【摘要】 随着城市化进程的加速和供水管网的老化，管网的维护和管理变得越来越困难，供水管网漏损问题逐渐凸显出来。针对这一问题，探讨了管道系统泄漏检测与定位技术的研究进展。基于瞬态理论的方法通过分析管道内流体压力的瞬态波动来识别泄漏，适用于检测小型泄漏。数据驱动方法通过分析传感器收集的实时数据识别异常，而模型驱动方法则利用物理或数学模型模拟管道系统的正常运行状态。混合泄漏检测方法结合了这两种技术的优势，提高了检测精度。研究者们应用了多种机器学习和统计分类技术，显著提升了泄漏检测性能。此外，总结了不同技术和数据处理方法的优缺点，旨在为未来的研究提供方向，推动更智能、高效的漏损控制技术的发展。更多还原

【Abstract】 With the acceleration of urbanization and the aging of water supply networks, the maintenance and management of these networks have become increasingly challenging, and the issue of water supply network leakage has gradually become more prominent. To address this problem, this paper explores the latest advancements in pipeline system leak detection and localization technologies. Methods based on transient theory identify leaks by analyzing the transient fluctuations in fluid pressure within the pipes, making them suitable for detecting small leaks. Data-driven methods identify anomalies by analyzing real-time data collected from sensors, while model-driven methods use physical or mathematical models to simulate the normal operating conditions of the pipeline system. Hybrid leak detection methods combine the advantages of both techniques, enhancing detection accuracy. Researchers have applied various machine learning and statistical classification techniques, significantly improving leak detection performance. Furthermore, this paper summarizes the strengths and weaknesses of different technologies and data processing methods, aiming to provide direction for future research and promote the development of more intelligent and efficient leak control technologies.更多还原