Abstract:

In modern warfare， drone/swarm air raids are gradually replacing the traditional air raid model and building a completely new battlefield situation. Whether the enemy's invasion can be accurately predicted is of decisive significance to the resource allocation， plan planning and efficiency-to-cost ratio control of anti-drone/swarm operations. It is directly related to the resource allocation efficiency and mission success or failure of air defense operations. Based on the above requirements， this paper proposes an air strike scale analysis method for drone/swarm combat： by constructing a multi-dimensional optimization function based on steady-state genetic algorithm， comprehensively considering core indicators such as enemy reconnaissance/strike mission completion rate， reconnaissance coverage rate， combat time and cost， and conducting a global search on the enemy's possible incoming scale marshalling scheme. Through quantitative comparison and analysis of indicators， select the enemy's optimal scale marshalling scheme， and finally form a scientific and effective conclusion on the scale analysis of air strikes.

Key words: drone/swarm, scale of air strikes, task completion rate, multidimensional function, operational timeliness, operational cost

摘要：

在现代战争中，无人机/蜂群空袭正逐步取代传统空袭模式并构建一种全新的战场态势。能否准确预判敌方来袭规模，对反无人机/蜂群作战的资源调配、方案筹划及效费比控制具有决定性意义，这直接关系到防空作战的资源分配效率与任务成败。基于上述需求，提出一种针对无人机/蜂群作战的空袭规模分析方法：通过构建基于稳态遗传算法的多维优化函数，综合考量敌方侦察/打击任务完成率、侦察覆盖率、作战时效及成本等核心指标，对敌方可能的来袭规模编组方案进行全局搜索。通过指标量化对比分析选择敌方最优的规模编组方案，最终形成科学有效的空袭规模分析结论。

关键词: 无人机/蜂群, 空袭规模, 任务完成率, 多维函数, 作战时效, 作战成本