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ONLINE MODEL PROMOTES DIGITAL TRANSFORMATION OF WATER SUPPLY ENTERPRISES: EXPLORATION AND PRACTICE
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摘要: 在"十四五"期间大力推动数字经济和数字技术改革创新的背景下，针对智慧水务发展中供水业务的数字化转型问题，探究了在线模型对于识别供水业务数字化转型中隐藏的问题、提高业务管理精度、提升数字信息完善程度等方面的作用。结合管网中面临的拓扑信息错误、管道堵塞、管道泄漏、设备异常、阀门情况不明等实际工程问题，具体阐述了在线模型在识别设施设备、业务系统、管理体制等方面堵点、断点的能力，讨论了在线模型对供水企业数字化转型的推动作用。Abstract: In the context of vigorously promoting the reform and innovation of the digital economy and digital technology during the "14th Five-Year Plan" period, the role of online modeling in identifying the hidden problems in the digital transformation of water supply business, improving the accuracy of business management, and enhancing the degree of perfection of digital information is explored with respect to the digital transformation of water supply business in the development of smart water services. Combined with actual engineering problems faced in water supply networks such as topological errors, pipe blockage, leakage, equipment abnormalities, and unknown valve conditions, we illustrated the role of the online model in identifying blockages and interruptions in facilities, equipment, business systems, management systems, and so on, and discussed the role of online models in promoting the digital transformation of water supply enterprises.
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Key words:
- smart water /
- digital transformation /
- hydraulic model /
- online model
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[1] 王雪峰.我国供水企业数字化转型实现路径的探索与思考[J].净水技术, 2022, 41(9):1-7. [2] 张金松, 李旭, 张炜博, 等.智慧水务视角下水务数字化转型的挑战与实践[J].给水排水, 2021, 57(6):1-8. [3] 王爱杰, 许冬件, 钱志敏, 等.我国智慧水务发展现状及趋势[J].环境工程, 2023, 41(9):46-53. [4] 何思源, 何志军.关于中小城市供水企业数字化转型的思考[J].水利技术监督, 2022(9):39-40, 84. [5] 侯煜堃.水务人工智能技术基础与应用趋势:数据、模型与优化[M].上海:同济大学出版社, 2021. [6] PEDERSEN A N, BORUP M, BRINK-KJR A, et al.Living and prototyping digital twins for urban water systems:towards multi-purpose value creation using models and sensors[J].Water, 2021, 13(5):592. [7] 熊少康.智慧水务视角下水务数字化转型面临的挑战及发展趋势[J].工程技术研究, 2021, 6(19):203-204. [8] ZHOU X, GUO S, XIN K, et al.Maintaining the long-term accuracy of water distribution models with data assimilation methods:a comparative study[J].Water Research, 2022, 226:119268. [9] ZHOU X, TANG Z, XU W, et al.Deep learning identifies accurate burst locations in water distribution networks[J].Water Research, 2019, 166:115058. [10] GAO J, QI S, NAN J, et al.Leakage control of multi-source water distribution system by network partition and optimal pump schedule in China[J].Journal of Water Supply:Research and Technology-Aqua, 2016, 66(1):62-74. -
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