基于模糊数学的多源异构数据融合模型

Multi-source Heterogeneous Data Fusion Model Based on Fuzzy Mathematics

针对多源异构型数据来源复杂、结构特殊,导致其融合难度较大的问题,为提升数据融合效率与准确性,提出一种基于模糊数学的多源异构数据融合模型.首先,利用联邦加权平均融合策略,整合从各传感器传输至数据级融合层中的元数据,得到数据级融合结果;其次,结合主成分分析法与典型相关分析法,提取出由网络本体语言统一后数据的特征,完成特征级数据融合;再次,根据模糊数学理论建立、更新模糊规则库,通过决策融合算法,得到决策级融合结果;最后,将上述不同层级的数据融合结果结合建立数据融合模型,得到最终的数据融合结果.实验结果表明,该方法的最大协方差值和绝对误差值不超过0.15,最短融合时间仅为12.6 ms.该方法的融合精度和稳定性较好,时效性与抗扰性均具有显著的优越性.

Aiming at the problem that multi-source heterogeneous data had the complex sources and unique structure,resulting in a greater difficulty in its fusion.In order to improve the efficiency and accuracy of data fusion,we proposed a multi-source heterogeneous data fusion model based on fuzzy mathematics.Firstly,by utilizing a federated weighted average fusion strategy,the metadata transmitted from various sensors to the data level fusion layer was integrated to obtain the data level fusion results.Secondly,combined with the principal component analysis method and canonical correlation analysis method,the features of data unified by Web Ontology Language were extracted to complete the feature level data fusion.Thirdly,a fuzzy rule library established and updated based on fuzzy mathematics theory was used to obtain decision level fusion results through decision fusion algorithms.Finally,we combined the data fusion results of above different levels to establish a data fusion model,and obtained the final data fusion result.The experimental results show that the maximum covariance value and absolute error value of the proposed method do not exceed 0.15,and the shortest fusion time is only 12.6 ms.The fusion accuracy and stability of this method are good,and both timeliness and anti-interference have significant advantages.