本文選題：案例推理 + 相似案例��； 參考：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：近些年,各種自然災(zāi)害及突發(fā)公共衛(wèi)生事件頻繁發(fā)生,為了做好災(zāi)后應(yīng)急救援工作,提高救援效率,最大化降低災(zāi)害帶來的損失。關(guān)鍵問題是能否在第一時間確定災(zāi)區(qū)所需的應(yīng)急物資數(shù)量,而后才能將珍貴且有限的物資按時按量的送達(dá)受災(zāi)人員手中,降低物資浪費,將物資發(fā)揮其最大的效用。做好應(yīng)急物資需求預(yù)測工作能給有關(guān)部門積極響應(yīng)和實施救援活動,提供理論依據(jù)。本文采用案例推理(Case Based on Reasoning)的方法解決這一問題,運用特定的案例數(shù)據(jù)作為當(dāng)前問題解的參照。在決策中用到了大量的形象思維,不是單純地依靠規(guī)則,還考慮到了專家的經(jīng)驗。與傳統(tǒng)的基于模型的方法相比,CBR不需要顯式表達(dá)的領(lǐng)域模型,適于解決不良結(jié)構(gòu)問題。CBR通過調(diào)整以往相關(guān)問題的成功解來解決新問題。確定案例的表達(dá)形式,不同的案例表達(dá)形式對整個案例推理過程有不同的影響。本體方法表示突發(fā)事件案例時,不僅可以提取數(shù)值型的特征屬性還可以提取語義型的特征屬性,能更詳細(xì)的描述案例的細(xì)節(jié)。其中每個特征屬性對于整體相似度的貢獻(xiàn)用權(quán)重來衡量,分別采用層次分析法和核距離法分別確定案例各個屬性的權(quán)重值,并將不同方法所得的權(quán)重值和用不同權(quán)重值所確定的相似度進(jìn)行比較,考察他們的異同。對于添加的語義信息運用基于概念實例的計算方法求解,不僅考慮概念實例之間的距離和概念實例的上層概念還考慮了概念實例所在層次的信息。克服了Wu-palmer算法中對上下層語義拆分計算的不足。整體相似度的值是數(shù)值型相似度和語義型相似度的加權(quán)和。檢索得到的相似案例將為問題案例的求解提供參考依據(jù)。在此基礎(chǔ)上構(gòu)建基于本體案例表示和案例推理的應(yīng)急物資需求預(yù)測系統(tǒng)框架。以框架結(jié)構(gòu)為基礎(chǔ),結(jié)合應(yīng)急物資需求領(lǐng)域?qū)I(yè)知識,使用Protégé構(gòu)建應(yīng)急物資需求領(lǐng)域本體。
[Abstract]:In recent years, a variety of natural disasters and public health emergencies occur frequently, in order to do a good job in post-disaster emergency rescue, improve the efficiency of rescue, maximize and reduce the losses caused by disasters. The key problem is whether the quantity of emergency materials needed in the disaster area can be determined in the first time, and then the precious and limited materials can be delivered to the affected persons on time, so as to reduce the waste of materials and bring the materials into full play. The work of forecasting the demand for emergency materials can provide theoretical basis for the departments concerned to respond positively and carry out rescue activities. In this paper, case based on reasoning (CBR) is used to solve this problem, and the specific case data is used as the reference to the solution of the current problem. A large number of image thinking is used in decision-making, not only relying on rules, but also taking into account the experience of experts. Compared with the traditional model-based approach, CBR does not require explicit representation of the domain model, and is suitable for solving ill-structured problems. CBR can solve new problems by adjusting the successful solutions of previous related problems. Different case expressions have different influences on the whole case-based reasoning process. Ontology method can not only extract the numerical feature attribute but also the semantic feature attribute, and describe the details of the case in more detail. The contribution of each feature attribute to the overall similarity is measured by weight, and the weight of each attribute is determined by the analytic hierarchy process (AHP) and the kernel distance method, respectively. The weights obtained by different methods are compared with the similarity determined by different weights, and their similarities and differences are investigated. The added semantic information is solved by the method based on the concept instance, which not only considers the distance between the concept instances and the upper concept of the concept instance, but also considers the information of the level where the concept instance is located. It overcomes the shortcomings of Wu-palmer algorithm for semantic splitting of upper and lower layers. The value of global similarity is the weighted sum of numerical similarity and semantic similarity. The similar cases retrieved will provide a reference for the solution of problem cases. On this basis, the framework of emergency material demand prediction system based on ontology case representation and case-based reasoning is constructed. Based on the framework structure and combining the expertise in the field of emergency material demand, Prot 茅 g 茅 is used to construct the ontology of emergency material demand domain.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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本文編號：1987675