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Integrated representation of geospatial data,model,and knowledge for digital twin railway 被引量:2
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作者 Hankan Li Qing Zhu +7 位作者 Liguo Zhang Yulin Ding Yongxin Guo Haoyu Wu Qiang Wang Runfang Zhou Mingwei Liu Yan Zhou 《International Journal of Digital Earth》 SCIE EI 2022年第1期1657-1675,共19页
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... 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. 展开更多
关键词 digital twin railway data-model-knowledge spatiotemporal relationships
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