基于多源数据融合的供水DMA泄漏诊断机理与方法研究

配水管线漏损严重，制约城市供水安全。利用数据驱动模型进行供水DMA泄漏诊断是近几年出现的新兴技术。然而，受机理认识和模型结构所限，常用的预测-诊断二阶段法存在误报率高等问题。.针对该问题，本项目以数据融合提高信息利用度为学术思想，以门控循环神经网络为实现手段，通过探究管网多源数据融合机制，构建基于深度学习的分钟级水量预测算法，解决DMA超短时水量高精度预测难题；通过厘清水量预测值与实测值残差的非均匀分布特征，开发基于累积残差相似度的异常数据检测算法，解决残差非均变条件下水量异常的检测难题；通过阐明不同工况下管网流量的差异性协同响应特征，揭示水量异常诱因的甄别机理，构建基于多源数据融合的供水DMA泄漏诊断方法，解决泄漏诊断误报率高的问题。.本项目旨在通过多学科交叉，形成DMA泄漏诊断的新理论与新方法，为供水管网漏损控制提供新思路。对于促进供水安全和水资源可持续管理，具有重要意义。

Water supply pipelines face serious leakage problem, which restricts the safety of urban water supply. Leak diagnosis using data driven model in district metering area (DMA) has been an emerging technology in recent years. However, the conventional predictive-diagnosis method suffers with high false alarm rate due to limited understanding of mechanism and the structure of model. ..To solve ultra-short time water demand prediction problem for DMAs, this project takes data fusion to improve the information utilization as principle. A minute level water prediction algorithm based on deep learning technique is developed using a gated recurrent units network (GRUN) model as the realization frame, by exploring the multi-source data fusion mechanism of the pipe network. To solve the problem of abnormal flow detection under the condition of non-uniform residuals, an outlier detection algorithm based on the similarity of accumulated residuals is developed by analyzing the inhomogeneous distribution characteristics of the predicted and measured residuals. By studying the differential collaborative response of pipe flow under different conditions, the discriminating mechanism of inducement for abnormal water demand is then revealed. Finally, the leak diagnosis problem can be solved by building a DMA leak diagnosis method based on multi-source data fusion. ..The purpose of this project is to form a new theory and new method for DMA leak diagnosis through multidisciplinary intersection. It will also provide a new solution for leakage control, which is of great significance for the safety of water supply and sustainable management of water resources.