面向航空母舰甲板航空保障决策支持的大模型关键技术与展望OA

[Objective]Aircraft carrier flight deck aviation operations constitute a critical process for gener-ating and sustaining the sortie generation capability of carrier-based aircraft.Their high-risk,highly con-strained,and tightly coupled nature places stringent demands on the safety,reliability,and interpretability of intelligent decision-support systems.[Method]Focusing on decision support for aircraft carrier flight deck aviation operations,this paper systematically reviews the foundational research on rule-based constraints,situ-ational awareness,and scheduling optimization.It further examines the key challenges associated with the ap-plication of large models in this domain,including trustworthy output,multimodal cross-domain fusion,adap-tation to diverse operational scenarios,and causal reasoning across multiple operational processes.Using two representative scenarios as case studies—pre-launch coordinated support involving aircraft towing,weapon loading,refueling,and catapult-window coordination,and dynamic replanning during recovery operations un-der conditions such as arresting-gear malfunctions or parking-space congestion—this paper analyzes task ob-jectives,input modalities,operational constraints,output forms,validation mechanisms,and performance evalu-ation metrics.Based on this analysis,the paper clarifies how large models can be integrated into practical sup-port workflows.The former scenario emphasizes resource coordination under efficiency and safety constraints,whereas the latter focuses on risk mitigation and recovery-oriented scheduling in the presence of operational disturbances.Together,these scenarios encompass both routine operational planning and contingency-driven reconstruction in flight deck aviation support.On this basis,this paper proposes a large-model-driven decision support framework for high-safety-level aviation support operations.The framework establishes a closed-loop decision-making process integrating generation,verification,screening,and confirmation.First,physical mechanisms,operational regulations,spatial constraints,safety separation requirements,and process depen-dencies are incorporated into the model generation process.In parallel,external verification is achieved through the integration of rule engines,collision detection,formal verification,discrete-event simulation,and digital-twin-based reasoning.Second,to handle heterogeneous information sources,such as flight plans,deck surveillance videos,target-position time series,voice commands,equipment status data,engineering geomet-ric constraints,and operational logs,a unified multimodal representation and encoding framework is devel-oped to support situational awareness,event correlation,and task-state representation.Third,retrieval-augmented generation,lightweight fine-tuning,prompt templates,and scenario recognition are integrated to es-tablish a domain-knowledge-driven cross-scenario adaptation mechanism,thereby enhancing the model's trans-fer capabilities across tasks such as launch,recovery,refueling,weapon loading,maintenance,and contingency response.Finally,for continuous operational processes including towing,support,launch,recovery,and main-tenance,a process-level causal reasoning and multi-agent collaboration framework is constructed to characterize how local disturbances propagate through downstream tasks,trigger resource reallocation,and drive process reconstruction.Furthermore,considering shipborne edge-computing conditions,an engineering deployment framework is proposed that integrates lightweight inference,local knowledge enhancement,rule-and simula-tion-based verification,and command workflow integration.Structured outputs are adopted to represent task objects,state evidence,candidate actions,verification outcomes,risk levels,and human-confirmation status.[Results]The results show that large models for flight deck aviation support should not be employed as direct generators of execution-commands.Instead,they should function as decision-support modules for candidate-scheme generation,situational interpretation,risk assessment,and process-level reasoning within a closed-loop framework incorporating local verification and human confirmation.[Conclusion]The pro-posed framework can effectively mitigate untrustworthy outputs,while enhancing multimodal situational awareness,cross-scenario adaptability,proactive replanning capability,and engineering integration capability.It provides both theoretical foundations and methodological guidance for the safe and controllable application of large models in complex military support systems.