4a8613b27e
- run_match 新增 skip_incomplete：批量缺省跳过画像不完整教师并记录 skipped，指定单教师仍 PROFILE_INCOMPLETE；汇总页出"画像待补录"行 - render-html 补 --report-id 行上下文（inputMapping $row.$pk），CLI 容错解析回退最近 10 份 - report_service 日志改容错 _log()，修复未 setup 时单跑测试崩溃的顺序依赖 - fixtures 替换为教师画像/赛道/期望结论金标准样例，新增 sample_tracks.json - 新增 4 个批量跳过用例（314 项，除 3 个已知 jsonschema 环境失败外全绿） - tests/README 增补本机无共享 runtime 的测试方式；TUTORIAL/ACTIONS.md 同步表述
378 lines
14 KiB
Python
378 lines
14 KiB
Python
"""赛道匹配内核（唯一业务内核）。
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匹配算法（对齐《核心建设内容：各部门"Skill"深度需求规格书》科研处 Skill 001）：
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H1/H2 硬门槛过滤（职称 / SCI 一区）→ 剔除并说明原因
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S1 标签覆盖率 = 教师标签 ∩ 赛道标签 / 赛道标签数
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S2 优先方向命中（每命中 +0.15，封顶 0.3）
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S3 综合得分 = 0.7×覆盖率 + 优先命中（百分数展示）
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T1 分层：≥80 强烈推荐；50~80 可尝试；<50 不建议
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T2 Top 3 排序输出
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E1 画像完整性：无研究标签 → 不匹配，提示补录
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全部得分确定性计算、理由可追溯到具体标签；推荐仅供参考，不构成申报决定。
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"""
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from __future__ import annotations
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import json
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import uuid
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from typing import Any, Optional
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from db.connection import get_conn, init_db
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from util.logging_config import get_skill_logger
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from util.timeutil import now_unix
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TIER_STRONG = "强烈推荐"
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TIER_TRY = "可尝试"
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TIER_NOT = "不建议"
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_TIER_RANK = {TIER_NOT: 0, TIER_TRY: 1, TIER_STRONG: 2}
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# 职称档位：数值越大越高职称；门槛 min_title 使用"X及以上"档位
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_TITLE_RANK = {
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"助教": 0,
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"讲师": 1,
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"助理研究员": 1,
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"博士后": 1,
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"副高及以上": 2,
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"副教授": 2,
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"副研究员": 2,
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"高级实验师": 2,
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"正高及以上": 3,
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"教授": 3,
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"研究员": 3,
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}
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_STRONG_THRESHOLD = 80
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_TRY_THRESHOLD = 50
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def _log():
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try:
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return get_skill_logger()
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except RuntimeError:
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return None
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def _split_tags(raw: Any) -> list[str]:
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"""把逗号/分号/顿号分隔的标签文本拆成去空格列表。"""
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if not raw:
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return []
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import re
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return [t for t in (s.strip() for s in re.split(r"[,，;；、\n]", str(raw))) if t]
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def _title_rank(title: str) -> Optional[int]:
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if not title:
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return None
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return _TITLE_RANK.get(str(title).strip())
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def _min_title_rank(min_title: str) -> Optional[int]:
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text = str(min_title or "").strip()
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if not text:
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return None
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if text in _TITLE_RANK:
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return _TITLE_RANK[text]
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if text.endswith("及以上"):
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return _TITLE_RANK.get(text[:-3])
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return None
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def _norm_teacher(raw: dict[str, Any]) -> dict[str, Any]:
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"""归一化教师画像键（兼容 camelCase / snake_case / 常见中文键）。"""
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aliases = {
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"teacherName": ("teacher_name", "姓名", "教师姓名"),
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"department": ("department", "学院", "所属学院"),
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"title": ("title", "职称"),
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"researchTags": ("research_tags", "研究标签", "标签"),
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"recentPapers": ("recent_papers", "近3年论文", "论文"),
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"projectHistory": ("project_history", "项目经历", "项目"),
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"sciQ1Count": ("sci_q1_count", "sci一区", "sci一区篇数"),
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"corePaperCount": ("core_paper_count", "核心论文", "核心论文篇数"),
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}
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out: dict[str, Any] = {}
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for key, value in raw.items():
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out.setdefault(str(key), value)
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for canonical, names in aliases.items():
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if str(key) in names or _camel(str(key)) == canonical:
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out.setdefault(canonical, value)
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return out
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def _norm_track(raw: dict[str, Any]) -> dict[str, Any]:
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aliases = {
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"year": ("year", "年度", "指南年度"),
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"agency": ("agency", "学部", "归口"),
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"programName": ("program_name", "项目类型"),
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"trackName": ("track_name", "赛道名称", "赛道"),
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"trackTags": ("track_tags", "需求标签"),
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"priorityKeywords": ("priority_keywords", "优先方向", "优先资助方向"),
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"minTitle": ("min_title", "职称门槛"),
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"minSciQ1": ("min_sci_q1", "sci一区门槛"),
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"quota": ("quota", "拟资助数"),
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"sourceDoc": ("source_doc", "指南来源"),
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}
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out: dict[str, Any] = {}
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for key, value in raw.items():
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out.setdefault(str(key), value)
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for canonical, names in aliases.items():
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if str(key) in names or _camel(str(key)) == canonical:
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out.setdefault(canonical, value)
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return out
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def _camel(name: str) -> str:
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parts = str(name).split("_")
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if len(parts) <= 1:
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return str(name)
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head, *rest = parts
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return head + "".join(p[:1].upper() + p[1:] for p in rest if p)
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def _to_int(value: Any) -> Optional[int]:
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if value is None or value == "":
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return None
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try:
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return int(float(value))
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except (TypeError, ValueError):
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return None
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def match_teacher(teacher: dict[str, Any], tracks: list[dict[str, Any]], season: str) -> dict[str, Any]:
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"""对单个教师执行硬门槛过滤 + 标签匹配，返回分层结果（不落库）。"""
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teacher = _norm_teacher(teacher)
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tracks = [_norm_track(t) for t in tracks]
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teacher_name = str(teacher.get("teacherName") or "").strip() or "未命名教师"
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teacher_tags = _split_tags(teacher.get("researchTags"))
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papers_text = str(teacher.get("recentPapers") or "")
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projects_text = str(teacher.get("projectHistory") or "")
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sci_q1 = _to_int(teacher.get("sciQ1Count")) or 0
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title_rank = _title_rank(str(teacher.get("title") or ""))
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if not teacher_tags and not papers_text:
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raise ValueError(f"教师 {teacher_name} 画像不完整：请至少填写研究标签或论文标题（E1）")
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eligible: list[dict[str, Any]] = []
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excluded: list[dict[str, Any]] = []
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for track in tracks:
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track_name = str(track.get("trackName") or "").strip()
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program = str(track.get("programName") or "").strip()
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label = f"{program}·{track_name}" if program else track_name
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min_title = str(track.get("minTitle") or "").strip()
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required_rank = _min_title_rank(min_title)
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if required_rank is not None and (title_rank is None or title_rank < required_rank):
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excluded.append({"track": label, "reason": f"职称门槛：要求{min_title}，现为{teacher.get('title') or '未填写'}（H1）"})
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continue
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min_sci = _to_int(track.get("minSciQ1")) or 0
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if min_sci > 0 and sci_q1 < min_sci:
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excluded.append({"track": label, "reason": f"SCI一区门槛：要求 {min_sci} 篇，现有 {sci_q1} 篇（H2）"})
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continue
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track_tags = _split_tags(track.get("trackTags"))
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if not track_tags:
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excluded.append({"track": label, "reason": "赛道未配置需求标签，无法匹配（数据待补）"})
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continue
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matched_tags = [t for t in track_tags if t in teacher_tags]
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coverage = round(len(matched_tags) / len(track_tags), 4)
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priority_hits = []
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haystack = ",".join(teacher_tags) + ";" + papers_text + ";" + projects_text
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for keyword in _split_tags(track.get("priorityKeywords")):
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if keyword and keyword in haystack:
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priority_hits.append(keyword)
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score = round(0.7 * coverage + min(len(priority_hits) * 0.15, 0.3), 4)
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match_pct = int(round(score * 100))
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reasons = []
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if matched_tags:
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reasons.append(f"命中需求标签：{'、'.join(matched_tags)}（覆盖 {len(matched_tags)}/{len(track_tags)}）")
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if priority_hits:
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reasons.append(f"优先资助方向命中：{'、'.join(priority_hits)}")
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if min_sci > 0:
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reasons.append(f"SCI一区余量：现有 {sci_q1} 篇 / 要求 {min_sci} 篇")
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if not reasons:
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reasons.append("与赛道方向存在部分相关性")
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if match_pct >= _STRONG_THRESHOLD:
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tier = TIER_STRONG
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elif match_pct >= _TRY_THRESHOLD:
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tier = TIER_TRY
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else:
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tier = TIER_NOT
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if tier == TIER_TRY:
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reasons.append("建议补充与赛道直接相关的成果说明后再申报")
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eligible.append(
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{
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"track": label,
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"track_name": track_name,
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"program_name": program,
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"agency": str(track.get("agency") or ""),
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"match_pct": match_pct,
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"tier": tier,
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"matched_tags": matched_tags,
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"coverage": coverage,
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"priority_hits": priority_hits,
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"reasons": reasons,
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}
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)
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eligible.sort(key=lambda x: (-x["match_pct"], x["track"]))
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tiers: dict[str, list[dict[str, Any]]] = {TIER_STRONG: [], TIER_TRY: [], TIER_NOT: []}
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for entry in eligible:
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tiers[entry["tier"]].append(entry)
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top3 = eligible[:3]
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return {
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"teacher_name": teacher_name,
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"department": str(teacher.get("department") or ""),
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"title": str(teacher.get("title") or ""),
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"season": str(season),
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"eligible_count": len(eligible),
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"excluded": excluded,
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"top3": top3,
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"tiers": {k: v for k, v in tiers.items() if v},
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}
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def _write_report(result: dict[str, Any], batch_no: str) -> str:
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from service.report_service import write_report_for_kernel
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return write_report_for_kernel(result=result, batch_no=batch_no)
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def run_match(
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teachers_raw: list[dict[str, Any]],
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tracks: list[dict[str, Any]],
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season: str,
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*,
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batch_no: str = "",
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skip_incomplete: bool = True,
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) -> dict[str, Any]:
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"""匹配内核：逐教师匹配 → 写库 → 生成建议书（多人时另出汇总页）。
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skip_incomplete=True（批量缺省）：画像不完整（E1）的教师被跳过并记入
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``skipped``，不中断批次；skip_incomplete=False（指定单教师）：直接抛
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ValueError，由 CLI 映射 PROFILE_INCOMPLETE。
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"""
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from service.report_service import build_batch_brief
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log = _log()
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init_db()
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if not teachers_raw:
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raise ValueError("没有可匹配的教师画像")
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if not tracks:
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raise ValueError("指南赛道库为空：请先录入当年指南赛道（add-track / import-tracks）")
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batch_no = batch_no or now_unix().to_bytes(5, "big").hex() + uuid.uuid4().hex[:6]
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if log:
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log.info("match_kernel_start batch_no=%s teachers=%s tracks=%s season=%s stage=kernel", batch_no, len(teachers_raw), len(tracks), season)
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results = []
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skipped: list[dict[str, str]] = []
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conn = get_conn()
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try:
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cur = conn.cursor()
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for teacher_raw in teachers_raw:
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try:
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result = match_teacher(teacher_raw, tracks, season)
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except ValueError as exc:
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if not skip_incomplete:
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raise
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raw_name = str((teacher_raw or {}).get("teacherName") or (teacher_raw or {}).get("teacher_name") or "").strip() or "未命名教师"
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skipped.append({"teacher_name": raw_name, "reason": str(exc)})
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if log:
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log.warning("match_teacher_skipped teacher=%s reason=%s stage=kernel", raw_name, exc)
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continue
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cur.execute(
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"""
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INSERT INTO match_reports
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(batch_no, teacher_name, department, season, top_json, tiers_json, eligible_count, excluded_json)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?)
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""",
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(
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batch_no,
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result["teacher_name"],
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result["department"],
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result["season"],
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json.dumps(result["top3"], ensure_ascii=False),
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json.dumps(result["tiers"], ensure_ascii=False),
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result["eligible_count"],
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json.dumps(result["excluded"], ensure_ascii=False),
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),
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)
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report_id = int(cur.lastrowid)
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result["report_id"] = report_id
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result["batch_no"] = batch_no
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report_file = _write_report(result, batch_no)
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cur.execute("UPDATE match_reports SET report_file = ? WHERE id = ?", (report_file, report_id))
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result["report_file"] = report_file
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results.append(result)
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conn.commit()
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finally:
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conn.close()
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brief_file = build_batch_brief(batch_no=batch_no, results=results, season=season, skipped=skipped)
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tier_counts: dict[str, int] = {}
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for result in results:
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for tier, entries in result["tiers"].items():
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tier_counts[tier] = tier_counts.get(tier, 0) + len(entries)
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payload = {
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"ok": True,
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"batch_no": batch_no,
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"season": season,
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"count": len(results),
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"skipped": skipped,
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"tier_counts": tier_counts,
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"brief_file": brief_file,
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"disclaimer": "推荐仅供参考，申报决策请结合个人规划与学院统筹。",
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"reports": results,
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}
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if log:
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log.info(
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"match_kernel_done batch_no=%s count=%s skipped=%s stage=kernel",
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batch_no,
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len(results),
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len(skipped),
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)
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return payload
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def load_tracks(year: Optional[int] = None) -> list[dict[str, Any]]:
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"""读取赛道库（缺省全部年度；可按年度筛选）。"""
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conn = get_conn()
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try:
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cur = conn.cursor()
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if year:
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cur.execute("SELECT * FROM guide_tracks WHERE year = ? ORDER BY id", (year,))
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else:
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cur.execute("SELECT * FROM guide_tracks ORDER BY year DESC, id")
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columns = [d[0] for d in cur.description]
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rows = [dict(zip(columns, row)) for row in cur.fetchall()]
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finally:
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conn.close()
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return rows
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def load_teachers(teacher_name: str = "") -> list[dict[str, Any]]:
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"""读取教师画像（可按姓名精确筛选）。"""
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conn = get_conn()
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try:
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cur = conn.cursor()
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if teacher_name:
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cur.execute("SELECT * FROM teacher_profiles WHERE teacher_name = ?", (teacher_name,))
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else:
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cur.execute("SELECT * FROM teacher_profiles ORDER BY id")
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columns = [d[0] for d in cur.description]
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rows = [dict(zip(columns, row)) for row in cur.fetchall()]
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finally:
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conn.close()
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return rows
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