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基于业务流程的财务数据自动化分类系统设计 被引量:4

Design of financial data automatic classification system based on business process
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摘要 针对当前的财务数据分类系统误分率较高的问题,设计一种基于业务流程的财务数据自动化分类系统。该系统以财务数据分类算法为中心,在程序加载和交叉编译模式下,采用分布式云计算技术对采集到的财务数据进行融合处理,提取其高阶统计特征量;采用分组样本检验分析方法分析财务数据间的关联性,结合业务流程进行财务数据的属性分类识别;以业务流程的模糊聚类分布为中心矢量,采用分段检测方法实现财务数据的自动化分类;将上述过程采用程序加载方式移植到处理器终端,进行财务数据分类系统的交叉编译控制,实现财务数据的自动化分类系统的设计。仿真实验结果表明,采用该系统进行财务数据自动化分类的准确性较高、误分率较低,提高了财务数据的业务管理和分析能力。 Aiming at the high error rate of current financial data classification system,An automatic financial data classification system is designed based on business process.The system is centered on the financial data classification algorithm.Under the mode of program loading and cross-compilation,the distributed cloud computing technology is used to fuse the collected financial data and extract its high-order statistical characteristic quantity.The correlation between financial data is analyzed by grouping sample test and analysis method,and the attribute classification and identification of financial data are carried out by combining business process.Taking the fuzzy clustering distribution of business process as the central vector,the segmentation detection method is used to realize the automatic classification of financial data.The above process is transplanted to the processor terminal by means of program loading,and the cross compilation control of financial data classification system is carried out to realize the design of automatic financial data classification system.The simulation results show that the accuracy of automatic classification of financial data is high and the error rate is low,which improves the business management and analysis ability of financial data.
作者 张景 ZHANG Jing(Shaanxi institute of technology,Xi'an 710300,China)
出处 《自动化与仪器仪表》 2020年第1期85-88,共4页 Automation & Instrumentation
基金 陕西会计学会教育专业委员会2018年教育财会科学研究计划—政府购买职业教育服务研究(No.18JC018)
关键词 数据分类 特征提取数据融合 云计算 分段检测 系统开发 data classification feature extraction data fusion cloud computing segment detection system devtlopmelt
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  • 1胡中波,熊盛武,胡付高,苏清华.改进的差分演化算法及其在函数优化中的应用[J].武汉理工大学学报,2007,29(4):125-128. 被引量:11
  • 2陆克中,刘应玲.一种线型无线传感器网络的节点布置方案[J].计算机应用,2007,27(7):1566-1568. 被引量:16
  • 3Han J,Kamber M.数据挖掘概念与技术[M].范明,译.北京:机械工业出版社,2007:32-59.
  • 4Wu Q X,Mc Ginnity M,Bell D A,et al.A self-organizing computing network for decision-making in data sets with a diversity of data types[J].IEEE Transactions on Knowledge and Data Engineering,2006,18(7):941-953.
  • 5Lin C J.On the convergence of the decomposition method for support vector machines[J].IEEE Transactions on Neural Networks,2001,12(6):1288-1298.
  • 6Duda R O,Hart P E,Stork D G.模式分类[M].李宏东,姚天翔,等译.2版.北京:机械工业出版社,2004.
  • 7Dean J,Ghemawat S.MapReduce:simplified data processing on large cluster[J].Communication of the ACM,2008,51(1):107-113.
  • 8Tan P N,Steinbach M,Kumar V.Introduction to Data Mining[M].Addison Westey,2005.
  • 9Quinlan J R.C4.5:Programs for Machine Learning[M].Morgan Kaufman,1993.
  • 10Langley P,Thompson K.An analysis of Bayesian classifiers[C]//Proceedings of the 10thNational Conference on Artificial Intelligence,1992:223-228.

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