Industrial process operation index intelligent forecasting method, device and equipment and storage medium

Abstract

The invention provides an industrial process operation index intelligent forecasting method, device and equipment and a storage medium. The industrial process operation index intelligent forecasting method comprises the steps of establishing an operation index dynamic model according to the characteristic that the change of an operation index depends on the dynamic characteristics of an industrial process control system, wherein the operation index dynamic model comprises an identifiable model and an unmodeled dynamic model; estimating parameters of the identifiable model in the operation index dynamic model; combining the identification error of the parameter of the identifiable model in the operation index dynamic model and the unmodeled dynamic state in the operation index dynamic model into an unknown nonlinear dynamic system; establishing an online intelligent forecasting model of the unknown nonlinear dynamic system; and obtaining a forecast value of the operation index according to output of the identifiable model in the operation index dynamic model and output of the online intelligent forecast model of the unknown nonlinear dynamic system. Aiming at the problem that industrial process operation indexes are difficult to forecast, a system identification method based on a mechanism model is combined with a deep learning method based on big data, an intelligent forecasting method of the industrial process operation indexes is provided, and the problem of forecasting of the industrial process operation indexes is solved.

Description

technical field [0001] The invention belongs to the technical field of industrial artificial intelligence, and in particular relates to a method, device, equipment and storage medium for intelligent forecasting of industrial process operation indicators. Background technique [0002] Accurate forecasting of operating indicators representing product quality, efficiency, and consumption of industrial process processing is crucial to the realization of optimal control of the industrial process. Due to the comprehensive complexity of the operation index and the input and output of the industrial process control system that affects the operation index, such as strong nonlinearity, multi-variable strong coupling, operating condition changes, and raw material fluctuations, the mechanism is unclear and it is difficult to establish a dynamic model. The existing system identification method and forecast method based on the mechanism model establishes the forecast model of the operatio...

AI Technical Summary

Problems solved by technology

Due to the comprehensive complexity of the operation index and the input and output of the industrial process control system that affects the operation index, such as strong nonlinearity, multi-variable strong coupling, operating condition changes, and raw material fluctuations, the mechanism is unclear and it is difficult to establish a dynamic model. The existing system identification method and forecast method based on the mechanism model establishes the forecast model of the operation index

Due to the dynamic changes in the industrial process in the production process, the operating indicators and the input and output data of the industrial process control system are in a changing, open, and uncertain information space, which cannot be established by using the existing deep learning technology with a complete information space. Forecasting Models for Running Indicators

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