JIANCai,GAOJin-liang,XUYong-peng.Iterative Partition Identification Algorithm for Anomaly Isolation of Water Distribution Network[J].China Water & Wastewater,2022,38(23):31-37.
使用迭代分区识别算法的供水管网异常隔离
- Title:
- Iterative Partition Identification Algorithm for Anomaly Isolation of Water Distribution Network
- Keywords:
- water distribution network; anomaly isolation; iterative partition; anomaly identification; convolutional neural network
- 摘要:
- 为了降低城市供水管网中异常事件（如爆管、漏失、非法用水等）风险，提出了一种能够及时、有效隔离供水管网异常事件的方法，该方法包括“分区”和“识别”两个步骤。首先，结合图论技术、聚类方法和人工微调将管网分为两个子区域，然后采用卷积神经网络算法、水力模拟判定异常事件的具体发生区域。分区和识别过程迭代进行，直到满足预设停止条件，使异常事件隔离在尽可能小的区域。为了提高计算效率，在水力模拟过程中使用了子组技术，同时在训练卷积神经网络算法中评估并选用最重要监测数据向量特征。案例分析结果表明，所提出的方法能够可靠、高效地隔离管网异常，在准确性及时效性方面均具有较好的表现。
- Abstract:
- To minimize abnormal event risks in urban water distribution network (such as pipe burst, leakage and illegal water use), a method for timely and effective isolation of abnormal events was proposed, which included two steps: partition and identification. First, the pipe network was divided into two sub-regions by combination graph theory technology, clustering method and manual fine tuning. Then, the specific occurrence area of abnormal events was determined by convolutional neural network algorithm and hydraulic simulation. The partitioning and identification process iterated until preset stop conditions were met, so as to isolate the abnormal event in the smallest possible area. To improve the computational efficiency, the subgroup technique was used in the hydraulic simulation process, and the most important monitoring data vector features were evaluated and selected in the training convolutional neural network algorithm. The results of case analysis showed that the proposed method was capable of isolating network anomalies reliably and efficiently, and had a good performance in terms of accuracy and timeliness.
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