针对大型体育赛事新冠疫情监测缺乏实时信息采集技术、疫情监测策略评估和场馆疫情预警预测手段，本项目拟利用大数据、人工智能等技术，自主扫描新冠疫情发生、发展过程中多个关键节点数据，为赛事及时发现风险或已出现的疫情“苗头”提供工具。构建国际新冠疫情本底数据池，建立集成智能数据采集和数据存储功能的信息系统；基于自然语言处理技术，对网络上获取的非结构化文本数据进行主题建模、语义分析、知识提取和规则挖掘，并融合气象等自然环境数据，量化并结构化多维多模态变量，采用深度学习算法对赛事相关人群进行风险分级，将赛事疫情防控策略和赛事相关人群疫情风险表征到策略评估模型，优选监测策略。建立大型赛事场馆疫情响应系统动力学仿真推演模型，模拟场馆疫情形态下的管控模式，对人员观赛、隔离、疏散进行初步仿真分析，发现主要因素间的内在关联，提出应对策略，为赛事运行指挥决策提供支持。

For infectious disease surveillance of large sports events is lack of real-time information monitoring technology and event management strategy evaluation method, this project intends to apply big data and artificial intelligence in scanning key information of the outbreak of development of COVID-19 epidemic. The project will develop an intelligent information system for data acquisition and storage, and timely perceiving risk or "symptom" of COVID-19 outbreak. Building theme model of unstructured network data, analyzing semantics, extracting knowledge and mining rules based on natural language processing technology. Quantitative and structure multidimensional variables based on yuan and manifold learning algorithm. Using dynamic model of infectious diseases and deep learning technology to evaluate risk of COVID-19 outbreak. Represent control and prevention strategy and related populations with the risk characterization to the early warning and forecasting model, build large event venues outbreak response system dynamics simulation model, simulate the outbreak of COVID-19 under different control measures, the personnel attending, isolation, evacuate a preliminary simulation analysis, found the inner relation between the main factors, put forward the coping strategies and provide support for decisions.