Construction method of high-horsepower tractor digital twin

Digital Twin Pub Date : 2022-09-13 DOI:10.12688/digitaltwin.17615.1

Yanan Zhang, Yuefeng Du, Zihan Yang, Du Chen, Zhenghe Song, Zhong-xiang Zhu

{"title":"Construction method of high-horsepower tractor digital twin","authors":"Yanan Zhang, Yuefeng Du, Zihan Yang, Du Chen, Zhenghe Song, Zhong-xiang Zhu","doi":"10.12688/digitaltwin.17615.1","DOIUrl":null,"url":null,"abstract":"Background: Agricultural machinery equipment is the core element of advanced agricultural productivity. The operation system of agricultural machinery equipment involves machine-human-environment-society interactions. Limited by the production mode, operation mode, etc., the design and development, manufacturing, operation and maintenance control, recycling and other links of agricultural machinery equipment are still independent of each other. The massive information in the agricultural production process has not been fully utilized, so there are some outstanding problems such as low operating performance, low production efficiency, and poor integration of agricultural machinery and agronomy. Methods: Focusing on the agricultural production process, this paper proposes the construction method for a high-horsepower tractor digital twin, and expounds on its operation mechanism. Taking high-horsepower tractor ploughing operation as an example, by deploying tractors, central servers, and an Internet of Things (IoT) platform, we developed a digital twin service platform for the agricultural production system of intelligent agricultural machinery equipment and built a tractor digital twin to verify the effectiveness of the proposed method. Results: The accuracy rate of the tractor ploughing quality prediction service based on this platform is 96.65%. Under open-loop control, the number of excellent, good, medium and poor sets of the tractor ploughing quality are 153, 955, 1470, and 1422, respectively. After adopting closed-loop control, the number of excellent and good sets increased by 378, and 821, respectively, and the number of medium and poor groups decreases by 119 and 1080, respectively. Through this platform, the operation quality can be effectively and accurately predicted and improved, which verifies the effectiveness of the proposed construction method of high-horsepower tractor digital twin. Conclusions: This research provides a method framework for the construction of the digital twin of tractor operation and maintenance control processes, and provides strong support for the vigorous development of intelligent agriculture.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Twin","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12688/digitaltwin.17615.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

Abstract

Background: Agricultural machinery equipment is the core element of advanced agricultural productivity. The operation system of agricultural machinery equipment involves machine-human-environment-society interactions. Limited by the production mode, operation mode, etc., the design and development, manufacturing, operation and maintenance control, recycling and other links of agricultural machinery equipment are still independent of each other. The massive information in the agricultural production process has not been fully utilized, so there are some outstanding problems such as low operating performance, low production efficiency, and poor integration of agricultural machinery and agronomy. Methods: Focusing on the agricultural production process, this paper proposes the construction method for a high-horsepower tractor digital twin, and expounds on its operation mechanism. Taking high-horsepower tractor ploughing operation as an example, by deploying tractors, central servers, and an Internet of Things (IoT) platform, we developed a digital twin service platform for the agricultural production system of intelligent agricultural machinery equipment and built a tractor digital twin to verify the effectiveness of the proposed method. Results: The accuracy rate of the tractor ploughing quality prediction service based on this platform is 96.65%. Under open-loop control, the number of excellent, good, medium and poor sets of the tractor ploughing quality are 153, 955, 1470, and 1422, respectively. After adopting closed-loop control, the number of excellent and good sets increased by 378, and 821, respectively, and the number of medium and poor groups decreases by 119 and 1080, respectively. Through this platform, the operation quality can be effectively and accurately predicted and improved, which verifies the effectiveness of the proposed construction method of high-horsepower tractor digital twin. Conclusions: This research provides a method framework for the construction of the digital twin of tractor operation and maintenance control processes, and provides strong support for the vigorous development of intelligent agriculture.

查看原文本刊更多论文

大马力拖拉机数字孪生的构造方法

背景:农业机械装备是先进农业生产力的核心要素。农业机械设备的运行系统涉及机器-人-环境-社会的相互作用。受限于生产方式、操作方式等，农机设备的设计开发、制造、运维控制、回收等环节仍然是相互独立的。农业生产过程中的海量信息没有得到充分利用，存在着经营效益不高、生产效率不高、农机农艺一体化程度低等突出问题。方法:针对农业生产过程，提出了大马力拖拉机数字孪生体的构建方法，并阐述了其运行机理。以大马力拖拉机犁耕作业为例，通过部署拖拉机、中央服务器和物联网平台，开发了智能农机装备农业生产系统的数字孪生服务平台，并构建了拖拉机数字孪生，验证了所提方法的有效性。结果:基于该平台的拖拉机耕作质量预测服务准确率为96.65%。开环控制下，优良套数153套，良好套数955套，中等套数1470套，差套数1422套。采用闭环控制后，优组和良组分别增加378组和821组，中组和差组分别减少119组和1080组。通过该平台，可以有效、准确地预测和提高拖拉机的运行质量，验证了所提出的大马力拖拉机数字孪生结构方法的有效性。结论:本研究为构建拖拉机运维控制过程数字孪生提供了方法框架，为智能农业的蓬勃发展提供了有力支撑。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Digital Twin digital twin technologies-

自引率

0.00%

发文量

期刊介绍： Digital Twin is a rapid multidisciplinary open access publishing platform for state-of-the-art, basic, scientific and applied research on digital twin technologies. Digital Twin covers all areas related digital twin technologies, including broad fields such as smart manufacturing, civil and industrial engineering, healthcare, agriculture, and many others. The platform is open to submissions from researchers, practitioners and experts, and all articles will benefit from open peer review.  The aim of Digital Twin is to advance the state-of-the-art in digital twin research and encourage innovation by highlighting efficient, robust and sustainable multidisciplinary applications across a variety of fields. Challenges can be addressed using theoretical, methodological, and technological approaches. The scope of Digital Twin includes, but is not limited to, the following areas:  ● Digital twin concepts, architecture, and frameworks ● Digital twin theory and method ● Digital twin key technologies and tools ● Digital twin applications and case studies ● Digital twin implementation ● Digital twin services ● Digital twin security ● Digital twin standards Digital twin also focuses on applications within and across broad sectors including: ● Smart manufacturing ● Aviation and aerospace ● Smart cities and construction ● Healthcare and medicine ● Robotics ● Shipping, vehicles and railways ● Industrial engineering and engineering management ● Agriculture ● Mining ● Power, energy and environment Digital Twin features a range of article types including research articles, case studies, method articles, study protocols, software tools, systematic reviews, data notes, brief reports, and opinion articles.