# DMS 各子任务在 Akida 上的适用性评估
dms_tasks_akida = {
    "face_detection": {
        "suitability": "⚠️ 中",
        "reason": "面部大部分时间静止不动 → 事件稀疏 → Akida 优势有限",
        "traditional_better": True,
        "note": "传统 CNN 在每帧检测更可靠"
    },
    "blink_detection": {
        "suitability": "✅ 高",
        "reason": "眨眼是快速事件（100-400ms）→ 事件驱动捕捉精准",
        "advantage": "微睡眠检测延迟 <5ms vs 传统 33ms（30fps）",
        "note": "DVS 摄像头 + Akida 可实现超低延迟眨眼检测"
    },
    "gaze_tracking": {
        "suitability": "⚠️ 中",
        "reason": "眼球微动持续产生事件 → 适合 SNN",
        "challenge": "精度要求 <2°，SNN 精度待验证",
        "note": "可能需要混合方案"
    },
    "drowsiness_detection": {
        "suitability": "✅ 高",
        "reason": "困倦是缓慢趋势 → 增量学习适配个人基线",
        "advantage": "片上学习可个性化 PERCLOS 阈值",
        "note": "解决跨被试域偏移问题"
    },
    "phone_use_detection": {
        "suitability": "✅ 高",
        "reason": "手机使用是离散事件 → 突发活动检测",
        "advantage": "仅在行为变化时触发推理，功耗极低",
        "note": "适合 always-on 后台监控"
    },
    "occupant_classification": {
        "suitability": "⚠️ 低",
        "reason": "需要全帧推理分类 → 不稀疏",
        "note": "传统 CNN 更适合"
    }
}

# 总体评估
print("=== Akida DMS 适用性排序 ===")
for task, info in sorted(dms_tasks_akida.items(), key=lambda x: x[1]["suitability"], reverse=True):
    print(f"  {task}: {info['suitability']} - {info['reason']}")

# 混合 DMS 架构设计
hybrid_architecture = {
    "always_on_layer": {
        "chip": "Akida AKD1500",
        "power": "0.1-0.5W",
        "tasks": [
            "眨眼事件检测（微睡眠监控）",
            "手机使用事件检测",
            "驾驶员身份识别（片上学习）",
            "困倦趋势跟踪（个性化基线）"
        ],
        "camera": "事件摄像头 (DVS) 或 低帧率 RGB",
        "advantage": "24/7 低功耗监控，不增加电池负担"
    },
    "high_performance_layer": {
        "chip": "QCS8255 Hexagon NPU",
        "power": "2-5W（按需唤醒）",
        "tasks": [
            "高精度视线估计 (<2°)",
            "面部关键点检测 (468点)",
            "乘员分类",
            "OOP 姿态估计",
            "安全带误用检测"
        ],
        "camera": "IR 940nm + RGB 双摄",
        "advantage": "仅在 always-on 层检测到风险时唤醒"
    },
    "power_management": {
        "strategy": "事件触发唤醒",
        "flow": "Akida 检测到异常 → 唤醒 QCS8255 → 高精度分析",
        "power_saving": "预计节省 60-70% 总功耗"
    }
}

# Akida 片上学习解决的核心问题
on_chip_learning = {
    "personal_baseline": {
        "problem": "不同驾驶员眨眼率/PERCLOS 基线不同",
        "traditional": "需大量数据训练通用模型",
        "akida": "片上增量学习，3-5分钟建立个人基线"
    },
    "new_scenario": {
        "problem": "遇到新眼镜/新帽子/新安装位置",
        "traditional": "需回传数据→重训练→OTA更新",
        "akida": "片上自适应，无需云端"
    },
    "privacy": {
        "problem": "面部数据上传云端训练有隐私风险",
        "traditional": "需脱敏处理",
        "akida": "数据不出车，天然隐私保护"
    }
}