智能健康报告系统设计与实践

class SmartHealthReportGenerator:
    def __init__(self, growth_data_service, reference_data_service, ai_model_service):
        self.growth_data_service = growth_data_service
        self.reference_data_service = reference_data_service
        self.ai_model_service = ai_model_service
    
    def generate_report(self, child_id: str) -> dict:
        """生成智能健康报告主流程"""
        # 1. 获取儿童所有生长记录
        child_records = self.growth_data_service.get_child_growth_records(child_id)
        if not child_records:
            return {"status": "error", "message": "未找到生长数据"}
        
        latest_record = sorted(child_records, 
                               key=lambda x: x.record_date, reverse=True)[0]
        
        # 2. 计算核心指标
        indicators = self._calculate_core_indicators(latest_record, child_records)
        
        # 3. 趋势分析
        trends = self._analyze_trends(child_records)
        
        # 4. 风险评估
        risk_warnings = self._assess_risks(indicators, trends)
        
        # 5. 生成个性化建议
        suggestions = self._generate_personalized_suggestions(
            risk_warnings, indicators
        )
        
        return {
            "child_id": child_id,
            "report_date": "2025-12-19",
            "indicators": indicators,
            "trends": trends,
            "risk_warnings": risk_warnings,
            "health_suggestions": suggestions,
            "growth_curve_url": f"/charts/{child_id}"
        }
    
    def _calculate_core_indicators(self, latest_record, records):
        """计算身高、体重、BMI的百分位和Z-score"""
        height_p, height_z = self._calc_percentile_zscore(
            latest_record, 'height'
        )
        weight_p, weight_z = self._calc_percentile_zscore(
            latest_record, 'weight'
        )
        bmi_p, bmi_z = self._calc_percentile_zscore(latest_record, 'bmi')
        
        return {
            "height_percentile": round(height_p, 2),
            "height_zscore": round(height_z, 2),
            "weight_percentile": round(weight_p, 2),
            "weight_zscore": round(weight_z, 2),
            "bmi_percentile": round(bmi_p, 2),
            "bmi_zscore": round(bmi_z, 2)
        }
    
    def _analyze_trends(self, records, months=3):
        """分析近期生长速率"""
        recent_records = [r for r in records 
                         if (date.today() - r.record_date).days <= months*30]
        
        if len(recent_records) < 2:
            return {"growth_rate_height": None, "growth_rate_weight": None}
        
        first = min(recent_records, key=lambda x: x.record_date)
        latest = max(recent_records, key=lambda x: x.record_date)
        
        return {
            "growth_rate_height_cm_per_month": round(
                (latest.height_cm - first.height_cm) / months, 2
            ),
            "growth_rate_weight_kg_per_month": round(
                (latest.weight_kg - first.weight_kg) / months, 2
            )
        }
    
    def _assess_risks(self, indicators, trends):
        """基于规则引擎的风险评估"""
        warnings = []
        
        # 规则1：生长迟缓预警
        if indicators["height_zscore"] < -2.0:
            warnings.append({
                "level": "HIGH",
                "type": "生长迟缓风险",
                "message": "身高显著低于同龄儿童平均水平，建议及时就医评估"
            })
        
        # 规则2：肥胖风险
        if indicators["bmi_percentile"] > 95.0:
            warnings.append({
                "level": "HIGH",
                "type": "肥胖风险",
                "message": "BMI指数过高，需调整饮食结构并增加运动"
            })
        
        # 规则3：生长过快预警(可能性早熟)
        if trends["growth_rate_height_cm_per_month"] and \
           trends["growth_rate_height_cm_per_month"] > 1.0:
            warnings.append({
                "level": "MEDIUM",
                "type": "生长速度过快",
                "message": "近期身高增长明显加速，建议关注性早熟可能性"
            })
        
        return warnings
    
    def _generate_personalized_suggestions(self, risks, indicators):
        """生成个性化健康建议"""
        suggestions = []
        
        for risk in risks:
            if risk["type"] == "生长迟缓风险":
                suggestions.extend([
                    "确保每日摄入足量蛋白质(鸡蛋、牛奶、瘦肉)",
                    "补充维生素D(每日400IU)和钙质",
                    "保证每晚8-10小时优质睡眠",
                    "每天进行纵向跳跃运动30分钟"
                ])
            elif risk["type"] == "肥胖风险":
                suggestions.extend([
                    "减少高糖高脂食物，控制零食摄入",
                    "每日户外活动时间不少于1小时",
                    "用全谷物替代精制米面",
                    "定期监测体重变化"
                ])
        
        return suggestions