针对电动垂直起降机（electric vertical take-off and landing, eVTOL）在复杂城市低空空域运行中存在的场景适应性不足和多约束耦合建模不足等突出问题，提出了融合不同任务场景和多目标优化的通用三维eVTOL飞行路径规划方法。首先，提出一种优化的结构化空域规划架构具体方法，以平衡低空空域航空器运行安全和空域容量之间的矛盾，确保空域资源的高效利用；其次，基于多旋翼eVTOL飞行器的飞行动力学与空气动力学特性，建立准确的能耗评估模型以进一步优化飞行路径，从而通过构建标准大气模型和障碍区环境建模，综合优化路径能耗、路径长度、路径平滑和计算时间，形成了全面的综合代价评估函数；最后，基于改进粒子群算法，提出了适用于不同运行场景并能应对各种交通活动需求的三维通用路径规划算法。仿真验证表明：通用三维全局路径规划方法能够在多种复杂场景下，针对不同任务需求规划出安全可行绿色的飞行路径；相较于传统路径规划算法，该改进方法在典型城市场景中展现出更优的能耗效率和航程优化等性能；在极端紧急任务场景下可快速生成应急路径，计算效率提升显著。研究结果为城市空中交通系统设计提供了兼顾安全性与经济性的解决方案，其多场景适应特性有助于推动立体化交通网络建设，为低空经济产业链发展提供技术支撑。

Aiming at the prominent issues of insufficient scenario adaptability and inadequate multi-constraint coupled modeling in electric vertical take-off and landing (eVTOL) operations within complex urban low-altitude airspace, a universal three-dimensional eVTOL flight path planning method integrating diverse mission scenarios and multi-objective optimization was proposed. Firstly, an optimized structured airspace planning framework was proposed to balance the contradiction between aircraft operation safety and airspace capacity in low-altitude airspace and ensure the efficient utilization of airspace resources. Secondly, based on the flight dynamics and aerodynamic characteristics of the rotor eVTOL aircraft, an accurate energy consumption evaluation model was established to further optimize the flight path. Through the construction of standard atmospheric models and obstacle zone environmental modeling, comprehensive optimization of path energy consumption, path length, path smoothness, and computational time was achieved, forming a holistic comprehensive cost evaluation function. Finally, based on the improved particle swarm optimization algorithm, a three-dimensional general path planning algorithm suitable for different operating scenarios and able to cope with various traffic activities was proposed. Simulation verification demonstrates that this universal 3D global path planning method can generate safe, feasible, and environmentally friendly flight paths for different mission requirements across multiple complex scenarios. Compared with traditional path planning algorithms, the improved method exhibits superior performance in energy efficiency and range optimization in typical urban scenarios, while demonstrating significant computational efficiency improvements in generating emergency paths for extreme urgent mission scenarios. The research outcomes provide a safety-economic balanced solution for urban air mobility system design, with its multi-scenario adaptability contributing to the development of multidimensional transportation networks and offering technical support for the low-altitude economy industrial chain development.