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作 者:马金锋[1] 陈求稳[2] 徐强[1] 饶凯锋[3] 安伟[1] 马梅[1] MA Jin-feng;CHEN Qiu-wen;XU Qiang;RAO Kai-feng;AN Wei;MA Mei(Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085 , China;Nanjing Hydraulic Research Institute, Nanjing 210029 , China;State Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085 , China)
机构地区:[1]中国科学院生态环境研究中心中国科学院饮用水科学与技术重点实验室,北京100085 [2]南京水利科学研究院,江苏南京210029 [3]中国科学院生态环境研究中心环境水质学国家重点实验室,北京100085
出 处:《中国给水排水》2019年第10期36-41,共6页China Water & Wastewater
摘 要:管网独立计量分区(DMA)管理是目前供水管网漏损控制的主要措施之一,日益受到自来水公司的重视。随着传感器成本的降低,数据采集越来越密集,产生的海量数据对传统数据处理和分析模式提出挑战,导致供水企业对传感器投入的效益比偏低。大数据技术的发展为DMA有效管理提供技术支撑,基于大数据技术指导DMA漏损控制决策成为必然趋势。阐述了大数据技术在DMA管理中的应用价值,从数据采集、汇聚、传输、存储、计算及应用角度,论述了适用于DMA数据处理和决策分析的大数据处理模式,并实例验证了模式的可行性。该模式具有通用性,能够实现整套模式的移植使用,可为大中型供水企业的DMA数据管理及应用提供技术支持。District Metered Area ( DMA) management is one of the main measures for leakage control of water supply network , which has been paid rising attention by water supply enteqDrises. As the cost of sensors decreases, data collection becomes more and more intensive. However, the huge amount of data generated poses a challenge to traditional data storage and processing paradigm, which has resulted in low profitability of water supply enterprises against sensor input. The development of big data technology may provide a promising support for the efficient management of DMA, and the decisionmaking of DMA leakage control based on big data technology has become an inevitable trend. This paper explored the application value of big data technology in DMA management, discussed the big data processing mode suitable for DMA data processing and decision analysis from a perspective of data collection, transmission, storage, calculation and application, and gave an example to verify the feasibility of the mode. The model had universal property and could be used in any waterworks. And, it could provide technical support for the management and application of DMA data in large- and mediumsized water supply enterprises.
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