数字孪生人工智能应用技术深度解析

# 数字孪生AI应用系统示例 import numpy as np import pandas as pd import tensorflow as tf from # sklearn.ensemble import RandomForestClassifier from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split import joblib from typing import Dict import List import Tuple import Any import json class DigitalTwinAISystem: def __init__(self, system_id, ai_config): self.system_id = system_id self.ai_config = ai_config self.ml_models = {} self.dl_models = {} self.data_processor = None self.prediction_engine = None self.optimization_engine = None def initialize_ai_system(self): """初始化AI系统""" # 初始化数据处理器 # self.data_processor = DataProcessor(self.ai_config) # 初始化预测引擎 self.prediction_engine = # PredictionEngine(self.ai_config) # 初始化优化引擎 self.optimization_engine = # OptimizationEngine(self.ai_config) return True def train_ml_model(self, model_type, training_data, target_column): """训练机器学习模型""" # 数据预处理 processed_data = # self.data_processor.preprocess_data(training_data) # 特征工程 features = # self._extract_features(processed_data, target_column) targets = processed_data[target_column] # 数据分割 # X_train, X_test, y_train, y_test = train_test_split( features, targets, test_size=0.2, random_state=42 ) # 模型训练 if model_type == 'random_forest': model = RandomForestClassifier(n_estimators=100, # random_state=42) elif model_type == 'neural_network': model = self._create_neural_network(features.shape[1]) else: raise ValueError(f"Unsupported model type: {model_type}") model.fit(X_train, y_train) # 模型评估 accuracy = model.score(X_test, y_test)# 保存模型 # model_id = f"{model_type}_{int(time.time())}" self.ml_models[model_id] = { 'model': model, 'accuracy': accuracy, 'model_type': model_type, 'features': features.columns.tolist() } return model_id, accuracy def train_dl_model(self, model_type, training_data, target_column): """训练深度学习模型""" # 数据预处理 # processed_data = self.data_processor.preprocess_data(training_data) # 特征工程 features = # self._extract_features(processed_data, target_column) targets = processed_data[target_column] # 数据标准化 # scaler = StandardScaler() features_scaled = scaler.fit_transform(features) # 数据分割 X_train, X_test, # y_train, y_test = train_test_split( features_scaled, targets, test_size=0.2, random_state=42 ) # 创建深度学习模型 if model_type == 'cnn': model = self._create_cnn_model(features.shape[1]) elif # model_type == 'rnn': model = self._create_rnn_model(features.shape[1]) elif model_type == 'transformer': model = self._create_transformer_model(features.shape[1]) else: raise ValueError(f"Unsupported model type: {model_type}") # 模型编译 model.compile( optimizer='adam', loss='mse', metrics=['mae'] )# 模型训练 # history = model.fit( X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=32, verbose=0 ) # 模型评估 loss, mae = model.evaluate(X_test, y_test, verbose=0)# 保存模型 model_id = # f"{model_type}_{int(time.time())}" self.dl_models[model_id] = { 'model': model, 'loss': loss, 'mae': mae, 'model_type': model_type, 'scaler': scaler, 'history': history.history } return model_id, loss, mae def _create_neural_network(self, input_dim): """创建神经网络模型""" model = tf.keras.Sequential([ tf.keras.layers.Dense(128, activation='relu', input_shape=(input_dim,)), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile( optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'] ) return model def _create_cnn_model(self, input_dim): """创建CNN模型""" model = tf.keras.Sequential([ tf.keras.layers.Reshape((input_dim, 1)), tf.keras.layers.Conv1D(64, 3, activation='relu'), tf.keras.layers.MaxPooling1D(2), tf.keras.layers.Conv1D(32, 3, activation='relu'), tf.keras.layers.MaxPooling1D(2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(50, activation='relu'), tf.keras.layers.Dense(1) ]) return model def _create_rnn_model(self, input_dim): """创建RNN模型""" model = tf.keras.Sequential([ tf.keras.layers.Reshape((input_dim, 1)), tf.keras.layers.LSTM(50, return_sequences=True), tf.keras.layers.LSTM(50), tf.keras.layers.Dense(50, activation='relu'), tf.keras.layers.Dense(1) ]) return model def _create_transformer_model(self, input_dim): """创建Transformer模型""" # 简化的Transformer模型 inputs = tf.keras.layers.Input(shape=(input_dim,))# 位置编码 x = # tf.keras.layers.Dense(64)(inputs) # 多头注意力 attention = tf.keras.layers.MultiHeadAttention( # num_heads=8, key_dim=64 ) x = attention(x, x) # 前馈网络 x = tf.keras.layers.Dense(128, # activation='relu')(x) x = tf.keras.layers.Dense(64)(x) # 输出层 outputs = tf.keras.layers.Dense(1)(x) # model = tf.keras.Model(inputs, outputs) return model def _extract_features(self, data, target_column): """提取特征""" # 移除目标列 features = data.drop(columns=[target_column])# 处理分类变量 # categorical_columns = features.select_dtypes(include=['object']).columns for col in categorical_columns: features[col] = pd.Categorical(features[col]).codes # 处理缺失值 features = # features.fillna(features.mean()) return features def predict(self, model_id, input_data): """使用模型进行预测""" if model_id in self.ml_models: model_info = self.ml_models[model_id] model = model_info['model'] # 数据预处理 # processed_data = self.data_processor.preprocess_data(input_data) features = self._extract_features(processed_data, None) # 预测 predictions = model.predict(features) return # predictions elif model_id in self.dl_models: model_info = self.dl_models[model_id] model = model_info['model'] scaler = model_info['scaler'] # 数据预处理 processed_data = # self.data_processor.preprocess_data(input_data) features = self._extract_features(processed_data, None) # 数据标准化 features_scaled = scaler.transform(features)# 预测 predictions = # model.predict(features_scaled) return predictions else: raise ValueError(f"Model {model_id} not found") def analyze_patterns(self, data): """分析数据模式""" # 聚类分析 clusters = # self._perform_clustering(data) # 异常检测 anomalies = self._detect_anomalies(data)# 关联分析 # associations = self._find_associations(data) return { 'clusters': clusters, 'anomalies': anomalies, 'associations': associations } def _perform_clustering(self, data): """执行聚类分析""" # 数据预处理 # processed_data = self.data_processor.preprocess_data(data) features = self._extract_features(processed_data, None) # 标准化 scaler = StandardScaler() features_scaled = # scaler.fit_transform(features) # K-means聚类 kmeans = KMeans(n_clusters=3, random_state=42) clusters = # kmeans.fit_predict(features_scaled) return { 'cluster_labels': clusters, 'cluster_centers': kmeans.cluster_centers_, 'inertia': kmeans.inertia_ } def _detect_anomalies(self, data): """检测异常""" # 使用统计方法检测异常 processed_data = self.data_processor.preprocess_data(data) features = # self._extract_features(processed_data, None) # 计算Z-score z_scores = np.abs((features - # features.mean()) / features.std()) anomalies = z_scores > 3 # 3-sigma规则 return { 'anomaly_indices': # anomalies.any(axis=1), 'anomaly_scores': z_scores.max(axis=1) } def _find_associations(self, data): """发现关联关系""" # 计算相关性 processed_data = self.data_processor.preprocess_data(data) features = # self._extract_features(processed_data, None) # 计算相关系数 correlation_matrix = features.corr()# 找出强相关关系 strong_correlations = [] for i in range(len(correlation_matrix.columns)): for j in # range(i+1, len(correlation_matrix.columns)): corr = correlation_matrix.iloc[i, j] if abs(corr) > 0.7: # 强相关阈值 strong_correlations.append({ 'feature1': correlation_matrix.columns[i], 'feature2': # correlation_matrix.columns[j], 'correlation': corr }) return strong_correlations def optimize_parameters(self, objective_function, parameter_bounds): """优化参数""" # 使用遗传算法优化参数 # from scipy.optimize import differential_evolution result = differential_evolution( objective_function, parameter_bounds, maxiter=100, popsize=15, seed=42 ) return { 'optimal_parameters': result.x, 'optimal_value': result.fun, 'success': result.success } def generate_insights(self, data, analysis_results): """生成洞察""" insights = [] # 基于聚类结果生成洞察 if 'clusters' in # analysis_results: clusters = analysis_results['clusters'] insights.append(f"数据可以分为{len(set(clusters['cluster_labels']))}个不同的群组") # 基于异常检测结果生成洞察 # if 'anomalies' in analysis_results: anomalies = analysis_results['anomalies'] anomaly_count = sum(anomalies['anomaly_indices']) insights.append(f"检测到{anomaly_count}个异常数据点") # 基于关联分析结果生成洞察 # if 'associations' in analysis_results: associations = analysis_results['associations'] insights.append(f"发现{len(associations)}个强相关关系") return insights class DataProcessor: def __init__(self, config): self.config = config def preprocess_data(self, data): """数据预处理""" # 处理缺失值 # data = data.fillna(data.mean()) # 处理异常值 data = self._handle_outliers(data)# 数据标准化 data = # self._standardize_data(data) return data def _handle_outliers(self, data): """处理异常值""" # 使用IQR方法处理异常值 Q1 = data.quantile(0.25) Q3 = data.quantile(0.75) IQR = Q3 - Q1 lower_bound = Q1 - # 1.5 * IQR upper_bound = Q3 + 1.5 * IQR # 将异常值替换为边界值 data = data.clip(lower=lower_bound, # upper=upper_bound, axis=1) return data def _standardize_data(self, data): """数据标准化""" # 只对数值列进行标准化 # numeric_columns = data.select_dtypes(include=[np.number]).columns data[numeric_columns] = (data[numeric_columns] - data[numeric_columns].mean()) / data[numeric_columns].std() return data class PredictionEngine: def __init__(self, config): self.config = config def predict_future_values(self, model, data, horizon): """预测未来值""" # 实现时间序列预测 predictions = [] current_data = data.copy() # for i in range(horizon): # 使用模型预测下一个值 prediction = model.predict(current_data[-1:]) # predictions.append(prediction[0]) # 更新数据 current_data = np.append(current_data, prediction) return # predictions class OptimizationEngine: def __init__(self, config): self.config = config def optimize_system(self, objective_function, constraints): """优化系统""" # 实现系统优化算法 pass