Distributed Management Method for Geographic and Geological Knowledge Base for Railway Digital Twin

ZHU Qing; ZHAO Yuanzhen; GUO Yongxin; DING Yulin; WANG Qiang; PAN Yan; CHEN Junhua; ZHANG Liguo

doi:10.3969/j.issn.0258-2724.20230389

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Journal of Southwest Jiaotong University > 2025 >

ZHU Qing, ZHAO Yuanzhen, GUO Yongxin, DING Yulin, WANG Qiang, PAN Yan, CHEN Junhua, ZHANG Liguo. Distributed Management Method for Geographic and Geological Knowledge Base for Railway Digital Twin[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230389

Citation:

ZHU Qing, ZHAO Yuanzhen, GUO Yongxin, DING Yulin, WANG Qiang, PAN Yan, CHEN Junhua, ZHANG Liguo. Distributed Management Method for Geographic and Geological Knowledge Base for Railway Digital Twin[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230389

Citation:

ZHU Qing, ZHAO Yuanzhen, GUO Yongxin, DING Yulin, WANG Qiang, PAN Yan, CHEN Junhua, ZHANG Liguo. Distributed Management Method for Geographic and Geological Knowledge Base for Railway Digital Twin[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230389

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Distributed Management Method for Geographic and Geological Knowledge Base for Railway Digital Twin

doi: 10.3969/j.issn.0258-2724.20230389

ZHU Qing^1
,,
ZHAO Yuanzhen^{1, 2},
GUO Yongxin¹,
DING Yulin^{1
,
,},
WANG Qiang^{1, 3},
PAN Yan¹,
CHEN Junhua¹,
ZHANG Liguo¹

1.
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2.
Third Surveying and Mapping Engineering Institute of Sichuan Province, Chengdu 730070, China
3.
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China

Received Date: 24 Aug 2023
Rev Recd Date: 15 Nov 2023

Available Online: 21 Feb 2025

Abstract

Abstract

Efficient geographic and geological knowledge services for railway engineering form a crucial foundation for supporting the multi-scale and multi-disciplinary intelligent applications of digital twin technology in railways. To improve the completeness of query results and enhance the capability for integrated analysis across the multi-scale “region–engineering project–construction site” applications of railway digital twins, a meta-network model for the dynamic distribution of geographic and geological knowledge was proposed. This model was designed as a distributed knowledge base network system, with railway agents, business departments, knowledge relationships, and multi-scale application scenarios as key nodes. A meta-network disruption algorithm for distribution optimization was implemented, with node importance assessed using degree centrality indicators. By analyzing the distribution influence through network perturbation, the influence range of nodes was calculated, obtaining the optimized distribution structure of the knowledge base. To validate this approach, it was applied to optimize the distribution of the knowledge base within the knowledge base management and application scenario for railway digital twins of a major railway bridge project. Experimental results show that, when processing knowledge retrieval tasks at the engineering and regional scales, the distribution optimization method increases the number of query results with reduced retrieval time and enhanced result matching accuracy.
- railway digital twin,
- geographic and geological knowledge base,
- distributed management,
- meta-network analysis

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References

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