QI Xiaoxiao, GUO Youmin, QI Jinping. Research on underground multi-system fusion method for coal mine safety monitoring and control[J]. Journal of Mine Automation, 2018, 44(12): 9-13. DOI: 10.13272/j.issn.1671-251x.2018040064
Citation: QI Xiaoxiao, GUO Youmin, QI Jinping. Research on underground multi-system fusion method for coal mine safety monitoring and control[J]. Journal of Mine Automation, 2018, 44(12): 9-13. DOI: 10.13272/j.issn.1671-251x.2018040064

Research on underground multi-system fusion method for coal mine safety monitoring and control

More Information
  • According to the requirements for underground multi-system fusion of Technology schemes of upgrading of coal mine safety monitoring and control system, and for the problem that fusion degree of underground monitoring and control system was not high, a new fusion sub-station with multi-communication interface and multi-sensor interface was designed. Based on the new fusion sub-station, an underground multi-system fusion method for coal mine safety monitoring and control was proposed. The fusion method adopts the new fusion sub-station to realize link level fusion, device level fusion and shared data level fusion according to field equipment acquisition interfaces, communication interfaces and fusion degree of different systems. The link level fusion can realize fusion of multiple sensor acquisition interfaces, device level fusion can realize equipment fusion of different systems, shared data level fusion can realize data sharing between different systems.
  • Related Articles

    [1]ZHANG Zenghui, MA Wenwei. Prediction of gas emission in mining face based on random forest regression algorithm[J]. Journal of Mine Automation, 2023, 49(12): 33-39. DOI: 10.13272/j.issn.1671-251x.2023020006
    [2]WEI Feng, MA Long. Locality-sensitive hashing K-means algorithm for large-scale datasets[J]. Journal of Mine Automation, 2023, 49(3): 53-62. DOI: 10.13272/j.issn.1671-251x.2022080018
    [3]ZHENG Xuezhao, LI Menghan, ZHANG Yanni, JIANG Peng, WANG Baoyuan. Research on the prediction model of coal spontaneous combustion temperature based on random forest algorithm[J]. Journal of Mine Automation, 2021, 47(5): 58-64. DOI: 10.13272/j.issn.1671-251x.17700
    [4]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
    [5]FENG Shuo, XIE Tingchuan, KANG Jing, LI Jianliang. Path planning of mine search and rescue robot based on two-particle swarm optimization algorithm[J]. Journal of Mine Automation, 2020, 46(1): 65-71. DOI: 10.13272/j.issn.1671-251x.2019050092
    [6]MO Shupei, TANG Jin, WANG Yu, LAI Pujian, JIN Limo. Underground personnel positioning algorithm based on clustering and K-nearest neighbor algorithm[J]. Journal of Mine Automation, 2019, 45(4): 43-48. DOI: 10.13272/j.issn.1671-251x.2018110072
    [7]YE Manyuan, HUANG Kaifeng. Power balance control strategy for staircase modulation based on improved particle swarm optimization algorithm[J]. Journal of Mine Automation, 2015, 41(9): 57-62. DOI: 10.13272/j.issn.1671-251x.2015.09.015
    [8]CHI Jing, YANG Zhen-yu, ZHANG Ting. Intrusion detection method based on Bayesian and decision tree[J]. Journal of Mine Automation, 2013, 39(2): 62-65.
    [9]WANG Jian-jun, WANG Shi-ying, LEI Meng. Application of Particle Swarm Optimization Algorithm in Prediction of Coal Calorific Value[J]. Journal of Mine Automation, 2012, 38(5): 50-53.
    [10]AN Feng-shua, . Optimization of PID Controller Parameters Based on Modified Particle Swarm Optimization Algorithm[J]. Journal of Mine Automation, 2010, 36(5): 54-57.
  • Cited by

    Periodical cited type(7)

    1. 孙吉平. 基于SF_6质量浓度变化特征的煤矿火灾状态识别分析. 山西煤炭. 2025(01): 42-49 .
    2. 邓军,李鑫,王凯,王伟峰,闫军,汤宗情,康付如,任帅京. 矿井火灾智能监测预警技术近20年研究进展及展望. 煤炭科学技术. 2024(01): 154-177 .
    3. 王刚,杨宝东,徐浩,孙路路,黄启铭. 煤自燃程序升温实验及其在实验教学中的应用. 实验技术与管理. 2024(03): 225-231 .
    4. 曹富荣,吴学松,李军,付天予,刘佳伟,李志辉,杨小彬. 基于机器学习的多气体指标煤自燃温度预测. 煤矿安全. 2024(04): 106-113 .
    5. 杨英兵,邢真强,张运增,郭佳策,李龙,鹿文勇,陈明浩. 煤自燃全阶段防控研究进展及趋势分析. 煤矿安全. 2024(07): 85-101 .
    6. 王玉怀,胡硕鹏,朱永兴,窦静文,张烜乐,张思佳. 机器学习在煤自燃预测预报中的应用现状及展望. 中国煤炭. 2024(10): 98-103 .
    7. 童保国,姜福领,毕寸光,王亮,田坤云. 基于LSTM改进Transformer的煤自燃温度预测模型. 金属矿山. 2024(12): 275-280 .

    Other cited types(2)

Catalog

    Article Metrics

    Article views (80) PDF downloads (18) Cited by(9)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return