223 lines
10 KiB
Python
223 lines
10 KiB
Python
"""移动端个人中心 - 训练统计 / 智能分析（读 fastapi 只读训练表）。
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- 4.3 临床核心能力指标 GET /api/user/competency-metrics/
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- 4.4 训练记录（统计信息） GET /api/user/training-records/
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- 4.5 智能分析（关联评价表）GET /api/user/analysis/
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数据源：training_record（managed=False 只读，user_id = 当前用户.id）。
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各能力维度得分 + 满分来自 training_record.ai_feedback_structured.dimension_scores
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（与 training_score_detail 同源，但 json 里带 max_score，便于算「得分率」）。
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"""
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import json
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from datetime import timedelta
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from django.db.models import Sum, Avg, Case, When, F, DecimalField
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from django.utils import timezone
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from rest_framework.decorators import api_view, permission_classes
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from rest_framework.permissions import IsAuthenticated
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from rest_framework.pagination import PageNumberPagination
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from rest_framework.response import Response
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from drf_spectacular.utils import extend_schema
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from apps.training.models import TrainingRecord
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# 临床胜任力标准 5 维（与 training_score_detail / ai_feedback_structured 的评分维度一致）
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STANDARD_DIMS = ['信息获取', '分析推理', '处置决策', '沟通人文', '临床整合']
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# 评分维度（fastapi 实际打分维度，名称随病例评分规则而变）→ 标准 5 维 的归并映射。
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# 维度名不完全统一（实测有 检查利用/临床推理/人文沟通 等同义写法），统一归并到 5 个标准维度。
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# 未在映射内的维度会被忽略。
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DIMENSION_MAP = {
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# 信息获取（病史/问诊）
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'信息获取': '信息获取', '病史采集': '信息获取', '问诊': '信息获取',
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# 分析推理（诊断/鉴别/推理）
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'分析推理': '分析推理', '临床推理': '分析推理', '诊断推理': '分析推理',
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'鉴别诊断': '分析推理', '诊断能力': '分析推理',
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# 处置决策（检查决策 + 治疗决策合并）
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'处置决策': '处置决策', '检查利用': '处置决策', '检查理解': '处置决策',
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'检查决策': '处置决策', '辅助检查': '处置决策', '检验决策': '处置决策',
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'治疗决策': '处置决策', '治疗': '处置决策',
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# 沟通人文
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'沟通人文': '沟通人文', '人文沟通': '沟通人文', '沟通技巧': '沟通人文',
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'医患沟通': '沟通人文', '沟通': '沟通人文',
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# 临床整合（含知识掌握/运用）
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'临床整合': '临床整合', '知识掌握': '临床整合', '知识运用': '临床整合',
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}
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# 诊断相关维度（诊断准确率口径：这些维度的平均得分率；与归并到「分析推理」的来源一致）
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DIAGNOSIS_DIMS = {'诊断推理', '诊断能力', '鉴别诊断', '分析推理', '临床推理'}
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def _completed_qs(user):
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return TrainingRecord.objects.filter(user_id=user.id, status='completed')
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# total_score 归一到百分制：五分制(0-5) ×20，其余按原值(percentage)。
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# 用于 avg_score / current_score / 趋势 / 较上周，避免百分制与五分制混平均失真。
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# （诊断准确率/雷达用各维度 score/max_score 比值自归一，不走这里。）
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def _norm_total(score, score_type):
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if score is None:
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return None
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return float(score) * 20 if score_type == 'five_point' else float(score)
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# DB 侧等价表达式，供 aggregate(Avg(...)) 使用
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_NORM_TOTAL_EXPR = Case(
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When(score_type='five_point', then=F('total_score') * 20),
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default=F('total_score'),
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output_field=DecimalField(max_digits=7, decimal_places=2),
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)
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def _week_start(offset_weeks=0):
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now = timezone.localtime()
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monday = now - timedelta(days=now.weekday(), weeks=-offset_weeks)
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return monday.replace(hour=0, minute=0, second=0, microsecond=0)
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def _dimension_scores(rec):
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"""从 ai_feedback_structured.dimension_scores 取 [(dimension, score, max_score)]。"""
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fb = rec.ai_feedback_structured
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if isinstance(fb, str):
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try:
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fb = json.loads(fb)
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except Exception:
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fb = {}
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if not isinstance(fb, dict):
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return []
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out = []
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for d in (fb.get('dimension_scores') or []):
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if isinstance(d, dict) and d.get('dimension'):
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out.append((d['dimension'], d.get('score'), d.get('max_score')))
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return out
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def _diagnosis_accuracy(records):
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"""诊断相关维度的平均得分率（%）。无数据返回 None。"""
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rates = []
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for rec in records:
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for dim, score, mx in _dimension_scores(rec):
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if dim in DIAGNOSIS_DIMS and score is not None and mx and float(mx) > 0:
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rates.append(float(score) / float(mx))
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return round(sum(rates) / len(rates) * 100) if rates else None
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def _hours(seconds):
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return round((seconds or 0) / 3600, 1)
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# ── 4.3 临床核心能力指标 ─────────────────────────────────────────────────────
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@extend_schema(summary='临床核心能力指标', tags=['个人中心'])
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@api_view(['GET'])
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@permission_classes([IsAuthenticated])
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def competency_metrics(request):
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qs = _completed_qs(request.user)
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agg = qs.aggregate(total=Sum('duration_seconds'), avg=Avg(_NORM_TOTAL_EXPR))
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avg = agg['avg']
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return Response({
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'completed_cases': qs.count(),
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'completed_cases_week': qs.filter(end_time__gte=_week_start()).count(),
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'total_hours': _hours(agg['total']),
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'avg_score': round(float(avg), 1) if avg is not None else 0,
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'diagnosis_accuracy': _diagnosis_accuracy(qs.only('ai_feedback_structured')),
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})
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# ── 4.4 训练记录（统计信息）─────────────────────────────────────────────────
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@extend_schema(summary='训练记录（统计信息）', tags=['个人中心'])
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@api_view(['GET'])
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@permission_classes([IsAuthenticated])
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def training_records(request):
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base = _completed_qs(request.user)
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search = (request.query_params.get('search') or '').strip()
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if search:
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from django.db.models import Q
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base = base.filter(Q(case__title__icontains=search) | Q(case__department__name__icontains=search))
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agg = base.aggregate(total=Sum('duration_seconds'))
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summary = {
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'total_cases': base.count(),
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'total_hours': _hours(agg['total']),
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'avg_accuracy': _diagnosis_accuracy(base.only('ai_feedback_structured')),
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}
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page_qs = base.select_related('case', 'case__department').order_by('-end_time', '-id')
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paginator = PageNumberPagination()
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page = paginator.paginate_queryset(page_qs, request)
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results = []
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for r in page:
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case = r.case if r.case_id else None # 孤立外键(本地无对应病例)时为 None
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dept = case.department if case else None # 病例无科室或科室缺失时为 None
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results.append({
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'record_id': r.id,
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'case_id': r.case_id,
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'case_title': case.title if case else '',
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'department': dept.name if dept else '',
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'trained_at': r.end_time or r.created_at,
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'score': float(r.total_score) if r.total_score is not None else None,
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'score_type': r.score_type,
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'evaluation_level': r.evaluation_level,
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'training_mode': r.training_mode,
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'case_type': r.case_type,
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})
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resp = paginator.get_paginated_response(results)
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resp.data['summary'] = summary
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return resp
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# ── 4.5 智能分析（关联评价表）─────────────────────────────────────────────────
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@extend_schema(summary='智能分析（关联评价表）', tags=['个人中心'])
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@api_view(['GET'])
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@permission_classes([IsAuthenticated])
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def analysis(request):
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recs = list(_completed_qs(request.user).order_by('end_time', 'id'))
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# 趋势 / 当前评分（按 total_score，按 score_type 归一到百分制）
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scored = [(r.end_time or r.created_at, _norm_total(r.total_score, r.score_type))
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for r in recs if r.total_score is not None]
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recent = scored[-7:]
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recent_trend = [{'label': (dt.strftime('%m-%d') if dt else ''), 'score': round(s)} for dt, s in recent]
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current_score = round(sum(s for _, s in recent) / len(recent), 1) if recent else 0
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# 较上周提升%
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ws, ws_prev = _week_start(), _week_start(offset_weeks=-1)
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this_week = [s for dt, s in scored if dt and dt >= ws]
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last_week = [s for dt, s in scored if dt and ws_prev <= dt < ws]
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score_delta_pct = None
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if this_week and last_week:
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a, b = sum(this_week) / len(this_week), sum(last_week) / len(last_week)
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if b:
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score_delta_pct = round((a - b) / b * 100)
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# 雷达：维度得分率归一到 0-100，按 A 组归并求均
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from collections import defaultdict
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buckets = defaultdict(list)
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for r in recs:
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for dim, score, mx in _dimension_scores(r):
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std = DIMENSION_MAP.get(dim)
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if std and score is not None and mx and float(mx) > 0:
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buckets[std].append(float(score) / float(mx) * 100)
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radar = [{'dimension': d, 'score': (round(sum(buckets[d]) / len(buckets[d])) if buckets[d] else 0)}
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for d in STANDARD_DIMS]
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# 仅在「有数据」的维度里排序取强/弱，避免无数据维度(0 分)被误判为薄弱项
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ordered = sorted((x for x in radar if buckets[x['dimension']]), key=lambda x: x['score'])
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weak_dimensions = [ordered[0]['dimension']] if ordered else []
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comment = ''
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if len(ordered) >= 2 and ordered[0]['score'] != ordered[-1]['score']:
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# 强弱维度不同才做对比，避免「最强==最弱」时出现自相矛盾文案
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comment = f"您的{ordered[-1]['dimension']}表现突出，但{ordered[0]['dimension']}仍有提升空间，建议增加相关模拟训练。"
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elif ordered:
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comment = f"各维度表现较为均衡，可针对{weak_dimensions[0]}做进一步强化。"
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return Response({
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'current_score': current_score,
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'score_delta_pct': score_delta_pct,
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'recent_trend': recent_trend,
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'radar': radar,
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'weak_dimensions': weak_dimensions,
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'comment': comment,
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})
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