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<p>Digital twin systems are increasingly challenged by rapid iteration cycles and high deployment costs, necessitating novel methodologies for agile development and maintenance. This study presents DTOps, an innovative framework that integrates DevOps principles into digital twin environments via a service-oriented architecture. By embedding continuous integration and delivery (CI/CD) pipelines tailored to the specific demands of digital twin systems, DTOps significantly enhances system scalability, adaptability, and operational consistency between virtual and physical entities. A case study on a gear production line—validated using open-source tools—demonstrates that DTOps can achieve up to a 30% improvement in evolution efficiency over traditional approaches, particularly under data-intensive conditions. The proposed framework not only streamlines digital twin development and maintenance but also offers a robust and scalable solution applicable in both academic research and industrial practice.</p>

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Streamlining digital twin development and operation with DTOps

  • 期刊论文

  • 发表日期：

    2025年4月23日

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  • Ronghua Miao,
  • Shimin Liu,
  • Yicheng Sun,
  • Ming Du,
  • Jinsong Bao

摘要

Digital twin systems are increasingly challenged by rapid iteration cycles and high deployment costs, necessitating novel methodologies for agile development and maintenance. This study presents DTOps, an innovative framework that integrates DevOps principles into digital twin environments via a service-oriented architecture. By embedding continuous integration and delivery (CI/CD) pipelines tailored to the specific demands of digital twin systems, DTOps significantly enhances system scalability, adaptability, and operational consistency between virtual and physical entities. A case study on a gear production line—validated using open-source tools—demonstrates that DTOps can achieve up to a 30% improvement in evolution efficiency over traditional approaches, particularly under data-intensive conditions. The proposed framework not only streamlines digital twin development and maintenance but also offers a robust and scalable solution applicable in both academic research and industrial practice.