Volume 48 Issue 3
Mar.  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
DU Zhigang, CHU Nan, LUO Ke. Underground location service system design[J]. Journal of Mine Automation，2022，48(3)：123-128, 134. DOI: 10.13272/j.issn.1671-251x.2021040070
Citation: DU Zhigang, CHU Nan, LUO Ke. Underground location service system design[J]. Journal of Mine Automation，2022，48(3)：123-128, 134. DOI: 10.13272/j.issn.1671-251x.2021040070
shu

Underground location service system design

  • 1.

    CCTEG Changzhou Research Institute, Changzhou 213015, China

  • 2.

    Tiandi(Changzhou) Automation Co., Ltd., Changzhou 213015, China

More Information
  • Received Date: April 20, 2021
  • Revised Date: March 12, 2022
  • Available Online: March 04, 2022
    Graphical Abstract
    • Abstract
  • The location service aims to provide accurate real-time position information of target objects, which is based on positioning. However, the current underground positioning system has some problems, such as low positioning precision, poor real-time performance, insufficient capacity, limited database carrying capacity, only supporting one-dimensional positioning and so on. In order to avoid the influence of underground positioning system on location service, an underground location service system is designed. The system adopts a Docker-based micro-service architecture, which overcomes the problems of development iteration and performance bottleneck of the traditional monolithic architecture and looses the coupling between businesses. The system adopts the simultaneous ranging method of multi-label and multi-anchor nodes, which improves the ranging efficiency and the capacity of the positioning system while ensuring the ranging accuracy. The system uses multi-source data fusion positioning algorithm to improve the discrimination accuracy of the direction of the sign card relative to the anchor base station. The system adopts the positioning algorithm based on Kalman filter and weighted LM method and the low-complexity characteristic extraction method to optimize the positioning results, reduce noise interference, remove redundant data and improve positioning precision. The system introduces the time series database for mixed data storage, and stores time series data such as historical track in InfluxDB to improve system data access performance. The system adopts the publish-subscribe mode for asynchronous message transmission, which increases the reusability and sharing of public information. The system adopts Bearer verification for the location service interface to protect system data security and underground sensitive data. The practical application results show that the system can provide high-precision real-time position information of various underground targets, and provide important data support for the working face limit monitoring system, human-machine approach protection device, auxiliary transportation system and automatic driving system.
    • FullText(HTML)
    • References (17)
  • [1]
    王国法,赵国瑞,任怀伟. 智慧煤矿与智能化开采核心技术分析[J]. 煤炭学报,2019,44(1):34-41.

    WANG Guofa,ZHAO Guorui,REN Huaiwei. Analysis on key technologies of intelligent coal mine and intelligent mining[J]. Journal of China Coal Society,2019,44(1):34-41.
    [2]
    李德毅,张天雷,黄立威. 位置服务:接地气的云计算[J]. 电子学报,2014,42(4):786-790. DOI: 10.3969/j.issn.0372-2112.2014.04.025

    LI Deyi,ZHANG Tianlei,HUANG Liwei. A down-to-earth cloud computing:location-based service[J]. Acta Electronica Sinica,2014,42(4):786-790. DOI: 10.3969/j.issn.0372-2112.2014.04.025
    [3]
    包建军. 智能矿山高精度位置服务系统研究现状与展望[J]. 智能矿山,2021(3):46-52.

    BAO Jianjun. Research status and prospect of intelligent mine high-precision location service system[J]. Journal of Intelligent Mine,2021(3):46-52.
    [4]
    荆诚,王爱军. 微服务在煤矿监控类软件开发框架中的应用[J]. 工矿自动化,2020,46(2):94-99.

    JING Cheng,WANG Aijun. Application of microservice in development framework of coal mine monitoring software series[J]. Industry and Mine Automation,2020,46(2):94-99.
    [5]
    曹宏宇,胡恒. 基于微服务架构的智能终端软件架构探讨[J]. 科技创新与应用,2019(20):17-19.

    CAO Hongyu,HU Heng. Discussion on the software architecture of intelligent terminal based on micro-service architecture[J]. Technology Innovation and Application,2019(20):17-19.
    [6]
    钟俊林. 基于微服务架构的自助微商城的研究与实现[D]. 北京: 北京邮电大学, 2019.

    ZHONG Junlin. Research and implementation of self-service wemall based on microservices architecture[D]. Beijing: Beijing University of Posts and Telecommunications, 2019.
    [7]
    霍振龙. 矿井定位技术现状和发展趋势[J]. 工矿自动化,2018,44(2):51-55.

    HUO Zhenlong. Status and development trend of mine positioning technology[J]. Industry and Mine Automation,2018,44(2):51-55.
    [8]
    MAO Guoqiang,FIDAN B,ANDERSON B D O. Wireless sensor network localization techniques[J]. Computer Networks,2007,51(10):2529-2553. DOI: 10.1016/j.comnet.2006.11.018
    [9]
    陈康,王军,包建军,等. 基于消息复用的TOF井下精确定位技术[J]. 工矿自动化,2019,45(2):1-5.

    CHEN Kang,WANG Jun,BAO Jianjun,et al. TOF underground accurate positioning technology based on message multiplexing[J]. Industry and Mine Automation,2019,45(2):1-5.
    [10]
    KIM H. Performance analysis of the SDS-TWR-MA algorithm[C]//International Conference on Wireless Communications & Mobile Computing: Connecting the World Wirelessly, Leipzig, 2009: 399-403.
    [11]
    包建军. 煤矿巷道多源数据融合定位算法研究[J]. 工矿自动化,2019,45(8):38-42.

    BAO Jianjun. Research on multi-source data fusion positioning algorithm for coal mine roadway[J]. Industry and Mine Automation,2019,45(8):38-42.
    [12]
    王伟. 基于卡尔曼滤波和加权LM法的井下精确定位算法[J]. 工矿自动化,2019,45(11):5-9.

    WANG Wei. Underground precise positioning algorithm based on Kalman filter and weighted LM algorithm[J]. Industry and Mine Automation,2019,45(11):5-9.
    [13]
    余修武. 矿井安全智能监测无线传感器网络关键技术研究[D]. 武汉: 武汉理工大学, 2013.

    YU Xiuwu. Study on key technologies of wireless sensor networks in mine underground safety intelligent monitoring[D]. Wuhan: Wuhan University of Technology, 2013.
    [14]
    杨婕. 时序数据库发展研究[J]. 广东通信技术,2020,40(3):46-48. DOI: 10.3969/j.issn.1006-6403.2020.03.011

    YANG Jie. Research on development of time series database[J]. Guangdong Communication Technology,2020,40(3):46-48. DOI: 10.3969/j.issn.1006-6403.2020.03.011
    [15]
    韩艺坤,李富年,陈志丹. 基于InfluxDB的桥梁监测系统设计与实现[J]. 现代电子技术,2020,43(16):21-25.

    HAN Yikun,LI Funian,CHEN Zhidan. Design and implementation of bridge monitoring system based on InfluxDB[J]. Modern Electronics Technique,2020,43(16):21-25.
    [16]
    徐化岩,初彦龙. 基于InfluxDB的工业时序数据库引擎设计[J]. 计算机应用与软件,2019,36(9):33-36. DOI: 10.3969/j.issn.1000-386x.2019.09.006

    XU Huayan,CHU Yanlong. Design of industrial time series database engine based on InfluxDB[J]. Computer Applications and Software,2019,36(9):33-36. DOI: 10.3969/j.issn.1000-386x.2019.09.006
    [17]
    吴龙波. 基于Oauth2.0构建空管云数据中心的资源认证授权机制[J]. 科技传播,2020,12(15):168-169. DOI: 10.3969/j.issn.1674-6708.2020.15.075

    WU Longbo. The resource authentication and authorization mechanism of air traffic control cloud data center is constructed based on Oauth2.0[J]. Public Communication of Science & Technology,2020,12(15):168-169. DOI: 10.3969/j.issn.1674-6708.2020.15.075
    • Related Articles

  • Related Articles

    [1]SHANG Weidong, WANG Haili, ZHANG Xiaoxia, WANG Hao, XU Hualong. A coal mine data acquisition, fusion and sharing system based on object model[J]. Journal of Mine Automation, 2024, 50(1): 17-24, 34. DOI: 10.13272/j.issn.1671-251x.2023070047
    [2]TAN Zhanglu, WANG Meijun. Research on the concept connotation, development goal and key technologies of data governance for smart mine[J]. Journal of Mine Automation, 2022, 48(5): 6-14. DOI: 10.13272/j.issn.1671-251x.2021120090
    [3]ZHAO Ha. Research on smart mine data sharing scheme based on blockchai[J]. Journal of Mine Automation, 2021, 47(S2): 45-48.
    [4]ZHANG Peng. Exploration on construction of big data system for intelligent mine[J]. Journal of Mine Automation, 2021, 47(S1): 21-23.
    [5]TAN Zhanglu, MA Yingying. Research on coal big data and its developing directio[J]. Journal of Mine Automation, 2018, 44(3): 49-52. DOI: 10.13272/j.issn.1671-251x.2017110059
    [6]ZHANG Xiaoyan, CAI Panliang. Research of informatization framework of transportation and sale for large coal enterprise[J]. Journal of Mine Automation, 2014, 40(4): 93-95. DOI: 10.13272/j.issn.1671-251x.2014.04.023
    [7]HAN A. Research of enterprise service bus technology[J]. Journal of Mine Automation, 2013, 39(11): 50-53. DOI: 10.7526/j.issn.1671-251X.2013.11.014
    [8]YAN Zhao-zhen, HE Yao-yi, DING Rui-qi. Research of Data Interaction Middleware Based on OPC UA[J]. Journal of Mine Automation, 2012, 38(12): 80-82.
    [9]ZHOU Jing. The Application of the Technology of Teradata Data Warehouse[J]. Journal of Mine Automation, 2005, 31(6): 97-99.
    [10]XIE Jian-fei. Using Shared RAM for Data Exchange Between Two CPU[J]. Journal of Mine Automation, 1997, 23(4): 65-68.

  • Cited by

    Periodical cited type(21)

    1. 赵冬. 基于机器视觉的煤矿井下火灾检测方法. 自动化应用. 2025(01): 101-104+107 .
    2. 孙继平，李小伟. 基于图像顺滑度的矿井外因火灾识别及抗干扰方法研究. 中国矿业大学学报. 2025(01): 215-226 .
    3. 耿哲，张德胜. 煤矿井下区域防灭火系统的设计. 矿山机械. 2025(03): 50-55 .
    4. 孙继平，李小伟. 矿井外因火灾图像凹陷度识别方法. 煤炭科学技术. 2025(01): 341-355 .
    5. 王炎林，裴晓东，王凯，徐光. 基于双光谱成像技术的矿井早期火源识别及抗干扰方法研究. 工矿自动化. 2025(03): 122-130 . 本站查看
    6. 胡纪年，李雨成，李俊桥，张巍. 基于CNN的矿井外因火灾火源定位方法研究. 中国安全生产科学技术. 2024(03): 134-140 .
    7. 李海龙. 基于机器视觉的煤矿用输送带跑偏检测方法. 矿山机械. 2024(05): 29-33 .
    8. 徐晓敏，朱正磊，刘鑫，郭青玄，赵一夫. 基于无人机监测的电力火灾起火点定位方法研究. 电力大数据. 2024(03): 50-56 .
    9. 孙继平，李小伟. 基于图像内凹度的矿井外因火灾识别及抗干扰方法. 煤炭学报. 2024(07): 3253-3264 .
    10. 于博，陈光波. 基于组合赋权-物元可拓模型的煤矿内因火灾安全评价. 煤矿安全. 2023(02): 61-70 .
    11. 李光宇，李守军，缪燕子. 基于机器视觉和灰色模型的矿井外因火灾辨识与定位方法. 矿业安全与环保. 2023(02): 82-87 .
    12. 程开. 选煤厂机械搅拌式浮选机智能控制系统的研究. 煤炭加工与综合利用. 2023(06): 6-12 .
    13. 孙继平，程继杰. 煤矿冲击地压和煤与瓦斯突出感知报警方法研究. 工矿自动化. 2022(01): 1-6 . 本站查看
    14. 郭玉柱. 斜沟煤矿火灾应急隔离系统设计研究. 煤. 2022(02): 13-16+29 .
    15. 范涌高，周怡敏，张玉玺，闫宋锟，朱丽娜. 井下复合定位系统框架. 计算机测量与控制. 2022(02): 201-206 .
    16. 孙继平，范伟强. MS-ADoG域结合ReNLU与VGG-16的矿井双波段图像融合算法. 光子学报. 2022(03): 1-15 .
    17. 孙继平，李小伟，徐旭，张森森. 矿井电火花及热动力灾害紫外图像感知方法研究. 工矿自动化. 2022(04): 1-4+95 . 本站查看
    18. 王勇，胡斌，康渴楠，孔庆东，葛小菡，李辰阳. 智能传感器在消防装备中的应用现状分析. 中国安全科学学报. 2022(S1): 184-188 .
    19. 高懿伟，郭志国，张俊，郑彪华. 我国金属矿山热动力灾害防治技术研究现状与展望. 金属矿山. 2022(12): 196-204 .
    20. 肖国强，范新丽，马砺，骆伟，吴明明. 矿井火灾环境信息动态感知与应急隔离系统. 工矿自动化. 2021(09): 65-69 . 本站查看
    21. 刘彦武. 基于智能通风的火灾和瓦斯突出灾变管控系统探讨. 山西焦煤科技. 2021(08): 47-52 .

    Other cited types(11)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (195) PDF downloads (34) Cited by(32)
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd. Visits：1225246    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return