收稿日期: 2018-03-05
修回日期: 2018-08-13
网络出版日期: 2019-04-30
基金资助
江西省教育厅科技项目“复杂应力环境下地下金属矿开采引起的岩层移动规律研究”(编号:GJJ170466)、国家自然科学基金青年基金项目“基于人工智能的矿山技术经济指标动态优化研究”(编号:51404305)、东华理工大学江西省数字国土重点实验室开放研究基金项目“基于红外遥感技术的地下工程岩爆灾害判别方法研究”(编号:DLLJ201706)、东华理工大学博士科研启动基金项目“地下金属矿无废开采规划方法与技术研究”(编号:DHBK2016125)和东华理工大学校级教改课题项目“《矿业系统工程》跨专业联合创新课程设计研究”(编号:1310100334)
Research on Mining Method Optimization Based on Concept Lattice Rough Set
Received date: 2018-03-05
Revised date: 2018-08-13
Online published: 2019-04-30
为了正确选择矿山初选的采矿方法,提出基于概念格粗糙集的采矿方法评价体系。综合考虑影响采矿方法选择的众多因素后,对指标进行分层处理,利用改进的粗糙集建立采矿方法评价体系,生成最少决策规则集。属性约简是粗糙集中的核心问题,选择概念格作为约简工具,对条件属性进行约简。将模型用于15种采矿方法的优选,得到了最大可约简属性集,决策规则集的分类质量为100%。最后,将约简概念格与传统粗糙集中的分辨矩阵进行对比,结果表明:概念格在属性约简方面比分辨矩阵更有效,利用概念格的粗糙集构建采矿方法评价体系对矿山生产具有一定的理论指导意义。
邬书良 , 杨珊 , 黄温钢 . 基于概念格粗糙集的采矿方法优选研究[J]. 黄金科学技术, 2019 , 27(2) : 181 -188 . DOI: 10.11872/j.issn.1005-2518.2019.02.181
While making rational use of mineral resources,it is necessary to make rational selection of mining methods so as to improve the utilization rate of mineral resources.The selection of mining methods has a decisive impact on the overall improvement of mine production capacity,safety and economic benefits,and also has direct effects on the degree of environmental damage caused by mining production.Therefore,it is particularly important to choose a scientific and reasonable mining method.In order to make a correct selection of the mining method for the primary section,to improve the efficiency of mining method optimization and perfect the process of mining method optimization,a mining method evaluation system based on concept lattice rough set is proposed.The evaluation system is aiming at the problems of strong subjective arbitrariness and insufficient analysis of index information in current mining method optimization,combining quantitative methods with qualitative ones,analyzing the relationship between evaluation index and mining method.Firstly,according to the idea of constructing evaluation system by analytic hierarchy process (AHP),many influencing factors of the selection of mining method were comprehensively considered and sliced,so as to obtain the evaluation index system of mining methods with complete information.Then,the improved rough sets were applied to establish mining method evaluation system.As attribute reduction is the kernel contents of rough set,using concept lattice as reduction tool can get the maximal reductions,and minimum decision rule set was generated by using reduced evaluation index.Finally,the model is applied to the evaluation of 15 mining methods,and the classification quality of 15 mining methods is 100
1 | 尹利平,刘金海,朱卓会.基于逼近理想解排序的采矿方法选择[J].矿冶工程,2010,30(3):12-15. |
1 | YinLiping,LiuJinhai,ZhuZhuohui.Mining method based on technique for order preference by similarity to ideal solution[J].Mining and Metallurgical Engineering,2010,30(3):12-15. |
2 | 王卫京.太白金矿深部矿体采矿方法选择研究[J].西安建筑科技大学学报(自然科学版),2010,42(6):907-912. |
2 | WangWeijing.Selection of mining methods for deep orebody in Taibai gold mine[J].Journal of Xi’an University of Architecture and Technology(Natural Science Edition),2010,42(6):907-912. |
3 | BakhtavarE,ShahriarK,OraeeK.Mining method selection and optimization of transition from open pit to underground in combined mining[J].Archives of Mining Sciences,2009,54(3):481-493. |
4 | YavuzM,AlpayS.Underground mining technique selection by multicriterion optimization methods[J].Journal of Mining Science,2008,44(4):391-401. |
5 | ?zf?ratM K.A fuzzy method for selecting underground coal mining method considering mechanization criteria[J].Journal of Mining Science,2012,48(3):533-544. |
6 | 王新民,赵彬,张钦礼.基于层次分析和模糊数学的采矿方法选择[J].中南大学学报(自然科学版),2008,39(5):875-880. |
6 | WangXinmin,ZhaoBin,ZhangQinli.Mining method choice based on AHP and fuzzy mathematics[J].Journal of Central South University (Science and Technology),2008,39(5):875-880. |
7 | 谭玉叶,宋卫东,雷远坤,等.基于模糊聚类及层次分析法的采矿方法综合评判优选[J].北京科技大学学报,2012,34(5):489-494. |
7 | TanYuye,SongWeidong,LeiYuankun,et al.Synthetic judgment for mining method optimization based on fuzzy cluster analysis and analytic hierarchy process[J].Journal of University of Science and Technology Beijing,2012,34(5):489-494. |
8 | MohammadA,HashemS,RezaM.Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method[J].International Journal of Mining Science and Technology,2013,23(4):573-578. |
9 | 陈建宏,刘浪,周智勇,等.基于主成分分析与神经网络的采矿方法优选[J].中南大学学报(自然科学版),2010,41(5):1967-1972. |
9 | ChenJianhong,LiuLang,ZhouZhiyong,et al.Optimization of mining methods based on combination of principal component analysis and neural networks[J].Journal of Central South University (Science and Technology),2010,41(5):1967-1972. |
10 | 陈建宏,郑海力,刘振肖,等.基于优势关系的粗糙集的巷道支护方案评价体系[J].中南大学学报(自然科学版),2011,42(6):1698-1703. |
10 | ChenJianhong,ZhengHaili,LiuZhenxiao,et al.Rough sets of laneway supporting schemes evaluation system based on dominance relation [J].Journal of Central South University (Science and Technology),2011,42(6):1698-1703. |
11 | 菅利荣.面向不确定性决策的杂合粗糙集方法及其应用[M].北京:科学出版社,2008:57-81. |
11 | JianLirong.Facing the Heterozygous Uncertainty Decision-Making Rough Set Method and Its Application[M].Beijing:Science Press,2008:57-81. |
12 | YeM Q,WuX D,HuX G,et al.Multi-level rough set reduction for decision rule mining[J].Applied Intelligence,2013,39(3):642-658. |
13 | 王萍,王学峰,吴谷丰.基于遗传算法的粗糙集属性约简算法[J].计算机应用与软件,2008,27(5):42-44. |
13 | WangPing,WangXuefeng,WuGufeng.Rough set attribute reduction algorithm base on GA [J].Computer Applications and Software,2008,27 (5):42-44. |
14 | 肖厚国,桑琳,丁守珍,等.基于遗传算法的粗糙集属性约简及其应用[J].计算机工程与应用,2008,44(15):228-230. |
14 | XiaoHouguo,SangLin,DingShouzhen,et al.Rough set attribute reduction algorithm based on GA and its application[J].Computer Engineering and Applications,2008,44(15):228-230. |
15 | 康向平,李德玉.一种基于形式概念分析的粗糙集中的知识获取方法[J].山西大学学报(自然科学版),2011,34(3):415-420. |
15 | KangXiangping,LiDeyu.One knowledge acquisition method based on formal concept analysis in rough set[J].Journal of Shanxi University ( Natural Science Edition),2011,34(3):415-420. |
16 | 胡学钢,薛峰,张玉红,等.基于概念格的决策表属性约简方法[J].模式识别与人工智能,2009,22(4):624-629. |
16 | HuXuegang,XueFeng,ZhangYuhong,et al.Attribute reduction methods of decision table based on concept lattice[J].Pattern Recognition and Artificial Intelligence,2009,22(4):624-629. |
17 | DiasS M,LuiseZ,VieiraN.Using iceberg concept lattices and implications rules to extract knowledge from Ann[J].Intelligent Automation and Soft Computing,2013,19(3):361-372. |
18 | MaJ M,ZhangW X.Axiomatic characterizations of dual concept lattices[J].International Journal of Approximate Reasoning,2013,54(5):690-697. |
19 | 李云.概念格分布处理及其框架下的知识发现研究[D].上海:上海大学,2005. |
19 | LiYun.Research on Distributed Treatment of Concept Lattices and Knowledge Discovery Based on Its Framework [D].Shanghai: Shanghai University,2005. |
20 | 杨凯,马垣.基于概念格的多层属性约简方法[J].模式识别与人工智能,2012,25(6):922-927. |
20 | YangKai,MaYuan.Multi-level attribute reduction methods based on concept lattice[J].Pattern Recognition and Artificial Intelligence,2012,25(6):922-927. |
21 | 黄加增.基于粗糙概念格的属性约简及规则获取[J].软件,2011,32(10):16-19. |
21 | HuangJiazeng.Based on rough concept lattice attribute reduction and rule acquisition[J].Software,2011,32(10):16-19. |
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