Industrial product quality prediction method based on XGBoost algorithm

The XGBoost algorithm-based method addresses inefficiencies in traditional quality detection by preprocessing data and optimizing model parameters, resulting in enhanced prediction accuracy and efficiency, supporting high-quality and efficient industrial production.

CN119648023APending Publication Date: 2025-03-18JINHUA INSTITUTE OF ZHEJIANG UNIVERSITY
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Patent Information

Application Number
CN202411574025.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-06
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

Traditional industrial product quality detection methods are inefficient, unable to handle complex variations and non-linear relationships, underutilize data, and are subjective, leading to low efficiency and high costs, which fail to meet modern industrial demands for high-quality and efficient production.

Method used

A method using the XGBoost algorithm, incorporating sparse matrices and optimized model parameters, for predicting industrial product quality by preprocessing data, extracting relevant features, and training with distributed gradient boosting to enhance prediction accuracy and efficiency.

Benefits of technology

The method significantly improves product quality prediction accuracy, increases production efficiency, reduces costs, and enhances decision-making, leading to improved quality management and market competitiveness.

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Abstract

The invention discloses an industrial product quality prediction method based on an XGBoost algorithm. The method comprises the following steps: collecting product quality data of a target industrial product, preprocessing and extracting useful features as a training set; inputting the training set into an XGBoost algorithm for training, optimizing model performance by introducing a sparse matrix into the algorithm, optimizing model parameters of the algorithm, and then selecting an optimal parameter combination to complete training; and preprocessing the product quality data of the industrial product to be subjected to quality prediction, inputting the product quality data into an algorithm for processing, and outputting a quality prediction result of the industrial product to realize industrial product quality prediction. The method can solve the problems of low efficiency, difficulty in processing complex variation and nonlinear relationship, insufficient data utilization, strong subjectivity and the like in the traditional quality detection method, can remarkably improve the accuracy of product quality prediction, improve the production efficiency, reduce the production cost and enhance the data utilization rate, and is suitable for popularization and application. And the production targets of high quality and high efficiency are achieved.
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