import numpy as np
from dataclasses import dataclass
from typing import Dict, Optional

@dataclass
class AirQualityReading:
    """车内空气质量读数"""
    pm25: float         # PM2.5 μg/m³
    pm10: float         # PM10 μg/m³
    voc_ppb: float      # 挥发性有机物 ppb
    co2_ppm: float      # CO2 ppm
    temp_c: float       # 温度 °C
    humidity_pct: float # 湿度 %
    
    @property
    def aqi_level(self) -> str:
        """空气质量等级判定"""
        if self.pm25 <= 12:
            return 'excellent'
        elif self.pm25 <= 35:
            return 'good'
        elif self.pm25 <= 55:
            return 'moderate'
        elif self.pm25 <= 150:
            return 'unhealthy'
        else:
            return 'hazardous'

class CabinAirQualityMonitor:
    """
    车内空气质量监测器
    
    数据源: Forbes 2026 + 市场报告
    核心: PM2.5是VIAQ的"黄金标准"指标
    
    WHO建议:
    - PM2.5 年均 < 5 μg/m³
    - PM2.5 24h < 15 μg/m³
    - CO2 < 1000 ppm (认知功能影响阈值)
    - VOC (甲醛) < 100 μg/m³
    """
    
    # WHO和EPA标准
    THRESHOLDS = {
        'pm25_24h_good': 15,       # WHO 24h准则
        'pm25_24h_moderate': 35,   # EPA
        'pm25_24h_unhealthy': 55,  # EPA
        'co2_normal': 800,          # 正常车内
        'co2_cognitive': 1000,     # 认知功能开始受影响
        'co2_drowsiness': 2000,    # 明显困倦
        'voc_formaldehyde': 100,   # μg/m³
        'voc_benzene': 5,          # μg/m³ (致癌)
    }
    
    def __init__(self):
        self.history = []
        self.filter_loading = 0.0  # 滤芯负载 0-1
        
    def add_reading(self, reading: AirQualityReading):
        """添加读数并分析"""
        self.history.append(reading)
        if len(self.history) > 1000:
            self.history = self.history[-1000:]
            
        return self.analyze()
    
    def analyze(self) -> Dict:
        """分析当前空气质量状态"""
        if not self.history:
            return {'status': 'no_data'}
        
        current = self.history[-1]
        
        # CO2与困倦风险
        co2_risk = self._assess_co2_risk(current.co2_ppm)
        
        # PM2.5健康风险
        pm_risk = self._assess_pm_risk(current.pm25)
        
        # VOC风险
        voc_risk = self._assess_voc_risk(current.voc_ppb)
        
        # 滤芯状态
        filter_status = self._assess_filter()
        
        # 综合建议
        recommendations = self._generate_recommendations(
            co2_risk, pm_risk, voc_risk, filter_status
        )
        
        return {
            'current': {
                'pm25': current.pm25,
                'co2': current.co2_ppm,
                'voc': current.voc_ppb,
                'aqi': current.aqi_level
            },
            'risks': {
                'co2_drowsiness': co2_risk,
                'pm_health': pm_risk,
                'voc_exposure': voc_risk
            },
            'filter': filter_status,
            'recommendations': recommendations
        }
    
    def _assess_co2_risk(self, co2: float) -> Dict:
        """CO2导致的认知/困倦风险"""
        if co2 < self.THRESHOLDS['co2_normal']:
            level = 'safe'
            cognitive_impact = 0
        elif co2 < self.THRESHOLDS['co2_cognitive']:
            level = 'elevated'
            cognitive_impact = 10  # 认知能力下降~10%
        elif co2 < self.THRESHOLDS['co2_drowsiness']:
            level = 'moderate'
            cognitive_impact = 25  # 认知能力下降~25%
        else:
            level = 'high'
            cognitive_impact = 50  # 认知能力下降~50%
            
        return {
            'level': level,
            'co2_ppm': co2,
            'cognitive_impact_pct': cognitive_impact,
            'action': 'increase_ventilation' if level != 'safe' else 'none'
        }
    
    def _assess_pm_risk(self, pm25: float) -> Dict:
        """PM2.5健康风险"""
        if pm25 <= 12:
            return {'level': 'safe', 'pm25': pm25, 'health_risk': 'minimal'}
        elif pm25 <= 35:
            return {'level': 'moderate', 'pm25': pm25, 'health_risk': 'low'}
        elif pm25 <= 55:
            return {'level': 'high', 'pm25': pm25, 'health_risk': 'moderate'}
        else:
            return {'level': 'critical', 'pm25': pm25, 'health_risk': 'high'}
    
    def _assess_voc_risk(self, voc: float) -> Dict:
        """VOC暴露风险"""
        if voc < 50:
            return {'level': 'safe', 'voc_ppb': voc}
        elif voc < 200:
            return {'level': 'moderate', 'voc_ppb': voc, 'advice': 'check_interior'}
        else:
            return {'level': 'high', 'voc_ppb': voc, 'advice': 'replace_filter'}
    
    def _assess_filter(self) -> Dict:
        """滤芯状态评估"""
        loading = min(1.0, self.filter_loading)
        if loading < 0.5:
            status = 'good'
        elif loading < 0.75:
            status = 'replace_soon'
        else:
            status = 'replace_now'
            
        return {
            'loading': loading,
            'status': status,
            'remaining_life_pct': (1 - loading) * 100
        }
    
    def _generate_recommendations(self, co2, pm, voc, filt):
        """生成建议"""
        recs = []
        if co2['level'] != 'safe':
            recs.append(f"CO2={co2['co2_ppm']:.0f}ppm → 切换新风模式")
        if pm['level'] in ('high', 'critical'):
            recs.append(f"PM2.5={pm['pm25']:.0f}μg/m³ → 内循环+HEPA")
        if voc['level'] in ('moderate', 'high'):
            recs.append(f"VOC={voc['voc_ppb']:.0f}ppb → 检查内饰/换滤芯")
        if filt['status'] != 'good':
            recs.append(f"滤芯负载{filt['loading']*100:.0f}% → 更换")
        return recs if recs else ['空气质量良好']


# 实际测试
if __name__ == "__main__":
    monitor = CabinAirQualityMonitor()
    
    # 模拟不同场景
    scenarios = [
        ("市区堵车", AirQualityReading(65, 85, 120, 1500, 35, 55)),
        ("高速公路", AirQualityReading(15, 25, 40, 700, 28, 45)),
        ("隧道内", AirQualityReading(120, 180, 200, 2500, 30, 60)),
        ("新车内饰", AirQualityReading(10, 15, 500, 800, 25, 50)),
    ]
    
    for name, reading in scenarios:
        monitor.filter_loading = 0.6  # 模拟60%滤芯负载
        result = monitor.add_reading(reading)
        
        print(f"\n=== {name} ===")
        print(f"PM2.5: {reading.pm25} μg/m³ ({result['current']['aqi']})")
        print(f"CO2: {reading.co2_ppm} ppm → {result['risks']['co2_drowsiness']['level']}")
        print(f"VOC: {reading.voc_ppb} ppb → {result['risks']['voc_exposure']['level']}")
        print(f"认知影响: -{result['risks']['co2_drowsiness']['cognitive_impact_pct']}%")
        for rec in result['recommendations']:
            print(f"  → {rec}")