用云边协同的深度学习监测污水溢流，断网也不停服
A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge
- 云端与边缘部署深度学习模型，实时预测溢流池水位
- 系统在断网时仍可运行，保障监控连续性
- 适合城市水务部门用于暴雨预警和防溢决策
许多历史城市的合流制排水系统因老化，在极端降雨事件下日益承压，易引发合流制溢流（CSO），带来显著环境与公共健康风险。准确预测溢流池的填充动态，对提前识别容量超限、及时采取预防措施至关重要。本文展示了一个基于Web的演示系统，将深度学习预测方法同时集成于云端与边缘计算环境，构建了具备交互功能的溢流监控仪表板，系统在网络中断时仍能保持运行。视频演示可通过 https://cloud.bht-berlin.de/index.php/s/b9xt4T3SdiLBiFZ 查看。
原文摘要 · Abstract (English)
Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows (CSO) with significant environmental and public health impacts. Forecasting the filling dynamics of overflow basins is critical for anticipating capacity exceedance and enabling timely preventive actions for CSO. We present a web-based demonstrator that integrates Deep Learning forecasting methods in both cloud and edge settings into an interactive monitoring dashboard for overflow monitoring, resilient to network outages. A video showcase is available online (https://cloud.bht-berlin.de/index.php/s/b9xt4T3SdiLBiFZ).
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