Sports training is an important part of daily life, and various injuries are prone to occur during the training process. If they are not dealt promptly, they are bound to affect daily life. Although our nationals are becoming more and more aware of participating in physical exercise, they are performing numerous sports activities at the time of unexpected events, and sports injuries are becoming more and more frequent. To realize the evaluation and automatic prediction of sports training injury risk factors, a sports training injury risk evaluation algorithm using big data analysis is proposed. Establish a training injury risk analysis model, analyze the relevant parameters of training injury risk assessment through statistical and quantitative analyses, extract the entropy characteristics of training injury risk big data, optimize the decision-making and assessment process of injury risk through stable result assessment and fuzzy decision-making, and establish an expert system analysis model of sports training injury risk assessment. The hierarchical analysis method is applied to evaluate the training injury risk, and the adaptive fuzzy control is optimized to realize the optimal design of training injury risk assessment. Results show that this method has good adaptive characteristics and high certainty.

中文翻译：

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基于人工智能和大数据的运动训练损伤风险评估系统

运动训练是日常生活的重要组成部分，在训练过程中容易发生各种伤害。如果不及时处理，势必影响日常生活。尽管我国国民对参加体育锻炼的意识越来越强，但在发生突发事件时，他们正在进行众多的体育活动，运动损伤也越来越频繁。为实现运动训练损伤风险因素的评估和自动预测，提出一种基于大数据分析的运动训练损伤风险评估算法。建立训练伤风险分析模型，通过统计和定量分析分析训练伤风险评估的相关参数，提取训练伤风险大数据的熵特征，通过稳定结果评估和模糊决策优化损伤风险决策评估流程，建立运动训练损伤风险评估专家系统分析模型。采用层次分析法对训练损伤风险进行评估，优化自适应模糊控制，实现训练损伤风险评估的优化设计。结果表明，该方法具有良好的自适应特性和较高的确定性。并对自适应模糊控制进行优化，实现训练损伤风险评估的优化设计。结果表明，该方法具有良好的自适应特性和较高的确定性。并对自适应模糊控制进行优化，实现训练损伤风险评估的优化设计。结果表明，该方法具有良好的自适应特性和较高的确定性。