让工业智能系统自动运行，减少配置，提升部署效率。
Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications
- 用数字孪生统一管理数据与AI模块，实现解耦
- 支持多模型并行与动态数据处理，加速智能服务上线
- 适合需要快速部署的复杂工业场景
工业领域中日益复杂的网络物理系统（CPS）带来了人工智能（AI）与机器学习（ML）技术集成的挑战。物联网（IoT）与工业物联网（IIoT）在通信协议、数据格式和设备能力上的碎片化，导致底层物理层与上层智能功能之间存在显著鸿沟。近年来，数字孪生（Digital Twin, DT）技术因其结构化、可互操作且语义丰富的数字表示能力，成为解决该问题的潜在方案。然而，现有方法多为孤立且强耦合，限制了AI功能的可扩展性与复用。本文提出一种模块化、可互操作的解决方案，通过最小化配置，将AI管道无缝集成至CPS中，实现数字孪生与AI组件的角色解耦。我们引入零配置（ZeroConf）AI管道概念，由数字孪生负责数据管理与智能增强。该方法在微工厂（MicroFactory）场景中得到验证，支持并发机器学习模型与动态数据处理，显著加速复杂工业环境中智能服务的部署。
原文摘要 · Abstract (English)
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.
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