基于自适应学习算法的档案信息自动化整合系统设计

Design of Archives Information Automatic Integration System Based on Adaptive Learning Algorithm

档案信息整合过程中,受到自动化整合模型的影响,使得系统整合耗费时间较长。因此,提出基于自适应学习算法的档案信息自动化整合系统设计。硬件方面,设计采集器和存储器,为软件设计提供支撑。利用粗糙集技术获取待整合档案信息,形成样本集并对该集合进行筛选。利用自适应学习算法,构建自适应学习整合模型。对组合逻辑延时分析后,完成档案信息高效率自动化整合。相同测试环境下,与传统整合系统相比,设计系统将档案信息整合耗费时间分别降低171 s、199 s,极大提升了系统性能。

In the process of file information integration,affected by the automatic integration model,the system integration takes a long time.Therefore,the design of a file information automation integration system based on an adaptive learning algorithm is proposed.In terms of hardware,the collector and memory are designed to support the software design.Rough set technology is used to obtain the file information to be integrated,form a sample set and screen the set.The adaptive learning integration model is constructed by using adaptive learning algorithm.After the delay analysis of combinational logic,the file information is integrated automatically and efficiently.Under the same test environment,compared with the traditional integration system,the design system reduces the file information integration time by 171 s and 199 s,which greatly improves the system performance.