Reservoir dam abutment rock mass deformation real-time monitoring method based on digital image correlation

Abstract

The invention provides a reservoir dam abutment rock mass deformation real-time monitoring method based on digital image correlation, and relates to the technical field of digital image monitoring. According to the monitoring method, high-precision, real-time, non-contact and full-field deformation monitoring of the dam abutment rock mass can be achieved only through one gray-scale camera and a computer, a high-precision synchronous measurement model of multiple measurement points in a large view field is constructed through the computer multi-thread parallel computing technology, the computing efficiency is greatly improved, the measurement and computing cost is saved, and the method is suitable for large-scale industrial production. In addition, by means of a high-performance machine vision hardware system, higher monitoring resolution can be achieved.

Description

Technical Field

[0001] The invention relates to the technical field of digital image monitoring, and in particular to a real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation. Background Art

[0002] During the service life of a reservoir dam, deformation or even collapse may occur due to the influence of many factors such as external forces, its own structure and the surrounding environment. In order to ensure the safe operation of the reservoir dam, it is particularly important to monitor the deformation of the dam shoulder rock mass in real time. There are generally two existing non-contact monitoring schemes: (1) based on the global positioning system (GPS) technology; (2) based on the synthetic aperture radar interferometry technology (InSAR).

[0003] GPS is a positioning technology based on satellite navigation. By receiving satellite signals through GPS receivers placed on the ground, combined with the transmission speed of electromagnetic waves and the signal reception time, the distance between the receiver and the satellite can be calculated, and then the position of the receiver can be determined. Reservoir dam deformation monitoring based on GPS technology generally involves deploying a series of GPS receivers that receive satellite signals in real time on the dam and connecting them to the base station. By comparing and analyzing the calculated data of the GPS receiver positions at different time points, the deformation of the dam can be obtained. The technical solution steps are as follows:

[0004] 1) Monitoring network layout: Several GPS observation stations are evenly arranged on and around the dam and connected to the base station.

[0005] 2) Monitoring data collection: Each GPS observation station regularly and synchronously receives satellite signals to calculate the location information of several stations within the dam monitoring area.

[0006] 3) Monitoring data processing: The collected data is processed, and the position change of each point is calculated by comparing the data at different time points to obtain the deformation information of the dam body.

[0007] 4) Monitoring data analysis: Analyze the processed monitoring data, evaluate the deformation of the dam body, determine whether there is abnormal deformation and take corresponding measures.

[0008] The time-series InSAR technology uses the phase difference between two (or more) consecutive SAR images taken at different times to monitor the deformation information of the target object.

[0009] In 2024, Guo Ziqi and others applied the time series ISAR technology to the inspection and early warning work of a reservoir in Luoyang City, and studied three application scenarios: dam deformation monitoring, reservoir area geological disaster survey, and urban surface settlement. In this technology, first, a series of descending orbit radar satellite image data needs to be obtained; second, in order to calculate the deformation phase between satellite image data, it is necessary to use the satellite orbit data of the same period and the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) data of the same region for registration and geocoding, and download the GACOS (General Atmospheric Correction Online Service) meteorological data of the same period to assist in atmospheric filtering processing; finally, the deformation phase is obtained.

[0010] In terms of data processing, this technology combines the high relative position accuracy of PS (permanent scatterer)-InSAR and the strong anti-decorrelation ability of SBAS (small baseline set method)-InSAR. The PS-InSAR technology is used to extract stable deformation monitoring points as the starting control points of the SBAS-InSAR technology. The starting control points are used as standard pixels, and the displacement and residual vector are zeroed. All pixels within the research range are calibrated with this point as the reference point to generate the final deformation monitoring results. The technical process is as follows Figure 1 shown.

[0011] However, the above non-contact monitoring solutions all have many defects. The defects of reservoir dam deformation monitoring technology based on global positioning system (GPS) technology are as follows:

[0012] 1) Climate has a great impact. GPS signals are easily affected by severe weather conditions, resulting in unstable signals or even failure to receive signals, which seriously affects the accuracy of reservoir dam deformation monitoring.

[0013] 2) Insufficient monitoring accuracy. The accuracy of GPS monitoring is subject to a variety of influencing factors, such as the number and distribution of satellites, atmospheric changes, etc., which makes it impossible to meet the needs of high-precision real-time monitoring.

[0014] 3) The equipment is complex and expensive. Deformation monitoring of reservoir dams based on GPS technology requires the deployment of more GPS signal receivers, which increases the monitoring cost, and the GPS measurement network is relatively complex.

[0015] The technical defects of reservoir dam deformation monitoring based on synthetic aperture radar interferometry (InSAR) are as follows:

[0016] 1) Changes in climate conditions, cloud cover, and vegetation cover may result in a reduction in available satellite image data, which in turn affects the reliability of measurement data.

[0017] 2) Due to limitations in satellite performance, continuous monitoring of certain monitoring areas may not be possible or may even fail.

[0018] 3) Satellite signals are easily affected by factors such as atmospheric fluctuations and satellite orbit deviations, resulting in spatial propagation delays that affect real-time measurements.

[0019] 4) Satellite images have insufficient resolution and low measurement accuracy. Summary of the invention

[0020] In view of the deficiencies in the prior art, the present invention provides a real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation, which solves the problems that reservoir dam deformation monitoring is easily affected by external factors, has low accuracy and high cost.

[0021] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0022] A real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation, the monitoring method comprising:

[0023] S1. Arrange a number of discrete target measuring points with speckle images at the dam abutment deformation observation points;

[0024] S2. Install anchor points at stable locations on the dam as standard base points;

[0025] S3. Install the visual camera and connect it to the computer, and debug the machine vision system;

[0026] S4. Use the chessboard to perform high-precision camera calibration and obtain the internal and external parameters of the visual camera;

[0027] S5. At t=t0, the overall image of the dam shoulder position is obtained in real time through the visual camera, the image data is imported into the self-written engineering application software for image preprocessing, and a number of discrete target measurement points of interest are marked;

[0028] S6. Take an overall image of the dam shoulder position at a certain time interval ΔT, and import it into the self-developed engineering application software for image preprocessing;

[0029] S7. Use DIC technology, sub-pixel resolution algorithm, multi-threaded parallel computing technology combined with camera calibration internal and external parameters to synchronously calculate the deformation data of each discrete target measuring point of the dam shoulder rock mass;

[0030] S8. Compare and analyze the deformation data of each target measuring point with the measurement data of the anchor point, check whether there is abnormal deformation at each measuring point and take corresponding early warning measures;

[0031] S9. Repeat S6-S8 to achieve high-precision real-time monitoring of the deformation displacement of the dam shoulder rock mass under a large field of view.

[0032] Preferably, the image preprocessing in S5 and S6 includes: image enhancement and image filtering.

[0033] Preferably, the image enhancement includes:

[0034] Decompose the original image I(x,y) taken by the camera into RGB three-channel components, and the i-channel image is:

[0035] I i (x,y)=L i (x,y)×R i (x,y);

[0036] Among them, R i (x, y) is the reflection component of the original image in channel i;

[0037] L i (x, y) is the incident component of the original image in channel i;

[0038] (x,y) is the pixel coordinate;

[0039] Taking the logarithm of both sides of the equation gives:

[0040] i i (x,y)=l i (x,y)+r i (x,y);

[0041] Among them, i i (x,y)=Log(I i (x,y));

[0042] l i (x,y)=Log(L i (x,y)), through the i-channel image I of the original image i (x,y) is approximately estimated by convolution with Gaussian function;

[0043] r i (x,y)=Log(R i (x,y)), is the logarithmic representation of the original image in channel i;

[0044] The logarithmic representation of the single-scale image enhancement of the i-channel of the original image is:

[0045]

[0046] Among them, g(x,y,σ) is the Gaussian filter model;

[0047] σ is the standard deviation of Gaussian filtering;

[0048] e is the base of the logarithm of natural numbers;

[0049] The original image I(x,y) taken by the CCD camera is decomposed into RGB three-channel components, and the logarithmic expression of the R channel image enhancement is:

[0050]

[0051] Where n represents the number of scales;

[0052] σ k Represents the standard deviation of the Gaussian function corresponding to scale k;

[0053] w k is the weight associated with the kth scale, w1+w2+…+w n =1;

[0054] Similarly, we can get r G (x,y),r B (x, y), convert each channel from the logarithmic domain to the real domain and perform linear transformation to obtain an 8-bit depth image R for each channel R (x,y),R G (x,y) and R B (x, y), the three are combined to obtain a three-channel color image R RGB (x,y), for the color image R RGB After converting (x, y) to grayscale, a single-channel grayscale image is obtained, that is, an 8-bit single-channel output image R(x, y) after image enhancement.

[0055] Preferably, the image filtering comprises:

[0056] Perform inverse filtering on the output image R(x,y):

[0057]

[0058] G(u,v)=H(u,v)F(u,v)+N(u,v);

[0059]

[0060] in, is the estimated value of F(u,v);

[0061] F(u,v) is the Fourier transform of the image before degradation, that is, the Fourier transform of the output image R(x,y);

[0062] G(u,v) is the Fourier transform of the image after degradation;

[0063] N(u,v) is the Fourier transform of the noise;

[0064] H(u,v) is the Fourier transform form of the atmospheric turbulence model;

[0065] H(u,v)=exp{[-k(u 2 +v 2 )] 5 / 6};

[0066] Among them, u and v are discrete variables in the frequency domain;

[0067] k is the turbulence factor. For slight thermal disturbance, the turbulence factor is taken as k=0.00025.

[0068] Preferably, the monitoring method limits the filtering frequency, and the filtering radius is selected to be 30 pixels.

[0069] Preferably, the comparison analysis in S8 specifically includes:

[0070] The anchor point is fixed at a stable location in the reservoir as a standard base point. The deformation data of each target measuring point is subtracted from the measurement data of the anchor point, and the displacement difference obtained is compared with the maximum safe displacement difference built into the software. If it is greater than the maximum safe displacement difference, it is marked as a dangerous point and corresponding early warning measures are taken.

[0071] A real-time monitoring system for reservoir dam shoulder rock deformation based on digital image correlation, the monitoring system comprising: a target arrangement module, an anchor point installation module, a visual system construction module, a parameter acquisition module, a target marking module, an image acquisition module, a data solution module and a data comparison and analysis module;

[0072] The target arrangement module is used to arrange a number of discrete target measurement points with speckle images at the dam shoulder deformation observation points;

[0073] The anchor point installation module is used to install the anchor point at a stable position of the dam as a standard base point;

[0074] The visual system building module is used to install a visual camera and connect it to a computer to debug the machine vision system;

[0075] The parameter acquisition module is used to perform high-precision camera calibration using a chessboard to obtain internal and external parameters of the visual camera;

[0076] The target marking module is used to obtain the overall image of the dam shoulder position in real time through the visual camera at the time t=t0, import the image data into the self-written engineering application software for image preprocessing, and mark a number of discrete target measurement points of interest;

[0077] The image acquisition module is used to take an overall image of the dam shoulder position at a certain time interval ΔT, and import it into the self-written engineering application software for image preprocessing;

[0078] The data solving module is used to solve the deformation data of each discrete target measuring point of the dam shoulder rock mass synchronously by using DIC technology, sub-pixel analysis algorithm, multi-threaded parallel computing technology and internal and external parameters calibrated by the camera;

[0079] The data comparison and analysis module is used to compare and analyze the deformation data of each target measuring point with the measurement data of the anchor point, check whether there is abnormal deformation at each measuring point and take corresponding early warning measures.

[0080] The present invention provides a method for real-time monitoring of reservoir dam shoulder rock deformation based on digital image correlation. Compared with the prior art, it has the following beneficial effects:

[0081] In the present invention, the monitoring method only requires a grayscale camera and a computer to achieve high-precision, real-time, non-contact, full-field deformation monitoring of the dam shoulder rock mass. By using computer multi-threaded parallel computing technology, a high-precision synchronous measurement model of multiple measuring points under a large field of view is constructed, which greatly improves the computing efficiency and saves the measurement and computing costs. In addition, with the help of a high-performance machine vision hardware system, a higher monitoring resolution can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0083] Figure 1 This is a flow chart of the time-series ImSAR technology in the background technology of the present invention.

[0084] Figure 2 It is a physical diagram of the object to be measured in an embodiment of the present invention.

[0085] Figure 3 FIG. 4 is a schematic diagram of image sub-area deformation in an embodiment of the present invention.

[0086] Figure 4 Schematic diagram of a large field of view multi-point selection scheme in an embodiment of the present invention.

[0087] Figure 5 Schematic diagram of deformation measurement of the object under test in an embodiment of the present invention.

[0088] Figure 6 This is a schematic diagram of multi-threaded parallel computing in an embodiment of the present invention. DETAILED DESCRIPTION

[0089] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] The embodiment of the present application solves the problem that reservoir dam deformation monitoring is easily affected by external factors, has low accuracy and high cost by providing a real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation.

[0091] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0092] Example:

[0093] like Figure 2 As shown in the figure, in order to solve the problem of high-precision real-time monitoring of reservoir dam shoulder rock deformation, machine vision and full-field digital image correlation technology (Digital Image Correlation, DIC) and computer multi-threaded parallel computing technology are used to realize real-time, non-contact, full-field deformation measurement of dam shoulder rock mass, establish real-time image processing and high-precision deformation data processing models, and write corresponding engineering application software, providing technical and theoretical support for safety early warning of dam shoulder rock deformation.

[0094] The basic principle of the DIC method is to use a visual camera to collect two speckle images of the test piece before and after deformation, and use the correlation criterion to perform correlation calculations on the sub-areas of the speckle images before and after deformation. Figure 3 As shown in the figure, in the reference image, a (2M+1)×(2M+1) pixel area is selected with (x0, y0) as the center as the reference sub-area, and then the sub-area is moved in the deformed image while performing correlation calculation according to a certain correlation scheme until the deformed sub-area at (x′0, y′0) in the deformed image reaches the peak of the correlation function. Then (x′0, y′0) is the best integer pixel matching point of the reference point (x0, y0) in the deformed image. However, when the object to be measured is deformed, the center position and shape of the deformed image sub-area will change. In order to further improve the measurement accuracy, the ICGN algorithm must be used to iterate the sub-pixel positioning of the integer pixel matching point to obtain a higher-precision sub-pixel matching point (x″0, y″0), and then the displacement of the point can be solved by combining the internal and external parameters calibrated by the camera.

[0095] As can be seen from the above, in the small field measurement environment, the full-field DIC matching calculation is firstly to select the effective area (ROI) of the test piece on the reference image according to certain selection rules, then to grid the ROI area, and finally to perform DIC matching calculation on several grid points in a certain order. However, the above scheme is obviously not suitable for the large field measurement environment such as the reservoir dam shoulder rock mass, for the following reasons:

[0096] 1) The object to be measured is large, and it is impossible to spray speckle on a large area on its surface;

[0097] 2) There are many invalid areas of the measured object, and the effective area field of view accounts for a small proportion and is irregularly and discontinuously distributed;

[0098] 3) In a large field of view measurement environment, the small field of view selection scheme will inevitably select more invalid areas. If the full-field DIC matching calculation is performed using this selection, it will cause a large waste of computing power, resulting in low computing efficiency and unable to meet the requirements of real-time deformation monitoring.

[0099] In order to effectively avoid invalid selection of the object being measured, such as Figure 4 As shown, the present invention makes appropriate changes to the DIC selection area. By arranging a number of discrete target measuring points in a number of discontinuous effective areas on the measured object, a number of discrete ROI areas can be obtained. Each discrete ROI area needs to be gridded. Combined with computer multi-threaded parallel computing technology, multi-area parallel matching calculation can be realized to improve computing efficiency and save computing power costs.

[0100] like Figure 5 , Figure 6 As shown in the figure, the present invention provides a real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation. The main scheme is to use a machine vision system, DIC technology, image enhancement technology, sub-pixel analysis algorithm and multi-threaded parallel computing technology to achieve high-precision synchronous measurement of deformation displacement of several discrete target measuring points of reservoir dam shoulder rock mass. The overall technical scheme is as follows Figure 5 As shown, the principle of multi-threaded parallel computing is as follows Figure 6 The overall technical solution is as follows:

[0101] S1. Arrange a number of discrete target measuring points with speckle images at the dam abutment deformation observation points;

[0102] S2. Install anchor points at stable locations on the dam as standard base points;

[0103] S3. Install the visual camera and connect it to the computer, and debug the machine vision system;

[0104] S4. Use the chessboard to perform high-precision camera calibration and obtain the internal and external parameters of the visual camera;

[0105] S5. At t=t0, the overall image of the dam shoulder position is obtained in real time through the visual camera, the image data is imported into the self-written engineering application software for image preprocessing, and a number of discrete target measurement points of interest are marked;

[0106] S6. Take an overall image of the dam shoulder position at a certain time interval ΔT, and import it into the self-developed engineering application software for image preprocessing;

[0107] S7. Use DIC technology, sub-pixel resolution algorithm, multi-threaded parallel computing technology combined with camera calibration internal and external parameters to synchronously calculate the deformation data of each discrete target measuring point of the dam shoulder rock mass;

[0108] S8. Compare and analyze the deformation data of each target measuring point with the measurement data of the anchor point, check whether there is abnormal deformation at each measuring point and take corresponding early warning measures;

[0109] S9. Repeat S6-S8 to achieve high-precision real-time monitoring of the deformation displacement of the dam shoulder rock mass under a large field of view.

[0110] The image preprocessing in S5 and S6 is used to improve the image matching accuracy of subsequent discrete measurement points, including: image enhancement and image filtering;

[0111] Since the ambient light during the day and at night has a great influence on the grayscale of the image, there will be errors in the DIC matching degree, which will affect the DIC measurement accuracy. Image enhancement uses a multi-scale Retinex algorithm to improve light balance to enhance image contrast and further improve DIC measurement accuracy.

[0112] The DIC method has high requirements for image quality. The external environment of the reservoir is an environment with constantly changing temperature. Especially in the hot summer, slight airflow disturbances will occur. These airflows form a microlens effect due to density differences, and the airflow disturbances intensify as the temperature rises, causing image blur, which seriously affects the image quality obtained by the visual monitoring system and affects the measurement accuracy. In order to improve the measurement accuracy and reduce the impact of thermal disturbances on the measurement results, the image filter abstracts the atmospheric turbulence degradation function, which is used to perform inverse filtering on the images affected by thermal disturbances, aiming to reduce the negative impact of thermal disturbances on image quality.

[0113] The image enhancement includes:

[0114] The main theoretical basis of the Retinex algorithm is the three primary color theory and color constancy theory.

[0115] In the Retinex model, the original single-channel 8-bit depth grayscale image captured by the camera is I(x, y), which is decomposed into RGB three-channel components. Assume that the image of a certain channel i is I i (x,y), then:

[0116] I i (x,y)=L i (x,y)×R i (x,y);

[0117] Among them, R i (x, y) is the reflection component of the original image in channel i, which represents the characteristic information of the object and should be retained to the greatest extent;

[0118] L i (x, y) is the incident component of the original image in channel i, which represents the brightness of the incident light and determines the dynamic range of the image pixels, and should be removed as much as possible;

[0119] (x,y) is the pixel coordinate;

[0120] Taking the logarithm of the left and right sides of the above formula gives:

[0121] Log(I i (x,y))=Log(L i (x,y))+Log(R i (x,y));

[0122] Let i i (x,y)=Log(I i (x,y));

[0123] l i (x,y)=Log(L i (x,y));

[0124] r i (x,y)=Log(R i (x,y));

[0125] Then we have:

[0126] i i (x,y)=l i (x,y)+r i (x,y);

[0127] Among them, r i (x, y) is the logarithmic representation of the original image in the i-th channel.

[0128] l i (x,y) passes through the i-channel image I of the original image i (x,y) is approximately estimated by convolution with Gaussian function;

[0129] Then the logarithmic representation of the single-scale image enhancement of the i-th channel of the original image is:

[0130]

[0131] Among them, G(x,y,σ) is the Gaussian filter model;

[0132] σ is the standard deviation of Gaussian filtering;

[0133] e is the base of the logarithm of natural numbers;

[0134] According to the basic principles of the Retinex model, the basic principles of the multi-scale Retinex algorithm are as follows:

[0135] The original image I(x,y) taken by the CCD camera is decomposed into RGB three-channel components. Taking the R channel as an example, the logarithmic expression of the image enhancement of the R channel under multiple scales is:

[0136]

[0137] Where n represents the number of scales;

[0138] σ k Represents the standard deviation of the Gaussian function corresponding to scale k;

[0139] w k is the weight associated with the kth scale, w1+w2+…+w n =1;

[0140] Similarly, we can get r G (x,y),r B (x, y), convert each channel from the logarithmic domain to the real domain and perform linear transformation to obtain an 8-bit depth image R for each channel R (x,y),R G (x,y) and R B (x, y), the three are combined to obtain a three-channel color image R RGB (x,y), for the color image R RGB After converting (x, y) to grayscale, a single-channel grayscale image is obtained, that is, an 8-bit single-channel output image R(x, y) after image enhancement.

[0141] The image filtering comprises:

[0142] H(u,v)=exp{[-k(u 2 +v 2 )] 5 / 6};

[0143] Among them, H(u,v) is the Fourier transform form of the atmospheric turbulence model;

[0144] u and v are discrete variables in the frequency domain;

[0145] k is the turbulence factor, and for slight thermal disturbances, the turbulence factor is set to k = 0.00025;

[0146] The Fourier transform of the output image R(x,y) is F(u,v), and the inverse filtering process includes:

[0147]

[0148] G(u,v)=H(u,v)F(u,v)+N(u,v);

[0149]

[0150] in, is the estimated value of F(u,v);

[0151] F(u,v) is the Fourier transform of the image before degradation;

[0152] G(u,v) is the Fourier transform of the image after degradation;

[0153] N(u,v) is the Fourier transform of the noise;

[0154] It can be seen from the above formula that even if the degradation function is known, the image cannot be accurately restored because N(u,v) is unknown. In even worse cases, if the degradation function is zero or a very small value, the value of N(u,v) / H(u,v) is relatively large, which can easily dominate the estimated value of F(u,v). In order to solve this problem, the present invention adopts a method of limiting the frequency of filtering. The value of the high-frequency component is close to 0, and H(0,0) is usually the highest value of H(u,v) in the frequency domain. Therefore, the filter radius can be shortened so that the passing frequency is close to the origin and the probability of encountering zero values ​​is reduced. According to prior knowledge, for slight atmospheric turbulence disturbances, the filter radius is generally selected to be 30 pixels.

[0155] The comparison analysis in S8 specifically includes:

[0156] The anchor point is fixed at a stable location in the reservoir as a standard base point. The deformation data of each target measuring point is subtracted from the measurement data of the anchor point, and the displacement difference obtained is compared with the maximum safe displacement difference built into the software. If it is greater than the maximum safe displacement difference, it is marked as a dangerous point and corresponding early warning measures are taken.

[0157] The present invention provides a real-time monitoring system for reservoir dam shoulder rock deformation based on digital image correlation, the monitoring system comprising: a target arrangement module, an anchor point installation module, a visual system construction module, a parameter acquisition module, a target marking module, an image acquisition module, a data solution module and a data comparison and analysis module;

[0158] The target arrangement module is used to arrange a number of discrete target measurement points with speckle images at the dam shoulder deformation observation points;

[0159] The anchor point installation module is used to install the anchor point at a stable position of the dam as a standard base point;

[0160] The visual system building module is used to install a visual camera and connect it to a computer to debug the machine vision system;

[0161] The parameter acquisition module is used to perform high-precision camera calibration using a chessboard to obtain internal and external parameters of the visual camera;

[0162] The target marking module is used to obtain the overall image of the dam shoulder position in real time through the visual camera at the time t=t0, import the image data into the self-written engineering application software for image preprocessing, and mark a number of discrete target measurement points of interest;

[0163] The image acquisition module is used to take an overall image of the dam shoulder position at a certain time interval ΔT, and import it into the self-written engineering application software for image preprocessing;

[0164] The data solving module is used to solve the deformation data of each discrete target measuring point of the dam shoulder rock mass synchronously by using DIC technology, sub-pixel analysis algorithm, multi-threaded parallel computing technology and internal and external parameters calibrated by the camera;

[0165] The data comparison and analysis module is used to compare and analyze the deformation data of each target measuring point with the measurement data of the anchor point, check whether there is abnormal deformation at each measuring point and take corresponding early warning measures.

[0166] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0167] In the embodiment of the present invention, the monitoring method only requires a grayscale camera and a computer to achieve high-precision, real-time, non-contact, full-field deformation monitoring of the dam shoulder rock mass. By using computer multi-threaded parallel computing technology, a high-precision synchronous measurement model of multiple measuring points under a large field of view is constructed, which greatly improves the computing efficiency and saves the measurement and computing costs. In addition, with the help of a high-performance machine vision hardware system, a higher monitoring resolution can be achieved.

[0168] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0169] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation, characterized in that: The monitoring method comprises: S1. Arrange a number of discrete target measuring points with speckle images at the dam abutment deformation observation points; S2. Install anchor points at stable locations on the dam as standard base points; S3. Install the visual camera and connect it to the computer, and debug the machine vision system; S4. Use the chessboard to perform high-precision camera calibration and obtain the internal and external parameters of the visual camera; S5. At t=t0, the overall image of the dam shoulder position is obtained in real time through the visual camera, the image data is imported into the self-written engineering application software for image preprocessing, and a number of discrete target measurement points of interest are marked; S6. Take an overall image of the dam shoulder position at a certain time interval ΔT, and import it into the self-developed engineering application software for image preprocessing; S7. Use DIC technology, sub-pixel resolution algorithm, multi-threaded parallel computing technology combined with camera calibration internal and external parameters to synchronously calculate the deformation data of each discrete target measuring point of the dam shoulder rock mass; S8. Compare and analyze the deformation data of each target measuring point with the measurement data of the anchor point, check whether there is abnormal deformation at each measuring point and take corresponding early warning measures; S9. Repeat S6-S8 to achieve high-precision real-time monitoring of the deformation displacement of the dam shoulder rock mass under a large field of view.

2. The method for real-time monitoring of reservoir dam shoulder rock deformation based on digital image correlation according to claim 1, characterized in that: The image preprocessing in S5 and S6 includes: image enhancement and image filtering.

3. The real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation according to claim 2 is characterized in that: The image enhancement includes: Decompose the original image I(x,y) taken by the camera into RGB three-channel components, and the i-channel image is: I i (x,y)=L i (x,y)×R i (x,y); Among them, R i (x, y) is the reflection component of the original image in channel i; L i (x, y) is the incident component of the original image in channel i; (x,y) is the pixel coordinate; Taking the logarithm of both sides of the equation gives: i i (x,y)=l i (x,y)+r i (x,y); where i i (x, y) = Log(I i (x, y)); l i (x,y)=Log(L i (x,y)), through the i-channel image I of the original image i (x,y) is approximately estimated by convolution with Gaussian function; r i (x,y)=Log(R i (x,y)), is the logarithmic representation of the original image in the i-th channel; The logarithmic representation of the single-scale image enhancement of the i-channel of the original image is: Among them, G(x,y,σ) is the Gaussian filter model; σ is the standard deviation of Gaussian filtering; e is the base of the logarithm of natural numbers; The original image I(x,y) taken by the CCD camera is decomposed into RGB three-channel components, and the logarithmic expression of the R channel image enhancement is: Where n represents the number of scales; σ k Represents the standard deviation of the Gaussian function corresponding to scale k; w k is the weight associated with the kth scale, w1+w2+…+w n =1; Similarly, we can get r G (x,y),r B (x, y), convert each channel from the logarithmic domain to the real domain and perform linear transformation to obtain an 8-bit depth image R for each channel R (x,y),R G (x,y) and R B (x, y), the three are combined to obtain a three-channel color image R RGB (x,y), for the color image R RGB After converting (x, y) to grayscale, a single-channel grayscale image is obtained, that is, an 8-bit single-channel output image R(x, y) after image enhancement.

4. The real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation according to claim 3 is characterized in that: The image filtering comprises: Perform inverse filtering on the output image R(x,y): G(u,v)=H(u,v)F(u,v)+N(u,v); in, is the estimated value of F(u,v); F(u,v) is the Fourier transform of the image before degradation, that is, the Fourier transform of the output image R(x,y); G(u,v) is the Fourier transform of the image after degradation; N(u,v) is the Fourier transform of the noise; H(u,v) is the Fourier transform form of the atmospheric turbulence model; H(u,v)=exp{[-k(u 2 +v 2 )] 5 / 6 }; Among them, u and v are discrete variables in the frequency domain; k is the turbulence factor. For slight thermal disturbance, the turbulence factor is taken as k=0.00025.

5. The real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation according to claim 4 is characterized in that: The monitoring method limits the frequency of filtering, and the filtering radius is selected to be 30 pixels.

6. The real-time monitoring method for reservoir dam shoulder rock deformation based on digital image correlation according to claim 1, characterized in that: The comparison analysis in S8 specifically includes: The anchor point is fixed at a stable location in the reservoir as a standard base point. The deformation data of each target measuring point is subtracted from the measurement data of the anchor point, and the displacement difference obtained is compared with the maximum safe displacement difference built into the software. If it is greater than the maximum safe displacement difference, it is marked as a dangerous point and corresponding early warning measures are taken.

7. A real-time monitoring system for reservoir dam shoulder rock deformation based on digital image correlation, characterized in that: The monitoring system includes: a target arrangement module, an anchor point installation module, a visual system construction module, a parameter acquisition module, a target marking module, an image acquisition module, a data solution module and a data comparison and analysis module; The target arrangement module is used to arrange a number of discrete target measurement points with speckle images at the dam shoulder deformation observation points; The anchor point installation module is used to install the anchor point at a stable position of the dam as a standard base point; The visual system building module is used to install a visual camera and connect it to a computer to debug the machine vision system; The parameter acquisition module is used to perform high-precision camera calibration using a chessboard to obtain internal and external parameters of the visual camera; The target marking module is used to obtain the overall image of the dam shoulder position in real time through the visual camera at the time t=t0, import the image data into the self-written engineering application software for image preprocessing, and mark a number of discrete target measurement points of interest; The image acquisition module is used to take an overall image of the dam shoulder position at a certain time interval ΔT, and import it into the self-written engineering application software for image preprocessing; The data solving module is used to solve the deformation data of each discrete target measuring point of the dam shoulder rock mass synchronously by using DIC technology, sub-pixel analysis algorithm, multi-threaded parallel computing technology and internal and external parameters calibrated by the camera; The data comparison and analysis module is used to compare and analyze the deformation data of each target measuring point with the measurement data of the anchor point, check whether there is abnormal deformation at each measuring point and take corresponding early warning measures.

Images