本文選題：資源共享 + 本體模型�。� 參考：《山東大學》2017年碩士論文

【摘要】：隨著人們需求的不斷變化,更多個性化的加工任務被提出,制造企業(yè)僅依靠自身擁有的加工資源很難滿足加工任務的要求。加工資源共享可促進異地企業(yè)間進行協(xié)同工作,增強企業(yè)生產(chǎn)能力,有效提高閑置加工資源的利用率,是降低產(chǎn)品生產(chǎn)成本和提高產(chǎn)品生產(chǎn)效率的重要途徑。近年來,為加快對市場需求的響應和提高企業(yè)的競爭力,多種網(wǎng)絡(luò)化制造模式先后被提出,如制造網(wǎng)格、敏捷制造、全球化制造等。網(wǎng)絡(luò)化制造促進了企業(yè)間的協(xié)同合作,實現(xiàn)了社會加工資源的集成與共享,有效提高了產(chǎn)品生產(chǎn)效率和加工質(zhì)量,降低了產(chǎn)品生產(chǎn)成本。然而,在運營模式及加工任務與加工資源匹配等方面依然存在不足。本文在現(xiàn)有網(wǎng)絡(luò)化制造技術(shù)的基礎(chǔ)上,結(jié)合云計算技術(shù)和語義網(wǎng)相關(guān)理論提出了一種運行于Hadoop架構(gòu)下的面向加工任務匹配的加工資源共享平臺。重點以機床設(shè)備資源為共享對象,研究了加工資源與加工任務的模型設(shè)計、加工資源信息存儲策略、加工資源與加工任務間的匹配技術(shù)等資源共享平臺涉及到的關(guān)鍵技術(shù),并通過原型系統(tǒng)的開發(fā)和具體實例驗證了所提方法的可行性和實用性,對我國制造業(yè)的向前發(fā)展進行了有益的探索。論文的主要創(chuàng)新點和研究成果如下:(1)對資源共享平臺涉及的關(guān)鍵問題進行分析,基于Hadoop平臺提出了面向加工任務匹配的加工資源共享平臺的體系結(jié)構(gòu)。對平臺的核心層云服務端結(jié)構(gòu)做了分析。(2)分析加工資源特點并對加工資源進行分類,從匹配的角度抽象加工資源和加工任務屬性,分別設(shè)計了加工資源模型和加工任務模型,并采用本體建模方法建立了加工資源模型和加工任務模型。為資源與任務間進行匹配實現(xiàn)資源共享做技術(shù)準備。(3)采用Hadoop架構(gòu)中的文件系統(tǒng)HDFS和HBase數(shù)據(jù)庫來存儲加工資源信息。結(jié)合加工資源信息特點,綜合考慮存儲空間和查詢效率兩方面因素設(shè)計了加工資源信息存儲方案,設(shè)計了相關(guān)HTable表結(jié)構(gòu),并闡述了加工資源存儲過程。(4)分析加工資源匹配所具有的特點,構(gòu)建匹配框架,建立了加工資源與加工任務的匹配模型,在MapReduce分布式計算框架下實現(xiàn)加工資源與加工任務的匹配算法,基于蟻群算法實現(xiàn)機床組合優(yōu)化。
[Abstract]:With the continuous change of people's demand, more and more individualized processing tasks have been put forward. It is very difficult for manufacturing enterprises to meet the requirements of processing tasks only on the basis of their own processing resources. The sharing of processing resources can promote the cooperative work among enterprises in different places, enhance the production capacity of enterprises, and effectively improve the utilization ratio of idle processing resources. It is an important way to reduce the production cost and improve the production efficiency of products. In recent years, in order to speed up the response to market demand and improve the competitiveness of enterprises, a variety of networked manufacturing models have been put forward successively, such as manufacturing grid, agile manufacturing, global manufacturing and so on. Networked manufacturing promotes the cooperative cooperation among enterprises, realizes the integration and sharing of social processing resources, effectively improves the production efficiency and processing quality of products, and reduces the production cost of products. However, there are still shortcomings in the operation mode and the matching of processing tasks and processing resources. Based on the existing networked manufacturing technology, combined with cloud computing technology and semantic web theory, this paper proposes a processing resource sharing platform based on Hadoop architecture for processing task matching. Focusing on the machine tool equipment resources as the sharing object, this paper studies the key technologies involved in the resource sharing platform, such as the model design of machining resources and processing tasks, the information storage strategy of machining resources, the matching technology between machining resources and machining tasks, etc. The feasibility and practicability of the proposed method are verified by the development of the prototype system and concrete examples, and the beneficial exploration of the forward development of the manufacturing industry in China is made. The main innovations and research results are as follows: (1) the key issues involved in the resource sharing platform are analyzed. Based on Hadoop platform, the architecture of the processing resource sharing platform for machining task matching is proposed. The core layer cloud service end structure of the platform is analyzed. (2) analyzing the characteristics of processing resources and classifying the processing resources, abstracting the processing resources and processing task attributes from the point of view of matching, designing the machining resource model and the machining task model, respectively. The machining resource model and the machining task model are established by ontology modeling method. (3) File system HDFS and HBase database in Hadoop architecture are used to store processing resource information. According to the characteristics of machining resource information and considering the storage space and query efficiency, the information storage scheme of machining resources is designed, and the related HTable table structure is designed. The storage procedure of machining resources is expounded. (4) the characteristics of machining resource matching are analyzed, the matching framework is constructed, and the matching model between machining resource and processing task is established. In the framework of MapReduce distributed computing, the matching algorithm of machining resource and machining task is realized, and the machine tool combination optimization is realized based on ant colony algorithm.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH186

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