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

S. Prokopchina
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引用次数: 1

摘要

在工业4.0概念的框架内,设想了感官系统智能化过程的密集发展。复杂系统中实际测量过程最重要的特性之一是,首先,它们在相当不确定的条件下实现。不确定性是由复杂测量对象及其运行环境的先验不完备、不准确、模糊信息引起的,这使得我们无法在进行测量实验之前建立适当的对象模型,无法识别和形式化外部环境的影响因素,无法开发有效的信息和测量系统运行算法。本文提出了一种基于贝叶斯智能技术(BITS)的智能传感器网络及其实现方法,以实现不确定条件下测量系统的智能化。考虑了这种网络的典型模块,即各种传感器的集成集和智能测量信息处理系统。这样的传感器组可以包括物理实现的测量装置和用于测量非定量或积分特性的虚拟传感器。网络工作的结果是对复杂对象的状况进行全面评估,并为确保其可持续运作提出建议。这种系统的一个重要组成部分是对所获得的所有解进行完整计量论证的内置手段。该系统具有与复杂对象的管理层次相对应的分层体系结构,具有根据新接收到的信息进行自开发的可能性。这要归功于具有动态约束的模型和测量尺度,这些网络中使用的所有算法都是基于这些模型和尺度构建的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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