Integrated representation of geospatial data, model, and knowledge for digital twin railway

Autor: Hankan Li, Qing Zhu, Liguo Zhang, Yulin Ding, Yongxin Guo, Haoyu Wu, Qiang Wang, Runfang Zhou, Mingwei Liu, Yan Zhou
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: International Journal of Digital Earth, Vol 15, Iss 1, Pp 1657-1675 (2022)
Druh dokumentu: article
ISSN: 1753-8947
1753-8955
17538947
DOI: 10.1080/17538947.2022.2127949
Popis: The real-time accurate description of all spatial features of railway and their spatiotemporal relationships is a crucial factor in realizing comprehensive management and related decision-making within the entire life cycle of railways. Nevertheless, available spatiotemporal data models mainly use static historical sequence data, which are insufficient to support multi-source heterogeneous real-time sensed data; they lack a systematic depiction of the interactive relationships among multiple feature entities, and are limited to low-level descriptive analysis. Therefore, this study proposes a data-model-knowledge integrated representation data model for a digital twin railway, which explicitly describes the spatiotemporal, and interaction relationships among railway features through a conceptual knowledge graph. This study first analyzes the characteristics of railway features from above ground to underground, and then constructs a conceptual model to clearly describe the complex relationships among railway features. Secondly, a logical model is developed to illustrate the basic data structure. Thirdly, an ontology model is constructed as a basic framework for further deepening the domain knowledge graph. Finally, considering the prevention of landslides as an example, it demonstrates the abundant spatiotemporal relationships among railway related features. The results of this study bring more clear understanding of the complex interactive relationships of railway entities.
Databáze: Directory of Open Access Journals
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