计算机科学
节点(物理)
事件(粒子物理)
网络数据包
数据挖掘
声誉
模糊逻辑
条件概率
人工智能
机器学习
计算机安全
统计
工程类
结构工程
社会学
物理
量子力学
社会科学
数学
标识
DOI:10.1142/s0218126623502791
摘要
The quantitative evaluation for communicable events has been a significant demand in digital society management. This paper takes the 2022 Winter Olympic Games as the object and proposes a quantitative evaluation method for the communication impact of sporting events based on the SIR dynamic diffusion model. Specifically, this study combinesa long short-term memory (LSTM) neural network, wavelet packet decomposition, and other techniques to propose a digital evaluation approach for the quantification of communication impact. Among these, the transfer probability is quantified and calculated by the user node reputation value algorithm. In the experimental simulation, the effects of different mechanisms of joining consensus nodes and blockchain on the propagation probability in the model are discussed, respectively. Some simulation experiments are conducted on the real-world scenes of social networks, and the simulation results show that the number of nodes spreading false information is reduced by 9.89% compared with baseline methods. Finally, a sports event communication effect evaluation index system was constructed, and the data characteristics of indicators at all levels were analyzed to preliminarily predict the communication effect, after which the fuzzy hierarchical comprehensive evaluation method, combined with the expert survey method, was used to empirically evaluate, and test the communication effect of its events.
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