发表机构
National Laboratory of Wireless Communications of CNIT; University of Bologna; Fibercop（CNIT无线通信国家实验室; 博洛尼亚大学; Fibercop）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种集成物理过程、通信与应用逻辑的模块化数字孪生框架，经BI-REX中试线的5G自主移动机器人实验验证，可用于AI驱动工业控制回路的预部署设计与验证。
AI 中文摘要
工业环境的特征日益表现为物理过程、通信基础设施与智能应用之间的紧密交互。在此背景下，数字孪生（Digital Twins, DTs）已成为系统分析与优化的关键技术。然而，现有数字孪生解决方案通常仅聚焦于工业过程或通信网络，缺乏集成且面向应用的视角。为填补这一空白，本文提出一种面向工业环境的模块化数字孪生框架，该框架在统一架构内联合建模物理过程、无线通信与应用逻辑。通过在BI-REX中试线实施的真实概念验证（Proof-of-Concept, PoC）对所提框架的可行性进行实验验证，该验证涉及一台由5G连接的自主移动机器人（Autonomous Mobile Robot, AMR），用于运输危险液体并由AI驱动应用进行远程控制。所提数字孪生被用于复现真实部署的行为，并研究AI应用不同部署位置的影响，包括本地部署、边缘部署及远程云执行场景。实验结果表明，数字孪生预测与概念验证测量结果在网络级指标（如参考信号接收功率（Reference Signal Received Power, RSRP）和延迟）以及端到端应用指标（包括应用级往返时间（Round-Trip-Time, RTT））方面均高度吻合。此外，分析显示网络建模的不准确会严重影响延迟敏感型工业控制回路的可行性，凸显集成数字孪生作为下一代工业系统预部署设计与验证工具的潜力。
英文摘要
Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intelligent applications. In this context, Digital Twins (DTs) have emerged as a key technology for system analysis and optimization. However, existing DT solutions typically focus either on industrial processes or communication networks, while lacking an integrated and application-aware perspective. To fill this gap, this paper proposes a modular DT framework for industrial environments that jointly models physical processes, wireless communications, and application logic within a unified architecture. The feasibility of the proposed framework is experimentally validated through a real-world Proof-of-Concept (PoC) implemented in the BI-REX pilot line, involving a 5G-connected Autonomous Mobile Robot (AMR) transporting hazardous liquids and remotely controlled by an AI-driven application. The proposed DT is used to reproduce the behaviour of the real deployment and to investigate the impact of different placements of the AI application, including on-premise, edge, and remote cloud execution scenarios. Experimental results demonstrate a close agreement between DT predictions and PoC measurements in terms of both network-level metrics, such as Reference Signal Received Power (RSRP) and latency, and end- to-end application metrics, including application-level Round- Trip-Time (RTT). Moreover, the analysis shows how inaccuracies of network modeling can critically affect the feasibility of latency-sensitive industrial control loops, highlighting the potential of integrated DTs as tools for the pre-deployment design and validation of next-generation industrial systems.
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