铝电解槽分班组指标统计分析研究与开发
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摘要
铝电解槽班组是电解铝生产中的基本生产单位,在铝电解生产实际中积累了大量与铝电解班组密切相关的生产数据。生产管理者需要通过分析以班组为单位的生产数据,来对班组工作情况进行评估,发现生产管理中存在的问题,以及不同班组在电解槽生产工艺参数管理上的异同,开展有针对性的工作。因此,对以铝电解班组为单位的生产数据统计与分析方面的研究就显得十分重要。
本论文主要对多维分析与数据挖掘在铝电解班组统计分析方面的应用进行了深入研究和探讨。本文的主要内容及创新如下:
1、首先对多维分析技术与数据挖掘的基本概念和相关应用进行了总结,对挖掘模型中的Apriori算法进行了详细描述,采用将多维分析与挖掘模型相结合的方法来统计分析以班组为单位的生产数据。
2、依据实际电解生产组织形式,在对生产数据间的关系进行分析的基础上,将电解班组,班次,生产数据联系在一起,实现动态分析班组生产数据的班报表和日报表。
3、构建了围绕班组的电解槽生产数据仓库,方便系统从多个角度查看分析班组,班次,电解槽生产数据之间的关系,实现对以班组为单位的生产数据发展趋势的监测、分析和管理。
4、通过运用关联规则模型,来发现班组、班次与生产数据之间的关联规则,对挖掘出的规律进行评价并加以利用。
在以上工作的基础上,本课题设计和实现了铝电解槽班组统计分析系统,集采集、抽取、处理、多维分析、挖掘、展示功能为一体,为电解生产管理者提供方便、快捷的班组分析、决策支持软件。
Teams are the basic organization at the proceeding of the aluminum electrolysis production. There are mass of data accumulated in the production of the aluminum electrolysis which is heavily related with the performance of the teams. The manager need to statistically analyze those mass of data to evaluate the performance of the teams and to compare the main index of the aluminum electrolysis production between several teams and to find the problem hidden in the production, then to directly solve those problems. From the above the research and application on the performance of the teams and groups of the aluminum electrolysis is very essential and required.
This article mainly introduces the research on the application of the multidimensional analysis and data mining technology in the aluminum electrolysis field. In this paper, content and innovation are as follows:
1、First this paper make summary of the application of multi-dimensional analysis and data mining technology. At the following chapter this article described the Apriori algorithm in detail. From previous research this paper will adopt the combination of the OLAP and data mining to statistical analysis the performance of the teams.
2、According to the actual organization of the production and the analyze on the relationship between a variety of the production data. Based on the above work, the application can dynamically generate the report about the data of production by day or by month.
3、This application design and construct one module which is lightweight data warehouse. This module can render several functions which can analyze the relationship between the team,class and the data from different views.
4、At last this paper talk sabout the association rule algorithm to digger the useful knowledge about the relationship between the teams and the data of production .
The design and the application of the system has met the demand of the actual production. The user can analyze the relationship between the teams and class in the production by various of method to discovery the valuable information and the knowledge which could be extracted from the massive data and can be used to instruct the management in aluminum electrolysis industry.
本论文主要对多维分析与数据挖掘在铝电解班组统计分析方面的应用进行了深入研究和探讨。本文的主要内容及创新如下:
1、首先对多维分析技术与数据挖掘的基本概念和相关应用进行了总结,对挖掘模型中的Apriori算法进行了详细描述,采用将多维分析与挖掘模型相结合的方法来统计分析以班组为单位的生产数据。
2、依据实际电解生产组织形式,在对生产数据间的关系进行分析的基础上,将电解班组,班次,生产数据联系在一起,实现动态分析班组生产数据的班报表和日报表。
3、构建了围绕班组的电解槽生产数据仓库,方便系统从多个角度查看分析班组,班次,电解槽生产数据之间的关系,实现对以班组为单位的生产数据发展趋势的监测、分析和管理。
4、通过运用关联规则模型,来发现班组、班次与生产数据之间的关联规则,对挖掘出的规律进行评价并加以利用。
在以上工作的基础上,本课题设计和实现了铝电解槽班组统计分析系统,集采集、抽取、处理、多维分析、挖掘、展示功能为一体,为电解生产管理者提供方便、快捷的班组分析、决策支持软件。
Teams are the basic organization at the proceeding of the aluminum electrolysis production. There are mass of data accumulated in the production of the aluminum electrolysis which is heavily related with the performance of the teams. The manager need to statistically analyze those mass of data to evaluate the performance of the teams and to compare the main index of the aluminum electrolysis production between several teams and to find the problem hidden in the production, then to directly solve those problems. From the above the research and application on the performance of the teams and groups of the aluminum electrolysis is very essential and required.
This article mainly introduces the research on the application of the multidimensional analysis and data mining technology in the aluminum electrolysis field. In this paper, content and innovation are as follows:
1、First this paper make summary of the application of multi-dimensional analysis and data mining technology. At the following chapter this article described the Apriori algorithm in detail. From previous research this paper will adopt the combination of the OLAP and data mining to statistical analysis the performance of the teams.
2、According to the actual organization of the production and the analyze on the relationship between a variety of the production data. Based on the above work, the application can dynamically generate the report about the data of production by day or by month.
3、This application design and construct one module which is lightweight data warehouse. This module can render several functions which can analyze the relationship between the team,class and the data from different views.
4、At last this paper talk sabout the association rule algorithm to digger the useful knowledge about the relationship between the teams and the data of production .
The design and the application of the system has met the demand of the actual production. The user can analyze the relationship between the teams and class in the production by various of method to discovery the valuable information and the knowledge which could be extracted from the massive data and can be used to instruct the management in aluminum electrolysis industry.
引文
[1]苟护生.我国电解铝工业的现状和国际竞争力分析.中国有色冶金,2004,5:1-4
[2]李劫,黄永忠,刘业翔.铝电解工艺参数采集系统及工艺参数测试与研究.轻金属,1992,9:21-22
[3]夏火松.数据仓库与数据挖掘技术.北京:科学出版社,2004.15-18
[4]邱竹贤.预焙槽炼铝(修订版).北京:冶金工业出版社,1998.20-22
[5]范双平.浅谈电解铝平稳生产.技术创新与应用,2007,4:20
[6]胡志坤,桂卫华,彭小奇.有色冶金过程的数据挖掘.有色金属,2003,55(2):47-55
[7]姚克娟.基于.Net技术的数据多维分析系统.回转窑生产数据在线分析系统[硕士学位论文].北京:北方工业大学2005
[8]张艳芳.铝电解槽焙烧启动数据挖掘的研究与开发[硕士学位论文].北京:北方工业大学,2006
[9]曹丹阳.数据挖掘在教务系统中应用研究[硕士学位论文].北京:北方工业大学,2006
[10]Jiawei Han,Micheline Kamber.数据挖掘概念与技术.机械工业出版社,2005
[11]魏金强.多维分析在教务管理系统应用与研究[硕士学位论文].北京:北方工业大学,2006
[12]冯玉才,冯剑琳.关联规则的增量式更新算法.软件学报,1998,9(4):301-306
[13]顾庆锋,宋顺林.Apriori算法在SQL中的改进与应用.计算机工程与设计,2007,28(13):3060-3062
[14]王珏.基于.Net技术的数据多维分析系统-长城铝业压力容器分析系统[硕士学位论文].北京:北方工业大学,2005
[15]毕建欣,张岐山.关联规则挖掘算法综述.中国工程科学,2005,7(4):88-90
[16]何胜文.基于数据仓库的关联规则挖掘算法的研究与应用[硕士学位论文].南昌:南昌大学.2007
[17]周虹.关联规则挖掘算法的研究[硕士学位论文].哈尔滨:哈尔滨大学2007
[18]裴芳,郑颖,杜丽芳.基于多维关联规则的教学管理系统的开发.数据库与信息管理,2007,7(10):103-105
[19]郑之开,张广凡,邵慧鹤.数据采掘与知识发现、回顾和展望.信息与控制,1999,28(5):357-359
[20]李晋宏,闰鹏,曹丹阳,苏志同,胡健,熊伟.铝电解槽生产数据的多维分析系统.北方工业大学学报,2002
[21]王德兴,胡学钢,刘晓平,王浩.基于概念格和Apriori的关联规则挖掘算法分析合肥工业大学校报,2006,29(6):699-702
[22]陈霞,邱桃荣,魏玲玲.基于数据挖掘的病历数据预处理计算机与现代化,2007,5:23-25
[23]罗英辉.利用SQL语言的数据挖掘系统的设计广东教育学院学报,2007,27(3):89-91
[24]李乃乾,沈钧毅.量化关联规则挖掘及算法小型微型计算机系统,2003,24(12):2275-2276
[25]O'Leary D E.Using AI in Knowledge Management:Knowledge Bases and Ontologies.IEEE Intelligent Systems,1996.34-39
[26]Agrawal R,Srikant R.Mining association rulesbetween sets of items in large databases[A].ProcACM SIGMOD Int'l Conf Management of data[C].Washington DC,May 1993.207-216
[27]Agrawal R,Srikant R.Fast algorithms for mining association rules[A].Proc 20th Int 'l Conf Very Large Database[C].Santiago,Chile,Sept 1994.487-499
[28]Park J S,Chen M S,Yu P S.An effective hash2based algorithm for mining association rules[A].Proceedings of ACM SIGMOD International Conference On Management of Data[C].San Jose,CA,May 1995.175-186
[29]Savasere A,Omiecinski E,Navathe S.An efficient algorithm for mining association rules in large databases[A].Proc of the 21th International Conference on Very Large Database[C].Zurich,Switzerland,Sept 1995.432-443
[30]Mannila H,Toivonen H,Verkamo A.Efficientalgorithm for discovering association rules[A].AAAIWorkshop on Knowledge Discovery in Databases[C],1994.181-192
[31]Brin S,Motwani R,Silverstein C.Beyond marketbaskets generalizing association rules to correlations[A].Proc of the 1997 ACM SIGMOD Int'l Confon
[32]Srikant R,Agrawal R.Mining generalized association rules[A].Proceedings of the 21th International Conference on Very Large Databases[C].Zurich,Switzerland,Sept 1995.407-419
[33]Srikant R,Agrawal R.Mining quantitative association rules in large relational tables[A].Proc of the 1996 ACM SIGMOD Int'l Confon Management Of Data [M].Montreal,Quebee,Canada:ACM Press,1996.1-12
[34]Savasere A,Omiecinski E,Navathe S B.Mining for strong negative associations in a large database of customer transactions[A].Proc of the 14th Int'l Conf on Data Engineering[M].Orlando,Florida,USA:IEEE Computer Society Press,1998.494-502
[35]Srikant R,Agrawal R.Mining association rules withitem constrains[A].Proc of the 3rd Int'l Conference on Knowledge Discovery in Data Bases and Data Mining[C].Newport Beach,California,August 1997.67-73
[36]Ng R,Lakshmanan L V S,Han J,et al.Exploratory mining and pruning optimizations of constrained associations rules[A].Proceedings of ACM SIGMOD International Conference on Management of Data[C].Seattle,Washington,J une 1998.13-24
[37]Fu Y,Han J.Meta2rule2guided mining of association rules in relational databases [A].Proc 1995 Int 'l Workshop on Knowledge Discovery and Deductive and Object2Oriented Databases(KDOOD'95)[C].Singapore,December 1995.39-46
[38]Park J S,Chen M S,Yu P S.An effective hash2based algorithm for mining association rules[A].Proceedings of ACM SIGMOD International Conference on Management of Data[C].San Jose,CA,May 1995.175-186
[39]Ralph Kimball,Laura Reeves,Margy Ross et al.数据仓库生命周期工具箱一设计、开发和部署数据仓库的专家方法(肖明,王永红等译).北京:电子工业出版社.2004.1
[40]陈湘涛,数据仓库与数据挖掘技术在新型铝电解控制系统中的应用研究.2004.4
[41]蔡伟杰,张晓辉,朱建秋,朱扬勇.关联规则挖掘综述.数据挖掘研究院
[2]李劫,黄永忠,刘业翔.铝电解工艺参数采集系统及工艺参数测试与研究.轻金属,1992,9:21-22
[3]夏火松.数据仓库与数据挖掘技术.北京:科学出版社,2004.15-18
[4]邱竹贤.预焙槽炼铝(修订版).北京:冶金工业出版社,1998.20-22
[5]范双平.浅谈电解铝平稳生产.技术创新与应用,2007,4:20
[6]胡志坤,桂卫华,彭小奇.有色冶金过程的数据挖掘.有色金属,2003,55(2):47-55
[7]姚克娟.基于.Net技术的数据多维分析系统.回转窑生产数据在线分析系统[硕士学位论文].北京:北方工业大学2005
[8]张艳芳.铝电解槽焙烧启动数据挖掘的研究与开发[硕士学位论文].北京:北方工业大学,2006
[9]曹丹阳.数据挖掘在教务系统中应用研究[硕士学位论文].北京:北方工业大学,2006
[10]Jiawei Han,Micheline Kamber.数据挖掘概念与技术.机械工业出版社,2005
[11]魏金强.多维分析在教务管理系统应用与研究[硕士学位论文].北京:北方工业大学,2006
[12]冯玉才,冯剑琳.关联规则的增量式更新算法.软件学报,1998,9(4):301-306
[13]顾庆锋,宋顺林.Apriori算法在SQL中的改进与应用.计算机工程与设计,2007,28(13):3060-3062
[14]王珏.基于.Net技术的数据多维分析系统-长城铝业压力容器分析系统[硕士学位论文].北京:北方工业大学,2005
[15]毕建欣,张岐山.关联规则挖掘算法综述.中国工程科学,2005,7(4):88-90
[16]何胜文.基于数据仓库的关联规则挖掘算法的研究与应用[硕士学位论文].南昌:南昌大学.2007
[17]周虹.关联规则挖掘算法的研究[硕士学位论文].哈尔滨:哈尔滨大学2007
[18]裴芳,郑颖,杜丽芳.基于多维关联规则的教学管理系统的开发.数据库与信息管理,2007,7(10):103-105
[19]郑之开,张广凡,邵慧鹤.数据采掘与知识发现、回顾和展望.信息与控制,1999,28(5):357-359
[20]李晋宏,闰鹏,曹丹阳,苏志同,胡健,熊伟.铝电解槽生产数据的多维分析系统.北方工业大学学报,2002
[21]王德兴,胡学钢,刘晓平,王浩.基于概念格和Apriori的关联规则挖掘算法分析合肥工业大学校报,2006,29(6):699-702
[22]陈霞,邱桃荣,魏玲玲.基于数据挖掘的病历数据预处理计算机与现代化,2007,5:23-25
[23]罗英辉.利用SQL语言的数据挖掘系统的设计广东教育学院学报,2007,27(3):89-91
[24]李乃乾,沈钧毅.量化关联规则挖掘及算法小型微型计算机系统,2003,24(12):2275-2276
[25]O'Leary D E.Using AI in Knowledge Management:Knowledge Bases and Ontologies.IEEE Intelligent Systems,1996.34-39
[26]Agrawal R,Srikant R.Mining association rulesbetween sets of items in large databases[A].ProcACM SIGMOD Int'l Conf Management of data[C].Washington DC,May 1993.207-216
[27]Agrawal R,Srikant R.Fast algorithms for mining association rules[A].Proc 20th Int 'l Conf Very Large Database[C].Santiago,Chile,Sept 1994.487-499
[28]Park J S,Chen M S,Yu P S.An effective hash2based algorithm for mining association rules[A].Proceedings of ACM SIGMOD International Conference On Management of Data[C].San Jose,CA,May 1995.175-186
[29]Savasere A,Omiecinski E,Navathe S.An efficient algorithm for mining association rules in large databases[A].Proc of the 21th International Conference on Very Large Database[C].Zurich,Switzerland,Sept 1995.432-443
[30]Mannila H,Toivonen H,Verkamo A.Efficientalgorithm for discovering association rules[A].AAAIWorkshop on Knowledge Discovery in Databases[C],1994.181-192
[31]Brin S,Motwani R,Silverstein C.Beyond marketbaskets generalizing association rules to correlations[A].Proc of the 1997 ACM SIGMOD Int'l Confon
[32]Srikant R,Agrawal R.Mining generalized association rules[A].Proceedings of the 21th International Conference on Very Large Databases[C].Zurich,Switzerland,Sept 1995.407-419
[33]Srikant R,Agrawal R.Mining quantitative association rules in large relational tables[A].Proc of the 1996 ACM SIGMOD Int'l Confon Management Of Data [M].Montreal,Quebee,Canada:ACM Press,1996.1-12
[34]Savasere A,Omiecinski E,Navathe S B.Mining for strong negative associations in a large database of customer transactions[A].Proc of the 14th Int'l Conf on Data Engineering[M].Orlando,Florida,USA:IEEE Computer Society Press,1998.494-502
[35]Srikant R,Agrawal R.Mining association rules withitem constrains[A].Proc of the 3rd Int'l Conference on Knowledge Discovery in Data Bases and Data Mining[C].Newport Beach,California,August 1997.67-73
[36]Ng R,Lakshmanan L V S,Han J,et al.Exploratory mining and pruning optimizations of constrained associations rules[A].Proceedings of ACM SIGMOD International Conference on Management of Data[C].Seattle,Washington,J une 1998.13-24
[37]Fu Y,Han J.Meta2rule2guided mining of association rules in relational databases [A].Proc 1995 Int 'l Workshop on Knowledge Discovery and Deductive and Object2Oriented Databases(KDOOD'95)[C].Singapore,December 1995.39-46
[38]Park J S,Chen M S,Yu P S.An effective hash2based algorithm for mining association rules[A].Proceedings of ACM SIGMOD International Conference on Management of Data[C].San Jose,CA,May 1995.175-186
[39]Ralph Kimball,Laura Reeves,Margy Ross et al.数据仓库生命周期工具箱一设计、开发和部署数据仓库的专家方法(肖明,王永红等译).北京:电子工业出版社.2004.1
[40]陈湘涛,数据仓库与数据挖掘技术在新型铝电解控制系统中的应用研究.2004.4
[41]蔡伟杰,张晓辉,朱建秋,朱扬勇.关联规则挖掘综述.数据挖掘研究院