A data processing method, device and system for surveying and mapping data

Abstract

The present invention relates to the field of data processing technology, and in particular to a data processing method, device and system for surveying and mapping data, the method comprising: clustering the surveying and mapping data of the overall terrain of a target mine; determining the target cluster according to the number of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster; determining the regional characteristic parameters of the first terrain area corresponding to the target cluster by using the first vector formed by the adjacent surveying and mapping data in the target cluster and the angle formed between the first vectors; determining the irregular terrain area from the first terrain area according to the regional characteristic parameters; determining the area type to which the second terrain area belongs according to the regional characteristic parameters of the irregular terrain area and the Euclidean distance between the centroids of the adjacent target clusters in the second terrain area; and performing lossless compression processing on the surveying and mapping data of the second terrain area according to the area type. The present invention improves the reliability of data compression for surveying and mapping data and ensures the high quality of compressed data.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a data processing method, device and system for surveying and mapping data. Background Art

[0002] The surveying and mapping data of mine terrain is the data that accurately measures and records the mine and its surrounding terrain. These surveying and mapping data provide data support for mine development, resource assessment, environmental monitoring and safety management. Surveying and mapping data include but are not limited to topographic maps, elevation data, geological structure information, land cover information and underground structure data.

[0003] In the process of mine mapping, with the continuous accumulation of mapping data, the complexity and scale of mapping data are increasing. In order to facilitate the storage and classification of mapping data, point cloud compression technology is often used to compress the mapping data losslessly. However, due to the complex terrain of the mine, especially the irregular areas such as steep slopes, cliffs, and areas caused by mining operations, the mapping data is too much and complex. When the mapping data of these areas are lossily compressed, some data will be lost, and the integrity and accuracy of the data will be affected. In this way, the reliability of data compression for mapping data is low, resulting in a decrease in data quality and failure to reflect the real mine terrain. Summary of the invention

[0004] In order to solve the technical problem that the reliability of data compression for surveying and mapping data is low, resulting in a decrease in data quality, the purpose of the present invention is to provide a data processing method, device and system for surveying and mapping data. The technical solutions adopted are as follows:

[0005] An embodiment of the present invention provides a data processing method for surveying and mapping data, comprising: clustering the surveying and mapping data of the overall terrain of a target mine to obtain multiple clusters; determining the target cluster according to the number of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster, wherein the density coefficient of the target cluster is greater than or equal to a first threshold; determining the regional characteristic parameters of a first terrain area corresponding to the target cluster using a first vector formed by adjacent surveying and mapping data in the target cluster and an angle formed between the first vectors; determining an irregular terrain area from the first terrain area according to the regional characteristic parameters; determining the regional type to which the second terrain area belongs according to the regional characteristic parameters of the irregular terrain area and the Euclidean distance between the centroids of adjacent target clusters in the second terrain area, wherein the second terrain area includes the irregular terrain areas corresponding to the multiple target clusters; and performing lossless compression processing on the surveying and mapping data of the second terrain area according to the regional type.

[0006] Optionally, determining the target cluster according to the number of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster includes: determining the point coordinates of each surveying and mapping data in the cluster in three-dimensional space; making a circumscribed polyhedron for the point coordinates of each surveying and mapping data, and determining the volume of the circumscribed polyhedron as the spatial volume occupied by the surveying and mapping data in the cluster; normalizing the ratio of the number of surveying and mapping data in the cluster to the spatial volume to obtain a density coefficient of the cluster; and determining a cluster having a density coefficient greater than or equal to a first threshold as a target cluster.

[0007] Optionally, determining the regional characteristic parameters of the first terrain area corresponding to the target cluster using a first vector formed by adjacent surveying and mapping data in the target cluster and the angle formed between the first vectors includes: selecting three consecutive adjacent surveying and mapping data from the target cluster; calculating the angle between one of the three adjacent surveying and mapping data and the first vector formed by the other two surveying and mapping data; when the angle is greater than a second threshold, determining that the three consecutive adjacent surveying and mapping data are the boundary lines of two terrain sub-areas in the first terrain area; dividing the first terrain area according to the boundary lines to obtain multiple terrain sub-areas, and determining a second vector formed between each surveying and mapping data of each terrain sub-area and the surveying and mapping data at the boundary line of the terrain sub-area; and determining the regional characteristic parameters of the first terrain area corresponding to the target cluster using the density coefficient of the target cluster, the first number of terrain sub-areas, the second number of second vectors in the terrain sub-areas, and the adjacent second vectors in each terrain sub-area.

[0008] Optionally, using the density coefficient of the target cluster, the first number of terrain sub-regions, the second number of second vectors in the terrain sub-regions, and the adjacent second vectors in each terrain sub-region, determining the regional characteristic parameters of the first terrain region corresponding to the target cluster includes: superimposing the dot products between adjacent second vectors in each terrain sub-region to obtain a first superposition value; calculating a first difference between the second number and a preset value and a first product of the first number, and a sum of the first product and the preset value; determining a first ratio of the first superposition value to the sum and a second product of the density coefficient as the regional characteristic parameter.

[0009] Optionally, determining the region type to which the second terrain region belongs based on the regional characteristic parameters of the irregular terrain region and the Euclidean distance between the centroids of adjacent target clusters in the second terrain region includes: performing height division based on the values ​​of the centroids of the target clusters in the second terrain region on the z-axis to obtain multiple height regions; calculating the Euclidean distance between the centroid of each target cluster and the centroid of its adjacent target cluster in the same height region; superimposing second ratios of the pairwise Euclidean distances in the second terrain region to obtain a second superposition value; determining the product of the regional characteristic parameter and the second superposition value as the distance dispersion coefficient between the centroids of the target clusters; and determining the region type to which the second terrain region belongs based on the mean value of the angle between the first vectors formed by adjacent surveying and mapping data in the target clusters in the second terrain region and the distance dispersion coefficient.

[0010] Optionally, determining the region type to which the second terrain region belongs based on the mean of the angles between first vectors formed by adjacent surveying and mapping data in the target cluster in the second terrain region and the distance dispersion coefficient includes: calculating a first mean of the angles between first vectors formed by adjacent surveying and mapping data in the current target cluster in the second terrain region, and a second mean of the angles between first vectors formed by adjacent surveying and mapping data in target clusters other than the current target cluster in the second terrain region; respectively calculating third ratios of the first mean and each second mean, and superimposing each third ratio to obtain a third superposition value; calculating a second difference between the product of the distance dispersion coefficient and the third superposition value and a preset value; normalizing the second difference to obtain an overall variation coefficient of the second terrain region; and determining that the region type to which the second terrain region belongs is a flat region when the overall variation coefficient is greater than or equal to a third threshold, and determining that the region type to which the second terrain region belongs is an uneven region when the overall variation coefficient is less than the third threshold.

[0011] Optionally, determining the irregular terrain region from the first terrain region according to the regional characteristic parameter includes: determining the first terrain region whose regional characteristic parameter is greater than or equal to a fourth threshold as the irregular terrain region.

[0012] Optionally, losslessly compressing the surveying and mapping data of the second terrain area according to the area type includes: when the area type to which the second terrain area belongs is a flat area, selecting core data from the surveying and mapping data in the second terrain area, and performing lossless compression on the core data; when the area type to which the second terrain area belongs is an uneven area, performing lossless compression on all the surveying and mapping data of the second terrain area.

[0013] In a second aspect, an embodiment of the present invention provides a data processing system for surveying and mapping data, including: a clustering module, used to cluster the surveying and mapping data of the overall terrain of a target mine to obtain multiple clusters; a determination module, used to determine the target cluster according to the number of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster, and the density coefficient of the target cluster is greater than or equal to a first threshold; the determination module is also used to determine the regional characteristic parameters of the first terrain area corresponding to the target cluster using the first vector formed by adjacent surveying and mapping data in the target cluster and the angle formed between the first vectors; the determination module is also used to determine the irregular terrain area from the first terrain area according to the regional characteristic parameters; the determination module is also used to determine the area type to which the second terrain area belongs according to the regional characteristic parameters of the irregular terrain area and the Euclidean distance between the centroids of adjacent target clusters in the second terrain area, and the second terrain area includes irregular terrain areas corresponding to multiple target clusters; a processing module is used to perform lossless compression processing on the surveying and mapping data of the second terrain area according to the area type.

[0014] In a third aspect, an embodiment of the present invention provides a data processing device for surveying and mapping data, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the data processing method for surveying and mapping data mentioned in the first aspect are implemented.

[0015] The present invention has the following beneficial effects: first, the surveying and mapping data of the overall terrain of the target mine are clustered to obtain multiple clusters. Then, the target cluster is determined according to the number of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster, and the data density in the target cluster is greater than or equal to the first threshold. Then, the regional characteristic parameters of the first terrain area corresponding to the target cluster are determined by using the first vector formed by the adjacent surveying and mapping data in the target cluster and the angle formed between the first vectors; secondly, the irregular terrain area is determined from the first terrain area according to the regional characteristic parameters, and the regional type to which the second terrain area belongs is determined according to the regional characteristic parameters of the irregular terrain area and the Euclidean distance between the centroids of the adjacent target clusters in the second terrain area, and the second terrain area includes the irregular terrain areas corresponding to the multiple target clusters. Finally, the surveying and mapping data of the second terrain area are losslessly compressed according to the regional type.

[0016] In this way, by analyzing the surveying and mapping data in the target mine, the differences in vectors of the target mine in different terrain areas are obtained, so as to determine the irregular terrain areas in the target mine. Further analysis is performed on the irregular terrain areas, and the Euclidean distance between the irregular terrain areas is used to characterize the similarity between different irregular terrain areas, and the regional type of the overall terrain area where these irregular terrain areas are located is determined, and the surveying and mapping data is losslessly compressed according to the regional type. When the surveying and mapping data of this part of the area is losslessly compressed, no data will be lost, and the integrity and accuracy of the data will not be affected. In this way, the reliability of data compression for surveying and mapping data is improved, and the high quality of compressed data is guaranteed to reflect the real mine terrain. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a data processing method for surveying and mapping data provided by one embodiment of the present invention;

[0019] Figure 2 A schematic diagram of a high-density clustered area provided by an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of the module composition of a data processing system for surveying and mapping data provided by an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of the structure of another data processing device for surveying and mapping data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a data processing method, device and system for surveying and mapping data proposed by the present invention, its specific implementation method, structure, features and effects in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0024] The following is a detailed description of a data processing method, device and system for surveying and mapping data provided by the present invention in conjunction with the accompanying drawings.

[0025] Embodiment 1:

[0026] See also Figure 1 , which shows a flow chart of a data processing method for surveying and mapping data provided by an embodiment of the present invention, including:

[0027] S101, clustering the surveying and mapping data of the overall terrain of the target mine to obtain a plurality of clusters.

[0028] Specifically, the overall terrain of the target mine includes natural terrain such as forests, hills, and steep cliffs, as well as unnatural terrain caused by human activities. The natural and unnatural terrain of the target mine includes steep slope areas, cliff areas, areas changed by mining operations, irregular terrain areas, and regular areas such as gentle slopes and flat land.

[0029] Furthermore, the surveying and mapping data can be point cloud data, which is a three-dimensional spatial data representation form, usually composed of a large number of discrete points, which have precise position coordinates (X, Y, Z) in three-dimensional space. When obtaining the surveying and mapping data of the overall terrain of the target mine, the navigation route of the drone for data collection can be designed according to the specific terrain of the target mine, so that these navigation routes can ensure full coverage of the entire mine area. At the same time, the distance between each navigation route should be accurately controlled within a range close to the data collection width, which can not only ensure the comprehensive coverage of the target mine, but also reduce the overlap between the collected data and improve the efficiency and quality of data collection. Then, the target mine is scanned by a drone equipped with a radar detection device along the set navigation route to obtain initial surveying and mapping data, and then the initial surveying and mapping data is converted into initial point cloud data in the form of point coordinates in three-dimensional space; finally, the initial point cloud data is preprocessed to obtain ordered point cloud data, which is the surveying and mapping data of the target mine.

[0030] Among them, the preprocessing process is as follows: first, the initial point cloud data is denoised using algorithms such as radius filtering to remove the noise points. Then, filtering is performed to separate the non-topographic information data points of the target mine, thereby improving the quality of the point cloud data. Finally, the point cloud data that has been denoised and filtered is arranged in order according to the linear scanning method used by the laser radar when scanning and detecting the target mine, and the data points of the point cloud data of the overall terrain of the target mine are arranged in order according to the order in which they are scanned, forming an orderly point cloud data for subsequent analysis and application. Furthermore, the initial mapping data contains the Global Positioning System (GPS) data, inertial navigation system (Inertial Navigation System) and terrain data of each location of the target mine.

[0031] Furthermore, in the target mine, there are many irregular areas, such as steep slopes and cliffs. These areas may be affected by construction and mining, and there are many protruding parts on the slope or cliff. This part of the terrain requires more data to represent when mapping. Compared with irregular areas such as steep slopes and cliffs, terrain areas such as gentle slopes and flat land are relatively easier to map and record, and the changes between the various areas in the terrain area are relatively small. Therefore, in the process of mapping the target mine, the mapping data points of the steep slopes, cliffs and other terrain areas of the mine are relatively dense, while the mapping data points on the gentle slopes, flat land and other terrain areas are relatively sparse compared to steep slopes and cliffs. Therefore, the mapping data of the overall terrain of the target mine can be clustered to distinguish different terrain areas. When clustering the mapping data of the overall terrain of the target mine, the K-means clustering algorithm can be used to cluster the mapping data, and the number of cluster centers K can be determined according to the elbow method. It is worth noting that the principle of using the K-means clustering algorithm to cluster data and using the elbow method to determine the number of cluster centers can be found in the prior art, and the embodiments of the present invention will not be repeated here.

[0032] S102: Determine a target cluster according to the amount of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster.

[0033] The density coefficient of the target cluster is greater than or equal to a first threshold.

[0034] Specifically, the data density in the target cluster is relatively high, and the corresponding area may be an irregular area such as a cliff or a steep slope. The first threshold can be determined according to the actual scenario, and in the embodiment of the present invention, the value is 0.6. If the density coefficient of the cluster is greater than or equal to 0.6, it means that the density of the surveying and mapping data in the cluster is relatively high, and the corresponding terrain area may be an irregular area such as a steep slope or a cliff. If the density coefficient of the cluster is less than 0.6, it means that the density of the surveying and mapping data in the cluster is relatively low, and the corresponding terrain area may be a regular area such as a gentle slope or a flat land.

[0035] Furthermore, the spatial volume occupied by the surveying and mapping data in the cluster refers to the space where the point set of the surveying and mapping data in the cluster is located, which is determined by making a circumscribed polyhedron for the surveying and mapping data in the cluster, and the circumscribed polyhedron is the smallest circumscribed polyhedron containing all the surveying and mapping data in the cluster.

[0036] Furthermore, when determining the target cluster based on the number of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster, as an optional embodiment of the present invention, firstly, the point coordinates of each surveying and mapping data in the cluster in the three-dimensional space are determined; then, a circumscribed polyhedron is made for the point coordinates of each surveying and mapping data, and the volume of the circumscribed polyhedron is determined to be the spatial volume occupied by the surveying and mapping data in the cluster; then, the ratio of the number of surveying and mapping data in the cluster to the spatial volume is normalized to obtain the density coefficient of the cluster; finally, the cluster whose density coefficient is greater than or equal to the first threshold is determined to be the target cluster.

[0037] Specifically, the density coefficient can be expressed as follows:

[0038]

[0039] In the above formula, is the cluster density coefficient, is the number of plotted data in the cluster. is the spatial volume occupied by the surveying and mapping data in this cluster. It is the ratio of the number of surveying and mapping data in a cluster to the spatial volume occupied by the surveying and mapping data in the cluster. The larger the ratio, the denser the surveying and mapping data in the cluster, and the higher the probability that the cluster is an irregular terrain area such as a steep cliff. Conversely, the higher the probability that it is a regular area such as a gentle slope or flat land. is a normalization function, which is used to Normalize it so that its value is in the range of 0 to 1.

[0040] Step S103 : determining the regional characteristic parameters of the first terrain area corresponding to the target cluster by using the first vectors formed by the adjacent surveying and mapping data in the target cluster and the angle formed between the first vectors.

[0041] Specifically, in the surveying and mapping data of the mine, high-density surveying and mapping data may appear in multiple terrain areas, such as mining roads, slopes, and steep cliffs. In these areas, the terrain of mining roads and slopes is relatively regular. They are formed around the mountain in the overall structure. The terrain between adjacent positions is similar, that is, the direction vectors formed by adjacent surveying and mapping data in their respective areas are similar or the same. However, due to the influence of long-term mining activities, the terrain in the steep cliff area has become extremely irregular, with complex landforms such as protruding rocks and sunken cavities, resulting in significant differences in terrain between adjacent positions, and the direction vectors formed by adjacent surveying and mapping data in the area are quite different. Therefore, in the surveying and mapping data, the adjacent surveying and mapping data in the cluster can be analyzed. If the vectors formed between multiple adjacent surveying and mapping data in the target cluster are similar or the same, it may be a regular terrain area such as a mining road or a slope, otherwise it may be an irregular terrain area such as a cliff. Then determine the regional characteristic parameters of the terrain area corresponding to each target cluster.

[0042] Furthermore, when determining the regional characteristic parameters of the first terrain area corresponding to the target cluster, firstly, three consecutive adjacent surveying and mapping data are selected from the target cluster; then, the angle between a first vector formed by one of the three adjacent surveying and mapping data and the other two surveying and mapping data is calculated; when the angle is greater than a second threshold, the three consecutive adjacent surveying and mapping data are determined to be the boundary line of two terrain sub-areas in the first terrain area; then, the first terrain area is divided according to the boundary line to obtain a plurality of terrain sub-areas, and a second vector formed between each surveying and mapping data of each terrain sub-area and the surveying and mapping data at the boundary line of the terrain sub-area is determined; finally, the regional characteristic parameters of the first terrain area corresponding to the target cluster are determined using the density coefficient of the target cluster, the first number of terrain sub-areas, the second number of second vectors in the terrain sub-areas, and the adjacent second vectors in each terrain sub-area.

[0043] Specifically, the angle between the first vectors can be expressed as follows:

[0044]

[0045] In the above formula, It is the angle between the first vectors formed by any three connected adjacent mapping data in the target cluster. For mapping data With adjacent surveying data The first vector formed between . Surveying and mapping data With adjacent surveying data The first vector formed. The arccosine function is used to obtain and The angle between.

[0046] Furthermore, the second threshold value can be set according to actual conditions. In the embodiment of the present invention, the second threshold value is .like Then three consecutive adjacent surveying and mapping data are the boundary lines of two terrain sub-regions in the first terrain region, and vice versa.

[0047] Further, all the surveying and mapping data in the target cluster are traversed to detect all the boundary lines, and the first terrain area is divided into s terrain sub-areas according to the number of boundary lines, and the surveying and mapping data points of each terrain sub-area and the surveying and mapping data at the boundary line of the terrain sub-area constitute a second vector, and there are k second vectors in total, and the regional characteristic parameters of the first terrain area are determined by combining the change relationship between adjacent second vectors. Adjacent second vectors refer to two vectors that are adjacent to each other.

[0048] Furthermore, when determining the regional characteristic parameters of the first terrain area corresponding to the target cluster, as an optional embodiment of the present invention, the dot products between adjacent second vectors in each terrain sub-area are first superimposed to obtain a first superposition value; then the first difference between the second quantity and the preset value and the first product of the first quantity, as well as the sum of the first product and the preset value, are calculated; finally, the first ratio of the first superposition value to the sum and the second product of the density coefficient are determined as the regional characteristic parameters.

[0049] Specifically, the preset value can be determined according to actual conditions, and in the embodiment of the present invention, the value is 1. The regional characteristic parameter can be expressed by the following formula:

[0050]

[0051] In the above formula, is the regional characteristic parameter of the first terrain area corresponding to the target cluster. is the density coefficient of the target cluster. is the first number of terrain sub-regions after the first terrain region is divided. is the second quantity of the second vector in the terrain sub-region. The target cluster corresponds to A terrain sub-region. In the terrain sub-area The second vector. For the In the terrain sub-area The second vector. For the In the terrain sub-area The second vector. For two adjacent second vectors and When the value is greater than 0, the directions of the two adjacent second vectors are similar, otherwise they are not similar. It is the mean of the dot products between the adjacent second vectors in each terrain sub-region in the target cluster. If the value is greater than 0, the directions of the two adjacent second vectors in each terrain sub-region are similar, otherwise they are not similar.

[0052] All target clusters are traversed using the above method to obtain regional characteristic parameters of the first terrain area corresponding to each target cluster.

[0053] Step S104: determining an irregular terrain region from the first terrain region according to the regional characteristic parameters.

[0054] Specifically, it can be determined that the first terrain region whose regional characteristic parameter is greater than or equal to the fourth threshold is an irregular terrain region. The fourth threshold can be determined according to actual conditions. In the embodiment of the present invention, the value is 0.6. , then the first terrain area corresponding to the target cluster is an irregular terrain area, otherwise it is not.

[0055] Step S105, determining the region type to which the second terrain region belongs according to the region characteristic parameters of the irregular terrain region and the Euclidean distance between the centroids of adjacent target clusters in the second terrain region, wherein the second terrain region includes irregular terrain regions corresponding to multiple target clusters.

[0056] Specifically, in the process of compressing the surveying and mapping data of the target cluster, due to the high data density in the target cluster, it is easy to blur the details between the surveying and mapping data, resulting in the loss of some terrain fluctuation information, which in turn causes the compressed data to lack details, affecting the accuracy and depth of subsequent research on the terrain data of the area. Therefore, before compressing the surveying and mapping data of this type of terrain, it is necessary to analyze the similarities between the first terrain areas, and then determine the area type to which the second terrain area containing each first terrain area belongs and perform targeted compression. Furthermore, in complex terrain areas such as steep cliffs, there are many uneven areas formed by mining activities. During the mining process of these terrains, in order to facilitate the mining of rock walls and the transportation of ore, the regularity of the rock walls will be maintained as much as possible during construction, or the mined ore will be made to present a polyhedron shape for easy transportation. Therefore, a high-density clustered area formed by terrain areas corresponding to multiple target clusters will be formed in the steep cliff area. As follows Figure 2 Schematic diagram of the high-density clustered area shown. Figure 2 A schematic diagram of a high-density clustered area provided by an embodiment of the present invention, where each point is a target cluster, and the distance between target clusters is the Euclidean distance in space, that is, the Euclidean distance between the centroids of the target clusters.

[0057] Furthermore, Figure 2The irregular high-density clustering area 110, the parallel high-density clustering area 111 and the high-density clustering area 112 are included. In the target mine, due to the mining operations and other activities, many irregular terrain areas appear in the mountain. The irregular terrain areas are relatively messy, and the Euclidean distances between the target clusters formed are quite different. The clustering area formed by each target cluster may be the irregular high-density clustering area 110. The distances between the target clusters formed at the intersection of gentle slopes and mining roads are similar. For example, the Euclidean distances between the target clusters on both sides of the adjacent roads in the same plane are similar. The clustering areas formed by each target cluster may be the parallel high-density clustering area 111 and the high-density clustering area 112. Among them, the clustering area formed by each target cluster is the second terrain area. Therefore, the region type to which the second terrain region belongs can be determined based on the regional characteristic parameters of the irregular terrain region and the Euclidean distance between the centroids of adjacent target clusters in the second terrain region, wherein the second terrain region may include an irregular high-density cluster region 110, a parallel high-density cluster region 111, and a high-density cluster region 112. The irregular high-density cluster region 110 may be an uneven region, and the parallel high-density cluster region 111 and the high-density cluster region 112 may be flat regions.

[0058] Further, when determining the region type to which the second terrain region belongs, as an optional embodiment of the present invention, first, height division is performed according to the value of the centroid of the target cluster in the second terrain region on the z-axis to obtain multiple height regions; then the Euclidean distance between the centroid of each target cluster and the centroid of its adjacent target cluster in the same height region is calculated; then the second ratio between the Euclidean distances in the second terrain region is superimposed to obtain a second superposition value; and the product of the regional characteristic parameter and the second superposition value is determined to be the distance dispersion coefficient between the centroids of the target clusters; finally, the region type to which the second terrain region belongs is determined according to the mean value of the angle between the first vectors formed by the adjacent surveying and mapping data in the target clusters in the second terrain region and the distance dispersion coefficient.

[0059] Specifically, when performing height division, the ground can be used as the initial plane, and the value of the z-axis in the three-dimensional coordinates of the centroid of each target cluster can be used as a split point to form multiple height regions. Furthermore, the distance dispersion coefficient can be expressed as follows:

[0060]

[0061] In the above formula, is the distance discrete parameter between the centroids of the target clusters. is the first cluster after being highly divided according to the centroid of the target cluster in the same plane. The centroid of the target cluster. is the number of height regions divided according to the centroid of the target cluster in the same plane. is the centroid of a target cluster The Euclidean distance between the centroids of adjacent target clusters in the same plane and at the same height. is the centroid of a target cluster The Euclidean distance between the centroids of adjacent target clusters in the same plane and at the same height. is the centroid of the target cluster , the centroid of the target cluster The ratio of the Euclidean distances between the centroids of the adjacent target clusters in the same plane and at the same height. The closer the value is to 1, the flatter the area is, otherwise it is not flat. is the regional characteristic parameter of the first terrain area corresponding to the target cluster.

[0062] Further, when determining the area type to which the second terrain area belongs, as an optional embodiment of the present invention, firstly, a first mean of the angles between first vectors formed by adjacent surveying and mapping data in the current target cluster in the second terrain area and a second mean of the angles between first vectors formed by adjacent surveying and mapping data in other target clusters other than the current target cluster in the second terrain area are calculated; then, third ratios of the first mean and each second mean are calculated respectively, and each third ratio is superimposed to obtain a third superposition value; then, a second difference between the product of the distance dispersion coefficient and the third superposition value and a preset value is calculated; secondly, the second difference is normalized to obtain an overall variation coefficient of the second terrain area; finally, when the overall variation coefficient is greater than or equal to a third threshold value, the area type to which the second terrain area belongs is determined to be a flat area; and when the overall variation coefficient is less than the third threshold value, the area type to which the second terrain area belongs is determined to be an uneven area.

[0063] Specifically, the preset value and the third threshold value can be determined according to actual conditions. In the embodiment of the present invention, the preset value is 1, and the third threshold value is 0.8. The overall variation coefficient of the second terrain area can be expressed by the following formula:

[0064]

[0065] In the above formula, Represents the overall variation coefficient of the second terrain area. is the distance discrete parameter between the centroids of the target clusters in the same plane. i is the number of the data group formed between the adjacent surveying and mapping data in the current target cluster in the second terrain area. n is the number of data groups formed between the adjacent surveying and mapping data in each target cluster in the second terrain area. Represents the first mean value of the angles between first vectors formed by adjacent surveying and mapping data in the i-th data group in the current target cluster of the second terrain area. The second mean value of the angles between the first vectors formed by adjacent surveying and mapping data in the (i+1)th data group in other target clusters in the second terrain area. It is used to indicate the flatness of the second terrain area. The closer the value is to 1, the flatter the second terrain area is, and vice versa. is a normalization function, which is Normalize it so that In the range of 0 to 1.

[0066] The second terrain area formed by the irregular terrain area is traversed and detected by the above method, and the third threshold is set to 0.8. , then the second terrain area is a flat area, otherwise it is an uneven area.

[0067] Step S106: Losslessly compress the surveying and mapping data of the second terrain area according to the area type.

[0068] Specifically, when the area type to which the second terrain area belongs is a flat area, core data is selected from the surveying and mapping data in the second terrain area, and lossless compression is performed on the core data; when the area type to which the second terrain area belongs is an uneven area, lossless compression is performed on all the surveying and mapping data in the second terrain area. The selection of core data from the surveying and mapping data in the second terrain area can be to divide the second terrain area into a plurality of sub-areas, calculate the average value of the surveying and mapping data in each sub-area, and finally use the average value of the surveying and mapping data in each sub-area as the core data. The lossless compression process can use Run-Length Encoding (RLE), Huffman coding, etc.

[0069] The embodiment of the present invention analyzes the surveying and mapping data in the target mine to obtain the differences in vectors of the target mine in different terrain areas, thereby determining the irregular terrain areas in the target mine. Further analysis is performed on the irregular terrain areas, and the Euclidean distance between the irregular terrain areas is used to characterize the similarity between different irregular terrain areas, and the area type of the overall terrain area where these irregular terrain areas are located is determined, and the surveying and mapping data is losslessly compressed according to the area type. When the surveying and mapping data of this part of the area is losslessly compressed, no data will be lost, and the integrity and accuracy of the data will not be affected. In this way, the reliability of data compression for surveying and mapping data is improved, and the high quality of compressed data is guaranteed to reflect the real mine terrain.

[0070] Embodiment 2:

[0071] The data processing method for surveying and mapping data provided in the above embodiment is based on the same technical concept, such as Figure 3 As shown, the embodiment of the present invention further provides a data processing system 200 for surveying and mapping data, including: a clustering module 201, used to cluster the surveying and mapping data of the overall terrain of the target mine to obtain multiple clusters; a determination module 202, used to determine the target cluster according to the number of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster, and the density coefficient of the target cluster is greater than or equal to a first threshold; the determination module 202 is also used to determine the regional characteristic parameters of the first terrain area corresponding to the target cluster by using the first vector formed by the adjacent surveying and mapping data in the target cluster and the angle formed between the first vectors; the determination module 202 is also used to determine the irregular terrain area from the first terrain area according to the regional characteristic parameters; the determination module 202 is also used to determine the area type to which the second terrain area belongs according to the regional characteristic parameters of the irregular terrain area and the Euclidean distance between the centroids of the adjacent target clusters in the second terrain area, and the second terrain area includes the irregular terrain areas corresponding to the multiple target clusters; the processing module 203 is used to perform lossless compression processing on the surveying and mapping data of the second terrain area according to the area type.

[0072] The embodiment of the present invention analyzes the surveying and mapping data in the target mine to obtain the differences in vectors of the target mine in different terrain areas, thereby determining the irregular terrain areas in the target mine. Further analysis is performed on the irregular terrain areas, and the Euclidean distance between the irregular terrain areas is used to characterize the similarity between different irregular terrain areas, and the area type of the overall terrain area where these irregular terrain areas are located is determined, and the surveying and mapping data is losslessly compressed according to the area type. When the surveying and mapping data of this part of the area is losslessly compressed, no data will be lost, and the integrity and accuracy of the data will not be affected. In this way, the reliability of data compression for surveying and mapping data is improved, and the high quality of compressed data is guaranteed to reflect the real mine terrain.

[0073] Optionally, the determination module 202 is further used to determine the point coordinates of each surveying and mapping data in the cluster in the three-dimensional space; make a circumscribed polyhedron for the point coordinates of each surveying and mapping data, and determine the volume of the circumscribed polyhedron as the spatial volume occupied by the surveying and mapping data in the cluster; normalize the ratio of the number of surveying and mapping data in the cluster to the spatial volume to obtain the density coefficient of the cluster; and determine the cluster whose density coefficient is greater than or equal to the first threshold as the target cluster.

[0074] Optionally, the determination module 202 is further used to select three consecutive adjacent surveying and mapping data from the target cluster; calculate the angle between a first vector formed by one surveying and mapping data of the three adjacent surveying and mapping data and the other two surveying and mapping data; when the angle is greater than a second threshold, determine that the three consecutive adjacent surveying and mapping data are the boundary lines of two terrain sub-regions in the first terrain region; divide the first terrain region according to the boundary lines to obtain multiple terrain sub-regions, and determine a second vector formed between each surveying and mapping data of each terrain sub-region and the surveying and mapping data at the boundary line of the terrain sub-region; and determine the regional characteristic parameters of the first terrain region corresponding to the target cluster by using the density coefficient of the target cluster, the first number of terrain sub-regions, the second number of second vectors in the terrain sub-regions, and the adjacent second vectors in each terrain sub-region.

[0075] Optionally, the determination module 202 is also used to superimpose the dot products between adjacent second vectors in each terrain sub-region to obtain a first superposition value; calculate the first difference between the second quantity and the preset value and the first product of the first quantity, and the sum of the first product and the preset value; determine the first ratio of the first superposition value to the sum and the second product of the density coefficient as the regional characteristic parameter.

[0076] Optionally, the determination module 202 is further used to perform height division according to the values ​​of the centroids of the target clusters in the second terrain area on the z-axis to obtain multiple height areas; calculate the Euclidean distance between the centroid of each target cluster and the centroid of its adjacent target cluster in the same height area; superimpose the second ratio between the Euclidean distances in the second terrain area to obtain a second superposition value; determine the product of the regional characteristic parameter and the second superposition value as the distance dispersion coefficient between the centroids of the target clusters; determine the area type to which the second terrain area belongs according to the mean value of the angle between the first vectors formed by the adjacent surveying and mapping data in the target clusters in the second terrain area and the distance dispersion coefficient.

[0077] Optionally, the determination module 202 is further used to calculate a first mean of angles between first vectors formed by adjacent surveying and mapping data in the current target cluster in the second terrain area, and a second mean of angles between first vectors formed by adjacent surveying and mapping data in target clusters other than the current target cluster in the second terrain area; respectively calculate third ratios of the first mean and each second mean, and superimpose each third ratio to obtain a third superposition value; calculate a second difference between the product of the distance dispersion coefficient and the third superposition value and a preset value; normalize the second difference to obtain an overall variation coefficient of the second terrain area; when the overall variation coefficient is greater than or equal to a third threshold, determine that the area type to which the second terrain area belongs is a flat area; when the overall variation coefficient is less than the third threshold, determine that the area type to which the second terrain area belongs is an uneven area.

[0078] Optionally, the determination module 202 is further configured to determine that the first terrain region whose regional characteristic parameter is greater than or equal to a fourth threshold is an irregular terrain region.

[0079] Optionally, the processing module 203 is also used to select core data from the surveying and mapping data in the second terrain area and perform lossless compression on the core data when the area type to which the second terrain area belongs is a flat area; and to perform lossless compression on all surveying and mapping data of the second terrain area when the area type to which the second terrain area belongs is an uneven area.

[0080] Embodiment three:

[0081] Corresponding to the data processing method for surveying and mapping data provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides a data processing device for surveying and mapping data, and the data processing device for surveying and mapping data is used to execute the above data processing method for surveying and mapping data. Figure 4 A schematic diagram of a data processing device for surveying and mapping data is provided to implement various embodiments of the present invention. Figure 4 As shown in the hardware level, the data processing device for surveying and mapping data includes a processor, and optionally, an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the data processing device for surveying and mapping data may also include hardware required for other services.

[0082] The processor, network interface and memory can be interconnected through an internal bus, which can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0083] The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operation commands. The memory may include internal memory and non-volatile memory, and provide instructions and data to the processor.

[0084] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a device for locating the specified user at the logical level. The processor executes the program stored in the memory and is specifically used to execute: Figure 4 The method disclosed in the illustrated embodiment implements the functions and beneficial effects of each method in the previous method embodiments, which will not be described in detail here.

[0085] It should be noted that the data processing device for surveying and mapping data provided in an embodiment of the present invention and the data processing method for surveying and mapping data provided in an embodiment of the present invention are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned data processing method for surveying and mapping data, and has the same or similar beneficial effects, and the repeated parts will not be repeated.

[0086] It should be noted that the sequence of the above 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 accompanying 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.

[0087] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A data processing method for surveying and mapping data, characterized in that: The data processing method for surveying and mapping data comprises: Clustering the surveying and mapping data of the overall topography of the target mine to obtain multiple clusters; Determine a target cluster according to the amount of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster, wherein a density coefficient of the target cluster is greater than or equal to a first threshold; Determine the regional characteristic parameters of the first terrain area corresponding to the target cluster by using the first vectors formed by the adjacent surveying and mapping data in the target cluster and the angle formed between the first vectors; Determine an irregular terrain region from the first terrain region according to the region characteristic parameter; Determining the region type to which the second terrain region belongs according to the region characteristic parameter of the irregular terrain region and the Euclidean distance between the centroids of adjacent target clusters in the second terrain region, wherein the second terrain region includes a plurality of irregular terrain regions corresponding to the target clusters; Performing lossless compression processing on the surveying and mapping data of the second terrain area according to the area type; The performing lossless compression processing on the surveying and mapping data of the second terrain area according to the area type comprises: In the case where the area type to which the second terrain area belongs is a flat area, core data is selected from the surveying and mapping data in the second terrain area, and losslessly compressed is performed on the core data; In the case that the area type to which the second terrain area belongs is an uneven area, lossless compression processing is performed on all surveying and mapping data of the second terrain area.

2. The data processing method for surveying and mapping data according to claim 1, characterized in that: Determining the target cluster according to the amount of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster comprises: Determine the point coordinates of each of the surveying and mapping data in the cluster in the three-dimensional space; Constructing a circumscribed polyhedron for the point coordinates of each of the surveying and mapping data, and determining the volume of the circumscribed polyhedron as the spatial volume occupied by the surveying and mapping data in the cluster; Normalizing the ratio of the number of surveying and mapping data in the cluster to the volume of the space to obtain a density coefficient of the cluster; A cluster whose density coefficient is greater than or equal to the first threshold is determined as the target cluster.

3. The data processing method for surveying and mapping data according to claim 2, characterized in that: Determining the regional characteristic parameters of the first terrain area corresponding to the target cluster by using the first vector formed by the adjacent surveying and mapping data in the target cluster and the angle formed between the first vectors includes: Select three consecutive adjacent mapping data from the target cluster; Calculating an angle between one of the three adjacent surveying and mapping data and a first vector formed by the other two surveying and mapping data; When the angle is greater than a second threshold, determining the three consecutive adjacent surveying and mapping data as boundary lines between two terrain sub-regions in the first terrain region; Divide the first terrain region according to the boundary line to obtain a plurality of terrain sub-regions, and determine a second vector formed between each surveying and mapping data of each terrain sub-region and the surveying and mapping data at the boundary line of the terrain sub-region; The regional characteristic parameter of the first terrain region corresponding to the target cluster is determined by using the density coefficient of the target cluster, the first number of the terrain sub-regions, the second number of the second vectors in the terrain sub-regions, and the adjacent second vectors in each of the terrain sub-regions.

4. The data processing method for surveying and mapping data according to claim 3, characterized in that: Determining the regional characteristic parameter of the first terrain area corresponding to the target cluster by using the density coefficient of the target cluster, the first number of the terrain sub-areas, the second number of the second vectors in the terrain sub-areas, and the adjacent second vectors in each of the terrain sub-areas includes: Superimposing the dot products of adjacent second vectors in each of the terrain sub-areas to obtain a first superposition value; Calculate a first product of a first difference between the second number and a preset value and the first number, and a sum of the first product and the preset value; A first ratio of the first superposition value to the sum value and a second product of the density coefficient are determined as the regional characteristic parameter.

5. The data processing method for surveying and mapping data according to any one of claims 1 to 4, characterized in that: Determining the region type to which the second terrain region belongs according to the region characteristic parameter of the irregular terrain region and the Euclidean distance between the centroids of adjacent target clusters in the second terrain region comprises: Performing height division according to the value of the centroid of the target cluster in the second terrain area on the z-axis to obtain a plurality of height areas; Calculating the Euclidean distance between the centroid of each target cluster and the centroid of its adjacent target cluster in the same height region; Superimposing the second ratios of the pairwise Euclidean distances in the second terrain area to obtain a second superimposed value; Determine the product of the regional characteristic parameter and the second superposition value as the distance dispersion coefficient between the centroids of the target clusters; The region type to which the second terrain region belongs is determined according to the mean value of the angles between first vectors formed by adjacent surveying and mapping data in the target cluster in the second terrain region and the distance dispersion coefficient.

6. The data processing method for surveying and mapping data according to claim 5, characterized in that: Determining the region type to which the second terrain region belongs according to the mean value of the angles between the first vectors formed by adjacent surveying and mapping data in the target cluster in the second terrain region and the distance dispersion coefficient comprises: Calculating a first mean value of angles between first vectors formed by adjacent surveying and mapping data in the current target cluster in the second terrain region, and a second mean value of angles between first vectors formed by adjacent surveying and mapping data in other target clusters in the second terrain region except the current target cluster; respectively calculating third ratios of the first mean value to each of the second mean values, and superimposing the third ratios to obtain a third superimposed value; Calculate a second difference between the product of the distance dispersion coefficient and the third superposition value and a preset value; normalizing the second difference to obtain an overall variation coefficient of the second terrain area; When the overall variation coefficient is greater than or equal to a third threshold, the region type to which the second terrain region belongs is determined to be a flat region; when the overall variation coefficient is less than the third threshold, the region type to which the second terrain region belongs is determined to be an uneven region.

7. The data processing method for surveying and mapping data according to claim 1, characterized in that: Determining the irregular terrain area from the first terrain area according to the regional characteristic parameters comprises: A first terrain region whose regional characteristic parameter is greater than or equal to a fourth threshold is determined as the irregular terrain region.

8. A data processing system for surveying and mapping data, characterized in that: include: A clustering module is used to cluster the surveying and mapping data of the overall terrain of the target mine to obtain multiple clusters; A determination module, configured to determine a target cluster according to the amount of surveying and mapping data in the cluster and the spatial volume occupied by the surveying and mapping data in the cluster, wherein a density coefficient of the target cluster is greater than or equal to a first threshold; The determination module is further used to determine the regional characteristic parameters of the first terrain area corresponding to the target cluster by using the first vectors formed by the adjacent surveying and mapping data in the target cluster and the angle formed between the first vectors; The determination module is further used to determine an irregular terrain area from the first terrain area according to the regional characteristic parameters; The determination module is further used to determine the region type to which the second terrain region belongs based on the region characteristic parameter of the irregular terrain region and the Euclidean distance between the centroids of adjacent target clusters in the second terrain region, wherein the second terrain region includes a plurality of irregular terrain regions corresponding to the target clusters; A processing module, configured to perform lossless compression processing on the surveying and mapping data of the second terrain area according to the area type; The performing lossless compression processing on the surveying and mapping data of the second terrain area according to the area type comprises: In the case where the area type to which the second terrain area belongs is a flat area, core data is selected from the surveying and mapping data in the second terrain area, and losslessly compressed is performed on the core data; In the case that the area type to which the second terrain area belongs is an uneven area, lossless compression processing is performed on all surveying and mapping data of the second terrain area.

9. A data processing device for surveying and mapping data, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the data processing method for surveying and mapping data are implemented as described in any one of claims 1 to 7.

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