中国水利

白蚁危害具有隐蔽性、持续性和突发转化风险，是影响土石坝、土质堤防等水利工程安全运行的重要隐患。针对现有白蚁防治中人工巡查依赖度高、监测覆盖不足、数据分散、预警不及时和跨层级协同能力弱等问题，设计了一套面向水利工程的白蚁监测预警系统并进行了实践。采用“云-边-端”协同架构，构建由前端智能监测装置、边缘网关、云端数据治理与智能分析平台、态势展示指挥中心和移动端应用组成的业务体系，实现白蚁活动数据的实时采集、传输、存储、分析、预警和处置反馈。以多源异构感知数据统一接入为基础，集成物联网、GIS、大数据分析、可视化展示和移动巡查功能；面向国家、省、市、县多级场景设计分层态势展示与协同处置机制；形成“监测—分析—预警—处置—评估”闭环。2024年1月，该系统在江西省多地50余座水库部署实践，设备运行率达98.26%，累计发出426次预警，现场核查处理率达到100%，效率较传统防治方式提升60%；报警准确率提升至98.5%以上，误报警率控制在1.5%以下，漏报警率降低至4%以内。研究表明，该系统能够提升白蚁隐患识别、风险预警和防治协同能力，为水利工程白蚁防治由经验型管理向数字化、网络化和智能化管理转变提供技术支撑。

Termite hazards are concealed, persistent, and capable of evolving rapidly into structural risks, making them a major hidden threat to the safe operation of earth-rock dams, earth embankments, and other water conservancy projects. To address the high dependence on manual inspection, insufficient monitoring coverage, scattered data resources, delayed risk warning, and weak cross-level coordination in existing termite control practices, this paper designed and implemented a termite monitoring and early warning system for water conservancy projects. Based on a “cloud-edge-terminal” collaborative architecture, the system established a business framework consisting of intelligent front-end monitoring devices, edge gateways, a cloud-based data governance and intelligent analysis platform, a situation display and command center, and mobile applications. It supported real-time collection, transmission, storage, analysis, early warning, and disposal feedback of termite activity data. Based on the unified access of multi-source heterogeneous sensing data, it integrated the functions of the Internet of Things, GIS, big data analysis, visualization, and mobile inspection; a hierarchical situation display and collaborative response mechanism was designed for multi-level scenarios at the national, provincial, municipal, and county levels, forming a closed loop of “monitoring, analysis, early warning, response, and evaluation”. In January 2024, the system was deployed across more than 50 reservoirs in multiple locations in Jiangxi Province. The equipment operation rate reached 98.26%, with a total of 426 warnings issued. The on-site verification and treatment rate reached 100%, and the efficiency was improved by 60% compared with traditional prevention methods. The warning accuracy increased to over 98.5%; the false warning rate was controlled within 1.5%, and the missed warning rate was reduced to within 4%. The study shows that the system enhances the capabilities of termite hazard identification, risk early warning, and collaborative prevention and control, providing technical support for the transformation of termite prevention and control in water conservancy projects from experience-based management to digital, networked, and intelligent management.