發(fā)布時(shí)間：2019-05-11 01:51

【摘要】：企業(yè)在事故隱患排查治理過程中積累了大量隱患數(shù)據(jù),為挖掘其潛在價(jià)值,實(shí)現(xiàn)事故隱患預(yù)警預(yù)控,針對(duì)隱患類型多、數(shù)量大的特點(diǎn),應(yīng)用垂直數(shù)據(jù)格式挖掘算法對(duì)高維隱患數(shù)據(jù)進(jìn)行關(guān)聯(lián)規(guī)則挖掘,并利用Kulc和不平衡比(IR)減小隱患出現(xiàn)頻率差異對(duì)規(guī)則的影響;在此基礎(chǔ)上,設(shè)計(jì)基于關(guān)聯(lián)規(guī)則的隱患預(yù)警評(píng)估模型,并對(duì)預(yù)警信息進(jìn)行可視化處理,最終構(gòu)建完整的企業(yè)隱患預(yù)警方法。以130家機(jī)械制造企業(yè)的53 029條隱患數(shù)據(jù)為例,驗(yàn)證所建預(yù)警方法的可行性。結(jié)果表明,該方法對(duì)事故隱患預(yù)警的準(zhǔn)確率為80.62%。
[Abstract]:Enterprises have accumulated a large number of hidden danger data in the process of accident hidden danger investigation and treatment. In order to excavate its potential value and realize the early warning and pre-control of accident hidden danger, in view of the characteristics of many types and large quantities of hidden dangers, The vertical data format mining algorithm is used to mine the association rules of high dimensional hidden danger data, and Kulc and imbalance ratio (IR) are used to reduce the influence of hidden trouble frequency difference on the rules. On this basis, a hidden danger early warning evaluation model based on association rules is designed, and the early warning information is visually processed, and finally a complete enterprise hidden danger early warning method is constructed. Taking 53 029 hidden danger data from 130 mechanical manufacturing enterprises as an example, the feasibility of the established early warning method is verified. The results show that the accuracy of this method is 80.62%.
【作者單位】： 中國地質(zhì)大學(xué)(北京)工程技術(shù)學(xué)院;國網(wǎng)吉林電力科學(xué)研究院;
【基金】：國家科技支撐計(jì)劃項(xiàng)目(2015BAK16B03) 國家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2016YCF0801906)
【分類號(hào)】：X928;TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 歐陽秋梅;吳超;黃浪;;大數(shù)據(jù)應(yīng)用于安全科學(xué)領(lǐng)域的基礎(chǔ)原理研究[J];中國安全科學(xué)學(xué)報(bào);2016年11期

2 胡建華;習(xí)智琴;周科平;;深部采空區(qū)尺寸效應(yīng)的危險(xiǎn)度正態(tài)云辨識(shí)模型[J];中國安全科學(xué)學(xué)報(bào);2016年10期

3 王玲;李樹林;吳璐璐;;基于定量關(guān)聯(lián)規(guī)則樹的分類及回歸預(yù)測算法[J];工程科學(xué)學(xué)報(bào);2016年06期

4 譚章祿;王澤;陳曉;;基于LDA的煤礦安全隱患主題發(fā)現(xiàn)研究[J];中國安全科學(xué)學(xué)報(bào);2016年06期

5 楊丹;宋英華;洪志坤;呂偉;王U，

本文編號(hào)：2474159