矿山物联网人员情境描述与不安全行为识别

冯仕民; 刘忠育; 俞啸; 孟磊; 赵志凯; 丁恩杰

doi:10.11959/j.issn.2096-3750.2018.00083

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矿山物联网人员情境描述与不安全行为识别

服务与应用 | 更新时间：2024-06-05

- 矿山物联网人员情境描述与不安全行为识别
- Workers’ context description and unsafe behavior recognition in Internet of things for mines
- 物联网学报 2018年2卷第4期页码：93-98
- 作者机构：
  
  1. 矿山互联网应用技术国家地方联合工程实验室，江苏徐州 221008
  2. 中国矿业大学物联网（感知矿山）研究中心，江苏徐州 221008
  3. 徐州市物联网产业发展研究中心，江苏徐州 221008
- 作者简介：
  
  [ "冯仕民（1983-），男，博士，中国矿业大学讲师，主要研究方向为物联网、传感器融合、人机交互、机器学习、人工智能等。" ]
  [ "刘忠育（1985-），男，中国矿业大学信息与控制工程学院博士生，主要研究方向为语义物联网、行为识别、3D场景语义理解。" ]
  [ "俞啸（1989-），男，中国矿业大学博士，主要研究方向为故障诊断方法、嵌入式系统与物联网技术、机器学习与人工智能技术。" ]
  [ "孟磊（1982-），男，博士，中国矿业大学助理研究员，主要研究方向为基于物联网的矿山环境感知、矿井排水优化理论与方法等。" ]
  [ "赵志凯（1983-），男，博士，中国矿业大学助理研究员，主要研究方向为物联网、机器学习、模式识别、数据挖掘等。" ]
  [ "丁恩杰（1962-），男，中国矿业大学教授、博士生导师，主要研究方向为矿山信息化、煤矿综合监测监控、矿井无线传感器网络、矿山物联网等。" ]
- 基金信息：
  
  国家重点研发计划项目“矿山安全生产物联网关键技术与装备研发”课题资助项目;National Key Research and Development Program Project “Research and Development of Key Technologies and Equipment for Internet of Things in Mine Safety Production”(2017YFC0804401)
- DOI：10.11959/j.issn.2096-3750.2018.00083
  中图分类号： TP181
- 纸质出版日期：2018-12-30，
  
  网络出版日期：2018-12，
- 稿件说明：
移动端阅览
冯仕民, 刘忠育, 俞啸, 等. 矿山物联网人员情境描述与不安全行为识别[J]. 物联网学报, 2018,2(4):93-98.

SHIMIN FENG, ZHONGYU LIU, XIAO YU, et al. Workers’ context description and unsafe behavior recognition in Internet of things for mines. [J]. Chinese journal on internet of things, 2018, 2(4): 93-98.
冯仕民, 刘忠育, 俞啸, 等. 矿山物联网人员情境描述与不安全行为识别[J]. 物联网学报, 2018,2(4):93-98. DOI： 10.11959/j.issn.2096-3750.2018.00083.

SHIMIN FENG, ZHONGYU LIU, XIAO YU, et al. Workers’ context description and unsafe behavior recognition in Internet of things for mines. [J]. Chinese journal on internet of things, 2018, 2(4): 93-98. DOI： 10.11959/j.issn.2096-3750.2018.00083.

摘要

矿山的环境复杂，智能化识别此类行为需要数据驱动方法和基于机器可读的领域知识的方法，然而矿山物联网的数据缺乏语义信息，矿山人员状态信息和不安全行为知识没有标准的表示方法。为解决上述问题，提出了一种基于语义本体的人员状态信息感知描述方法及一种数据驱动和知识驱动相结合的矿山人员不安全行为识别框架。首先介绍了语义本体在物联网领域的概况，阐述了矿山人员不安全行为识别的重要性和必要性，介绍了人员行为识别、情境感知和语义本体等研究背景，引出了矿山人员状态信息感知描述方法。基于此情境建模，提出了一种矿山人员不安全行为识别框架并应用于矿山人员不佩戴防护与安全装备的不安全行为识别，最后总结并展望了人工智能方法在矿山物联网应用层研究中的前景。

Abstract

The mine is a complicated environment.The intelligent recognition of such behavior requires not only the data-driven activity recognition method

but also the machine-readable domain knowledge based approach.However

the data of IoT for mines lacks the semantic information.Besides

there is no standard way of describing the worker’s context and representing the knowledge of worker’s unsafe behavior.In order to solve the above problems

a semantic ontology based approach to describing the worker’s context and a hybrid method based framework for worker’s unsafe behavior recognition were presented.This method combines the data-driven approach and the knowledge-driven approach.Firstly

an introduction to the semantic ontology in the Internet of things was given.Then

the importance and necessity of worker’s unsafe behavior recognition was introduced.After that

the research background on human activity recognition

context-awareness and semantic ontology was presented.This was followed by the semantic ontology based approach to the worker’s context description.Based on the context modeling

the framework that combined the data-driven method and the knowledge-driven method for the worker’s unsafe behavior recognition was proposed.The application of the framework was illustrated with the recognition of a kind of worker’s unsafe behavior who don’t wear the protective and safety equipment.Finally

the conclusions were drawn and the prospect of using the artificial intelligence method in the application layer of mine IoT was presented.

关键词

矿山物联网不安全行为行为识别本体人工智能

Keywords

mine Internet of thingsunsafe behavioractivity recognitionontologyartificial intelligence

references

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