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基于虚拟图像与物联网的大学生短视频传播教育后期制作的心理学分析

The Psychology Analysis for Post-production of College Students' Short Video Communication Education Based on Virtual Image and Internet of Things.

作者信息

Tang Wufeng

机构信息

Zhejiang University of Finance & Economics Dongfang College, Haining, China.

Faculty Communication and Media Studies, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.

出版信息

Front Psychol. 2022 Mar 25;13:781802. doi: 10.3389/fpsyg.2022.781802. eCollection 2022.

Abstract

To improve the understanding of film and television postproduction for college students in the era of intelligent media, a study is conducted on college students' short video communication education and audience psychology based on the rapid development of virtual image and the Internet of Things (IoT). Primarily, the collaborative filtering algorithm (CFA) is optimized and combined with the principle of Spark and Hadoop platforms as well as the IoT and virtual image technologies. Then, a hybrid computing model is proposed, and the two algorithms are improved and combined, with 90,000 network video records as data samples. Finally, the push accuracy of the hybrid algorithm and the traditional algorithm is calculated and compared, and based on this, a questionnaire survey on the audience psychology of short video production is carried out for college students. The results show that the time user of the combined algorithm is always at least 0.4 s faster than that of a single algorithm and the running speed of the algorithm with five nodes is nearly 80% higher than the algorithm with a single node. The Spark algorithm with multinode has good versatility in image recording and processing of large groups of college students. When processing more than 100,000 image records, the deviation values of Spark and Hadoop with a single node exceeded 1.1, but the deviation value of the hybrid algorithm was still lower than 1.1. With the increase of data volume, the deviation values of the three algorithms are increasing. Compared with the traditional CFA algorithm, the optimized algorithm has a higher speed in processing data and is more accurate in content pushing. From the questionnaire survey of college students, it is found that contemporary college students are not active in learning knowledge of virtual images. Hence, it is concluded that colleges must carry out relevant courses based on short video communication education and strengthen the short video communication education of college students. A reference is provided for the development of college students' short video communication education in the digital age.

摘要

为提升智能媒体时代大学生对影视后期制作的理解,基于虚拟影像与物联网(IoT)的快速发展,开展了一项关于大学生短视频传播教育与受众心理的研究。首先,对协同过滤算法(CFA)进行优化,并结合Spark和Hadoop平台的原理以及物联网和虚拟影像技术。然后,提出一种混合计算模型,对两种算法进行改进与结合,以90000条网络视频记录作为数据样本。最后,计算并比较混合算法与传统算法的推送准确率,并在此基础上针对大学生开展短视频制作受众心理的问卷调查。结果表明,组合算法的用户用时始终比单一算法至少快0.4秒,且五节点算法的运行速度比单节点算法快近80%。多节点的Spark算法在大量大学生图像记录与处理方面具有良好的通用性。在处理超过100000条图像记录时,单节点的Spark和Hadoop的偏差值超过1.1,但混合算法的偏差值仍低于1.1。随着数据量的增加,三种算法的偏差值都在增大。与传统CFA算法相比,优化后的算法在数据处理速度上更快,内容推送更准确。从对大学生的问卷调查中发现,当代大学生对虚拟影像知识的学习积极性不高。因此得出结论,高校必须开展基于短视频传播教育的相关课程,加强大学生的短视频传播教育。为数字时代大学生短视频传播教育的发展提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f92a/8992796/a7b0ac4469e9/fpsyg-13-781802-g001.jpg

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