An emergency decision support framework for flight-deck accidents: integrating dynamic adjudication, multi-agent cooperation, and hierarchical path planning

The flight deck of a naval vessel is a high-risk operational environment in which a single initiating event can precipitate coupled hazard cascades encompassing concurrent fire propagation, progressive casualty deterioration, and damage-induced stability degradation. Conventional pre-plan-driven response approaches are architecturally ill-suited to such multi-hazard, temporally evolving scenarios, as they cannot adapt to stochastic hazard interactions in real time. This paper presents the Intelligent Emergency Decision-support Framework for Flight-deck Safety (IEDFS), a closed-loop architecture comprising three tightly coupled modules: (1) a knowledge–data dual-driven dynamic reasoning engine that performs online fire propagation modelling, casualty condition state estimation, real-time confidence updating, and damage stability constraint evaluation; (2) an attention-enhanced multi-agent cooperative decision module in which the task-allocation problem is formalised as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP) and solved via an Attention-QMIX network preserving heterogeneous agent role attributes; and (3) a hierarchical time-varying path planning module combining a temporally extended A* algorithm for global route generation with D* Lite for local dynamic replanning, enabling simultaneous hazard avoidance, stretcher-carry task scheduling, and inter-agent conflict resolution. At each decision cycle, the reasoning engine furnishes updated situational assessments to the decision module, whose cooperative policy outputs are consumed by the path planner—completing a feedback loop that sustains adaptive response under continuously evolving hazard states. Validation on the FDAS-Sim platform across five progressively escalating accident scenarios demonstrates that IEDFS improves the critical-casualty golden-hour treatment rate by 26.3 percentage points , reduces fire containment time by 40.1% , decreases flight deck operational recovery time by 36.1% , and lowers the damage stability exceedance rate by 53.2% relative to static rule-based baselines. Ablation studies confirm the independent contribution of each module and quantify the synergistic performance gain from system integration. These results position IEDFS as a viable methodological foundation for intelligent decision support and simulation-based proficiency training in flight deck emergency response.