面向低空智联网的多维信息统一表征技术综述

董超; 崔灿; 贾子晔; 朱奕安; 张磊; 吴启晖

doi:10.11999/JEIT240835

面向低空智联网的多维信息统一表征技术综述

doi: 10.11999/JEIT240835

南京航空航天大学电子信息工程学院电磁频谱空间认知动态系统工信部重点实验室南京 211106

基金项目: 国家重点研发计划(2022YFB3104502)，国家自然科学基金(62301251)，航空科学基金(2023Z071052007)

详细信息

作者简介:
董超：男，教授，博士生导师，研究方向为电磁频谱空间认知管控、无人机通信与组网、低空智联网、空天地一体智联网、边缘网络智能、无人机协同智能应用、大规模无线网络跨域仿真等

崔灿：女，硕士生，研究方向为低空智联网等

贾子晔：女，副教授，硕士生导师，研究方向为空天地一体化网络、低空智联网、卫星网络等

朱奕安：男，硕士生，研究方向为低空智联网

张磊：男，教授，硕士生导师，研究方向为嵌入式系统与边缘计算、人工智能与无线自组网

吴启晖：男，教授，博士生导师，研究方向为认知科学与应用、认知信息论、天地一体化智能信息网络、电磁空间频谱认知智能管控、无人机认知集群

通讯作者:
贾子晔　jzy@nuaa.edu.cn

中图分类号: TN92
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出版历程
- 收稿日期: 2024-10-08
- 修回日期: 2025-03-20
- 网络出版日期: 2025-04-01

Survey of Unified Representation Technology of Multi-dimensional Information for Low Altitude Intelligent Network

Key Laboratory of Electromagnetic Spectrum Spatial Cognitive Dynamic System, Ministry of Industry and Information Technology, College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Funds: The National Key R&D Program of China (2022YFB3104502), The National Natural Science Foundation of China (62301251), The Aeronautical Science Foundation of China (2023Z071052007)

摘要

摘要: 作为新质生产力的低空智联网(LAIN)，通过构建多种应用场景下的3维网络体系，可协助实现泛在覆盖和万物互联的美好愿景。然而，随着LAIN的快速发展，在数据采集和利用过程中，分布式飞行器和地面设备在运营过程中所产生的数据来源广泛、格式各异，但由于尚未形成对数据的统一表征标准，极大地限制了LAIN中信息共享和有效利用。因此，该文首先总结了当前国内外相关研究现状，分析了LAIN下潜在的异构数据类型，指明其主要特征和应用场景；然后，设计了LAIN数据集成与融合的示范平台；其次，剖析了实现LAIN下多维异构信息统一表征所面临的挑战；进而，基于数据融合技术、时空栅格化技术、多模态协同推理以及知识图谱，提出潜在的融合与集成表征方法，构建统一的知识表征模型框架，以期实现不同信息源数据的语义对齐和集成；最后，对所述内容进行总结，并展望了未来的研究方向，旨在为LAIN的进一步发展提供理论基础和技术支持，推动LAIN信息资源的高效利用和智能化发展。
- 低空智联网 /
- 信息融合 /
- 统一表征 /
- 多模态技术 /
- 知识图谱
Abstract: Significance The Low Altitude Intelligent Network (LAIN) has emerged as a critical productive force in recent years, particularly with the growing strategic role of the low-altitude economy in national development plans. As an integral part of smart city infrastructure and advanced air mobility systems, LAIN contributes both to economic growth and to airspace security. By integrating unmanned aerial vehicles, fifth-generation communication technologies, and artificial intelligence, LAIN enables real-time monitoring and provides services for urban traffic, agriculture, and disaster management. This integration optimizes resource allocation and enhances public safety. However, the rapid development of LAIN results in a vast array of distributed aircraft and ground equipment that generate large volumes of heterogeneous data in various formats. The absence of a unified representation standard significantly hinders the efficient utilization of data within the LAIN ecosystem, presenting substantial challenges for its widespread application in complex real-world scenarios. Therefore, the development of a unified data representation model for multi-dimensional and heterogeneous information within LAIN is essential to eliminate data heterogeneity, enhance data utilization efficiency, and promote the deep integration of the low-altitude economy with the digital economy. Process Existing research has explored innovative methods and technologies for information representation and addressing potential challenges in the LAIN. However, current solutions remain domain-specific and lack adaptability to the dynamic environment of LAIN. The absence of targeted research and standards makes it difficult to establish a unified representation for multi-source data. To bridge this gap, a heterogeneous information unified representation model is proposed for LAIN. This paper aims to address the challenges posed by complex data and information in the LAIN environment, particularly within the context of the sixth generation of communication technologies, and to provide new approaches for data management and application in LAIN. First, the heterogeneous data types within LAIN are categorized, highlighting their key characteristics and application scenarios. A platform for LAIN data integration and fusion is then developed, incorporating multiple technologies to facilitate efficient data collection, transmission, processing, and visual display. Additionally, the challenges of achieving a unified representation of multi-dimensional and heterogeneous information within LAIN are analyzed. Finally, promising methods for data fusion and representation are discussed, including data fusion, spatiotemporal gridding data technology, multi-mode technology, and knowledge graphs. These methods aim to establish a unified knowledge representation model and achieve semantic alignment, enabling the integration of data from diverse sources. Specifically, multi-source data are preprocessed to enhance understandability and availability through multi-level fusion, integrating multi-dimensional information from various sensors and data sources within a unified framework. Spatiotemporal gridding standardizes data formats and captures spatiotemporal changes, thereby effectively processing and integrating multi-source, multi-dimensional spatial data. Furthermore, integrating multi-mode data through multi-mode technology is expected to improve decision-making accuracy, while the knowledge graph links multi-source data, constructing a knowledge network that standardizes and correlates information from various sources, formats, and semantics. Prospects With the advancement of multi-dimensional data unified representation technology, the LAIN is poised to integrate with edge computing, radio knowledge description languages, large language models, and other emerging technologies to enable intelligent analysis and autonomous decision-making for low-altitude systems. Specifically, data processing can be optimized through edge computing. By positioning edge devices closer to the terminal, edge computing facilitates preprocessing and preliminary analysis at the data source. This technology enhances response speed and efficiency, providing high-quality services for the rapid acquisition and unified representation of LAIN information. Data from various sensors and systems can be structured and represented in an organized manner, facilitating data exchange between different systems, enabling readable spectrum management policies, and reducing interference incidents. Additionally, large language models can assist in constructing and refining knowledge graphs, advancing the intelligent operation and management of low-altitude aircraft. These promising technologies are expected to support further fusion and unified representation of LAIN data, laying a foundation for future research in the LAIN field. Conclusions This paper systematically addresses the challenges of multi-dimensional data representation in the LAIN through a combination of theoretical innovation and technological integration. The main contributions of this paper include: (1) A summary of related works in the field, with an introduction to potential heterogeneous data types, their key characteristics, and relevant application scenarios. (2) The proposal of a low-altitude information fusion and monitoring system, with an analysis of the challenges in achieving unified data representation. (3) The introduction of key technologies such as data fusion, spatiotemporal gridding data technology, multi-mode technology, and knowledge graphs. Additionally, edge computing technology, radio knowledge description language, and large language model technology are integrated to enhance data fusion and unified representation in LAIN. The findings of this study provide both theoretical and technical support for the development of LAIN, fostering the efficient utilization and intelligent advancement of information resources.
- Low Altitude Intelligent Network (LAIN) /
- Data fusion /
- Unified representation /
- Multi-mode technology /
- Knowledge graph

HTML全文

图 1 低空信息融合与监控系统

下载: 全尺寸图片幻灯片

图 2 低空智联网异构数据统一表征技术路线

下载: 全尺寸图片幻灯片

图 3 低空智联网多维数据信息特征级融合

下载: 全尺寸图片幻灯片

图 4 低空智联网多模态数据融合表征技术

下载: 全尺寸图片幻灯片

图 5 低空飞行器知识图谱构建示意图

下载: 全尺寸图片幻灯片

表 1 低空智联网多维数据融合方法

融合方法	主要特点	优缺点	应用场景
数据级融合	整合原始数据，保留完整信息，存储、处理开销高	信息完整性高，同时对数据质量要求高	数据量大且多源数据具有高度互补性，需要直接整合原始数据的场景
特征级融合	整合数据主要特征，可提高数据表示和分析效果，实时性强	鲁棒性更强，能综合多源数据特征，但特征提取算法依赖度高，可能导致信息损失	智能交通、农业监测、公共安全等需要从多源数据中提取和综合多源数据特征的场景
决策级融合	不同数据源的数据或特征进行分析和决策后，整合独立决策结果，可有效提高决策准确性和可靠性，灵活性高，鲁棒性强，抗干扰能力强	灵活性高，鲁棒性强，但融合算法相对复杂	UAV导航、目标识别、灾害预警等需进行独立决策以提高决策准确性和可靠性的场景

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