- Chinese Core Periodicals
- Chinese Core Journals of Science and Technology
- RCCSE Chinese Authoritative Academic Journals
| Citation: |
ZHANG Jing, ZHA Dou, QU Shijia, et al. Sparse crowd sensing method for methane concentration field in coal mine fully mechanized mining face[J]. Safety in Coal Mines, 2025, 56（3）: 1−11. DOI: 10.13347/j.cnki.mkaq.20241098
|
In view of the lack of methane monitoring points in coal mine fully mechanized mining face, it is difficult to achieve for high coverage methane concentration monitoring in the mining face area. We propose a methane concentration field sensing method in the mining face based on sparse crowd sensing. Through the monitoring data of methane concentration in some sub-cells, the correlation of sub-cells is explored, and the methane concentration field of fully mechanized mining face is inferred. Firstly, based on the sub-cell division of fully mechanized mining face, we propose a deep reinforcement learning based on distributed weighted self-attention mechanism (DSA-DQN) methane concentration sensing cell optimization selection algorithm. The cell optimization selection algorithm uses the distributed weighted self-attention mechanism (DSA) to capture the key information of the mining face environment, to assist the optimization and decision-making of the cell optimization selection algorithm based on DQN. Secondly, in the aspect of methane concentration inference in the unsensing area, a Kriging interpolation based on particle swarm optimization support vector regression (PS-Kriging) methane concentration inference method is proposed. The support vector regression is combined with the Kriging interpolation method to solve the problem that the traditional method fall into the local optimal solution in the interpolation process. Particle swarm optimization is used to improve the kernel parameters and penalty parameters of the support vector regression model, which improves the inference accuracy of the model. Finally, we choosed a fully mechanized mining face in a Shanxi province mine, and made experimental verification through the combination of on-site measurement and simulation experiments. The experimental results show that the sparse crowd sensing method can infer the methane concentration in all cells of the mining face by selecting some key sub cells, with an mean absolute error of 0.07%.
| [1] |
王国法,巩师鑫,申凯. 煤矿智能安控技术体系与高质量发展对策[J]. 矿业安全与环保,2023,50(5):1−8.
WANG Guofa, GONG Shixin, SHEN Kai. Intelligent security control technology system and high-quality development countermeasures for coal mines[J]. Mining Safety & Environmental Protection, 2023, 50(5): 1−8.
|
| [2] |
孙继平,余星辰. 基于CEEMD分量样本熵与SVM分类的煤矿瓦斯和煤尘爆炸声音识别方法[J]. 采矿与安全工程学报,2022,39(5):1061−1070.
SUN Jiping, YU Xingchen. Sound recognition method of coal mine gas and coal dust explosion based on CEEMD component sample entropy and SVM classification[J]. Journal of Mining & Safety Engineering, 2022, 39(5): 1061−1070.
|
| [3] |
王凯,杜锋. 煤岩瓦斯复合动力灾害机理研究进展与展望[J]. 安全,2022,43(1):1−10.
WANG Kai, DU Feng. Research progress and prospect on coal-rock-gas compound dynamic disaster mechanism[J]. Safety & Security, 2022, 43(1): 1−10.
|
| [4] |
王国法. 煤矿智能化最新技术进展与问题探讨[J]. 煤炭科学技术,2022,50(1):1−27. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201001
WANG Guofa. New technological progress of coal mine intelligence and its problems[J]. Coal Science and Technology, 2022, 50(1): 1−27. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201001
|
| [5] |
解北京,李晓旭,张景顺,等. 井工煤矿甲烷排放精准监测与核算[J]. 煤炭科学技术,2024,52(4):119−130. doi: 10.12438/cst.2023-1992
XIE Beijing, LI Xiaoxu, ZHANG Jingshun, et al. Accurate monitoring and accounting of methane emission in underground coal mine[J]. Coal Science and Technology, 2024, 52(4): 119−130. doi: 10.12438/cst.2023-1992
|
| [6] |
翟成,丛钰洲,陈爱坤,等. 中国煤矿瓦斯突出灾害治理的若干思考及展望[J]. 中国矿业大学学报,2023,52(6):1146−1161.
ZHAI Cheng, CONG Yuzhou, CHEN Aikun, et al. Reflection and prospect on the prevention of gas outburst disasters in China’s coal mines[J]. Journal of China University of Mining & Technology, 2023, 52(6): 1146−1161.
|
| [7] |
屈世甲,吴雪菲. 基于物联终端的工作面甲烷数字场实现方法研究[J]. 煤炭科学技术,2021,49(12):112−118. doi: 10.3969/j.issn.0253-2336.2021.12.mtkxjs202112014
QU Shijia, WU Xuefei. Study on realization of methane digital field in working face based on IoT terminal[J]. Coal Science and Technology, 2021, 49(12): 112−118. doi: 10.3969/j.issn.0253-2336.2021.12.mtkxjs202112014
|
| [8] |
王健,王超,郑赟,等. 煤与瓦斯突出机理探讨及其预报方法[J]. 煤矿安全,2016,47(6):173−177.
WANG Jian, WANG Chao, ZHENG Yun, et al. Discussion on coal and gas outburst mechanism and its forecasting method[J]. Safety in Coal Mines, 2016, 47(6): 173−177.
|
| [9] |
杨秀贵,潘昌树,陈云. 煤与瓦斯突出机理及主要影响因素研究现状[J]. 采矿技术,2024,24(1):203−213. doi: 10.3969/j.issn.1671-2900.2024.01.041
YANG Xiugui, PAN Changshu, CHEN Yun. Research status of coal and gas outburst mechanism and main influencing factors[J]. Mining Technoloy, 2024, 24(1): 203−213. doi: 10.3969/j.issn.1671-2900.2024.01.041
|
| [10] |
梁运涛,陈成锋,田富超,等. 甲烷气体检测技术及其在煤矿中的应用[J]. 煤炭科学技术,2021,49(4):40−48.
LIANG Yuntao, CHEN Chengfeng, TIAN Fuchao, et al. Methane gas detection technology and its application in coal mines[J]. Coal Science and Technology, 2021, 49(4): 40−48.
|
| [11] |
屈世甲,武福生. 基于边缘计算的煤矿工作面甲烷监测模式探讨[J]. 煤炭科学技术,2020,48(12):161−167.
QU Shijia, WU Fusheng. Research on methane monitoring mode of coal mining face based on edge computing[J]. Coal Science and Technology, 2020, 48(12): 161−167.
|
| [12] |
RESTUCCIA F, FERRARO P, SANDERS T S, et al. FIRST: A framework for optimizing information quality in mobile crowdsensing systems[J]. ACM Transactions on Sensor Networks (TOSN), 2018, 15(1): 1−35.
|
| [13] |
ZHANG Y, ZHANG X. Price learning-based incentive mechanism for mobile crowd sensing[J]. ACM Transactions on Sensor Networks (TOSN), 2021, 17(2): 1−24.
|
| [14] |
WANG L, ZHANG D, WANG Y, et al. Sparse mobile crowdsensing: challenges and opportunities[J]. IEEE Communications Magazine, 2016, 54(7): 161−167. doi: 10.1109/MCOM.2016.7509395
|
| [15] |
LIU W, YANG Y, WANG E, et al. User recruitment for enhancing data inference accuracy in sparse mobile crowdsensing[J]. IEEE Internet of Things Journal, 2019, 7(3): 1802−1814.
|
| [16] |
HAN L, YU Z, WANG L, et al. Keeping cell selection model up-to-date to adapt to time-dependent environment in sparse mobile crowdsensing[J]. IEEE Internet of Things Journal, 2021, 8(18): 13914−13925. doi: 10.1109/JIOT.2021.3068415
|
| [17] |
LIU W B, WANG L Y, WANG E, et al. Reinforcement learning-based cell selection in sparse mobile crowdsensing[J]. Computer Networks, 2019, 161: 102−114. doi: 10.1016/j.comnet.2019.06.010
|
| [18] |
YANTO, APRIYONO A, SANTOSO P B, et al. Landslide susceptible areas identification using IDW and Ordinary Kriging interpolation techniques from hard soil depth at middle western Central Java, Indonesia[J]. Natural Hazards, 2022, 110(2): 1405−1416. doi: 10.1007/s11069-021-04982-5
|
| [19] |
LIU T, ZHU Y, YANG Y, et al. ALC2: When active learning meets compressive crowdsensing for urban air pollution monitoring[J]. IEEE Internet of Things Journal, 2019, 6(6): 9427−9438. doi: 10.1109/JIOT.2019.2939552
|
| [20] |
FAN J C, CHENG J Y. Matrix completion by deep matrix factorization[J]. Neural Networks, 2018, 98: 34−41. doi: 10.1016/j.neunet.2017.10.007
|
| [21] |
WANG E, LIU W, LIU W, et al. Spatiotemporal urban inference and prediction in sparse mobile crowdsensing: A graph neural network approach[J]. IEEE Transactions on Mobile Computing, 2022, 22(11): 6784−6799.
|
| [22] |
宋子晖,李卓,陈昕. 基于压缩感知的移动群智感知任务分发机制[J]. 计算机应用,2019,39(1):15−21.
SONG Zihui, LI Zhuo, CHEN Xin. Mobile crowdsensing task distribution mechanism based on compressed sensing[J]. Journal of Computer Applications, 2019, 39(1): 15−21.
|
| [23] |
胡健,陶丹. 移动群智感知质量度量与保障理论和方法[J]. 小型微型计算机系统,2019,40(5):918−923. doi: 10.3969/j.issn.1000-1220.2019.05.002
HU Jian, TAO Dan. Theories and methods of quality measure and assurance for mobile crowd sensing[J]. Journal of Chinese Computer Systems, 2019, 40(5): 918−923. doi: 10.3969/j.issn.1000-1220.2019.05.002
|
| [24] |
WANG E, ZHANG M, CHENG X, et al. Deep learning-enabled sparse industrial crowdsensing and prediction[J]. IEEE Transactions on Industrial Informatics, 2020, 17(9): 6170−6181.
|
| [25] |
ZHAO S, QI G, HE T, et al. A survey of sparse mobile crowdsensing: Developments and opportunities[J]. IEEE Open Journal of the Computer Society, 2022, 3: 73−85. doi: 10.1109/OJCS.2022.3177290
|
| [26] |
涂淳钰,於志勇,韩磊,等. 基于强化学习的稀疏群智感知参与者招募策略[J]. 计算机学报,2022,45(7):1539−1556. doi: 10.11897/SP.J.1016.2022.01539
TU Chunyu, YU Zhiyong, HAN Lei, et al. Direct participant recruitment strategy in sparse mobile crowdsensing[J]. Chinese Journal of Computers, 2022, 45(7): 1539−1556. doi: 10.11897/SP.J.1016.2022.01539
|
| [27] |
YANG Y, JUNTAO L, LINGLING P. Multi-robot path planning based on a deep reinforcement learning DQN algorithm[J]. CAAI Transactions on Intelligence Technology, 2020, 5(3): 177−183. doi: 10.1049/trit.2020.0024
|
| [28] |
ZHANG Z, WANG Y, PENG J, et al. An improved self-attention for long-sequence time-series data forecasting with missing values[J]. Neural Computing and Applications, 2024, 36(8): 3921−3940. doi: 10.1007/s00521-023-09347-6
|
| [29] |
NIU Z, ZHONG G, YU H. A review on the attention mechanism of deep learning[J]. Neurocomputing, 2021, 452: 48−62. doi: 10.1016/j.neucom.2021.03.091
|
| [30] |
OLIVER M A, WEBSTER R. Kriging. a method of interpolation for geographical information systems[J]. International Journal of Geographical Information Science, 1990, 4(3): 313−332. doi: 10.1080/02693799008941549
|
| [31] |
李鹏,靳德武,程建远,等. 基于Open CASCADE和普通克里金插值的透明矿井水文地质建模软件开发[J]. 煤矿安全,2020,51(5):138−141.
LI Peng, JIN Dewu, CHENG Jianyuan, et al. Development of transparent mine hydro-geological modeling software based on Open CASCADE and ordinary Kriging algorithm[J]. Safety in Coal Mines, 2020, 51(5): 138−141.
|
| [32] |
郭钊吾,宋晓夏,任海青,等. 东周窑井田多尺度三维地质建模[J]. 煤矿安全,2023,54(1):161−171.
GUO Zhaowu, SONG Xiaoxia, REN Haiqing, et al. Multi-scale 3D geological modeling of Dongzhouyao Coal Mine[J]. Safety in Coal Mines, 2023, 54(1): 161−171.
|
| [33] |
王金凤,翟雪琪,冯立杰. 基于SVR的煤矿安全资源与安全状态作用机理模型[J]. 煤矿安全,2014,45(6):222−225.
WANG Jinfeng, ZHAI Xueqi, FENG Lijie. Mechanism model for coal mine safety resources and safety status based on support vector regression[J]. Safety in Coal Mines, 2014, 45(6): 222−225.
|
| [34] |
成小雨,周爱桃,郭焱振,等. 基于随机森林与支持向量机的回采工作面瓦斯涌出量预测方法[J]. 煤矿安全,2022,53(10):205−211.
CHENG Xiaoyu, ZHOU Aitao, GUO Yanzhen, et al. Prediction method of gas emission based on random forest and support vector machine[J]. Safety in Coal Mines, 2022, 53(10): 205−211.
|
| [35] |
吕文玉,王海金,伍永平,等. 基于PSO-SVR预测模型的综采工作面周期来压研究[J]. 煤炭工程,2022,54(4):86−91.
LYU Wenyu, WANG Haijin, WU Yongping, et al. PSO-SVR prediction model of periodic weighting in fully mechanized working face[J]. Coal Engineering, 2022, 54(4): 86−91.
|
| [36] |
吴旋,来兴平,郭俊兵,等. 综采面区段煤柱宽度的PSO-SVM预测模型[J]. 西安科技大学学报,2020,40(1):64−70.
WU Xuan, LAI Xingping, GUO Junbing, et al. PSO-SVM prediction model of coal pillar width in fully mechanized mining face[J]. Journal of Xi’an University of Science and Technology, 2020, 40(1): 64−70.
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: