152 lines
5.1 KiB
Python
152 lines
5.1 KiB
Python
"""推荐引擎：找出薄弱知识点并生成“下一步任务”。"""
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from __future__ import annotations
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import json
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from sqlalchemy.orm import Session
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from models import (
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AttemptItem,
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Chapter,
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DailyCompletion,
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ErrorEntry,
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Knowledge,
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Question,
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Textbook,
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UserKnowledge,
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)
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from services.mastery_engine import snapshot
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from services.knowledge_service import chapter_knowledge_names
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def _error_counts(db: Session, user_id: int) -> dict[str, int]:
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counts: dict[str, int] = {}
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for primary, raw_names in (
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db.query(ErrorEntry.knowledge_name, ErrorEntry.knowledge_names)
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.filter(ErrorEntry.user_id == user_id)
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.all()
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):
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try:
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names = json.loads(raw_names or "[]")
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if not isinstance(names, list) or not names:
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names = [primary]
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except (TypeError, ValueError):
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names = [primary]
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for item in names:
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counts[str(item)] = counts.get(str(item), 0) + 1
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return counts
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def build_plan(db: Session, user_id: int) -> dict:
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knowledge = snapshot(db, user_id)
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counts = _error_counts(db, user_id)
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weak = [k for k in knowledge if k.mastery < 75]
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strong = [k for k in knowledge if k.mastery >= 75]
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focus_names = [k.name for k in weak]
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if not focus_names:
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focus_names = [strong[0].name] if strong else []
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focus_text = " / ".join(focus_names[:2]) or "当前知识点"
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notice = f"系统根据已有答题记录，将“{focus_text}”设为当前强化重点。"
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steps: list[str] = []
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if weak:
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group = weak[:2]
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step1 = " / ".join(k.name for k in group)
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steps.append(f"① 重点强化 {step1}：巩固定义 → 点的对应 → 图像性质判断。")
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if len(weak) > 2:
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steps.append(f"② {weak[2].name}：从表达式判断图像性质，补做针对性小题。")
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else:
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steps.append("② 再完成一次章节测试，检验本轮掌握情况。")
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steps.append("③ 间隔复习：在后续测试中穿插旧知识，防止遗忘。")
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else:
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steps.append("① 保持当前节奏，进入下一章节的学习。")
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steps.append("② 每周做一次章节测试，维持各知识点掌握度。")
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steps.append("③ 错题清零后开始新主题，建立更完整的知识地图。")
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top_error = sorted(knowledge, key=lambda k: (-counts.get(k.name, 0), k.mastery))
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return {
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"notice": notice,
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"focus_names": focus_names,
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"steps": steps,
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"top_error": (
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top_error[0].name
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if top_error and counts.get(top_error[0].name, 0)
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else None
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),
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}
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def build_daily_recommendation(
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db: Session, user_id: int
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) -> dict | None:
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"""推荐今日章节：无掌握度且未做过的章优先，然后按薄弱反馈/错误加权。"""
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chapters = (
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db.query(Chapter, Textbook)
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.join(Textbook, Textbook.id == Chapter.textbook_id)
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.order_by(Textbook.position.asc(), Chapter.position.asc())
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.all()
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)
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candidates: list[dict] = []
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for chapter, textbook in chapters:
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question_count = (
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db.query(Question.id).filter(Question.chapter_id == chapter.id).count()
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)
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if question_count == 0:
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continue
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tags = chapter_knowledge_names(db, chapter.id)
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masteries = [
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mastery
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for mastery, in db.query(UserKnowledge.mastery)
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.join(Knowledge, Knowledge.id == UserKnowledge.knowledge_id)
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.filter(
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UserKnowledge.user_id == user_id,
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Knowledge.name.in_(tags),
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)
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.all()
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]
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answered = (
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db.query(AttemptItem.id)
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.join(Question, Question.id == AttemptItem.question_id)
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.filter(
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AttemptItem.user_id == user_id,
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Question.chapter_id == chapter.id,
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)
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.count()
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)
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candidates.append(
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{
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"chapter": chapter,
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"textbook": textbook,
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"tags": tags,
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"mastery_avg": sum(masteries) / len(masteries)
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if masteries
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else None,
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"answered": answered,
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"knowledge_count": len(tags),
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}
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)
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if not candidates:
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return None
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# 未开始章节优先；同等情况按章节顺序
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candidates.sort(
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key=lambda item: (
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0 if item["answered"] == 0 and item["mastery_avg"] is None else 1,
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item["mastery_avg"] if item["mastery_avg"] is not None else 50,
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)
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)
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choice = candidates[0]
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reason = (
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"你还没有练习过这一章，建议从本章开始建立基础"
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if choice["answered"] == 0
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else "该章知识点掌握度较低，需要优先强化"
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)
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return {
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"chapter_id": choice["chapter"].id,
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"chapter_name": choice["chapter"].name,
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"book_name": choice["textbook"].name,
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"knowledge_names": choice["tags"],
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"reason": reason,
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}
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