针对飞行器任务指令序列生成和优化问题，提出一种约束分级的任务指令序列处理框架，并进一步设计融合拓扑优化和优先级编码遗传的序列编排方法。首先，将指令及其约束建模成有向图，通过引入虚拟节点替代图中的强连通分量，实现去环效果。然后，针对生成的有向无环图，通过拓扑优化构建指令序列的基本初始框架。对于抽取的强连通分量，对其节点的优先级进行编码，并在遗传过程中将其作为交叉对象的基因索引，不断迭代生成优化的指令序列片段。最后，将片段集成到初始框架中，实现任务指令序列的生成和优化。仿真结果表明：在不同规模和复杂度的指令集合场景中，相较于其它方法，本文所提方法能够显著降低指令序列的生成时间，并压缩指令序列的长度。

To address the problem of aircraft mission instruction sequence generation and optimization， we propose a hierarchical-constraint mission instruction sequence processing framework， and further design a sequencing？ method that integrates topological optimization and the priority-encoded genetic algorithm. First， instructions and their constraints are modeled as a directed graph； virtual nodes are introduced to replace Strongly Connected Components （SCCs） in the graph， achieving cycle elimination. Then， for the generated Directed Acyclic Graph （DAG）， a basic initial framework of the instruction sequence is constructed through topological optimization. For the extracted strongly connected components， the priorities of their nodes are encoded and used as the gene indexes of crossover objects， thereby iteratively generating optimized instruction sequence snippets. Finally， the snippets are integrated into the initial framework to achieve the generation and optimization of the mission instruction sequence. Simulation results display that in the scenarios with instruction sets of varying scales and complexities， the proposed method significantly reduces the generation time and compresses the length of instruction sequences compared to other encoding methods.