This paper designs and implements a quality inspection information management system based on the B/S architecture to address issues in traditional quality inspection methods, such as susceptibility to human error and the inconvenience of managing paper documents. The system adopts a three-tier architecture: the presentation layer uses HTML5, CSS3, and JavaScript; the business logic layer is developed with Spring Boot; and the data access layer employs MySQL and MyBatis. Key functionalities include inspection task management, nonconforming product management, equipment management, document management, and system administration. The system’s reliability and effectiveness were validated through comprehensive testing. Results indicate significant improvements in inspection efficiency and accuracy, enhanced data analysis, and better support for quality management decisions. Future enhancements will include machine learning for data analysis, a mobile application, and exploring blockchain for quality traceability.

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B/S Architecture-Based Quality Inspection Information Management System

  • Nan Hu,
  • Hening Niu

摘要

This paper designs and implements a quality inspection information management system based on the B/S architecture to address issues in traditional quality inspection methods, such as susceptibility to human error and the inconvenience of managing paper documents. The system adopts a three-tier architecture: the presentation layer uses HTML5, CSS3, and JavaScript; the business logic layer is developed with Spring Boot; and the data access layer employs MySQL and MyBatis. Key functionalities include inspection task management, nonconforming product management, equipment management, document management, and system administration. The system’s reliability and effectiveness were validated through comprehensive testing. Results indicate significant improvements in inspection efficiency and accuracy, enhanced data analysis, and better support for quality management decisions. Future enhancements will include machine learning for data analysis, a mobile application, and exploring blockchain for quality traceability.