地理空间数字孪生与时空智能

doi:10.11947/j.AGCS.2025.20240515

摘要/Abstract

摘要：

地理空间数字孪生是地理空间信息服务的基础支撑，也是社会智能化建设、规划与发展的重要辅助系统。地理空间数字孪生系统与其他工业数字孪生系统相比，在精确性、系统性和可靠性方面有更特殊的要求。本文提出了地理空间数字孪生系统建设的基本准则，涉及地理实体感知、描述、映射、统计、预测与动态推演全过程。特别强调，地理空间数字孪生实体感知必须精确、时空基准必须一致、属性描述必须正确、历史信息必须可信、映射关系必须严密、规律统计必须系统、变化预测必须可靠、辅助决策必须科学。粗略划分了地理空间实体感知、映射和动态推演所涉及的研究主题，论述了地理空间数字孪生与时空智能的关系及其地理空间智能研究的主要方向，梳理了地理空间数字智能建设需要解决的关键技术问题，特别指出，需要探讨数据驱动与模型驱动在大模型生成中的贡献率以及历史数据与现实数据在大模型生成中的贡献率优化分配问题。

关键词: 数字孪生, 地理空间信息系统, 时空智能, 地理空间实体映射, 动态推演

Abstract:

The digital twin system of geospatial information is an important support system for geospatial information service and an important foundation for the development of intelligent society. The digital twin system of geospatial information has more special requirements in terms of accuracy, systemization and reliability, compared to other industrial digital twin systems. This paper describes the basic rules of the establishment of the geospatial digital twin system from perception, description and mapping to statistics, prediction and deduction. It is emphasized that the perception of geographic entities should be accurate, the space-time reference should be consistent, the attribute description should be correct, the historical information should be dependable, the mapping relationship should be complete, the statistic trend should be systematic, the variation prediction should be rigorous, and the auxiliary decision making should be scientific. The related research topics of the geospatial digital twins are generally classified. The problems to be paid attention are listed. Finally, the relationship between the geospatial digital twin and spatio-temporal intelligence is discussed. The basic process and key technologies in the construction of geospatial digital intelligence system are pointed out.

Key words: digital twin, geospatial information system, spatio-temporal intelligence, geospatial entity mapping, dynamic deduction

中图分类号:

杨元喜. 地理空间数字孪生与时空智能[J]. 测绘学报, 2025, 54(2): 213-220.

Yuanxi YANG. Digital twin and spatio-temporal intelligence of geospatial information system[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(2): 213-220.

图/表 3

参考文献 31

[1]	李双成, 张文彬, 陈立英, 等. 孪生空间及其应用：兼论地理研究空间的重构[J]. 地理学报, 2022, 77(3): 507-517.
	LI Shuangcheng, ZHANG Wenbin, CHEN Liying, et al. Digital twin space and its applications: concurrent discussion on the space reconstruction of geographical research[J]. Acta Geographica Sinica, 2022, 77(3): 507-517.
[2]	GRIEVES M. Digital twin: manufacturing excellence through virtual factory replication[EB/OL]. [2022-11-30]. https://www.researchgate.net/publication/275211047.
[3]	林珲, 徐丙立, 肖昕, 等. 数字孪生人文地理环境[J]. 遥感学报, 2024, 28(5): 1131-1144.
	LIN Hui, XU Bingli, XIAO Xin, et al. Digital twin human geographic environment[J]. National Remote Sensing Bulletin, 2024, 28(5): 1131-1144.
[4]	陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18.
	TAO Fei, LIU Weiran, LIU Jianhua, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18.
[5]	VANDERHORN E, MAHADEVAN S. Digital twin: generalization, characterization and implementation[J]. Decision Support Systems, 2021, 145: 113524.
[6]	TAO Fei, ZHANG He, LIU Ang, et al. Digital twin in industry: state-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2405-2415.
[7]	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[8]	陈军, 艾廷华, 闫利, 等. 智能化测绘的混合计算范式与方法研究[J]. 测绘学报, 2024, 53(6): 985-998. DOI:. doi: 10.11947/j.AGCS.2024.20240131
	CHEN Jun, AI Tinghua, YAN Li, et al. Hybrid computational paradigm and methods for intelligentized surveying and mapping[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 985-998. DOI:. doi: 10.11947/j.AGCS.2024.20240131
[9]	缪青海, 王兴霞, 杨静, 等. 基础智能到通用智能：基于大模型的GenAI和AGI之现状与展望[J]. 自动化学报, 2024, 50(4): 674-687.
	MIAO Qinghai, WANG Xingxia, YANG Jing, et al. From foundation intelligence to general intelligence: the state-of-art and perspectives of GenAI and AGI based on foundation models[J]. ACTA Automatic Sinica, 2024, 50(4): 674-687.
[10]	龚健雅, 季顺平. 摄影测量与深度学习[J]. 测绘学报, 2018, 47(6): 693-704. DOI:. doi: 10.11947/j.AGCS.2018.20170640
	GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 693-704. DOI:. doi: 10.11947/j.AGCS.2018.20170640
[11]	龚健雅, 张觅, 胡翔云, 等. 智能遥感深度学习框架与模型设计[J]. 测绘学报, 2022, 51(4): 475-487. DOI:. doi: 10.11947/j.AGCS.2022.20220027
	GONG Jianya, ZHANG Mi, HU Xiangyun, et al. The design of deep learning framework and model for intelligent remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(4): 475-487. DOI:. doi: 10.11947/j.AGCS.2022.20220027
[12]	张兵. 遥感大数据时代与智能信息提取[J]. 武汉大学学报(信息科学版), 2018, 43(12): 1861-1871.
	ZHANG Bing. Remotely sensed big data era and intelligent information extraction[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1861-1871.
[13]	张永军, 李彦胜, 党博, 等. 多模态遥感基础大模型：研究现状与未来展望[J]. 测绘学报, 2024, 53(10): 1942-1954. DOI:. doi: 10.11947/j.AGCS.2024.20240019
	ZHANG Yongjun, LI Yansheng, DANG Bo, et al. Multi-modal remote sensing large foundation models: current research status and future prospect[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(10): 1942-1954. DOI:. doi: 10.11947/j.AGCS.2024.20240019
[14]	王密, 程昫, 潘俊, 等. 大模型赋能智能摄影测量：现状、挑战与前景[J]. 测绘学报, 2024, 53(10): 1955-1966. DOI:. doi: 10.11947/j.AGCS.2024.20240068
	WANG Mi, CHENG Xu, PAN Jun, et al. Large models enabling intelligent photogrammetry: status, challenges and prospects[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(10): 1955-1966. DOI:. doi: 10.11947/j.AGCS.2024.20240068
[15]	燕琴, 顾海燕, 杨懿, 等. 智能遥感大模型研究进展与发展方向[J]. 测绘学报, 2024, 53(10): 1967-1980. DOI:. doi: 10.11947/j.AGCS.2024.20240053
	YAN Qin, GU Haiyan, YANG Yi, et al. Research progress and trend of intelligent remote sensing large model[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(10): 1967-1980. DOI:. doi: 10.11947/j.AGCS.2024.20240053
[16]	朱庆, 张利国, 丁雨淋, 等. 从实景三维建模到数字孪生建模[J]. 测绘学报, 2022, 51(6): 1040-1049. DOI:. doi: 10.11947/j.AGCS.2022.20210640
	ZHU Qing, ZHANG Liguo, DING Yulin, et al. From real 3D modeling to digital twin modeling[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 1040-1049. DOI:. doi: 10.11947/j.AGCS.2022.20210640
[17]	陈荣, 曹永锋, 孙洪. 基于主动学习和半监督学习的多类图像分类[J]. 自动化学报, 2011, 37(8): 954-962.
	CHEN Rong, CAO Yongfeng, SUN Hong. Multi-class image classification with active learning and semi-supervised learning[J]. Acta Automatica Sinica, 2011, 37(8): 954-962.
[18]	ZHANG Hao, XU Jinjian, CUI Hongwei, et al. When geoscience meets foundation models: toward a general geoscience artificial intelligence system[J/OL]. IEEE Geoscience and Remote Sensing Magazine.[2024-10-11]. https://doi.org/10.1109/MGRS.2024.3496478.
[19]	张永生, 张振超, 童晓冲, 等. 地理空间智能研究进展和面临的若干挑战[J]. 测绘学报, 2021, 50(9): 1137-1146. DOI:. doi: 11947/j.AGCS.2021.20200420
	ZHANG Yongsheng, ZHANG Zhenchao, TONG Xiaochong, et al. Progress and challenges of geospatial artificial intelligence[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9): 1137-1146. DOI:. doi: 11947/j.AGCS.2021.20200420
[20]	杨元喜, 任夏, 王建荣. 集成型与智能型测绘卫星工程发展及其关键技术[J]. 测绘学报, 2022, 51(6): 854-861. DOI:. doi: 10.11947/j.JGGS.2023.0401
	YANG Yuanxi, REN Xia, WANG Jianrong. Development of integrated and intelligent surveying and mapping satellite project with corresponding key technology[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 854-861. DOI:. doi: 10.11947/j.JGGS.2023.0401
[21]	程涛, 张洋, James Haworth. 基于网络和图的时空智能：概念、方法和应用[J]. 测绘学报, 2022, 51(7): 1629-1639. DOI:. doi: 10.11947/j.AGCS.2022.20220236
	CHENG Tao, ZHANG Yang, HAWORTH J. Network and graph-based SpaceTimeAI: conception, method and applications[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1629-1639. DOI:. doi: 10.11947/j.AGCS.2022.20220236
[22]	吴晨昊, 向隆刚, 张叶廷, 等. 基于地理空间感知型表征学习的轨迹相似度计算[J]. 测绘学报, 2023, 52(4): 670-678. DOI:. doi: 10.11947/j.AGCS.2023.20220026
	WU Chenhao, XIANG Longgang, ZHANG Yeting, et al. Geography-aware representation learning for trajectory similarity computation[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(4): 670-678. DOI:. doi: 10.11947/j.AGCS.2023.20220026
[23]	李彦胜, 武康, 欧阳松, 等. 地学知识图谱引导的遥感影像语义分割[J]. 遥感学报, 2024, 28(2): 455-469.
	LI Yansheng, WU Kang, OUYANG Song, et al. Geographic knowledge graph-guided remote sensing image semantic segmentation[J]. National Remote Sensing Bulletin, 2024, 28(2): 455-469.
[24]	YANG Yuanxi. Robust Bayesian estimation[J]. Bulletin Géodésique, 1991, 65(3): 145-150.
[25]	YANG Yuanxi, GAO Weiguang. An optimal adaptive Kalman filter[J]. Journal of Geodesy, 2006, 80(4): 177-183.
[26]	杨元喜, 王建荣. 泛在感知与航天测绘[J]. 测绘学报, 2023, 52(1): 1-7. DOI:. doi: 10.11947/j.AGCS.2023.20220405
	YANG Yuanxi, WANG Jianrong. Ubiquitous perception and space mapping[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(1): 1-7. DOI:. doi: 10.11947/j.AGCS.2023.20220405
[27]	董裕民, 张静, 谢昌佐, 等. 云边端架构下边缘智能计算关键问题综述：计算优化与计算卸载[J]. 电子与信息学报, 2024, 46(3): 765-776.
	DONG Yumin, ZHANG Jing, XIE Changzuo, et al. A survey of key issues in edge intelligent computing under cloud-edge-terminal architecture: computing optimization and computing offloading[J]. Journal of Electronics & Information Technology, 2024, 46(3): 765-776.
[28]	DeepSeek-A1, GUO D, YANG D, et al. DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning[EB/OL]. [2025-02-10]. https://doi.org/10.48550/arXiv.2501.12948.
[29]	杨元喜, 杨诚, 任夏. PNT智能服务[J]. 测绘学报, 2021, 50(8): 1006-1012. DOI:. doi: 10.11947/j.AGCS.2021.20210051
	YANG Yuanxi, YANG Cheng, REN Xia. PNT intelligent services[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 1006-1012. DOI:. doi: 10.11947/j.AGCS.2021.20210051
[30]	朱彬, 杨诚, 刘岩. GNSS多径信号3种非监督学习法分析与比较[J]. 测绘学报, 2021, 50(12): 1762-1771. DOI:. doi: 10.11947/j.AGCS.2021.20210233
	ZHU Bin, YANG Cheng, LIU Yan. Analysis and comparison of three unsupervised learning clustering methods for GNSS multipath signals[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(12): 1762-1771. DOI:. doi: 10.11947/j.AGCS.2021.20210233
[31]	张雨童, 邓欣, 徐迈. 动态场景下深度自监督多曝光图像融合方法[J]. 电子学报, 2024, 52(1): 264-273.
	ZHANG Yutong, DENG Xin, XU Mai. Deep self-supervised multi-exposure image fusion for dynamic scenes[J]. Acta Electronica Sinica, 2024, 52(1): 264-273.

编辑推荐 0

Metrics

阅读次数

全文

295

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	37	0	0	258

来源	本网站	其他网站

次数	295	0
比例	100%	0%

摘要

215

最新录用	在线预览	正式出版

0	0	215

	来源	本网站

	次数	215
	比例	100%