Details

Autor(en) / Beteiligte

• Yongxin Guo
• Qing Zhu
• Yulin Ding
• Yun Li
• Haoyu Wu
• Yang He
• Zhihong Li
• Hankan Li
• Liguo Zhang
• Yuanzhen Zhao
• Yan Pan
• Ting Yang
• Mingwei Liu
• Haowei Zeng

Titel

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

Ist Teil von

• International journal of digital earth, 2024-12, Vol.17 (1)

Ort / Verlag

Taylor & Francis Group

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• ABSTRACTDigital 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 ([Formula: see text]) that explicitly associates temporal, spatial, and interactive relationships. Subsequently, an association management architecture was proposed, accompanied by a global association graph index ([Formula: see text]) 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.