Method and system for identifying deterioration of mining slope rock mass structure

Abstract

The invention discloses an identification method and system for mining slope rock mass structure degradation, particularly relates to the technical field of mining engineering rock mass monitoring, and is used for solving the problem of hidden damage missed judgment or misjudgment caused by dependence on overall statistical characteristics of signals in an existing cross-hole sound wave test method. The method comprises the following steps: collecting sound wave signals among drill holes, performing multi-scale wavelet decomposition, and extracting sub-signal energy distribution of a characteristic frequency band; in combination with skewness and kurtosis statistical maps of the sound wave travel time sequence, comparing the complete rock mass reference maps in a segmented manner to mark abnormal path segments; synchronously fusing direction matching verification of the micro-fracture group observed by the borehole television, eliminating misjudgment data and generating a micro-fracture chain density model; and finally, constructing a rock mass deterioration risk map through multi-source data fusion and a three-dimensional visualization technology. Accurate positioning and dynamic evaluation of a rock mass degradation high-risk area are achieved, and the reliability and engineering applicability of mining slope disaster early warning are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rock mass monitoring in mining engineering, and more specifically, to a method and system for identifying structural degradation of rock mass in mining slopes. Background Art

[0002] In mining projects, dynamic degradation monitoring of rock structures is one of the key technologies to ensure slope stability. Existing technologies usually use cross-hole acoustic wave testing methods to evaluate the degree of rock fracture development by analyzing the mean wave velocity or amplitude attenuation characteristics of acoustic wave propagation between boreholes. This method relies on the overall statistical characteristics of acoustic wave signals and combines borehole observation data to form a macroscopic judgment of rock damage, which is applied to rock stability monitoring in mining areas.

[0003] However, the above method has significant limitations when dealing with hidden damage to rock mass. When there are micro-fracture chains that have not formed macro-cracks but are continuously distributed inside the rock mass, the acoustic wave signal will be overall attenuated due to the cumulative effect of the micro-fractures on the cross-hole propagation path, causing the test data to present pseudo-uniform characteristics. It is difficult to distinguish the difference in acoustic response between such hidden damage and intact rock mass, resulting in missed or misjudgment of high-risk areas of rock mass degradation, which restricts the accuracy of early warning of mining slope disasters. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for identifying structural degradation of rock mass in mining slopes to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for identifying structural degradation of rock mass in a mining slope comprises the following steps: S1, collecting acoustic wave signals between boreholes in the target rock mass area, and adjusting the signal collection time window according to the spacing between adjacent boreholes; S2, performing multi-scale wavelet decomposition on the acoustic wave signal to extract the energy distribution of the sub-signals in each frequency band, and screening the characteristic frequency band corresponding to the micro-fracture chain response based on the energy gradient; S3, calculating the skewness and kurtosis of the acoustic wave travel time series of the characteristic frequency band sub-signal to generate a statistical spectrum, segmenting the acoustic wave propagation path, and performing a time domain sliding comparison with the complete rock mass benchmark spectrum to mark the abnormal path segment; S4, fitting the density parameters of the microfracture chain according to the acoustic wave travel time data of the abnormal path segment to generate a preliminary density model; S5, extracting the coordinates of microcracks that do not penetrate the borehole wall in the borehole television observation data, and screening the microcrack groups that match the direction of the sound wave propagation path; S6. Compare and verify the spatial density of the microcrack group with the preliminary density model, and remove the misjudged data in the direction mismatching area; S7. Generate a three-dimensional rock mass degradation risk map based on the verified microfracture chain density and microcrack group distribution.

[0006] In a preferred embodiment, S1 comprises: Determine the spacing between adjacent boreholes and calculate the theoretical propagation time of sound waves between adjacent boreholes based on the preset average sound velocity of the rock mass; Determine the total duration of the signal acquisition time window based on the sum of the theoretical propagation time and a preset redundant time threshold; The signal acquisition time window is divided into a start time and an end time according to the total duration of the signal acquisition time window, and the cross-hole acoustic wave device is controlled to collect acoustic wave signals according to the divided time windows.

[0007] In a preferred embodiment, S2 includes: Calculate the energy proportion of each frequency band sub-signal after multi-scale wavelet decomposition to generate an energy distribution sequence; Generate an energy gradient sequence according to the absolute value of the difference between the energy proportions of adjacent frequency bands; Mark the frequency bands whose absolute values ​​of differences in the energy gradient sequence exceed a preset gradient threshold as candidate frequency bands; The target frequency band that matches the frequency band characteristics of the micro-fracture chain response in the candidate frequency bands is extracted as the final feature frequency band.

[0008] In a preferred embodiment, the final characteristic frequency band is determined by a frequency band range of a preset micro-fracture chain response or a frequency band characteristic library of historical data statistics.

[0009] In a preferred embodiment, S3 includes: The acoustic wave propagation path is divided into multiple continuous sub-path segments according to the spacing between adjacent boreholes and the preset segmentation rules, and the length of each sub-path segment is dynamically adjusted based on the uniformity of the rock mass structure; The acoustic wave travel time statistics spectrum in each sub-path segment is segmented into time domain windows, and the window length is dynamically matched with the acoustic wave propagation time of the sub-path segment; Extract the benchmark data window corresponding to the current sub-path segment from the complete rock mass benchmark map, and calculate the joint difference between the skewness and kurtosis of the statistical map in the current window and the benchmark data window; According to the comparison result between the joint difference value and the preset difference threshold, the sub-path segment whose joint difference value exceeds the preset difference threshold is marked as an abnormal path segment.

[0010] In a preferred embodiment, S4 includes: Extract the skewness and kurtosis data of the acoustic wave travel time sequence corresponding to the abnormal path segment, and generate the statistical feature vector of the abnormal path segment; Based on the mapping relationship between the acoustic wave data and density parameters in the historical microfracture chain development area, a regression model of skewness-kurtosis and density parameters was constructed. The statistical feature vector of the abnormal path segment is input into the regression model, and the density parameter of the micro-fracture chain is output; According to the spatial distribution of density parameters, a preliminary density model is generated by the Kriging interpolation algorithm. During the interpolation process, the acoustic wave propagation path of the abnormal path segment is used as the spatial constraint condition.

[0011] In a preferred embodiment, S5 includes: Extract the spatial coordinates of micro-cracks that do not penetrate the borehole wall in the borehole television observation data to generate a micro-crack coordinate set; Calculate the corresponding extension direction of each microcrack in the microcrack coordinate set, and the extension direction is determined by fitting the direction of the straight line of the coordinates of the two end points of the microcrack; Calculate the direction of the sound wave propagation path in the borehole television observation coordinate system. The direction of the sound wave propagation path is determined by the spatial coordinate connection line between the transmitting probe and the receiving probe. Microcracks whose extension direction and the direction of the sound wave propagation path have an angle less than a preset angle are screened to generate a group of microcracks with matching directions.

[0012] In a preferred embodiment, S6 includes: The spatial density is calculated based on the number and distribution volume of microcracks in the microcrack group. The distribution volume is determined according to the three-dimensional boundary of the rock mass area covered by the microcrack group. Define the verification index of the preliminary density model in the corresponding rock mass area, which is the absolute value of the relative error between the predicted value of the preliminary density model and the spatial density of the microcrack group; The areas where the screening verification index exceeds the preset error threshold and the direction of the microcrack group does not match the acoustic wave propagation path are marked as misjudged areas; The density parameter data of the misjudged area is removed from the preliminary density model, and the corrected density model is retained.

[0013] In a preferred embodiment, S7 includes: The verified microfracture chain density parameter and the microfracture group spatial density are weightedly fused according to the spatial coordinates to generate the fused density parameter; Divide the three-dimensional grid cells based on the drilling positions and acoustic wave propagation paths in the rock mass area, and the grid cell size is determined according to the drilling spacing and the preset accuracy requirements; The fusion density parameter in each grid cell is normalized, and the normalization range is 0 to 1, where 0 indicates no degradation risk and 1 indicates the highest risk; The risk level threshold is set according to the normalized results, and the grid units are divided into low-risk, medium-risk, and high-risk areas according to the threshold interval; The risk level is mapped into a color gradient through three-dimensional visualization technology to generate a three-dimensional map containing spatial coordinates and risk levels.

[0014] In another aspect, the present invention provides a system for identifying structural degradation of a mining slope rock mass, comprising: Acoustic time window module: collects acoustic wave signals between boreholes in the target rock mass area and adjusts the signal acquisition time window according to the spacing between adjacent boreholes; Frequency band screening module: Perform multi-scale wavelet decomposition on the acoustic wave signal to extract the sub-signal energy distribution of each frequency band, and screen the characteristic frequency band corresponding to the micro-fracture chain response based on the energy gradient; Path comparison module: Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the characteristic frequency band sub-signal to generate a statistical spectrum, segment the acoustic wave propagation path, perform time domain sliding comparison with the complete rock mass benchmark spectrum, and mark the abnormal path segment; Density modeling module: Fit the density parameters of the microfracture chain according to the acoustic travel time data of the abnormal path segment to generate a preliminary density model; Fracture screening module: extracts the coordinates of micro-fractures that do not penetrate the borehole wall in the borehole TV observation data, and screens the micro-fracture groups that match the direction of the sound wave propagation path; False positive elimination module: compares and verifies the spatial density of microcrack groups with the preliminary density model, and eliminates false positive data in areas with mismatched directions; Three-dimensional map module: Generate a three-dimensional rock mass degradation risk map based on the verified microfracture chain density and microcrack group distribution.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Accurately locate high-risk areas of rock mass degradation by constructing statistical maps of skewness and kurtosis of acoustic wave travel time series and comparing the paths in segmented time domain. At the same time, combined with the spatial matching verification of the direction of microcrack groups and the acoustic wave propagation path in borehole television observation data, a multi-source data collaborative misjudgment elimination mechanism is constructed, which eliminates the limitations of single data source analysis from the perspective of physical correlation and greatly improves the reliability of detection results. 2. Through dynamic density modeling and three-dimensional map generation technology, based on the spatial fusion analysis of micro-fracture chain density parameters and micro-crack group distribution, it can dynamically reflect the evolution trend of rock damage and provide a forward-looking basis for early warning of mining slope disasters; through multi-level cross-validation of acoustic and optical data, it not only solves the risk of missed judgment caused by data isolation in traditional methods, but also realizes the visual grading expression of rock degradation risk level, providing intuitive and quantitative decision-making support for the formulation of engineering protection measures; while improving the monitoring accuracy, it significantly optimizes the engineering applicability of rock stability evaluation under complex geological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1A flow chart of a method for identifying structural degradation of rock mass in a mining slope according to the present invention; Figure 2 The present invention is a structural schematic diagram of a system for identifying structural degradation of rock mass in mining slopes. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0018] Embodiment 1: Figure 1 The present invention provides a method for identifying structural degradation of rock mass in mining slopes, which comprises the following steps: S1, collecting acoustic wave signals between boreholes in the target rock mass area, and adjusting the signal collection time window according to the spacing between adjacent boreholes; S2, performing multi-scale wavelet decomposition on the acoustic wave signal to extract the energy distribution of the sub-signals in each frequency band, and screening the characteristic frequency band corresponding to the micro-fracture chain response based on the energy gradient; S3, calculating the skewness and kurtosis of the acoustic wave travel time series of the characteristic frequency band sub-signal to generate a statistical spectrum, segmenting the acoustic wave propagation path, and performing a time domain sliding comparison with the complete rock mass benchmark spectrum to mark the abnormal path segment; S4, fitting the density parameters of the microfracture chain according to the acoustic wave travel time data of the abnormal path segment to generate a preliminary density model; S5, extracting the coordinates of microcracks that do not penetrate the borehole wall in the borehole television observation data, and screening the microcrack groups that match the direction of the sound wave propagation path; S6. Compare and verify the spatial density of the microcrack group with the preliminary density model, and remove the misjudged data in the direction mismatching area; S7. Generate a three-dimensional rock mass degradation risk map based on the verified microfracture chain density and microcrack group distribution.

[0019] S1. Collect acoustic wave signals between boreholes in the target rock mass area and adjust the signal acquisition time window according to the spacing between adjacent boreholes, including: When determining the spacing between adjacent boreholes, the hole coordinates of adjacent boreholes in the target rock mass area are measured and the spacing between adjacent boreholes is calculated based on the three-dimensional space distance formula.

[0020] When calculating the theoretical propagation time of sound waves between adjacent boreholes according to a preset average sound velocity of the rock mass, the preset average sound velocity of the rock mass is obtained by averaging historical sound wave test data of the target rock mass area.

[0021] When determining the total duration of the signal acquisition time window based on the sum of the theoretical propagation time and the preset redundant time threshold, the preset redundant time threshold is set according to the background noise level of the target rock area, and the background noise level is determined by the energy statistics of the acoustic wave signal during the period without mining operations.

[0022] When the start time and the end time of the signal acquisition time window are divided according to the total duration of the signal acquisition time window, the start time is set as the sound wave emission time, and the end time is set as the sum of the sound wave emission time and the total duration.

[0023] When controlling the cross-hole acoustic wave device to collect acoustic wave signals according to the divided time window, the transmitting probe and the receiving probe of the cross-hole acoustic wave device are respectively placed at the set depths of adjacent boreholes, the transmitting probe transmits the acoustic wave signal at the set frequency, and the receiving probe synchronously collects the acoustic wave signal within the time window.

[0024] S2. Perform multi-scale wavelet decomposition on the acoustic wave signal to extract the sub-signal energy distribution of each frequency band, and screen the characteristic frequency band corresponding to the micro-fracture chain response based on the energy gradient, including: When performing multi-scale wavelet decomposition on the sound wave signal, the db4 basis function in the Daubechies wavelet basis function is used to decompose the sound wave signal into three layers. The decomposed sub-signals include the first layer high-frequency sub-signal, the second layer high-frequency sub-signal, the third layer high-frequency sub-signal and the third layer low-frequency sub-signal.

[0025] When extracting the sub-signal energy distribution of each frequency band, the energy proportion of each sub-signal in the time window is calculated. The energy proportion is calculated by dividing the sum of the squares of the sub-signal amplitudes by the total sum of the squares of all sub-signal amplitudes. The time window length is consistent with the signal acquisition time window in step S1.

[0026] When generating an energy gradient sequence according to the absolute value of the difference in energy proportions of adjacent frequency bands, adjacent frequency bands are arranged in order from low to high frequency as the third layer low-frequency sub-signal, the third layer high-frequency sub-signal, the second layer high-frequency sub-signal, and the first layer high-frequency sub-signal, and the absolute value of the difference in energy proportions of two adjacent sub-signals is calculated in turn.

[0027] When the frequency band whose absolute value of difference in the energy gradient sequence exceeds the preset gradient threshold is marked as a candidate frequency band, the preset gradient threshold is set by collecting the acoustic wave signal of the microfracture chain development area in the historical data of the target mining area, and calculating the 95% quantile value of its energy gradient sequence as the threshold.

[0028] When extracting the target frequency band that matches the frequency band characteristics of the microfracture chain response from the candidate frequency bands, the frequency band feature library is constructed through laboratory rock sample fracture tests and historical microfracture chain data of the mining area. The specific method is: the acoustic wave signal is collected when the rock sample is loaded to the microfracture chain development stage, and the frequency band range of the energy gradient mutation is statistically analyzed after wavelet decomposition, and 50 Hz to 200 Hz is screened out as the typical frequency band range.

[0029] The final characteristic frequency band is determined by comparing the candidate frequency band with the typical frequency band range in the frequency band feature library. If the overlapping part of the candidate frequency band and the typical frequency band range accounts for more than 70% of the candidate frequency band length, it is determined as the target frequency band, otherwise it is eliminated.

[0030] S3. Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the characteristic frequency band sub-signal to generate a statistical spectrum, segment the acoustic wave propagation path, and perform a time domain sliding comparison with the complete rock mass benchmark spectrum to mark the abnormal path segment, including: When the sound wave propagation path is divided into multiple continuous sub-path segments according to the adjacent borehole spacing and the preset segmentation rules, the preset segmentation rules are determined based on the geological exploration data of the target rock mass area, and the geological exploration data include the rock mass elastic modulus distribution, the historical fracture development location and the stress field distribution; for example, in areas where the rock mass elastic modulus varies greatly or the historical fractures are dense, the segmentation rules give priority to shortening the sub-path segment length to improve monitoring accuracy, while in areas with good rock mass uniformity, the segmentation rules extend the sub-path segment length to improve efficiency.

[0031] The length of each sub-path segment is dynamically adjusted based on the uniformity of the rock mass structure. The uniformity of the rock mass structure is evaluated by the coefficient of variation of the historical wave velocity of the cross-hole acoustic wave test. For example, if the coefficient of variation of the historical wave velocity in a certain area is less than 0.1, it is judged to be a uniform rock mass, and the length of the sub-path segment is set to 1 / 5 to 1 / 3 of the spacing between adjacent boreholes. If the coefficient of variation is greater than 0.1, it is judged to be a non-uniform rock mass, and the length of the sub-path segment is shortened to 1 / 10 to 1 / 5 of the spacing between adjacent boreholes.

[0032] When the acoustic wave travel time statistical spectrum in each sub-path segment is segmented into time domain windows, the window length is dynamically matched with the acoustic wave propagation time of the sub-path segment; for example, for a sub-path segment with a propagation time of 0.1 second, the window length is set to 0.02 seconds to 0.033 seconds, and the window sliding step is set to 1 / 2 of the window length, that is, 0.01 seconds to 0.016 seconds; if the sub-path segment propagation time is 0.2 seconds, the window length is adjusted to 0.04 seconds to 0.066 seconds, and the corresponding sliding step is 0.02 seconds to 0.033 seconds.

[0033] When extracting the benchmark data window corresponding to the current sub-path segment in the complete rock mass benchmark map, the complete rock mass benchmark map is generated by collecting acoustic wave data of the complete rock mass area that is not disturbed by mining in the mining area; for example, a cross-hole acoustic wave testing device is arranged in the unmined area at the edge of the mining area, and a location more than 50 meters away from the boundary of the goaf and where drilling television observation shows no visible cracks in the borehole wall is selected, at least 10 groups of acoustic wave signals are collected, and the skewness and kurtosis mean of their travel time series are calculated to generate a benchmark map.

[0034] When calculating the joint difference between the skewness and kurtosis of the statistical map in the current window and the benchmark data window, the joint difference is the weighted sum of the absolute value of the skewness difference and the absolute value of the kurtosis difference; the weight coefficient is determined according to the correlation between the microfracture chain response and the skewness and kurtosis in the historical data. For example, by analyzing the acoustic wave data of 10 groups of microfracture chain development areas, it is found that the skewness is more sensitive to the microfracture chain than the kurtosis, so the skewness weight coefficient is 0.6 and the kurtosis weight coefficient is 0.4.

[0035] When marking abnormal path segments based on the comparison results of the joint difference value and the preset difference threshold, the preset difference threshold is determined by laboratory rock sample tests. For example, cross-hole acoustic wave tests are performed on complete granite samples and rock samples containing micro-fracture chains. The complete rock samples are tested under unloaded conditions to obtain data, and the rock samples containing micro-fracture chains are prepared and tested after triaxial loading to an axial strain of 0.5%. The joint difference value distribution of the two types of rock samples is calculated, and the 95% quantile value of the difference value distribution of the complete rock samples is taken as the threshold. If the joint difference value of the current sub-path segment exceeds the threshold, it is marked as an abnormal path segment.

[0036] S4. Fit the density parameters of the microfracture chain according to the acoustic travel time data of the abnormal path segment to generate a preliminary density model, including: When extracting the skewness and kurtosis data of the acoustic travel time sequence corresponding to the abnormal path segment, the abnormal path segment is determined by the abnormal path segment marked in step S3, the skewness of the acoustic travel time sequence is the mean skewness of all the acoustic travel time data in the path segment, and the kurtosis is the mean kurtosis of all the acoustic travel time data in the path segment; for example, in a mining area with poor rock mass uniformity, the abnormal path segments may be concentrated in the stress concentration area at the edge of the goaf, and their skewness mean is significantly negatively skewed, and the kurtosis mean is higher than that of the intact rock mass area.

[0037] When generating the statistical feature vector of the abnormal path segment, the statistical feature vector is a two-dimensional vector containing the mean skewness and the mean kurtosis. For example, in the historical data of a mining area, the statistical feature vectors of the microfracture chain development area are usually distributed in the skewness interval [-1.5, 0.5] and the kurtosis interval [3.0, 6.0], while the feature vectors of the intact rock mass area are concentrated in the skewness [-0.2, 0.2] and the kurtosis [2.8, 3.2].

[0038] When constructing a regression model based on the mapping relationship between the acoustic wave data and density parameters in the historical micro-fracture chain development area, the historical data are selected from the cross-hole acoustic wave test data and the corresponding drill core density measurement results in the micro-fracture chain development area in the historical monitoring of the mining area; for example, in a certain iron ore area, the historical data include 10 groups of acoustic wave test records in the micro-fracture chain development area, and each group of data corresponds to the core density measurement values ​​at different depths (50 meters to 200 meters). The mapping equation of skewness-kurtosis and density parameters is established through multivariate linear regression, and the equation form is a linear combination.

[0039] Among them, the regression model is constructed based on the correspondence between the acoustic wave characteristics of the microfracture chain development area in the historical data and the core density parameters; the historical data include the mean skewness and kurtosis of the acoustic wave travel time recorded by the cross-hole acoustic wave test in the mining area, and the microfracture chain density parameters measured by the core laboratory at the corresponding drilling location. The core density is converted into a physical density value by analyzing the microfracture chain distribution through CT scanning images.

[0040] The model adopts the multivariate linear regression method, takes the mean skewness and the mean kurtosis as input variables, and the density parameter as the output variable. The weight coefficient and constant term of each variable are determined by fitting historical data through the least squares method. For example, in the historical data of a certain iron ore area, for every 1 unit increase in the mean skewness, the density parameter increases by 0.8 units; for every 1 unit increase in the mean kurtosis, the density parameter increases by 0.3 units. The constant term is 0.5, and the error between the model-predicted density value and the measured value is less than 0.2g / cm³.

[0041] If the lithology difference leads to a nonlinear relationship between acoustic parameters and density (such as sandstone mining area), the model is expanded to include the square term of the mean skewness and the square term of the mean kurtosis, and the fitting accuracy is improved by adding higher-order terms; the weight coefficient is dynamically allocated according to the contribution of skewness and kurtosis to the density parameter in historical data. For example, in the granite mining area, the skewness weight accounts for 70% and the kurtosis accounts for 30%, and in the sandstone mining area, the skewness and kurtosis are adjusted to 50% each; after the model is constructed, it needs to be cross-validated with historical data to ensure the stability of the prediction results.

[0042] If a copper mining area has a nonlinear relationship between microfracture chain density and acoustic parameters due to lithological differences, the regression model can be expanded to a polynomial form, for example, by introducing a squared skewness term to improve the fitting accuracy.

[0043] When the statistical feature vector of the abnormal path segment is input into the regression model, the regression model outputs the estimated value of the density parameter of the microfracture chain; for example, the statistical feature vector of an abnormal path segment is (skewness -0.8, kurtosis 4.2), and the output density parameter after input into the model is 1.8g / cm³, while the feature vector of another path segment is (skewness 0.3, kurtosis 3.1), and the output density parameter is 1.2g / cm³. When the preliminary density model is generated according to the spatial distribution of density parameters, the spatial distribution is the set of density parameters and their spatial coordinates corresponding to each abnormal path segment, and the spatial coordinates are determined by the drilling position and the acoustic wave propagation path of the cross-hole acoustic wave device; for example, an abnormal path segment is distributed along the straight line from borehole A (coordinates X1, Y1, Z1) to borehole B (X2, Y2, Z2), and the spatial coordinates of its density parameters are distributed in proportion to the path length, the starting point coordinates are (X1, Y1, Z1), the end point is (X2, Y2, Z2), and the coordinates of the intermediate points are determined by linear interpolation.

[0044] When the preliminary density model is generated by the Kriging interpolation algorithm, the acoustic wave propagation path is used as a spatial constraint in the interpolation process; for example, if there are multiple intersecting acoustic wave propagation paths in a mining area, the interpolation range is limited to the rock mass area covered by all paths, and non-path areas (such as untested isolated rock layers) are not involved in the interpolation calculation.

[0045] When the sound wave propagation paths are unevenly distributed, the search radius of the interpolation algorithm is dynamically adjusted according to the path density. For example, the search radius is set to 5 meters in path-dense areas (spacing less than 10 meters) and expanded to 10 meters in sparse areas (spacing greater than 20 meters).

[0046] The variogram parameters of the interpolation algorithm are determined according to the spatial variation characteristics of the microfracture chain density parameters in the historical data. For example, the nugget value of a certain mining area is set to 0.1 (reflecting the measurement error), the base value is set to 1.5 (reflecting the total variation), and the range is set to 20 meters (reflecting the range of spatial autocorrelation).

[0047] S5. Extract the coordinates of microcracks that do not penetrate the borehole wall in the borehole TV observation data, and select the microcrack groups that match the direction of the sound wave propagation path, including: When extracting the spatial coordinates of micro-cracks that do not penetrate the borehole wall in the borehole television observation data, the borehole television observation data collects the borehole wall image through the downhole camera, and the image resolution meets the accuracy requirement of 0.1 mm per pixel corresponding to the actual size.

[0048] The criterion for determining microcracks that do not penetrate the hole wall is that the crack depth is less than the drilling radius and does not penetrate the hole wall. For example, when the drilling radius is 50 mm, microcracks with a crack depth less than 50 mm are included in the coordinate set.

[0049] When generating a microcrack coordinate set, each microcrack coordinate in the coordinate set is converted into the global coordinate system of the mining area through the local coordinate system of the borehole television; the conversion method is: take the borehole mouth as the origin of the global coordinate system, the borehole axis direction is the vertical downward direction, the due north direction is the positive direction of the X-axis, and the due east direction is the positive direction of the Y-axis. The longitude, latitude and elevation of the borehole mouth are measured by a total station or a global navigation satellite system device, and the coordinate conversion is performed in combination with the borehole inclination and azimuth; for example, the radial distance of a microcrack in the local coordinate system of the borehole is 1.2 meters, the circumferential angle is 30 degrees, and the vertical depth is 50 meters. After conversion to the global coordinate system, the coordinates are X=1000.5 meters, Y=2000.3 meters, and Z=-50.0 meters.

[0050] When calculating the corresponding extension direction of each microcrack in the microcrack coordinate set, the extension direction is determined by fitting the straight line direction of the coordinates of the two end points of the microcrack; specifically, the local coordinates of the starting point and the ending point of the microcrack in the borehole wall image are extracted, converted into three-dimensional global coordinates, and the horizontal coordinate difference and vertical coordinate difference between the starting point and the ending point are calculated. The horizontal direction difference is the X coordinate of the ending point minus the X coordinate of the starting point, and the Y coordinate difference is the same. The vertical direction difference is the Z coordinate of the ending point minus the Z coordinate of the starting point.

[0051] The horizontal azimuth of the extension direction is obtained by calculating the arc tangent of the horizontal coordinate difference, and the vertical inclination is obtained by calculating the arc tangent of the vertical direction difference and the horizontal distance. If the microcrack is a curve, the main extension direction is extracted by segmented fitting, for example, the crack image is divided into multiple sub-segments, the directions of each sub-segment are calculated respectively, and the average is taken as the main extension direction.

[0052] When calculating the direction of the sound wave propagation path in the borehole television observation coordinate system, the direction of the sound wave propagation path is determined by connecting the spatial coordinates of the transmitting probe and the receiving probe; for example, the transmitting probe is located at the global coordinates of borehole A (Xa=1000.0m, Ya=2000.0m, Za=-50.0m), and the receiving probe is located at the global coordinates of borehole B (Xb=1050.0m, Yb=2050.0m, Zb=-60.0m). The horizontal azimuth of the sound wave propagation path is 45 degrees east of north, and the vertical inclination is 5 degrees downward.

[0053] If the acoustic wave path is a broken line across multiple layers of boreholes, the direction of each segment is calculated segment by segment. For example, from borehole A to borehole C is the first segment, and from borehole C to borehole B is the second segment. The horizontal azimuth and vertical inclination of each segment are calculated separately.

[0054] When screening microcracks whose extension direction and the direction of the sound wave propagation path have an angle less than a preset angle, the preset angle is set according to the microcrack chain development characteristics of the target rock mass area; for example, in a layered rock mass area, the microcrack chain usually extends along the bedding plane, and the preset angle is set to 30 degrees; in a massive rock mass area, the preset angle is relaxed to 45 degrees; the angle is calculated by calculating the absolute value of the horizontal azimuth difference and the absolute value of the vertical inclination difference between the microcrack extension direction and the sound wave propagation path direction. If both are less than the preset angle, it is determined to be a direction match; for example, a microcrack extension direction is The horizontal azimuth of the extension direction is 50 degrees east of north, and the vertical inclination is 10 degrees downward. The horizontal azimuth of the sound wave path direction is 55 degrees east of north, and the vertical inclination is 8 degrees downward. The preset angle is 30 degrees. The horizontal azimuth difference is 5 degrees, and the vertical inclination difference is 2 degrees, both of which are less than 30 degrees, and it is judged to be a match; if the horizontal azimuth of the extension direction of a microcrack is 80 degrees east of north, and the vertical inclination is 15 degrees downward, the sound wave path direction is 30 degrees east of north, and the vertical inclination is 5 degrees downward, then the horizontal azimuth difference is 50 degrees, which exceeds the preset angle and is judged to be a mismatch.

[0055] When generating direction-matched microcrack groups, the spatial distribution of the microcrack groups is merged through spatial proximity and directional consistency. Spatial proximity is determined when the global coordinate distance between two microcracks is less than the preset cluster radius. For example, when the borehole spacing is 20 meters, the cluster radius is set to 5 meters. Directional consistency is determined when the angle between the extension directions of the microcracks is less than the preset angle and the vertical inclination difference is less than 10 degrees; for example, if the global coordinate distance between two microcracks is 3 meters, the horizontal azimuth difference in the extension direction is 10 degrees, and the vertical inclination difference is 5 degrees, they are merged into the same microcrack group; if a microcrack group contains 10 microcracks, and its spatial distribution is concentrated along the direction of the acoustic wave propagation path, it is marked as a high-risk microfracture chain area.

[0056] S6. Compare and verify the spatial density of the microcrack group with the preliminary density model, and eliminate the misjudged data in the direction mismatch area, including: When calculating the spatial density based on the number and distribution volume of microcracks within a microcrack group, the distribution volume is determined according to the three-dimensional boundary of the rock mass area covered by the microcrack group.

[0057] The method for determining the three-dimensional boundary is: extract the global coordinates of all microcracks in the microcrack group, calculate the length, width, and height of its smallest enclosing cube as the approximate value of the distribution volume; for example, the microcrack coordinates of a microcrack group range from 1000.0 meters to 1005.0 meters in the X direction, 2000.0 meters to 2008.0 meters in the Y direction, and -50.0 meters to -45.0 meters in the Z direction, then the distribution volume is 5 meters (length) × 8 meters (width) × 5 meters (height) = 200 cubic meters, and the spatial density is the number of microcracks (such as 50) divided by 200 cubic meters, which is 0.25 per cubic meter. If the microcrack group is irregularly distributed, the actual occupied volume is calculated using the three-dimensional convex hull algorithm, for example, using the microcrack coordinate point set in the borehole TV observation data to generate the minimum convex polyhedron and calculate its volume.

[0058] When defining the verification index of the preliminary density model in the corresponding rock mass area, the verification index is the absolute value of the relative error between the predicted value of the preliminary density model and the spatial density of the microcrack group.

[0059] The absolute value of the relative error is calculated by subtracting the spatial density of the microcrack group from the model prediction density, taking the absolute value and dividing it by the spatial density of the microcrack group to convert it into a percentage error; for example, the model prediction density is 0.30 per cubic meter, the spatial density of the microcrack group is 0.25 per cubic meter, and the absolute value of the relative error is |0.30-0.25| / 0.25=20%. The preset error threshold is determined based on the statistical distribution of the model prediction error in the historical data of the mining area. For example, the 95% percentile value of the historical error data is taken as the threshold. If 95% of the errors in the historical data are less than 25%, the threshold is set to 25%.

[0060] When screening and verifying areas where the error threshold is exceeded and the direction of the microcrack group does not match the acoustic wave propagation path, the criterion for direction mismatch is that the angle between the average extension direction of the microcrack group and the direction of the acoustic wave propagation path exceeds the preset angle; for example, if the model prediction error of a certain area is 30% (exceeding the threshold of 25%), and the angle between the average extension direction of the microcrack group and the acoustic wave path is 40 degrees (exceeding the preset angle of 30 degrees), it is determined to be a misjudged area. If the error of a certain area is 20% (not exceeding the threshold) but the direction angle is 35 degrees (exceeding the angle), or the error is 30% (exceeding the threshold) but the direction angle is 25 degrees (not exceeding the angle), it is not marked as a misjudged area.

[0061] When removing the density parameter data of the misjudged area from the preliminary density model, the removal method is to set the density parameter corresponding to the misjudged area to zero or replace it with the interpolation result of the adjacent area; for example, the density parameter of a misjudged area is 2.0g / cm³, and the density parameters of its adjacent areas are 1.5g / cm³, 1.8g / cm³ and 1.6g / cm³. After setting it to zero, the area will not participate in the subsequent analysis in the corrected model, or it will be replaced by the average value of the adjacent areas, 1.63g / cm³.

[0062] When the revised density model is retained, the revised model only contains the density parameters and interpolation completion data of the areas not marked as misjudgment areas, which are used to generate the final three-dimensional rock mass degradation risk map.

[0063] S7. Generate a three-dimensional rock mass degradation risk map based on the verified microfracture chain density and microfracture group distribution, including: When the verified microfracture chain density parameters and the spatial density of the microfracture group are weightedly fused according to the spatial coordinates, the weight coefficient of the weighted fusion is determined according to the historical data correlation between the microfracture chain density parameters and the spatial density of the microfracture group.

[0064] For example, in the historical data of a certain iron ore area, the contribution of microfracture chain density parameters to rock mass degradation accounts for 70%, and the spatial density of microcracks accounts for 30%. The calculation formula of fusion density parameters is fusion density = 0.7 × microfracture chain density + 0.3 × microcrack spatial density; if a copper ore area is more sensitive to seepage due to microcracks, the weights are adjusted to 60% for microfracture chain density and 40% for microcrack spatial density. The fusion density parameters after weighted fusion are stored in association with spatial coordinates. For example, the coordinates of the center point of a grid unit are (X=1000.5m, Y=2000.3m, Z=-50.0m), and the fusion density is 1.8g / cm³.

[0065] When dividing the three-dimensional grid units based on the drilling positions and sound wave propagation paths in the rock mass area, the grid unit size is determined according to the drilling spacing and the preset accuracy requirements; for example, when the drilling spacing is 20 meters, the preset accuracy requirement is 5-meter resolution, and the grid unit size is set to 5 meters × 5 meters × 5 meters; if the drilling spacing is 50 meters and the accuracy requirement is 10 meters, the grid size is set to 10 meters × 10 meters × 10 meters.

[0066] For areas where the acoustic wave propagation paths intersect or overlap, the grid cell size can be locally refined, for example, the grid size can be reduced to 1m×1m×1m within 1m around the path intersection. The boundaries of the grid cells are aligned with the borehole locations. For example, if borehole A is located within the grid cell (1000-1005m, 2000-2005m, -50 to -45m), the grid division will be expanded around the borehole.

[0067] When the fusion density parameter within each grid cell is normalized, the normalization range is 0 to 1, where 0 indicates that the cell has no degradation risk (fusion density ≤ 1.0 g / cm³) and 1 indicates the highest risk (fusion density ≥ 3.0 g / cm³).

[0068] The normalization method is: subtract the minimum value of 1.0g / cm³ from the grid unit fusion density, and then divide it by the difference between the maximum value of 3.0g / cm³ and the minimum value of 1.0g / cm³; for example, in a grid unit fusion density of 2.5g / cm³, the normalized value is (2.5-1.0) / (3.0-1.0) = 0.75. If there are extremely high-density areas in the mining area (such as fusion density > 3.0g / cm³), the normalized value is forced to be set to 1.0.

[0069] When setting the risk level threshold according to the normalization result, the risk level threshold is determined according to the historical disaster data of the mining area; for example, in the historical data of a certain iron mining area, there is no disaster record in the area with a normalized value of <0.3, local rock spalling occurs in the area of ​​0.3-0.6, and roof collapse occurs in the area of ​​>0.6, so the low risk threshold is set to 0.3, the medium risk is 0.6, and the high risk is 1.0. If a copper mining area adjusts the threshold due to lithology differences, the low risk is set to 0.4, the medium risk is 0.7, and the high risk is 1.0.

[0070] After the grid cells are divided according to the threshold interval, the low-risk area is marked as green, the medium-risk area is marked as yellow, and the high-risk area is marked as red, forming a color coding rule.

[0071] When risk levels are mapped to color gradients using 3D visualization technology, the color gradients use transparency overlays to enhance spatial recognition; for example, the transparency of low-risk green areas is set to 50%, medium-risk yellow is 30%, and high-risk red is 0% (completely opaque). The generated 3D map contains the spatial coordinates, risk levels, and color codes of the grid cells, and supports rotation, scaling, and cross-sectional viewing.

[0072] For example, users can view the color distribution of the horizontal slice at Z=-50 meters, or analyze the risk gradient change along the profile view from borehole A to borehole B; the 3D map output format is general point cloud data or grid model, which is compatible with mainstream geological software (such as Surpac, Micromine) for import.

[0073] Embodiment 2: Figure 2 A structural schematic diagram of a system for identifying structural degradation of a mining slope rock mass is provided in the present invention. The system for identifying structural degradation of a mining slope rock mass comprises: Acoustic time window module: collects acoustic wave signals between boreholes in the target rock mass area and adjusts the signal acquisition time window according to the spacing between adjacent boreholes; Frequency band screening module: Perform multi-scale wavelet decomposition on the acoustic wave signal to extract the sub-signal energy distribution of each frequency band, and screen the characteristic frequency band corresponding to the micro-fracture chain response based on the energy gradient; Path comparison module: Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the characteristic frequency band sub-signal to generate a statistical spectrum, segment the acoustic wave propagation path, perform time domain sliding comparison with the complete rock mass benchmark spectrum, and mark the abnormal path segment; Density modeling module: Fit the density parameters of the microfracture chain according to the acoustic travel time data of the abnormal path segment to generate a preliminary density model; Fracture screening module: extracts the coordinates of micro-fractures that do not penetrate the borehole wall in the borehole TV observation data, and screens the micro-fracture groups that match the direction of the sound wave propagation path; False positive elimination module: compares and verifies the spatial density of microcrack groups with the preliminary density model, and eliminates false positive data in areas with mismatched directions; Three-dimensional map module: Generate a three-dimensional rock mass degradation risk map based on the verified microfracture chain density and microcrack group distribution.

[0074] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0075] It should be noted that the present invention can be deployed on the device itself to realize embedded applications, and can also be run on a PC or other terminal with a user interface, so as to meet various hardware environments and usage requirements.

[0076] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0079] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0081] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0082] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0083] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying structural degradation of rock mass in mining slopes, characterized in that: The steps include: S1, collecting acoustic wave signals between boreholes in the target rock mass area, and adjusting the signal collection time window according to the spacing between adjacent boreholes; S2, performing multi-scale wavelet decomposition on the acoustic wave signal to extract the energy distribution of the sub-signals in each frequency band, and screening the characteristic frequency band corresponding to the micro-fracture chain response based on the energy gradient; S3, calculating the skewness and kurtosis of the acoustic wave travel time series of the characteristic frequency band sub-signal to generate a statistical spectrum, segmenting the acoustic wave propagation path, and performing a time domain sliding comparison with the complete rock mass benchmark spectrum to mark the abnormal path segment; S4, fitting the density parameters of the microfracture chain according to the acoustic wave travel time data of the abnormal path segment to generate a preliminary density model; S5, extracting the coordinates of microcracks that do not penetrate the borehole wall in the borehole television observation data, and screening the microcrack groups that match the direction of the sound wave propagation path; S6. Compare and verify the spatial density of the microcrack group with the preliminary density model, and remove the misjudged data in the direction mismatching area; S7. Generate a three-dimensional rock mass degradation risk map based on the verified microfracture chain density and microcrack group distribution.

2. A method for identifying structural degradation of rock mass in mining slopes according to claim 1, characterized in that: S1 includes: Determine the spacing between adjacent boreholes and calculate the theoretical propagation time of sound waves between adjacent boreholes based on the preset average sound velocity of the rock mass; Determine the total duration of the signal acquisition time window based on the sum of the theoretical propagation time and a preset redundant time threshold; The signal acquisition time window is divided into a start time and an end time according to the total duration of the signal acquisition time window, and the cross-hole acoustic wave device is controlled to collect acoustic wave signals according to the divided time windows.

3. The method for identifying structural degradation of rock mass in mining slopes according to claim 1, characterized in that S2 include: Calculate the energy proportion of each frequency band sub-signal after multi-scale wavelet decomposition to generate an energy distribution sequence; Generate an energy gradient sequence according to the absolute value of the difference between the energy proportions of adjacent frequency bands; Mark the frequency bands whose absolute values ​​of differences in the energy gradient sequence exceed a preset gradient threshold as candidate frequency bands; The target frequency band that matches the frequency band characteristics of the micro-fracture chain response in the candidate frequency bands is extracted as the final feature frequency band.

4. A method for identifying structural degradation of rock mass in mining slopes according to claim 3, characterized in that: The final characteristic frequency band is determined by presetting the frequency band range of the micro-fracture chain response or the frequency band characteristic library of historical data statistics.

5. The method for identifying structural degradation of rock mass in mining slopes according to claim 1, characterized in that: S3 includes: The acoustic wave propagation path is divided into multiple continuous sub-path segments according to the spacing between adjacent boreholes and the preset segmentation rules, and the length of each sub-path segment is dynamically adjusted based on the uniformity of the rock mass structure; The acoustic wave travel time statistics spectrum in each sub-path segment is segmented into time domain windows, and the window length is dynamically matched with the acoustic wave propagation time of the sub-path segment; Extract the benchmark data window corresponding to the current sub-path segment from the complete rock mass benchmark map, and calculate the joint difference between the skewness and kurtosis of the statistical map in the current window and the benchmark data window; According to the comparison result between the joint difference value and the preset difference threshold, the sub-path segment whose joint difference value exceeds the preset difference threshold is marked as an abnormal path segment.

6. The method for identifying structural degradation of rock mass in mining slopes according to claim 1, characterized in that S4 include: Extract the skewness and kurtosis data of the acoustic wave travel time sequence corresponding to the abnormal path segment, and generate the statistical feature vector of the abnormal path segment; Based on the mapping relationship between the acoustic wave data and density parameters in the historical microfracture chain development area, a regression model of skewness-kurtosis and density parameters was constructed. The statistical feature vector of the abnormal path segment is input into the regression model, and the density parameter of the micro-fracture chain is output; According to the spatial distribution of density parameters, a preliminary density model is generated by the Kriging interpolation algorithm. During the interpolation process, the acoustic wave propagation path of the abnormal path segment is used as the spatial constraint condition.

7. The method for identifying structural degradation of rock mass in mining slopes according to claim 1, characterized in that S5 include: Extract the spatial coordinates of micro-cracks that do not penetrate the borehole wall in the borehole television observation data to generate a micro-crack coordinate set; Calculate the corresponding extension direction of each microcrack in the microcrack coordinate set, and the extension direction is determined by fitting the direction of the straight line of the coordinates of the two end points of the microcrack; Calculate the direction of the sound wave propagation path in the borehole television observation coordinate system. The direction of the sound wave propagation path is determined by the spatial coordinate connection line between the transmitting probe and the receiving probe. Microcracks whose extension direction and the direction of the sound wave propagation path have an angle less than a preset angle are screened to generate a group of microcracks with matching directions.

8. The method for identifying structural degradation of a mining slope rock mass according to claim 1, characterized in that S6 include: The spatial density is calculated based on the number and distribution volume of microcracks in the microcrack group. The distribution volume is determined according to the three-dimensional boundary of the rock mass area covered by the microcrack group. Define the verification index of the preliminary density model in the corresponding rock mass area, which is the absolute value of the relative error between the predicted value of the preliminary density model and the spatial density of the microcrack group; The areas where the screening verification index exceeds the preset error threshold and the direction of the microcrack group does not match the acoustic wave propagation path are marked as misjudged areas; The density parameter data of the misjudged area is removed from the preliminary density model, and the corrected density model is retained.

9. The method for identifying structural degradation of rock mass in mining slopes according to claim 1, characterized in that: S7 includes: The verified microfracture chain density parameter and the microfracture group spatial density are weightedly fused according to the spatial coordinates to generate the fused density parameter; Divide the three-dimensional grid cells based on the drilling positions and acoustic wave propagation paths in the rock mass area, and the grid cell size is determined according to the drilling spacing and the preset accuracy requirements; The fusion density parameter in each grid cell is normalized, and the normalization range is 0 to 1, where 0 indicates no degradation risk and 1 indicates the highest risk; The risk level threshold is set according to the normalized results, and the grid units are divided into low-risk, medium-risk, and high-risk areas according to the threshold interval; The risk level is mapped into a color gradient through three-dimensional visualization technology to generate a three-dimensional map containing spatial coordinates and risk levels.

10. A system for identifying structural degradation of a mining slope rock mass, used to implement a method for identifying structural degradation of a mining slope rock mass as claimed in any one of claims 1 to 9, characterized in that: include: Acoustic time window module: collects acoustic wave signals between boreholes in the target rock mass area and adjusts the signal acquisition time window according to the spacing between adjacent boreholes; Frequency band screening module: Perform multi-scale wavelet decomposition on the acoustic wave signal to extract the sub-signal energy distribution of each frequency band, and screen the characteristic frequency band corresponding to the micro-fracture chain response based on the energy gradient; Path comparison module: Calculate the skewness and kurtosis of the acoustic wave travel time sequence of the characteristic frequency band sub-signal to generate a statistical spectrum, segment the acoustic wave propagation path, perform time domain sliding comparison with the complete rock mass benchmark spectrum, and mark the abnormal path segment; Density modeling module: Fit the density parameters of the microfracture chain according to the acoustic travel time data of the abnormal path segment to generate a preliminary density model; Fracture screening module: extracts the coordinates of micro-fractures that do not penetrate the borehole wall in the borehole TV observation data, and screens the micro-fracture groups that match the direction of the sound wave propagation path; False positive elimination module: compares and verifies the spatial density of microcrack groups with the preliminary density model, and eliminates false positive data in areas with mismatched directions; Three-dimensional map module: Generate a three-dimensional rock mass degradation risk map based on the verified microfracture chain density and microcrack group distribution.

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