基于深度学习的运动员训练量监测信息整合

Integration of athlete training quantity monitoring information based on deep learning

为方便运动员训练量监测信息的挖掘和分析,对信息进行整合具有重要意义。因此提出基于深度学习的运动员训练量监测信息整合方法。将Agent部署到各个数据源节点之上,采集各个数据源中的运动员训练量信息对监测信息实施去噪和降维;以深度学习中的卷积神经网络为基础,构建信息整合模型;通过卷积层提取监测信息特征,通过输出层分类器将拥有同类特征的信息融合到同一类别中,完成运动员训练量监测信息整合。结果表明:所研究方法的平均基尼系数更大,说明该方法的整合准确度更高。

In order to facilitate the mining and analysis of athletes’training amount monitoring information,the integration of information has important practical significance.Therefore,an integration method of athlete training volume monitoring information based on deep learning is proposed.The Agent is deployed on each data source node to collect the athlete training quantity information in each data source.And the monitoring information would be denoised and reduced dimensionally.Based on the convolution neural network in deep learning,an information integration model is constructed.In this model,the monitoring information features are extracted through the convolution layer,and the information with similar features is fused into the same category through the output layer classifier to complete the integration of athlete training amount monitoring information.The results show that the average Gini coefficient of the studied method is larger,indicating that the integration accuracy of this method is higher.