S系列综合保障标准数据模型中的数据元素映射关系研究
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摘要
国内相关装备综合保障标准信息间的交互性差,难以实现数据重用。S系列综合后勤保障标准体系通过UML模式定义了装备全寿命周期的信息格式,构建了综合保障信息的数据重用框架,为综合保障信息的集成利用奠定了基础。然而,S系列标准并未给出不同标准间数据的映射关系,且各标准的数据元素数量多,映射关系寻找难度大。针对这一问题,在SX002D基础上,使用基于Levenshtein距离的字符串模糊匹配方法来缩小映射范围,之后通过人工筛选找出S系列综合后勤保障标准数据模型间的映射关系。实验表明,这种方法能够在维持一定匹配准确度的前提下提高映射关系的寻找效率。
The information interoperability between domestic related equipment integrated support standards is poor,and it is difficult to realize data reuse.The S Series Integrated Logistics Support System defines the equipment life cycle information format through the UML model,builds a data reuse framework that integrates safeguard information and lays the foundation for the integrated use of comprehensive assurance information.However,S-series standards do not provide mappings between data from different standards and there are too many data elements in each standard,so it is difficult to find the mapping relationship.To solve this problem,use,firstly,SX002 D and Levenshtein distance-based string fuzzy matching method to narrow the mapping range.Afterwards,through manual screening,the mapping relationship between the data models of the S Series Integrated Logistics Support Standards was found. Experiments show that this method can improve the search efficiency of the mapping relationship while maintaining certain matching accuracy.
The information interoperability between domestic related equipment integrated support standards is poor,and it is difficult to realize data reuse.The S Series Integrated Logistics Support System defines the equipment life cycle information format through the UML model,builds a data reuse framework that integrates safeguard information and lays the foundation for the integrated use of comprehensive assurance information.However,S-series standards do not provide mappings between data from different standards and there are too many data elements in each standard,so it is difficult to find the mapping relationship.To solve this problem,use,firstly,SX002 D and Levenshtein distance-based string fuzzy matching method to narrow the mapping range.Afterwards,through manual screening,the mapping relationship between the data models of the S Series Integrated Logistics Support Standards was found. Experiments show that this method can improve the search efficiency of the mapping relationship while maintaining certain matching accuracy.
引文
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[3] Yujian L, Bo L. A normalized Levenshtein distance metric.[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1091.
[4] Schimke S,Vielhauer C,Dittmann J. Using Adapted Levenshtein Distance for On-Line Signature Authentication[C]. Pattern Recognition,International Conference on.IEEE Computer Society,2004:931-934.
[5] ASD/AIA S1000D.International specification for technical publications utilizing acommon source database. Issue4. 1[M].ASD-AIA,2009.
[6] ASD/AIA S2000M.International specification for material management.Issue6. 0[M].ASD-AIA,2015.
[7] ASD/AIA S3000L.International procedure specification for logistics support analysis.Issue1. 1[M].ASD-AIA,2014.
[8] ASD/AIA S4000P. International specification for developing and continuously improving preventive maintenance.Issue1. 0[M].ASD-AIA,2014.
[9] ASD/AIA S5000F. International specification for inservice data feedback.Issue1. 0[M].ASD-AIA,2016.
[10] ASD/AIA SX002D.Common data model for the S-Series ILS specifications.Issue1. 1[M].ASD-AIA,2015.