A method for denoising and early warning of radar monitoring data of open pit slope

Abstract

The application discloses a kind of open pit slope radar monitoring data denoising and early warning method, by obtaining the deformation basis data of slope radar monitoring in the process of open pit production, obtain initial deformation matrix sequence;Further, the initial deformation matrix sequence is carried out interval difference operation denoising processing, and the denoised slope deformation difference matrix sequence is obtained, and the denoising method effectively eliminates environmental factor noise;Further, the average value of each matrix element of denoised slope deformation difference matrix sequence is calculated, and the sequence with time characteristics is obtained;Finally, based on critical slowing down theory, the variance and autocorrelation coefficient of time characteristic sequence are calculated, the precursor warning information of slope failure is identified by the rapid change characteristics on the variance and autocorrelation coefficient and time relationship curve, with the advantages of effectively improving the signal-to-noise ratio and early warning accuracy of slope radar monitoring, important significance for open pit safety production, etc., suitable for slope radar monitoring deformation data denoising and early warning in the process of open pit mining.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of open-pit mine disaster remote sensing monitoring and early warning, and particularly relates to a kind of open-pit slope radar monitoring data denoising and early warning method. BACKGROUND

[0002] Open-pit mining is one of the main mining methods for developing mineral resources in China. With the consumption of shallow resources, open-pit mining has gradually moved to deep concave mining or underground mining. With the continuous expansion of the scale of mining depth and area, slope deformation instability disasters caused thereby are becoming more and more prominent, and in severe cases, cause personnel casualties and property losses. Rapid and accurate analysis of the deformation characteristics of the slope surface is an important guarantee for the safety production of open-pit mines, so how to realize long-term scientific and effective deformation monitoring of open-pit slopes and further grasp their deformation trend and stability condition has become a major problem in slope engineering.

[0003] Slope radar monitoring is a non-destructive, non-contact, real-time slope deformation monitoring technology, and plays an important role in the field of open-pit mine slope remote sensing monitoring and early warning. However, when it is applied in actual open-pit mining engineering, random noise pollution often occurs due to weather, blasting and other environmental factors, which reduces the signal-to-noise ratio and resolution of slope radar monitoring data, and causes problems such as false alarms in monitoring and early warning.

[0004] After searching by the research group, no prior document was found that is the same as or similar to the present subject.

[0005] Therefore, it is of great significance to develop an open-pit slope radar monitoring data denoising and early warning method. SUMMARY

[0006] The task of the present application is to overcome the shortcomings of the prior art and provide an open-pit slope radar monitoring data denoising and early warning method that can improve the signal-to-noise ratio and resolution of slope radar monitoring data and prevent false alarms in monitoring and early warning.

[0007] The task of the present application is accomplished by the following technical solution:

[0008] An open-pit slope radar monitoring data denoising and early warning method is provided for the problem of open-pit slope deformation monitoring. By improving the signal-to-noise ratio of slope radar deformation monitoring data, the accuracy of slope monitoring and early warning precursor information recognition is achieved. The specific process steps and conditions are as follows:

[0009] Step 1: Collect the slope deformation basic data of open-pit mine slope radar monitoring and convert it into the corresponding point cloud data sequence;

[0010] Step 2: In the radar monitoring area of the slope, a rectangular frame is used to define a deformation data processing range, and a group of initial deformation matrix sequences of the processing range is obtained by using point cloud data sequences, and the i-th frame of the initial deformation matrix is represented as:

[0011]

[0012] In the formula, D i is the i-th frame of the initial deformation matrix sequence of the deformation data processing range defined by the rectangular frame; x i mn is the matrix element of the mth row and nth column of the i-th frame of the initial deformation matrix; wherein the maximum row number and column number of the initial deformation matrix are denoted as M and N respectively;

[0013] Step 3: The obtained initial deformation matrix sequence is used for interval difference value operation denoising processing to obtain a denoised slope deformation difference matrix sequence;

[0014] Step 4: The average value of each frame of the denoised slope deformation difference matrix sequence is calculated and denoted as

[0015] (i=1, 2, …, k, k is the total frame number of the denoised slope deformation difference matrix sequence), to obtain a sequence about time characteristics

[0016] Step 5: Then, the variance and autocorrelation coefficient of the above sequence about time characteristics are calculated by using the critical slowing down theory, the curves of the variance and autocorrelation coefficient changing with time are drawn, the rapid change of the curve is regarded as the precursor information of the damage danger of the rectangular frame selected range on the slope, and the precursor information is used for monitoring and early warning of the slope.

[0017] The working principle of the present application is: on the basis of the radar monitoring deformation data of the slope, a differential denoising method is adopted, and a method for identifying the precursor information of the slope deformation damage danger is proposed based on the critical slowing down theory, which can improve the understanding of the slope disaster process, so as to respond and prevent in time.

[0018] Compared with the prior art, the present application has the following advantages or effects:

[0019] (1) Because the deformation basic data itself is used for denoising, the noise pollution caused by environmental factors is reduced, and the method is easy to operate.

[0020] (2) Since the variance and autocorrelation coefficient indexes for monitoring and early warning are calculated based on the critical slowing down theory, the accuracy of identifying the precursor information of the slope instability and damage is improved.

[0021] The critical slowing down theory described in the application file is a theory based on the critical slowing down phenomenon. In a dynamic system in nature, if the phase state of the system changes, that is, the system changes from an old phase state to a new phase state, a dispersion fluctuation phenomenon that is beneficial to the formation of the new phase state will appear near the critical point. The dispersion fluctuation is manifested as an increase in amplitude and a lengthening of fluctuation time, a slower recovery speed after disturbance, and a smaller ability to recover to the old phase. This phenomenon is called critical slowing down phenomenon. In the critical slowing down theory, the critical slowing down phenomenon is often observed by variance and autocorrelation coefficient. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 A process flow diagram of a radar monitoring data denoising and early warning method for an open pit slope according to the present application is shown.

[0023] Figure 2 For Figure 1 A schematic diagram of the process of denoising and calculating the variance and autocorrelation coefficient of the deformation basis data is shown.

[0024] The present application will be described in further detail below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] As Figures 1-2 shown, a radar monitoring data denoising and early warning method for an open pit slope, aiming at the problem of open pit slope deformation monitoring, improves the signal-to-noise ratio of slope radar deformation monitoring data, realizes the accuracy of slope monitoring and early warning precursor information recognition, and the specific process steps and conditions are as follows:

[0026] Step 1: Collecting slope deformation basis data of open pit slope radar monitoring and converting it into corresponding point cloud data sequence;

[0027] Step 2: Defining a deformation data processing range with a rectangular frame in the slope radar monitoring area, obtaining an initial deformation matrix sequence of the processing range using the point cloud data sequence, and the i-th frame of the initial deformation matrix is represented as:

[0028]

[0029] In the formula, D i is the i-th frame of the initial deformation matrix sequence defined by the rectangular frame of the deformation data processing range; x i mn is the matrix element of the m-th row and n-th column of the i-th frame of the initial deformation matrix; wherein the maximum number of rows and columns of the initial deformation matrix are denoted as M and N, respectively;

[0030] Step 3: Using the obtained initial deformation matrix sequence to perform interval difference operation denoising processing to obtain a denoised slope deformation difference matrix sequence;

[0031] Step 4: calculate the average value of each frame of the denoised side slope deformation difference matrix sequence, denoted as (i = 1, 2, …, k, k is the total frame number of the denoised side slope deformation difference matrix sequence), to obtain a sequence of time characteristics

[0032] Step 5: Then, the variance and autocorrelation coefficient of the above sequence of time characteristics are calculated by using the critical slowing down theory, and the curves of the variance and autocorrelation coefficient changing with time are drawn, the rapid change of the curve is the precursor information of the danger of the existence of the rectangular frame selection range on the side slope, and the monitoring and early warning of the side slope are realized by using the precursor information.

[0033] The process of the application can be further:

[0034] The expression of the step 3 denoising processing of interval difference operation on the initial deformation matrix sequence is:

[0035]

[0036] In the formula, ID i is the i-th frame of the denoised side slope deformation difference matrix; D i and D i-1 are the i-th frame and the i-1-th frame of the initial deformation matrix sequence respectively; y i mn is the matrix element of the m-th row and the n-th column of the i-th frame of the denoised side slope deformation difference matrix; wherein the maximum row number and the maximum column number of the denoised side slope deformation difference matrix are denoted as M and N respectively;

[0037] The method for calculating the average value of each frame of the denoised side slope deformation difference matrix sequence in the step 4 is to calculate the average value of the elements in the denoised side slope deformation difference matrix, and the expression is:

[0038]

[0039] In the formula, is the average value of the i-th frame of the denoised side slope deformation difference matrix; y i mn is the matrix element of the m-th row and the n-th column of the i-th frame of the denoised side slope deformation difference matrix;

[0040] The step 5 calculates the variance and autocorrelation coefficient of the denoised side slope deformation difference matrix sequence by using the critical slowing down theory, and the calculation expression is:

[0041]

[0042]

[0043] In the formula, is a variance; is an autocorrelation coefficient; lag is a lag step length of the characteristic time series; k is a total frame number of the denoised slope deformation difference matrix sequence, representing the length of the time characteristic sequence used for calculating the variance and autocorrelation coefficient.

[0044] As described above, the application can be better implemented. The above-mentioned embodiments are only the best mode of the application, but the embodiments of the application are not limited by the above-mentioned embodiments, and other changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the application should be equivalent replacement methods, which are all included in the protection scope of the application.

Claims

1. A method for denoising and early warning of open-pit slope radar monitoring data, aiming at the problem of open-pit slope deformation monitoring, characterized in that By improving the signal-to-noise ratio of slope radar deformation monitoring data, the accuracy of slope monitoring and early warning precursor information recognition is realized, and the specific process steps and conditions are as follows: Step 1: Collecting the slope deformation basic data of open-pit mine slope radar monitoring, and converting it into the corresponding point cloud data sequence; Step 2: In the slope radar monitoring area, a deformation data processing range is defined by using a rectangular frame, and a set of initial deformation matrix sequences of the processing range is obtained by using the point cloud data sequence, and the i-th frame of the initial deformation matrix is represented as: ； In the formula, initial deformation matrix sequence of the deformation data processing range defined by the rectangular frame i-th frame; the matrix element of the m-th row and n-th column of the initial deformation matrix of the i-th frame; wherein the maximum row number and column number of the initial deformation matrix are denoted as M and N, respectively. Step 3: The obtained initial deformation matrix sequence is used for interval difference value operation denoising processing to obtain the denoised slope deformation difference matrix sequence; Step 4: Calculate the average value of each frame of the denoised side slope deformation difference matrix sequence, denoted as i = 1, 2, …, k, k is the total frame number of the denoised side slope deformation difference matrix sequence, and a sequence about time characteristics is obtained , … ; Step 5: Then, the variance and autocorrelation coefficient of the above sequence about time characteristics are calculated by using the critical slowing down theory, the curve of the variance and autocorrelation coefficient changing with time is drawn, the rapid change of the curve is regarded as the precursor information of the danger of damage in the rectangular frame selected range on the slope, and the monitoring and early warning of the slope are realized by using the precursor information; The calculation expression of the variance and autocorrelation coefficient of the denoised slope deformation difference matrix sequence calculated by using the critical slowing down theory is: ； ； wherein is the variance; is the autocorrelation coefficient; lag is the lag step of the characteristic time series; k is the total frame number of the denoised slope deformation difference matrix sequence, representing the length of the time characteristic series used to calculate the variance and autocorrelation coefficient.

2. The method according to claim 1, characterized in that The expression of the denoising processing of the initial deformation matrix sequence in step 3 by using interval difference value operation is: ； In the formula, is the i-th frame of the denoised side slope deformation difference matrix; and are the i-th frame and the i-1-th frame of the initial deformation matrix sequence respectively; is the m-th row and n-th column matrix element of the i-th frame of the denoised side slope deformation difference matrix; wherein the maximum row number and column number of the denoised side slope deformation difference matrix are denoted as M and N respectively.

3. The method according to claim 1, characterized in that The method for calculating the average value of each frame of the denoised slope deformation difference matrix sequence in step 4 is to calculate the average value of the elements in the denoised slope deformation difference matrix, and the expression is: ； In the formula, is the average value of the edge slope deformation difference matrix after denoising of the i-th frame; is the matrix element of the m-th row and n-th column of the edge slope deformation difference matrix after denoising of the i-th frame.

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