"""SQLite 连接与幂等初始化（申报赛道推荐助手）。 业务表 3 张： - teacher_profiles（教师画像，可编辑） - guide_tracks（指南赛道，可编辑） - match_reports（推荐报告，只读，匹配自动写入） 表间禁止 SQLite 外键，仅存业务 id（见 references/SCHEMA.md）。 """ from __future__ import annotations import sqlite3 from db.display_metadata import ( init_display_metadata, seed_column_metadata_batch, upsert_table_metadata, ) from db.timestamp_columns import init_updated_at_trigger from util.runtime_paths import get_db_path def get_conn() -> sqlite3.Connection: return sqlite3.connect(get_db_path()) _TEACHER_PROFILES_DDL = """ CREATE TABLE IF NOT EXISTS teacher_profiles ( id INTEGER PRIMARY KEY AUTOINCREMENT, teacher_name TEXT NOT NULL UNIQUE, department TEXT, title TEXT, research_tags TEXT, recent_papers TEXT, project_history TEXT, sci_q1_count INTEGER, core_paper_count INTEGER, created_at INTEGER NOT NULL DEFAULT (unixepoch()), updated_at INTEGER NOT NULL DEFAULT (unixepoch()) ) """ _GUIDE_TRACKS_DDL = """ CREATE TABLE IF NOT EXISTS guide_tracks ( id INTEGER PRIMARY KEY AUTOINCREMENT, year INTEGER NOT NULL, agency TEXT, program_name TEXT, track_name TEXT NOT NULL, track_tags TEXT, priority_keywords TEXT, min_title TEXT, min_sci_q1 INTEGER NOT NULL DEFAULT 0, quota INTEGER, source_doc TEXT, created_at INTEGER NOT NULL DEFAULT (unixepoch()), updated_at INTEGER NOT NULL DEFAULT (unixepoch()), UNIQUE (year, agency, program_name, track_name) ) """ _MATCH_REPORTS_DDL = """ CREATE TABLE IF NOT EXISTS match_reports ( id INTEGER PRIMARY KEY AUTOINCREMENT, batch_no TEXT NOT NULL, teacher_name TEXT, department TEXT, season TEXT, top_json TEXT, tiers_json TEXT, eligible_count INTEGER, excluded_json TEXT, report_file TEXT, created_at INTEGER NOT NULL DEFAULT (unixepoch()) ) """ _MATCH_REPORTS_INDEX = """ CREATE INDEX IF NOT EXISTS idx_match_reports_batch ON match_reports(batch_no) """ _TS_COLUMN_NAMES = {"created_at", "updated_at"} def _col(column_name: str, display_name: str, description: str, **extra: object) -> dict[str, object]: base: dict[str, object] = { "column_name": column_name, "display_name": display_name, "description": description, "visible": 1, "searchable": 0, "editable": 0, } base.update(extra) if column_name in _TS_COLUMN_NAMES: base["display_type"] = "datetime_unix_seconds" return base _TEACHER_PROFILES_COLUMNS: tuple[dict[str, object], ...] = ( _col("id", "编号", "主键", searchable=1), _col("teacher_name", "教师姓名", "教师姓名（唯一）。", searchable=1), _col("department", "所属学院", "二级学院/单位。", searchable=1, editable=1), _col("title", "职称", "讲师/副教授/教授等，用于职称门槛比对。", searchable=1, editable=1, options_json='[{"value":"讲师","label":"讲师"},{"value":"副教授","label":"副教授"},{"value":"教授","label":"教授"}]'), _col("research_tags", "研究标签", "研究方向关键词，逗号分隔（匹配主依据）。", searchable=1, editable=1), _col("recent_papers", "近3年论文", "近 3 年论文标题，分号分隔。", editable=1), _col("project_history", "项目经历", "在研/结题项目名称，分号分隔。", editable=1), _col("sci_q1_count", "SCI一区篇数", "SCI 一区论文数量（硬门槛比对）。", searchable=1, editable=1, display_type="number"), _col("core_paper_count", "核心论文篇数", "核心期刊论文数量。", searchable=1, editable=1, display_type="number"), _col("created_at", "创建时间", "记录创建时间（Unix 秒级，自动生成）。"), _col("updated_at", "更新时间", "记录最后更新时间（Unix 秒级，自动维护）。"), ) _GUIDE_TRACKS_COLUMNS: tuple[dict[str, object], ...] = ( _col("id", "编号", "主键", searchable=1), _col("year", "指南年度", "指南发布/申报年度。", searchable=1), _col("agency", "学部/归口", "如 数理学部、科研处。", searchable=1, editable=1), _col("program_name", "项目类型", "如 面上项目、青年项目。", searchable=1, editable=1), _col("track_name", "赛道名称", "指南中的方向/赛道名。", searchable=1, editable=1), _col("track_tags", "需求标签", "赛道方向关键词，逗号分隔（匹配主依据）。", searchable=1, editable=1), _col("priority_keywords", "优先方向", "优先资助方向关键词，逗号分隔（加分项）。", editable=1), _col("min_title", "职称门槛", "讲师及以上 / 副高及以上 / 正高及以上。", editable=1, options_json='[{"value":"讲师及以上","label":"讲师及以上"},{"value":"副高及以上","label":"副高及以上"},{"value":"正高及以上","label":"正高及以上"}]'), _col("min_sci_q1", "SCI一区门槛", "要求的 SCI 一区最少篇数。", editable=1, display_type="number"), _col("quota", "拟资助数", "该赛道拟资助项数（参考）。", editable=1, display_type="number"), _col("source_doc", "指南来源", "指南文件名/文号。", editable=1), _col("created_at", "创建时间", "记录创建时间（Unix 秒级，自动生成）。"), _col("updated_at", "更新时间", "记录最后更新时间（Unix 秒级，自动维护）。"), ) _MATCH_REPORTS_COLUMNS: tuple[dict[str, object], ...] = ( _col("id", "报告编号", "主键，推荐报告编号。", searchable=1), _col("batch_no", "匹配批次号", "同一次匹配请求的批次号。", searchable=1), _col("teacher_name", "教师姓名", "被匹配教师。", searchable=1), _col("department", "所属学院", "教师所属学院。", searchable=1), _col("season", "指南年度", "匹配所用指南年度。", searchable=1), _col("top_json", "Top3推荐", "Top 3 赛道（JSON，含得分与理由）。", visible=0, display_type="textarea"), _col("tiers_json", "分层清单", "强烈推荐/可尝试/不建议分层（JSON）。", visible=0, display_type="textarea"), _col("eligible_count", "可匹配赛道数", "通过硬门槛过滤后的赛道数。", searchable=1, display_type="number"), _col("excluded_json", "剔除清单", "被硬门槛剔除的赛道及原因（JSON）。", visible=0, display_type="textarea"), _col("report_file", "建议书文件", "《申报策略建议书》HTML 文件路径。", visible=0), _col("created_at", "创建时间", "报告创建时间（Unix 秒级，自动生成）。"), ) def _init_teacher_profiles_metadata(cursor) -> None: cursor.execute(_TEACHER_PROFILES_DDL) init_updated_at_trigger(cursor, "teacher_profiles") upsert_table_metadata( cursor, table_name="teacher_profiles", display_name="教师画像", description="教师学术画像：研究标签、成果与职称，用于赛道匹配。", grid_help="研究标签是匹配主依据（逗号分隔）；顶栏按钮可录入，行内可编辑。", visible=1, readonly=0, default_sort_json='[{"column":"id","direction":"desc"}]', default_visible_columns_json='["teacher_name","department","title","research_tags","sci_q1_count"]', ) seed_column_metadata_batch( cursor, table_name="teacher_profiles", columns=_TEACHER_PROFILES_COLUMNS, valid_column_names=[str(c["column_name"]) for c in _TEACHER_PROFILES_COLUMNS], ) def _init_guide_tracks_metadata(cursor) -> None: cursor.execute(_GUIDE_TRACKS_DDL) init_updated_at_trigger(cursor, "guide_tracks") upsert_table_metadata( cursor, table_name="guide_tracks", display_name="指南赛道", description="当年申报指南的结构化赛道库：需求标签、优先方向与硬门槛。", grid_help="按年度管理；需求标签为匹配主依据，硬门槛用于过滤不满足条件的教师。", visible=1, readonly=0, default_sort_json='[{"column":"year","direction":"desc"},{"column":"id","direction":"asc"}]', default_visible_columns_json='["year","program_name","track_name","track_tags","min_title","min_sci_q1"]', ) seed_column_metadata_batch( cursor, table_name="guide_tracks", columns=_GUIDE_TRACKS_COLUMNS, valid_column_names=[str(c["column_name"]) for c in _GUIDE_TRACKS_COLUMNS], ) def _init_match_reports_metadata(cursor) -> None: cursor.execute(_MATCH_REPORTS_DDL) cursor.execute(_MATCH_REPORTS_INDEX) upsert_table_metadata( cursor, table_name="match_reports", display_name="推荐报告", description="每次匹配的 Top 3 赛道、分层清单与剔除原因；可重建建议书。", grid_help="推荐仅供参考；顶栏按钮可重建《申报策略建议书》。", visible=1, readonly=1, default_sort_json='[{"column":"id","direction":"desc"}]', default_visible_columns_json='["id","season","teacher_name","department","eligible_count"]', ) seed_column_metadata_batch( cursor, table_name="match_reports", columns=_MATCH_REPORTS_COLUMNS, valid_column_names=[str(c["column_name"]) for c in _MATCH_REPORTS_COLUMNS], ) def init_db() -> dict[str, int]: """幂等创建/迁移本地 SQLite 与数据管理元数据。""" conn = get_conn() try: cur = conn.cursor() init_display_metadata(cur) _init_teacher_profiles_metadata(cur) _init_guide_tracks_metadata(cur) _init_match_reports_metadata(cur) conn.commit() return {} finally: conn.close()