{"title":"工业5.0中的智能传感器网络。基于正则贝叶斯方法的复杂系统管理数字平台创建的广义概念","authors":"S. Prokopchina","doi":"10.1109/scm55405.2022.9794889","DOIUrl":null,"url":null,"abstract":"Within the framework of the Industry 4.0 concept, intensive development of the processes of intellectualization of sensory systems is envisaged. Among the most important specific properties of real measuring processes in complex systems is, first of all, their implementation in conditions of considerable uncertainty. The uncertainty is caused by a priori incompleteness, inaccuracy, vagueness of information about a complex measuring object and its functioning environment, which does not allow us to build an adequate model of the object before conducting a measuring experiment, identify and formalize the influencing factors of the external environment and develop effective algorithms for the functioning of information and measurement systems.The article proposes an approach to the intellectualization of measurement systems in conditions of uncertainty by creating intelligent sensor networks based on Bayesian intelligent technologies (BITS) and means of their implementation. Typical modules of such networks are considered, which are integrated sets of various sensors and intelligent measurement information processing systems.Such sensor sets may include both physically implemented measuring devices and virtual sensors for measuring non-quantitative or integral characteristics. The results of the work of the networks are comprehensive assessments of the state of complex objects and recommendations for ensuring their sustainable functioning. An important part of such systems is the built-in means of a complete metrological justification of all the solutions obtained. The systems have a hierarchical architecture corresponding to the levels of management of complex objects, which has the possibility of self-development based on newly received information. This is achieved thanks to models and measurement scales with dynamic constraints, on which all algorithms used in these networks are built.","PeriodicalId":162457,"journal":{"name":"2022 XXV International Conference on Soft Computing and Measurements (SCM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Intelligent Sensor Networks in Industry 5.0. Generalized Concept of Creating Digital Platforms for Managing Complex Systems Based on a Regularizing Bayesian Approach\",\"authors\":\"S. Prokopchina\",\"doi\":\"10.1109/scm55405.2022.9794889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Within the framework of the Industry 4.0 concept, intensive development of the processes of intellectualization of sensory systems is envisaged. Among the most important specific properties of real measuring processes in complex systems is, first of all, their implementation in conditions of considerable uncertainty. The uncertainty is caused by a priori incompleteness, inaccuracy, vagueness of information about a complex measuring object and its functioning environment, which does not allow us to build an adequate model of the object before conducting a measuring experiment, identify and formalize the influencing factors of the external environment and develop effective algorithms for the functioning of information and measurement systems.The article proposes an approach to the intellectualization of measurement systems in conditions of uncertainty by creating intelligent sensor networks based on Bayesian intelligent technologies (BITS) and means of their implementation. Typical modules of such networks are considered, which are integrated sets of various sensors and intelligent measurement information processing systems.Such sensor sets may include both physically implemented measuring devices and virtual sensors for measuring non-quantitative or integral characteristics. The results of the work of the networks are comprehensive assessments of the state of complex objects and recommendations for ensuring their sustainable functioning. An important part of such systems is the built-in means of a complete metrological justification of all the solutions obtained. The systems have a hierarchical architecture corresponding to the levels of management of complex objects, which has the possibility of self-development based on newly received information. This is achieved thanks to models and measurement scales with dynamic constraints, on which all algorithms used in these networks are built.\",\"PeriodicalId\":162457,\"journal\":{\"name\":\"2022 XXV International Conference on Soft Computing and Measurements (SCM)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 XXV International Conference on Soft Computing and Measurements (SCM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/scm55405.2022.9794889\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 XXV International Conference on Soft Computing and Measurements (SCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/scm55405.2022.9794889","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intelligent Sensor Networks in Industry 5.0. Generalized Concept of Creating Digital Platforms for Managing Complex Systems Based on a Regularizing Bayesian Approach
Within the framework of the Industry 4.0 concept, intensive development of the processes of intellectualization of sensory systems is envisaged. Among the most important specific properties of real measuring processes in complex systems is, first of all, their implementation in conditions of considerable uncertainty. The uncertainty is caused by a priori incompleteness, inaccuracy, vagueness of information about a complex measuring object and its functioning environment, which does not allow us to build an adequate model of the object before conducting a measuring experiment, identify and formalize the influencing factors of the external environment and develop effective algorithms for the functioning of information and measurement systems.The article proposes an approach to the intellectualization of measurement systems in conditions of uncertainty by creating intelligent sensor networks based on Bayesian intelligent technologies (BITS) and means of their implementation. Typical modules of such networks are considered, which are integrated sets of various sensors and intelligent measurement information processing systems.Such sensor sets may include both physically implemented measuring devices and virtual sensors for measuring non-quantitative or integral characteristics. The results of the work of the networks are comprehensive assessments of the state of complex objects and recommendations for ensuring their sustainable functioning. An important part of such systems is the built-in means of a complete metrological justification of all the solutions obtained. The systems have a hierarchical architecture corresponding to the levels of management of complex objects, which has the possibility of self-development based on newly received information. This is achieved thanks to models and measurement scales with dynamic constraints, on which all algorithms used in these networks are built.