Deep well construction of big data platform based on multi-source heterogeneous data fusion

Q4 Engineering
Yu Zhang, Yange Wang, Hongwei Ding, Yongzhen Li, Yan-ping Bai
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引用次数: 3

Abstract

At present, energy saving and emission reduction had become a problem of great concern for mankind. At the same time, there were some problems in the mining industry, such as waste of resources, low efficiency and easy occurrence of industrial accidents. Therefore, this paper had designed a deep well construction big data platform. The high precision and bear great pressure sensors were added to the system to solve the difficult problem of collecting information in deep wells by ordinary sensors. The multi-source heterogeneous data fusion algorithm was added to the system to solve the problem that the format of the data acquisition was different. In conclusion, the completion of the platform could achieve data monitoring in the process of mines. It not only helps to enhance the safety of mine construction, but also provides data analytical tools for further theoretical research of mine construction.
基于多源异构数据融合的大数据平台深井建设
当前,节能减排已成为人类高度关注的问题。同时也存在着资源浪费、效率低下、易发生工业事故等问题。为此,本文设计了深井施工大数据平台。为解决普通传感器在深井中采集信息困难的问题,在系统中加入了高精度、承受压力大的传感器。系统中加入了多源异构数据融合算法,解决了数据采集格式不同的问题。综上所述,该平台的建成可以实现矿山生产过程中的数据监控。这不仅有助于提高矿山建设的安全性,而且为矿山建设的进一步理论研究提供了数据分析工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Internet Manufacturing and Services
International Journal of Internet Manufacturing and Services Engineering-Industrial and Manufacturing Engineering
CiteScore
0.70
自引率
0.00%
发文量
7
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