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基于多传感器信息融合的城市边坡监测数据异常事件检测

刘刚 叶立新 陈麒玉 陈根深 范文遥

刘刚, 叶立新, 陈麒玉, 陈根深, 范文遥. 基于多传感器信息融合的城市边坡监测数据异常事件检测[J]. 地质科技通报, 2022, 41(2): 13-25. doi: 10.19509/j.cnki.dzkq.2022.0060
引用本文: 刘刚, 叶立新, 陈麒玉, 陈根深, 范文遥. 基于多传感器信息融合的城市边坡监测数据异常事件检测[J]. 地质科技通报, 2022, 41(2): 13-25. doi: 10.19509/j.cnki.dzkq.2022.0060
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

基于多传感器信息融合的城市边坡监测数据异常事件检测

doi: 10.19509/j.cnki.dzkq.2022.0060
基金项目: 

国家自然科学基金项目 U1711267

"地学长江计划"核心项目 CUGCJ1810

湖北省创新群体项目 2019CFA023

生物地质与环境地质国家重点实验室自主研究课题资助项目 2021

详细信息
    作者简介:

    刘刚(1967—), 男, 教授, 博士生导师, 主要从事地学大数据、地学信息工程方面的教学与科研工作。E-mail: liugang@cug.edu.cn

  • 中图分类号: X830.3

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

  • 摘要: 为预防和管控城市突发地质灾害造成的人民生命和财产损失, 国家针对城市地质灾害易发地区部署了大量的各类传感器, 用来感知和监测城市边坡等地质体的变化情况, 以支持对地质灾害的预警。从边坡监测数据特点和时序数据分析技术出发, 针对监测数据噪声混杂、模式分析困难、预警阈值的不确定性等问题, 给出了一种基于多传感器信息融合的边坡监测数据异常事件检测方法。主要工作包括: ①边坡监测数据变化模式可以归结为周期项、趋势项以及噪声项的叠加, 实践中在预处理基础上对边坡监测数据进行周期为24 h的重采样, 同时趋势项可以近似看作是经典的牛顿运动, 以此构建形变运动模型, 为卡尔曼滤波的状态转移提供理论支持; ②采用集中式衰减记忆卡尔曼滤波, 引入衰减记忆因子, 对多传感器边坡监测数据进行特征级融合, 降低了噪声的影响, 提高了边坡监测数据的可靠性; ③引入惩罚系数, 应用改进的动态时间弯曲算法对于周期序列数据进行相似性度量。在此基础上基于K-means聚类和局部异常因子分析对边坡监测数据进行异常检测, 并基于3σ准则确定预警阈值。该方法能将正常模式和异常模式的时序数据进行区分, 有效检测出边坡监测数据的异常, 为灾害预防提供支持。最后以深圳市典型边坡监测数据为例验证了此方法的可行性。

     

  • 图 1  实验区传感器布置示意图

    Figure 1.  Schematic diagram of the sensor layout in the experimental area

    图 2  实验数据小波去噪结果

    Figure 2.  Wavelet denoising results of the experimental data

    图 3  实验区水平位移监测点LX1(a)及倾角监测点QJ2(b)监测数据变化曲线

    Figure 3.  Monitoring data of horizontal displacement LX1 (a) and dip angle QJ2 (b)

    图 4  实验区水平位移监测点LX1(a)及倾角监测点QJ2(b)监测数据配准结果

    Figure 4.  Registration results of the horizontal displacement LX1 (a) and dip angle QJ2 (b) monitoring data in monitoring area

    图 5  本例中卡尔曼滤波数据融合结果

    Figure 5.  Results of Kalman filter data fusion

    图 6  各水平位移监测点地表水平位移曲线

    Figure 6.  Surface horizontal displacement curve of each monitoring point

    图 7  各水平位移监测点地表水平位移及卡尔曼滤波数据融合结果曲线

    Figure 7.  Surface horizontal displacement and Kalman filter data fusion curve of each monitoring point

    图 8  实验区边坡表面位移(近一年)

    Figure 8.  Slope surface displacement in the experimental area(nearly one year)

    图 9  监测数据每日采样次数统计直方图

    Figure 9.  Histogram of daily sampling times of monitoring data

    图 10  不同簇下的聚类结果

    a.簇1(11个元素);b.簇2(27个元素);
    c.簇3(230个元素);d.簇4(58个元素);
    e.簇5(39个元素)

    Figure 10.  Clustering results under different clusters

    图 11  聚类结果示意图

    Figure 11.  Schematic diagram of clustering results

    图 12  表面位移异常分布及日降雨量分布对比

    Figure 12.  Comparison of surface displacement anomalies and daily rainfall

    图 13  LOF异常检测结果(k=10)

    Figure 13.  Results of LOF anomaly detection(k=10)

    图 14  突变异常仿真数据

    Figure 14.  Simulation data of abrupt anomalies

    图 15  仿真实验局部异常因子分析结果图(LOF≥1.615 1为异常)

    Figure 15.  Result graph of the local anomaly factor of this simulation experiment(LOF≥1.615 1 for anomalies)

    表  1  边坡监测实验数据

    Table  1.   Monitoring experimental data of the slope

    期数 位移量/mm 期数 位移量/mm 期数 位移量/mm 期数 位移量/mm
    1 186.251 5 186.251 9 186.150 13 186.091
    2 186.253 6 186.256 10 186.096 14 186.066
    3 186.256 7 186.257 11 186.033
    4 186.256 8 186.260 12 186.098 290 188.378
    下载: 导出CSV

    表  2  实验区水平位移监测点LX1及倾角监测点QJ2监测数据

    Table  2.   Monitoring data of horizontal displacement and dip angle in monitoring area

    时间 LX1位移/mm 时间 QJ2倾角/(°)
    00∶04∶31 186.258 00∶04∶43 -0.945
    00∶14∶02 186.258 00∶15∶24 -0.947
    00∶24∶44 186.257 00∶24∶57 -0.948
    00∶35∶23 186.257 00∶35∶36 -0.947
    00∶45∶00 186.258 00∶46∶20 -0.948
    00∶56∶13 186.258 00∶54∶58 -0.947
    23∶26∶28 186.257 23∶48∶30 -0.949
    23∶36∶30 186.256 23∶59∶07 -0.949
    23∶47∶06 186.257
    23∶56∶44 186.255
    下载: 导出CSV
    Algorithm 1: Improved centralized Kalman filter algorithm
    Inputs:
    A过程误差矩阵W
    B观测误差矩阵V
    C状态转移矩阵A
    D测量矩阵H
    E误差协方差矩阵P
    F初始状态向量X
    Algorithm steps
      1 While Get(Lk) do
      2 $ \hat{\boldsymbol{X}}_{k}^{-}=\boldsymbol{A} \hat{\boldsymbol{X}}_{k-1}+\boldsymbol{B} \boldsymbol{u}_{k-1} $//预测 先验估计X
      3 $\boldsymbol{P}_{k}^{-}=\boldsymbol{A}\left(\lambda \boldsymbol{P}_{k-1}\right) \boldsymbol{A}^{\mathrm{T}}+\boldsymbol{Q} $//预测 先验估计误差协方差
      4 $\boldsymbol{K}_{k}=\boldsymbol{P}_{k}^{-} \boldsymbol{H}_{k}^{\mathrm{T}}\left(\boldsymbol{H}_{k} \boldsymbol{P}_{k}^{-} \boldsymbol{H}_{k}{}^{\mathrm{T}}+\boldsymbol{R}\right)-1 $//校正 后验估计卡尔曼增益
      5 $\hat{\boldsymbol{X}}_{k}=\hat{\boldsymbol{X}}{}_{k}^{-}+\boldsymbol{K}_{k}\left[\boldsymbol{Z}_{k}-\boldsymbol{H}_{k} \hat{\boldsymbol{X}}{}_{k}^{-}\right] $//校正 后验估计状态向量X
      6 $\boldsymbol{P}_{k}=\left[I-\boldsymbol{K}_{k} \boldsymbol{H}_{k}\right] \boldsymbol{P}_{k}^{-} $//校正 后验估计误差协方差
      7 End While
    Outputs:
    A最优估计状态向量$\hat{X} $
    下载: 导出CSV

    表  3  仿真实验模拟数据

    Table  3.   Simulation experiment data

    序号 真实值 传感器1测量值 传感器2测量值 融合值
    1 25.003 24.519 24.527 24.570
    2 25.038 24.829 26.115 25.254
    3 25.055 26.308 25.166 25.712
    4 25.127 24.303 25.867 25.457
    5 24.977 24.414 24.335 24.894
    6 24.818 24.314 24.808 24.677
    48 32.456 258 67 31.489 33.981 32.569
    49 32.911 428 72 32.389 34.334 33.258
    50 33.593 419 76 33.824 33.252 33.723
    下载: 导出CSV

    表  4  不同方法的RMSE

    Table  4.   RMSE of different methods

    方法 RMSE
    传感器1 0.710 2
    传感器2 0.758 9
    平均(传感器) 0.734 5
    改进的自适应集中式卡尔曼滤波 0.335 8
    下载: 导出CSV

    表  5  实验区3个边坡表面水平位移监测点的原始数据

    Table  5.   Original data from three monitoring points in the experimental area  位移/mm

    监测日期 LX1 LX2 LX3
    2020-03-18 188.294 187.763 187.543
    2020-03-25 188.256 187.716 187.732
    2020-04-01 188.224 187.674 186.910
    2020-04-08 188.756 188.402 187.762
    2020-04-15 188.792 188.239 187.790
    2020-04-22 188.346 187.663 187.443
    2020-04-29 188.745 187.861 187.542
    2020-05-06 188.156 187.263 187.145
    2021-03-03 186.147 185.333 184.981
    2021-03-10 186.182 185.744 185.061
    2021-03-17 186.214 185.912 185.355
    下载: 导出CSV
    Algorithm 2:K-nearest neighbor local anomaly detection algorithm
    Inputs:
    A Timeseries TS
    B Number of nearest neighbors K
    Algorithm steps
      1  L= len(TS);
      2  for i in range(1, L)
      3    for j in range(1, L)
      4      if i!=j:
      5        Data(i).add(point, dist(i, j))
      6  Sorted(Data(i))
      7  k-dist(p)= Data(i)[k][1]
      8  for i in Data(i):
      9    if Data(i)[1] < k-dist(p)
      10      Nk(p).add(Data(i)[k][0])
      11  for i in range(1, L)
      12    Data(i).lrd=getLrd
      13  for i in range(1, L)
      14    Data(i).lof=getLof
    Outputs:
    A Local Outlier Factor LOF
    下载: 导出CSV

    表  6  混淆矩阵

    Table  6.   Confusion matrix

    类别 正常 异常预警
    正常(100) 98 2
    异常(50) 12 38
    下载: 导出CSV
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