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Gold Science and Technology ›› 2019, Vol. 27 ›› Issue (2): 181-188.doi: 10.11872/j.issn.1005-2518.2019.02.181

• Mining Technology and Mine Management • Previous Articles    

Research on Mining Method Optimization Based on Concept Lattice Rough Set

Shuliang WU1,2(),Shan YANG3,Wengang HUANG1   

  1. 1. School of Earth Sciences,East China University of Technology,Nanchang 330013,Jiangxi,China
    2. Key Laboratory for Digital Land and Resources of Jiangxi Province,East China University of Technology,Nanchang 330013,Jiangxi,China
    3. School of Resources and Safety Engineering,Central South University,Changsha 410083,Hunan, China
  • Received:2018-03-05 Revised:2018-08-13 Online:2019-04-30 Published:2019-04-30

Abstract:

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% according to the decision rule set.In order to verify the data processing ability of concept lattice for attribute reduction,the reduced concept lattice is compared with that of the traditional rough set.The results show that the concept lattice is more effective than the resolution matrix in attribute reduction.It can carry out deep data mining on evaluation indexes of mining methods.Based on the relationship between condition attributes and decision attribute,it can also reduce the indexes needed for evaluation of mining methods.As to the ability of rough set to deal with uncertainties,the minimum decision rule set generated by rough set can classify the pros and cons of alternative mining methods.The construction of mining method evaluation system by using the rough set based on concept lattice has a certain theoretical significance for mine production.

Key words: mining method, rough set, decision rules, attribute reduction, knowledge discovery, concept lattice, discernable matrix

CLC Number: 

  • TD853.3

Fig.1

Structural diagram of evaluation indexes for mining method"

Table 1

Comparison criteria for the importance of indicators"

标准值意义
1xixj重要性相同
3xi重要性稍微高于xj
5xi重要性明显高于xj
7xi重要性强烈高于xj
9xi重要性绝对高于xj
2,4,6,8介于1和3,3和5,5和7,7和9之间的值

Table 2

Evaluation decision-making table of mining methods"

方案条件属性决策属性E
开采条件A

经济指标

B

地压控制

程度C

技术指标

D

n188.0487.1692.6580.4287.3302
n286.3682.2587.7582.7884.6848
n385.7979.6585.6290.2784.9722
n480.6378.2175.4788.7280.2976
n578.2680.1265.3185.2376.6058
n679.2568.4782.4586.3278.6728
n778.3466.2666.4672.8769.8392
n880.4562.9668.6568.1568.7110
n972.3263.1371.2467.6768.1230
n1088.6592.3695.1888.7891.7532
n1190.1588.2692.7988.6290.0078
n1288.3289.3595.6880.5888.9794
n1381.4675.6278.6476.4577.6596
n1485.1377.9877.3275.2478.2684
n1582.9682.6180.1381.3581.6196

"

方案条件属性决策属性E

开采条件

A

经济指标

B

地压控制

程度C

技术指标

D

n1
n2
n3
n4
n5
n6
n7
n8
n9
n10
n11
n12
n13
n14
n15

Table 4

Formal context of knowledge expression system"

A_1A_2A_3A_4B_1E_3E_4
n1×
n2×
n3×
n4×
n5××
n6××
n7××
n8××
n9××
n10××
n11×
n12×
n13××
n14××
n15×

Fig.2

Concept lattice of formal context"

Table 5

D≥ probability decision rules"

概率规则集支持数置信度/%
if开采条件≥优∧经济指标≥良∧技术指标≥良then综合评价=优2100
if开采条件≥良∧经济指标≥良∧技术指标≥良then综合评价≥良6100
if开采条件≥中∧经济指标≥良∧技术指标≥中then综合评价≥中4100
if开采条件≤中∧经济指标≤差∧技术指标≤中then综合评价=差3100

Table 6

D≤ probability decision rules"

概率规则集支持数置信度/%
if开采条件≥优∧经济指标≥良∧技术指标≥良then综合评价=优2100
if开采条件≤良∧经济指标≤良∧技术指标≤良then综合评价≤良6100
if开采条件≤良∧经济指标≤中∧技术指标≤中then综合评价≤中2100
if开采条件≤中∧经济指标≤良∧技术指标≤良then综合评价≤中2100
if开采条件≤中∧经济指标≤差∧技术指标≤中then综合评价=差3100

Table 7

Discernable matrix"

n1n2n3n4n5n6n7n8n9n10n11n12n13n14

n2

n3

C

BCD

BD
n4BCBCCD
n5ACACABCDABC
n6ABCABABDABCBC
n7ABCDABCDABCDABCDBDCD
n8ABCDABCDABCDABCDABDCDD
n9ABCDABCDABCDABDBCDCDCDC
n10BBCBCDBCABCABCABCDABCDABCD
n11AACABCDABCACABCABCDABCDABCDAB
n12CBCDBCACABCABCDABCDABCDBA
n13BCDBCDCDDABCDABCDABCABCDACDBCDABCDBCD
n14BCDBCDCDDABCDABCDABCABCDACDBCDABCDBCD
n15CBDBCACABABCDABCDABCDBCDACCBCDBCD

Table 8

Comparison of reduced concept lattice and discernable matrix reduction"

对比内容约简概念格分辨矩阵约简
约简结果{经济指标,地压控制程度,技术指标}和{开采条件,经济指标,技术指标},通过概念对比,选择后者为最终约简结果,该结果符合实际未能搜寻到约简,仍然需要4个条件属性进行决策
约简效率

通过求得相融可辨概念的亏属性,

得到约简结果,搜寻速度快

需根据条件属性进行两两对比,执行效率低,搜寻时间较概念格要长
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.
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.
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 ÖzfıratM 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
HuangJiazeng.Based on rough concept lattice attribute reduction and rule acquisition[J].Software,2011,32(10):16-19.
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