基于LSTM网络的矿山压力时空混合预测

doi:10.16180/j.cnki.issn1007-7820.2023.02.010

摘要/Abstract

摘要：

矿山压力失衡引起的顶板事故是矿山重大事故之一,超前感知综采工作面矿山压力的变化对保证煤层安全高效具有重要意义。为了提高矿压预测准确性,文中提出了一种基于LSTM网络的时空混合预测模型。该模型采用两个独立的LSTM网络分别提取采空区侧和支架移架侧的压力特征,然后将得到的数据通过全连接层融合,从而实现对矿压的共同预测。文中以MSE和MAE来评估基于LSTM的时空混合模型的预测效果,实验结果表明MSE和MAE分别下降了24.49%和35.24%,说明基于LSTM的时空混合预测模型优于传统LSTM预测模型,且时空混合模型预测方法较传统模型具有更高的可靠性和准确性,能够实现工作面推进过程中对矿压变化的有效预测。

关键词: 综采工作面, 液压支架, 矿压分析, 深度学习, 矿压预测, 时间序列预测, LSTM神经网络, 混合LSTM

Abstract:

The roof accident caused by the unbalanced pressure of the mine is one of the major mine accidents, so it is of great significance to perceive the change of the mine pressure in the fully mechanized mining face in advance to ensure the safety and efficiency of the coal seam. To improve the accuracy of mine pressure prediction, a spatiotemporal hybrid prediction model based on LSTM network is proposed in this study. Two independent LSTM networks are used to extract the pressure characteristics on the goaf side and the support moving side respectively, and the obtained data are merged through the fully connected layer, so as to realize the common prediction of mine pressure. Two indicators, MSE and MAE, are used to evaluate the prediction effect of the spatio-temporal mixture model based on LSTM. The experimental results show that MSE and MAE are reduced by 24.49% and 35.24%, respectively. The spatio-temporal mixture prediction model based on LSTM is excellent when compared with the traditional LSTM prediction model, and the prediction method of the spatio-temporal hybrid model has higher reliability and accuracy than the traditional model, and can predict the changes in the mine pressure during the advancement of the working face.

Key words: fully-mechanized mining face, hydraulic support, mine pressure analysis, deep learning, mine pressure prediction, time series prediction, LSTM neural network, hybrid LSTM

中图分类号:

TP18

余琼芳,牛冬阳. 基于LSTM网络的矿山压力时空混合预测[J]. 电子科技, 2023, 36(2): 67-72.

YU Qiongfang,NIU Dongyang. Mixed Prediction of Mine Pressure Time and Space Based on LSTM Network[J]. Electronic Science and Technology, 2023, 36(2): 67-72.

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参考文献 18

[1]	程敬义, 万志军, Peng Syd S, 等. 基于海量矿压监测数据的采场支架与顶板状态智能感知技术[J]. 煤炭学报, 2020, 45(6):2090-2103.
	Cheng Jingyi, Wan Zhijun, Peng Syd S, et al. Technology of intelligent sensing longwall shield supports status and roof strata based on massive shield pressure monitoring data[J]. Journal of China Coal Society, 2020, 45(6):2090-2103.
[2]	郭玉峰, 浦仕江, 付巍, 等. 综放仰采工作面覆岩移动规律及支架工作阻力确定[J]. 工矿自动化, 2021, 47(3):89-94.
	Guo Yufeng, Pu Shijiang, Fu Wei, et al. Movement law of overburden in upward fully mechanized working face anddetermination of support working resistance[J]. Industry and Mine Automation, 2021, 47(3):89-94.
[3]	常峰. 基于GA-BP神经网络的工作面顶板矿压预测模型应用研究[D]. 北京: 中国矿业大学, 2019.
	Chang Feng. Application research of the prediction model for the coal working face roof pressure based on GA-BP neural networks[D]. Beijing: China University of Mining and Technology, 2019.
[4]	张守祥, 张学亮, 刘帅, 等. 智能化放顶煤开采的精确放煤控制技术[J]. 煤炭学报, 2020, 45(6):2008-2020.
	Zhang Shouxiang, Zhang Xueliang, Liu Shuai, et al. Intelligent precise control technology of fully mechanized top coal caving face[J]. Journal of China Coal Society, 2020, 45(6):2008-2020.
[5]	程海星, 朱磊, 宋立平, 等. 基于逆向传播神经网络的工作面顶板矿压数据预测[J]. 煤矿安全, 2021, 52(5):216-220.
	Cheng Haixing, Zhu Lei, Song Liping, et al. Roof pressure data prediction for working face based on back propagation neural network[J]. Safety in Coal Mines, 2021, 52(5):216-220.
[6]	王润沛. 基于机器学习的分布式光纤监测覆岩变形矿压预测研究[D]. 西安: 西安科技大学, 2020.
	Wang Runpei. Research on the prediction of underground pressure based on distributed optical fiber monitoring of overburden rock deformation based on machine learning[D]. Xi'an: Xi'an University of Science and Technology, 2020.
[7]	赵仕艳, 谢子殿, 丁康康, 等. 粒子群优化BP-PID的矿井提升机调速系统[J]. 电子科技, 2021, 34(1):43-49.
	Zhao Shiyan, Xie Zidian, Ding Kangkang, et al. Particle swarm optimization BP-PID of rotor variable frequency speed in mine hoisting system[J]. Electronic Science and Technology, 2021, 34(1):43-49.
[8]	苏志毅. 胜利煤矿综采工作面矿山压力监测分析[J]. 煤炭与化工, 2019, 42(7):8-10.
	Su Zhiyi. Monitoring and analysis of mining pressure in fully mechanized face of Shengli Mine[J]. Coal and Chemical Industry, 2019, 42(7):8-10.
[9]	郭晋平. 煤矿综合机械化采煤工艺的发展与应用探析[J]. 冶金与材料, 2019, 39(5):67-68.
	Guo Jinping. Development and application of comprehensive mechanized coal mining technology in coal mines[J]. Metallurgy and Materials, 2019, 39(5):67-68.
[10]	张通, 赵毅鑫, 朱广沛, 等. 神东浅埋工作面矿压显现规律的多因素耦合分析[J]. 煤炭学报, 2016, 41(S2):287-296.
	Zhang Tong, Zhao Yixin, Zhu Guangpei, et al. A multi-coupling analysis of mining-induced pressure characteristics of shallow-depth coal face in Shendong mining area[J]. Journal of China Coal Society, 2016, 41(S2):287-296.
[11]	赵毅鑫, 杨志良, 马斌杰, 等. 基于深度学习的大采高工作面矿压预测分析及模型泛化[J]. 煤炭学报, 2020, 45(1):54-65.
	Zhao Yixin, Yang Zhiliang, Ma Binjie, et al. Deep learning prediction and model generalization of ground pressure for deep longwall face with large mining height[J]. Journal of China Coal Society, 2020, 45(1):54-65.
[12]	李泽萌. 时间窗设置对综采工作面矿压预测精度的影响研究[J]. 电脑与电信, 2020(4):14-18.
	Li Zemeng. Time window setting on prediction accuracy of rock pressure in fully mechanized working face[J]. Computers and Telecommunications, 2020(4):14-18.
[13]	章黎明. 王家山煤矿大倾角厚煤层综放采场矿压规律研究[J]. 煤炭科学技术, 2007, 35(12):22-26.
	Zhang Liming. Research on ground behavior law offully mechanized caving mining face in deep inclined seam of Wangjiashan Mine[J]. Coal Science and Technology, 2007, 35(12):22-26.
[14]	朱思润, 徐涛. 付村煤矿3_下1007工作面设备运输及安装工艺[J]. 山东煤炭科技, 2021, 39(3):114-115.
	Zhu Sirun, Xu Tao. Equipment transportation and installation technology of 3_Xia1007 face in Fucun Coal Mine[J]. Shandong Coal Science and Technology, 2021, 39(3):114-115.
[15]	郭文彬, 孙志文. 矿山压力与岩层控制原理[J]. 内蒙古煤炭经济, 2017(22):35-45.
	Guo Wenbin, Sun Zhiwen. Principles of mine pressure and strata control[J]. Inner Mongolia Coal Economy, 2017(22):35-45.
[16]	葛靖, 刘子龙. 基于CNN和LSTM的睡眠呼吸暂停检测算法[J]. 电子科技, 2021, 34(2):21-26.
	Ge Jing, Liu Zilong. The algorithm based on CNN and LSTM for sleep apnea syndrome detection[J]. Electronic Science and Technology, 2021, 34(2):21-26.
[17]	冯天晨. 基于深度学习的恶意评论检测[J]. 通讯世界, 2019, 26(3):193-194.
	Feng Tianchen. Malicious comment detection based on deep learning[J]. Telecom World, 2019, 26(3):193-194.
[18]	石文浩, 孟军, 张朋, 等. 融合CNN和Bi-LSTM的miRNA-lncRNA互作关系预测模型[J]. 计算机研究与发展, 2019, 56(8):1652-1660.
	Shi Wenhao, Meng Jun, Zhang Peng, et al. Prediction of miRNA-lncRNA interaction by combining CNN and Bi-LSTM[J]. Journal of Computer Research and Development, 2019, 56(8):1652-1660.

模型名称	MSE	MAE
混合LSTM	0.003 7	0.022 6
LSTM	0.004 9	0.034 9