基于长时间序列预测的计量区给水管网爆管识别  

Burst detection in district metering areas based on long sequence time-series forecasting

作　　者：文思齐 龙天渝[1] WEN Siqi;LONG Tianyu(College of Environment and Ecology,Chongqing University,Chongqing 400044,P.R.China)

机构地区：[1]重庆大学环境与生态学院,重庆400044

出　　处：《重庆大学学报》2023年第5期62-71,共10页Journal of Chongqing University

基　　金：国家自然科学基金(41877472)。

摘　　要：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-Z-score算法。Infomer-Z-score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-Z-score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。For the rapid detection of burst pipes in water supply networks of district metering area,a new prediction-classification-correction three-stage Infomer-Z-score algorithm was proposed.The Infomer-Z-score algorithm solves the problem of low data processing efficiency and abnormally low water consumption in traditional methods.In the prediction stage,the deep learning Informer algorithm was used to predict long sequence time-series of pressure data for the pipe network so as to improve the accuracy of water pressure prediction and the efficiency of data processing.In the classification stage,the robustness of water pressure data over time was improved by using a multi-threshold classification method.In the pipe burst simulation test,the Infomer-Z-score algorithm achieved a 99.5%(DA)detection accuracy with a 90.9%true positive rate(TPR),and a 1.7%false positive rate(FPR).Long sequence time-series pressure forecasting can be used not only for burst detection,but also for effective pressure control in the network to reduce the risk of bursts.

关 键 词：给水系统 爆管识别 深度学习 统计过程控制 压力控制 

分 类 号：TU991[建筑科学—市政工程]