Volume 41 Issue 2
Mar.  2022
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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.


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