滑坡裂缝计时序数据实时异常检测分析

doi:10.13722/j.cnki.jrme.2023.0291

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Abstract In order to address the issue of effectively identifying abnormal data in real-time monitoring of landslide cracks，occasional abnormal thresholds based on the various deformation stages of landslides are established. Additionally，a real-time anomaly detection method for time series data，utilizing interval prediction，is proposed. This method takes into consideration the temporal logic relationship between data and the correlated information of landslide deformation stages. Firstly，the time series characteristics of cumulative displacement of crack gauge are extracted using the autoregressive integrated moving average(ARIMA) model. Subsequently，an interval prediction model is constructed，and the sliding window algorithm is employed to predict sub-sequences. Secondly，to determine prospective abnormal points，a modified confidence interval(with ? = 0.05) is utilized，and occasional abnormal thresholds are established for different deformation stages of landslides. Finally，exceptional information is obtained through combined anomaly recognition. The research results indicate that this method accurately identifies abnormal data values and demonstrates universal applicability in real-time anomaly detection of time series data. By comparing the predicted interval of the model with the abnormal values，the real-time possibility of data abnormalities can be obtained. Furthermore，this method provides valuable data reference for intelligent decision-making in landslide monitoring and early warning.

Key words： slope engineering landslide time series model real-time anomaly detection monitoring and early warning

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	Articles by authors
	ZHANG Lei1
	JU Nengpan1
	HE Chaoyang1
	XIE Mingli1
	ZHANG Chengqiang1
	LIU Yang2

Cite this article:

ZHANG Lei1,JU Nengpan1,HE Chaoyang1, et al. Real-time anomaly detection and analysis of time series data for crack gauge in landslides[J]. , 2024, 43(1): 206-215.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2023.0291 OR https://rockmech.whrsm.ac.cn/EN/Y2024/V43/I1/206

[1]	何满潮，任树林，陶志刚. 滑坡地质灾害牛顿力远程监测预警系统及工程应用[J]. 岩石力学与工程学报，2021，40(11)：2 161-2 172. (HE Manchao，REN Shulin，TAO Zhigang. Remote monitoring and forecasting system of Newton force for landslide geological hazards and its engineering application[J]. Chinese Journal of Rock Mechanics and Engineering，2021，40(11)：2 161-2 172.(in Chinese))
[2]	陈贺，李亚军，房锐，等. 滑坡深部位移监测新技术及预警预报研究[J]. 岩石力学与工程学报，2015，34(增2)：4 063-4 070. (CHEN He，LI Yajun，FANG Rui，et al. A novel technique for monitoring deep displacement and early-warning of landslide[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(Supp.2)：4 063-4 070.(in Chinese))
[3]	朱鸿鹄，施斌，张诚成. 地质和岩土工程光电传感监测研究新进展——第六届OSMG国际论坛综述[J]. 工程地质学报，2020，28(1)：178-188.(ZHU Honghu，SHI Bin，ZHANG Chengcheng. Current progress and trends in opto-electronic sensor-based monitoring in geo-engineering—a summary of 6th OSMG-2017[J] Journal of Engineering Geology，2020，28(1)：178-188.(in Chinese))
[4]	ZHANG W. Geological disaster monitoring and early warning system based on big data analysis[J]. Arabian Journal of Geosciences，2020，13(18)：946.
[5]	何朝阳. 滑坡实时监测预警系统关键技术及其应用研究[博士学位论文]D]. 成都：成都理工大学，2020.(HE Chaoyang. Research on key technology and application of real-time monitoring and early warning system of landslide[Ph. D. Thesis][D]. Chengdu：Chengdu University of Technology，2020.(in Chinese))
[6]	LIU G，YE L X，CHEN Q Y，et al. Abnormal event detection of city slope monitoring data based on multi-sensor information fusion[J]. Bulletin of Geological Science and Technology，2022，41(2)：13-25.
[7]	王德文，杨力平. 智能电网大数据流式处理方法与状态监测异常检测[J]. 电力系统自动化，2016，40(14)：122-128.(WANG Dewen，YANG Liping. Stream processing method and condition monitoring anomaly detection for big data in smart grid[J]. Automation of Electric Power Systems，2016，40(14)：122-128.(in Chinese))
[8]	严英杰，盛戈皞，陈玉峰，等. 基于大数据分析的输变电设备状态数据异常检测方法[J]. 中国电机工程学报，2015，35(1)：52-59.(YAN Yingjie，SHENG Gehao，CHEN Yufeng，et al. An method for anomaly detection of state information of power equipment based on big data[J]. Proceedings of the CSEE，2015，35(1)：52-59.(in Chinese))
[9]	RASSAM M A，MAAROF M A，ZAINAL A，et al. Adaptive and online data anomaly detection for wireless sensor systems[J]. Knowledge-based Systems，2014，60(1)：44-57.
[10]	COOK A A，MISIRLI G，FAN Z，et al. Anomaly detection for IoT time-series data：A survey[J]. IEEE Internet of Things Journal，2020，7(7)：6 481-6 494.
[11]	王磊，何江涛，张振国，等. 基于信息筛选和拉依达准则识别地下水主要组分水化学异常的方法研究[J]. 环境科学学报，2018，8(3)：919-929.(WANG Lei，HE Jiangtao，ZHANG Zhenguo，et al. Research on the method to identify the outliers of the main components of groundwater based on the information screening coupled with PauTa criterion[J]. Acta Scientiae Circumstantiae，2018，8(3)：919-929.(in Chinese))
[12]	ZHAO C，YANG J. A robust skewed boxplot for detecting outliers in rainfall observations in real-time flood forecasting[J]. Advances in Meteorology，2019，2019：1-7.
[13]	吴国强，姚建锋，管敏渊，等. 基于DBSCAN的电力变压器故障诊断[J]. 武汉大学学报：工学版，2021，54(12)：1 172-1 179.(WU Guoqiang，YAO Jianfeng，GUAN Minyuan，et al. Fault diagnosis of power transformer based on DBSCAN[J]. Engineering Journal of Wuhan University，2021，54(12)：1 172-1 179.(in Chinese))
[14]	WANG Q Y，CAO S R，CHEN B Y，et al. Study on fault diagnosis of high voltage circirt breaker based on spectral kurtosis analysis and particle swarm optimization kmeans clustering algorithm[J]. High Voltage Apparatus，2019，55(5)：23-28，34.
[15]	KIM D，KOU H，KIM H J. Analysis of an SVM with resampling techniques for anomaly detection in a DSMS environment[J]. Kiise Transactions on Computing Practices，2018，24(9)：442-455.
[16]	吴金娥，王若愚，段倩倩，等. 基于反向k近邻过滤异常的群数据异常检测[J]. 上海交通大学学报，2021，55(5)：598-606.(WU Jin?e，WANG Ruoyu，DUAN Qianqian，et al. Collective data anomaly detection based on reverse k-nearest neighbor filtering[J]. Journal of Shanghai Jiao Tong University，2021，55(5)：598-606.(in Chinese))
[17]	梁桂兰，徐卫亚，谈小龙，等. 岩石高边坡安全监测数据的小波变换去噪处理[J]. 岩石力学与工程学报，2008，202(9)：1 837-1 844. (LIANG Guilan，XU Weiya，TAN Xiaolong，et al. Denoising processing of safety monitoring data for high rock slope based on wavelet transform[J]. Chinese Journal of Rock Mechanics and Engineering，2008，202(9)：1 837-1 844.(in Chinese))
[18]	SUN P，LI J，KOU X K，et al. Wind turbine status parameter anomaly detection based on prediction models and fuzzy theory[J]. Electric Power Automation Equipment，2017，37(8)：90-98.
[19]	王橙，许沛尧，马爱军，等. 基于ARIMA模型对传染病监测数据异常点的探测研究[J]. 现代预防医学，2018，45(4)：577-581.(WANG Chen，XU Peiyao，MA Aijun，et al. Detection of outliers in infectious disease surveillance data based on Arima model[J]. Modern Preventive Medicine，2018，45(4)：577-581.(in Chinese))
[20]	麻文刚，张亚东，郭进. 基于LSTM与改进残差网络优化的异常流量检测方法[J]. 通信学报，2021，42(5)：23-40.(MA Wengang，ZHANG Yadong，GUO Jin. Abnormal traffic detection method based on LSTM and improved residual neural network optimization[J]. Journal of Communications，2021，42(5)：23-40.(in Chinese))
[21]	贺可强，陈为公，张朋. 蠕滑型边坡动态稳定性系数实时监测及其位移预警判据研究[J]. 岩石力学与工程学报，2016，35(7)：1 377-1 385.(HE Keqiang，CHEN Weigong，ZHANG Peng. Real-time monitoring of dynamic stability coefficient and displacement criterion of the creep slope[J]. Chinese Journal of Rock Mechanics and Engineering，2016，35(7)：1 377-1 385.(in Chinese))
[22]	谢凡，夏开文，黄会宝，等. 基于多重散射波波速变化的滑坡实时监测方法与应用研究[J]. 岩石力学与工程学报，2020，39(11)：2 274-2 282.(XIE Fan，XIA Kaiwen，HUANG Huibao，et al. A new rock mass wave velocity monitoring method of slopes based on multiple scattering waves and its field application[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(11)：2 274-2 282.(in Chinese))
[23]	ZHENG T Y，CHEN G，WANG X Y，et al. Real-time intelligent big data processing：technology，platform and applications[J]. Science China-information Sciences，2019，62(8)：1-12.
[24]	宋杰. 大数据处理平台[M]. 北京：人民邮电出版社，2017：176-189.(SONG Jie. Big data processing platform[M]. Beijing：Posts and Telecom Press，2017：176-189.(in Chinese))
[25]	亓开元，赵卓峰，房俊，等. 针对高速数据流的大规模数据实时处理方法[J]. 计算机学报，2012，35(3)：477-490.(QI Kaiyuan，ZHAO Zhuofeng，FANG Jun，et al. Real-time processing for high speed data stream over large scale data[J]. Chinese Journal of Computers，2012，35(3)：477-490.(in Chinese))
[26]	YANG J，CAO Y D，SUN G C，et al. GF-3 data real-time processing method based on multi-satellite distributed data processing system[J]. Journal of Central South University，2020，27(3)：842-852.
[27]	邢保印，张炜怡，章广成，等. 基于变形速率分解的阶跃型滑坡预测——以呷爬滑坡为例[J].岩石力学与工程学报，2023，42(3)：685-697.(XING Baoyin，ZHANG Weiyi，ZHANG Guangcheng，et al. Prediction of step-type landslides based on deformation rate decomposition——a case study of Gapa landslide[J]. Chinese Journal of Rock Mechanics and Engineering，2023，42(3)：685-697.(in Chinese))
[28]	许强，曾裕平，钱江澎，等. 一种改进的切线角及对应的滑坡预警判据[J].地质通报，2009，28(4)：501-505.(XU Qiang，ZENG Yuping，QIAN Jiangpeng，et al. Study on a improved tangential angle and the corresponding landslide pre-warning criteria[J]. Geological Bulletin of China，2009，28(4)：501-505.(in Chinese))
[29]	许强，彭大雷，何朝阳，等. 突发型黄土滑坡监测预警理论方法研究——以甘肃黑方台为例[J]. 工程地质学报，2020，28(1)：111-121.(XU Qiang，PENG Dalei，HE Chaoyang，et al. Theory and method of monitoring and early warning for sudden loess landslide—a case study at Heifangtai terrace[J]. Journal of Engineering Geology，2020，28(1)：111-121.(in Chinese))
[30]	亓星. 突发型黄土滑坡监测预警研究[博士学位论文][D]. 成都：成都理工大学，2017.(QI Xing. Sudden loess landslide monitoring and early warning research—a case study of Gansu landslide in Heifangtai Loess[Ph. D. Thesis][D]. Chengdu：Chengdu University of Technology，2017.(in Chinese))