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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.

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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.