class StressInterventionScheduler:
    """
    通用汽车专利US2026/0285323A1的干预调度器
    
    在触发压力缓解练习前，必须评估:
    1. 当前驾驶状态是否安全
    2. 可用时间窗口是否足够
    3. 环境条件是否允许分心
    """
    
    # 安全练习条件
    SAFE_CONDITIONS = {
        'vehicle_speed': 0,        # 必须停车
        'gear': 'P',                # 停车挡
        'traffic_light': 'red',      # 红灯等待
        'parked': True,              # 路边停车
    }
    
    # 练习时长映射
    EXERCISE_DURATIONS = {
        'breathing_basic': 60,       # 基础呼吸：60秒
        'breathing_478': 90,         # 4-7-8呼吸：90秒
        'progressive_relax': 180,    # 渐进放松：3分钟
        'mindfulness': 120,         # 正念：2分钟
    }
    
    def check_environment(self, vehicle_data: dict) -> dict:
        """
        评估环境是否适合练习
        
        Returns:
            suitability: 环境适宜性评估
            available_time: 可用时间（秒）
        """
        speed = vehicle_data.get('speed', 0)
        gear = vehicle_data.get('gear', 'D')
        traffic_light = vehicle_data.get('traffic_light', 'green')
        parked = vehicle_data.get('parked', False)
        
        # 条件1: 车辆必须静止
        is_stationary = (speed == 0 and (gear == 'P' or parked))
        
        # 条件2: 红灯等待
        at_red_light = (speed == 0 and traffic_light == 'red')
        
        # 条件3: 环境安全
        safe = is_stationary or at_red_light
        
        # 估算可用时间
        if at_red_light:
            available = vehicle_data.get('red_light_remaining', 30)
        elif is_stationary:
            available = 600  # 停车时默认10分钟
        else:
            available = 0
        
        return {
            'suitable': safe,
            'available_time': available,
            'reason': 'parked' if is_stationary else 'red_light' if at_red_light else 'moving'
        }
    
    def select_exercise(self, stress_level: float, 
                        available_time: int) -> dict:
        """
        根据压力等级和可用时间选择练习
        
        Args:
            stress_level: 0-1
            available_time: 秒
        """
        candidates = []
        for name, duration in self.EXERCISE_DURATIONS.items():
            if duration <= available_time:
                candidates.append((name, duration))
        
        if not candidates:
            return None
        
        # 高压力→长练习; 低压力→短练习
        if stress_level > 0.7:
            preferred = [c for c in candidates if c[1] >= 120]
        else:
            preferred = [c for c in candidates if c[1] < 120]
        
        selected = preferred[0] if preferred else candidates[0]
        
        return {
            'exercise': selected[0],
            'duration': selected[1],
            'instructions': self._get_instructions(selected[0])
        }
    
    def _get_instructions(self, name: str) -> str:
        instructions = {
            'breathing_basic': '吸气4秒→屏息2秒→呼气6秒，重复',
            'breathing_478': '吸气4秒→屏息7秒→呼气8秒，重复4次',
            'progressive_relax': '从脚到头依次收紧→放松各肌肉群',
            'mindfulness': '关注呼吸，觉察当下，不评判'
        }
        return instructions.get(name, '')


# 测试
if __name__ == "__main__":
    scheduler = StressInterventionScheduler()
    
    # 场景1: 高速行驶，高压力
    env = scheduler.check_environment({
        'speed': 100, 'gear': 'D', 'traffic_light': 'green'
    })
    print(f"场景1 (高速): suitable={env['suitable']}, time={env['available_time']}s")
    
    # 场景2: 红灯等待，高压力
    env = scheduler.check_environment({
        'speed': 0, 'gear': 'D', 'traffic_light': 'red',
        'red_light_remaining': 45
    })
    exercise = scheduler.select_exercise(stress_level=0.8, 
                                         available_time=env['available_time'])
    print(f"场景2 (红灯): suitable={env['suitable']}, time={env['available_time']}s")
    print(f"  推荐练习: {exercise['exercise']} ({exercise['duration']}s)")
    print(f"  指导: {exercise['instructions']}")
    
    # 场景3: 路边停车，极高压力
    env = scheduler.check_environment({
        'speed': 0, 'gear': 'P', 'parked': True
    })
    exercise = scheduler.select_exercise(stress_level=0.9, 
                                         available_time=env['available_time'])
    print(f"场景3 (停车): suitable={env['suitable']}, time={env['available_time']}s")
    print(f"  推荐练习: {exercise['exercise']} ({exercise['duration']}s)")