AI智能运维在医学教育行业的最佳实践

# 继续医学教育场景核心检测逻辑
from prophet import Prophet
from sklearn.ensemble import IsolationForest
import pandas as pd
import numpy as np
def detect_cme_anomaly(metric_data, system_priority='high', learning_context=None):
    """
    继续医学教育系统异常检测
    system_priority: 'critical'(考试/学分系统), 'high'(直播/LMS), 'normal'(其他)
    learning_context: 学习上下文，包含考试日历、热门课程、直播安排等
    """
    # Prophet预测，考虑医务人员学习周期性
    model = Prophet(
        changepoint_prior_scale=0.05,
        seasonality_prior_scale=10,
        seasonality_mode='multiplicative',
        daily_seasonality=True,
        weekly_seasonality=True
    )
    # 添加医务人员学习特殊时段
    if learning_context:
        # 晚间学习高峰（19:00-23:00）
        model.add_seasonality(
            name='evening_peak',
            period=1,
            fourier_order=5,
            condition_name='is_evening'
        )
        # 周末学习高峰
        model.add_seasonality(
            name='weekend_peak',
            period=7,
            fourier_order=3,
            condition_name='is_weekend'
        )
        # 考试周期
        exam_dates = pd.DataFrame({
            'holiday': 'exam_period',
            'ds': learning_context['exam_dates'],
            'lower_window': -1,
            'upper_window': 1,
        })
        model.add_country_holidays(country_name='CN')
    metric_data['is_evening'] = (metric_data['ds'].dt.hour >= 19) & (metric_data['ds'].dt.hour <= 23)
    metric_data['is_weekend'] = metric_data['ds'].dt.dayofweek >= 5
    model.fit(metric_data[['ds', 'y', 'is_evening', 'is_weekend']])
    forecast = model.predict(metric_data[['ds', 'is_evening', 'is_weekend']])
    # 计算残差
    metric_data['residual'] = metric_data['y'] - forecast['yhat']
    metric_data['residual_std'] = metric_data['residual'].rolling(window=100).std()
    # 根据系统优先级和时段调整敏感度
    contamination_rate = {
        'critical': 0.002,  # 考试/学分系统最严格
        'high': 0.006,      # 直播/LMS系统
        'normal': 0.012     # 其他系统
    }
    # 考试期间和学习高峰期进一步提高敏感度
    if is_exam_period(learning_context) or is_peak_learning_time(metric_data):
        contamination_rate = {k: v * 0.5 for k, v in contamination_rate.items()}
    # Isolation Forest检测
    iso_forest = IsolationForest(
        contamination=contamination_rate.get(system_priority, 0.01),
        random_state=42,
        n_estimators=150
    )
    features = metric_data[['residual', 'residual_std']].values
    predictions = iso_forest.fit_predict(features)
    metric_data['is_anomaly'] = predictions == -1
    metric_data['severity'] = metric_data['residual'].abs() / (metric_data['residual_std'] + 1e-6)
    # 添加学习影响评估
    metric_data['learning_impact'] = evaluate_learning_impact(
        metric_data, system_priority, learning_context
    )
    return metric_data[metric_data['is_anomaly']].sort_values('severity', ascending=False)
def is_exam_period(learning_context):
    """判断是否在考试期间"""
    if not learning_context or 'exam_dates' not in learning_context:
        return False
    current_date = pd.Timestamp.now()
    for exam_date in learning_context['exam_dates']:
        if abs((exam_date - current_date).days) <= 1:
            return True
    return False
def is_peak_learning_time(metric_data):
    """判断是否在学习高峰时段"""
    latest_hour = metric_data['ds'].iloc[-1].hour
    latest_dow = metric_data['ds'].iloc[-1].dayofweek
    # 晚间19:00-23:00 或 周末
    return (19 <= latest_hour <= 23) or (latest_dow >= 5)
def evaluate_learning_impact(metric_data, system_priority, learning_context):
    """评估学习影响程度"""
    impact_scores = []
    for idx, row in metric_data.iterrows():
        score = row['severity']
        # 考试期间影响加倍
        if is_exam_period(learning_context):
            score *= 2.5
        # 学习高峰时段影响加倍
        hour = row['ds'].hour
        if 19 <= hour <= 23:
            score *= 2.0
        # 周末影响加倍
        if row['ds'].dayofweek >= 5:
            score *= 1.8
        # 学分系统影响最高
        if system_priority == 'critical':
            score *= 3.0
        impact_scores.append(score)
    return impact_scores

# 继续医学教育场景根因分析核心算法
from causalnex.structure import StructureModel
from causalnex.network import BayesianNetwork
def cme_root_cause_analysis(alert_info, knowledge_graph, learning_context):
    """
    继续医学教育场景根因分析
    learning_context: {
        'affected_courses': ['心血管内科学', '急诊医学'],
        'affected_users': 1800,
        'learning_phase': 'exam_week',
        'business_impact': '考试提交失败',
        'user_regions': ['基层医院', '县级医院', '三甲医院']
    }
    """
    # 获取受影响的学习业务链路
    affected_services = knowledge_graph.get_learning_business_chain(
        alert_info['service'],
        learning_context['business_impact']
    )
    # 考虑地域因素（基层网络差）
    if '基层医院' in learning_context['user_regions']:
        affected_services.extend(knowledge_graph.get_cdn_nodes('rural_area'))
    # 构建继续医学教育场景因果图
    sm = StructureModel()
    # 添加教育业务节点和技术节点
    for service in affected_services:
        sm.add_node(
            service['name'],
            service_type=service['type'],
            learning_priority=service.get('learning_priority', 'normal'),
            affects_credits=service.get('affects_credits', False)
        )
    # 添加因果边，考虑教育业务依赖
    for edge in knowledge_graph.get_causal_edges(affected_services):
        sm.add_edge(
            edge['source'],
            edge['target'],
            weight=edge['confidence'],
            learning_critical=edge.get('learning_critical', False)
        )
    # 贝叶斯推断
    bn = BayesianNetwork(sm)
    bn.fit_node_states(alert_data)
    # 计算每个节点的异常概率，优先排查关键路径
    probabilities = bn.predict_probability(affected_services)
    # 考试期间和学分系统提高权重
    weight_multiplier = 3.0 if learning_context['learning_phase'] == 'exam_week' else 1.0
    if learning_context['business_impact'] == '学分同步失败':
        weight_multiplier *= 2.0
    # 按概率和教育影响排序
    root_causes = sorted(
        probabilities.items(),
        key=lambda x: (
            x[1] * 
            learning_context.get('user_impact_weight', 1.0) * 
            weight_multiplier *
            (3.0 if affected_services[x[0]].get('affects_credits') else 1.0)
        ),
        reverse=True
    )[:5]
    # 生成教育业务影响描述
    impact_description = generate_learning_impact(root_causes, learning_context)
    # 判断是否需要CDN切换
    cdn_recommendation = None
    if any('CDN' in cause[0] for cause in root_causes):
        cdn_recommendation = recommend_cdn_switch(root_causes, learning_context)
    return {
        'root_causes': root_causes,
        'learning_impact': impact_description,
        'affected_courses': get_affected_courses(root_causes, knowledge_graph),
        'affected_users': learning_context['affected_users'],
        'urgency_level': calculate_learning_urgency(learning_context),
        'cdn_recommendation': cdn_recommendation
    }
def calculate_learning_urgency(learning_context):
    """计算紧急程度"""
    if learning_context.get('business_impact') == '学分同步失败':
        return 'critical'  # 学分问题最高优先级
    elif learning_context['learning_phase'] == 'exam_week':
        return 'critical'  # 考试期间
    elif learning_context['affected_users'] > 1000:
        return 'high'      # 影响人数多
    elif learning_context['learning_phase'] == 'evening_peak':
        return 'medium'    # 学习高峰
    return 'low'
def recommend_cdn_switch(root_causes, learning_context):
    """推荐CDN切换策略"""
    affected_regions = learning_context.get('user_regions', [])
    recommendations = []
    for region in affected_regions:
        if region == '基层医院':
            recommendations.append({
                'region': region,
                'action': '切换到运营商边缘节点',
                'expected_improvement': '延迟降低40%，卡顿率降低60%'
            })
    return recommendations

# 学习高峰预测模型
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, Embedding, Attention
import numpy as np
def build_learning_load_model(sequence_length=60, features=12):
    """
    学习负载预测模型
    features包括：历史并发数、星期、时段、考试日历、课程热度、用户增长等
    """
    model = Sequential([
        LSTM(128, return_sequences=True, input_shape=(sequence_length, features)),
        Dropout(0.2),
        LSTM(64, return_sequences=False),
        Dropout(0.2),
        Dense(32, activation='relu'),
        Dense(1)  # 预测未来并发数
    ])
    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
    return model
def predict_learning_capacity(historical_data, learning_calendar, future_days=14):
    """
    预测未来学习容量需求
    learning_calendar: 包含考试、热门课程上线等关键时间点
    """
    model = build_learning_load_model()
    # 特征工程：加入继续教育相关特征
    features = prepare_cme_features(historical_data, learning_calendar, [
        'concurrent_users',          # 并发用户数
        'day_of_week',              # 星期
        'hour_of_day',              # 小时
        'is_exam_period',           # 是否考试期间
        'is_credit_deadline',       # 是否学分申报截止期
        'active_courses',           # 活跃课程数
        'live_broadcast_count',     # 直播场次
        'popular_course_factor',    # 热门课程因子
        'total_registered_users',   # 注册用户数
        'holiday_flag',             # 节假日标记
        'weather',                  # 天气（影响基层医生学习）
        'exam_type'                 # 考试类型（执医/职称等）
    ])
    model.fit(
        features['X_train'], 
        features['y_train'],
        epochs=50,
        batch_size=32,
        validation_split=0.2
    )
    # 预测未来负载
    predictions = model.predict(features['X_test'])
    # 转换为资源需求
    resource_demand = convert_to_cme_resource(predictions, {
        'cpu_per_user': 0.2,            # 每个用户平均CPU需求
        'memory_per_user': 0.5,         # 每个用户平均内存需求（GB）
        'storage_per_user': 20,         # 每个用户平均存储需求（MB）
        'bandwidth_per_video': 1.5,     # 视频点播每路带宽（Mbps）
        'bandwidth_per_live': 3.0,      # 视频直播每路带宽（Mbps）
        'cdn_bandwidth_reserve': 1.3    # CDN带宽预留系数
    })
    # 识别关键学习活动
    critical_events = identify_critical_learning_events(
        predictions, 
        learning_calendar
    )
    return {
        'predicted_load': predictions,
        'resource_demand': resource_demand,
        'peak_periods': identify_peak_periods(predictions),
        'critical_events': critical_events,
        'scaling_recommendation': generate_cme_scaling_plan(
            resource_demand, 
            critical_events
        ),
        'cdn_recommendation': recommend_cdn_preparation(resource_demand)
    }
def identify_critical_learning_events(predictions, learning_calendar):
    """识别关键学习活动并评估资源需求"""
    events = []
    # 执业医师考试
    for exam in learning_calendar['physician_exams']:
        events.append({
            'event': '执业医师继续教育考试',
            'date': exam['date'],
            'expected_users': exam['expected_participants'],
            'expected_load_multiplier': 4.5,
            'systems': ['考试系统', '学分系统', '防作弊系统'],
            'recommendation': '提前扩容100%计算资源，启用三副本部署，CDN预热'
        })
    # 学分申报截止期
    for deadline in learning_calendar['credit_deadlines']:
        events.append({
            'event': '学分申报截止',
            'date': deadline,
            'expected_load_multiplier': 3.2,
            'systems': ['学分系统', 'LMS', '证书系统'],
            'recommendation': '提前扩容60%计算资源，数据库读写分离'
        })
    # 热门课程上线
    for course in learning_calendar['popular_courses']:
        events.append({
            'event': f'热门课程上线：{course["name"]}',
            'date': course['release_date'],
            'expected_load_multiplier': 2.5,
            'systems': ['LMS', 'CDN', '视频转码'],
            'recommendation': '提前转码多码率版本，CDN预热，扩容30%带宽'
        })
    # 大型学术直播
    for live in learning_calendar['major_live_events']:
        events.append({
            'event': f'学术直播：{live["topic"]}',
            'date': live['date'],
            'expected_users': live['expected_viewers'],
            'expected_load_multiplier': 5.0,
            'systems': ['直播系统', 'CDN', '互动系统'],
            'recommendation': f'预估{live["expected_viewers"]}人同时在线，扩容150%推流带宽，CDN准备{live["expected_viewers"]*3}Mbps带宽'
        })
    return events
def recommend_cdn_preparation(resource_demand):
    """推荐CDN准备策略"""
    total_bandwidth = resource_demand['total_bandwidth_demand']
    return {
        'bandwidth_preparation': total_bandwidth * 1.3,  # 预留30%余量
        'node_distribution': {
            '一线城市': '40%带宽',
            '二三线城市': '35%带宽',
            '县级及基层': '25%带宽（重点保障）'
        },
        'cache_strategy': '热门课程提前缓存到边缘节点',
        'fallback_plan': '准备备用CDN服务商'
    }

# CDN智能调度算法
import requests
from typing import List, Dict
def intelligent_cdn_dispatch(user_info: Dict, content_info: Dict, cdn_nodes: List[Dict]):
    """
    基于用户位置、网络质量、CDN节点负载智能调度
    """
    # 获取用户网络质量
    user_network_quality = detect_user_network(user_info)
    # 筛选候选CDN节点
    candidate_nodes = filter_cdn_nodes(
        cdn_nodes,
        user_info['location'],
        user_network_quality
    )
    # 评分排序
    scored_nodes = []
    for node in candidate_nodes:
        score = calculate_node_score(
            node,
            user_info,
            user_network_quality,
            content_info
        )
        scored_nodes.append((node, score))
    # 选择最优节点
    best_node = max(scored_nodes, key=lambda x: x[1])[0]
    # 弱网优化
    if user_network_quality == 'weak':
        content_info['bitrate'] = 'low'  # 降低码率
        content_info['preload'] = True   # 预加载
    return {
        'cdn_node': best_node,
        'content_url': generate_cdn_url(best_node, content_info),
        'optimization': get_weak_network_optimization(user_network_quality)
    }
def detect_user_network(user_info: Dict) -> str:
    """检测用户网络质量"""
    # 基于历史数据和实时探测
    if user_info.get('is_rural', False):
        return 'weak'  # 基层医院默认弱网
    # 实时网络探测
    latency = user_info.get('network_latency', 100)
    bandwidth = user_info.get('network_bandwidth', 10)
    if latency > 200 or bandwidth < 2:
        return 'weak'
    elif latency > 100 or bandwidth < 5:
        return 'medium'
    return 'good'
def calculate_node_score(node: Dict, user_info: Dict, network_quality: str, content: Dict) -> float:
    """计算CDN节点评分"""
    score = 0.0
    # 地理距离评分（40%权重）
    distance_score = 100 - min(node['distance_km'] / 10, 100)
    score += distance_score * 0.4
    # 节点负载评分（30%权重）
    load_score = 100 - node['current_load_percent']
    score += load_score * 0.3
    # 网络质量评分（20%权重）
    if network_quality == 'weak' and node.get('weak_network_optimized', False):
        score += 100 * 0.2
    elif network_quality == 'good':
        score += 80 * 0.2
    else:
        score += 50 * 0.2
    # 内容缓存评分（10%权重）
    if content['id'] in node.get('cached_contents', []):
        score += 100 * 0.1
    return score
def get_weak_network_optimization(network_quality: str) -> Dict:
    """弱网优化策略"""
    if network_quality == 'weak':
        return {
            'adaptive_bitrate': True,
            'buffer_size': 'large',
            'preload_duration': 30,  # 预加载30秒
            'retry_strategy': 'aggressive',
            'compression': 'high'
        }
    return {}