Dual-Scale Attributed Graph Transformer for Extracting Spatial-Temporal Features With Applications in Quality Index Prediction

Abstract

This paper presents a novel deep learning architecture, the Dual-scale Attribute Graph Transformer (DAGT), for extracting spatial-temporal features from attributed graph data. DAGT addresses the challenge of inconsistent sampling periods in industrial data streams by utilizing two key modules: 1) Dual-Scale Spatial-temporal Graph Convolution Network (DSGCN): This module captures both spatial and temporal information within attributed graphs, enabling effective feature extraction for tasks like quality index prediction. 2) Spatial-temporal Graph Attention Block (SGAB): This module employs an attention mechanism to selectively focus on crucial areas of the graph sequence. By assigning higher weights to regions with significant spatial-temporal features, SGAB refines the feature representation. The contributions of DAGT lie in the construction of a dual-scale adjacency matrix for efficient temporal and spatial dimensionality reduction and the design of a graph pooling module via spatial clustering. These innovations enhance the model's ability to learn from attributed graph sequences. The proposed method for quality index prediction is validated using real-world industrial data of the mineral processing process and various comparative experiments.