ANALYSIS OF THE INFLUENCE OF BIOCHEMICAL INDEXES OF ATHLETES UNDER TRAINING BASED ON THE INTERNET OF THINGS AND CLOUD COMPUTING

ABSTRACT For athletes under training, it is more efficient to use the Internet of Things (IoT) and cloud computing methods to collect and process biochemical indicators, and this study is about research based on the IoT and cloud computing technology for athletes under training. The problems are put forward in this study. The requirements of related algorithm design and the communication model properties are comprehensively analyzed. Scheduling the link and allocating the transmit power of the nodes are comprehensively considered, with design and analysis of wireless sensor network scheduling algorithm. The factors influencing the scheduling efficiency of the algorithm are analyzed, considering the node density and the influence of different power allocation schemes on the scheduling result. This study shows that the algorithm of this thesis can collect the biochemical index data of athletes during training period. As the number of nodes increases, the running results will gradually move towards the optimal value. This research study is of important theoretical significance for the application of IoT and cloud computing technology and the improvement of athlete training effect.

摘要 针对参训运动员，采用物联网（Internet of Things, IoT）与云计算（cloud computing）技术采集、处理生化指标的方式具备更高效率，本研究正是围绕物联网与云计算技术，针对参训运动员展开相关研究。本研究首先提出相关问题，全面分析了相关算法设计的需求以及通信模型的特性；综合考量链路调度与节点发射功率分配问题，完成了无线传感器网络（Wireless Sensor Network, WSN）调度算法的设计与分析。本研究分析了影响算法调度效率的各类因素，涵盖节点密度与不同功率分配方案对调度结果的作用。本研究表明，本文所提出的算法可有效采集运动员训练期间的生化指标数据；随着节点数量的增加，算法运行结果将逐步趋近最优值。本研究对于物联网与云计算技术的应用推广以及运动员训练效果的提升均具有重要的理论意义。