{"title":"有效实施任务自动化以支持CPS的多学科工程","authors":"R. Maier, S. Unverdorben, M. Gepp","doi":"10.1109/COASE.2018.8560507","DOIUrl":null,"url":null,"abstract":"Developing cyber-physical systems (CPS) as well as cyber-physical production systems (CPPS), as a base for concepts like Industry 4.0 and smart factories, requires a close interaction between several engineering disciplines. However, efficient collaboration between the different technical disciplines and engineering tools remains a challenge. Especially the seamless and automated information exchange between tools is seen as one of the core problems to be solved on the way to Industry 4.0. Current state of the art approaches are relying on task automation functionalities embedded in single tools and on tool independent approaches that use physical addresses to handle data. The main challenge regarding these state of the art tools is that the needs of Industry 4.0 with respect to flexibility and efficiency can't be fulfilled. In order to overcome these challenges a new task automation approach is introduced. Compared to today's approaches this new approach supports logical addressing of data based on taxonomic ordering schemes, flexible usage of best fitting base techniques, predefined tool specific interface functions as well as self aware task execution. The new approach offers the advantage of exchanging data between tools, supporting dynamic workflows, automating tasks, and easily implementing extensions. Several tasks in the areas of data or document generation, mass data handling, seamless tool usage and automated decision making, have already been implemented and were proven to work.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"15 1","pages":"1388-1393"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Efficient Implementation of Task Automation to Support Multidisciplinary Engineering of CPS\",\"authors\":\"R. Maier, S. Unverdorben, M. Gepp\",\"doi\":\"10.1109/COASE.2018.8560507\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Developing cyber-physical systems (CPS) as well as cyber-physical production systems (CPPS), as a base for concepts like Industry 4.0 and smart factories, requires a close interaction between several engineering disciplines. However, efficient collaboration between the different technical disciplines and engineering tools remains a challenge. Especially the seamless and automated information exchange between tools is seen as one of the core problems to be solved on the way to Industry 4.0. Current state of the art approaches are relying on task automation functionalities embedded in single tools and on tool independent approaches that use physical addresses to handle data. The main challenge regarding these state of the art tools is that the needs of Industry 4.0 with respect to flexibility and efficiency can't be fulfilled. In order to overcome these challenges a new task automation approach is introduced. Compared to today's approaches this new approach supports logical addressing of data based on taxonomic ordering schemes, flexible usage of best fitting base techniques, predefined tool specific interface functions as well as self aware task execution. The new approach offers the advantage of exchanging data between tools, supporting dynamic workflows, automating tasks, and easily implementing extensions. Several tasks in the areas of data or document generation, mass data handling, seamless tool usage and automated decision making, have already been implemented and were proven to work.\",\"PeriodicalId\":6518,\"journal\":{\"name\":\"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)\",\"volume\":\"15 1\",\"pages\":\"1388-1393\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COASE.2018.8560507\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COASE.2018.8560507","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient Implementation of Task Automation to Support Multidisciplinary Engineering of CPS
Developing cyber-physical systems (CPS) as well as cyber-physical production systems (CPPS), as a base for concepts like Industry 4.0 and smart factories, requires a close interaction between several engineering disciplines. However, efficient collaboration between the different technical disciplines and engineering tools remains a challenge. Especially the seamless and automated information exchange between tools is seen as one of the core problems to be solved on the way to Industry 4.0. Current state of the art approaches are relying on task automation functionalities embedded in single tools and on tool independent approaches that use physical addresses to handle data. The main challenge regarding these state of the art tools is that the needs of Industry 4.0 with respect to flexibility and efficiency can't be fulfilled. In order to overcome these challenges a new task automation approach is introduced. Compared to today's approaches this new approach supports logical addressing of data based on taxonomic ordering schemes, flexible usage of best fitting base techniques, predefined tool specific interface functions as well as self aware task execution. The new approach offers the advantage of exchanging data between tools, supporting dynamic workflows, automating tasks, and easily implementing extensions. Several tasks in the areas of data or document generation, mass data handling, seamless tool usage and automated decision making, have already been implemented and were proven to work.