Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Ying Zhu,Wei Wang

doi:10.1155/2021/5566647

Abstract

In this paper, an adaptive edge service placement mechanism based on online learning and a predictive edge service migration method based on factor graph model are proposed to solve the edge computing service placement problem from the edge computing dimension. First, the time series of the development of online chaotic public opinion is a platform for vectorized collection of keyword index trends using the theory of chaotic phase space reconstruction. Secondly, it is necessary to use the main index method to judge whether the time series has the chaotic characteristics of the network public opinion data. The simulation results show that network public opinion is the development characteristic of chaotic time series. Finally, the prediction model is improved by using complex network topology. Through the simulation experiment of network public opinion and chaotic time series, the results show that the improved model has the advantages of accuracy, rapidity, and self-adaptability and can be applied to other fields.

Highlights

In recent years, with the rapid development of social economy and science and technology in the world, many new technologies have emerged in the information and communication technology industry
It shows how to use the factor graph model, an emerging artificial intelligence technique, to achieve user location prediction to improve the quality of dynamic migration decisions for edge services
The phase space development time series of network public opinion was reconstructed by using CAO method and autocorrelation function method in phase space reconstruction theory, and the prediction model was established by using reconstructed time delay vector and reserve pool neural network

Summary

Introduction

With the rapid development of social economy and science and technology in the world, many new technologies have emerged in the information and communication technology industry. The mechanism can adapt to complex user behavior and changeable edge network environment through online learning artificial intelligence technology, so as to assist users to make efficient service migration decisions. It shows how to use the factor graph model, an emerging artificial intelligence technique, to achieve user location prediction to improve the quality of dynamic migration decisions for edge services. The time series representing the development trend information of network public opinion is collected, and Fourier transform is carried out. The phase space development time series of network public opinion was reconstructed by using CAO method and autocorrelation function method in phase space reconstruction theory, and the prediction model was established by using reconstructed time delay vector and reserve pool neural network. The ability to implement algorithms jumps out of local optimality

Related Work

Results and Analysis

Conclusion