Abstract: To address key challenges in time-sensitive target(TST) striking－rigid modeling and opaque decision-making of traditional optimization methods,and high sample demand,complex reward design and poor result verifiability of deep reinforcement learning－this paper proposes a novel hybrid intelligent decision method combining order-based striking with large language mo-del(LLM) empowerment.Built on a dynamic order-centric adaptive firepower scheduling model,it adopts LLMs as strategic advisors and develops two core algorithms:STIRA-LLM accelerates constrained optimization via LLM logical reasoning,while KPSA-LLM generates tactically consistent natural language explanations to enhance decision interpretability.Experiments show the framework delivers significant improvements on key metrics,maintains stable efficient performance in large-scale dynamic adversarial environments,and produces hierarchical,auditable tactical intentions and scheme rationales,effectively boosting human-machine collaboration transparency and command trust.By fusing mechanism models with cognitive intelligence,this work provides a practical technical approach for efficient,reliable and interpretable adaptive decision-making in intelligent warfare.

Key words: Time-sensitive target, Kill chain, Weapon-target assignment, Large language model, Decision intelligence, Mixed-integer programming, Human-machine collaboration