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针对全球大规模GNSS网高精度与高效率解算需求，基于2021—2022年全球约650个GNSS站的观测数据，提出了以数据质量为核心的聚类分析方法进行子网划分，并设计了双层并行计算模型以优化解算效率。采用3种子网划分方案对基线解算和网平差结果进行比较，并设计多线程并行解算实验，以评估解算用时、加速比和效率表现。结果表明，聚类分析方法有效提升了子网划分的精度与一致性，基线解算精度达10-8～10-9量级，网平差内符合精度控制在毫米级范围内，X、Y、Z方向坐标误差比其他方案降低约30%～40%。双层并行计算模型能显著提升解算效率，综合考虑加速比与效率，确定24线程为最佳配置。研究结果可为超大规模GNSS网的高效、高精度解算提供重要理论支撑和技术参考。
Abstract：Aiming at the demands of high-precision and high-efficiency solutions in global large scale GNSS networks, based on observational data from approximately 650 GNSS stations worldwide from 2021 to 2022, we proposed a clustering analysis method focusing on data quality for sub-network division, and designed a dual-level parallel computation model to optimize computation efficiency. We used three sub-network division schemes for comparative analysis of baseline solution and network adjustment results, and conducted multi-threaded parallel computation experiments to evaluate acceleration and efficiency performance. The results show that the clustering analysis method effectively enhances the precision and consistency of sub-network division, achieving baseline solution precision at the 10-8to 10-9level. The internal agreement precision is controlled within the millimeter range, with errors in the X, Y and Z directions reduced by approximately 30% ～40% compared to other schemes. The dual-level parallel computation model significantly improves solution efficiency. The optimal configuration is determined to be24 threads, balancing computational efficiency and resource utilization. This research provides critical theoretical support and technical references for high-efficiency and high-precision solutions in ultra-large scale GNSS networks.
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基本信息:
中图分类号:P228.4
引用信息:
[1]李秀龙.全球大规模GNSS子网划分与高效解算方法研究[J].地理空间信息,2025,23(11):22-26.
基金信息:
国家自然科学基金资助项目（42274031）