AI 中文总结
本文提出基于RM-ODP的多视点DT集成建模框架，结合LLM辅助的不匹配检测，实现结构化兼容性推理，提升互操作性，支持异构DT生态系统的可扩展模型重用。
AI 中文摘要
数字孪生（DT）生态系统集成异构计算模型，以表示在不断演进的特定目标下的复杂系统。现有高质量模型与数据集的系统性重用对可扩展的DT开发至关重要，但受到语义意图、数据结构、行为接口及执行环境的异质性限制。因此，集成成为跨模型、跨视点的一致性问题，难以在设计选择间预测、量化与比较。现有标准与集成平台分别解决这些问题，在模型为新DT目标重用时，对结构化、目标感知的兼容性评估及早期可行性分析支持有限。本文提出基于开放分布式处理参考模型（RM-ODP）的多视点集成建模框架，该框架在域、信息、计算、工程及技术视点间组织与集成相关的知识，将跨视点依赖表示为明确的、机器可执行的元数据。框架包含两部分：（i）用于系统模型描述与发现的视点结构化模型元模型；（ii）感知模式的不匹配检测器，其通过集成模式将跨视点兼容性约束付诸实施，结合确定性规则生成与大语言模型（LLM）辅助推理。这使得能够系统识别语义、信息及运行时不一致性，并在实施前对集成可行性与工作量进行推理。专家验证及环境建模案例研究表明，该方法可实现结构化兼容性推理，提升集成假设的透明度，增强跨视点互操作性，并支持异构DT生态系统中的可扩展重用。
英文摘要
Digital Twin (DT) ecosystems integrate heterogeneous computational models to represent complex systems under evolving, purpose-specific objectives. Systematic reuse of existing high-quality models and datasets is essential for scalable DT development, yet is constrained by heterogeneity in semantic intent, data structures, behavioral interfaces, and execution environments. As a result, integration becomes a cross-model, cross-view consistency problem that is hard to predict, quantify, and compare across design choices. Existing standards and integration platforms address these concerns separately, offering limited support for structured, purpose-aware compatibility assessment and early feasibility analysis when models are reused under new DT objectives. This paper introduces a multi-viewpoint integration modeling framework grounded in the Reference Model of Open Distributed Processing (RM-ODP). The framework structures integration-relevant knowledge across domain, information, computational, engineering, and technology viewpoints, representing cross-view dependencies as explicit, machine-actionable metadata. It comprises (i) a viewpoint-structured Model Metamodel for systematic model description and discovery, and (ii) a pattern-aware Mismatch Detector that operationalizes cross-view compatibility constraints via integration patterns, combining deterministic rule generation with Large Language Model (LLM)-assisted reasoning. This enables systematic identification of semantic, informational, and runtime inconsistencies and supports reasoning about integration feasibility and effort before implementation. Expert validation and an environmental modeling case study show that the approach enables structured compatibility reasoning, improves transparency of integration assumptions, strengthens cross-view interoperability, and supports scalable reuse in heterogeneous DT ecosystems.
Comments45 page, 14 figs, 6 tables, 2 Appendix, Submitted to the International Journal on Software and Systems Modeling (SoSyM)
