问题 | RAIM + LSTM 你怎么看？？？

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github：https://github.com/MichaelBeechan
CSDN：https://blog.csdn.net/u011344545

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import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense# RAIM-LSTM 融合模型
class RAIM_LSTM(tf.keras.Model):def __init__(self, input_dim, hidden_units):super(RAIM_LSTM, self).__init__()self.lstm = LSTM(hidden_units, return_sequences=False)self.dense = Dense(1, activation='sigmoid')  # 输出阈值缩放因子def call(self, inputs):# 输入形状: (batch_size, time_steps, input_dim)h = self.lstm(inputs)scale_factor = self.dense(h)  # 动态缩放因子∈[0.5, 1.5]return scale_factor# RAIM-LSTM 完整流程
def raim_lstm_detection(z_sequence, H_sequence, model, T_base):""":param z_sequence: 时序伪距观测 (k+1, n):param H_sequence: 时序几何矩阵 (k+1, n, 4):param model: 预训练RAIM-LSTM模型:param T_base: 基础卡方阈值:return: 故障告警标志, 动态阈值"""# 计算奇偶矢量序列p_sequence = []for t in range(len(z_sequence)):H = H_sequence[t]P = np.linalg.pinv(H.T @ H) @ H.T  # 奇偶空间矩阵p = P @ z_sequence[t]p_sequence.append(p)p_sequence = np.array(p_sequence)# 提取特征：奇偶矢量范数 + 几何因子features = np.concatenate([np.linalg.norm(p_sequence, axis=1).reshape(-1,1),np.sqrt(np.trace(H_sequence @ H_sequence.T, axis1=1, axis2=2))], axis=1)# LSTM预测动态阈值scale = model.predict(features[np.newaxis, ...])T_dynamic = T_base * (0.5 + scale[0][0])  # 缩放至[0.5*T_base, 1.5*T_base]# 当前时刻检测T_SSE = np.linalg.norm(p_sequence[-1])**2alarm = T_SSE > T_dynamicreturn alarm, T_dynamic# 训练示例
model = RAIM_LSTM(input_dim=2, hidden_units=32)
model.compile(optimizer='adam', loss='mse')
# 假设已生成训练数据（特征序列+标签）
# model.fit(X_train, y_train, epochs=50)