Interpolation Analysis of Industrial Big Data Based on KDR Knowledge Recognition Algorithm Considering Singular Value Decomposition Theory

Abstract

Although various algorithms have made some progress in the current research of industrial big data interpolation, most of them are only suitable for static KDR operation methods. In fact, most of the data is not achieved overnight, but in an incremental manner. For example, the data will increase with the passage of time. In the process of data collection, in order to ensure the consistency of KDR calculation results under dynamic conditions, the same and different information in the old and new data must be merged, so as to disperse the dynamic data. According to the increasing property of data in Industrial big data analysis, a dynamic KDR operation model is established by considering singular value decomposition (SVD) theory. In order to ensure the consistency before and after static separation, a rough set method based on Manilkara is used. Under the influence of Yalo' s singular value decomposition (SVD) theory, the conventional interval is divided into two parts: the core and the blank to express the unstable interval. This method uses the method based on the middle interval. By dividing the middle interval again, the interval between the old and the new data is combined.