【AI大模型】在健康睡眠监测中的深度融合与实践案例

# 模拟实时数据采集def collect_real_time_data():    heart_rate = np.random.normal(60, 5)    respiration_rate = np.random.normal(16, 2)    temperature = np.random.normal(36.5, 0.5)    movement = np.random.normal(0, 1)    return np.array([heart_rate, respiration_rate, temperature, movement])
# 模拟实时数据采集real_time_data = collect_real_time_data()print("实时数据采集:", real_time_data)

# 实时监测和睡眠阶段预测def real_time_sleep_monitor(model):    data_window = []
    while True:        new_data = collect_real_time_data()        data_window.append(new_data)        if len(data_window) > 100:            data_window.pop(0)  # 保持固定窗口大小
        if len(data_window) == 100:            data_window_array = np.expand_dims(np.array(data_window), axis=0)            sleep_stage = model.predict(data_window_array)            print(f"当前睡眠阶段: {np.argmax(sleep_stage)}")
            # 提供实时反馈            if np.argmax(sleep_stage) == 2:  # 假设2代表深睡                print("进入深睡状态，请保持安静环境。")            elif np.argmax(sleep_stage) == 0:  # 假设0代表浅睡                print("浅睡状态，建议放松。")                time.sleep(1)  # 模拟每秒采集一次数据
# 启动实时监测# real_time_sleep_monitor(model)

from keras.models import Sequentialfrom keras.layers import LSTM, Dense, Dropout, Bidirectional
def build_optimized_lstm_model(input_shape):    model = Sequential()    model.add(Bidirectional(LSTM(64, return_sequences=True), input_shape=input_shape))    model.add(Dropout(0.2))    model.add(Bidirectional(LSTM(64, return_sequences=False)))    model.add(Dropout(0.2))    model.add(Dense(3, activation='softmax'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])    return model
input_shape = (None, 4)  # 4个特征：心率、呼吸率、体温、运动optimized_model = build_optimized_lstm_model(input_shape)

from keras.models import Sequentialfrom keras.layers import Conv1D, MaxPooling1D, Flatten, Dense
def build_optimized_cnn_model(input_shape):    model = Sequential()    model.add(Conv1D(64, kernel_size=3, activation='relu', input_shape=input_shape))    model.add(MaxPooling1D(pool_size=2))    model.add(Conv1D(128, kernel_size=3, activation='relu'))    model.add(MaxPooling1D(pool_size=2))    model.add(Flatten())    model.add(Dense(128, activation='relu'))    model.add(Dense(3, activation='softmax'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])    return model
input_shape = (100, 4)  # 100个时间步，4个特征optimized_cnn_model = build_optimized_cnn_model(input_shape)

from keras.models import Modelfrom keras.layers import Input, Dense, MultiHeadAttention, LayerNormalization, Dropout
def build_optimized_transformer_model(input_shape, num_heads=4, ff_dim=64):    inputs = Input(shape=input_shape)    attention_output = MultiHeadAttention(num_heads=num_heads, key_dim=ff_dim)(inputs, inputs)    attention_output = LayerNormalization(epsilon=1e-6)(attention_output)    ffn_output = Dense(ff_dim, activation='relu')(attention_output)    ffn_output = Dense(input_shape[-1])(ffn_output)    outputs = LayerNormalization(epsilon=1e-6)(ffn_output)    model = Model(inputs, outputs)    model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])    return model
input_shape = (100, 4)optimized_transformer_model = build_optimized_transformer_model(input_shape)

from cryptography.fernet import Fernet
# 生成密钥key = Fernet.generate_key()cipher_suite = Fernet(key)
# 加密数据data = b"Sensitive user data"encrypted_data = cipher_suite.encrypt(data)
# 解密数据decrypted_data = cipher_suite.decrypt(encrypted_data)

import pandas as pd
# 模拟用户数据data = pd.DataFrame({    'user_id': ['user1', 'user2', 'user3'],    'heart_rate': [70, 65, 80],    'sleep_stage': ['deep', 'light', 'REM']})
# 匿名化处理data['user_id'] = data['user_id'].apply(lambda x: 'user_' + str(hash(x)))print(data)

from flask import Flask, request, jsonifyfrom functools import wraps
app = Flask(__name__)
# 模拟用户数据存储user_data = {    'user_1': {'heart_rate': 70, 'sleep_stage': 'deep'},    'user_2': {'heart_rate': 65, 'sleep_stage': 'light'}}
# 模拟访问控制def requires_auth(f):    @wraps(f)    def decorated(*args, **kwargs):        auth = request.headers.get('Authorization')        if auth != 'Bearer secret-token':            return jsonify({"message": "Unauthorized"}), 403        return f(*args, **kwargs)    return decorated
@app.route('/api/data', methods=['GET'])@requires_authdef get_data():    user_id = request.args.get('user_id')    return jsonify(user_data.get(user_id, {"message": "User not found"}))
if __name__ == '__main__':    app.run()

# 模拟多模态数据采集def collect_multimodal_data():    heart_rate = np.random.normal(60, 5)    respiration_rate = np.random.normal(16, 2)    temperature = np.random.normal(36.5, 0.5)    movement = np.random.normal(0, 1)    skin_conductance = np.random.normal(5, 1)  # 皮肤电反应    blood_oxygen = np.random.normal(98, 1)  # 血氧饱和度    noise_level = np.random.normal(30, 5)  # 噪音水平    return np.array([heart_rate, respiration_rate, temperature, movement, skin_conductance, blood_oxygen, noise_level])
# 模拟数据采集multimodal_data = collect_multimodal_data()print("多模态数据采集:", multimodal_data)

from sklearn.linear_model import SGDClassifierimport numpy as np
# 模拟数据X_train = np.random.rand(100, 7)  # 7个特征y_train = np.random.randint(0, 3, 100)  # 3个睡眠阶段
# 初始训练model = SGDClassifier()model.fit(X_train, y_train)
# 模拟新的数据X_new = np.random.rand(10, 7)y_new = np.random.randint(0, 3, 10)
# 在线学习更新模型model.partial_fit(X_new, y_new)