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面向CPS的高速公路凝冰短時預(yù)測與主動式防冰控制

發(fā)布時間:2018-11-19 11:06
【摘要】:針對影響中國高速公路行車安全與運營效率的冰雪凍雨氣象災(zāi)害,基于信息物理系統(tǒng)(CPS)理論,設(shè)計了面向高速公路路表凝冰短時預(yù)測與主動式防冰控制的CPS模型.不同于傳統(tǒng)的事后處置方式,該CPS可以通過主動預(yù)測和預(yù)先處置手段,防止冰雪災(zāi)害發(fā)生或降低其危害程度.圍繞該CPS模型,提出了其信息世界的路表凝冰短時預(yù)測算法,并設(shè)計了相應(yīng)的主動式防冰控制策略.其中,凝冰短時預(yù)測算法基于熱力學(xué)理論建?諝鉁囟群吐繁頊囟鹊鸟詈献兓P(guān)系,并從凝冰事件的發(fā)生可能性及強度兩個方面對預(yù)測的凝冰風(fēng)險進行描述;防冰控制策略主要針對一款自研的以噴撒融雪劑方式防冰融冰的CPS物理世界裝置,根據(jù)凝冰風(fēng)險的可能性及強度分量遠程控制該裝置按照一定的模式進行工作.基于實測數(shù)據(jù)的實驗分析以及針對實際路段的示范應(yīng)用表明:該CPS具有較精確的凝冰預(yù)測精度,30min內(nèi)的平均預(yù)測誤差約為±0.2℃;融冰處置可以覆蓋高速公路車道約3/4的有效行車區(qū)域,能夠事先避免凝冰或積雪形成或主動降低其危害,因此在我國高速公路的信息化與效率提升建設(shè)當中具有較高的實用價值.
[Abstract]:Based on the theory of information physical system (CPS), a CPS model for short-term prediction and active anti-icing control of freeway surface ice is designed, which affects the safety and operation efficiency of freeway in China. Different from the traditional post-disposal method, the CPS can prevent the occurrence of ice and snow disaster or reduce its harm degree by active prediction and pre-disposal. Based on the CPS model, the short-time prediction algorithm of road surface ice coagulation is proposed, and the corresponding active anti-ice control strategy is designed. Based on the thermodynamic theory, the short-time prediction algorithm is used to model the coupling relationship between air temperature and road surface temperature, and to describe the ice coagulation risk from two aspects: the possibility and the intensity of the ice coagulation event. The anti-ice control strategy is mainly aimed at a self-developed CPS physical world device which sprays snow melting agent to prevent ice melting. According to the possibility of ice agglomeration risk and its intensity component, the device can be remotely controlled to work according to a certain mode. The experimental analysis based on the measured data and the demonstration application to the actual section show that the CPS has more accurate prediction accuracy of ice coagulation, and the average prediction error in 30min is about 鹵0.2 鈩,

本文編號:2342116

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