铁道运营技术2024,Vol.30Issue(3) :13-16.DOI:10.13572/j.cnki.tdyy.2024.03.004

数字孪生在铁路货运生产组织中的应用研究

A Simulation Scheme for the Operation Flow of Railway Freight Handling Cars Based on Digital Twins

张岚 樊文翔 颜欢 严安 宁涛
铁道运营技术2024,Vol.30Issue(3) :13-16.DOI:10.13572/j.cnki.tdyy.2024.03.004

数字孪生在铁路货运生产组织中的应用研究

A Simulation Scheme for the Operation Flow of Railway Freight Handling Cars Based on Digital Twins

张岚 1樊文翔 1颜欢 1严安 1宁涛1
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作者信息

  • 1. 中国铁路南宁局集团有限公司 信息技术所,广西 南宁 530029
  • 折叠

摘要

铁路调度日(班)计划与货运作业是铁路运输生产经营中两个关键组成部分,它们之间存在着密切的关系.为推动铁路货运高质量发展,从调度日(班)计划与货运作业的关系出发,提出一种基于数字孪生的铁路货运装卸车作业流程仿真方案,充分利用计划、货运作业等相关数据,构建相应的铁路货运装卸车作业流程模型,通过仿真工具进行仿真,生成货运作业数字孪生结果,并利用机器学习优化模型,提高货运作业仿真预测结果的准确性.该仿真方案可应用于顺向推演模拟、阶段作业研判和逆向推演研判等场景,经试验验证,提出的方案和应用场景能够有效提高生产组织效率.

Abstract

The railway dispatch daily(shift)planning and freight operations are two key components of railway transportation production and operation,closely intertwined with each other.To advance the high-quality development of railway freight transportation,this paper proposes a simulation solution based on digital twin for railway freight loading and unloading operations,starting from the relationship between the dis-patch daily(shift)planning and freight operations.By fully utilizing relevant data such as plans and freight operations,a corresponding railway freight loading and unloading operation process model is con-structed,and the simulation tool is used for simulation to generate a digital twin result of freight opera-tions,moreover,machine learning is used to optimize the model to improve the accuracy of freight opera-tions simulation prediction results.This simulation solution can be applied to scenarios such as forward de-duction simulation,stage operation judgment,and reverse deduction judgment.The test verifies that the proposed scheme and application scenarios can effectively improve production organization efficiency.

关键词

铁路调度日(班)计划/货运作业/数字孪生/仿真

Key words

railway dispatch daily(shift)planning/freight operations/digital twin/simulation

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出版年

2024
铁道运营技术
广西铁道学会

铁道运营技术

影响因子:0.151
ISSN:1006-8686
参考文献量6
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