Abstract: This disclosure relates to the technical field of fault diagnosis, in particular, to a method and system for industrial equipment fault diagnosis based on graph structure joint optimization. The method includes: acquiring an original equipment dataset; constructing an original graph structure based on the original equipment dataset; extracting two basic views based on the original graph structure, calculating graph node embeddings of the basic views using a GCN, and recalculating a probability of an edge in the graph structure based on the graph node embeddings; performing view fusion based on the probability of the edge in the graph structure to obtain a preliminarily optimized view; and processing a fused view through a GAT network to obtain an enhanced view. According to this disclosure, the problems of low prediction accuracy, poor robustness, the like in traditional fault diagnosis are optimized, and thus the stability of industrial Internet equipment is greatly improved.

Abstract: The present invention relates to the technical field of expression recognition, and in particular, to an expression recognition method and system based on multi-scale features and spatial attention. The method includes: performing feature extraction on acquired facial image data by using an HNFER neural network model to obtain an original input feature map; performing pooling and concatenation on extracted features based on a CoordAtt attention mechanism to obtain a feature map; performing deep convolution processing on the feature map to obtain an attention map, and then performing element-by-element multiplication to obtain a final feature map; and performing feature transformation and normalization on the final feature map to obtain an expression category probability and output the expression category probability.

Abstract: The present invention discloses a human-robot collaboration method based on a multi-scale graph convolutional neural network. The method includes the following steps: S1, data acquisition: acquiring a dataset of a human skeleton in human-robot collaboration scenes, and performing pre-processing to obtain pre-processed data; S2, model training: loading the pre-processed data, and obtaining a human behavior recognition network model by training a multi-scale graph convolutional neural network; S3, human behavior recognition: predicting human behaviors through a trained deep learning network model; and S4, human-robot interaction: sending predicted information to a robot system through a communication algorithm, and enabling a robot to make action plans based on the human behaviors. By the human-robot collaboration method based on a multi-scale graph convolutional neural network disclosed by the present invention, a robot can predict human behaviors and intents in real scenes and make correct interaction.