【摘要】：針對(duì)大數(shù)據(jù)下的人力流動(dòng)區(qū)域的估算問(wèn)題,提出基于大數(shù)據(jù)分析的人力流動(dòng)區(qū)域估計(jì)仿真模型。引進(jìn)了K近鄰非參數(shù)估計(jì)仿真模型,對(duì)大數(shù)據(jù)背景下的人力流動(dòng)區(qū)域進(jìn)行標(biāo)準(zhǔn)估算。同時(shí)能夠?qū)值進(jìn)行預(yù)留計(jì)算,避免大數(shù)據(jù)干擾的發(fā)生,優(yōu)化了分類(lèi)近鄰子集生成模塊,有效地提高了估算能力以及估算的范圍,對(duì)人力流動(dòng)區(qū)域的估算準(zhǔn)確性有極大的幫助。并進(jìn)行實(shí)驗(yàn)分析,由實(shí)驗(yàn)分析可知,提出的方法能夠準(zhǔn)確地對(duì)人力流動(dòng)的區(qū)域進(jìn)行系統(tǒng)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.
【作者單位】： 中國(guó)石油大學(xué)勝利學(xué)院;
【分類(lèi)號(hào)】：F249.27;TP311.13

【相似文獻(xiàn)】

相關(guān)期刊論文 前2條

1 得州;;挖出不一樣的秘密 數(shù)據(jù)分析師[J];電腦愛(ài)好者;2010年13期

2 龍瀛;張宇;崔承印;;利用公交刷卡數(shù)據(jù)分析北京職住關(guān)系和通勤出行[J];地理學(xué)報(bào);2012年10期

相關(guān)重要報(bào)紙文章 前1條

1 本報(bào)記者 李韶文;數(shù)據(jù)分析人才將成電商行業(yè)“黃金職業(yè)”[N];國(guó)際商報(bào);2013年

，

本文編號(hào)：2308396