基于云模型的煤矿安全大数据多粒度表示方法及应用
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重庆邮电大学

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中图分类号:

A

基金项目:

国家重点研发计划重点专项 -基于大数据的区域煤矿安全态势智能分析与预警技术


Multi-granularity representation method of big data in coal mine safety based on cloud model and its application
Author:
Affiliation:

CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS

Fund Project:

National Key R&D Program of China

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    摘要:

    以大规模物联网为支撑的新一代信息技术的深入应用,为基于海量大数据挖掘的煤矿安全知识发现提供了实现可能。现有的针对煤矿安全大数据的研究大多基于定量模型,其解决问题的角度单一且一定程度上忽略了煤矿监管中多时空、多粒度的管控需求,使得数据中蕴含的煤矿风险知识未得到客观、全面的发现。 本文从煤矿监管中的多粒度需求出发,借助云模型定量数据与定性概念间良好的转换能力,从煤矿监管中的时间、空间监管架构角度,提出了基于自适应混合云变换的面向煤矿安全大数据的多粒度表示方法,该方法能够有效满足煤矿监管中其基于宏观、微观,不同时间、空间维度的变粒度需求,实现煤矿安全大数据在不同粒度认知结构中的特性的深入挖掘。通过在煤矿数据概念提取中的应用并与高斯云变换算法对比,其提取的概念覆盖度更全且更客观,验证了本方法的合理性;在煤矿监测数据预测应用中,其预测精度相较于arima算法更高,验证了本方法的可行性。

    Abstract:

    Abstract: the in-depth application of information technology supported by the large-scale Internet of things provides the possibility for the discovery of coal mine safety knowledge based on massive data mining. The existing researches on big data of coal mine safety are mostly based on quantitative models, which solve problems with a single perspective and neglect the multi-granularity management needs in coal mine supervision to some extent, so that the risk knowledge of coal mine contained in the data has not been found objectively and comprehensively. From the view of multi-granularity demand in coal mine supervision, this paper with the advantage of cloud model which could have a good conversion between quantitative data and qualitative concepts, a multi-granularity representation method for coal mine safety big data based on adaptive hybrid cloud transformation is proposed. This method can effectively meet the variable granularity requirements of coal mine supervision based on macro, micro, different time and space dimensions, and realize the deep mining of the characteristics of coal mine safety big data in different granularity cognitive structure. Through the application in the concept extraction of coal mine data and comparison with the gaussian cloud transformation algorithm, we can see our method’s concept coverage of the extraction is more complete and more objective, which verifies the rationality of this method. In the application of coal mine monitoring data prediction, our method’s prediction accuracy is higher than that of arima algorithm, which verifies the feasibility of this method.

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  • 收稿日期:2020-03-20
  • 最后修改日期:2020-06-30
  • 录用日期:2020-07-01
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