Merge branch 'dev' of http://106.13.42.156:33077/lei_y601/yj_resume

# Conflicts:
#	main.py

Dockerfile

@@ -0,0 +1,9 @@
+FROM docker.m.daocloud.io/python:3.12-slim
+WORKDIR /app
+COPY . /app
+ENV TZ=Asia/Shanghai \
+    LANG=C.UTF-8
+RUN rm -rf  logs .git .idea .venv && apt-get update && apt-get install -y vim curl sqlite3 && pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+RUN mkdir -p /app/uploads
+EXPOSE 3006
+CMD ["python", "main.py"]

db/sql_db.py

@@ -0,0 +1,48 @@
+from sqlalchemy import Column, DateTime, Integer, BigInteger, String, create_engine, Boolean, Text
+from sqlalchemy.orm import declarative_base, sessionmaker
+# 申明基类对象
+Base = declarative_base()
+from decouple import config
+
+DB_PATH = config('DB_PATH', default='D://PycharmProject//yj_resume//main.sqlite3')
+
+
+class DBTASK(Base):
+    __tablename__ = 'db_task'
+    id = Column(String(100), primary_key=True)
+    name = Column(String(100), nullable=False)
+    create_time = Column(DateTime, nullable=False, )
+    # 0 代表待执行，1 成功，2 失败
+    status = Column(Integer, nullable=False, default=0)
+    success_num = Column(Integer, nullable=False, default=0)
+    total_num = Column(Integer, nullable=False, default=0)
+    fail_num = Column(Integer, nullable=False, default=0)
+    message = Column(Text, nullable=True)
+
+
+class DBRESUME(Base):
+    __tablename__ = 'db_resume'
+    id = Column(String(100), primary_key=True)
+    # 每个任务对应一个文件夹ID
+    task_id = Column(String(100), nullable=False)
+    # 0 代表待执行，1 成功，2 失败
+    status = Column(Integer, nullable=False, default=0)
+    file_name = Column(String(100), nullable=True)
+    # 可以用json表示提取的数据
+    data_info = Column(Text, nullable=True)
+    # 错误信息等
+    message = Column(Text, nullable=True)
+
+
+class SqliteSqlalchemy(object):
+    def __init__(self):
+        # 创建sqlite连接引擎
+        engine = create_engine(f'sqlite:///{DB_PATH}', echo=True)
+        # 创建表
+        Base.metadata.create_all(engine, checkfirst=True)
+        # 创建sqlite的session连接对象
+        self.session = sessionmaker(bind=engine)()
+
+
+
+

logging_config.py

@@ -0,0 +1,62 @@
+# logging_config.py
+import logging
+import logging.config
+from pathlib import Path
+
+# 确保 logs 目录存在
+log_dir = Path("logs")
+log_dir.mkdir(exist_ok=True)
+
+LOGGING_CONFIG = {
+    "version": 1,
+    "disable_existing_loggers": False,
+    "formatters": {
+        "default": {
+            "format": "%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s",
+        },
+        "detailed": {
+            "format": "%(asctime)s - %(name)s - %(levelname)s - %(funcName)s - %(message)s",
+        }
+    },
+    "handlers": {
+        "console": {
+            "class": "logging.StreamHandler",
+            "level": "INFO",
+            "formatter": "default",
+            "stream": "ext://sys.stdout"
+        },
+        "file": {
+            "class": "logging.handlers.RotatingFileHandler",  # 自动轮转
+            "level": "INFO",
+            "formatter": "detailed",
+            "filename": "logs/resume.log",
+            "maxBytes": 10485760,  # 10MB
+            "backupCount": 5,      # 保留5个备份
+            "encoding": "utf8"
+        },
+    },
+    "root": {
+        "level": "INFO",
+        "handlers": ["console", "file"]
+    },
+    "loggers": {
+        "uvicorn": {
+            "level": "INFO",
+            "handlers": ["console", "file"],
+            "propagate": False
+        },
+        "uvicorn.error": {
+            "level": "INFO",
+            "handlers": ["console", "file"],
+            "propagate": False
+        },
+        "uvicorn.access": {
+            "level": "WARNING",  # 只记录警告以上，避免刷屏
+            "handlers": ["file"],  # 只写入文件
+            "propagate": False
+        }
+    }
+}
+
+# 应用配置
+logging.config.dictConfig(LOGGING_CONFIG)

main.py

@@ -1,5 +1,17 @@
 from fastapi import FastAPI
-import  uvicorn
+import uvicorn
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from typing import List
+from service.file_service import check_and_create_directory, upload_and_save_file,fetch_files
+from service import excel_service
+from service.db_service import get_task_list
+from fastapi.responses import FileResponse
+import threading
+from logging_config import LOGGING_CONFIG
+import logging
+
+logger = logging.getLogger(__name__)
+
 app = FastAPI()
 
 
@@ -8,7 +20,36 @@ def read_root():
     return {"Hello": "World"}
 
 
+# 上传文件并解析，解析是异步错误
+@app.post("/upload_files_and_parse")
+async def create_upload_files(files: List[UploadFile] = File(...)):
+    dir_id = check_and_create_directory(files)
+    if not dir_id:
+        return {"result": False, "code": 500, "message": "create directory failed"}
+    flag, message= await upload_and_save_file(dir_id, files)
+    logger.info(f"flag is {flag}")
+    if flag:
+       flag,message = await fetch_files(dir_id)
+    return {"result": flag, "message": message,"task_id": dir_id}
+
+
+@app.get("/export_task_data_to_excel")
+def export_task_data_to_excel(task_id: str):
+    path_xx = excel_service.export_task_data_to_excel(task_id)
+    if not path_xx:
+        raise HTTPException(status_code=404, detail="file not found")
+    return FileResponse(
+        path=path_xx,
+        media_type="application/octet-stream",  # 通用二进制流
+        filename=f"{task_id}.xlsx"  # 浏览器下载时使用的文件名
+    )
+
+
+@app.get("/parse_task_list")
+def parse_task_list():
+    data = get_task_list()
+    return {"data": data, "code": 200, }
 
 
 if __name__ == '__main__':
-    uvicorn.run(app, host="127.0.0.1", port=3006)
+    uvicorn.run(app, host="127.0.0.1", port=3006)

requirements.txt

@@ -1,3 +1,18 @@
-python-docx== 0.8.11
+python-docx
 fastapi
-uvicorn
+uvicorn
+docxtpl
+SQLAlchemy
+python-decouple
+python-multipart
+pandas
+openpyxl
+
+python-multipart
+PyMuPDF>=1.23.0
+paddlepaddle>=2.5.0
+paddleocr>=2.7.0.3
+opencv-python>=4.8.0
+numpy>=1.24.0
+pdf2image>=1.16.3
+Pillow>=10.0.0

service/db_service.py

@@ -0,0 +1,18 @@
+from db.sql_db import DBTASK, DBRESUME, SqliteSqlalchemy
+
+
+def get_task_list():
+    task_list = SqliteSqlalchemy().session.query(DBTASK).order_by(DBTASK.create_time.desc()).all()
+    result=[]
+    for task in task_list:
+        result.append({
+            "id": task.id,
+            "name": task.name,
+            "success_num": task.success_num,
+            "fail_num": task.fail_num,
+            "status": task.status,
+            "total_num": task.total_num,
+            "message": task.message,
+            "create_time": task.create_time.strftime("%Y-%m-%d %H:%M:%S") if task.create_time else None,
+        })
+    return result

service/excel_service.py

@@ -0,0 +1,35 @@
+from db.sql_db import DBTASK, DBRESUME, SqliteSqlalchemy
+import json
+import pandas as pd
+import pathlib
+from decouple import config
+
+# BASE_PATH = config('BASE_PATH', default='E://pyptoject//yj_resume//')
+BASE_PATH = config('BASE_PATH', default='D://PycharmProject//yj_resume//')
+
+
+# 导出数据到excel
+def export_to_excel(task_id):
+    # 获取所有成功的信息
+    list_data = SqliteSqlalchemy().session.query(DBRESUME).filter_by(task_id=task_id, status=1).all()
+    pd_data = []
+    for data in list_data:
+        pd_data.append(json.loads(data.data_info))
+    data_frame = pd.DataFrame(pd_data)
+    # 导出到excel
+    pathxx = pathlib.Path(BASE_PATH).joinpath(task_id)
+    pathxx = pathxx.joinpath(f"{task_id}.xlsx")
+    data_frame.to_excel(pathxx, index=False)
+
+
+def export_task_data_to_excel(task_id):
+    pathxx = pathlib.Path(BASE_PATH).joinpath(task_id)
+    pathxx = pathxx.joinpath(f"{task_id}.xlsx")
+    if pathxx.exists():
+        return pathxx
+    session = SqliteSqlalchemy().session
+    task = session.query(DBTASK).filter_by(id=task_id).first()
+    if not task or task.status == 0 or task.status == 2:
+        return None
+    export_to_excel(task_id)
+    return pathxx

service/file_service.py

@@ -0,0 +1,139 @@
+import json
+
+from pymupdf import message
+
+from db.sql_db import DBTASK, DBRESUME, SqliteSqlalchemy
+import uuid
+from datetime import datetime
+from decouple import config
+import pathlib
+from fastapi import File, UploadFile
+from typing import List
+import os
+import asyncio
+import logging
+
+from service.parse_resume2_doc import extra_resume
+
+logger = logging.getLogger(__name__)
+BASE_PATH = config('BASE_PATH', default='D://PycharmProject//yj_resume//')
+
+
+
+
+def check_and_create_directory(files):
+    logger.info("check_and_create_directory in service")
+    # 先创建一个task
+    if not files or len(files) == 0:
+        return None
+    id = str(uuid.uuid4())
+    current_time = datetime.now()
+    # 格式化时间为字符串
+    formatted_time = current_time.strftime("%Y-%m-%d-%H-%M-%S")
+    task = DBTASK(id=id, create_time=datetime.now(), status=0, success_num=0, total_num=len(files),
+                  fail_num=0,name=f"解析任务({formatted_time})")
+
+    session = SqliteSqlalchemy().session
+    try:
+        session.add(task)
+        session.commit()
+    except Exception as e:
+        print(f"Failed to save DBTASK info  error {e}")
+        session.rollback()
+        return None
+    finally:
+        session.close()
+    return id
+
+
+async def upload_and_save_file(dir_id, files: List[UploadFile]) -> (bool, str):
+    logger.info(f"upload_and_save_file in service dir_id {dir_id}")
+    pathxx = pathlib.Path(BASE_PATH).joinpath(dir_id)
+    pathxx.mkdir(parents=True, exist_ok=True)
+    data = []
+    for file in files:
+        name, fix = os.path.splitext(file.filename)
+        id = str(uuid.uuid4())
+        if fix not in ['.doc', '.docx']:
+            continue
+        with open(pathxx.joinpath(id + fix), 'wb') as f:
+            file_content = await file.read()
+            f.write(file_content)
+
+        data.append(DBRESUME(id=id, task_id=dir_id, status=0, file_name=id + fix))
+    session = SqliteSqlalchemy().session
+    try:
+        session.bulk_save_objects(data)
+        session.commit()
+    except Exception as e:
+        print(f"Failed to save DBRESUME  error {e}")
+        session.rollback()
+        return False, f"Failed to save DBRESUME  error {e}",[]
+    finally:
+        session.close()
+    return True, "success"
+
+async def fetch_files(dir_id) -> (bool, str):
+
+    logger.info(f"start fetching files task {dir_id} in service")
+    if not os.path.exists(BASE_PATH):
+        logger.info(f"目录{BASE_PATH}不存在")
+        return None
+    file_extensions = ['.docx', '.doc']
+    files_list = []
+    dir_path = pathlib.Path(BASE_PATH).joinpath(dir_id)
+    for root,dirs,files in os.walk(dir_path):
+        for file in files:
+            _,ext = os.path.splitext(file)
+            if file_extensions and ext not in file_extensions:
+                logger.error(f"文件{file}格式不符合预期")
+                continue
+            file_path = os.path.join(root,file)
+            if os.path.isfile(file_path):
+                files_list.append(file_path)
+            else:
+                logger.error(f"路径下{file_path}不是文件")
+    update_success_mapping = []
+    update_fail_mapping = []
+    for file in files_list:
+        logger.info(f"file is {file} {os.path.basename(file)}")
+        file_name = os.path.basename(file)
+        id = os.path.splitext(file_name)[0]
+        result = extra_resume(file)
+        result = json.dumps(result, ensure_ascii=False)
+        logger.info(f"result type is {type(result)}")
+        logger.info(f"file content is {result}")
+        if not result:
+            logger.warning(f"file {file_name} 提取为空")
+            update_fail_mapping.append({'id':id, 'status':0,
+                                   'message': f"task {dir_id} => file {file_name} 提取为空"})
+            continue
+        update_success_mapping.append({'id':id, 'status':1,'data_info': result})
+    session = SqliteSqlalchemy().session
+    logger.info(f"update success mapping => {update_success_mapping}")
+    logger.info(f"update fail mapping => {update_fail_mapping}")
+    success_num = len(update_success_mapping)
+    fail_num = len(update_fail_mapping)
+    try:
+        update_data = update_success_mapping + update_fail_mapping
+        session.bulk_update_mappings(DBRESUME, update_data)
+
+        if update_fail_mapping:
+            session.bulk_update_mappings(DBTASK, [{'id':dir_id, 'status':2, 'success_num':success_num,
+                                                   'fail_num':fail_num,'message':f'fail => {update_fail_mapping}'}])
+        else:
+            session.bulk_update_mappings(DBTASK, [{'id': dir_id, 'status': 1,
+                                                   'success_num': success_num, 'fail_num': fail_num}])
+        session.commit()
+    except Exception as e:
+        logger.error(f"update failed => task {dir_id} error {e}")
+        session.rollback()
+        return False, f"Failed to update DBRESUME  error {e}"
+    finally:
+        session.close()
+
+    return True, 'success'
+
+
+
+

service/format_template_resume.py

@@ -0,0 +1,35 @@
+from docxtpl import DocxTemplate
+from pathlib import Path
+
+context = {
+    'name': '张三',
+    'sex': '男',
+    'nation': '汉族',
+    'brith': '1990-01-01',
+    'address': '北京市海淀区西二旗',
+    'education': '本科',
+    'degree': '学士',
+    # 籍贯
+    'origin': '山东',
+    'politics': '党员',
+    # 部门
+    'department': '数信部',
+    'position': '助理开发工程师',
+    'phone': '13812345678',
+    'title': '后端开发工程师',
+    'start_work_time': '2018-01-01',
+    # 身份证
+    'id_number': '500221199001010010101',
+    # 荣誉，交给大模型
+    'honor': '一等奖',
+    # 工作内容
+    'work_text': '''
+    2023.12-2024.10：负责《边缘计算+5G自组网的水电物联网系统建设与研究》项目异常检测算法和项目实施：利用5G自组网技术、自建边缘计算单元等，实际实现在线异常检测、时间序列趋势分析、模型轻量化等功能，缓解通信带宽压力；在观音岩、彭水、渝能等场站实施应用。完成项目科技成果凝练、项目报奖等工作。本项目工作获得第六届全国设备管理与技术创新成果一等奖、中电联职工创新成果二等奖。
+2024.04-2025.至今：广西河池源网荷储一体化项目/大唐西藏玉曲河扎拉电厂可行性研究报告&方案编写、AI支持中心方案策划
+'''
+
+}
+file_path = Path.cwd().joinpath('template.docx')
+template = DocxTemplate(file_path)
+template.render(context)
+template.save('E://resu//output.docx')

service/parse_resume2_doc.py

@@ -0,0 +1,384 @@
+import re
+import json
+from pathlib import Path
+
+from docx import Document
+from typing import Dict, List, Any, Tuple
+from collections import defaultdict
+
+class EnhancedDocxExtractor:
+    def __init__(self):
+        # 定义字段名称的多种变体
+        self.field_variants = {
+            '姓名': ['姓名', '姓 名', '姓  名', '姓名：', '姓 名：','姓    名'],
+            '性别': ['性别', '性 别', '性  别', '性别：', '性 别：','性    别'],
+            '出生年月': ['出生年月', '出生年月：', '出生日期', '出生日期：'],
+            '民族': ['民族', '民族：', '民 族'],
+            '政治面貌': ['政治面貌', '政治面貌：', '政治面貌：'],
+            '现任职单位及部门': ['现任职单位及部门', '单位及部门', '工作单位', '现任职单位'],
+            '联系电话': ['联系电话', '电话', '手机', '联系电话：', '手机号'],
+            '联系地址': ['联系地址', '地址', '联系地址：', '家庭地址'],
+            '学历学位': ['学历', '学历：', '学 历', '学历\n学位','学位','学位：','学 位'],
+            '毕业院校': ['毕业院校', '毕业学校', '毕业院校：','毕业院校系及专业'],
+            '专业': ['专业', '专业：', '系及专业', '所学专业'],
+        }
+
+    def extract_with_table_structure(self, docx_path: str) -> Dict[str, Any]:
+        """
+        提取 .docx 中的表格结构数据
+        """
+        doc = Document(docx_path)
+        results = defaultdict(dict)
+        # 分析每个表格
+        for table_idx, table in enumerate(doc.tables):
+            print(f"\n处理表格 {table_idx + 1} ({len(table.rows)}行 × {len(table.columns)}列)")
+
+            # 获取表格结构
+            table_structure = self._analyze_table_structure(table)
+            # 提取键值对
+            kv_pairs = self._extract_from_table_structure(table, table_structure)
+            # 分类存储
+            for key, value in kv_pairs:
+                category = self._categorize_field(key)
+                results[category][key] = value
+        # 提取段落中的信息
+        paragraph_info = self._extract_from_paragraphs(doc.paragraphs)
+        for key, value in paragraph_info:
+            category = self._categorize_field(key)
+            results[category][key] = value
+
+        return dict(results)
+
+    def _analyze_table_structure(self, table) -> List[List[Dict]]:
+        """
+        分析表格结构，返回每个单元格的元信息
+        """
+        structure = []
+
+        for row_idx, row in enumerate(table.rows):
+            row_info = []
+            for col_idx, cell in enumerate(row.cells):
+                cell_text = cell.text.strip()
+                # 分析单元格属性
+                cell_info = {
+                    'text': cell_text,
+                    'row': row_idx,
+                    'col': col_idx,
+                    'rowspan': 1,
+                    'colspan': 1,
+                    'is_key': self._is_likely_key(cell_text),
+                    'is_value': self._is_likely_value(cell_text),
+                }
+                row_info.append(cell_info)
+            structure.append(row_info)
+
+        return structure
+
+    def _extract_from_table_structure(self, table, structure) -> List[Tuple[str, str]]:
+        """
+        从表格结构中提取键值对
+        """
+        kv_pairs = []
+        visited = set()
+        key_recode = []
+        for row_idx, row in enumerate(structure):
+            for col_idx, cell in enumerate(row):
+                print(f"visited is {visited} ")
+                print(f'row {row_idx} col {col_idx} all cell is {cell}')
+                if (row_idx, col_idx) in visited:
+                    print(f'---{row_idx}, {col_idx}  ')
+                    print(f'cell is {cell}')
+                    continue
+
+                if cell['is_key']:
+                    next_cell = structure[row_idx][col_idx+1]
+                    # 寻找对应的值
+                    print(f"cell2 is {cell} row {row_idx} col {col_idx}")
+                    value = self._find_value_for_key(table, structure, row_idx, col_idx, visited, kv_pairs)
+                    if value:
+                        key = self._normalize_key(cell['text'])
+                        found = False
+                        kv_pairs = [(k,v+","+value)if k == cell['text'] else (k, v) for k,v in kv_pairs ]
+                        for i, (k,v) in enumerate(kv_pairs):
+                            if k == cell['text']:
+                                kv_pairs[i] = (k,value)
+                                found = True
+                        if not found:
+                            kv_pairs.append((key, value))
+
+                else:
+                    print("不是key")
+        return kv_pairs
+
+    def _find_value_for_key(self, table, structure, key_row, key_col, visited, kv_pairs) -> str:
+        """
+        为键找到对应的值
+        """
+        # 尝试右侧单元格
+        if key_col + 1 < len(structure[key_row]):
+            value_cell = structure[key_row][key_col + 1]
+            current_key_cell = structure[key_row][key_col]
+            if value_cell['is_key']:
+                return None
+            # 特殊处理学历
+            spec_coll = ['全日制教育','在职教育']
+            if current_key_cell['text'].replace('\n','') in spec_coll :
+                if not value_cell['text']:
+                    value_cell['text'] = 'False'
+                else:
+                    value_cell['text'] = 'True'
+
+            if value_cell['text'] and (key_row, key_col + 1) not in visited:
+                # 检查这个值是否与前一个键提取的值相同（可能是合并单元格）
+                if not self._is_key_duplicate_merged_cell(structure[key_row][key_col]['text'], kv_pairs):
+                    print("前一个不重复")
+                    print(f"visited add {key_row} {key_col + 1}")
+                    visited.add((key_row, key_col + 1))
+                    return value_cell['text']
+                else:
+                    current_key = structure[key_row][key_col]['text']
+                    print(f"key值重复------------------------------key {current_key}")
+                    for key, value in kv_pairs:
+                        if key == current_key:
+                            return value+","+value_cell['text']
+
+
+        # 尝试下方单元格
+        if key_row + 1 < len(structure):
+            value_cell = structure[key_row + 1][key_col]
+            if value_cell['text'] and (key_row + 1, key_col) not in visited:
+                # 检查这个值是否与前一个键提取的值相同（可能是合并单元格）
+                if not self._is_key_duplicate_merged_cell(structure[key_row][key_col]['text'],  kv_pairs):
+                    print("下一个不重复")
+                    print(f"visited add {key_row} {key_col + 1}")
+                    visited.add((key_row + 1, key_col))
+                    return value_cell['text']
+
+        # 尝试合并单元格的情况
+        for row_idx in range(len(structure)):
+            for col_idx in range(len(structure[row_idx])):
+                cell = structure[row_idx][col_idx]
+                if (row_idx, col_idx) not in visited and cell['text']:
+                    # 检查是否在键的附近
+                    if abs(row_idx - key_row) <= 2 and abs(col_idx - key_col) <= 2:
+                        # 检查这个值是否与前一个键提取的值相同
+                        if not self._is_key_duplicate_merged_cell(structure[key_row][key_col]['text'], kv_pairs):
+                            print("合并不重复")
+                            print(f"visited add {key_row} {key_col + 1}")
+                            visited.add((row_idx, col_idx))
+                            return cell['text']
+        return None
+
+
+    def _is_key_duplicate_merged_cell(self, text, kv_pairs) -> bool:
+        """
+        检查当前文本value是否可能和已收录的kv集合里的key值重复
+        如下例：1行0列 ，2行0列 都是毕业院校
+        第一次 1行0列:1行2列组成key:value
+        第二次到2行0列，检测到 毕业院校已出现在kv_pairs中，不再组合2行0列:2行1列
+                | 硕士学位/研究生学历：中国科学院计算技术研究所计算机技术专业
+        毕业院校 |——————————————————————————————————————————————————
+               |
+               |————————————————————————————————————————————————————
+        """
+
+        for k, v in kv_pairs:
+            if text == k:
+                return True
+
+        return False
+
+    def extract_parentheses_content(self, text):
+        # 使用正则表达式提取括号内的所有内容
+        matches = re.findall(r'[（(]([^）)]*)[）)]', text)
+
+        return matches # 返回列表，可能包含多个括号
+
+    def _is_likely_key(self, text: str) -> bool:
+        """判断文本是否可能是键"""
+        if not text or len(text) > 20:
+            return False
+
+        # 检查是否包含常见字段词
+        key_indicators = ['籍贯','籍 贯','政治面貌','政治\n面貌','姓名','性别','姓 名', '性 别', '出生年月', '民族','民 族', '单位', '部门','联系地址','主要学习经历','全日制教育','在职教育',
+                          '职务','职 务','职\n务', '职称','职 称', '电话', '地址', '学历', '学位','现任职务','职业资格','奖惩情况（近三年主要奖惩信息）'
+                          '专业', '岗位', '经历', '时间', '资格','现任职单位及部门','身份证号','婚姻状况','健康状况','应聘岗位','应聘部门/岗位','毕业院校系及专业']
+
+        for indicator in key_indicators:
+            translation_table = str.maketrans('', '', ' \t\n\r\f\v')
+            indicator = indicator.translate(translation_table)
+            text = text.translate(translation_table)
+            if indicator in text:
+                return True
+
+        # 检查是否有冒号（中文文档常用）
+        if '：' in text or ':' in text:
+            key_part = text.split('：')[0].split(':')[0]
+            if any(indicator in key_part for indicator in key_indicators):
+                return True
+
+        for indicator in key_indicators:
+            print("indicator is ===============================", indicator)
+            print("text is ===============================", text)
+            translation_table = str.maketrans('', '', ' \t\n\r\f\v')
+            indicator = indicator.translate(translation_table)
+            text = text.translate(translation_table)
+            clean_text = self.extract_parentheses_content(text)
+            print(text)
+            clean_indicator = self.extract_parentheses_content(indicator)
+            print(indicator)
+            if not clean_text:
+                print("特殊匹配失败")
+                return False
+            if clean_indicator:
+                print("开始匹配=========")
+                clean_text = clean_text[0]
+                clean_indicator = clean_indicator[0]
+                if clean_indicator in clean_text:
+                    print(f"特殊情况匹配成功======={text}")
+                    return True
+            else:
+                print("继续匹配")
+                continue
+
+        return False
+
+    def _is_likely_value(self, text: str) -> bool:
+        """判断文本是否可能是值"""
+        if not text:
+            return False
+
+        # 值通常不是常见的字段名
+        if self._is_likely_key(text):
+            return False
+
+        # 值可能包含特定内容
+        if re.match(r'^\d{11}$', text):  # 手机号
+            return True
+        if re.match(r'^\d{4}年', text):  # 日期
+            return True
+        if len(text) > 10:  # 长文本可能是值
+            return True
+
+        return True
+
+    def _normalize_key(self, key_text: str) -> str:
+        """标准化键名"""
+        # 移除冒号和空格
+        key_text = re.sub(r'[：:\s]+$', '', key_text)
+
+        # 映射到标准键名
+        for std_key, variants in self.field_variants.items():
+            for variant in variants:
+                if variant == key_text or key_text in variant:
+                    return std_key
+
+        return key_text
+
+    def _categorize_field(self, key: str) -> str:
+        """将字段分类"""
+        categories = {
+            '基本信息': ['姓名', '性别', '出生年月', '民族', '政治面貌','学历学位','毕业院校系及专业','全日制教育','在职教育'
+                         '婚姻状况', '健康状况', '籍贯', '身份证号','联系电话','婚姻状况','健康状况','身份证号','联系电话（手机）','毕业院校系及专业','联系地址','主要学习经历','奖惩情况（近三年主要奖惩信息）'],
+            '工作信息': ['现任职单位及部门', '现任职务', '职称', '职业资格',
+                         '参加工作时间', '职称取得时间','应聘部门/岗位','是否接受调剂职级/岗位','奖惩情况（近三年主要奖惩信息）'],
+        }
+
+        for category, fields in categories.items():
+            translation_table = str.maketrans('', '', ' \t\n\r\f\v')
+            key = key.translate(translation_table)
+            if key in fields:
+                # print(f"filed is {fields} key is {key} ")
+                return category
+
+        return '其他信息'
+
+    def _extract_from_paragraphs(self, paragraphs) -> List[Tuple[str, str]]:
+        """从段落中提取信息"""
+        kv_pairs = []
+
+        for para in paragraphs:
+            text = para.text.strip()
+            if not text:
+                continue
+
+            # 尝试提取冒号分隔的键值对
+            if '：' in text or ':' in text:
+                separator = '：' if '：' in text else ':'
+                parts = text.split(separator, 1)
+
+                if len(parts) == 2:
+                    key = parts[0].strip()
+                    value = parts[1].strip()
+
+                    if self._is_likely_key(key) and value:
+                        normalized_key = self._normalize_key(key)
+                        kv_pairs.append((normalized_key, value))
+
+        return kv_pairs
+
+
+# 快速使用示例
+def quick_extract(docx_path: str):
+    """快速提取并显示结果"""
+    extractor = EnhancedDocxExtractor()
+
+    try:
+        result = extractor.extract_with_table_structure(docx_path)
+        print("\n提取结果 (键值对格式):")
+        print("=" * 60)
+
+        for category, fields in result.items():
+            if fields:
+                print(f"\n{category}:")
+                for key, value in fields.items():
+                    print(f"  {key}: {value}")
+        return result
+
+    except Exception as e:
+        print(f"提取失败: {e}")
+
+
+base_map = ['姓名','性别','籍贯','政治面貌','出生年月','身份证号','现居住地','民族','学历','学位','学历学位','特长','联系电话','联系电话（手机）',
+            '婚姻状况','健康状况','毕业院校系及专业','主要学习经历','联系地址','入党/团时间','全日制教育','在职教育','奖惩情况（近三年主要奖惩信息）']
+work_map = ['参加工作时间','现任职单位及部门','职务','现任职务','职称','奖惩','工作经历','主要工作经历','职称取得时间','职业资格','应聘部门/岗位']
+other_map = ['工作经历','主要工作经历','职称取得时间','职业资格','应聘部门/岗位','是否接受调剂职级/岗位']
+
+
+
+def fetch_info(data):
+    map_word = base_map + work_map + other_map
+    print("data is {0}".format(data))
+    print("map_word is {0}".format(map_word))
+    final_res = {}
+    for key, value in data.items():
+        translation_table = str.maketrans('', '', ' \t\n\r\f\v')
+        clean_key = key.translate(translation_table)
+        print(f"key is {clean_key} ")
+        if clean_key in map_word:
+            # clean_value = value.translate(translation_table)
+            final_res[clean_key] = value
+
+    return final_res
+
+
+def extra_resume(file_path):
+    result = quick_extract(file_path)
+    print(result)
+    base_data = result['基本信息']
+    work_data = result['工作信息']
+    other_data = result['其他信息']
+    data = {}
+    data.update(base_data)
+    data.update(work_data)
+    data.update(other_data)
+    res = fetch_info(data)
+    return res
+
+
+# if __name__ == "__main__":
+#     # 使用方法
+#     docx_file = "../1.报名登记表.docx"  # 替换为你的文件
+#     print(extra_resume(docx_file))
+
+