Knowledge Map Construction of Multi-source Heterogeneous Power Grid Data Fusion in the Power Internet of Things Environment

Abstract

In order to realize the dynamic monitoring of multi-source heterogeneous power grid data in the power Internet of Things environment, it is necessary to construct the knowledge map. A knowledge map construction method of multi-source heterogeneous power grid data fusion in the power Internet of Things environment based on the combination and correction of re labeling method and projection method is proposed, and the knowledge clustering map features of multi-source heterogeneous power grid data generated by the basic clustering algorithm are used. The phase space reconstruction of multi-source heterogeneous power grid data is realized by projection method, and the subspace noise reduction and feature clustering model of multi-source heterogeneous power grid data in the power Internet of Things environment is established by combining the method of identification of grid connected installed capacity parameters. According to the probability distributed reorganization of the set of discrete variables of multi-source heterogeneous power grid data, the output status of multi-source heterogeneous power grid data at different times is calculated. According to the fusion method of state distribution difference, the dynamic correction and adaptive feedback adjustment of power grid data are realized by using the combination and correction method of re marking method and projection method to improve the accuracy of heterogeneous reconstruction of the knowledge map. The simulation test results show that this method can be used to construct the knowledge map of multi-source heterogeneous power grid data fusion, which has a good ability to express the internal structure information parameters of power grid data, a strong noise reduction performance for redundant data, and improves the dynamic monitoring and evaluation ability of power grid data.