Article Gold Open Access 2024

Innovative cultivation path of great craftsmanship based on logistic regression model

Applied Mathematics and Nonlinear Sciences
Journal · Vol. 9 · Issue 1 · Art. 20230234
Abstract

The word "craftsmanship"appeared for the first time in the 2016 Chinese government work report, bringing it back to the public eye. In the Industry 4.0 era, China has accelerated the transformation from manufacturing power to manufacturing energy, and various parties in the society have higher and higher requirements for talent. The "craftsmanship"fits the content of patriotism and professionalism in the core socialist values and the new concept of contemporary China's development. Therefore, the cultivation of craftsmanship should be vigorously advocated in society, and a new path of cultivating craftsmanship should be explored. This paper analyzes the factors influencing the cultivation of craftsmanship through a logistic regression model based on a large number of vivid cases, and the research on the cultivation path around the cultivation status of "craftsmanship"can more vividly explain the essence of craftsmanship to people; it proposes to use the multiple logistic regression correlation in statistics to estimate Then, the logistic regression algorithm is applied to the product quality assurance, and the success rate of each node is calculated by analyzing the correlation between various characteristic factors and product quality to obtain the best cultivation path of craftsmanship innovation. This study shows that after one month of craftsmanship training, the product perfection rate of CM employees reached 93%, and the defect rate decreased by 13.6%, reflecting that craftsmanship has dramatically improved the product quality and laid a solid foundation for the long-term development and brand influence of the company. © 2023 Wenhui Wang, published by Sciendo.

Keywords

Author Keywords

Industry 4.0 Innovation cultivation Logistic regression Multiple logistic regression Statistics

Index Keywords

Personnel training Quality control Industry 4.0 Energy Power Paper analysis Logistic regression Quality assurance Model-based OPC Logistic Regression modeling Linear regression Chinese Government Innovation cultivation Logistics regressions Multiple logistic regression Products quality
Author Affiliations
College of Marxism, Ordos Institute of Technology, Ordos, Nei Mongol, China
Funding & Acknowledgements
No funding information
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