数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (4): 99-110.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.04.007

doi: 10.11871/jfdc.issn.2096-742X.2026.04.007

• • 上一篇    下一篇

原位数据集的质量评价模型构建及应用

王梦乐1(),武新乾1,*(),陈祖刚2,李静2,李国庆2   

  1. 1 河南科技大学， 数学与统计学院， 河南 洛阳 471023
    2 中国科学院空天信息创新研究院， 北京 100094
  • 收稿日期:2025-11-18 出版日期:2026-08-20 发布日期:2026-08-21
  • 通讯作者: 武新乾（E-mail: wuxinqian1001@163.com）
  • 作者简介:王梦乐，河南科技大学数学与统计学院，硕士研究生，主要研究方向为大数据分析和原位数据集的质量评价方法研究。
    本文中主要负责研究方法的提出、实验代码的实现与运行，以及论文的撰写工作。
    WANG Mengle is a master’s student at the School of Mathematics and Statistics, Henan University of Science and Technology. Her main research interests include big data analysis and quality evaluation methods for in situ datasets.
    In this paper, she is primarily responsible for proposing the research methodology, implementing and running the experimental code, and drafting the manuscript.
    E-mail：w18739451429@163.com|武新乾，河南科技大学数学与统计学院，教授，主要研究方向为大数据分析方法与应用。
    本文中主要负责研究方法的总体指导，以及对论文结构与内容的审阅和修改工作。
    WU Xinqian is a professor at the School of Mathematics and Statistics, Henan University of Science and Technology. His research interests focus on big data analysis methods and their applications.
    In this paper, he iss primarily responsible for the overall guidance of the research methodology as well as reviewing and revising the structure and content of the manuscript.
    E-mail：wuxinqian1001@163.com
  • 基金资助:
    国家自然科学基金项目(42201505);国家重点研发项目(2024YFB3908404-03);河南省高校人文社会科学研究一般项目(2024-ZZJH-281)

Development and Application of a Quality Evaluation Model for In Situ Datasets

WANG Mengle1(),WU Xinqian1,*(),CHEN Zugang2,LI Jing2,LI Guoqing2   

  1. 1 School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, Henan 471023, China
    2 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • Received:2025-11-18 Online:2026-08-20 Published:2026-08-21

RichHTML

2

PDF

47

摘要：

【目的】 随着环境监测精度提升与多源观测系统发展，原位数据在大气科学、地球系统建模和环境评估中的作用愈加重要。但其在采集、传输与处理过程中易出现缺测、异常及格式不规范等问题，影响可靠性与应用价值。【方法】 本研究基于国家对地观测科学数据中心发布的大气成分变化探测网络和地基气溶胶全球自动观测网络Level 1.5数据集，构建了一套多维度原位数据集质量评价体系，从数据合规情况、缺测情况、错测情况和潜在伪造情况4个准则层出发设计15项细化指标，系统划分各质量指标内涵，利用MSCRED*无监督异常检测模型、K近邻等方法开展质量指标量化研究。【结果】 对该原位数据集的1,468个观测站点进行实证分析，结果表明该体系可有效揭示站点间质量差异及其全球与区域分布特征。【结论】 本研究提出了面向原位数据集的多维度质量评价体系与定量化集成方法，可为原位数据质量现状诊断、质量控制优化及后续科学应用提供方法支撑。

关键词: 原位数据集, 质量评价, 多维指标体系, 无监督异常检测, 数据真实性

Abstract:

[Objective] With the increasing precision of environmental monitoring and the rapid advancement of multi-source observation systems, in situ observational data have become increasingly essential for atmospheric sciences, Earth system modeling, and environmental assessment. However, during data acquisition, transmission, and processing, in situ datasets are prone to missing observations, abnormal values, and non-standard formatting issues, which may degrade their reliability and limit their scientific utility. [Methods] This study utilizes the Level 1.5 datasets from the Atmospheric Composition Monitoring Network and the Aerosol Robotic Network, both released by the National Earth Observation Science Data Center of China, to construct a multi-dimensional quality evaluation framework for in situ datasets. The evaluation system is organized along four major criteria—data compliance, data missing conditions, data mis-measurement conditions, and potential data falsification—and is further operationalized into 15 refined quality indicators. The conceptual connotations of each quality indicator are systematically defined, and quantitative assessment is conducted by applying the MSCRED* unsupervised anomaly detection model as well as the K-nearest neighbor method for indicator-specific quantitative evaluation. [Results] Empirical evaluations were conducted on 1,468 observational stations included in the target dataset. The results show that the proposed evaluation system is capable of effectively revealing the quality heterogeneity among stations, and is able to characterize both the global and regional distribution patterns of in situ data quality levels. [Conclusions] This study proposes a multidimensional quality assessment framework and a quantitative integration approach tailored for in situ datasets. The framework can provide methodological support for diagnosing the current status of data quality, optimizing quality control procedures, and facilitating subsequent scientific applications.

Key words: in situ datasets, quality assessment, multidimensional indicator system, unsupervised anomaly detection, data authenticity