基于队列调度算法的民机驾驶舱人机功能分配模型

任波西; 孙有朝; 刘威成; 曾喆; 曾一宁

doi:10.3963/j.jssn.1674-4861.2025.01.010

基于队列调度算法的民机驾驶舱人机功能分配模型

doi: 10.3963/j.jssn.1674-4861.2025.01.010

南京航空航天大学民航学院南京 211106

基金项目:

国家自然科学基金项目 52172387

国家自然科学基金民航联合基金项目 U2033202

国家自然科学基金民航联合基金项目 U1333119

中央高校基本科研业务费 ILA22032-1A

航空科学基金项目 2022Z071052001

南京航空航天大学研究生科研与实践创新计划 xcxjh20230728

南京航空航天大学研究生科研与实践创新计划 xcxjh20230729

详细信息

作者简介:
任波西（2000—），硕士研究生. 研究方向：交通运输. E-mail: 13688516902@nuaa.edu.cn

通讯作者:
孙有朝（1964—），博士，教授. 研究方向：飞机虚拟设计技术、航空器可靠性工程等. E-mail: sunyc@nuaa.edu.cn

中图分类号: V19
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出版历程
- 收稿日期: 2024-07-04
- 网络出版日期: 2025-06-27

A Human Machine Function Allocation Model Based on Queue Scheduling Algorithm in the Cockpit of a Civil Aircraf

College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

摘要

摘要: 针对飞机驾驶舱人机系统（aircraft cockpit human-machine system，ACHMS）高复杂性导致飞行员信息流负载越来越高的现实问题，研究了基于队列调度算法的驾驶舱人机功能分配方法。基于熵值法量化了飞行任务流程操作复杂度与人机交互过程飞行员资源需求复杂度，提出了1种融合时空动态影响因子的飞行员信息流负载强度量化方法，并将信息流负载强度计算结果作为信息交互网络的有向边权重和信息流调度的依据，旨在通过网络的形式直观地描述飞行员与驾驶舱人机接口间的信息流通过程与信息流耦合作用关系。基于ACHMS和计算机操作系统的映射关系，扩展了加权轮询调度（weighted round robin，WRR）算法的串行调度机制，建立了基于队列权重的认知资源差异化分配与信息流入队调度机制，提出了基于改进WRR算法的驾驶舱人机功能分配策略。以波音737起飞任务作为分析案例，对起飞任务全过程进行信息流提取，建立了起飞过程人机耦合信息交互网络，利用改进WRR算法调度信息流并触发人机功能分配，最后对人机功能分配前后网络性能进行评估，结果显示：人机功能分配后，飞行员节点接近中心性提高了4.82倍、介数中心性提高了0.47%，网络鲁棒性提高了4.24倍，飞行员节点信息流负载强度最大降幅为86.8%，信息流耦合度最大降幅为93.5%，表明该模型能够对ACHMS功能进行有效分配，并辅助降低关键时刻飞行员信息流负载，提高飞行安全性。
- 人机交互 /
- 人机系统 /
- 人机功能分配 /
- 队列调度算法 /
- 信息流负载
Abstract: The high complexity of the aircraft cockpit human-machine system (ACHMS) has resulted in increasingly heavy information flow loads for pilots. To address this challenge, a human-machine function allocation model is proposed based on queue scheduling algorithms. The operational complexity of flight procedures and pilot resource requirements during human-machine interactions are quantitatively evaluated using entropy analysis. A spatiotemporal dynamic factor-integrated metric is proposed to measure pilot information flow load intensity, serving dual purposes as directed edge weights in information interaction networks and scheduling criteria. These networks are subsequently constructed to visually represent information transmission processes and coupling relationships between pilots and cockpit interfaces. Building on the mapping relationship between ACHMS and computer operating systems, the serial scheduling mechanism of the weighted round robin (WRR) algorithm is extended. A queue-weight-based cognitive resource differential allocation and information flow queuing scheduling mechanism is established, while a human-machine function allocation strategy for cockpits is proposed based on the improved WRR algorithm. The Boeing 737 takeoff procedure serves as a validation case, with information flows systematically extracted throughout operational phases. A human-machine coupled information interaction network is constructed for takeoff procedures, with the enhanced WRR algorithm deployed for dynamic scheduling and function allocation triggering. Post-allocation analysis reveals significant improvements: pilot node closeness centrality improves by 4.82 times, betweenness centrality rises by 0.47%, and network robustness enhances 4.24 times. Maximum reductions of 86.8% in pilot load intensity and 93.5% in information coupling degree are achieved. The case verified the effectiveness of the proposed human-machine function allocation model in reducing the pilot information flow load at critical moments, thereby improve flight safety.
- human-machine interaction /
- human-machine system /
- human-machine function allocation /
- queue scheduling algorithm /
- information flow load

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图 1 ACHMS与计算机操作系统调度流程对比

Figure 1. Comparison of ACHMS and computer operating system scheduling processes

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图 2 WRR算法工作流程

Figure 2. WRR algorithm workflow

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图 3 改进WRR算法工作流程

Figure 3. Improved WRR algorithm workflow

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图 4 起飞任务飞行员信息流时序图

Figure 4. Timing diagram of pilot information flow for take-off missions

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图 5 “滑行程序”动作控制图

Figure 5. "Taxi process" motion control diagram

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图 6 “滑行程序”资源需求结构图

Figure 6. "Taxi process" structural diagram of resource requirements

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图 7 起飞任务人机耦合信息交互网络

Figure 7. Human-machine coupled information interaction network for take-off missions

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图 8 人机功能分配前后飞行员节点信息流负载强度对比

Figure 8. Comparison of pilot node information flow load intensity before and after human machine function allocation

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图 9 人机功能分配前后信息流耦合度对比

Figure 9. Comparison of information flow coupling degree before and after human machine function allocation

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表 1 ACHMS自动化等级划分

Table 1. ACHMS level of automation classification

系统自动化等级	任务承担情况	飞行员任务比重/%
1	飞行员独立执行所有子功能	100
2	飞机系统提供基本的感知辅助，飞行员仍需独立决策和执行	90
3	飞机系统自动感知飞行状态，飞行员根据系统提供的信息做出决策并执行	80
4	飞机系统自动感知并提供决策建议，飞行员确认后执行操作	60
5	飞机系统自动感知和决策，飞行员负责执行	50
6	飞机系统独立感知和决策，自动执行操作，飞行员负责监控，并在必要时干预	30
7	飞机系统独立执行所有子功能	0

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表 2 操作逻辑复杂度节点分类情况

Table 2. Operational logic complexity node classification case

节点	节点输入数	节点输出数	节点数量占比
A	0	1	1/11
B、C、E、F、H、I、J	1	1	7/11
D	1	2	1/11
G	2	1	1/11
K	1	0	1/11

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表 3 操作步长复杂度节点分类情况

Table 3. Operational step complexity node classification case

节点	邻居节点	节点数量占比
A	B	1/11
B	A、C	1/11
C	B、D	1/11
D	F、C、E	1/11
E、F	D、G	2/11
G	F、H、E	1/11
H	I、G	1/11
I	J、H	1/11
J	I、K	1/11
K	J	1/11

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表 4 RDC节点分类情况

Table 4. RDC node classification case

节点	节点输入数	节点输出数	节点数量占比
A	10	11	1/2
B	10	9	1/2

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表 5 起飞任务子程序信息流负载强度权重因子计算结果

Table 5. Take-off task subroutine information flow load intensity weighting factor calculation results

起飞任务子程序	权重因子
驾驶舱准备程序	1.858 1
推出程序	1.029 3
开车程序	3.130 2
滑行程序	2.347 8
起飞程序	4.020 1

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表 6 “滑行程序”相关人机接口信息量

Table 6. Amount of information on the human-machine interface related to the "taxi process"

人机接口	信息量
外部灯开关	1 bit/次
停机刹车手柄	1 bit/次
油门杆	log₂⁶bit
襟翼手柄	1 bit/次
发动机启动和点火面板	1 bit/次
MCP面板	1 bit/次
TCAS面板	1 bit/次
气象雷达控制面板	1 bit/次
后缘襟翼指示仪表	1 bit

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表 7 “滑行程序”信息流负载强度

Table 7. "Taxi process" information flow load intensity

信息流	信息流负载强度
滑行灯打开	6.591 1
停留刹车松开	6.591 1
柔和增加推力	17.037 8
襟翼手柄放到5位	6.591 1
核实手柄位置、指示器指示一致	13.182 3
内侧着陆灯打开、固定着陆灯打开	6.591 1
启动电门连续位	6.591 1
频闪灯打开	6.591 1
自动油门预位	6.591 1
应答机打开	6.591 1
气象雷达打开	6.591 1

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表 8 过载时刻信息流调度结果

Table 8. Information flow scheduling results at the moment of overload

时刻	信息流	过载量
47	加油门至N1	4.695 0
47	监控V1、VR	16.934 1
50	核实高度与速度①	8.116 5
51	VR时柔和地以2.5~3 °/s的速率抬机头	4.695 0
51	核实高度与速度①	25.251 3
52	核实高度与速度②	8.116 5
53	VR时柔和地以2.5~3 °/s的速率抬机头	4.695 0
53	核实高度与速度②	25.251 3

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表 9 起飞任务ACHMS人机功能分配方案

Table 9. ACHMS human machine function allocation programme for take-off missions

时刻	信息流	自动化等级调整情况
47	加油门至N1	飞行员可继续自行执行，需要时飞机系统可进行协助执行，自动化等级由“1”调整到“2”
47	监控V1、VR	飞行员视觉资源需求过高，飞机系统需给予适当辅助提示，自动化等级由“1”调整到“4”
50	核实高度与速度①	飞行员脑力资源和视觉资源需求轻微过载，飞机系统可向飞行员提供一些优化后的执行方案，飞行员需选择其中1个方案，自动化等级由“1”调整到“3”
51	VR时柔和地以2.5~3 °/s的速率抬机头	飞行员可继续自行执行，需要时飞机系统可进行协助执行，自动化等级由“1”调整到“2”
51	核实高度与速度①	飞行员脑力资源和视觉资源需求重度过载，PFD的监控工作直接交由飞机系统执行，自动化等级由“1”调整到“5”
52	核实高度与速度②	飞行员脑力资源和视觉资源需求轻微过载，飞机系统可向飞行员提供一些优化后的执行方案，飞行员需选择其中1个方案，自动化等级由“1”调整到“3”
53	VR时柔和地以2.5~3 °/s的速率抬机头	飞行员可继续自行执行，需要时飞机系统可进行协助执行，自动化等级由“1”调整到“2”
53	核实高度与速度②	飞行员脑力资源和视觉资源需求重度过载，PFD的监控工作直接交由飞机系统执行，自动化等级由“1”调整到“5”

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表 10 人机功能分配前后网络性能对比

Table 10. Comparison of network performance before and after human machine function allocation

性能指标	人机功能分配前	人机功能分配后
飞行员节点接近中心性	0.000 924 6	0.004 461
飞行员节点介数中心性	0.279 2	0.280 5
网络密度	0.020 46	0.020 46
网络鲁棒性	0.002 560	0.0108 5

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