针对复杂城市环境下无人机集群飞行面临的多机耦合制约、冲突风险频发和运行效率低下等难题，提出了城市低空无人机飞行计划协同推演与最优调配方法。首先，面向无人机“个体”，考虑无人机与城市静态环境目标之间的冲突问题，构建了基于概率风险地图的无人机三维路径规划方法，实现了无人机飞行计划“初始生成”；然后，面向无人机“群体”，考虑无人机与无人机之间的冲突问题，构建了无人机飞行计划多类型冲突协同推演模型，设计并度量了冲突发生率、冲突风险等级和栅格占用比例等冲突特征指标；最后，设计了调整飞行路径、飞行速度、飞行时间等多元化调配策略，建立了基于多元策略自适应配置的无人机飞行计划优化调配模型，验证了所提方法的优化性能和参数敏感性，实现了不同交通密度场景下城市低空无人机飞行计划的优化调配。实验表明，所提方法可在有效控制风险成本和时间成本的基础上，将飞行计划冲突降低96.2%，对于40架以内的无人机运行场景，可得到完全无冲突的飞行计划，而100架以内的无人机最低飞行冲突解脱率可控制在95%以上。所提方法是科学有效的，可为复杂城市低空无人机飞行活动安全和高效管理提供理论基础和方法指导。

To address the problems of multiple aircraft coordination constraints， frequent conflict risks， and operational inefficiencies faced by UAV cluster flights in complex urban environments， a collaborative deduction and optimal allocation method for urban low-altitude UAV flight plan is proposed. Firstly， for the UAV “individual”， the conflict between UAVs and urban static environment targets is considered， and a three-dimensional path planning method based on probabilistic risk maps is constructed to achieve the initial generation of UAV flight plan. Then， for the UAV “group”， considering the conflict between UAVs， a multi-type conflict cooperative deduction model for UAV flight plan is constructed， and the conflict characteristic indicators such as conflict occurrence rate， conflict risk level， and grid occupancy ratio are designed and metricized. Finally， diversified allocation strategies such as adjusting flight paths， flight speeds， and flight times are designed. Based on the multi-strategy self-adaptive configuration， an optimization and allocation model is established. The optimization performance and parameter sensitivity of the proposed method are verified， demonstrating the optimization performance and parameter sensitivity of urban low-altitude flight plans under different traffic density scenarios. Experiments show that the proposed method can reduce flight plan conflicts by 96.2% on the basis of effectively controlling the risk cost and time cost， completely conflict free flight plan can be obtained for UAV operation scenarios with less than 40 UAVs， and the minimum flight conflict resolution rate for UAVs with less than 100 UAVs can be controlled above 95%. The proposed method is scientific and effective， and can provide a theoretical basis and methodological guidance for safe and efficient management of low-altitude UAV flight activities in complex cities.