Speakers
Description
Space systems often have commonalities, and they might share comparable assumptions and requirements across projects. The assessment of new missions can be supported by model-based approaches, making use of new methods and tools so that consistency across models and at different phases is maintained. In this context, a Digital Assistant accessible to engineers can be used to ensure quality, accuracy, completeness, and correctness. The AI-Powered Digital Assistant for Space System Engineering TDE activity aims to develop a solution to identify common concepts from different models and propose suggestions for new designs, speeding up the mission and spacecraft definition phases and avoiding the repetition of previous mistakes.
Three different use cases have been selected for this activity. They are mainly focused on learning from a previous set of models and requirement specifications to identify potential links between requirements and from requirements to model artefacts, as well as to detect incompleteness or inconsistencies in the models. The application of the latest Natural Language Processing (NLP) techniques is being investigated, fine-tuned for the space domain, to track, analyse and determine the relationships among requirements and other Model Based System Engineering (MBSE) artefacts. After the development of the Digital Assistant, the solution will be integrated with the MBSE Hub, and conceptual interoperability with the Space System Ontology (SSO) will be also ensured.
