Coal mine tunnel deformation calculation method and device

Abstract

The present disclosure proposes a method and device for calculating the deformation of a coal mine tunnel, including: obtaining source point cloud data and target point cloud data of the tunnel to be estimated; establishing an action set; extracting any action factor from the action set without callback, selecting a deformation instruction from the action factor to adjust the posture of the source point cloud data and / or target point cloud data, and calculating a measurement value, and determining whether there is an optimal adjustment posture based on the measurement value; repeating the above steps until it is determined that there is an optimal adjustment posture, and calculating the deformation value of the tunnel to be estimated based on the source point cloud data and the target point cloud data. By extracting action factors from the action set without callback, and applying the deformation instructions in these action factors to adjust the posture of the source point cloud data and the target point cloud data, and finally finding the optimal adjustment posture to calculate the deformation value of the tunnel to be estimated, the registration accuracy can be improved, and the local optimal solution can be avoided. At the same time, it can adapt to the complex working environment underground and enhance flexibility and adaptability.

Description

Technical Field

[0001] The present disclosure relates to the field of computer vision technology, and in particular to a method and device for calculating deformation of a coal mine tunnel. Background Art

[0002] As coal mines continue to increase in depth and scale, monitoring coal mine roadway deformation is crucial for ensuring safe production and protecting the ecological environment. Traditional methods for monitoring coal mine roadway deformation include manual observation and measurement, convergence monitoring instruments, bolt and cable force monitoring, and roof separation instruments to detect roof separation deformation. However, manual observation and measurement are significantly affected by human factors, resulting in limited accuracy and efficiency. Furthermore, methods such as convergence monitoring, bolt and cable force monitoring, and roof separation monitoring struggle to provide a comprehensive, real-time understanding of the overall deformation status of the roadway. Due to the complex geological conditions of coal mines, traditional, single-source monitoring methods struggle to accurately estimate coal mine roadway deformation. Summary of the Invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, one purpose of the present disclosure is to propose a method for calculating coal mine tunnel deformation.

[0005] The second objective of the present disclosure is to provide a coal mine tunnel deformation calculation device.

[0006] A third objective of the present disclosure is to provide an electronic device.

[0007] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0008] A fifth object of the present disclosure is to provide a computer program product.

[0009] To achieve the above-mentioned purpose, the first embodiment of the present disclosure proposes a method for calculating the deformation of a coal mine tunnel, comprising: obtaining source point cloud data and target point cloud data of the tunnel to be estimated, wherein the source point cloud data and the target point cloud data are collected in the tunnel to be estimated at different timestamps; establishing an action set, the action set including multiple action factors, the action factors including multiple deformation instructions, and the deformation instructions are arranged in an operation order; extracting any action factor from the action set without release and callback, selecting a deformation instruction from the action factor based on the operation order to adjust the posture of the source point cloud data and / or the target point cloud data, and calculating the measurement value of the deformation instruction after the posture adjustment, and determining whether there is an optimal adjustment posture based on all the measurement values ​​of the action factors; in response to the absence of the optimal adjustment posture, repeating the above-mentioned step of extracting any action factor from the action set without release and callback and its subsequent steps until it is determined that there is an optimal adjustment posture, and calculating the deformation value of the tunnel to be estimated based on the source point cloud data and target point cloud data after the optimal adjustment posture.

[0010] According to one embodiment of the present disclosure, the calculation of the measurement value of the deformation instruction after the posture adjustment includes: establishing a three-dimensional coordinate system based on the source point cloud data and the target point cloud data after the posture adjustment; projecting the source point cloud data and the target point cloud data in a direction perpendicular to the YOZ plane and a direction perpendicular to the XOZ plane, respectively, to obtain first source point cloud projection data of the source point cloud data projected on the YOZ plane and second source point cloud projection data projected on the XOZ plane, as well as obtaining first target point cloud projection data of the target point cloud data projected on the YOZ plane and second target point cloud projection data projected on the XOZ plane; calculating the measurement value based on the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data and the second target point cloud projection data.

[0011] According to one embodiment of the present disclosure, the calculating the measurement value based on the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data and the second target point cloud projection data includes: for the i-th point position in each of the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data and the second target point cloud projection data, calculating the first distance between the first source point cloud projection data of the i-th point position and the first target point cloud projection data of the i-th point position, and calculating the second distance between the second source point cloud projection data of the i-th point position and the second target point cloud projection data of the i-th point position; adding the first distance and the second distance, and taking the square root of the sum to obtain the square root sum of the i-th point position; and taking the average of the square root sums of all points to calculate the measurement value.

[0012] According to one embodiment of the present disclosure, the determining whether there is an optimal adjustment posture based on all the metric values ​​of the action factor includes: for the j-th deformation instruction in the action factor, obtaining the first metric value of the j-th deformation instruction, and obtaining the second metric value of the j-1-th deformation instruction; determining the reward and punishment value of the j-th deformation instruction based on the first metric value and the second metric value; and determining whether the posture after the j-th deformation instruction adjusts the posture of the source point cloud data and / or the target point cloud data based on the reward and punishment value is the optimal adjustment posture.

[0013] According to one embodiment of the present disclosure, determining the reward or penalty value of the j-th deformation instruction based on the first metric value and the second metric value includes: comparing the first metric value and the second metric value; in response to the first metric value being greater than the second metric value, determining the reward or penalty value to be the first reward or penalty value; or, in response to the first metric value being less than the second metric value, determining the reward or penalty value to be the second reward or penalty value; or, in response to the first metric value being equal to the second metric value, determining the reward or penalty value to be the third reward or penalty value.

[0014] According to one embodiment of the present disclosure, determining whether the posture after the j-th deformation instruction adjusts the posture of the source point cloud data and / or the target point cloud data based on the reward and punishment value is the optimal adjustment posture includes: in response to the reward and punishment value being a third reward and punishment value, determining that the posture after the j-th deformation instruction adjusts the posture of the source point cloud data and / or the target point cloud data is the optimal adjustment posture.

[0015] According to one embodiment of the present disclosure, the deformation value of the to-be-estimated lane is calculated based on the source point cloud data and the target point cloud data after the optimal adjustment posture, including: for the kth point in the source point cloud data after the optimal adjustment posture, selecting two comparison points closest to the kth point from the target point cloud data after the optimal adjustment posture; respectively calculating the comparison distance value between the kth point and the two comparison points; calculating the candidate deformation value of the kth point based on the two comparison distance values; and taking the average of the candidate deformation values ​​of all points in the source point cloud data after the optimal adjustment posture as the deformation value of the to-be-estimated lane.

[0016] According to one embodiment of the present disclosure, the selecting deformation instructions from the action factors based on the operation sequence to adjust the posture of the source point cloud data and / or the target point cloud data includes: obtaining the displacement instructions and rotation instructions in the deformation instructions; performing translation processing on the source point cloud data and / or the target point cloud data based on the displacement instructions, and performing rotation processing on the source point cloud data and / or the target point cloud data based on the rotation instructions.

[0017] To achieve the above-mentioned purpose, the second embodiment of the present disclosure proposes a coal mine tunnel deformation calculation device, comprising: an acquisition module, configured to acquire source point cloud data and target point cloud data of a tunnel to be estimated, wherein the source point cloud data and the target point cloud data are collected in the tunnel to be estimated at different timestamps; an establishment module, configured to establish an action set, wherein the action set includes multiple action factors, and the action factors include multiple deformation instructions, and the deformation instructions are arranged in an operation order; an adjustment module, configured to retrieve any action factor from the action set without callback, select a deformation instruction from the action factor based on the operation order, perform posture adjustment on the source point cloud data and / or the target point cloud data, calculate a measurement value of the deformation instruction after the posture adjustment, and determine whether there is an optimal adjustment posture based on all measurement values ​​of the action factors; a calculation module, configured to, in response to the absence of an optimal adjustment posture, repeat the above-mentioned step of retrieval of any action factor from the action set without callback and subsequent steps until it is determined that an optimal adjustment posture exists, and calculate the deformation value of the tunnel to be estimated based on the source point cloud data and target point cloud data after the optimal adjustment posture.

[0018] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the coal mine tunnel deformation calculation method as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, the fourth embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the coal mine tunnel deformation calculation method as described in the first embodiment of the present disclosure.

[0020] To achieve the above-mentioned purpose, the fifth embodiment of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the coal mine tunnel deformation calculation method as described in the first embodiment of the present disclosure.

[0021] Therefore, by extracting action factors from the action set without callback and applying the deformation instructions in these action factors to adjust the pose of the source point cloud data and the target point cloud data, the optimal adjustment pose is finally found to calculate the deformation value of the estimated tunnel. This can improve the alignment accuracy and avoid local optimal solutions. At the same time, it can adapt to the complex working environment underground and enhance flexibility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a schematic diagram of a method for calculating deformation of a coal mine roadway according to one embodiment of the present disclosure;

[0023] Figure 2 is a schematic diagram of another method for calculating coal mine roadway deformation according to one embodiment of the present disclosure;

[0024] Figure 3 is a schematic diagram of another method for calculating coal mine roadway deformation according to an embodiment of the present disclosure;

[0025] Figure 4 is a schematic diagram of another method for calculating coal mine roadway deformation according to one embodiment of the present disclosure;

[0026] Figure 5 is a schematic diagram of a coal mine tunnel deformation calculation device according to one embodiment of the present disclosure;

[0027] Figure 6 is a schematic diagram of an electronic device according to one embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0029] The acquisition, storage, use, and processing of data in the technical solution disclosed herein are in compliance with the relevant provisions of relevant laws and regulations.

[0030] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0031] In recent years, rapid advances in computing and artificial intelligence technologies have enabled the powerful modeling capabilities of deep learning neural networks to be widely applied to coal mine roadway deformation monitoring and mine slope deformation monitoring. A coal mine roadway deformation monitoring method based on the deep learning VoxelNet model converts unordered point cloud data into high-dimensional feature data and calculates roadway deformation using the Alphashape algorithm. This addresses the limitations of traditional coal mine roadway deformation monitoring methods, such as poor accuracy and the inability to continuously monitor deformation across the entire roadway. A vision-based 3D point cloud identifier and 3D registration method automatically extracts mine road contours and enables mine deformation monitoring. A mine slope deformation monitoring method based on the DBSCAN clustering algorithm uses cluster mining on acquired 3D radar data to identify different types of slope deformation regions based on temporal and spatial attributes. The method then estimates displacement deformation characteristics to determine the degree of slope deformation, effectively providing more accurate monitoring and early warning results for the degree of slope deformation, the scale, and the pattern of deformation regions.

[0032] Figure 1 is a schematic diagram of a method for calculating coal mine roadway deformation according to an embodiment of the present disclosure, such as Figure 1 As shown, the coal mine tunnel deformation calculation method includes the following steps:

[0033] S101 , obtaining source point cloud data and target point cloud data of a lane to be estimated, wherein the source point cloud data and the target point cloud data are collected in the lane to be estimated at different time stamps.

[0034] The coal mine roadway deformation calculation method of the embodiment of the present application can be applied to scenarios such as coal mine roadway deformation monitoring and mine slope deformation monitoring research. The executor of the coal mine roadway deformation calculation of the embodiment of the present application can be the coal mine roadway deformation calculation device of the embodiment of the present application, and the coal mine roadway deformation calculation device can be set on an electronic device.

[0035] In the embodiment of the present disclosure, point cloud data can be collected by a radar device installed underground. The radar device can be of various types and is not limited here.

[0036] Among current technologies, LiDAR scanning technology can quickly acquire the three-dimensional coordinates of the surface of an object being measured. The resulting point cloud data is highly accurate and dense, making it widely used in many industries. Using LiDAR to scan the estimated roadway quickly acquires roadway shape data and employs methods to calculate coal mine roadway deformation, achieving both high speed and accuracy while minimizing the impact on production.

[0037] In actual operation, due to the complex geographical environment and production needs underground, the estimated tunnel is in dynamic change. Therefore, the point cloud data collected for the estimated tunnel at different timestamps may be different. By analyzing the differences between the point cloud data at different timestamps, the overall deformation of the estimated tunnel can be estimated and analyzed.

[0038] It should be noted that the source point cloud data was collected earlier than the target point cloud data. The source and target point cloud data are not aligned, and they need to be aligned before subsequent deformation analysis can be performed.

[0039] In the disclosed embodiments, after point cloud data is collected by a radar device, the point cloud data can be preprocessed to improve data quality, reduce noise, and enhance feature extraction. This preprocessing can be performed in a variety of ways. For example, the point cloud data can be preprocessed using one or more methods such as denoising, sampling, feature extraction, and matching.

[0040] S102: Establish an action set, where the action set includes multiple action factors, each action factor includes multiple transformation instructions, and the transformation instructions are arranged in an operation order.

[0041] It should be noted that the action factor is a complete action or step for adjusting the point cloud data. The adjustment may include rotation, displacement, deformation, etc. Each deformation instruction includes and only includes one action.

[0042] It should be noted that the action factors are generated by arranging the deformation instructions in an operation sequence. The operation sequence is designed in advance or generated randomly.

[0043] In the embodiment of the present disclosure, the action set can be randomly established. By setting the translation range and rotation angle range in advance, a plurality of deformation instructions can be randomly generated, and then the action set can be established based on the plurality of randomly generated deformation instructions. For example, the coordinate system axis, axis, The translation range of the three axes can be set to , and , randomly generate deformation instructions from the above three coordinate ranges.

[0044] S103, take any action factor from the action set without callback, select deformation instructions from the action factors based on the operation sequence to adjust the posture of the source point cloud data and / or target point cloud data, and calculate the measurement value of the deformation instruction after the posture adjustment, and determine whether there is an optimal adjustment posture based on all the measurement values ​​of the action factors.

[0045] It should be noted that non-recall retrieval means that when selecting an action factor from an action set, the action factor is removed from the set or marked as used after each selection, thereby ensuring that the selected action factor will not be called repeatedly in subsequent selections.

[0046] It should be noted that the metric value is an evaluation value used to evaluate whether the source point cloud data and the target point cloud data are aligned after the posture of the source point cloud data and / or the target point cloud data is adjusted through the deformation instruction.

[0047] In the embodiment of the present disclosure, there are many methods for calculating the measurement value of the deformation instruction after posture adjustment, and no limitation is made here.

[0048] In one possible implementation, the source point cloud data and the target point cloud data after pose adjustment may be compared to determine the displacement value between corresponding points, and then the measurement value of the deformation instruction after pose adjustment is calculated based on the displacement value.

[0049] In another possible implementation, a preset metric algorithm can be used to calculate the pose-adjusted source and target point cloud data to obtain metric values ​​for the pose-adjusted deformation instructions based on the displacement values. It should be noted that this metric algorithm is pre-designed and can be modified based on actual design needs, and is not limited here.

[0050] In the disclosed embodiment, the pose of the source point cloud data can be adjusted through deformation instructions, the pose of the target point cloud data can also be adjusted through deformation instructions, and the pose of the source point cloud data and the target point cloud data can also be adjusted simultaneously. There is no limitation here, and the specific setting can be made according to actual design needs.

[0051] In the disclosed embodiments, after obtaining the metric values ​​of a deformation instruction, the metric values ​​may be processed based on preset determination conditions to determine whether the source and target point cloud data have achieved an optimally adjusted pose after the deformation instruction is executed. It should be noted that when the source and target point cloud data have achieved an optimally adjusted pose, the source and target point cloud data may be considered aligned or have met the desired alignment conditions.

[0052] S104, in response to the absence of an optimal adjustment posture, repeat the above-mentioned steps of extracting any action factor from the action set without release and callback and its subsequent steps until it is determined that an optimal adjustment posture exists, and calculate the deformation value of the roadway to be estimated based on the source point cloud data and the target point cloud data after the optimal adjustment posture.

[0053] In an embodiment of the present disclosure, source point cloud data and target point cloud data of a to-be-estimated lane are first acquired, wherein the source point cloud data and the target point cloud data are collected in the to-be-estimated lane at different timestamps, and then an action set is established, the action set including a plurality of action factors, the action factors including a plurality of deformation instructions, the deformation instructions being arranged in an operation order, and then any action factor is taken from the action set without callback, and a deformation instruction is selected from the action factor based on the operation order to perform posture adjustment on the source point cloud data and / or target point cloud data, and a measurement value of the deformation instruction after the posture adjustment is calculated, and based on all the measurement values ​​of the action factor, it is determined whether there is an optimal adjustment posture, and finally, in response to the absence of an optimal adjustment posture, the above-mentioned step of taking any action factor from the action set without callback and its subsequent steps are repeated until it is determined that there is an optimal adjustment posture, and the deformation value of the to-be-estimated lane is calculated based on the source point cloud data and the target point cloud data after the optimal adjustment posture. Therefore, by extracting action factors from the action set without callback and applying the deformation instructions in these action factors to adjust the pose of the source point cloud data and the target point cloud data, the optimal adjustment pose is finally found to calculate the deformation value of the estimated tunnel. This can improve the alignment accuracy and avoid local optimal solutions. At the same time, it can adapt to the complex working environment underground and enhance flexibility and adaptability.

[0054] In an embodiment of the present disclosure, deformation instructions are selected from action factors based on the operation sequence to adjust the posture of source point cloud data and / or target point cloud data. The displacement instructions and rotation instructions in the deformation instructions can be first obtained, and then the source point cloud data and / or target point cloud data are translated based on the displacement instructions, and the source point cloud data and / or target point cloud data are rotated based on the rotation instructions.

[0055] In the above embodiment, the metric value of the deformation instruction after posture adjustment is calculated, and the Figure 2 Explaining further, the method includes:

[0056] S201, establishing a three-dimensional coordinate system based on the source point cloud data and the target point cloud data after posture adjustment.

[0057] S202, projecting the source point cloud data and the target point cloud data in a direction perpendicular to the YOZ plane and a direction perpendicular to the XOZ plane, respectively, to obtain first source point cloud projection data of the source point cloud data projected on the YOZ plane and second source point cloud projection data projected on the XOZ plane, as well as obtaining first target point cloud projection data of the target point cloud data projected on the YOZ plane and second target point cloud projection data projected on the XOZ plane.

[0058] S203 , calculating a metric value based on the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data, and the second target point cloud projection data.

[0059] In an embodiment of the present disclosure, the first distance between the first source point cloud projection data of the i-th point and the first target point cloud projection data of the i-th point can be calculated for each of the i-th point in the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data, and the second target point cloud projection data, as well as the second distance between the second source point cloud projection data of the i-th point and the second target point cloud projection data of the i-th point can be calculated. Then, the first distance and the second distance are added, and the square root of the sum is taken to obtain the square root sum of the i-th point. Finally, the square root sums of all points are averaged to calculate the measurement value.

[0060] In the embodiment of the present disclosure, the formula for calculating the metric value is:

[0061]

[0062] in, and Respectively represent the first source point cloud projection data of the i-th point and the first target point cloud projection data of the i-th point, and Represents the second source point cloud projection data of the i-th point and the second target point cloud projection data of the i-th point, represents the total number of corresponding points, Represents a measurement value.

[0063] In the disclosed embodiment, a three-dimensional coordinate system is first established based on the pose-adjusted source and target point cloud data. The source and target point cloud data are then projected perpendicular to the YOZ and XOZ planes, respectively, to obtain first source point cloud projection data for the source point cloud data projected onto the YOZ plane and second source point cloud projection data for the target point cloud data projected onto the XOZ plane. Furthermore, first target point cloud projection data for the target point cloud data projected onto the YOZ plane and second target point cloud projection data for the target point cloud data projected onto the XOZ plane are obtained. Finally, a metric value is calculated based on the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data, and the second target point cloud projection data. By projecting onto two different planes (YOZ and XOZ), the differences between point clouds can be evaluated from multiple perspectives, providing a more comprehensive evaluation criterion. The projection operation also simplifies a three-dimensional problem into a two-dimensional one, making computations simpler and more efficient, particularly when processing large-scale point cloud data.

[0064] In the above embodiment, it is determined whether there is an optimal adjustment posture based on all the metric values ​​of the action factors. Figure 3 Explaining further, the method includes:

[0065] S301 : For the j-th deformation instruction in the action factor, obtain a first metric value of the j-th deformation instruction, and obtain a second metric value of the j-1-th deformation instruction.

[0066] S302 : Determine a reward or penalty value for the j-th deformation instruction based on the first metric value and the second metric value.

[0067] In an embodiment of the present disclosure, a first metric value and a second metric value may be compared, and in response to the first metric value being greater than the second metric value, the reward or punishment value may be determined to be the first reward or punishment value; or in response to the first metric value being less than the second metric value, the reward or punishment value may be determined to be the second reward or punishment value; or in response to the first metric value being equal to the second metric value, the reward or punishment value may be determined to be the third reward or punishment value.

[0068] In one possible implementation, the formula for determining the reward or penalty value of the j-th deformation instruction based on the first metric value and the second metric value is:

[0069]

[0070] represents the second metric value of the j-1th deformation instruction, represents the first metric value of j deformation instructions, is the first reward and punishment value, is the second reward and punishment value, It is the third reward and punishment value.

[0071] S303 , determining whether the pose after the j-th deformation instruction adjusts the pose of the source point cloud data and / or the target point cloud data based on the reward and penalty values ​​is the optimal adjusted pose.

[0072] In an embodiment of the present disclosure, in response to the reward and penalty value being the third reward and penalty value, it is determined that the pose after the j-th deformation instruction adjusts the source point cloud data and / or the target point cloud data is the optimal adjusted pose.

[0073] In the embodiment disclosed herein, first, for the j-th deformation instruction in the action factor, the first metric value of the j-th deformation instruction is obtained, and the second metric value of the j-1-th deformation instruction is obtained. Then, based on the first metric value and the second metric value, the reward and penalty value of the j-th deformation instruction is determined. Finally, based on the reward and penalty value, it is determined whether the posture after the j-th deformation instruction adjusts the posture of the source point cloud data and / or the target point cloud data is the optimal adjustment posture. Thus, by introducing a reward and penalty mechanism to dynamically evaluate the effect of each deformation instruction, and based on this, deciding whether to accept the posture adjustment result brought by the current instruction, this not only improves the accuracy and efficiency of point cloud registration, but also enhances the robustness and adaptability of the system.

[0074] In the above embodiment, the deformation value of the roadway to be estimated is calculated based on the source point cloud data and the target point cloud data after the optimal adjustment of the posture, and the deformation value of the roadway to be estimated can also be calculated by Figure 4 Explaining further, the method includes:

[0075] S401 : For a k-th point in the source point cloud data after the optimal adjustment of the posture, two comparison points closest to the k-th point are selected from the target point cloud data after the optimal adjustment of the posture.

[0076] S402: Calculate the comparison distance between the k-th point and the two comparison points respectively.

[0077] S403: Calculate the candidate deformation value of the k-th point based on the two comparison distance values.

[0078] In the embodiment of the present disclosure, after obtaining the two comparison distances, the two comparison distances can be calculated according to a preset algorithm to calculate the candidate deformation value of the k-th point. The algorithm can be changed according to actual design needs.

[0079] In a possible implementation, after obtaining two comparison distances, an average value of the two comparison distances may be calculated as the candidate deformation value of the k-th point.

[0080] S404 , calculating an average of candidate deformation values ​​of all points in the source point cloud data after the optimally adjusted posture, and using the average as the deformation value of the roadway to be estimated.

[0081] In the disclosed embodiment, for the kth point in the source point cloud data after optimal adjustment, the two closest comparison points to the kth point are selected from the target point cloud data after optimal adjustment. The comparison distances between the kth point and the two comparison points are then calculated. A candidate deformation value for the kth point is then calculated based on the two comparison distances. Finally, the average of the candidate deformation values ​​for all points in the source point cloud data after optimal adjustment is taken as the deformation value of the roadway to be estimated. This method effectively improves the accuracy and reliability of deformation estimation by meticulously comparing the distance between each point in the source point cloud data and its two closest comparison points in the target point cloud data. Furthermore, the use of local calculations not only enhances the robustness of the system but also optimizes computational efficiency.

[0082] Corresponding to the coal mine tunnel deformation calculation methods provided in the above-mentioned embodiments, an embodiment of the present disclosure also provides a coal mine tunnel deformation calculation device. Since the coal mine tunnel deformation calculation device provided in the embodiment of the present disclosure corresponds to the coal mine tunnel deformation calculation methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned coal mine tunnel deformation calculation methods are also applicable to the coal mine tunnel deformation calculation device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0083] Figure 5This is a schematic diagram of a coal mine tunnel deformation calculation device according to an embodiment of the present disclosure. As shown in FIG5 , the coal mine tunnel deformation calculation device 500 includes: an acquisition module 510 , an establishment module 520 , an adjustment module 530 and a calculation module 540 .

[0084] The acquisition module 510 is configured to acquire source point cloud data and target point cloud data of the lane to be estimated, wherein the source point cloud data and the target point cloud data are collected in the lane to be estimated at different time stamps.

[0085] The establishing module 520 is used to establish an action set, where the action set includes a plurality of action factors, and the action factors include a plurality of transformation instructions, and the transformation instructions are arranged in an operation order.

[0086] The adjustment module 530 is used to retrieve any action factor from the action set without callback, select deformation instructions from the action factors based on the operation order to adjust the posture of the source point cloud data and / or target point cloud data, and calculate the measurement value of the deformation instruction after the posture adjustment, and determine whether there is an optimal adjustment posture based on all the measurement values ​​of the action factors.

[0087] The calculation module 540 is used to, in response to the absence of the optimal adjustment posture, repeat the above-mentioned steps of extracting any action factor from the action set without callback and subsequent steps until it is determined that the optimal adjustment posture exists, and calculate the deformation value of the roadway to be estimated based on the source point cloud data and the target point cloud data after the optimal adjustment posture.

[0088] According to one embodiment of the present disclosure, the measurement value of the deformation instruction after posture adjustment is calculated, including: establishing a three-dimensional coordinate system based on the source point cloud data and the target point cloud data after posture adjustment; projecting the source point cloud data and the target point cloud data in a direction perpendicular to the YOZ plane and a direction perpendicular to the XOZ plane, respectively, to obtain first source point cloud projection data of the source point cloud data projected on the YOZ plane and second source point cloud projection data projected on the XOZ plane, as well as obtaining first target point cloud projection data of the target point cloud data projected on the YOZ plane and second target point cloud projection data projected on the XOZ plane; calculating the measurement value based on the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data and the second target point cloud projection data.

[0089] According to one embodiment of the present disclosure, a measurement value is calculated based on first source point cloud projection data, second source point cloud projection data, first target point cloud projection data, and second target point cloud projection data, including: for the i-th point position in each of the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data, and the second target point cloud projection data, calculating a first distance between the first source point cloud projection data of the i-th point position and the first target point cloud projection data of the i-th point position, and calculating a second distance between the second source point cloud projection data of the i-th point position and the second target point cloud projection data of the i-th point position; adding the first distance and the second distance, and taking the square root of the sum to obtain the square root sum of the i-th point position; and taking the average of the square root sums of all the points to calculate the measurement value.

[0090] According to one embodiment of the present disclosure, determining whether there is an optimal adjustment posture based on all metric values ​​of the action factor includes: for the j-th deformation instruction in the action factor, obtaining the first metric value of the j-th deformation instruction, and obtaining the second metric value of the j-1-th deformation instruction; determining the reward and punishment value of the j-th deformation instruction based on the first metric value and the second metric value; determining whether the posture after the j-th deformation instruction adjusts the posture of the source point cloud data and / or the target point cloud data based on the reward and punishment value is the optimal adjustment posture.

[0091] According to one embodiment of the present disclosure, determining the reward or penalty value of the j-th deformation instruction based on the first metric value and the second metric value includes: comparing the first metric value and the second metric value; in response to the first metric value being greater than the second metric value, determining the reward or penalty value to be the first reward or penalty value; or, in response to the first metric value being less than the second metric value, determining the reward or penalty value to be the second reward or penalty value; or, in response to the first metric value being equal to the second metric value, determining the reward or penalty value to be the third reward or penalty value.

[0092] According to one embodiment of the present disclosure, whether the posture after the j-th deformation instruction adjusts the posture of the source point cloud data and / or the target point cloud data is the optimal adjusted posture is determined based on the reward and penalty value, including: in response to the reward and penalty value being a third reward and penalty value, determining that the posture after the j-th deformation instruction adjusts the posture of the source point cloud data and / or the target point cloud data is the optimal adjusted posture.

[0093] According to one embodiment of the present disclosure, the deformation value of the to-be-estimated lane is calculated based on the source point cloud data and the target point cloud data after the optimal adjustment posture, including: for the kth point in the source point cloud data after the optimal adjustment posture, selecting two comparison points closest to the kth point from the target point cloud data after the optimal adjustment posture; respectively calculating the comparison distance value between the kth point and the two comparison points; calculating the candidate deformation value of the kth point based on the two comparison distance values; and obtaining the average value of the candidate deformation values ​​of all points in the source point cloud data after the optimal adjustment posture as the deformation value of the to-be-estimated lane.

[0094] According to one embodiment of the present disclosure, deformation instructions are selected from action factors based on an operation sequence to adjust the posture of source point cloud data and / or target point cloud data, including: obtaining displacement instructions and rotation instructions in the deformation instructions; translating the source point cloud data and / or target point cloud data based on the displacement instructions, and rotating the source point cloud data and / or target point cloud data based on the rotation instructions.

[0095] In order to implement the above embodiment, the present disclosure further provides an electronic device 600. Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 6 As shown, the electronic device 600 includes: a processor 601 and a memory 602 in communication with the processor, the memory 602 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 601 to implement the present disclosure. Figures 1-4 A method for calculating coal mine tunnel deformation according to an embodiment.

[0096] In order to implement the above embodiment, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiment. Figures 1-4 A method for calculating coal mine tunnel deformation according to an embodiment.

[0097] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program, which implements the above embodiments when executed by a processor. Figures 1-4 A method for calculating coal mine tunnel deformation according to an embodiment.

[0098] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0099] This application contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0100] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples, unless they are mutually inconsistent.

[0101] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0102] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0103] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0104] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0105] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0106] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0107] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for calculating deformation of coal mine tunnels, characterized in that: include: Acquire source point cloud data and target point cloud data of the lane to be estimated, wherein the source point cloud data and the target point cloud data are collected in the lane to be estimated at different time stamps; Establishing an action set, wherein the action set includes multiple action factors, the action factors include multiple deformation instructions, and the deformation instructions are arranged in an operation order, wherein the action factor is a complete action or step for adjusting the point cloud data, and the adjustment includes: rotation, displacement, and deformation, and each deformation instruction includes and only includes one action; extracting any action factor from the action set without callback, selecting a deformation instruction from the action factor based on the operation sequence to adjust the pose of the source point cloud data and / or the target point cloud data, calculating a metric value of the deformation instruction after the pose adjustment, and determining whether there is an optimal adjustment pose based on all metric values ​​of the action factor; In response to the absence of an optimal adjustment pose, repeating the above-mentioned steps of extracting any action factor from the action set without release and subsequent steps until an optimal adjustment pose is determined to exist, and calculating the deformation value of the roadway to be estimated based on the source point cloud data and the target point cloud data after the optimal adjustment pose; The calculation of the measurement value of the deformation instruction after the posture adjustment includes: establishing a three-dimensional coordinate system based on the source point cloud data and the target point cloud data after the posture adjustment; projecting the source point cloud data and the target point cloud data in a direction perpendicular to the YOZ plane and a direction perpendicular to the XOZ plane, respectively, to obtain first source point cloud projection data of the source point cloud data projected on the YOZ plane and second source point cloud projection data projected on the XOZ plane, as well as obtaining first target point cloud projection data of the target point cloud data projected on the YOZ plane and second target point cloud projection data projected on the XOZ plane; calculating the measurement value based on the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data and the second target point cloud projection data.

2. The method according to claim 1, characterized in that The calculating the metric value based on the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data, and the second target point cloud projection data includes: For each of the i-th point in the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data, and the second target point cloud projection data, calculate a first distance between the first source point cloud projection data at the i-th point and the first target point cloud projection data at the i-th point, and calculate a second distance between the second source point cloud projection data at the i-th point and the second target point cloud projection data at the i-th point; Adding the first distance and the second distance, and taking the square root of the sum to obtain the square root sum of the i-th point; The metric value is calculated by taking the average of the square roots of all the points.

3. The method according to any one of claims 1 to 2, characterized in that The determining whether there is an optimal adjustment posture based on all the metric values ​​of the action factors includes: For the j-th deformation instruction in the action factor, obtain a first metric value of the j-th deformation instruction, and obtain a second metric value of the j-1-th deformation instruction; Determining a reward or penalty value for the j-th deformation instruction based on the first metric value and the second metric value; Based on the reward and penalty values, it is determined whether the pose after the j-th deformation instruction adjusts the source point cloud data and / or the target point cloud data is the optimal adjusted pose.

4. The method according to claim 3, characterized in that The determining the reward or penalty value of the j-th deformation instruction based on the first metric value and the second metric value includes: comparing the first metric value and the second metric value; In response to the first metric value being greater than the second metric value, determining the reward or penalty value to be a first reward or penalty value; or In response to the first metric value being smaller than the second metric value, determining the reward or penalty value to be the second reward or penalty value; or In response to the first metric value being equal to the second metric value, the reward or penalty value is determined to be a third reward or penalty value.

5. The method according to claim 4, characterized in that The determining, based on the reward and penalty values, whether the pose after the j-th deformation instruction performs pose adjustment on the source point cloud data and / or the target point cloud data is the optimal adjusted pose includes: In response to the reward and penalty value being a third reward and penalty value, determining a pose after the j-th deformation instruction performs pose adjustment on the source point cloud data and / or the target point cloud data as the optimal adjusted pose.

6. The method according to claim 1, characterized in that The calculating the deformation value of the to-be-estimated roadway based on the source point cloud data and the target point cloud data after the optimally adjusted posture comprises: For the kth point in the source point cloud data after the optimal adjustment posture, select two comparison points that are closest to the kth point from the target point cloud data after the optimal adjustment posture; Calculating the comparison distance between the k-th point and the two comparison points respectively; Calculating a candidate deformation value of the k-th point based on the two compared distance values; An average value of candidate deformation values ​​of all points in the source point cloud data after the optimal adjustment of the posture is calculated as the deformation value of the roadway to be estimated.

7. The method according to claim 1, characterized in that The selecting deformation instructions from the action factors based on the operation sequence to adjust the pose of the source point cloud data and / or the target point cloud data includes: Obtaining a displacement instruction and a rotation instruction in the deformation instruction; The source point cloud data and / or the target point cloud data are translated based on the displacement instruction, and the source point cloud data and / or the target point cloud data are rotated based on the rotation instruction.

8. A coal mine tunnel deformation calculation device, characterized in that: include: An acquisition module, configured to acquire source point cloud data and target point cloud data of the lane to be estimated, wherein the source point cloud data and the target point cloud data are collected in the lane to be estimated at different time stamps; An establishment module is used to establish an action set, wherein the action set includes multiple action factors, the action factors include multiple deformation instructions, and the deformation instructions are arranged in an operation order, wherein the action factor is a complete action or step for adjusting the point cloud data, and the adjustment includes: rotation, displacement, and deformation, and each deformation instruction includes and only includes one action; an adjustment module, configured to extract any action factor from the action set without callback, select a deformation instruction from the action factor based on the operation sequence to perform pose adjustment on the source point cloud data and / or the target point cloud data, calculate a metric value of the deformation instruction after the pose adjustment, and determine whether there is an optimal adjustment pose based on all the metric values ​​of the action factor; a calculation module, configured to, in response to the absence of an optimal adjustment pose, repeat the above-mentioned steps of extracting any action factor from the action set without release and subsequent steps until it is determined that an optimal adjustment pose exists, and calculate the deformation value of the roadway to be estimated based on the source point cloud data and the target point cloud data after the optimal adjustment pose; The calculation of the measurement value of the deformation instruction after the posture adjustment includes: establishing a three-dimensional coordinate system based on the source point cloud data and the target point cloud data after the posture adjustment; projecting the source point cloud data and the target point cloud data in a direction perpendicular to the YOZ plane and a direction perpendicular to the XOZ plane, respectively, to obtain first source point cloud projection data of the source point cloud data projected on the YOZ plane and second source point cloud projection data projected on the XOZ plane, as well as obtaining first target point cloud projection data of the target point cloud data projected on the YOZ plane and second target point cloud projection data projected on the XOZ plane; calculating the measurement value based on the first source point cloud projection data, the second source point cloud projection data, the first target point cloud projection data and the second target point cloud projection data.

9. An electronic device, characterized in that: Including memory and processor; The processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method according to any one of claims 1 to 7.

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