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Article(id=1251226695886385719, tenantId=1146029695717560320, journalId=1251194772300279900, issueId=1251226682309423223, articleNumber=null, orderNo=null, doi=10.20079/j.issn.1001-893x.240226002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1708876800000, receivedDateStr=2024-02-26, revisedDate=1725292800000, revisedDateStr=2024-09-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1776245290966, onlineDateStr=2026-04-15, pubDate=1764259200000, pubDateStr=2025-11-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776245290966, onlineIssueDateStr=2026-04-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776245290966, creator=13041195026, updateTime=1776245290966, updator=13041195026, issue=Issue{id=1251226682309423223, tenantId=1146029695717560320, journalId=1251194772300279900, year='2025', volume='65', issue='11', pageStart='1729', pageEnd='1954', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776245287729, creator=13041195026, updateTime=1776246742124, updator=13041195026, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1251232782568080068, tenantId=1146029695717560320, journalId=1251194772300279900, issueId=1251226682309423223, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1251232782568080069, tenantId=1146029695717560320, journalId=1251194772300279900, issueId=1251226682309423223, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1828, endPage=1835, ext={EN=ArticleExt(id=1251226696242901597, articleId=1251226695886385719, tenantId=1146029695717560320, journalId=1251194772300279900, language=EN, title=A Multi-dimensional Telemetry Data Pattern Mining Method Based on Matrix Profile, columnId=1251226683223781499, journalTitle=Telecommunication Engineering, columnName=Application Fundamental Research and Advanced Technology, runingTitle=null, highlight=null, articleAbstract=

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 Mueen'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 150000 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.

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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（Mueen'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。

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娄乐 Email:
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娄乐 男，1994年生于河北保定，2021年获硕士学位，现为工程师，主要研究方向为卫星测运控与任务规划。

柳震 男，1985年生于黑龙江哈尔滨，2011年获硕士学位，现为高级工程师，主要研究方向为卫星测运控与任务规划。

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2中国星网网络创新研究院有限公司，北京 100029, bio={"content":"

娄乐 男，1994年生于河北保定，2021年获硕士学位，现为工程师，主要研究方向为卫星测运控与任务规划。

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娄乐 男，1994年生于河北保定，2021年获硕士学位，现为工程师，主要研究方向为卫星测运控与任务规划。

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柳震 男，1985年生于黑龙江哈尔滨，2011年获硕士学位，现为高级工程师，主要研究方向为卫星测运控与任务规划。

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柳震 男，1985年生于黑龙江哈尔滨，2011年获硕士学位，现为高级工程师，主要研究方向为卫星测运控与任务规划。

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运行百分比/%运行时间/s位置索引Sm
稀有模式1稀有模式2稀有模式3稀有模式1稀有模式2稀有模式3
0.12.9702990754672002.26501.04600.7646
1.027.2702990722671922.26501.04221.0341
10.0255.8702990725671962.26501.03971.0337
20.0509.3702490726671632.25871.03931.0324
30.0762.5702890705671892.25671.03911.0317
40.01013.6700190725671792.23731.03861.0309
50.01267.2702990705671972.23421.03791.0304
60.01514.1702490711672112.21691.03821.0289
70.01774.9702490715672112.21691.03651.0289
80.02042.3701790715671852.20191.03651.0274
90.02309.3701790715671822.20191.03651.0274
99.02547.9708690791670612.21541.03411.0243
99.82569.2716590503670362.21481.02671.0019
100.0（精确）2574.3666290554670360.75010.73520.6508
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程序迭代过程中得到的稀有模式近似解

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运行百分比/%运行时间/s位置索引Sm
稀有模式1稀有模式2稀有模式3稀有模式1稀有模式2稀有模式3
0.12.9702990754672002.26501.04600.7646
1.027.2702990722671922.26501.04221.0341
10.0255.8702990725671962.26501.03971.0337
20.0509.3702490726671632.25871.03931.0324
30.0762.5702890705671892.25671.03911.0317
40.01013.6700190725671792.23731.03861.0309
50.01267.2702990705671972.23421.03791.0304
60.01514.1702490711672112.21691.03821.0289
70.01774.9702490715672112.21691.03651.0289
80.02042.3701790715671852.20191.03651.0274
90.02309.3701790715671822.20191.03651.0274
99.02547.9708690791670612.21541.03411.0243
99.82569.2716590503670362.21481.02671.0019
100.0（精确）2574.3666290554670360.75010.73520.6508
), ArticleFig(id=1251226708532211910, tenantId=1146029695717560320, journalId=1251194772300279900, articleId=1251226695886385719, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
运行百分比/%运行时间/s位置索引C
频繁模式1频繁模式2频繁模式3频繁模式1频繁模式2频繁模式3
0.12.9[57419,110102][57359,141259][31870,137044]0.49710.47060.4566
1.027.2[58037,76212][31094,58053][47044,68042]0.53980.50890.4822
10.0255.8[58079,76254][30850,57809][47005,68003]0.54080.52970.4830
20.0509.3[58060,76235][30850,57809][47017,68016]0.54080.52970.4881
30.0762.5[58054,76229][30850,57809][47017,68016]0.54080.52970.4903
40.01013.6[58054,76229][30850,57809][47017,68016]0.54080.52970.4903
50.01267.2[58054,76229][30850,57809][47017,68016]0.54080.52970.4903
60.01514.1[58057,76232][30850,57809][47020,68018]0.54110.52970.4905
70.01774.9[58057,76232][30850,57809][47019,68018]0.54110.52970.4905
80.02042.3[58057,76232][30851,57810][47019,68018]0.54110.53020.4905
90.02309.3[58057,76232][30851,57810][47019,68018]0.54110.53020.4905
99.02547.9[58057,76232][47019,68018][47019,68018]0.54110.54110.4905
99.82569.2[58057,76232][47019,68018][47019,68018]0.54110.54110.4905
100.0（精确）2574.3[58057,76232][30851,57810][47019,68018]0.54110.55020.4905
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程序迭代过程中得到的频繁模式近似解

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运行百分比/%运行时间/s位置索引C
频繁模式1频繁模式2频繁模式3频繁模式1频繁模式2频繁模式3
0.12.9[57419,110102][57359,141259][31870,137044]0.49710.47060.4566
1.027.2[58037,76212][31094,58053][47044,68042]0.53980.50890.4822
10.0255.8[58079,76254][30850,57809][47005,68003]0.54080.52970.4830
20.0509.3[58060,76235][30850,57809][47017,68016]0.54080.52970.4881
30.0762.5[58054,76229][30850,57809][47017,68016]0.54080.52970.4903
40.01013.6[58054,76229][30850,57809][47017,68016]0.54080.52970.4903
50.01267.2[58054,76229][30850,57809][47017,68016]0.54080.52970.4903
60.01514.1[58057,76232][30850,57809][47020,68018]0.54110.52970.4905
70.01774.9[58057,76232][30850,57809][47019,68018]0.54110.52970.4905
80.02042.3[58057,76232][30851,57810][47019,68018]0.54110.53020.4905
90.02309.3[58057,76232][30851,57810][47019,68018]0.54110.53020.4905
99.02547.9[58057,76232][47019,68018][47019,68018]0.54110.54110.4905
99.82569.2[58057,76232][47019,68018][47019,68018]0.54110.54110.4905
100.0（精确）2574.3[58057,76232][30851,57810][47019,68018]0.54110.55020.4905
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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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娄乐 1, 2 , 柳震 1
电讯技术 | 应用基础与前沿技术 2025,65(11): 1828-1835
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电讯技术 | 应用基础与前沿技术 2025, 65(11): 1828-1835
基于矩阵轮廓的多维遥测数据模式挖掘方法
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娄乐1, 2 , 柳震1
作者信息
  • 1航天科工海鹰集团有限公司，北京 100071
  • 2中国星网网络创新研究院有限公司，北京 100029

    • 娄乐 男，1994年生于河北保定，2021年获硕士学位，现为工程师，主要研究方向为卫星测运控与任务规划。

    柳震 男，1985年生于黑龙江哈尔滨，2011年获硕士学位，现为高级工程师，主要研究方向为卫星测运控与任务规划。

通讯作者:

娄乐 Email:
A Multi-dimensional Telemetry Data Pattern Mining Method Based on Matrix Profile
Le LOU1, 2 , Zhen LIU1
Affiliations
  • 1Aerospace Science and Industry Hiwing Group Co., Ltd., Beijing 100071, China
  • 2China Satellite Network Innovation Co., Ltd., Beijing 100029, China
出版时间: 2025-11-28 doi: 10.20079/j.issn.1001-893x.240226002
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摘要
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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（Mueen'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。

卫星遥测  /  频繁模式挖掘  /  稀有模式挖掘  /  异常检测  /  矩阵轮廓

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 Mueen'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 150000 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.

satellite telemetry  /  frequeat pattern mining  /  rare pattern mining  /  anomaly detection  /  matrix profile
娄乐, 柳震. 基于矩阵轮廓的多维遥测数据模式挖掘方法. 电讯技术, 2025 , 65 (11) : 1828 -1835 . DOI: 10.20079/j.issn.1001-893x.240226002
Le LOU, Zhen LIU. A Multi-dimensional Telemetry Data Pattern Mining Method Based on Matrix Profile[J]. Telecommunication Engineering, 2025 , 65 (11) : 1828 -1835 . DOI: 10.20079/j.issn.1001-893x.240226002
正文
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0　引言
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在卫星系统工程中，遥测数据是星载计算机进行星上自主管理的重要数据来源，也是地面系统人员进行卫星运行管理、状态监测和故障诊断的唯一依据。遥测数据来源于卫星的各个分系统，这些数据与传感器测量值有关，涉及温度、电流、电压、轨道、姿态等数百个测量参数，包含丰富的卫星在轨状态和系统行为信息[1-2]。由于空间遥测数据的高度复杂性和固有特性，常规的极值检测法、基于模型的诊断方法和基于规则的专家系统等过度依赖卫星设计的先验知识，通用性较差。此外，卫星各分系统的工工作模式和遥测参数逐渐增多，在卫星设计和研制过程中准备卫星系统模型和规则库成本非常高昂[3]。随着卫星寿命的不断变长，预先准备的先验知识和卫星在空间中的实际状态和行为也会产生较大的差异。模式挖掘是指利用统计学、机器学习、人工智能等技术从数据中发现有用信息，因为具有不依赖于先验知识、通用性强、扩展性强的特点，在卫星运行管理和状态监测方面成为研究热点。文献[4]提出一种基于模糊贝叶斯的启发式滤波选择算法，用于卫星电气系统中故障特征的发现。文献[5]以遥测数据为实验对象，采用自回归模型（Autoregressive Model，AR）、误差反向传播（Error Back Propagation Training，BP）神经网络、非参数回归法对卫星长期性能趋势进行预测，然后对3种预测数据进行融合，用于性能趋势的预测。文献[6]提出一种逐段回归近似的方法用于卫星遥测时序遥测数据中的相似性子序列挖掘。文献[7]使用最小稀有模式挖掘算法（Minimal Rare Pattern based on Top-down and Bottom-up，MRP-TB）挖掘卫星遥测数据中的稀有模式。文献[8]提出了一种基于物理分割和改进时间序列表示的遥测数据聚类框架，用于遥测数据片段聚类分析。文献[9]通过傅里叶变换将时域遥测数据转化到频域，用于检测气象卫星制冷机测温异常时的波形抖动。文献[10]提出对传统的动态时间弯曲（Dynamic Time Warping，DTW）算法进行改进，提出一种新的DTW算法用于捕获NS2卫星遥测序列中的相关性片段和趋势性信息。文献[11]以卫星遥测数据典型时间序列作为训练集，将BP神经网络模型与小世界网络模型进行组合，用于卫星遥测数据的故障检测。文献[12]引入时间卷积和自编码器神经网络，同时建立不同时刻、多元特征参数间的依赖关系，实现融合多元特征参数进行卫星故障的检测。文献[13]首先基于原型的负混合对比学习异常检测（Contrastive Learning with Prototype-based Negative Mixing for Anomaly Detection，CLPNM-AD）框架采集随机特征来增强样本，然后构建正态轮廓，最后构造异常评分函数，用于遥测数据异常相关决策。
上述大多数模式挖掘或故障检测方法只用于一维遥测的单类模式挖掘，未用到多个遥测参数之间相关信息，且计算量大，难以进行遥测数据流的在线挖掘。本文面向多维遥测数据，提出一种基于矩阵轮廓的模式挖掘方法，用于挖掘多维遥测数据中的频繁模式、稀有模式。该方法可随时中断得到挖掘近似解，能够在较短的时间内挖掘出高质量的模式信息。
1　基于矩阵轮廓的模式挖掘方法
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1.1　遥测数据模式描述
卫星遥测数据中的模式信息是指能表征卫星的行为和在轨状态的时间序列片段[14]，通常包括频繁模式、稀有模式和趋势模式。由于卫星的周期运动性、空间环境的循环平稳性和业务模式的重复性，多类遥测数据会出现大量的频繁模式，但频繁模式序列并不完全相同，需采用距离度量和相关性进行比较。图1（a）所示为某卫星的动量轮温度数据，红色方框中为数据中出现的频繁模式。在发生突发性故障、空间环境剧烈变化、卫星业务状态改变时，遥测数据中就会出现稀有模式，体现为某些遥测参数的幅值和频率发生突发性变化，后续可能恢复也可能不恢复。图1（b）所示为某卫星负载电流数据，红色方框中为稀有模式，为卫星载荷执行上注指令导致负载电流突然增大，充放电速度加快。趋势模式具有长周期性的特点，与卫星机械及电器部件劣化相对应。图1（c）所示为某卫星某测点的温度变化，很容易从平均幅值看出趋势变化。其中趋势模式的识别与挖掘相对简单，可以使用低通滤波器处理，频繁模式、稀有模式种类多样，容易隐匿在噪声中。此外，遥测参数众多，遥测数据体现多维性，且各维度数据相互关联。多维关联的频繁模式和稀有模式挖掘更能表征卫星的在轨状态。图1（d）所示为某卫星太阳能电池阵电流和电池输入电流参数，呈现强关联性。
1.2　基于矩阵轮廓的模式挖掘方法
矩阵轮廓（Matrix Profile，MP）算法是2016年提出的时间序列快速挖掘算法[15-16]。本文对Matrix Profile算法进行改进，将算法扩展到多维，用于卫星多维遥测数据模式挖掘，流程如图2所示。
步骤1对多维遥测数据进行傅里叶变换，对各维度数据进行频谱数据，找到频繁模式特征频率点fo。
步骤2根据频繁模式特征频率点fo和采样频率fs,计算模板长度m。
步骤3保持遥测数据各维度索引一致，每隔一个采样点，对各维度遥测数据进行滑窗取模板，得到各维度数据模板库。图3所示为一维遥测数据的模板集合，假设一维遥测数据S长度为T，随机截取长度为m的任意子序列，每隔一个采样点进行滑动，共有T-m+1个模板。
步骤4随机选取多维模板集合Q中的一个模板qi，采用MASS算法快速计算qi相对于遥测数据S的距离轮廓列向量，直至计算完所有模板qi相对于遥测数据S的距离路面看列向量di，形成多维距离矩阵D。多维距离矩阵D是由各维度的距离矩阵进行归一化，并赋予不同权重相加得到。
距离轮廓是时间序列数据挖掘和机器学习的基础元素，它测量查询序列和较长时间序列中的子序列之间的相似性[17-18]。MASS算法利用快速傅里叶变换、卷积定量和滑动标准差来快速计算矩阵轮廓，其伪代码如下：
图4所示为二维距离矩阵的计算过程。以维度1的距离矩阵为例，该矩阵的元素表示数据维度1模板间的欧氏距离，表示模板与模板的欧氏距离。
步骤5将多维距离矩阵D中与主对角线元素绝对距离小于m/2的元素置为无穷大inf,如图4中黑线所示。
步骤6将矩阵D按列取最小值，得到矩阵轮廓曲线MDMP，并记录MDMP极值对应矩阵D的位置索引。
步骤 7分析MDMP曲线和位置索引，得到多维数据的关联稀有模式和频繁模式。
2　实验验证
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本文实验数据来源于某在轨卫星连续7天的延时遥测数据，遥测数据采样率为0.25 Hz，总时长为610724 s，共有152681个采样点。该卫星为太阳同步轨道卫星，测试时间内的平均轨道高度为500 km，平均轨道周期为5640 s。通过卫星地面站上注遥控延时指令，控制卫星在经过下个卫星地面站时通过高速数据传输通道发送延时遥测数据，同时地面站在该圈次进行跟踪接收并获得遥测源码。地面系统根据遥测链路协议，解析得到遥测数据。取其中的3个遥测参数构成三维遥测数据，分别为负载电流、锂电池电压、母线电压。
对其进行频谱分析，如图5所示，可以看到每个维度遥测数据的频谱高峰均为0.0001764 Hz，使用公式（1），频繁模式采样点数m=1417，时间为5668 s，与平均轨道周期相近。
使用本文提出的方法，程序运行到100%时得到的精确解如图6所示。为了展示更加清晰，在图中标注了一对优先级最高的关联频繁模式，4个优先级最高的关联稀有模式进行分析。MDMP曲线中幅值相同的极小值点对应的坐标索引为一组关联频繁模式出现的位置；取MDMP曲线中4个幅值最大的极大值点，其位置索引对应着4个优先级最高的稀有模式：稀有模式1从3个维度的波形上都可以看出频率突然变小；稀有模式2从3个维度的波形上都可以看出频率突然变大；稀有模式3也可以看出频率的突然变化，但差别较小；稀有模式4体现为负载电流参数幅值的突然增大。
图7为挖掘出的一对多维频繁模式细节图，三个维度分别为负载电流、锂电池电压、母线电压。其中，黄色实线为一组模式，紫色虚线为一组模式，可以看出这两组模式在波形上非常相似。
稀有模式x的评价指标采用与模板集合库的最小相似性Sm进行定量描述，Sm越大，表示该模式越稀有。
式中：yi是模板集合库中任意模板；m为模式的维度；n为模式在每个维度上的采样点个数；
一组频繁模式a、b的评价指标采用互相关值C进行定量描述，C越大，表示该组频繁模式相关性越高。
式中：m为模式的维度；n为模式在每个维度上的采样点个数。
该方法支持实时在线的快速挖掘，多维距离矩阵D和MDMP曲线的计算采用逐步随机迭代计算的方式，可对运行中的程序暂停，得到当前的近似解。图8为程序迭代过程中得到的MDMP曲线。
表1为程序迭代过程中挖掘出的稀有模式位置索引和定量指标，表2为程序迭代过程中挖掘出的频繁模式位置索引和定量指标，可以看出，15万个采样点，算法运行1%所用时间为27.2 s，也可有效挖掘出稀有模式和频繁模式。稀有模式位置误差分别为第367、168、156个采样点，频繁模式位置误差分别为20、243、25个采样点。
3　结束语
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针对海量多维遥测数据模式挖掘问题，本文分析了卫星遥测数据中稀有模式、频繁模式和趋势模式表征的物理状态，提出了基于矩阵轮廓的多维遥测数据模式挖掘方法。利用真实在轨卫星15万采样点连续7天的延时遥测数据进行实验验证，得到以下结论：基于矩阵轮廓的多维遥测数据模式挖掘方法可有效挖掘到多维遥测数据中的相关频繁模式和稀有模式。算法可中途进行停止，得到挖掘的近似解。比较算法运行1%得到的近似解与精确解的误差：稀有模式位置误差分别为第367、168、156个采样点；频繁模式位置误差分别为20、243、25个采样点。
本文采用卫星地面站接收的延时遥测进行实验，分析了运行过程中得到的近似解和精确解，结果表明对于15万个采样点的三维遥测数据，挖掘程度1%得到的近似解与精确解的位置误差控制400个采样点以内，为卫星在轨异常检测提供了新的思路。后续将开发实时的卫星在轨状态监测与预警软件进行在轨验证。
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2025年第65卷第11期
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doi: 10.20079/j.issn.1001-893x.240226002
  • 接收时间：2024-02-26
  • 首发时间：2026-04-15
  • 出版时间：2025-11-28
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  • 收稿日期：2024-02-26
  • 修回日期：2024-09-03
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    1航天科工海鹰集团有限公司，北京 100071
    2中国星网网络创新研究院有限公司，北京 100029

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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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