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基于多传感器预测模型的煤矿长壁工作面瓦斯状态分析与安全预警策略研究

Research on coal mine longwall face gas state analysis and safety warning strategy based on multi-sensor forecasting models.

作者信息

Chang Haoqian, Meng Xiangrui, Wang Xiangqian, Hu Zuxiang

机构信息

School of Economics and Management, Anhui University of Science & Technology, Huainan, 232000, China.

出版信息

Sci Rep. 2024 Jun 14;14(1):13795. doi: 10.1038/s41598-024-64181-7.

DOI:10.1038/s41598-024-64181-7
PMID:38877166
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11178851/
Abstract

Intelligent computing is transforming safety inspection methods and response strategies in coal mines. Due to the significant safety hazards associated with mining excavation, this study proposes a multi-source data based predictive model for assessing gas risk and implementing countermeasures. By examining the patterns of gas dispersion at the longwall face, utilizing both temporal and spatial correlation, a predictive model is crafted that incorporates safety thresholds for gas concentrations, four-level early warning method and response strategy are devised by integrating weighted predictive confidence with these correlations. Initially tested using a public dataset from Poland, this method was later verified in coal mine in China. This paper discusses the validity and correlation of multi-source monitoring data in temporal and spatial correlation and proposes a risk warning mechanism based on it, which can be applied not only for safety warning but also for regulatory management.

摘要

智能计算正在改变煤矿的安全检查方法和应对策略。由于采矿挖掘存在重大安全隐患，本研究提出了一种基于多源数据的预测模型，用于评估瓦斯风险并实施应对措施。通过研究长壁工作面瓦斯扩散模式，利用时间和空间相关性，构建了一个包含瓦斯浓度安全阈值的预测模型，并通过将加权预测置信度与这些相关性相结合，设计了四级预警方法和应对策略。该方法最初使用来自波兰的公共数据集进行测试，后来在中国的煤矿中得到验证。本文讨论了多源监测数据在时间和空间相关性方面的有效性和相关性，并提出了基于此的风险预警机制，该机制不仅可用于安全预警，还可用于监管管理。

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本文引用的文献

1
Fuzzy multi-criteria decision-making framework for controlling methane explosions in coal mines.煤矿瓦斯爆炸控制的模糊多准则决策框架。
Environ Sci Pollut Res Int. 2024 Feb;31(6):9045-9061. doi: 10.1007/s11356-023-31782-0. Epub 2024 Jan 6.
2
Data on methane concentration collected by underground coal mine sensors.地下煤矿传感器收集的甲烷浓度数据。
Data Brief. 2021 Oct 11;39:107457. doi: 10.1016/j.dib.2021.107457. eCollection 2021 Dec.
3
Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine.
基于概率密度估计机的煤矿生产中瓦斯浓度的早期预警。
Sensors (Basel). 2021 Aug 25;21(17):5730. doi: 10.3390/s21175730.
4
Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks.基于人工神经网络的长壁区域甲烷浓度预测。
Int J Environ Res Public Health. 2019 Apr 18;16(8):1406. doi: 10.3390/ijerph16081406.
5
Long short-term memory.长短期记忆
Neural Comput. 1997 Nov 15;9(8):1735-80. doi: 10.1162/neco.1997.9.8.1735.