Collaborative path optimization model of power material supply chain based on hash index spatio-temporal graph neural network

The daily dispatching of materials in power systems involves multifaceted operations, including data analysis and logistics warehouse management. Current research on intelligent IoT mainly focuses on the static management of electrical materials and isolated dynamic dispatching schemes. It lacks a comprehensive spatio-temporal circulation design throughout the IoT-enabled distribution process. This gap hinders the implementation of efficient allocation mechanisms. This paper considers the coupling relationship between logistics collaborative data and spatio-temporal correlations. Using the Hash Index algorithm, the logistics data are transformed into multi-objective optimization composite functions. The proposed framework integrates Spatio-Temporal Graph Neural Networks (STGNNs) to model spatio-temporal relationships among nodes adjacent to abnormal coordinates in distribution paths. By aggregating information from neighboring collaborative nodes to update node embeddings, the framework leverages the enhanced external functions of multiple adjacent nodes in decision-making processes. This approach effectively resolves optimal path selection challenges under emergency conditions while ensuring global model optimization. Experimental results show that, compared to mainstream graph neural network models, the proposed model reduces path prediction errors by an average of approximately 12.3 %, as measured by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Moreover, it shortens the path length by 17.6 % in multi-objective collaborative route optimization. These results confirm the model's effectiveness and superiority in routing tasks within the electric power material supply chain. The proposed solution also exhibits notable technical advantages over mainstream approaches. Additionally, it not only ensures operational efficiency in power logistics but also offers technical support for multi-vehicle and multi-station collaborative operations under emergency conditions in the logistics industry.