跨层可靠性分析和边缘适应性多目标优化策略,用于智能农业CPS管理中的网络物理建模
1School of Economics and Management, Huzhou College; m18058652297@163.com.
Journal of visualized experiments : JoVE
|February 9, 2026
概括
这项研究引入了智能农业的新战略,增强了温室的网络物理系统 (CPS). 它通过综合建模和优化来提高食品生产的可靠性和适应性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 系统工程 系统工程
背景情况:
- 全球日益增长的粮食需求和气候变化需要智能农业的进步.
- 现有的农业网络物理系统 (CPS) 在动态环境中难以实现跨层集成和实时适应性.
研究的目的:
- 为智能温室农业开发一个跨层网络物理建模和优化战略.
- 提高农业CPS在动态农业条件下的可靠性和适应性.
主要方法:
- 综合物理层建模 (土壤-植物-大气连续体,集成卡尔曼波器) 用于土壤湿度预测.
- 网络层分析使用多协议融合和随机彼得里网来提高通信可靠性.
- 通过随机混合系统进行控制层协调,通过多目标增强学习和贝叶斯元学习进行优化.
- 边缘智能部署,在通信中断期间提供可靠的控制.
主要成果:
- 温室番茄在产量预测和用水效率方面表现出可复制和稳定的性能.
- 即使在具有挑战性的环境和通信条件下,也实现了低控制延迟.
- 验证了该方法在真实世界温室环境中的适用性,在中国的Shouguang.
结论:
- 拟议的方法提供了一个实用和可复制的工作流程,用于开发适应性和可靠的农业CPS.
- 这种方法解决了当前农业CPS的关键局限性,为更有弹性的智能农业铺平了道路.
- 综合战略提高了系统的性能和稳定性,这对于在气候压力下满足未来的粮食需求至关重要.
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