CN113899478B - Digital image-based ground stress/historical stress measuring method - Google Patents
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Abstract
The invention aims to provide a digital image-based ground stress/historical stress measuring method and system, which are used for carrying out twice loading on a cuboid rock sample to be measured and acquiring an image of the surface of a standard cuboid rock sample in real time in the process; calculating the displacement value of each pixel point in the image at each moment; converting the displacement data of all pixel points in the two-time loading into gray data, then calculating the gray difference of the most relevant pixel points under the same moment of the two-time loading, making a gray difference-moment scatter diagram of all the most relevant pixel points, and setting a sliding window to traverse the scatter diagram; and fitting scattered points falling in the sliding window by adopting a linear function, and judging the crustal stress/historical stress according to the sudden change condition of a slope fitting coefficient k. The method realizes the measurement of the historical stress/ground stress by utilizing the digital image for the first time, makes a gray level difference-time curve by combining the displacement data of the rock sample, introduces the statistics to judge the mutation point, and improves the accuracy rate of judging the ground stress/historical stress point.
Description
Technical Field
The invention relates to a historical stress/ground stress measuring method and system based on digital images.
Background
The ground stress refers to an initial stress inside the rock formed by the compression due to the self weight of the rock and geological actions. The ground stress is the fundamental action force for causing collapse and damage of engineering such as underground caverns, tunnels, mine tunnels and the like. Because these caverns are excavated and lose support, the ground stress can stress the rock to deform into the cavern. For tunnel deformation failure, rock parameters and tunnel parameters are internal causes, and then the crustal stress is an external cause. The ground stress is very complex and varies with geographical location and burial. Therefore, the measurement of the ground stress data of the area is very important for the design, construction, operation safety analysis and calculation of the underground engineering tunnel of the area, and the like.
Disclosure of Invention
The invention aims to provide a historical stress/ground stress measuring method and system based on a surface strain digital image.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
the digital image-based geostress/historical stress measuring method comprises the following steps:
s1, applying a prior stress to the standard cuboid rock sample by using an indoor loading instrument as a historical stress, or obtaining a standard cuboid rock sample to be measured for ground stress from actual underground engineering;
the standard cuboid rock sample is loaded twice indoors, the two times of loading are uniform linear loading, the loading rate, the maximum force value and the loading time length T are the same, and T isxMoment T of loading stress under momentx，x＝[1，2，…，N]；
S2, setting the image acquisition frequency as the frequency of stress data acquisition, and acquiring the image of the surface of the standard cuboid rock sample in real time in the two loading processes;
the collected image is a real-time image of any surface of a standard cuboid rock sample parallel to the load direction;
s3, calculating the displacement value of each pixel point in the image at each moment, wherein N displacement values are the total displacement values at N moments;
s4, converting displacement data of all pixel points under N moments in two times of loading into gray data, then calculating the difference value of the gray data of the most relevant pixel points under the same moment of two times of loading to obtain gray difference, and making a gray difference-moment scatter diagram of all the most relevant pixel points;
s5, setting a sliding window traversal scatter diagram with the length of H in each gray level difference-moment scatter diagram, wherein the moving step length of the sliding window is H/4; fitting scattered points falling in a sliding window by adopting a linear function and obtaining a slope fitting coefficient k;
s6, when more than 50% of fitting coefficient k suddenly changes at a certain time t, determining the force at the time t + [ H/2], and correspondingly determining the stress normal stress component or the historical stress normal stress component;
s7, particularly, for the geostress, acquiring real-time images of standard cuboid rock samples in six different directions based on the method from S2 to S6, acquiring positive stress components in the six different directions, and calculating to obtain a three-dimensional geostress tensor.
As a further improvement of the invention, when the picture is collected, the surface to be shot of the standard cuboid rock sample is irradiated by a light source. The method can enhance the definition of the collected picture and reduce the field error of the comparison of the front picture and the rear picture.
As a further improvement of the present invention, in S2, an RGB image of the surface of a standard rectangular parallelepiped rock sample is acquired.
As a further improvement of the present invention, in S3, the real-time displacement value of the most relevant pixel point at each loading time is calculated by searching the most relevant pixel point of the image at each loading time and by the position change of the most relevant pixel point.
Further, the pixel displacement calculation method is as follows:
selecting a monitoring point a in the first graph, and constructing a square pixel block A by taking the monitoring point a as a central point;
selecting a pixel point at the coordinate position such as the monitoring point a from the second graph as a central point, constructing a square pixel block as a search range, optionally selecting a point in the search range, and constructing a square pixel block B with the same size as the square pixel block A by using the point as the central point;
calculating the normalized cross-correlation coefficient of the pixel blocks A and B;
and calculating correlation coefficients of all pixel points in the search range according to the steps, finding out a pixel point corresponding to the maximum correlation coefficient from the correlation coefficients as a corresponding point b of the point a in the second graph, and calculating the displacement of the point a based on the coordinates of the points a and b.
As a further improvement of the present invention, the side length of the search range is determined according to the load change; the larger the load, the larger the search range.
As a further improvement of the present invention, in S4, the manner of converting the displacement data into the grayscale data is as follows: the displacement data is mapped to a value range of [0, 255 ].
As a further improvement of the present invention, in S5, the linear function form is: y is kx + m; wherein k and m are fitting coefficients.
As a further improvement of the present invention, in S6, the mutation of k is determined by:
setting a judgment threshold value C;
when | kx+1－kx|/kx>If C is true, the fitting coefficient k is mutated; otherwise, no mutation occurs;
kxfinger TxThe slope of the time of day fits the coefficient k.
Another object of the present invention is to provide a digital image-based geostress/historical stress measurement system, comprising:
the indoor loading instrument is used for carrying out twice loading on the standard cuboid rock sample to be measured; the standard cuboid rock sample to be measured is a standard cuboid rock sample to which early stress is applied by an indoor loading instrument or a standard cuboid rock sample obtained from actual underground engineering;
the shooting device is used for shooting the standard cuboid rock sample to be measured in the two loading processes, and the shot and collected image is a real-time image of any surface of the standard cuboid rock sample parallel to the load direction and is transmitted to the terminal;
the terminal comprises an image processing module used for image processing;
the image processing module includes:
the pixel displacement calculation unit is used for calculating the pixel displacement of the image shot at the initial moment in each loading process and obtaining a displacement numerical matrix;
the image conversion unit is used for converting the displacement numerical matrix into gray data;
the stress calculation unit is used for calculating the gray level difference of the same pixel point at the same moment in two times of loading and making a gray level difference-moment scatter diagram, wherein the stress is the load applied by the loading instrument at the corresponding moment; and searching a gray level difference-stress scatter diagram of each pixel point by adopting a sliding window with the length of H, wherein the moving step length of the sliding window is H/4, fitting scatter points falling in the sliding window by adopting a linear function to obtain a fitting coefficient k, and determining the force at the t + [ H/2] moment when more than 50% of the pixel points are subjected to sudden change in the fitting coefficient k of the sliding window, so as to correspond to the normal stress component or the historical stress.
The invention has the following beneficial effects:
(1) the digital image technology is applied to the historical stress and the ground stress measurement for the first time, and a digital image-based ground stress/historical stress measurement method is provided;
(2) the ground stress/ground stress is found based on the gray level difference-time curve through the displacement mutation point of each point of the rock sample, and the mutation point is judged by introducing statistics, namely, the gray level difference-time curve of more than 50% of all pixel points of one image has mutation at a certain time, and the mutation is considered as the mutation of the whole rock sample. In terms of probability, the probability that only one pixel point has errors is very high, but the probability that more than 50% of pixels have errors is very low, and the method improves the accuracy of judging the ground stress/historical stress point.
(3) The method is a method for measuring the ground stress and the historical stress in a non-contact mode.
Drawings
Fig. 1 is a gray scale difference-stress scatter plot.
Fig. 2 is a graph of k-value change.
Detailed Description
The technical scheme of the invention is further explained by the following description and the specific implementation mode in combination with the attached drawings.
Example 1
The historical stress and ground stress measuring method based on the surface strain digital image comprises the following steps:
s1, applying a pre-stress to the standard cuboid rock sample by using an indoor loading instrument, or obtaining a rock sample to be measured for the ground stress from the actual underground engineering, and making the rock sample into standard cuboid rock samples in 6 different directions;
the standard cuboid rock sample is loaded twice indoors, the two times of loading are uniform linear loading, the loading rate, the maximum force value and the loading time length T are the same, and T isxMoment T of loading stress under momentx，x＝[1，2，…，N]；
S2, irradiating one side surface of each standard cuboid rock sample by using a light source, then collecting pictures of the real-time state of an observation target by using a set high-definition camera, setting the image collection frequency as the stress data collection frequency, and collecting the images of the surface of the standard cuboid rock sample in real time in the two loading processes.
The collected image is transmitted to an image processing module, and the computer image processing module carries out digital processing on the obtained image and converts the image into a matrix containing RGB values of each pixel point;
s3, digital image correlation analysis is carried out on the collected images, and the most relevant pixel points of the two images in each loading are searched and the displacement of the pixel points is calculated.
Taking the first loading of the first and second images as an example: a monitoring point of the first image is used as a central point to construct a square pixel block A with the side length L of one pixel, and the monitoring point is set as a point a. And selecting any point B in a search range on the newly added image, and constructing a square pixel block B with the same side length L as pixels by taking the point B as a central point, wherein the search range is a square area with the half side length of 500 pixels on the newly added image by taking a pixel point at the position of the point a as the center.
The normalized cross-correlation coefficients of pixel blocks a and B are calculated as follows:
in the formula: rnccThe gray scale correlation coefficients of the R color components of the two pixel blocks with the same size are respectively calculated to obtain corresponding correlation coefficient values for the G, B color components, and then the average value of the three is taken as the correlation coefficient values of the two pixel blocks;
f (x, y) -the gray value of the pixel color component at coordinate (x, y) on the image, ranging from 0-255;
g (x ', y') — the gray value of the pixel color component at coordinate (x ', y') on the image, ranging from 0-255;
m is an integer obtained by dividing the side length L of the pixel block by 2;
and calculating the correlation coefficient of all the pixel points in the search range defined on the image according to the formula, and then finding out the pixel point corresponding to the maximum correlation coefficient from the correlation coefficient as the corresponding point b of the point a on the first image on the newly added image. Respectively recording the coordinates of the point a and the coordinates of the point b, calculating according to the following formula to obtain the displacement value of the point, and analyzing the displacement of other monitoring points to be the same as the displacement value; each pixel point has N displacement values at N moments;
in the formula: x is the number ofa、ya-the abscissa and the ordinate of the point a on the first image respectively;
x’b、y’b-respectively the abscissa and ordinate of the point b on the newly added image;
Δ x-displacement of point a in the lateral direction;
Δ y — displacement of point a in the vertical direction;
Δ s-the total displacement of point a.
S4, converting the displacement data of all the pixel points at N moments into gray data, then calculating the difference value of the gray data of the most relevant pixel points loaded twice at the same moment to obtain the gray difference, and making a gray difference-moment scatter diagram of all the most relevant pixel points.
S5, searching a gray difference-stress scatter diagram of each pixel point by using a sliding window with the length of H, wherein the moving step length of the sliding window is H/4, and the sliding window is shown in figure 1.
Fitting scattered points falling on the sliding window by adopting a linear function to obtain a fitting coefficient k, wherein a fitting curve of the sliding window adopts the following formula:
y＝kx+m
in the formula: k and m are fitting coefficients.
S6, setting a change threshold C of the fitting coefficient k, wherein the change threshold C of the fitting coefficient k is used for representing whether the scatter fitting coefficient k in the two sliding windows is mutated or not, and the judgment standard is as follows:
|k2－k1|/k1>C
if the above formula is true, the fitting coefficient k is mutated; otherwise, no mutation occurs.
And counting the pixel points of which the fitting coefficient k of the sliding window exceeds a change threshold C, and determining the force at the t + [ H/2] moment when more than 50% of the fitting coefficient k has sudden change at a certain moment t, so as to correspondingly determine the normal stress component or the historical stress of the stress.
S7 particularly, for the ground stress, positive stress tensors in 6 different directions are transformed by an elastic force calculation formula to obtain three principal stress tensors, that is, a three-dimensional ground stress tensor.
Claims (10)
1. The method for measuring the crustal stress/historical stress based on the digital image is characterized by comprising the following steps:
s1, applying a prior stress to the standard cuboid rock sample by using an indoor loading instrument as a historical stress, or obtaining a standard cuboid rock sample to be measured for ground stress from actual underground engineering;
the standard cuboid rock sample is loaded twice indoors, the two times of loading are uniform linear loading, the loading rate, the maximum force value and the loading time length T are the same, and T isxStress under load at time = load rate at time Tx，x=[1，2，…，N]；
S2, setting the image acquisition frequency as the frequency of stress data acquisition, and acquiring the image of the surface of the standard cuboid rock sample in real time in the two loading processes;
the collected image is a real-time image of any surface of a standard cuboid rock sample parallel to the load direction;
s3, calculating the displacement value of each pixel point in the image at each moment, wherein N displacement values are the total displacement values at N moments;
s4, converting displacement data of all pixel points under N moments in two times of loading into gray data, then calculating the difference value of the gray data of the most relevant pixel points under the same moment of two times of loading to obtain gray difference, and making a gray difference-moment scatter diagram of all the most relevant pixel points;
s5, setting a sliding window traversal scatter diagram with the length of H in each gray level difference-moment scatter diagram, wherein the moving step length of the sliding window is H/4; fitting scattered points falling in a sliding window by adopting a linear function and obtaining a slope fitting coefficientk；
S6 at a certain momenttLower, fitting coefficient of more than 50%kIf a mutation occurs, the determination is madet+[H/2]The force at the moment, corresponding to the stress normal stress component or the historical stress normal stress component;
s7, acquiring real-time images of standard cuboid rock samples in six different directions based on the method of S2-S6 for the geostress, acquiring positive stress components in the six different directions, and calculating to obtain a three-dimensional geostress tensor.
2. The method as claimed in claim 1, characterized in that, when the pictures are taken, the surface to be photographed of the standard cuboid rock sample is illuminated by means of a light source.
3. The method according to claim 1, wherein in the S2, RGB images of a standard cuboid rock sample surface are acquired.
4. The method according to claim 1, wherein in S3, the real-time displacement value of the most relevant pixel point at each loading time is calculated by searching for the most relevant pixel point of the image at each loading time and by the position change of the most relevant pixel point.
5. The method according to claim 4, wherein in the step S3, the pixel displacement is calculated as follows:
selecting a monitoring point a in the first graph, and constructing a square pixel block A by taking the monitoring point a as a central point;
selecting a pixel point at the coordinate position such as the monitoring point a from the second graph as a central point, constructing a square pixel block as a search range, optionally selecting a point in the search range, and constructing a square pixel block B with the same size as the square pixel block A by using the point as the central point;
calculating the normalized cross-correlation coefficient of the pixel blocks A and B;
and calculating correlation coefficients of all pixel points in the search range according to the steps, finding out a pixel point corresponding to the maximum correlation coefficient from the correlation coefficients as a corresponding point b of the point a in the second graph, and calculating the displacement of the point a based on the coordinates of the points a and b.
6. The method of claim 5, wherein the side length of the search range is determined according to load variation; the larger the load, the larger the search range.
7. The method according to claim 1, wherein in S4, the displacement data is converted into grayscale data by: the displacement data is mapped to a value range of [0, 255 ].
8. The method according to claim 1, wherein in S5, the linear function is in the form of:y=kx+m(ii) a Whereink，mAre fitting coefficients.
9. The method according to claim 1, wherein in the step S6, it is determinedkThe mode of mutation was:
setting a judgment threshold value C;
when | tok x+1 －k x ｜/ k x > CIf true, then the fitting coefficientkMutation occurs; otherwise, no mutation occurs;
k x finger TxSlope fitting coefficient of timek。
10. A digital image-based geostress/historical stress measurement system for use in the method of any of claims 1-9, comprising:
the indoor loading instrument is used for carrying out twice loading on the standard cuboid rock sample to be measured; the standard cuboid rock sample to be measured is a standard cuboid rock sample subjected to early stress by an indoor loading instrument or a standard cuboid rock sample obtained from actual underground engineering;
the shooting device is used for shooting the standard cuboid rock sample to be measured in the two loading processes, and the shot and collected image is a real-time image of any surface of the standard cuboid rock sample parallel to the load direction and is transmitted to the terminal;
the terminal comprises an image processing module for processing images;
the image processing module includes:
the pixel displacement calculation unit is used for calculating the pixel displacement of the image shot at the initial moment in each loading process and obtaining a displacement numerical matrix;
the image conversion unit is used for converting the displacement numerical matrix into gray data;
the stress calculation unit is used for calculating the gray level difference of the same pixel point at the same moment in two times of loading and making a gray level difference-moment scatter diagram, wherein the stress is the load applied by the loading instrument at the corresponding moment; searching a gray level difference-stress scatter diagram of each pixel point by adopting a sliding window with the length of H, wherein the moving step length of the sliding window is H/4, fitting scatter points falling in the sliding window by adopting a linear function and obtaining a fitting coefficientkWhen the pixel point exceeds 50 percent, the fitting coefficient of the sliding windowkWhen a mutation occurs, determiningt+[H/2]The force at the moment corresponds to the stress normal stress component or the historical stress normal stress component.
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