摘要
现代战争需要对多源异构的装备数据进行高效集成。针对不同来源数据中装备名称不一致的问题,设计了装备数据的聚合模型和聚合流程,在综合分析现有算法的基础上,结合装备名称特点为该模型提供了一种新的相似度匹配算法,算法将Jaro-Winkler和最长公共子序列相结合,以提高匹配的精度。最后通过实验进行了验证,结果表明该算法与传统相似度算法相比具有较高的适配性和鲁棒性,可以为装备数据聚合工作提供有效支撑。
Modern warfare requires efficient integration of multi-source heterogeneous equipment data.To solve the problem of inconsistent equipment names of data from different sources,the aggregation model and aggregation process of equipment data are studied and designed.Based on the comprehensive analysis of existing algorithms and the characteristics of equipment data,a new similarity algorithm is provided for the model,the algorithm combines Jaro-Winkler and the longest common subsequence to improve the matching accuracy.Finally,experiments show that the algorithm has high adaptability and robustness compared with the traditional similarity algorithm,and can provide effective support for equipment data aggregation.
作者
杨杉
YANG Shan(Unit 32683,Shenyang 110000,China)
出处
《空军工程大学学报》
CSCD
北大核心
2023年第2期98-103,共6页
Journal of Air Force Engineering University
基金
国家自然科学基金(71701205)。
