Methods, apparatus, computer equipment, media and products for detecting misalignment in tunnel blasting.

Abstract

This invention relates to the field of tunnel engineering technology and discloses a method, apparatus, computer equipment, medium, and product for detecting misalignment during tunnel blasting. The method includes: after tunnel blasting excavation, collecting tunnel point clouds and calculating the over-excavation and under-excavation values ​​of the tunnel point clouds; dividing the tunnel point clouds into regions based on a preset division method and calculating the average over-excavation and under-excavation values ​​for each point cloud region; performing curve fitting on the average over-excavation and under-excavation values ​​of the regions to obtain an over-excavation and under-excavation fitting curve; and determining the misalignment between tunnel blasting cycles based on the peaks and troughs of the over-excavation and under-excavation fitting curve. This invention can improve the efficiency and accuracy of blasting misalignment detection.

Description

Technical Field

[0001] This invention relates to the field of tunnel engineering technology, specifically to methods, devices, computer equipment, media, and products for detecting misalignments in tunnel blasting. Background Technology

[0002] Tunnel engineering refers to structures built underground, underwater, or within mountains to lay railways or construct highways for motor vehicles. Tunnel blasting is a common excavation method in tunnel and underground engineering, using explosives to break rock or soil to achieve a predetermined excavation profile. When using blasting for tunnel excavation, the peripheral blasting boreholes are typically angled outwards at a certain angle to ensure the excavated tunnel diameter meets requirements. Therefore, misalignment can occur between blasting cycles, resulting in uneven tunnel walls and affecting the quality of subsequent construction processes.

[0003] Current methods for detecting misalignments mainly rely on manual, localized sampling and measurement. This involves carefully inspecting the excavated rock surface under natural or artificial light, recording the location and approximate dimensions of the misalignment, or using tools such as rulers and feeler gauges to measure the specific values ​​of the misalignment. These current methods are inefficient and inaccurate. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, computer equipment, medium and product for detecting misalignment in tunnel blasting, so as to improve the efficiency and accuracy of blasting misalignment detection.

[0005] In a first aspect, the present invention provides a method for detecting misalignment during tunnel blasting. The method includes: after tunnel blasting excavation, collecting tunnel point clouds and calculating the over-excavation and under-excavation values ​​of the tunnel point clouds; dividing the tunnel point clouds into regions based on a preset division method and calculating the average over-excavation and under-excavation values ​​for each point cloud region; performing curve fitting on the average over-excavation and under-excavation values ​​to obtain an over-excavation and under-excavation fitting curve; and determining the misalignment between tunnel blasting cycles based on the peaks and troughs of the over-excavation and under-excavation fitting curve.

[0006] In this implementation, a 3D point cloud detection method is used to collect tunnel point clouds, and the overall tunnel point cloud is divided. The average over-excavation and under-excavation value of a region is used to characterize the tunnel blasting status within that region, and this average value is representative of the region. Furthermore, a continuous over-excavation and under-excavation fitting curve is obtained, which can convert discrete point data into continuous data and characterize the tunnel blasting status of the entire continuous region, thereby achieving comprehensive tunnel detection. Based on the relationship between the peaks and troughs of the over-excavation and under-excavation fitting curve, existing faults are identified. The intelligent detection method has high detection efficiency and reliable detection results.

[0007] In one optional implementation, the tunnel point cloud is divided into regions based on a preset division method, and the average over-excavation and under-excavation value of each point cloud region is calculated. This includes: determining tunnel strips at preset locations on the cross-sectional contour of the tunnel, sampling the tunnel point cloud to obtain the corresponding point cloud strips; dividing the tunnel point cloud on the point cloud strips into regions based on the preset division method, and calculating the average over-excavation and under-excavation value of each point cloud region.

[0008] In this implementation, strip-shaped point cloud sampling is performed according to preset locations. The preset locations are determined based on actual detection needs, ensuring that the selected tunnel point cloud is representative and reducing the amount of data and computation. Based on the point cloud strips, regions are divided, and the average over-excavation and under-excavation values ​​of each region accurately characterize the blasting conditions of each area within the tunnel strip.

[0009] In one optional implementation, the tunnel point cloud on the point cloud strip is divided into regions based on a preset division method, and the average over-excavation and under-excavation value of each point cloud region is calculated. This includes: dividing the tunnel point cloud on the point cloud strip according to the interval range of the station number to obtain multiple point cloud regions. Each point cloud region includes multiple target station number point clouds, and the station numbers of adjacent point cloud regions are adjacent; obtaining the point cloud over-excavation and under-excavation value of the target station number point cloud, and calculating the average over-excavation and under-excavation value of all target station number point clouds in the point cloud region to obtain the corresponding average over-excavation and under-excavation value of the region.

[0010] In this implementation, point cloud strips are divided according to station numbers, providing a dividing benchmark. Point cloud strips can be planned according to station direction, laying the foundation for subsequent fitting of over-excavation and under-excavation curves. By calculating the average over-excavation and under-excavation values ​​of all target station point clouds within the point cloud area, the blasting status within that station area can be accurately characterized, thereby improving the efficiency of subsequent misalignment detection.

[0011] In one optional implementation, curve fitting is performed on the average over-excavation and under-excavation values ​​of the region to obtain an over-excavation and under-excavation fitting curve, including: curve fitting is performed on the average over-excavation and under-excavation values ​​of the point cloud region according to the station number order to obtain an over-excavation and under-excavation fitting curve.

[0012] In this implementation, curve fitting is performed according to the station number sequence, so that the fitted curve of over-excavation and under-excavation can accurately characterize the changes of the tunnel surface along the station number sequence.

[0013] In one optional implementation, determining the misalignment between tunnel blasting cycles based on the peaks and troughs of the over- and under-excavation fitting curve includes: determining local peaks and troughs based on the peaks and troughs of the over- and under-excavation fitting curve; deleting troughs within local peaks and peaks with the smallest average over- and under-excavation values ​​in the region, and deleting peaks within local troughs and troughs with the largest average over- and under-excavation values ​​in the region, to obtain updated peaks and updated troughs; determining that there is a misalignment between tunnel blasting cycles between adjacent updated peaks and updated troughs, and calculating the difference between the average average over- and under-excavation values ​​in the region of adjacent updated peaks and updated troughs to obtain the misalignment height.

[0014] In this implementation, peaks and troughs are used to identify existing misalignments. At the same time, the impact of smaller peaks and troughs on misalignment detection is considered. Peaks and troughs with small continuous fluctuations are deleted, while peaks and troughs with larger fluctuations and greater distances are retained. This can effectively filter peaks and troughs and improve the accuracy of subsequent misalignment detection.

[0015] In one optional implementation, determining local peaks and local troughs based on the peaks and troughs of the over-dumping and under-dumping fitting curve includes: obtaining the peak station number difference between adjacent peaks; when the peak station number difference is less than a first station number threshold, determining that the area between adjacent peaks is a local peak; obtaining the trough station number difference between adjacent troughs; when the trough station number difference is less than a second station number threshold, determining that the area between adjacent troughs is a local trough.

[0016] In this implementation, the presence of local peaks and troughs is determined by the distance between station numbers. The detection method is simple and accurate, improving detection efficiency.

[0017] Secondly, the present invention provides a tunnel blasting misalignment detection device, which includes: a data acquisition module for acquiring tunnel point clouds after tunnel blasting excavation and calculating the over-excavation and under-excavation values ​​of the tunnel point clouds; a division module for dividing the tunnel point clouds into regions based on a preset division method and calculating the average over-excavation and under-excavation values ​​of each point cloud region; a fitting module for curve fitting the average over-excavation and under-excavation values ​​of the regions to obtain an over-excavation and under-excavation fitting curve; and a determination module for determining the misalignment between tunnel blasting cycles based on the peaks and troughs of the over-excavation and under-excavation fitting curve.

[0018] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the tunnel blasting misalignment detection method described in the first aspect or any corresponding embodiment.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the tunnel blasting misalignment detection method of the first aspect or any corresponding embodiment described above.

[0020] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the tunnel blasting misalignment detection method described in the first aspect or any corresponding embodiment. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of a tunnel blasting platform according to an embodiment of the present invention;

[0023] Figure 2 This is a flowchart of a method for detecting misaligned tunnel blasting according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of a tunnel point cloud according to an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of a tunnel cross-section according to an embodiment of the present invention;

[0026] Figure 5 This is a flowchart of another method for detecting misaligned tunnel blasting according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of a tunnel strip according to an embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of the average value of regional over-excavation and under-excavation according to an embodiment of the present invention;

[0029] Figure 8 This is a schematic diagram of an over- or under-drilling fitting curve according to an embodiment of the present invention;

[0030] Figure 9 This is a structural block diagram of a tunnel blasting misalignment detection device according to an embodiment of the present invention;

[0031] Figure 10 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0033] When tunnels are excavated using blasting, the surrounding blasting boreholes are typically offset outwards at a certain angle to ensure the excavated tunnel diameter meets requirements. Therefore, misalignment occurs between blasting cycles, resulting in unevenness on the tunnel's inner wall and affecting the quality of subsequent construction processes. Please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of a tunnel blasting fault platform according to an embodiment of the present invention. Figure 1 As shown, the dashed line represents the tunnel boundary, i.e., the initial designed tunnel boundary line. However, due to the uncertainties in the actual blasting process, it is impossible to excavate the tunnel strictly according to the tunnel boundary to obtain the expected tunnel. Generally, the actual excavated tunnel will be near the tunnel boundary, forming a shape like... Figure 1 The tunnel is shown. Specifically, during tunnel blasting excavation, multiple blasting boreholes are formed along the tunnel excavation direction, from the outside to the inside, creating blasting misalignments between two boreholes. Timely detection and evaluation of these misalignments can be used to assess current blasting parameters and design more reasonable blasting parameters for subsequent blasting cycles to control misalignments between blasting cycles. Therefore, this application proposes a tunnel blasting misalignment detection method that utilizes intelligent detection methods to improve detection efficiency and reliability.

[0034] According to embodiments of the present invention, a method, apparatus, computer equipment, medium, and product method embodiment for detecting misaligned tunnel blasting is provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] This embodiment provides a method for detecting misalignment during tunnel blasting. Figure 2 This is a flowchart of a tunnel blasting misalignment detection method according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily reflect that misalignment. Figure 2 The illustrated process sequence is limited. For example... Figure 2 As shown, the process includes the following steps:

[0036] Step S201: After the tunnel is blasted and excavated, collect the tunnel point cloud and calculate the over-excavation and under-excavation values ​​of the tunnel point cloud.

[0037] After the tunnel blasting and excavation are completed, 3D laser scanning technology is used to collect tunnel point cloud data and generate a 3D point cloud model of the actual tunnel. Please refer to [link / reference]. Figure 3 , Figure 3 This is a schematic diagram of a tunnel point cloud according to an embodiment of the present invention, such as... Figure 3 As shown, a three-dimensional point cloud model of the actual tunnel is generated based on the collected tunnel point cloud, according to the tunnel blasting direction.

[0038] In one implementation, this application further optimizes the generated actual tunnel 3D point cloud model. Specifically, the actual tunnel 3D point cloud model is transformed to a unified engineering coordinate system, and noise reduction processing is performed on all tunnel point clouds in the actual tunnel 3D point cloud model to remove invalid tunnel point clouds. For example, outlier noisy tunnel point clouds can be identified through density-based spatial clustering.

[0039] Furthermore, the actual tunnel 3D point cloud model is compared with the designed tunnel model, and the over-excavation and under-excavation values ​​of each tunnel point cloud in the actual 3D point cloud model are calculated.

[0040] In earthwork engineering, over-excavation and under-excavation refer to the difference between the actual excavation volume and the designed excavation volume. Over-excavation includes both over-excavation and under-excavation. Over-excavation refers to the portion of the actual excavation volume exceeding the designed excavation volume, while under-excavation refers to the portion of the actual excavation volume falling short of the designed excavation volume.

[0041] The point cloud over-excavation and under-excavation values ​​include point cloud over-excavation value and point cloud under-excavation value. Point cloud over-excavation value is the distance by which the tunnel point cloud exceeds the designed tunnel cross-section, and point cloud under-excavation value is the distance by which the tunnel point cloud does not reach the designed tunnel cross-section.

[0042] Please see Figure 4 , Figure 4 This is a schematic diagram of a tunnel cross-section according to an embodiment of the present invention. Figure 4 As shown, the cross-section of the designed tunnel model is a regular straight line segment in the ground area and a regular curved cross-section segment in the non-ground area. However, the actual excavated tunnel has an irregular cross-section. The difference between the cross-section point cloud in the actual 3D point cloud model and the designed tunnel model is the over-excavation / under-excavation value of the point cloud.

[0043] In one implementation, this application uses the cross-sectional projection method to calculate the over- and under-excavation values ​​of the point cloud.

[0044] Specifically, each tunnel point cloud in the actual tunnel 3D point cloud model is vertically projected onto the cross-section of the initial design tunnel model, and the minimum distance between the projected point and all points on the initial design tunnel model is calculated as the over-excavation / under-excavation value of the point cloud. Further, when the projected point is outside the initial design tunnel model, the minimum distance is the over-excavation value of the tunnel point cloud, i.e., the over-excavation / under-excavation value is positive; when the projected point is inside the initial design tunnel model, the minimum distance is the under-excavation value of the tunnel point cloud, i.e., the over-excavation / under-excavation value is negative.

[0045] Step S202: Divide the tunnel point cloud into regions based on a preset division method, and calculate the average over-excavation and under-excavation values ​​for each point cloud region.

[0046] The preset division method includes a preset division direction and a preset division distance. The preset division direction is determined according to the misalignment research direction, and the tunnel point cloud is divided into regions according to the preset division distance to obtain each point cloud region. The average value of the over-excavation and under-excavation of each tunnel point cloud within the point cloud region is calculated to obtain the average value of over-excavation and under-excavation of the region.

[0047] In one implementation, the tunnel point cloud is divided into multiple point cloud regions according to the tunnel excavation direction and a preset division distance, and the multiple point cloud regions are sequentially numbered to calculate the average value of over-excavation and under-excavation in the region.

[0048] In another implementation, the tunnel point cloud is divided into multiple point cloud regions according to the tunnel arc direction and a preset division distance, and the multiple point cloud regions are sequentially numbered to calculate the average value of over-excavation and under-excavation in the region.

[0049] Step S203: Perform curve fitting on the average over-excavation and under-excavation values ​​of the region to obtain the over-excavation and under-excavation fitting curve.

[0050] Curve fitting is performed according to the point cloud region sequence using the curve fitting method to obtain the over-excavation and under-excavation fitting curves.

[0051] Curve fitting methods include polynomial fitting and moving average methods.

[0052] In one implementation, when dividing the point cloud region according to the tunnel excavation direction and a preset division distance, the average over-excavation and under-excavation values ​​of the region are curve-fitted according to the region number of the tunnel excavation direction to obtain the over-excavation and under-excavation fitting curve. The horizontal axis of the over-excavation and under-excavation fitting curve is the region number, and the vertical axis is the corresponding average over-excavation and under-excavation value of the region.

[0053] In another implementation, when dividing the point cloud region according to the tunnel arc direction and the preset division distance, the average over-excavation and under-excavation values ​​of the region are curve-fitted according to the region number in the tunnel arc direction to obtain the over-excavation and under-excavation fitting curve. The horizontal axis of the over-excavation and under-excavation fitting curve is the region number, and the vertical axis is the corresponding average over-excavation and under-excavation value of the region.

[0054] Step S204: Determine the misalignment between tunnel blasting cycles based on the peaks and troughs of the under-excavation fitting curve.

[0055] Specifically, the peaks and troughs in the over- and under-excavation fitting curves are identified, and the average over- and under-excavation values ​​of adjacent peaks and troughs are compared to determine the misalignment between tunnel blasting cycles. The difference between the average over- and under-excavation values ​​of the regions between the peaks and troughs is taken as the misalignment height.

[0056] In one implementation, when the difference between the average over-excavation and under-excavation values ​​of adjacent peaks and troughs is greater than a preset value, it is determined that there is a misalignment between adjacent peaks and troughs, and the difference is further used as the height of the misalignment.

[0057] In another implementation, the identified peaks and troughs are filtered out, and peaks and troughs with smaller fluctuations are deleted. Furthermore, the misalignment is determined based on the difference between the average over-excavation and under-excavation values ​​of adjacent peaks and troughs after filtering.

[0058] The tunnel blasting misalignment detection method provided in this embodiment utilizes a three-dimensional point cloud detection method to collect tunnel point clouds and divide the overall tunnel point cloud. The average over- and under-excavation value of a region is used to characterize the tunnel blasting status within that region, and this average value is representative of the region. Furthermore, a continuous over- and under-excavation fitting curve is obtained, which can convert discrete point data into continuous data and characterize the overall tunnel blasting status of a continuous region, thus achieving comprehensive tunnel detection. Misalignments are identified based on the relationship between the peaks and troughs of the over- and under-excavation fitting curve. This intelligent detection method has high detection efficiency and reliable results.

[0059] This embodiment provides a method for detecting misaligned blasting platforms in tunnels. Figure 5 This is a flowchart of another method for detecting misaligned tunnel blasting according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily reflect that misalignment. Figure 5 The illustrated process sequence is limited. For example... Figure 5 As shown, the process includes the following steps:

[0060] Step S501: After the tunnel is blasted and excavated, collect the tunnel point cloud and calculate the over-excavation and under-excavation values ​​of the tunnel point cloud.

[0061] Please see details Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0062] Step S502: Divide the tunnel point cloud into regions based on a preset division method, and calculate the average over-excavation and under-excavation values ​​for each point cloud region.

[0063] In this implementation, the tunnel point cloud is divided into multiple point cloud regions according to the tunnel excavation direction and a preset division distance, and the multiple point cloud regions are sequentially numbered to calculate the average value of over-excavation and under-excavation in the region.

[0064] Specifically, step S502 includes:

[0065] Step S5021: Determine tunnel strips at preset locations on the cross-sectional profile of the tunnel, sample the tunnel point cloud, and obtain the corresponding point cloud strips.

[0066] The preset area is a region of preset width. The preset area can be customized according to the blasting detection requirements.

[0067] One or more point cloud intervals are determined on the cross-sectional point cloud of the irregular tunnel section obtained from actual excavation. One or more tunnel strips are then determined on the tunnel cross-sectional outline according to the point cloud intervals along the tunnel excavation direction. The point cloud intervals can be uniformly distributed or non-uniformly distributed; that is, the resulting tunnel strips can be uniformly distributed or non-uniformly distributed.

[0068] Specifically, please refer to Figure 6 , Figure 6 This is a schematic diagram of a tunnel strip according to an embodiment of the present invention. Figure 6 As shown, in one implementation, eight tunnel strips are obtained based on multiple point cloud intervals.

[0069] Step S5022: Divide the tunnel point cloud on the point cloud strip into regions based on the preset division method, and calculate the average over-excavation and under-excavation value of each point cloud region.

[0070] In one implementation, the tunnel point cloud on the point cloud strip is divided into regions according to the station number as an interval.

[0071] Specifically, for a point cloud strip, at a preset distance of one station, multiple point cloud regions corresponding to multiple station numbers are divided according to the tunnel excavation direction. Each point cloud region includes multiple target station point clouds, and the station numbers of adjacent point cloud regions are adjacent.

[0072] Furthermore, the over-excavation and under-excavation values ​​of all target station point clouds within each point cloud region are obtained, and the average value is calculated to obtain the regional over-excavation and under-excavation average value corresponding to each point cloud region.

[0073] For example, with a preset distance of 5cm, for a point cloud strip, a point cloud region is divided every 5cm along the tunnel excavation direction, and these regions are named sequentially according to their station numbers. The average over-excavation / under-excavation value for each station number is then calculated. Please refer to [link to relevant documentation]. Figure 7 , Figure 7This is a schematic diagram of the average value of regional over-excavation and under-excavation according to an embodiment of the present invention. Figure 7 As shown, the horizontal axis represents the station number, and the vertical axis represents the average over-excavation and under-excavation values ​​for the region, which vary along the station number.

[0074] In this implementation, strip-shaped point cloud sampling is performed according to preset locations. These preset locations are determined based on actual detection needs, ensuring the selected tunnel point cloud is representative and reducing data volume and computational complexity. Based on the point cloud strips, regions are divided, and the average over-excavation / under-excavation value of each region accurately characterizes the blasting conditions of each area within the tunnel strip. Specifically, dividing the point cloud strips according to station numbers provides a dividing benchmark, enabling the planning of point cloud strips along station directions. This lays the foundation for subsequent fitting of over-excavation / under-excavation curves. By calculating the average over-excavation / under-excavation value of all target station point clouds within the point cloud region, the blasting conditions within that station region can be accurately characterized, thereby improving the efficiency of subsequent misalignment detection.

[0075] Step S503: Perform curve fitting on the average over-excavation and under-excavation values ​​of the region to obtain the over-excavation and under-excavation fitting curve.

[0076] For a point cloud strip, curve fitting is performed on the average over- and under-excavation values ​​of the continuous area using the curve fitting method to obtain the over- and under-excavation fitting curve.

[0077] In this implementation, curve fitting is performed according to the station number sequence, so that the fitted curve of over-excavation and under-excavation can accurately characterize the changes of the tunnel surface along the station number sequence.

[0078] For example, please refer to Figure 8 , Figure 8 This is a schematic diagram of an over-draining / under-draining fitting curve according to an embodiment of the present invention. Figure 8 As shown, for Figure 7 The average over-excavation and under-excavation values ​​in the shown area were obtained by curve fitting along the continuous direction of station number. Figure 8 The continuous curve is the corresponding over-dumping and under-dumping fitting curve.

[0079] Step S504: Determine the misalignment between tunnel blasting cycles based on the peaks and troughs of the under-excavation fitting curve.

[0080] Specifically, step S504 includes:

[0081] Step S5041: Determine local peaks and local troughs based on the peaks and troughs of the over-dumping and under-dumping fitting curves.

[0082] First, calculate the peaks and troughs of the over-dumping and under-dumping fitting curves.

[0083] In one implementation, a direct comparison method is used to calculate the peaks and troughs of the over-excavation and under-excavation fitting curve. Specifically, on the over-excavation and under-excavation fitting curve, when... and Then the x-coordinate is at the peak at point x. and If the x-coordinate is at point x, then the trough is the point on the x-coordinate.

[0084] In another implementation, the peaks and troughs of the over- and under-excavation fitting curve are calculated using a first-order and second-order derivative method. Specifically, on the over- and under-excavation fitting curve, when the first derivative of the x-coordinate at point x is equal to zero and the second derivative is less than zero, the x-coordinate at point x is a peak; when the first derivative of the x-coordinate at point x is equal to zero and the second derivative is greater than zero, the x-coordinate at point x is a trough.

[0085] Furthermore, the regions with relatively small continuous fluctuations on the over-dumping and under-dumping fitting curves were identified as local peaks and local troughs.

[0086] Specifically, the station distance between adjacent peaks is obtained. When the station distance is less than a first station threshold, the adjacent peaks are determined to be local peaks. The station distance between adjacent troughs is obtained. When the station distance is less than a second station threshold, the adjacent troughs are determined to be local troughs. In one implementation, the first station threshold is equal to the second station threshold.

[0087] For example, please refer to Figure 8 Near station 1175, there are multiple consecutive and closely spaced peaks and troughs, indicating the existence of local peaks and troughs.

[0088] Step S5042: Delete the troughs within the local peaks and the peaks with the smallest average over-excavation and under-excavation values ​​in the region; delete the peaks within the local troughs and the troughs with the largest average over-excavation and under-excavation values ​​in the region; and obtain updated peaks and updated troughs.

[0089] Specifically, if a local peak region contains two peaks and a trough, delete the trough. Then, on the over- and under-excavation fitting curve, identify the peak with the smallest average over- and under-excavation value among the two peaks and delete that peak. Similarly, if a local trough region contains two troughs and a peak, delete the peak. Then, on the over- and under-excavation fitting curve, identify the trough with the largest average over- and under-excavation value among the two troughs and delete that trough.

[0090] After deleting some peaks and troughs, we get updated peaks and updated troughs.

[0091] Step S5043: Determine the misalignment between the blasting cycles between adjacent renewal peaks and troughs, and calculate the difference between the average over-excavation and under-excavation values ​​of adjacent renewal peaks and troughs to obtain the misalignment height.

[0092] Updated peaks and troughs are peaks and troughs with prominent features, and misalignment between adjacent updated peaks and troughs is determined. For example, please refer to... Figure 8 Between chainages 1165 and 1170, there are renewal peaks and renewal troughs. There is a misalignment between the chainages of the renewal peaks and the chainages of the renewal troughs. The height difference between the average over-excavation and under-excavation values ​​of the corresponding chainages is the misalignment difference.

[0093] The tunnel blasting misalignment detection method provided in this embodiment uses wave peaks and troughs to identify existing misalignments. It also considers the influence of smaller wave peaks and troughs on misalignment detection, and determines the existence of local wave peaks and troughs by station distance. The detection method is simple and accurate, improves detection efficiency, deletes wave peaks and troughs with small continuous fluctuations, and retains wave peaks and troughs with larger fluctuations that are farther away. This can effectively screen wave peaks and troughs and improve the accuracy of subsequent misalignment detection.

[0094] This embodiment also provides a tunnel blasting misalignment detection device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0095] This embodiment provides a tunnel blasting misalignment detection device. Figure 9 This is a structural block diagram of a tunnel blasting misalignment detection device according to an embodiment of the present invention, as shown below. Figure 9 As shown, the tunnel blasting misalignment detection device includes:

[0096] The acquisition module 901 is used to acquire tunnel point clouds after tunnel blasting and excavation, and to calculate the over-excavation and under-excavation values ​​of the tunnel point clouds.

[0097] The partitioning module 902 is used to partition the tunnel point cloud into regions based on a preset partitioning method and calculate the average over-excavation and under-excavation values ​​for each point cloud region.

[0098] The fitting module 903 is used to perform curve fitting on the average value of over-excavation and under-excavation in the region to obtain the over-excavation and under-excavation fitting curve.

[0099] Module 904 is used to determine the misalignment between tunnel blasting cycles based on the peaks and troughs of the over- and under-excavation fitting curve.

[0100] In some alternative implementations, the partitioning module 902 includes:

[0101] The sampling unit is used to determine tunnel strips at preset locations on the cross-sectional profile of the tunnel, sample the tunnel point cloud, and obtain the corresponding point cloud strips.

[0102] The calculation unit is used to divide the tunnel point cloud on the point cloud strip into regions based on a preset division method, and to calculate the average over-excavation and under-excavation value of each point cloud region.

[0103] In some alternative implementations, the computing unit includes:

[0104] The stationing sub-unit is used to divide the tunnel point cloud on the point cloud strip according to the stationing interval range to obtain multiple point cloud regions. Each point cloud region includes multiple target stationing point clouds, and the stationings of adjacent point cloud regions are adjacent.

[0105] The calculation sub-unit is used to obtain the over-excavation and under-excavation values ​​of the target station point cloud, and calculate the average over-excavation and under-excavation values ​​of all target station point clouds in the point cloud region to obtain the corresponding regional average over-excavation and under-excavation values.

[0106] In some alternative implementations, the fitting module 903 includes:

[0107] The station number fitting unit is used to perform curve fitting on the average over-excavation and under-excavation values ​​of the point cloud area according to the station number order, so as to obtain the over-excavation and under-excavation fitting curve.

[0108] In some alternative implementations, the determining module 904 includes:

[0109] The first determining unit is used to determine local peaks and local troughs based on the peaks and troughs of the over-dumping and under-dumping fitting curve.

[0110] The deletion unit is used to delete the troughs within local peaks and the peaks with the smallest average over-excavation and under-excavation values ​​in the region, and to delete the peaks within local troughs and the troughs with the largest average over-excavation and under-excavation values ​​in the region, thus obtaining updated peaks and updated troughs.

[0111] The second determining unit is used to determine the misalignment between blasting cycles between adjacent renewal peaks and troughs, and to calculate the difference between the average over-excavation and under-excavation values ​​of adjacent renewal peaks and troughs to obtain the misalignment height.

[0112] In some optional implementations, the first determining unit includes:

[0113] The first acquisition subunit is used to acquire the peak station distance between adjacent peaks. When the peak station distance is less than the first station threshold, the adjacent peaks are determined to be local peaks.

[0114] The second acquisition subunit is used to acquire the valley station distance between adjacent valleys. When the valley station distance is less than the second station threshold, the valleys between adjacent valleys are determined to be local valleys.

[0115] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0116] In this embodiment, the tunnel blasting misalignment detection device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit), a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0117] This invention also provides a computer device having the above-described features. Figure 9 The tunnel blasting misalignment detection device shown.

[0118] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 10 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 10 Take a processor 10 as an example.

[0119] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0120] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0121] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0122] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0123] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 10 Taking the example of a connection between China and Israel via a bus.

[0124] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0125] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0126] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0127] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for detecting misalignment during tunnel blasting, characterized in that, The method includes: After the tunnel is blasted and excavated, the tunnel point cloud is collected, and the over-excavation and under-excavation values ​​of the tunnel point cloud are calculated. The tunnel point cloud is divided into regions based on a preset division method, and the average over-excavation and under-excavation value of each point cloud region is calculated. Curve fitting is performed on the average over-excavation and under-excavation values ​​of the region to obtain the over-excavation and under-excavation fitting curve; The misalignment between tunnel blasting cycles is determined based on the peaks and troughs of the over- and under-excavation fitting curve. The process of dividing the tunnel point cloud into regions based on a preset division method and calculating the average over-excavation and under-excavation values ​​for each point cloud region includes: Along the tunnel excavation direction, a tunnel strip is determined at a predetermined location on the cross-sectional outline of the tunnel. The tunnel point cloud is sampled to obtain the corresponding point cloud strip. The predetermined location is an area of ​​predetermined width. The tunnel point cloud on the point cloud strip is divided according to the range of station numbers to obtain multiple point cloud regions. Each point cloud region includes multiple target station point clouds, and the station numbers of adjacent point cloud regions are adjacent. Obtain the over-excavation and under-excavation values ​​of the target station point cloud, and calculate the average over-excavation and under-excavation values ​​of all target station point clouds in the point cloud region to obtain the corresponding average over-excavation and under-excavation values ​​of the region.

2. The method for detecting misaligned tunnel blasting blasting dams according to claim 1, characterized in that, The step of curve fitting the average over-excavation and under-excavation values ​​of the region to obtain the over-excavation and under-excavation fitting curve includes: The average over- and under-excavation values ​​of the point cloud region are curve-fitted according to the station number order to obtain the over- and under-excavation fitting curve.

3. The method for detecting misaligned tunnel blasting blasting dams according to claim 2, characterized in that, The determination of the misalignment between tunnel blasting cycles based on the peaks and troughs of the undercut / overcut fitting curve includes: Local peaks and local troughs are determined based on the peaks and troughs of the over-dumping and under-dumping fitting curve; Delete the troughs within the local peaks and the peak with the smallest average over-excavation and under-excavation values ​​in the region; delete the peaks within the local troughs and the troughs with the largest average over-excavation and under-excavation values ​​in the region; and obtain updated peaks and updated troughs. The misalignment height is obtained by determining the misalignment between the blasting cycles between adjacent renewal peaks and valleys, and calculating the difference between the average over-excavation and under-excavation values ​​of the region for adjacent renewal peaks and valleys.

4. The method for detecting misalignment in tunnel blasting according to claim 3, characterized in that, The determination of local peaks and local troughs based on the peaks and troughs of the under-drilling fitting curve includes: Obtain the peak station distance between adjacent peaks. When the peak station distance is less than the first station threshold, determine that the area between the adjacent peaks is the local peak. Obtain the valley station distance between adjacent valleys. When the valley station distance is less than the second station threshold, determine that the adjacent valleys are local valleys.

5. A tunnel blasting misalignment detection device, characterized in that, The device includes: The acquisition module is used to acquire tunnel point cloud after tunnel blasting and excavation, and to calculate the over-excavation and under-excavation values ​​of the tunnel point cloud. The segmentation module is used to segment the tunnel point cloud into regions based on a preset segmentation method and calculate the average over-excavation and under-excavation values ​​for each point cloud region. The segmentation of the tunnel point cloud into regions based on the preset segmentation method and the calculation of the average over-excavation and under-excavation values ​​for each point cloud region includes: determining a tunnel strip at a preset location on the cross-sectional outline of the tunnel along the tunnel excavation direction; sampling the tunnel point cloud to obtain the corresponding point cloud strip, where the preset location is a region of preset width; dividing the tunnel point cloud on the point cloud strip according to the interval range of station numbers to obtain multiple point cloud regions, each point cloud region including multiple target station point clouds, with adjacent point cloud regions having adjacent station numbers; obtaining the over-excavation and under-excavation values ​​of the target station point clouds and calculating the average over-excavation and under-excavation values ​​of all target station point clouds in the point cloud region to obtain the corresponding average over-excavation and under-excavation values ​​for the region. The fitting module is used to perform curve fitting on the average value of over-excavation and under-excavation in the region to obtain the over-excavation and under-excavation fitting curve. The determination module is used to determine the misalignment between tunnel blasting cycles based on the peaks and troughs of the undercut fitting curve.

6. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the tunnel blasting misalignment detection method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the tunnel blasting misalignment detection method according to any one of claims 1 to 4.

8. A computer program product, characterized in that, Includes computer instructions, which are used to cause a computer to execute the tunnel blasting misalignment detection method according to any one of claims 1 to 4.

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