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| class FatigueBiologyPathways:
"""
跨疾病疲劳的五大共享生物学通路
每条通路都有可量化的生物标志物
→ 理论上可转化为车内可监测指标
"""
def __init__(self):
self.pathways = {
"1_immune_inflammatory": {
"name": "免疫/炎症信号",
"markers": {
"CRP": {"type": "血液炎症标志物", "level": "升高", "normal": "<3 mg/L"},
"IL_6": {"type": "白细胞介素6", "level": "升高", "normal": "<7 pg/mL"},
"TNF_alpha": {"type": "肿瘤坏死因子", "level": "升高", "normal": "<8 pg/mL"},
},
"vehicle_proxy": "HRV(交感/副交感平衡) → 炎症水平间接反映",
"ims_applicability": "⭐⭐⭐ 高 - HRV可车内测量"
},
"2_mitochondrial_energy": {
"name": "线粒体能量生产",
"markers": {
"ATP": {"type": "三磷酸腺苷", "level": "降低", "normal": "细胞内正常"},
"lactate": {"type": "乳酸", "level": "升高", "normal": "<2 mmol/L"},
"NAD_NADH_ratio": {"type": "氧化还原比", "level": "降低", "normal": "组织特异"},
},
"vehicle_proxy": "肌肉疲劳 → 方向盘握力变化 → 压力传感器",
"ims_applicability": "⭐⭐ 中 - 需方向盘压力传感器"
},
"3_metabolic": {
"name": "代谢调节",
"markers": {
"glucose": {"type": "血糖", "level": "波动", "normal": "70-100 mg/dL"},
"insulin": {"type": "胰岛素", "level": "变化", "normal": "2-25 μU/mL"},
"cortisol": {"type": "皮质醇(应激)", "level": "异常", "normal": "5-25 μg/dL"},
},
"vehicle_proxy": "血糖低 → 认知功能下降 → 反应时间增加",
"ims_applicability": "⭐ 低 - 需可穿戴连续监测"
},
"4_stress_response": {
"name": "应激反应",
"markers": {
"cortisol": {"type": "皮质醇", "level": "升高", "normal": "5-25 μg/dL"},
"DHEA_S": {"type": "脱氢表雄酮", "level": "降低", "normal": "年龄性别特异"},
"alpha_amylase": {"type": "唾液淀粉酶", "level": "升高", "normal": "30-100 U/mL"},
},
"vehicle_proxy": "唾液淀粉酶 → 口干 → 语音变化 → 语音分析",
"ims_applicability": "⭐⭐ 中 - 语音分析已有"
},
"5_neuroendocrine": {
"name": "神经内分泌",
"markers": {
"melatonin": {"type": "褪黑素", "level": "节律紊乱", "normal": "夜间高"},
"dopamine": {"type": "多巴胺", "level": "降低", "normal": "脑脊液特异"},
"serotonin": {"type": "血清素", "level": "变化", "normal": "血小板特异"},
},
"vehicle_proxy": "褪黑素 → 昼夜节律 → 驾驶时间关联",
"ims_applicability": "⭐⭐ 中 - 时间+HRV间接估计"
}
}
def get_ims_relevant_markers(self):
"""筛选车内可监测的标志物"""
relevant = []
for pathway_id, pathway in self.pathways.items():
proxy = pathway["vehicle_proxy"]
applicability = pathway["ims_applicability"]
if "⭐⭐⭐" in applicability or "⭐⭐" in applicability:
relevant.append({
"pathway": pathway["name"],
"proxy": proxy,
"applicability": applicability
})
return relevant
bio = FatigueBiologyPathways()
ims_relevant = bio.get_ims_relevant_markers()
print(f"车内可监测的疲劳生物标志物通路: {len(ims_relevant)}/5")
for r in ims_relevant:
print(f" {r['pathway']}: {r['proxy']}")
print(f" 适用性: {r['applicability']}")
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