基于ANP-MEA模型的智能化开采工作面适应性评价研究  

Study on intelligent adaptability evaluation of intelligent coal mining working face based on ANP and matter-element extension model

作　　者：张科学 闫星辰[1,3,6] 何满潮 陈学习[1,3,6] 姜耀东[4] 孙健东[1,3,6] 李东 王晓玲[1,3,6] 亢磊 杨海江 朱俊傲 吴永伟 李举然 尹宇航 ZHANG Kexue;YAN Xingchen;HE Manchao;CHEN Xuexi;JIANG Yaodong;SUN Jiandong;LI Dong;WANG Xiaoling;KANG Lei;YANG Haijiang;ZHU Jun'ao;WU Yongwei;LI Juran;YIN Yuhang(Hebei Key Laboratory of Mine Intelligent Unmanned Mining Technology,North China Institute of Science and Technology,Beijing 101601,China;State Key Laboratory for Geomechanics and Deep Underground Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China;Institute of Intel-ligent Unmanned Mining,North China Institute of Science and Technology,Beijing 101601,China;State Key Laboratory of Coal Resources and Safe Mining,China University of Mining and Technology(Beijing),Beijing 100083,China;China Coal Research Institute,Beijing 100013,China;School of Mine Safety,North China Institute of Science and Technology,Beijing 101601,China)

机构地区：[1]华北科技学院河北省矿山智能化开采技术重点实验室,北京101601 [2]中国矿业大学(北京)深部岩土力学与地下工程国家重点实验室,北京100083 [3]华北科技学院智能化无人开采研究所,北京101601 [4]中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京100083 [5]煤炭科学研究总院,北京100013 [6]华北科技学院矿山安全学院,北京101601

出　　处：《采矿与岩层控制工程学报》2023年第2期89-98,共10页Journal of Mining and Strata Control Engineering

基　　金：中国科协科技智库青年人才计划资助项目(20220615ZZ07110397);深部岩土力学与地下工程国家重点实验室(北京)开放基金资助项目(SKLGDUEK1822);中央高校基本科研业务费资助项目(3142021007,3142019009);国家自然科学基金资助项目(51804160);河北省自然科学基金资助项目(E2019508209);煤炭资源与安全开采国家重点实验室开放基金资助项目(SKLCRSM16KFD08)。

摘　　要：为更好地解决煤矿智能化开采工作面适应性评价模型的关联性和模糊性问题,提出了由地质条件、开采技术条件、关键技术条件以及管理保障条件等4个一级影响因素及16个二级影响因素,构建的煤矿智能化开采工作面适应性评价指标体系,并建立了煤矿智能化开采工作面适应性ANP网络模型。将煤矿智能化开采工作面适应性评价等级划分为I级(好)、Ⅱ级(较好)、Ⅲ级(一般)和Ⅳ级(差)等4个等级。采用网络层次分析法(ANP)研究影响因素之间的相互联系,并使用YAANP软件计算得到煤矿智能化开采工作面适应性影响因素的权重。为有效降低个人因素对各影响因素评分的影响,将网络层次分析法与物元可拓模型相结合,对煤矿智能化开采工作面适应性影响因素进行评价,计算得到各影响因素的关联度及综合关联度,最后由综合关联度对煤矿智能化开采工作面适应性进行等级评定。将煤矿智能化开采工作面适应性ANP网络模型在陕西黄陵1号煤矿的810智能化工作面进行应用,得出该煤矿智能化开采工作面适应性综合关联度为K_(1)=0.06,K_(2)=-0.05,K_(3)=-0.61,K_(4)=-0.77,对应评价标准得到煤矿智能化开采工作面适应性评价等级为Ⅰ级(好),分析结果与现场实际情况相吻合,说明构建的煤矿智能化开采工作面适应性ANP网络模型具有一定的可行性与科学性。To better address the relevance and fuzzy problems in the adaptability evaluation model for intellectualized mining of coal mine working faces,this paper proposes an evaluation index system consisting of four primary influence factors(geological conditions,mining technical conditions,key technologies and security,and management conditions)and their corresponding 16 secondary influence factors.An adaptive ANP network model for intelligent mining face is established to evaluate the adaptability of coal mine working faces.The adaptability evaluation is divided into four grades:I(best),II(good),III(general),and IV(poor).The relationship between the influencing factors is studied using the network analytic hierarchy process(ANP)and the YAANP software is used to calculate the weight of the influencing factors.To reduce the influence of personal factors on the evaluation score,a combination of ANP and matter-element extension model is used to evaluate the factors affecting adaptability.The various influence factors are evaluated for correlation degree and relational grade,and the adaptability of intellectualized mining of coal mine working faces is rated through comprehensive correlation.The ANP network model is applied to analyze 810 intelligent working faces of Shaanxi Huangling No.1 coal mine.The comprehensive correlation degree of adaptability of intelligent mining face is calculated as K_(1)=0.06,K_(2)=-0.05,K_(3)=-0.61,and K_(4)=-0.77.The evaluation grade of adaptability of intelligent coal mining working face is determined to be I(best)through comparison with the evaluation standard analysis.The analysis results are consistent with the actual situation on site,indicating the feasibility and scientific basis of the established ANP network model for adaptability of intellectualized mining of coal mine working faces.

关 键 词：智能化开采 无人开采 智能化工作面 ANP 物元可拓模型 适应性 

分 类 号：TD82[矿业工程—煤矿开采]