云计算和人工智能数据筛选算法分析
Cloud Computing and Artificial Intelligence Data Screening Algorithm Analysis
张兵 1黄中杰 1郭莎莎1
作者信息
摘要
为进一步优化云计算人工智能数据筛选算法的数据收敛效果、用户满意度和资源处理效率,解决传统算法在实际应用中存在的问题,本文结合数据筛选的目的以及具体完成过程中数据抽取、数据清理、数据加载的3个环节,分析当前超欧里几何数据筛选算法和时间片积累调度筛选算法在实际应用中存在的缺陷,并运用经济模型研究数据筛选算法的优化思路,通过优化设计与效果检验发现其具有显著的应用优势,可为相关工作提供参考.
Abstract
In order to further optimize the data convergence effect,user satisfaction and resource processing efficiency of cloud computing artificial intelligence data screening algorithm,and solve the existing problems in practical application of traditional algorithms,this paper analyzes the shortcomings of the current Ultra-Euclidean Geometric Data Screening Algorithm and Time Slice Cumulative Scheduling Filtering Algorithm in practical application by combining the purpose of data screening and three links of data extraction,data cleaning and data loading in the specific completion process,and uses the economic model to study the optimization ideas of data screening algorithm.Through the optimized design and effect test,it is found to have significant application advantages,which can provide reference for related work.
关键词
云计算/人工智能/数据筛选算法Key words
cloud computing/artificial intelligence/data screening algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据