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Research and Application of Active Quality Improvement Model Based on Data Decision



In view of the current situation and demand of enterprise quality management activities, this paper combines quality management and data mining technology, and makes full use of the large amount of quality data obtained by the enterprise to establish data model, and digs out the data that is conducive to improving product quality and reducing the rate of product disqualification. The model realizes regular and automatic analysis of existing quality data, and identifies opportunities for improvement, and automatically pushes the responsible department to improve. Finally, the improvement effect is verified and evaluated. The improved model is introduced into the production, and the improvement opportunities are found, and injected into the production process to provide data decision support for quality improvement. The quantity improvement model is based on machine learning algorithm to identify abnormal and send out early warning information. According to a large number of existing historical quality data of EMU, business objects are determined and business mining objectives are defined. All the data related to business objects are found, and selected the original data suitable for data mining applications. In this paper, a high generalization intelligent early warning quality improvement model is established by constructing quality improvement index features to complete automatic identification of quality problems and provide intelligent early warning and data decision support for quality improvement. Through the actual production data to verify the established early warning quality improvement model, the results are in good agreement with the experimental situation.


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