数字孪生与零配置AI：为工业应用构建自动化智能流水线
Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications
AI总结：
本文提出零配置AI流水线，通过模块化和解耦DTs与AI组件，实现工业CPS中AI功能的无缝集成与高效部署。
摘要：
随着物理信息系统（CPS）复杂性的增加，特别是在工业领域，有效整合人工智能（AI）和机器学习（ML）技术的挑战变得更加严峻。物联网和工业物联网技术的碎片化，通过多样的通信协议、数据格式和设备能力表现出来，导致低层物理层与高层智能功能之间存在显著差距。最近，数字孪生（DT）技术作为一种有前景的解决方案出现，提供结构化、互操作性和语义丰富的物理资产数字表示。当前的方法往往孤立且紧密耦合，限制了AI功能的扩展性和重用性。本文提出了一种模块化和互操作性的解决方案，通过最小化配置并解耦DTs和AI组件的角色，使CPS中的AI流水线集成变得无缝。我们引入了零配置（ZeroConf）AI流水线的概念，其中DTs协调数据管理和智能增强。该方法在MicroFactory场景中得到演示，展示了对同时ML模型和动态数据处理的支持，有效加速了复杂工业环境中的智能服务部署。
英文摘要：
The increasing complexity of Cyber-Physical Systems (CPS), particularly in the industrial domain, has amplified the challenges associated with the effective integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Fragmentation across IoT and IIoT technologies, manifested through diverse communication protocols, data formats and device capabilities, creates a substantial gap between low-level physical layers and high-level intelligent functionalities. Recently, Digital Twin (DT) technology has emerged as a promising solution, offering structured, interoperable and semantically rich digital representations of physical assets. Current approaches are often siloed and tightly coupled, limiting scalability and reuse of AI functionalities. This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into CPS by minimizing configuration and decoupling the roles of DTs and AI components. We introduce the concept of Zero Configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation. The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing, effectively accelerating the deployment of intelligent services in complex industrial settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
