A manufacturing industry big data-oriented regression modeling method

Abstract

The invention discloses a manufacturing industry big data-oriented regression modeling method, which comprises the following steps: S1, obtaining low-dimensional features suitable for modeling through data preprocessing; s2, converting low-dimensional data of different service domains into a latent variable form; s3, establishing a regression equation among different latent variables through partial least square regression analysis, calculating to obtain the latent variables according to the maximum covariance among the latent variables, and determining the number of the latent variables by adopting a predicted residual sum of squares, so as to realize simultaneous regression analysis of multiple dependent variables on multiple independent variables; and S4, establishing a binomial regression equation between the latent variables to obtain a standard regression coefficient beta of each independent variable acting on each dependent variable, and further obtaining a single service predicted value. According to the invention, the latent structure model among the business domains is established, the influence relationship among different business domain data is mined, and different types of data of a plurality of business domains are communicated, so that the modeling effect of a single business is better, and the business is helped to improve quality and efficiency.

Description

technical field [0001] The invention relates to the technical field of big data analysis and modeling, in particular to a regression modeling method for manufacturing big data. Background technique [0002] The manufacturing industry is one of the pillar industries of the national economy, the guarantee for realizing modernization and the embodiment of comprehensive national strength. With the increasing development of economy and technology, the amount of data generated by modern manufacturing has grown exponentially, making the potential and value of big data gradually recognized and accepted by the society. The combination of big data and manufacturing will promote manufacturing design, Comprehensive reform of management, manufacturing and service models. However, this type of manufacturing data usually has the characteristics of multi-source, heterogeneous, and complex, which is also one of the main problems that manufacturing companies need to face when performing big ...

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Problems solved by technology

However, this type of manufacturing data usually has the characteristics of multi-source, heterogeneous, and complex, which is also one of the main problems that manufacturing companies need to face when performing big data modeling.

[0003] The existing big data model is only for a single business of manufacturing enterprises, without considering the correlation between businesses, ignoring the impact of design, management, service and other businesses on the manufacturing process, and not establishing the relationship between manufacturing business and other businesses relationship, so that the data between the various businesses of the manufacturing enterprise is not fully utilized, resulting in the inability to strictly control and rationally plan the entire manufacturing process

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