Design and implementation of a fast integration method for multi-source data in high-speed network

Abstract

The data collected by the distributed high-speed network has multiple sources. Therefore, in order to realize the rapid integration of multi-source data, this paper designs a rapid data integration method based on the characteristics of the distributed high-speed network. First, we use linear regression analysis to build a distributed perceptual data model, so that network nodes can only transmit the parameter information of the regression model, so as to simplify the data collection. Then, a dead band amplitude limiting nonlinear link is added at the high frequency channel side to filter and assimilate the data. Finally, the data feature vectors are extracted as the training samples of the neural network to obtain the mapping relationship between different feature vectors, and then the decision level data integration is achieved by training the neural network. The experimental results show that this method can accurately collect high-speed network data, and the data collection deviation is always less than 5 μrad; This method has good filtering effect on data and can eliminate the interference of burr signal; The convergence speed of this method is fast, and the data assimilation can be completed within 0.4 s, which is conducive to improving the speed of data integration; With the increase of network size, the average traffic load of this method increases less.