AI与人类注意力流：未来的工作场所与注意力流管理

    import heapq
    from dataclasses import dataclass
    
    @dataclass
    class Task:
    id: int
    importance: int
    urgency: int
    
    def calculate_priority(task: Task) -> float:
    # 根据重要性和紧急性计算优先级
    return (task.importance + task.urgency) / 2
    
    def manage_attention(tasks: list[Task]) -> list[int]:
    # 使用优先级队列管理任务
    priority_queue = []
    for task in tasks:
        heapq.heappush(priority_queue, (-calculate_priority(task), task.id))
    
    completed_tasks = []
    while priority_queue:
        _, task_id = heapq.heappop(priority_queue)
        completed_tasks.append(task_id)
    
    return completed_tasks
    
    # 示例任务
    tasks = [
    Task(id=1, importance=3, urgency=2),
    Task(id=2, importance=2, urgency=3),
    Task(id=3, importance=4, urgency=1),
    ]
    
    # 管理任务
    completed_tasks = manage_attention(tasks)
    print("Completed tasks:", completed_tasks)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import numpy as np
    import matplotlib.pyplot as plt
    
    def plot_hrvg(hrv_data: list[float]) -> None:
    # 绘制心率变异性数据
    time = np.arange(0, len(hrv_data))
    plt.plot(time, hrv_data)
    plt.xlabel('Time (s)')
    plt.ylabel('HRV (ms)')
    plt.title('Heart Rate Variability')
    plt.show()
    
    def calculate_hrv(rpeaks: list[float]) -> list[float]:
    # 计算心率变异性
    interbeat_durations = np.diff(rpeaks)
    hr = 60 / interbeat_durations
    sdnn = np.std(hr)
    return sdnn
    
    # 示例心率数据
    rpeaks = [0.5, 0.7, 0.9, 1.2, 1.5, 1.8, 2.0, 2.3, 2.5, 2.7]
    
    # 计算心率变异性
    hrv = calculate_hrv(rpeaks)
    print("HRV:", hrv)
    
    # 绘制心率变异性数据
    plot_hrvg(hrv)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import datetime
    
    def tomato_timer(duration: int) -> None:
    # 番茄工作法计时器
    end_time = datetime.datetime.now() + datetime.timedelta(minutes=duration)
    print("开始工作，计时：{}分钟".format(duration))
    while datetime.datetime.now() < end_time:
        time_left = (end_time - datetime.datetime.now()).seconds
        print("剩余时间：{}秒".format(time_left), end="\r")
        time.sleep(1)
    print("\n完成工作，休息时间！")
    
    # 设置工作周期时长（分钟）
    work_duration = 25
    tomato_timer(work_duration)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import numpy as np
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score
    
    # 示例数据
    X = np.array([[0.5, 0.6], [0.7, 0.5], [0.8, 0.7], [0.3, 0.4]])
    y = np.array([0, 1, 1, 0])  # 0 表示未疲劳，1 表示疲劳
    
    # 划分训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # 训练随机森林模型
    model = RandomForestClassifier(n_estimators=100)
    model.fit(X_train, y_train)
    
    # 预测测试集
    y_pred = model.predict(X_test)
    
    # 计算准确率
    accuracy = accuracy_score(y_test, y_pred)
    print("Accuracy:", accuracy)
    
    # 实时预测
    user_data = np.array([[0.6, 0.5]])
    user_prediction = model.predict(user_data)
    if user_prediction == 1:
    print("用户可能出现疲劳，建议休息。")
    else:
    print("用户未出现疲劳，可以继续工作。")
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import random
    
    def optimize_tasks(tasks: list[str], attention_duration: int) -> list[str]:
    # 优化任务执行顺序
    optimized_tasks = []
    while tasks:
        max_attention = -1
        max_attention_task = None
        for task in tasks:
            # 计算任务对注意力的消耗
            attention_cost = random.uniform(0.5, 1.5)
            if attention_cost > max_attention:
                max_attention = attention_cost
                max_attention_task = task
        optimized_tasks.append(max_attention_task)
        tasks.remove(max_attention_task)
    
    return optimized_tasks
    
    # 示例任务
    tasks = ["任务1", "任务2", "任务3", "任务4", "任务5"]
    
    # 优化任务执行顺序
    optimized_tasks = optimize_tasks(tasks, attention_duration=25)
    print("优化后的任务顺序：", optimized_tasks)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def attention_management_strategy(strategy: str, tasks: list[str]) -> list[str]:
    if strategy == "优先级排序":
        return sorted(tasks, key=lambda x: (x[-1], x))
    elif strategy == "情境式管理":
        return sorted(tasks, key=lambda x: (x.startswith("紧急"), x))
    elif strategy == "分段式管理":
        return ["紧急任务", "常规任务", "放松任务"]
    else:
        return tasks
    
    # 示例任务
    tasks = ["紧急任务1", "常规任务2", "紧急任务3", "常规任务4"]
    
    # 应用不同的注意力管理策略
    optimized_tasks1 = attention_management_strategy("优先级排序", tasks)
    optimized_tasks2 = attention_management_strategy("情境式管理", tasks)
    optimized_tasks3 = attention_management_strategy("分段式管理", tasks)
    
    print("优先级排序后的任务顺序：", optimized_tasks1)
    print("情境式管理后的任务顺序：", optimized_tasks2)
    print("分段式管理后的任务顺序：", optimized_tasks3)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import datetime
    import random
    
    def ai协同学（human_attention: float, task_difficulty: float） -> float:
    # AI 基于人类注意力和任务难度调整工作效率
    efficiency = human_attention * (1 - task_difficulty / 10)
    return efficiency
    
    def work_session(human_attention: float, tasks: list[str]) -> float:
    total_efficiency = 0
    for task in tasks:
        task_difficulty = random.uniform(0.5, 2.0)
        efficiency = ai协同学（human_attention, task_difficulty）
        print(f"执行任务：{task}，效率：{efficiency:.2f}")
        total_efficiency += efficiency
    return total_efficiency
    
    # 示例任务
    tasks = ["任务1", "任务2", "任务3"]
    
    # 设置初始注意力水平
    human_attention = 0.8
    
    # 进行工作会话
    total_efficiency = work_session(human_attention, tasks)
    print("总工作效率：", total_efficiency)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def predict_future_trends() -> str:
    trends = [
        "智能化",
        "个性化",
        "可穿戴设备",
        "协作化",
        "整合化",
    ]
    return "未来趋势包括：\n- " + "，\n- ".join(trends) + "。"
    
    print(predict_future_trends())
    
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def assess_mental_health(attention_management_strategy: str) -> str:
    if attention_management_strategy == "主动式管理":
        return "心理健康状况良好。"
    elif attention_management_strategy == "被动式管理":
        return "心理健康状况需加强自我管理。"
    else:
        return "心理健康状况尚可，但需注意调整。"
    
    # 示例注意力管理策略
    attention_management_strategy = "主动式管理"
    
    print(assess_mental_health(attention_management_strategy))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def calculate_work_efficiency(attention_management_strategy: str, work_hours: int) -> float:
    if attention_management_strategy == "主动式管理":
        return work_hours * 1.2
    elif attention_management_strategy == "被动式管理":
        return work_hours * 1.0
    else:
        return work_hours * 1.1
    
    # 示例工作小时数
    work_hours = 8
    
    # 计算工作效率
    efficiency = calculate_work_efficiency("主动式管理", work_hours)
    print("工作效率：", efficiency)
    
    efficiency = calculate_work_efficiency("被动式管理", work_hours)
    print("工作效率：", efficiency)
    
    efficiency = calculate_work_efficiency("结合式管理", work_hours)
    print("工作效率：", efficiency)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def remote_work_management(attention_management_strategy: str) -> str:
    if attention_management_strategy == "主动式管理":
        return "远程工作表现优秀。"
    elif attention_management_strategy == "被动式管理":
        return "远程工作表现一般，需加强自我管理。"
    else:
        return "远程工作表现尚可，但需注意调整。"
    
    # 示例注意力管理策略
    attention_management_strategy = "主动式管理"
    
    print(remote_work_management(attention_management_strategy))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def educational_impact(attention_management_strategy: str) -> str:
    if attention_management_strategy == "主动式管理":
        return "学习效果显著提高。"
    elif attention_management_strategy == "被动式管理":
        return "学习效果一般，需加强自我管理。"
    else:
        return "学习效果尚可，但需注意调整。"
    
    # 示例注意力管理策略
    attention_management_strategy = "主动式管理"
    
    print(educational_impact(attention_management_strategy))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def career_development_role(attention_management_strategy: str) -> str:
    if attention_management_strategy == "主动式管理":
        return "有助于提升职业竞争力。"
    elif attention_management_strategy == "被动式管理":
        return "对职业发展有一定帮助，但需加强自我管理。"
    else:
        return "对职业发展有积极影响，但需注意调整。"
    
    # 示例注意力管理策略
    attention_management_strategy = "主动式管理"
    
    print(career_development_role(attention_management_strategy))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def innovation_impact(attention_management_strategy: str) -> str:
    if attention_management_strategy == "主动式管理":
        return "有助于提高创新思维质量。"
    elif attention_management_strategy == "被动式管理":
        return "对创新思维有一定帮助，但需加强自我管理。"
    else:
        return "对创新思维有积极影响，但需注意调整。"
    
    # 示例注意力管理策略
    attention_management_strategy = "主动式管理"
    
    print(innovation_impact(attention_management_strategy))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def enterprise_management_role(attention_management_strategy: str) -> str:
    if attention_management_strategy == "主动式管理":
        return "有助于提高企业竞争力。"
    elif attention_management_strategy == "被动式管理":
        return "对企业运营有一定帮助，但需加强管理。"
    else:
        return "对企业运营有积极影响，但需注意调整。"
    
    # 示例注意力管理策略
    attention_management_strategy = "主动式管理"
    
    print(enterprise_management_role(attention_management_strategy))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def project_management_role(attention_management_strategy: str) -> str:
    if attention_management_strategy == "主动式管理":
        return "有助于提高项目执行效率。"
    elif attention_management_strategy == "被动式管理":
        return "对项目执行有一定帮助，但需加强管理。"
    else:
        return "对项目执行有积极影响，但需注意调整。"
    
    # 示例注意力管理策略
    attention_management_strategy = "主动式管理"
    
    print(project_management_role(attention_management_strategy))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def job_satisfaction(attention_management_strategy: str) -> str:
    if attention_management_strategy == "主动式管理":
        return "工作满意度较高。"
    elif attention_management_strategy == "被动式管理":
        return "工作满意度一般，需加强自我管理。"
    else:
        return "工作满意度较低，但需注意调整。"
    
    # 示例注意力管理策略
    attention_management_strategy = "主动式管理"
    
    print(job_satisfaction(attention_management_strategy))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def evaluate_attention_tool(tool_accuracy: float, user_friendliness: float, flexibility: float, efficiency: float, extensibility: float, cost效益：float）-> str:
    score = (tool_accuracy * 0.3 + user_friendliness * 0.2 + flexibility * 0.2 + efficiency * 0.2 + extensibility * 0.1 + cost效益 * 0.1) 
    return "评价得分：{:.1f}/10。".format(score)
    
    # 示例工具指标
    tool_accuracy = 0.9
    user_friendliness = 0.8
    flexibility = 0.7
    efficiency = 0.85
    extensibility = 0.6
    cost效益 = 0.75
    
    # 评价注意力流管理工具
    evaluation_result = evaluate_attention_tool(tool_accuracy, user_friendliness, flexibility, efficiency, extensibility, cost效益）
    print(evaluation_result)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def attention_management_challenges(solutions: list[str]) -> str:
    return "注意力流管理中可能遇到的挑战及解决方案：\n- " + "，\n- ".join(solutions) + "。"
    
    # 示例解决方案
    solutions = [
    "技术障碍：选择兼容性强的工具，确保数据安全。",
    "用户坚持：提供个性化的激励措施。",
    "需求适应：开发多样化的工具。",
    "更新优化：定期评估和改进工具。"
    ]
    
    # 输出挑战与解决方案
    print(attention_management_challenges(solutions))
    
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def future_trends(trends: list[str]) -> str:
    return "注意力流管理未来的发展趋势：\n- " + "，\n- ".join(trends) + "。"
    
    # 示例趋势
    trends = [
    "智能化",
    "可穿戴设备普及",
    "跨平台整合",
    "心理健康支持",
    "隐私保护",
    ]
    
    # 输出未来发展趋势
    print(future_trends(trends))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def collaboration_role(attention_management_role: str) -> str:
    if attention_management_role == "辅助人类识别和管理注意力需求":
        return "有助于提高人类和AI的协作效率。"
    elif attention_management_role == "合理分配任务":
        return "有助于确保每个任务都有足够的注意力投入。"
    else:
        return "有助于优化人类和AI在协作过程中的注意力分配，提高整体效果。"
    
    # 示例注意力管理角色
    attention_management_role = "辅助人类识别和管理注意力需求"
    
    print(collaboration_role(attention_management_role))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def educational_impact(attention_management_impact: str) -> str:
    if attention_management_impact == "提高教学质量":
        return "有助于提升学生的学习效果。"
    elif attention_management_impact == "减少课堂干扰":
        return "有助于提高学生的学习体验。"
    else:
        return "有助于培养良好的学习习惯和心理健康。"
    
    # 示例注意力管理影响
    attention_management_impact = "提高教学质量"
    
    print(educational_impact(attention_management_impact))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def remote_work_role(attention_management_role: str) -> str:
    if attention_management_role == "识别和管理注意力需求":
        return "有助于提高远程办公效率。"
    elif attention_management_role == "优化工作流程":
        return "有助于提升远程办公的工作体验。"
    else:
        return "有助于团队协作和情绪调节。"
    
    # 示例注意力管理角色
    attention_management_role = "识别和管理注意力需求"
    
    print(remote_work_role(attention_management_role))
    
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def design_principles(principles: list[str]) -> str:
    return "设计注意力流管理工具时应遵循以下原则：\n- " + "，\n- ".join(principles) + "。"
    
    # 示例设计原则
    principles = [
    "用户体验优先",
    "个性化定制",
    "数据隐私保护",
    "准确性",
    "可扩展性",
    "实时性",
    ]
    
    # 输出设计原则
    print(design_principles(principles))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def work_efficiency(attention_management_impact: str) -> str:
    if attention_management_impact == "提高专注度":
        return "有助于提高工作效率。"
    elif attention_management_impact == "任务优化":
        return "有助于提高工作效率。"
    elif attention_management_impact == "情绪调节":
        return "有助于提高工作效率。"
    else:
        return "有助于时间管理和心理状态监测，从而提高工作效率。"
    
    # 示例注意力管理影响
    attention_management_impact = "提高专注度"
    
    print(work_efficiency(attention_management_impact))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def attention_management_functions(functions: list[str]) -> str:
    return "注意力流管理工具的常见功能包括：\n- " + "，\n- ".join(functions) + "。"
    
    # 示例功能
    functions = [
    "注意力监测",
    "任务管理",
    "休息提醒",
    "情绪分析",
    "数据分析",
    "个性化设置",
    "跨平台支持",
    ]
    
    # 输出功能列表
    print(attention_management_functions(functions))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def application_scenarios(scenarios: list[str]) -> str:
    return "注意力流管理在不同工作场景中的应用包括：\n- " + "，\n- ".join(scenarios) + "。"
    
    # 示例应用场景
    scenarios = [
    "办公室场景",
    "远程办公场景",
    "教育场景",
    "医疗场景",
    "科研场景",
    ]
    
    # 输出应用场景
    print(application_scenarios(scenarios))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def employee_satisfaction_role(attention_management_role: str) -> str:
    if attention_management_role == "减轻工作压力":
        return "有助于提高员工满意度。"
    elif attention_management_role == "提升工作效率":
        return "有助于提高员工满意度。"
    elif attention_management_role == "改善情绪管理":
        return "有助于提高员工满意度。"
    else:
        return "有助于提高工作与生活的平衡度和企业认可度，从而提高员工满意度。"
    
    # 示例注意力管理角色
    attention_management_role = "减轻工作压力"
    
    print(employee_satisfaction_role(attention_management_role))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def enterprise_management_role(attention_management_role: str) -> str:
    if attention_management_role == "员工绩效评估":
        return "有助于提高企业管理效率。"
    elif attention_management_role == "团队协作优化":
        return "有助于提高团队整体效率。"
    elif attention_management_role == "工作压力管理":
        return "有助于提高员工的工作满意度。"
    else:
        return "有助于培养企业内部专注文化和企业战略规划。"
    
    # 示例注意力管理角色
    attention_management_role = "员工绩效评估"
    
    print(enterprise_management_role(attention_management_role))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def team_work_efficiency_role(attention_management_role: str) -> str:
    if attention_management_role == "任务优先级排序":
        return "有助于提高团队工作效率。"
    elif attention_management_role == "协作优化":
        return "有助于提高团队整体效率。"
    elif attention_management_role == "情绪管理":
        return "有助于提高团队的积极性和创造力。"
    else:
        return "有助于优化工作节奏和压力管理，从而提高团队工作效率。"
    
    # 示例注意力管理角色
    attention_management_role = "任务优先级排序"
    
    print(team_work_efficiency_role(attention_management_role))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读