arXiv:2504.00286cs.HCcs.AI2025-04综述被引 4

让数字孪生更好帮人,提升生物制药生产中的协作效率

Digital Twins in Biopharmaceutical Manufacturing: Review and Perspective on Human-Machine Collaborative Intelligence

  • 构建人机协同智能框架,融合操作员与数字孪生系统
  • 强调界面易用性与信任建立,解决信息过载问题
  • 适合生物制药工厂管理者和数字孪生设计者参考

生物制药行业正日益发展数字孪生技术以数字化和自动化制造流程,应对不断增长的市场需求。然而,这一转变对人工操作员带来巨大挑战,复杂且海量的信息可能超出其有效管理能力。当数字孪生系统未考虑与操作员的交互与协作时,问题更加严重,尤其是在异常情况下,操作员需负责过程监控与态势评估。本文综述当前生物制药数字孪生发展的趋势,发现多数研究聚焦技术实现,却忽视了操作员的关键作用。为此,本文提出一种人机协同智能框架,强调操作员与数字孪生系统的深度融合。文章介绍了提升操作员信任度与人机界面可用性的系统设计方法,并探讨了帮助操作员理解与使用数字孪生的创新培训方案。该框架旨在通过充分发挥双方潜力,有效提升生物制药制造过程的韧性与生产效率。

原文摘要 · Abstract (English)

The biopharmaceutical industry is increasingly developing digital twins to digitalize and automate the manufacturing process in response to the growing market demands. However, this shift presents significant challenges for human operators, as the complexity and volume of information can overwhelm their ability to manage the process effectively. These issues are compounded when digital twins are designed without considering interaction and collaboration with operators, who are responsible for monitoring processes and assessing situations, particularly during abnormalities. Our review of current trends in biopharma digital twin development reveals a predominant focus on technology and often overlooks the critical role of human operators. To bridge this gap, this article proposes a collaborative intelligence framework that emphasizes the integration of operators with digital twins. Approaches to system design that can enhance operator trust and human-machine interface usability are presented. Moreover, innovative training programs for preparing operators to understand and utilize digital twins are discussed. The framework outlined in this article aims to enhance collaboration between operators and digital twins effectively by using their full capabilities to boost resilience and productivity in biopharmaceutical manufacturing.

数字孪生人机协同生物制药智能制造

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