标题： 基于LLMOps的社会健康智能锻炼与反馈系统 显示英文标题

标题： Intelligent Exercise and Feedback System for Social Healthcare using LLMOps

摘要： 这项研究通过引入基于LLMOps的系统，实现了自动化的运动分析和个性化推荐，以满足医疗保健平台和社会社区中对个性化反馈日益增长的需求。 当前的医疗保健平台在很大程度上依赖于人工分析和通用的健康建议，这限制了用户的参与度和健康促进的效果。 我们开发了一个系统，利用大型语言模型（LLM）来自动分析“Ounwan”运动记录社区的用户活动数据。 该系统将LLMOps与LLM API、容器化基础设施和CI/CD实践相结合，以高效处理大规模用户活动数据，识别模式并生成个性化推荐。 该架构确保了大规模医疗保健社区的可扩展性、可靠性和安全性。 评估结果展示了系统在三个关键指标上的有效性：运动分类、持续时间预测和卡路里消耗估计。 这种方法提高了社区管理的效率，同时为用户提供更准确和个性化的反馈，解决了传统人工分析方法的局限性。 显示英文摘要

摘要： This study addresses the growing demand for personalized feedback in healthcare platforms and social communities by introducing an LLMOps-based system for automated exercise analysis and personalized recommendations. Current healthcare platforms rely heavily on manual analysis and generic health advice, limiting user engagement and health promotion effectiveness. We developed a system that leverages Large Language Models (LLM) to automatically analyze user activity data from the "Ounwan" exercise recording community. The system integrates LLMOps with LLM APIs, containerized infrastructure, and CI/CD practices to efficiently process large-scale user activity data, identify patterns, and generate personalized recommendations. The architecture ensures scalability, reliability, and security for large-scale healthcare communities. Evaluation results demonstrate the system's effectiveness in three key metrics: exercise classification, duration prediction, and caloric expenditure estimation. This approach improves the efficiency of community management while providing more accurate and personalized feedback to users, addressing the limitations of traditional manual analysis methods.