A geographic information surveying and mapping data intelligent management method and system

Abstract

The present application relates to the field of point cloud data processing, and particularly relates to a geographic information surveying and mapping data intelligent management method and system.The method comprises the following steps: obtaining geographic information surveying and mapping data of a region to be measured; the geographic information surveying and mapping data comprises a plurality of data points, each data point comprising three-dimensional coordinate information and echo intensity; an improved octree is constructed according to the plurality of data points; and the storage and query of the geographic information surveying and mapping data are completed by using the improved octree.The scheme of the present application can reduce the over-division or insufficient division of each node in the octree caused by complex terrain.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of point cloud data processing. More particularly, the present application relates to a geographic information surveying and mapping data intelligent management method and system. BACKGROUND

[0002] Geographic information surveying and mapping data is the information of the position and attributes of the earth's surface obtained through various measurement techniques and means, including spatial coordinates, topography, and feature characteristics. These data form the basis of a geographic information system (GIS); geographic surveying and mapping is commonly used in urban planning, resource management, environmental monitoring, disaster warning, and other fields. Therefore, the accuracy and completeness of geographic information surveying and mapping data directly affect the results of subsequent applications. Therefore, it is necessary to intelligently manage geographic information surveying and mapping data to achieve efficient storage and processing of data, while ensuring the security and integrity of the data, it can also support fast query and visualization, improving the speed and accuracy of data analysis.

[0003] Since geographic information surveying and mapping data is three-dimensional point cloud data, the data volume is huge, and when managing geographic information surveying and mapping data, an octree algorithm is commonly used to represent and compress point cloud data in layers to reduce storage space.

[0004] In related technologies, such as the patent application file with publication number CN117036621A, a geographic information surveying and mapping data management method based on the Internet of Things is disclosed. The method constructs an octree from the collected three-dimensional point cloud data set; obtains the terrain criticality and terrain information purity of the leaf nodes of the octree; then obtains the terrain criticality of the leaf nodes, and compresses the octree according to the terrain criticality of the leaf nodes. The geographic information data contained in the compressed octree is stored in a database and indexed, and the target surveying and mapping area topography is visualized using geographic information system software or visualization tools.

[0005] The above method only solves the problem of insufficient data storage precision when managing geographic information surveying and mapping data, and does not consider that due to the complex and variable terrain in space, the same type of terrain located at different positions is affected by the changes in the surrounding terrain, resulting in differences in data point density in the segmentation strategy, and a large difference in the segmentation strategy, which leads to over-segmentation or insufficient segmentation of some point cloud data in the octree, resulting in increased storage costs. SUMMARY

[0006] The present application aims to provide a geographic information surveying and mapping data intelligent management method and system to solve the problem of over-segmentation or insufficient segmentation of some point cloud data in the octree when managing geographic information surveying and mapping data in the prior art. To this end, the present application provides solutions in the following two aspects.

[0007] In a first aspect, the present application provides a geographic information mapping data intelligent management method, comprising:

[0008] Obtaining geographic information mapping data of a region to be measured; the geographic information mapping data comprises a plurality of data points, each data point comprising three-dimensional coordinate information and echo intensity;

[0009] Constructing an improved octree according to the plurality of data points; and storing and querying the geographic information mapping data by using the improved octree;

[0010] The splitting condition of the subdivided cubes in the improved octree comprises that the density of each node is greater than or equal to a set value; the density is a ratio of a weighted density sum of the data points in each node to the volume of the cube corresponding to the node; the weighted density is a mean value of all first densities, the first density is a weighted sum of the measured density of a target point in any cluster and the measured density of each data point in any remaining cluster; the weight in the weighting is a density reference factor of the target point in any cluster and each data point in any remaining cluster, and the target point is any data point in any cluster;

[0011] The density reference factor is a product of a spatial neighborhood coincidence degree and a corresponding proximity factor; the proximity factor represents the similarity of any two clusters; and the spatial neighborhood coincidence degree represents the similarity of the spatial neighborhood points of any data point and the spatial neighborhood points of each data point in all clusters.

[0012] The above scheme analyzes the proximity factor of any two clusters in a plurality of clusters of a plurality of data points in the geographic information mapping data clustering, and then obtains the density reference factor of any data point and each data point in each cluster, which is used to calibrate the measured density of the any data point to obtain the weighted density of the corresponding data point, and the weighted density of the data points in the node is used to obtain the density of the node, which can determine the density of the node from the collected global data, and the data points of the corresponding node are split by using the density of the node to obtain the final octree. That is, the scheme of the present application can reduce the over-splitting or insufficient splitting of the data points in each node of the octree caused by complex terrain.

[0013] Optionally, the first density is:

[0014] ;

[0015] wherein, is the density reference factor of the i-th data point in the j-th cluster and the k-th data point in the i-th cluster; ,​​​​ The first In the cluster, the th The data point, the first In the cluster, the th The measured density of each data point; For the first The total number of data points in each cluster.

[0016] The above scheme provides a method for accurately calculating the weighted density.

[0017] Optionally, the spatial neighborhood consistency for:

[0018] ; For the first The cluster and the first The similarity factor of each cluster; For the first In the cluster, the th The set of spatial neighborhood points of the nth data point belongs to the nth data point. The total number of data points in each cluster; For the first In the cluster, the th The set of spatial neighborhood points of the nth data point belongs to the nth data point. The total number of data points in each cluster; The total number of clusters; The function is a normalization function; the set of spatial neighborhood points consists of other data points whose spatial distance from any data point to other data points is less than a set neighborhood radius.

[0019] The above scheme provides a method for accurately calculating the density reference factor.

[0020] Optionally, the similarity factor is the difference between 1 and a first value; the first value is the normalized value of the product of the distance between the cluster centers of any two clusters and the spatial distance, the spatial distance being the sum of a first distance and a second distance; the first distance is the sum of the minimum spatial distances between all data points in the first cluster and the second cluster in any two clusters; the second distance is the sum of the minimum spatial distances between all data points in the second cluster and the first cluster in any two clusters.

[0021] The similarity factor calculated in the above scheme can assess the similarity between two clusters.

[0022] Optionally, the spatial distance is the Euclidean distance between any data point and the three-dimensional coordinate information of all other data points.

[0023] Optionally, the split condition further comprises condition two; the condition two is that the recursive depth of the node is less than the maximum recursive depth or the number of data points in each node is greater than a set number.

[0024] Optionally, the specific process of subdividing the cube in the improved octree is as follows:

[0025] The normalized value of the density of each node at any recursive time is obtained.

[0026] When the normalized value of the density of each node obtained is greater than or equal to a set threshold and the condition two is met, the split of the node is continued; otherwise, the split is not performed; the new node after the split of each node is obtained, and the normalized value of the density of each node is obtained by using the maximum-minimum value normalization method.

[0027] The above scheme can accurately split the data points of the corresponding node by calculating the normalized value of the density of the node at the recursive time.

[0028] Optionally, the cluster cluster is obtained by using the DBSCAN algorithm on the echo intensity of the plurality of data points in the geographic information surveying and mapping data clustering.

[0029] Optionally, the process of the query is as follows:

[0030] The query range is defined.

[0031] The root node of the octree is started, and it is checked whether the space represented by the root node intersects with the query range.

[0032] If the intersection is not found, the query is immediately terminated; if the root node partially intersects with the query range, the eight child nodes of the root node are recursively queried one by one; if the root node is completely covered by the query range, it is checked whether the data stored in the root node meets the query condition, and the data points meeting the query condition are counted into the result set; the root node is sequentially traversed to realize the query operation.

[0033] In a second aspect, a geographic information surveying and mapping data intelligent management system comprises:

[0034] A processor;

[0035] A memory storing computer instructions for intelligent management of geographic information surveying and mapping data, when the computer instructions are run by the processor, the system executes the above-mentioned geographic information surveying and mapping data intelligent management method.

[0036] The present application has the following beneficial effects:

[0037] The scheme of the present application can adjust the fineness of the segmentation according to the actual situation of the data points when constructing the octree of the geographic information mapping data, by calculating the weighted density of the data points in each node in each recursive process to obtain the density of the corresponding node, and then setting the corresponding segmentation strategy according to the density of each node to segment all data points in each node. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A step flowchart of a geographic information mapping data intelligent management method in the embodiment is schematically shown;

[0039] Figure 2 A structural block diagram of a geographic information mapping data intelligent management system in the embodiment is schematically shown. DETAILED DESCRIPTION

[0040] Specifically, taking a certain to-be-measured region as an example, as shown in the figure, Figure 1 A geographic information mapping data intelligent management method in the embodiment includes the following steps:

[0041] Step S1: Obtain the geographic information mapping data of the to-be-measured region.

[0042] In the embodiment, the geographic information mapping data of the to-be-measured region is obtained by a laser scanner. The to-be-measured region is any region to be measured and mapped, such as a mountainous area or a canyon in a certain region.

[0043] It should be noted that the laser scanner can accurately measure the three-dimensional coordinates of each point on the surface of the target object in the to-be-measured region, especially for topographic mapping, which can capture the changes in complex terrain such as mountainous areas and canyons in detail, providing an accurate terrain model basis for subsequent geographic analysis. Therefore, the laser scanner is selected to obtain the geographic information mapping data.

[0044] Specifically, the to-be-measured region is scanned by using an airborne laser scanner to obtain the point cloud data and the echo intensity of each data point of the to-be-measured region, and the point cloud data and the echo intensity of each data point of the to-be-measured region are taken as the geographic information mapping data. The point cloud data contains the three-dimensional position information of each data point; that is, each data point in the geographic information mapping data contains four dimensions of x, y, z three-axis coordinate information and echo intensity.

[0045] Of course, as other embodiments, the dimensions of each data point in the geographic information mapping data can also include image information of the to-be-measured region.

[0046] Step S2: Construct an improved octree according to the plurality of data points in the geographic information mapping data.

[0047] The octree is a tree data structure for describing and managing a three-dimensional space. The tree data structure is suitable for processing a sparse data set, and can subdivide a region containing data, thereby saving storage space and improving query efficiency.

[0048] Specifically, the steps of the octree include setting a maximum recursion depth, finding a maximum size of a scene, placing a unit element into a cube, subdividing the cube, and stopping the subdivision. That is, the octree is obtained by recursively dividing a cube space into eight smaller sub-cubes (referred to as "nodes"), each of which can continue to be divided until a termination condition is reached. The node at the first division is referred to as a root node, and the sub-nodes divided on the basis of the root node are referred to as nodes. Since the specific implementation steps of the octree are prior art, they will not be described in detail here. The existing termination condition is that the recursion depth reaches the set maximum recursion depth.

[0049] However, due to the complexity of the terrain in space, the same type of terrain located at different positions is affected by the changes in the surrounding terrain, and the density is different, and the division strategy difference is large. Therefore, it is necessary to improve the step of subdividing the cube in the existing octree to reduce the over-division or insufficient division caused by the complexity of the terrain, and improve the division accuracy of the geographic information surveying and mapping data.

[0050] It should be noted that the improvement point of the present embodiment is only in the step of subdividing the cube, and no improvement is made to other steps.

[0051] Specifically, the process of subdividing the cube in the improved octree in the present embodiment includes the following steps:

[0052] Step S21, obtaining a normalized value of the density of each node at any recursive time.

[0053] In the present embodiment, the density of the node is the ratio of the sum of the weighted densities of the data points in each node to the volume of the cube corresponding to the node.

[0054] The method for obtaining the weighted density of each data point in each node is as follows:

[0055] Step S211, dividing all data points in the geographic information surveying and mapping data into clustering clusters, and obtaining a proximity factor between the clustering clusters.

[0056] In the present embodiment, the echo intensity of all data points is clustered by the DBSCAN algorithm to obtain each initial clustering cluster and each isolated point. Each isolated point is separately taken as a separate clustering cluster, thereby obtaining a plurality of clustering clusters, and the total number of clustering clusters is denoted as K. It should be noted that a large number of isolated points may exist in the clustering process of the DBSCAN algorithm, and the relationship between the clusters is considered in the subsequent calculation process, and each isolated point is calculated as a single cluster, which does not affect the subsequent calculation, so each isolated point can be taken as a separate cluster.

[0057] The reason for clustering by using the echo intensity is that the echo intensity of each data point in the geographic information mapping data can effectively reflect the terrain represented by the data point, so the echo intensity of all data points can be clustered to distinguish different terrains.

[0058] In this embodiment, the reason for selecting the BSCAN algorithm for clustering is that the DBSCAN algorithm does not need to know the number of clusters to be formed in advance, and can find clusters of any shape, and has good clustering effect on various irregularly distributed same terrain data points that may appear in complex terrain.

[0059] After obtaining a plurality of clustering clusters, the specific process of calculating the proximity factor of any two clustering clusters is as follows:

[0060] First, the cluster center distance of any two clustering clusters is obtained. Specifically, the position corresponding to the mean value of the parameters of all data points in each dimension in each clustering cluster is taken as the cluster center of the corresponding clustering cluster, and the Euclidean distance between the cluster centers of any two different clustering clusters is calculated as the cluster center distance of the two clustering clusters.

[0061] Since the clustering clusters with similar cluster centers are more likely to be similar terrains, the distance between the cluster centers of each clustering cluster is also obtained to measure whether each clustering cluster is similar. Therefore, the similarity of two clustering clusters is measured by using the cluster center distance.

[0062] Secondly, the minimum spatial distance between the target point and each clustering cluster is calculated, wherein the target point is any data point in any clustering cluster.

[0063] In this embodiment, in order to measure whether the clustering cluster where the target point is located is similar to other clustering clusters, it is necessary to consider whether the target point can find spatially clustered similar data points in another clustering cluster. Therefore, it is also necessary to calculate the minimum spatial distance between the target point and each clustering cluster.

[0064] Specifically, the Euclidean distance between the three-dimensional coordinate information of the target point and each data point in each clustering cluster is obtained, and the Euclidean distance is recorded as the spatial distance; the minimum value of the Euclidean distance between the target point and all data points in each clustering cluster is taken as the minimum spatial distance between the target point and each clustering cluster.

[0065] For example, the Euclidean distances between data point A and each data point in cluster a and cluster b are calculated to obtain the minimum Euclidean distance a' between data point A and cluster a, and the minimum Euclidean distance b' between data point A and cluster b. Here, the minimum Euclidean distance a' is the minimum spatial distance between data point A and cluster a, and the minimum Euclidean distance b' is the minimum spatial distance between data point A and cluster b. It should be noted that data point A can be a point in either cluster a or cluster b, or it can be a point outside of either cluster a or cluster b.

[0066] Then, based on the cluster center spacing and the minimum spatial distance, the similarity factor is obtained.

[0067] Specifically, when the target point is the first In the cluster, the th When there are 10 data points, the similarity factor is:

[0068] ;

[0069] In the formula, For the first The cluster and the first The similarity factor between clusters; For the first The cluster and the first The distance between the cluster centers of each cluster; For the first In the cluster, the th The data point and the first The minimum spatial distance between clusters; For the first In the cluster, the th The data point and the first The minimum spatial distance between clusters; For the first The total number of data points in each cluster; For the first The total number of data points in each cluster; This is the normalization function.

[0070] The above As the first value, Indicates the first The cluster and the first The sum of all minimum spatial distances between clusters, when The smaller the value, the more... The cluster and the first The data points in each cluster are spatially similar, and the higher the spatial similarity between two clusters, the better; when the... The cluster and the first The higher the spatial proximity between clusters, the more likely the i-th cluster is to be clustered together. The cluster and the first The closer the distance between the cluster centers of two clusters, the higher the similarity factor between the two clusters.

[0071] In this embodiment, the closer the cluster centers of two clusters are, the higher the probability that the two clusters are similar terrain. Since similar terrain usually has similar density, by obtaining the similarity factor between the cluster where the target point is located and any other cluster, it is possible to identify whether each cluster is similar to the cluster where the target point is located.

[0072] Step S212: Obtain the density reference factors of the target point and each data point in each cluster.

[0073] The process of obtaining the density reference factor in this embodiment is as follows:

[0074] First, obtain the spatial neighborhood points of the target point and each data point in each cluster.

[0075] Specifically, taking each data point in any cluster as an example, all data points whose Euclidean distance to each data point is less than or equal to the set neighborhood radius are taken as the spatial neighborhood point set of the corresponding data point, and the total number of data points belonging to each cluster in the spatial neighborhood point set of each data point is obtained.

[0076] The radius set above is the radius of the empirical neighborhood range, and its value can be... .

[0077] Secondly, calculate the spatial neighborhood similarity between the target point and any data point in any cluster.

[0078] Specifically, when the target point is the first In the cluster, the th When there are 10 data points, the formula for calculating the spatial neighborhood consistency is as follows:

[0079] ;

[0080] In the formula, For the first In the cluster, the th The data point and the first In the cluster, the th Spatial neighborhood consistency of each data point; For the first The cluster and the first The similarity factor of each cluster; For the first In the cluster, the th The set of spatial neighborhood points of the nth data point belongs to the nth data point. The total number of data points in each cluster; the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster; the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster; the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster; the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster; the total number of clusters; the normalization function.

[0081] The normalization function can be a max-min normalization.

[0082] In the formula, n represents the total number of clusters, and k represents the sequence number of the i-th cluster. represents the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster. represents the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster. represents the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster. represents the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster. represents the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster. represents the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster. represents the total number of data points belonging to the i-th cluster in the spatial neighborhood point set of the j-th data point in the i-th cluster.

[0083] For example, it is assumed that there are totally 6 clusters, wherein the spatial neighborhood point set of data point A in the 1st cluster includes 1, 2, 3 and 4. Among them, there are 2 spatial neighborhood points belonging to the 3rd cluster, which are 1 and 2 respectively, and there are 2 spatial neighborhood points belonging to the 5th cluster, which are 3 and 4 respectively. The spatial neighborhood point set of data point B in the 2nd cluster includes 1', 2', 3', 4' and 5'. Among them, there are 2 spatial neighborhood points belonging to the 4th cluster, which are 1' and 2' respectively, there is 1 spatial neighborhood point belonging to the 3rd cluster, which is 3', and there is 1 spatial neighborhood point belonging to the 6th cluster, which is 5'.

[0084] At this time, when k = 1 or 2, for any sequence number, the matching mode is 0;

[0085] when k = 3, the sequence numbers of are 3, 4, 5 or 6 respectively, and the corresponding matching modes are 2, 4, 0, 2; when k = 4, for any sequence number, the corresponding matching mode is 0; when k = 5, the sequence numbers of are 3, 4, 5 or 6 respectively, and the corresponding matching modes are 2, 4, 0, 2; when k = 6, for any sequence number, the matching mode is 0.

[0086] At this time, the spatial neighborhood coincidence degree of data point A and data point B can be obtained.

[0087] Then, the density reference factor of the target point and any data point in any cluster is obtained.

[0088] Specifically, when the target point is the i-th data point in the j-th cluster, the calculation formula of the density reference factor is as follows:

[0089] In the formula, d is the density reference factor of the i-th data point in the j-th cluster and the i'-th data point in the j'-th cluster; is the proximity factor between the j-th cluster and the j'-th cluster; is the spatial neighborhood coincidence degree of the i-th data point in the j-th cluster and the i'-th data point in the j'-th cluster.

[0090] In the embodiment, for the data point in the cluster with a low proximity factor to the cluster where the target point is located, when the difference between the cluster where the spatial neighborhood point of the data point belongs and the cluster where the spatial neighborhood point of the target point belongs is high, the measured density at the data point is low in reference for the subsequent calculation of the weighted density at the target point, and thus the density reference factor of the target point and any data point in any cluster needs to be evaluated to determine the reference of any data point.

[0091] Step S213: obtaining the weighted density of the target point.

[0092] Specifically, when the target point is the i-th data point in the j-th cluster, the calculation formula of the weighted density of the target point is as follows:

[0093] ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​a density reference factor of the data point; , is a measured density of the data point in the i-th cluster, is a measured density of the data point in the i-th cluster, and is a measured density of the data point in the i-th cluster. is a total number of data points in the i-th cluster. is a total number of clusters.

[0094] The measured density is the reciprocal of the sum of the spatial distances of each data point to other remaining data points.

[0095] Since the measured density of the data points with similar terrain categories and similar positions is higher, the reference degree is higher when calculating the weighted density of the target point. Therefore, the measured density of the data points with similar terrain categories and similar positions is also introduced when calculating the weighted density.

[0096] Meanwhile, since the data points with high density reference factors can better represent the overall density level of the same terrain, the measured density of each data point can be calibrated by the density reference factors of each data point and other data points, so as to reduce the problem of over-segmentation or under-segmentation when establishing the octree later, caused by the influence of complex terrain on each data point (for example, data point A and data point B actually belong to the same terrain, but the terrain near data point A and the terrain near data point B are different, resulting in a difference in the density of data point A and data point B).

[0097] In this embodiment, by calculating the weighted density of each data point and determining the density of each node (the density is the sum of the weighted densities of all data points in the node), and then setting the corresponding segmentation strategy according to the density of each node, the octree of the geographic information mapping data of the region to be measured is constructed, which can adjust the degree of segmentation according to the actual situation of the data points, so that the segmentation is more in line with the real characteristics of the data. For complex terrain regions, the weighted density is usually high, and a more fine segmentation strategy can better capture the terrain details, for example, in mountainous, canyon and other terrain regions with large terrain undulations, the unique topographic features can be accurately reflected, and important geographic information can be avoided to be lost; for relatively flat or simple regions, the weighted density is low, and a coarser segmentation strategy can avoid unnecessary over-segmentation, improve processing efficiency and reduce waste of storage resources.

[0098] In step S22, when the normalized value of the density of each node is greater than or equal to the set threshold and the second condition is met, the segmentation of the single node is continued; otherwise, the segmentation is not performed; and the new nodes after the segmentation of each node are obtained.

[0099] ​​​​​The recursive depth of the two nodes is less than the maximum recursive depth or the number of data points in each node is greater than the set number.

[0100] Exemplarily, in any recursive process of establishing the octree, the density of each node obtained is linearly normalized, when the density of the linearly normalized node is greater than or equal to a set value and the number of data points in the corresponding node is greater than a set number, the splitting is continued; otherwise, the splitting is not performed, so as to obtain the octree of the geographic information surveying and mapping data through recursion.

[0101] The set value is The set number is .

[0102] In the embodiment, the method for linearly normalizing the density of the node is the maximum-minimum value normalization method or the Sigmoid function normalization.

[0103] In step S3, the geographic information surveying and mapping data is stored by using the improved octree, and the data query is completed.

[0104] Specifically, the process of the query is as follows:

[0105] Firstly, the query range is defined, for example, all the points located in a specific cube or spherical region are searched. Secondly, starting from the root node of the octree, it is checked whether the space represented by the root node intersects with the query range, if not, the query is terminated immediately, because the entire octree does not contain any data point meeting the condition; if the root node is completely covered by the query range, it is further checked whether the data points stored in the node meet the query condition, and the data points are added to the result set; if the root node partially intersects with the query range, the eight child nodes of the root node need to be recursively checked one by one. Then, the query operation of the target data point is realized by traversing the above operation, so as to realize the intelligent management of the geographic information surveying and mapping data.

[0106] The scheme of the present application can adjust the degree of splitting according to the actual situation of the data points, so that the splitting is more in line with the real characteristics of the data, and an accurate octree is obtained.

[0107] The present application also provides an intelligent management system of geographic information surveying and mapping data. Figure 2As shown, the system comprises a processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, a kind of geographic information surveying and mapping data intelligent management method according to the above-mentioned application is realized.

[0108] The system also includes communication bus and communication interface and other components well known to those skilled in the art, which are set and functions are known in the art, therefore will not be repeated here.

[0109] In the present application, the aforementioned memory can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, device or apparatus. For example, the computer readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory RRAM (Resistive Random Access Memory), dynamic random access memory DRAM (Dynamic Random Access Memory), static random access memory SRAM (Static Random-Access Memory), enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), high bandwidth memory HBM (High-Bandwidth Memory), hybrid memory cube HMC (Hybrid Memory Cube) and the like, or any other medium that can be used to store desired information and can be accessed by an application, module or both. Any such computer storage medium can be part of a device or accessible or connectable to a device. Any application or module described in the present application can be implemented by computer readable / executable instructions stored or otherwise held by such computer readable medium.

[0110] Although the present specification has shown and described a number of embodiments of the application, it will be apparent to those skilled in the art that many modifications, variations, and alternatives to the embodiments described herein can be made in the light of the teachings herein. It is therefore to be understood that changes can be made in the form, details, and / or arrangement of the embodiments of the application without departing from the spirit and intent of the application.

Claims

1. A method for intelligent management of geographic information mapping data, characterized in that, The application relates to a method for constructing an improved octree and a method for querying the improved octree. The method comprises the following steps: acquiring geographic information survey data of a region to be measured; the geographic information survey data comprises a plurality of data points, and each data point comprises three-dimensional coordinate information and echo intensity; constructing an improved octree according to the plurality of data points; storing and querying the geographic information survey data by using the improved octree; a splitting condition for subdividing a cube in the improved octree comprises that a normalized value of the density of each node is greater than or equal to a set value; the density is a ratio of the sum of weighted densities of the data points in each node to the volume of the cube corresponding to the node; the weighted density is the mean value of all first densities, the first density is a weighted sum of the measured density of a target point in any cluster and the measured density of each data point in any other cluster; the weight in the weighting is a density reference factor of the target point in any cluster and each data point in any other cluster; the target point is any data point in any cluster; the cluster is obtained by clustering the echo intensities of the plurality of data points; the measured density is the reciprocal of the sum of the spatial distances of each data point in each node from other data points; 2.The geographic information survey data intelligent management method according to claim 1, characterized in that, The first density is: ； wherein, is the number of clusters, is the number of data points in the th cluster, is the number of data points in the th cluster, , is the observed density of the th data point in the th cluster, th data point in the th cluster, is the total number of data points in the th cluster. 3.The geographic information survey data intelligent management method according to claim 2, characterized in that, The spatial neighborhood correspondence is: ; the similarity factor of the i-th cluster to the j-th cluster; the i-th data point in the j-th cluster; the total number of data points in the j-th cluster in the spatial neighborhood of the i-th data point in the j-th cluster; the i-th data point in the j-th cluster; the total number of data points in the j-th cluster in the spatial neighborhood of the i-th data point in the j-th cluster; the i-th data point in the j-th cluster; the total number of data points in the j-th cluster in the spatial neighborhood of the i-th data point in the j-th cluster; the i-th data point in the j-th cluster; the total number of data points in the j-th cluster in the spatial neighborhood of the i-th data point in the j-th cluster; the total number of clusters; the total number of clusters; the total number of clusters; the total number of clusters; 4. The geographic information survey data intelligent management method according to claim 3, characterized in that, the density reference factor is the product of a spatial neighborhood coincidence degree and a corresponding proximity factor; the proximity factor represents the similarity of any two clusters; the spatial neighborhood coincidence degree represents the similarity of the spatial neighborhood points of any data point and the spatial neighborhood points of all data points in all clusters.

5. The geographic information survey data intelligent management method according to claim 1 or 3, characterized in that, the proximity factor is the difference between 1 and a first value; the first value is a normalized value of the product of the cluster center distance between any two clusters and a spatial distance; the spatial distance is the sum of a first distance and a second distance; the first distance is the sum of the minimum spatial distances from all data points in a first cluster in any two clusters to a second cluster; the second distance is the sum of the minimum spatial distances from all data points in the second cluster to the first cluster.

6. The geographic information survey data intelligent management method according to claim 1, wherein, the spatial distance is the Euclidean distance between the three-dimensional coordinate information of any data point and all other data points.

7. The geographic information survey data intelligent management method according to claim 6, characterized in that, the splitting condition further comprises condition two; the condition two is that the recursive depth of the node is less than the maximum recursive depth or the number of data points in each node is greater than a set number. the specific process for subdividing a cube in the improved octree is as follows: acquiring the normalized value of the density of each node at any recursive time; 8.The geographic information survey data intelligent management method according to claim 1, wherein, when the acquired normalized value of the density of each node is greater than or equal to a set threshold and the condition two is met, the node is continuously split; otherwise, the node is not split; new nodes are obtained after the nodes are split; the normalized value of the density of each node is obtained by using the maximum-minimum value normalization method.

9. The geographic information survey data intelligent management method according to claim 1, wherein, the cluster is obtained by clustering the echo intensities of the plurality of data points in the geographic information survey data by using a DBSCAN algorithm. the querying process is as follows: defining a query range; starting from the root node of the octree, checking whether the space represented by the root node intersects with the query range; If the root node is not intersected with the query range, the query is terminated immediately; if the root node is intersected with the query range, the eight child nodes of the root node are queried recursively; if the root node is completely covered by the query range, it is checked whether the data stored in the root node satisfies the query condition, and the data points satisfying the query condition are counted into the result set; the root node is traversed in turn to realize the query operation.

10. A geographic information mapping data intelligent management system, characterized in that, Comprise: A processor; A memory which stores computer instructions for intelligent management of geographic information surveying and mapping data, and when the computer instructions are run by the processor, the system executes the intelligent management method of geographic information surveying and mapping data according to any one of claims 1-9.

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