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A method for forecasting industrial production process

A prediction method and process technology, applied in the direction of comprehensive factory control, electrical program control, comprehensive factory control, etc., can solve the problems of difficult production operation prediction, low degree of standardization, relying on manual experience, etc., so as to improve the level of standardized operations and solve production problems. Problems, the effect of improving production efficiency

Inactive Publication Date: 2018-08-07
UNIV OF SCI & TECH BEIJING
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a method for predicting industrial production processes to solve the problems existing in the prior art that the operation process only relies on manual work, process control all relies on manual experience, production operation prediction is difficult, and the standardization degree is low.

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  • A method for forecasting industrial production process
  • A method for forecasting industrial production process
  • A method for forecasting industrial production process

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Embodiment 1

[0059] see figure 1 As shown, a method for predicting an industrial production process provided by an embodiment of the present invention includes:

[0060] S1: Obtain historical production data;

[0061] S2: According to the relationship between reactants and products in industrial production, use machine learning to establish a simplified model;

[0062] S3: use a data mining method to mine and analyze the historical production data, and solve the coefficients of the simplified model according to the analysis result to obtain an operation prediction model;

[0063] S4: Predict the production operation through the operation prediction model.

[0064] The method for predicting an industrial production process according to the embodiment of the present invention uses a machine learning method to establish a simplified model according to the relationship between reactants and products in industrial production; uses a data mining method to mine and analyze historical production...

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Abstract

The invention provides a method for predicting industrial production processes, which can predict production operations and improve the standardized operation level of industrial production. The method includes: obtaining historical production data; establishing a simplified model by using machine learning methods according to the relationship between reactants and products in industrial production; mining and analyzing the historical production data by using data mining methods, and according to the analysis results , solving the coefficients of the simplified model to obtain an operation prediction model; predicting the production operation through the operation prediction model. The invention is applicable to the field of industrial production process control.

Description

technical field [0001] The invention relates to the field of industrial production process control, in particular to an industrial production process prediction method. Background technique [0002] The difficulty in industrial production process control is that large-scale industrial production processes are complex, and there are many factors that affect the production process. Moreover, most industrial production sites have low levels of electronics. In the prior art, the operation process only relies on manual labor, and the process control all relies on manual experience judgment. , the degree of standardization is low. Although large-scale industrial production has certain standardized production operations, these processes are only used as indicators to measure operations, and they are too rigid in production operation guidance. [0003] Take the production flow of copper, steel, and mining as an example. Such production processes often involve dozens and hundreds of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B19/418
CPCY02P90/02
Inventor 石志国宋兴剑任晖
Owner UNIV OF SCI & TECH BEIJING
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