数字孪生铁路地理地质知识库分布式管理方法OA

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.