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| """
智能健康座舱感知系统
三层架构: 环境感知 → 体征感知 → AI干预
依赖: numpy
"""
import numpy as np
from typing import Dict
class SmartHealthCabin:
"""
智能健康座舱系统
三层架构:
1. 环境管理: 空气质量+温湿度+气味
2. 体征监测: 心率/呼吸/疲劳/压力
3. 主动干预: AI推荐+自动调节
"""
def __init__(self):
self.env_sensors = {
'pm25': {'type': 'particulate', 'range': '0-500 μg/m³', 'hz': 1},
'co2': {'type': 'gas', 'range': '400-5000 ppm', 'hz': 1},
'tvoc': {'type': 'gas', 'range': '0-10 mg/m³', 'hz': 1},
'temp': {'type': 'temperature', 'range': '-20~+60°C', 'hz': 1},
'humidity': {'type': 'humidity', 'range': '0-100%RH', 'hz': 1},
'uv': {'type': 'light', 'range': '0-11 UV index', 'hz': 1},
}
self.bio_sensors = {
'mmwave_radar': {
'type': '77GHz FMCW',
'measures': ['heart_rate', 'breathing_rate', 'HRV'],
'accuracy': {'hr': '±3 BPM', 'br': '±0.5 BPM'},
},
'ir_camera': {
'type': '940nm IR',
'measures': ['fatigue', 'distraction', 'emotion'],
'fps': 30,
},
'seat_pressure': {
'type': 'pressure_mat',
'measures': ['posture', 'weight', 'OOP'],
},
'microphone': {
'type': 'array',
'measures': ['voice_emotion', 'cough', 'respiratory'],
},
}
self.actuators = {
'air_purifier': {'type': 'HEPA+activated_carbon', 'cadre': 50},
'fragrance': {'type': 'multi_scent', 'options': 5},
'ac': {'type': 'climate_control', 'zones': 4},
'lighting': {'type': 'ambient_LED', 'colors': 16},
'seat': {'type': 'massage+ventilation'},
'hud': {'type': 'AR-HUD', 'brightness': 12000},
}
def health_assessment(self, bio_data, env_data):
"""
综合健康评估
输入: 生物信号 + 环境数据
输出: 健康分数 + 干预建议
"""
score = 100
interventions = []
if bio_data['perclos'] > 0.3:
score -= 20
interventions.append({
'type': 'air_conditioner',
'action': 'cool_mode',
'temp': 20,
'reason': '降低温度提神'
})
interventions.append({
'type': 'fragrance',
'action': 'peppermint',
'reason': '薄荷提神'
})
interventions.append({
'type': 'hud',
'action': 'cool_blue',
'reason': '冷色调HUD提神'
})
elif bio_data['perclos'] > 0.15:
score -= 10
interventions.append({
'type': 'fragrance',
'action': 'lemon',
'reason': '柠檬清新'
})
if bio_data['hrv'] < 30:
score -= 15
interventions.append({
'type': 'lighting',
'action': 'warm_orange',
'reason': '暖色调放松'
})
interventions.append({
'type': 'seat',
'action': 'massage_gentle',
'reason': '轻柔按摩'
})
if env_data['pm25'] > 75:
score -= 10
interventions.append({
'type': 'air_purifier',
'action': 'max_power',
'reason': 'PM2.5超标'
})
if env_data['co2'] > 1000:
score -= 8
interventions.append({
'type': 'ac',
'action': 'fresh_air',
'reason': 'CO2超标,引入新风'
})
if bio_data.get('emotion') == 'angry':
score -= 10
interventions.append({
'type': 'fragrance',
'action': 'lavender',
'reason': '薰衣草舒缓情绪'
})
interventions.append({
'type': 'lighting',
'action': 'soft_blue',
'reason': '蓝色平静'
})
return {
'health_score': max(score, 0),
'level': self._classify(score),
'interventions': interventions,
}
def _classify(self, score):
if score >= 85:
return 'excellent'
elif score >= 70:
return 'good'
elif score >= 50:
return 'attention'
else:
return 'warning'
if __name__ == "__main__":
cabin = SmartHealthCabin()
print("=" * 60)
print("智能健康座舱 2026")
print("=" * 60)
print(f"\n环境传感器 ({len(cabin.env_sensors)}个):")
for name, spec in cabin.env_sensors.items():
print(f" {name}: {spec['range']}")
print(f"\n体征传感器 ({len(cabin.bio_sensors)}个):")
for name, spec in cabin.bio_sensors.items():
print(f" {name}: {spec['measures']}")
print(f"\n执行器 ({len(cabin.actuators)}个):")
for name, spec in cabin.actuators.items():
print(f" {name}: {spec['type']}")
print(f"\n--- 场景: 疲劳驾驶+高CO2 ---")
bio = {'perclos': 0.35, 'hrv': 25, 'heart_rate': 90, 'emotion': 'neutral'}
env = {'pm25': 45, 'co2': 1200, 'temp': 28, 'humidity': 60}
result = cabin.health_assessment(bio, env)
print(f" 健康分数: {result['health_score']}/100 ({result['level']})")
print(f" 干预建议 ({len(result['interventions'])}项):")
for iv in result['interventions']:
print(f" {iv['type']}: {iv['action']} - {iv['reason']}")
|
