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.

翻译：随着信息物理系统（CPS）复杂性的日益增加，尤其在工业领域，人工智能（AI）与机器学习（ML）技术的有效集成面临更大挑战。物联网（IoT）与工业物联网（IIoT）技术间的碎片化——体现在多样化的通信协议、数据格式与设备能力上——在底层物理层与高层智能功能间造成了显著鸿沟。近年来，数字孪生（DT）技术作为一种前景广阔的解决方案崭露头角，能够为物理资产提供结构化、可互操作且语义丰富的数字化表征。现有方法常呈孤岛化且紧耦合，限制了AI功能的可扩展性与复用性。本研究提出一种模块化、可互操作的解决方案，通过最小化配置需求并解耦DT与AI组件的角色，实现AI管道在CPS中的无缝集成。我们引入零配置（ZeroConf）AI管道概念，由DT协调数据管理与智能增强。该方法在一个微工厂场景中得到验证，展示了对并发ML模型与动态数据处理的支持，有效加速了复杂工业环境中智能服务的部署。