Operating performance assessment for complex industrial processes based on multi-block sparse spatiotemporal graph transformer

Energy and material transfer and physicochemical reactions in complex industrial processes exhibit spatiotemporal propagation characteristics, and spatiotemporal relationships among variables can reveal operating patterns and improve operating performance assessment. However, numerous redundant spatiotemporal relationships interfere with operating performance representation, reducing model interpretability and cross-distribution performance stability. To address this issue, this paper proposes an operating performance assessment method for complex industrial processes based on multi-block sparse spatiotemporal graph transformer. Specifically, a perturbation response mechanism based on a conditional variational autoencoder is designed to measure the sensitivity of edge relationships to local representation changes and select perturbation-responsive edges that carry critical operating performance discriminative information. Meanwhile, a masked decay matrix is designed to impose temporal decay constraints on spatiotemporal information propagation, reflecting the continuous evolution of industrial process variables. Finally, quantile thresholds are constructed based on the sparse dynamic graph obtained from the optimal operating performance level, and key non-optimal variables are traced by analyzing edge-intensity variations. Experimental results on a coal preparation process demonstrate that the proposed method can effectively prune weakly responsive redundant relationships, achieving a pruning rate of 93.42%, while maintaining high operating performance assessment accuracy and performance stability under cross-distribution scenarios.