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In order to research the methods used in the design of electronic products, such as defect severity assessment, probability calculation of defect distribution and inspection of influencing factors of defects, the inspection of the design defects is connected with the defect tracking system, and then the defect tracking system is designed. The defect tracking record of the completed project is extracted from the defect tracking system based on big data. As a sample data, Bayesian network inference model is constructed, and then the structure and parameters of the network inference model are adjusted according to the needs of the current similar projects so as to improve the reasoning accuracy. In addition, the reasoning model is used to evaluate the severity of design defect and to calculate the defect distribution probability. The results show that the Bayesian network model can calculate the occurrence probability of each defect type. Based on the centralized research and development (R&D) power, the defects of these processes and modules can be detected and eliminated, and the design defects can be prevented and controlled in time. Through the probability calculation for project defect severity, it can effectively improve the evaluation of project defect status, and provide a factual basis for correctly evaluating the quality of project development and modifying the product development process.

RETRACTED ARTICLE: Inspection of design defects of big multimedia data electronic products based on Bayesian network inference model