中国机械工程

• 智能制造 • 上一篇    下一篇

工业物联网环境下隐式人机交互消息传播方法

杨林;李文锋;段莹;罗云;杨文超   

  1. 武汉理工大学物流工程学院,武汉,430063
  • 出版日期:2018-02-25 发布日期:2018-02-27
  • 基金资助:
    国家自然科学基金资助项目(61571336)
    National Natural Science Foundation of China (No. 61571336)

Message Dissemination Methods for Implicit Human-Machine Interactions Based on Industrial Internet of Things

YANG Lin;LI Wenfeng;DUAN Ying;LUO Yun;YANG Wenchao   

  1. School of Logistics Engineering,Wuhan University of Technology,Wuhan,430063
  • Online:2018-02-25 Published:2018-02-27
  • Supported by:
    National Natural Science Foundation of China (No. 61571336)

摘要: 针对工业4.0环境下人与周边物联网制造资源交互具有临时再配置、网络拓扑复杂多变的特点,以群集、社会化自组织的工业物联网智能设备为研究对象,构建简洁、高效的隐式人机交互机制。考虑人的因素对物联网节点交互的影响,引入设备社会化网络,提出了一种社会化设备对设备的隐式交互消息传播方法。以汽车轮毂的客户定制过程为例,进行了隐式交互分析;引入Caveman社会关系构建方法与概率模型,以消息完成率与体感网连接设备数量为指标,对所提隐式交互方法的性能进行了分析。仿真结果表明,与随机消息转发机制相比,所提社会化设备对设备交互的消息传播的最大平均完成率提高7%;在给定用户满意度条件下,周边直接连接智能设备数量可进行数值计算。研究结果为工业物联网环境下的人机交互系统参数选择提供了理论依据。

关键词: 以人为中心的工业物联网, 设备对设备网络, 隐式人机交互, 设备社会化网络

Abstract: For the features of industry 4.0 ecosystem, interactions among human and internet of things (IoT)-based manufacturing resources were reconfigured temporary, and the network topology of human-IoT was complex and dynamic. To address the issues in crowdsourcing and social self-organized smart devices in industrial internet of things (IIoT), an efficient and clear human-IoT implicit interaction mechanism was designed. Moreover, considering the factors of human in IoT interactions, based on concepts of machine social networks, a message dissemination method in social device-to-device network was proposed. Finally, a case study of implicit interactions for car wheel customized manufacturing was illustrated. And by using the classic method of Caveman for social network modeling and probability theory, average delivery rate and number of connection for wireless body networks were introduced as matrices to analyse performance of proposed mechanism. The simulation results show that, the maximum average completion rate of proposed message dissemination is improved by 7% compared with the random message forwarding mechanism. Under the given user satisfaction rate, the numbers of directly connected smart devices may be calculated quantitatively. The results may be used to provide the theoretical basis of the parameter selections for human-computer interaction systems in IIoT.

Key words: people-centric industrial internet of things, device-to-device network, implicit human machine interaction, machine social network

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