【摘要】：云計算、互聯(lián)網(wǎng)、物聯(lián)網(wǎng)等高新技術(shù)的發(fā)展帶動了傳統(tǒng)行業(yè)的快速轉(zhuǎn)型。云制造是助力“中國制造2025”計劃實(shí)現(xiàn)制造業(yè)發(fā)展得重要手段。對于制造企業(yè)供應(yīng)商選擇而言,云制造與傳統(tǒng)制造相比擁有更大的選擇范圍,能夠主動實(shí)時的匹配個性化需求,使分布的制造資源高度共享。同時,云制造龐大的服務(wù)資源為供應(yīng)商選擇帶來新的難題,其服務(wù)匹配是亟待解決的問題。目前傳統(tǒng)的匹配方法缺乏針對性,缺少針對供應(yīng)商領(lǐng)域匹配方法的研究；同時云制造中存在大量功能相似的供應(yīng)商,需要根據(jù)需求的QoS (Quality of Service)信息進(jìn)行判斷,傳統(tǒng)匹配機(jī)制缺乏對模糊QoS信息的選擇和排序。另外,傳統(tǒng)匹配方法多推薦唯一的最優(yōu)服務(wù),難以滿足需求方自主選擇的需要。針對傳統(tǒng)匹配方法的弊端,本文以云制造環(huán)境下的供應(yīng)商為研究對象,結(jié)合本體和模糊QoS提出三階段匹配方法,具體工作如下：首先,分析云制造及其供應(yīng)鏈特點(diǎn)、與傳統(tǒng)網(wǎng)絡(luò)制造的區(qū)別,以及云平臺的服務(wù)匹配流程和方法,找到問題的重點(diǎn)和難點(diǎn),提出本文要解決的問題。然后,定義服務(wù)匹配的內(nèi)容。設(shè)計了包含功能性信息和QoS信息的供應(yīng)商描述模型。在本體建模研究的基礎(chǔ)上定義了供應(yīng)商服務(wù)本體的結(jié)構(gòu),作為匹配算法的語義支持。接著,設(shè)計供應(yīng)商服務(wù)匹配算法,算法分為三個階段,每個階段匹配的內(nèi)容分別是服務(wù)的功能性信息、QoS信息和前兩個階段的綜合信息。針對功能性信息的資源概念,采用了基于語義相似度的匹配算法,同時為避免了由于表達(dá)方式不同造成概念不能匹配到本體模型,從而造成錯誤或片面匹配結(jié)果的問題,引入了屬性影響因素。針對QoS信息匹配,考慮了QoS的模糊性和用戶偏好,采用優(yōu)化的FCM (Fuzzy C Means Clustering)算法。匹配方法結(jié)果為一定數(shù)量的基于綜合匹配度的排序結(jié)果集合,滿足需求方自主挑選供應(yīng)商的需求。最后,實(shí)現(xiàn)了服務(wù)匹配系統(tǒng)并對匹配算法進(jìn)行實(shí)驗分析。通過實(shí)例驗證了匹配方法的有效性,能較好的滿足現(xiàn)實(shí)需求。并對比分析了查全率和查準(zhǔn)率,同時驗證了優(yōu)化的算法提高了服務(wù)匹配的效率。本研究為探索如何解決云制造環(huán)境下的供應(yīng)商匹配問題提出了一種新思路。
[Abstract]:Cloud computing, Internet of things, Internet of things and other high-tech development has driven the rapid transformation of traditional industries. Cloud manufacturing is an important means to help the "made in China 2025" plan to realize the development of manufacturing industry. For supplier selection of manufacturing enterprises, cloud manufacturing has a larger selection range than traditional manufacturing, and can actively and real-time match personalized requirements, so that distributed manufacturing resources are highly shared. At the same time, the huge service resources of cloud manufacturing bring new problems for supplier selection, and service matching is an urgent problem to be solved. At present, the traditional matching methods are lack of pertinence and lack of research on supplier domain matching methods. At the same time, there are a large number of similar suppliers in cloud manufacturing, which need to be judged according to the required QoS (Quality of Service) information. The traditional matching mechanism lacks the selection and ranking of fuzzy QoS information. In addition, the traditional matching methods often recommend the only optimal service, which is difficult to meet the needs of the demand side. Aiming at the disadvantages of traditional matching methods, this paper takes suppliers in cloud manufacturing environment as the research object, combines ontology with fuzzy QoS, and proposes a three-stage matching method. The specific work is as follows: firstly, the characteristics of cloud manufacturing and its supply chain are analyzed. The difference from the traditional network manufacturing, as well as the service matching flow and method of the cloud platform, find out the key and difficult points of the problem, and put forward the problems to be solved in this paper. Then, define the content of the service match. A supplier description model including functional information and QoS information is designed. On the basis of ontology modeling, the structure of supplier service ontology is defined as the semantic support of matching algorithm. Then, the supplier service matching algorithm is designed. The algorithm is divided into three stages. The content of each phase matching is the functional information of the service, the QoS information and the synthesis information of the first two stages. For the resource concept of functional information, a matching algorithm based on semantic similarity is adopted, and in order to avoid the problem that concepts can not match to ontology model because of different expression methods, thus causing errors or one-sided matching results. The influence factor of attribute is introduced. Considering the fuzziness and user preference of QoS, the optimized FCM (Fuzzy C Means Clustering) algorithm is adopted. The result of the matching method is a set of ranking results based on the comprehensive matching degree to meet the demand of the demand side. Finally, the service matching system is implemented and the matching algorithm is analyzed experimentally. The effectiveness of the matching method is verified by an example, and it can meet the practical requirements. The recall rate and precision ratio are compared and analyzed, and the optimized algorithm is verified to improve the efficiency of service matching. This study provides a new way to solve the problem of supplier matching in cloud manufacturing environment.
【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】：2016
【分類號】：F274

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