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基于数字孪生的路桥一体化数字底座建设研究

Research on the Construction of an Integrated Digital Pedestal for Road and Bridge Based on Digital Twins
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摘要 针对当前路桥数字孪生应用中多源数据融合效率低、场景交互性能不足及编码体系碎片化等问题,提出基于数字孪生的路桥一体化数字底座建设框架。通过全要素三维场景服务、物理真实实时渲染引擎、云端协同渲染与自适应压缩技术,以及动态编码映射与分级评价技术,攻克多源模型融合效率低、轻量化渲染性能弱和跨平台协同能力差等核心难题。研究构建了覆盖静态建模、动态仿真与多源数据可视化的标准化技术体系,实现了高精度时空数据融合与实时交互,验证了其在桥梁抗风响应模拟与承重极限分析中的预警精度与决策支持能力。研究成果为交通基础设施从经验驱动向数据驱动的智能化转型提供了可复用的技术范式,助力交通强国战略目标的实现。 To address the problems of low multi-source data fusion efficiency,insufficient scene interaction performance,and fragmented coding systems in current road and bridge Digital Twins applications,a framework is developed for constructing an integrated digital pedestal based on Digital Twins.The technologies including full-element 3D scene services,physically-based real-time rendering engines,cloud collaborative rendering with adaptive compression,and dynamic coding mapping with hierarchical evaluation are developed to resolve core problems such as inefficient model fusion,weak lightweight rendering performance,and poor cross-platform collaboration.The study establishes a standardized technical system covering static modeling,dynamic simulation,and multi-source data visualization,achieving high-precision spatiotemporal data fusion and real-time interaction and validating the early warning precision and decision-making support capability in wind resistance simulation and load-bearing limit analysis.The research results provide a reusable technical paradigm for intelligent transformation of transportation infrastructure from experience-driven to data-driven,contributing to the realization of Chinese transportation power strategy.
作者 罗晶 肖德广 周阳 LUO Jing;XIAO Deguang;ZHOU Yang(Guizhou Expressway Group Co.,Ltd.,Guiyang 550025,China;Guizhou Zhongnan Transport Technology Co.,Ltd.,Guiyang 550214,China)
出处 《现代信息科技》 2025年第17期143-146,151,共5页 Modern Information Technology
基金 贵州省交通运输厅2023年度科技项目(2023-122-016)。
关键词 数字孪生 路桥一体化 数字底座 Digital Twins road and bridge integration digital pedestal
作者简介 罗晶(1992-),男,汉族,贵州遵义人,高级工程师,硕士研究生,研究方向:智慧交通领域的深度学习图像处理。
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