论文标题
MIP AI分布式建筑模型，以在网络物理系统（CPS）中引入认知计算功能
MIP An AI Distributed Architectural Model to Introduce Cognitive computing capabilities in Cyber Physical Systems (CPS)
论文作者
论文摘要
本文介绍了MIP平台体系结构模型，这是一种基于AI的新型认知计算平台体系结构。提议应用MIP的目的是减轻用于使用网络物理生产系统中制造过程中认知计算和流利的HMI相互作用的AI算法的实施负担。 MIP的认知推理引擎是一个确定性的认知模块，该模块可以处理声明性目标，标识意图和实体，选择合适的动作和相关算法，并为执行中配置在内部函数-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-AS-SAS-sasvice或连接引擎中的执行。针对绩效标准的持续观察和评估评估了许多场景和各种情况的Lambda的性能。具有明确定义的接口的模块化设计可以使FAAS组件的可重复性和可扩展性。一个集成的BigData平台实现了这种模块化设计，该设计由Docker，Kubernetes等技术提供支持，用于虚拟化和编排各个组件及其交流。使用现实世界中的用例来评估体系结构的实现，以后在本文中讨论。
This paper introduces the MIP Platform architecture model, a novel AI-based cognitive computing platform architecture. The goal of the proposed application of MIP is to reduce the implementation burden for the usage of AI algorithms applied to cognitive computing and fluent HMI interactions within the manufacturing process in a cyber-physical production system. The cognitive inferencing engine of MIP is a deterministic cognitive module that processes declarative goals, identifies Intents and Entities, selects suitable actions and associated algorithms, and invokes for the execution a processing logic (Function) configured in the internal Function-as-aService or Connectivity Engine. Constant observation and evaluation against performance criteria assess the performance of Lambda(s) for many and varying scenarios. The modular design with well-defined interfaces enables the reusability and extensibility of FaaS components. An integrated BigData platform implements this modular design supported by technologies such as Docker, Kubernetes for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case later discussed in this paper.
