Abnormal event detection of city slope monitoring data based on multi-sensor information fusion
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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。
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关键词:
- 时序数据 /
- 多传感器信息融合 /
- 卡尔曼滤波 /
- 动态时间弯曲 /
- 边坡监测数据异常事件检测
Abstract: 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. -
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 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 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 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 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 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 
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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
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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} $
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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
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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
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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
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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
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