用可视化语言让非程序员也能构建可复用的实时数字孪生流水线。
Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines
- 提出FDF可视化语言，显式定义机器学习模块并支持组合复用。
- 用户研究显示工具对领域专家友好，可用性与功能完整度良好。
- 扩展出分层版FDF，支持迭代式复杂流水线的规范设计。
数字孪生（DT）越来越多地依赖人工智能与机器学习流水线，以从高保真仿真中构建实时数字孪生，并通过历史数据实例化。然而，这些流水线的工程实现仍高度依赖经验：难以明确指定、验证和复用，缺乏专用工具支持。本文提出的函数+数据流（FDF）通过定义一种可视化领域特定语言（DSL），显式表示函数（如机器学习模型），支持其组合与复用。我们在DesCartes Builder中实现了FDF，该集成建模环境支持基于FDF的数字孪生合成与验证。本文报告了一项实证用户研究，评估FDF与DesCartes Builder是否能让基于AI的数字孪生开发更易用且可靠。参与者在环境中实现了一个典型实时数字孪生原型，我们通过定量与定性指标测量了感知可用性与功能完备性。结果表明，DesCartes Builder与FDF在广泛用户群体中展现出良好可用性，尤其适合目标用户——领域专家。研究还揭示了工具与框架的具体优势与改进空间。基于此，我们提出了H-FDF——FDF的分层扩展，支持迭代与模块化流水线，能够形式化规范如双阶段训练等更复杂的数字孪生流水线。研究结果表明，集成化的模型驱动平台是将基于AI的数字孪生工程转变为系统化建模实践的可行方向。
原文摘要 · Abstract (English)
Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instantiate them with historical data. However, engineering these pipelines remains largely ad-hoc: pipelines are hard to specify, validate, and reuse, with scarce dedicated tooling. Function+Data Flow (FDF) addresses this by defining a visual domain-specific language (DSL) that represents functions (ML models) explicitly, enabling their composition and reuse. We implemented FDF in DesCartes Builder, an integrated modeling environment supporting FDF-based DT synthesis and validation. In this paper, we report on an empirical user study evaluating whether FDF and DesCartes Builder can make AI-based DT development more accessible and reliable. Participants implemented a representative real-time DT prototype within DesCartes Builder, and we measured perceived usability and feature adequacy through quantitative and qualitative measures. Our results indicate that DesCartes Builder and FDF achieve a good level of usability across a broad range of potential users, and particularly for the intended audience of domain experts. The study additionally surfaces concrete strengths and areas for improvement of both the tool and the underlying FDF framework. Informed by these findings, we propose H-FDF, a Hierarchical extension of FDF supporting iterative and modular pipelines, enabling the formal specification of more complex DT pipelines such as dual training. Our findings suggest that integrated, model-driven platforms are a promising direction to transform AI-based DT engineering into a disciplined modeling practice.
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