## 4 Results
Additionally, the camera is equipped with a wireless transmission module, enabling real-time uploading of classroom data to the cloud server for further system analysis and processing. The data pre-processing process comprises the following steps. The first is noise reduction using a median filtering algorithm to eliminate potential noise artifacts during data collection. Next, the video is segmented into 2 s time windows with a 0.5 s overlap. Data normalization is then applied to video frames by adjusting pixel values to the range [0, 1] to meet the input requirements of the CogVLM2-Video model. Static features are extracted from the pre-processed data using RGB images, while dynamic features are derived from optical flow images. The Farneback method is used to generate optical flow images by calculating motion vectors between consecutive frames. Figure 4: System for evaluating teaching behaviors in PE classrooms. 1.3 Platform Construction: Front-End and Back-End Technologies with MySQL Database Used to Create an Efficient, Compatible, and Stable System Environment  
The platform developed for the evaluation system targeting teaching behaviors in PE classrooms was designed to ensure efficiency, compatibility, and stability by integrating advanced front-end and back-end technologies with a robust MySQL 8.0.32 database. At the front end, Vue, HTML, CSS, JavaScript, and Ajax technologies are used to deliver an intuitive, user-friendly interface. Vue serves as the primary framework, enabling component-based development to enhance code modularity, reusability, and readability. HTML and CSS are used to establish a clear structural layout and an esthetically pleasing interface design, ensuring cross-platform responsiveness on desktop, tablet, and mobile devices. JavaScript and Ajax boost the system with asynchronous data interaction capabilities, enabling real-time updates, which significantly enhance user experience by ensuring smooth and responsive system operations. The back-end framework uses the SSM architecture (Spring 2.6.13, SpringMVC 2.6.13, and MyBatis 3.5.16), thus facilitating lightweight and modular development of web applications. Core business logic is managed via the Spring framework, which simplifies the configuration and integration of components through dependency injection. SpringMVC handles user requests, facilitating efficient front-end/back-end interaction. For instance, when users upload classroom videos via the Vue interface, HTTP POST requests are sent from the front end to the back-end server. The server processes these videos, stores them in a cloud database, analyzes their content using the CogVLM2-Video model, and returns the results in JSON format. These results are then visualized at the front-end interface, enabling users to interact with and interpret the data easily. The persistence layer framework, MyBatis, bridges the application logic and the MySQL database, translating SQL queries into objects and simplifying data management operations. The MySQL database serves as the backbone of the system's data management infrastructure. As an open-source relational database management system, MySQL is renowned for its high performance, stability, and scalability. The system leverages MySQL's support for SQL-based data operations, enabling flexible and customizable configurations to manage and analyze data related to PE teaching behaviors, thus facilitating the evaluation and improvement of teaching practices. This allows for tailored analysis and reporting to meet the diverse needs of physical education evaluation. Additionally, MySQL offers advanced transaction management and concurrency control mechanisms, ensuring data integrity and consistency, even under high-concurrency scenarios. Its cross-platform compatibility allows deployment across multiple operating systems, including Windows, Linux, and macOS, further enhancing its adaptability and portability.