Application of IoT detection based on deep self coding multidimensional feature fusion in sports training

Abstract

In response to the shortcomings of existing centralized IoT detection activities, this article proposes a new IoT detection scheme based on programmable switches and deep self coding, which combines programmable switches with IoT MQTT protocol and multidimensional feature fusion algorithm. Based on this point, a comprehensive programmable switch and machine learning centralized IoT detection system are designed. This system is different from traditional centralized IoT detection systems. The IoT detection module discussed in this article is located on a programmable switch between IoT nodes and servers. This system utilizes the potential of programmable switches to quickly collect required information from packet data and pre detect fault data from the front end of the server, thereby quickly making IoT node packet processing decisions (i.e. redirecting or directly discarding) based on programmable switches to minimize the delay in transmitting data packets. Finally, in order to apply this technology to the field of sports training, we examined different directions of sports training. Therefore, based on the development of simulation systems, this article greatly enhances the practicality of such projects, effectively increasing the usage time and meeting the actual training needs. Therefore, The deep self coding computer simulation of sports training is an issue that we must pay attention to. The article conducted research on IoT detection based on deep self coding multidimensional feature fusion, and applied the research results to sports training, promoting the rapid development of sports training.