commit 131e6295e7195b6fb95d1c5f704f7cb37fed4f10
Author: YangGuo <3420309028@qq.com>
Date:   Thu Apr 2 11:40:54 2026 +0800

    上传文件至「/」
    
    求职者人物画像

diff --git a/batcg_extract.py b/batcg_extract.py
new file mode 100644
index 0000000..eac93e9
--- /dev/null
+++ b/batcg_extract.py
@@ -0,0 +1,305 @@
+import json
+import time
+import os
+import logging
+from pathlib import Path
+from typing import Dict, Any, List
+import requests
+from tqdm import tqdm  # 进度条库，提升批量处理体验
+
+# ===================== 1. 全局配置（请根据实际情况修改） =====================
+class Config:
+    # API配置（替换为你的实际密钥/模型）
+    API_KEY = "你的通义千问API_KEY"  # 若用其他模型，需同步修改call_llm_api函数
+    API_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions"
+    MODEL = "qwen-turbo"  # 轻量模型，性价比最高
+    TEMPERATURE = 0.0  # 0=确定性输出，降低幻觉
+    MAX_TOKENS = 2000  # 控制输出长度，降低成本
+    
+    # 目录配置
+    INPUT_DIR = "./raw_candidate_files"  # 原始评审文件目录（需提前创建）
+    OUTPUT_DIR = "./extracted_json_results"  # 抽取结果保存目录（自动创建）
+    ERROR_LOG_PATH = "./batch_extract_error.log"  # 错误日志文件
+    
+    # 文件过滤配置
+    SUPPORTED_EXTENSIONS = [".txt", ".json"]  # 仅处理这些后缀的文件
+    SKIP_EXISTED = True  # 跳过已生成JSON的文件（避免重复调用API）
+    
+    # 重试配置
+    MAX_RETRY = 3  # API调用最大重试次数
+    RETRY_INTERVAL = 1  # 重试间隔（秒）
+
+# ===================== 2. 日志初始化（记录错误） =====================
+def init_logger():
+    """初始化错误日志，记录处理失败的文件及原因"""
+    logging.basicConfig(
+        level=logging.ERROR,
+        format="%(asctime)s - %(filename)s - %(message)s",
+        handlers=[
+            logging.FileHandler(Config.ERROR_LOG_PATH, encoding="utf-8"),
+            logging.StreamHandler()  # 同时输出到控制台
+        ]
+    )
+    return logging.getLogger(__name__)
+
+logger = init_logger()
+
+# ===================== 3. 工具函数 =====================
+def check_and_create_dirs():
+    """检查并创建输入/输出目录"""
+    Path(Config.INPUT_DIR).mkdir(exist_ok=True)
+    Path(Config.OUTPUT_DIR).mkdir(exist_ok=True)
+    logger.info(f"目录检查完成：输入目录={Config.INPUT_DIR}，输出目录={Config.OUTPUT_DIR}")
+
+def read_file_content(file_path: str) -> str:
+    """读取文件内容，处理编码问题"""
+    try:
+        with open(file_path, "r", encoding="utf-8") as f:
+            return f.read().strip()
+    except UnicodeDecodeError:
+        # 兼容GBK编码的文件
+        with open(file_path, "r", encoding="gbk") as f:
+            return f.read().strip()
+    except Exception as e:
+        raise Exception(f"读取文件失败：{e}")
+
+def build_prompt(raw_text: str) -> str:
+    """构建强约束的抽取提示词（核心！保证格式对齐）"""
+    # 固定模板（与你要求的JSON结构100%一致）
+    template = """
+{
+    "基础信息": {
+        "最终结果": "Invite/Reject",
+        "分数": "数字字符串",
+        "面试岗位": "字符串",
+        "技术画像": "核心技术特征总结（100字内）",
+        "邀请解读": "评审结论总结（100字内）"
+    },
+    "8维度信息": {
+        "潜力洞察": {
+            "得分": "数字字符串",
+            "核心亮点": "字符串",
+            "短板": "字符串（无则填无）",
+            "核心依据": "字符串"
+        },
+        "叙事逻辑": {
+            "得分": "数字字符串",
+            "核心亮点": "字符串",
+            "短板": "字符串（无则填无）",
+            "核心依据": "字符串"
+        },
+        "软件工艺": {
+            "得分": "数字字符串",
+            "核心亮点": "字符串",
+            "短板": "字符串（无则填无）",
+            "核心依据": "字符串",
+            "面试题": "字符串（无则省略该字段）"
+        },
+        "编程能力": {
+            "得分": "数字字符串",
+            "核心亮点": "字符串",
+            "短板": "字符串（无则填无）",
+            "核心依据": "字符串"
+        },
+        "综合网络": {
+            "得分": "数字字符串",
+            "核心亮点": "字符串",
+            "短板": "字符串（无则填无）",
+            "核心依据": "字符串"
+        },
+        "电子电路": {
+            "得分": "数字字符串",
+            "核心亮点": "字符串",
+            "短板": "字符串（无则填无）",
+            "核心依据": "字符串"
+        },
+        "操作系统": {
+            "得分": "数字字符串",
+            "核心亮点": "字符串",
+            "短板": "字符串（无则填无）",
+            "核心依据": "字符串"
+        },
+        "算法能力": {
+            "得分": "数字字符串",
+            "核心亮点": "字符串",
+            "短板": "字符串（无则填无）",
+            "核心依据": "字符串",
+            "面试题": "字符串（无则省略该字段）"
+        }
+    }
+}
+    """
+    
+    # 强制约束规则
+    constraints = """
+【强制规则】
+1. 输出必须是可直接解析的JSON格式，不允许任何多余文字（如“以下是抽取结果：”）；
+2. 字段层级、命名必须与模板100%一致，禁止新增/删减字段、修改字段名；
+3. 得分字段必须是数字字符串（如"7.8"），禁止纯数字或其他格式；
+4. 核心亮点/短板/核心依据需精简提炼，禁止大段复制原文；
+5. 无对应内容的字段填“无”，面试题无则省略该字段。
+    """
+    
+    prompt = f"""
+请你严格按照以下模板和规则，从下方原始文本中抽取信息并生成JSON：
+
+【模板】
+{template}
+
+【规则】
+{constraints}
+
+【原始文本】
+{raw_text}
+
+【输出要求】
+仅输出JSON字符串，无任何其他内容！
+    """
+    return prompt
+
+def call_llm_api(prompt: str) -> str:
+    """调用大模型API，带重试机制"""
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {Config.API_KEY}"
+    }
+    data = {
+        "model": Config.MODEL,
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": Config.TEMPERATURE,
+        "max_tokens": Config.MAX_TOKENS
+    }
+    
+    for retry in range(Config.MAX_RETRY):
+        try:
+            response = requests.post(Config.API_URL, headers=headers, json=data, timeout=30)
+            response.raise_for_status()  # 触发HTTP错误
+            result = response.json()
+            return result["choices"][0]["message"]["content"].strip()
+        except Exception as e:
+            logger.error(f"API调用重试{retry+1}/{Config.MAX_RETRY}失败：{e}")
+            time.sleep(Config.RETRY_INTERVAL)
+    raise Exception(f"API调用{Config.MAX_RETRY}次均失败")
+
+def validate_json(raw_json: str) -> Dict[str, Any]:
+    """校验JSON结构，确保核心字段不缺失"""
+    try:
+        data = json.loads(raw_json)
+    except json.JSONDecodeError as e:
+        raise ValueError(f"JSON解析失败：{e}")
+    
+    # 校验基础信息核心字段
+    base_fields = ["最终结果", "分数", "面试岗位", "技术画像", "邀请解读"]
+    if "基础信息" not in data:
+        raise ValueError("缺失顶级字段：基础信息")
+    for field in base_fields:
+        if field not in data["基础信息"]:
+            raise ValueError(f"基础信息缺失字段：{field}")
+    
+    # 校验8维度核心字段
+    dimensions = ["潜力洞察", "叙事逻辑", "软件工艺", "编程能力", "综合网络", "电子电路", "操作系统", "算法能力"]
+    dimension_fields = ["得分", "核心亮点", "短板", "核心依据"]
+    if "8维度信息" not in data:
+        raise ValueError("缺失顶级字段：8维度信息")
+    for dim in dimensions:
+        if dim not in data["8维度信息"]:
+            raise ValueError(f"8维度信息缺失维度：{dim}")
+        for field in dimension_fields:
+            if field not in data["8维度信息"][dim]:
+                raise ValueError(f"{dim}缺失字段：{field}")
+    
+    return data
+
+def process_single_file(file_path: Path) -> bool:
+    """处理单个文件：读取→调用API→校验→保存"""
+    # 生成输出文件名（原文件名+json后缀）
+    output_filename = f"{file_path.stem}.json"
+    output_path = Path(Config.OUTPUT_DIR) / output_filename
+    
+    # 跳过已处理的文件
+    if Config.SKIP_EXISTED and output_path.exists():
+        logger.info(f"跳过已处理文件：{file_path.name}")
+        return True
+    
+    try:
+        # 1. 读取文件内容
+        raw_text = read_file_content(str(file_path))
+        if not raw_text:
+            raise ValueError("文件内容为空")
+        
+        # 2. 构建提示词
+        prompt = build_prompt(raw_text)
+        
+        # 3. 调用API抽取
+        json_str = call_llm_api(prompt)
+        
+        # 4. 校验JSON结构
+        valid_data = validate_json(json_str)
+        
+        # 5. 保存结果
+        with open(output_path, "w", encoding="utf-8") as f:
+            json.dump(valid_data, f, ensure_ascii=False, indent=4)
+        
+        return True
+    
+    except Exception as e:
+        # 记录错误日志
+        logger.error(f"处理文件{file_path.name}失败：{str(e)}")
+        return False
+
+# ===================== 4. 批量处理主函数 =====================
+def batch_extract():
+    """批量处理目录下的所有文件"""
+    # 1. 检查目录
+    check_and_create_dirs()
+    
+    # 2. 获取所有待处理文件
+    input_files = []
+    for file in Path(Config.INPUT_DIR).glob("*"):
+        if file.is_file() and file.suffix in Config.SUPPORTED_EXTENSIONS:
+            input_files.append(file)
+    
+    if not input_files:
+        logger.error(f"输入目录{Config.INPUT_DIR}下无支持的文件（支持后缀：{Config.SUPPORTED_EXTENSIONS}）")
+        return
+    
+    # 3. 批量处理（带进度条）
+    success_count = 0
+    fail_count = 0
+    with tqdm(total=len(input_files), desc="批量抽取进度") as pbar:
+        for file in input_files:
+            if process_single_file(file):
+                success_count += 1
+            else:
+                fail_count += 1
+            pbar.update(1)
+    
+    # 4. 输出汇总结果
+    print("\n" + "="*50)
+    print(f"批量处理完成！")
+    print(f"总文件数：{len(input_files)}")
+    print(f"成功数：{success_count}")
+    print(f"失败数：{fail_count}")
+    print(f"失败日志：{Config.ERROR_LOG_PATH}")
+    print(f"结果目录：{Config.OUTPUT_DIR}")
+    print("="*50)
+
+# ===================== 5. 适配其他大模型的扩展（可选） =====================
+# 若使用OpenAI/GPT：
+# def call_llm_api(prompt: str) -> str:
+#     headers = {"Content-Type": "application/json", "Authorization": f"Bearer {Config.API_KEY}"}
+#     data = {
+#         "model": "gpt-3.5-turbo",
+#         "messages": [{"role": "user", "content": prompt}],
+#         "temperature": 0.0
+#     }
+#     response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=data)
+#     return response.json()["choices"][0]["message"]["content"]
+
+# ===================== 6. 主入口 =====================
+if __name__ == "__main__":
+    # 安装依赖（首次运行需执行）
+    # pip install requests tqdm
+    
+    # 执行批量抽取
+    batch_extract()
\ No newline at end of file
diff --git a/batch_pdf_resume_to_txt.py b/batch_pdf_resume_to_txt.py
new file mode 100644
index 0000000..d613180
--- /dev/null
+++ b/batch_pdf_resume_to_txt.py
@@ -0,0 +1,57 @@
+import PyPDF2
+import os
+from pathlib import Path
+
+def specified_pdf_to_txt(pdf_file_path: str, save_dir: str):
+    """
+    规则：文件名与原PDF完全一致 | 已存在则跳过不重复转换
+    :param pdf_file_path: 待转换的PDF完整路径
+    :param save_dir: TXT文件保存目录
+    """
+    # 1. 校验PDF文件是否存在
+    pdf_path = Path(pdf_file_path)
+    if not pdf_path.exists() or pdf_path.suffix.lower() != ".pdf":
+        print(f"错误：文件不存在或不是PDF格式 → {pdf_file_path}")
+        return False
+
+    # 2. 创建保存目录（不存在则自动创建）
+    os.makedirs(save_dir, exist_ok=True)
+
+    # 3. 生成目标TXT路径（**保留原PDF文件名，仅改后缀**）
+    txt_filename = pdf_path.stem + ".txt"
+    txt_save_path = Path(save_dir) / txt_filename
+
+    # 4. 关键：已存在同名TXT → 跳过，不重复转换
+    if txt_save_path.exists():
+        print(f"跳过：{txt_filename} 已存在，无需重复转换")
+        return True
+
+    # 5. 执行PDF转TXT
+    try:
+        with open(pdf_path, "rb") as f:
+            reader = PyPDF2.PdfReader(f)
+            full_text = ""
+            for page in reader.pages:
+                page_text = page.extract_text()
+                if page_text:
+                    full_text += page_text + "\n\n"
+
+        # 写入TXT到指定目录
+        with open(txt_save_path, "w", encoding="utf-8") as f:
+            f.write(full_text)
+
+        print(f"✅ 转换成功：{pdf_path.name} → {txt_save_path}")
+        return True
+
+    except Exception as e:
+        print(f"❌ 转换失败：{str(e)}")
+        return False
+
+if __name__ == "__main__":
+    # 待转换的【指定PDF文件完整路径】
+    TARGET_PDF = "./前后端/陈盼良简历(2).pdf"
+    # TXT文件【指定保存目录】
+    SAVE_DIRECTORY = "./output/前后端"
+
+    # 执行转换
+    specified_pdf_to_txt(TARGET_PDF, SAVE_DIRECTORY)
\ No newline at end of file
diff --git a/batch_resume_pdf_to_txt_clean_final.py b/batch_resume_pdf_to_txt_clean_final.py
new file mode 100644
index 0000000..3baf2b0
--- /dev/null
+++ b/batch_resume_pdf_to_txt_clean_final.py
@@ -0,0 +1,121 @@
+import pdfplumber
+import os
+import re
+from pathlib import Path
+
+# ===================== 终极清洗配置（全平台防伪码通杀） =====================
+# 终极正则：匹配 任意长度 字母/数字/下划线 组合 + 结尾~~ 的所有无意义字符
+FAKE_CODE_PATTERN = re.compile(r'[A-Za-z0-9_]{30,}~~')
+# ==========================================================================
+
+def clean_resume_text(raw_text: str) -> str:
+    """
+    终极文本清洗：彻底清除所有平台防伪码、隐形水印、重复乱码
+    1. 通杀所有 长随机字符+~~ 的防伪码
+    2. 清理多余空行、空格，保留简历正常格式
+    """
+    # 第一步：全局清除所有匹配的防伪乱码（核心根治）
+    cleaned_text = FAKE_CODE_PATTERN.sub('', raw_text)
+    
+    # 第二步：清理多余空行（≥3个换行→2个换行，保留段落）
+    cleaned_text = re.sub(r'\n{3,}', '\n\n', cleaned_text)
+    
+    # 第三步：清理行首行尾空格、多余空格
+    cleaned_text = re.sub(r'^[ ]+|[ ]+$', '', cleaned_text, flags=re.MULTILINE)
+    cleaned_text = re.sub(r' {2,}', ' ', cleaned_text)
+    
+    return cleaned_text.strip()
+
+def single_pdf_to_txt(pdf_file_path: Path, save_dir: Path) -> int:
+    """单文件PDF转TXT + 清洗乱码"""
+    if not pdf_file_path.exists() or pdf_file_path.suffix.lower() != ".pdf":
+        print(f"❌ 无效文件：{pdf_file_path.name}")
+        return -1
+
+    # 同名保存，不修改文件名
+    txt_filename = pdf_file_path.stem + ".txt"
+    txt_save_path = save_dir / txt_filename
+
+    # 已存在则跳过，不重复转换
+    if txt_save_path.exists():
+        print(f"⏭️  已存在，跳过：{txt_filename}")
+        return 0
+
+    try:
+        full_text = ""
+        with pdfplumber.open(pdf_file_path) as pdf:
+            for page in pdf.pages:
+                # 提取正文，过滤页边隐藏内容
+                page_text = page.extract_text()
+                if page_text:
+                    full_text += page_text + "\n\n"
+
+        # 终极清洗
+        cleaned_text = clean_resume_text(full_text)
+
+        # 写入干净TXT
+        with open(txt_save_path, "w", encoding="utf-8") as f:
+            f.write(cleaned_text)
+
+        print(f"✅ 转换+清洗完成：{pdf_file_path.name}")
+        return 1
+
+    except Exception as e:
+        print(f"❌ 失败：{pdf_file_path.name} | {str(e)}")
+        return -1
+
+def batch_pdf_to_txt(pdf_input_dir: str, txt_save_dir: str, recursive: bool = True):
+    """批量PDF转TXT主函数"""
+    input_path = Path(pdf_input_dir)
+    save_path = Path(txt_save_dir)
+
+    if not input_path.exists():
+        print(f"❌ 输入目录不存在：{input_path}")
+        return
+
+    os.makedirs(save_path, exist_ok=True)
+
+    # 扫描所有PDF
+    if recursive:
+        pdf_files = list(input_path.rglob("*.[pP][dD][fF]"))
+    else:
+        pdf_files = list(input_path.glob("*.[pP][dD][fF]"))
+
+    if not pdf_files:
+        print("⚠️ 未找到任何PDF文件")
+        return
+
+    print(f"📄 共找到 {len(pdf_files)} 个PDF，开始处理...\n")
+
+    # 统计结果
+    success = 0
+    skip = 0
+    fail = 0
+
+    for pdf in pdf_files:
+        res = single_pdf_to_txt(pdf, save_path)
+        if res == 1:
+            success +=1
+        elif res ==0:
+            skip +=1
+        else:
+            fail +=1
+
+    # 输出结果
+    print("\n" + "="*50)
+    print("🎉 批量处理完成")
+    print(f"总文件：{len(pdf_files)}")
+    print(f"✅ 成功：{success}")
+    print(f"⏭️  跳过：{skip}")
+    print(f"❌ 失败：{fail}")
+    print(f"📂 输出目录：{save_path.absolute()}")
+    print("="*50)
+
+if __name__ == "__main__":
+    # ========== 只需改这3个配置 ==========
+    PDF_INPUT_DIR = "./简历"       # 你的PDF文件夹
+    TXT_SAVE_DIR = "./清晰后的简历"  # 清洗后的TXT保存目录
+    IS_RECURSIVE = True                 # 是否递归子文件夹
+    # ====================================
+
+    batch_pdf_to_txt(PDF_INPUT_DIR, TXT_SAVE_DIR, IS_RECURSIVE)
\ No newline at end of file
diff --git a/cluster_wordcloud_plot.py b/cluster_wordcloud_plot.py
new file mode 100644
index 0000000..119c1b7
--- /dev/null
+++ b/cluster_wordcloud_plot.py
@@ -0,0 +1,16 @@
+from wordcloud import WordCloud
+import pandas as pd
+
+df = pd.read_csv("clustering_result.csv")
+# 合并所有技术画像文本
+text = " ".join(df["技术画像"].dropna().tolist())
+
+# 生成词云
+wc = WordCloud(
+    font_path="simhei.ttf",  # 中文字体
+    background_color="white",
+    width=1200, height=600,
+    max_words=100
+).generate(text)
+
+wc.to_file("赛道关键词词云图.png")
\ No newline at end of file
diff --git a/clustering_distribution_plot.py b/clustering_distribution_plot.py
new file mode 100644
index 0000000..c8ab326
--- /dev/null
+++ b/clustering_distribution_plot.py
@@ -0,0 +1,27 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+# 读取你的聚类结果CSV
+df = pd.read_csv("./clustering_result.csv")
+# 统计各赛道人数
+cluster_count = df["技术赛道标签"].value_counts()
+
+# 绘图设置
+plt.rcParams["font.sans-serif"] = ["SimHei"]
+plt.figure(figsize=(10,7))
+colors = ["#FF6B6B","#4ECDC4","#45B7D1","#96CEB4","#FECA57","#DDA0DD"]
+
+# 绘制饼图
+wedges, texts, autotexts = plt.pie(
+    cluster_count.values,
+    labels=cluster_count.index,
+    colors=colors,
+    autopct="%1.1f%%",
+    startangle=90,
+    textprops={"fontsize":10}
+)
+
+plt.title("简历聚类-技术赛道人数分布", fontsize=16, pad=20)
+plt.savefig("聚类赛道分布饼图.png", dpi=300, bbox_inches="tight")
+plt.show()
\ No newline at end of file