基于大数据分析的人力流动区域估计模型仿真
发布时间：2018-11-03 17:03
【摘要】：针对大数据下的人力流动区域的估算问题,提出基于大数据分析的人力流动区域估计仿真模型。引进了K近邻非参数估计仿真模型,对大数据背景下的人力流动区域进行标准估算。同时能够对K值进行预留计算,避免大数据干扰的发生,优化了分类近邻子集生成模块,有效地提高了估算能力以及估算的范围,对人力流动区域的估算准确性有极大的帮助。并进行实验分析,由实验分析可知,提出的方法能够准确地对人力流动的区域进行系统的估算。
[Abstract]:In order to solve the problem of estimating manpower flow region under big data, a simulation model of manpower flow region estimation based on big data analysis is proposed. In this paper, a simulation model of K-nearest neighbor nonparametric estimation is introduced to estimate the human flow region under the background of big data. At the same time, the K value can be reserved to avoid the interference of big data, and the generation module of classification nearest neighbor subset can be optimized, which can effectively improve the estimation ability and the estimation range, which is of great help to the estimation accuracy of the human flow region. The experimental analysis shows that the proposed method can estimate the area of labor flow accurately.
【作者单位】： 中国石油大学胜利学院;
【分类号】：F249.27;TP311.13
本文编号：2308396
[Abstract]:In order to solve the problem of estimating manpower flow region under big data, a simulation model of manpower flow region estimation based on big data analysis is proposed. In this paper, a simulation model of K-nearest neighbor nonparametric estimation is introduced to estimate the human flow region under the background of big data. At the same time, the K value can be reserved to avoid the interference of big data, and the generation module of classification nearest neighbor subset can be optimized, which can effectively improve the estimation ability and the estimation range, which is of great help to the estimation accuracy of the human flow region. The experimental analysis shows that the proposed method can estimate the area of labor flow accurately.
【作者单位】： 中国石油大学胜利学院;
【分类号】：F249.27;TP311.13
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