Distributed government data sharing and exchange system based on geographic-aware routing and intelligent agents

WANG Yun; TA Na; GUO Yifeng; ZHOU Wuai; ZHANG Wanzhe; FENG Jianhua

doi:10.16511/j.cnki.qhdxxb.2026.26.034

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Journal of Tsinghua University(Science and Technology) ›› 2026, Vol. 66 ›› Issue (6) : 1249-1264. DOI: 10.16511/j.cnki.qhdxxb.2026.26.034

COMPUTER SCIENCE AND TECHNOLOGY

Distributed government data sharing and exchange system based on geographic-aware routing and intelligent agents

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Abstract

[Objective] Data sharing and exchange play a critical role in promoting the intelligent and digital transformation of government services. However, existing government data sharing and exchange systems typically adopt cascaded architectures, resulting in long supply-demand paths at the architectural level. In addition, limited capabilities in data quality inspection and rapid construction of underlying routing mechanisms lead to low real-time performance, difficulty in ensuring data quality, and inadequate support for scenarios involving large volumes of frequently used data. [Methods] To address these challenges, a systematic research approach is adopted. First, guided by the principles of distribution, high reliability, and flexible configuration, a distributed architecture for government data sharing and exchange is proposed. A distributed data exchange network composed of peer nodes is constructed, in which node relationships are equal, thereby shortening data forwarding paths and improving exchange efficiency. By decoupling the control layer from the transport layer, the control layer is dedicated to routing management and node status monitoring, while the transport layer focuses on efficient and reliable data transmission, clarifying the functional structure and operational mechanism of the architecture. Second, a data quality inspection algorithm based on large-model intelligent agents is introduced. Using a unified inspection strategy, the algorithm evaluates data quality across four dimensions—semantic consistency, format standardization, logical consistency, and data integrity—ensuring high-quality data provision. Third, a geographic-aware routing algorithm is proposed by integrating administrative geographic information into distributed Hash tables. A hybrid routing strategy is designed, combining cross-layer routing based on a multiway tree structure with intralayer routing based on a binary tree structure, thereby reducing routing hops during data addressing. Finally, a series of experimental validation processes is employed to verify the effectiveness of key algorithms and the overall architecture. [Results] Compared with the benchmark method, the proposed geographic-aware routing algorithm reduced the average hop count by 76.82%. The intelligent-agent-based data quality inspection algorithm achieved an average precision of 93.06%, an average recall of 93.50%, and an average F₁-score of 0.72. Based on the distributed government data sharing and exchange architecture, three typical business scenarios—real-time transactions, unstructured transactions, and batch transactions—were evaluated. Deployment in Heilongjiang Province enabled on-site performance testing under real-world conditions. The results showed that the average response time for real-time transaction scenarios was 722 ms, with a median of 594 ms. In unstructured transaction scenarios, large-file upload speeds reached 220.0-235.0 MB/s, while batch transaction scenarios achieved an average throughput of 1.5 MB/s and an average write speed of 1 961 records/s. Compared with the theoretical performance peak of traditional cascaded systems, the performance was improved by 50% and 91%, respectively. [Conclusions] The proposed distributed government data sharing and exchange system significantly enhances real-time performance and ensures data quality in government data sharing and exchange. It provides a new technical pathway for intelligent and digital government transformation and offers valuable insights into the large-scale application of “artificial intelligence + data elements” in the public sector.

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geographic-aware routing / government data / data sharing / distributed system / intelligent agents

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WANG Yun, TA Na, GUO Yifeng, ZHOU Wuai, ZHANG Wanzhe, FENG Jianhua. Distributed government data sharing and exchange system based on geographic-aware routing and intelligent agents[J]. Journal of Tsinghua University(Science and Technology). 2026, 66(6): 1249-1264 https://doi.org/10.16511/j.cnki.qhdxxb.2026.26.034

References

[1] 国家市场监督管理总局, 国家标准化管理委员会. 信息安全技术政务信息共享数据安全技术要求: GB/T 39477—2020[S]. 北京: 中国标准出版社, 2020. State Administration for Market Regulation, National Standardization Administration. Information security technology government information sharing data security technology requirements: GB/T 39477—2020[S]. Beijing: Standards Press of China, 2020. (in Chinese)
[2] 李军, 乔立民, 王加强, 等. 智慧政务框架下大数据共享的实现与应用研究[J]. 电子政务, 2019(2): 34-44. LI J, QIAO L M, WANG J Q, et al. Research on the implementation and application of big data sharing under the smart government framework [J]. E-Government, 2019(2): 34-44. (in Chinese)
[3] 程军. 政务信息资源共享交换平台研究[J]. 电子政务, 2009(2-3): 120-126. CHENG J. Research on the government information resource sharing and exchange platform [J]. E-Government, 2009(2-3): 120-126. (in Chinese)
[4] 魏房忠, 李萍, 朱春琴, 等. 省级政务数据共享交换平台体系和信息资源库建设实践[J]. 信息系统工程, 2021(7): 82-85. WEI F Z, LI P, ZHU C Q, et al. Practice in building a provincial government data sharing and exchange platform system and information resource database [J]. China CIO News, 2021(7): 82-85. (in Chinese)
[5] ZRELLI A, EZZEDINE T. Collect tree protocol for SHM system using wireless sensor networks [C]// 201713^th International Wireless Communications and Mobile Computing Conference (IWCMC). Valencia, Spain: IEEE, 2017: 1797-1801.
[6] ZHANG L M, NG G W, LEAU Y B. A real-time information exchange strategy for big data volume systems based on internet of things [C]// 20207^th International Conference on Dependable Systems and Their Applications (DSA). Xi'an, China: IEEE, 2020: 58-63.
[7] JESUS P, BAQUERO C, ALMEIDA P S. Flow updating: Fault-tolerant aggregation for dynamic networks [J]. Journal of Parallel and Distributed Computing, 2015, 78: 53-64.
[8] DENG X Z. Urgent need for a data sharing platform to promote ecological research in China [J]. Ecosystem Health and Sustainability, 2016, 2(9): 11879048.
[9] WU Z H, WANG Y Z, WANG L M. GAM: A scalable and efficient multi-chain data sharing scheme [J]. Information Processing & Management, 2025, 62(3): 104004.
[10] AMJAD M, TAYLOR G, LAI C S, et al. Scalability and reliability analysis of a novel cloud platform for TSO-DSO information and data exchange [C]// 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). Novi Sad, Serbia: IEEE, 2022: 1-5.
[11] 武群惠, 王叶华, 王浩. 跨层级网络、跨架构、跨平台的数据共享交换关键技术研究与系统建设[J]. 军民两用技术与产品, 2022(5): 30-33. WU Q H, WANG Y H, WANG H. Research on key technologies and system construction for cross hierarchical network, cross architecture, and cross platform data sharing and exchange [J]. Dual Use Technologies & Products, 2022(5): 30-33. (in Chinese)
[12] 肖炯恩, 吴应良. 大数据背景下的政府数据治理: 共享机制、管理机制研究[J]. 科技管理研究, 2018, 38(17): 188-194. XIAO J E, WU Y L. Data governance in the context of big data: Sharing mechanism and management mechanism research [J]. Science and Technology Management Research, 2018, 38(17): 188-194. (in Chinese)
[13] 熊瑰. 浅谈政府数据共享交换平台建设[J]. 信息通信, 2018(2): 254-255. XIONG G. On the construction of government data sharing and exchange platform [J]. Information & Communications, 2018(2): 254-255. (in Chinese)
[14] 张翠梅, 方宜. 区块链架构下政府数据开放共享治理研究[J]. 南通大学学报(社会科学版), 2021, 37(6): 60-70. ZHANG C M, FANG Y. On government data open sharing and its management from the architecture of block chain [J]. Journal of Nantong University (Social Sciences Edition), 2021, 37(6): 60-70. (in Chinese)
[15] 杨文霞, 刘文云, 孙志腾, 等. 基于区块链与ROMA的政府数据共享模型构建研究[J]. 情报理论与实践, 2021, 44(5): 115-121. YANG W X, LIU W Y, SUN Z T, et al. Research on the construction of government data sharing model based on blockchain and ROMA [J]. Information Studies: Theory & Application, 2021, 44(5): 115-121. (in Chinese)
[16] 国务院. 国务院关于加强数字政府建设的指导意见[EB/OL]. (2022-06-23) [2025-12-14]. https://www.gov.cn/zhengce/zhengceku/2022-06/23/content_5697299.htm. The State Council. Guiding opinions of the State Council on strengthening the construction of digital government [EB/OL]. (2022-06-23) [2025-12-14]. https://www.gov.cn/zhengce/zhengceku/2022-06/23/content_5697299.htm. (in Chinese)
[17] 中共中央, 国务院. 中共中央国务院关于构建数据基础制度更好发挥数据要素作用的意见[EB/OL]. (2022-12-02) [2025-12-14]. http://www.gov.cn/zhengce/2022-12/19/ content_5732695.htm. Central Committee of the Communist Party of China, the State Council. Opinions of the Central Committee of the Communist Party of China and the State Council on building a data infrastructure system to better utilize the role of data elements [EB/OL]. (2022-12-02) [2025-12-14]. http://www.gov.cn/zhengce/2022-12/19/content_5732695.htm. (in Chinese)
[18] 国务院. 国务院关于进一步优化政务服务提升行政效能推动“高效办成一件事”的指导意见[EB/OL]. (2024-01-16) [2025-12-14]. https://www.gov.cn/zhengce/zhengceku/202401/ content_6926256.htm. The State Council. The State Council's guidelines on further optimizing government services, enhancing administrative efficiency, and promoting “getting things done efficiently” [EB/OL]. (2024-01-16) [2025-12-14]. https://www.gov.cn/zhengce/zhengceku/202401/content_6926256.htm. (in Chinese)
[19] GHEMAWAT S, GOBIOFF H, LEUNG S T. The Google file system [J]. ACM SIGOPS Operating Systems Review, 2003, 37(5): 29-43.
[20] LAMPORT L. Time, clocks, and the ordering of events in a distributed system [J]. Communications of the ACM, 1978, 21(7): 558-565.
[21] BATINI C, CAPPIELLO C, FRANCALANCI C, et al. Methodologies for data quality assessment and improvement [J]. ACM Computing Surveys, 2009, 41(3): 16.
[22] BROWN T B, MANN B, RYDER N, et al. Language models are few-shot learners [C]// Proceedings of the 34^th International Conference on Neural Information Processing Systems. Vancouver, Canada: Curran Associates Inc., 2020: 159.
[23] OpenAI, ACHIAM J, ADLER S, et al. GPT-4 technical report [J/OL]. arXiv. (2023-03-15) [2025-12-14]. https://arxiv.org/abs/2303.08774.
[24] ARIFF M A M, MOHAMAD S, AMRAN A R. A review of recent advancement in Kademlia and Chord algorithm [J]. AIP Conference Proceedings, 2019, 2129(1): 020131.
[25] MAYMOUNKOV P, MAZIōRES D. Kademlia: A peer-to-peer information system based on the XOR metric [C]// The First International Workshop on Peer-to-Peer Systems. Berlin, Germany: Springer, 2002: 53-65.
[26] STOICA I, MORRIS R, LIBEN-NOWELL D, et al. Chord: A scalable peer-to-peer lookup protocol for Internet applications [J]. IEEE/ACM Transactions on Networking, 2003, 11(1): 17-32.
[27] ROWSTRON A, DRUSCHEL P. Pastry: Scalable, decentralized object location, and routing for large-scale peer-to-peer systems [C]// Proceedings of the IFIP/ACM International Conference on Distributed Systems Platforms. Heidelberg, Germany: Springer, 2001: 329-350.
[28] WIRTZ H, HEER T, HUMMEN R, et al. Mesh-DHT: A locality-based distributed look-up structure for wireless mesh networks [C]// 2012 IEEE International Conference on Communications (ICC). Ottawa, Canada: IEEE, 2012: 653-658.
[29] CHERBAL S, BOUKERRAM A, BOUBETRA A. A survey of locality-awareness solutions in mobile DHT systems [C]// 201512^th International Symposium on Programming and Systems (ISPS). Algiers, Algeria: IEEE, 2015: 1-7.
[30] JEDDA A, MOUFTAH H T. Enhancing DHT-based object naming service architectures with geographic-awareness [C]// 20156^th International Conference on the Network of the Future (NOF). Montreal, Canada: IEEE, 2015: 1-6.
[31] LI R D, HARAI H, ASAEDA H. An aggregatable name-based routing for energy-efficient data sharing in big data era [J]. IEEE Access, 2015, 3: 955-966.
[32] XIE J J, GUO D K, SHI X F, et al. A fast hybrid data sharing framework for hierarchical mobile edge computing [C]// IEEE INFOCOM 2020-IEEE Conference on Computer Communications. Toronto, Canada: IEEE, 2020: 2609-2618.
[33] ZOELS S, DESPOTOVIC Z, KELLERER W. Cost-based analysis of hierarchical DHT design [C]// 6^th IEEE International Conference on Peer-to-Peer Computing (P2P'06). Cambridge, UK: IEEE, 2006: 233-239.
[34] ZOELS S, DESPOTOVIC Z, KELLERER W. On hierarchical DHT systems: An analytical approach for optimal designs [J]. Computer Communications, 2008, 31(3): 576-590.
[35]IT时报. “随申办”每日访问量超2000万“公共数据治理”上海样本初步形成[EB/OL]. (2021-05-10)[2025-12-14]. https:// baijiahao.baidu.com/s?id=1699342093437099145&wfr=spider&for=pc. IT Times. The daily traffic of “Suishenban” exceeds 20 million, and the Shanghai sample of “public data governance” has preliminarily formed [EB/OL]. (2021-05-10) [2025-12-14]. https://baijiahao.baidu.com/s?id=1699342093437099145&wfr=spider&for=pc. (in Chinese)
[36] 徐汇区人民政府. 日均数据交换量约5.8亿条！徐汇探索如何让数据“活”起来[EB/OL]. (2025-08-22) [2025-12-14]. https://www.shanghai.gov.cn/nw15343/20250822/8094ccdb 9a9b45cdaf8ba8ce30a04066.html. Xuhui District People's Government. The daily average data exchange volume is about 580 million! Xuhui explores how to make data “live” [EB/OL]. (2025-08-22) [2025-12-14]. https://www.shanghai.gov.cn/nw15343/20250822/8094 ccdb9a9b45cdaf8ba8ce30a04066.html. (in Chinese)
[37] 窦悦, 易成岐, 黄倩倩, 等. 打造面向全国统一数据要素市场体系的国家数据要素流通共性基础设施平台：构建国家“数联网”根服务体系的技术路径与若干思考[J]. 数据分析与知识发现, 2022, 6(1): 1-12. DOU Y, YI C Q, HUANG Q Q, et al. Constructing a common data circulation infrastructure platform for the national unified data factor market: Technical path and policy thinking of constructing the national “data networking” root service system [J]. Data Analysis and Knowledge Discovery, 2022, 6(1): 1-12. (in Chinese)
[38] LIU F, NIU Y, ZHANG Q H, et al. A foundational architecture for AI agents in healthcare [J]. Cell Reports Medicine, 2025, 6(10): 102374.
[39] TANG Z R, WANG W Z, ZHOU Z H, et al. LLM/agent-as-data-analyst: A survey [J/OL]. arXiv. (2025-09-28) [2025-12-14]. https://arxiv.org/abs/2509.23988.
[40] ALEXANDER R, KATZ L, MOORE C, et al. Evaluating the decency and consistency of data validation tests generated by LLMs [J/OL]. arXiv. (2023-10-02) [2025-12-14]. https://arxiv.org/abs/2310.01402.
[41] MONTEIRO J, COSTA P Á, LEIT?O J, et al. Enriching Kademlia by partitioning [C]// 2022 IEEE 42^nd International Conference on Distributed Computing Systems Workshops (ICDCSW). Bologna, Italy: IEEE, 2022: 33-38
[42] ZHANG Y Q, BOJJA VENKATAKRISHNAN S. Kadabra: Adapting Kademlia for the decentralized Web [C]// 27^th International Conference on Financial Cryptography and Data Security. Bol, Croatia: Springer, 2024: 327-345.
[43] BAYER R, MCCREIGHT E. Organization and maintenance of large ordered indices [C]// Proceedings of the 1970 ACM SIGFIDET (now SIGMOD) Workshop on Data Description, Access and Control. Houston, USA: ACM, 1970: 107-141.
[44] CULIK K, OTTMANN T, WOOD D. Dense multiway trees [J]. ACM Transactions on Database Systems, 1981, 6(3): 486-512.
[45] YANG D L, VICOL P, QI X J, et al. QDM: Quadtree-based region-adaptive sparse diffusion models for efficient image super-resolution [J/OL]. arXiv. (2025-03-15) [2025-12-14]. https://arxiv.org/abs/2503.12015.
[46] FU C Y, LI G, SONG R, et al. OctAttention: Octree-based large-scale contexts model for point cloud compression [C]// Proceedings of the 36^th AAAI Conference on Artificial Intelligence. California, USA: AAAI, 2022: 625-633.
[47] 韩株桃, 石杰锋, 吴金华, 等. 基于POI数据及四叉树思想的“三生空间”识别方法[J]. 地球信息科学学报, 2022, 24(6): 1107-1119. HAN Z T, SHI J F, WU J H, et al. Recognition method of “the production, living and ecological space” based on POI data and quad-tree idea [J]. Journal of Geo-Information Science, 2022, 24(6): 1107-1119. (in Chinese)
[48] TAKEICHI M. Operation-based collaborative data sharing for distributed systems [J/OL]. arXiv. (2021-12-01) [2025-12-14]. https://arxiv.org/abs/2112.00288.
[49] ISHIHARA Y, KATO H, NAKANO K, et al. Toward BX-based architecture for controlling and sharing distributed data [C]// 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). Kyoto, Japan: IEEE, 2019: 1-5.
[50] ZHUGE H, LIU J, FENG L, et al. Semantic-based query routing and heterogeneous data integration in peer-to-peer semantic link networks [C]// 1^st International Conference on Semantics for the Networked World. Paris, France: Springer, 2004: 91-107.
[51] KULKARNI S S, JAIN U. Causal consistency protocols for adaptive multi-region data sharing in distributed clouds [J]. International Journal of Research in Humanities and Social Sciences, 2025, 13(3): 1-15.
[52] ZHANG H R, ZHANG Z H, MU S, et al. CausalMesh: A formally verified causal cache for stateful serverless computing [J/OL]. arXiv. (2025-08-21) [2025-12-14]. https://arxiv.org/abs/2508.15647.
[53] ROOS S, SALAH H, STRUFE T. Comprehending Kademlia routing: A theoretical framework for the hop count distribution [J/OL]. arXiv. (2013-07-26) [2025-12-14]. https://arxiv.org/abs/1307.7000.
[54] ROOS S, SALAH H, STRUFE T. On the routing of Kademlia-type systems [M]// SHA K, STRIEGEL A, SONG M. Advances in computer communications and networks from green, mobile, pervasive networking to big data computing. New York: River Publishers, 2022.
[55] CORTES-GOICOECHEA M, KIRALY C, RYAJOV D, et al. Scalability limitations of Kademlia DHTs when enabling data availability sampling in ethereum [C]// Proceedings of the 20246^th Blockchain and Internet of Things Conference. Fukuoka, Japan: 2024: 83-91.
[56] VARALAKSHMI K,PRAKASH V D, LOURDHU D N. Multipurpose file transfer and file inquiry [J]. Journal of Science, Computing and Engineering Research, 2023, 6(4): 90-96.
[57] ZHENG L, ZHAO C S, WANG Z W. The SSH protocol audit system based on proxy technology [C]// 2013 International Conference on Computational and Information Sciences. Shiyan, China: IEEE, 2013: 1489-1492.