class AlertFatigueAnalysis:
    """
    警报疲劳分析模型
    
    基于北海风电场事故：每小时6次警报 → 被静音
    核心问题：误报率过高导致操作员对真实威胁失去敏感性
    """
    
    def __init__(self):
        self.alert_types = {
            'ais_offline': {'freq_per_hour': 2, 'is_threat': False},
            'radar_minor': {'freq_per_hour': 2, 'is_threat': False},
            'vessel_approach': {'freq_per_hour': 1, 'is_threat': True},
            'safety_zone_breach': {'freq_per_hour': 0.5, 'is_threat': True},
            'system_fault': {'freq_per_hour': 0.5, 'is_threat': False},
        }
    
    def analyze_alert_fatigue(self) -> dict:
        """分析警报疲劳风险"""
        total_alerts = sum(a['freq_per_hour'] for a in self.alert_types.values())
        threat_alerts = sum(a['freq_per_hour'] for a in self.alert_types.values() if a['is_threat'])
        false_alerts = total_alerts - threat_alerts
        
        # 信号噪声比
        snr = threat_alerts / (false_alerts + 1e-6)
        
        # 警报疲劳指数 (0-100, 越高越危险)
        if total_alerts > 5:
            fatigue_index = min(100, (total_alerts - 5) * 20)
        else:
            fatigue_index = 0
        
        return {
            'total_alerts_per_hour': total_alerts,
            'threat_alerts_per_hour': threat_alerts,
            'false_alerts_per_hour': false_alerts,
            'signal_to_noise_ratio': snr,
            'fatigue_index': fatigue_index,
            'recommendation': '静音警报' if fatigue_index > 60 else '正常',
        }


if __name__ == "__main__":
    analysis = AlertFatigueAnalysis()
    result = analysis.analyze_alert_fatigue()
    
    print("=== 北海事故警报疲劳分析 ===")
    print(f"总警报频率: {result['total_alerts_per_hour']:.1f} 次/小时")
    print(f"真实威胁警报: {result['threat_alerts_per_hour']:.1f} 次/小时")
    print(f"误报: {result['false_alerts_per_hour']:.1f} 次/小时")
    print(f"信噪比: {result['signal_to_noise_ratio']:.2f}")
    print(f"警报疲劳指数: {result['fatigue_index']}/100")
    print(f"建议: {result['recommendation']}")
    
    print("\n=== 对 DMS 的启示 ===")
    print("""
    DMS 同样面临警报疲劳风险：
    1. 如果 PERCLOS 告警频繁但非真实疲劳 → 驾驶员会忽略
    2. 分心告警过于敏感 → 驾驶员关闭/不信任系统
    3. 解决方案：分级告警 + 自适应阈值 + 上下文融合
    
    海事教训：风电场警报被静音是因为6次/小时的误报率
    DMS 教训：如果误报率 > 5次/小时，驾驶员将不信任系统
    """)

class DMSAlertDesign:
    """
    基于海事事故教训的 DMS 警报设计
    
    核心原则：
    1. 警报必须能唤醒疲劳驾驶员（桥楼警报失败教训）
    2. 误报率必须可控（风电场警报静音教训）
    3. 分级告警避免警报疲劳
    4. 多模态告警确保感知
    """
    
    ALERT_LEVELS = {
        'info': {
            'trigger': '轻度疲劳迹象',
            'modalities': ['视觉:仪表盘图标'],
            'volume_db': 0,
            'duration': '2s闪烁',
            'max_per_hour': 3,
        },
        'warning': {
            'trigger': 'PERCLOS 20-30% 或分心',
            'modalities': ['视觉:红色图标', '听觉:语音提醒'],
            'volume_db': 65,
            'duration': '3s',
            'max_per_hour': 2,
        },
        'critical': {
            'trigger': 'PERCLOS >30% 或微睡眠',
            'modalities': ['视觉:全屏红色', '听觉:高音警报', '触觉:方向盘震动'],
            'volume_db': 85,
            'duration': '持续至驾驶员响应',
            'max_per_hour': 1,
        },
        'emergency': {
            'trigger': '持续闭眼 >3s 或无响应',
            'modalities': ['全感官', 'ADAS 介入'],
            'volume_db': 95,
            'duration': '持续',
            'max_per_hour': 0,  # 不应有
        },
    }
    
    def evaluate_alert_strategy(self, current_level: str,
                                alerts_last_hour: int) -> dict:
        """评估告警策略"""
        level_config = self.ALERT_LEVELS[current_level]
        max_hourly = level_config['max_per_hour']
        
        if alerts_last_hour >= max_hourly:
            return {
                'action': 'escalate',
                'reason': f'{current_level}已达上限{max_hourly}/h，升级至更高级别',
                'new_level': self._escalate(current_level),
            }
        
        return {
            'action': 'alert',
            'level': current_level,
            'modalities': level_config['modalities'],
            'volume_db': level_config['volume_db'],
        }
    
    def _escalate(self, level: str) -> str:
        levels = ['info', 'warning', 'critical', 'emergency']
        idx = levels.index(level)
        return levels[min(idx + 1, len(levels) - 1)]


# 测试
if __name__ == "__main__":
    dms = DMSAlertDesign()
    
    print("=== DMS 警报设计（基于海事教训）===")
    for level, config in dms.ALERT_LEVELS.items():
        print(f"\n{level.upper()}:")
        print(f"  触发: {config['trigger']}")
        print(f"  模态: {config['modalities']}")
        print(f"  音量: {config['volume_db']} dB")
        print(f"  上限: {config['max_per_hour']}/h")
    
    print("\n=== 警报疲劳防护测试 ===")
    # 模拟过去1小时已触发2次 warning
    result = dms.evaluate_alert_strategy('warning', 2)
    print(f"第3次 warning 触发: {result}")
    
    result = dms.evaluate_alert_strategy('warning', 1)
    print(f"第2次 warning 触发: {result}")

class CumulativeFatigueTracker:
    """
    累积疲劳追踪器
    
    海事教训：船长是慢性疲劳，非即时疲劳
    DMS 需要：检测累积疲劳，非仅当前帧状态
    """
    
    def __init__(self):
        self.fatigue_history = []  # 每分钟PERCLOS记录
        self.driving_duration = 0  # 连续驾驶分钟
        self.rest_history = []     # 休息记录
    
    def update(self, perclos: float, is_driving: bool = True):
        """每分钟更新"""
        if is_driving:
            self.driving_duration += 1
            self.fatigue_history.append(perclos)
        else:
            self.rest_history.append({'duration_min': 1, 'timestamp': time.time()})
            self.driving_duration = 0  # 重置连续驾驶
    
    def get_fatigue_risk(self) -> dict:
        """评估累积疲劳风险"""
        if not self.fatigue_history:
            return {'risk': 'unknown'}
        
        recent_1h = self.fatigue_history[-60:] if len(self.fatigue_history) >= 60 else self.fatigue_history
        recent_3h = self.fatigue_history[-180:] if len(self.fatigue_history) >= 180 else self.fatigue_history
        
        avg_1h = sum(recent_1h) / len(recent_1h)
        avg_3h = sum(recent_3h) / len(recent_3h)
        
        # 累积疲劳指数
        cumulative_index = (avg_1h * 0.4 + avg_3h * 0.3 + 
                           min(self.driving_duration / 120, 1) * 30)
        
        if cumulative_index > 50:
            return {'risk': 'high', 'index': cumulative_index,
                    'recommendation': '强制休息20min'}
        elif cumulative_index > 30:
            return {'risk': 'moderate', 'index': cumulative_index,
                    'recommendation': '建议休息'}
        else:
            return {'risk': 'low', 'index': cumulative_index,
                    'recommendation': '继续监控'}


import time
if __name__ == "__main__":
    tracker = CumulativeFatigueTracker()
    
    # 模拟2小时驾驶，疲劳逐渐积累
    for minute in range(120):
        perclos = 5 + minute * 0.2  # 从5%逐渐增加到29%
        tracker.update(perclos)
    
    risk = tracker.get_fatigue_risk()
    print(f"连续驾驶: {tracker.driving_duration} 分钟")
    print(f"疲劳风险: {risk['risk']} (指数: {risk['index']:.1f})")
    print(f"建议: {risk['recommendation']}")