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基于物联网技术的矿山地质环境监测与实时传输

Mining Geological Environment Monitoring and Real-Time Transmission Based on Internet of Things Technology.

作者信息

Wang Yi, Zhang Deqiang, Pei Shengliang, He Peiyong

机构信息

China Institute of Geological Environment Monitoring, Beijing 100081, China.

Key Laboratory of Mine Ecological Effects and Systematic Restoration, Ministry of Natural Resources, Beijing 100081, China.

出版信息

Int J Anal Chem. 2022 Jul 7;2022:1393024. doi: 10.1155/2022/1393024. eCollection 2022.

Abstract

A landslide monitoring technology based on BeiDouand, a wireless sensor network, is proposed in order to solve the problem that landslide has brought severe threat to people's life and safety of property. The landslide monitoring system based on the BeiDouand wireless sensor network is analyzed and designed from the point of view of hardware and software. BeiDou message and GPRS double redundancy transmission mode are adopted to improve the reliability of transmission. It also adopts the factor compression method to improve the effectiveness of BeiDoutransmission and adopts queue management and packet scheduling mechanism to improve the real-time and reliability of wireless sensor network transmission. The result is that, as the number of visible satellites increases, the efficiency of the algorithm decreases. However, the overall efficiency of the algorithm has been significantly improved. When there are 28 visible satellites, the number of algorithm times decreases from 20,475 to 1,140, and the efficiency of the algorithm increases by 16.9 times. The GDOP simulated by the fast star selection algorithm proposed in this paper is less than 3.9, reaching an excellent grade.

摘要

为解决滑坡对人民生命财产安全造成严重威胁的问题,提出了一种基于北斗和无线传感器网络的滑坡监测技术。从硬件和软件角度对基于北斗无线传感器网络的滑坡监测系统进行了分析与设计。采用北斗报文和GPRS双冗余传输模式提高传输可靠性。还采用因子压缩方法提高北斗传输的有效性,并采用队列管理和分组调度机制提高无线传感器网络传输的实时性和可靠性。结果表明,随着可见卫星数量的增加,算法效率降低。然而,算法的整体效率有了显著提高。当有28颗可见卫星时,算法次数从20475次减少到1140次,算法效率提高了16.9倍。本文提出的快速选星算法模拟的GDOP小于3.9,达到优秀等级。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/514a/9283076/4f0f100a856c/IJAC2022-1393024.001.jpg

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