Management (Tourism) School of Guangzhou University, Guangzhou 510006, Guangdong, China.
Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, Johor, Malaysia.
Comput Intell Neurosci. 2022 Jul 21;2022:3948221. doi: 10.1155/2022/3948221. eCollection 2022.
With the rapid development of image video and tourism economy, tourism economic data are gradually becoming big data. Therefore, how to schedule between data has become a hot topic. This paper first summarizes the research results on image video, cloud computing, tourism economy, and data scheduling algorithms. Secondly, the origin, structure, development, and service types of cloud computing are expounded in detail. And in order to solve the problem of tourism economic data scheduling, this paper regards the completion time and cross-node transmission delay as the constraints of tourism economic data scheduling. The constraint model of data scheduling is established, the fitness function is improved on the basis of an artificial immune algorithm combined with the constraint model, and the directional recombination of excellent antibodies is carried out by using the advantages of gene recombination so as to obtain the optimal solution to the problem more appropriately. When the resource node scale is 100, the response time of EDSA is 107.92 seconds.
随着图像视频和旅游经济的快速发展，旅游经济数据逐渐成为大数据。因此，如何在数据之间进行调度成为热门话题。本文首先总结了图像视频、云计算、旅游经济和数据调度算法的研究成果。其次，详细阐述了云计算的起源、结构、发展和服务类型。并且，为了解决旅游经济数据调度问题，本文将完成时间和跨节点传输延迟作为旅游经济数据调度的约束条件。建立数据调度的约束模型，在结合约束模型的人工免疫算法的基础上，改进适应度函数，利用基因重组的优势进行优秀抗体的定向重组，从而更恰当地得到问题的最优解。当资源节点规模为 100 时，EDSA 的响应时间为 107.92 秒。
