SysML v2 的不确定性建模
Uncertainty Modeling for SysML v2
AI总结：
本文提出将PSUM元模型扩展至SysML v2，以实现系统建模中的不确定性表示与传播分析。
摘要：
现代工程系统中的不确定性无处不在，包括物理-网络系统、自主系统以及大规模软件密集型基础设施（如基于微服务的系统），这些系统在动态且部分可观察的环境中运行。最近，由对象管理组发布的《精确语义不确定性建模》（PSUM）是首个标准化的不确定性建模规范，为基于模型的系统工程（MBSE）社区提供了形式化定义的语义，用于表示和推理不确定性。同时，系统建模语言（SysML v2）的第二版作为下一代系统建模语言发布，提供了改进的语义严谨性和可重用性，但缺乏与PSUM对应的原生构造，无法实现第一类的不确定性表示。本文提出了一种系统扩展SysML v2，将其PSUM元模型纳入其建模框架中。该扩展使能够显式指定不确定性来源，结构化地表征不确定性，并在系统模型中一致地传播不确定性，同时保持与SysML v2语法和语义的兼容性。我们通过七个案例研究验证了该方法。结果表明，所提出的扩展（PSUM-SysMLv2）在不确定性感知的MBSE中具有表达性和适用性，并可能实现不确定性及不确定性传播分析。
英文摘要：
Uncertainty is inherent in modern engineered systems, including cyber-physical systems, autonomous systems, and large-scale software-intensive infrastructures (such as microservice-based systems) operating in dynamic and partially observable environments. The recent publication of Precise Semantics for Uncertainty Modeling (PSUM) by the Object Management Group represents the first standardized specification for uncertainty modeling within the Model-Based Systems Engineering (MBSE) community, providing formally defined semantics for representing and reasoning about uncertainty in models. In parallel, the second version of Systems Modeling Language (SysML v2) was released as the next-generation systems modeling language, offering improved semantic rigor and reusability, yet lacking native constructs aligned with PSUM for first-class uncertainty representation. This paper proposes a systematic extension of SysML v2 that incorporates the PSUM metamodel into its modeling framework. The extension enables explicit specification of indeterminacy sources, structured characterization of uncertainties, and consistent propagation of uncertainty within system models, while preserving conformance with SysML v2 syntax and semantics. We validate the approach through seven case studies. Results demonstrate that the proposed extension (PSUM-SysMLv2) is expressive and applicable for uncertainty-aware MBSE, and potentially enables uncertainty and uncertainty propagation analyses.
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。
