The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamic foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook.

翻译：生产领域的数字化转型需要新的数据集成与存储方法，以及能够在开发、生产和使用周期中纵向与横向运作的决策支持系统。本文提出面向生产的数据到知识（及知识到数据）流水线，作为基于数字影子网络（一种增强数字孪生的概念）的通用框架。我们展示了一个概念验证系统，该系统基于并桥接现有基础设施，实现：1）在数据湖仓中捕获来自不同组织及用例中多个相似但独立机器人的轨迹数据并进行语义标注；2）通过独立流程动态查询匹配数据，用于训练机器人控制的反向动力学基础模型。本文讨论了该方法的挑战与优势，并展望了数据到知识流水线如何在全球实验室框架下提升效率与工业可扩展性。