【摘要】：目前，我國(guó)制造業(yè)正經(jīng)歷由生產(chǎn)型主導(dǎo)向服務(wù)型轉(zhuǎn)軌的過(guò)程，實(shí)現(xiàn)制造業(yè)的全面服務(wù)化成為新的經(jīng)濟(jì)增加熱點(diǎn)，然而要實(shí)現(xiàn)由傳統(tǒng)生產(chǎn)模式轉(zhuǎn)型發(fā)展成技術(shù)服務(wù)型為主導(dǎo)的生產(chǎn)模式需要相關(guān)的先進(jìn)技術(shù)、嶄新的服務(wù)平臺(tái)以及全新模式的作支撐。隨著云計(jì)算，物聯(lián)網(wǎng)等新一代IT技術(shù)蓬勃發(fā)展，一些先進(jìn)的制造模式和技術(shù)與其相融合形成了現(xiàn)在的云制造模式。云制造旨在匯聚全球資源，實(shí)現(xiàn)資源優(yōu)化配置，為加快經(jīng)濟(jì)轉(zhuǎn)型和傳統(tǒng)制造業(yè)的升級(jí)提供了核心動(dòng)力。 服務(wù)搜索作為云制造的核心技術(shù)之一是云制造實(shí)現(xiàn)資源優(yōu)化配置和按需使用的關(guān)鍵使能技術(shù)。服務(wù)的搜索與發(fā)現(xiàn)為實(shí)現(xiàn)服務(wù)的組合與優(yōu)選提供數(shù)據(jù)支撐，其性能好壞直接影響資源服務(wù)的優(yōu)化配置。而現(xiàn)有服務(wù)搜索的研究大多數(shù)是針對(duì)計(jì)算資源和Web服務(wù)，在制造領(lǐng)域關(guān)于服務(wù)搜索的研究不多。由于制造資源的異構(gòu)性、復(fù)雜性，導(dǎo)致不能直接應(yīng)用現(xiàn)有的一些服務(wù)搜索方法。因此，本文將以Web服務(wù)發(fā)現(xiàn)研究方法為基礎(chǔ)，結(jié)合制造領(lǐng)域資源的特點(diǎn)，對(duì)云制造服務(wù)搜索做出了深入的研究，提出了制造資源搜索整體系統(tǒng)框架，在制造資源虛擬化和服務(wù)化的基礎(chǔ)上建立了基于QoS的服務(wù)搜索模型，并研究了基于蜜蜂算法等群智能優(yōu)化算法的服務(wù)搜索方法。 首先，，在研究現(xiàn)有的服務(wù)組合與優(yōu)選體系、關(guān)鍵技術(shù)和服務(wù)流程的基礎(chǔ)上，提出了基于QoS服務(wù)搜索的系統(tǒng)架構(gòu)，并且給出了實(shí)現(xiàn)基于QoS服務(wù)搜索的三個(gè)環(huán)節(jié)，即服務(wù)QoS的提取、服務(wù)QoS的評(píng)估以及服務(wù)QoS的比較。該體系給出了實(shí)現(xiàn)資源準(zhǔn)確發(fā)現(xiàn)的技術(shù)路線(xiàn)和工作流程，為制造云服務(wù)匹配提供完整的信息支撐。 然后，針對(duì)現(xiàn)有的服務(wù)描述在描述制造資源服務(wù)信息不全面，分類(lèi)不清晰的問(wèn)題，著重分析云制造資源有的特征，并且用本體建模語(yǔ)言描述了制造資源的基本信息、制造功能信息、QoS信息。按照用戶(hù)對(duì)服務(wù)性?xún)r(jià)比的要求，給出了QoS評(píng)價(jià)指標(biāo)及計(jì)算公式并且建立一個(gè)四元組的服務(wù)發(fā)現(xiàn)模型和約束指標(biāo)。 由于蜜蜂算法在收斂性和穩(wěn)定性上都要優(yōu)于傳統(tǒng)的群智能優(yōu)化算法，并能更快的發(fā)現(xiàn)復(fù)雜優(yōu)化問(wèn)題的最優(yōu)解，并且具有較強(qiáng)的魯棒性、易于與其他方法結(jié)合、優(yōu)良的分布式算機(jī)制等優(yōu)點(diǎn)。本文將增強(qiáng)型蜜蜂算法作為云制造資源服務(wù)搜索的算法。 最后，搭建了基于QoS信息云服務(wù)發(fā)現(xiàn)仿真測(cè)試環(huán)境。用改進(jìn)后的蜜蜂算法對(duì)其進(jìn)行測(cè)試與性能比較，對(duì)比實(shí)驗(yàn)表明，本文的搜索算法在收斂時(shí)間和收斂精度上更為快速和穩(wěn)定。
[Abstract]:At present, China's manufacturing industry is undergoing a process of transition from production-oriented to service-oriented, and the realization of full-scale service-oriented manufacturing has become a new hot point in economic growth. However, in order to realize the transformation from traditional production mode to technology-service-oriented production mode, the related advanced technology, new service platform and new mode are needed. With the rapid development of cloud computing, Internet of things and other new generation of IT technology, some advanced manufacturing models and technologies are combined to form the cloud manufacturing model. Cloud manufacturing aims to pool global resources and achieve optimal allocation of resources, which provides the core power for accelerating economic transformation and upgrading traditional manufacturing industries. As one of the core technologies of cloud manufacturing, service search is a key enabling technology for resource optimization and on-demand deployment in cloud manufacturing. The search and discovery of services provides data support for service composition and optimal selection, and its performance directly affects the optimal configuration of resource services. However, most of the existing research on service search is focused on computing resources and Web services, but there are few researches on service search in manufacturing field. Due to the heterogeneity and complexity of manufacturing resources, some existing service search methods can not be directly applied. Therefore, based on the research method of Web service discovery and the characteristics of manufacturing resources, this paper makes a deep research on cloud manufacturing service search, and puts forward the overall system framework of manufacturing resource search. Based on the virtualization and service of manufacturing resources, the service search model based on QoS is established, and the service search method based on swarm intelligence optimization algorithm such as bee algorithm is studied. First of all, on the basis of studying the existing service composition and optimization system, key technology and service flow, the system architecture based on QoS service search is put forward, and the three links to realize QoS service search are given, that is, the extraction of service QoS. Service QoS evaluation and service QoS comparison. The system provides the technical route and workflow to realize the accurate discovery of resources, and provides complete information support for manufacturing cloud service matching. Then, aiming at the problem that the service description is not comprehensive and the classification is not clear, the characteristics of cloud manufacturing resources are analyzed, and the basic information of manufacturing resources is described by ontology modeling language. Manufacturing function information, QoS information. According to the requirement of service performance to price ratio, the QoS evaluation index and calculation formula are given, and a quaternion service discovery model and constraint index are established. Because the bee algorithm is superior to the traditional swarm intelligence optimization algorithm in convergence and stability, it can find the optimal solution of the complex optimization problem more quickly, and has strong robustness, which is easy to combine with other methods. Excellent distributed computing mechanism and other advantages. In this paper, the enhanced honeybee algorithm is used as a search algorithm for cloud manufacturing resource services. Finally, a simulation test environment based on QoS information cloud service discovery is built. The improved honeybee algorithm is used to test and compare its performance. The comparison experiment shows that the search algorithm in this paper is faster and more stable in the convergence time and accuracy.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.09

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孫其博;劉杰;黎

本文編號(hào)：2406481