云-雾-边-端协同的农业装备数字孪生系统研究
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国家自然科学基金项目(52175258)和现代农业装备设计与智能制造创新平台项目(2021XDRHXMPT29)


Digital Twin System for Agricultural Machinery with Cloud-Fog-Edge-Terminal Architecture
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    摘要:

    数字孪生是一种实现虚实融合的先进理念,能够解决农业装备全生命周期中的复杂性和不确定性问题,促进农业机械化和农业装备产业的转型升级。目前,农业装备数字孪生尚处起步阶段,缺乏实用解决方案和典型应用案例。为此,基于数字孪生和农业装备的特点,融合五维模型和移动边缘计算技术,提出一种云-雾-边-端协同的数字孪生系统架构与运行机制。以籽粒直收型玉米联合收获机为对象,针对脱粒过程中籽粒破碎率高的问题,开发大型联合收获机的数字孪生原型系统,实现模型预测、模型更新、实时监测和优化决策等功能,并开展田间试验。试验结果显示:数字孪生系统有效提高了虚拟模型的适应能力,使虚拟模型保持良好的预测效果;基于数字孪生的决策优化方法有效降低了籽粒破碎率,相较于手动收获模式,籽粒破碎率平均值降低24.24%;相较于反馈控制模式,籽粒破碎率平均值降低15.78%,说明原型系统能够有效改善玉米籽粒收获质量,所提出的系统架构和实现方法可行。

    Abstract:

    The concept of digital twin represents a cutting-edge approach that seamlessly integrates virtual and real-world environments, effectively addressing complexity and uncertainty issues encountered throughout the lifecycle of agricultural equipment. This innovation is poised to accelerate the transformation and modernization of the agricultural mechanization and equipment industry. However, the practical application of digital twin technology for agricultural machinery is still in its nascent stages, and typical case studies and practical solutions are yet to be developed. In light of the unique characteristics of digital twin and agricultural machinery, a cloud-fog-edge-terminal collaborative digital twin system architecture and operation mechanism was proposed, integrating the 5D model and mobile edge computing technology. Specially, a digital twin prototype system for a large corn harvester with grain direct harvesting capabilities was developed, focusing on the high broken grain rate during the threshing process. This system enabled functions such as model prediction, model update, real-time monitoring, and optimization decision-making. Field experiments were conducted, the results showed that the digital twin system effectively enhanced the adaptability of the virtual model, maintaining good predictive performance. Furthermore, the decision optimization method based on digital twin can reduce the broken grain rate by an average of 24.24% compared with manual harvesting mode, and by an average of 15.78% compared with feedback control mode. These findings confirmed that the prototype system can effectively improve the quality of corn grain harvesting. Overall, the proposed system architecture and implementation method were feasible and can provide a reference for further research and application of digital twin in the agricultural machinery industry.

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郭大方,杜岳峰,栗晓宇,李国润,陈度,宋正河.云-雾-边-端协同的农业装备数字孪生系统研究[J].农业机械学报,2023,54(10):133-141. GUO Dafang, DU Yuefeng, LI Xiaoyu, LI Guorun, CHEN Du, SONG Zhenghe. Digital Twin System for Agricultural Machinery with Cloud-Fog-Edge-Terminal Architecture[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(10):133-141.

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  • 收稿日期:2023-03-14
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  • 在线发布日期: 2023-06-21
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