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| """
车辆健康监测管道
方向盘ECG + 安全带呼吸 + rPPG 融合
依赖:
pip install numpy scipy
"""
import numpy as np
from scipy.signal import butter, filtfilt, find_peaks
from dataclasses import dataclass
from typing import Optional, Tuple
@dataclass
class HealthMetrics:
"""健康指标"""
heart_rate: float
hrv_sdnn: float
respiratory_rate: float
stress_index: float
anomaly_flag: bool
source: str
class SteeringWheelECG:
"""方向盘 ECG 传感器"""
def __init__(self, sample_rate: int = 256):
self.fs = sample_rate
def generate(self, hr: float = 72, duration_sec: int = 30) -> Tuple[np.ndarray, bool]:
"""生成 ECG 信号"""
t = np.arange(0, duration_sec, 1/self.fs)
hands_on = np.random.random() > 0.15
if not hands_on:
return np.zeros(len(t)), False
rr = 60 / hr + np.random.normal(0, 0.04, int(duration_sec * hr / 60))
r_times = np.cumsum(rr)
ecg = np.zeros(len(t))
for r_t in r_times:
idx = int(r_t * self.fs)
if 0 <= idx < len(ecg):
ecg[idx] = 1.5
if idx > 5: ecg[idx-5] = -0.15
if idx+8 < len(ecg): ecg[idx+8] = -0.35
if idx+15 < len(ecg): ecg[idx+15] = 0.1
return ecg + np.random.normal(0, 0.02, len(ecg)), True
def extract_hr_hrv(self, ecg: np.ndarray) -> Tuple[float, float]:
"""提取心率和 HRV"""
peaks, _ = find_peaks(ecg, height=0.5, distance=0.3*self.fs)
if len(peaks) < 3:
return 0, 0
rr = np.diff(peaks) / self.fs
hr = 60 / np.mean(rr)
hrv = np.std(rr) * 1000
return hr, hrv
class SeatbeltRespiration:
"""安全带呼吸传感器"""
def __init__(self, sample_rate: int = 50):
self.fs = sample_rate
def generate(self, resp_rate: float = 16, duration_sec: int = 30) -> np.ndarray:
"""生成呼吸信号"""
t = np.arange(0, duration_sec, 1/self.fs)
signal = np.sin(2 * np.pi * resp_rate / 60 * t)
signal += 0.05 * np.random.randn(len(t))
return signal
def extract_resp_rate(self, signal: np.ndarray) -> float:
"""提取呼吸频率"""
fft = np.abs(np.fft.rfft(signal))
freqs = np.fft.rfftfreq(len(signal), 1/self.fs)
resp_freq = freqs[np.argmax(fft)]
return resp_freq * 60
class rPPGCamera:
"""rPPG 摄像头"""
def __init__(self, sample_rate: int = 30):
self.fs = sample_rate
def generate(self, hr: float = 72, duration_sec: int = 30) -> np.ndarray:
"""生成 rPPG 信号"""
t = np.arange(0, duration_sec, 1/self.fs)
hr_freq = hr / 60
signal = 0.5 * np.sin(2 * np.pi * hr_freq * t)
signal += 0.3 * np.sin(2 * np.pi * hr_freq * 2 * t)
signal += 0.1 * np.random.randn(len(t))
signal += 0.2 * np.sin(2 * np.pi * 0.1 * t)
return signal
def extract_hr(self, signal: np.ndarray) -> Tuple[float, float]:
"""提取心率和置信度"""
nyq = self.fs / 2
b, a = butter(2, [0.7/nyq, 3.0/nyq], btype='band')
filtered = filtfilt(b, a, signal)
fft = np.abs(np.fft.rfft(filtered))
freqs = np.fft.rfftfreq(len(filtered), 1/self.fs)
peak_idx = np.argmax(fft)
hr = freqs[peak_idx] * 60
confidence = fft[peak_idx] / (np.sum(fft) + 1e-8) * 100
return hr, confidence
class VehicleHealthMonitor:
"""车辆健康监测融合系统"""
def __init__(self):
self.ecg_sensor = SteeringWheelECG()
self.resp_sensor = SeatbeltRespiration()
self.rppg_sensor = rPPGCamera()
self.baseline_hr = 72
self.baseline_hrv = 45
self.baseline_resp = 16
def monitor(self, duration_sec: int = 30) -> HealthMetrics:
"""监测健康状态"""
ecg, hands_on = self.ecg_sensor.generate(72, duration_sec)
resp = self.resp_sensor.generate(16, duration_sec)
rppg = self.rppg_sensor.generate(72, duration_sec)
ecg_hr, ecg_hrv = self.ecg_sensor.extract_hr_hrv(ecg) if hands_on else (0, 0)
resp_rate = self.resp_sensor.extract_resp_rate(resp)
rppg_hr, rppg_conf = self.rppg_sensor.extract_hr(rppg)
if hands_on and ecg_hr > 0:
hr = ecg_hr
hrv = ecg_hrv
source = 'ecg'
else:
hr = rppg_hr
hrv = 0
source = 'rppg'
hr_dev = abs(hr - self.baseline_hr)
resp_dev = abs(resp_rate - self.baseline_resp)
stress_index = min(100, hr_dev * 3 + resp_dev * 5)
anomaly = (hr_dev > 20 or resp_dev > 8 or hr < 40 or hr > 120)
return HealthMetrics(
heart_rate=round(hr, 1),
hrv_sdnn=round(hrv, 1),
respiratory_rate=round(resp_rate, 1),
stress_index=round(stress_index, 1),
anomaly_flag=anomaly,
source=source
)
if __name__ == "__main__":
print("=" * 70)
print("车辆健康监测管道")
print("方向盘ECG + 安全带呼吸 + rPPG")
print("=" * 70)
monitor = VehicleHealthMonitor()
print("\n=== 正常驾驶 ===")
result = monitor.monitor(30)
print(f" 心率: {result.heart_rate} bpm ({result.source})")
print(f" HRV: {result.hrv_sdnn} ms")
print(f" 呼吸: {result.respiratory_rate} 次/分")
print(f" 压力指数: {result.stress_index}/100")
print(f" 异常: {'⚠️' if result.anomaly_flag else '✅'}")
print(f"\n{'='*70}")
print("传感器特性对比")
print(f"{'='*70}")
print(f"\n{'传感器':<15s} {'信号':<15s} {'精度':<10s} {'持续':<8s} {'接触':<8s}")
print("-" * 55)
print(f"{'方向盘ECG':<15s} {'心电':<15s} {'±5ms':<10s} {'间歇':<8s} {'接触':<8s}")
print(f"{'安全带电极':<15s} {'呼吸':<15s} {'±2次/分':<10s} {'持续':<8s} {'接触':<8s}")
print(f"{'rPPG摄像头':<15s} {'心率':<15s} {'±15ms':<10s} {'持续':<8s} {'非接触':<8s}")
print(f"{'融合系统':<15s} {'全信号':<15s} {'最佳':<10s} {'持续':<8s} {'混合':<8s}")
print(f"\n{'='*70}")
print("IMS 部署路线")
print(f"{'='*70}")
print("""
Phase 1 (2026-2027): 方向盘ECG + 安全带呼吸
- 成本: +$5-8 (电极)
- 精度: HR ±2bpm, 呼吸 ±1次/分
- 限制: 手离开方向盘时ECG中断
Phase 2 (2027-2028): + rPPG 摄像头
- 成本: 摄像头已有(DMS复用)
- 精度: HR ±5bpm (rPPG备份)
- 优势: ECG中断时无缝切换
Phase 3 (2028-2029): + AI 异常检测
- 功能: 心律失常/心率突变/呼吸异常
- 警报: 健康风险 → 靠边停车建议
Phase 4 (2029+): 完整健康监测
- 功能: 心血管/呼吸/压力/疲劳综合评估
- 价值: 预防性健康干预
""")
|
