A method and system for constructing a three-dimensional model of a digitalized coal mine underground tunneling working face

Abstract

The present application relates to the technical field of three-dimensional modeling, and particularly relates to a method and system for constructing a three-dimensional model of a digitalized underground tunneling working face in a coal mine. The method comprises: performing the same grid division on a first point cloud of the tunneling working face before support work starts after a section of cutting work is completed, and on multiple second point clouds of the tunneling working face during a next section of cutting work after the support work is completed; comparing the grid regions of each second point cloud with the corresponding grid regions of the first point cloud to determine the vibration influence degree of each grid region of each second point cloud; determining the vibration trend coefficient of each grid region according to the change in 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; filtering the point cloud according to the data accuracy of each grid region, and performing three-dimensional modeling according to the filtered point cloud, thereby improving the accuracy of three-dimensional modeling of the tunneling working face.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional modeling, in particular to a digital coal mine underground tunneling working face three-dimensional model construction method and system. BACKGROUND

[0002] The underground tunneling working face is the core scene of coal mine production, with narrow space, high dust concentration, easy gas accumulation, frequent equipment movement, complex geological conditions and harsh environment, which is a high-risk area of coal mine safety accidents. Therefore, realizing the digitization and intelligentization of the tunneling working face is the key to safe and efficient production of coal mines, and the three-dimensional modeling of the tunneling working face is needed to realize the visualization of the coal mine operation state and risk prediction.

[0003] In the process of three-dimensional modeling of the coal mine underground tunneling working face, the accuracy of the three-dimensional modeling model depends on the accuracy of the three-dimensional data reflecting the actual conditions of the working face. In the process of coal mine underground tunneling, the two major works of cutting and supporting are alternately performed, and when the working face advances a certain distance, the cutting of coal needs to be stopped for supporting. Since the cutting operation of the tunneling machine will cause vibration of the tunneling working face, resulting in displacement fluctuation of the tunneling working face, and the vibration influence degree of different regions is different, the traditional three-dimensional model construction accuracy of the coal mine underground tunneling working face is insufficient, which affects the risk identification and judgment in the process of coal mine underground tunneling. SUMMARY

[0004] In order to solve the technical problem of inaccurate three-dimensional modeling of the coal mine underground tunneling working face, the purpose of the present application is to provide a digital coal mine underground tunneling working face three-dimensional model construction method and system, and the technical solution adopted is as follows:

[0005] The present application provides a digital coal mine underground tunneling working face three-dimensional model construction method, which comprises:

[0006] The first point cloud of the tunneling working face before the supporting operation starts after completing a cutting operation, and a plurality of second point clouds of the tunneling working face in the process of the next cutting operation after completing the supporting operation are subjected to the same grid division;

[0007] The grid regions of each of the second point clouds are compared with the corresponding grid regions of the first point cloud to determine the vibration influence degree of each grid region of each second point cloud;

[0008] According to the change of the vibration influence degree of each grid region in each of the second point clouds, the vibration trend coefficient of each grid region is determined;

[0009] According to the vibration trend coefficient of each grid region, the data accuracy of each grid region is determined;

[0010] According to the data accuracy of each grid area, the first point cloud and the second point cloud are filtered, and three-dimensional modeling is performed according to the filtered point cloud.

[0011] According to the digital coal mine underground tunneling working face three-dimensional model construction method provided by the application, the grid area of each second point cloud is compared with the corresponding grid area of the first point cloud, and the vibration influence degree of each grid area of each second point cloud is determined.

[0012] According to the relative position of each grid area of each second point cloud relative to the corresponding tunneling position of the second point cloud, and the position difference and point number difference of the corresponding grid area of the first point cloud, the vibration influence degree of each grid area of each second point cloud is determined.

[0013] According to the digital coal mine underground tunneling working face three-dimensional model construction method provided by the application, the grid area of each second point cloud is compared with the corresponding grid area of the first point cloud, and the vibration influence degree of each grid area of each second point cloud is determined.

[0014] According to the position difference and point number difference of each grid area of each second point cloud and the corresponding grid area of the first point cloud, the vibration performance difference degree of each grid area of each second point cloud is determined.

[0015] According to the vibration performance difference degree of each grid area of each second point cloud and the relative position relative to the corresponding tunneling position of the second point cloud, the vibration influence degree of each grid area of each second point cloud is determined.

[0016] According to the digital coal mine underground tunneling working face three-dimensional model construction method provided by the application, the grid area of each second point cloud is compared with the corresponding grid area of the first point cloud, and the vibration influence degree of each grid area of each second point cloud is determined.

[0017] According to the digital coal mine underground tunneling working face three-dimensional model construction method provided by the application, the grid area of each second point cloud is compared with the corresponding grid area of the first point cloud, and the vibration influence degree of each grid area of each second point cloud is determined.

[0018] According to the digital coal mine underground tunneling working face three-dimensional model construction method provided by the application, the grid area of each second point cloud is compared with the corresponding grid area of the first point cloud, and the vibration influence degree of each grid area of each second point cloud is determined.

[0019] The method for constructing a three-dimensional model of a digitalized underground tunneling working face in a coal mine according to the present application comprises the following steps:

[0020] For each grid region of each second point cloud, a position influence coefficient of the grid region of the second point cloud is determined according to 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.

[0021] The vibration influence degree of the grid region of the second point cloud is determined according to the vibration performance difference degree of the grid region of the second point cloud and the position influence coefficient.

[0022] The method for constructing a three-dimensional model of a digitalized underground tunneling working face in a coal mine according to the present application comprises the following steps:

[0023] For each grid region, the difference between the vibration influence degrees of the grid region in two adjacent second point clouds is calculated to obtain an adjacent vibration influence degree difference.

[0024] The vibration trend coefficient of the grid region is determined according to the difference between the adjacent vibration influence degree differences.

[0025] The method for constructing a three-dimensional model of a digitalized underground tunneling working face in a coal mine according to the present application comprises the following steps:

[0026] For each grid region, the data reliability of the grid region is determined according to the difference between the vibration trend coefficients of the grid region and each adjacent grid region.

[0027] The data accuracy of the grid region is determined according to the data reliability and the vibration trend coefficient of the grid region.

[0028] The method for constructing a three-dimensional model of a digitalized underground tunneling working face in a coal mine according to the present application comprises the following steps:

[0029] The overall vibration influence degree of the grid region is determined according to the data reliability and the vibration trend coefficient of the grid region.

[0030] determine a data accuracy of the grid region according to the overall vibration influence degree of the grid region.

[0031] According to the data accuracy of each grid region, the first point cloud and the second point cloud are filtered.

[0032] According to the data accuracy of each grid region, a filtering weight of each grid region is determined.

[0033] According to the filtering weight of each grid region, a basic filtering parameter is adjusted to obtain an adjusted filtering parameter of each grid region.

[0034] According to the adjusted filtering parameter of each grid region, the point cloud data in the grid region is filtered for the first point cloud and the second point cloud.

[0035] The application provides a digital coal mine underground tunneling working face three-dimensional model construction system, the system comprises a memory and a processor, the memory is used for storing executable program code, the processor is used for calling and running the executable program code from the memory to realize the digital coal mine underground tunneling working face three-dimensional model construction method provided by the application.

[0036] The application has the following beneficial effects: the first point cloud of the tunneling working face after completing a section of cutting operation and before starting the supporting operation, and a plurality of second point clouds of the tunneling working face in the next section of cutting operation after completing the supporting operation are subjected to the same grid division, the grid regions of each second point cloud are compared with the corresponding grid regions of the first point cloud, the vibration influence degree of each grid region of each second point cloud is determined, the vibration trend coefficient of each grid region is determined according to the change of the vibration influence degree of each grid region in each second point cloud, the data accuracy of each grid region is determined according to the vibration trend coefficient of each grid region, the point cloud data of the tunneling working face is compared and analyzed to realize the vibration performance, thereby quantifying the data accuracy of each grid region, and the point cloud data of the tunneling working face can be accurately filtered according to the data accuracy of each grid region, the accuracy of the three-dimensional modeling of the coal mine underground tunneling working face is improved, and the risk identification and judgment ability of the three-dimensional model in the coal mine underground tunneling process is improved. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0038] Figure 1 A flowchart of a digital coal mine underground tunneling working face three-dimensional model construction method provided by an embodiment of the present application;

[0039] Figure 2 A flowchart of determining the vibration influence degree of the grid area of the second point cloud provided by an embodiment of the present application;

[0040] Figure 3 A flowchart of determining the vibration performance difference degree of the grid area of the second point cloud provided by an embodiment of the present application;

[0041] Figure 4 A flowchart of determining the vibration influence degree of the grid area of the second point cloud in combination with the relative position relationship provided by an embodiment of the present application;

[0042] Figure 5 A structural block diagram of a digital coal mine underground tunneling working face three-dimensional model construction system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purposes, the specific embodiments, structures, features and effects of the digital coal mine underground tunneling working face three-dimensional model construction method and system according to the present application are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0044] 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 the present application belongs.

[0045] The specific scheme of the digital coal mine underground tunneling working face three-dimensional model construction method and system provided by the present application is specifically described below with reference to the drawings.

[0046] Please refer to Figure 1 which shows a flowchart of a digital coal mine underground tunneling working face three-dimensional model construction method provided by an embodiment of the present application, including the following steps:

[0047] Step 101, the first point cloud of the excavation face before the support operation starts after completing a section of cutting operation, and a plurality of second point clouds of the excavation face during the next section of cutting operation after completing the support operation are subjected to the same grid division.

[0048] Wherein, cutting and supporting are two alternating works in the process of underground excavation in coal mines. After the working face is excavated for a certain distance, cutting coal needs to be stopped for support. The first point cloud refers to the point cloud data of the excavation face before the support operation starts after completing a section of cutting operation. The second point cloud refers to the point cloud data of the excavation face during the next section of cutting operation after completing the support operation. After the support equipment completes the support operation of the working face, scanning is performed from the start of the next section of cutting operation to capture the point cloud of the working face under the continuous influence of the vibration load of the roadheader, and a set of second point clouds is obtained. The support equipment can be an anchor drill or a hydraulic support, etc. The scanning points, resolution and coordinate system during scanning of the first point cloud and each second point cloud remain the same. The first point cloud and each second point cloud are divided into the same number of uniform grid regions.

[0049] In one embodiment, an intrinsically safe three-dimensional laser scanner conforming to the coal mine explosion-proof standard can be used to scan the excavation face to obtain the point cloud data of the excavation face. The scanning range of the intrinsically safe three-dimensional laser scanner needs to cover the whole space of the excavation face. According to the "narrow and long" spatial characteristics of the excavation face, the scanning points are planned, and at least three global coordinate reference points are selected, for example, the roadway roof anchor 5 meters behind the working face, and the permanent support structure on the two sides.

[0050] Step 102, comparing the grid regions of each second point cloud with the corresponding grid regions of the first point cloud to determine the vibration influence degree of each grid region of each second point cloud.

[0051] Wherein, the vibration influence degree is used to measure the degree of vibration influence on the grid region in the second point cloud.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] Step 104: Determine the data accuracy of each grid area based on the vibration trend coefficient of each grid area.

[0056] Data accuracy is used to measure the accuracy of point cloud data within a grid area.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] In one embodiment, steps 101 to 105 are performed for each cutting operation, so as to analyze the vibration performance according to the point cloud data before and after each support, and then filter the point cloud data and perform three-dimensional modeling, thereby improving the accuracy of three-dimensional modeling.

[0061] In the above method for constructing a three-dimensional model of a digitalized coal mine underground excavation face, the first point cloud of the excavation face before support operation starts after a cutting operation is completed, and the plurality of second point clouds of the excavation face during a cutting operation of the next section after the support operation is completed are subjected to the same grid division, the grid regions of each second point cloud are compared with the corresponding grid regions of the first point cloud, the vibration influence degree of each grid region of each second point cloud is determined, the vibration trend coefficient of each grid region is determined according to the change of the vibration influence degree of each grid region in each second point cloud, the data accuracy of each grid region is determined according to the vibration trend coefficient of each grid region, the vibration performance is analyzed by comparing the point cloud data of the excavation face before and after the support, thereby quantifying the data accuracy of each grid region, and the point cloud data of the excavation face can be accurately filtered according to the data accuracy of each grid region, thereby improving the accuracy of three-dimensional modeling of the coal mine underground excavation face, and improving the risk identification and judgment ability of the three-dimensional model during the coal mine underground excavation process.

[0062] In one embodiment, comparing the grid regions of each second point cloud with the corresponding grid regions of the first point cloud to determine the vibration influence degree of each grid region of each second point cloud comprises: determining the vibration influence degree of each grid region of each second point cloud according to the relative position of each grid region of each second point cloud relative to the excavation position corresponding to the second point cloud, and the position difference and point number difference of the corresponding grid region of the first point cloud.

[0063] The excavation position corresponding to the second point cloud refers to the position being excavated when the second point cloud is scanned. The point number refers to the number of points in the point cloud data in the grid region.

[0064] In one embodiment, the excavation position corresponding to the second point cloud can be the position of the excavation head when the second point cloud is scanned.

[0065] In one embodiment, the position of the grid region can be the characteristic value coordinates of the grid region. The centroid coordinates of the grid region can be calculated according to the coordinates of each point in the grid region, as the characteristic value coordinates of the grid region.

[0066] According to the relative position of each grid region of each second point cloud relative to the corresponding tunneling position of the second point cloud, and the difference in position and the difference in number of points of the corresponding grid region of the first point cloud, the degree of vibration influence of each grid region of each second point cloud can be accurately determined.

[0067] In one embodiment, referring to Figure 2 According to the relative position of each grid region of each second point cloud relative to the corresponding tunneling position of the second point cloud, and the difference in position and the difference in number of points of the corresponding grid region of the first point cloud, the degree of vibration influence of each grid region of each second point cloud is determined, including the following steps:

[0068] Step 201, according to the difference in position and the difference in number of points of each grid region of each second point cloud and the corresponding grid region of the first point cloud, the vibration performance difference degree of each grid region of each second point cloud is determined.

[0069] Wherein, the vibration performance difference degree is used to measure the difference degree of the grid region of the second point cloud after being affected by the vibration relative to the grid region in the point cloud data before the support operation (i.e. the corresponding grid region of the first point cloud).

[0070] Step 202, according to the vibration performance difference degree of each grid region of each second point cloud and the relative position relative to the corresponding tunneling position of the second point cloud, the degree of vibration influence of each grid region of each second point cloud is determined.

[0071] In one embodiment, according to the relative position of the grid region of the second point cloud relative to the corresponding tunneling position of the second point cloud, the position influence coefficient of the grid region of the second point cloud is determined, and then according to the vibration performance difference degree of the grid region of the second point cloud and the position influence coefficient, the degree of vibration influence of the grid region of the second point cloud is determined.

[0072] Wherein, the position influence coefficient is used to measure the significant degree of the position of the grid region of the second point cloud being affected by the vibration.

[0073] 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.

[0074] 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:

[0075] 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.

[0076] 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.

[0077] 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.

[0078] In one embodiment, the maximum point cloud density difference in the grid region can be determined according to the following formula:

[0079]

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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:

[0085]

[0086] 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 characteristic value coordinates of the grid region. represent the first characteristic value coordinates of the grid region of the first point cloud.

[0087] It can be understood that reflects the spatial position change of the first grid region before and after supporting, that is, the local slight displacement caused by vibration. The greater the value, the greater the overall spatial deformation degree of the grid region caused by vibration, and therefore the greater the vibration performance difference . The greater the vibration performance difference , the worse the structure of the tunneling working face on the grid region itself, and the more susceptible to vibration.

[0088] In the above embodiment, since the tunneling process in the coal mine underground is an alternating process of tunneling and supporting, the cutting operation of the tunneling machine will cause the tunneling working face to vibrate, resulting in vibration of the tunneling working face, which is manifested as displacement fluctuation of the coal wall. During the tunneling operation, the relevant data obtained will be disturbed by vibration, water mist, dust and other factors, thereby making it difficult to guarantee the accuracy of the data. Before the supporting operation is implemented, the tunneling working face is in a non-vibrating state, and after a certain period of static state, the interference of water mist, dust and other factors on data acquisition is greatly reduced, so the static data obtained in this stage has relatively high accuracy. In addition, the supporting operation can effectively improve the stability of the coal and rock, and enhance the ability of the coal and rock to resist vibration. Therefore, the position difference and density difference of the point clouds before and after supporting can accurately quantify the vibration performance difference degree, and the greater the vibration performance difference degree, the worse the integrity of the coal seam structure and the higher the degree of loosening, the more sensitive to vibration, that is, more susceptible to vibration and structural instability risk.

[0089] In one embodiment, referring to Figure 4 , step 202 determines the vibration influence degree of each grid region of each second point cloud according to the vibration performance difference degree of each grid region of each second point cloud and the relative position relative to the tunneling position corresponding to the second point cloud, including the following steps:

[0090] Step 2021, for each grid region of each second point cloud, the position influence coefficient of the grid region of the second point cloud is determined according to 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.

[0091] ​​In one embodiment, the position coordinates (global coordinates) of the current heading of the tunneling can be obtained by the positioning function of the scanner or the positioning system of the tunneling machine, as the position coordinates of the tunneling position corresponding to the second point cloud obtained by the current scanning.

[0092] It can be understood that the tunneling machine is the main source of vibration, and the vibration generated by the cutting operation will propagate from the heading to the surrounding space, and the energy will gradually attenuate during the propagation process. Therefore, the closer the coal block is to the heading, the stronger the vibration energy it directly bears, and the more obvious the vibration performance is; while in the area far away from the heading, the vibration energy has been greatly attenuated, and the vibration influence is weakened. Therefore, the grid areas are in different positions, and the vibration performance is different, the closer the grid area is to the current tunneling position, the more obvious the vibration performance is. Therefore, the position influence coefficient of the grid area of the second point cloud can be determined according to the relative position relationship between the grid area of the second point cloud and the tunneling position, and then the vibration influence degree of the grid area of the second point cloud is obtained based on the position influence coefficient adjusting the vibration performance difference.

[0093] The position influence coefficient of the grid area of the second point cloud is negatively correlated with the difference between the position coordinates of the grid area of the second point cloud and the position coordinates of the tunneling position corresponding to the second point cloud.

[0094] In one embodiment, the Euclidean distance between the position coordinates of the grid area of the second point cloud and the position coordinates of the tunneling position corresponding to the second point cloud can be taken as the difference between the position coordinates of the grid area of the second point cloud and the position coordinates of the tunneling position corresponding to the second point cloud.

[0095] In one embodiment, the position influence coefficient of the grid area of the second point cloud can be determined according to the following formula:

[0096]

[0097] wherein, represents the position influence coefficient of the i-th grid area of the j-th second point cloud. represents the eigenvalue coordinates of the i-th grid area of the j-th second point cloud. represents the position coordinates of the tunneling position corresponding to the j-th second point cloud, i.e. the position coordinates of the position where the heading is located when the j-th second point cloud is scanned. represents the Euclidean distance between the position coordinates of the i-th grid area of the j-th second point cloud and the position coordinates of the tunneling position corresponding to the second point cloud.

[0098] It can be understood that,​​​​​​​​ 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.

[0099] 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.

[0100] 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.

[0101] 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:

[0102]

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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:

[0109]

[0110] 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.

[0111] 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.

[0112] In the above embodiment, the difference between the vibration influence degrees of the grid region in the second point cloud of the adjacent two scans is calculated to obtain the adjacent vibration influence degree difference, and the vibration trend coefficient of the grid region can be accurately determined according to the difference between the adjacent adjacent vibration influence degree differences.

[0113] In one embodiment, the data accuracy of each grid region is determined according to the vibration trend coefficient of each grid region, including: for each grid region respectively, the data reliability of the grid region is determined according to the difference between the vibration trend coefficients of the grid region and each adjacent grid region; and the data accuracy of the grid region is determined according to the data reliability and the vibration trend coefficient of the grid region.

[0114] It can be understood that, since the adjacent grid regions may belong to the same coal seam structure, they should have similar vibration responses, and the data reliability of a grid region can be calculated by comparing the vibration trend coefficients of the adjacent grid regions, the better the vibration consistency performance of the grid region and its adjacent grid regions, the higher the data reliability of the grid region, and the higher the data accuracy accordingly.

[0115] In one embodiment, the difference between the vibration trend coefficients of the grid region and the adjacent grid regions can be taken as the difference between the vibration trend coefficients of the grid region and the adjacent grid regions.

[0116] In one embodiment, the difference between the vibration trend coefficients of the grid region and each adjacent grid region can be averaged to obtain the vibration trend coefficient difference average, and then the data reliability of the grid region is determined according to the vibration trend coefficient difference average. The data reliability of the grid region is negatively correlated with the vibration trend coefficient difference average.

[0117] In one embodiment, the data reliability of the grid region can be determined according to the following formula:

[0118]

[0119] wherein, represents the data reliability of the i-th grid region. represents the vibration trend coefficient of the i-th grid region. represents the vibration trend coefficient of the j-th adjacent grid region of the i-th grid region. represents the difference between the vibration trend coefficients of the i-th grid region and the j-th adjacent grid region. represents the vibration trend coefficient of the i-th grid region. represents the vibration trend coefficient of the j-th adjacent grid region of the i-th grid region. represents the difference between the vibration trend coefficients of the i-th grid region and the j-th adjacent grid region. represents the total number of adjacent grid regions of the i-th grid region, and the j represents the j-th adjacent grid region of the i-th grid region. represents the total number of adjacent grid regions of the i-th grid region, and the j represents the j-th adjacent grid region of the i-th grid region. represents the total number of adjacent grid regions of the i-th grid region, and the j represents the j-th adjacent grid region of the i-th grid region. represents the total number of adjacent grid regions of the i-th grid region, and the j represents the j-th adjacent grid region of the i-th grid region. represents the total number of adjacent grid regions of the i-th grid region, and the j represents the j-th adjacent grid region of the i-th grid region. The adjacent grid area of the kth grid area refers to the eight adjacent grid areas of the kth grid area. The adjacent grid area of the kth grid area refers to the eight adjacent grid areas of the kth grid area.

[0120] It can be understood that The smaller the value is, the better the vibration consistency performance of the grid area and its adjacent grid area is, and the higher the data reliability of the grid area is.

[0121] In the above embodiment, the data reliability of the grid area is determined according to the difference between the vibration trend coefficients of the grid area and each adjacent grid area, and then the data accuracy of the grid area is accurately determined according to the data reliability of the grid area and the vibration trend coefficient.

[0122] In one embodiment, the data accuracy of the grid area is determined according to the data reliability of the grid area and the vibration trend coefficient, including: determining the overall vibration influence degree of the grid area according to the data reliability of the grid area and the vibration trend coefficient; determining the data accuracy of the grid area according to the overall vibration influence degree of the grid area.

[0123] In one embodiment, the overall vibration influence degree of the grid area is positively correlated with the data reliability and the vibration trend coefficient of the grid area.

[0124] In one embodiment, the overall vibration influence degree of the grid area can be determined according to the following formula:

[0125]

[0126] Wherein, The overall vibration influence degree of the kth grid area is represented by k. The data reliability of the kth grid area is represented by k. The vibration trend coefficient of the kth grid area is represented by k.

[0127] The greater the overall vibration influence degree is, the higher the degree of influence of the vibration on the kth grid area is. The greater the overall vibration influence degree is, the higher the degree of influence of the vibration on the kth grid area is.

[0128] The greater the vibration influence degree is, the higher the degree of vibration interference on the grid area is, and the lower the accuracy of the three-dimensional data is. Therefore, the data accuracy of the grid area is negatively correlated with the overall vibration influence degree of the grid area.

[0129] In one embodiment, the data accuracy of the grid area can be determined according to the following formula:

[0130] ​​​​​

[0131] wherein, represents the data accuracy of the first grid region. represents the overall vibration influence degree of the first grid region.

[0132] In the above embodiment, the overall vibration influence degree of the grid region can be accurately determined according to the data reliability and the vibration trend coefficient of the grid region. Since the greater the vibration influence degree, the higher the degree of the grid region being interfered by vibration, and the lower the accuracy of the three-dimensional data, the data accuracy of the grid region can be accurately determined according to the overall vibration influence degree of the grid region.

[0133] In one embodiment, the first point cloud and the second point cloud are filtered according to the data accuracy of each grid region, including: determining the filtering weight of each grid region according to the data accuracy of each grid region; adjusting the basic filtering parameters according to the filtering weight of each grid region to obtain the adjusted filtering parameters of each grid region; and filtering the point cloud data in the grid region according to the adjusted filtering parameters of each grid region for the first point cloud and the second point cloud.

[0134] It can be understood that the higher the data accuracy of the grid region, the smaller the interference of the grid region by vibration, and the more reliable the data, and the more original details should be retained, and the lower the filtering strength should be. Therefore, the filtering weight of the grid region is negatively correlated with the data accuracy of the grid region. That is, the higher the data accuracy of the grid region, the lower the filtering strength, that is, the smaller the filtering weight, so as to retain more original details; the lower the data accuracy of the grid region, the higher the filtering strength, that is, the larger the filtering weight, so as to suppress noise.

[0135] In one embodiment, the filtering weight of the grid region can be determined according to the following formula:

[0136]

[0137] wherein, represents the filtering weight of the first grid region. represents the data accuracy of the first grid region.

[0138] 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, respectively, to obtain the adjusted filtering parameters (the adjusted first filtering parameter and the adjusted second filtering parameter) of each grid region. The formula is as follows:

[0139]

[0140]

[0141] wherein, represents the adjusted first filter parameter of the th grid region. represents the first base filter parameter. represents the filter weight of the th grid region. represents the adjusted second filter parameter of the th grid region. represents the second base filter parameter.

[0142] In the above embodiments, the filter weight of each grid region is determined according to the data accuracy of each grid region, the base filter parameter is adjusted according to the filter weight of each grid region, and the adjusted filter parameter of each grid region is obtained, thereby realizing the adaptive adjustment of the filter parameter. 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 filter parameter of each grid region, which can improve the accuracy of the point cloud data and further improve the accuracy of the three-dimensional model constructed according to the point cloud data.

[0143] It should be understood that, although each step in the flowchart involved in each of the above embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each of the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0144] Referring to Figure 5The application provides a kind of digital coal mine underground tunneling working face three-dimensional model construction system, the system includes memory and processor;Memory is used to store executable program code (referred to as executable code);Processor is used to call and run executable program code from memory, to realize the following steps: the first point cloud of tunneling working face after completing a section cutting operation and before supporting operation starts, and multiple second point clouds of tunneling working face during the next section cutting operation after completing supporting operation are subjected to the same grid division;The grid area of each second point cloud is compared with the corresponding grid area of the first point cloud, and the vibration influence degree of each grid area of each second point cloud is determined;According to the change of the vibration influence degree of each grid area in each second point cloud, the vibration trend coefficient of each grid area is determined;According to the vibration trend coefficient of each grid area, the data accuracy of each grid area is determined;According to the data accuracy of each grid area, the first point cloud and the second point cloud are filtered, and three-dimensional modeling is carried out according to the filtered point cloud.

[0145] In one embodiment, comparing the grid area of each second point cloud with the corresponding grid area of the first point cloud to determine the vibration influence degree of each grid area of each second point cloud comprises: determining the vibration influence degree of each grid area of each second point cloud according to the relative position of each grid area of each second point cloud relative to the tunneling position corresponding to the second point cloud, and the position difference and point number difference of the corresponding grid area of the first point cloud.

[0146] In one embodiment, according to the relative position of each grid area of each second point cloud relative to the tunneling position corresponding to the second point cloud, and the position difference and point number difference of the corresponding grid area of the first point cloud, the vibration influence degree of each grid area of each second point cloud is determined, comprising: determining the vibration performance difference degree of each grid area of each second point cloud according to the position difference and point number difference of the corresponding grid area of the first point cloud;According to the vibration performance difference degree of each grid area of each second point cloud and the relative position relative to the tunneling position corresponding to the second point cloud, the vibration influence degree of each grid area of each second point cloud is determined.

[0147] In one embodiment, the vibration performance difference degree of each grid region of each second point cloud is determined according to the position difference and the point number difference of each grid region of each second point cloud and the corresponding grid region of the first point cloud, including: respectively for each grid region, determining the maximum point cloud density difference of the grid region according to the maximum value in the difference between the number of points of the grid region in each second point cloud and in the first point cloud; respectively for each grid region of each second point cloud, determining the vibration performance difference degree of the grid region of the second point cloud according to 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 maximum point cloud density difference of the grid region.

[0148] In one embodiment, the vibration influence degree of each grid region of each second point cloud is determined according to the vibration performance difference degree of each grid region of each second point cloud and the relative position relative to the tunneling position corresponding to the second point cloud, including: respectively for each grid region of each second point cloud, determining the position influence coefficient of the grid region of the second point cloud according to 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; determining the vibration influence degree of the grid region of the second point cloud according to the vibration performance difference degree of the grid region of the second point cloud and the position influence coefficient.

[0149] In one embodiment, the vibration trend coefficient of each grid region is determined according to the change of the vibration influence degree of each grid region in each second point cloud, including: respectively for each grid region, calculating the difference between the vibration influence degrees of the grid region in the second point clouds of adjacent two times of scanning to obtain adjacent vibration influence degree differences; determining the vibration trend coefficient of the grid region according to the difference between the adjacent adjacent vibration influence degree differences.

[0150] In one embodiment, the data accuracy of each grid region is determined according to the vibration trend coefficient of each grid region, including: respectively for each grid region, determining the data reliability of the grid region according to the difference between the vibration trend coefficients of the grid region and each adjacent grid region; determining the data accuracy of the grid region according to the data reliability and the vibration trend coefficient of the grid region.

[0151] In one embodiment, the data accuracy of each grid region is determined according to the data reliability and the vibration trend coefficient of the grid region, including: determining the overall vibration influence degree of the grid region according to the data reliability and the vibration trend coefficient of the grid region; determining the data accuracy of the grid region according to the overall vibration influence degree of the grid region.

[0152] In one embodiment, filtering the first point cloud and the second point cloud according to the data accuracy of each grid region comprises: determining a filtering weight of each grid region according to the data accuracy of each grid region; adjusting a basic filtering parameter according to the filtering weight of each grid region to obtain an adjusted filtering parameter of each grid region; and filtering point cloud data in each grid region according to the adjusted filtering parameter of each grid region for the first point cloud and the second point cloud.

[0153] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0154] The above embodiments only express several implementation manners of the present disclosure, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the present patent. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure.

[0155] It should be noted that: the above sequence of the embodiments of the present disclosure is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0156] Each embodiment in the specification is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other. Each embodiment mainly describes the differences from other embodiments.

Claims

1. A method for constructing a three-dimensional model of a digitized underground coal mine tunneling working face, characterized in that, The method comprises: The first point cloud of the tunneling working face after a cutting operation is completed and before a supporting operation starts, and a plurality of second point clouds of the tunneling working face during a next cutting operation after the supporting operation is completed are subjected to the same grid division; The grid regions of each of the second point clouds are compared with the corresponding grid regions of the first point cloud to determine the vibration influence degree of each grid region of each second point cloud; According to the change of the vibration influence degree of each grid region in each of the second point clouds, a vibration trend coefficient of each grid region is determined; According to the vibration trend coefficients of the grid regions, data accuracy of the grid regions is determined; According to the data accuracy of the grid regions, the first point cloud and the second point clouds are filtered, and three-dimensional modeling is performed according to the filtered point clouds; The comparison of the grid regions of each of the second point clouds with the corresponding grid regions of the first point cloud to determine the vibration influence degree of each grid region of each second point cloud comprises: According to the relative position of each grid region of each second point cloud relative to the tunneling position corresponding to the second point cloud, and the position difference and point number difference of the corresponding grid region of the first point cloud, the vibration influence degree of each grid region of each second point cloud is determined; The comparison of the grid regions of each of the second point clouds with the corresponding grid regions of the first point cloud to determine the vibration influence degree of each grid region of each second point cloud comprises: According to the position difference and point number difference of each grid region of each second point cloud and the corresponding grid region of the first point cloud, a vibration performance difference degree of each grid region of each second point cloud is determined; According to the vibration performance difference degree of each grid region of each second point cloud and the relative position relative to the tunneling position corresponding to the second point cloud, the vibration influence degree of each grid region of each second point cloud is determined; The determination of the vibration trend coefficient of each grid region according to the change of the vibration influence degree of each grid region in each of the second point clouds comprises: For each grid region, the difference between the vibration influence degrees of the grid region in the second point clouds of adjacent two times of scanning is calculated to obtain adjacent vibration influence degree differences; According to the difference between adjacent adjacent vibration influence degree differences, the vibration trend coefficient of the grid region is determined.

2. The method of claim 1, wherein, The determination of the vibration performance difference degree of each grid region of each second point cloud according to the position difference and point number difference of each grid region of each second point cloud and the corresponding grid region of the first point cloud comprises: For each grid region, according to the maximum value in the difference between the number of points of the grid region in each second point cloud and in the first point cloud, a maximum point cloud density difference of the grid region is determined; respectively for each grid region of each second point cloud, determine a vibration performance difference degree of the grid region of the second point cloud according to a difference between position coordinates of the grid region of the second point cloud and corresponding grid region of the first point cloud, and the maximum point cloud density difference of the grid region.

3. The method of claim 1, wherein, The determining, according to the vibration performance difference degree of each grid region of each second point cloud and a relative position relative to a corresponding tunneling position of the second point cloud, of a vibration influence degree of each grid region of each second point cloud comprises: respectively for each grid region of each second point cloud, determine a position influence coefficient of the grid region of the second point cloud according to a difference between position coordinates of the grid region of the second point cloud and a corresponding tunneling position of the second point cloud; determine a vibration influence degree of the grid region of the second point cloud according to the vibration performance difference degree of the grid region of the second point cloud and the position influence coefficient.

4. The method of claim 1, wherein, The determining, according to the vibration trend coefficient of each grid region, of a data accuracy of each grid region comprises: respectively for each grid region, determine a data reliability of the grid region according to a difference between the vibration trend coefficient of the grid region and each adjacent grid region; determine a data accuracy of the grid region according to the data reliability and the vibration trend coefficient of the grid region.

5. The method of claim 4, wherein, The determining, according to the data reliability and the vibration trend coefficient of the grid region, of a data accuracy of the grid region comprises: determine an overall vibration influence degree of the grid region according to the data reliability and the vibration trend coefficient of the grid region; determine a data accuracy of the grid region according to the overall vibration influence degree of the grid region.

6. The method of claim 1 to 5, characterized in that, The filtering, according to the data accuracy of each grid region, of the first point cloud and the second point cloud comprises: determine a filtering weight of each grid region according to the data accuracy of each grid region; adjust a basic filtering parameter according to the filtering weight of each grid region to obtain an adjusted filtering parameter of each grid region; for the first point cloud and the second point cloud, filter point cloud data in each grid region according to the adjusted filtering parameter of the grid region.

7. A system for constructing a three-dimensional model of a digitized underground coal mine tunneling working face, characterized in that, The system comprises 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 realize the digital coal mine underground tunneling working face three-dimensional model construction method in any one of claims 1 to 6.

Images