AI 中文总结
本文针对数字孪生缺乏模型层面数据需求指定与运行时执行方法的问题，提出基于契约的质量管理方法，定义契约理论、架构定位及领域特定语言，以监控数据质量并提升数字孪生服务可靠性。
AI 中文摘要
数字孪生（Digital Twin, DT）整合多源数据，模型消费数据并支撑仿真、假设分析、机器学习驱动预测等数字孪生服务。为保障数字孪生正常运行，数据驱动服务要求数据具备可靠性、高质量等特性，包括准确性、完整性、时效性等。但目前缺乏在模型层面指定数据需求并在运行时执行这些规范的系统方法。针对该不足，本文提出数字孪生中基于契约的质量管理方法，正式定义此类契约的理论，在架构中定位其在数字孪生内的位置，并提出用于指定契约的领域特定语言。该方法支持持续数据质量监控，从而提升数字孪生服务的可靠性与质量。
英文摘要
Digital Twins (DT) integrate data from multiple sources. Models consume data and enable DT services such as simulations, what-if analyses, and ML-driven predictions. To ensure proper DT operation, data-driven services require data to exhibit traits such as reliability and high quality (including, e.g., accuracy, completeness, and timeliness). Yet, there is no systematic way to specify data requirements at the model level, and subsequently enact those specifications at runtime. To address this shortcoming, we propose an approach to contract-based quality management in DTs. We formally define a theory of such contracts, situate them architectually within DTs, and propose a domain-specific language to specify contracts. Our approach enables continuous data quality monitoring, thereby improving the reliability and quality of DT services.
