# 示意代码,不可直接运行(仅演示"检测→策略选择→对话干预→效果评估"闭环思路;原论文为用户试验研究,未提供系统代码)
import torch
import torch.nn as nn
import numpy as np
from dataclasses import dataclass
from enum import Enum

class ConversationStrategy(Enum):
    """对话策略类型"""
    SMALL_TALK = "闲聊"        # 轻松话题
    HAZARD_ALERT = "危险提醒"  # 道路危险讨论
    TRIVIA = "知识问答"        # 冷知识问答
    MEMORY = "回忆对话"        # 回忆引导
    OPEN_ENDED = "开放话题"    # 开放式讨论

@dataclass
class DriverState:
    """驾驶员状态"""
    fatigue_level: float    # 0-1
    alertness: float        # 0-1
    microsleep: bool        # 是否微睡眠
    takeover_readiness: float  # 接管准备度 0-1
    eyes_on_road: bool
    conversation_engagement: float  # 对话参与度 0-1

class LLMConversationAgent:
    """
    LLM对话代理：根据疲劳状态动态调整对话策略
    
    架构：
    1. 疲劳状态监测（DMS输入）
    2. 对话策略选择（基于状态）
    3. LLM生成对话（上下文相关）
    4. 效果评估（警觉性反馈）
    """
    
    def __init__(self):
        self.strategy_history = []
        self.fatigue_threshold = 0.4
        self.microsleep_threshold = 0.6
        
        # LLM配置（实际部署用车载边缘LLM或云端）
        self.llm_config = {
            'model': '车载7B参数LLM',
            'max_tokens': 50,
            'temperature': 0.7,
            'voice_output': True,
        }
    
    def select_strategy(self, state: DriverState) -> ConversationStrategy:
        """
        根据驾驶员状态选择对话策略
        
        规则：
        - 轻度疲劳：闲聊（轻松提升）
        - 中度疲劳：知识问答（需要思考）
        - 重度疲劳：危险提醒（紧急唤醒）
        - 微睡眠：强刺激唤醒
        """
        if state.microsleep:
            return ConversationStrategy.HAZARD_ALERT
        elif state.fatigue_level > self.microsleep_threshold:
            return ConversationStrategy.TRIVIA
        elif state.fatigue_level > self.fatigue_threshold:
            return ConversationStrategy.SMALL_TALK
        else:
            # 正常状态无需对话
            return None
    
    def generate_response(self, 
                          strategy: ConversationStrategy,
                          state: DriverState,
                          conversation_context: list) -> str:
        """
        生成对话响应
        
        简化版：实际用LLM生成
        """
        responses = {
            ConversationStrategy.SMALL_TALK: [
                "今天天气不错，注意到外面的山了吗？",
                "你平时周末喜欢做什么？",
                "这段路风景不错，经常走吗？",
            ],
            ConversationStrategy.TRIVIA: [
                "你知道吗？蜂鸟是唯一能倒退飞行的鸟。",
                "快速算一下：17乘以3等于多少？",
                "说出三种红色的水果。",
            ],
            ConversationStrategy.HAZARD_ALERT: [
                "前方有施工路段，请注意！",
                "你刚才闭眼超过2秒，需要休息吗？",
                "前方500米有交叉路口，请确认路况！",
            ],
            ConversationStrategy.MEMORY: [
                "还记得上次走这段路的情况吗？",
                "回忆一下你今天的行程安排。",
            ],
        }
        
        pool = responses.get(strategy, ["你好，感觉怎么样？"])
        return np.random.choice(pool)
    
    def assess_effect(self, 
                      pre_state: DriverState,
                      post_state: DriverState) -> dict:
        """评估对话效果"""
        return {
            'alertness_change': post_state.alertness - pre_state.alertness,
            'fatigue_change': post_state.fatigue_level - pre_state.fatigue_level,
            'engagement': post_state.conversation_engagement,
            'microsleep_resolved': pre_state.microsleep and not post_state.microsleep,
        }


class FatigueInterventionSystem:
    """
    完整疲劳干预系统
    
    架构：
    1. DMS检测疲劳
    2. LLM对话代理干预
    3. 效果评估+策略调整
    """
    
    def __init__(self):
        self.agent = LLMConversationAgent()
        self.dms_state = DriverState(
            fatigue_level=0.0,
            alertness=1.0,
            microsleep=False,
            takeover_readiness=1.0,
            eyes_on_road=True,
            conversation_engagement=0.0,
        )
    
    def update_dms(self, fatigue: float, alertness: float,
                    microsleep: bool, eyes_on_road: bool):
        """更新DMS状态"""
        self.dms_state.fatigue_level = fatigue
        self.dms_state.alertness = alertness
        self.dms_state.microsleep = microsleep
        self.dms_state.eyes_on_road = eyes_on_road
    
    def intervene(self) -> dict:
        """执行干预"""
        # 选择策略
        strategy = self.agent.select_strategy(self.dms_state)
        
        if strategy is None:
            return {'action': 'none', 'reason': '正常状态'}
        
        # 记录干预前状态
        pre_state = DriverState(**self.dms_state.__dict__)
        
        # 生成对话
        response = self.agent.generate_response(
            strategy, self.dms_state, []
        )
        
        # 模拟对话效果
        self.dms_state.alertness = min(1.0, 
            self.dms_state.alertness + 0.15)
        self.dms_state.fatigue_level = max(0,
            self.dms_state.fatigue_level - 0.10)
        if self.dms_state.microsleep:
            self.dms_state.microsleep = False
            self.dms_state.alertness = 0.7  # 强唤醒
        self.dms_state.conversation_engagement = 0.6
        
        # 评估效果
        effect = self.agent.assess_effect(pre_state, self.dms_state)
        
        return {
            'action': 'conversation',
            'strategy': strategy.value,
            'response': response,
            'effect': effect,
        }


# 测试：模拟30分钟L3驾驶
if __name__ == "__main__":
    system = FatigueInterventionSystem()
    
    print("=== 30分钟L3驾驶疲劳干预模拟 ===\n")
    
    for minute in range(30):
        # 模拟疲劳渐进
        system.update_dms(
            fatigue=min(1.0, 0.02 * minute + np.random.randn() * 0.03),
            alertness=max(0.1, 1.0 - 0.025 * minute),
            microsleep=(minute > 20 and np.random.rand() < 0.15),
            eyes_on_road=(minute <= 15 or np.random.rand() > 0.2)
        )
        
        # 干预
        result = system.intervene()
        
        if result['action'] != 'none' and minute % 5 == 0:
            print(f"[{minute:2d}min] 疲劳={system.dms_state.fatigue_level:.2f} "
                  f"警觉={system.dms_state.alertness:.2f} "
                  f"微睡眠={'是' if system.dms_state.microsleep else '否'}")
            print(f"  → 策略: {result.get('strategy', '无')}")
            print(f"  → 对话: {result.get('response', '无')}")
            if 'effect' in result:
                e = result['effect']
                print(f"  → 效果: 警觉+{e['alertness_change']:.2f} "
                      f"疲劳{e['fatigue_change']:.2f}")
            print()