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