Research on Macroscopic Quality Intelligent System Integration Model Based on Machine Learning

This paper explores an innovative macroscopic quality control system, which is based on the random forest algorithm and includes modules such as user authentication, data storage, data purification, statistical analysis, data analysis, data retrieval, and report generation. It is suitable for integrated quality control and monitoring in the production process of consumer goods. The system can automatically detect and real-time collect quality related data during the production process of consumer goods through machine learning models, including production parameters, material characteristics, environmental factors, etc., and store and preprocess various types of data collected; Furthermore, BP neural network algorithm and self-organizing map (SOM) algorithm are used to conduct in-depth analysis on the preprocessed data, forming a quality control analysis report for the production process of consumer goods, providing scientific basis for production process optimization.