# 示例：多模态数据对齐与特征融合
import numpy as np
from sklearn.decomposition import PCA

def preprocess_data(uhf_signal, ae_signal, tev_signal):
    # 特高频信号降噪处理
    uhf_clean = butter_bandpass_filter(uhf_signal, 300e6, 1.5e9, fs=2e9)

    # 超声波信号时频分析
    ae_spectrogram = stft(ae_signal, fs=20e3)

    # 暂态地电压特征提取
    tev_features = extract_tevas(tev_signal)

    # 多模态特征拼接
    combined_features = np.hstack((uhf_clean, ae_spectrogram.flatten(), tev_features))
    return combined_features

# 主成分分析降维
pca = PCA(n_components=0.95)
reduced_data = pca.fit_transform(combined_features)

# 示例：多模态Transformer-CNN-LSTM模型
import tensorflow as tf
from tensorflow.keras import layers

class MultiModalPDModel(tf.keras.Model):
    def __init__(self):
        super(MultiModalPDModel, self).__init__()

        # 特高频信号处理分支
        self.uhf_branch = tf.keras.Sequential([
            layers.Conv1D(64, 3, activation='relu'),
            layers.MaxPooling1D(2),
            layers.LSTM(128)
        ])

        # 超声波信号处理分支
        self.ae_branch = tf.keras.Sequential([
            layers.Conv2D(32, (3,3), activation='relu'),
            layers.MaxPooling2D(2),
            layers.GlobalAveragePooling2D()
        ])

        # 暂态地电压处理分支
        self.tev_branch = layers.Dense(64, activation='relu')

        # 跨模态注意力机制
        self.attention = layers.Attention()

        # 顶层分类器
        self.classifier = tf.keras.Sequential([
            layers.Dense(256, activation='relu'),
            layers.Dropout(0.5),
            layers.Dense(5, activation='softmax')  # 5类故障类型
        ])

    def call(self, inputs):
        uhf_out = self.uhf_branch(inputs['uhf'])
        ae_out = self.ae_branch(inputs['ae'])
        tev_out = self.tev_branch(inputs['tev'])

        # 注意力融合
        fused = self.attention([uhf_out, ae_out, tev_out])
        return self.classifier(fused)

# 示例：基于TOA的放电定位算法
def toa_localization(sensor_readings, sensor_positions):
    # 计算时间差
    delta_times = [r - sensor_readings[0] for r in sensor_readings[1:]]

    # 构建非线性方程组
    equations = []
    for i in range(1, len(sensor_positions)):
        dx = sensor_positions[i][0] - sensor_positions[0][0]
        dy = sensor_positions[i][1] - sensor_positions[0][1]
        dz = sensor_positions[i][2] - sensor_positions[0][2]
        equations.append(
            f"sqrt((x-{sensor_positions[0][0]})^2 + (y-{sensor_positions[0][1]})^2 + (z-{sensor_positions[0][2]})^2) " +
            f"- sqrt((x-{sensor_positions[i][0]})^2 + (y-{sensor_positions[i][1]})^2 + (z-{sensor_positions[i][2]})^2) = {delta_times[i-1]*c}"
        )

    # 使用数值方法求解
    solution = solve_nonlinear_system(equations)
    return solution