In the grand scene of the smart factory, the integration of computer vision and digital media technology, this project to create an advanced print defect comprehensive detection system. The system takes advanced computer vision technology as the core, combined with efficient data analysis methods, and is committed to improving the production efficiency and product quality of the printing industry. The system uses a powerful YOLOv10 target detection model to achieve high-precision defect recognition. It integrates a variety of detection methods such as image detection, real-time camera monitoring and video stream analysis, providing an efficient and reliable quality control tool for the printing industry. At the technical architecture level, the back-end is carefully constructed using Python and Django frameworks, and the front-end uses Vue.js to create a user-friendly interface and successfully implement large-screen display functions, making real-time monitoring and data analysis more intuitive and vivid. At the same time, the system uses pyecharts for data visualization to help users quickly gain insight into defect distribution and trends. All detection records are safely stored in the MySql database, which supports fast query and in-depth data analysis. The main functions of the system include image upload detection, real-time camera monitoring, video stream processing, detection record management and data analysis. Among them, the data analysis function comprehensively covers defect types and severity distribution, overall detection statistics and related factor analysis, helping users to accurately identify common problems and provide scientific basis for priority treatment. In addition, the system actively integrates the energy efficiency management system, and improves the efficiency and intelligent level of energy management by providing a convenient interface and integrating the energy consumption data of industrial equipment in the workshop.