Mine laneway deformation monitoring method and system

Abstract

The invention relates to the technical field of deformation monitoring, in particular to a mine laneway deformation monitoring method and system.The tolerance of updating point cloud data missing areas in each monitoring area in the model updating process is determined by analyzing the number of intersections contained in each monitoring area in a mine laneway three-dimensional model; screening the successfully obtained updated point cloud data; then, for the successfully obtained updated point cloud data, determining the validity degree of the updated point cloud data by analyzing the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained last time; on the basis of the relevance of the point cloud data between the monitoring area and the adjacent monitoring area, determining whether the acquisition position of the monitoring area needs to deviate in the updating process of the three-dimensional model and determining the corresponding deviation direction and deviation degree; therefore, new updated three-dimensional point cloud data are acquired, and a more accurate monitoring result is obtained during subsequent monitoring of mine laneway deformation.

Description

Technical Field

[0001] The present invention relates to the technical field of deformation monitoring, and particularly relates to a method and system for monitoring the deformation of mine roadways. Background Art

[0002] At present, the deformation monitoring of mine roadways is changing from traditional manual detection to new technologies with high precision and real-time performance. Traditional monitoring methods have problems such as low data collection efficiency, insufficient analysis ability, and imperfect real-time response mechanism, making it difficult to meet the needs of modern mine safety. Emerging technologies such as GNSS, laser ranging, 3D laser scanning, fiber optic sensing, and the Internet of Things have been widely used in the deformation monitoring of mine roadways, with advantages such as high precision, real-time performance, and high degree of automation, providing a favorable guarantee for mine safety.

[0003] Currently, when monitoring the deformation of mine roadways based on emerging technologies, multiple radars are usually installed in the mine roadways to obtain high-precision monitoring data. Due to the complex geological sections in mines, a large amount of fog and dust is usually generated after the excavation face advances, and the original monitoring points are often damaged. Therefore, monitoring blind spots often occur in complex geological sections, resulting in abnormal updating of the 3D model of the mine roadway during the subsequent update process, and thus abnormal deformation monitoring of the mine roadway. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method and system for monitoring the deformation of mine roadways.

[0005] According to the first aspect of the embodiments of the present invention, a method for monitoring the deformation of mine roadways is provided, and the technical solution adopted is specifically as follows: Collect the point cloud data of multiple monitoring areas in the mine roadway to obtain the overall 3D model of the mine roadway; Based on the overall 3D model, count the number of intersections included in the monitoring area and determine the tolerance of the missing area in the monitoring area; Re-collect the updated point cloud data of the monitoring area to obtain the number and location of the missing areas in the monitoring area, and combine with the tolerance to judge whether the acquisition of the updated point cloud data of the monitoring area is successful; If so, analyze the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring to obtain the effectiveness of the updated point cloud data of the monitoring area; Based on the effectiveness, analyze the correlation between the updated point cloud data of the monitoring area and the adjacent monitoring areas to judge whether the updated point cloud data of the monitoring area is effective; If not, analyze the acquisition position offset of the monitoring area based on the updated point cloud data of the monitoring area and the position coordinates of the point cloud data obtained in the previous monitoring, offset the acquisition position, and re-acquire new updated point cloud data until it is valid; Complete the update of the overall three-dimensional model based on the valid updated point cloud data.

[0006] In some embodiments of the present invention, re-acquire the updated point cloud data of the monitoring area, obtain the number and positions of the missing areas in the monitoring area, and combine the tolerance to determine whether the acquisition of the updated point cloud data of the monitoring area is successful, including: Re-acquire the updated point cloud data of the monitoring area at the previous monitoring position; Perform grid processing on the updated point cloud data, and use the connected component labeling algorithm to obtain the number and positions of the missing areas in the monitoring area; Determine whether the number of the missing areas is less than the tolerance; If not, the acquisition of the updated point cloud data of the monitoring area fails; If so, based on the missing positions, determine whether the missing areas are at intersections; If so, the acquisition of the updated point cloud data of the monitoring area fails; If not, the acquisition of the updated point cloud data of the monitoring area is successful.

[0007] In some embodiments of the present invention, if the acquisition of the updated point cloud data of the monitoring area fails, re-acquire the updated point cloud data of the monitoring area.

[0008] In some embodiments of the present invention, analyze the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring to obtain the effectiveness of the updated point cloud data of the monitoring area, including: Calculate the quantity difference between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring to obtain the degree of quantity difference; Obtain the matching point cloud data and non-matching point cloud data between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring, and analyze the dispersion degree of the non-matching point cloud data to obtain the matching degree between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring; According to the degree of quantity difference and the matching degree, obtain the effectiveness of the updated point cloud data of the monitoring area.

[0009] In some embodiments of the present invention, based on the effectiveness, analyze the relevance of the updated point cloud data between the monitoring area and adjacent monitoring areas, and determine whether the updated point cloud data of the monitoring area is valid, including: Mark the monitoring area where the point cloud data coincides with the monitoring area as the monitoring area to be matched; Analyze the change relationship between the updated point cloud data of the monitoring area to be matched and the point cloud data obtained in the previous monitoring to obtain the effectiveness of the updated point cloud data of the monitoring area to be matched; Analyze the magnitude correlation between the effectiveness corresponding to the monitoring area and the monitoring area to be matched, and determine whether the updated point cloud data of the monitoring area is initially invalid; If not, the updated point cloud data of the monitoring area is valid; If so, based on the point cloud data obtained during the measurement in the monitoring area, obtain the number of matching point clouds between the monitoring area and the monitoring area to be matched; And based on the updated point cloud data obtained during the measurement in the monitoring area to be matched, obtain the number of updated matching point clouds between the monitoring area and the monitoring area to be matched; Analyze the difference degree between the number of matching point clouds and the number of updated matching point clouds, and determine whether the updated point cloud data of the monitoring area is valid; If so, the updated point cloud data of the monitoring area is valid; If not, the updated point cloud data of the monitoring area is invalid.

[0010] In some embodiments of the present invention, analyzing the magnitude correlation between the effectiveness corresponding to the monitoring area and the monitoring area to be matched, and determining whether the updated point cloud data of the monitoring area is initially invalid includes: Set an effectiveness threshold, and determine whether the effectiveness corresponding to the monitoring area and the monitoring area to be matched are both less than the effectiveness threshold; If so, the updated point cloud data of the monitoring area is valid; If not, the updated point cloud data of the monitoring area is initially invalid.

[0011] In some embodiments of the present invention, analyzing the difference degree between the number of matching point clouds and the number of updated matching point clouds, and determining whether the updated point cloud data of the monitoring area is valid includes: Set a difference threshold, and determine whether the difference between the number of matching point clouds and the number of updated matching point clouds is less than the difference threshold; If so, the updated point cloud data of the monitoring area is valid; If not, the updated point cloud data of the monitoring area is invalid.

[0012] In some embodiments of the present invention, based on the position coordinates of the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring, analyze the offset situation of the acquisition position of the monitoring area, and offset the acquisition position, including: Calculate the mean of the position coordinates of the matching point cloud data in the point cloud data of the monitoring area during the last measurement process, and denote it as the first feature position; Calculate the mean of the position coordinates of the updated point cloud data of the monitoring area, and denote it as the second feature position; Calculate the difference between the first feature position and the second feature position, and combine it with the preset displacement of the acquisition position to obtain the offset distance; Based on the acquisition position, obtain the acquisition center line; Denote the feature position corresponding to the maximum value of the matching point cloud quantity and the updated matching point cloud quantity as the offset feature coordinate; According to the direction of the offset feature coordinate relative to the current acquisition position, use it as the offset direction; According to the offset distance and the offset direction, offset the acquisition position on the acquisition center line.

[0013] According to the second aspect of the embodiments of the present invention, a mine roadway deformation monitoring system is provided, including: a memory and a processor, wherein: The memory is used to store program codes; The processor is used to read the program codes stored in the memory and execute the method described in the first aspect of the embodiments of the present invention.

[0014] In some embodiments of the present invention, the processor includes: A three-dimensional model construction module, which is used to collect the point cloud data of multiple monitoring areas in the mine roadway to obtain an overall three-dimensional model of the mine roadway; A tolerance analysis module, which is used to count the number of intersections included in the monitoring area based on the overall three-dimensional model and determine the tolerance of the missing area of the monitoring area; An updated point cloud data acquisition module, which is used to re-collect the updated point cloud data of the monitoring area, obtain the number and position of the missing areas of the monitoring area, and combine the tolerance to judge whether the acquisition of the updated point cloud data of the monitoring area is successful; An updated point cloud data validity judgment module, which is used to analyze the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained from the last monitoring when the acquisition of the updated point cloud data is successful, to obtain the validity degree of the updated point cloud data of the monitoring area; and based on the validity degree, analyze the relevance of the updated point cloud data between the monitoring area and adjacent monitoring areas to judge whether the updated point cloud data of the monitoring area is valid; Update point cloud data re - acquisition module, which is used to analyze the acquisition position offset of the monitoring area based on the updated point cloud data of the monitoring area and the position coordinates of the point cloud data obtained in the previous monitoring when the updated point cloud data is invalid, and offset the acquisition position; and after the acquisition position is offset, re - acquire new updated point cloud data until it is valid; Three - dimensional model update module, which is used to complete the update of the overall three - dimensional model based on the valid updated point cloud data.

[0015] Compared with the prior art, a mine roadway deformation monitoring method and system provided by the present invention have the following beneficial effects: The present invention determines the missing area tolerance of the updated point cloud data in each monitoring area during the update process by analyzing the degree of intersections included in each monitoring area in the three - dimensional model of the mine roadway; re - acquires the updated point cloud data of the monitoring area, obtains the number and positions of the missing areas in the monitoring area, and combines with the tolerance to judge whether the acquisition of the updated point cloud data in the monitoring area is successful; determines the updated point cloud data that fails to be acquired through the tolerance and directly re - acquires it, saving subsequent computing resources. If the acquisition of the updated point cloud data is successful, then analyze the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring to obtain the effectiveness of the updated point cloud data in the monitoring area; then based on the effectiveness, analyze the correlation between the updated point cloud data of the monitoring area and adjacent monitoring areas to judge whether the updated point cloud data in the monitoring area is valid, eliminating the influence of the operation of the mining machine on the effectiveness of the acquired updated point cloud data. For the updated point cloud data that fails to be acquired or is invalid, analyze the acquisition position offset of the monitoring area based on the position coordinates of the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring, offset the acquisition position, and re - acquire more accurate new updated point cloud data, so as to obtain more accurate monitoring results when monitoring the deformation of the mine roadway subsequently. Through the present invention, accurate and reliable mine roadway deformation information can be obtained, and then maintenance personnel can timely maintain the dangerous areas of the mine roadway, ensuring the progress of the mine roadway excavation work and the safety of the staff in the mine roadway. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a schematic diagram of the basic process of a mine roadway deformation monitoring method provided by an embodiment of the present invention; Figure 2 Schematic diagram of an overall three-dimensional model of a mine roadway provided by an embodiment of the present invention; Figure 3 Schematic diagram of a center line provided by an embodiment of the present invention; Figure 4 Schematic diagram of the display of deformation information on the three-dimensional model of the mine roadway provided by an embodiment of the present invention; Figure 5 Schematic diagram of the basic composition of a mine roadway deformation monitoring system provided by an embodiment of the present invention. Specific embodiments

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the drawings and preferred embodiments to detail the specific embodiments, structures, features and effects of a mine roadway deformation monitoring method and system proposed according to the present invention. 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.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. Terms such as "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the article or device including the element.

[0020] The following specifically describes the specific solution of a mine roadway deformation monitoring method provided by the present invention with reference to the drawings.

[0021] Please refer to Figure 1 , which shows the basic process of a mine roadway deformation monitoring method provided by an embodiment of the present invention.

[0022] As Figure 1 shown, a mine roadway deformation monitoring method provided by an embodiment of the present invention specifically includes: S100: Collect point cloud data of multiple monitoring areas in the mine roadway to obtain an overall three-dimensional model of the mine roadway.

[0023] During the tunneling process of mine roadways, there are various potential dangers. For example, the roof may fall during the operation of drilling with a drilling machine and cleaning loose stones, and there is a risk of roof collapse during blasting operations. Therefore, for the operation safety of the mine and the safety of the staff, a portable radar monitoring system is used to monitor the roadway.

[0024] Install the portable radar monitoring system about 20 cm away from the monitoring area. After completing the system assembly, wiring, and power-on, start the portable radar monitoring system to collect three-dimensional data of the mine roadway in the monitoring area. Collect for about 20 to 30 minutes to obtain the point cloud data of the monitoring area. Then, after performing preprocessing operations such as denoising and resampling on the obtained point cloud data, obtain the point cloud representing the removal of sundries such as pipelines, cables, and human figures inside the mine roadway, and further obtain a point cloud dataset that only retains the roadway wall.

[0025] After the collection in a certain monitoring area is completed, record the collection position of the portable radar monitoring system, and then transfer the portable radar monitoring system to another monitoring area to continue the collection according to the above method. In particular, to ensure the integrity of the subsequent mine roadway modeling, there will be a certain degree of overlap in the point cloud data obtained from different monitoring areas.

[0026] After the collection is completed, input the point cloud data of all monitoring areas into the three-dimensional model construction system for modeling, and then obtain the three-dimensional model of each monitoring area in the mine roadway and the overall three-dimensional model of the mine roadway, as Figure 2 shown.

[0027] After obtaining the complete mine roadway model according to the above steps, during the subsequent deformation monitoring of the mine roadway, the mine roadway model needs to be updated. Specifically, it includes steps S200 to S600.

[0028] S200: Based on the overall three-dimensional model, count the number of intersections in the monitoring area and determine the tolerance of the missing area in the monitoring area.

[0029] Since the geological sections in the mine are relatively complex, a large amount of fog and dust are usually generated after the excavation face advances. Therefore, monitoring blind spots often occur in complex geological sections, resulting in missing areas in data collection, and the tolerance of missing areas varies for different mines. Therefore, first, based on the overall three-dimensional model, count the number of intersections in the monitoring area and determine the tolerance of the missing area in the monitoring area. The specific implementation method is as follows: In the overall three-dimensional model of the mine roadway, count the number of intersections contained in each monitoring area, and record the coordinates of each intersection (represented by the coordinates of the center point at the intersection). Among them, the more intersections a monitoring area contains, the greater the complexity of the corresponding monitoring area. Therefore, in order to render the detailed features more accurately when constructing the three-dimensional model subsequently, the missing area of this monitoring area should be less during the data collection process. Construct the tolerance formula for the missing area of the th monitoring area is: In the formula, represents the tolerance of the missing area of the th monitoring area; represents the number of intersections contained in the th monitoring area; represents the total number of monitoring areas during the previous measurement; represents the total number of intersections contained in all monitoring areas during the previous measurement; represents the number threshold of the missing area (the value can be 3, and it is taken according to experience). represents the total number of intersections contained in all monitoring areas during the previous measurement; represents the number threshold of the missing area (the value can be 3, and it is taken according to experience).

[0030] represents the relative number of intersections contained in the th monitoring area relative to the total number of intersections contained in all monitoring areas. The larger this value is, the more intersections the monitoring area contains, and the greater the complexity of the corresponding monitoring area. Therefore, in order to render the detailed features more accurately when constructing the three-dimensional model subsequently, the missing area of this monitoring area should be less; by weighting the number threshold of the missing area, the tolerance of the missing area of the monitoring area is obtained. weighting the number threshold of the missing area, the tolerance of the missing area of the monitoring area is obtained.

[0031] S300: Re-collect the updated point cloud data of the monitoring area, obtain the number and location of the missing areas of the monitoring area, and combine with the tolerance to judge whether the acquisition of the updated point cloud data of the monitoring area is successful.

[0032] Re-collect the updated point cloud data of the monitoring area, obtain the number and location of the missing areas of the monitoring area, and combine with the tolerance to judge whether the acquisition of the updated point cloud data of the monitoring area is successful. Further include: First, re-collect the updated point cloud data of the monitoring area at the previous monitoring position. Specifically, during the subsequent update process of the mine roadway model, for the th monitoring area, re-collect the point cloud data of the mine roadway at the previous monitoring position, and preprocess the point cloud data to obtain the updated point cloud data of the th monitoring area.

[0033] Then, grid the updated point cloud data, and use the connected component labeling algorithm to obtain the number and positions of the missing areas in the monitoring area. Specifically, grid the updated point cloud data of the th monitoring area (prior art), use the connected component labeling algorithm to identify the missing areas in the updated point cloud data, and then obtain the number and positions of the missing areas of the updated point cloud data of the th monitoring area; since the updated point cloud data has been gridded, the number of missing areas is the number of grids with missing values, denoted as , and the missing positions are the central point coordinates of the corresponding grids.

[0034] Then, determine whether the number of missing areas is less than the tolerance; if not, the acquisition of the updated point cloud data for the monitoring area fails; if so, based on the missing positions, determine whether the missing areas are at intersections; if so, the acquisition of the updated point cloud data for the monitoring area fails; if not, the acquisition of the updated point cloud data for the monitoring area is successful.

[0035] Specifically, taking the th monitoring area as an example, when the number of missing areas of the updated point cloud data of the th monitoring area is greater than the tolerance of the missing areas of the th monitoring area, that is, , it means that the missing areas of the updated point cloud data obtained for the th monitoring area are too large, and the updated point cloud data for this monitoring area needs to be acquired again.

[0036] When the number of missing areas of the updated point cloud data of the th monitoring area is less than or equal to the tolerance of the missing areas of the th monitoring area, that is, , since the missing areas should be avoided in the intersection areas as much as possible, it is necessary to further consider whether the missing areas are in the critical intersection areas. More specifically: Calculate the distances between the position coordinates of each missing area of the th monitoring area and all intersection coordinates, and record the minimum value of the distances as the degree value indicating the degree to which each missing area is in the critical intersection area. Then, record the mean value of the degree values indicating the degree to which all missing areas are in the critical intersection area as the degree value indicating the degree to which the missing areas in the th monitoring area are in the critical intersection path area, denoted as X. Preset a degree value threshold , and the specific value can be 0.5m (determined based on experience). When the value of X is less than , it means that the The missing area of the updated point cloud data obtained from a monitoring area is too biased towards the intersection path area, indicating that the acquisition of the updated point cloud data for the th monitoring area fails, and the updated point cloud data for this monitoring area needs to be collected again. On the contrary, it indicates that the acquisition of the updated point cloud data for the th monitoring area is successful. At this time, it is necessary to quantify whether the obtained updated point cloud data for the th monitoring area is valid. Specifically, it includes step S400 and step S500.

[0037] S400: Analyze the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained from the previous monitoring to obtain the validity degree of the updated point cloud data of the monitoring area.

[0038] Analyze the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained from the previous monitoring to obtain the validity degree of the updated point cloud data of the monitoring area, which further includes: First, calculate the quantity difference between the updated point cloud data of the monitoring area and the point cloud data obtained from the previous monitoring to obtain the quantity difference degree. The specific implementation method is: calculate the quantity difference between the updated point cloud data of the th monitoring area and the point cloud data of this monitoring area measured last time, representing the quantity difference degree, denoted as ; where is positive and the larger it is, the more data volume of the updated point cloud data is obtained compared with the point cloud data volume obtained from the previous measurement. Furthermore, more detailed feature information can be shown when constructing a 3D model subsequently. Therefore, after obtaining the quantity difference degree, it also includes: judging that the updated point cloud data of the monitoring area is initially valid according to the quantity difference degree. Specifically, a preset quantity difference threshold is set, the value of which can be 200 (obtained according to experience). When the difference of is less than or equal to , it indicates that the quantity of the updated point cloud data collected for the th monitoring area does not meet the requirements, that is, the updated point cloud data for the th monitoring area is invalid, and it is necessary to offset the acquisition position of the portable radar monitoring system in the th monitoring area and collect new updated point cloud data. The specific offset method is the same as step S600 and will not be elaborated here. If is greater than the preset quantity difference threshold , then it is judged that the updated point cloud data for the th monitoring area is initially valid. Then, further determine the matching degree between the updated point cloud data for the th monitoring area and the point cloud data of this monitoring area measured last time.

[0039] Then, obtain the matching point cloud data and the non-matching point cloud data of the updated point cloud data in the monitoring area and the point cloud data obtained in the previous monitoring, and analyze the dispersion degree of the non-matching point cloud data to obtain the matching degree between the updated point cloud data in the monitoring area and the point cloud data obtained in the previous monitoring. The specific implementation method is as follows: Since a large amount of fog and dust will be generated during the operation of the roadheader in the mine roadway when obtaining the updated point cloud data. Therefore, it is necessary to determine whether there is a serious loss of the obtained updated point cloud data.

[0040] Specifically, use the ICP algorithm to calculate the matching situation between the updated point cloud data of the th monitoring area and the point cloud data measured in this monitoring area last time, record the number of matching point clouds as , and record the number of non-matching point cloud data as (divided into the non-matching point cloud data set in the updated point cloud data of the th monitoring area and the non-matching point cloud data set in the point cloud data measured in this monitoring area last time).

[0041] Denote the average Euclidean distance between all updated point cloud data in the non-matching point cloud data set as the dispersion degree of the non-matching point cloud data set . Similarly, denote the average Euclidean distance between all point cloud data in the non-matching point cloud data set as the dispersion degree of the non-matching point cloud data set . Denote the average of the dispersion degree of the non-matching point cloud data set and the dispersion degree of the non-matching point cloud data set as the dispersion degree of the non-matching point cloud data in the th monitoring area, denoted as . The larger the value of , the higher the dispersion degree of the non-matching point cloud data, indicating that the non-matching point cloud data does not show an obvious aggregation form, and thus it is less likely to have a missing area in the future. When the more matching point cloud data there is between the updated point cloud data obtained in the th monitoring area and the point cloud data obtained in this monitoring area last time, and at the same time the higher the dispersion degree of the non-matching point cloud data, the higher the matching degree between the updated point cloud data obtained in the th monitoring area and the point cloud data obtained in this monitoring area last time. The calculation formula for the matching degree between the updated point cloud data obtained in the th monitoring area and the point cloud data obtained in this monitoring area last time is constructed as follows: where The matching degree between the updated point cloud data obtained from a monitoring area and the point cloud data obtained from the previous measurement in the same monitoring area; Indicates the number of matching point clouds between the updated point cloud data of the th monitoring area and the point cloud data obtained from the previous measurement in the same monitoring area; Indicates the number of non - matching point clouds between the updated point cloud data of the th monitoring area and the point cloud data obtained from the previous measurement in the same monitoring area;

[0042] Indicates the proportion of the matching point cloud data in the updated point cloud data obtained from the th monitoring area in all the point cloud data. The larger this value, the more matching point cloud data there is between the updated point cloud data obtained from the th monitoring area and the point cloud data obtained from the previous measurement in the same monitoring area; The larger the value, the higher the degree of dispersion of the non - matching point cloud data, indicating that the non - matching point cloud data does not show an obvious aggregation pattern, and thus it is less likely to have missing areas in the future; Therefore

[0043] Finally, based on the degree of quantity difference and the matching degree, the effectiveness of the updated point cloud data of the monitoring area is obtained. Specifically, the matching degree between the updated point cloud data obtained from the th monitoring area and the point cloud data obtained from the previous measurement in the same monitoring area and are combined to determine the effectiveness of the updated point cloud data obtained from the In the formula, Indicates the effectiveness of the updated point cloud data obtained from the th monitoring area; Indicates the matching degree between the updated point cloud data obtained from the th monitoring area and the point cloud data obtained from the previous measurement in the same monitoring area; Indicates the preset threshold of the quantity difference, that is, the minimum threshold of the quantity difference; Indicates the normalization function.

[0044] The larger the value, the more the number of updated point cloud data obtained from the th monitoring area can show the detailed features of the mine roadway, indicating that the effective degree of the updated point cloud data obtained from this monitoring area is greater; the matching degree The larger the value, the greater the reliability of the updated point cloud data obtained from the th monitoring area, indicating that the effective degree of the updated point cloud data obtained from this monitoring area is greater.

[0045] S500: Based on the effective degree, analyze the relevance of the updated point cloud data between the monitoring area and adjacent monitoring areas, and judge whether the updated point cloud data of the monitoring area is effective.

[0046] The effective degree of the updated point cloud data of the th monitoring area calculated in the above steps is determined by the correlation with the historical point cloud data at the same location. Since when using a portable radar monitoring system to construct a three-dimensional model of a mine roadway, in order to ensure comprehensive coverage of the roadway, there is usually a certain overlap between the point cloud data obtained from adjacent monitoring areas. When the staff is collecting data using a portable radar monitoring system, the mining machine is also working synchronously. Therefore, there is a situation where after the th monitoring area is measured, the mining machine is mining in the th monitoring area, which affects the correctness of the subsequently collected updated point cloud data.

[0047] Based on the above analysis, in the embodiments of the present invention, based on the effective degree, analyze the relevance of the updated point cloud data between the monitoring area and adjacent monitoring areas, and judge whether the updated point cloud data of the monitoring area is effective. Further including: First, mark the monitoring areas with overlapping point cloud data with the monitoring area as the to-be-matched monitoring areas. Specifically, for the th monitoring area, assume that there are a total of monitoring areas among the remaining monitoring areas that have overlapping point cloud data with the th monitoring area. Mark these monitoring areas as the to-be-matched monitoring areas of the th monitoring area.

[0048] Then, analyze the change relationship between the updated point cloud data of the to-be-matched monitoring areas and the point cloud data obtained from the previous monitoring to obtain the effective degree of the updated point cloud data of the to-be-matched monitoring areas. The specific method is the same as the effective degree of the updated point cloud data obtained from the th monitoring area in step S400, and obtain the effective degree of the updated point cloud data of the th to-be-matched monitoring area , which will not be elaborated here.

[0049] Then, analyze the size correlation of the effectiveness corresponding to the monitoring area and the monitoring area to be matched, and determine whether the updated point cloud data of the monitoring area is initially invalid; if not, the updated point cloud data of the monitoring area is valid; if so, based on the point cloud data obtained during the measurement process in the monitoring area, obtain the number of matching point clouds between the monitoring area and the monitoring area to be matched; and based on the updated point cloud data obtained during the measurement process in the monitoring area to be matched, obtain the number of updated matching point clouds between the monitoring area and the monitoring area to be matched; analyze the difference degree between the number of matching point clouds and the number of updated matching point clouds, and determine whether the updated point cloud data of the monitoring area is valid; if so, the updated point cloud data of the monitoring area is valid; if not, the updated point cloud data of the monitoring area is invalid. The specific implementation method is as follows: Set the effectiveness threshold , The value can be 0.5 (obtained according to experience), and determine whether the effectiveness corresponding to the monitoring area and the monitoring area to be matched is less than the effectiveness threshold; if so, that is, , and , indicating that the effectiveness values of the updated point cloud data of the th monitoring area and the th monitoring area to be matched are relatively small, then it means that the th monitoring area and the th monitoring area to be matched may have had the distribution of the point cloud data in the two monitoring areas changed due to the excavation of the mining machine. Therefore, the updated point cloud data of the th monitoring area obtained at this time is valid; if not, that is, when the value of and the value of are not all less than or are both greater than , then the updated point cloud data of the monitoring area is initially invalid, and further determine whether the initially invalid updated point cloud data of the monitoring area is valid. Specifically, after the measurement of the th monitoring area is completed, the staff will then go to the next monitoring area for measurement, and the next monitoring area is the monitoring area to be matched of the th monitoring area, which is set as the th monitoring area to be matched. After the measurement of the th monitoring area to be matched is completed, then use the ICP algorithm to calculate the number of updated matching point clouds of the updated point cloud data of the th monitoring area to be matched and the updated point cloud data of the th monitoring area, denoted as ; record the number of matching point clouds of the th monitoring area and the th monitoring area to be matched in the previous measurement process as . Set the difference threshold , The value can be 0.2 (based on experience) to determine the number of matching point clouds Update the number of matching point clouds Is the difference less than the difference threshold? If so, , then it means the The effectiveness of the updated point cloud data of the monitoring area is good, and the updated point cloud data of the monitoring area is effective; if not, that is, , then it means the The effectiveness of the updated point cloud data of each monitoring area is poor, and the updated point cloud data of the monitoring area is invalid. It is necessary to offset the collection position and re-collect new updated point cloud data. The specific method is step S600.

[0050] S600: Based on the updated point cloud data of the monitoring area and the position coordinates of the point cloud data obtained in the last monitoring, the acquisition position offset of the monitoring area is analyzed, the acquisition position is offset, and new updated point cloud data is re-collected until it is valid.

[0051] After steps S400 and S500, invalid data in the updated point cloud data is obtained. It is necessary to offset the acquisition position and then re-acquire new updated point cloud data. Therefore, based on the updated point cloud data of the monitoring area and the position coordinates of the point cloud data obtained from the last monitoring, the acquisition position offset of the monitoring area is analyzed, the acquisition position is offset, and new updated point cloud data is re-acquired to complete the overall 3D model update. The specific implementation method is as follows: First, calculate the number of The mean value of the position coordinates of the matching point cloud data in the point cloud data of the monitoring area is recorded as the first feature position; and the The average of the updated point cloud data position coordinates of the monitoring area is recorded as the second feature position; the difference between the first feature position and the second feature position is calculated and recorded as the offset distance , combined with the preset displacement of the acquisition position (Assume that the acquisition position deviates by y meters each time, the corresponding offset distance is ), acquisition position preset displacement The value can be 10 meters (based on experience), so the distance the collection position needs to be offset is , that is, the offset distance .

[0052] Then, since the distance between the acquisition position and the edge of the roadway remains unchanged during the offset process, its movement is relative motion. Therefore, based on the acquisition position, the acquisition center line is obtained, such as Figure 3 As shown, the acquisition position is offset on the center line.

[0053] Then, the number of matching point clouds will be And update the number of matching point clouds The feature position corresponding to the maximum value of is recorded as the offset feature coordinate, that is, if the number of matching point clouds is updated Relative to the number of matching point clouds If it is larger, the The average value of the updated point cloud data position coordinates of the monitoring area (the second feature position) is recorded as the offset feature coordinate; if the number of matching point clouds Relative to updating the number of matching point clouds If it is larger, the The mean value of the position coordinates of the point cloud data of each monitoring area (the first feature position) is recorded as the offset feature coordinate.

[0054] The offset direction is determined by the direction of the offset feature coordinate relative to the last acquisition position. Specifically, the offset feature coordinate is projected onto a horizontal plane, and the centerline is divided into two parts, front and back, by the current acquisition position. If the offset feature coordinate projection position is in front of the centerline relative to the original acquisition position, the corresponding offset direction is forward along the centerline; otherwise, the offset direction is backward.

[0055] Then, the acquisition position is offset on the acquisition center line according to the offset distance and offset direction.

[0056] Finally, the new updated point cloud data is collected again to complete the overall 3D model update. Specifically, after the migration is completed, the first The new updated point cloud data of the monitoring area is obtained, and then the method of step S200 to step S500 is continued to determine the obtained The validity of the new updated point cloud data of each monitoring area is determined until the new updated point cloud data is valid. Collection of updated point cloud data for each monitoring area.

[0057] S700: Complete the overall 3D model update based on valid updated point cloud data.

[0058] Based on the valid updated point cloud data, the overall 3D model is updated. The valid updated point cloud data includes the valid updated point cloud data obtained in step S500 and the new valid updated point cloud data obtained in step S600. When the updated point cloud data of all monitoring areas are collected, the overall 3D model of the mine tunnel is updated. After the update is completed, the deformation monitoring system automatically superimposes the deformation information on the 3D model for display, such as Figure 4 The figure below shows deformation information displayed on a 3D model of a mine tunnel. Different colors represent different deformation values. When the deformation speed or value in the monitoring area exceeds the set warning threshold, the system issues an alarm, and maintenance personnel perform maintenance on the dangerous areas of the mine tunnel based on the alert.

[0059] Based on the same inventive concept as the above method, this embodiment also provides a mine roadway deformation monitoring system.

[0060] Please refer to Figure 5 , which shows the basic composition of a mine roadway deformation monitoring system provided by an embodiment of the present invention.

[0061] As Figure 5 shown, a mine roadway deformation monitoring system includes: a memory 10 and a processor 20, where: The memory 10 is used to store program codes; The processor 20 is configured to read the program codes stored in the memory 10, and execute the steps of collecting point cloud data of multiple monitoring areas in the mine roadway to obtain an overall three-dimensional model of the mine roadway; based on the overall three-dimensional model, counting the number of intersections included in the monitoring area, determining the tolerance of the missing area in the monitoring area; re-collecting the updated point cloud data of the monitoring area, obtaining the number and position of the missing areas in the monitoring area, and combining with the tolerance to judge whether the acquisition of the updated point cloud data of the monitoring area is successful; if so, analyzing the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring to obtain the effectiveness of the updated point cloud data of the monitoring area; based on the effectiveness, analyzing the correlation between the updated point cloud data of the monitoring area and the adjacent monitoring areas to judge whether the updated point cloud data of the monitoring area is valid; if not, analyzing the offset situation of the acquisition position of the monitoring area based on the position coordinates of the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring, offsetting the acquisition position, and re-collecting new updated point cloud data until it is valid; based on the valid updated point cloud data, completing the update of the overall three-dimensional model.

[0062] Further, the processor 20 includes: a three-dimensional model construction module 21, a tolerance analysis module 22, an updated point cloud data acquisition module 23, an updated point cloud data validity judgment module 24, an updated point cloud data re-acquisition module 25, and a three-dimensional model update module 26. Wherein: The three-dimensional model construction module 21 is configured to collect point cloud data of multiple monitoring areas in the mine roadway to obtain an overall three-dimensional model of the mine roadway; The tolerance analysis module 22 is configured to count the number of intersections included in the monitoring area based on the overall three-dimensional model, and determine the tolerance of the missing area in the monitoring area; The updated point cloud data acquisition module 23 is configured to re-collect the updated point cloud data of the monitoring area, obtain the number and position of the missing areas in the monitoring area, and combine with the tolerance to judge whether the acquisition of the updated point cloud data of the monitoring area is successful; Update the point cloud data validity judgment module 24, which is used to analyze the change relationship between the updated point cloud data in the monitoring area and the point cloud data obtained in the previous monitoring when the updated point cloud data is successfully obtained, so as to obtain the validity degree of the updated point cloud data in the monitoring area; and based on the validity degree, analyze the relevance of the updated point cloud data between the monitoring area and the adjacent monitoring areas, and judge whether the updated point cloud data in the monitoring area is valid; Update the point cloud data re-acquisition module 25, which is used to analyze the offset situation of the acquisition position in the monitoring area based on the position coordinates of the updated point cloud data in the monitoring area and the point cloud data obtained in the previous monitoring when the updated point cloud data is invalid, and offset the acquisition position; and after the acquisition position is offset, re-acquire new updated point cloud data until it is valid; 3D model update module 26, which is used to complete the overall 3D model update based on the valid updated point cloud data.

[0063] It should be noted that: the above sequence of embodiments of the present invention 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, multitasking and parallel processing are also possible or may be advantageous.

[0064] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. The key points of each embodiment are the differences from other embodiments.

Claims

1. A method for monitoring the deformation of mine roadways, characterized in that, The method includes: Collecting point cloud data of multiple monitoring areas in a mine roadway to obtain an overall three-dimensional model of the mine roadway; Based on the overall three-dimensional model, counting the number of intersections included in the monitoring area and determining the tolerance of the missing area in the monitoring area; Re-collecting the updated point cloud data of the monitoring area, obtaining the number and positions of the missing areas in the monitoring area, and combining with the tolerance to determine whether the acquisition of the updated point cloud data of the monitoring area is successful; If so, analyzing the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring to obtain the effectiveness of the updated point cloud data of the monitoring area; Based on the effectiveness, analyzing the correlation of the updated point cloud data between the monitoring area and adjacent monitoring areas to determine whether the updated point cloud data of the monitoring area is effective; If not, based on the position coordinates of the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring, analyzing the offset situation of the acquisition position of the monitoring area, offsetting the acquisition position, and re-collecting new updated point cloud data until it is effective; Based on the effective updated point cloud data, complete the update of the overall three-dimensional model.

2. The mine roadway deformation monitoring method according to claim 1, wherein Re-collecting the updated point cloud data of the monitoring area, obtaining the number and positions of the missing areas in the monitoring area, and combining with the tolerance to determine whether the acquisition of the updated point cloud data of the monitoring area is successful, including: Re-collecting the updated point cloud data of the monitoring area at the previous monitoring position; Performing grid processing on the updated point cloud data and using the connected component labeling algorithm to obtain the number and positions of the missing areas in the monitoring area; Determining whether the number of missing areas is less than the tolerance; If not, the acquisition of the updated point cloud data of the monitoring area fails; If so, based on the missing positions, determining whether the missing areas are at intersections; If so, the acquisition of the updated point cloud data of the monitoring area fails; If not, the acquisition of the updated point cloud data of the monitoring area is successful.

3. The mine roadway deformation monitoring method according to claim 2, wherein If the acquisition of the updated point cloud data of the monitoring area fails, re-collect the updated point cloud data of the monitoring area.

4. The mine tunnel deformation monitoring method according to claim 3, characterized in that: Analyzing the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring to obtain the effectiveness of the updated point cloud data of the monitoring area, including: Calculating the quantity difference between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring to obtain the degree of quantity difference; Obtaining the matching point cloud data and non-matching point cloud data between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring, and analyzing the dispersion degree of the non-matching point cloud data to obtain the matching degree between the updated point cloud data of the monitoring area and the point cloud data obtained in the previous monitoring; Based on the degree of quantity difference and the matching degree, obtaining the effectiveness of the updated point cloud data of the monitoring area.

5. The mine roadway deformation monitoring method according to claim 4, wherein Based on the effectiveness, analyzing the correlation of the updated point cloud data between the monitoring area and adjacent monitoring areas to determine whether the updated point cloud data of the monitoring area is effective, including: Marking the monitoring areas with overlapping point cloud data with the monitoring area as the monitoring areas to be matched; Analyze the change relationship between the updated point cloud data of the monitoring area to be matched and the point cloud data obtained from the last monitoring to obtain the effectiveness of the updated point cloud data of the monitoring area to be matched; Analyzing the size correlation between the effectiveness of the monitoring area and the monitoring area to be matched, and determining whether the updated point cloud data of the monitoring area is initially invalid; If not, the updated point cloud data of the monitoring area is valid; If yes, obtaining the number of matching point clouds between the monitoring area and the monitoring area to be matched based on the point cloud data acquired during the measurement of the monitoring area; and obtaining the number of updated matching point clouds between the monitoring area and the monitoring area to be matched based on the updated point cloud data obtained during the measurement of the monitoring area to be matched; Analyzing the difference between the number of the matched point cloud and the number of the updated matched point cloud to determine whether the updated point cloud data of the monitoring area is valid; If yes, the updated point cloud data of the monitoring area is valid; If not, the updated point cloud data of the monitoring area is invalid.

6. The mine roadway deformation monitoring method according to claim 5, characterized in that, Analyzing the correlation between the effectiveness of the monitoring area and the monitoring area to be matched, and determining whether the updated point cloud data of the monitoring area is preliminarily invalid, including: Setting a validity threshold to determine whether the validity levels corresponding to the monitoring area and the monitoring area to be matched are both less than the validity threshold; If yes, the updated point cloud data of the monitoring area is valid; If not, the updated point cloud data of the monitoring area is preliminarily invalid.

7. The mine roadway deformation monitoring method according to claim 5, wherein Analyzing the difference between the number of the matched point cloud and the number of the updated matched point cloud to determine whether the updated point cloud data of the monitoring area is valid includes: Setting a difference threshold to determine whether the difference between the number of matching point clouds and the number of updated matching point clouds is less than the difference threshold; If yes, the updated point cloud data of the monitoring area is valid; If not, the updated point cloud data of the monitoring area is invalid.

8. The mine roadway deformation monitoring method according to claim 5, characterized in that Analyzing the offset of the acquisition position of the monitoring area based on the updated point cloud data of the monitoring area and the position coordinates of the point cloud data obtained in the last monitoring, and offsetting the acquisition position, including: Calculate the average value of the position coordinates of the matching point cloud data in the point cloud data of the monitoring area during the last measurement process, and record it as the first feature position; Calculate the mean value of the position coordinates of the updated point cloud data of the monitoring area and record it as the second characteristic position; Calculating the difference between the first characteristic position and the second characteristic position, and combining the preset displacement of the acquisition position to obtain an offset distance; Based on the acquisition position, the acquisition center line is obtained; Recording a feature position corresponding to a maximum value of the number of matching point clouds and the number of updated matching point clouds as an offset feature coordinate; The direction of the offset feature coordinates relative to the current acquisition position is used as the offset direction; The acquisition position is offset on the acquisition center line according to the offset distance and the offset direction.

9. A deformation monitoring system for mine roadways, characterized in that, The system comprises: a memory and a processor, wherein: The memory is used to store program codes; The processor is configured to read the program code stored in the memory and execute the method according to any one of claims 1 to 8.

10. The mine roadway deformation monitoring system according to claim 9, characterized in that, The processor includes: A three-dimensional model construction module, which is used to collect point cloud data of multiple monitoring areas in a mine roadway to obtain an overall three-dimensional model of the mine roadway; A tolerance analysis module, which is used to count the number of intersections included in the monitoring area based on the overall three-dimensional model and determine the tolerance of the missing area in the monitoring area; An updated point cloud data acquisition module, which is used to re-collect the updated point cloud data of the monitoring area, obtain the number and positions of the missing areas in the monitoring area, and combine with the tolerance to judge whether the acquisition of the updated point cloud data of the monitoring area is successful; An updated point cloud data validity judgment module, which is used to analyze the change relationship between the updated point cloud data of the monitoring area and the point cloud data obtained from the previous monitoring when the acquisition of the updated point cloud data is successful, to obtain the validity degree of the updated point cloud data of the monitoring area; and based on the validity degree, analyze the relevance of the updated point cloud data between the monitoring area and adjacent monitoring areas to judge whether the updated point cloud data of the monitoring area is valid; An updated point cloud data re-acquisition module, which is used to analyze the offset situation of the acquisition position of the monitoring area based on the position coordinates of the updated point cloud data of the monitoring area and the point cloud data obtained from the previous monitoring when the updated point cloud data is invalid, and offset the acquisition position; and after the acquisition position is offset, re-collect new updated point cloud data until it is valid; A three-dimensional model update module, which is used to complete the update of the overall three-dimensional model based on the valid updated point cloud data.

Images