摘要
图书馆借阅量具有大规模、混沌性等变化特点,当前图书馆借阅量没有综合考虑该特点,导致图书馆借阅量预测结果与实际不相符,为了获得更加可靠的图书馆借阅量预测结果,设计基于大数据的图书馆借阅量预测模型。首先,分析图书馆借阅量的预测原理,并收集图书馆借阅量的历史数据;然后,引入大数据技术对图书馆借阅量特性进行分析和重建,将原始数据变换为更加有利于图书馆借阅量建模的数据;最后,采用极限学习机对图书馆借阅量进行预测,并采用VC++6.0编写图书馆借阅量预测程序进行仿真实验。结果表明,所提模型的图书馆借阅量预测精度高,图书馆借阅量预测速度快,完全可以满足图书馆借阅量分析研究,并且图书馆借阅量的整体预测结果明显优于传统图书馆借阅量预测模型,为图书馆借阅量预测建模提供了一种新的研究工具。
The library circulation data is characterized by large scale and chaos.However,the characters have not been comprehensively taken into account in present library circulation data,resulting in the prediction result of the library circulation data being inconsistent with the fact.In view of the above,a library circulation data prediction model based on big data is designed to obtain more reliable prediction results.First of all,the prediction principle of library circulation data is analyzed and the historical data of library circulation data are collected.Then,the big data technology is introduced to analyze and reconstruct the characteristics of library circulation data,and transform the original data into data more conducive to the modeling of library circulation data.Finally,the extreme learning machine is used to predict the library circulation data,and the VC++6.00 is adopted to compile the library circulation data prediction program and perform simulation experiments.The results show that the proposed model is of high prediction accuracy and fast prediction speed for library circulation data,which can completely satisfy the analysis and research of library circulation data.In addition,the overall prediction result of library circulation data of the proposed model is obviously superior to that of traditional library circulation data prediction model,providing a new research tool for library circulation data prediction modeling.
作者
刘洋
LIU Yang(Pingdingshan University,Pingdingshan 467000,China)
出处
《现代电子技术》
北大核心
2020年第5期105-108,共4页
Modern Electronics Technique
关键词
图书馆管理系统
图书借阅量
大数据特征
历史数据重建
混沌变化
预测精度
library management system
book circulation data
big data characteristic
historical data reconstruction
chaotic change
prediction accuracy
