数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (2): 15-24.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.02.002

doi: 10.11871/jfdc.issn.2096-742X.2026.02.002

• 专刊：冰冻圈大数据挖掘分析关键技术及应用 • 上一篇    下一篇

基于时空元胞的冰冻圈多源异构数据融合与智能管理

王慈枫1(),刘力云1,艾鸣浩2,张鑫鹏1,赵珏1,路长发1,*()   

  1. 1 中国科学院计算机网络信息中心， 北京 100083
    2 中国科学院西北生态环境资源研究院， 甘肃 兰州 730000
  • 收稿日期:2025-05-13 出版日期:2026-04-20 发布日期:2026-04-23
  • 通讯作者: *路长发（E-mail: luchangfa@cnic.cn）
  • 作者简介:王慈枫，中国科学院计算机网络信息中心，大数据技术与应用发展部科学数据软件体系实验室，博士，工程师。主要研究方向为计算机应用技术，先后参与了国家重点研发计划“空间科学大数据智能管理与分析挖掘关键技术及应用”“空间天气典型事件知识挖掘与智能建模研究”等项目的研发和实施。
    本文负责论文初稿的撰写及修订。
    WANG Cifeng, Ph.D., is an engineer of the Scientific Data Software System Lab, Department of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences. Her main research direction is computer application technologies. She has participated in National Key R&D Program of China, “Key Technologies and Applications of Intelligent Management, Analysis and Mining of Space Science Big Data”, “Study on Knowledge Mining and Intelligent Modeling of Space Weather Events”, and other projects' technical research and development.
    In this paper, she is responsible for drafting and revising the manuscript.
    E-mail: cfwang@cnic.cn|路长发，中国科学院计算机网络信息中心，大数据技术与应用发展部科学数据软件体系实验室主任，硕士，高级工程师。主要研究方向为大数据管理技术，先后主持或参与了“大数据中台”“中国科协大数据知识管理与服务平台”“烟草科技知识图谱”“国家空间科学中心领域大数据知识图谱服务平台”“智慧中科院知识图谱与专家画像系统”“中国科学院学部专家人才推荐系统”等项目的技术研发和工程实施。
    本文负责学术指导、关键技术创新及论文终稿审定。
    LU Changfa, holding a master’s degree, is a senior engineer and director of the Scientific Data Software System Lab, Department of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences. His main research direction is big data management technology. He has presided over or participated in the “Big Data Platform”, “CAST Big Data Knowledge Management and Service Platform”, “Tobacco Science and Technology Knowledge Graph”, “National Space Science Center Big Data Knowledge Graph Service Platform”, “Smart CAS Knowledge Graph and Expert Portrait System”, “Expert Talent Recommendation System of CAS” and other projects' technical research and development and engineering implementation.
    In this paper, he is responsible for academic supervision, key technological innovations, and final approval of the paper.
    E-mail: luchangfa@cnic.cn
  • 基金资助:
    国家重点研发计划“冰冻圈大数据挖掘分析关键技术及应用”(2022YFF0711700)

Spatio-Temporal Cell-Based Data Fusion and Intelligent Management for Multi-Source Heterogeneous Data in the Cryosphere

WANG Cifeng1(),LIU Liyun1,AI Minghao2,ZHANG Xinpeng1,ZHAO Jue1,LU Changfa1,*()   

  1. 1 Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
    2 Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
  • Received:2025-05-13 Online:2026-04-20 Published:2026-04-23

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摘要：

【背景】 冰冻圈研究面临多源异构数据时空基准不统一、语义关联薄弱等核心挑战，严重制约了数据价值的深度挖掘。【目的】 本文提出了一种基于时空元胞的多模态数据融合与智能管理框架，旨在通过标准化时空单元构建与语义增强技术，提升海量科学数据的可操作性与知识发现效率。【方法】 首先基于统一时空基准，按预设分辨率剖分观测区域为规则化的时空元胞，并通过气象、遥感等多模态数据的标准化处理确保数据在元胞内的时空一致性；其次设计元胞特征计算模型，结合统计算法与关联分析方法挖掘元胞内、跨元胞的数据变化规律及交互关系；进一步集成大语言模型与语义嵌入技术，构建“精确条件+语义推理”双模式检索机制，支持结构化查询与自然语言驱动的深层关联挖掘。【结论】 应用表明，该框架有效解决了多源数据融合与语义关联薄弱的问题，显著提升了冰冻圈科学数据的智能化管理水平。研究人员可通过交互界面动态探索数据演化规律，辅助科学发现与决策制定，为气候系统研究与可持续发展提供高效工具支撑。

关键词: 时空元胞, 数据融合, 特征关联, 语义推理

Abstract:

[Background] Cryosphere research faces core challenges such as inconsistent spatiotemporal references and weak semantic correlations in multi-source heterogeneous data, which severely hinder the in-depth mining of data value. [Objective] This study proposes a spatiotemporal cell-based framework for multimodal data fusion and intelligent management, aiming to enhance the operability and knowledge discovery efficiency of massive scientific data through standardized spatiotemporal unit construction and semantic enhancement technologies. [Methods] First, based on unified spatiotemporal references, observation areas are divided into regularized spatiotemporal cells with preset resolutions, ensuring spatiotemporal consistency of meteorological, remote sensing, and other multimodal data through standardized processing. Second, a cell feature calculation model is designed to mine data variation patterns and interaction relationships within and across cells using statistical algorithms and association analysis. Furthermore, large language models and semantic embedding technologies are integrated to establish a “precise condition + semantic reasoning” dual-mode retrieval mechanism, supporting structured queries and natural language-driven deep correlation mining. [Conclusions] The framework effectively addresses multi-source data fusion and weak semantic correlation issues, significantly improving the intelligent management level of cryosphere scientific data. Researchers can dynamically explore data evolution patterns through interactive interfaces, supporting scientific discovery and decision-making, thereby providing efficient tool support for climate system research and sustainable development.

Key words: spatio-temporal cells, data fusion, feature correlation analysis, semantic reasoning