Using semantic web technologies for developing an intelligent automatic production line

• Main Authors: WANG FANG, 王方
• Other Authors: Yu-Liang Chi
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/5zkcf2

碩士 === 中原大學 === 資訊管理研究所 === 105 === Semantic web technology transform heterogeneous data in virtual environment of Cyber-Physical Systems (CPS) into linked data. It’s makes application systems understand the data and construct the ontology of the domain Knowledge. Through the knowledge model combined the inference information to achieve the purpose of intelligent production. Industrial 4.0 is the future of the enterprise production direction, the core concept for the CPS, the current business information and more in the form of a database or form, these heterogeneous data will be stored in the CPS virtual space, but all the application systems can’t understand these heterogeneous data, That can’t meet the industrial demand for the intelligent production of 4.0. Thought this study, there are three designs based on the above problems: (1) By using linked data technology to convert heterogeneous data into linked data, so that application systems in different fields or decentralized environment can perform, identify and understanding information. (2) Ameliorate TOVE (Toronto Virtual Enterprise) ontology design method and using ontology language OWL (Web Ontology Language) to build knowledge model. (3) Design SPARQL query language, knowledge model combined with the linked data endpoint query inference to explore the implied fact by using application systems. Finally, this study integrates heterogeneous industrial data into an understandable linked data of application systems and constructs the traditional knowledge and experience into a knowledge model available to application systems. The application model uses the knowledge model to deduce the above linked data instead of human Knowledge and experience to deal with information, to achieve human intervention without the need for intelligent production.