Method and system for constructing three-dimensional model of digitized underground coal mine driving working face

Abstract

The invention relates to the technical field of three-dimensional modeling, in particular to a digital coal mine underground driving working face three-dimensional model construction method and system. Comprising the following steps: performing same grid division on a first point cloud of a driving working face after a section of cutting operation is completed and before supporting operation is started, and a plurality of second point clouds of the driving working face in a next section of cutting operation process after the supporting operation is completed; comparing the grid area of each second point cloud with the corresponding grid area of the first point cloud, and determining the vibration influence degree of each grid area of each second point cloud; determining the vibration trend coefficient of each grid region according to the change condition of the vibration influence degree of each grid region in each second point cloud; determining the data accuracy of each grid region according to the vibration trend coefficient of each grid region; according to the data accuracy of each grid region, the point cloud is filtered, and three-dimensional modeling is performed according to the filtered point cloud, so that the accuracy of three-dimensional modeling of the driving working face is improved.

Description

Technical Field

[0001] This invention relates to the field of 3D modeling technology, specifically to a method and system for constructing a 3D model of a digital coal mine underground tunneling face. Background Technology

[0002] Underground tunneling faces are the core of coal mine production. They are characterized by narrow spaces, high dust concentrations, easy gas accumulation, frequent equipment movement, complex geological conditions, and harsh environments, making them high-risk areas for coal mine safety accidents. Therefore, achieving the digitalization and intelligentization of tunneling faces is crucial for safe and efficient coal mine production. This requires visualizing the operational status and predicting risks through 3D modeling of the tunneling faces.

[0003] In the process of 3D modeling of underground coal mine tunneling faces, the accuracy of the 3D model depends on the accuracy of the acquired 3D data in reflecting the actual conditions of the working face. During underground coal mine tunneling, cutting and support are carried out alternately. When the working face has been tunneled a certain distance, cutting and coal cutting must be stopped for support. Because the cutting operation of the tunneling machine causes vibrations in the tunneling face, resulting in displacement fluctuations, and the degree of vibration varies in different areas, the accuracy of traditional 3D models of underground coal mine tunneling faces is insufficient, affecting risk identification and assessment during underground coal mine tunneling. Summary of the Invention

[0004] To address the technical problem of inaccurate 3D modeling of underground coal mine tunneling faces, the present invention aims to provide a method and system for constructing a digital 3D model of an underground coal mine tunneling face. The specific technical solution adopted is as follows: This invention provides a method for constructing a three-dimensional model of a digital coal mine underground tunneling face, the method comprising: The same grid division is applied to the first point cloud of the tunneling face before the support operation begins after the completion of a section of cutting operation, and to the multiple second point clouds of the tunneling face during the next section of cutting operation after the completion of the support operation. The grid regions of each second point cloud are compared with the corresponding grid regions of the first point cloud to determine the degree of vibration influence of each grid region of each second point cloud. Based on the changes in the degree of vibration influence of each grid region within each second point cloud, determine the vibration trend coefficient of each grid region; The data accuracy of each grid region is determined based on the vibration trend coefficient of each grid region. Based on the data accuracy of each grid region, the first point cloud and the second point cloud are filtered, and 3D modeling is performed based on the filtered point cloud.

[0005] According to the method for constructing a three-dimensional model of a digital coal mine underground tunneling face provided by the present invention, the step of comparing the grid regions of each second point cloud with the corresponding grid regions of the first point cloud to determine the degree of vibration influence of each grid region of each second point cloud includes: The degree of vibration impact of each grid region of each second point cloud is determined based on the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, as well as the positional differences and point number differences with the corresponding grid regions of the first point cloud.

[0006] According to the method for constructing a three-dimensional model of a digital coal mine underground tunneling face provided by the present invention, the step of determining the vibration influence degree of each grid region of each second point cloud based on the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, and the positional differences and point number differences with the corresponding grid regions of the first point cloud, includes: Based on the positional differences and the differences in the number of points between each grid region of each second point cloud and the corresponding grid region of the first point cloud, the vibration performance difference of each grid region of each second point cloud is determined; The degree of vibration impact of each grid region of each second point cloud is determined based on the difference in vibration performance of each grid region of each second point cloud and its relative position with respect to the tunneling position corresponding to the second point cloud.

[0007] According to the method for constructing a three-dimensional model of a digital coal mine underground tunneling face provided by the present invention, determining the vibration performance difference of each grid region of each second point cloud based on the positional difference and the difference in the number of points between each grid region of each second point cloud and the corresponding grid region of the first point cloud includes: For each grid region, the maximum point cloud density difference of the grid region is determined based on the maximum value of the difference between the number of points in each second point cloud and the number of points in the first point cloud for that grid region. For each grid region of each second point cloud, the vibration performance difference of the grid region of the second point cloud is determined based on the difference between the position coordinates of the grid region of the second point cloud and the corresponding grid region of the first point cloud, and the difference in the maximum point cloud density of the grid region.

[0008] According to the method for constructing a three-dimensional model of a digital coal mine underground tunneling face provided by the present invention, determining the degree of vibration influence of each grid region of each second point cloud based on the vibration performance difference of each grid region of each second point cloud and its relative position with respect to the tunneling position corresponding to the second point cloud includes: For each grid region of each second point cloud, the positional influence coefficient of the grid region of the second point cloud is determined based on the difference between the position coordinates of the grid region of the second point cloud and the position coordinates of the corresponding tunneling position of the second point cloud. The degree of vibration influence of the grid region of the second point cloud is determined based on the difference in vibration performance and the positional influence coefficient.

[0009] According to the method for constructing a three-dimensional model of a digital coal mine underground tunneling face provided by the present invention, the step of determining the vibration trend coefficient of each grid region based on the change in the degree of vibration influence within each second point cloud includes: For each grid region, the difference in the degree of vibration influence within the second point cloud of two adjacent scans is calculated to obtain the difference in the degree of vibration influence between adjacent scans. The vibration trend coefficient of the grid region is determined based on the difference between the degree of influence of adjacent vibrations.

[0010] According to the method for constructing a three-dimensional model of a digital coal mine underground tunneling face provided by the present invention, the step of determining the data accuracy of each grid region based on the vibration trend coefficient of each grid region includes: For each grid region, the data reliability of the grid region is determined based on the difference between the vibration trend coefficients of the grid region and each of its adjacent grid regions. The data accuracy of the grid region is determined based on the data reliability and the vibration trend coefficient of the grid region.

[0011] According to the method for constructing a three-dimensional model of a digital coal mine underground tunneling face provided by the present invention, the step of determining the data accuracy of the grid region based on the data reliability and the vibration trend coefficient of the grid region includes: The overall vibration impact of the grid area is determined based on the data reliability and vibration trend coefficient of the grid area. The data accuracy of the grid region is determined based on the overall vibration impact level of the grid region.

[0012] According to the method for constructing a 3D model of a digital coal mine underground tunneling face provided by the present invention, the step of filtering the first point cloud and the second point cloud based on the data accuracy of each of the grid regions includes: The filtering weights for each grid region are determined based on the data accuracy of each grid region. Based on the filtering weights of each grid region, the basic filtering parameters are adjusted to obtain the adjusted filtering parameters for each grid region. For the first point cloud and the second point cloud, the point cloud data within the grid area is filtered according to the adjusted filtering parameters for each grid area.

[0013] This invention provides a system for constructing a three-dimensional model of a digital coal mine underground tunneling face. The system includes a memory and a processor. The memory is used to store executable program code. The processor is used to call and run the executable program code from the memory to implement the method for constructing a three-dimensional model of a digital coal mine underground tunneling face provided by this invention.

[0014] This invention has the following beneficial effects: It divides the first point cloud of the tunneling face before the start of support work after the completion of a section of cutting operations, and multiple second point clouds of the tunneling face during the next section of cutting operations after the completion of support work, into the same grid. The grid regions of each second point cloud are compared with the corresponding grid regions of the first point cloud to determine the vibration impact degree of each grid region in each second point cloud. Based on the change in the vibration impact degree of each grid region within each second point cloud, the vibration trend coefficient of each grid region is determined. Based on the vibration trend coefficient of each grid region, the data accuracy of each grid region is determined. This enables the analysis of vibration performance by comparing the point cloud data of the tunneling face before and after support, thereby quantifying the data accuracy of each grid region. Furthermore, based on the data accuracy of each grid region, the point cloud data of the tunneling face can be accurately filtered, improving the accuracy of 3D modeling of underground tunneling faces in coal mines, and thus enhancing the ability of the 3D model to identify and judge risks during underground tunneling processes in coal mines. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a process for determining the degree of vibration influence on a grid region of a second point cloud, provided as an embodiment of the present invention. Figure 3This is a schematic diagram of a process for determining the vibration performance difference of a grid region of a second point cloud according to an embodiment of the present invention. Figure 4 This is a schematic diagram of a process for determining the degree of vibration influence on the grid region of a second point cloud based on relative positional relationships, according to an embodiment of the present invention. Figure 5 This is a structural block diagram of a digital coal mine underground tunneling face three-dimensional model construction system provided in one embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and system for constructing a three-dimensional model of a digital coal mine underground tunneling face according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method and system for constructing a three-dimensional model of a digital coal mine underground tunneling face provided by the present invention.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to an embodiment of the present invention, including the following steps: Step 101: Perform the same mesh division on the first point cloud of the tunneling face before the start of the support operation after the completion of a section of the cutting operation, and on multiple second point clouds of the tunneling face during the next section of the cutting operation after the completion of the support operation.

[0021] Cutting and support are two alternating tasks in underground coal mine tunneling. After the working face has been tunneled a certain distance, cutting and coal cutting must be stopped for support. The first point cloud refers to the point cloud data of the tunneling face after the completion of a section of cutting operation and before the start of support operation. The second point cloud refers to the point cloud data of the tunneling face during the next section of cutting operation after the completion of support operation. After the support equipment completes the working face support operation, a scan is performed from the start of the next section of cutting operation to capture the working face point cloud under the continuous influence of the tunneling machine's vibration load, resulting in the set of second point clouds. The support equipment can be a rock bolt drilling rig or a hydraulic support, etc. The scanning point position, resolution, and coordinate system are kept the same when scanning the first point cloud and each second point cloud. The first point cloud and each second point cloud are divided into several identical uniform grid areas.

[0022] In one embodiment, an intrinsically safe 3D laser scanner conforming to the explosion-proof standards for underground coal mines can be used to scan the tunneling face and obtain point cloud data of the tunneling face. The scanning range of the intrinsically safe 3D laser scanner needs to cover the entire space of the tunneling face. Based on the "narrow and long" spatial characteristics of the tunneling face, the scanning points are planned, and at least three global coordinate reference points are selected. For example, the roof anchor bolts 5 meters behind the face can be selected, as well as the permanent support structures on both sides.

[0023] Step 102: Compare the grid regions of each second point cloud with the corresponding grid regions of the first point cloud to determine the degree of vibration influence of each grid region of each second point cloud.

[0024] Among them, the degree of vibration impact is used to measure the extent to which the grid area in the second point cloud is affected by vibration.

[0025] It is understandable that the two main tasks of cutting and supporting are carried out alternately. When the working face has advanced a certain distance, cutting and coal cutting must be stopped for support. The cutting operation of the tunneling machine will cause vibration of the tunneling face, which manifests as displacement and fluctuation of the coal wall. Moreover, the tunneling operation is gradually implemented in local areas, and the vibration is more pronounced in areas closer to the current tunneling position. After the coal blocks in the tunneling face are collected and transported, the original stress balance of the coal and rock is disrupted, the overall integrity is weakened, and the constraint capacity of the remaining coal body is weakened, leading to increased stress in these areas. Under the vibration of the tunneling machine cutting, the coal body that has not been tunneled is more prone to loosening and deformation, and in severe cases, even destruction, with a greater degree of vibration impact. That is, the vibration characteristics of the tunneling face show that the vibration tends to increase as the tunneling work progresses, and adjacent grid areas may belong to the same coal seam structure, thus having similar vibration responses. The better the consistency of vibration between a grid area and its adjacent areas, the higher the reliability of the data in that area. Therefore, the accuracy of the three-dimensional data of different grid areas can be quantified by analyzing the degree of vibration impact of different grid areas.

[0026] Step 103: Determine the vibration trend coefficient of each grid region based on the changes in the degree of vibration influence of each grid region within each second point cloud.

[0027] Among them, the vibration trend coefficient is used to measure the change trend of the degree of vibration influence in the same grid area in each second point cloud over time.

[0028] Step 104: Determine the data accuracy of each grid area based on the vibration trend coefficient of each grid area.

[0029] Data accuracy is used to measure the accuracy of point cloud data within a grid area.

[0030] In one embodiment, for each grid region, the data accuracy is determined by comparing the difference between the vibration trend coefficients of that grid region and its neighboring grid regions. Here, neighboring grid regions refer to the grid regions adjacent to the targeted grid region.

[0031] Step 105: Filter the first and second point clouds based on the data accuracy of each grid region, and perform 3D modeling based on the filtered point clouds.

[0032] In one embodiment, the basic filtering parameters are adjusted according to the data accuracy of each grid region to obtain the adjusted filtering parameters for each grid region. Then, for the first point cloud and the second point cloud, the point cloud data within each grid region is filtered according to the adjusted filtering parameters for each grid region.

[0033] In one embodiment, steps 101 to 105 are executed for each segment cutting operation, thereby enabling the analysis of vibration performance based on point cloud data before and after each support operation, and then filtering and performing three-dimensional modeling on the point cloud data to improve the accuracy of three-dimensional modeling.

[0034] In the aforementioned method for constructing a 3D model of a digital coal mine underground tunneling face, the first point cloud of the tunneling face before the start of support operations after the completion of a section of cutting operations, and multiple second point clouds of the tunneling face during the next section of cutting operations after the completion of support operations, are divided into the same grid. The grid regions of each second point cloud are compared with the corresponding grid regions of the first point cloud to determine the degree of vibration influence of each grid region of each second point cloud. Based on the change in the degree of vibration influence of each grid region within each second point cloud, the vibration trend coefficient of each grid region is determined. Based on the vibration trend coefficient of each grid region, the data accuracy of each grid region is determined. This method enables the analysis of vibration performance by comparing the point cloud data of the tunneling face before and after support, thereby quantifying the data accuracy of each grid region. Furthermore, based on the data accuracy of each grid region, the point cloud data of the tunneling face can be accurately filtered, improving the accuracy of 3D modeling of underground coal mine tunneling faces and thus enhancing the ability of the 3D model to identify and judge risks during underground coal mine tunneling.

[0035] In one embodiment, the vibration impact degree of each grid region of each second point cloud is determined by comparing the grid regions of each second point cloud with the corresponding grid regions of the first point cloud. This includes determining the vibration impact degree of each grid region of each second point cloud based on the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, as well as the positional differences and differences in the number of points with the corresponding grid regions of the first point cloud.

[0036] Here, the tunneling location corresponding to the second point cloud refers to the location where tunneling was underway when the second point cloud was obtained through scanning. The number of points refers to the number of points in the point cloud data within the grid area.

[0037] In one embodiment, the tunneling position corresponding to the second point cloud can be the position of the tunneling face when the second point cloud is obtained by scanning.

[0038] In one embodiment, the location of the grid region can be determined using the eigenvalue coordinates of the grid region. The centroid coordinates of the grid region can be calculated based on the coordinates of each point within the grid region and used as the eigenvalue coordinates of the grid region.

[0039] In the above embodiments, based on the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, as well as the positional differences and point number differences with the corresponding grid regions of the first point cloud, the vibration impact degree of each grid region of each second point cloud can be accurately determined.

[0040] In one embodiment, see Figure 2Based on the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, and the differences in position and number of points with the corresponding grid regions of the first point cloud, the vibration impact degree of each grid region of each second point cloud is determined, including the following steps: Step 201: Determine the vibration performance difference of each grid region of each second point cloud based on the positional difference and the difference in the number of points between each grid region of each second point cloud and the corresponding grid region of the first point cloud.

[0041] Among them, the vibration performance difference is used to measure the degree of difference between the grid area of ​​the second point cloud after being affected by vibration and the grid area in the point cloud data before the start of the support operation (i.e. the corresponding grid area of ​​the first point cloud).

[0042] Step 202: Determine the degree of vibration impact of each grid region of each second point cloud based on the difference in vibration performance of each grid region of each second point cloud and its relative position to the tunneling position corresponding to the second point cloud.

[0043] In one embodiment, the positional influence coefficient of the grid region of the second point cloud is determined based on the relative position of the grid region of the second point cloud with respect to the corresponding tunneling position of the second point cloud. Then, the degree of vibration influence of the grid region of the second point cloud is determined based on the vibration performance difference of the grid region of the second point cloud and the positional influence coefficient.

[0044] Among them, the position influence coefficient is used to measure the degree to which the position of the grid region of the second point cloud is affected by vibration.

[0045] In the above embodiments, based on the positional differences and the number of points between each grid region of each second point cloud and the corresponding grid region of the first point cloud, the vibration performance difference of each grid region of each second point cloud is determined. This can accurately measure the degree of difference between the grid regions of the second point cloud after being affected by vibration and the grid regions in the point cloud data before the start of the support operation. Then, combined with the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, the vibration impact degree of each grid region of each second point cloud is determined. This can comprehensively consider the vibration performance of the structure of the grid region of the second point cloud itself and the degree of vibration impact on its location, and accurately determine the vibration impact degree of the grid region of the second point cloud.

[0046] In one embodiment, see Figure 3 Step 201 determines the vibration performance difference of each grid region of each second point cloud based on the positional differences and the differences in the number of points between each grid region of each second point cloud and the corresponding grid region of the first point cloud, including the following steps: Step 2011: For each grid region, determine the maximum point cloud density difference of the grid region based on the maximum difference between the number of points in each second point cloud and the number of points in the first point cloud.

[0047] It is understandable that cutting operations will cause vibrations at the tunneling face, leading to loosening of the surrounding rock and displacement of the coal face. This may result in more or fewer fine particles being scanned, manifesting as changes in the grid point cloud density. Therefore, the difference in point cloud density within a grid area quantifies the degree of vibration impact on that area before and after support. A larger value indicates a more significant difference in the number of points scanned in that grid area before and after support, meaning that the grid area is more significantly affected by vibration and is more sensitive to it.

[0048] In one embodiment, the absolute value of the difference between the number of points in the second point cloud and the number of points in the first point cloud for the same grid region can be used as the difference between the number of points in the second point cloud and the number of points in the first point cloud for that grid region.

[0049] In one embodiment, the maximum point cloud density difference in the grid region can be determined according to the following formula: in, Indicates the first The maximum point cloud density difference in each grid region. Indicates the first The second point cloud The number of points within a grid area. Indicates the first point cloud The number of points within a grid area. Indicates the first The grid region in the first The difference between the number of points in the second point cloud and the number of points in the first point cloud. Indicates from various corresponding Take the maximum value from the middle, that is, the first... The maximum value among the differences between the number of points in each grid region within the second point cloud and the number of points in the first point cloud.

[0050] Step 2012: For each grid region of each second point cloud, determine the degree of difference in vibration performance of the grid region of the second point cloud based on the difference in position coordinates between the grid region of the second point cloud and the corresponding grid region of the first point cloud, as well as the difference in the maximum point cloud density of the grid region.

[0051] It is understandable that cutting operations will cause vibrations at the tunneling face, manifesting as displacement and fluctuations in the coal wall. Therefore, the difference in vibration performance can be quantified by considering the positional differences of the point clouds before and after support, combined with the difference in maximum point cloud density. The difference in vibration performance of the grid area of ​​the second point cloud is positively correlated with the difference in positional coordinates between the grid area of ​​the second point cloud and the corresponding grid area of ​​the first point cloud, and is also positively correlated with the difference in maximum point cloud density of the grid area.

[0052] In one embodiment, the Euclidean distance between the position coordinates of the grid region of the second point cloud and the corresponding grid region of the first point cloud is used as the difference between the position coordinates of the grid region of the second point cloud and the corresponding grid region of the first point cloud.

[0053] In one embodiment, the vibration performance difference of the grid regions of the second point cloud is determined by multiplying the difference in position coordinates between the grid regions of the second point cloud and the corresponding grid regions of the first point cloud by the difference in maximum point cloud density of the grid regions. The formula is as follows: in, Indicates the first The second point cloud The difference in vibration performance among the grid regions. Indicates the first The maximum point cloud density difference in each grid region. Indicates the first The second point cloud The feature coordinates of each grid region. Indicates the first point cloud The feature coordinates of each grid region. Indicates the first The second point cloud The grid region and the first point cloud The Euclidean distance between the position coordinates of each grid region.

[0054] Understandable. Reflects the first The spatial position change of a grid area before and after support, i.e., the local micro-displacement caused by vibration, the larger the value, the greater the degree of overall spatial deformation of the grid area caused by vibration, and therefore the greater the difference in vibration performance. The larger the value, the greater the difference in vibration performance. The larger the value, the worse the structure of the tunneling face in that grid area is, making it more susceptible to vibration.

[0055] In the above embodiments, since the underground tunneling process in coal mines involves alternating tunneling and support operations, the cutting operation of the tunneling machine causes vibration of the tunneling face, resulting in displacement fluctuations of the coal wall. During tunneling operations, the acquired data is affected by various factors such as vibration, water mist, and dust, making it difficult to guarantee data accuracy. Before the support operation, the tunneling face is in a vibration-free state, and after a certain period of settling, the interference of factors such as water mist and dust on data acquisition is significantly reduced. Therefore, the static data acquired at this stage has relatively high accuracy. In addition, the support operation can effectively improve the stability of the coal and rock, enhancing their resistance to vibration. Therefore, the difference in vibration performance can be accurately quantified by the position and density differences of the point clouds before and after support. The greater the difference in vibration performance, the worse the integrity of the coal seam structure and the higher the degree of loosening, making it more sensitive to vibration and more susceptible to structural instability risks.

[0056] In one embodiment, see Figure 4 Step 202 determines the degree of vibration impact of each grid region of each second point cloud based on the difference in vibration performance of each grid region and its relative position with respect to the corresponding tunneling position of the second point cloud, including the following steps: Step 2021: For each grid region of each second point cloud, determine the positional influence coefficient of the grid region of the second point cloud based on the difference between the positional coordinates of the grid region of the second point cloud and the positional coordinates of the corresponding tunneling position of the second point cloud.

[0057] In one embodiment, the current position coordinates (global coordinates) of the tunneling face can be obtained through the positioning function of the scanner or the positioning system of the tunneling machine, and used as the position coordinates of the tunneling position corresponding to the second point cloud obtained by the current scan.

[0058] It is understandable that the tunneling machine is the primary source of vibration. The vibration generated by its cutting operation propagates from the tunneling face into the surrounding space, and the energy gradually attenuates during propagation. Therefore, the closer the coal block is to the tunneling face, the stronger the vibration energy it directly experiences, and the more pronounced the vibration. Conversely, in areas farther from the face, the vibration energy has significantly attenuated, and the vibration impact is weakened. Thus, the vibration behavior varies depending on the location of the grid area; the closer the grid area is to the current tunneling position, the more pronounced the vibration. Therefore, based on the relative positional relationship between the grid areas of the second point cloud and the tunneling position, the positional influence coefficient of the grid areas in the second point cloud can be determined. Then, by adjusting the vibration behavior difference based on the positional influence coefficient, the degree of vibration influence of the grid areas in the second point cloud can be obtained.

[0059] The positional influence coefficient of the grid region of the second point cloud is negatively correlated with the difference between the positional coordinates of the grid region of the second point cloud and the positional coordinates of the corresponding tunneling location of the second point cloud.

[0060] In one embodiment, the Euclidean distance between the position coordinates of the grid region of the second point cloud and the position coordinates of the corresponding tunneling position of the second point cloud can be used as the difference between the position coordinates of the grid region of the second point cloud and the position coordinates of the corresponding tunneling position of the second point cloud.

[0061] In one embodiment, the positional influence coefficient of the grid region of the second point cloud can be determined according to the following formula: in, Indicates the first The second point cloud The positional influence coefficient of each grid region. Indicates the first The second point cloud The feature coordinates of each grid region. Indicates the first The location coordinates of the tunneling position corresponding to the second point cloud, that is, the coordinates of the tunneling position obtained during the scan. The coordinates of the location of the tunnel face at the second point cloud. Indicates the first The second point cloud The Euclidean distance between the position coordinates of the grid region and the position coordinates of the tunneling position corresponding to the second point cloud.

[0062] Understandable. The smaller the value, the better. The second point cloud The closer a grid area is to the tunnel face, the more significant the impact of vibration; therefore, the positional influence coefficient... The larger.

[0063] Step 2022: Determine the degree of vibration influence of the grid region of the second point cloud based on the difference in vibration performance and the positional influence coefficient of the grid region of the second point cloud.

[0064] The degree of vibration influence on the grid area of ​​the second point cloud is positively correlated with the degree of difference in vibration performance and also positively correlated with the position influence coefficient.

[0065] In one embodiment, the degree of vibration influence of the second point cloud's grid region can be determined by multiplying the vibration performance difference of the grid region by the positional influence coefficient. The formula is as follows: in, Indicates the first The second point cloud The degree of vibration impact in each grid area. Indicates the first The second point cloud The positional influence coefficient of each grid region. Indicates the first The second point cloud The difference in vibration performance among the grid regions.

[0066] In the above embodiments, the vibration impact of different grid areas under vibration is calculated by the spatial location and vibration performance of the grid areas. The closer the grid area is to the current tunneling operation area, the more significant the vibration impact and the greater the degree of vibration impact. Therefore, the degree of vibration impact can be accurately determined.

[0067] In one embodiment, the vibration trend coefficient of each grid region is determined based on the change in the degree of vibration influence of each grid region within each second point cloud. This includes: calculating the difference in the degree of vibration influence of each grid region within the second point cloud of two adjacent scans for each grid region, thereby obtaining the difference in the degree of vibration influence between adjacent grid regions; and determining the vibration trend coefficient of the grid region based on the difference in the degree of vibration influence between adjacent grid regions.

[0068] It is understandable that during the mining process at a coal mine face, after the coal is collected and transported, the stress balance between coal and rock is disrupted, weakening the constraint on the remaining coal body. At this point, the vibration effect of the tunneling process has a greater impact; that is, the vibration characteristics of the area show an increasing trend in vibration impact as the tunneling process progresses. The vibration trend enhancement coefficient can be calculated by observing the changes in vibration trends during the scanning process.

[0069] In one embodiment, the difference between the degree of vibration influence of the grid region within the second point cloud of two adjacent scans can be used as the difference in the degree of vibration influence between adjacent scans. Then, the ratio between the differences in the degree of vibration influence between adjacent scans can be used as the difference between the differences in the degree of vibration influence between adjacent scans.

[0070] In one embodiment, the product of the difference between adjacent vibration influence levels and the vibration influence level of the corresponding grid region of the second point cloud is calculated. Then, the product of these products for each grid region in the respective second point clouds is summed to obtain the vibration trend coefficient of the grid region. The formula is as follows: in, Indicates the first Vibration trend coefficients for each grid area. Indicates the first The second point cloud The degree of vibration impact in each grid area. Indicates the first The second point cloud The degree of vibration impact in each grid area. Indicates the first The second point cloud The degree of vibration impact in each grid area. and Indicates the first The difference in the degree of vibration influence between two adjacent scans of a grid region within the second point cloud, i.e., the difference in the degree of adjacent vibration influence. This indicates the difference between adjacent vibrations in terms of their degree of influence. This indicates the total number of points in the second cloud.

[0071] It can be understood that the ratio of the difference in vibration influence between two adjacent scans can be used to determine the trend of vibration influence in the grid area. The larger the value, the stronger the vibration trend in the later scan data. That is, as the coal seam is collected, the surrounding rock constraint weakens, and the vibration influence tends to increase. Therefore, the vibration trend coefficient... The larger the value, the more pronounced the vibration behavior in that grid region.

[0072] In the above embodiments, the difference in the degree of vibration influence between the grid region and the second point cloud in two adjacent scans is calculated to obtain the difference in the degree of vibration influence between adjacent regions. Based on the difference in the degree of vibration influence between adjacent regions, the vibration trend coefficient of the grid region can be accurately determined.

[0073] In one embodiment, determining the data accuracy of each grid region based on the vibration trend coefficient of each grid region includes: determining the data reliability of each grid region based on the difference between the vibration trend coefficients of the grid region and each adjacent grid region; and determining the data accuracy of the grid region based on the data reliability and the vibration trend coefficient.

[0074] It is understandable that since adjacent grid areas may belong to the same coal seam structure, they should have similar vibration responses. The data reliability of a grid area can be calculated by comparing the vibration trend coefficients of adjacent grid areas. The better the vibration consistency between a grid area and its adjacent grid areas, the higher the data reliability and the higher the data accuracy of that grid area.

[0075] In one embodiment, the difference between the vibration trend coefficients of the grid region and the adjacent grid regions can be used as the difference between the vibration trend coefficients of the grid region and the adjacent grid regions.

[0076] In one embodiment, the average difference between the vibration trend coefficients of a grid area and its adjacent grid areas can be calculated to obtain the average difference in vibration trend coefficients. The data reliability of the grid area can then be determined based on this average difference in vibration trend coefficients. The data reliability of a grid area is negatively correlated with the average difference in vibration trend coefficients.

[0077] In one embodiment, the data reliability of a grid region can be determined according to the following formula: in, Indicates the first Data reliability of each grid area. Indicates the first Vibration trend coefficients for each grid area. Indicates the first The first grid region Vibration trend coefficients of adjacent grid regions. Indicates the first The grid region and the first The difference between the vibration trend coefficients of adjacent grid regions. Indicates the first The total number of adjacent grid regions in a given grid region, the first The adjacent grid regions of the first grid region refer to the first grid region. The grid area has eight neighboring grid areas.

[0078] Understandable. The smaller the value, the better the vibration consistency between the grid region and its adjacent grid regions, and the higher the data reliability of that grid region. The higher.

[0079] In the above embodiments, the data reliability of the grid area is determined based on the difference between the vibration trend coefficients of the grid area and each adjacent grid area. Then, based on the data reliability and vibration trend coefficient of the grid area, the data accuracy of the grid area can be accurately determined.

[0080] In one embodiment, determining the data accuracy of a grid area based on the data reliability and vibration trend coefficient of the grid area includes: determining the overall vibration impact level of the grid area based on the data reliability and vibration trend coefficient of the grid area; and determining the data accuracy of the grid area based on the overall vibration impact level of the grid area.

[0081] In one embodiment, the overall vibration impact of the grid area is positively correlated with both the data reliability and the vibration trend coefficient of the grid area.

[0082] In one embodiment, the overall vibration impact of the grid region can be determined according to the following formula: in, Indicates the first The overall vibration impact of each grid area. Indicates the first Data reliability of each grid area. Indicates the first Vibration trend coefficients for each grid area.

[0083] Overall vibration impact The larger it is, the more likely it is to be the first The greater the degree to which a grid area is affected by vibration.

[0084] The greater the degree of vibration impact, the higher the degree of vibration disturbance in the grid area, and the lower the accuracy of the 3D data. Therefore, the data accuracy of the grid area is negatively correlated with the overall degree of vibration impact in the grid area.

[0085] In one embodiment, the data accuracy of the grid region can be determined according to the following formula: in, Indicates the first Data accuracy for each grid area. Indicates the first The overall vibration impact of each grid area.

[0086] In the above embodiments, the overall vibration impact of the grid area can be accurately determined based on the data reliability and vibration trend coefficient of the grid area. Since the greater the vibration impact, the higher the degree of vibration interference in the grid area and the lower the accuracy of the three-dimensional data, the data accuracy of the grid area can be accurately determined based on the overall vibration impact of the grid area.

[0087] In one embodiment, filtering the first point cloud and the second point cloud based on the data accuracy of each grid region includes: determining the filtering weight of each grid region based on the data accuracy of each grid region; adjusting the basic filtering parameters based on the filtering weight of each grid region to obtain the adjusted filtering parameters of each grid region; and filtering the point cloud data within each grid region based on the adjusted filtering parameters of each grid region for the first point cloud and the second point cloud.

[0088] It's understandable that higher data accuracy in a grid area indicates less vibration interference and higher data reliability. Therefore, more original details should be preserved, and the filtering intensity should be lower. Consequently, the filtering weight of a grid area is negatively correlated with its data accuracy. That is, grid areas with higher data accuracy should have lower filtering intensity (i.e., smaller filtering weight) to retain more original details; conversely, grid areas with lower data accuracy should have higher filtering intensity (i.e., larger filtering weight) to suppress noise.

[0089] In one embodiment, the filtering weights of the grid region can be determined according to the following formula: in, Indicates the first The filtering weights for each grid region. Indicates the first Data accuracy for each grid area.

[0090] In one embodiment, the two basic filtering parameters (the first basic filtering parameter and the second basic filtering parameter) of the bilateral filtering algorithm can be adjusted according to the filtering weight of each grid region to obtain the adjusted filtering parameters (the adjusted first filtering parameter and the adjusted second filtering parameter) for each grid region. The formula is as follows: in, Indicates the first The first filter parameter after adjustment for each grid region. This represents the first basic filter parameter. Indicates the first The filtering weights for each grid region. Indicates the first The adjusted second filter parameters for each grid region. This represents the second basic filter parameter.

[0091] In the above embodiments, the filtering weight of each grid region is determined based on the data accuracy of each grid region. The basic filtering parameters are adjusted according to the filtering weight of each grid region to obtain the adjusted filtering parameters of each grid region. This achieves adaptive adjustment of the filtering parameters. Then, for the first point cloud and the second point cloud, the point cloud data in each grid region is filtered according to the adjusted filtering parameters of each grid region, which can improve the accuracy of the point cloud data and thus improve the accuracy of the 3D model constructed based on the point cloud data.

[0092] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0093] See Figure 5 This invention provides a digital coal mine underground tunneling face 3D model construction system. The system includes a memory and a processor. The memory stores executable program code (hereinafter referred to as executable code). The processor calls and runs the executable program code from the memory to achieve the following steps: dividing the first point cloud of the tunneling face after the completion of a section of cutting operation and before the start of support operation, and multiple second point clouds of the tunneling face during the next section of cutting operation after the completion of support operation, into the same mesh; comparing the mesh regions of each second point cloud with the corresponding mesh regions of the first point cloud to determine the vibration influence degree of each mesh region of each second point cloud; determining the vibration trend coefficient of each mesh region based on the change in the vibration influence degree of each mesh region within each second point cloud; determining the data accuracy of each mesh region based on the vibration trend coefficient of each mesh region; filtering the first and second point clouds based on the data accuracy of each mesh region, and performing 3D modeling based on the filtered point clouds.

[0094] In one embodiment, the vibration impact degree of each grid region of each second point cloud is determined by comparing the grid regions of each second point cloud with the corresponding grid regions of the first point cloud. This includes determining the vibration impact degree of each grid region of each second point cloud based on the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, as well as the positional differences and differences in the number of points with the corresponding grid regions of the first point cloud.

[0095] In one embodiment, determining the vibration impact degree of each grid region of each second point cloud based on the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, and the positional difference and the difference in the number of points with respect to the corresponding grid region of the first point cloud, includes: determining the vibration performance difference degree of each grid region of each second point cloud based on the positional difference and the difference in the number of points with respect to the corresponding grid region of the first point cloud; and determining the vibration impact degree of each grid region of each second point cloud based on the vibration performance difference degree of each grid region of each second point cloud and its relative position with respect to the corresponding tunneling position of the second point cloud.

[0096] In one embodiment, determining the vibration performance difference of each grid region of each second point cloud based on the positional difference and the difference in the number of points between each grid region of each second point cloud and the corresponding grid region of the first point cloud includes: determining the maximum point cloud density difference of each grid region based on the maximum value among the differences in the number of points of the grid region in each second point cloud and in the first point cloud; and determining the vibration performance difference of each grid region of each second point cloud based on the difference between the positional coordinates of the grid region of the second point cloud and the corresponding grid region of the first point cloud, and the maximum point cloud density difference of the grid region.

[0097] In one embodiment, determining the vibration impact degree of each grid region of each second point cloud based on the vibration performance difference of each grid region of each second point cloud and its relative position with respect to the tunneling position corresponding to the second point cloud includes: determining the positional influence coefficient of each grid region of each second point cloud based on the difference between the position coordinates of the grid region of the second point cloud and the position coordinates of the tunneling position corresponding to the second point cloud; and determining the vibration impact degree of the grid region of the second point cloud based on the vibration performance difference of the grid region of the second point cloud and the positional influence coefficient.

[0098] In one embodiment, the vibration trend coefficient of each grid region is determined based on the change in the degree of vibration influence of each grid region within each second point cloud. This includes: calculating the difference in the degree of vibration influence of each grid region within the second point cloud of two adjacent scans for each grid region, thereby obtaining the difference in the degree of vibration influence between adjacent grid regions; and determining the vibration trend coefficient of the grid region based on the difference in the degree of vibration influence between adjacent grid regions.

[0099] In one embodiment, determining the data accuracy of each grid region based on the vibration trend coefficient of each grid region includes: determining the data reliability of each grid region based on the difference between the vibration trend coefficients of the grid region and each adjacent grid region; and determining the data accuracy of the grid region based on the data reliability and the vibration trend coefficient.

[0100] In one embodiment, determining the data accuracy of a grid area based on the data reliability and vibration trend coefficient of the grid area includes: determining the overall vibration impact level of the grid area based on the data reliability and vibration trend coefficient of the grid area; and determining the data accuracy of the grid area based on the overall vibration impact level of the grid area.

[0101] In one embodiment, filtering the first point cloud and the second point cloud based on the data accuracy of each grid region includes: determining the filtering weight of each grid region based on the data accuracy of each grid region; adjusting the basic filtering parameters based on the filtering weight of each grid region to obtain the adjusted filtering parameters of each grid region; and filtering the point cloud data within each grid region based on the adjusted filtering parameters of each grid region for the first point cloud and the second point cloud.

[0102] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

[0104] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for constructing a three-dimensional model of a digital coal mine underground tunneling face, characterized in that, The method includes: The same grid division is applied to the first point cloud of the tunneling face before the support operation begins after the completion of a section of cutting operation, and to the multiple second point clouds of the tunneling face during the next section of cutting operation after the completion of the support operation. The grid regions of each second point cloud are compared with the corresponding grid regions of the first point cloud to determine the degree of vibration influence of each grid region of each second point cloud. Based on the changes in the degree of vibration influence of each grid region within each second point cloud, determine the vibration trend coefficient of each grid region; The data accuracy of each grid region is determined based on the vibration trend coefficient of each grid region. Based on the data accuracy of each grid region, the first point cloud and the second point cloud are filtered, and 3D modeling is performed based on the filtered point cloud.

2. The method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to claim 1, characterized in that, The step of comparing the grid regions of each second point cloud with the corresponding grid regions of the first point cloud to determine the degree of vibration influence of each grid region of each second point cloud includes: The degree of vibration impact of each grid region of each second point cloud is determined based on the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, as well as the positional differences and point number differences with the corresponding grid regions of the first point cloud.

3. The method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to claim 2, characterized in that, The determination of the vibration impact degree of each grid region of each second point cloud based on the relative position of each grid region of each second point cloud with respect to the corresponding tunneling position of the second point cloud, and the positional differences and point number differences with the corresponding grid regions of the first point cloud, includes: Based on the positional differences and the differences in the number of points between each grid region of each second point cloud and the corresponding grid region of the first point cloud, the vibration performance difference of each grid region of each second point cloud is determined; The degree of vibration impact of each grid region of each second point cloud is determined based on the difference in vibration performance of each grid region of each second point cloud and its relative position with respect to the tunneling position corresponding to the second point cloud.

4. The method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to claim 3, characterized in that, The step of determining the vibration performance difference of each grid region of each second point cloud based on the positional difference and the difference in the number of points between each grid region of each second point cloud and the corresponding grid region of the first point cloud includes: For each grid region, the maximum point cloud density difference of the grid region is determined based on the maximum value of the difference between the number of points in each second point cloud and the number of points in the first point cloud for that grid region. For each grid region of each second point cloud, the vibration performance difference of the grid region of the second point cloud is determined based on the difference between the position coordinates of the grid region of the second point cloud and the corresponding grid region of the first point cloud, and the difference in the maximum point cloud density of the grid region.

5. The method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to claim 3, characterized in that, The determination of the vibration impact degree of each grid region of each second point cloud based on the vibration performance difference of each grid region of each second point cloud and its relative position with respect to the corresponding tunneling position of the second point cloud includes: For each grid region of each second point cloud, the positional influence coefficient of the grid region of the second point cloud is determined based on the difference between the position coordinates of the grid region of the second point cloud and the position coordinates of the corresponding tunneling position of the second point cloud. The degree of vibration influence of the grid region of the second point cloud is determined based on the difference in vibration performance and the positional influence coefficient.

6. The method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to claim 1, characterized in that, The step of determining the vibration trend coefficient for each grid region based on the change in the degree of vibration influence within each of the second point clouds includes: For each grid region, the difference in the degree of vibration influence within the second point cloud of two adjacent scans is calculated to obtain the difference in the degree of vibration influence between adjacent scans. The vibration trend coefficient of the grid region is determined based on the difference between the degree of influence of adjacent vibrations.

7. The method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to claim 1, characterized in that, The step of determining the data accuracy of each grid region based on the vibration trend coefficient of each grid region includes: For each grid region, the data reliability of the grid region is determined based on the difference between the vibration trend coefficients of the grid region and each of its adjacent grid regions. The data accuracy of the grid region is determined based on the data reliability and the vibration trend coefficient of the grid region.

8. The method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to claim 7, characterized in that, Determining the data accuracy of the grid region based on the data reliability and the vibration trend coefficient of the grid region includes: The overall vibration impact of the grid area is determined based on the data reliability and vibration trend coefficient of the grid area. The data accuracy of the grid region is determined based on the overall vibration impact level of the grid region.

9. The method for constructing a three-dimensional model of a digital coal mine underground tunneling face according to any one of claims 1 to 8, characterized in that, The step of filtering the first point cloud and the second point cloud based on the data accuracy of each of the grid regions includes: The filtering weights for each grid region are determined based on the data accuracy of each grid region. Based on the filtering weights of each grid region, the basic filtering parameters are adjusted to obtain the adjusted filtering parameters for each grid region. For the first point cloud and the second point cloud, the point cloud data within the grid area is filtered according to the adjusted filtering parameters for each grid area.

10. A system for constructing a three-dimensional model of a digital coal mine underground tunneling face, characterized in that, The system includes a memory and a processor; the memory is used to store executable program code; the processor is used to call and run the executable program code from the memory to implement the method for constructing a three-dimensional model of a digital coal mine underground tunneling face as described in any one of claims 1 to 9.

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