In recent years, with the development of the Internet of Things technology and the popularization of smart phones, wearable devices have gradually expanded their applications. Sensor technology can effectively manage dynamic motion data systems. Wearable devices are widely used in sports, fitness and other fields, and users can use such devices to monitor target movement status data in real time. At present, although contemporary students have a strong sense of movement, they cannot fully understand their physical endurance. Therefore, they can solve this problem by using wearable devices. Under this background, this research completed the construction of the dynamic management system of college students' sports data based on wearable devices by introducing the Internet of Things technology. The system data management module and storage module are implemented by Hadoop end and web end, and can complete data interaction between different ends through set communication methods. Among them, the wearable device system can achieve data collection, use mobile terminals to complete software loading, and use cloud storage technology to achieve data storage. The data transmission process between the three parts is also different. For example, GPRS is used to complete the interaction between the mobile terminal and the data storage module, and Bluetooth can be used to transmit data between the mobile terminal and the device data collection platform. Through the design of simulation experiments, we can know that the system algorithm has good classification accuracy, and can effectively reduce the training model time. This paper completes the dynamic management of sports data for college students by combining wearable devices and Internet of Things technology.

Based on dual-core technology and the theory of nontransmission and noninterference of information flow, this paper conducts a detailed system trust environment investigation on key issues such as trust routing design and reliability analysis in the embedded structure. At the same time, we will study the method of extracting features from speech data. When dealing with abnormal audio detection, firstly, the preprocessed valid audio clips are framed and windowed and have stable short-term characteristics; after the feature is stable, the frequency feature and time domain feature of audio data are analyzed and compared. And then combined with specific applications, a detailed demand analysis is carried out, and the development plan and implementation method of the college physical education network auxiliary system are proposed. The teaching system follows a three-tier system structure. In order to expand the existing functions, this paper uses the principle of modularity, starting from the following four aspects: educational resources, online question and answer, coursework, and test-related modules. We choose object-oriented and easy-to-expandable modules, such as the development and implementation of programming language environments and database systems. By applying the abnormal audio detection technology to the embedded system of college P.E. classroom, it can effectively optimize the traditional P.E. teaching mode and promote the teaching efficiency and the new development of P.E. teaching in the information age.