卫星监测时空大数据蠕变特征提取及预警算法

doi:10.11896/jsjkx.201000071

计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 258-264.doi: 10.11896/jsjkx.201000071

卫星监测时空大数据蠕变特征提取及预警算法

刘亚臣¹, 黄雪莹²

1 北京工业大学北京市物联网软件与系统工程技术研究中心北京100124
2 卡内基梅隆大学工程院宾夕法尼亚州15213

出版日期:2021-11-10 发布日期:2021-11-12
通讯作者: 刘亚臣(liuyachennn@emails.bjut.edu.cn)

Research on Creep Feature Extraction and Early Warning Algorithm Based on Satellite MonitoringSpatial-Temporal Big Data

LIU Ya-chen¹, HUANG Xue-ying²

1 Beijing Engineering Research Center for IoT Software and Systems,Beijing University of Technology,Beijing 100124,China
2 Carnegie Institute of Technology,Carnegie Mellon University ,PA 15213,USA

Online:2021-11-10 Published:2021-11-12
About author:LIU Ya-chen,born in 1997,postgraduate.Her main research interests include BeiDou high-accuracy position and navigation,and IoT software and system.

摘要/Abstract

摘要： 针对山体滑坡等地质灾害发生时间、趋势难以及时精准预警的难题,提出采用最新北斗卫星高精度形变监测技术,开展蠕变运动特征提取及预警算法研究。对卫星监测高精度时空大数据进行分析、清洗,重点研究监测点数据的时间属性、空间属性、不同监测点之间的变化规律;提取蠕变运动多维特征,如位移、位移方向角、瞬间速度、加速度等,并以多维的方式展示监测数据内在的变化趋势。蠕变灾害预警算法能够发现和预警形变过程中的潜在灾害,确保及时防治地质灾害,保障人员生命和财产安全。该研究成果可以在多个不同领域得到广泛的应用,具有很大的理论意义和应用价值。

关键词: 蠕变特征提取, 山体滑坡地质灾害, 时空大数据, 卫星监测, 预警算法

Abstract: This paper presents a new methodology for overcoming the difficulties to issue warning of the occurrence time and tendency of the geologic hazards timely and accurately,employs the latest BeiDou satellite deformation monitoring technology,and considers a novel feature extraction of creep deformation method and algorithm for hazards warning.Based on the data analysis and data cleansing of the data from satellite monitoring spatial-temporal big data,we focus on time and spatial attribute and variation between different monitoring points.Moreover,we extract multi-dimensional of creep deformation such as displacement,displacement angle,instantaneous velocity,acceleration,etc.And the internal variation trend of the monitored data is displayed in a multi-dimensional manner.Research on creep disaster warning algorithm can find and warn potential disasters in deformation process,this finding is helpful to take measures to ensure the personal and property safety timely.Our research findings have important application value and theoretical significance in many fields.

Key words: Creep feature extraction, Early warning algorithm, Landslide geological hazard, Satellite monitoring, Spatial-temporal big data

中图分类号:

TP391

刘亚臣, 黄雪莹. 卫星监测时空大数据蠕变特征提取及预警算法[J]. 计算机科学, 2021, 48(11A): 258-264. https://doi.org/10.11896/jsjkx.201000071

LIU Ya-chen, HUANG Xue-ying. Research on Creep Feature Extraction and Early Warning Algorithm Based on Satellite MonitoringSpatial-Temporal Big Data[J]. Computer Science, 2021, 48(11A): 258-264. https://doi.org/10.11896/jsjkx.201000071

参考文献

[1]ELLIOTT D K,CHRISTOPHER H.Understanding GPS:Principles and Applications,Second Edition[M].The United States:Artech House Publishers,2006:14.
[2]WANG M,CAI H,PAN Z.BDS/GPS relative positioning for long baseline with undifferenced observations[J].Advances in Space Research,2015,55(1):113-124.
[3]WANG X J,TAO Z P,PENG H L.Introduction of BeiDou high precision location service platform and its application[C]//China Satellite Navigation Conference(CSNC 2017).Academic Exchange Center of China Satellite Navigation System Management Office:Organizing Committee of China Satellite Navigation Annual Conference,2017:5.
[4]LIU R L.Research on the Application of DGPS Based on BDS[J].Telecom Power Technology,2020,37(8):4-6.
[5]ZHANG H Y.A system design of landslide early warning based on BeiDou satellite navigation system[J].Electronic Test,2017,(24):11,13.
[6]YANG R G.Stability theory of rock and soil structure andLandslide prediction [M].Geology Press,2010.
[7]SUN X Y,JIN Y S,CHEN B,et al.Analysis on practices of dual-coupling aging curve and prediction criterion for landslide[J].Architecture Technology,2015,46(10):886-890.
[8]ZHANG M S,ZHANG X Z,CHEN B P,et al.A new self-adaptive Kalman filtering method for GPS kinematic positioning[J].Journal of Central South University,2003,34(5):543-546.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

卫星监测时空大数据蠕变特征提取及预警算法

Research on Creep Feature Extraction and Early Warning Algorithm Based on Satellite MonitoringSpatial-Temporal Big Data

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

Metrics

本文评价

推荐阅读 0

[1]	滕建, 滕飞, 李天瑞. 基于3D卷积和LSTM编码解码的出行需求预测 Travel Demand Forecasting Based on 3D Convolution and LSTM Encoder-Decoder 计算机科学, 2021, 48(12): 195-203. https://doi.org/10.11896/jsjkx.210400022
[2]	张新, 胡晓东, 魏嘉伟. 基于云计算的地理信息服务技术 Cloud Computing Based Geographical Information Service Technologies 计算机科学, 2019, 46(6A): 532-536.