LIU Haipeng, ZHOU Shuqiu. Research on application of network slice technology in mine communication network[J]. Journal of Mine Automation, 2020, 46(8): 28-31. DOI: 10.13272/j.issn.1671-251x.2020050074
Citation: LIU Haipeng, ZHOU Shuqiu. Research on application of network slice technology in mine communication network[J]. Journal of Mine Automation, 2020, 46(8): 28-31. DOI: 10.13272/j.issn.1671-251x.2020050074

Research on application of network slice technology in mine communication network

More Information
  • Aiming at problems of low resource sharing efficiency, difficult maintenance and upgrade, and inflexible and inefficient vertical service deployment and configuration caused by multi-source heterogeneous structure of mine communication network, network slice(NS) technology was applied to mine communication network. Mine communication network architecture based on NS was constructed. Deployment of NS in mine communication network is divided into service instance layer, network slice instance(NSI) layer and resource layer, which respectively correspond to application layer, transmission layer and perception layer in mine communication network. The service instance layer is responsible for underground service creation and life cycle maintenance. By abstracting device into independent NS, the NSI layer generates customizable, logically independent and service isolated NSI for customized use by service subscribers with different requirements. The resource layer is responsible for providing computing, storage, forwarding, wireless access and other functions to the NSI layer. By dynamically modifying key performance indicators parameters of each network in NS template, management of NSI life cycle is realized, which meets service requirements of customization and flexible use of NS. Through dynamic configuration and management of NSI isolation attributes and global operation of NSI isolation requirements, NS isolation is realized to achieve purpose of non-interference among services carried by NS. NS was divided according to latency sensitive, bandwidth sensitive and connection quantity sensitive underground services, and NS working state under typical underground services was given, which could meet flexible configuration requirements of mine communication network for differentiated vertical services.
  • Related Articles

    [1]SUN Yongxin. Research on bearing residual life prediction method of coal mine machinery equipment[J]. Journal of Mine Automation, 2021, 47(11): 126-130. DOI: 10.13272/j.issn.1671-251x.17834
    [2]QIU Xingguo, WANG Ruizhi, ZHANG Weiguo, ZHANG Zhaozhao, ZHANG Jing. Discrimination of mine inrush water source based on PCA -CRHJ model[J]. Journal of Mine Automation, 2020, 46(11): 65-71. DOI: 10.13272/j.issn.1671 -251x.2020040089
    [3]WU Yaqin, LI Huijun, XU Danni. Prediction algorithm of coal and gas outburst based on IPSO-Powell optimized SVM[J]. Journal of Mine Automation, 2020, 46(4): 46-53. DOI: 10.13272/j.issn.1671-251x.2019110018
    [4]ZHANG Linfeng, TIAN Muqin, SONG Jiancheng, HE Ying, FENG Junling, YANG Xiang. Feature extraction of vibration signal of roadheader based on singular value decompositio[J]. Journal of Mine Automation, 2019, 45(1): 28-34. DOI: 10.13272/j.issn.1671-251x.2018070035
    [5]MI Qiang, XU Yan, LIU Bin, XU Yunjie. Extraction method of texture feature of images of coal and gangue[J]. Journal of Mine Automation, 2017, 43(5): 26-30. DOI: 10.13272/j.issn.1671-251x.2017.05.007
    [6]WU Yunxia, ZHANG Haopeng, DU Dongbi. Feature extraction method for human ear image and its application in miner identificatio[J]. Journal of Mine Automation, 2015, 41(11): 30-34. DOI: 10.13272/j.issn.1671-251x.2015.11.008
    [7]WANG Jinfeng, QIN Ying, ZHAI Xueqi, FENG Lijie. Safety evaluation model of coal mine based on principal component and cluster analysis[J]. Journal of Mine Automation, 2015, 41(6): 29-34. DOI: 10.13272/j.issn.1671-251x.2015.06.008
    [8]WANG Yang. Statistics of coal mine safety accidents cause based on principal component analysis[J]. Journal of Mine Automation, 2013, 39(5): 90-92.
    [9]ZHANG Ning, REN Mao-wen, LIU Ping. Identification of coal-rock interface based on principal component analysis and BP neural network[J]. Journal of Mine Automation, 2013, 39(4): 55-58.
    [10]LEI Meng~, LI Ming~, XU Zhi-bin~. Application of Genetic Neural Network in Coal Quality Analysis with Near-infrared Spectroscopy[J]. Journal of Mine Automation, 2010, 36(2): 41-44.
  • Cited by

    Periodical cited type(1)

    1. 杨宁. 不连沟煤矿F6225工作面电阻率监测实践研究. 能源与环保. 2024(10): 119-123+134 .

    Other cited types(1)

Catalog

    Article Metrics

    Article views (137) PDF downloads (26) Cited by(2)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return