本文選題：云制造 + 資源建模�。� 參考：《浙江工業(yè)大學(xué)》2014年博士論文

【摘要】：隨著計(jì)算機(jī)網(wǎng)絡(luò)技術(shù)的飛速發(fā)展,制造業(yè)的全球化、信息化日益突顯,制造資源的共享度和利用率不斷提高,網(wǎng)絡(luò)化制造、制造網(wǎng)格等利用計(jì)算機(jī)網(wǎng)絡(luò)技術(shù)的新型制造模式逐漸成為現(xiàn)代制造業(yè)的主要模式。近年來(lái),隨著云計(jì)算等“云理論”的發(fā)展,云制造的概念開始興起。云制造是云計(jì)算在制造業(yè)的落地和延伸,它體現(xiàn)了“分散資源集中使用”和“集中資源分散服務(wù)”的思想,以“多對(duì)多”的服務(wù)模式,匯聚分布式資源進(jìn)行集中管理,實(shí)現(xiàn)制造資源的高度共享和高效利用,提高制造企業(yè)的生產(chǎn)效益,為用戶提供更高滿意度和更環(huán)保的產(chǎn)品及服務(wù)。 云制造是一種大規(guī)模網(wǎng)絡(luò)化分布式制造,與網(wǎng)絡(luò)化制造、制造網(wǎng)格等一脈相承,并具有三方面特征：云制造資源海量性、異構(gòu)性、復(fù)雜性和粗粒度性；云制造的用戶具有高度的參與性和多樣性；云制造具有較強(qiáng)的自修復(fù)性。依據(jù)這三個(gè)特征,本文在國(guó)家自然科學(xué)基金(60970021)和浙江省科技計(jì)劃項(xiàng)目(2007C21013)的支持下,著重研究了云制造環(huán)境下制造資源的定義、發(fā)布、匹配和選擇問題,包括以下幾方面： (1)針對(duì)云資源海量、復(fù)雜、異構(gòu)的特征,提出了云制造體系結(jié)構(gòu)、雙層資源模型,以及基于資源云服務(wù)(RVCS:Resources Via Cloud Service)的資源封裝、發(fā)布和發(fā)現(xiàn)機(jī)制。以WSRF體系架構(gòu)為基礎(chǔ),提出了云制造體系結(jié)構(gòu),并詳細(xì)分析了其中云端系統(tǒng)和云制造平臺(tái)系統(tǒng)的邏輯層次和邏輯關(guān)系。在此基礎(chǔ)上,根據(jù)云端和云制造平臺(tái)不同的功能需求,提出了云端-云制造平臺(tái)相分離的雙層資源模型(BCMRM:Bilayer Cloud Manufacturing Resource Model):在云端建立面向資源基本屬性的基礎(chǔ)數(shù)據(jù)模型,在云制造平臺(tái)建立面向資源服務(wù)屬性的功能數(shù)據(jù)模型,并存在邏輯映射關(guān)系,實(shí)現(xiàn)了復(fù)雜、異構(gòu)資源的統(tǒng)一數(shù)據(jù)模型。并且,根據(jù)云資源動(dòng)態(tài)屬性的功能特征和更新頻率分為三層次兩類別,即資源屬性、服務(wù)屬性和提供者屬性三個(gè)層次,以及特征屬性和狀態(tài)屬性兩個(gè)類別。根據(jù)動(dòng)態(tài)屬性劃分,引入具有實(shí)時(shí)偵聽和屬性甄別功能的連接器和云代理,進(jìn)一步提出了基于RVCS的云資源封裝、發(fā)布和發(fā)現(xiàn)機(jī)制,以便實(shí)現(xiàn)海量云資源數(shù)據(jù)的分布式存儲(chǔ)和快速獨(dú)立更新。通過(guò)對(duì)比實(shí)驗(yàn),證明在大規(guī)模數(shù)據(jù)環(huán)境下,能夠較好地提高資源發(fā)布和更新效率。 (2)針對(duì)云資源粗粒度性的特征,提出了一種基于多維可拓理論的云資源性能相似度計(jì)算方法。首先提出特征屬性匹配的核心是云服務(wù)資源性能屬性的匹配。通過(guò)物元模型描述云資源的性能屬性和用戶需求,并根據(jù)性能指標(biāo)數(shù)分為一維、二維、三維和多維等四類性能模塊,從而將云資源性能匹配問題轉(zhuǎn)化為多維空間中點(diǎn)與多維體的可拓距計(jì)算問題,并進(jìn)一步建立關(guān)聯(lián)函數(shù)計(jì)算云資源性能模塊的匹配度。同時(shí),提出了自定義權(quán)重、結(jié)構(gòu)權(quán)重和實(shí)例權(quán)重相結(jié)合權(quán)重確定方法,從而獲得云服務(wù)資源性能屬性綜合相似度。通過(guò)實(shí)驗(yàn),驗(yàn)證了該方法具有較好的最佳資源命中性,且降低了粗粒度云資源指標(biāo)權(quán)重計(jì)算的復(fù)雜度。 (3)針對(duì)云制造用戶高度參與性和多樣性的特征,以Beth信任關(guān)系理論為基礎(chǔ),提出了一種基于用戶自主預(yù)測(cè)評(píng)估和其他用戶推薦評(píng)估的云資源QoS評(píng)估方法。首先提出了狀態(tài)屬性匹配的核心是云服務(wù)資源的QoS評(píng)估,并建立了一種基于預(yù)測(cè)評(píng)估和推薦評(píng)估的云資源QoS評(píng)估方法：對(duì)于預(yù)測(cè)評(píng)估,引入時(shí)效性經(jīng)驗(yàn)因子和經(jīng)驗(yàn)修正因子,優(yōu)化了基于用戶歷史評(píng)價(jià)的預(yù)測(cè)模型；對(duì)于推薦評(píng)估,建立了基于歷史經(jīng)歷的推薦用戶粗選方法,和基于評(píng)價(jià)相似度與客觀度的推薦用戶群精選方法。最后通過(guò)變異系數(shù),獲得更為全面的QOS評(píng)估。通過(guò)對(duì)比實(shí)驗(yàn),證明了預(yù)測(cè)評(píng)估能夠充分反應(yīng)歷史評(píng)價(jià)且具有較好的靈敏性,滿足了高度的用戶參與性；用戶篩選算法能夠很好地過(guò)濾惡意評(píng)價(jià)和劣質(zhì)評(píng)價(jià),避免了用戶多樣性帶來(lái)的干擾。 (4)針對(duì)云制造節(jié)點(diǎn)突發(fā)故障時(shí)替代資源選取問題,建立了基于三角模糊數(shù)互補(bǔ)判斷矩陣的制造風(fēng)險(xiǎn)可拓評(píng)價(jià)模型,并進(jìn)一步提出了云資源動(dòng)態(tài)調(diào)整策略。替代資源的選取從制造風(fēng)險(xiǎn)和動(dòng)態(tài)屬性匹配度兩個(gè)方面進(jìn)行考量：首先在可拓物元模型的基礎(chǔ)上建立云資源制造風(fēng)險(xiǎn)評(píng)價(jià)模型,通過(guò)三角模糊數(shù)互補(bǔ)判斷矩陣計(jì)算獲得風(fēng)險(xiǎn)指標(biāo)權(quán)重,并給出了三種生產(chǎn)關(guān)系的組合風(fēng)險(xiǎn)計(jì)算方法；在動(dòng)態(tài)屬性匹配度計(jì)算中,采用變異系數(shù)和使用門限相結(jié)合的方法,解決了性能匹配度與QoS評(píng)估值的不可比問題和QOS評(píng)估的客觀性問題。通過(guò)實(shí)驗(yàn),驗(yàn)證了該方法能夠較為全面地反映資源的制造風(fēng)險(xiǎn)和可替代性。
[Abstract]:With the rapid development of computer network technology, the globalization of manufacturing industry, the increasingly prominent information technology, the increasing sharing and utilization of manufacturing resources, network manufacturing, manufacturing grid and other new manufacturing modes using computer network technology have gradually become the main mode of modern manufacturing. In recent years, with cloud computing, "cloud theory" and so on. The concept of cloud manufacturing is beginning to rise. Cloud manufacturing is the ground and extension of cloud computing in the manufacturing industry. It embodies the idea of "centralized use of scattered resources" and "centralized resource decentralization service", with "multi to many" service patterns, centralized management of distributed resources, and high sharing and efficiency of manufacturing resources. Use, improve production efficiency of manufacturing enterprises, provide users with higher satisfaction and more environmentally friendly products and services.
Cloud manufacturing is a kind of large-scale networked distributed manufacturing, which is connected with networked manufacturing and manufacturing grid. It has three characteristics: cloud manufacturing resources are massive, heterogeneous, complex and coarse granularity; cloud manufacturing users have high participation and diversity; cloud manufacturing has strong self-repair and complex. Based on these three With the support of the National Natural Science Foundation (60970021) and the Zhejiang science and technology project (2007C21013), this paper focuses on the research on the definition, release, matching and selection of manufacturing resources in the cloud manufacturing environment, including the following aspects:
(1) in view of the mass, complex and heterogeneous features of cloud resources, the cloud manufacturing architecture, double resource model, and resource encapsulation, release and discovery mechanism based on RVCS:Resources Via Cloud Service are proposed. Based on the WSRF architecture, the cloud manufacturing architecture is proposed and the cloud system and cloud are analyzed in detail. On this basis, based on the different functional requirements of cloud and cloud manufacturing platforms, a double layer resource model (BCMRM:Bilayer Cloud Manufacturing Resource Model), which is separated from cloud manufacturing platform, is proposed. The basic data model for the basic resources of resources is established in the cloud, and the cloud system is used in the cloud system. The building platform establishes a functional data model of resource oriented service attributes, and has a logical mapping relationship. It realizes a unified data model of complex and heterogeneous resources. According to the functional features and update frequency of the dynamic properties of the cloud resources, it can be divided into three levels and two categories, namely, resource attributes, service attributes and provider attributes, and features, and features. Two categories of attribute and state property. Based on dynamic attribute partition, the connector and cloud agent with real-time listening and attribute discrimination are introduced. The mechanism of cloud resource encapsulation, release and discovery based on RVCS is further proposed in order to realize the distributed storage and fast independent update of mass cloud resource data. Large scale data environment can improve the efficiency of resource release and update.
(2) aiming at the coarse-grained characteristics of cloud resources, a method of computing the similarity of cloud resources based on multidimensional extension theory is proposed. Firstly, the core of feature attribute matching is the matching of performance attributes of cloud service resources. The performance attributes and user requirements of cloud resources are described by the matter element model, and the number of performance indexes is divided into one dimension. Four kinds of performance modules, such as two-dimensional, three-dimensional and multidimensional, are used to transform the problem of cloud resource performance matching into the extension distance calculation problem of multi-dimensional space middle point and multidimensional body, and to further establish the correlation function to calculate the matching degree of the cloud resource performance module. At the same time, the definition of custom weight, structure weight and weight of instance is put forward to determine the weight. The method is used to obtain the comprehensive similarity of performance attributes of cloud service resources. Through experiments, it is proved that the method has better optimal resource neutrality and reduces the complexity of the weight calculation of coarse grain cloud resource index.
(3) in view of the highly participatory and diversity characteristics of cloud manufacturing users, based on the Beth trust relationship theory, a cloud resource QoS evaluation method based on user independent prediction assessment and other user recommendation evaluation is proposed. The core of the state attribute matching is the QoS evaluation of cloud service resources and a prediction based on the prediction. The evaluation and evaluation method of cloud resource QoS assessment: for the prediction evaluation, the timeliness experience factor and the experience correction factor are introduced, and the prediction model based on the user history evaluation is optimized. The recommended user coarse selection method based on the history experience and the recommended user group based on the evaluation similarity and objective degree are established for the recommendation evaluation. Finally, a more comprehensive QOS evaluation is obtained through the coefficient of variation. Through a comparative experiment, it is proved that the prediction evaluation can fully respond to historical evaluation and have good sensitivity to meet the high user participation. The user screening algorithm can filter malicious evaluation and inferior evaluation well and avoid user diversity belt. The interference from it.
(4) in view of the alternative resource selection problem for cloud manufacturing nodes, a manufacturing risk assessment model based on triangular fuzzy number complementary judgement matrix is established, and the dynamic adjustment strategy of cloud resources is further proposed. The selection of alternative resources is considered from two aspects: manufacturing risk and dynamic attribute matching degree: first, extension On the basis of matter-element model, the risk evaluation model of cloud resource manufacturing is established. The weight of risk index is obtained by the triangular fuzzy number complementary judgement matrix, and the combined risk calculation method of three kinds of production relations is given. In the calculation of dynamic attribute matching, the performance matching is solved by combining the variation coefficient and the use threshold. The problem of matching degree and QoS evaluation value and the objectivity of QOS evaluation are discussed. Through experiments, it is proved that this method can reflect the manufacturing risk and substitutability of resources more comprehensively.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09

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