Industrial production process target data prediction method of multi-feature fusion deep neural network

Abstract

The invention discloses an industrial production process target data prediction method of a multi-feature fusion deep neural network. Collecting other variable time sequence data related to the key variable in the industrial equipment through equal-interval sampling by utilizing a sensor, and performing predictive analysis on the time sequence data of the key variable in the process industry; inputting into a pre-designed and pre-constructed deep convolutional neural network for training; segmenting historical data of the key variables according to time steps and then inputting the segmented historical data into a deep gated recurrent neural network for learning; a multi-feature fusion method is utilized to fuse output features obtained by two networks and then input the output features into a full connection layer, network parameters are optimized through back propagation, and prediction precision is improved. According to the method, reliable and effective target variable parameter prediction is provided for process monitoring in industrial production, and the hysteresis quality of measurement of key variables such as the silicon content of molten iron in industrial production isrelieved.

Description

technical field [0001] The invention belongs to a method for predicting industrial parameters in the field of process industry production, and in particular relates to a method for predicting key parameters in an industrial production process based on multi-feature fusion. Background technique [0002] The current process industry system is gradually becoming more and more intelligent, integrated, and automated, and the functions of the entire industrial system are becoming more and more perfect. Therefore, the correlation between variables in the system is getting closer and closer. At the same time, with the emergence of various sensors, more and more parameters are available in the process industry, which provides a data source for industrial big data processing. However, there is still a huge lag in the measurement of key parameters in the process industry, such as the silicon content of molten iron in blast furnace ironmaking. Such key parameters can only be obtained by...

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Problems solved by technology

However, the gated recurrent neural network used in this method is not good at capturing the characteristics of the data dimension, so the informa

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