面向无人机支线物流的起降场容量模型与协同调度

江波; 李蝶; 郑远; 李诚龙; 张照轩

doi:10.13700/j.bh.1001-5965.2025.0422

面向无人机支线物流的起降场容量模型与协同调度

doi: 10.13700/j.bh.1001-5965.2025.0422

江波^{1, 2},
李蝶³,
郑远^4, ,,
李诚龙¹,
张照轩³

1.
中国民用航空飞行学院飞行技术学院，广汉 618307
2.
中国民用航空飞行学院国产民机飞行与运行支持四川省工程研究中心，广汉 618307
3.
中国民用航空飞行学院空中交通管理学院，广汉 618307
4.
中国民用航空飞行学院计算机与人工智能学院，广汉 618307

基金项目:

国家自然科学基金青年基金项目(52502410)；中国民用航空飞行学院本科生科研创新基金项目(X202510624277)

详细信息

通讯作者:
E-mail：ranchozy@cafuc.edu.cn

中图分类号: V355
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出版历程
- 收稿日期: 2025-07-01
- 录用日期: 2025-10-20
- 网络出版日期: 2025-12-31
- 整期出版日期: 2026-09-01

Capacity model and collaborative scheduling of droneports for drone regional logistics

1.
College of Flight Technology，Civil Aviation Flight University of China，Guanghan 618307，China
2.
Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support，Civil Aviation Flight University of China，Guanghan 618307，China
3.
College of Air Traffic Management，Civil Aviation Flight University of China，Guanghan 618307，China
4.
School of Computer Science and Artificial Intelligence，Civil Aviation Flight University of China，Guanghan 618307，China

Funds:

Youth Fund Project of National Natural Science Foundation of China (52502410); Training Program of Innovation for Undergraduates of Civil Aviation Flight University of China (X202510624277)

More Information

Corresponding author: E-mail：ranchozy@cafuc.edu.cn

摘要

摘要:
针对公共垂直起降场终端区空域资源紧张与运行效率不足的问题，考虑多场站协同调度机制缺失及支线物流区域的负载均衡性需求，以精准评估容量和优化区域协同调度能力为目标，提出一种串联耦合排队模型与静动态协同调度策略。构建差异化排队模型，刻画各环节阻塞效应，突破传统独立分析局限；在降落排队系统中引入非抢占式优先级策略，保障高优先级任务；设计“静态流量预分配+动态容流调配”策略，通过基于排队论的双路径决策实现多场站协同。仿真结果表明：所提串联耦合排队模型的整体容量评估偏差控制在6%以内，显著优于传统独立分析模型，且在等待层空域容量评估中，同样显著优于基于冲突阈值的模型；非抢占式优先级策略下，高优先级任务平均延误时间始终低于30 s，但随其在总任务中占比的增加，系统平均延误显著上升；静动态协同调度策略在突发流量下使区域负载均衡度提升至95.9%，无人机总延误时间平均减少39.49%。研究成果可为低空物流网络规划与实时调度提供理论支撑。
- 无人机 /
- 支线物流 /
- 垂直起降场 /
- 排队论 /
- 容量评估 /
- 协同调度
Abstract:
Droneport terminal airspace faces the critical issues of scarce airspace resources and insufficient operational efficiency. A series-coupled queuing model and a static-dynamic collaborative scheduling strategy are established to achieve accurate capacity assessment and effective regional collaborative scheduling, taking into account the lack of collaborative scheduling mechanisms for multiple droneports and the need for load balancing in regional logistics areas. First, a differentiated queuing model is constructed to characterize blockage effects in each operational link, breaking through the limitations of traditional independent analysis. Second, a non-preemptive priority strategy is introduced into the landing system to safeguard high-priority tasks. Finally, a "static flow pre-allocation + dynamic capacity-flow regulation" mechanism is designed to achieve multi-droneport collaboration through dual-path decision-making based on queuing theory. The static results show that the overall capacity assessment deviation of the proposed series-coupled queuing model is controlled within 6%, which is significantly superior to the traditional independent analysis model. Moreover, in the capacity assessment of the waiting layer airspace, it also significantly outperforms the conflict threshold-based model. Under the non-preemptive priority strategy, the average delay time of high-priority tasks consistently stays below 30 seconds. However, with the increase in their proportion of total tasks, the system’s average delay rises significantly. Under surge traffic conditions, the cooperative scheduling approach lowers overall drone delay by 39.49% on average and increases regional load balance to 95.9%. The research results can provide theoretical support for low-altitude logistics network planning and real-time dispatch.
- drone /
- regional logistics /
- droneport /
- queueing theory /
- capacity evaluation /
- collaborative scheduling

HTML全文

图 1 无人机起降场空域结构

Figure 1. Airspace structure of droneport

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图 2 排队过程

Figure 2. Queueing process

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图 3 容流调配流程

Figure 3. Flowchart for capacity-flow regulation

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图 4 不同延时下的系统容量变化曲线

Figure 4. System capacity variation curves under different delays

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图 5 3种不同策略下的容量变化曲线

Figure 5. Capacity variation curves under three different strategies

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图 6 2种策略下的无人机平均延误时间曲线

Figure 6. Curves of average waiting duration of drones under two strategies

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图 7 物流波次性突发需求下的多起降场协同调度

Figure 7. Multi-droneport coordinated scheduling under logistic wave-like sudden demands

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图 8 2种策略下的无人机平均延误时间

Figure 8. Average drone delay time under two strategies

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表 1 本文在不同延时条件下的各排队系统容量

Table 1. Capacity of each queuing system under different delays in this research

可接受延误时间/s	容量/（架次·h⁻¹）
可接受延误时间/s	降落	地面	起飞
30	60.0	68.8	61.1
60	77.5	84.6	86.0
90	85.6	95.4	105.0
120	89.8	115.2	115.0
150	92.6	129.6	119.4
180	94.7	155.4	120.0

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表 2 对照组在不同延时条件下的各排队系统容量

Table 2. Capacity of each queuing system under different delays in control group

可接受延误时间/s	容量/（架次·h⁻¹）
可接受延误时间/s	降落	地面	起飞
30	60.0	64.6	61.1
60	79.8	73.7	86.0
90	90.0	80.8	105.0
120	96.0	83.6	115.0
150	100.2	85.5	119.4
180	102.8	87.3	120.0

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表 3 起降场终端区静态流量预分配

Table 3. Droneport terminal area static flow pre-allocation 架次/h

起降场	高优先级任务总量	低优先级任务总量	预分配任务总量	实际容量
起降场a	13.0	30.0	43.0	85.6
起降场b	17.0	39.0	56.0	110.0
起降场c	14.0	32.0	46.0	92.0
起降场d	10.0	25.0	35.0	70.0

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