Knowledge-Guided Multi-Modal Modular Satellite Network for Space-Air-Ground Integrated Intelligent Satellite Computing

LI Qian; WANG Shangguang

doi:10.12263/DZXB.20260290

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Knowledge-Guided Multi-Modal Modular Satellite Network for Space-Air-Ground Integrated Intelligent Satellite Computing

PAPERS | 更新时间：2026-10-16

- Knowledge-Guided Multi-Modal Modular Satellite Network for Space-Air-Ground Integrated Intelligent Satellite Computing
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 2740-2762(2026)
- 作者机构：
  
  北京邮电大学网络与交换技术国家重点实验室，北京100876
- 作者简介：
- 基金信息：
  
  Science and Technology Innovation Program of Xiongan New Area(2024XAGG0025);National Natural Science Foundation of China(62402054)
- DOI：10.12263/DZXB.20260290
  CLC： TP391;
- Received：21 April 2026，
  
  Accepted：16 May 2026，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
李倩, 王尚广. 面向空天地一体化的多模态知识增强卫星智能计算方法[J]. 电子学报, 2026, 54(06): 2740-2762.

LI Qian, WANG Shangguang. Knowledge-Guided Multi-Modal Modular Satellite Network for Space-Air-Ground Integrated Intelligent Satellite Computing[J]. Acta Electronica Sinica, 2026, 54(06): 2740-2762.
李倩, 王尚广. 面向空天地一体化的多模态知识增强卫星智能计算方法[J]. 电子学报, 2026, 54(06): 2740-2762. DOI：10.12263/DZXB.20260290

LI Qian, WANG Shangguang. Knowledge-Guided Multi-Modal Modular Satellite Network for Space-Air-Ground Integrated Intelligent Satellite Computing[J]. Acta Electronica Sinica, 2026, 54(06): 2740-2762. DOI：10.12263/DZXB.20260290

摘要

随着空天地一体化体系的快速发展，卫星端智能计算逐渐成为支撑遥感感知、态势分析与应急决策的重要技术基础，但其同时面临星载算力与存储受限、多模态异构数据噪声复杂以及任务语义动态变化等挑战。现有多模态融合方法多强调视觉-文本对齐或大模型驱动推理，知识图谱方法强调结构化语义建模，但二者大多面向地面高算力环境设计，缺乏面向卫星边缘计算场景的统一部署机制。针对上述问题，本文提出一种面向空天地一体化的知识引导多模态模块化卫星网络（Knowledge-guided Multi-Modal Modular Satellite Network，KM³-SatNet）。与直接引入全局知识图谱或采用固定融合结构的既有方法不同，本文围绕“任务相关知识裁剪-知识约束鲁棒融合-预算感知动态推理-端云协同自适应部署”开展一体化设计：首先构建卫星任务知识图谱，

并通过语义匹配、关系感知扩展与个性化PageRank实现动态知识子图抽取；其次设计知识约束门控融合机制，在视觉、文本与知识之间建立可解释的动态交互约束；进一步提出模块化可裁剪推理结构与算力感知控制器，实现星载预算约束下的自适应模块选择；最后给出考虑链路波动与中断回退的端云协同优化策略。在多模态遥感任务数据集上的实验结果表明，与典型多模态融合模型相比，本文方法在目标识别任务上精度提高6.3%，在灾害场景识别任务上提高5.8%，同时相较于轻量级遥感多模态大语言模型（Remote Sensing Multimodal Large Language Model Lite，RS-MLLM-Lite）参数量减少50.0%，相较于Swin-

，参数量减少98.7%，推理时延降低67.7%。结果表明，KM³-SatNet能够在保持较高精度的同时兼顾模型规模、时延、能耗与鲁棒性，适用于卫星边缘环境下的多模态智能计算部署。

Abstract

With the rapid development of space-air-ground integrated systems

intelligent on-board satellite computing has become an important technical foundation for supporting remote sensing perception

situation analysis

and emergency decision making. However

it faces several challenges

including limited on-board computing power and storage

complex noise in multimodal heterogeneous data

and dynamic changes in task semantics. Existing multimodal fusion methods mainly emphasize vision-text alignment or large-model-driven reasoning

while knowledge graph methods focus on structured semantic modeling. Most of these methods are designed for ground-side high-performance computing environments and lack a unified deployment mechanism for satellite edge computing scenarios. To address these problems

this paper proposes a knowledge-guided multi-modal modular satellite network (KM³-SatNet) for space-air-ground integrated systems. Different from existing methods that directly introduce a global knowledge graph or adopt a fixed fusion structure

this paper develops an integrated design around “task-related knowledge pruning

knowledge-constrained robust fusion

budget-aware dynamic reasoning

and edge-cloud collaborative adaptive deployment”. First

a satellite task knowledge graph is constructed

and dynamic knowledge subgraph extraction is achieved through semantic matching

relation-aware expansion

and personalized PageRank. Second

a knowledge-constrained gated fusion mechanism is designed to establish interpretable dynamic interact

ion constraints among vision

text

and knowledge. Furthermore

a modular and prunable inference structure and a computing-power-aware controller are proposed to realize adaptive module selection under on-board budget constraints. Finally

an edge-cloud collaborative optimization strategy considering link fluctuation and interruption fallback is presented. Experimental results on multimodal remote sensing task datasets show that

compared with typical multimodal fusion models

the proposed method improves accuracy by 6.3% on the object recognition task and by 5.8% on the disaster scene recognition task. Meanwhile

compared with the lightweight remote sensing multimodal large language model lite (RS-MLLM-Lite)

the number of model parameters is reduced by 50.0%; compared with Swin-

the number of parameters is reduced by 98.7%

and inference latency is reduced by 67.7%. The results demonstrate that KM³-SatNet can maintain high accuracy while balancing model scale

latency

energy consumption

and robustness

making it suitable for multimodal intelligent computing deployment in satellite edge environments.

关键词

Keywords

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