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作 者:秦祯研 张抗抗 QIN Zhen-yan;ZHANG Kang-kang(Art College,Anhui Jianzhu University,Hefei Anhui,230022)
出 处:《山西大同大学学报(自然科学版)》2021年第1期33-36,76,共5页Journal of Shanxi Datong University(Natural Science Edition)
基 金:安徽建筑大学精品开放课程与智慧课堂建设项目[2017MOOC100]。
摘 要:为了提高材料动画资源管理和调度能力,提出基于KVM虚拟化资源池的材料动画资源管理模型,构建材料动画资源融合的大数据分析模型,通过模糊信息融合的方法建立材料动画资源管理KVM虚拟化资源池采样和统计分析模型,结合统计分析和量化回归分析方法进行材料动画资源管理的资源池构造,采用大数据库模型调度的方法,进行材料动画资源管理的KVM虚拟化资源池融合,采用差异性融合和特征检测方法进行材料动画资源管理和动画资源虚拟化特征重组,结合模糊聚类和信息融合方法进行材料动画资源虚拟化资源池的特征分类识别,通过大数据挖掘方法,进行材料动画资源管理和资源优化调度。仿真结果表明,采用该方法进行材料动画资源管理的资源融合度水平较好,虚拟化资源池的信息分类能力较强,提高了KVM虚拟化资源池的信息化综合管理能力。In order to improve the management and scheduling ability of material animation resources,a material animation resource management model based on KVM virtual resource pool is proposed,and a big data analysis model of material animation resource fusion is constructed.The sampling and statistical analysis model of material animation resource management KVM virtual resource pool is established by using fuzzy information fusion method In the construction of resource pool of material animation resource management,the method of large database model scheduling is used to fuse KVM virtualization resource pool of material animation resource management.The difference fusion and feature detection methods are used to reconstruct the characteristics of material animation resource management and animation resource virtualization.The characteristics of material animation resource virtualization resource pool are combined with fuzzy clustering and information fusion methods Classification identification,through the big data mining method,material animation resource management and resource optimization scheduling.The simulation results show that the resource fusion level of this method is good,the information classification ability of virtual resource pool is strong,and the information comprehensive management ability of KVM virtual resource pool is improved.
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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