【摘要】：近年來各類突發(fā)事件頻繁發(fā)生,造成了嚴(yán)重的災(zāi)害后果。突發(fā)事件是隨時(shí)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(duì)的突發(fā)事件狀態(tài)不斷變化,因此,在信息不全、時(shí)間緊迫的情況下,如果能事先知道突發(fā)事件隨時(shí)間發(fā)生發(fā)展的趨勢(shì),也就是對(duì)突發(fā)事件進(jìn)行推演,并根據(jù)推演結(jié)果制定應(yīng)急行動(dòng),以達(dá)到提高效率,降低損失,避免災(zāi)害擴(kuò)大化的目的。 現(xiàn)有對(duì)突發(fā)事件推演的研究多針對(duì)突發(fā)事件情景,但是對(duì)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(duì)象是災(zāi)害后果,決策者需要對(duì)災(zāi)害后果的嚴(yán)重程度及其發(fā)展趨勢(shì)有所了解。因此,推演的內(nèi)涵是災(zāi)害后果及其走向趨勢(shì)。由于突發(fā)事件的發(fā)生是不可預(yù)測(cè)的,而且在不同的區(qū)域會(huì)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(gè)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(jī)理的不同,應(yīng)分別對(duì)其構(gòu)建推演模型。對(duì)于單個(gè)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(rèn)為災(zāi)害后果是由承災(zāi)體體現(xiàn)的,單個(gè)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(dòng)態(tài)系統(tǒng)的演化,該特性與元胞自動(dòng)機(jī)模型相符。因此,提出基于元胞自動(dòng)機(jī)模型的單個(gè)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(huì)受損提出三種不同類型承災(zāi)體的推演規(guī)則,并分別對(duì)這三種類型的承災(zāi)體進(jìn)行仿真實(shí)驗(yàn)。對(duì)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(duì)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(yùn)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(yàn)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(shí)刻的災(zāi)害后果情況,評(píng)估災(zāi)害損失,預(yù)測(cè)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(dòng)。
[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(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：X913

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