Tunnel blasting outer insertion angle calculation method and system based on point cloud features

Abstract

The invention discloses a tunnel blasting extrapolation angle calculation method and system based on point cloud features, and relates to the technical field of tunnel drilling. Point cloud section data is obtained through laser scanning, an expansion graph is analyzed, the corresponding relation between pixels and stubble height is established, data is segmented according to advanced size, the stubble height difference is calculated through a K-means clustering algorithm, and the tunnel blasting extrapolation angle is obtained. Calculating an extrapolation angle according to the blasting stubble height difference and the advanced mileage; according to the method, the external insertion angle numerical modeling formula is established through point cloud analysis, the influence of the external insertion angle numerical modeling formula on the blasting design is analyzed, the blasting effect is evaluated in an auxiliary mode, dynamic adjustment of the blasting design and construction is facilitated, the construction quality is improved, the cost is reduced, more scientific and accurate technical support is provided for tunnel blasting construction, and the method has important practical application value and significance.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel drilling, and more particularly to a method and system for calculating an external interpolation angle of tunnel blasting based on point cloud features. Background Art

[0002] At present, tunnel excavation usually adopts the drilling and blasting method. In the drilling and blasting construction, the drilling of the tunnel perimeter holes is limited by the site, and a certain external insertion angle is required when the drill rod is drilling holes. The reasonable external insertion angle plays an important role in the over-excavation and under-excavation during the tunnel excavation process. The drilling accuracy is an important link in controlling the excavation effect. The drilling hole and the external insertion angle determine the result of over-excavation and under-excavation. At present, the construction unit summarizes the experience and adjusts the blasting parameters in time according to the blasting effect, continuously improves the drilling accuracy, and ensures the blasting effect.

[0003] In the initial construction, the smooth blasting effect was not ideal due to the external insertion angle: a large external insertion angle resulted in over-excavation; a small external insertion angle resulted in under-excavation. The non-uniform external insertion angle made the blasting tunnel contour irregular. During the construction process, the external insertion angle was generally too large, and there was a significant height difference between the recommended blasting shots, and even the height difference between the two shots exceeded 30cm, resulting in widespread over-excavation.

[0004] Many scholars have conducted research on the control of external insertion angles. For example, Hu Weiping improved the top plate structure of the drilling rig, Zhang Weiguang used advanced equipment to control the drilling angle, Zhang Yuan controlled the excavation surface contour by controlling the external insertion angle, and Xiao Xian proposed relevant technologies to alleviate the problems of over-excavation and under-excavation.

[0005] Therefore, how to accurately calculate the angle of the external interpolation angle is a problem that technical personnel in this field need to solve urgently. Summary of the invention

[0006] In view of this, the present invention provides a method and system for calculating the external interpolation angle of tunnel blasting based on point cloud features to solve the problems existing in the background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A method for calculating the external interpolation angle of tunnel blasting based on point cloud features, comprising:

[0009] Obtain point cloud section data through laser scanning of the tunnel section, and obtain a point cloud analysis expansion diagram of the tunnel section based on the point cloud data;

[0010] Establishing a corresponding relationship between pixels and blasting height on the tunnel section point cloud analysis expansion diagram;

[0011] According to the advanced dimensions, the point cloud section data is segmented;

[0012] According to the advanced size, the point cloud boundary line from the starting mileage to the end of the advanced is obtained, and the K-means clustering algorithm is used to calculate the average height of the stubble within 0.5m of the starting and ending boundary lines. The difference between the average values ​​of the end point and the starting point is the stubble height difference;

[0013] The external interpolation angle is calculated based on the stubble height difference and the advanced mileage.

[0014] Optionally, the formula for calculating the external insertion angle is:

[0015] α=arctan(C / D)*180 / π

[0016] Where D represents the step length, C represents the undercut value, and α represents the external insertion angle of the peripheral hole.

[0017] Optionally, the calculation formula of the under-digging value is:

[0018]

[0019] In the formula, The average height of the stubble within ±0.5 meters around the starting point of the advanced mileage. It is the average height of the stubble within ±0.5 meters around the end point of the advanced mileage.

[0020] Optionally, the K-means clustering algorithm is specifically:

[0021] Cluster the point cloud section data set to obtain K data sets;

[0022] Randomly select K points from the data set as centroids;

[0023] For each point in the data set, calculate its Euclidean distance to each center point, and divide it into the set to which the center point belongs based on the distance and the proximity principle.

[0024] Optionally, it also includes determining whether the current advanced mileage has reached the terminal mileage. If so, end the external pin calculation; if not, continue to calculate the next advanced mileage.

[0025] A tunnel blasting external interpolation angle calculation system based on point cloud features, comprising:

[0026] The data acquisition module obtains point cloud section data by laser scanning the tunnel section, and obtains the tunnel section point cloud analysis expansion diagram based on the point cloud data;

[0027] A relationship building module is used to build a corresponding relationship between pixels and blasting heights on the tunnel section point cloud analysis expansion diagram;

[0028] Data segmentation module, which segments the point cloud cross-section data according to the advanced dimensions;

[0029] The stubble height difference calculation module obtains the point cloud boundary line from the starting mileage to the end of the advancement according to the advanced size, and uses the K-means clustering algorithm to calculate the average stubble height within 0.5m of the starting and ending boundary lines. The difference between the average value of the end point and the starting point is the stubble height difference;

[0030] The external interpolation angle calculation module calculates the external interpolation angle according to the stubble height difference and the advanced mileage.

[0031] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for calculating the external interpolation angle of tunnel blasting based on point cloud features. Through theoretical analysis of point clouds and the correspondence between the point cloud expansion diagram and the blasting stub height, a numerical modeling formula for the external interpolation angle of the peripheral holes is established to analyze the influence of the external interpolation angle on the blasting design. By analyzing the auxiliary evaluation of the blasting effect by the external interpolation angle, it is beneficial to dynamically adjust the blasting design and construction according to the data, improve the construction quality and reduce the construction cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 A schematic diagram of a calculation method provided by the present invention;

[0034] Figure 2 A schematic diagram of the peripheral hole external plug-in angle structure provided by the present invention;

[0035] Figure 3 (a)-(f) are schematic diagrams of the K-Means clustering iterative process provided by the present invention;

[0036] Figure 4 This is the expanded diagram of the tunnel section point cloud analysis provided by the present invention. DETAILED DESCRIPTION

[0037] 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.

[0038] The embodiment of the present invention discloses a method for calculating the external interpolation angle of tunnel blasting based on point cloud features, such as Figure 1 As shown, including:

[0039] Obtain point cloud section data through laser scanning of the tunnel section, and obtain a point cloud analysis expansion diagram of the tunnel section based on the point cloud data;

[0040] Establish the corresponding relationship between pixels and blasting height on the tunnel section point cloud analysis expansion diagram;

[0041] According to the advanced dimensions, the point cloud section data is segmented;

[0042] According to the advanced size, the point cloud boundary line from the starting mileage to the end of the advanced is obtained, and the K-means clustering algorithm is used to calculate the average height of the stubble within 0.5m of the starting and ending boundary lines. The difference between the average values ​​of the end point and the starting point is the stubble height difference;

[0043] The external interpolation angle is calculated based on the stub height difference and the advanced mileage.

[0044] In a specific embodiment, the formula for calculating the external insertion angle is:

[0045] α=arctan(C / D)*180 / π

[0046] Where D represents the step length, C represents the over-excavation depth, and α represents the external insertion angle of the peripheral hole.

[0047] In a specific embodiment, the calculation formula of the over-excavation depth value is:

[0048]

[0049] In the formula, The average height of the stubble within ±0.5 meters around the starting point of the advanced mileage. It is the average height of the stubble within ±0.5 meters around the end point of the advanced mileage.

[0050] In a specific embodiment, the K-means clustering algorithm is an iterative clustering algorithm that randomly selects K objects as the initial cluster centers, calculates the distance between the objects and the cluster centers, and assigns them to the nearest cluster centers. Whenever the sample allocation is completed, the cluster centers are actively recalculated based on the objects in the cluster. This process is repeated until the termination condition is met: the cluster center does not change and the error sum of squares is a local minimum.

[0051] Implementation steps of K-Means algorithm:

[0052] (1) First, determine the K value, that is, cluster the point cloud cross-section data set to obtain K data sets.

[0053] (2) Randomly select K points from the data set as centroids.

[0054] (3) For each point in the data set, calculate its Euclidean distance to each center point, and divide it into the set to which the center point belongs based on the distance and the proximity principle.

[0055] like Figure 3 As shown in (a)-(f) in Figure 1, the K-Means clustering iterative process:

[0056] Figure (a) shows an initial data set with K = 2 categories. In Figure (b), the center points corresponding to two categories are randomly selected and distinguished by red and blue center points. Then, the Euclidean distance between each point in the data set and the center point is calculated, and each point is assigned to the category of the nearest center point according to the proximity principle.

[0057] Looking at Figure (c), after the first iteration, each point is assigned to the red or blue class based on its distance from the center point. Now, new center points are calculated for both classes.

[0058] As shown in Figure (d), the positions of the new center points of both categories have changed compared to the initial center points. Figures (e) and (f) show the iterative process, repeating the steps of assigning points to the nearest center point and updating the center point until convergence.

[0059] Finally, in Figure f, the final clustering result is obtained, that is, each point is assigned to the red or blue category according to the distance from the center point. This iterative process of updating the center point and redistributing the points continues until the center point tends to be stable and the categories tend to be consistent.

[0060] The choice of K value has a great impact on K-means, which is also the biggest disadvantage of K-means. Common methods for selecting K value include elbow method and gap statistics. Given a sample, there are objectively J "natural subclasses" and these real subclasses are hidden in the data. Under three-dimensional data, you can still use the naked eye to draw a picture to distinguish the approximate number of J, but higher dimensions cannot be seen intuitively. You can only start from a relatively small K, such as K-2, to try to approximate the true value of J.

[0061] (1) When K is smaller than the actual number of clusters J of the sample, the degree of clustering of each cluster will increase greatly and the sum of distances will decrease greatly with each increase in K.

[0062] (2) When K is close to J, the aggregation return value obtained by increasing K will decrease rapidly, and the decrease in the sum of distances will also decrease;

[0063] (3) As K increases, the change in the total distance will be gradual. In a specific embodiment, it also includes determining whether the current advanced mileage has reached the terminal mileage. If so, the external pin calculation is terminated; if not, the next advanced mileage is continued to be calculated.

[0064] In order to verify the practical application effect of the present invention, the DK61+215.00-DK61+235.00 section of the excavation mileage of the Changgangling Tunnel was selected, and the calculation error was compared and analyzed using the method of the present invention and manual measurement. The derived tunnel section point cloud analysis expansion diagram is shown in the figure below: Figure 4 As shown:

[0065] By comparing the color and height, inputting the advanced start and end mileage, the K-means clustering algorithm calculates the height of the excavation advanced blasting stubble according to the calculation range, and then calculates the external interpolation angle in combination with the width of the advanced mileage. In the actual application of Changgangling Tunnel, the advanced blasting stubble is 4m, and the specific calculated blasting stubble height difference and external interpolation angle are shown in the following table:

[0066] Table 1 Results of external insertion angle and stubble height difference / (m3)

[0067]

[0068] As shown in Table 1, in the DK61+215.00-DK61+235.00 section of the excavation mileage of the Changgangling Tunnel, the error range of the blasting height difference between the method of the present invention and the manual measurement is in the range of [-0.04, +0.04] meters, and the error range of the external insertion angle is controlled within the acceptable error range of [-0.2, +0.2] degrees, which meets the standard requirements for the external insertion angle of the construction, and greatly effectively improves the efficiency of measurement, which provides strong support for the control of tunnel over-excavation and under-excavation. When drilling peripheral holes, the drill rig drills parallel to the designed contour line in an ideal state.

[0069] Due to limited working space, the drilling direction often deviates from the outside of the designed contour line by a certain angle, which is the external interpolation angle (α). Figure 2 shown.

[0070] A tunnel blasting external interpolation angle calculation system based on point cloud features, comprising:

[0071] The data acquisition module obtains point cloud section data by laser scanning the tunnel section, and obtains the tunnel section point cloud analysis expansion diagram based on the point cloud data;

[0072] The relationship building module establishes the corresponding relationship between pixels and blasting height on the tunnel section point cloud analysis expansion diagram;

[0073] Data segmentation module, which segments the point cloud cross-section data according to the advanced dimensions;

[0074] The stubble height difference calculation module obtains the point cloud boundary line from the starting mileage to the end of the advancement according to the advanced size, and uses the K-means clustering algorithm to calculate the average stubble height within 0.5m of the starting and ending boundary lines. The difference between the average value of the end point and the starting point is the stubble height difference;

[0075] The external interpolation angle calculation module calculates the external interpolation angle based on the stubble height difference and advanced mileage.

[0076] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0077] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating the external interpolation angle of tunnel blasting based on point cloud features, characterized in that: include: Obtain point cloud section data through laser scanning of the tunnel section, and obtain a point cloud analysis expansion diagram of the tunnel section based on the point cloud data; Establishing a corresponding relationship between pixels and blasting height on the tunnel section point cloud analysis expansion diagram; According to the advanced dimensions, the point cloud section data is segmented; According to the advanced size, the point cloud boundary line from the starting mileage to the end of the advanced is obtained, and the K-means clustering algorithm is used to calculate the average height of the stubble within 0.5m of the starting and ending boundary lines. The difference between the average values ​​of the end point and the starting point is the stubble height difference; The external interpolation angle is calculated based on the stubble height difference and the advanced mileage.

2. The method for calculating the external interpolation angle of tunnel blasting based on point cloud features according to claim 1 is characterized in that: The formula for calculating the external insertion angle is: α=arctan(C / D)*180 / π Where D represents the step length, C represents the undercut value, and α represents the external insertion angle of the peripheral hole.

3. The method for calculating the external interpolation angle of tunnel blasting based on point cloud features according to claim 2 is characterized in that: The calculation formula of the undercut value is: In the formula, The average height of the stubble within ±0.5 meters around the starting point of the advanced mileage. It is the average height of the stubble within ±0.5 meters around the end point of the advanced mileage.

4. The method for calculating the external interpolation angle of tunnel blasting based on point cloud features according to claim 1, characterized in that: The K-means clustering algorithm is specifically: Cluster the point cloud section data set to obtain K data sets; Randomly select K points from the data set as centroids; For each point in the data set, calculate its Euclidean distance to each center point, and divide it into the set to which the center point belongs based on the distance and the proximity principle.

5. The method for calculating the external interpolation angle of tunnel blasting based on point cloud features according to claim 1, characterized in that: It also includes judging whether the current advanced mileage has reached the terminal mileage, if so, ending the external pin calculation; if not, continuing to calculate the next advanced mileage.

6. A tunnel blasting external interpolation angle calculation system based on point cloud features, characterized in that: A method for calculating the external interpolation angle of tunnel blasting based on point cloud features according to any one of claims 1 to 5, comprising: The data acquisition module obtains point cloud section data by laser scanning the tunnel section, and obtains the tunnel section point cloud analysis expansion diagram based on the point cloud data; A relationship building module is used to build a corresponding relationship between pixels and blasting heights on the tunnel section point cloud analysis expansion diagram; Data segmentation module, which segments the point cloud cross-section data according to the advanced dimensions; The stubble height difference calculation module obtains the point cloud boundary line from the starting mileage to the end of the advancement according to the advanced size, and uses the K-means clustering algorithm to calculate the average stubble height within 0.5m of the starting and ending boundary lines. The difference between the average value of the end point and the starting point is the stubble height difference; The external interpolation angle calculation module calculates the external interpolation angle according to the stubble height difference and the advanced mileage.

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