面向应急救灾的意图驱动无人机集群组网

doi:10.7527/S1000-6893.2026.33471

Abstract

Abstract:

Facing the severe challenge of major natural disasters such as earthquakes and floods， which damage ground communication infrastructure and turn disaster-stricken areas into “information islands”， this paper proposes an intent-driven emergency networking architecture for Unmanned Aerial Vehicle （UAV） swarms. By integrating natural language processing and knowledge graph technologies， this architecture achieves intelligent representation and parsing of high-level rescue instructions at the intent layer， translating them into executable commands that are issued to the UAV swarm at the data layer. A multi-objective optimization problem is formulated to jointly optimize the dynamic deployment of the UAV swarm， aiming to minimize the energy consumption and transmission delay of both UAVs and ground users while maximizing network coverage. An Intent-Driven Artificial Neural Network （ID-ANN） self-optimization algorithm is designed， which employs K-means for region partitioning and introduces the Fermat point theory to optimize the matching between UAVs and users. Simulation results demonstrate that the proposed algorithm achieves an average improvement of 20.2% in network signal coverage， a 33.33% reduction in average reconfiguration delay， and a 12.72% increase in throughput， significantly outperforming baseline algorithms. This study provides theoretical support and a feasible technical pathway for the construction of space-based emergency communication systems， offering valuable insights for enhancing communication support capabilities in disaster emergency rescue operations.

Key words: information islands, intent-driven, UAV swarms, emergency networking, natural language, network signal coverage, ID-ANN

CLC Number:

TN929.5
V279

Liangqi GAN, Chao DONG, Wei WANG, Chang TIAN. Intent-driven UAV swarm network for emergency disaster relief[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(18): 533471.

Figures/Tables 28

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Symbol table

符号	含义	单位
$L$	区域边长	m
$H$	无人机飞行高度	m
$U t$	$t$ 时刻用户集合	-
$I t$	$t$ 时刻无人机集合	-
$θ u, m$	地面用户U到任务无人机 $j m s$ 时间的仰角	rad
$P U E$	用户发射功率	w
$f$	工作频率	2.4 GHz
$g$	重力加速度	m/s²
$M U A V$	无人机的质量	kg
$τ N L o S$	链路的平均附加损耗	-
$τ L o S$	链路的平均附加损耗	-
$V p t$	无人机旋翼的叶尖速度	m/s
$v z$	无人机垂直方向速度	m/s
$v x, y$	无人机水平方向速度	m/s
$f 0$	机身阻力比	-
$ρ 0$	空气密度	kg/m³
$S 0$	旋翼盘面积	m²
$K 0$	旋翼实度	-
$λ 2$	平均噪声功率谱密度	dBm/Hz
$α$	环境中的S曲线参数	-
$β$	环境中的S曲线参数	-
$B m, s$	任务无人机和头部无人机之间的带宽	MHz
$T u - m a x$	地面用户的最大时延	s
$D u$	地面用户的数据大小	Mb
$p p c p$	悬停状态下叶片功率	W
$p i p$	悬停状态下叶片轮廓诱导功率	W
$P u - m a x$	地面用户的最大功率	W
$P m - m a x$	无人机的最大功率	W
$P u - m i n$	地面用户的最小功率	W
$P m - m i n$	无人机的最小功率	W

Table 1

Fig.5

Fig.6

Table 2

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig. 13

Table 3

Table 4

Simulation parameters

实验参数	数值
区域大小	1 km×1 km
无人机数量	4/6/8/10/12/14/16/18
速率/Kbps	7 000
物理层	802.11b
MAC层	802.11
模拟时间/s	300
天线类型	全向天线
无人机拓扑	网格
f	2.4 GHz
g	9.8 m/s²
M _UAV	5.6 kg
τ _NLoS	30.5
τ _LoS	1
V _pt	100 m/s²
$v z$	7 m/s
$v x, y$	12 m/s
$f 0$	0.51
$ρ 0$	1.225 kg/m³
$S 0$	0.6 m²
$K 0$	$1 × 10 - 4$
$λ 2$	-189 dBm/Hz
$α$	9.8
$β$	0.32
$B m, s$	10 MHz
$T u - m a x$	0.8 s
$D u$	10 Mb
$p p c p$	110.6 W
$p i p$	136.6 W
$P u - m a x$	1.6 W
$P m - m a x$	5.6 W
$P u - m i n$	$1 × 10 - 2$ W
$P m - m i n$	$1 × 10 - 2$ W

Table 4

Table 5

Fig.14

Fig.15

Fig. 16

Fig.17

Fig.18

Fig.19

Fig.20

Fig.21

Fig.22

Fig.23

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源地址	目的地址	源端口号	目的端口号	业务类型	业务等级	时间
倒塌的建筑区A	避难所	10	20	话音业务	普通	2026年1月16日
笔架山南门	莲花山南门	30	40	数据业务	重要	2026年1月17日
洪湖公园东门	人民公园东门	50	60	视频业务	普通	2026年1月20日
…	…	…	…	…	…	…

组件	型号	速度/（T·s^-1）
CPU	AMD Ryzen 9 7950X	11.2
GPU	RTX 4090	139
内存	64 GB
存储	2TB NVMe SSD+冷存储

实验参数	数值
优化器	Adam
学习率	1×10^-4
Batch size	64
训练轮次	200epoch
早停策略	验证集损失函数连续10轮小于1×10^-6则停止
收敛判定	损失函数相对变化小于1×10^-6

Intent-driven UAV swarm network for emergency disaster relief

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