为了提高大型车联网数据库检索的查准率,提出一种基于模糊数据聚类的大型车联网数据库高效查询技术.分析大型车联网的网络分布式结构和数据结构,对大型车联网数据库的数据信息流进行特征空间重组,采用关联语义融合方法进行数据库检索的特征量提取,结合模糊C均值聚类算法对提取的大型车联网数据库的语义本体特征进行分类检索,实现数据库的高效查询.仿真结果表明,采用该方法进行大型车联网数据库查询的查准率和查全率较高,查询过程的收敛性较好.%In order to improve the retrieval precision ratio of large vehicle networking database,a large vehicle networking database query technology based on fuzzy data clustering is presented. The network distributed structure and data structure of large vehicle networking are analyzed. The feature space reorganization of information flow for large vehicle networking database is conducted. The associated semantic fusion method is used to perform feature extraction of database retrieval. The classification retrieval for the extracted semantic ontology features from large vehicle networking database is carried out in combination with fuzzy C means clustering algorithm to realize efficient query of the database. The simulation results show that the method has high the precision ratio and high recall ratio for large vehicle networking database query,and excellent convergence in the query process.
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