AI与人类注意力流：未来的工作生活与AI的融合

    import pandas as pd
    
    # 假设我们已经收集了用户使用AI系统前后的任务完成时间和注意力分散数据
    data = pd.DataFrame({
    'user_id': [1, 2, 3],
    'task_time_before': [30, 45, 60],
    'task_time_after': [25, 40, 55],
    'attention_spread': [0.2, 0.3, 0.4]
    })
    
    # 计算平均任务完成时间
    avg_task_time_before = data['task_time_before'].mean()
    avg_task_time_after = data['task_time_after'].mean()
    
    print(f"平均任务完成时间（使用前）：{avg_task_time_before}秒")
    print(f"平均任务完成时间（使用后）：{avg_task_time_after}秒")
    
    # 绘制注意力分散分布
    import matplotlib.pyplot as plt
    
    plt.hist(data['attention_spread'], bins=3, edgecolor='black')
    plt.xlabel('注意力分散')
    plt.ylabel('用户数')
    plt.title('用户注意力分散分布')
    plt.show()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个用户研究团队，收集了用户的反馈数据
    feedback_data = pd.DataFrame({
    'user_id': [1, 2, 3, 4],
    'feature': ['UI clarity', 'response time', 'task automation', 'attention allocation'],
    'rating': [4, 3, 5, 2]
    })
    
    # 分析用户对各个特征的满意度
    import seaborn as sns
    
    sns.barplot(x='feature', y='rating', data=feedback_data)
    plt.xlabel('Feature')
    plt.ylabel('Rating')
    plt.title('User Feedback on System Features')
    plt.show()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个疲劳监测系统，记录了员工的注意力状态
    attention_data = pd.DataFrame({
    'user_id': [1, 2, 3],
    'attention_level': [0.6, 0.8, 0.3],
    'timestamp': ['10:00', '14:00', '18:00']
    })
    
    # 绘制员工的注意力变化趋势
    import matplotlib.pyplot as plt
    
    plt.plot(attention_data['timestamp'], attention_data['attention_level'], marker='o')
    plt.xlabel('Timestamp')
    plt.ylabel('Attention Level')
    plt.title('Attention Level Trend Over Time')
    plt.show()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个自适应AI系统，根据用户的行为调整其交互方式
    interaction_data = pd.DataFrame({
    'user_id': [1, 2, 3],
    'interaction_type': ['text', 'image', 'video'],
    'response_time': [2, 4, 6]
    })
    
    # 根据用户的交互类型和响应时间，调整交互方式
    def adjust_interaction(data):
    if data['response_time'] < 3:
        return 'text'
    elif data['response_time'] < 5:
        return 'image'
    else:
        return 'video'
    
    interaction_data['adjusted_interaction'] = interaction_data.apply(adjust_interaction, axis=1)
    
    print(interaction_data)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个数据集，包含用户的交互记录和注意力水平
    data = pd.DataFrame({
    'user_id': [1, 2, 3],
    'interaction_time': [10, 20, 30],
    'attention_level': [0.4, 0.6, 0.8],
    'predicted_attention': [0.5, 0.7, 0.6]
    })
    
    # 训练一个线性回归模型来预测注意力水平
    from sklearn.linear_model import LinearRegression
    from sklearn.model_selection import train_test_split
    
    X = data[['interaction_time']]
    y = data['predicted_attention']
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    model = LinearRegression()
    model.fit(X_train, y_train)
    
    # 评估模型性能
    print("Model coefficients:", model.coef_)
    print("Model intercept:", model.intercept_)
    
    y_pred = model.predict(X_test)
    print("Test set R-squared:", model.score(X_test, y_test))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个高负荷任务的队列
    task_queue = pd.DataFrame({
    'task_id': [1, 2, 3, 4],
    'status': ['queued', 'queued', 'processing', 'completed'],
    'priority': [2, 1, 3, 0]
    })
    
    # 根据任务的优先级重新排列队列
    task_queue.sort_values(by='priority', inplace=True, ascending=False)
    
    # 向用户显示当前任务队列的状态
    print("Current Task Queue:")
    print(task_queue)
    
    # 假设我们有一个异步处理机制，处理任务的完成状态
    def process_task(task_id):
    # 模拟任务处理
    time.sleep(2)
    return "completed"
    
    # 异步处理最高优先级的任务
    import asyncio
    
    async def process_high_priority_tasks():
    for index, row in task_queue.iterrows():
        if row['status'] == 'queued' and row['priority'] == 0:
            asyncio.create_task(async_process_task(row['task_id']))
            task_queue.loc[index, 'status'] = 'processing'
    
    async def async_process_task(task_id):
    # 模拟任务处理
    time.sleep(2)
    task_queue.loc[task_queue['task_id'] == task_id, 'status'] = 'completed'
    
    # 执行异步处理
    asyncio.run(process_high_priority_tasks())
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个感知用户状态的模块
    class UserAttentionMonitor:
    def __init__(self):
        self.attention_levels = []
    
    def update_attention_level(self, level):
        self.attention_levels.append(level)
    
    def get_average_attention_level(self):
        if not self.attention_levels:
            return 0
        return sum(self.attention_levels) / len(self.attention_levels)
    
    # 假设我们有一个基于平均注意力水平的交互策略调整模块
    class InteractionStrategyAdjuster:
    def __init__(self, monitor):
        self.monitor = monitor
    
    def adjust_strategy(self):
        average_attention = self.monitor.get_average_attention_level()
        if average_attention < 0.5:
            # 用户注意力较低，减少互动信息量
            return 'less_interactive'
        elif average_attention < 0.8:
            # 用户注意力中等，保持常规互动
            return 'normal_interactive'
        else:
            # 用户注意力高，增加互动信息量
            return 'more_interactive'
    
    # 模拟用户注意力状态的更新和交互策略的调整
    monitor = UserAttentionMonitor()
    monitor.update_attention_level(0.3)
    monitor.update_attention_level(0.7)
    
    adjuster = InteractionStrategyAdjuster(monitor)
    print("Adjusted Interaction Strategy:", adjuster.adjust_strategy())
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个长期跟踪研究的数据集
    long_term_data = pd.DataFrame({
    'user_id': [1, 2, 3],
    'days_used': [30, 60, 90],
    'average_attention_level': [0.4, 0.5, 0.6],
    'task_completion_time': [25, 20, 22]
    })
    
    # 绘制用户在长期使用AI系统期间的注意力水平和任务完成时间趋势
    import matplotlib.pyplot as plt
    
    plt.plot(long_term_data['days_used'], long_term_data['average_attention_level'], label='Attention Level')
    plt.plot(long_term_data['days_used'], long_term_data['task_completion_time'], label='Task Completion Time')
    plt.xlabel('Days Used')
    plt.ylabel('Level')
    plt.legend()
    plt.title('Long-Term Impact of AI System on Attention and Task Completion')
    plt.show()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个用户行为分析模块
    class UserBehaviorAnalyzer:
    def __init__(self):
        self.behavior_data = []
    
    def record_behavior(self, behavior):
        self.behavior_data.append(behavior)
    
    def get_preference_model(self):
        # 模拟基于行为数据构建用户偏好模型
        preference_model = {'prefer_short_texts': True, 'prefer_visuals': False}
        return preference_model
    
    # 假设我们有一个交互策略调整模块
    class InteractionAdjuster:
    def __init__(self, analyzer):
        self.analyzer = analyzer
    
    def adjust_interaction(self):
        preference_model = self.analyzer.get_preference_model()
        if preference_model['prefer_short_texts']:
            return 'text'
        else:
            return 'visual'
    
    # 模拟用户交互行为记录和交互策略调整
    analyzer = UserBehaviorAnalyzer()
    analyzer.record_behavior('text')
    analyzer.record_behavior('image')
    
    adjuster = InteractionAdjuster(analyzer)
    print("Adjusted Interaction Type:", adjuster.adjust_interaction())
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个用于注意力流预测的数据集
    prediction_data = pd.DataFrame({
    'user_id': [1, 2, 3],
    'interaction_time': [10, 20, 30],
    'predicted_attention': [0.5, 0.7, 0.6]
    })
    
    # 使用线性回归模型进行预测
    from sklearn.linear_model import LinearRegression
    
    X = prediction_data[['interaction_time']]
    y = prediction_data['predicted_attention']
    
    # 进行K折交叉验证
    from sklearn.model_selection import cross_val_score
    
    model = LinearRegression()
    scores = cross_val_score(model, X, y, cv=5)
    
    print("Cross-validation scores:", scores)
    print("Mean score:", scores.mean())
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个用户研究团队，收集了用户的反馈数据
    user_feedback = pd.DataFrame({
    'user_id': [1, 2, 3],
    'feature': ['notification frequency', 'interface clarity', 'data privacy'],
    'rating': [3, 4, 5]
    })
    
    # 绘制用户对各个特征的满意度评分
    import seaborn as sns
    
    sns.barplot(x='feature', y='rating', data=user_feedback)
    plt.xlabel('Feature')
    plt.ylabel('Rating')
    plt.title('User Feedback on System Features')
    plt.show()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个多任务处理系统，记录了用户完成各个任务的注意力分配
    task_data = pd.DataFrame({
    'user_id': [1, 2, 3],
    'task_id': [1, 2, 3],
    'attention分配': [0.3, 0.5, 0.7]
    })
    
    # 根据任务的重要性和紧急程度调整注意力分配
    def adjust_attention(task_data):
    priority_weights = {'high': 1.5, 'medium': 1.0, 'low': 0.5}
    for index, row in task_data.iterrows():
        priority = row['task_id'] // 10  # 假设任务ID除以10表示优先级
        task_data.at[index, 'attention分配'] *= priority_weights[priority]
    return task_data
    
    adjusted_data = adjust_attention(task_data)
    print("Adjusted Attention Distribution:")
    print(adjusted_data)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个感知用户注意力的模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_data = []
    
    def record_attention(self, level):
        self.attention_data.append(level)
    
    def predict_attention_change(self):
        # 模拟基于历史数据预测注意力变化
        if len(self.attention_data) < 3:
            return 0
        return sum(self.attention_data[-3:]) / 3
    
    # 假设我们有一个自适应调整模块
    class AdaptiveSystem:
    def __init__(self, monitor):
        self.monitor = monitor
    
    def adjust_interactive_content(self):
        attention_change = self.monitor.predict_attention_change()
        if attention_change < 0.5:
            return 'simple_content'
        elif attention_change < 0.8:
            return 'complex_content'
        else:
            return 'detailed_content'
    
    # 模拟用户注意力变化和自适应调整
    monitor = AttentionMonitor()
    monitor.record_attention(0.3)
    monitor.record_attention(0.6)
    monitor.record_attention(0.8)
    
    adaptive_system = AdaptiveSystem(monitor)
    print("Adjusted Interactive Content:", adaptive_system.adjust_interactive_content())
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个任务管理模块
    class TaskManager:
    def __init__(self):
        self.tasks = []
    
    def add_task(self, task_id, priority):
        self.tasks.append({'task_id': task_id, 'priority': priority})
    
    def execute_tasks(self, attention_level):
        # 模拟基于注意力水平调整任务执行优先级
        self.tasks.sort(key=lambda x: x['priority'] * (1 - attention_level))
        return [task['task_id'] for task in self.tasks]
    
    # 假设我们有一个注意力监测模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_levels = []
    
    def record_attention(self, level):
        self.attention_levels.append(level)
    
    def get_average_attention(self):
        return sum(self.attention_levels) / len(self.attention_levels)
    
    # 模拟用户任务执行和注意力监测
    task_manager = TaskManager()
    task_manager.add_task(1, 2)
    task_manager.add_task(2, 1)
    task_manager.add_task(3, 3)
    
    monitor = AttentionMonitor()
    monitor.record_attention(0.4)
    monitor.record_attention(0.7)
    
    tasks_to_execute = task_manager.execute_tasks(monitor.get_average_attention())
    print("Tasks to Execute:", tasks_to_execute)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个员工注意力监测模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_data = []
    
    def record_attention(self, level):
        self.attention_data.append(level)
    
    def get_attention_profile(self):
        # 模拟根据注意力数据生成注意力分布图
        return [level for level in self.attention_data]
    
    # 假设我们有一个任务分配模块
    class TaskAllocator:
    def __init__(self, monitor):
        self.monitor = monitor
    
    def allocate_tasks(self, tasks, attention_profile):
        # 模拟根据注意力分布调整任务分配
        tasks.sort(key=lambda x: x['priority'] * (1 - attention_profile[x['id']]))
        return tasks
    
    # 模拟员工注意力监测和任务分配
    monitor = AttentionMonitor()
    monitor.record_attention(0.4)
    monitor.record_attention(0.7)
    monitor.record_attention(0.6)
    
    tasks = [
    {'id': 1, 'name': '编写报告', 'priority': 2},
    {'id': 2, 'name': '处理邮件', 'priority': 1},
    {'id': 3, 'name': '参加会议', 'priority': 3}
    ]
    
    allocator = TaskAllocator(monitor)
    allocated_tasks = allocator.allocate_tasks(tasks, monitor.get_attention_profile())
    
    print("Allocated Tasks:", allocated_tasks)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个实时数据采集模块
    class RealtimeDataCollector:
    def __init__(self):
        self.data_stream = []
    
    def add_data(self, data):
        self.data_stream.append(data)
    
    def get_realtime_data(self):
        return self.data_stream
    
    # 假设我们有一个实时数据处理和模型更新模块
    class RealtimeProcessor:
    def __init__(self):
        self.model = None
    
    def update_model(self, data):
        # 模拟基于新数据更新模型
        self.model = data
    
    def predict_attention(self, data):
        # 模拟基于模型预测注意力水平
        return sum(data) / len(data)
    
    # 假设我们有一个实时交互调整模块
    class RealtimeAdjuster:
    def __init__(self, processor):
        self.processor = processor
    
    def adjust_interaction(self, data):
        attention_level = self.processor.predict_attention(data)
        if attention_level < 0.5:
            return 'less_interactive'
        elif attention_level < 0.8:
            return 'normal_interactive'
        else:
            return 'more_interactive'
    
    # 模拟实时数据采集和交互调整
    collector = RealtimeDataCollector()
    collector.add_data([0.3, 0.5, 0.7])
    
    processor = RealtimeProcessor()
    processor.update_model(collector.get_realtime_data())
    
    adjuster = RealtimeAdjuster(processor)
    print("Adjusted Interaction Type:", adjuster.adjust_interaction(collector.get_realtime_data()))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个用户分群模块
    class UserProfiler:
    def __init__(self):
        self.user_profiles = {}
    
    def add_user_profile(self, user_id, age, occupation):
        self.user_profiles[user_id] = {'age': age, 'occupation': occupation}
    
    def get_user_profile(self, user_id):
        return self.user_profiles.get(user_id, None)
    
    # 假设我们有一个个性化调整模块
    class PersonalizedAdjuster:
    def __init__(self, profiler):
        self.profiler = profiler
    
    def adjust_interaction(self, user_id):
        profile = self.profiler.get_user_profile(user_id)
        if profile and profile['age'] < 30:
            return 'youth_mode'
        elif profile and profile['occupation'] == 'engineer':
            return 'technical_mode'
        else:
            return 'default_mode'
    
    # 模拟用户分群和交互调整
    profiler = UserProfiler()
    profiler.add_user_profile(1, 25, 'student')
    profiler.add_user_profile(2, 35, 'engineer')
    
    adjuster = PersonalizedAdjuster(profiler)
    print("Adjusted Interaction Mode for User 1:", adjuster.adjust_interaction(1))
    print("Adjusted Interaction Mode for User 2:", adjuster.adjust_interaction(2))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个注意力监测模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_data = []
    
    def record_attention(self, level):
        self.attention_data.append(level)
    
    def get_attention_profile(self):
        return self.attention_data
    
    # 假设我们有一个学习任务分配模块
    class TaskAllocator:
    def __init__(self, monitor):
        self.monitor = monitor
    
    def allocate_tasks(self, tasks, attention_profile):
        # 模拟根据注意力分布调整任务分配
        tasks.sort(key=lambda x: x['difficulty'] * (1 - attention_profile[x['id']]))
        return tasks
    
    # 模拟学习任务分配
    tasks = [
    {'id': 1, 'name': '阅读资料', 'difficulty': 1},
    {'id': 2, 'name': '练习编程', 'difficulty': 2},
    {'id': 3, 'name': '完成报告', 'difficulty': 3}
    ]
    
    monitor = AttentionMonitor()
    monitor.record_attention(0.4)
    monitor.record_attention(0.7)
    monitor.record_attention(0.6)
    
    allocator = TaskAllocator(monitor)
    allocated_tasks = allocator.allocate_tasks(tasks, monitor.get_attention_profile())
    
    print("Allocated Tasks:", allocated_tasks)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    # 假设我们有一个注意力监测模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_data = []
    
    def record_attention(self, level):
        self.attention_data.append(level)
    
    def get_attention_profile(self):
        return self.attention_data
    
    # 假设我们有一个智能提醒模块
    class SmartReminder:
    def __init__(self, monitor):
        self.monitor = monitor
    
    def remind(self, attention_profile):
        if attention_profile[-1] < 0.3:
            return "时间到了，休息一下吧！"
        else:
            return "保持专注，继续努力！"
    
    # 模拟员工注意力监测和智能提醒
    monitor = AttentionMonitor()
    monitor.record_attention(0.4)
    monitor.record_attention(0.2)
    monitor.record_attention(0.7)
    
    reminder = SmartReminder(monitor)
    print(reminder.remind(monitor.get_attention_profile()))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个数据分析和预测模块
    class DecisionAssistant:
    def __init__(self):
        self.data = []
    
    def add_data(self, data_point):
        self.data.append(data_point)
    
    def predict_outcome(self):
        # 模拟基于历史数据预测决策结果
        if len(self.data) < 3:
            return None
        return sum(self.data[-3:]) / 3
    
    # 假设我们有一个决策支持系统
    class DecisionSupportSystem:
    def __init__(self, assistant):
        self.assistant = assistant
    
    def make_decision(self, data_point):
        prediction = self.assistant.predict_outcome()
        if prediction is not None and prediction > 0.5:
            return "执行决策"
        else:
            return "暂缓决策"
    
    # 模拟决策数据添加和决策支持
    assistant = DecisionAssistant()
    assistant.add_data(0.3)
    assistant.add_data(0.5)
    assistant.add_data(0.7)
    
    support_system = DecisionSupportSystem(assistant)
    print("Decision Recommendation:", support_system.make_decision(0.6))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个任务调度模块
    class TaskScheduler:
    def __init__(self):
        self.tasks = []
    
    def add_task(self, task):
        self.tasks.append(task)
    
    def schedule_tasks(self, resources):
        # 模拟基于资源限制的任务调度
        scheduled_tasks = []
        for task in self.tasks:
            if task['required_resources'] <= resources:
                scheduled_tasks.append(task)
                resources -= task['required_resources']
        return scheduled_tasks
    
    # 假设我们有一个资源管理模块
    class ResourceAllocator:
    def __init__(self, capacity):
        self.capacity = capacity
    
    def allocate_resources(self, scheduled_tasks):
        total_resources = sum(task['required_resources'] for task in scheduled_tasks)
        if total_resources <= self.capacity:
            return True
        else:
            return False
    
    # 模拟任务添加和资源分配
    scheduler = TaskScheduler()
    scheduler.add_task({'name': '任务A', 'required_resources': 5})
    scheduler.add_task({'name': '任务B', 'required_resources': 10})
    scheduler.add_task({'name': '任务C', 'required_resources': 3})
    
    allocator = ResourceAllocator(20)
    if allocator.allocate_resources(scheduler.schedule_tasks()):
    print("Tasks Scheduled Successfully:")
    print(scheduler.tasks)
    else:
    print("Not enough resources for all tasks.")
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个注意力监测模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_data = []
    
    def record_attention(self, level):
        self.attention_data.append(level)
    
    def get_attention_level(self):
        if not self.attention_data:
            return 0
        return sum(self.attention_data) / len(self.attention_data)
    
    # 假设我们有一个状态识别和交互调整模块
    class AttentionAdjuster:
    def __init__(self, monitor):
        self.monitor = monitor
    
    def adjust_interaction(self, level):
        if level < 0.3:
            return "您的注意力较低，请适当休息一下。"
        elif level < 0.6:
            return "您目前保持专注，继续努力！"
        else:
            return "您的注意力很高，是否需要挑战更复杂的任务？"
    
    # 模拟注意力监测和交互调整
    monitor = AttentionMonitor()
    monitor.record_attention(0.2)
    monitor.record_attention(0.5)
    monitor.record_attention(0.8)
    
    adjuster = AttentionAdjuster(monitor)
    print(adjuster.adjust_interaction(monitor.get_attention_level()))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个用户行为数据集
    user_behavior = pd.DataFrame({
    'user_id': [1, 2, 3],
    'system_usage_before': [10, 15, 20],
    'system_usage_after': [12, 18, 22],
    'task_completion_time_before': [30, 45, 60],
    'task_completion_time_after': [25, 40, 55]
    })
    
    # 绘制用户使用AI系统前后的行为变化
    import matplotlib.pyplot as plt
    
    plt.figure(figsize=(10, 5))
    plt.subplot(2, 2, 1)
    plt.scatter(user_behavior['user_id'], user_behavior['system_usage_before'], label='Before')
    plt.scatter(user_behavior['user_id'], user_behavior['system_usage_after'], label='After')
    plt.xlabel('User ID')
    plt.ylabel('System Usage')
    plt.title('System Usage Over Time')
    plt.legend()
    
    plt.subplot(2, 2, 2)
    plt.scatter(user_behavior['user_id'], user_behavior['task_completion_time_before'], label='Before')
    plt.scatter(user_behavior['user_id'], user_behavior['task_completion_time_after'], label='After')
    plt.xlabel('User ID')
    plt.ylabel('Task Completion Time')
    plt.title('Task Completion Time Over Time')
    plt.legend()
    
    plt.tight_layout()
    plt.show()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    # 假设我们有一个任务管理模块
    class TaskManager:
    def __init__(self):
        self.tasks = []
    
    def add_task(self, task):
        self.tasks.append(task)
    
    def process_tasks(self):
        # 模拟根据任务优先级和负载情况处理任务
        self.tasks.sort(key=lambda x: x['priority'] * (1 - x['load']))
        return [task['name'] for task in self.tasks]
    
    # 假设我们有一个负载监控模块
    class LoadMonitor:
    def __init__(self, capacity):
        self.capacity = capacity
    
    def check_load(self, tasks):
        total_load = sum(task['load'] for task in tasks)
        return total_load <= self.capacity
    
    # 模拟任务添加和负载监控
    task_manager = TaskManager()
    task_manager.add_task({'name': '任务A', 'priority': 1, 'load': 0.3})
    task_manager.add_task({'name': '任务B', 'priority': 2, 'load': 0.6})
    task_manager.add_task({'name': '任务C', 'priority': 1, 'load': 0.1})
    
    load_monitor = LoadMonitor(1.0)
    if load_monitor.check_load(task_manager.process_tasks()):
    print("Tasks processed successfully.")
    else:
    print("System load too high, some tasks may be deferred.")
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # 假设我们有一个任务分配模块
    class TaskAllocator:
    def __init__(self):
        self.tasks = []
    
    def add_task(self, task):
        self.tasks.append(task)
    
    def allocate_tasks(self, employee_profile):
        # 模拟根据员工特征分配任务
        self.tasks.sort(key=lambda x: x['difficulty'] * (1 - employee_profile['skill']))
        return [task['name'] for task in self.tasks]
    
    # 假设我们有一个日程规划模块
    class SchedulePlanner:
    def __init__(self, allocator):
        self.allocator = allocator
    
    def create_schedule(self, tasks, availability):
        # 模拟根据任务和员工可用时间创建日程
        schedule = []
        for task in tasks:
            if availability >= task['duration']:
                schedule.append(task['name'])
                availability -= task['duration']
            else:
                break
        return schedule
    
    # 模拟任务分配和日程规划
    allocator = TaskAllocator()
    allocator.add_task({'name': '任务A', 'difficulty': 1, 'duration': 2})
    allocator.add_task({'name': '任务B', 'difficulty': 2, 'duration': 4})
    allocator.add_task({'name': '任务C', 'difficulty': 1, 'duration': 1})
    
    planner = SchedulePlanner(allocator)
    print("Allocated Tasks:", allocator.allocate_tasks({'skill': 0.8}))
    print("Work Schedule:", planner.create_schedule(allocator.allocate_tasks({'skill': 0.8}), 10))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    # 假设我们有一个注意力监测模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_data = []
    
    def record_attention(self, level):
        self.attention_data.append(level)
    
    def get_attention_level(self):
        if not self.attention_data:
            return 0
        return sum(self.attention_data) / len(self.attention_data)
    
    # 假设我们有一个状态预测和交互调整模块
    class AttentionAdjuster:
    def __init__(self, monitor):
        self.monitor = monitor
        self.model = None
    
    def train_model(self, data):
        # 模拟基于注意力数据训练预测模型
        self.model = data
    
    def predict_attention_change(self, current_level):
        if self.model:
            return self.model[current_level]
        else:
            return 0
    
    def adjust_interaction(self, current_level):
        prediction = self.predict_attention_change(current_level)
        if prediction < 0.3:
            return "您的注意力较低，请适当休息。"
        elif prediction < 0.6:
            return "您保持专注，请继续。"
        else:
            return "您的注意力很高，是否需要挑战更复杂的任务？"
    
    # 模拟注意力监测和交互调整
    monitor = AttentionMonitor()
    monitor.record_attention(0.4)
    monitor.record_attention(0.5)
    monitor.record_attention(0.8)
    
    adjuster = AttentionAdjuster(monitor)
    adjuster.train_model([0.3, 0.4, 0.7])
    print(adjuster.adjust_interaction(monitor.get_attention_level()))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    # 假设我们有一个注意力监测模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_data = []
    
    def record_attention(self, level):
        self.attention_data.append(level)
    
    def get_average_attention(self):
        if not self.attention_data:
            return 0
        return sum(self.attention_data) / len(self.attention_data)
    
    # 假设我们有一个智能提醒模块
    class SmartReminder:
    def __init__(self, monitor):
        self.monitor = monitor
    
    def remind_if_distracted(self, current_attention):
        average_attention = self.monitor.get_average_attention()
        if average_attention < 0.5:
            return "您的注意力似乎分散了，请集中精力。"
        else:
            return "您目前保持专注，继续努力！"
    
    # 模拟注意力监测和智能提醒
    monitor = AttentionMonitor()
    monitor.record_attention(0.4)
    monitor.record_attention(0.7)
    monitor.record_attention(0.2)
    monitor.record_attention(0.5)
    
    reminder = SmartReminder(monitor)
    print(reminder.remind_if_distracted(0.3))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    # 假设我们有一个注意力监测模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_data = []
    
    def record_attention(self, level):
        self.attention_data.append(level)
    
    def get_average_attention(self):
        if not self.attention_data:
            return 0
        return sum(self.attention_data) / len(self.attention_data)
    
    # 假设我们有一个任务难度调整模块
    class TaskAdjuster:
    def __init__(self, monitor):
        self.monitor = monitor
        self.difficulty_thresholds = {0.2: 1, 0.4: 2, 0.6: 3, 0.8: 4}
    
    def adjust_task_difficulty(self, current_attention):
        return self.difficulty_thresholds.get(current_attention, 2)
    
    # 模拟注意力监测和任务难度调整
    monitor = AttentionMonitor()
    monitor.record_attention(0.3)
    monitor.record_attention(0.5)
    monitor.record_attention(0.7)
    
    adjuster = TaskAdjuster(monitor)
    current_difficulty = adjuster.adjust_task_difficulty(monitor.get_average_attention())
    print(f"Adjusted Task Difficulty: {current_difficulty}")
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    # 假设我们有一个日程规划模块
    class SchedulePlanner:
    def __init__(self):
        self.tasks = []
    
    def add_task(self, task):
        self.tasks.append(task)
    
    def optimize_schedule(self):
        # 模拟根据任务优先级和时间预测优化日程
        self.tasks.sort(key=lambda x: x['priority'] * (1 - x['duration']))
        return self.tasks
    
    # 假设我们有一个时间预测模块
    class TimePredictor:
    def __init__(self):
        self.time_data = []
    
    def record_time_taken(self, task_name, time_taken):
        self.time_data[task_name] = time_taken
    
    def predict_task_duration(self, task_name):
        if task_name in self.time_data:
            return self.time_data[task_name]
        else:
            return 1  # 假设未记录的任务需要1小时
    
    # 模拟任务添加和日程规划
    planner = SchedulePlanner()
    planner.add_task({'name': '任务A', 'priority': 1, 'duration': 2})
    planner.add_task({'name': '任务B', 'priority': 2, 'duration': 4})
    planner.add_task({'name': '任务C', 'priority': 1, 'duration': 1})
    
    predictor = TimePredictor()
    predictor.record_time_taken('任务A', 2)
    predictor.record_time_taken('任务B', 3)
    predictor.record_time_taken('任务C', 1)
    
    # 优化日程
    planner.tasks = planner.optimize_schedule()
    print("Optimized Schedule:", planner.tasks)
    
    # 预测任务完成时间
    for task in planner.tasks:
    task['predicted_duration'] = predictor.predict_task_duration(task['name'])
    print("Predicted Task Durations:", {task['name']: task['predicted_duration'] for task in planner.tasks})
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    # 假设我们有一个注意力监测模块
    class AttentionMonitor:
    def __init__(self):
        self.attention_data = []
    
    def record_attention(self, level):
        self.attention_data.append(level)
    
    def get_average_attention(self):
        if not self.attention_data:
            return 0
        return sum(self.attention_data) / len(self.attention_data)
    
    # 假设我们有一个交互策略调整模块
    class InteractionAdjuster:
    def __init__(self, monitor):
        self.monitor = monitor
        self.strategy_library = {
            0.2: 'simple',
            0.4: 'normal',
            0.6: 'complex',
            0.8: 'detailed'
        }
    
    def adjust_interaction(self, current_attention):
        return self.strategy_library.get(current_attention, 'normal')
    
    # 模拟注意力监测和交互调整
    monitor = AttentionMonitor()
    monitor.record_attention(0.3)
    monitor.record_attention(0.5)
    monitor.record_attention(0.7)
    
    adjuster = InteractionAdjuster(monitor)
    current_strategy = adjuster.adjust_interaction(monitor.get_average_attention())
    print(f"Adjusted Interaction Strategy: {current_strategy}")
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读