Rainy season land slide deformation monitoring method and rainy season land slide deformation monitoring system

Abstract

The invention provides a rainy season land slide deformation monitoring method and a rainy season land slide deformation monitoring system. A data integration center is used to acquire a displacement value and a speed value of a monitoring point of a current sampling time point according to a displacement sensor and a speed sensor, and is used to acquire displacement values and speed values of the previous one or two time point / s, and two given error covariance matrixes, and is used to acquire a measurement noise covariance matrix estimation value and a process noise covariance matrix estimation value of the current sampling time point according to the ordinal number of the current sampling time point and the above mentioned parameters. Kalman filtering of the displacement value and the speed value of the current time point can be carried out by adopting the acquired matrix estimation values, and then the optimal displacement estimation value and the optimal speed estimation value of the current sampling time point can be acquired. The displacement value and the speed value can be the displacement weighted average and the speed weighted average. The two covariance matrixes can be estimated according to the actually acquired displacement value and the actually acquired speed value, and are not given, and therefore the matrix estimation values are real, and the filtering result is accurate. The invention also relates to the rainy season land slide deformation monitoring system.

Description

technical field [0001] The invention relates to a monitoring method and system for landslide deformation in rainy season. Background technique [0002] Data fusion technology occupies a very important position in wireless sensor networks. Among them, Kalman data fusion technology is a well-known fusion technology. [0003] At present, the technology applied to landslide deformation monitoring in rainy season mainly uses wireless sensors to measure the displacement and velocity of landslides for a long time. The wireless sensors send the measured displacement data and velocity data to the data fusion center in the wireless sensor network. The center processes the displacement data and velocity data sent by the sensor, then artificially gives the process noise covariance matrix and measurement noise covariance matrix, and finally converts the given process noise covariance matrix and measurement noise covariance matrix Substituting into the Kalman filter algorithm, the measu...

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Problems solved by technology

[0004] The above-mentioned process noise covariance matrix and measurement noise covariance matrix are mainly given by the surveyors based on experience. However, due to the complexity and unpredictability of the actual environment, in the monitoring of landslide deformation, especially the annual rainfall is different. During the rainy season, the process noise and measurement noise of the system are different

Using the wrong process noise covariance matrix and measurement noise covariance matrix will inevitably lead to inaccurate landslide deformation monitoring data obtained by the data fusion center, thus affecting the overall monitoring effect of the landslide

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