Abstract:

In the digital transformation of power equipment manufacturing enterprises， it is necessary to mine and analyze the high-dimensional data of multi-value chain collaboration in the data space. The preprocessing of purchase， sales and inventory big data in power equipment manufacturing industry is studied. Firstly， the preprocessing method of data outlier checking and correction based on the combination of change point method and Local Outlier Factor method （LOF method） is given. Secondly， the data dimensionality reduction processing method of Stack Sparse Auto Encoder （SSAE） based on LASSO （Least Absolute Shrinkage and Selection Operator） deep learning algorithm is proposed （SSAE-LASSO）， which can compress and reduce the features， remove the noise information that seriously interferes with the data regression analysis， and filter the fault-tolerant redundant data with low influence， so as to realize the denoising and dimensionality reduction processing of the data. Finally， the method proposed in this paper is applied to different algorithms to test. By comparing the two preprocessed data， it is found that the method proposed in this paper can effectively improve the accuracy of the intelligent prediction of the sales volume of electric power products.

Key words: power equipment enterprises, multi value chain collaboration, data mining preprocessing method, deep learning