Online Prediction of Industrial Process Index: An Edge-Cloud Collaborated Approach

In this article, an edge-cloud collaborative prediction algorithm is proposed for the problem of industrial process index forecasting. In the proposed algorithm, the cloud-based models are updated periodically, while the edge-side models are updated multiple times using the latest data during each cloud update cycle. We use a linear model for the edge-side and a nonlinear long short term memory model for the cloud-based model and design a novel synchronization mechanism to update the models of both sides alternatively. In order to mitigate potential oscillations caused by frequent updates to the edge-side models, two additional coefficients are introduced. We provide a formal analysis of the convergence of prediction errors. The proposed algorithm is validated using both simulation and real industrial data. The results show that the forecast accuracy and stability are remarkably improved compared to other methods.