关键词: 生产-物流协同, 时空特征, 图注意力网络, 门控循环单元

Abstract: In order to solve the problem that the state prediction is difficult to perform due to the complex production process and frequent abnormal disturbances in the discrete workshop,a collaborative state prediction method of workshop production-logistics based on spatio-temporal feature was proposed. Firstly,based on the production-logistics operation logic and real-time manufacturing data,the production-logistics collaboration relationship was analyzed,and the prediction index of the production-logistics collaboration state was determined. Secondly,according to the spatio-temporal feature relationship between production and logistics,a production-logistics time sequence graph model was established,and then the spatio-temporal fusion network based on Graph Attention network-Gated Recurrent Unit (GAT-GRU) was used to predict the prediction indicators of the cooperative state. Finally,a typical mixed-flow production workshop was used as a case study,and the experimental results show that the proposed prediction method was better than the deep neural network,denoising autoencoder,gated recurrent unit and other models in terms of accuracy and efficiency,and can more effectively realize the production-logistics collaborative state prediction.

Key words: production-logistics collaboration, spatio-temporal feature, graph attention network, gated recurrent unit