Carbonate rock slope degradation evaluation method based on hyperspectral imaging and surface hardness

Abstract

The invention provides a carbonate rock slope degradation evaluation method based on hyperspectral imaging and surface hardness, which comprises the following steps: acquiring a hyperspectral image and corresponding rock rebound data to obtain a corresponding spectral curve; calculating a correlation coefficient according to the spectrum curve to obtain an optimal spectrum index; carrying out characteristic wavelength screening on the optimal spectral index through a screening algorithm; constructing a Schmidt rebound hardness prediction model, and taking the characteristic wavelength as input to obtain hardness data; establishing a fracture opening sensing spectral index according to the spectral index to obtain a rock mass discontinuous surface spectral index, and grading the rock discontinuous surface to obtain micro-fracture and opening areas; obtaining water level data, and obtaining water level fluctuation data according to the water level data; according to the rock rebound data, the hardness data, the micro-crack and opening area and the water level fluctuation data, carbonate rock slope degradation evaluation is conducted. According to the method, quantitative identification of rock discontinuous surface features is realized by constructing spectral indexes such as NDSI, FPSI and YGSI.

Description

Technical Field

[0001] The invention relates to the technical field of rock mass structural surface feature assessment, and in particular to a carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness. Background Art

[0002] The water level of the reservoir fluctuates periodically, and a water level fluctuation zone is easily formed in the riverside area. Due to the complex interaction of multiple factors such as structure, erosion, weathering and gravity, the rock mass in the HFB exhibits progressive deterioration behavior. Many studies have explored the characteristics of rock mass degradation from the aspects of water-rock interaction and slope stability, but the evolution of carbonate rock discontinuities, as an important manifestation of degradation in the water level fluctuation zone, is affected by physical, chemical and mechanical changes, and related research still needs to be deepened. Accurately assessing the degradation differences in the water level fluctuation zone requires a detailed understanding of the characteristics of the rock mass discontinuities, but existing research has limitations in this regard. Therefore, it is of great practical significance to develop an effective method for predicting the instability of carbonate rock slopes. Summary of the invention

[0003] Based on this, it is necessary to provide a carbonate slope degradation assessment method based on hyperspectral imaging and surface hardness to address the above technical issues.

[0004] The carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness includes the following steps:

[0005] Step S1, obtaining a hyperspectral image of a rock sample in the area to be evaluated and rock rebound data of a Schmidt test area, and extracting a spectral curve corresponding to the Schmidt hammer test area by a regional averaging method based on the hyperspectral image;

[0006] Step S2, calculating the correlation coefficient between the spectral index composed of different bands and the rock rebound data according to the spectral curve to obtain the optimal spectral index;

[0007] Step S3, screening the characteristic wavelength of the optimal spectral index by competitive adaptive reweighted sampling, continuous projection algorithm and random frog leaping algorithm to obtain the characteristic wavelength;

[0008] Step S4, constructing a Schmidt rebound hardness prediction model, inputting the characteristic wavelength into the Schmidt rebound hardness prediction model to obtain hardness data;

[0009] Step S5, establishing an opening perception spectral index according to the spectral index, obtaining a spectral index of a rock mass discontinuity surface, and classifying the rock discontinuity surface to obtain microcracks and opening areas;

[0010] Step S6, acquiring water level data, and obtaining water level fluctuation data according to the water level data;

[0011] Step S7, performing carbonate rock slope degradation assessment based on the rock rebound data, the hardness data, the microcracks and opening areas, and the water level fluctuation data.

[0012] In one embodiment, step S1 comprises:

[0013] Preprocessing the hyperspectral image to obtain a preprocessed hyperspectral image;

[0014] A spectral curve corresponding to the Schmidt hammer test area is extracted according to the preprocessed hyperspectral image by using a regional averaging method.

[0015] In one embodiment, step S2 comprises:

[0016] Calculate the spectrum index composed of different bands according to the spectrum curve;

[0017] The correlation coefficient with the rock rebound data is calculated according to the spectral index to obtain the optimal spectral index.

[0018] In one embodiment, calculating the spectral index composed of different bands according to the spectral curve includes:

[0019] The spectral index is calculated by the following formula:

[0020]

[0021] Where NDSI stands for Normalized Difference Spectral Index, Indicates the s p Band reflectivity, Indicates the s q Band reflectance, DSI stands for Difference Spectral Index, and RSI stands for Ratio Spectral Index.

[0022] In one embodiment, calculating the correlation coefficient with the rock rebound data according to the spectral index to obtain the optimal spectral index includes:

[0023] The correlation coefficient is calculated using the following formula:

[0024]

[0025] Where r represents the correlation coefficient, n represents the sample size, i represents the i-th spectral index, and x i represents the spectral index value, represents the average value of the spectral index, y i Represents rock rebound data, It represents the average value of Schmidt hammer rebound value;

[0026] The correlation coefficients are compared, and the spectral index composed of the band combination with the highest correlation coefficient is taken as the optimal spectral index.

[0027] In one embodiment, step S4 comprises:

[0028] The Schmidt rebound hardness prediction model is constructed as the SG-CARS-PLS model.

[0029] In one embodiment, step S5 comprises:

[0030] Establishing the opening perception spectral index:

[0031]

[0032] Where FPSI stands for Open Perception Spectral Index, D 700 Indicates the spectral reflectance at a wavelength of 700nm, D 450 Indicates the spectral reflectance at a wavelength of 450nm;

[0033] The spectral index of the rock mass discontinuity surface is calculated according to the opening perception spectral index:

[0034]

[0035] Among them, YGSI represents the rock mass discontinuity spectral index, NDSI represents the current normalized difference spectral index, and NDSI min Represents the minimum value of the normalized difference spectral index, NDSI max represents the maximum value of the normalized difference spectral index, FPSI represents the slit opening perception spectral index, and FPSI min Indicates the minimum value of the slit opening perception spectral index, FPSI max Indicates the maximum value of the perceived spectral index of the crack opening;

[0036] Classifying rock discontinuities according to the rock mass discontinuity spectral index to obtain microcracks and opening data;

[0037] The microcrack and opening areas are calculated based on the microcrack and opening data.

[0038] The carbonate rock slope degradation assessment system based on hyperspectral imaging and surface hardness is used to implement the carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness as described above, including:

[0039] A rebound acquisition module is used to obtain a hyperspectral image of a rock sample in the area to be evaluated and rock rebound data in the Schmidt test area, and extract a spectral curve corresponding to the Schmidt hammer test area by using a regional averaging method based on the hyperspectral image;

[0040] An index acquisition module is used to calculate the correlation coefficient between the spectral index composed of different bands and the Schmidt rebound value according to the spectral curve to obtain the optimal spectral index;

[0041] The wavelength selection module is used to screen the characteristic wavelength of the optimal spectral index through competitive adaptive reweighted sampling, continuous projection algorithm and random frog leaping algorithm to obtain the characteristic wavelength;

[0042] A hardness acquisition module is used to construct a Schmidt rebound hardness prediction model, input the characteristic wavelength into the Schmidt rebound hardness prediction model, and obtain hardness data;

[0043] A degradation calculation module is used to establish an opening perception spectral index according to the spectral index, obtain a spectral index of a rock mass discontinuity surface, and classify the rock discontinuity surface to obtain microcracks and opening areas;

[0044] A flood acquisition module, used to acquire water level data and obtain water level fluctuation data according to the water level data;

[0045] A degradation assessment module is used to perform carbonate rock slope degradation assessment based on the rock rebound data, the hardness data, the microcracks and opening areas, and the water level fluctuation data.

[0046] A device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the carbonate slope degradation assessment method based on hyperspectral imaging and surface hardness described in the above-mentioned embodiments are implemented.

[0047] A storage medium stores a computer program, which, when executed by a processor, implements the steps of the carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness described in the above-mentioned embodiments.

[0048] Compared with the prior art, the advantages and beneficial effects of the present invention are: the present invention can integrate hyperspectral imaging and Schmidt rebound hardness test technology to comprehensively and accurately characterize the changes in carbonate rocks under water-rock interaction, and provide a reliable basis for the assessment of water level drawdown zone degradation. By constructing a variety of spectral indices such as the normalized difference spectral index (NDSI), the crack opening perception spectral index (FPSI), and the rock mass discontinuity spectral index (YGSI), the quantitative identification of rock discontinuity characteristics is achieved. It can effectively distinguish different types of discontinuities such as high hardness, low hardness, microcracks and openings, overcoming the limitations of general methods in quantitatively describing rock degradation characteristics. Revealing the influence of water level fluctuation frequency on crack development and the relationship between degradation and rock layer structure provides a basis for understanding the degradation mechanism and helps in early warning and prevention of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a process of a carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness in one embodiment;

[0050] Figure 2 A schematic diagram of YGSI segmentation in one embodiment;

[0051] Figure 3 Schematic diagram of the structure of a carbonate rock slope degradation assessment system based on hyperspectral imaging and surface hardness in one embodiment;

[0052] Figure 4 Schematic diagram of the internal structure of a device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0054] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0055] For ease of understanding, the terms involved in the embodiments of the present invention are explained below:

[0056] CARS: Competitive Adaptive Reweighted Sampling.

[0057] SPA: Successive Projection Algorithm.

[0058] RF: Random Frog Leaping Algorithm.

[0059] PLS: Partial Least Squares.

[0060] FPSI: Fracture Opening Perception Spectral Index.

[0061] NDSI: Normalized Difference Spectral Index.

[0062] YGSI: Rock mass discontinuity spectral index.

[0063] DSI: Difference spectral index.

[0064] RSI: Ratio Spectral Index.

[0065] SG: Savitzky-Golay smoothing filter.

[0066] In one embodiment, Figure 1 As shown, a carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness is provided, including the following steps:

[0067] Step S1, obtaining a hyperspectral image of a rock sample in the area to be evaluated and rock rebound data of a Schmidt test area, and extracting a spectral curve corresponding to the Schmidt hammer test area by a regional averaging method based on the hyperspectral image.

[0068] Specifically, a portable hyperspectral imaging system is used to collect images of rock samples in the area to be evaluated on site to obtain a hyperspectral image.

[0069] In one embodiment, a portable hyperspectral imager with a wavelength range of 400-1000nm is used to collect hyperspectral data of rock samples. The acquisition parameters are initially set, with an exposure value of 80, a target gray value of 100, and an interval channel of 5nm. In each sampling area, no less than 5 spectral images are collected according to the set parameters, and there must be a certain degree of overlap between adjacent images to ensure the integrity of the data.

[0070] In the rock area where the hyperspectral image is collected, the Schmidt test is performed using a Schmidt hammer. Appropriate test points are selected, the surface is cleaned and tested, and rock rebound data is obtained and relevant information is recorded.

[0071] In one embodiment, 160 measuring points are arranged in each test area, and the tapping time of each point is about 5 minutes. The final rebound data is the average value of 5 tests to improve the reliability and representativeness of the data. For sub-areas with cracks and openings, the rebound data of the flat surface of the same sub-area is used instead to avoid the interference of cracks and other factors on the test results.

[0072] The spectral image is divided into 160 small areas with 10 rows and 16 columns, which correspond to the rock rebound values ​​one by one. The spectral data in each test area are averaged using the regional averaging method to extract the spectral curve corresponding to the Schmidt hammer test area.

[0073] In this embodiment, the Schmidt hammer has the advantages of being fast, economical, and slightly destructive, and is widely used in rock surface hardness testing. The measured value can reflect the relationship between rock strength and hardness.

[0074] In terms of hardness, the Schmidt rebound data directly reflects the hardness of the rock surface. The data of the rock surface with higher hardness is relatively large under the impact of the Schmidt hammer; on the contrary, the data of the rock surface with lower hardness is smaller. By measuring the Schmidt rebound values ​​of different areas (such as high hardness, low hardness, microcracks and opening areas), the differences in hardness of different areas can be distinguished, thus providing a quantitative basis for the evaluation of rock surface hardness.

[0075] In terms of strength, there is a certain positive correlation between Schmidt data and the compressive strength of rock. The size of the rebound data can indicate the ability of rock to resist pressure to a certain extent. By conducting Schmidt rebound tests on rocks at different elevations and combining the empirical conversion formula of rocks in the area, the compressive strength of rock can be estimated. The formula is:

[0076] UCS = 6.97 × e (0.014×RN×ρ)

[0077] Among them, UCS is the uniaxial compressive strength, RN is the Schmidt rebound value, and ρ is the rock density.

[0078] On this basis, step S1 includes:

[0079] Preprocessing the hyperspectral image to obtain a preprocessed hyperspectral image;

[0080] A spectral curve corresponding to the Schmidt hammer test area is extracted according to the preprocessed hyperspectral image by using a regional averaging method.

[0081] Specifically, the preprocessing includes black and white correction, SG smoothing and arithmetic averaging.

[0082] In this embodiment, black and white correction is a correction method in digital image processing, which can eliminate background noise and unevenness in the image. Savitzky-Golay smoothing filter is a commonly used digital signal processing method for smoothing data. The local polynomial fitting method in SG smoothing can maintain the characteristics of spectral data.

[0083] Step S2, calculating the correlation coefficient between the spectral index composed of different bands and the rock rebound data according to the spectral curve to obtain the optimal spectral index.

[0084] Specifically, the normalized difference spectral index (NDSI), difference spectral index (DSI) and ratio spectral index (RSI) were calculated, and the correlation between each index and the Schmidt rebound value at all preprocessed wavelengths was determined by the ergodic method. Based on this, the distribution of rock surface strength can be indirectly reflected through spectral characteristics, thereby more comprehensively evaluating the strength characteristics of different parts of the slope.

[0085] On this basis, step S2 includes:

[0086] Calculate the spectrum index composed of different bands according to the spectrum curve;

[0087] The correlation coefficient with the rock rebound data is calculated according to the spectral index to obtain the optimal spectral index.

[0088] Specifically, the optimal spectral index is selected by calculating the person correlation between the spectral index composed of different bands and the rock rebound data.

[0089] On this basis, the spectral indexes composed of different bands are calculated according to the spectral curve, including:

[0090] The spectral index is calculated by the following formula:

[0091]

[0092] Where NDSI stands for Normalized Difference Spectral Index, Indicates the s p Band reflectivity, Indicates the s q Band reflectance, DSI stands for Difference Spectral Index, and RSI stands for Ratio Spectral Index.

[0093] Specifically, multiple spectral indices such as the normalized difference spectral index (NDSI), the difference spectral index (DSI) and the ratio spectral index (RSI) are calculated. p The wavelength is the visible light band, s q The band is short-wave infrared light band.

[0094] On this basis, the correlation coefficient between the spectral index and the rock rebound data is calculated, and the optimal spectral index is obtained, including:

[0095] The correlation coefficient is calculated using the following formula:

[0096]

[0097] Where r represents the correlation coefficient, n represents the sample size, i represents the i-th spectral index, and x i represents the spectral index value, represents the average value of the spectral index, y i Represents rock rebound data, It represents the average value of Schmidt hammer rebound value;

[0098] The correlation coefficients are compared, and the spectral index composed of the band combination with the highest correlation coefficient is taken as the optimal spectral index.

[0099] Specifically, all wavelength combinations within the wavelength range of 400-1000nm are traversed, and the correlation coefficients between the spectral index under different wavelength combinations and the Schmidt rebound value at each wavelength point within the wavelength range of 400-1000nm are calculated to determine the strength of the correlation between the spectral characteristics and the Schmidt rebound value. The calculation formula is:

[0100]

[0101] Among them, r is the correlation coefficient, x i is the spectral index value, is the average value of the spectral index, y i is the rebound value of the Schmidt hammer, is the average rebound value of the Schmidt hammer, and n is the number of samples.

[0102] The correlation coefficients under all combinations are compared, and the spectral index composed of the band combination with the highest correlation coefficient is taken as the optimal spectral index.

[0103] Step S3, screening the characteristic wavelength of the optimal spectral index by competitive adaptive reweighted sampling, continuous projection algorithm and random frog leaping algorithm to obtain the characteristic wavelength.

[0104] Specifically, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and random frog leaping algorithm (RF) are used to screen the characteristic wavelengths of spectral data and screen the sensitive wavelengths related to the surface hardness of carbonate rocks. The extracted characteristic variables (characteristic wavelengths) can be used as input of the model.

[0105] When screening characteristic wavelengths, the three algorithms find the wavelength combination with the highest correlation with target attributes such as rock surface hardness. Through multiple iterations and optimizations, the algorithm gradually screens out the characteristic wavelength that contributes most to the model performance and uses it as the final screening result. In order to reduce the randomness of the algorithm, each algorithm is run multiple times, and the characteristic wavelength with a higher frequency is selected as the final screening result.

[0106] In this embodiment, CARS uses an adaptive reweighted sampling technique to effectively screen out characteristic wavelengths that contribute more to the model.

[0107] The SPA algorithm is based on vector space projection. It projects high-dimensional spectral data into low-dimensional space and selects mutually orthogonal vectors as characteristic wavelengths during the projection process.

[0108] The RF algorithm is based on random selection and iterative optimization. By utilizing the characteristics of random selection, it can more comprehensively explore potential feature combinations in the data.

[0109] Step S4, constructing a Schmidt rebound hardness prediction model, inputting the characteristic wavelength into the Schmidt rebound hardness prediction model to obtain hardness data.

[0110] Specifically, the optimal Schmidt rebound hardness prediction model is constructed by combining the characteristic wavelength and partial least squares (PLS) method, the characteristic wavelength of the unknown rock is input into the trained PSL model, the corresponding predicted hardness value is calculated, and the hardness data is obtained.

[0111] In the process of establishing the PLS model, the input data is first standardized, the covariance matrix of the characteristic wavelength data is calculated, the eigenvalues ​​and eigenvectors are solved, and the number of principal components is determined according to the cumulative contribution rate. The formula for calculating the principal component score is: i =p 1 x i1 +p 2 x i2 +…+p m x im . (Wherein, t i is the principal component score of the i-th sample, p j is the loading vector of the jth principal component, x ij is the spectral value of the ith sample at the jth wavelength, and m is the number of characteristic wavelengths) and the prediction model is obtained by establishing a linear regression relationship between the principal component and the Schmidt hammer rebound hardness.

[0112] On this basis, step S4 includes:

[0113] The Schmidt rebound hardness prediction model is constructed as the SG-CARS-PLS model.

[0114] Specifically, the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R 2 )Evaluate the accuracy of the model. The smaller the RMSE and MAE, the better the R 2 The closer it is to 1, the higher the prediction accuracy of the model. For the three models of SG-CARS-PLS, SG-RF-PLS and SG-SPA-PLS, their determination coefficients (R 2 ), root mean square error (RMSE) and mean absolute error (MAE) to evaluate the performance of the model. After calculation and comparison, it was found that the R 2 reached 0.96, RMSE was 2.29, and MAE was 1.74; the R 2 is 0.95, RMSE is 2.36, and MAE is 1.76; the R 2The SG-CARS-PLS model showed high accuracy, so it was determined to be the best model.

[0115] Step S5, establishing a crack opening perception spectral index according to the spectral index, obtaining a rock mass discontinuity surface spectral index, and classifying the rock discontinuity surface to obtain microcracks and opening areas.

[0116] Specifically, feature extraction and preprocessing are performed on the spectral data of microcracks and openings, and the crack opening perception spectral index (FPSI) is established.

[0117] When analyzing rock discontinuities using methods such as the Normalized Difference Spectral Index (NDSI), there are certain limitations (surface roughness, mineral composition, and lighting conditions have an impact on the spectral response). The development of cracks and openings is one of the important manifestations of slope degradation, and FPSI can better quantify the extent of cracks and openings in rocks.

[0118] NDSI is related to surface hardness and can reflect changes in the mechanical properties of rocks; FPSI can identify microcracks and opening areas. Compared with a single evaluation index, the rock mass discontinuity spectral index (YGSI) constructed by combining the two can comprehensively consider the physical and structural characteristics of rocks and evaluate the degradation of rock discontinuities from multiple angles.

[0119] On this basis, step S5 includes:

[0120] Establishing the slit opening perception spectral index:

[0121]

[0122] Where FPSI represents the slit opening perception spectral index, D 700 Indicates the spectral reflectance at a wavelength of 700nm, D 450 Indicates the spectral reflectance at a wavelength of 450nm;

[0123] The spectral index of the rock mass discontinuity surface is calculated based on the fracture opening perception spectral index:

[0124]

[0125] Among them, YGSI represents the rock mass discontinuity spectral index, NDSI represents the current normalized difference spectral index, and NDSI min Represents the minimum value of the normalized difference spectral index, NDSI max represents the maximum value of the normalized difference spectral index, FPSI represents the slit opening perception spectral index, and FPSI minIndicates the minimum value of the slit opening perception spectral index, FPSI max Indicates the maximum value of the perceived spectral index of the crack opening;

[0126] Classifying rock discontinuities according to the rock mass discontinuity spectral index to obtain microcracks and opening data;

[0127] The microcrack and opening areas are calculated based on the microcrack and opening data.

[0128] Specifically, the spectral reflectance data of high hardness, low hardness, microcracks and opening areas were extracted from the hyperspectral images, and the areas were accurately divided according to the image features and field observation information. The spectral reflectance data of each area was statistically analyzed, and the spectral curve characteristics of different areas were compared. It was found that the spectral curves of high hardness and low hardness areas were similar, but the reflectance of the low hardness area was slightly lower, and the spectral curve trends of microcracks and opening areas were similar, and the reflectance of the opening area was lower.

[0129] In this step, by comparing the sensitivity of NDSI, DSI and RSI to cracks and openings, it is determined that the wavelengths with the highest sensitivity to cracks and openings are 700nm and 450nm, and the crack opening perception spectral index (FPSI) is established. The calculation formula is: According to the actual situation in the study area, the FPSI threshold was determined to be 1.95. The FPSI value was calculated pixel by pixel for the hyperspectral image to generate a binary image of the crack opening area.

[0130] The rock quality discontinuity spectral index (YGSI) was constructed by combining FPSI and NDSI. The NDSI and FPSI were normalized and the calculation formula was: Rock discontinuities are classified according to the YGSI value, accurately distinguishing openings, microcracks, high hardness and low hardness areas. The specific distinction criteria are as follows Figure 2 As shown:

[0131] Low hardness area (0.0≤YGSI<0.3): The NDSI value is relatively low, and the FPSI value is also low (lower than 1.95). The rock surface in the low hardness area is rougher, resulting in a lower spectral reflectivity than the high hardness area, but no obvious cracks and openings have been formed, and its physical properties are between the high hardness area and the crack opening area.

[0132] High hardness area (0.3≤YGSI<0.5): corresponds to a higher NDSI value and a relatively low FPSI value (below 1.95). This means that the rock surface in this area is relatively flat, has a higher hardness, and has a spectral reflectivity characteristic similar to that of intact rock, with fewer cracks and openings.

[0133] Microcrack area (0.5≤YGSI<0.8): The characteristics of the microcrack area are shown in the YGSI as a change in the NDSI value, which may be between the high hardness and low hardness areas. At the same time, the FPSI value begins to increase, but has not yet reached 1.95. This indicates that microcracks begin to appear on the rock surface, affecting the spectral reflectance characteristics, making this area different from the intact rock area.

[0134] Open area (0.8≤YGSI<1.0): The open area has a higher FPSI value (greater than 1.95), and the NDSI value will also change due to the presence of the opening, which is significantly different from other areas. The spectral reflectance of the open area is different from that of intact rocks and other areas due to the influence of cracks and openings. YGSI can accurately identify and distinguish this area.

[0135] Based on the constructed YGSI, the image segmentation algorithm is used to process the hyperspectral image. The image is divided into different regions according to the YGSI threshold, and the pixel statistics method is used to count the number of pixels in each region. Combined with the image resolution (2048×2046 pixels corresponds to 1.44m 2 ) Calculate the actual area of ​​microcracks and openings on rock discontinuities at different elevations.

[0136] Step S6, acquiring water level data, and obtaining water level fluctuation data according to the water level data.

[0137] Specifically, water level data is obtained, including multi-year water level monitoring data in the area to be evaluated. The statistics of water level inundation frequency are based on multi-year water level monitoring data in the area to be evaluated. The number of times rocks at different elevations are inundated by water levels per unit time is calculated. The microcracks and opening areas of the rock discontinuities are identified through the established YGSI, and the changes in the proportion of microcracks and openings in the image are analyzed. From the correlation analysis between the inundation frequency and the deterioration ratio, the relationship between the degree of crack development and the water level inundation frequency is analyzed.

[0138] Collect multi-year water level monitoring data of the area to be evaluated, and count the number of times rocks at different elevations are submerged by water within a unit time (such as a year), that is, the water level submersion frequency.

[0139] Step S7, performing carbonate rock slope degradation assessment based on the rock rebound data, the hardness data, the microcracks and opening areas, and the water level fluctuation data.

[0140] Specifically, the rock surface strength data (such as rock rebound data, hardness data, etc.) is combined with other parameters (such as crack distribution, microcracks and opening area, and water level fluctuation data) to establish a comprehensive slope stability assessment index system. By analyzing the changes in indicators over time and in the environment, the stability state of the slope can be judged and the potential risk of geological disasters can be predicted. For example, if the hardness of the rock surface is found to decrease significantly in a certain area, while the microcracks and opening area increase (or exceed the predetermined threshold), and the area is in an elevation range where the water level changes frequently, it can be judged that the slope stability in the area is poor and corresponding reinforcement or monitoring measures need to be taken.

[0141] The carbonate slope degradation assessment method based on hyperspectral imaging and surface hardness provided by the present invention innovatively integrates hyperspectral imaging and Schmidt rebound hardness test technology to comprehensively and accurately characterize the changes of carbonate rocks under water-rock interaction, and provide a reliable basis for the degradation assessment of the water level drawdown zone. By constructing multiple spectral indices such as the normalized difference spectral index (NDSI), the crack-opening perception spectral index (FPSI), and the rock mass discontinuity spectral index (YGSI), the quantitative identification of the characteristics of rock discontinuities is achieved. It can effectively distinguish different types of discontinuities such as high hardness, low hardness, microcracks and openings, overcoming the limitations of general methods in quantitatively describing rock degradation characteristics. Revealing the influence of water level fluctuation frequency on crack development and the relationship between degradation and rock layer structure provides a basis for understanding the degradation mechanism and helps in early warning and prevention of geological disasters.

[0142] It should be noted that the method of the embodiment of the present invention can be performed by a single device, such as a computer or a server. The method of this embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present invention, and the multiple devices will interact with each other to complete the described method.

[0143] It should be noted that some embodiments of the present invention are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0144] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present invention also provides a carbonate rock slope degradation assessment system based on hyperspectral imaging and surface hardness.

[0145] refer to Figure 3 The carbonate rock slope degradation assessment system based on hyperspectral imaging and surface hardness comprises:

[0146] The rebound acquisition module 301 is used to obtain a hyperspectral image of a rock sample in the area to be evaluated and rock rebound data in the Schmidt test area, and extract a spectral curve corresponding to the Schmidt hammer test area by using a regional averaging method based on the hyperspectral image;

[0147] An index acquisition module 302 is used to calculate the correlation coefficient between the spectral index composed of different bands and the rock rebound data according to the spectral curve to obtain the optimal spectral index;

[0148] The wavelength selection module 303 is used to screen the characteristic wavelength of the optimal spectral index through competitive adaptive reweighted sampling, continuous projection algorithm and random frog leaping algorithm to obtain the characteristic wavelength;

[0149] A hardness acquisition module 304 is used to construct a Schmidt rebound hardness prediction model, input the characteristic wavelength into the Schmidt rebound hardness prediction model, and obtain hardness data;

[0150] The degradation calculation module 305 is used to establish a crack opening perception spectral index according to the spectral index, obtain degradation data of the rock discontinuity surface, and classify the rock discontinuity surface to obtain microcracks and opening areas;

[0151] A flood acquisition module 306 is used to acquire water level data and obtain water level fluctuation data according to the water level data;

[0152] The degradation assessment module 307 is used to perform carbonate rock slope degradation assessment based on the rock rebound data, the hardness data, the microcracks and opening areas, and the water level fluctuation data.

[0153] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0154] The system of the above embodiment is used to implement the corresponding carbonate slope degradation assessment method based on hyperspectral imaging and surface hardness in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0155] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the carbonate slope degradation assessment method based on hyperspectral imaging and surface hardness described in any of the above embodiments is implemented.

[0156] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0157] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0158] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0159] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0160] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0161] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0162] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0163] The electronic device of the above embodiment is used to implement the corresponding carbonate slope degradation assessment method based on hyperspectral imaging and surface hardness in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0164] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the carbonate slope degradation assessment method based on hyperspectral imaging and surface hardness as described in any of the above embodiments.

[0165] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0166] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the carbonate slope degradation assessment method based on hyperspectral imaging and surface hardness as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0167] The technical solutions in the present invention will be described clearly and completely below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. 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.

[0168] Embodiment 1

[0169] Take a carbonate slope assessment as an example:

[0170] A portable hyperspectral imaging system was used for data acquisition. The camera was connected to the computer, the spectral processing software was opened, and the acquisition parameters were set. The exposure value was set to 80, the target grayscale value was set to 100, and the interval channel was 5nm. First, a standard grayscale plate was placed in front of the rock sample in the area to be evaluated, and the white plate data was collected. Then, the camera lens was covered with a light shield to collect dark current data for subsequent light intensity correction. After removing the white plate, the camera position was kept unchanged, and the hyperspectral image of the rock sample surface was collected. The image resolution obtained was 2048×2046. In the same area, the hyperspectral image was collected five times according to the above steps to ensure the reliability and representativeness of the data.

[0171] Since the original data may be affected by factors such as air disturbance, unstable light source and uneven sensor preheating, the Savitzky-Golay (SG) smoothing filter is used to process the spectral curve. This filter uses a local polynomial fitting method to effectively retain the characteristics of the spectral data while reducing the interference of random noise on the data. The smoothed spectral curves are averaged to obtain a representative spectral curve for each test area.

[0172] Select rock areas with good integrity and relatively flat surfaces as Schmidt hammer test points. Before the test, use a clean brush to carefully clean dust, debris and other impurities on the rock surface to ensure that the test surface is clean and tidy. Hold an N-type HD-225S Schmidt hammer (impact energy of 2.207J), slowly apply pressure perpendicular to the rock surface, so that the hammer head hits the rock surface, and record the rebound value. In each test area, a total of 160 measuring points are set in an evenly spaced arrangement of 16×10, and the area of ​​a single test area is 1.44m 2 The striking time for each test is about 5 minutes. For each measuring point, the Schmidt hammer test is repeated five times, and the final rebound value is the average of the five test results. When testing a sub-area containing cracks and openings, if the cracks or openings affect the rebound data measurement, the rebound data of the flat surface in the same sub-area is used instead, and the location information of the test point and the corresponding rock surface characteristics are recorded in detail to ensure that the rebound data and the spectral image can accurately correspond.

[0173] The normalized difference spectral index (NDSI), difference spectral index (DSI) and ratio spectral index (RSI) are calculated according to the formula. In the calculation process, all wavelength combinations in the wavelength range of 400-1000nm are traversed, and the spectral index under different wavelength combinations is calculated respectively. According to the correlation coefficient between the spectral index calculation and the rock rebound data, the optimal spectral index is obtained.

[0174] The three algorithms of competitive adaptive reweighted sampling (CARS), continuous projection algorithm (SPA) and random frog leaping (RF) were used to screen the characteristic wavelengths of the optimal spectral index after preprocessing. The 160 optimal spectral indices were randomly divided into training sets and test sets, of which 112 data were used as training sets for model training and characteristic wavelength screening; the remaining 48 data were used as test sets to verify the accuracy and generalization ability of the model. The training set data were analyzed by these three algorithms, and the importance weight of each wavelength for characterizing the hardness of the rock surface was determined according to the principles and calculation rules of the algorithms, and the characteristic wavelengths sensitive to hardness were screened out.

[0175] The characteristic wavelength obtained by screening is used as the input variable, and the following Schmidt rebound hardness prediction model is constructed in combination with the partial least squares (PLS) method to calculate the hardness data.

[0176] The normalized difference spectral index (NDSI), difference spectral index (DSI) and ratio spectral index (RSI) of the spectral data were calculated. By traversing all possible combinations in the wavelength range of 400-1000nm, the correlation coefficients of NDSI, DSI and RSI with the Schmidt rebound value in each combination were calculated. The results showed that the absolute value of the maximum correlation coefficient of NDSI in 121 wavelength combinations reached 0.6928, and the wavelengths with the highest correlation were mainly concentrated between 550nm and 650nm. After further optimization, the optimal estimated parameters were determined to be 560nm and 645nm. In contrast, the absolute values ​​of the correlation coefficients of DSI and RSI were 0.1148 and 0.3319, respectively, which were significantly lower than NDSI. Therefore, NDSI was selected as the optimal spectral index for the subsequent characterization and analysis of rock surface hardness and discontinuity characteristics.

[0177] According to the correlation established between the Schmidt rebound value (hardness data) and the spectral characteristics, an intelligent algorithm is used to select characteristic wavelengths on the spectral curve after SG smoothing to identify the discontinuity of carbonate rocks. A detailed analysis of the spectral reflectance curves of different hardness (high hardness and low hardness) and different crack states (microcracks and openings) areas found that the spectral curves of high hardness and low hardness areas have certain similarities in overall shape and reflectivity values, while the spectral curves of microcracks and opening areas have similar characteristics and relatively low reflectivity. This is because factors such as surface roughness, mineral composition and lighting conditions affect the spectral response of rocks. In microcracks and opening areas, the increase in surface roughness leads to a decrease in spectral reflectivity, and the larger shadow area of ​​the opening area further reduces the reflectivity.

[0178] Based on the above analysis, in order to more accurately distinguish microcracks and opening areas, by comparing NDSI, DSI and RSI, it is determined to use the ratio spectral index (RSI) to establish the crack opening perception spectral index (FPSI). After feature extraction and analysis of the spectral data of microcracks and opening areas, it is found that the wavelengths of 700nm and 450nm are most sensitive to cracks and openings, so the FPSI calculation formula is determined as FPSI=D 700 / D 450 Based on a large amount of experimental data and statistical analysis, it is determined that the area with FPSI greater than 1.95 can be regarded as crack and opening area. The FPSI is used to process the collected spectral images to obtain the identification results of crack and opening areas, which provides an important basis for further analysis of discontinuity degradation.

[0179] The NDSI and FPSI were combined to construct the rock mass discontinuity spectral index (YGSI), which was used to more accurately classify and identify rock discontinuities. The YGSI was normalized to better reflect the characteristics of the discontinuities. The constructed YGSI was used to analyze the rock discontinuities at different elevations in the study area, and it was found that the development of microcracks and opening areas was closely related to the fluctuation of reservoir water levels. For example, at elevations of 161.2m and 162.2m, it was observed that the cracks mainly developed along the rock strata, forming a deep trough-type degradation phenomenon. This is because the strata are more susceptible to mechanical erosion, chemical dissolution, and dry-wet cycles under water-rock interaction; at an elevation of 169.5m, small trough-type degradation appeared, which is speculated to be the early stage of opening formation; at an elevation of 174.2m, the rock surface was mainly characterized by a small number of cracks caused by weathering and rainfall, and no obvious opening characteristics were formed. Combined with the statistical analysis of water level data over the years, it is found that with the increase of water level flooding frequency, the proportion of microcracks and opening area shows a regular change, gradually decreasing from 12.84% at an elevation of 161.2m to 6.03% at an elevation of 174.5m. In addition, the crack development trend is positively correlated with the frequency of water level flooding, that is, the longer the water level flooding time and the higher the frequency, the more obvious the crack development, which shows that the water-rock interaction has a significant impact on the degradation of rock discontinuities, and the degradation trend is largely controlled by the structure of the rock layer. With the increase of reservoir water level, the rock mass gradually tends to be saturated under immersion. The compressive and tensile properties of the rock mass and the shear properties of the discontinuity surface will be greatly reduced, resulting in a certain deformation of the rock mass.

[0180] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention (including the claims) is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0181] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0182] In addition, to simplify the description and discussion, and in order not to obscure the embodiments of the present invention, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the system may be shown in the form of a block diagram so as to avoid obscuring the embodiments of the present invention, and this also takes into account the fact that the details of the implementation of these block diagram systems are highly dependent on the platform on which the embodiments of the present invention will be implemented (i.e., these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present invention, it will be apparent to those skilled in the art that embodiments of the present invention may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0183] Although the invention has been described in conjunction with specific embodiments of the invention, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0184] The embodiments of the present invention are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the present invention.

Claims

1. A carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness, characterized in that: include: Step S1, obtaining a hyperspectral image of a rock sample in the area to be evaluated and rock rebound data of a Schmidt test area, and extracting a spectral curve corresponding to the Schmidt hammer test area by a regional averaging method based on the hyperspectral image; Step S2, calculating the correlation coefficient between the spectral index composed of different bands and the rock rebound data according to the spectral curve to obtain the optimal spectral index; Step S3, screening the characteristic wavelength of the optimal spectral index by competitive adaptive reweighted sampling, continuous projection algorithm and random frog leaping algorithm to obtain the characteristic wavelength; Step S4, constructing a Schmidt rebound hardness prediction model, inputting the characteristic wavelength into the Schmidt rebound hardness prediction model to obtain hardness data; Step S5, establishing a crack opening perception spectral index according to the spectral index, obtaining a rock mass discontinuity surface spectral index, and classifying the rock discontinuity surface to obtain microcracks and opening areas; Step S6, acquiring water level data, and obtaining water level fluctuation data according to the water level data; Step S7, performing carbonate rock slope degradation assessment based on the rock rebound data, the hardness data, the microcracks and opening areas, and the water level fluctuation data.

2. The carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness according to claim 1 is characterized in that: The step S1 comprises: Preprocessing the hyperspectral image to obtain a preprocessed hyperspectral image; A spectral curve corresponding to the Schmidt hammer test area is extracted according to the preprocessed hyperspectral image by using a regional averaging method.

3. The carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness according to claim 1 is characterized in that: The step S2 comprises: Calculate the spectrum index composed of different bands according to the spectrum curve; The correlation coefficient with the rock rebound data is calculated according to the spectral index to obtain the optimal spectral index.

4. The carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness according to claim 3 is characterized in that: The calculating of spectral indexes composed of different wavebands according to the spectral curve comprises: The spectral index is calculated by the following formula: Where NDSI stands for Normalized Difference Spectral Index, Indicates the s p Band reflectivity, Indicates the s q Band reflectance, DSI stands for Difference Spectral Index, and RSI stands for Ratio Spectral Index.

5. The carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness according to claim 3 is characterized in that: The calculation of the correlation coefficient between the spectral index and the rock rebound data to obtain the optimal spectral index includes: The correlation coefficient is calculated by the following formula: Where r represents the correlation coefficient, n represents the sample size, i represents the i-th spectral index, and x i represents the spectral index value, represents the average value of the spectral index, y i Represents rock rebound data, It represents the average value of Schmidt hammer rebound value; The correlation coefficients are compared, and the spectral index composed of the band combination with the highest correlation coefficient is taken as the optimal spectral index.

6. The carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness according to claim 1 is characterized in that: The step S4 comprises: The Schmidt rebound hardness prediction model is the SG-CARS-PLS model.

7. The carbonate rock slope degradation assessment method based on hyperspectral imaging and surface hardness according to claim 1 is characterized in that: The step S5 comprises: Establishing the slit opening perception spectral index: Where FPSI represents the slit opening perception spectral index, D 700 Indicates the spectral reflectance at a wavelength of 700nm, D 450 Indicates the spectral reflectance at a wavelength of 450nm; The spectral index of the rock mass discontinuity surface is calculated based on the fracture opening perception spectral index: Among them, YGSI represents the rock mass discontinuity spectral index, NDSI represents the current normalized difference spectral index, and NDSI min Represents the minimum value of the normalized difference spectral index, NDSI max represents the maximum value of the normalized difference spectral index, FPSI represents the slit opening perception spectral index, and FPSI min Indicates the minimum value of the slit opening perception spectral index, FPSI max Indicates the maximum value of the perceived spectral index of the crack opening; Classifying rock discontinuities according to the rock mass discontinuity spectral index to obtain microcracks and opening data; The microcrack and opening areas are calculated based on the microcrack and opening data.

8. Carbonate rock slope degradation assessment system based on hyperspectral imaging and surface hardness, characterized by: The method for evaluating carbonate slope degradation based on hyperspectral imaging and surface hardness according to any one of claims 1 to 7 comprises: A rebound acquisition module is used to obtain a hyperspectral image of a rock sample in the area to be evaluated and rock rebound data in the Schmidt test area, and extract a spectral curve corresponding to the Schmidt hammer test area by using a regional averaging method based on the hyperspectral image; An index acquisition module is used to calculate the correlation coefficient between the spectral index composed of different bands and the rock rebound data according to the spectral curve to obtain the optimal spectral index; The wavelength selection module is used to screen the characteristic wavelength of the optimal spectral index through competitive adaptive reweighted sampling, continuous projection algorithm and random frog leaping algorithm to obtain the characteristic wavelength; A hardness acquisition module is used to construct a Schmidt rebound hardness prediction model, input the characteristic wavelength into the Schmidt rebound hardness prediction model, and obtain hardness data; A degradation calculation module is used to establish a crack opening perception spectral index according to the spectral index, obtain a spectral index of a rock mass discontinuity surface, and classify the rock discontinuity surface to obtain microcracks and opening areas; A flood acquisition module, used to acquire water level data and obtain water level fluctuation data according to the water level data; A degradation assessment module is used to perform carbonate rock slope degradation assessment based on the rock rebound data, the hardness data, the microcracks and opening areas, and the water level fluctuation data.

9. A device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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