开发支持AI的IIoT平台--从早期用例验证中吸取的经验教训
Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation
摘要：
为了在工业生产中更广泛地采用人工智能，足够的基础设施能力至关重要。这包括简化人工智能与工业设备的集成，支持分布式部署，监控和一致的系统配置。现有的IIoT平台仍然缺乏以开放的、基于生态系统的方式灵活集成可重用AI服务和相关标准（如资产管理Shell或OPC UA）所需的能力。这正是我们的下一级智能工业生产生态圈（IIP-Ecosphere）平台所解决的问题，采用高度可配置的低代码方法。在本文中，我们介绍了这个平台的设计，并讨论了一个早期的评估，在一个示范的AI启用的视觉质量检测。在这一早期评价活动中获得的深刻见解和经验教训对此进行了补充。
英文摘要：
For a broader adoption of AI in industrial production, adequate infrastructure capabilities are crucial. This includes easing the integration of AI with industrial devices, support for distributed deployment, monitoring, and consistent system configuration. Existing IIoT platforms still lack required capabilities to flexibly integrate reusable AI services and relevant standards such as Asset Administration Shells or OPC UA in an open, ecosystem-based manner. This is exactly what our next level Intelligent Industrial Production Ecosphere (IIP-Ecosphere) platform addresses, employing a highly configurable low-code based approach. In this paper, we introduce the design of this platform and discuss an early evaluation in terms of a demonstrator for AI-enabled visual quality inspection. This is complemented by insights and lessons learned during this early evaluation activity.
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。
