基于分布式并行计算的测绘成果质量检查及分析策略  

Quality inspection and analysis strategy of surveying and mapping results based on distributed parallel computing

作　　者：张赟 汪星辰 ZHANG Yun;WANG Xingchen(Inner Mongolia Autonomous Region Institute of Metrology and Testing,Hohhot,Inner Mongolia 010050,China)

机构地区：[1]内蒙古自治区计量测试研究院,内蒙古呼和浩特010050

出　　处：《北京测绘》2025年第4期541-547,共7页Beijing Surveying and Mapping

基　　金：内蒙古自治区自然科学基金(2023MS04011)。

摘　　要：为充分利用集群中的计算资源,提升整体数据处理能力,并满足快速响应需求,研究基于分布式并行计算的测绘成果质量检查及分析策略,在保证数据准确性的同时,提高质量检查的稳定性和可靠性。通过分布式并行计算的Canopy-Kmeans聚类算法,设计分层抽样方案,抽取具有代表性的测绘成果样本;利用质量元素评分法,计算各具有代表性测绘成果样本内质量元素的评分,依据质量元素评分,进行测绘成果质量检查及分析。实验证明:该策略可有效抽取具有代表性的测绘成果样本,并计算测绘成果样本内各质量元素的评分,完成测绘成果质量检查及分析。To fully utilize the computing resources in a cluster,enhance overall data processing capabilities,and meet the demand for rapid response,this paper explored a quality inspection and analysis strategy for surveying and mapping results based on distributed parallel computing.The strategy aims to improve the stability and reliability of quality inspection while ensuring data accuracy.Using the distributed parallel computing-based Canopy-Kmeans clustering algorithm,a stratified sampling scheme was designed to extract representative surveying result samples.The quality element scoring method was then employed to calculate the scores of quality elements within these representative samples.Based on these quality element scores,quality inspection and analysis of the surveying results were carried out.Experimental results show that this strategy effectively extracts representative surveying result samples and calculates the scores of quality elements within the samples,completing the quality inspection and analysis of surveying results.

关 键 词：分布式 并行计算 测绘成果 质量检查 分析策略 Canopy-Kmeans聚类算法 

分 类 号：TP271[自动化与计算机技术—检测技术与自动化装置] P205[自动化与计算机技术—控制科学与工程]