Research progress and prospect of mine fire intelligent monitoring and early warning technology in recent 20 years
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摘要:
为加强矿井火灾智能监测预警系统建设，提出了矿井火灾智能监测预警技术研究思路，从矿井火灾智能感知技术及装备、预测技术及模型、智能预警系统及平台3方面综述了矿井火灾智能监测预警技术研究进展。首先，总结了内外因火灾信息监测技术及装备，归纳了基于图像视频识别的识别流程，阐述了多源信息融合在火灾监测过程中的应用情况。其次，介绍了矿井火灾的预测技术及模型，包括支持向量机、人工神经网络、随机森林算法等机器学习算法。然后，阐述了矿井火灾预警系统及平台：在总结矿井煤自燃和外因火灾分级预警技术的基础上，介绍了矿井火灾预警系统平台的感知层、网络层、服务融合层以及应用层方面的进展内容；归纳了预警系统各层的内涵及应用框架；搭建了矿井火灾智能监测预警系统。最后，展望了我国矿井火灾智能监测预警技术的未来发展趋势，具体包括：在矿井火灾信息智能感知技术方面，提出加强传感技术及装备研发；在矿井火灾智能预测技术方面，提出加强隐蔽火源的位置探寻方法研究，构建火灾灾害透明化模型；在矿井火灾智能预警系统建设方面，提出将大数据融入智能判识，查明矿井火灾风险源及隐蔽火源位置的预报，实现特殊条件下煤自燃的预警。在矿井火灾智能判识与防控技术联动方面，提出利用大型语言模型在智能判识的基础上实现对矿井火灾的自适应防控。
Abstract:In order to strengthen the construction of mine fire intelligent monitoring and early warning system, the research idea of mine fire intelligent monitoring and early warning technology, and summarizes the research progress of mine fire intelligent monitoring and early warning technology is put forward. The research progress from three aspects of mine fire intelligent perception technology and equipment, prediction technology and modeling, and intelligent early warning system and platform is summarized. First, the internal and external cause fire information monitoring technology and equipment are summarized, the recognition process based on image-video recognition is summarized, and the content of multi-source information fusion and its application in the fire monitoring process are described. Secondly, the prediction techniques and models for mine fires are introduced, including machine learning algorithms such as support vector machine, artificial neural network, and random forest algorithms. Then, the mine fire early warning system and platform are elaborated. On the basis of summarizing the graded early warning technology for spontaneous coal combustion and exogenous fires in mines, the progress of the mine fire early warning system platform in terms of perception layer, network layer, service fusion layer and application layer is introduced. The connotation of each layer of the early warning system and the application framework are summarized, and the intelligent monitoring and early warning system for fires in mines is constructed. Finally, the future development trend of mine fire intelligent monitoring and early warning technology in China is outlooked including following aspects. In terms of intelligent perception technology of mine fire information, it is proposed to strengthen the research and development of sensing technology and equipment. In terms of intelligent prediction technology for mine fires, it is proposed to strengthen the research on the location exploration method of hidden fire sources and construct a transparent model of fire disasters. In terms of the construction of an intelligent early warning system for mine fires, it is proposed to integrate big data into intelligent judgment, identify the risk sources of mine fire and forecast the sources’ location of hidden fire, and realize the early warning of spontaneous coal combustion under special conditions. In terms of the linkage between the intelligent judgment and prevention and control technology for mine fires, it is proposed to utilize large-scale linguistic models to realize the self-adaptive prevention and control of mine fires on the basis of intelligent judgment.
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图 22 基于物联网的火灾监测预警系统3层结构
Figure 22. Three-layer structure of IoT-based fire monitoring and early warning system
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表 1 近5年部分矿井火灾发生事故统计
Table 1 Statistics of some mine fire accidents in the past 5 years
日期 地点 火灾原因 火灾结果 2019−03−15 东升阳胜煤业有限公司 15203综采工作面上隅角堆积的瓦斯被引燃 3人被困 2019−05−24 龙成煤综合利用有限公司 809号传送带处的煤粉自燃引发火灾 2人死亡
6人受伤2019−06−03 山西保利平山煤业股份有限公司 31016运输巷掘进工作面施工瓦斯抽采钻孔过程中，因干打眼引发钻孔内着火 9人受伤，2人重伤 2019−11−19 山东能源肥城矿业梁宝寺煤矿 3306掘进工作面发生火灾 11人被困 2019−11−22 华宁县华盖山煤矿 掘进工作面违规采用气焊切割，乙炔泄漏并着火燃烧 8人受伤 2019−12−12 云南恩洪煤矿 工作面采空区发生火灾 8人受伤 2020−07−20 湖南高峰煤业团结煤矿 巷道式采煤造成局部瓦斯积聚，失爆的煤电钻短路产生火花，引燃瓦斯 1人死亡，2人重伤 2020−09−27 重庆渝新能源松藻煤矿 输送带与煤混合燃烧产生一氧化碳超限 16人死亡，42人受伤 2020−12−04 重庆吊水洞煤业公司 违规使用氧气/液化石油气切割，高温熔渣引燃吸水井内沉积油垢 23人死亡，1人重伤 2021−01−19 贵州大方瑞丰煤矿 穿孔误穿采空区导致一氧化碳超限 3人死亡，1人受伤 2022−02−07 宁夏煤业枣泉煤矿 采煤机割煤引发煤尘爆燃 1人死亡，8人受伤 
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阶段 预警等级 温度范围/℃ 判定临界值 潜伏阶段 预警
初值$ {T}_{0} < 30 $ $ {R}_{0}=\left\{\begin{array}{l}\varphi \left({{\mathrm{O}}}_{2}\right) > 18\%\cap \\ \varphi \left({\mathrm{CO}}\right) > 0.005\%\end{array}\right\}\cup $
$ \left\{ \left(\begin{array}{l}\varphi({{\mathrm{O}}}_{2})\in \left(15\%,18\%\right)\cap \\ \varphi \left({\mathrm{CO}}\right) > 0.01\%\end{array}\right)\right\}\cup $
$ \left\{ \left(\begin{array}{l}\varphi({{\mathrm{O}}}_{2})\in \left(12\%,15\%\right)\cap \\ \varphi \left({\mathrm{CO}}\right) > 0.015\%\end{array}\right)\right\}\cup $
$ \left\{\begin{array}{l}\varphi \left({{\mathrm{O}}}_{2}\right) < 12\%\cap \\ \varphi \left({\mathrm{CO}}\right) > 0.02\%\end{array}\right\} $复合阶段 灰色 $ {T}_{1}\in \left(\mathrm{30,50}\right) $ $ {R}_{1}={R}_{0}\cap \left\{\dfrac{100\varphi \left({\mathrm{CO}}\right)}{\varphi \left({{\mathrm{O}}}_{2}\right)} > 0.2～0.3\right\} $ 自热阶段 蓝色 $ {T}_{2}\in \left(\mathrm{50,70}\right) $ $ {R}_{2}={R}_{1}\cap \left\{\dfrac{100\varphi \left({\mathrm{CO}}\right)}{\varphi \left({{\mathrm{O}}}_{2}\right)} > 0.4～0.5\right\} $ 临界阶段 黄色 $ {T}_{3}\in \left(\mathrm{70,100}\right) $ $ {R}_{3}={R}_{2}\cap \left\{\dfrac{100\varphi \left({\mathrm{CO}}\right)}{\varphi \left({{\mathrm{O}}}_{2}\right)} > 0.6～0.8\right\} $ 热解阶段 橙色 $ {T}_{4}\in \left(\mathrm{100,150}\right) $ $ {R}_{4}={R}_{3}\cap \left\{\varphi \left({{\mathrm{C}}}_{2}{{\mathrm{H}}}_{4}\right) > 0\right\} $ 裂变阶段 红色 $ {T}_{5}\in \left(\mathrm{150,210}\right) $ $ {R}_{5}={R}_{4}\cap \left\{k=\dfrac{\varphi \left({{\mathrm{C}}}_{2}{{\mathrm{H}}}_{4}\right)}{{\varphi \left({{\mathrm{C}}}_{2}{{\mathrm{H}}}_{6}\right)}_{\max}}\right\} $ 燃烧阶段 黑色 $ {T}_{6} > 210 $ $ {R}_{6}={R}_{5}\cap \left\{\mathrm{明}\mathrm{火}\mathrm{、}\mathrm{明}\mathrm{烟}\mathrm{及}\mathrm{其}\mathrm{他}\mathrm{现}\mathrm{象}\right\} $
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自燃阶段 预警级别 温度范围/℃ 气体指标阈值 采取措施 复合阶段 灰色 T∈[30,50) $ {R}_{1}={R}_{0}\cap \left\{100\times{\mathrm{ CO}}/\Delta {{\mathrm{O}}}_{2}\in \left(\mathrm{0.2,0.3}\right)\right\} $ 监测警报 自加热阶段 蓝色 T∈[50,70) $ {R}_{2}={R}_{0}\cap \left\{100\times {\mathrm{CO}}/\Delta {{\mathrm{O}}}_{2}\in \left(\mathrm{0.3,0.5}\right)\right\} $ 注氮隔氧降温 活化阶段 黄色 T∈[70,110) $ {R}_{3}={R}_{0}\cap \left\{100\times {\mathrm{CO}}/\Delta {{\mathrm{O}}}_{2} > 0.5\right\}\bigcap \left\{{{\mathrm{C}}}_{2}{{\mathrm{H}}}_{4} > 0\right\} $ 热分解阶段 橙色 T∈[110,150) $ {R}_{4}={R}_{3}\bigcap \left\{{{\mathrm{C}}}_{2}{{\mathrm{H}}}_{4} > 0\right\}\cap {\left({{\mathrm{C}}}_{2}{{\mathrm{H}}}_{4}/{{\mathrm{C}}}_{2}{{\mathrm{H}}}_{6}\right)}_{\min} $ 降温隔氧降温 裂变阶段 红色 T∈[150,250) $ {R}_{5}={R}_{4}\bigcap {\left({\mathrm{CO}}/{\mathrm{C}}{{\mathrm{O}}}_{2}\right)}_{\max} $ 综合协同灭火 燃烧阶段 黑色 T∈[250,400) $ {R}_{6}={R}_{5}\bigcap \left\{{\left({{\mathrm{C}}}_{2}{{\mathrm{H}}}_{4}/{{\mathrm{C}}}_{2}{{\mathrm{H}}}_{6}\right)}_{\max}\right\} $
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表 4 浸水煤低温氧化分段特性
Table 4 Characteristics of low-temperature oxidizing segments of water-soaked coal
阶段 温度/℃ 指标 吸氧蓄热阶段 30～100 $ \dfrac{\varphi \left(\mathrm{C}\mathrm{O}\right)}{\varphi \left(\mathrm{C}\mathrm{O}_2\right)}\leqslant 0.1 或$$ \dfrac{\varphi \left(\mathrm{O}_2\right)}{ \varphi (\mathrm{C}\mathrm{O}_2)- \varphi(\mathrm{C}\mathrm{O})}\geqslant 0.02 $ 自热氧化阶段 100～140 $ 0.8\leqslant \dfrac{\varphi \left({\mathrm{C}}_{2}{\mathrm{H}}_{4}\right)}{\varphi \left({\mathrm{C}}_{2}{\mathrm{H}}_{6}\right)}\leqslant 1.10 $ 加速氧化阶段 140～230 $ \dfrac{\varphi \left(\mathrm{C}\mathrm{O}\right)}{\varphi \left(\mathrm{C}\mathrm{O}_2\right)}\geqslant 0.5 或$$ \dfrac{\varphi \left({\mathrm{O}}_{2}\right)}{\varphi (\mathrm{C}{\mathrm{O}}_{2})-\varphi(\mathrm{C}\mathrm{O})}\leqslant 0.005 $
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表 5 电缆火灾分级防控
Table 5 Cable fire hierarchy prevention and control
阶段 现象 极早期 电缆线芯导体发热，热量聚集 前期 阴燃 早期 明火 中期 电缆群燃烧 晚期 电缆烧尽
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表 6 输送带火灾分级防控
Table 6 Tape fire hierarchy prevention and control
阶段 现象 指标 正常阶段 无 $ 1.5\;{t}_{{\mathrm{p}}}\geqslant {t}_{{\mathrm{c}}} $ 初始阶段 出现发热现象 $ 3.4\;{\rm{^\circ C}}\geqslant {t}_{{\mathrm{c}}}\geqslant 1.5\;{t}_{{\mathrm{p}}} $ 发展阶段 出现烟雾伴有焦糊味 $ {t}_{{\mathrm{R}}}\geqslant {t}_{{\mathrm{c}}}\geqslant 3.4\;{\rm{^\circ C}} $ 燃烧阶段 出现明火，烟雾并伴有气体异常、
断电、断带、设备停转等现象$ {t}_{{\mathrm{c}}} > {t}_{{\mathrm{R}}} $
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[1] 王双明,申艳军,宋世杰,等. “双碳”目标下煤炭能源地位变化与绿色低碳开发[J]. 煤炭学报,2023,48(7):2599−2612. WANG Shuangming,SHEN Yanjun,SONG Shijie,et al. Change of coal energy status and green and low-carbon development under the “dual carbon” goal[J]. Journal of China Coal Society,2023,48(7):2599−2612.
[2] 国家统计局. 中华人民共和国2022年国民经济和社会发展统计公报 [EB/OL]. [2023−02−28]. https://www.stats.gov.cn/sj/zxfb/202302/t20230228_1919011.html. [3] 王德明. 矿井火灾学 [M]. 徐州:中国矿业工学出版社,2008:1−30. [4] 丁 震,李浩荡,张庆华. 煤矿灾害智能预警架构及关键技术研究[J]. 工矿自动化,2023,49(4):15−22. DING Zhen,LI Haodang,ZHANG Qinghua. Research on intelligent hazard early warning architecture and key technologies for coal mine[J]. Journal of Mine Automation,2023,49(4):15−22.
[5] 邓 军. 煤田火灾防治理论与技术 [M]. 徐州:中国矿业大学出版社,2014:1−26. [6] 白光星,陈炜乐,孙 勇,等. 煤矿带式输送机运输火灾风险智能监测与早期预警技术研究进展[J]. 煤矿安全,2022,53(9):47−54. BAI Guangxing,CHEN Weile,SUN Yong,et al. Research progress on intelligent monitoring and early warning technologyof fire risk in coal mine belt conveyor transportation[J]. Safety in Coal Mines,2022,53(9):47−54.
[7] MOHAMMAD Ali Moridi,MOSTAFA Sharifzadeh,YOUHEI Kawamura,et al. Development of wireless sensor networks for underground communication and monitoring systems (the cases of underground mine environments) [J]. Tunnelling and Underground Space Technology,2018,73:127−138.
[8] MUDULI Lalatendu,MISLIRA Devi Prasad,JANA Prisanta K. Application of wireless sensor network for environmental monitoring in underground coal mines:a systematic review[J]. Journal of Network and Computer Applications,2018,106:48−67. doi: 10.1016/j.jnca.2017.12.022
[9] WU Bing,WANG Jingxin,ZHONG Mingyu,et al. Multidimensional analysis of coal mine safety accidents in China-70 years review [J] Mining Metallurgy & Exploration,2023,40(1):253−262.
[10] 中华人民共和国中央人民政府. 关于印发《关于加快煤矿智能化发展的指导意见》的通知 [EB/OL]. [2020-02-25]. https://www.gov.cn/zhengce/zhengceku/2020-03/05/content_5487081.htm. [11] 王国法. 《煤矿智能化建设指南(2021年版)》解读——从编写组视角进行解读[J]. 智能矿山,2021,2(4):2−9. WANG Guofa. Interpretation of the coal mine intelligent construction guidelines (2021 Edition) - from the perspective of the writing group[J]. Journal of Intelligent Mine,2021,2(4):2−9.
[12] LU Peizhong,HUANG Yuxuan,JIN Peng,et al. Optimization of a marker gas for analyzing and predicting the spontaneous combustion period of coking coal [J]. Energies,2023,16(23):7802.
[13] 邓 军,白祖锦,肖 旸,等. 煤自燃指标体系试验研究[J]. 安全与环境学报,2018,18(5):1756−1761. DENG Jun,BAI Zujin,XIAO Yang,et al. Experimental investigation and examination for the indexical system of the coal spontaneous combustion[J]. Journal of Safety and Environment,2018,18(5):1756−1761.
[14] WANG Beifang,LYU Yuanhao,LIU Chunbao. Research on fire early warning index system of coal mine goaf based on multi-parameter fusion [J]. Research Square,2024,14(1):485.
[15] 易 欣,葛 龙,张少航,等. 基于指标气体法对水浸煤的氧化特性研究[J]. 煤炭科学技术,2023,51(3):130−136. YI Xin,GE Long,ZHANG Shaohang,et al. Research on oxidation characteristics of aqueous coal based on index gas method[J]. Coal Science and Technology,2023,51(3):130−136.
[16] 张军杰. 煤矿束管监测系统的现状与发展趋势[J]. 煤矿安全,2019,50(12):89−92. ZHANG Junjie. Current situation and development trend of coal mine beam tube monitoring system[J]. Safety in Coal Mines,2019,50(12):89−92.
[17] 梁运涛,田富超,冯文彬,等. 我国煤矿气体检测技术研究进展[J]. 煤炭学报,2021,46(6):1701−1714. LIANG Yuntao,TIAN Fuchao,FENG Wenbin,et al. Research progress of coal mine gas detection technology in China[J]. Journal of China Coal Society,2021,46(6):1701−1714.
[18] KONG Biao,WANG Enyuan,LI Zenghua,et al. Time-varying characteristics of electromagnetic radiation during the coal-heating process [J]. International Journal of Heat and Mass Transfer,2017,108:434−442.
[19] 王 栋,陆 伟,李金亮,等. 煤矿输气与控制共用管线的高正压束管监测系统研究 [J]. 煤炭科学技术,2019,47(12):141−144. WANG Dong,LU Wei,LI Jinliang,et al. Study on high positive pressure beam tube monitoring system of sharing pipeline for gastransmission and pump control [J]. Coal Science and Technology 2019,47(12):141−144.
[20] 赵晓夏. 正压束管监测系统输气关键部件的研发[J]. 煤矿安全,2020,51(7):92−95. ZHAO Xiaoxia. Research and development of key components of positive pressure beam tube monitoring system[J]. Safety in Coal Mines,2020,51(7):92−95.
[21] 姜 龙. 基于TDLAS的煤矿井下激光型束管监测系统设计 [D]. 济南:山东大学,2018. JIANG Long. Design of laser beam tube monitoring system in coal mine based on TDLAS [D]. Jinan:Shandong University,2018.
[22] 陈晓坤. 煤自燃多源信息融合预警研究 [D]. 西安:西安科技大学,2013. CHEN Xiaokun. Study on early warning method for coal spontaneous combustion based on multi-information fusion [D]. Xi’an :Xi’an University of Science and Technology,2013.
[23] CAI Yin,ZHANG Bingbing,WANG Jingyuan,et al. Research on a bimetallic-sensitized FBG temperature sensor [J]. Review of Scientific Instruments,2023,94(3):035010.
[24] 程永新. 煤矿带式输送机火灾光纤传感检测技术研究 [J]. 煤炭科学技术,2019,47(2):131−135. CHENG Yongxin. Technology research on optical fiber sensing detection for belt conveyor fire in coal mine [J]. Coal Science and Technology,2019,472):131−135.
[25] GUO Junyi,SUN Mengya,FANG Jinhui,et al. High-sensitivity seawater salinity sensing with cladding etched fiber bragg grating technology [J]. Ieee Sensors Journal,2023,23(13):14182−14192.
[26] LIU Qinpeng,WANG Danyang,WANG Chunfang,et al. Ultrasensitive temperature sensor based on optic fiber Fabry-Perot interferometer with Vernier effect [J]. Optics Communications,2023,541:129567.
[27] YANG Yu,NIU Yanxiong,WANG Botao,et al. The research on improving the spatial resolution of radiant optical fiber temperature sensor [J]. Measurement Science and Technology,2023,34(3):035111.
[28] RODOLFO A. CARRILLO-BETANCOURT,A. DARIO Lopez-Camero,JUAN Hernandez-Cordero. Luminescent polymer composites for optical fiber sensors [J]. Polymers,2023,15(3):505.
[29] 袁俊杰,刘喜银,张萌颖,等. 干涉型光纤传感器相位生成载波技术研究进展[J]. 激光杂志,2023,44(9):1−10. YUAN Junjie,LIU Xiyin,ZHANG Mengying,et al. Research progress of phase generation carrier technology for interferometric fiber optic sensor[J]. Laser Journal,2023,44(9):1−10.
[30] SEKINE Masashi,FURUYA Masahiro,FURUYA Masahiro. Development of measurement method for temperature and velocity field with optical fiber sensor [J]. Sensors,2023,23(3):1627.
[31] ZHANG Xuebing,ZHENG Zhizhou,WANG Li,et al. A Quasi-Distributed optic fiber sensing approach for interlayer performance analysis of ballastless track-type II plate [J]. Optics & Laser Technology,2024,170:110237.
[32] 王 宁,朱 永,张 洁. 高温高压环境下光纤法布里-珀罗传感技术研究现状 [J]. 激光与光电子学进展,2023,60(11):70−82. WANG Ning,ZHU Yong,ZHANG Jie. Fiber-optic fabry-perot sensing technology in high-temperature environments:a review,2023,60(11):70−82.
[33] GUI Xin,LI Zhengying,FU Xuelei,et al. Distributed optical fiber sensing and applications based on large-scale fiber bragg grating array:review [J]. Journal of Lightwave Technology,2023,41(13):4187-4200.
[34] ZHANG Chao,BAO Yan,CUI Tao,et al. Polarization independent phase-OFDR in rayleigh-based distributed sensing [J]. Journal of Lightwave Technology,2023,41(8):2518-2525.
[35] EKECHUKWU Gerald,SHARMA Jyotsna. Degradation analysis of single-mode and multimode fibers in a full-scale wellbore and its impact on DAS and DTS measurements [J]. Ieee Sensors Journal,2023,23(9):9287-9300.
[36] ZHANG Fengjie,HAN Dongyang,QIN Yueping,et al. Optimization of the monitoring of coal spontaneous combustion degree using a distributed fiber optic temperature measurement system:field application and evaluation [J]. Fire-Switzerland,2023,6(11):410.
[37] 张辛亥,刘 强,郑学召,等. 基于ZigBee的采空区无线自组网测温系统分析[J]. 煤炭工程,2012(9):122−124. doi: 10.3969/j.issn.1671-0959.2012.09.044 ZHANG Xinhai,LIU Qiang,ZHENG Xuezhao,et al. Analysis of ZigBee-based wireless self-organized network temperature measurement system in mining area[J]. Coal Engineering,2012(9):122−124. doi: 10.3969/j.issn.1671-0959.2012.09.044
[38] 文 虎,吴 慷,马 砺,等. 分布式光纤测温系统在采空区煤自燃监测中的应用 [J]. 煤矿安全,2014,45(5):100−102,105. WEN Hu,WU Kang,MA Li,et al. Application of distributed optical fiber temperature measurement system in monitorino goaf coal spontaneous combustion [J]. Safety in Coal Mines,2014,45(5):100−102,105.
[39] LIU Zhaojun,TIAN Bian,JIANG Zhuangde,et al. Flexible temperature sensor with high sensitivity ranging from liquid nitrogen temperature to 1200 °C [J]. International Journal of Extreme Manufacturing,2022,5(1):015601.
[40] 魏元焜. 基于压缩感知理论的声学层析成像温度场重建研究 [D]. 沈阳:沈阳工业大学,2023. WEI Yuankun. Research on temperature field reconstruction of acoustictomography based on compressed sensing theory [D]. Shenyang: ShenYang University of Technology,2023.
[41] TANG Chenggang,WANG Yuqiang,LI Yuning,et al. A review of graphene-based temperature sensors [J]. Microelectronic Engineering,2023,278:112015.
[42] 杨 飞. 高庄煤矿近距离煤层开采采空区遗煤自燃防控技术研究 [D]. 济南:山东科技大学,2018. YANG Fei. Study on prevention and control technology of sspontaneous combustion of coal in goaf of short diatance seam mining in Gao Zhuang Mine [D]. Jinan:Shandong University of Science and Technology,2018.
[43] TANG Qianying,ZHONG Fang,LI Qing,et al. Infrared photodetection from 2D/3D van der waals heterostructures [J]. Nanomaterials,2023,13(7):1169.
[44] 孙继平,孙雁宇,范伟强. 基于可见光和红外图像的矿井外因火灾识别方法[J]. 工矿自动化,2019,45(5):1−5,21. SUN Jiping,SUN Yanyu,FAN Weiqiang. Recognition of exogenous fires in mines based on visible and infrared images[J]. Industry and Mine Automation,2019,45(5):1−5,21.
[45] 赵勇毅,常建华,沈 婉,等. 矿井内CH4与CO2双组分NDIR传感器的设计与实现[J]. 红外技术,2019,41(8):778−785. ZHAO Yongyi,CHANG Jianhua,SHEN Wan,et al. NDIR sensor for CH,and CO,gas concentration detection in mines[J]. Infrared Technology,2019,41(8):778−785.
[46] 王伟峰,邓 军,侯媛彬,等. 基于PSO-SVM的矿用CO传感器非线性补偿方法研究[J]. 仪表技术与传感器,2017(9):5−7,51. doi: 10.3969/j.issn.1002-1841.2017.09.002 WANG Weifeng,DENG Jun,HOU Yuanbin,et al. Study on nonlinear compensation method of mine carbon monoxide sensor based on PSO-SVM[J]. Instrument Technique and Sensor,2017(9):5−7,51. doi: 10.3969/j.issn.1002-1841.2017.09.002
[47] 孙瑞彩,龙秉政. 基于SolidWorks Flow Simulation的矿用烟雾传感器气室结构流体仿真分析[J]. 煤矿机械,2023,44(10):92−94. SUN Ruicai,LONG Bingzheng. Fluid simulation analysis of gas chamber structure of mine smoke sensor based on solid works flow simulation[J]. Coal Mine Machinery,2023,44(10):92−94.
[48] 马 砺,范新丽,张晓龙,等. 矿用CH4-CO2红外传感器温度补偿算法模型研究[J]. 激光与红外,2020,50(12):1456−1462. doi: 10.3969/j.issn.1001-5078.2020.12.006 MA Li,FAN Xinli,ZHANG Xiaolong,et al. Study on temperature compensation algorithm model of mine CH4-CO2 infraredsensor[J]. Laser & Infrared,2020,50(12):1456−1462. doi: 10.3969/j.issn.1001-5078.2020.12.006
[49] GONG Weihua,HU Jie,WANG Zhaowei,et al. Recent advances in laser gas sensors for applications to safety monitoring in intelligent coal mines [J]. Frontiers in Physics,2022,10:1058475.
[50] WANG Xuwei,HU Xiangming,LIANG Yuntao,et al. Early Warning of coal spontaneous combustion:a study of CO response mechanism based on PANI/Ti3AlC2 composite gas sensing film [J]. Chemistry Select,2022,7(26):e202201563.
[51] 张子良. 基于AHP多特征融合的矿用烟雾传感器设计[J]. 煤矿机械,2023,44(6):6−10. ZHANG Ziliang. Design of mine smoke sensor based on AHP multi feature fusion[J]. Coal Mine Machinery,2023,44(6):6−10.
[52] 王晓强,米万升,杨永辰.基于非接触模式的采空区遗煤自燃预测红外有效探测距离研究[J/OL].红外技术:1-8[2023-12-20].http://kns.cnki.net/kcms/detail/53.1053.TN.20231206.1117.002.html. WANG Xiaoqiang, MI Wansheng, YANG Yongchen. Research on infrared effective detection distance for predicting spontaneous combustion of goaf residual coal based on non-contact mode [J/OL]. Infrared Technology:1−8[2023-12-20]. https://link.cnki.net/urlid/53.1053.TN.20231206.1117.002.
[53] 范伟强. 矿井外因火灾双光谱图像监测方法研究 [D]. 徐州:中国矿业大学,2022. FAN Weiqiang. Research on dual-spectrum image monitoring method for mine external fire [D]. Xuzhou:China University of Mining and Technology,2022.
[54] 李光宇,李守军,缪燕子. 基于机器视觉和灰色模型的矿井外因火灾辨识与定位方法[J]. 矿业安全与环保,2023,50(2):82−87. LI Guangyu,LI Shoujun,MIAO Yanzi. ldentification and positioning method of mine external fire based on machine vision and grey model[J]. Mining Safety & Environmental Protection,2023,50(2):82−87.
[55] 刘孝军,王 飞. 基于AI的煤矿视频智能分析技术[J]. 煤炭科学技术,2022,50(S2):260−264. LIU Xiaojun,WANG Fei. Application of video intelligent analysis technology in coal mine based oncomputer vision[J]. Coal Science and Technology,2022,50(S2):260−264.
[56] 孙继平,李小伟,徐 旭,等. 矿井电火花及热动力灾害紫外图像感知方法研究[J]. 工矿自动化,2022,48(4):1−4,95. SUN Jiping,LI Xiaowei,XU Xu,et al. Research on ultraviolet image perception method of mine electric spark and thermal power disaster[J]. Industry and Mine Automation,2022,48(4):1−4,95.
[57] 范伟强,李晓宇,刘 毅,等. 基于可见光视觉特征融合的矿井外因火灾监测方法[J]. 矿业科学学报,2023,8(4):529−537. FAN Weiqiang,LI Xiaoyu,LIU Yi,et al. Mine external fire monitoring method using the fusion of visible visual features[J]. Journal of Mining Science and Technology,2023,8(4):529−537.
[58] 孙继平,崔佳伟. 矿井外因火灾感知方法 [J]. 工矿自动化,2021,47(4):1−5,38. SUN Jiping,CUl Jiawei. Mine external fire sensing method [J]. Industry and Mine Automation,2021,47(4):1−5,38.
[59] 刘晓琴. 基于视频图像的矿井火灾火焰识别方法研究 [D]. 西安:西安建筑科技大学,2023. LIU Xiaoqin. Research on mine fire flame recognition method based onvideo image [D]. Xi’an:Xi’an University of Architecture and Technology,2023.
[60] 袁 洁,袁 伟,贾 阳,等. 一种基于纹理特征的主动红外烟雾识别方法[J]. 安全与环境学报,2016,16(2):86−89. YUAN Jie,YUAN Wei,JIA Yang,et al. Renovated identifying method of the active infrared smoke based on the texture feature analysis[J]. Journal of Safety and Environment,2016,16(2):86−89.
[61] 单亚锋,马艳娟,付 华,等. 分布式光纤测温系统在煤矿火灾监测中的应用[J]. 传感技术学报,2014,27(5):704−708. doi: 10.3969/j.issn.1004-1699.2014.05.025 SHAN Yafeng,MA Yanjuan,FU Hua,et al. Application of distributed optical fiber temperature measurement system in coal mine fire monitoring system[J]. Chinese Journal of Sensors and Actuators,2014,27(5):704−708. doi: 10.3969/j.issn.1004-1699.2014.05.025
[62] 赵 端,李 涛,董彦强,等. 基于边缘智能的煤矿外因火灾感知方法[J]. 工矿自动化,2022,48(12):108−115. ZHAO Duan,LI Tao,DONG Yanqiang,et al. Coal mine external fire detection method based on edge intelligence[J]. Industry and Mine Automation,2022,48(12):108−115.
[63] QIU Xuanbing,LI Jie,WEI Yongbo,et al. Study on the oxidation and release of gases in spontaneous coal combustion using a dual-species sensor employing laser absorption spectroscopy [J]. Infrared Physics & Technology,2019,102:103042.
[64] 王伟峰,张宝宝,王志强,等. 基于YOLOv5的矿井火灾视频图像智能识别方法[J]. 工矿自动化,2021,47(9):53−57. WANG Weifeng,ZHANG Baobao,WANG Zhiqiang,et al. Intelligent identification method of mine fire video images based on YOLOv5[J]. Industry and Mine Automation,2021,47(9):53−57.
[65] WANG Zilong,ZHANG Tianhang,HUANG Xinyan. Predicting real-time fire heat release rate by flame images and deep learning [J]. Proceedings of the Combustion Institute,2023,39(3):4115−4123.
[66] LI Sen,YUN Junying,FENG Chunyong,et al. An indoor autonomous inspection and firefighting robot based on SLAM and flame image recognition [J]. Fire-Switzerland,2023,6(3):93.
[67] 李 涛. 矿井火灾边缘智能检测系统设计与研究 [D]. 徐州:中国矿业大学,2023. LI Tao. Design and research of mine fire edge intelligent detection system [D]. Xuzhou:China University of Mining and Technology,2023.
[68] 朱红青,杨成轶,秦晓峰,等. 瞬变电磁法——整合矿井火区探测的有效方法[J]. 科技导报,2014,32(25):2. ZHU Hongqing,YANG Chengyi,QIN Xiaofeng,et al. Integrated coal mine fire district detecting method based on transient electromagnetic method[J]. Science & Technology Review,2014,32(25):2.
[69] CHEN Youying,SHEN Yixin,XIAO Shiyun,et al. A detailed magnetic characterization of combustion products from various metamorphic grade coals [J]. Journal of Applied Geophysics,2023,217:105168.
[70] 张辛亥,王 辉,郭 戎,等. 松散煤岩中放射性氡多角度扩散试验装置研制及应用[J]. 安全与环境学报,2016,16(6):80−84. ZHANG Xinhai,WANG Hui,GUO Rong,et al. Development and application of multiangle diffusion test device for radioactive radon in loose coal rock[J]. Journal of Safety and Environment,2016,16(6):80−84.
[71] 周 斌,周文强,董智宇,等. 氧化升温过程中煤岩介质体氡析出特性实验研究[J]. 煤炭学报,2020,45(S2):859−866. ZHOU Bin,ZHOU Wenqiang,DONG Zhiyu,et al. Experimental study on radon exhalation characteristics of coal and rock duringoxidation and heating[J]. Journal of China Coal Society,2020,45(S2):859−866.
[72] 刘思鑫,李洪先,王国芝,等. 基于SF6示踪试验的孤岛面采空区漏风规律研究 [J]. 煤炭技术,2021,40(12):166−170. LIU Sixin,LI Hongxian,WANG Guozhi,et al. Study on leakage law of lsolated lsland surface mining area based on SF6 tracer test [J]. Coal Technology 2021,40(12):166−170.
[73] 叶庆树,戴广龙,李 鹏,等. 基于双示踪技术浅埋煤层采空区地表漏风规律研究[J]. 煤炭工程,2020,52(7):83−87. YE Qingshu,DAl Guanglong,LI Peng,et al. Air leakage law of surface above shallow coal seam goaf based on dual-element tracing[J]. Coal Engineering,2020,52(7):83−87.
[74] 牟 义. 神府矿区隐蔽采空区相关致灾因素分析及勘查技术[J]. 地球物理学进展,2020,35(3):1017−1024. doi: 10.6038/pg2020DD0268 MU Yi. Analysis of disaster-causing factors and exploration techniques in concealed minedareas in Shenfu mining area[J]. Progress in Geophysics,2020,35(3):1017−1024. doi: 10.6038/pg2020DD0268
[75] GUO Jun,SHANG Haoyu,CAI Guobin,et al. Early detection of coal spontaneous combustion by complex acoustic waves in a concealed fire source [J]. Acs Omega,2023,8(19):16519−16531.
[76] YIN Jueli,SHI Linchao,LIU Zhen,et al. Study on the variation laws and fractal characteristics of acoustic emission during coal spontaneous combustion [J]. Processes,2023,11(3):786.
[77] 陈 欢,杨永亮. 煤自燃预测技术研究现状[J]. 煤矿安全,2013,44(9):1−26. CHEN Huan,YANG Yongliang. Research status of predicting coal spontaneous combustion[J]. Safety in Coal Mines,2013,44(9):1−26.
[78] LIANG Yuntao,SONG Shuanglin,GUO Baolong,et al. Study on the coupling characteristics of infrasound-temperature-gas in the process of coal spontaneous combustion and a new early warning method [J]. Combustion Science and Technology,2023:1−21.
[79] 段锁林,杨 可,毛 丹,等. 基于模糊证据理论算法在火灾检测中的应用[J]. 计算机工程与应用,2017,53(5):231−235. doi: 10.3778/j.issn.1002-8331.1507-0231 DUAN Suolin,YANG Ke,MAO Dan,et al. Fuzzy evidence theory-based algorithm in application of fire detection[J]. Computer Engineering and Applications,2017,53(5):231−235. doi: 10.3778/j.issn.1002-8331.1507-0231
[80] ZHAI Xiaowei,HAO Le,MA Teng,et al. Non-linear soft sensing method for temperature of coal spontaneous combustion [J] Process Safety and Environmental Protection,2023,170:1023−1031.
[81] 董 寅. 基于BP神经网络的DS证据理论模型在火灾探测中的应用研究 [D]. 杭州:浙江工业大学,2017. DONG Yin. The research on application of ds evidence theory model based on BP neuarl in fire detection [D]. Hangzhou:Zhejiang University of Technology,2017.
[82] 项平川. 基于LSTM与多传感器信息融合的火灾检测研究 [D]. 桂林:桂林电子科技大学,2023. XIANG Pingchuan. Research on fire detection based on LSTM and multi-sensor information fusion[D]. Guilin:Guilin University of Electronic Science and Technology,2023.
[83] 李正周,方朝阳,顾园山,等. 基于无 线多传感器信息融合的火灾检测系统[J]. 数据采集与处理,2014,29(5):694−698. doi: 10.3969/j.issn.1004-9037.2014.05.005 LI Zhengzhou,FANG Chaoyang,GU Yuanshan,et al. Fire detection system based on wireless multi-sensor lnformation fusion[J]. Journal of Data Acquisition & Processing,2014,29(5):694−698. doi: 10.3969/j.issn.1004-9037.2014.05.005
[84] 陈婷婷,赵世忠. 多传感器信息融合模糊控制模型设计[J]. 传感技术学报,2023,36(6):911−915. doi: 10.3969/j.issn.1004-1699.2023.06.009 CHEN Tingting,ZHAO Shizhong. Design of multi-sensor information fusion fuzzy control model[J]. Chinese Journal of Sensors and Actuators,2023,36(6):911−915. doi: 10.3969/j.issn.1004-1699.2023.06.009
[85] 邓 军. 徐精彩,阮国强,等. 国内外煤炭自然发火预测预报技术综述[J]. 西安矿业学院学报,1999(4):293−297,337. DENG Jun,XU Jingcai,RUAN Guoqiang,et al. Review of the prediction and forecasting technioues of coal self heating both at home and abroad[J]. Journal of Xi'an University of Science and Technology,1999(4):293−297,337.
[86] 王福生,张志明,董宪伟. 基于BP神经网络的煤自燃倾向性预测:以唐山矿及荆各庄矿为例[J]. 唐山学院学报,2020,33(3):16−20. WANG Fusheng,ZHANG Zhiming,DONG Xianwei. Forecast of coal spontaneous combustion tendency based on bp neural network:with tangshan mine and jinggezhuang mine as an example[J]. Tangshan Xueyuan Xuebao,2020,33(3):16−20.
[87] 昝军才,魏成才,蒋可娟,等. 基于BP神经网络的煤自燃温度预测研究[J]. 煤炭工程,2019,51(10):113−117. ZAN Juncai,WEl Chengcai,JIANG Kejuan,et al. Prediction of coal spontaneous combustion temperature based on BP neural network[J]. Coal Engineering,2019,51(10):113−117.
[88] 刘永立,刘晓伟,王海涛. 基于LSTM神经网络的煤矿火灾预测[J]. 黑龙江科技大学学报,2023,33(1):1−5. doi: 10.3969/j.issn.2095-7262.2023.01.001 LIU Yongli,LIU Xiaowei,WANG Haitao. Coal mine fire prediction based on LSTM neural network[J]. Journal of Heilongjiang University of Science and Technology,2023,33(1):1−5. doi: 10.3969/j.issn.2095-7262.2023.01.001
[89] 贾澎涛,林开义,郭风景. 基于PSO-SRU深度神经网络的煤自燃温度预测模型[J]. 工矿自动化,2022,48(4):105−113. JIA Pengtao,LIN Kaiyi,GUO Fengjing. A temperature prediction model for coal spontaneous combustion based on PSO-SRU deep artificial neural networks[J]. Journal of Mine Automation,2022,48(4):105−113.
[90] 孔 彪,朱思想,胡相明,等. 基于改进鲸鱼算法优化BP神经网络的煤自燃预测研究[J]. 矿业安全与环保,2023,50(5):30−36. KONG Biao,ZHU Sixiang,HU Xiangming,et al. Study on prediction of coal spontaneous combustion based on MSWOA-BP[J]. Mining Safety & Environmental Protection,2023,50(5):30−36.
[91] 邓 军,雷昌奎,曹 凯,等. 煤自燃预测的支持向量回归方法[J]. 西安科技大学学报,2018,38(2):175−180. DENG Jun,LEl Changkui,CAO Kai,et al. Support vector regression approach for predicting coal spontaneous combustion[J]. Journal of Xi’an University of Science and Technology,2018,38(2):175−180.
[92] 董天文. 矿井采空区内因火灾动态预警方法研究 [D]. 沈阳:沈阳航空航天大学,2020. DONG Tianwen. Research on dynamic early warning method of fire in goaf [D]. Shenyang:Shenyang University of Aeronautics and Astronautics,2020.
[93] 郭 军,王凯旋,金永飞,等. 煤自燃进程精细划分方法及其智能监测预警:煤火精准防控技术变革[J]. 煤炭学报,2023,48(S1):111−121. GUO Jun,WANG Kaixuan,JIN Yongfei,et al. Fine division method of coal spontaneous combustion process and its intelligent monitoring and early warning:technological change in precise prevention and control ofcoal fires[J]. Journal of China Coal Society,2023,48(S1):111−121.
[94] WANG Wei,LIANG Ran,QI Yun,et al. Study on the prediction model of coal spontaneous combustion limit parameters and its application[J]. Fire-Switzerland,2023,6(10):381.
[95] KAMRAN Muhammad,SHAHANI Niaz Muhammad. Decision support system for the prediction of mine fire levels in underground coal mining using machine learning approaches [J]. Mining Metallurgy & Exploration,2022,39(2):591−601.
[96] 王媛彬,马宪民. 煤矿外因火灾早期探测方法研究[J]. 工矿自动化,2015,41(9):63−66. WANG Yuanbin,MA Xianmin. Research of early prediction method for exogenous fire in coal mine[J]. Industry and Mine Automation,2015,41(9):63−66.
[97] 翟小伟,罗金雷,张羽琛,等. 基于数据填补的煤自燃温度预测模型[J]. 工矿自动化,2023,49(1):28−35,98. ZHAI Xiaowei,LUO Jinlei,ZHANG Yuchen,et al. Prediction model of coal spontaneous combustion temperature based on data filling[J]. Journal of Mine Automation,2023,49(1):28−35,98.
[98] 郑学召,李梦涵,张嬿妮,等. 基于随机森林算法的煤自燃温度预测模型研究[J]. 工矿自动化,2021,47(5):58−64. ZHENG Xuezhao,LI Menghan,ZHANG Yanni,et al. 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.
[99] 邓 军,雷昌奎,曹 凯,等. 采空区煤自燃预测的随机森林方法[J]. 煤炭学报,2018,43(10):2800−2808. DENG Jun,LEI Changkui,CAO Kai,et al. Random forest method for predicting coal spontaneous combustion in gob[J]. Journal of China Coal Society,2018,43(10):2800−2808.
[100] 邓 军,张燕妮,徐通模,等. 煤自然发火期预测模型研究[J]. 煤炭学报,2004,29(5):568−571. doi: 10.3321/j.issn:0253-9993.2004.05.013 DENG Jun,ZHANG Yanni,XU Tongmo,et al. Study on prediction model of coal spontaneous combustion stage[J]. Journal of China Coal Society,2004,29(5):568−571. doi: 10.3321/j.issn:0253-9993.2004.05.013
[101] 张 春,题正义,李宗翔. 基于采空区漏风量的遗煤温度预测模拟分析[J]. 防灾减灾工程学报,2015,35(3):328−332,424. ZHANG Chun,TI Zhengyi,LI Zongxiang. Simulation analysis of residual coal temperature prediction Based on air leakage volume of goaf[J]. Journal of Disaster Prevention and Mitigation Engineering,2015,35(3):328−332,424.
[102] 周 旭,朱 毅,张九零,等. 基于PSO-XGBoost的煤自燃程度预测研究[J]. 矿业安全与环保,2022,49(6):79−84. ZHOU Xu,ZHU Yi,ZHANG Jiuling,et al. Study on prediction model of coal spontaneous combustion based on PSO-XGBoost[J]. Mining Safety & Environmental Protection,2022,49(6):79−84.
[103] 朱令起,邵静静,刘 聪,等. 指标气体与温度耦合的烟煤自燃预测模型研究 [J]. 煤矿安全,2016,47(1):44−46,50. ZHU Lingqi,SHAO Jingjing,LIU Cong,et al. Research on forecasting model of bituminous coal spontaneous combustion combining indicator gases and temperature [J]. Safety in Coal Mines,2016,47(1):44−46,50.
[104] 汪 伟,贾宝山,祁 云. 改进CRITIC修正G2-TOPSIS的钻孔自燃预测模型及应用[J]. 中国安全科学学报,2019,29(11):26−31. WANG Wei,JIA Baoshan,QI Yun. Prediction model of spontaneous combustion risk of extraction driling based on improved CRITIC modifed G2-TOPSIS method and its application[J]. China Safety Science Journal,2019,29(11):26−31.
[105] 郑学召,童 鑫,郭 军,等. 煤矿智能监测与预警技术研究现状与发展趋势[J]. 工矿自动化,2020,46(6):35−40. ZHENG Xuezhao,TONG Xin,GUO Jun,et al. Research status and development trend of intelligent monitoring and early warningtechnology in coal mine[J]. Journal of Mine Automation,2020,46(6):35−40.
[106] 朱建国,戴广龙,唐明云,等. 水浸长焰煤自燃预测预报指标气体试验研究 [J]. 煤炭科学技术,2020,48(5):89−94. ZHU Jianguo,DAI Guanglong,TANG Mingyun,et al. Experimental study on spontaneous combustion prediction index gas of water immersed long flame coal [J]. Coal Science and Technology,2020,48(5):89−94.
[107] 王福生,王建涛,顾 亮,等. 煤自燃预测预报多参数指标体系研究[J]. 中国安全生产科学技术,2018,14(6):45−51. WANG Fusheng,WANG Jiantao,GU Liang,et al. Study on multi-parameter index system for prediction and forecast of coal spontaneous combustion[J]. Journal of Safety Science and Technology,2018,14(6):45−51.
[108] 贾海林,崔 博,焦振营,等. 基于TG/DSC/MS技术的煤氧复合全过程及气体产物研究[J]. 煤炭学报,2022,47(10):3704−3714. JIA Hailin,CUI Bo,JIAO Zhenying,et al. Study on the whole process and gas products of coal-oxygen complex reactionbased on TG/DSC/MS technology[J]. Journal of China Coal Society,2022,47(10):3704−3714.
[109] KONG Biao,NIU Siyu,CAO Huimin,et al. Study on the application of coal spontaneous combustion positive pressure beam tube classification monitoring and early warning [J]. Environmental Science and Pollution Research,2023,30(30):75735−75751.
[110] ZHU Hongqing,SHENG Kai,ZHANG Yilong,et al. The stage analysis and countermeasures of coal spontaneous combustion based on “five stages” division [J]. Plos One,2018,13(8):e0202724.
[111] GUO Jun,QUAN Yanping,CAI Guobin,et al. Meticulous graded and early warning system of coal spontaneous combustion based on index gases and characteristic temperature [J]. Acs Omega,2023,8(7):6801−6812.
[112] 仲晓星,王建涛,周 昆. 矿井煤自燃监测预警技术研究现状及智能化发展趋势[J]. 工矿自动化,2021,47(9):7−17. ZHONG Xiaoxing,WANG Jiantao,ZHOU Kun. Monitoring and early warning technology of coal spontaneous combustion in coalmines:research status and intelligent development trends[J]. Journal of Mine Automation,2021,47(9):7−17.
[113] 疏义国,赵庆伟,郁亚楠. 易自燃煤层预测预报气体指标体系研究 [J]. 煤炭科学技术,2019,47(10):229−234. SHU Yiguo,ZHAO Qingwei,YU Ya’nan. Research on prediction and forecast indicators system of easy spontaneous combustion coal seam [J]. Coal Science and Technology,2019,47(10):229−234.
[114] XU Xuefeng,ZHANG Fengjie. Evaluation and optimization of multi-parameter prediction index for coal spontaneous combustion combined with temperature programmed experiment [J]. Fire-Switzerland,2023,6(9):368.
[115] 郭 军,金 彦,王 帆,等. 基于Logistic回归分析的煤自燃多级预警方法研究[J]. 中国安全生产科学技术,2022,18(2):88−93. GUO Jun,JIN Yan,WANG Fan,et al. Research on multilevel warning method of coal spontaneous combustion based on Logistic regression analysis[J]. Journal of Safety Science and Technology,2022,18(2):88−93.
[116] WANG Kai,LI Yang,ZHAI Xiaowei,et al. A method for evaluating the coal spontaneous combustion index by the coefficient of variation and Kruskal-Wallis test:a case study [J]. Environmental Science and Pollution Research,2023,30(20):58956−58966.
[117] YANG Yong,FEI Jinbiao,LUO Zhenmin,et al. Experimental study on characteristic temperature of coal spontaneous combustion [J]. Journal of Thermal Analysis and Calorimetry,2023,148(19):10011−10019.
[118] 岳宁芳,金 彦,孙明福,等. 基于多指标气体的煤自燃进程分级预警研究[J]. 安全与环境学报,2020,20(6):2139−2146. YUE Ningfang,JIN Yan,SUN Mingfu,et al. Multi-staged warning system for controlling the coal spontaneous combustion based on the various index gases[J]. Journal of Safety and Environment,2020,20(6):2139−2146.
[119] 周 旭,王认卓,代亚勋,等. 基于BO-XGBoost的煤自燃分级预警研究[J]. 煤炭工程,2022,54(8):108−114. ZHOU Xu,WANG Renzhuo,DAI Yaxun,et al. Classified early warning of coal spontaneous combustion based on BO-XGBoost[J]. Coal Engineering,2022,54(8):108−114.
[120] 邓 军,杨囡囡,王彩萍,等. 采空区煤自燃“防−抑−灭”协同防灭火关键技术[J]. 煤矿安全,2022,53(9):1−8. DENG Jun,YANG Nannan,WANG Caiping,et al. Key technology of “preventing-suppressing-extinguishing” coordinated fire preventingand extinquishing for coal spontaneous combustion in goaf[J]. Safety in Coal Mines,2022,53(9):1−8.
[121] GUO Chaowei,JIANG Shuguang,SHAO Hao,et al. Effect of secondary oxidation of pre-oxidized coal on early warning value for spontaneous combustion of coal [J]. Applied Sciences-Basel,2023,13(5):3154.
[122] ZHANG Zhenya,DONG Ziwen,KONG Song,et al. Influence of long-term immersion in water at different temperatures on spontaneous combustion characteristics of coal [J]. Acs Omega,2023,8(35):31683−31697.
[123] 李东发,臧燕杰,师吉林. 矿井火灾智能预警系统[J]. 工矿自动化,2022,48(S1):112−115,120. LI Dongfa,ZANG Yanjie,SHI Jilin. Intelligent mine fire early warning system[J]. Journal of Mine Automation,2022,48(S1):112−115,120.
[124] 何勇军,易 欣,王伟峰,等. 煤矿井下电气火灾智能监控与灭火技术综述[J]. 煤矿安全,2022,53(9):55−64. HE Yongjun,YI Xin,WANG Weifeng,et al. Review of intelligent monitoring and extinguishing technology of electricafire in coal mine[J]. Safety in Coal Mines,2022,53(9):55−64.
[125] 张 伟,陈 红,李陈莹,等. 高压电力电缆隧道火灾早期预警判据的实验研究[J]. 火灾科学,2021,30(4):232−241. doi: 10.3969/j.issn.1004-5309.2021.04.06 ZHANG Wei,CHEN Hong,LI Chenying,et al. Experimental study on early warning criteria of fire in high voltage power cable tunnels[J]. Fire Safety Science,2021,30(4):232−241. doi: 10.3969/j.issn.1004-5309.2021.04.06
[126] WANG Weifeng,HUO Yuhang,KANG Furu,et al. Study on hazard of smoke generated by mining cable fires [J]. Journal of Thermal Analysis and Calorimetry,2023:1−11.
[127] CHEN Xiaolong,HUANG Guozhong,GAO Xuehong,et al. BN-RA:a hybrid model for risk analysis of overload-induced early cable fires [J]. Applied Sciences-Basel,2021,11(19):8922.
[128] LI Chenying,CHEN Jie,PU Ziheng,et al. Research on fire prediction method of high-voltage power cable tunnel based on abnormal characteristic quantity monitoring [J]. Frontiers in Energy Research,2022,10:836588.
[129] XIE Qiyuan,CHEN Hong,YUAN Yanhua. Heat blockage of air gap for inner overheating of high-voltage power cable and delay of early detection [J]. Journal of Fire Sciences,2020,38(4):363−376.
[130] LIU Haonan,ZHU Guoqing,PAN Rongliang,et al. Experimental investigation of fire temperature distribution and ceiling temperature prediction in closed utility tunnel [J]. Case Studies in Thermal Engineering,2019,14:100493.
[131] 王彦文,张旭然,高 彦,等. 三芯矿用电缆线芯温度预测及故障预警方法[J]. 煤炭学报,2023,48(3):1439−1448. WANG Yanwen,ZHANG Xuran,GAO Yan,et al. Prediction of core temperature and early warning of fault of three-core mining cable[J]. Journal of China Coal Society,2023,48(3):1439−1448.
[132] ZHANG Duo,LIU Maoxia,WEN Hu,et al. Use of coupled TG-FTIR and Py-GC/MS to study combustion characteristics of conveyor belts in coal mines [J]. Journal of Thermal Analysis and Calorimetry,2023,148(11):4779−4789.
[133] WANG Weifeng,LIU Hanfei,YANG Bo,et al. Pyrolysis characteristics and dynamics analysis of a coal mine roadway conveyor belt [J]. Journal of Thermal Analysis and Calorimetry,2023,148(11):4823−4832.
[134] 丁伟杰,刘昱廷,李建英,等. 基于窄带物联网技术的智能火灾报警系统设计[J]. 电工技术,2023(18):19−21,120. DING Weijie,LIU Yuting,LI Jianying,et al. Design of intelligent fire alarm system based on narrowband internet of things technology[J]. Electric Engineering,2023(18):19−21,120.
[135] 贺耀宜,陈晓晶,郝振宇,等. 智能矿山低代码工业物联网平台设计 [J]. 工矿自动化,2023,49(6):141−148,174. HE Yaoyi,CHEN Xiaojing,HAO Zhenyu,et al. Design of intelligent mine low code industrial loT platform [J]. Mining Safety & Environmental Protection,2023,49(6):141−148,174.
[136] 张 静,聂章龙. 基于物联网的煤矿安全监测与预警平台设计[J]. 煤炭技术,2021,40(10):209−211. ZHANG Jing,NIE Zhanglong. Design of coal mine safety monitoring and early warning platform based on internet of things[J]. Coal Technology,2021,40(10):209−211.
[137] 贺耀宜,刘丽静,赵立厂,等. 基于工业物联网的智能矿山基础信息采集关键技术与平台[J]. 工矿自动化,2021,47(6):17−24. HE Yaoyi,LIU Lijing,ZHAO Lichang,et al. Key technology and platform of intelligent mine basic information acquisition based on industrial nternet of things[J]. Mining Safety & Environmental Protection,2021,47(6):17−24.
[138] 陈珍萍,黄友锐,唐超礼,等. 占空比机制下煤矿井下物联网感知层时间同步[J]. 煤炭学报,2015,40(1):232−238. CHEN Zhenping,HUANG Yourui,TANG Chaoli,et al. Underground coalmine loTs perception layer time synchronization under dutycycle mechanism[J]. Journal of China Coal Society,2015,40(1):232−238.
[139] ZHAO Yifan,TIAN Shuicheng. Hazard identification and early warning system based on stochastic forest algorithm in underground coal mine [J]. Journal of Intelligent & Fuzzy Systems,2021,41(1):1193−1202.
[140] 降 华. 基于云计算的煤炭自燃安全监测系统设计[J]. 煤炭技术,2023,42(8):154−158. JIANG Hua. Design of coal spontaneous combustion safety monitoring system based on cloud computing[J]. Coal Technology,2023,42(8):154−158.
[141] 丁恩杰,金 雷,陈 迪. 互联网+感知矿山安全监控系统研究[J]. 煤炭科学技术,2017,45(1):129−134. DING Enjie,JIN Lei,CHEN Di. Study on safety monitoring and control system of internet + perception mine[J]. Coal Science and Technology,2017,45(1):129−134.
[142] 曹允钦. 基于云计算和物联网的煤矿安全动态诊断系统研究[J]. 煤炭科学技术,2016,44(7):135−139. CAO Yunqin. Study on dynamic diagnosis system of mine safety based on cloud computingand internet of things[J]. Coal Science and Technology,2016,44(7):135−139.
[143] 周福宝,时国庆,王雁鸣,等. 矿井密闭全生命周期安全风险监测预警[J]. 工矿自动化,2023,49(6):48−56. ZHOU Fubao,SHI Guoqing,WANG Yanming,et al. Safety risks monitoring and warning throughout the full lifecycle of mine airstopping[J]. Journal of Mine Automation,2023,49(6):48−56.
[144] 卢万杰,付 华,赵洪瑞. 基于深度学习算法的矿用巡检机器人设备识别[J]. 工程设计学报,2019,26(5):527−533. doi: 10.3785/j.issn.1006-754X.2019.05.005 LU Wanjie,FU Hua,ZHAO Hongrui. Equipment recognition of mining patrol robot based on deep learning algorithm[J]. Chinese Journal of Engineering Design,2019,26(5):527−533. doi: 10.3785/j.issn.1006-754X.2019.05.005
[145] 靳德武,乔 伟,李 鹏,等. 煤矿防治水智能化技术与装备研究现状及展望[J]. 煤炭科学技术,2019,47(3):10−17. JIN Dewu,QIAO Wei,LI Peng,et al. Research status and prospects on intelligent technology and equipment for minewater hazard prevention and control[J]. Coal Science and Technology,2019,47(3):10−17.
[146] 葛明臣,刘大同. 基于BP神经网络的井下电弧火灾预警研究[J]. 煤炭技术,2020,39(9):195−198. GE Mingchen,LIU Datong. Study on downhole arc fire warning based on BP neural network[J]. Coal Technology,2020,39(9):195−198.
[147] 肖粲俊,刘红梅,石发强,等. 基于数字孪生的煤矿智能管控平台架构研究与实现[J]. 矿业安全与环保,2023,50(5):43−49. XIAO Canjun,LIU Hongmei,SHI Faqiang,et al. Research and implementation of intelligent control platform architecture for coal mine based on digital twin[J]. Mining Safety & Environmental Protection,2023,50(5):43−49.
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