面向加工任务匹配的加工资源共享平台关键技术研究

发布时间：2018-07-12 18:08

本文选题：资源共享 + 本体模型　；参考：《山东大学》2017年硕士论文

【摘要】：随着人们需求的不断变化,更多个性化的加工任务被提出,制造企业仅依靠自身拥有的加工资源很难满足加工任务的要求。加工资源共享可促进异地企业间进行协同工作,增强企业生产能力,有效提高闲置加工资源的利用率,是降低产品生产成本和提高产品生产效率的重要途径。近年来,为加快对市场需求的响应和提高企业的竞争力,多种网络化制造模式先后被提出,如制造网格、敏捷制造、全球化制造等。网络化制造促进了企业间的协同合作,实现了社会加工资源的集成与共享,有效提高了产品生产效率和加工质量,降低了产品生产成本。然而,在运营模式及加工任务与加工资源匹配等方面依然存在不足。本文在现有网络化制造技术的基础上,结合云计算技术和语义网相关理论提出了一种运行于Hadoop架构下的面向加工任务匹配的加工资源共享平台。重点以机床设备资源为共享对象,研究了加工资源与加工任务的模型设计、加工资源信息存储策略、加工资源与加工任务间的匹配技术等资源共享平台涉及到的关键技术,并通过原型系统的开发和具体实例验证了所提方法的可行性和实用性,对我国制造业的向前发展进行了有益的探索。论文的主要创新点和研究成果如下:(1)对资源共享平台涉及的关键问题进行分析,基于Hadoop平台提出了面向加工任务匹配的加工资源共享平台的体系结构。对平台的核心层云服务端结构做了分析。(2)分析加工资源特点并对加工资源进行分类,从匹配的角度抽象加工资源和加工任务属性,分别设计了加工资源模型和加工任务模型,并采用本体建模方法建立了加工资源模型和加工任务模型。为资源与任务间进行匹配实现资源共享做技术准备。(3)采用Hadoop架构中的文件系统HDFS和HBase数据库来存储加工资源信息。结合加工资源信息特点,综合考虑存储空间和查询效率两方面因素设计了加工资源信息存储方案,设计了相关HTable表结构,并阐述了加工资源存储过程。(4)分析加工资源匹配所具有的特点,构建匹配框架,建立了加工资源与加工任务的匹配模型,在MapReduce分布式计算框架下实现加工资源与加工任务的匹配算法,基于蚁群算法实现机床组合优化。
[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.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TH186

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