Mine data processing method and device and storage medium

Abstract

The embodiment of the invention provides a mine data processing method and device and a storage medium. The method comprises the steps of obtaining to-be-processed point cloud data of a mine, wherein the to-be-processed point cloud data comprises multiple pieces of global point cloud data and multiple pieces of local point cloud data; for each piece of local point cloud data, acquiring environment information of an environment where the local point cloud data is located, equipment type information of the adopted equipment and feature information of the local point cloud data; determining the data quality of each piece of local point cloud data according to the environment information, and / or the equipment type information, and / or the feature information of each piece of local point cloud data; and fusing the plurality of local point cloud data with the plurality of global point cloud data based on the data acquisition time and the data quality of each local point cloud data to obtain the target point cloud data of the mine, so that the overall situation of the mine can be accurately and completely described.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and storage medium for processing mining data. Background Art

[0002] The exploration, development and management of mining geological resources require a large amount of geological information, but traditional geological survey and exploration methods are often limited by factors such as complex terrain and surface coverage and cannot meet actual needs. Advanced remote sensing technologies such as LiDAR provide an effective way to solve this problem. By collecting a large amount of point cloud data, it is possible to achieve refined investigation and monitoring of mining geological resources.

[0003] In the actual production process, due to the data collection cost of the mine and the meteorological conditions of the mine environment, the exploration and monitoring of mine geological resources usually need to be carried out at different times through different equipment. However, the data collected by different equipment often have different formats and standards, and the data collected by a single device is not complete and accurate, and it is difficult to manage and process uniformly, which makes it difficult to accurately and completely portray the overall situation of the mine. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device and storage medium for processing mine data, so as to solve the problem of unreasonable mine data processing in the prior art.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a method for processing mine data, comprising:

[0006] Acquire the point cloud data to be processed of the mine, where the point cloud data to be processed includes a plurality of global point cloud data and a plurality of local point cloud data;

[0007] For each local point cloud data, obtain environmental information of the environment in which the local point cloud data is collected, device type information of the device used, and feature information of the local point cloud data;

[0008] Determine the data quality of each local point cloud data according to the environment information, and / or the device type information, and / or the feature information of each local point cloud data;

[0009] Based on the data collection time and data quality of each local point cloud data, multiple local point cloud data are fused with multiple global point cloud data to obtain target point cloud data of the mine.

[0010] In an embodiment of the present application, the target point cloud data of a mine includes neighborhood point cloud data of multiple neighborhood ranges, and the multiple local point cloud data are fused with multiple global point cloud data based on the data collection time and data quality of each local point cloud data to obtain the target point cloud data of the mine, including: dividing the multiple local point cloud data and the multiple global point cloud data into multiple neighborhood ranges based on a preset spatial range where the mine is located; for each neighborhood range, weighting the data collection time and the data quality of each local point cloud data in the neighborhood range respectively, and determining the weighted mean of the corresponding local point cloud data; for each neighborhood range, filtering out target local point cloud data from all local point cloud data in the neighborhood range based on the weighted mean of each local point cloud data in the neighborhood range; for each neighborhood range, fusing the target local point cloud data in the neighborhood range with the global point cloud data in the neighborhood range to obtain the neighborhood point cloud data of the neighborhood range.

[0011] In an embodiment of the present application, the feature information includes geometric feature information and color feature information. Determining the data quality of each local point cloud data based on the environmental information, and / or device type information, and / or feature information of each local point cloud data includes: determining the environmental level of the environment in which each local point cloud data is located based on the environmental information of each local point cloud data; and / or determining the sensor confidence of each local point cloud data based on the device type information of each local point cloud data; and / or determining the data confidence of each local point cloud data based on the geometric feature information and / or color feature information of each local point cloud data; determining the data quality of each local point cloud data based on the environmental level and / or sensor confidence and / or data confidence corresponding to each local point cloud data.

[0012] In an embodiment of the present application, the processing method also includes: performing spatial segmentation processing on the target point cloud data based on the octree spatial segmentation algorithm to obtain multiple target space levels corresponding to the target point cloud data; performing thinning processing on the point cloud data of each target subspace under each target space level to obtain each thinned point cloud data; for each target space level, performing index coding processing on the target space level and each target subspace under the target space level; generating a point cloud data query table for the mine according to the target space level and target subspace after index coding processing, and the thinned point cloud data included in each target subspace.

[0013] In an embodiment of the present application, based on the octree space segmentation algorithm, the target point cloud data is spatially segmented to obtain multiple target space levels corresponding to the target point cloud data, including: based on the octree space segmentation algorithm, and according to the minimum recursive depth, the target point cloud data is divided into multiple basic space levels, each basic space level includes a preset number of data subspaces; for each data subspace included in the highest basic space level, based on a preset density division threshold and the operation requirement parameters of the mine, it is determined whether the data subspace contains point cloud data of the area of ​​interest of the mine; for each data subspace included in the highest basic space level, when it is determined that the data subspace contains point cloud data of the area of ​​interest, the data subspace is recursively divided until the maximum recursive depth is reached; it is determined that the spatial segmentation processing of the target point cloud data is completed, and multiple target space levels corresponding to the target point cloud data are obtained, and each target space level includes a preset number of target subspaces.

[0014] In an embodiment of the present application, the point cloud data of each target subspace under each target space level is thinned out to obtain each point cloud data after thinning out, including: for a preset number of target subspaces included in each target space level, judging whether each point cloud data in the target subspace contains at least one of preset geomorphic feature information, mine facility information and mine excavation surface information; when each point cloud data in the target subspace contains at least one of preset geomorphic feature information, mine facility information and mine excavation surface information, determining that the point cloud data is the target point cloud data to be retained in the target subspace; for each target point cloud data under each target space level, determining the retention priority of the target point cloud data according to the preset geomorphic feature information and / or mine facility information and / or mine excavation surface information contained in the target point cloud data; for each target space level, retaining the corresponding target point cloud data according to the retention priority of each target point cloud data under the target space level, and deleting the point cloud data outside the target point cloud data, so as to complete the thinning out of the point cloud data for each target subspace under the target space level.

[0015] In an embodiment of the present application, for each target space level, index coding processing is performed on the target space level and each target subspace under the target space level, including: determining the level index value of each target space level based on the central point cloud of each target space level; determining the auxiliary index value of each target subspace under each target space level based on the central point cloud of each target subspace under each target space level; mapping each target space level to a root node, and mapping each target subspace under each target space level to an octree node; associating the level index value of each target space level with the corresponding root node, and associating the auxiliary index value of each target subspace under each target space level with the corresponding octree node, so as to complete the index coding processing of each target subspace under each target space level.

[0016] In an embodiment of the present application, the processing method also includes: determining a tile data structure for the mine based on a point cloud data query table, the tile data structure including a tile level corresponding to the target space level, and a data tile corresponding to each target subspace; optimizing and verifying each data tile and the boundaries and indexes of each data tile to obtain a data tile that meets preset conditions; converting the point cloud data in each data tile that meets the preset conditions into a preset format according to a preset specification standard; creating a tile entry file for the mine based on each tile level and the data tiles that meet the preset conditions under each tile level; generating a three-dimensional tile data set for the mine based on the point cloud data after format conversion included in the tile entry file and each data tile that meets the preset conditions.

[0017] In an embodiment of the present application, the processing method also includes: when new point cloud data of the mine is acquired, the new point cloud data and the corresponding point cloud data are merged based on an incremental update algorithm to obtain new target point cloud data; the spatial hierarchy of the new target point cloud data is reconstructed, and the point cloud data query table is updated based on the new spatial hierarchy; the target data tile whose correlation degree with the new target point cloud data is greater than a preset degree is determined, and the target data tile is preferentially updated based on the new target point cloud data to generate a new tile entry file.

[0018] A second aspect of the present application provides a processing device for mining data, comprising:

[0019] a memory configured to store instructions; and

[0020] The processor is configured to call instructions from the memory and implement the above-mentioned processing method for mining data when executing the instructions.

[0021] A third aspect of the present application provides a machine-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned processing method for mining data.

[0022] Through the above technical scheme, the point cloud data to be processed of the mine is obtained, and the point cloud data to be processed includes multiple global point cloud data and multiple local point cloud data; for each local point cloud data, the environmental information of the environment in which the local point cloud data is collected, the equipment type information of the equipment used, and the characteristic information of the local point cloud data are obtained; the data quality of each local point cloud data is determined according to the environmental information, and / or equipment type information, and / or characteristic information of each local point cloud data; based on the data collection time and data quality of each local point cloud data, the multiple local point cloud data are merged with the multiple global point cloud data to obtain the target point cloud data of the mine, which can accurately and completely depict the overall situation of the mine.

[0023] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0025] Figure 1 A schematic diagram of a process flow of a method for processing mine data according to an embodiment of the present application is schematically shown;

[0026] Figure 2 A schematic diagram of a process flow of a method for processing mine data according to another embodiment of the present application is schematically shown;

[0027] Figure 3 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0029] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0030] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0031] Figure 1 The following schematically shows a flow chart of a method for processing mining data according to an embodiment of the present application. Figure 1 As shown, in one embodiment of the present application, a method for processing mine data is provided, comprising the following steps:

[0032] Step 101: Obtain the point cloud data to be processed of the mine, where the point cloud data to be processed includes a plurality of global point cloud data and a plurality of local point cloud data.

[0033] When the mine data needs to be processed, the processor can obtain the point cloud data to be processed of the mine. The point cloud data to be processed includes multiple global point cloud data and multiple local point cloud data. The point cloud data to be processed refers to the pre-processed point cloud data for the mine. The global point cloud data refers to the point cloud data of the entire mine after pre-processing. The local point cloud data refers to the point cloud data of a certain area in the entire mine that is collected by a mobile device and pre-processed.

[0034] Specifically, the point cloud data of the entire mine can be collected through unmanned equipment, and the point cloud data of a certain area in the entire mine can be collected through both mobile scanning equipment and fixed-point scanning equipment. Among them, the unmanned equipment can be equipped with sensors such as laser radar that have the function of collecting point cloud data. Fixed-point scanning equipment refers to fixed-point scanning equipment installed in the corresponding area, which can be equipped with sensors such as laser radar that have the function of collecting point cloud data. Mobile scanning equipment includes handheld scanning equipment, and handheld scanning equipment can be equipment equipped with laser radar and other equipment that have the function of collecting point cloud data.

[0035] The unmanned equipment can send the collected point cloud data of the entire mine to the processor. The movable scanning equipment and the fixed point scanning equipment can send the collected point cloud data of a certain area in the entire mine to the processor. After the processor obtains the point cloud data of the entire mine and the point cloud data of any area in the entire mine, it can pre-process the point cloud data of the entire mine and the point cloud data of a certain area in the entire mine respectively, so as to obtain multiple global point cloud data and multiple local point cloud data included in the data to be processed. Among them, the pre-processing methods include filtering and elimination processing, point cloud alignment processing and statistical processing.

[0036] When filtering and eliminating the point cloud data of the entire mine and the point cloud data of a certain area in the entire mine respectively, the processor can eliminate the noise data in all the acquired point cloud data based on a hybrid denoising algorithm based on a statistical filtering method and a normal vector denoising method.

[0037] Specifically, the processor can set the parameters of statistical filtering based on the empirical parameters of the mine point cloud and the actual status of the target mine. The parameters of statistical filtering mainly include neighborhood size and threshold. Based on the set parameters of statistical filtering, outliers and noise in all acquired point cloud data can be removed, and the main structure and features of all acquired point cloud data can be retained. For the point cloud data after statistical filtering, denoising can be performed based on the denoising method of the normal vector, and the filtered and denoised point cloud data can be evaluated by visual method and specific parameters. If the filtered and denoised point cloud data meets the evaluation criteria, point cloud alignment processing can be performed, otherwise the parameters can be readjusted and noise removal can be performed.

[0038] When performing point cloud alignment processing on the filtered and denoised point cloud data, due to the complex terrain of the mine, uneven data density, and huge data volume, a multi-scale registration method can be used to convert the filtered and denoised point cloud data from multiple sources and at different times into the same coordinate system. Specifically, the initial base point coordinates and reference coordinate system can be set, and the initial large-scale matching of the entire mine scene can be performed based on the global registration method to quickly obtain preliminary registration results. Afterwards, local registration can be performed based on local feature descriptions to improve the accuracy and reliability of the registration.

[0039] After the point cloud alignment process is completed, the point cloud data after the point cloud alignment process may be statistically processed. Specifically, the boundary range and color information of the point cloud data after the point cloud alignment process may be extracted based on the statistical information, geometric information and metadata generated during the data acquisition process of the point cloud data after the point cloud alignment process. Among them, the statistical information of the point cloud data after the point cloud alignment process may include the number of point clouds, the average density of point clouds, the four-dimensional coordinate values ​​of point clouds, etc. The geometric information of the point cloud data after the point cloud alignment process may include the normal vector information and curvature information of each point cloud data, etc. The metadata generated during the data acquisition process may include the acquisition time, acquisition location, acquisition equipment information, etc.

[0040] Step 102: For each local point cloud data, obtain environmental information of the environment in which the local point cloud data is collected, device type information of the device used, and feature information of the local point cloud data.

[0041] When collecting point cloud data of a certain area in a mine, the light intensity, humidity, and temperature during collection may affect the clarity and accuracy of the local point cloud data. Therefore, the processor may collect environmental information of each local point cloud data including the light intensity, humidity, and temperature of the environment where the local point cloud data is located.

[0042] The local point cloud data can be collected based on different collection devices, for example, it can be collected by a movable scanning device and a fixed point scanning device. The point cloud data obtained by the sensors installed on different collection devices, such as laser radar, may be different. For example, a fixed point scanning device is not prone to jitter when scanning, and a movable scanning device may jitter when scanning. Therefore, the point cloud data obtained by the fixed point scanning device may be clearer than that of the movable scanning device. Therefore, the processor can obtain the device type information of the device used to collect the local point cloud data. For example, the device type information may include fixed type and movable type.

[0043] For each local point cloud data, the processor obtains the feature information of the collected local point cloud data. The feature information of the local point cloud data may include geometric feature information and color feature information. The geometric feature information can be used to characterize the terrain and other information of the area where the corresponding local point cloud data is located, and the color feature information can be used to characterize the type of the area where the corresponding local point cloud data is located. For example, the color of the point cloud data of important operating areas such as the mining operation surface, blasting location, and actual operation lane of the mine may be darker.

[0044] Step 103: Determine the data quality of each local point cloud data according to the environment information, and / or device type information, and / or feature information of each local point cloud data.

[0045] The processor may determine the data quality of each local point cloud data according to the environment information, and / or the device type information, and / or the feature information of each local point cloud data.

[0046] In an embodiment of the present application, the feature information includes geometric feature information and color feature information. Determining the data quality of each local point cloud data based on the environmental information, and / or device type information, and / or feature information of each local point cloud data includes: determining the environmental level of the environment in which each local point cloud data is located based on the environmental information of each local point cloud data; and / or determining the sensor confidence of each local point cloud data based on the device type information of each local point cloud data; and / or determining the data confidence of each local point cloud data based on the geometric feature information and / or color feature information of each local point cloud data; determining the data quality of each local point cloud data based on the environmental level and / or sensor confidence and / or data confidence corresponding to each local point cloud data.

[0047] The feature information includes geometric feature information and color feature information. The processor can determine the environmental level of the environment in which each local point cloud data is located according to the environmental information of each local point cloud data. For example, if the environmental information includes light intensity, the local point cloud data with greater light intensity has a higher environmental level. If the light intensity of the environment in which the local point cloud data is collected is the same, the lower the humidity or the higher the temperature of the environment, the higher the environmental level of the environment in which the local point cloud data is collected. Among them, the environmental level can be quantified based on numerical values. For example, different numerical values ​​can be configured for different environmental levels. The higher the environmental level, the larger the corresponding numerical value. Based on the corresponding numerical value, the environmental level of the environment in which any two local point cloud data are located can be compared.

[0048] The processor may determine the sensor confidence of each local point cloud data according to the device type information of each local point cloud data. If the device type information may include fixed type and movable type, and the clarity and accuracy of the point cloud data need to be paid attention to, the sensor confidence of the local point cloud data collected by the fixed type acquisition device may be set to be greater than the sensor confidence of the local point cloud data collected by the movable type acquisition device. It should be noted that the confidence of the unmanned device in this solution is higher than that of the fixed scanning device and the movable scanning device.

[0049] The processor may determine the data confidence of each local point cloud data based on the geometric feature information and / or color feature information of each local point cloud data. Specifically, the processor may determine the normal vector of each local point cloud data based on the geometric feature information of each local point cloud data. If the normal vector of each local point cloud data is consistent with the normal vector of the local point cloud data collected by the same type of device at the same time within its neighborhood or the similarity between the two is within a preset range, the data confidence of the local point cloud data is high. The processor may determine the color value of each local point cloud data based on the color feature information of each local point cloud data. If the color value is not within the corresponding range, the color of the local point cloud data is abnormal.

[0050] It should be noted that the processor can determine the data confidence of each local point cloud data based on the geometric feature information and color feature information of each local point cloud data. At this time, the processor can perform weighted processing or comprehensive judgment based on the results obtained based on the geometric feature information and color feature information, respectively, to obtain the data confidence of each local point cloud data. For example, if the normal vector of each local point cloud data is consistent with the normal vector of the local point cloud data collected by the same type of device at the same time within its neighborhood, the local point cloud data whose color value is in the corresponding range has a greater data confidence than the local point cloud data whose color value is not in the corresponding range.

[0051] The processor may determine the data quality of each local point cloud data according to the environment level and / or sensor confidence and / or data confidence corresponding to each local point cloud data. That is, the processor may determine the data quality of each local point cloud data according to the environment level corresponding to each local point cloud data. The higher the environment level, the better the corresponding data quality. The processor may determine the data quality of each local point cloud data according to the sensor confidence corresponding to each local point cloud data. The higher the sensor confidence, the better the corresponding data quality. The processor may determine the data quality of each local point cloud data according to the data confidence corresponding to each local point cloud data. The higher the data confidence, the better the corresponding data quality.

[0052] The processor may determine the data quality of each local point cloud data according to the environment level, sensor confidence, and data confidence corresponding to each local point cloud data. For example, the environment level, sensor confidence, and data confidence of each local point cloud data may have corresponding weights, and then all weights may be summed or averaged, so that the data quality may be determined based on the total weight or average weight. For example, the larger the total weight or average weight, the higher the corresponding data quality.

[0053] Step 104: Based on the data collection time and data quality of each local point cloud data, multiple local point cloud data are merged with multiple global point cloud data to obtain target point cloud data of the mine.

[0054] The point cloud data to be processed of the mine acquired by the processor is a set of point cloud data under the same reference coordinate system. Since it is collected by different acquisition devices at different acquisition times, the processor can fuse multiple local point cloud data with multiple global point cloud data based on the data acquisition time and data quality of each local point cloud data to obtain the target point cloud data of the mine. The target point cloud data of the mine is more complete and accurate original point cloud data of the mine.

[0055] In an embodiment of the present application, the target point cloud data of a mine includes neighborhood point cloud data of multiple neighborhood ranges, and the multiple local point cloud data are fused with multiple global point cloud data based on the data collection time and data quality of each local point cloud data to obtain the target point cloud data of the mine, including: dividing the multiple local point cloud data and the multiple global point cloud data into multiple neighborhood ranges based on a preset spatial range where the mine is located; for each neighborhood range, weighting the data collection time and the data quality of each local point cloud data in the neighborhood range respectively, and determining the weighted mean of the corresponding local point cloud data; for each neighborhood range, filtering out target local point cloud data from all local point cloud data in the neighborhood range based on the weighted mean of each local point cloud data in the neighborhood range; for each neighborhood range, fusing the target local point cloud data in the neighborhood range with the global point cloud data in the neighborhood range to obtain the neighborhood point cloud data of the neighborhood range.

[0056] The target point cloud data of the mine includes neighborhood point cloud data of multiple neighborhood ranges. The processor may divide the multiple local point cloud data and the multiple global point cloud data into multiple neighborhood ranges based on the preset spatial range where the mine is located. The preset spatial range may be determined based on the boundary range of the point cloud data to be processed in the mine. For each neighborhood range, the processor may weight the data collection time and data quality of each local point cloud data in the neighborhood range, and determine the weighted mean of the corresponding local point cloud data.

[0057] The processor may filter out target local point cloud data from all local point cloud data within the neighborhood based on the weighted mean of each local point cloud data within the neighborhood. For example, local point cloud data whose weighted mean is within a preset range may be filtered out as target local point cloud data corresponding to the neighborhood. The processor may fuse the target local point cloud data within the neighborhood with the global point cloud data within the neighborhood to obtain neighborhood point cloud data of the neighborhood. Specifically, the processor may perform mean processing on the three-dimensional coordinates of the target local point cloud data and the global point cloud data within the neighborhood to obtain new three-dimensional coordinates, and the point corresponding to the new three-dimensional coordinates is the neighborhood point cloud data of the neighborhood.

[0058] The above scheme fuses multiple local point cloud data with multiple global point cloud data according to the time and quality of the point cloud data, which can ensure that the newer point cloud data can cover and update the older point cloud data, and the point cloud data with higher data quality can cover and update the point cloud data with lower quality. Since the quality of the point cloud data obtained by the unmanned equipment in this scheme is better than the quality of the point cloud data obtained by the fixed point scanning device and the movable scanning device, the point cloud data obtained by the unmanned equipment is supplemented and updated by the point cloud data collected by the fixed point scanning device and the movable scanning device, which avoids the large-scale update of the data, so that the point cloud data obtained by the unmanned equipment meets the needs of dynamic update. First, the neighborhood point cloud data in the neighborhood range obtained by weighted fusion is updated more in time than the point cloud data obtained by the unmanned equipment. Secondly, after the target local point cloud data in the neighborhood range and the global point cloud data in the neighborhood range are fused, the point cloud data obtained by the unmanned equipment is supplemented. The timeliness of the data is better than that of the point cloud data obtained by the unmanned equipment. Therefore, the quality of the neighborhood point cloud data in the neighborhood range will definitely be better than the original point.

[0059] In an embodiment of the present application, the processing method also includes: performing spatial segmentation processing on the target point cloud data based on the octree spatial segmentation algorithm to obtain multiple target space levels corresponding to the target point cloud data; performing thinning processing on the point cloud data of each target subspace under each target space level to obtain each thinned point cloud data; for each target space level, performing index coding processing on the target space level and each target subspace under the target space level; generating a point cloud data query table for the mine according to the target space level and target subspace after index coding processing, and the thinned point cloud data included in each target subspace.

[0060] After obtaining the target point cloud data of the mine, the processor can perform spatial segmentation processing on the target point cloud data based on the octree spatial segmentation algorithm to obtain multiple target spatial levels corresponding to the target point cloud data. Among them, the octree spatial segmentation algorithm is a spatial non-uniform grid partitioning algorithm. In this solution, the algorithm can divide the spatial cube containing the entire mine scene into eight cubic grids in three directions and organize them into an octree.

[0061] In an embodiment of the present application, based on the octree space segmentation algorithm, the target point cloud data is spatially segmented to obtain multiple target space levels corresponding to the target point cloud data, including: based on the octree space segmentation algorithm, and according to the minimum recursive depth, the target point cloud data is divided into multiple basic space levels, each basic space level includes a preset number of data subspaces; for each data subspace included in the highest basic space level, based on a preset density division threshold and the operation requirement parameters of the mine, it is determined whether the data subspace contains point cloud data of the area of ​​interest of the mine; for each data subspace included in the highest basic space level, when it is determined that the data subspace contains point cloud data of the area of ​​interest, the data subspace is recursively divided until the maximum recursive depth is reached; it is determined that the spatial segmentation processing of the target point cloud data is completed, and multiple target space levels corresponding to the target point cloud data are obtained, and each target space level includes a preset number of target subspaces.

[0062] The processor may divide the target point cloud data into a plurality of basic spatial levels based on the octree spatial segmentation algorithm and according to the minimum recursive depth, each basic spatial level including a preset number of data subspaces. The minimum recursive depth refers to the basic division level for the target point cloud data. The preset number is 8. For each data subspace included in the highest basic spatial level, the processor may determine whether the data subspace contains point cloud data of the area of ​​interest of the mine based on a preset density division threshold and the operation requirement parameters of the mine.

[0063] The preset density division threshold can be customized according to the actual situation, and the preset density division threshold can be used to determine whether the density of the point cloud data in the corresponding data subspace is high or low. The operation demand parameters may include the mining operation surface, the blast pile location, and the actual operation lane. The area of ​​interest refers to the area with high density and high demand in the mine, and the high or low demand of the area can be determined based on the operation demand parameters.

[0064] For each data subspace included in the highest basic spatial level, when it is determined that the data subspace contains point cloud data of the area of ​​interest, the processor can recursively divide the data subspace until the maximum recursion depth is reached. Among them, the multiple basic spatial levels corresponding to the target point cloud data have corresponding division times, and the basic spatial level corresponding to the latest division time can be determined as the highest basic spatial level. The maximum recursion depth refers to the division threshold level, and the maximum recursion depth is greater than the minimum recursion depth.

[0065] After recursively dividing the target point cloud data to the maximum recursion depth, the processor may determine that the spatial segmentation processing of the target point cloud data is completed, and obtain multiple target space levels corresponding to the target point cloud data, each target space level including a preset number of target subspaces, wherein the preset number is 8.

[0066] After performing spatial segmentation processing on the target point cloud data, the processor can perform thinning processing on the point cloud data of each target subspace under each target space level to obtain each thinned point cloud data. The thinning processing can remove redundant point cloud data in each target subspace, thereby reducing the amount of point cloud data in each target subspace and reducing the subsequent calculation and storage amount.

[0067] In an embodiment of the present application, the point cloud data of each target subspace under each target space level is thinned out to obtain each point cloud data after thinning out, including: for a preset number of target subspaces included in each target space level, judging whether each point cloud data in the target subspace contains at least one of preset geomorphic feature information, mine facility information and mine excavation surface information; when each point cloud data in the target subspace contains at least one of preset geomorphic feature information, mine facility information and mine excavation surface information, determining that the point cloud data is the target point cloud data to be retained in the target subspace; for each target point cloud data under each target space level, determining the retention priority of the target point cloud data according to the preset geomorphic feature information and / or mine facility information and / or mine excavation surface information contained in the target point cloud data; for each target space level, retaining the corresponding target point cloud data according to the retention priority of each target point cloud data under the target space level, and deleting the point cloud data outside the target point cloud data, so as to complete the thinning out of the point cloud data for each target subspace under the target space level.

[0068] For each target space level including a preset number of target subspaces, the processor may determine whether each point cloud data in the target subspace contains at least one of preset geomorphic feature information, mine facility information, and mine excavation face information. The preset geomorphic feature information may include information such as the location, shape, and size of mine areas such as mine slopes, mine roads, and mine dangerous areas. The mine facility information may include information such as the location, shape, and size of mine facilities such as transportation equipment, houses, ventilation equipment, and monitoring equipment. The mine excavation face information may include information such as the location, shape, size, and excavation progress of the excavation face.

[0069] In the case where each point cloud data in the target subspace contains at least one of preset geomorphic feature information, mine facility information, and mine excavation face information, the processor may determine that the point cloud data is the target point cloud data that needs to be retained in the target subspace. For each target point cloud data under each target space level, the processor may determine the retention priority of the target point cloud data according to the preset geomorphic feature information and / or mine facility information and / or mine excavation face information contained in the target point cloud data. Specifically, the feature importance between the preset geomorphic feature information, mine facility information, and mine excavation face information may be set. For example, the importance of mine excavation face information is higher than that of mine facility information, and the importance of mine equipment information is higher than that of preset geomorphic feature information. Then, the retention priority of the point cloud data including the mine excavation face information is higher than that of the point cloud data including the mine facility information, and the retention priority of the point cloud data including the mine facility information is higher than that of the point cloud data including the mine excavation face information.

[0070] For each target space level, the processor can retain the corresponding target point cloud data according to the retention priority of each target point cloud data under the target space level, and delete the point cloud data other than the target point cloud data, so as to complete the thinning processing of the point cloud data for each target subspace under the target space level. For example, the target point cloud data including the mining face information can be retained first, and then the target point cloud data including the mining facility information can be retained, and finally the target point cloud data including the preset landform feature information can be retained. In the above scheme, during the thinning process, the target point cloud data with high priority is retained first, and the target point cloud data with low priority is reduced layer by layer, so that the important point cloud data under different target space levels can be retained and the amount of data can be reduced.

[0071] For each target space level, the processor may perform index encoding processing on the target space level and each target subspace under the target space level.

[0072] In an embodiment of the present application, for each target space level, index coding processing is performed on the target space level and each target subspace under the target space level, including: determining the level index value of each target space level based on the central point cloud of each target space level; determining the auxiliary index value of each target subspace under each target space level based on the central point cloud of each target subspace under each target space level; mapping each target space level to a root node, and mapping each target subspace under each target space level to an octree node; associating the level index value of each target space level with the corresponding root node, and associating the auxiliary index value of each target subspace under each target space level with the corresponding octree node, so as to complete the index coding processing of each target subspace under each target space level.

[0073] The processor may determine the hierarchical index value of each target spatial level according to the central point cloud of each target spatial level. The processor may determine the auxiliary index value of each target subspace under each target spatial level according to the central point cloud of each target subspace under each target spatial level. Among them, the hierarchical index value may be a geohash value (GeoHash) determined according to the longitude and latitude corresponding to the central point cloud of each target spatial level. The auxiliary index value may be a geohash value determined according to the longitude and latitude corresponding to the central point cloud of each target subspace under each target spatial level.

[0074] The processor may map each target space level to a root node, and map each target subspace under each target space level to an octree node. The processor may associate the level index value of each target space level with the corresponding root node, and associate the auxiliary index value of each target subspace under each target space level with the corresponding octree node, so as to complete the index encoding processing of each target subspace under each target space level.

[0075] The processor can generate a point cloud data query table for the mine according to the target space level and target subspace after index coding processing, and the point cloud data after sparse processing included in each target subspace. Among them, the point cloud data query table can be a hash table, through which the corresponding root node and octree node can be quickly located according to the level index value and the auxiliary index value, and the corresponding level index value and auxiliary index value can also be quickly obtained according to the root node and the octree node.

[0076] In an embodiment of the present application, the processing method also includes: determining a tile data structure for the mine based on a point cloud data query table, the tile data structure including a tile level corresponding to the target space level, and a data tile corresponding to each target subspace; optimizing and verifying each data tile and the boundaries and indexes of each data tile to obtain a data tile that meets preset conditions; converting the point cloud data in each data tile that meets the preset conditions into a preset format according to a preset specification standard; creating a tile entry file for the mine based on each tile level and the data tiles that meet the preset conditions under each tile level; generating a three-dimensional tile data set for the mine based on the point cloud data after format conversion included in the tile entry file and each data tile that meets the preset conditions.

[0077] The processor can determine the tile data structure for the mine based on the point cloud data query table. Among them, the tile data structure includes a tile level corresponding to the target space level, and a data tile corresponding to each target subspace. The processor can optimize and verify each data tile and the boundaries and indexes of each data tile to obtain a data tile that meets the preset conditions. Among them, the boundary verification of the data tile includes absolute positioning verification and relative positioning verification. Specifically, the processor can determine whether the four boundaries of the data tile are within the mine area, and can determine whether the four boundaries of the tile are seamlessly connected with the four boundaries of the adjacent tiles, without overlap and gaps.

[0078] The index verification of the data tile includes verifying the integrity and uniqueness of the index value. Specifically, the processor can verify from top to bottom whether each level of nodes contains eight child node indexes, and can verify the uniqueness of the index value generated based on the GeoHash algorithm. If the boundaries of adjacent tiles of the data tile are seamlessly connected without overlap or gaps, and the four boundaries of the data tile are within the mining area, and the index value of the data tile is unique, and the data tile complies with the GeoHash rules, that is, the index encoding rules, then it can be determined that the data tile meets the preset conditions.

[0079] The processor can convert the point cloud data in each data tile that meets the preset conditions into a preset format according to the preset specification standard. Among them, the preset specification standard refers to the specification standard that meets the 3D Tiles, and the preset format is the GLB format. The processor can create a tile entry file for the mine based on each tile level and the data tiles that meet the preset conditions under each tile level. The tile entry file refers to the Tileset JSON file. The tile entry file can be used to describe the organizational structure and hierarchical relationship of the point cloud data contained in all target subspaces under the target space level. The tile entry file includes information such as the boundary range of the point cloud data in each target subspace, the boundary range of each tile, and the GLB file path. The processor can generate a three-dimensional tile data set for the mine based on the point cloud data after format conversion included in the tile entry file and each data tile that meets the preset conditions. Among them, the three-dimensional tile data set refers to the 3D Tiles file.

[0080] In one embodiment, the three-dimensional tile dataset may be deployed on a server so as to be subsequently accessed and loaded through a network.

[0081] In an embodiment of the present application, the processing method also includes: when new point cloud data of the mine is acquired, the new point cloud data and the corresponding point cloud data are merged based on an incremental update algorithm to obtain new target point cloud data; the spatial hierarchy of the new target point cloud data is reconstructed, and the point cloud data query table is updated based on the new spatial hierarchy; the target data tile whose correlation degree with the new target point cloud data is greater than a preset degree is determined, and the target data tile is preferentially updated based on the new target point cloud data to generate a new tile entry file.

[0082] When new point cloud data of the mine is acquired, the processor can fuse the new point cloud data with the corresponding point cloud data based on the incremental update algorithm to obtain new target point cloud data. Specifically, the incremental update algorithm refers to updating the point cloud data that has changed, while retaining the point cloud data that has not changed. The processor can reconstruct the spatial hierarchy of the new target point cloud data, and update the point cloud data query table based on the new spatial hierarchy. The processor can determine the target data tile whose correlation with the new target point cloud data is greater than a preset degree, and preferentially update the target data tile based on the new target point cloud data to generate a new tile entry file.

[0083] When determining the target data tile whose correlation with the new target point cloud data is greater than the preset degree, it can be based on two parts of information. On the one hand, the influence degree of the target data tile and the unit density of the point cloud data. When the point cloud density is higher than the set threshold, it enters the emergency update queue, and when the point cloud density is lower than the set threshold, it enters the normal update queue. On the other hand, it is the data type set by the user when the point cloud is updated, including the blasting area type, the operation area type, the road data, etc. The data can be updated according to the built-in priority order of the system to realize the dynamic update and management of the mine's point cloud data. According to the needs and changes of real-time point cloud data, the mine's point cloud data can be dynamically updated and adjusted to maintain the timeliness and accuracy of the point cloud data.

[0084] like Figure 2 As shown, a flow chart of another method for processing mine data is provided.

[0085] The point cloud data of the mine can be laser point cloud data. The point cloud data can be small-area laser point cloud data obtained by a fixed-point laser radar scanner, or large-area laser point cloud data obtained by a laser radar scan carried by a drone. After obtaining the point cloud data of the mine, data preprocessing, data fusion processing, space segmentation processing and format conversion processing can be performed in sequence. Then, after obtaining new point cloud data, the updated part of the point cloud data can be dynamically updated locally.

[0086] Among them, after data preprocessing, a point cloud data set under the same reference coordinates can be obtained. Data preprocessing may include the following steps:

[0087] S1-1: Noise removal. A hybrid denoising algorithm based on statistical filtering method and normal vector denoising method is used to remove noise from all the acquired point cloud data. Specifically, the parameters of statistical filtering can be set based on the empirical parameters of the mine point cloud and the actual status of the target mine. The parameters of statistical filtering mainly include neighborhood size and threshold. Based on the set statistical filtering parameters, outliers and noise in all the acquired point cloud data can be removed, and the main structure and characteristics of all the acquired point cloud data can be retained. For the point cloud data after statistical filtering, denoising can be performed based on the normal vector denoising method, and the filtered denoised point cloud data can be evaluated by visual method and specific parameters. If the filtered denoised point cloud data meets the evaluation criteria, point cloud alignment can be performed, otherwise the parameters are readjusted and noise removal is performed again.

[0088] S1-2: Point cloud alignment. Due to the complex terrain, uneven data density, and huge data volume of the mine, a multi-scale registration method can be used to convert the filtered and denoised point cloud data from multiple sources and at different times into the same coordinate system. Specifically, the initial base point coordinates and reference coordinate system can be set, and the initial large-scale matching of the entire mine scene can be performed based on the global registration method to quickly obtain preliminary registration results. After that, local registration can be performed based on local feature descriptions to improve the accuracy and reliability of the registration.

[0089] S1-3: Statistical processing. Based on the statistical information, geometric information and metadata generated during the data collection process of the point cloud data after the point cloud alignment process, the boundary range and color information of the point cloud data after the point cloud alignment process are extracted. Among them, the statistical information of the point cloud data after the point cloud alignment process may include the number of point clouds, the average density of point clouds, the four-dimensional coordinate values ​​of point clouds, etc. The geometric information of the point cloud data after the point cloud alignment process may include the normal vector information and curvature information of each point cloud data. The metadata generated during the data collection process may include the collection time, collection location, collection equipment information, etc.

[0090] After data fusion processing, point cloud data sets collected from different sensors or at different times can be combined to obtain complete original point cloud data of the mine. Data fusion processing can include the following steps:

[0091] S2-1: Set up the data base. In the actual production process, due to the data collection cost, meteorological conditions and other factors of the mine, the point cloud data set of the mine is usually obtained by airborne laser radar scanning and fixed point or handheld laser radar scanning. This method uses the point cloud data obtained by the airborne laser radar regularly as the point cloud base of the target mine. Before the airborne laser radar point cloud data is obtained in the next update cycle, all fixed point or handheld laser radar data will be fused on this base.

[0092] S2-2: Fusion priority calculation. For each point cloud data, record its acquisition timestamp and data quality parameters. In the point cloud data fusion process, fusion calculation is performed according to the time sequence of the point cloud data and the weighted data quality parameters.

[0093] S2-3: Coverage fusion. The point cloud data are fused according to the fusion priority of each point cloud data to ensure that the newer point cloud data can cover and update the older data, and the point cloud data with high data quality can cover and update the point cloud data with low data quality, and record the scope of data fusion and its density information.

[0094] After the spatial segmentation process, point cloud data with divided spatial levels, spatial indexes and auxiliary indexes are obtained. The point cloud data is organized into a more structured form, and a spatial index is constructed, which is conducive to spatial query and retrieval of point cloud data. The spatial segmentation process may include the following steps:

[0095] S3-1: Density division threshold. According to the point cloud metadata obtained in the above steps, such as acquisition range, point cloud density, point cloud quality assessment, and combined with actual operation demand parameters, such as mining operation surface, blasting pile location, actual operation lane, etc., set the density division threshold to obtain areas with high density and high demand, and areas with low density and low demand.

[0096] S3-2: Octree segmentation. Areas with high density and high demand are divided in detail, and areas with low density and low demand are divided roughly. Specifically, the space where the entire point cloud data is located is recursively divided into eight subspaces each time, and a node is generated for each subspace. The point cloud data in the subspace is stored in the node. For non-leaf nodes that meet the division threshold conditions, the subspace is recursively divided until the accuracy requirements are met. For non-leaf nodes that do not meet the division threshold conditions, the recursive division is stopped.

[0097] S3-3: Spatial index. Perform layered thinning on the mine point cloud. In the point cloud thinning stage, important geomorphological features, facility information, mining surface and other feature information of the mine area are input, and retention rules and priorities are set to retain important point cloud information at different levels and reduce the amount of data. Based on the above octree spatial segmentation strategy, an octree spatial index is constructed, and its level identifier is determined according to the level of the node. The level identifier of the root node can be set to 1, and the level identifiers of its child nodes are incremented in sequence.

[0098] S3-4: Auxiliary index. For each spatial region represented by an octree node, calculate its corresponding 3D coordinate range and convert its mapping into a local GeoHash code. In this way, each subspace region corresponds to a unique local GeoHash code.

[0099] S3-5: Index mapping. Associate each octree node with its corresponding GeoHash code and manage it using a hash table. This allows you to quickly locate the corresponding octree node based on the GeoHash code, and quickly obtain the corresponding GeoHash code based on the node.

[0100] After format conversion, the point cloud data with divided spatial levels, spatial indexes and auxiliary indexes can be converted into a data format that complies with the 3D Tiles standard for subsequent cross-platform and cross-device data sharing and application. The format conversion process may include the following steps:

[0101] S4-1: Determine the tile structure. Based on the multi-level point cloud data obtained by the octree space segmentation method and the point cloud thinning method, a recursive method is used to divide the subspace contained in the level into tiles according to its hierarchical structure, and the boundary range of the tile is determined to ensure that each tile has a unique boundary range. At the same time, a tile index is generated based on the spatial index obtained above, and the boundary range of each tile and the corresponding point cloud data are recorded.

[0102] S4-2: Point cloud tile encoding: Convert the point cloud data in each tile into GLB format data in a manner that complies with the 3D Tiles specification standard.

[0103] S4-3: Create an entry file (Tileset JSON file). According to the above steps, the hierarchical structure is obtained, and a Tileset JSON file is iteratively created. The Tileset JSON file can be used to describe the organizational structure and hierarchical relationship of the point cloud data contained in all subspaces under this level. The Tileset JSON file can include information such as the boundary range of the point cloud data, the boundary range of each tile, and the GLB file path.

[0104] S4-4: Generate 3D Tiles files. Optimize and verify the generated tiles, including the size, accuracy, and integrity of the tiles. At the same time, the boundaries and indexes of the tiles need to be verified to ensure that the generated tiles meet expectations and specifications, so as to generate 3D Tiles files based on the tiles that meet expectations and specifications and the corresponding point cloud data. The generated 3D Tiles data can be deployed on the server for easy access and loading through the network.

[0105] After acquiring new point cloud data, the updated part of the point cloud data can be dynamically partially updated. The dynamic partial update of the updated part of the point cloud data includes the following steps:

[0106] S5-1: Calculate the coding index. Generate the corresponding GeoHash code according to the collection range of the collected point cloud data.

[0107] S5-2: Index mapping. Then use GeoHash coding to quickly locate all the corresponding octree nodes under the collection range, and pop up the corresponding point cloud data.

[0108] S5-3: Incremental update. Overlay and fuse the latest point cloud data with the corresponding point cloud data, and only update the changed data while retaining the unchanged data.

[0109] S5-4: Update point cloud positioning. Update the boundaries of the fused point cloud data and its geometric center in geographic space.

[0110] S5-5: Spatial index update: Based on the method of spatial segmentation and spatial index construction, the spatial hierarchy covering the fused point cloud data is reconstructed and the spatial index, auxiliary index and their mapping relationship are updated.

[0111] S5-6: Dynamic tile update. Dynamically update the corresponding tile data based on the fused point cloud data. Based on the update strategy and priority, prioritize the tiles that are most affected or change faster, delay the update of tiles that change less or change slower, and regenerate the Tileset JSON file.

[0112] S5-7: Deploy the application. Deploy the 3D Tiles data regenerated based on the new Tileset JSON file to the server for easy access and loading over the network.

[0113] Through the above technical scheme, the point cloud data to be processed of the mine is obtained, and the point cloud data to be processed includes multiple global point cloud data and multiple local point cloud data; for each local point cloud data, the environmental information of the environment in which the local point cloud data is collected, the equipment type information of the equipment used, and the characteristic information of the local point cloud data are obtained; the data quality of each local point cloud data is determined according to the environmental information, and / or equipment type information, and / or characteristic information of each local point cloud data; based on the data collection time and data quality of each local point cloud data, the multiple local point cloud data are merged with the multiple global point cloud data to obtain the target point cloud data of the mine, which can accurately and completely depict the overall situation of the mine.

[0114] Figure 1 and 2 FIG. 1 is a flow chart of a method for processing mining data in one embodiment. It should be understood that although Figure 1 and 2The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 and 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0115] In one embodiment, a device for processing mine data is provided, comprising:

[0116] a memory configured to store instructions; and

[0117] The processor is configured to call instructions from the memory and implement the above-mentioned processing method for mining data when executing the instructions.

[0118] In one embodiment, a storage medium is provided, on which a program is stored, and when the program is executed by a processor, the above-mentioned processing method for mining data is implemented.

[0119] In one embodiment, a processor is provided, and the processor is used to run a program, wherein the program executes the above-mentioned processing method for mining data when it is run.

[0120] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02 and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data such as target point cloud data of the mine. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a method for processing mine data is implemented.

[0121] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0122] An embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, processing steps for mining data are implemented.

[0123] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps.

[0124] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0129] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0130] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0131] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0132] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for processing mine data, characterized in that: The processing method comprises: Acquire the point cloud data to be processed of the mine, wherein the point cloud data to be processed includes a plurality of global point cloud data and a plurality of local point cloud data; For each local point cloud data, obtain environmental information of the environment in which the local point cloud data is collected, device type information of the device used, and feature information of the local point cloud data; Determine the data quality of each local point cloud data according to the environment information, and / or the device type information, and / or the feature information of each local point cloud data; The multiple local point cloud data are fused with the multiple global point cloud data based on the data collection time and data quality of each local point cloud data to obtain the target point cloud data of the mine.

2. The method for processing mine data according to claim 1, characterized in that: The target point cloud data of the mine includes neighborhood point cloud data of a plurality of neighborhood ranges, and the fusion of the plurality of local point cloud data with the plurality of global point cloud data based on the data acquisition time and data quality of each local point cloud data to obtain the target point cloud data of the mine includes: Dividing the plurality of local point cloud data and the plurality of global point cloud data into a plurality of neighborhood ranges based on a preset spatial range where the mine is located; For each neighborhood range, weighting the data collection time and data quality of each local point cloud data within the neighborhood range, and determining a weighted mean of the corresponding local point cloud data; For each neighborhood range, based on the weighted mean of each local point cloud data within the neighborhood range, select target local point cloud data from all local point cloud data within the neighborhood range; For each neighborhood range, the target local point cloud data within the neighborhood range and the global point cloud data within the neighborhood range are fused to obtain the neighborhood point cloud data of the neighborhood range.

3. The method for processing mining data according to claim 1 or 2, characterized in that: The feature information includes geometric feature information and color feature information. The determining of the data quality of each local point cloud data according to the environment information of each local point cloud data, and / or the device type information, and / or the feature information includes: Determine the environment level of the environment where each local point cloud data is located according to the environment information of each local point cloud data; and / or Determine the sensor confidence of each local point cloud data according to the device type information of each local point cloud data; and / or Determine the data confidence of each local point cloud data according to the geometric feature information and / or color feature information of each local point cloud data; The data quality of each local point cloud data is determined according to the environment level and / or sensor confidence and / or data confidence corresponding to each local point cloud data.

4. The method for processing mine data according to claim 1, characterized in that: The processing method also includes: Based on the octree spatial segmentation algorithm, the target point cloud data is spatially segmented to obtain a plurality of target spatial levels corresponding to the target point cloud data; Performing thinning processing on the point cloud data of each target subspace at each target space level to obtain each thinned point cloud data; For each target space level, index coding is performed on the target space level and each target subspace under the target space level; A point cloud data query table for the mine is generated according to the target space level and the target subspace after index coding processing, and the point cloud data after thinning processing included in each target subspace.

5. The method for processing mine data according to claim 4, characterized in that: The octree-based spatial segmentation algorithm is used to perform spatial segmentation processing on the target point cloud data to obtain a plurality of target spatial levels corresponding to the target point cloud data, including: Based on an octree space segmentation algorithm and according to a minimum recursive depth, the target point cloud data is divided into a plurality of basic space levels, each basic space level including a preset number of data subspaces; For each data subspace included in the highest basic spatial level, judging whether the data subspace includes point cloud data of an area of ​​interest of the mine based on a preset density division threshold and an operation requirement parameter of the mine; For each data subspace included in the highest basic spatial level, when it is determined that the data subspace contains the point cloud data of the region of interest, recursively divide the data subspace until a maximum recursion depth is reached; It is determined that the spatial segmentation processing of the target point cloud data is completed, and a plurality of target space levels corresponding to the target point cloud data are obtained, each target space level including a preset number of target subspaces.

6. The method for processing mine data according to claim 4, characterized in that: The thinning process of the point cloud data of each target subspace at each target space level to obtain each thinned point cloud data includes: For a preset number of target subspaces included in each target space level, determining whether each point cloud data in the target subspace contains at least one of preset geomorphic feature information, mine facility information, and mine excavation face information; In the case where each point cloud data in the target subspace contains at least one of preset geomorphic feature information, mine facility information and mine excavation face information, determining that the point cloud data is the target point cloud data to be retained in the target subspace; For each target point cloud data under each target spatial level, determining the retention priority of the target point cloud data according to preset geomorphic feature information and / or mining facility information and / or mining face information contained in the target point cloud data; For each target space level, the corresponding target point cloud data is retained according to the retention priority of each target point cloud data under the target space level, and the point cloud data outside the target point cloud data is deleted to complete the thinning processing of the point cloud data for each target subspace under the target space level.

7. The method for processing mine data according to claim 4, characterized in that: For each target space level, performing index coding processing on the target space level and each target subspace under the target space level includes: Determine a level index value of each target spatial level according to a central point cloud of each target spatial level; Determine an auxiliary index value of each target subspace at each target space level according to a central point cloud of each target subspace at each target space level; Map each target space level to a root node, and map each target subspace under each target space level to an octree node; The hierarchical index value of each target space level is associated with the corresponding root node, and the auxiliary index value of each target subspace under each target space level is associated with the corresponding octree node to complete the index encoding processing of each target subspace under each target space level.

8. The method for processing mine data according to claim 4, characterized in that: The processing method also includes: Determining a tile data structure for the mine based on the point cloud data query table, the tile data structure comprising a tile level corresponding to a target space level, and a data tile corresponding to each target subspace; Optimize and verify each data tile and the boundaries and indexes of each data tile to obtain data tiles that meet preset conditions; Convert the point cloud data in each data tile that meets the preset conditions into a preset format according to the preset specification standard; Creating a tile entry file for the mine according to each tile level and the data tiles under each tile level that meet the preset conditions; A three-dimensional tile data set for the mine is generated according to the point cloud data after format conversion included in the tile entry file and each data tile that meets the preset conditions.

9. The method for processing mine data according to claim 8, characterized in that: The processing method also includes: When new point cloud data of the mine is obtained, the new point cloud data is fused with the corresponding point cloud data based on an incremental update algorithm to obtain new target point cloud data; Reconstructing the spatial hierarchy of the new target point cloud data, and updating the point cloud data query table based on the new spatial hierarchy; Determine a target data tile that has a correlation degree with the new target point cloud data greater than a preset degree, and preferentially update the target data tile based on the new target point cloud data to generate a new tile entry file.

10. A processing device for mining data, characterized in that: The processing device comprises: a memory configured to store instructions; and A processor is configured to call the instructions from the memory and implement the method for processing mine data according to any one of claims 1 to 9 when executing the instructions.

11. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for causing a machine to execute the method for processing mining data according to any one of claims 1 to 9.

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