fix: 缺省匹配年度收敛最新年度 + 批量坏文件跳过 + 匹配理由据实化 + 职称档位补全（P1+P2）
This commit is contained in:
chenfuqiang
10
DEMO.md
10
DEMO.md
@@ -4,6 +4,11 @@ demo_video_url: ""
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演示视频录制后请把地址填入上方 `demo_video_url`。以下是无视频时的图文演示脚本。
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## 演示数据准备
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- 可直接导入 `tests/fixtures/` 三份脱敏样例：教师画像、指南赛道、期望匹配结论
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- 缺省匹配取赛道库**最新年度**赛道：演示前确认"指南赛道"表已录入当年赛道，往年赛道不会混入结果
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## 演示一：教师问推荐（对话）
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1. 对话任务："我是张教授，研究钙钛矿、光伏、半导体和界面调控，SCI 一区 3 篇，今年适合报什么？"
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@@ -15,3 +20,8 @@ demo_video_url: ""
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1. "指南赛道"表批量导入 2027 年各学部赛道；"教师画像"表维护本院教师。
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2. 顶栏「赛道匹配」一键匹配；批量模式出汇总页，强推荐名单一目了然。
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3. 「生成建议书」重建 HTML 供打印分发。
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## 演示三：批量容错（可选）
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1. input 目录放入多位教师画像 JSON，混入一个格式损坏的文件。
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2. 发起批量匹配：坏文件被跳过并逐条告警说明，其余教师正常出报告与汇总页。
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@@ -28,19 +28,28 @@ 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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# 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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# 助理教授按中级档从审处理（预聘制档次各校不一，宁缺勿滥）；
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# 未识别职称遇门槛赛道一律剔除并说明原因（审慎非激进）。
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_TITLE_RANK = {
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"助教": 0,
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"初级": 0,
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"讲师": 1,
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"中级": 1,
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"助理研究员": 1,
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"助理教授": 1,
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"工程师": 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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"高级工程师": 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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"教授级高级工程师": 3,
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"正高级工程师": 3,
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}
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_STRONG_THRESHOLD = 80
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@@ -197,7 +206,7 @@ def match_teacher(teacher: dict[str, Any], tracks: list[dict[str, Any]], season:
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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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reasons.append("现有研究标签与该赛道需求标签暂无重合，补充研究标签后可能改善匹配")
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if match_pct >= _STRONG_THRESHOLD:
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tier = TIER_STRONG
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@@ -346,14 +355,17 @@ def run_match(
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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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"""读取赛道库（缺省取赛道库最新年度，与 actions.json 契约一致；显式传 year 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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if year is None:
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cur.execute("SELECT MAX(year) FROM guide_tracks")
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row = cur.fetchone()
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if not row or row[0] is None:
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return []
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year = int(row[0])
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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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@@ -361,6 +373,35 @@ def load_tracks(year: Optional[int] = None) -> list[dict[str, Any]]:
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return rows
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def collect_input_teachers(input_dir: str) -> tuple[list[dict[str, Any]], list[dict[str, str]]]:
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"""读取 input/ 目录下的教师画像 JSON；坏文件跳过并记录原因，不中断整批。
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返回 (教师列表, 跳过文件列表)；跳过文件形如 {"file": 名称, "reason": 原因}。
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"""
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import os
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files = sorted(
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os.path.join(input_dir, name)
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for name in os.listdir(input_dir)
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if name.lower().endswith(".json")
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)
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teachers: list[dict[str, Any]] = []
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skipped_files: list[dict[str, str]] = []
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for path in files:
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name = os.path.basename(path)
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try:
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with open(path, encoding="utf-8") as f:
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payload = json.load(f)
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if isinstance(payload, dict):
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payload = payload.get("teachers") or [payload]
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if not isinstance(payload, list):
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raise ValueError("内容不是教师对象数组")
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teachers.extend(item for item in payload if isinstance(item, dict))
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except (OSError, ValueError) as exc:
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skipped_files.append({"file": name, "reason": str(exc)})
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return teachers, skipped_files
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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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@@ -478,6 +478,9 @@ def cmd_match(*, teacher: str = "", year: Optional[int] = None) -> int:
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print(f"ERROR:TEACHER_NOT_FOUND {message}")
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return 1
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if not tracks:
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if year:
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message = f"指南赛道库中没有 {year} 年度赛道：请核对年度或先录入（add-track / import-tracks）"
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else:
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message = "指南赛道库为空：请先录入赛道（add-track / import-tracks）"
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log.warning("task_failed task_type=match error_code=TRACK_EMPTY stage=match")
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finish(status="failed", message=message, skill=SKILL_SLUG, error_code="TRACK_EMPTY", stage="match")
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@@ -529,9 +532,13 @@ def cmd_match(*, teacher: str = "", year: Optional[int] = None) -> int:
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def cmd_match_batch_input() -> int:
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"""批量匹配（async Action 入口）：匹配 input/ 目录中的教师画像 JSON 与库内赛道。"""
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"""批量匹配（async Action 入口）：匹配 input/ 目录中的教师画像 JSON 与库内赛道。
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坏文件（解析失败/非对象数组）跳过并告警，不中断整批；画像不完整教师同样跳过并记录（E1）。
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缺省匹配赛道库最新年度赛道。
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"""
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log = _get_task_logger()
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from service.match_service import load_tracks, run_match
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from service.match_service import collect_input_teachers, load_tracks, run_match
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from util.runtime_paths import get_input_dir
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with job_context(skill=SKILL_SLUG):
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@@ -546,26 +553,17 @@ def cmd_match_batch_input() -> int:
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return 1
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input_dir = get_input_dir()
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files = sorted(
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os.path.join(input_dir, name)
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for name in os.listdir(input_dir)
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if name.lower().endswith(".json")
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)
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if not files:
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message = f"input 目录没有教师画像 JSON：{input_dir}"
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teachers, skipped_files = collect_input_teachers(input_dir)
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for entry in skipped_files:
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log.warning("batch_input_file_skipped file=%s reason=%s stage=batch", entry["file"], entry["reason"])
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emit(f"{entry['file']}：{entry['reason']}，该文件已跳过", type="warn", skill=SKILL_SLUG, stage="batch")
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if not teachers:
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message = f"input 目录没有可用的教师画像 JSON（{len(skipped_files)} 个文件无法解析）：{input_dir}"
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log.warning("task_failed task_type=match_batch error_code=TEACHER_NOT_FOUND stage=batch")
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finish(status="failed", message=message, skill=SKILL_SLUG, error_code="TEACHER_NOT_FOUND", stage="batch")
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print(f"ERROR:TEACHER_NOT_FOUND {message}")
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return 1
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teachers = []
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for path in files:
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with open(path, encoding="utf-8") as f:
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payload = json.load(f)
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if isinstance(payload, dict):
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payload = payload.get("teachers") or [payload]
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teachers.extend(payload)
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tracks = load_tracks()
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if not tracks:
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message = "指南赛道库为空：请先录入赛道（add-track / import-tracks）"
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@@ -597,6 +595,7 @@ def cmd_match_batch_input() -> int:
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"batch_no": result["batch_no"],
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"count": result["count"],
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"skipped": result.get("skipped") or [],
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"skipped_files": skipped_files,
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"tier_counts": result["tier_counts"],
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"brief_file": result["brief_file"],
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}
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@@ -2,6 +2,9 @@
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"""赛道匹配内核金标准用例（离线、确定性）。"""
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from __future__ import annotations
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import json
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import os
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import tempfile
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import unittest
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from _support import IsolatedDataRoot
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@@ -80,13 +83,29 @@ class TestScoring(MatchRuleTestBase):
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self.assertEqual(top["match_pct"], 100)
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self.assertEqual(top["tier"], "强烈推荐")
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def test_t1_try_band(self) -> None:
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def test_coverage_only_below_try_band(self) -> None:
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result = self._match(_teacher(researchTags="钙钛矿,光伏", recentPapers=""))
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entry = next(e for e in result["top3"] if e["track"] == "面上项目·光伏材料")
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# coverage 2/3 → 0.7*0.667=0.4667 → 47% 不建议？ + 无 priority → 47%；二维材料 coverage 1/3=0.333→23%
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# 仅覆盖率 2/3 → 0.7×0.6667=46.7% → 47%，低于 50% 可尝试档 → 不建议
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self.assertEqual(entry["match_pct"], 47)
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self.assertEqual(entry["tier"], "不建议")
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def test_t1_try_band_with_supplement_hint(self) -> None:
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result = self._match(_teacher(researchTags="钙钛矿,光伏,界面调控"))
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entry = next(e for e in result["top3"] if e["track"] == "面上项目·光伏材料")
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# 覆盖率 2/3 → 0.7×0.6667=46.7% + 优先方向 1 命中 0.15 = 61.7% → 62%，落在可尝试档
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self.assertEqual(entry["match_pct"], 62)
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self.assertEqual(entry["tier"], "可尝试")
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self.assertTrue(any("建议补充" in r for r in entry["reasons"]))
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def test_zero_match_reason_is_factual(self) -> None:
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result = self._match(_teacher(researchTags="量子计算", recentPapers="", projectHistory=""))
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entry = next(e for e in result["top3"] if e["track"] == "面上项目·光伏材料")
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reasons_text = "".join(entry["reasons"])
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self.assertEqual(entry["match_pct"], 0)
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self.assertIn("暂无重合", reasons_text)
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self.assertNotIn("部分相关性", reasons_text)
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def test_t1_strong_boundary(self) -> None:
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# 恰好 80% 属强烈推荐
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result = self._match(_teacher(researchTags="钙钛矿,光伏,半导体", recentPapers=""))
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@@ -210,5 +229,77 @@ class TestBatchSkipIncomplete(MatchRuleTestBase):
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run_match([_teacher(researchTags="", recentPapers="")], _tracks(), "2027", skip_incomplete=False)
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class TestTrackYearScope(MatchRuleTestBase):
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"""缺省取赛道库最新年度（actions.json 契约）；显式年度不受影响。"""
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def _seed_two_years(self) -> None:
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from service.profile_service import upsert_track
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upsert_track({"year": 2026, "track_name": "旧方向", "program_name": "面上项目", "track_tags": "钙钛矿,光伏"})
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upsert_track({"year": 2027, "track_name": "新方向", "program_name": "面上项目", "track_tags": "量子计算"})
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def test_load_tracks_defaults_to_latest_year(self) -> None:
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from service.match_service import load_tracks
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self._seed_two_years()
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self.assertEqual({t["year"] for t in load_tracks()}, {2027})
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def test_load_tracks_explicit_year(self) -> None:
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from service.match_service import load_tracks
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self._seed_two_years()
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self.assertEqual({t["year"] for t in load_tracks(year=2026)}, {2026})
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def test_load_tracks_empty_library(self) -> None:
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from service.match_service import load_tracks
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self.assertEqual(load_tracks(), [])
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def test_kernel_default_matches_latest_year_only(self) -> None:
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from service.match_service import load_teachers, load_tracks, run_match
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from service.profile_service import upsert_teacher
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self._seed_two_years()
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upsert_teacher({"teacher_name": "张教授", "title": "教授", "research_tags": "量子计算,量子信息"})
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payload = run_match(load_teachers(), load_tracks(), "2027")
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report = payload["reports"][0]
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all_tracks = [e["track"] for entries in report["tiers"].values() for e in entries]
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all_tracks += [e["track"] for e in report["top3"]]
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self.assertIn("面上项目·新方向", all_tracks)
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self.assertNotIn("面上项目·旧方向", all_tracks)
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class TestBatchInputCollection(MatchRuleTestBase):
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"""input/ 目录收集：坏文件跳过并记录原因，不中断整批。"""
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def test_collect_input_teachers_skips_bad_files(self) -> None:
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from service.match_service import collect_input_teachers
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with tempfile.TemporaryDirectory() as tmp:
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with open(os.path.join(tmp, "a.json"), "w", encoding="utf-8") as f:
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json.dump([_teacher(), _teacher(teacherName="李教授")], f, ensure_ascii=False)
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with open(os.path.join(tmp, "single.json"), "w", encoding="utf-8") as f:
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json.dump({"teacherName": "王教授"}, f, ensure_ascii=False)
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with open(os.path.join(tmp, "bad.json"), "w", encoding="utf-8") as f:
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f.write("{not-json")
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with open(os.path.join(tmp, "scalar.json"), "w", encoding="utf-8") as f:
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json.dump("just-a-string", f, ensure_ascii=False)
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teachers, skipped = collect_input_teachers(tmp)
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names = [t.get("teacherName") for t in teachers]
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self.assertEqual(names, ["张教授", "李教授", "王教授"])
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self.assertEqual({e["file"] for e in skipped}, {"bad.json", "scalar.json"})
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self.assertTrue(all(e["reason"] for e in skipped))
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def test_collect_input_teachers_empty_dir(self) -> None:
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from service.match_service import collect_input_teachers
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with tempfile.TemporaryDirectory() as tmp:
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teachers, skipped = collect_input_teachers(tmp)
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self.assertEqual(teachers, [])
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self.assertEqual(skipped, [])
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if __name__ == "__main__":
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unittest.main()
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