f71d6f0"
+                    }
+                }
+            ]
+        }
+    },
+    "size": 1  # 每次返回的数据量
+}
+def query_es_data_batch(es_client):
+    """分批从ES获取数据"""
+    print("开始从ES获取数据...")
+    es_ids = []
+    start_time = time.time()
+    response = es_client.search(index="bidding", body=es_query)
+    hits = response['hits']['hits']
+    try:
+        for hit in hits:
+            es_ids.append(hit['_id'])
+            if len(es_ids) % BATCH_SIZE == 0:
+                yield es_ids
+                es_ids = []
+
+        if es_ids:
+            yield es_ids
+
+        print(f"ES数据获取完成，耗时: {time.time() - start_time:.2f}秒")
+    except Exception as e:
+        print(f"ES查询出错: {str(e)}")
+        raise
+
+
+def process_mongo_batch(bidding_collection, es_ids):
+    """处理一批MongoDB数据"""
+    results = []
+
+    try:
+        cursor = bidding_collection.find(
+            {"_id": {"$in": es_ids}}
+        )
+
+        for doc in cursor:
+            attachments = doc.get('projectinfo', {}).get('attachments', {})
+
+            for key, att in attachments.items():
+                if isinstance(att, dict) and att.get('org_url') and not att.get('fid'):
+                    _id = doc['_id']
+                    href = SE3.encode_string(_id)
+                    jybxhref = f"https://www.jianyu360.cn/article/entservice/{href}.html?appid"
+                    results.append({
+                        "_id": doc['_id'],
+                        "href":doc['href'],
+                        "site": doc['site'],
+                        "spidercode": doc['spidercode'],
+                        "jybxhref":jybxhref,
+                        "attachment_key": key,
+                        "attachment_data": att,
+                        "created_at": datetime.now(),
+                        "source": "es_and_mongo_sync"
+                    })
+    except Exception as e:
+        print(f"MongoDB查询出错: {str(e)}")
+        raise
+
+    return results
+
+
+def main():
+    es,mongo_client_bid, mongo_client = init_connections()
+    bidding_collection = mongo_client_bid.qfw['bidding']
+    new_collection = mongo_client.data_quality['missing_fid_attachments']
+
+    total_processed = 0
+    total_found = 0
+
+    try:
+        with tqdm(desc="处理进度", unit="batch") as pbar:
+            for batch_es_ids in query_es_data_batch(es):
+                batch_results = process_mongo_batch(bidding_collection, batch_es_ids)
+
+                if batch_results:
+                    try:
+                        new_collection.insert_many(batch_results, ordered=False)
+                        total_found += len(batch_results)
+                    except Exception as e:
+                        print(f"批量插入出错: {str(e)}")
+                        # 可以改为单条插入尝试
+                        for doc in batch_results:
+                            try:
+                                new_collection.insert_one(doc)
+                                total_found += 1
+                            except:
+                                continue
+
+                total_processed += len(batch_es_ids)
+                pbar.update(1)
+                pbar.set_postfix({
+                    '已处理': total_processed,
+                    '发现': total_found
+                })
+
+        print(f"\n处理完成! 共处理 {total_processed} 条记录，发现 {total_found} 条符合条件的附件")
+    except KeyboardInterrupt:
+        print("\n用户中断执行，已处理数据将被保存")
+    except Exception as e:
+        print(f"\n程序执行出错: {str(e)}")
+    finally:
+        # 可以在这里添加资源清理代码
+        pass
+
+
+if __name__ == "__main__":
+    main()

tools/标讯数据附件为空数量统计/附件缺失率第二版_新.py

@@ -0,0 +1,321 @@
+from elasticsearch import Elasticsearch
+from pymongo import MongoClient, UpdateOne
+from pymongo.errors import BulkWriteError
+from lib.detail_url import SE3
+import time
+
+
+def process_es_to_mongo(es_host, es_index, es_username, es_password,mongo_uri, mongo_db, mongo_collection, batch_size=1000):
+    """
+    优化后的数据迁移脚本，主要改进：
+    1. 更清晰的统计分类
+    2. 更健壮的批量处理
+    3. 更完善的错误处理
+    """
+
+    # 初始化客户端
+    es = Elasticsearch(
+        es_host,
+        http_auth=(es_username, es_password),
+        port=19800,
+        timeout=10000,
+        max_retries=3
+    )
+
+    mongo_client = MongoClient(mongo_uri, unicode_decode_error_handler = "ignore")
+    db = mongo_client[mongo_db]
+    collection = db[mongo_collection]
+
+    # 初始化统计
+    stats = {
+        'total_processed': 0,
+        'org_url_no_fid': 0,  # org_url有值且fid为空（错误情况）
+        'org_url_with_fid': 0,  # org_url有值且fid有值（正确情况）
+        'no_attachments': 0,  # 没有attachments的文档（保存）
+        'no_org_url': 0,  # org_url为空（需要检查的情况）
+        'saved_docs': 0,  # 实际保存的文档数
+        'errors': 0
+    }
+
+    # 保持原有的ES查询
+    # query = {
+    #     "track_total_hits": True,
+    #     "query": {
+    #         "bool": {
+    #             "must": [{
+    #                 "range": {
+    #                     "publishtime": {
+    #                         "gte": 1748534400,
+    #                         "lte": 1751212800
+    #                     }
+    #                 }
+    #             },
+    #                 {
+    #                     "bool": {
+    #                         "should": [{
+    #                             "multi_match": {
+    #                                 "query": "详情请下载附件",
+    #                                 "type": "phrase",
+    #                                 "fields": [
+    #                                     "detail"
+    #                                 ]
+    #                             }
+    #                         },
+    #                             {
+    #                                 "multi_match": {
+    #                                     "query": "详见附件",
+    #                                     "type": "phrase",
+    #                                     "fields": [
+    #                                         "detail"
+    #                                     ]
+    #                                 }
+    #                             },
+    #                             {
+    #                                 "multi_match": {
+    #                                     "query": "xlsx",
+    #                                     "type": "phrase",
+    #                                     "fields": [
+    #                                         "detail"
+    #                                     ]
+    #                                 }
+    #                             },
+    #                             {
+    #                                 "multi_match": {
+    #                                     "query": "xls",
+    #                                     "type": "phrase",
+    #                                     "fields": [
+    #                                         "detail"
+    #                                     ]
+    #                                 }
+    #                             },
+    #                             {
+    #                                 "multi_match": {
+    #                                     "query": "pdf",
+    #                                     "type": "phrase",
+    #                                     "fields": [
+    #                                         "detail"
+    #                                     ]
+    #                                 }
+    #                             },
+    #                             {
+    #                                 "multi_match": {
+    #                                     "query": "合同附件",
+    #                                     "type": "phrase",
+    #                                     "fields": [
+    #                                         "detail"
+    #                                     ]
+    #                                 }
+    #                             },
+    #                             {
+    #                                 "multi_match": {
+    #                                     "query": "公示附件",
+    #                                     "type": "phrase",
+    #                                     "fields": [
+    #                                         "detail"
+    #                                     ]
+    #                                 }
+    #                             },
+    #                             {
+    #                                 "multi_match": {
+    #                                     "query": "明细附件",
+    #                                     "type": "phrase",
+    #                                     "fields": [
+    #                                         "detail"
+    #                                     ]
+    #                                 }
+    #                             },
+    #                             {
+    #                                 "multi_match": {
+    #                                     "query": "见附件",
+    #                                     "type": "phrase",
+    #                                     "fields": [
+    #                                         "detail"
+    #                                     ]
+    #                                 }
+    #                             },
+    #                             {
+    #                                 "multi_match": {
+    #                                     "query": "详情请访问原网页！",
+    #                                     "type": "phrase",
+    #                                     "fields": [
+    #                                         "detail"
+    #                                     ]
+    #                                 }
+    #                             }
+    #                         ]
+    #                     }
+    #                 }
+    #             ],
+    #             "must_not": []
+    #         }
+    #     },
+    #     "highlight": {
+    #         "pre_tags": [
+    #             ""
+    #         ],
+    #         "post_tags": [
+    #             ""
+    #         ],
+    #         "fields": {
+    #             "detail": {
+    #                 "fragment_size": 115,
+    #                 "number_of_fragments": 1
+    #             }
+    #         }
+    #     },
+    #     "sort": [{
+    #         "dataweight": "desc"
+    #     },
+    #         {
+    #             "publishtime": "desc"
+    #         }
+    #     ],
+    #     "from": 0
+    # }
+    query = {"track_total_hits": True,
+                "query": {"bool": {"must": [{"range": {"comeintime": {"from": "1748534400", "to": "1751212800"}}}]}}}
+
+    try:
+        # 初始查询
+        resp = es.search(
+            index=es_index,
+            scroll='5m',
+            size=batch_size,
+            body=query
+        )
+        scroll_id = resp['_scroll_id']
+        total_docs = resp['hits']['total']['value']
+
+        bulk_ops = []
+        print(f"开始处理 {total_docs} 条文档...")
+
+        while True:
+            hits = resp['hits']['hits']
+            if not hits:
+                break
+
+            for hit in hits:
+                stats['total_processed'] += 1
+                doc = hit.get('_source', {})
+                attachments = doc.get('projectinfo', {}).get('attachments', {})
+
+                should_save = True
+                if attachments is None:
+                    stats['no_attachments'] += 1
+                elif not attachments:
+                    stats['no_attachments'] += 1
+                else:
+                    first_att = next(iter(attachments.values()))
+
+                    # org_url = first_att.get('org_url')
+                    # fid = first_att.get('fid')
+                    # 关键判断逻辑
+                    has_org_url = 'org_url' in first_att
+                    has_fid = 'fid' in first_att
+                    # 分类统计
+                    if has_org_url:
+                        if has_fid:
+                            stats['org_url_with_fid'] += 1
+                            should_save = False
+                        else:
+                            stats['org_url_no_fid'] += 1
+                            should_save = False
+                    else:
+                        stats['no_org_url'] += 1
+
+                    # 保存逻辑
+                if should_save:
+                    # 准备MongoDB文档
+                    _id = hit['_id']
+                    mongo_doc = {
+                        '_id': _id,
+                        'attachments':attachments,
+                        'site': doc['site'],
+                        'spidercode': doc['spidercode'],
+                        'href': doc.get('href', ''),
+                        'jybxhref': f"https://www.jianyu360.cn/article/entservice/{SE3.encode_string(_id)}.html?appid",
+                        'processed_at': time.time()
+                    }
+
+                    bulk_ops.append(UpdateOne(
+                        {'_id': _id},
+                        {'$set': mongo_doc},
+                        upsert=True
+                    ))
+
+                # 批量执行
+                if len(bulk_ops) >= batch_size:
+                    execute_bulk(collection, bulk_ops, stats)
+                    bulk_ops = []
+
+            # 进度报告
+            if stats['total_processed'] % 100 == 0:
+                print_progress(stats, total_docs)
+
+            # 获取下一批
+            resp = es.scroll(scroll_id=scroll_id, scroll='5m')
+            scroll_id = resp['_scroll_id']
+
+        # 最后一批
+        if bulk_ops:
+            execute_bulk(collection, bulk_ops, stats)
+
+    except Exception as e:
+        print(f"处理失败: {str(e)}")
+        stats['errors'] += 1
+    finally:
+        mongo_client.close()
+        print_final_report(stats, total_docs)
+
+
+def execute_bulk(collection, operations, stats):
+    print(f"\n进入execute_bulk，待处理操作数: {len(operations)}")  # 先确认函数被调用
+    """执行批量操作"""
+    try:
+        result = collection.bulk_write(operations)
+        stats['saved_docs'] += (result.upserted_count + result.modified_count)
+        print(f"已处理批次: 插入{result.upserted_count} 更新{result.modified_count}")
+    except BulkWriteError as bwe:
+        stats['errors'] += len(bwe.details['writeErrors'])
+        print(f"部分失败: {len(bwe.details['writeErrors'])}条错误")
+
+
+def print_progress(stats, total):
+    """打印处理进度"""
+    progress = stats['total_processed'] / total * 100
+    print(f"\n进度: {progress:.1f}%")
+    print(f"已处理: {stats['total_processed']}/{total}")
+    print(f"正确附件(有org_url和fid): {stats['org_url_with_fid']}")
+    print(f"错误附件(有org_url无fid): {stats['org_url_no_fid']}")
+    print(f"没有附件: {stats['no_attachments']}")
+    print(f"待检查附件(无org_url): {stats['no_org_url']}")
+    print(f"已保存文档: {stats['saved_docs']}")
+
+
+def print_final_report(stats, total):
+    """打印最终统计"""
+    print("\n" + "=" * 50)
+    print("处理结果统计".center(50))
+    print("=" * 50)
+    print(f"总文档数: {total}")
+    print(f"已处理文档: {stats['total_processed']}")
+    print(f"正确附件(有org_url和fid): {stats['org_url_with_fid']}")
+    print(f"错误附件(有org_url无fid): {stats['org_url_no_fid']}")
+    print(f"没有附件: {stats['no_attachments']}")
+    print(f"待检查附件(无org_url): {stats['no_org_url']}")
+    print(f"实际保存文档: {stats['saved_docs']}")
+    print(f"错误数量: {stats['errors']}")
+    print("=" * 50)
+
+
+if __name__ == "__main__":
+    process_es_to_mongo(
+        es_host="http://127.0.0.1:19800",
+        es_index="bidding",
+        es_username="jianyuGr",
+        es_password="we3g8glKfe#",
+        mongo_uri="mongodb://172.20.45.129:27002",
+        mongo_db="data_quality",
+        mongo_collection="missing_fid_attachments",
+        batch_size=1000
+    )

tools/标讯数据附件为空数量统计/附件缺失率第二版_新_bak.py

@@ -0,0 +1,289 @@
+from elasticsearch import Elasticsearch
+from pymongo import MongoClient
+from pymongo.errors import BulkWriteError
+from lib.detail_url import SE3
+
+
+def process_es_to_mongo(es_host, es_index, es_username, es_password,mongo_uri, mongo_db, mongo_collection, batch_size=1000):
+    """
+    从Elasticsearch中筛选数据，并将符合条件的数据插入到MongoDB中
+
+    :param es_host: Elasticsearch连接地址
+    :param es_index: Elasticsearch索引名
+    :param mongo_uri: MongoDB连接URI
+    :param mongo_db: MongoDB数据库名
+    :param mongo_collection: MongoDB集合名
+    :param batch_size: 每批处理的数据量
+    """
+
+    # 初始化Elasticsearch客户端（带认证）
+    es = Elasticsearch(
+        es_host,
+        http_auth=(es_username, es_password),
+        port=19800,  # 根据实际情况调整
+        timeout=10000  # 设置超时时间
+    )
+    # 初始化统计计数器
+    stats = {
+        'total_processed': 0,
+        'org_url_no_fid': 0,  # org_url有值且fid为空（错误情况）
+        'org_url_with_fid': 0,  # org_url有值且fid有值（正确情况）
+        'no_org_url': 0,  # org_url为空（需要检查的情况）
+        'saved_docs': 0,  # 实际保存的文档数
+        'errors': 0
+    }
+    # 初始化MongoDB客户端
+    mongo_client = MongoClient(mongo_uri)
+    db = mongo_client[mongo_db]
+    collection = db[mongo_collection]
+
+    # 构建Elasticsearch查询 - 查询attachments存在且不为空的数据
+    query = {
+            "track_total_hits": True,
+            "query": {
+                "bool": {
+                    "must": [{
+                            "range": {
+                                "publishtime": {
+                                    "gte": 1748534400,
+                                    "lte": 1751212800
+                                }
+                            }
+                        },
+                        {
+                            "bool": {
+                                "should": [{
+                                        "multi_match": {
+                                            "query": "详情请下载附件",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    },
+                                    {
+                                        "multi_match": {
+                                            "query": "详见附件",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    },
+                                    {
+                                        "multi_match": {
+                                            "query": "xlsx",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    },
+                                    {
+                                        "multi_match": {
+                                            "query": "xls",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    },
+                                    {
+                                        "multi_match": {
+                                            "query": "pdf",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    },
+                                    {
+                                        "multi_match": {
+                                            "query": "合同附件",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    },
+                                    {
+                                        "multi_match": {
+                                            "query": "公示附件",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    },
+                                    {
+                                        "multi_match": {
+                                            "query": "明细附件",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    },
+                                    {
+                                        "multi_match": {
+                                            "query": "见附件",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    },
+                                    {
+                                        "multi_match": {
+                                            "query": "详情请访问原网页！",
+                                            "type": "phrase",
+                                            "fields": [
+                                                "detail"
+                                            ]
+                                        }
+                                    }
+                                ]
+                            }
+                        }
+                    ],
+                    "must_not": []
+                }
+            },
+            "highlight": {
+                "pre_tags": [
+                    ""
+                ],
+                "post_tags": [
+                    ""
+                ],
+                "fields": {
+                    "detail": {
+                        "fragment_size": 115,
+                        "number_of_fragments": 1
+                    }
+                }
+            },
+            "sort": [{
+                    "dataweight": "desc"
+                },
+                {
+                    "publishtime": "desc"
+                }
+            ],
+            "from": 0
+        }
+
+    # 使用scroll API进行批量查询
+    resp = es.search(
+        index=es_index,
+        scroll='2m',  # 保持scroll上下文2分钟
+        size=batch_size,
+        body=query
+    )
+
+    scroll_id = resp['_scroll_id']
+    total_docs = resp['hits']['total']['value']
+    print(f"开始处理 {total_docs} 条文档...")
+
+    processed = 0
+    batch_count = 0
+    bulk_operations = []
+
+    while True:
+        # 处理当前批次的数据
+        hits = resp['hits']['hits']
+        if not hits:
+            break
+
+        for hit in hits:
+            processed += 1
+            doc= hit.get('_source', {})
+            attachments = doc.get('projectinfo', {}).get('attachments', {})
+
+            if not attachments:
+                continue
+
+            valid_attachments = {}
+            first_att = next(iter(attachments.values()))
+            org_url = first_att.get('org_url')
+            fid = first_att.get('fid')
+
+            # 分类统计
+            if org_url:
+                if fid:
+                    stats['org_url_with_fid'] += 1
+                    continue  # 正确情况，不保存
+                else:
+                    stats['org_url_no_fid'] += 1
+                    continue  # 错误情况，不保存
+            else:
+                stats['no_org_url'] += 1
+            # 只有这种情况才保存
+            # 准备要插入MongoDB的数据
+            _id = hit['_id']
+            href = SE3.encode_string(_id)
+            jybxhref = f"https://www.jianyu360.cn/article/entservice/{href}.html?appid"
+            mongo_doc = {
+                '_id': _id,
+                'fid': fid or '',
+                'org_url': org_url or '',
+                'site' :doc['site'],
+                'spidercode':doc['spidercode'],
+                'href': doc.get('href','' ),
+                'jybxhref' : jybxhref
+            }
+
+            bulk_operations.append(mongo_doc)
+            batch_count += 1
+
+            # 批量插入达到batch_size时执行
+            if batch_count >= batch_size:
+                try:
+                    collection.insert_many(bulk_operations,ordered=False)
+                    print(f"已批量插入 {batch_count} 条数据到MongoDB")
+                except BulkWriteError as bwe:
+                    print(f"批量插入时部分失败: {bwe.details}")
+
+                bulk_operations = []
+                batch_count = 0
+
+        # 获取下一批数据
+        resp = es.scroll(scroll_id=scroll_id, scroll='2m')
+        scroll_id = resp['_scroll_id']
+
+        print(f"已处理 {processed}/{total_docs} 条数据")
+
+    # 插入最后一批可能不足batch_size的数据
+    if bulk_operations:
+        try:
+            collection.insert_many(bulk_operations)
+            print(f"最后批量插入 {batch_count} 条数据到MongoDB")
+        except BulkWriteError as bwe:
+            print(f"最后批量插入时部分失败: {bwe.details}")
+
+    print(f"处理完成！总共处理了 {processed} 条数据")
+    print(f"被排除的附件数(org_url有值且fid为空): {stats['org_url_num']}")
+    print(f"被排除的附件数(org_url有值且fid不为空): {stats['org_url_fid_num']}")
+    print(f"保存的有效附件数: {stats['saved_attachments']}")
+
+
+# 使用示例
+if __name__ == "__main__":
+    ES_HOST = "http://127.0.0.1:19800" # Elasticsearch地址
+    ES_INDEX = "bidding"  # Elasticsearch索引名
+    ES_USERNAME = "jianyuGr"  # ES用户名
+    ES_PASSWORD = "we3g8glKfe#"  # ES密码
+    MONGO_URI = "mongodb://172.20.45.129:27002"  # MongoDB地址
+    MONGO_DB = "data_quality"  # MongoDB数据库名
+    MONGO_COLLECTION = "fid_attachments"  # MongoDB集合名
+
+    process_es_to_mongo(
+        es_host=ES_HOST,
+        es_index=ES_INDEX,
+        es_username=ES_USERNAME,
+        es_password=ES_PASSWORD,
+        mongo_uri=MONGO_URI,
+        mongo_db=MONGO_DB,
+        mongo_collection=MONGO_COLLECTION,
+        batch_size=1000
+    )

tools/爬虫数据质量二期/spider_quality.py

@@ -0,0 +1,311 @@
+import pandas as pd
+from pymongo import MongoClient
+from datetime import datetime, timedelta
+import smtplib
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.application import MIMEApplication
+import pymysql
+from collections import defaultdict
+
+
+# MySQL 配置信息
+MYSQL_CONFIG = {
+    "host": "172.20.45.129",
+    "user": "root",
+    "password": "=PDT49#80Z!RVv52_z",
+    "database": "quality",
+    "port": 4000
+}
+
+# 建立 MySQL 连接get_mysql_connection
+def get_mysql_connection():
+    return pymysql.connect(
+        host=MYSQL_CONFIG["host"],
+        user=MYSQL_CONFIG["user"],
+        password=MYSQL_CONFIG["password"],
+        database=MYSQL_CONFIG["database"],
+        port=MYSQL_CONFIG["port"]
+    )
+# ✅ MongoDB 连接配置
+MongodbConfig = {
+    "ip_port": "172.20.45.129:27002",
+    "db": "data_quality",
+    "col": "spider_analysis"
+}
+# ✅ 邮件配置信息
+EMAIL_CONFIG = {
+    "smtp_server": "smtp.163.com",  # SMTP 服务器
+    "smtp_port": 465,                   # 端口
+    "sender_email": "liumm_6064@163.com",  # 发件人邮箱
+    "sender_password": "TPVBPYSETVHWTIDH",       # 发件人邮箱密码
+    # "recipient_emails": ["liumiaomiao@topnet.net.cn", "lizhikun@topnet.net.cn","yanpeizhu@topnet.net.cn","zhangjinkun@topnet.net.cn"],  # 收件人列表
+    "recipient_emails": ["liumiaomiao@topnet.net.cn"],  # 收件人列表
+    "email_subject": "爬虫数据异常报告",
+    "email_body": "您好，请查收昨日的爬虫数据异常报告，详情见附件。"
+}
+# 连接 MongoDB
+client = MongoClient(MongodbConfig["ip_port"])
+db = client[MongodbConfig["db"]]
+collection = db[MongodbConfig["col"]]
+
+# ✅ 需要检查的字段
+fields_to_check = [
+    "bidtype_count", "projectname_count", "budget_count", "buyer_count",
+    "s_winner_count", "projectcode_count", "bidamount_count", "area_count",
+    "city_count", "agency_count", "subtype_count", "toptype_count", "projectinfo_count"
+]
+def generate_and_send_report():
+    """ 生成异常数据表格并发送邮件 """
+    # ✅ 计算时间段
+    now_date = (datetime.now()).strftime("%Y-%m-%d")
+    yesterday_date = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
+    # 计算上一周周一到周五的时间段
+    last_week_start = (datetime.now() - timedelta(days=datetime.now().weekday() + 14)).strftime("%Y-%m-%d")
+    last_week_end = (datetime.now() - timedelta(days=datetime.now().weekday() + 10)).strftime("%Y-%m-%d")
+    print(f"计算上一周周一到周五的时间段：{last_week_start} -- {last_week_end} ")
+
+    # ✅ 计算近三天（排除周六、周日）
+    recent_days = []
+    date_cursor = datetime.now() - timedelta(days=1)  # 从昨天开始往前推
+
+    while len(recent_days) < 3:
+        if date_cursor.weekday() < 5:  # 0-4 表示周一到周五
+            recent_days.append(date_cursor.strftime("%Y-%m-%d"))
+        date_cursor -= timedelta(days=1)
+
+    recent_days.sort()  # **正序排列（从旧到新）**
+
+    # # ✅ 获取近三天的开始和结束日期
+    # recent_three_days_start = recent_days[0]
+    # recent_three_days_end = recent_days[-1]
+
+    print(f"近两天（排除周六周日）：{recent_days}")
+
+    # target_spidercode = "a_gdsggzyjypt_gycq_jggg"  # 你要调试的爬虫代码
+    # ✅ 1. 查询上一周周一到周五的数据
+    last_week_data = list(collection.find({
+        "datefarmat": {"$gte": last_week_start, "$lte": last_week_end},
+        # "spidercode": target_spidercode  # 仅查询特定爬虫
+    }))
+    # ✅ 2. 查询近三天的数据
+    last_three_data = list(collection.find({
+        # "datefarmat": {"$gte": recent_three_days_start, "$lte": recent_three_days_end},
+        # "spidercode": target_spidercode,  # 仅查询特定爬虫
+        "datefarmat": {"$in": recent_days},
+    }))
+    # 转换为 DataFrame 处理
+    last_week_df = pd.DataFrame(last_week_data)
+    last_three_df = pd.DataFrame(last_three_data)
+    # # ✅ 2. 查询昨天的数据
+    # yesterday_data = list(collection.find({
+    #     "datefarmat": yesterday_date,
+        # "spidercode": target_spidercode  # 仅查询特定爬虫
+    # }))
+    #
+    # # 转换为 DataFrame 处理
+    # yesterday_df = pd.DataFrame(yesterday_data)
+
+    # ✅ 3. 检查上一周是否每天都有数据 且 每天字段值不为 0
+    anomalies = []  # 存储异常数据
+    if not last_week_df.empty:
+        # 统计上周 `spidercode` 和 `bidtype` 的所有数据
+        grouped_data = last_week_df.groupby(["spidercode", "bidtype", "datefarmat","site_important"])[fields_to_check].sum().reset_index()
+        # groupedthree_data = last_three_df.groupby(["spidercode", "bidtype", "datefarmat","site_important"])[fields_to_check].sum().reset_index()
+        # grouped_data = grouped_data[grouped_data["spidercode"] == target_spidercode]
+
+        # A. 数量问题检查
+        # 1. 分析上周数据有效性
+        spider_info = {}
+        for (spidercode, bidtype), group in grouped_data.groupby(["spidercode", "bidtype"]):
+            if len(group) == 5:  # 确保有5天完整数据
+                if spidercode not in spider_info:
+                    spider_info[spidercode] = {
+                        "site_important": group["site_important"].iloc[0],
+                        # "valid_fields": {},
+                        "bidtype_fields": {},  # 存储各bidtype的有效字段
+                        "all_bidtypes": set()
+                    }
+                spider_info[spidercode]["all_bidtypes"].add(bidtype)
+                valid_fields = [field for field in fields_to_check if (group[field] != 0).all()]
+                spider_info[spidercode]["bidtype_fields"][bidtype] = valid_fields
+
+        # 2. 检查近三天异常
+        for spidercode, info in spider_info.items():
+            print(f"\n检查爬虫 {spidercode} 的异常...")
+
+            # A. 数量问题检查
+            problematic_bidtypes = []
+            for bidtype in info["all_bidtypes"]:
+                # 检查该bidtype近三天是否有数据
+                has_data = not last_three_df[
+                    (last_three_df["spidercode"] == spidercode) &
+                    (last_three_df["bidtype"] == bidtype)
+                    ].empty
+
+                if not has_data:
+                    problematic_bidtypes.append(bidtype)
+                    print(f"{bidtype} 类型: 近三天无数据")
+
+            # 生成数量问题报告
+            if problematic_bidtypes:
+                if len(problematic_bidtypes) == len(info["all_bidtypes"]):
+                    content = f"{yesterday_date} 日，发现该爬虫所有类型已连续三天无数据，请关注！"
+                else:
+                    bidtypes_str = "，".join(problematic_bidtypes)
+                    content = f"{yesterday_date} 日，发现{bidtypes_str}类型已连续三天无数据，请关注！"
+
+                anomalies.append({
+                    "日期": now_date,
+                    "爬虫代码": spidercode,
+                    "是否重点网站": info["site_important"],
+                    "问题类型": "数量问题",
+                    "问题内容": content
+                })
+
+            # B. 质量问题检查（优化合并逻辑）
+            # 先按字段分组收集问题
+            field_problems = {}  # {field: [bidtypes]}
+            # 先获取该爬虫涉及的所有bidtype（无论是否有有效字段）
+            all_bidtypes = set(last_week_df[last_week_df["spidercode"] == spidercode]["bidtype"].unique())
+
+            # 收集所有有问题的字段和对应的bidtypes
+            for bidtype in all_bidtypes:
+                # 获取该bidtype上周有效的字段（如果没有则为空列表）
+                valid_fields = info["bidtype_fields"].get(bidtype, [])
+                # 检查近三天该字段是否全为0
+                threeday_data = last_three_df[
+                    (last_three_df["spidercode"] == spidercode) &
+                    (last_three_df["bidtype"] == bidtype)]
+
+                for field in valid_fields:
+                    if field in threeday_data.columns:
+                        if not threeday_data.empty and (threeday_data[field] == 0).all():
+                            if field not in field_problems:
+                                field_problems[field] = []
+                            if bidtype not in field_problems[field]:
+                                field_problems[field].append(bidtype)
+            # 生成合并后的质量问题报告
+            problem_messages = []
+            for field, bidtypes in field_problems.items():
+                # 计算该字段在上周有效的bidtype总数
+                total_valid_for_field = sum(
+                    1 for bt in all_bidtypes
+                    if bt in info["bidtype_fields"] and field in info["bidtype_fields"][bt]
+                )
+
+                if len(all_bidtypes) == total_valid_for_field:
+                    problem_messages.append(f"所有类型的 {field} 字段")
+                else:
+                    bidtypes_str = "，".join(bidtypes)
+                    problem_messages.append(f"{bidtypes_str}类型的 {field} 字段")
+            # 如果有质量问题，合并为一条记录
+            if problem_messages:
+                problems_str = "，".join(problem_messages)
+                content = f"{yesterday_date} 日，发现{problems_str}连续三天为0，请关注！"
+
+                anomalies.append({
+                    "日期": now_date,
+                    "爬虫代码": spidercode,
+                    "是否重点网站": info["site_important"],
+                    "问题类型": "质量问题",
+                    "问题内容": content
+                })
+            else:
+                # 当没有有效字段时的处理逻辑
+                print(f"  警告：爬虫 {spidercode} 没有有效的字段数据（上周检查未通过或所有字段无效）")
+    else:
+        print("⚠️ 上周数据为空，未找到有效数据！")
+
+    # ✅ 5. 输出异常并导出 Excel
+    if anomalies:
+        anomalies_df = pd.DataFrame(anomalies)
+
+        # 导出 Excel 文件
+        output_filename = f"spider_anomalies_{yesterday_date}.xlsx"
+        anomalies_df.to_excel(output_filename, index=False)
+        print(f"⚠️ 异常数据已导出：{output_filename}")
+
+        # 保存异常数据到 MySQL
+        save_anomalies_to_mysql(anomalies)
+
+        send_email_with_attachment(output_filename)
+    else:
+        print("✅ 没有发现异常数据。")
+
+def save_anomalies_to_mysql(anomalies):
+    """ 将异常数据存入 MySQL 数据库 """
+    if not anomalies:
+        return
+
+    # 获取 MySQL 连接
+    connection = get_mysql_connection()
+    cursor = connection.cursor()
+
+    # 插入异常数据的 SQL 语句
+    insert_sql = """
+    INSERT INTO spider_issues (date, spider_code, is_key_site, issue_type, issue_content)
+    VALUES (%s, %s, %s, %s, %s)
+    """
+
+    # 插入数据
+    for anomaly in anomalies:
+        date = anomaly["日期"]
+        spider_code = anomaly["爬虫代码"]
+        is_key_site = anomaly["是否重点网站"]
+        issue_type = anomaly["问题类型"]
+        issue_content = anomaly["问题内容"]
+
+        # 执行插入操作
+        cursor.execute(insert_sql, (date, spider_code, is_key_site, issue_type, issue_content))
+
+    # 提交事务并关闭连接
+    connection.commit()
+    cursor.close()
+    connection.close()
+
+    print(f"📥 {len(anomalies)} 条异常数据已存入 MySQL 数据库！")
+
+
+def send_email_with_attachment(file_path):
+    """ 发送带附件的邮件 """
+    try:
+        # 创建邮件对象
+        msg = MIMEMultipart()
+        msg["From"] = EMAIL_CONFIG["sender_email"]
+        msg["To"] = ", ".join(EMAIL_CONFIG["recipient_emails"])
+        msg["Subject"] = EMAIL_CONFIG["email_subject"]
+
+        # 邮件正文
+        msg.attach(MIMEText(EMAIL_CONFIG["email_body"], "plain"))
+
+        # 添加附件
+        with open(file_path, "rb") as attachment:
+            part = MIMEApplication(attachment.read(), Name=file_path)
+            part["Content-Disposition"] = f'attachment; filename="{file_path}"'
+            msg.attach(part)
+
+        # 连接 SMTP 服务器并发送邮件
+        server = smtplib.SMTP_SSL(EMAIL_CONFIG["smtp_server"], EMAIL_CONFIG["smtp_port"])
+        # server.starttls()
+        server.login(EMAIL_CONFIG["sender_email"], EMAIL_CONFIG["sender_password"])
+        server.sendmail(
+            EMAIL_CONFIG["sender_email"],
+            EMAIL_CONFIG["recipient_emails"],
+            msg.as_string(),
+        )
+        server.quit()
+        print("📧 邮件发送成功！")
+
+    except Exception as e:
+        print(f"❌ 发送邮件失败，错误信息：{e}")
+
+# # ✅ 7. 定时任务：每天 7 点执行
+# schedule.every().day.at("10:00").do(generate_and_send_report)
+#
+# # ⏰ 持续运行
+# print("⏰ 任务已启动，将在每天 10:00 自动执行...")
+# while True:
+#     schedule.run_pending()
+#     time.sleep(60)
+generate_and_send_report()

tools/生成三级页地址/生成三级页地址.py

@@ -0,0 +1,29 @@
+import base64
+
+
+
+class SimpleEncrypt:
+    def __init__(self, key):
+        self.key = key
+
+    def do_encode(self, bs):
+        tmp = bytearray(self.key.encode())
+        i = 0
+        while i < len(bs):
+            for j in range(len(tmp)):
+                if i >= len(bs):
+                    break
+                bs[i] = bs[i] ^ tmp[j]
+                i += 1
+
+    def encode_string(self, s):
+        data = bytearray(s.encode())
+        self.do_encode(data)
+        return base64.b64encode(data).decode()
+
+    def decode_string(self, s):
+        data = bytearray(base64.b64decode(s))
+        self.do_encode(data)
+        return data.decode()
+
+SE3 = SimpleEncrypt(key="entservice")