经期AI助手技术解析：2026年4月9日深度科普与面试指南

 传统方式：基于固定周期的简单预测
class SimplePeriodTracker:
    def __init__(self):
        self.cycle_length = 28   默认周期长度
        self.last_period_start = None
    
    def log_period(self, start_date):
        self.last_period_start = start_date
         简单更新周期长度
        if hasattr(self, 'previous_start'):
            self.cycle_length = (start_date - self.previous_start).days
        self.previous_start = start_date
    
    def predict_next_period(self):
        if self.last_period_start:
            return self.last_period_start + timedelta(days=self.cycle_length)
        return None

 多模态数据融合示例
class MultimodalFusion:
    def __init__(self):
        self.temperature_weight = 0.4       体温特征权重
        self.symptom_weight = 0.3           症状特征权重
        self.sleep_weight = 0.3             睡眠特征权重
    
    def fuse_features(self, temp_data, symptom_data, sleep_data):
         1. 将不同模态数据标准化到同一量纲
        temp_normalized = self._normalize(temp_data, range=(35.5, 37.5))
        symptom_encoded = self._encode_symptoms(symptom_data)   文本→向量
        sleep_normalized = self._normalize(sleep_data, range=(4, 10))
        
         2. 加权融合
        fused = (self.temperature_weight  temp_normalized +
                 self.symptom_weight  symptom_encoded +
                 self.sleep_weight  sleep_normalized)
        return fused

 极简版经期AI助手 - ML预测模块
import numpy as np
from datetime import datetime, timedelta
from sklearn.linear_model import LinearRegression

class MenstrualAIAssistant:
    """经期AI助手核心预测模块"""
    
    def __init__(self):
        self.model = LinearRegression()
        self.history = []   存储历史周期数据
        self.avg_cycle = 28   默认周期
    
    def log_cycle(self, start_date, end_date, symptoms=None, temperature=None):
        """记录一个完整周期"""
        cycle_length = (end_date - start_date).days
        self.history.append({
            'start': start_date,
            'length': cycle_length,
            'symptoms': symptoms or [],
            'temperature': temperature
        })
         更新平均周期
        if len(self.history) >= 2:
            lengths = [h['length'] for h in self.history]
            self.avg_cycle = np.mean(lengths)
            self._train_model()
    
    def _train_model(self):
        """基于历史周期训练ML模型"""
        if len(self.history) < 2:
            return
         构建特征：X为周期序号，y为周期长度
        X = np.array(range(len(self.history))).reshape(-1, 1)
        y = np.array([h['length'] for h in self.history])
        self.model.fit(X, y)
    
    def predict_next_period(self):
        """预测下一次月经开始日期"""
        if not self.history:
            return datetime.now() + timedelta(days=self.avg_cycle)
        
         方法1：简单平均值（基线）
        baseline_pred = self.history[-1]['start'] + timedelta(days=self.avg_cycle)
        
         方法2：ML预测（更智能）
        next_idx = len(self.history)
        ml_pred_length = int(self.model.predict([[next_idx]])[0])
        ml_pred_date = self.history[-1]['start'] + timedelta(days=ml_pred_length)
        
         方法3：体温辅助修正（如果提供了体温数据）
        if self.history[-1]['temperature']:
            temp = self.history[-1]['temperature']
            if temp > 37.0:   体温升高可能接近排卵
                ml_pred_date -= timedelta(days=1)
        
        return {
            'baseline': baseline_pred.strftime('%Y-%m-%d'),
            'ml_prediction': ml_pred_date.strftime('%Y-%m-%d'),
            'confidence': min(0.9, 0.5 + len(self.history)  0.05)
        }
    
    def get_insights(self):
        """生成个性化健康洞察"""
        if len(self.history) < 2:
            return "数据不足，继续记录将获得更精准预测"
        
        avg_length = np.mean([h['length'] for h in self.history])
        std_length = np.std([h['length'] for h in self.history])
        
        insights = []
        if std_length > 5:
            insights.append("周期波动较大，建议关注压力管理和作息规律")
        elif avg_length < 24:
            insights.append("周期偏短，建议咨询专业医师")
        elif avg_length > 35:
            insights.append("周期偏长，建议关注内分泌健康")
        else:
            insights.append("周期规律良好")
        
        return insights

 使用示例
if __name__ == "__main__":
    assistant = MenstrualAIAssistant()
    
     模拟记录3个周期
    base = datetime(2026, 3, 1)
    assistant.log_cycle(base, base + timedelta(days=28), symptoms=['cramps'], temperature=36.6)
    assistant.log_cycle(base + timedelta(days=28), base + timedelta(days=56), symptoms=[], temperature=36.7)
    assistant.log_cycle(base + timedelta(days=56), base + timedelta(days=85), symptoms=['bloating'], temperature=36.8)
    
     预测下一个周期
    result = assistant.predict_next_period()
    print(f"📅 ML预测下次经期: {result['ml_prediction']}")
    print(f"📊 置信度: {result['confidence']:.0%}")
    print(f"💡 健康洞察: {assistant.get_insights()}")