联合收获机轻量级数字孪生系统构建方法研究  

Construction Method and Application Example of Lightweight Digital Twin System of Combine Harvester

作　　者：马博文 刘孟楠 尹彦鑫 孟志军 张宾[1] 张亚伟[1] 温昌凯 张安琪 MA Bowen;LIU Mengnan;YIN Yanxin;MENG Zhijun;ZHANG Bin;ZHANG Yawei;WEN Changkai;ZHANG Anqi(College of Engineering,China Agricultural University,Beijing 100083,China;State Key Laboratory of Intelligent Agricultural Power Equipment,Beijing 100097,China;Intelligent Equipment Research Center,Beijing Academy of Agriculture and Forestry Science,Beijing 100097,China;Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China)

机构地区：[1]中国农业大学工学院,北京100083 [2]智能农业动力装备全国重点实验室,北京100097 [3]北京市农林科学院智能装备技术研究中心,北京100097 [4]北京市农林科学院信息技术研究中心,北京100097

出　　处：《农业机械学报》2024年第5期108-120,共13页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家重点研发计划项目(2021YFD200050302);智能农业动力装备全国重点实验室开放课题项目(SKLIAPE2023005);国家自然科学基金面上项目(32171907)。

摘　　要：针对现有农机装备数字孪生系统开发难度大、配置要求高以及资源占用过高的问题,提出基于轻量级网络的联合收获机数字孪生系统构建方法,包含物理、虚拟、数据交互、模型计算以及人机交互等多个子系统的实现方法。基于数字孪生的技术特点和联合收获机的作业特性,设计了一种基于JavaScript语言的轻量级数字孪生系统框架。通过采用Solidworks和CMdevelopment kit工具进行数字孪生系统的模型轻量化处理及坐标系整合,实现了在不影响模型精度和功能的前提下,显著降低系统对硬件要求和内存占用量。以雷沃GM100型联合收获机为对象,开发基于轻量级网络的联合收获机数字孪生系统,为联合收获机孪生系统性能分析、实时监控、瞬时计算以及远程操纵提供联合仿真、分析以及验证平台。为验证数字孪生系统性能和功能,开展了孪生系统性能测试及油耗预测实验。实验结果表明,在数据更新频率20 Hz下,响应时间在78 ms以内,内存占用量在331 MB以内;性能测试中,系统在运行状态下CPU和GPU的平均占用率分别为17%和30%;即使在高强度操作下,系统帧率仍可保持在75.6 f/s;在正常作业下油耗预测模型平均误差为0.34 L/h,平均相对误差仅为2.51%。本系统提供了一种低成本、高效率的数字孪生轻量化构建方案。Aiming at the problems of the existing agricultural equipment digital twin system development difficulty,high configuration requirements and poor portability,a lightweight network-based digital twin system construction method for combine harvester was proposed,which contained the realization of multiple subsystems such as physical,virtual,data interaction,model computation and human-computer interaction.Based on the technical characteristics of digital twin and the operational characteristics of combine harvester,a lightweight digital twin system framework was designed based on JavaScript language.By adopting Solidworks and CMdevelopment kit tools for the model lightweight processing and coordinate system integration of the digital twin system,it achieved a significant reduction of the system's hardware requirements and memory occupation without affecting the model's accuracy and functionality.The lightweight network-based combine harvester digital twin system was developed by using a Lovol GM100 combine harvester as an object to provide a joint simulation,analysis,and validation platform for performance analysis,real-time monitoring,instantaneous computation,and remote manipulation of the combine harvester digital twin system.To verify the performance and functionality of the digital twin system,twin system performance tests and fuel consumption prediction experiments were conducted.Tests showed that the response speed was within 78 ms at a data update frequency of 20 Hz,and the memory occupation was within 331 MB in the performance test,and the average occupancy of the system's CPU and GPU in the running state was 17% and 30%,respectively;and the system's frame rate can be maintained at 75.6 f/s even under high-intensity operation.Under normal operation,the average error of the fuel consumption prediction model was 0.34 L/h,with an average relative error of only 2.51%.This system can provide a low-cost,high-efficiency digital twin lightweight construction scheme,which provided a useful reference for the further

关 键 词：联合收获机 数字孪生 轻量级 运动逻辑建模 齐次矩阵 

分 类 号：S24[农业科学—农业电气化与自动化] TP272[农业科学—农业工程]