Abstract
Today's industrial production is developing in the direction of intelligence, in order to improve production efficiency and supervise production. Nowadays, traditional manufacturing industry is gradually paying attention to the role of data twin technology in industrial production. Real-time and accurate production forecasts can help factories generate rough expectations for production results and help factories troubleshoot problems in the production process. Therefore, it is of great significance to improve the prediction accuracy of production results and provide reliable information for factories. This article describes a digital twin technology that leverages machine learning to analyze industrial production processes. Use data processing, clustering, classification and regression algorithms to model the production process, and use GUI to make a visual interface for display. Specifically, preprocessing production data through correlation analysis and data cleaning brings valuable datasets for modeling. And established a regression model based on KNeighborsClassifier algorithm to predict the target variable. This enables accurate prediction of production results.
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