Complex industrial system forecasting model construction method and device based on deep learning, equipment and storage medium

Abstract

The invention provides a complex industrial system forecasting model construction method and device with unknown model structure, parameter and variable order, equipment and a storage medium. The complex industrial system forecasting model construction method comprises the steps of creating a dynamic model of an industrial system, determining an input variable and an output variable of the dynamicmodel, wherein the output variable is a forecasted variable; establishing a forecasting model by adopting LSTM, taking the input variable of the dynamic model as single neuron input of the LSTM, taking output data of the dynamic model as label data, expressing unknown variable orders of the dynamic model by using the number of neurons of the LSTM, and by using a training algorithm, determining amodel parameter of the LSTM according to an error between the label data and the forecast model output. The invention solves the problem that a dynamic mathematical model is difficult to establish fora complex industrial system, a dynamic system modeling method is combined with a deep learning technology of a complete information space, and the construction of a complex industrial dynamic systemforecasting model is realized.

Description

technical field [0001] The invention belongs to the technical field of industrial artificial intelligence, and relates to a method, a device, a device and a storage medium for constructing a prediction model of a complex industrial dynamic system based on deep learning. Background technique [0002] Since it is difficult to establish a dynamic mathematical model for complex industrial systems, its operational decisions still rely on knowledge workers to make manual decisions based on experience and knowledge to optimize the operational decisions of industrial systems. The key is to predict the product quality of the system reflected in the operational actions of the decision. , efficiency, consumption of production indicators and key process parameters are within the target value range. Due to the complexity of the manufacturing process, most production indicators and key process parameters cannot be accurately measured online, such as the concentration and particle size of ...

AI Technical Summary

Problems solved by technology

Due to the complexity of the manufacturing process, most production indicators and key process parameters cannot be accurately measured online, such as the concentration and particle size of the grinding process, the detection of key process parameters such as the caustic concentration in the alumina production process, and the production of fused magnesia. The rate of change of demand in the process, etc., can only be obtained through manual testing or post-event statistics

Therefore, there is a serious lag, and knowledge workers can only make decisions based on experience and knowledge based on lagging information, resulting in problems such as unstable product quality and high consumption

To achieve accurate forecasting of production indicators and key process parameters, it is necessary to solve the modeling problem that existing system identification methods and deep learning methods cannot be used for complex dynamic system forecasting models with unknown model structure and unknown order of input and output variables.

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