江西科学2024,Vol.42Issue(1) :66-71.DOI:10.13990/j.issn1001-3679.2024.01.011

铁路隧道构件级数字孪生模型渐进式高效更新方法

A Progressive and Efficient Updating Method of Railway Tunnel Component-level Digital Twin Model

黄贤喆 朱庆 郭永欣 陈俊桦 丁雨淋
江西科学2024,Vol.42Issue(1) :66-71.DOI:10.13990/j.issn1001-3679.2024.01.011

铁路隧道构件级数字孪生模型渐进式高效更新方法

A Progressive and Efficient Updating Method of Railway Tunnel Component-level Digital Twin Model

黄贤喆 1朱庆 1郭永欣 1陈俊桦 1丁雨淋1
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作者信息

  • 1. 西南交通大学地球科学与环境工程学院,611756,成都
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摘要

铁路隧道构件级数字孪生模型高效动态更新是铁路工程精细化管控的关键基础,现有替代式更新方法重复冗余处理大量已有数据、更新代价高且时间效率低,为此,提出一种铁路隧道构件级数字孪生模型渐进式更新方法,设计了可支撑模型高效更新的铁路隧道多细节层次三维瓦片结构,提出了适用于级联更新的多细节层次渐进式更新算法,利用典型大型铁路隧道数字孪生模型数据进行实验验证.结果表明,本文方法有效提升了铁路隧道构件级数字孪生模型在GIS环境中更新过程的时间效率,可有效支撑模型高效动态更新与高性能可视化分析应用.

Abstract

Efficient and dynamic updating of the component-level digital twin model of railway tun-nels is a key foundation for refined control of railway engineering.However,existing replacement-based update methods involve redundant processing of a large amount of existing data,resulting in high update costs and low efficiency.To address these issues,this paper proposes a progressive up-date method for the component-level digital twin model of railway tunnels,and designs a three-di-mensional tile structure for railway tunnels with multiple levels of detail that can support efficient model updating.A multi-level progressive update algorithm suitable for cascading updates is also proposed and experimental verification is conducted using typical large-scale digital twin model data of railway tunnels.The results show that the proposed method effectively improves the time efficiency of updating the component-level digital twin model of railway tunnels in the GIS environment and can effectively support efficient dynamic updating of the model and high-performance visualization and analysis applications.

关键词

数字孪生铁路/隧道/渐进式更新/三维瓦片/多细节层次

Key words

digital twin railway/tunnel/progressive update/3D tiles/LOD(Level of Detail)

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基金项目

国家自然科学基金资助项目(41941019)

出版年

2024
江西科学
江西省科学院

江西科学

影响因子:0.286
ISSN:1001-3679
参考文献量6
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