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

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.

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.

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 1961 records/s. Compared with the theoretical performance peak of traditional cascaded systems, the performance was improved by 50% and 91%, respectively.

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.