Ultrasonic detection method for integrity of sealing layer of mine tunnel

Abstract

The present application belongs to the technical field of ultrasonic detection, and discloses a mine roadway sealing layer integrity ultrasonic detection method; the method comprises the following steps: dividing a roadway area, and dividing a regional risk level of each roadway area; generating a sealing layer stress thermal diagram, performing stress partition, and generating a measuring point according to the regional risk level of the roadway area and the sealing layer stress partition; obtaining individual difference characteristics of the measuring point, and dividing the measuring point into different types according to the individual difference characteristics of the measuring point; executing a differentiated detection scheme according to the measuring point type; and adjusting the measuring point type and the regional risk level according to the detection result; the present application is characterized by multi-dimensional risk quantization division and individual differentiation focusing, which improves the detection accuracy, reliability and resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic testing technology, and more specifically, to a method for ultrasonic testing of the integrity of sealing layers in mine roadways. Background Technology

[0002] During long-term operation, mine roadways require sealing layers on the surface of the surrounding rock, the surface of the shotcrete support layer, and the areas where the surrounding rock is filled, to seal tiny cracks in the rock surface, prevent harmful gases, methane, and water from seeping through these cracks, and reduce air contact with the surrounding rock to avoid oxidation. If cracks, delamination, or holes appear in the sealing layer, the seal will fail, easily leading to safety hazards such as gas leakage, air short circuits, or spontaneous combustion due to rock oxidation.

[0003] Ultrasonic testing is the mainstream technology for detecting defects in mine sealing layers, and the scientific nature of the measurement point division directly determines the accuracy of the test results and the utilization efficiency of testing resources.

[0004] While existing technologies have moved beyond the early, purely linear distribution point model and incorporate geological structures, local monitoring data, or field experience for point placement, key limitations remain: regional risk classification relies heavily on single risk indicators and fails to systematically integrate and quantify various core risk factors, resulting in a lack of unified standards for determining regional risk levels and a lack of scientific allocation logic for feature weights and judgment criteria.

[0005] This results in the inability to accurately focus on high-risk areas and provide key coverage for critical measurement points, making it difficult to adapt to the complex risk scenarios of mine sealing layers.

[0006] In view of this, the present invention proposes an ultrasonic testing method for the integrity of the sealing layer in mine roadways to solve the above problems. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: an ultrasonic testing method for the integrity of a sealing layer in a mine roadway, comprising:

[0008] Divide the roadways into zones and classify the risk levels of each zone;

[0009] Based on the stress measurement data collected by the mine pressure sensors deployed in the sealing layer area and the spatial coordinates of each mine pressure sensor, the stress scalar of each sensor is obtained, and the stress scalar and spatial coordinates are interpolated by gridding to generate a stress heat map of the sealing layer and perform stress zoning.

[0010] Based on the regional risk level of the roadway area and the stress zoning of the sealing layer, the corresponding grid size of the stress zone is set, and the measuring points for sealing layer detection are generated based on the grid size of the stress zone.

[0011] Individual difference characteristics of measuring points used to characterize the differences in the integrity of the sealing layer are obtained, and the measuring points are classified into types based on the individual difference characteristics. The types of measuring points include core measuring points, key measuring points, and routine measuring points.

[0012] Implement differentiated testing plans based on the type of testing point;

[0013] The types of monitoring points and the risk levels of the areas will be adjusted based on the test results.

[0014] Furthermore, the method for dividing the roadway areas is as follows:

[0015] Based on the mine construction drawings and records, the entire roadway was divided into multiple initial zones according to the construction time sequence.

[0016] Based on the changes in the direction and cross-sectional shape of the tunnels, the initial area was further divided into various tunnel regions.

[0017] Furthermore, the method for classifying regional risk levels is as follows:

[0018] S101: Extract the core risk dimension parameters of the roadway area where the sealing layer is located, including stress concentration coefficient, gas concentration fluctuation value and wind pressure fluctuation amplitude, normalize the three and map them to the [0,1] interval, and splice them to construct the regional risk vector;

[0019] S102: The K-means clustering algorithm is used to cluster the regional risk vectors, calculate the mean of the regional risk vector of each cluster center, and classify the regional risk level. The cluster with the highest mean of regional risk vector is marked as a high-risk core area, the cluster with the medium mean of regional risk vector is marked as a medium-risk key area, and the cluster with the lowest mean of regional risk vector is marked as a low-risk normal area.

[0020] Furthermore, the method for obtaining the stress-thermal diagram is as follows:

[0021] S111: Collect stress data from mine pressure sensors deployed within the sealing layer area;

[0022] S112: For the stress data of each mine pressure sensor, the daily peak sequence within a preset time period is exponentially smoothed, and the stress scalar is calculated based on the smoothed peak value.

[0023] S113: Obtain the sealing layer layout information based on the mine roadway construction drawings, including the roadway plan layout, longitudinal section, and roadway cross-section.

[0024] In the plan layout, determine the longitudinal position (X coordinate) of each mine pressure sensor along the roadway, and measure the distance with the starting point of the roadway as the reference.

[0025] Based on the plan layout and roadway cross-section diagram, determine the lateral position (Y coordinate) of each mine pressure sensor within the roadway cross-section, using the roadway centerline as the reference.

[0026] Read the elevation of the sealing layer and the bottom plate on the longitudinal section diagram, and calculate the vertical height (Z coordinate) of each mine pressure sensor by combining the sealing layer thickness.

[0027] The obtained X, Y, and Z coordinates are correlated with the stress data from the mine pressure sensor to form a complete three-dimensional spatial stress data set;

[0028] S114: Spatial discretization of the sealing layer region is performed, dividing it into regular grid cells;

[0029] S115: Interpolate the stress scalar of each mine pressure sensor with its three-dimensional spatial coordinates to generate a stress thermogram.

[0030] Furthermore, methods for stress zoning include:

[0031] S121: Using the stress distribution of all grid cells in the stress-thermal diagram as the overall sample, a quantile threshold is set, and grid cells above the threshold are clustered into high-stress clusters;

[0032] S122: The stress values ​​within each high-stress cluster are further divided into concentrated areas, transition areas, and edge areas;

[0033] S123: For the remaining areas not included in the high-stress clusters, define them as background areas as a whole.

[0034] Furthermore, based on the regional risk level of the roadway area and the stress zoning of the sealing layer, the corresponding grid size of the stress zone is set, and the method for generating the measuring points for sealing layer detection based on the grid size of the stress zone is as follows:

[0035] S131: Set the corresponding mesh size based on the combination of regional risk level and stress zoning;

[0036] S132: Divide all the sealing layers to be tested into a grid according to the grid size set in step S131, with each grid having four vertices as measurement points;

[0037] S133: Obtain the stress extreme points in the concentrated area based on the stress heat map, and add an additional measuring point at the center of the grid where the stress extreme point is located.

[0038] Furthermore, methods for obtaining individual differences in measurement points include:

[0039] S141: Obtain surface state characteristics, the surface state characteristics including surface defect marker values ​​and moisture content characteristic assignments;

[0040] S142: Obtain structural parameter features, including thickness feature assignment and stress feature assignment:

[0041] S143: By splicing surface state features with structural parameter features, individual differences of measurement points can be obtained.

[0042] Furthermore, the method for classifying measurement point types is as follows:

[0043] The individual difference characteristic value is obtained by weighting and summing the surface defect mark value, moisture content characteristic value, thickness characteristic value, and stress characteristic value of the measuring point with the corresponding preset weighting weight;

[0044] Different thresholds are set for different regional risk levels;

[0045] Individual difference feature values ​​are compared with corresponding preset judgment thresholds, and the measurement points are divided into core measurement points, key measurement points and routine measurement points based on the comparison results.

[0046] Furthermore, implementing a differentiated testing protocol includes:

[0047] The core measuring points use high-frequency ultrasonic probes and perform three or more repeated acquisition processes simultaneously, resulting in the highest detection frequency.

[0048] The conventional measurement points use low-frequency ultrasonic probes to perform a single rapid acquisition process with the lowest detection frequency.

[0049] For key measurement points, conventional ultrasonic probes are used, and two repeated acquisition processes are performed. The data consistency verification results are used as valid detection data, and the detection frequency is between the two.

[0050] Furthermore, the method for adjusting the types of monitoring points and the risk level of the area based on the test results is as follows:

[0051] A detection cycle is defined as m consecutive detections. If an integrity defect in the sealing layer is detected at a detection point within a detection cycle, the detection point is upgraded to a core detection point; otherwise, the detection point is downgraded by one level. If the detection point is already a regular detection point, it will not be downgraded further.

[0052] If a region has more than the first proportion threshold of measurement points that are upgraded to core measurement points within a detection cycle, the risk level of the region will be raised by one level, and the measurement points will be reclassified according to the new regional risk level while retaining the core measurement points.

[0053] If no monitoring points are upgraded within a monitoring cycle and the number of monitoring points downgraded reaches the second proportion threshold, the risk level of the region will be reduced by one level, and the monitoring points will be reclassified according to the new regional risk level while retaining the core monitoring points.

[0054] The technical effects and advantages of the ultrasonic testing method for the integrity of the sealing layer in mine roadways proposed in this invention are as follows:

[0055] First, this invention constructs a regional risk vector by extracting three core risk dimensions—stress concentration coefficient, gas concentration fluctuation value, and wind pressure fluctuation amplitude—into the area where the sealing layer is located. Then, a clustering algorithm is used to classify the risk areas. This multi-dimensional integration and quantification strategy breaks through the limitations of single-indicator judgment and provides a scientific and unified basis for regional risk level classification.

[0056] Secondly, this invention formulates a measurement point generation scheme through two dimensions: regional risk level and stress zoning. This ensures dense coverage of measurement points in high-risk areas and streamlined distribution in low-risk areas. It avoids the waste of resources associated with traditional evenly distributed points and ensures that no key risk points are missed, thus achieving a precise match between measurement points and risks.

[0057] Finally, this invention optimizes resource utilization through a mechanism that classifies measurement points by individual differences and dynamically adjusts their risk levels. Combining surface state characteristics and structural parameter characteristics, and using weighted summation and regional differentiation thresholds, measurement points are categorized into three types: core, key, and routine. Corresponding differentiated solutions are then implemented, and the type of measurement point and the risk level of the region are dynamically adjusted based on defect detection. This dynamic adaptation strategy ensures both the detection accuracy and frequency of high-risk measurement points while reducing resource consumption in low-risk areas.

[0058] In summary, multi-dimensional risk quantification lays the foundation for accurate detection, dual-dimensional measurement point generation enables the focus on key areas, provides detailed basis for the identification of individual differences, and differentiated dynamic adjustment ensures the optimization of resource allocation. The synergy of these four elements enables the entire detection system to not only solve the problems of ambiguous judgment, unbalanced coverage, and waste of resources in existing technologies, but also adapt to the complex risk scenarios of mine roadway sealing layers, significantly improving detection accuracy, reliability, and resource utilization efficiency. Attached Figure Description

[0059] Figure 1 This is a flowchart of the ultrasonic testing method for the integrity of the sealing layer in a mine roadway according to Embodiment 1 of the present invention;

[0060] Figure 2 This is a schematic diagram of the measurement point grid size setting in Embodiment 1 of the present invention;

[0061] Figure 3 This is a schematic diagram of the classification of measurement point types in Embodiment 1 of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Example 1

[0064] See Figure 1 As shown, this embodiment provides an ultrasonic testing method for the integrity of the sealing layer in mine roadways, including:

[0065] The tunnel areas were divided into zones, and the risk levels of each tunnel area were classified.

[0066] The method for dividing the roadway area is as follows:

[0067] Based on the mine construction drawings and records, the entire roadway was divided into multiple initial zones according to the construction time sequence.

[0068] Based on the changes in the direction of the tunnel (e.g., straight or turning, dividing into two areas) and the changes in the cross-sectional shape of the tunnel (semi-circular, rectangular, trapezoidal, etc.), the initial area is further divided into various tunnel areas.

[0069] The method for classifying regional risk levels is as follows:

[0070] S101: Extract the core risk dimension parameters of the roadway area where the sealing layer is located, including stress concentration coefficient, gas concentration fluctuation value and wind pressure fluctuation amplitude, normalize the three and map them to the [0,1] interval, and splice them to construct the regional risk vector.

[0071] The stress concentration coefficient is the percentage of times the stress value at all monitoring points in the region exceeds the preset stress threshold in the mine pressure monitoring data of the past month; the gas concentration fluctuation value is the difference between the maximum and minimum gas concentration values ​​collected by all fixed gas sensors in the region in the past month; and the wind pressure fluctuation amplitude is the standard deviation of the wind pressure monitoring data collected by all micro-pressure sensors in the region in the past month.

[0072] Normalization is obtained by calculating the ratio between the difference between the above-obtained value and the minimum value within the collection period, and the difference between the maximum and minimum values ​​within the collection period. For the stress concentration coefficient, the maximum and minimum values ​​are used from the percentage of times the daily stress value exceeds the preset stress threshold in the past month. For the gas concentration fluctuation value, the minimum and maximum values ​​are used from the daily gas concentration fluctuation value in the past month. For the wind pressure fluctuation amplitude, the minimum and maximum values ​​are used from the daily wind pressure fluctuation amplitude in the past month.

[0073] S102: The K-means clustering algorithm is used to cluster the regional risk vectors, with a cluster size of 3. The Euclidean distance between the regional risk vectors is calculated using the following formula:

[0074] ;

[0075] Where d is the Euclidean distance between regional risk vectors. , These are the normalized values ​​of the core risk dimension parameters for the two regions, where i is the risk dimension number, ranging from 1 to 3, corresponding to the proportion of stress exceeding the threshold frequency, gas concentration fluctuation value, and wind pressure fluctuation amplitude, respectively.

[0076] Using the Euclidean distance between regional risk vectors as a similarity measure, the cluster centers are iteratively optimized until the change in cluster centers between two adjacent iterations is less than a preset convergence threshold, at which point the iteration stops.

[0077] Calculate the mean regional risk vector of each cluster center to classify the regional risk level. The cluster with the highest mean regional risk vector is marked as a high-risk core area, the cluster with a medium mean regional risk vector is marked as a medium-risk key area, and the cluster with the lowest mean regional risk vector is marked as a low-risk normal area.

[0078] Generate a stress thermal map of the sealing layer and perform stress zoning.

[0079] The method for obtaining the stress thermogram is as follows:

[0080] S111: Collect stress time-series data of mine pressure sensors deployed in the sealing layer area;

[0081] S112: For each mine pressure sensor, the stress time series data is grouped by natural day within a preset time period, the daily peak sequence is extracted and exponential smoothing is performed to obtain a smoothed peak sequence. The preset time period is preferably thirty days. Specifically, the maximum stress value of each day within the preset time period is taken as the peak value of that day;

[0082] The daily peak values ​​are arranged sequentially by date to form a daily peak value sequence. Exponential smoothing is then applied to this sequence, calculated recursively: the smoothed peak value for the first day is the peak value of the first day; from the second day onwards, the smoothed peak value for each day is obtained by weighting the current day's peak value with the smoothed peak value of the previous day. Preferably, the weight of the current day's peak value is 0.3, and the weight of the previous day's smoothed peak value is 0.7. This exponential smoothing process increases the weight of recent daily peak values ​​in the smoothed peak values, gradually diminishing the influence of earlier peak values ​​and thus suppressing the disturbance of abnormal daily peak values ​​to subsequent stress zones.

[0083] The median of all smoothed peak values ​​within the preset time period is used as the current stress scalar.

[0084] S113: Obtain the sealing layer layout information based on the mine roadway construction drawings, including the roadway plan layout, longitudinal section, and roadway cross-section.

[0085] In the plan layout, determine the longitudinal position (X coordinate) of each mine pressure sensor along the roadway, and measure the distance with the starting point of the roadway as the reference.

[0086] Based on the plan layout and roadway cross-section diagram, determine the lateral position (Y coordinate) of each mine pressure sensor within the roadway cross-section, using the roadway centerline as the reference.

[0087] Read the elevation of the sealing layer and the elevation of the base plate on the longitudinal section. Combined with the thickness of the sealing layer, specifically, read the elevation of the sealing layer at the corresponding position of the sensor installation point as the vertical height coordinate of the sensor. When the sensor is installed inside the sealing layer and the elevation of the installation point is not directly marked on the longitudinal section, first read the elevation of the sealing layer, then calculate the elevation of the installation point based on the burial depth of the sensor relative to the top interface of the sealing layer, and use the elevation of the installation point as the vertical height coordinate, i.e., the Z coordinate.

[0088] The obtained X, Y, and Z coordinates are correlated with the stress data from the mine pressure sensor to form a complete three-dimensional spatial stress data set;

[0089] S114: Spatial discretization is performed on the sealing layer area, dividing it into regular grid units. The grid size is selected according to the roadway scale and the accuracy requirements of the measuring points, preferably a square grid of 0.25 to 2 meters.

[0090] S115: Spatial interpolation is performed between the stress scalar of each mine pressure sensor and its three-dimensional spatial coordinates to generate a stress heat map. Specifically: the center point of each grid cell in the regular grid is taken as the location point to be estimated; for each location point to be estimated, the nearest multiple mine pressure sensors are selected as sample points according to the three-dimensional spatial distance from near to far, preferably 6 to 8; the three-dimensional spatial distance from the location point to be estimated to each sample point is calculated, the weight of each sample point is set to the square of the inverse of the distance, and all weights are normalized; the stress scalar of each sample point is weighted and summed according to the normalized weights to obtain the estimated stress value of the location point to be estimated; the center points of all grid cells are traversed to obtain the gridded stress distribution and output as a stress heat map.

[0091] Stress zoning based on stress thermograms includes the following specific methods:

[0092] S121: Using the stress distribution of all grid cells in the stress-thermal diagram as the overall sample, set a quantile threshold, such as the 80th quantile threshold, and cluster high-stress grid cells above this threshold into high-stress clusters;

[0093] During the clustering process, a region growing algorithm based on spatial continuity is used to merge high-stress grid cells with Euclidean distance less than a preset adjacency threshold (preferably 1.5 times the grid resolution) into the same high-stress cluster;

[0094] S122: The stress values ​​within each high-stress cluster are further divided into concentrated areas, transition areas, and edge areas to reflect the internal risk gradient. For example, the 90%, 70%, and 50% quantiles of the stress values ​​within the cluster are calculated respectively, and a three-level nested region is formed accordingly: areas above the 90% quantile are concentrated areas, areas between the 70% and 90% quantiles are transition areas, and areas below the 70% quantile are edge areas.

[0095] S123: For the remaining areas not included in the high-stress clusters, define them as background areas as the lowest risk level.

[0096] See Figure 2 As shown, measuring points are generated based on the regional risk level and sealing layer stress zoning of the roadway area. The specific method is as follows:

[0097] S131: Based on the combination of regional risk level and stress zoning, a corresponding grid size is set. The grid is square, and the side length is the spacing between measurement points. To ensure reproducibility, this embodiment first determines the "minimum defect scale to be covered" under each risk level, then determines the grid side length of the concentrated area based on the minimum defect scale, and then obtains the grid side lengths of the transition area, edge area, and background area according to a fixed magnification. Finally, the final grid side length of any point is obtained by stress zoning selection.

[0098] The setting of the grid side length in the concentrated area is defined as the minimum defect scale value that needs to be identified under the corresponding risk level. The minimum defect scale is the minimum plane scale of the defect that needs to be triggered for handling or review in the integrity judgment of the sealing layer. The value is given in advance by the engineering acceptance requirements or industry standards, and in this embodiment, it is fixed into three levels according to the risk level.

[0099] After obtaining the grid side length of the concentrated area, the grid side lengths of the transition area, edge area, and background area within the same risk level are progressively enlarged at a fixed ratio to ensure a continuous change from dense to sparse. Preferably, the grid side length of the transition area is 1.6 times the grid side length of the concentrated area and rounded up, the grid side length of the edge area is 2.4 times the grid side length of the concentrated area and rounded up, and the grid side length of the background area is 4.8 times the grid side length of the concentrated area and rounded up.

[0100] Preferably, the minimum defect size is 500 mm in the high-risk core area, 600 mm in the medium-risk key area, and 800 mm in the low-risk general area. The grid side length in the concentrated area is half of the minimum defect size, rounded down to the nearest 50 mm to ensure that defects do not fall into the gaps between adjacent measuring points and are thus crossed. Therefore, the grid side length in the concentrated area of ​​the high-risk core area is 250 mm, in the concentrated area of ​​the medium-risk key area is 300 mm, and in the concentrated area of ​​the low-risk general area is 400 mm.

[0101] S132: Divide all the sealing layers to be tested into a grid according to the grid size set in step S131. The horizontal grid lines are parallel to the direction of the roadway, and the four vertices of each grid are set as measuring points.

[0102] S133: Obtain the stress extreme points in the concentrated area based on the stress heat map, and add an additional measuring point at the center of the grid where the stress extreme point is located.

[0103] See Figure 3 As shown, individual differences in measuring points are obtained, and the measuring points are classified into different types based on these individual differences. These individual differences include surface state characteristics and structural parameter characteristics.

[0104] The methods for obtaining the individual difference characteristics of the measuring points include:

[0105] S141: Obtain surface state features, including:

[0106] S1411: Observe and record whether there are cracks, bulges, or repair marks at the measuring points. If any of these are present, the surface defect mark value is recorded as 1; otherwise, it is recorded as 0.

[0107] S1412: Measure the surface moisture content at the measuring point using a contact humidity sensor; the surface moisture content is below [value missing]. When the assigned moisture content characteristic is 0, the surface moisture content is greater than or equal to the moisture content threshold. At that time, the assigned moisture content characteristic is 1, where The selectable value is 5%, which is set based on dividing the surface condition of the measuring point into two categories: no visible water film and visible water film. The moisture content threshold is... Set to 5%, this is used to numerically distinguish between the two states mentioned above; under different surrounding rock materials and sensor installation conditions, the results of on-site judgment based on whether a visible water film or continuous wet marks appears can be used to determine the state. Make minor adjustments;

[0108] S1413: Concatenate the surface defect markers with the moisture content feature assignments to form surface state features, such as {1,0};

[0109] S142: Obtain structural parameter features, including:

[0110] S1421: Use an ultrasonic thickness gauge to detect the thickness of the sealing layer at the measuring point, and calculate the ratio of the actual thickness to the design thickness. When the ratio is less than... A thickness feature value of 1 indicates a thin thickness; when the ratio is greater than or equal to the first thickness threshold... And less than the second thickness threshold When the thickness characteristic is assigned a value of 0.5, it indicates that the thickness is normal. When the ratio is greater than or equal to... When the thickness feature is assigned a value of 0, it indicates that the thickness is too high, where the first thickness threshold is... With the second thickness threshold The method for obtaining this information is as follows: During the system deployment phase, read the allowable deviation requirements for the construction and acceptance of the sealing layer of the project, and convert the lower limit of the allowable deviation into the first thickness threshold. The upper limit of allowable deviation is converted into a second thickness threshold. When the project does not provide explicit allowable deviation requirements, the default allowable deviation band is used as the source of the threshold. The optional value is 80%. The optional value is 120%;

[0111] S1422: Assign stress characteristics based on the stress zone where the measuring point is located. When the measuring point is in the concentrated zone, the assigned stress characteristic is 3; when the measuring point is in the transition zone, the assigned stress characteristic is 2; when the measuring point is in the edge zone, the assigned stress characteristic is 1; and when the measuring point is in the concentrated background zone, the assigned stress characteristic is 0.

[0112] S1423: Concatenate the thickness feature assignment with the stress feature assignment to obtain the structural parameter features, such as {0,3};

[0113] S143: Concatenate the surface state features with the structural parameter features to obtain the individual difference features of the measurement points, such as {1,0,0,3}.

[0114] The method for classifying the types of measuring points is as follows:

[0115] The individual difference feature value is obtained by weighting and summing the surface defect mark value, moisture content feature value, thickness feature value, and stress feature value of the measuring point with the corresponding preset weighting weight.

[0116] Preset weighting weights are set to , , and For each measurement point i, calculate the individual difference characteristic value:

[0117] ;

[0118] in, Individual difference characteristic values, This is the surface defect marker value. Assign values ​​to the moisture content characteristic. Assign values ​​to the thickness feature. Assign values ​​to stress characteristics.

[0119] It should be noted that, , , and All four numbers are greater than 0, and their sum is 1.

[0120] Different judgment thresholds are set for different regional risk levels, with the preset judgment threshold being [value missing]. , and .

[0121] Individual difference feature values ​​are compared with corresponding preset judgment thresholds, and the measurement points are divided into core measurement points, key measurement points, and routine measurement points based on the comparison results:

[0122] For monitoring points within high-risk core areas, if the individual difference characteristic value is ≥ If the value is less than 1, it is marked as a core measurement point; individual difference characteristic value < These are then marked as key measurement points;

[0123] For monitoring points within medium-risk key areas, individual difference eigenvalues ​​≥ Marked as key measurement points; individual difference characteristic values ​​< Marked as a regular measuring point;

[0124] For measurement points within low-risk routine areas, individual difference eigenvalues ​​≥ The points marked as key measuring points; all others are marked as regular measuring points.

[0125] The individual differences of measurement points focus on the unique attributes of a single measurement point. By quantifying and assigning values ​​to surface state and structural parameters, it achieves a refinement from regional clustering to individual positioning, avoiding the problem of indiscriminately classifying good and bad points within the same region, and providing micro-level basis for measurement point priority labeling.

[0126] The preset weighting weights and preset judgment thresholds are set by professionals in the field based on their own experience. For example, the preset weighting weights and preset judgment thresholds can be set by professionals in the field based on the contribution of historical defect detection in the roadway sealing layer.

[0127] Differentiated testing plans are implemented based on the type of testing point, specifically including:

[0128] Regarding the allocation of detection frequency, time resources are dynamically allocated based on the risk level of the detection point area. Core detection points, as key nodes for risk prevention and control, adopt the highest detection frequency, such as once a week, to ensure real-time capture of risk evolution trends and changes in the integrity of the sealing layer. Key detection points balance monitoring accuracy and resource consumption, for example, the detection frequency is set to once every two weeks to achieve effective tracking of risk status. Regular detection points aim at basic investigation, for example, the detection frequency is set to once a month, reducing detection costs while meeting basic risk management needs.

[0129] Regarding ultrasonic testing parameters and operating strategies, customized technical solutions are configured for different levels of testing points: core testing points use high-frequency ultrasonic probes to maximize defect resolution and accurately identify hidden defects such as micro-cracks and local delamination, while performing three or more repeated acquisition processes to eliminate random errors through data redundancy, ensuring the reliability of the detection signal and the accuracy of the data; key testing points use conventional ultrasonic probes to balance defect identification accuracy and detection efficiency, and perform two repeated acquisition processes; routine testing points use low-frequency ultrasonic probes to balance penetration depth and survey efficiency, and perform a single rapid acquisition process to improve the overall detection coverage efficiency while ensuring the identification capability of basic defects (such as through cracks and large-area delamination).

[0130] The types of monitoring points and the risk levels of the areas will be adjusted based on the test results. Specifically, this includes:

[0131] A detection cycle is defined as m consecutive detections (m can be 3). If an integrity defect is detected in the sealing layer at a detection point within a detection cycle, the detection point is upgraded to a core detection point; otherwise, the detection point is downgraded. If the detection point is already a regular detection point, it will not be downgraded further.

[0132] It should be noted that ultrasonic testing for the integrity defects of the sealing layer is existing technology and will not be discussed further here.

[0133] If a region has more than the first proportion threshold of measurement points that are upgraded to core measurement points within a detection cycle, the risk level of the region will be raised by one level, and the measurement points will be re-divided according to the new regional risk level while retaining the core measurement points to ensure detection coverage.

[0134] If no monitoring points in a region are upgraded within a monitoring cycle and the number of monitoring points downgraded reaches the second proportion threshold, the risk level of the region will be reduced by one level. Based on the new regional risk level, the monitoring points will be re-divided while retaining the core monitoring points to save monitoring resources.

[0135] The first and second proportional thresholds are set by those skilled in the art based on their own experience.

[0136] In this embodiment, by comprehensively judging multiple consecutive test results as a cycle, the impact of single abnormal data or occasional errors on the determination of test point types can be effectively reduced, improving the stability and reliability of test point classification. When a test point has an integrity defect within a cycle, it is upgraded to a core test point, allowing priority to be paid to potentially high-risk locations, enabling real-time monitoring of key risk points and improving the overall safety control level. When a large number of test points in an area are upgraded to core test points, the area's risk level is raised by one level and the test points are reclassified, ensuring sufficient test point density and coverage in high-risk areas, thereby gaining a more comprehensive understanding of the sealing layer status. For areas where there is no change in risk level within the detection cycle but the number of downgraded test points reaches a preset proportion, reducing the area's risk level and adjusting the test point layout helps save detection resources, concentrating limited monitoring capabilities on areas with real risks. Overall, this method, by combining test point and area division with actual detection results, realizes a dynamic optimization mechanism driven by results, ensuring key monitoring of high-risk areas while avoiding resource waste, and enhancing the scientific nature and operability of the detection system.

[0137] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0138] In conclusion, the above description is merely 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 within the protection scope of the present invention.

Claims

1. An ultrasonic testing method for the integrity of the sealing layer in mine roadways, characterized in that, include: Divide the roadways into zones and classify the risk levels of each zone; Based on the stress measurement data collected by the mine pressure sensors deployed in the sealing layer area and the spatial coordinates of each mine pressure sensor, the stress scalar of each sensor is obtained, and the stress scalar and spatial coordinates are interpolated by gridding to generate a stress heat map of the sealing layer and perform stress zoning. Based on the regional risk level of the roadway area and the stress zoning of the sealing layer, the corresponding grid size of the stress zone is set, and the measuring points for sealing layer detection are generated based on the grid size of the stress zone. Individual difference characteristics of measuring points used to characterize the differences in the integrity of the sealing layer are obtained, and the measuring points are classified into types based on the individual difference characteristics. The types of measuring points include core measuring points, key measuring points, and routine measuring points. Differentiated testing schemes are implemented based on the type of testing points to obtain testing results; The types of monitoring points and the risk levels of the areas will be adjusted based on the test results.

2. The ultrasonic testing method for the integrity of the sealing layer in mine roadways according to claim 1, characterized in that, The method for dividing the roadway area is as follows: Based on the mine construction drawings and records, the entire roadway was divided into multiple initial zones according to the construction time sequence. Based on the changes in the direction and cross-sectional shape of the tunnels, the initial area was further divided into various tunnel regions.

3. The ultrasonic testing method for the integrity of the sealing layer in mine roadways according to claim 1, characterized in that, The method for classifying regional risk levels is as follows: S101: Extract the core risk dimension parameters of the roadway area where the sealing layer is located, including stress concentration coefficient, gas concentration fluctuation value and wind pressure fluctuation amplitude, normalize the three and map them to the [0,1] interval, and splice them to construct the regional risk vector; S102: The K-means clustering algorithm is used to cluster the regional risk vectors, calculate the mean of the regional risk vector of each cluster center, and classify the regional risk level. The cluster with the highest mean of regional risk vector is marked as a high-risk core area, the cluster with the medium mean of regional risk vector is marked as a medium-risk key area, and the cluster with the lowest mean of regional risk vector is marked as a low-risk normal area.

4. The ultrasonic testing method for the integrity of the sealing layer in mine roadways according to claim 1, characterized in that, The method for obtaining the stress thermogram is as follows: S111: Collect stress data from mine pressure sensors deployed within the sealing layer area; S112: For the stress data of each mine pressure sensor, the daily peak sequence within a preset time period is exponentially smoothed, and the stress scalar is calculated based on the smoothed peak value. S113: Obtain the sealing layer layout information based on the mine roadway construction drawings, including the roadway plan layout, longitudinal section, and roadway cross-section. In the plan layout, determine the longitudinal position (X coordinate) of each mine pressure sensor along the roadway, and measure the distance with the starting point of the roadway as the reference. Based on the plan layout and roadway cross-section diagram, determine the lateral position (Y coordinate) of each mine pressure sensor within the roadway cross-section, using the roadway centerline as the reference. Read the elevation of the sealing layer and the bottom plate on the longitudinal section diagram, and calculate the vertical height (Z coordinate) of each mine pressure sensor by combining the sealing layer thickness. The obtained X, Y, and Z coordinates are correlated with the stress data from the mine pressure sensor to form a complete three-dimensional spatial stress data set; S114: Spatial discretization of the sealing layer region is performed, dividing it into regular grid cells; S115: Interpolate the stress scalar of each mine pressure sensor with its three-dimensional spatial coordinates to generate a stress thermogram.

5. The ultrasonic testing method for the integrity of the sealing layer in mine roadways according to claim 4, characterized in that, The method for stress zoning includes: S121: Using the stress distribution of all grid cells in the stress-thermal diagram as the overall sample, a quantile threshold is set, and grid cells above the threshold are clustered into high-stress clusters; S122: The stress values ​​within each high-stress cluster are further divided into concentrated areas, transition areas, and edge areas; S123: For the remaining areas not included in the high-stress clusters, define them as background areas as a whole.

6. The ultrasonic testing method for the integrity of the sealing layer in mine roadways according to claim 1, characterized in that, The method for setting the grid size of the corresponding stress zone based on the regional risk level of the roadway area and the stress zone of the sealing layer, and generating the measuring points for sealing layer detection based on the grid size of the stress zone is as follows: S131: Set the corresponding mesh size based on the combination of regional risk level and stress zoning; S132: Divide all the sealing layers to be tested into a grid according to the set grid size, and the four vertices of each grid are the measurement points; S133: Obtain the stress extreme points in the concentrated area based on the stress heat map, and add an additional measuring point at the center of the grid where the stress extreme point is located.

7. The ultrasonic testing method for the integrity of the sealing layer in mine roadways according to claim 1, characterized in that, The methods for obtaining the individual difference characteristics of the measuring points include: S141: Obtain surface state characteristics, the surface state characteristics including surface defect marker values ​​and moisture content characteristic assignments; S142: Obtain structural parameter features, including thickness feature assignment and stress feature assignment: S143: By splicing surface state features with structural parameter features, individual differences of measurement points can be obtained.

8. The ultrasonic testing method for the integrity of the sealing layer in mine roadways according to claim 1, characterized in that, The method for classifying the types of measuring points is as follows: The individual difference characteristic value is obtained by weighting and summing the surface defect mark value, moisture content characteristic value, thickness characteristic value, and stress characteristic value of the measuring point with the corresponding preset weighting weight; Different thresholds are set for different regional risk levels; Individual difference feature values ​​are compared with corresponding preset judgment thresholds, and the measurement points are divided into core measurement points, key measurement points and routine measurement points based on the comparison results.

9. The ultrasonic testing method for the integrity of the sealing layer in a mine roadway according to claim 8, characterized in that, The implementation of the differential detection scheme includes: The core measuring points use high-frequency ultrasonic probes and perform three or more repeated acquisition processes simultaneously, resulting in the highest detection frequency. The conventional measurement points use low-frequency ultrasonic probes to perform a single rapid acquisition process with the lowest detection frequency. For key measurement points, conventional ultrasonic probes are used, and two repeated acquisition processes are performed. The data consistency verification results are used as valid detection data, and the detection frequency is between the two.

10. The ultrasonic testing method for the integrity of the sealing layer in a mine roadway according to claim 1, characterized in that, The method for adjusting the type of monitoring point and the risk level of the area based on the detection results is as follows: A detection cycle is defined as m consecutive detections. If an integrity defect in the sealing layer is detected at a detection point within a detection cycle, the detection point is upgraded to a core detection point; otherwise, the detection point is downgraded by one level. If the detection point is already a regular detection point, it will not be downgraded further. If a region has more than the first proportion threshold of measurement points that are upgraded to core measurement points within a detection cycle, the risk level of the region will be raised by one level, and the measurement points will be reclassified according to the new regional risk level while retaining the core measurement points. If no monitoring points are upgraded within a monitoring cycle and the number of monitoring points downgraded reaches the second proportion threshold, the risk level of the region will be reduced by one level, and the monitoring points will be reclassified according to the new regional risk level while retaining the core monitoring points.

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