import asyncio

class RailCrossingManager:
    def __init__(self):
        self.perception_agent = PerceptionAgent()
        self.prediction_agent = PredictionAgent()
        self.decision_agent = DecisionAgent()
        self.execution_agent = ExecutionAgent()
        self.memory = SharedMemory()

    async def run_loop(self):
        while True:
            # step 1: 感知
            sensor_data = await self.perception_agent.sense()
            self.memory.update_short_term(sensor_data)

            # step 2: 预测（并行）
            train_pred = await self.prediction_agent.predict_train_arrival(sensor_data)
            vehicle_pred = await self.prediction_agent.predict_vehicle_crossing(sensor_data)
            risk = self.prediction_agent.evaluate_collision_risk(train_pred, vehicle_pred)

            # step 3: 决策
            action = self.decision_agent.choose_action(risk, state=self.memory)

            # step 4: 执行
            result = await self.execution_agent.execute(action)
            self.memory.log_result(action, result)

            # 根据新状态调整循环频率
            await asyncio.sleep(0.1)  # 100ms周期

import asyncio
import random

class SharedMemory:
    def __init__(self):
        self.train_distance = None
        self.train_speed = None
        self.vehicle_present = False
        self.vehicle_speed = None
        self.boom_gate_down = False

class PerceptionAgent:
    async def sense(self):
        # 模拟传感器读数
        return {
            'train_distance': round(random.uniform(0.5, 3.0), 2),  # km
            'train_speed': round(random.uniform(60, 120), 1),  # km/h
            'vehicle_present': random.choice([True, False]),
            'vehicle_speed': round(random.uniform(10, 50), 1) if random.random() > 0.3 else 0
        }

class PredictionAgent:
    def predict_collision_risk(self, data, memory):
        if not data['vehicle_present']:
            return 0.0
        time_to_train_arrival = data['train_distance'] / data['train_speed'] * 3600  # 秒
        time_to_vehicle_clear = 5.0 / (data['vehicle_speed'] / 3.6) if data['vehicle_speed'] > 0 else float('inf')  # 假设道口宽度5m
        # 简单碰撞条件
        risk = 0.0
        if time_to_train_arrival < 30 and time_to_vehicle_clear > time_to_train_arrival:
            risk = 0.95
        elif time_to_train_arrival < 60 and time_to_vehicle_clear > time_to_train_arrival * 0.7:
            risk = 0.5
        # 考虑机械历史（记忆）
        if memory.get('boom_gate_failure_rate', 0) > 0.1:
            risk = min(1.0, risk * 1.2)
        return risk

class DecisionAgent:
    def choose_action(self, risk):
        if risk > 0.9:
            return {'type': 'emergency_brake', 'priority': 1}
        elif risk > 0.4:
            return {'type': 'lower_gate_and_warn', 'priority': 2}
        else:
            return {'type': 'no_action', 'priority': 99}

class ExecutionAgent:
    async def execute(self, action):
        if action['type'] == 'emergency_brake':
            print("[执行] 向列车发送紧急制动指令")
            # 模拟执行耗时
            await asyncio.sleep(0.2)
            return {'success': True, 'message': 'brake signal sent'}
        elif action['type'] == 'lower_gate_and_warn':
            print("[执行] 降下护栏并点亮警示灯")
            await asyncio.sleep(0.5)
            return {'success': True, 'message': 'gate down'}
        return {'success': True, 'message': 'no action'}

async def main_loop():
    memory = SharedMemory()
    percep = PerceptionAgent()
    predict = PredictionAgent()
    decide = DecisionAgent()
    exec_agent = ExecutionAgent()

    for _ in range(5):
        print("\n--- 新周期 ---")
        data = await percep.sense()
        print(f"感知: {data}")
        risk = predict.predict_collision_risk(data, memory)
        print(f"碰撞风险: {risk:.2f}")
        action = decide.choose_action(risk)
        print(f"决策: {action}")
        result = await exec_agent.execute(action)
        print(f"执行结果: {result}")
        await asyncio.sleep(1)

if __name__ == "__main__":
    asyncio.run(main_loop())