Objectives The launch and recovery of carrier-based aircraft are characterized by sparse incident occurrences, high unpredictability, and severe consequences. Consequently, flight deck commanders cannot effectively accumulate experiential knowledge solely through existing incident cases. To address this limitation, 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 related literature. Additional domain-specific knowledge was integrated to enhance the professionalism and relevance of the large model’s generated content. Through prompt engineering, the large model was guided to generate latent situational trends representing potential event evolutions given a specific causative factor. A decision-making model, augmented with the launch and recovery knowledge base, was then employed to evaluate these latent trends, filter out unreasonable evolutions, and update the contingency state iteratively.

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

Conclusions The proposed approach effectively leverages the extensive prior knowledge and strong logical reasoning capabilities of large models to overcome the scarcity of real-world carrier-based contingency data. The generated case simulations provide valuable reference material and learning scenarios that enhance the emergency decision-making capabilities of flight deck commanders.