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.

中文翻译：

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基于边缘计算和人工智能新范式的网络舆情预测与控制

提出了一种基于在线学习的自适应边缘服务放置机制和一种基于因子图模型的预测边缘服务迁移方法，从边缘计算维度解决了边缘计算服务放置问题。首先，在线混沌舆论发展的时间序列是一个使用混沌相空间重构理论对关键词索引趋势进行矢量化收集的平台。其次，有必要使用主指标法来判断时间序列是否具有网络舆情数据的混沌特征。仿真结果表明，网络舆情是混沌时间序列的发展特征。最后，通过使用复杂的网络拓扑来改进预测模型。