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

Author:

Zhu Ying1ORCID

Affiliation:

1. Hubei University of Technology, Wuhan Hubei 430060, China

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.

Funder

Provincial Department of Education Project, Internet Public Opinion Prediction and Control in Big Data Environment

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Reference28 articles.

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