Cloud-edge collaboration forecasting system and method for aluminum oxide production indexes

Abstract

Provided is a cloud-edge collaboration forecasting system and method for aluminum oxide production indexes. The forecasting system performs forecasting algorithm selection, parameter configuration and model training on indexes and variables of the aluminum oxide production process at a cloud model training server, performs evaluation and parameter correction on the trained model to obtain an optimal training model, and pre-processes the data in the aluminum oxide production process at an aluminum oxide production index forecasting computer at an edge end. The trained model parameters are imported from the cloud, and further the trained forecasting model is used for forecasting aluminum oxide production indexes for different production processes. The forecasting system and method can provide powerful calculating resources by training an aluminum oxide production index forecasting model through the cloud model training server, and real-time convenient aluminum oxide production index forecasting through the computer at the edge end.