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Research and design of machine room management system based on digital twin

Published: 15 March 2023 Publication History

Abstract

Machine room management and operation work involves a number of contents. In order to maintain the normal operation of the machine room, the machine room administrator needs to patrol the machine room equipment frequently, this will lead to low work efficiency. And the quality of the administrator 's work is difficult to guarantee, Sometimes unable to find and solve problems in time. Based on the digital twin technology, this paper studies and designs the solution of the intelligent management system of the machine room. The system provides remote visualization and intelligent management. The machine room administrator remotely view the real-time situation of each equipment and see detailed historical operation data of each equipment through the machine room management website or the equipment model based on digital twin. The system can timely alarm for equipment failure and troubleshoot the equipment by analysis of historical data. The experiment shows that this system solution can improve the operation and maintenance quality and the work efficiency of the machine room administrator.

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EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 March 2023

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Author Tags

  1. Digital twin
  2. Digital twin model
  3. Intelligent management system
  4. Machine room management
  5. Visualization

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EITCE 2022

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Overall Acceptance Rate 508 of 972 submissions, 52%

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