让工业AI在边缘设备快速落地，本地推理降低延迟。
An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0
- 基于智能体架构，分角色分工实现灵活部署
- 实测食品行业场景下部署时间与适应性显著提升
- 轻量设计支持模块化集成，适合工业界快速应用
我们提出一种面向工业5.0的新框架，简化AI模型在各类工业场景中边缘设备的部署。通过本地推理与实时处理，减少延迟并避免外部数据传输。系统采用基于智能体的设计，由人、算法或协同体负责明确任务，提升灵活性并简化集成。框架支持模块化扩展，资源占用低。在真实食品行业场景中的初步评估显示，部署时间与系统适应性均有明显改善。源代码已公开于 https://github.com/AI-REDGIO-5-0/ci-component。
原文摘要 · Abstract (English)
We present a novel framework for Industry 5.0 that simplifies the deployment of AI models on edge devices in various industrial settings. The design reduces latency and avoids external data transfer by enabling local inference and real-time processing. Our implementation is agent-based, which means that individual agents, whether human, algorithmic, or collaborative, are responsible for well-defined tasks, enabling flexibility and simplifying integration. Moreover, our framework supports modular integration and maintains low resource requirements. Preliminary evaluations concerning the food industry in real scenarios indicate improved deployment time and system adaptability performance. The source code is publicly available at https://github.com/AI-REDGIO-5-0/ci-component.
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