科技创新与应用2026，Vol.16Issue(15)：83-87,5.DOI:10.19981/j.CN23-1581/G3.2026.15.020
基于BIM显示引擎的多专业数据融合与业务赋能研究
摘要
Abstract
In response to the problems of information silos,inconsistent model formats,and low collaboration efficiency in multi-disciplinary railway engineering,this paper proposes a technical solution for multi-disciplinary data integration and business empowerment based on BIM display engines.Firstly,to address the three core demands of semantic unification,spatial alignment,and precision balance,three key technologies including a dual-layer mapping mechanism,affine transformation for coordinate unification,and hybrid geometric representation are developed to achieve semantic interoperability,precise spatial alignment,and lightweight adaptation of multi-source data.Secondly,a four-level technical process of"data input-data fusion-cross-disciplinary detection-business empowerment"is constructed,enabling real-time collaborative verification during the design stage,optimizing work procedures through progress visualization and AR handover during the construction stage,and integrating multi-disciplinary data to build a digital twin during the operation and maintenance stage.Finally,an experimental verification was conducted on a 10-kilometer high-speed railway section project.The results show that the semantic matching accuracy of the dual-layer mapping mechanism is 98.1%,the coordinate transformation deviation is≤0.005 m,the maximum model lightweight compression ratio is 8.7:1,and the cross-disciplinary conflict detection coverage rate is 98.2%,which can reduce ineffective communication time by more than 70%and lower the construction rework rate by 30%.This solution provides efficient technical support for multi-disciplinary collaboration throughout the railway engineering life cycle,and is both practically applicable and innovative.关键词
BIM/多专业协同/数据融合/业务赋能/铁路工程Key words
BIM/multi-disciplinary collaboration/data fusion/business empowerment/railway engineering分类
交通工程引用本文复制引用
郑云水,于晨,蔡博..基于BIM显示引擎的多专业数据融合与业务赋能研究[J].科技创新与应用,2026,16(15):83-87,5.基金项目
国家自然基金重点课题(N2024S013) （N2024S013）
