Efficient distributed association management method of data, model, and knowledge for digital twin railway

ABSTRACT

Digital twin railway is a pivotal foundation for the intelligent construction and maintenance of railway engineering projects within extensive open spaces. Its essence is the integrated representation and association management of multi-granularity spatiotemporal data, executable analysis models, and professional knowledge. These elements are characterized by the prominent characteristics of multi-source, heterogeneity, and massive volume. However, current decentralized and independent management strategies often neglect the dynamic coupling relationships between them, and numerous multi-path joins and conversion aggregation operations exist across various spatial scale applications. Consequently, this results in challenges such as the inability to dynamically couple data-model-knowledge and conduct global association retrieval, thereby limiting the potential for real-time analysis and intelligent application capabilities. To address these problems, we first constructed a tripartite graph model ($\mathrm{DMK}-\mathrm{Graph}\mathrm{model}$) that explicitly associates temporal, spatial, and interactive relationships. Subsequently, an association management architecture was proposed, accompanied by a global association graph index ($\mathrm{DMK}-\mathrm{Graph}\mathrm{index}$) and a global-local indexing mechanism. Finally, a prototype system for railway data-model-knowledge association management was developed. The effectiveness of the distributed association management method was demonstrated by employing a case study of high-temperature safety risk analysis in railway tunnel engineering with multi-physics field coupling.

KEYWORDS:

• Digital twin railway
• data-model-knowledge
• association management
• graph index
• tripartite graph

Acknowledgments

The authors wish to express their profound gratitude to both the editors and the anonymous reviewers.

Data and codes availability statement

Supplemental online video, encapsulating the prototype system and the intermediate operation process of association retrieval, is accessible via the following link: https://youtu.be/Pv2CykRNGF0. The data and codes that support the findings of this study are available in figshare at the private link: https://figshare.com/s/49c77bf58a0e5a1dc242.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors would like to thank the support of the National Key Research and Development Program of China under Grant Number 2022YFB3904100.