突發(fā)事件災(zāi)害后果推演模型研究
發(fā)布時間：2018-10-05 07:13
【摘要】：近年來各類突發(fā)事件頻繁發(fā)生,造成了嚴重的災(zāi)害后果。突發(fā)事件是隨時間不斷向前演變的復(fù)雜系統(tǒng),這種演變使得應(yīng)急決策主體所面對的突發(fā)事件狀態(tài)不斷變化,因此,在信息不全、時間緊迫的情況下,如果能事先知道突發(fā)事件隨時間發(fā)生發(fā)展的趨勢,也就是對突發(fā)事件進行推演,并根據(jù)推演結(jié)果制定應(yīng)急行動,以達到提高效率,降低損失,避免災(zāi)害擴大化的目的。 現(xiàn)有對突發(fā)事件推演的研究多針對突發(fā)事件情景,但是對情景的定義卻很少和決策者的需求聯(lián)系起來,也沒有建立關(guān)于突發(fā)事件與推演的關(guān)聯(lián)。從應(yīng)急需求考慮,應(yīng)急處置的對象是災(zāi)害后果,決策者需要對災(zāi)害后果的嚴重程度及其發(fā)展趨勢有所了解。因此,推演的內(nèi)涵是災(zāi)害后果及其走向趨勢。由于突發(fā)事件的發(fā)生是不可預(yù)測的,而且在不同的區(qū)域會產(chǎn)生不同的災(zāi)害后果。因此,本文將從災(zāi)害后果的角度出發(fā),根據(jù)災(zāi)害后果的共性特征,構(gòu)建突發(fā)事件災(zāi)害后果的推演模型。 突發(fā)事件的災(zāi)害后果不僅包括單個突發(fā)事件的災(zāi)害后果,還包括該事件引發(fā)的連鎖反應(yīng),由于兩者發(fā)生機理的不同,應(yīng)分別對其構(gòu)建推演模型。對于單個突發(fā)事件災(zāi)害后果推演模型,本文依據(jù)災(zāi)害系統(tǒng)論,認為災(zāi)害后果是由承災(zāi)體體現(xiàn)的,單個突發(fā)事件災(zāi)害后果的推演也就是區(qū)域內(nèi)大量的承災(zāi)體之間通過簡單的規(guī)則構(gòu)成的復(fù)雜動態(tài)系統(tǒng)的演化,該特性與元胞自動機模型相符。因此,提出基于元胞自動機模型的單個突發(fā)事件災(zāi)害后果推演模型。該模型用元胞表示承災(zāi)體,根據(jù)承災(zāi)體是否具有恢復(fù)能力以及恢復(fù)后是否還會受損提出三種不同類型承災(zāi)體的推演規(guī)則,并分別對這三種類型的承災(zāi)體進行仿真實驗。對于突發(fā)事件連鎖反應(yīng)推演模型,本文在突發(fā)事件關(guān)聯(lián)網(wǎng)絡(luò)模型的基礎(chǔ)上,針對從該網(wǎng)絡(luò)中很難找出突發(fā)事件連鎖反應(yīng)路徑的問題,提出了基于Hopfield (?)申經(jīng)網(wǎng)絡(luò)的突發(fā)事件連鎖反應(yīng)路徑推演模型,該模型用神經(jīng)元代表突發(fā)事件,通過建模將突發(fā)事件連鎖反應(yīng)路徑推演過程映射為Hopfield (?)神經(jīng)網(wǎng)絡(luò)的演化過程,通過運行Hopfield網(wǎng)絡(luò),推演出初始事件引發(fā)的連鎖反應(yīng)事件。最后,推演了地震引發(fā)的連鎖反應(yīng)事件,驗證了該模型的合理性。 本文提出的突發(fā)事件災(zāi)害后果推演模型,便于決策主體提前了解突發(fā)事件各時刻的災(zāi)害后果情況,評估災(zāi)害損失,預(yù)測突發(fā)事件可能引發(fā)的次生突發(fā)事件,并制定行之有效的應(yīng)急行動。
[Abstract]:In recent years, a variety of emergencies occurred frequently, resulting in serious disaster consequences. Emergency is a complex system that evolves continuously with time. This kind of evolution makes the emergency decision makers face constant changes in the state of emergencies. Therefore, in the case of incomplete information and tight time, If we can know in advance the trend of sudden events with time, that is to say, we can extrapolate the sudden events, and make emergency action according to the result of deduction, in order to improve efficiency, reduce losses and avoid the expansion of disasters. The current research on emergency inference mostly focuses on emergency scenarios, but the definition of scenarios is rarely related to the needs of decision makers, and there is no correlation between emergencies and extrapolation. Considering the emergency demand, the object of emergency treatment is the disaster consequence, and the decision-makers need to understand the severity of the disaster consequence and its development trend. Therefore, the implication of inference is disaster consequence and its trend. The occurrence of unexpected events is unpredictable and has different disaster consequences in different regions. Therefore, from the point of view of disaster consequences and according to the common characteristics of disaster consequences, this paper will construct the inference model of disaster consequences of unexpected events. The disaster consequences of a sudden event not only include the disaster consequences of a single emergency, but also include the chain reaction caused by the event. Due to the difference of their occurrence mechanism, the inference model should be constructed separately. According to the disaster system theory, this paper considers that the disaster consequence is reflected by the disaster bearing body. The evolution of complex dynamic systems formed by simple rules between a large number of disaster bearing bodies in a region is the deduction of the disaster consequences of a single unexpected event, which is consistent with the cellular automata model. Therefore, a model based on cellular automata (CA) is proposed to predict the disaster consequences of a single emergency. In this model, the disaster bearing body is represented by cells. According to whether the disaster bearing body has the ability to recover and whether the disaster bearing body will be damaged after recovery, the inference rules of three different types of disaster bearing bodies are proposed, and the simulation experiments are carried out on the three types of disaster bearing bodies. For the emergency chain reaction deduction model, based on the emergency correlation network model, aiming at the problem that it is difficult to find out the chain reaction path of the emergency from the network, this paper proposes a method based on Hopfield (?) In this model, neurons are used to represent the sudden events, and by modeling, the inference process of the chain reaction path of the sudden events is mapped to Hopfield (?) The evolution process of neural network, by running the Hopfield network, deduces the chain reaction event caused by the initial event. Finally, the chain reaction caused by earthquake is deduced and the rationality of the model is verified. The model proposed in this paper is convenient for the decision makers to understand the disaster consequences at each moment in advance, to evaluate the disaster losses, to predict the secondary emergencies that may be caused by the emergencies. And formulate effective emergency action.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：X913
本文編號：2252484
[Abstract]:In recent years, a variety of emergencies occurred frequently, resulting in serious disaster consequences. Emergency is a complex system that evolves continuously with time. This kind of evolution makes the emergency decision makers face constant changes in the state of emergencies. Therefore, in the case of incomplete information and tight time, If we can know in advance the trend of sudden events with time, that is to say, we can extrapolate the sudden events, and make emergency action according to the result of deduction, in order to improve efficiency, reduce losses and avoid the expansion of disasters. The current research on emergency inference mostly focuses on emergency scenarios, but the definition of scenarios is rarely related to the needs of decision makers, and there is no correlation between emergencies and extrapolation. Considering the emergency demand, the object of emergency treatment is the disaster consequence, and the decision-makers need to understand the severity of the disaster consequence and its development trend. Therefore, the implication of inference is disaster consequence and its trend. The occurrence of unexpected events is unpredictable and has different disaster consequences in different regions. Therefore, from the point of view of disaster consequences and according to the common characteristics of disaster consequences, this paper will construct the inference model of disaster consequences of unexpected events. The disaster consequences of a sudden event not only include the disaster consequences of a single emergency, but also include the chain reaction caused by the event. Due to the difference of their occurrence mechanism, the inference model should be constructed separately. According to the disaster system theory, this paper considers that the disaster consequence is reflected by the disaster bearing body. The evolution of complex dynamic systems formed by simple rules between a large number of disaster bearing bodies in a region is the deduction of the disaster consequences of a single unexpected event, which is consistent with the cellular automata model. Therefore, a model based on cellular automata (CA) is proposed to predict the disaster consequences of a single emergency. In this model, the disaster bearing body is represented by cells. According to whether the disaster bearing body has the ability to recover and whether the disaster bearing body will be damaged after recovery, the inference rules of three different types of disaster bearing bodies are proposed, and the simulation experiments are carried out on the three types of disaster bearing bodies. For the emergency chain reaction deduction model, based on the emergency correlation network model, aiming at the problem that it is difficult to find out the chain reaction path of the emergency from the network, this paper proposes a method based on Hopfield (?) In this model, neurons are used to represent the sudden events, and by modeling, the inference process of the chain reaction path of the sudden events is mapped to Hopfield (?) The evolution process of neural network, by running the Hopfield network, deduces the chain reaction event caused by the initial event. Finally, the chain reaction caused by earthquake is deduced and the rationality of the model is verified. The model proposed in this paper is convenient for the decision makers to understand the disaster consequences at each moment in advance, to evaluate the disaster losses, to predict the secondary emergencies that may be caused by the emergencies. And formulate effective emergency action.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：X913
【參考文獻】
相關(guān)期刊論文 前10條
1 關(guān)超;彭云;袁文燕;;基于元胞自動機帶有干預(yù)機制的傳染病模型[J];北京化工大學(xué)學(xué)報(自然科學(xué)版);2011年06期
2 黃華國,張曉麗,王蕾;基于三維曲面元胞自動機模型的林火蔓延模擬[J];北京林業(yè)大學(xué)學(xué)報;2005年03期
3 劉哲民,劉引鴿,劉玉娥;災(zāi)害后果分析[J];寶雞文理學(xué)院學(xué)報(自然科學(xué)版);1998年04期
4 袁一凡;;四川汶川8.0級地震損失評估[J];地震工程與工程振動;2008年05期
5 徐國棟;方偉華;史培軍;袁藝;;汶川地震損失快速評估[J];地震工程與工程振動;2008年06期
6 吳廣謀;趙偉川;江億平;;城市重特大事故情景再現(xiàn)與態(tài)勢推演決策模型研究[J];東南大學(xué)學(xué)報(哲學(xué)社會科學(xué)版);2011年01期
7 榮莉莉;蔡瑩瑩;王鐸;;基于共現(xiàn)分析的我國突發(fā)事件關(guān)聯(lián)研究[J];系統(tǒng)工程;2011年06期
8 姜卉;黃鈞,;;罕見重大突發(fā)事件應(yīng)急實時決策中的情景演變[J];華中科技大學(xué)學(xué)報(社會科學(xué)版);2009年01期
9 余雷;薛惠鋒;高曉燕;李剛;;基于元胞自動機的傳染病傳播模型研究[J];計算機工程與應(yīng)用;2007年02期
10 王建偉;榮莉莉;;突發(fā)事件的連鎖反應(yīng)網(wǎng)絡(luò)模型研究[J];計算機應(yīng)用研究;2008年11期
相關(guān)會議論文 前1條
1 楊保華;胡明禮;方志耕;;具有不確定性參數(shù)的非常規(guī)突發(fā)事件情景推演GERTS網(wǎng)絡(luò)互力耦合模型[A];第十三屆中國管理科學(xué)學(xué)術(shù)年會論文集[C];2011年
相關(guān)碩士學(xué)位論文 前2條
1 鄭營;基于元胞自動機模型的火災(zāi)環(huán)境下人員疏散研究[D];北京交通大學(xué);2011年
2 譚華;突發(fā)事件災(zāi)害后果時空矩陣構(gòu)建研究[D];大連理工大學(xué);2012年
，本文編號：2252484
本文鏈接：http://sikaile.net/kejilunwen/anquangongcheng/2252484.html
最近更新
教材專著
