CareerNexus 职联：岗位能力图谱构建与动态演化分析平台（挑战杯 XH-202621）

- 全链路：多源JD接入/清洗交叉验证/结构化抽取(星火+离线双通道)/时序知识图谱/新岗位发现/演化分析/人岗匹配/学习路径 - WebUI 五视图：驾驶舱/图谱工作台/岗位演化/匹配(单人+批量)/学习路径 - 设置中心(config.json)/Token鉴权/SQLite状态库/语义向量通道(BGE-M3本地或联网API) - 71 个 pytest 用例；数据目录仅保留结构(.gitkeep)，数据由 scripts/generate_dataset.py 复现
2026-08-03 13:15:35 +08:00
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+"""简历解析：.txt 直接读，.docx 用 python-docx，.pdf 用 pypdf。
+
+解析后做四件事：脱敏（姓名/手机/邮箱/住址打码）、技能抽取并归一化到词表、
+工作年限与场景识别、结构化信息抽取（性别/学历/住址/分节/证件照），
+供规范简历模板展示与导出。
+"""
+from __future__ import annotations
+
+import base64
+import logging
+import re
+from pathlib import Path
+
+from app.extract.base import SkillMatcher, load_lexicon
+from app.models import ResumeProfile
+
+logger = logging.getLogger(__name__)
+
+_PHONE_RE = re.compile(r"1[3-9]\d{9}")
+_EMAIL_RE = re.compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}")
+_NAME_RE = re.compile(r"姓名[:：]\s*([一-龥]{2,4})")
+_YEARS_RE = re.compile(r"(\d+(?:\.\d+)?)\s*年(?:以上)?[^\n，。；]{0,6}?经验")
+_GENDER_RE = re.compile(r"性别[:：]?\s*(男|女)")
+_EDU_RE = re.compile(r"(博士后|博士|硕士|研究生|MBA|本科|大专|专科)")
+_ADDR_RE = re.compile(r"(?:家庭住址|现居(?:住)?(?:地|址)?|联系地址|通讯地址|籍贯)[:：]\s*([^\n，。；]{2,30})")
+
+# 常见简历分节标题（整行匹配，可带冒号）
+_SECTION_TITLES = {
+    "个人信息", "基本信息", "求职意向", "教育经历", "教育背景",
+    "工作经历", "工作经验", "实习经历", "项目经历", "项目经验",
+    "技能清单", "专业技能", "核心技能", "技能特长", "技能",
+    "自我评价", "个人评价", "荣誉证书", "荣誉奖项", "证书",
+}
+
+
+def mask_text(text: str) -> str:
+    """脱敏：手机、邮箱、姓名、住址打码。"""
+    text = _PHONE_RE.sub(lambda m: m.group(0)[:3] + "****" + m.group(0)[-2:], text)
+    text = _EMAIL_RE.sub(lambda m: m.group(0)[0] + "***@" + m.group(0).split("@")[1], text)
+    text = _NAME_RE.sub(lambda m: "姓名：" + m.group(1)[0] + "*" * (len(m.group(1)) - 1), text)
+    text = _ADDR_RE.sub(lambda m: m.group(0)[:2] + m.group(1)[:6] + "***"
+                        if len(m.group(1)) > 6 else m.group(0), text)
+    return text
+
+
+def split_sections(text: str) -> list[dict]:
+    """按常见标题把简历正文分节，返回 [{title, text}]；无标题时整体归入"简历全文"。"""
+    sections: list[dict] = []
+    cur_title, cur_lines = "基本信息", []
+    for ln in text.splitlines():
+        key = ln.strip().rstrip(":：").strip()
+        if key in _SECTION_TITLES:
+            if "\n".join(cur_lines).strip():
+                sections.append({"title": cur_title, "text": "\n".join(cur_lines).strip()})
+            cur_title, cur_lines = key, []
+        else:
+            cur_lines.append(ln)
+    if "\n".join(cur_lines).strip():
+        sections.append({"title": cur_title, "text": "\n".join(cur_lines).strip()})
+    if not sections and text.strip():
+        sections = [{"title": "简历全文", "text": text.strip()}]
+    return sections
+
+
+def extract_photo_b64(path: str | Path) -> str:
+    """从 docx/pdf 中提取第一张图片作为证件照，返回 data URI；无图片返回空串。"""
+    path = Path(path)
+    suffix = path.suffix.lower()
+    try:
+        if suffix == ".docx":
+            import zipfile
+            with zipfile.ZipFile(str(path)) as z:
+                names = sorted(n for n in z.namelist()
+                               if n.startswith("word/media/")
+                               and n.lower().endswith((".png", ".jpg", ".jpeg")))
+                if not names:
+                    return ""
+                data = z.read(names[0])
+                mime = "image/png" if names[0].lower().endswith(".png") else "image/jpeg"
+                return f"data:{mime};base64,{base64.b64encode(data).decode()}"
+        if suffix == ".pdf":
+            from pypdf import PdfReader
+            reader = PdfReader(str(path))
+            for page in reader.pages[:2]:
+                for img in getattr(page, "images", []):
+                    mime = "image/png" if img.name.lower().endswith(".png") else "image/jpeg"
+                    return f"data:{mime};base64,{base64.b64encode(img.data).decode()}"
+    except Exception as exc:  # 照片是增强信息，提取失败不阻断解析
+        logger.warning("简历照片提取失败（已跳过）: %s", exc)
+    return ""
+
+
+def read_resume_file(path: str | Path) -> str:
+    """按扩展名读取简历文件文本。"""
+    path = Path(path)
+    suffix = path.suffix.lower()
+    if suffix == ".txt":
+        return path.read_text(encoding="utf-8")
+    if suffix == ".docx":
+        from docx import Document  # python-docx，requirements 已声明
+        doc = Document(str(path))
+        return "\n".join(p.text for p in doc.paragraphs)
+    if suffix == ".pdf":
+        try:
+            from pypdf import PdfReader
+        except ImportError as exc:
+            raise RuntimeError(
+                "解析 .pdf 需要可选依赖 pypdf，请先执行：pip install pypdf"
+            ) from exc
+        reader = PdfReader(str(path))
+        return "\n".join(page.extract_text() or "" for page in reader.pages)
+    raise ValueError(f"不支持的简历格式：{suffix}（支持 .txt/.docx/.pdf）")
+
+
+def _mask_addr(addr: str) -> str:
+    """住址脱敏：保留前 6 字（大致到区/县），其余打码。"""
+    return addr[:6] + "***" if len(addr) > 6 else addr
+
+
+def parse_resume(text: str, matcher: SkillMatcher | None = None,
+                 photo_b64: str = "") -> ResumeProfile:
+    """从简历文本解析脱敏画像（含结构化基本信息与分节）。"""
+    matcher = matcher or SkillMatcher(load_lexicon())
+    name_m = _NAME_RE.search(text)
+    phone_m = _PHONE_RE.search(text)
+    email_m = _EMAIL_RE.search(text)
+    years_m = _YEARS_RE.search(text)
+    gender_m = _GENDER_RE.search(text)
+    edu_m = _EDU_RE.search(text)
+    addr_m = _ADDR_RE.search(text)
+    masked = mask_text(text)
+    return ResumeProfile(
+        name=(name_m.group(1)[0] + "*" * (len(name_m.group(1)) - 1)) if name_m else "",
+        phone=(phone_m.group(0)[:3] + "****" + phone_m.group(0)[-2:]) if phone_m else "",
+        email=(email_m.group(0)[0] + "***@" + email_m.group(0).split("@")[1]) if email_m else "",
+        years=float(years_m.group(1)) if years_m else 0.0,
+        skills=matcher.find_names(text),
+        scenarios=matcher.find_scenarios(text),
+        raw_masked_text=masked,
+        gender=gender_m.group(1) if gender_m else "",
+        education=edu_m.group(1) if edu_m else "",
+        address=_mask_addr(addr_m.group(1).strip()) if addr_m else "",
+        photo_b64=photo_b64,
+        sections=split_sections(masked),
+    )
+
+
+def parse_resume_file(path: str | Path, matcher: SkillMatcher | None = None) -> ResumeProfile:
+    """读取文件并解析（含证件照提取）。"""
+    return parse_resume(read_resume_file(path), matcher,
+                        photo_b64=extract_photo_b64(path))