工业复杂系统日渐应用于我们的日常生活，如健康医疗、航空航天等关键领域。其广泛应用给软件工程学科提出了前所未有的挑战，如高复杂度、高动态、大规模、持续集成与部署。此外，伴随着急速发展的人工智能技术在工业产品中的应用，会对此类产品自身与其运行的环境造成更多的“客观不确定性”；由于系统的高复杂性及开发人员的知识局限性，导致在开发过程中引入“主观不确定性”；由于系统的持续集成与部署，在系统设计与开发阶段，无法完全预测未来可能部署，从而引入了另外一个层面的主观与客观不确性。实践证明基于模型的系统工程（MBSE）以及广泛应用的系统建模语言SysML能极大提高系统开发效率。但是目前SysML并不具备不确定性建模的能力。本课题的目标是扩展SysML以弥补其在工业复杂系统MBSE开发过程的针对不确定性建模的严重不足，从理论、方法、和工具几个层面提出适合工业复杂系统MBSE开发的不确定性建模与演化。

Industrial complex systems are increasingly used in our daily lives, such as healthcare, aerospace and other safety-critical areas. Its extensive application presents unprecedented challenges to software engineering such as high complexity, being highly dynamic, large-scale, continuous integration and deployment. In addition, the application of the artificial intelligence technology with rapid development in industrial complex systems will cause more "objective uncertainty" for the systems themselves and the environment in which they operate; the high complexity of the systems and the limitations of the developer's knowledge lead to the introduction of “subjective uncertainty” in the development process; due to the continuous integration and deployment of the system, it is impossible to fully predict the future possible deployments during the system design and development phase, thus introducing another level of subjective and objective uncertainty. Evidence has shown that Model-Based Systems Engineering (MBSE) and the widely used System Modeling Language (SysML) can greatly improve system development efficiency. However, SysML currently does not have the ability to model uncertainty. The goal of this project is to effectively expand SysML to make up for its serious deficiency in uncertainty modeling in the MBSE development process of industrial complex systems, from the aspects of theory, method, and tools.