Multi-modal anomaly detection method based on graph attention network and time convolution network

• Main Authors: LIU PENG, SONG HONGTAO, LU DAN, LU XINKAI, HAN QILONG
• Format: Patent
• Language: Chinese; English
• Published: 05.09.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

The invention discloses a multi-modal anomaly detection method based on a graph attention network and a time convolution network, and belongs to the technical field of multi-modal data anomaly detection, and the method comprises the steps: obtaining a multi-modal data set based on a plurality of sensors, and carrying out the preprocessing of the multi-modal data set, dividing the preprocessed multi-modal data set into a training set and a test set; constructing a spatial feature extraction module for extracting spatial correlation features based on the graph attention network and the multi-head attention mechanism, constructing a time feature extraction module based on the time convolution generative adversarial network, and constructing a multi-modal anomaly detection model according to the spatial feature extraction module and the time feature extraction module; performing training and parameter optimization on the multi-modal anomaly detection model through the training set to obtain a target network; and