Volume 41 Issue 2
Mar.  2022
Turn off MathJax
Article Contents
Liu Gang, Ye Lixin, Chen Qiyu, Chen Genshen, Fan Wenyao. 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. doi: 10.19509/j.cnki.dzkq.2022.0060
Citation: Liu Gang, Ye Lixin, Chen Qiyu, Chen Genshen, Fan Wenyao. 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. doi: 10.19509/j.cnki.dzkq.2022.0060

Abnormal event detection of city slope monitoring data based on multi-sensor information fusion

doi: 10.19509/j.cnki.dzkq.2022.0060
  • Received Date: 22 Aug 2021
  • To prevent and control the loss of people's lives and property caused by sudden urban geological disasters, China has deployed a large number of sensors for urban geological disaster-prone areas to perceive changes in urban underground space. In this article, based on the characteristics of slope monitoring data and the analysis technology of time series data, aiming at problems such as noise mixtures in monitoring data, the difficulty of mode analysis and the uncertainty of early warning thresholds, a method of abnormal event detection in slope monitoring data based on multisensor information fusion is proposed. The results show that: ① Aiming at the disadvantage that the optimal estimation of the Kalman filter requires known noise information, the attenuation memory factor is introduced, and the centralized attenuation memory Kalman filter is used to fuse the multisensor slope monitoring data, which reduces the influence of noise and improves the reliability of slope monitoring data. ② The change mode of slope monitoring data can be summed up as the superposition of periodic term, trend term and noise term. The period is 24 hours, and the trend term can be approximately regarded as the classic Newtonian motion. Based on this, the deformation motion model can be constructed to provide theoretical support for the state transfer of the Kalman filter. ③ The penalty coefficient is introduced to make the improved DTW have a better measurement effect for the periodic sequence. On this basis, anomaly detection is carried out on the slope monitoring data based on K-means clustering, and local anomaly factors are used to analyse the abnormal conditions of the monitoring data. This method can distinguish the time series data of thenormal mode and abnormal mode better, detect abnormal slope monitoring data effectively, and provide guarantees for disaster prevention. Therefore, in view of the insufficiency of slope monitoring data processing and analysis processes, different information fusion technologies are adopted to improve the reliability and robustness of slope monitoring data. The feasibility of the proposed method is verified by slope monitoring data in Shenzhen.

     

  • loading
  • [1]
    吴冲龙, 刘刚, 王力哲, 等. 基于大数据的城市地质环境智能监管思路与方法[J]. 地质科技通报, 2020, 39(1): 157-163. doi: 10.19509/j.cnki.dzkq.2020.0117

    Wu C L, Liu G, Wang L Z, et al. Thinking and methods of intelligent supervision of urban geological environment based on big data[J]. Bulletin of Geological Science and Technology, 2020, 39(1): 157-163(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2020.0117
    [2]
    刘军旗, 刘强, 刘千慧, 等. 大数据时代地质灾害数据管理及应用模式探讨[J]. 地质科技通报, 2021, 40(6): 276-282, 292. doi: 10.19509/j.cnki.dzkq.2021.0627

    Liu J Q, Liu Q, Liu Q H, et al. Discussion of geological hazard data management and application model in big data era[J]. Bulletin of Geological Science and Technology, 2021, 40(6): 276-282, 292(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2021.0627
    [3]
    许强. 对滑坡监测预警相关问题的认识与思考[J]. 工程地质学报, 2020, 28(2): 360-374. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202002017.htm

    Xu Q. Understanding the landslide monitoring and early warning: Consideration to practical issues[J]. Journal of Engineering Geology, 2020, 28(2): 360-374(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202002017.htm
    [4]
    仝德富, 谭飞, 苏爱军, 等. 基于多源数据的谭家湾滑坡变形机制及稳定性评价[J]. 地质科技通报, 2021, 40(4): 162-170. doi: 10.19509/j.cnki.dzkq.2021.0432

    Tong D F, Tan F, Su A J, et al. Deformation mechanism and stability evaluation of Tanjiawan landslide based on multi-source data[J]. Bulletin of Geological Science and Technology, 2021, 40(4): 162-170(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2021.0432
    [5]
    熊寄然. GNSS技术在城市边坡监测中的应用[J]. 重庆建筑, 2019, 18(8): 47-49. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJZ201908021.htm

    Xiong J R. Application of GNSS technology in urban slope monitoring[J]. Chongqing Architecture, 2019, 18(8): 47-49(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-CQJZ201908021.htm
    [6]
    王腾军, 赖百炼, 叶俊华, 等. 基于GM(1, 1)数据融合算法的滑坡预测研究[J]. 测绘通报, 2012(5): 63-65. https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB201205021.htm

    Wang T J, Lai B L, Ye J H, et al. Research on landslide prediction based on GM(1, 1) data fusion algorithm[J]. Bulletin of Surveying and Mapping, 2012(5): 63-65(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-CHTB201205021.htm
    [7]
    侯圣山, 李昂, 陈亮, 等. 基于普适型仪器的滑坡监测预警初探: 以甘肃兰州岷县三处滑坡为例[J]. 中国地质灾害与防治学报, 2020, 31(6): 47-53. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202006006.htm

    Hou S S, Li A, Chen L, et. al. Application of universal geo-hazard monitoring instruments in landslides and early warning of three landslides in Gansu Province: A case study in Minxian County and Lanzhou City of Gansu Province[J]. The Chinese Journal of Geological Hazard and Control, 2020, 31(6): 47-53(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH202006006.htm
    [8]
    王智伟, 王利, 黄观文, 等. 基于BP神经网络的滑坡监测多源异构数据融合算法研究[J]. 地质力学学报, 2020, 26(4): 575-582. https://www.cnki.com.cn/Article/CJFDTOTAL-DZLX202004014.htm

    Wang Z W, Wang L, Huang G W, et al. Research on multi-source heterogeneous data fusion algorithm of landslide monitoring based on BP neural network[J]. Journal of Geomechanics, 2020, 26(4): 575-582(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZLX202004014.htm
    [9]
    刘超云, 尹小波, 张彬. 基于Kalman滤波数据融合技术的滑坡变形分析与预测[J]. 中国地质灾害与防治学报, 2015, 26(4): 30-35. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201504007.htm

    Liu C Y, Yin X B, Zhang B. Analysis and prediction of landslide deformations based on data fusion technology of Kalman-filter[J]. The Chinese Journal of Geological Hazard and Control, 2015, 26(4): 30-35(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDH201504007.htm
    [10]
    张福荣. 自适应卡尔曼滤波在变形监测数据处理中的应用研究[D]. 西安: 长安大学, 2009.

    Zhang F R. Application of adaptive kalman filter in deformation monitoring data processing[D]. Xi'an: Chang'an University, 2009(in Chinese with English abstract).
    [11]
    朱自强, 吴顺川, 刘洋, 等. 基于自适应Kalman滤波融合技术的边坡变形分析[J]. 矿业研究与开发, 2020, 40(1): 16-21. https://www.cnki.com.cn/Article/CJFDTOTAL-KYYK202001004.htm

    Zhu Z Q, Wu S C, Liu Y, et al. Slope deformation analysis based on adaptive Kalman-filter fusion technology[J]. Mining Research and Development, 2020, 40(1): 16-21(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-KYYK202001004.htm
    [12]
    吴艳. 多传感器数据融合算法研究[D]. 西安: 西安电子科技大学, 2003.

    Wu Y. Study of multisensor data fusion algorithms[D]. Xi'an: Xidian University, 2003(in Chinese with English abstract).
    [13]
    Novikov I Y. Asymptotics of the roots of bernstein polynomials used in the construction of modified daubechies wavelets[J]. Mathematical Notes, 2002, 71(1): 217-229.
    [14]
    李秀珍. 滑坡变形突变异常的小波识别方法[J]. 自然灾害学报, 2015, 24(6): 50-56. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH201506006.htm

    Li X Z. Wavelet identification method for deformation abnormality of landslides[J]. Journal of Natural Disasters, 2015, 24(6): 50-56(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH201506006.htm
    [15]
    李新源, 贺可强, 贾玉跃, 等. 堆积层滑坡位移矢量角异常变化分析: 以新滩滑坡为例[J]. 价值工程, 2010, 29(17): 88-89. https://www.cnki.com.cn/Article/CJFDTOTAL-JZGC201017060.htm

    Li X Y, He K Q, Jia Y Y, et al. Analysis on the displacement vector angle abnormal of colluvial landslide: Xintan landslide as an example[J]. Value Engineering, 2010, 29(17): 88-89(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-JZGC201017060.htm
    [16]
    袁勇, 许强, 陈聆. 基于人工免疫算法的数据压缩技术在滑坡异常提取中的应用研究[J]. 成都理工大学学报: 自然科学版, 2007, 34(6): 621-625. https://www.cnki.com.cn/Article/CJFDTOTAL-CDLG200706008.htm

    Yuan Y, Xu Q, Chen L. Application of data compression based on AIS to the extraction of landslide anomaly[J]. Journal of Chengdu University of Technology: Science & Technology Edition, 2007, 34(6): 621-625(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-CDLG200706008.htm
    [17]
    Li D, Liu S, Zhang H. A boundary-fixed negative selection algorithm with online adaptive learning under small samples for anomaly detection[J]. Engineering Applications of Artificial Intelligence, 2016, 50: 93-105. doi: 10.1016/j.engappai.2015.12.014
    [18]
    Safa M, Sari P A, Shariati M, et al. Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes[J]. Physica A: Statistical Mechanics and Its Applications, 2020, 550: 124046.
    [19]
    陈小惠, 万德钧, 王庆. 模糊逻辑在分布式多目标跟踪融合中的应用研究[J]. 东南大学学报: 自然科学版, 2003, 33(6): 754-757. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX200306017.htm

    Chen X H, Wan D Y, Wang Q. Study for distributed multitarget tracking fusion using fuzzy logic[J]. Journal of Southeast University: Natural Science Edition, 2003, 33(6): 754-757(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX200306017.htm
    [20]
    卢鋆, 吴忠望, 王宇, 等. 基于KNN算法的异常行为检测方法研究[J]. 计算机工程, 2007, 33(7): 133-134, 138. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC200707048.htm

    Lu Y, Wu Z W, Wang Y, et al. Research on abnormal behavior detection based on KNN algorithm[J]. Computer Engineering, 2007, 33(7): 133-134, 138(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC200707048.htm
    [21]
    Breunig M M, Kriegel H P, Raymond T N, et al. LOF: Identifying density-based local outliers[C]//Proc. ACM SIGMOD Int. Conf. on Management of Data. Dalles, TX, USA: [s. n. ], 2000: 1-12.
    [22]
    武小年, 彭小金, 杨宇洋, 等. 入侵检测中基于SVM的两级特征选择方法[J]. 通信学报, 2015, 36(4): 23-30. https://www.cnki.com.cn/Article/CJFDTOTAL-TXXB201504003.htm

    Wu X N, Peng X J, Yang Y Y, et al. Two-level feature selection method based on SVM for intrusion detection[J]. Journal on Communications, 2015, 36(4): 23-30(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-TXXB201504003.htm
    [23]
    王思敬, 杨志法, 刘竹华. 地下工程岩体稳定分析[M]. 北京: 科学出版社, 1984.

    Wang S J, Yang Z F, Liu Z H. Stability analysis of underground engineering rock mass[M]. Beijing: Science Press, 1984(in Chinese).
    [24]
    Thuy H, Anh D T, Chau V. Efficient segmentation-based methods for anomaly detection in static and streaming time series under dynamic time warping[J]. Journal of Intelligent Information Systems, 2021, 56(3): 121-146.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article Views(499) PDF Downloads(75) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return