广州供水管网既有二级分区的优化重构方法  

Optimized Reconstruction Method for Existing Secondary Partitions in Guangzhou's Water Distribution Network

在线阅读下载全文

作  者:龙志宏 蔡爽爽 邵煜[2] 姚华奇 Long Zhihong;Cai Shuangshuang;Shao Yu;Yao Huaqi(Guangzhou Water Supply Company,Guangzhou 510600,China;College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,China)

机构地区:[1]广州市自来水有限公司,广州510600 [2]浙江大学建筑工程学院,杭州310058

出  处:《科技通报》2025年第3期51-57,共7页Bulletin of Science and Technology

基  金:国家自然科学基金项目(52270095)。

摘  要:供水管网的分区管理是实现压力调控和主动漏损控制的重要技术手段,已在众多大城市得以实践。然而,传统分区方案大多依赖人工经验,缺乏科学依据。随着优化分区算法的发展,供水管网智能化分区具有显著优势,但这些算法通常用于未分区管网的初始划分,而如何对既有分区进行优化尚待研究。本文提出一种基于混合社区发现算法的既有管网分区优化重构方法,该方法创新性地融入虚拟管径以提高算法对既有边界的再利用率,并引入Jaccard指标量化既有分区与智能分区的相似度。结果表明:该方法能够综合考虑分区质量、边界再利用率和方案相似度;获得最优的重构方案,为既有管网的分区重构提供了技术支撑。Partition management of water distribution networks is critical for effective pressure regulation and active leakage control,widely implemented in many big cities.Traditional zoning approaches,however,largely rely on manual experience,often lacking scientific rigor.Recent advancements in optimized partition algorithms demonstrate significant potential for intelligent network partitioning,although their applications are generally based on unpartitioned networks.This study proposes an optimized reconstruction method for existing network partitions based on a hybrid community detection algorithm,which integrates virtual diameters to enhance the reutilization of existing boundaries.Besides,the Jaccard index is proposed to quantify the similarity between existing partitions and intelligent partitions.The results demonstrate the effectiveness of the method in obtaining an optimal reconstruction scheme that comprehensively considers partition quality,boundary reutilization,and scheme similarity,providing a more scientific guidance for the reconstruction of existing network partitions.

关 键 词:供水管网 独立计量分区 社区发现算法 分区优化重构 

分 类 号:TU991[建筑科学—市政工程]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象