数字双胞胎驱动的智能工厂:使用边缘AI进行实时基于物理的共模拟. 和联合学习
V Padmavathi1, R Kanimozhi2, R Saminathan3
1Department of Information Technology, A. V. C. College of Engineering, Mannampandal, Mayiladuthurai, Tamil Nadu, India. vvpadhu@hotmail.com.
Scientific reports
|December 9, 2025
概括
本研究介绍了一种使用边缘人工智能和联合学习 (FL) 进行智能工厂的新数字双胞胎方法. 这种方法增强了实时共模拟,通过本地处理数据来提高效率和数据安全性.
科学领域:
- 智能制造 智能制造是一种智能制造.
- 工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是一个
- 数字双胞胎技术的数字双胞胎技术
背景情况:
- 传统的数字双胞胎 (DT) 方法通常依赖于云解决方案,导致高延迟,可扩展性问题和数据隐私问题.
- 工厂快速决策的实时物理模拟受到计算速度和网络限制的阻碍.
研究的目的:
- 开发一种结合数字双胞胎,边缘人工智能和联合学习 (FL) 的新方法,在智能工厂中实现高效和安全的共模拟.
- 解决与以云为中心的DT架构相关的延迟,可扩展性和数据隐私的挑战.
主要方法:
- 实施一个利用边缘AI和FL进行实时数据处理和推断的框架,用于工厂附近的边缘设备.
- 利用联合学习 (FL) 实现边缘设备之间的协作学习,同时保持敏感的生产数据的本地化.
- 开发基于FMI的引擎,实时共模拟物理和虚拟事件,以测试系统行为.
主要成果:
- 与仅云架构相比,延迟时间减少了多达35%.
- 降低了28%的云使用量,并增加了13.2%的吞吐量.
- 证明了拟议框架的增强可扩展性和工业适用性.
结论:
- 数字双胞胎,边缘人工智能和FL的集成提供了一个快速,高效和安全的解决方案,用于智能工厂的共模拟.
- 将计算转移到边缘显著提高了性能和数据隐私.
- 未来的改进可能包括区块链集成,自动学习更新和跨工厂知识传输.
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