使用自然语言处理方法对 IIoT 中的自动 M2M 翻译进行深度本体统一
Saleha Javed1, Muhammad Usman2, Fredrik Sandin1
1Machine Learning, Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了一种自学模型,用于在工业4.0和5.0中无整合设备. 它可以实现动态的机器对机器翻译,克服工业物联网网络中的互操作性挑战.
科学领域:
- 工业自动化 工业自动化
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 人工智能的人工智能
背景情况:
- 现代工业4.0和5.0依赖于相互连接的设备来优化资源管理.
- 工业物联网 (IIoT) 面临的挑战是异质的设备缺乏兼容的设计和互操作性.
- 传统的设备集成解决方案是昂贵的,需要大量的工程工作.
研究的目的:
- 为确定设备分类学和实现无集成提出一种自学模型.
- 为应对将具有不同本体学的新设备集成到现有物联网网络中的挑战.
- 为了促进动态机器对机器 (M2M) 翻译,而不需要额外的工程或硬件.
主要方法:
- 使用自学模型来分析设备的本体学元数据和结构信息.
- 采用自然语言处理 (NLP) 来匹配基于语言上下文的不同本体.
- 将本体网络视为知识图来理解元数据结构和消息制定.
- 在具有相似的上下文和结构的本体图中对齐实体.
主要成果:
- 该模型成功地确定了设备分类学,并确定了不同实体学之间的匹配.
- 它可以实现动态的M2M翻译,提高IIoT网络内的互操作性.
- 这种方法减少了对手工工程和设备集成额外硬件资源的需求.
结论:
- 拟议的自学模型为工业4.0/5.0.0中的设备互操作性提供了一种高效且具有成本效益的解决方案.
- 它促进了异质设备的动态翻译和集成,这对智能城市和工业自动化至关重要.
- 这一进步支持物联网网络的无扩展,并优化能源分配和资源管理.
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