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基于矩阵轮廓的多维遥测数据模式挖掘方法.

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Title: 基于矩阵轮廓的多维遥测数据模式挖掘方法. (Chinese)
Alternate Title: A Multi-dimensional Telemetry Data Pattern Mining Method Based on Matrix Profile. (English)
Authors: 娄乐, 柳震
Source: Telecommunication Engineering; 11/28/2025, Vol. 65 Issue 11, p1828-1835, 8p
Subject Terms: Telemetry, Stochastic processes, Sequential pattern mining, Spectral theory, Artificial satellite tracking, Spectrum analysis instruments, Matrices (Mathematics), Pattern perception
Abstract (English): Multi-dimensional telemetry data pattern mining holds significant importance for satellite status monitoring. However, the sheer volume of telemetry parameters and data poses a challenge in obtaining precise solutions within a short timeframe. To address this issue, the authors propose a matrix profile-based pattern mining approach that employs stochastic principles to search for approximate solutions, which can serve as surrogates for precise solutions within an acceptable error margin. Firstly, spectral analysis is performed on the multi-dimensional telemetry data to determine the template length based on the characteristic frequencies of the patterns. Subsequently, the Mueen̓ s algorithm for similarity search (MASS) is iteratively applied in a stochastic manner to compute elements within the distance matrix. A crucial step involves zeroing out elements near the main diagonal to form the multi-dimensional distance matrix. Finally, the minimum values are extracted from each column to generate the multi-dimensional distance matrix profile (MDMP). On this profile, the locations of the maximum and minimum values correspond to the identified rare and frequent patterns, respectively. Experimental analysis indicates that when processing three-dimensional telemetry data containing 150 000 sampling points, the proposed method, at a 1% mining depth, is able to constrain the positional error between the approximate and precise solutions within 400 sampling points. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 多维遥测数据模式挖掘对卫星状态监测具有重要意义, 但遥测参数多、数据量大, 很难在短 时间内得到精确解。 针对这一问题, 提出了一种基于矩阵轮廓的模式挖掘方法, 利用随机思想搜索 近似解, 在误差允许的范围内替代精确解。 首先对多维遥测数据进行频谱分析, 根据模式特征频率 计算得到模板长度。 然后按照随机的原则循环使用聚类相似性搜索算法 (Mueen̓ s Algorithm for Similarity Search, MASS) 计算距离矩阵中的元素, 并对主对角线附近的元素进行归零处理, 形成多维 距离矩阵。 最后按列取最小值, 生成多维距离矩阵轮廓曲线 (Multi-dimensional Distance Matrix Profile, MDMP)。 在该曲线上, 极大值和极小值分别对应于挖掘出的稀有模式和频繁模式位置。 实 验分析显示, 在处理包含 15 万个采样点的三维遥测数据时, 通过该方法在 1% 的挖掘程度下获得的 近似解与精确解之间的位置误差控制在 400 个采样点以内。 [ABSTRACT FROM AUTHOR]
Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 基于矩阵轮廓的多维遥测数据模式挖掘方法. (Chinese)
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  Data: A Multi-dimensional Telemetry Data Pattern Mining Method Based on Matrix Profile. (English)
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  Data: Telecommunication Engineering; 11/28/2025, Vol. 65 Issue 11, p1828-1835, 8p
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  Data: <searchLink fieldCode="DE" term="%22Telemetry%22">Telemetry</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+pattern+mining%22">Sequential pattern mining</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+theory%22">Spectral theory</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellite+tracking%22">Artificial satellite tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis+instruments%22">Spectrum analysis instruments</searchLink><br /><searchLink fieldCode="DE" term="%22Matrices+%28Mathematics%29%22">Matrices (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+perception%22">Pattern perception</searchLink>
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  Label: Abstract (English)
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  Data: Multi-dimensional telemetry data pattern mining holds significant importance for satellite status monitoring. However, the sheer volume of telemetry parameters and data poses a challenge in obtaining precise solutions within a short timeframe. To address this issue, the authors propose a matrix profile-based pattern mining approach that employs stochastic principles to search for approximate solutions, which can serve as surrogates for precise solutions within an acceptable error margin. Firstly, spectral analysis is performed on the multi-dimensional telemetry data to determine the template length based on the characteristic frequencies of the patterns. Subsequently, the Mueen̓ s algorithm for similarity search (MASS) is iteratively applied in a stochastic manner to compute elements within the distance matrix. A crucial step involves zeroing out elements near the main diagonal to form the multi-dimensional distance matrix. Finally, the minimum values are extracted from each column to generate the multi-dimensional distance matrix profile (MDMP). On this profile, the locations of the maximum and minimum values correspond to the identified rare and frequent patterns, respectively. Experimental analysis indicates that when processing three-dimensional telemetry data containing 150 000 sampling points, the proposed method, at a 1% mining depth, is able to constrain the positional error between the approximate and precise solutions within 400 sampling points. [ABSTRACT FROM AUTHOR]
– Name: AbstractNonEng
  Label: Abstract (Chinese)
  Group: Ab
  Data: 多维遥测数据模式挖掘对卫星状态监测具有重要意义, 但遥测参数多、数据量大, 很难在短 时间内得到精确解。 针对这一问题, 提出了一种基于矩阵轮廓的模式挖掘方法, 利用随机思想搜索 近似解, 在误差允许的范围内替代精确解。 首先对多维遥测数据进行频谱分析, 根据模式特征频率 计算得到模板长度。 然后按照随机的原则循环使用聚类相似性搜索算法 (Mueen̓ s Algorithm for Similarity Search, MASS) 计算距离矩阵中的元素, 并对主对角线附近的元素进行归零处理, 形成多维 距离矩阵。 最后按列取最小值, 生成多维距离矩阵轮廓曲线 (Multi-dimensional Distance Matrix Profile, MDMP)。 在该曲线上, 极大值和极小值分别对应于挖掘出的稀有模式和频繁模式位置。 实 验分析显示, 在处理包含 15 万个采样点的三维遥测数据时, 通过该方法在 1% 的挖掘程度下获得的 近似解与精确解之间的位置误差控制在 400 个采样点以内。 [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.20079/j.issn.1001-893x.240226002
    Languages:
      – Code: chi
        Text: Chinese
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      Pagination:
        PageCount: 8
        StartPage: 1828
    Subjects:
      – SubjectFull: Telemetry
        Type: general
      – SubjectFull: Stochastic processes
        Type: general
      – SubjectFull: Sequential pattern mining
        Type: general
      – SubjectFull: Spectral theory
        Type: general
      – SubjectFull: Artificial satellite tracking
        Type: general
      – SubjectFull: Spectrum analysis instruments
        Type: general
      – SubjectFull: Matrices (Mathematics)
        Type: general
      – SubjectFull: Pattern perception
        Type: general
    Titles:
      – TitleFull: 基于矩阵轮廓的多维遥测数据模式挖掘方法.
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            NameFull: 娄乐
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            NameFull: 柳震
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              M: 11
              Text: 11/28/2025
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              Y: 2025
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