供水管网爆管时有发生，快速爆管识别是长期以来困扰供水企业亟待解决的问题。为了讯速识别计量分区给水管网中的爆管，提出了一种新的预测-分类-校核的三阶段Infomer-Zscore算法。Infomer-Zscore算法解决了传统方法数据处理效率低、不正常低用水量不处理的问题。在预测阶段中使用深度学习Informer算法预测管网长时间用水压力数据，提高用水压力预测的准确性和数据处理的效率。在分类阶段使用多阈值的分类方法提高了对用水压力数据随时间变化的鲁棒性。Infomer-Zscore算法在爆管模拟检验中的真阳性率（TPR）为90.9%、假阳性率（FPR）为1.7%、检测准确率（DA）为99.5%。长时间序列的压力预测不仅能用于爆管识别，而且还能有效的进行管网中的压力控制使爆管风险降低。

Pipe bursts occur in water supply networks, and the identification quick detection of bursts is a long-standing problem for water supply companies. For the rapid detection of burst pipes in metering sub-district water supply networks of district metering area, a new prediction-classification-correction three-stage Infomer-Zscore algorithm is proposed. The Infomer-Zscore algorithm solves the problem of low data processing efficiency and abnormally low water consumption in traditional methods. In the prediction stage, using the deep learning Informer algorithm to predict long sequence time-series Long Sequence Time-series of pressure data for the pipe network, improving the accuracy of water pressure prediction and the efficiency of data processing. In the classification stage, improved robustness of water pressure data over time by using a multi-threshold classification method .The Infomer-Zscore algorithm achieved a 90.9% true positive rate (TPR) ,a 1.7% false positive rate (FPR) and a 99.5% detection accuracy (DA) in the pipe burst simulation test. Long sequence Sequence timeTime-series pressure forecasting can be used not only for burst identificationdetection, but also for effective pressure control in the network to reduce the risk of bursts.

水动力介导下微塑料影响藻毒素环境行为的复合机制