作为现代都市的生命线，供水管网在人们的日常生活和城市的发展中占据不可替代的地位，然而爆管问题一直困扰着整个供水行业。针对供水管网爆管事故，该文以流量监测数据为基础，通过分析计量分区（district metering area，DMA）各个出入口流量计的数据关联性，使用聚类算法检测各种事件引起的异常流量数据，然后依据其出入口流量的变化特征识别管线爆管事故。结果表明：该方法应用在多出入口DMA时，识别效果会受到DMA出入口的数量与位置的影响，在出入口数量较少且位置适宜的前提下，能够准确识别爆管事故，并具有较低误报率。
Pipe bursts are a universal problem in water supply systems, which can severely disrupt daily life and urban development. This study correlates flow measurements collected from inlets and outlets of a district metering area (DMA) to quickly identify bursts. The analysis uses a clustering algorithm to detect abnormal flow data i.e., outliers. Then, some outliers are identified as bursts according to the inlet and outlet flow fluctuation characteristics. The results indicate that the number and locations of the inlets and outlets affect the detection performance. The system can accurately detect bursts and insure a low false positive rate when there are a relatively small number of inlets and outlets and the locations are well placed.
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