计算机与现代化

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基于多传感器数据融合的煤矿安全状态评估

  

  1.  
    (安徽理工大学计算机科学与工程学院,安徽淮南 232001)
  • 收稿日期:2013-08-30 出版日期:2014-02-14 发布日期:2014-02-14
  • 作者简介:熊博杰(1989-),男,江苏连云港人,安徽理工大学计算机科学与工程学院硕士研究生,研究方向:多传感器数据融合; 周华平(1979-),女,安徽淮南人,副教授,博士,研究方向:计算机在矿业中的应用。
  • 基金资助:
    国家自然科学基金资助项目(51174257); 安徽高校省级自然科学研究重点项目(KJ2010A083)

 
Assessment of Coal Mine Safety State Based on Multi-sensor Data Fusion

  1.  
    (School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China)
  • Received:2013-08-30 Online:2014-02-14 Published:2014-02-14

摘要: 为了保障矿井工人的生命安全,减少经济损失,提出一种基于多传感器数据融合技术的煤矿安全状态评估方法。先使用基于均值的分批估计预处理方法对井下的瓦斯浓度、温度、风速、一氧化碳、粉尘等多种传感器采集的数据进行综合处理,得到第一级融合结果,再利用D-S证据理论消除评估过程中的不确定性,提高评估的准确性。通过具体的案例,验证了本方法的可行性。实验结果表明,该评估方法的准确性很高,能够为矿井安全状态的评估与判断提供决策支持。

关键词: 传感器, 数据融合, 瓦斯浓度, 分批估计, D-S证据理论

Abstract: In order to protect the safety of the coal mine workers and reduce economic losses, an assessment method for coal mine safety status based on multi-sensor data fusion was proposed. The data from sensors of gas concentration, temperature, wind speed, carbon monoxide, dust and others from the underground was first handled, the batch estimate pretreatment methods based on the average value were used to get the first level fusion result, and then the D-S evidence theory was used to eliminate the uncertainty in the assessment and improve the accuracy. At last, a specific case was used to verify the feasibility of the proposed method. The result shows that the assessment method is of a high degree of accuracy, and it can provide decision support for the assessment and judgment of the state of coal mine safety.

Key words: sensor, data fusion, gas concentration, batch estimation, D-S evidence theory

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