用因子图优化技术，实现供水网漏水的高效精准定位。
Factor Graph Optimization for Leak Localization in Water Distribution Networks
- 构建双因子图架构，融合压力与用水量数据
- 比非线性卡尔曼方法快数倍，定位精度更高
- 适合智能水务系统、城市管网运维人员使用
在供水管网中检测和定位泄漏是具有重大环境、经济和社会影响的重要课题。本文首次探索使用因子图优化技术进行供水管网泄漏定位，实现压力与用水量传感器数据的融合，并估计网络所有节点在时间和结构上的状态演变。提出包含特定供水网因子的新架构，由无泄漏状态估计因子图和泄漏定位因子图组成。新传感器数据到来时，因子图可同时更新当前与历史状态，不同于仅估计当前状态的卡尔曼及插值类方法。在Modena、L-TOWN和合成网络上的实验表明，因子图比非线性卡尔曼方法（如UKF）快得多，同时在定位性能上优于现有先进方法。代码与基准测试已公开于https://github.com/pirofti/FGLL。
原文摘要 · Abstract (English)
Detecting and localizing leaks in water distribution network systems is an important topic with direct environmental, economic, and social impact. Our paper is the first to explore the use of factor graph optimization techniques for leak localization in water distribution networks, enabling us to perform sensor fusion between pressure and demand sensor readings and to estimate the network's temporal and structural state evolution across all network nodes. The methodology introduces specific water network factors and proposes a new architecture composed of two factor graphs: a leak-free state estimation factor graph and a leak localization factor graph. When a new sensor reading is obtained, unlike Kalman and other interpolation-based methods, which estimate only the current network state, factor graphs update both current and past states. Results on Modena, L-TOWN and synthetic networks show that factor graphs are much faster than nonlinear Kalman-based alternatives such as the UKF, while also providing improvements in localization compared to state-of-the-art estimation-localization approaches. Implementation and benchmarks are available at https://github.com/pirofti/FGLL.
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