Objectives The launch and recovery of carrier-based aircraft are characterized by rare incident occurrences, high unpredictability, and severe consequences. As a result, flight deck commanders cannot rely solely on past incident cases to accumulate experiential knowledge. To address this challenge, this study proposes a large-model-driven framework for comprehensive case-based simulation of carrier-based aircraft launch and recovery contingencies.

Methods To analyze the evolution of contingency events under specific causative factors, a launch and recovery knowledge base was first constructed using carrier-based aircraft operation manuals and relevant literature. Additional domain-specific knowledge was incorporated to enhance the professionalism and relevance of content generated by the large model. Using prompt engineering, the large model was guided to generate latent situational trends, representing potential event evolutions under given causative factors. A decision-making model, augmented with the launch and recovery knowledge base, was then employed to evaluate these latent trends, filter out implausible evolutions, and update the contingency state iteratively.

Results Experimental results demonstrate that the proposed framework can, through multiple iterations, generate a complete set of contingency evolution processes corresponding to specific causative factors. By simulating diverse incident triggers and decision-making paths, the framework facilitates the creation of a comprehensive contingency case database for carrier-based aircraft operations.

Conclusions The proposed approach effectively utilizes the extensive prior knowledge and strong logical reasoning capabilities of large models to address the scarcity of real-world data on carrier-based contingencies. The generated case simulations serve as valuable reference material and learning scenarios, enhancing the emergency decision-making capabilities of flight deck commanders.