Geographic information surveying and mapping data intelligent management method and system

Abstract

The invention relates to the field of point cloud data processing, in particular to an intelligent management method and system for geographic information surveying and mapping data. The method comprises the following steps: acquiring geographic information surveying and mapping data of a to-be-measured area; the geographic information surveying and mapping 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; and completing storage and query of geographic information surveying and mapping data by using an improved octree. According to the scheme provided by the invention, the condition of excessive segmentation or insufficient segmentation of each node in the octree caused by complex terrain can be reduced.

Description

Technical Field

[0001] This invention relates to the field of point cloud data processing. More specifically, this invention relates to an intelligent management method and system for geographic information mapping data. Background Technology

[0002] Geographic information mapping data is information about the location and attributes of the Earth's surface, including spatial coordinates, topography, and landform features, obtained through various measurement techniques and methods. This data forms the foundation of Geographic Information Systems (GIS); geographic mapping is commonly used in urban planning, resource management, environmental monitoring, and disaster early warning. Therefore, the accuracy and completeness of geographic information mapping data directly affect the results of subsequent applications. Thus, intelligent management of geographic information mapping data is necessary to achieve efficient storage and processing, ensuring data security and integrity while supporting rapid querying and visualization, thereby improving the speed and accuracy of data analysis.

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

[0004] In related technologies, such as the patent application document with publication number CN117036621A, which discloses a data management method for geographic information mapping instruments based on the Internet of Things, the method constructs an octree by collecting a three-dimensional point cloud dataset; obtains the terrain treacherousness and terrain information purity of the leaf nodes of the octree; then obtains the terrain criticality of the leaf nodes; compresses the octree according to the terrain criticality of the leaf nodes; stores the geographic information data contained in the compressed octree in a database and establishes an index; and uses geographic information system software or visualization tools to visualize the landform and terrain of the target mapping area.

[0005] The above-mentioned approach to managing geographic information mapping data only addresses the issue of insufficient data storage accuracy. It does not consider that existing octrees, when storing point cloud data, suffer from various problems. Due to the complex and varied spatial terrain, even the same type of terrain located in different places can be affected by changes in their surrounding terrain, resulting in differences in the density of data points in the segmentation strategy. This leads to significant differences in segmentation strategies, causing some point cloud data in the octree to be over-segmented or under-segmented, thus increasing storage costs. Summary of the Invention

[0006] The purpose of this invention is to propose an intelligent management method and system for geographic information mapping data, in order to solve the problem of over-segmentation or under-segmentation of some point cloud data octrees in the management of geographic information mapping data in the prior art; to this end, this invention provides solutions in the following two aspects.

[0007] In a first aspect, the present invention provides an intelligent management method for geographic information mapping data, comprising: Acquire geographic information mapping data of the area to be measured; the geographic information mapping data includes multiple data points, each of which includes three-dimensional coordinate information and echo intensity. An improved octree is constructed based on multiple data points; the improved octree is used to store and query geographic information mapping data. The improved octree's subdivision criteria for cubes include that the normalized density of each node is greater than or equal to a set value; the density is the ratio of the weighted density sum of the data points within each node to the cube volume of the corresponding node; the weighted density is the mean of all first densities, where the first density is the weighted sum of the measured density of the target point in any cluster and the measured density of each data point in any remaining cluster; the weights used in the weighting are density reference factors for the target point in any cluster and each data point in any remaining cluster, where the target point is any data point in any cluster. The density reference factor is the product of the spatial neighborhood similarity and the corresponding proximity factor; the proximity factor represents the similarity between any two clusters; the spatial neighborhood similarity represents the similarity between the spatial neighborhood of any data point and the spatial neighborhood of all data points in all clusters.

[0008] The above-described scheme analyzes the similarity factors between any two clusters of multiple data points in geographic information mapping data clustering. This allows for the acquisition of density reference factors between any given data point and all data points within each cluster. These reference factors are used to calibrate the measured density of the given data point, obtaining its weighted density. The weighted density of the data points within a node is then used to determine the node's density. This allows for the determination of node density based on collected global data, and the segmentation of data points within that node is then performed using the node's density to obtain the final octree. In other words, the scheme of this invention can reduce the over-segmentation or under-segmentation of data points within nodes of the octree caused by complex terrain.

[0009] Optionally, the first density for: ; in, For the first In the cluster, the th The data point and the first In the cluster, the th Density reference factor for each data point; , 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.

[0010] The above scheme provides a method for accurately calculating the weighted density.

[0011] Optionally, the spatial neighborhood consistency for: ; 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.

[0012] The above scheme provides a method for accurately calculating the density reference factor.

[0013] 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.

[0014] The similarity factor calculated in the above scheme can assess the similarity between two clusters.

[0015] Optionally, the spatial distance is the Euclidean distance between any data point and the three-dimensional coordinate information of all other data points.

[0016] Optionally, the segmentation condition further includes condition two; condition two is that the recursion depth of a node is less than the maximum recursion depth or the number of data points in each node is greater than a set number.

[0017] Optionally, the specific process of subdividing the cube in the improved octree is as follows: Get the normalized value of the density of each node for any number of recursions; If the normalized density value of each node is greater than or equal to the set threshold and condition two is met, then the node is further segmented; otherwise, no segmentation is performed; new nodes are obtained after each node is segmented, and the normalized density value of each node is obtained by using the maximum-minimum normalization method.

[0018] The above scheme can accurately segment the data points of the corresponding nodes by calculating the normalized value of the node density during recursion.

[0019] Optionally, the clustering clusters are obtained by using the DBSCAN algorithm to obtain the echo intensity of multiple data points in the geographic information mapping data clustering.

[0020] Optionally, the query process is as follows: Define the query range; Starting from the root node of the octree, check whether the space represented by the root node intersects with the query range; If the nodes do not intersect, the query is terminated immediately. If the root node partially intersects with the query range, the eight child nodes of the root node are queried one by one recursively. If the root node is completely covered by the query range, the data stored in the root node is checked to see if it meets the query conditions, and the data points that meet the query conditions are added to the result set. The root node is traversed in turn to perform the query operation.

[0021] In the second aspect, a geographic information mapping data intelligent management system includes: processor; The memory stores computer instructions for intelligent management of geographic information mapping data. When the computer instructions are executed by the processor, the system performs the aforementioned intelligent management method for geographic information mapping data.

[0022] The beneficial effects of this invention are as follows: In constructing an octree for geographic information mapping data, the present invention calculates the weighted density of data points within each node in each recursive process to obtain the density of the corresponding node. Then, based on the density of each node, a corresponding segmentation strategy is set to segment all data points within each node. In other words, the present invention can adjust the fineness of the segmentation according to the actual situation of the data points. Attached Figure Description

[0023] Figure 1 This illustration schematically shows a flowchart of the steps of an intelligent management method for geographic information mapping data in this embodiment; Figure 2 The schematic diagram illustrates the structural block diagram of an intelligent management system for geographic information mapping data in this embodiment. Detailed Implementation

[0024] Specifically, taking a certain area to be tested as an example, such as Figure 1 As shown in this embodiment, a method for intelligent management of geographic information mapping data includes the following steps: Step S1: Obtain geographic information mapping data of the area to be measured.

[0025] In this embodiment, geographic information mapping data of the area to be measured is acquired using a laser scanner. The area to be measured can be any region to be mapped, such as mountainous areas or canyons in a certain region.

[0026] It should be noted that laser scanners can accurately measure the three-dimensional coordinates of each point on the surface of a target object within the area to be measured. Especially for topographic mapping, for complex terrains such as mountains and canyons, they can capture the changes in terrain undulations in detail, providing an accurate terrain model basis for subsequent geographic analysis. Therefore, laser scanners are chosen to acquire geographic information mapping data.

[0027] Specifically, an airborne laser scanner is used to scan a pre-defined area to be measured, and point cloud data of the area to be measured and the echo intensity of each data point are obtained. The point cloud data of the area to be measured and the echo intensity of each data point are used as 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: x, y, z axis coordinate information and echo intensity.

[0028] Of course, as another implementation method, the dimensions of each data point in the geographic information mapping data can also include the image information of the area to be measured.

[0029] Step S2: Construct an improved octree based on multiple data points in the geographic information mapping data.

[0030] An octree is a tree-like data structure used to describe and manage three-dimensional space. This tree-like data structure is suitable for processing sparsely distributed datasets, and can subdivide the regions containing data, thereby saving storage space and improving query efficiency.

[0031] Specifically, the steps of an octree include setting a maximum recursion depth, finding the maximum size of the scene, placing unit elements into the cube, subdividing the cube, and stopping the subdivision. That is, an octree recursively divides a cubic space into eight smaller sub-cubes (called "nodes"). Each child node can continue to be subdivided until a termination condition is reached. The node at the time of the first subdivision is called the root node, and the child nodes subdivided from the root node are called nodes. Since the specific implementation steps of an octree are existing technology, they will not be described in detail here. The existing termination condition is that the recursion depth reaches the set maximum recursion depth.

[0032] However, due to the spatial complexity and variability of terrain, even terrain of the same type located in different places can vary in density due to changes in their surrounding terrain, leading to significant differences in segmentation strategies. Therefore, it is necessary to improve the subdivision cube steps in the existing octree to reduce over-segmentation or under-segmentation caused by terrain complexity, thereby improving the segmentation accuracy of geographic information mapping data.

[0033] It should be noted that the improvement in this embodiment is only in the step of subdividing the cube, and no improvement is made to other steps.

[0034] Specifically, the process of subdividing the cube in the improved octree in this embodiment includes the following steps: Step S21: Obtain the normalized value of the density of each node for any number of recursions.

[0035] In this embodiment, the density of a node is the ratio of the weighted density of the data points within each node to the cubic volume of the corresponding node.

[0036] The method for obtaining the weighted density of each data point within each node is as follows: Step S211: Divide all data points in the geographic information mapping data into clusters and obtain the similarity factor between clusters.

[0037] In this embodiment, the DBSCAN algorithm is used to cluster the echo intensity of all data points, resulting in initial clusters and isolated points. Each isolated point is then treated as a separate cluster, thus generating multiple clusters. The total number of clusters is denoted as . It should be noted that the DBSCAN algorithm may have a large number of outliers during the clustering process. However, subsequent calculations will take into account the relationships between clusters and treat each outlier as a separate cluster. This will not affect subsequent calculations, so each outlier can be treated as a separate cluster.

[0038] The reason for using echo intensity for clustering is that the echo intensity of each data point in geographic information mapping data can effectively reflect the terrain represented by the data point. Therefore, the echo intensity of all data points can be clustered to distinguish different terrains.

[0039] In this embodiment, the BSCAN algorithm is chosen for clustering because it does not require prior knowledge of the number of clusters to be formed, can discover clusters of arbitrary shapes, and has a good clustering effect on various irregularly distributed similar terrain data points that may appear in complex terrain.

[0040] After obtaining multiple clusters, the specific process for calculating the similarity factor between any two clusters is as follows: First, obtain the cluster center distance between any two clusters. Specifically, take the position corresponding to the mean of the parameters of all data points in each dimension in each cluster as the cluster center of the corresponding cluster, and calculate the Euclidean distance between the cluster centers of any two different clusters as the cluster center distance between the two clusters.

[0041] Since clusters with similar cluster centers are more likely to have similar topography, it is also necessary to obtain the distance between the cluster centers of each cluster to measure the similarity between the clusters. Therefore, the above-mentioned cluster center distance is used to measure the similarity between two clusters.

[0042] Secondly, calculate the minimum spatial distance between the target point and each cluster, where the target point is any data point in any cluster.

[0043] In this embodiment, to measure whether the cluster in which the target point is located is similar to other clusters, it is necessary to consider whether the target point can find spatially similar data points in another cluster. Therefore, it is also necessary to calculate the minimum spatial distance between the target point and each cluster.

[0044] Specifically, the Euclidean distance between the target point and the three-dimensional coordinates of each data point in each cluster is obtained, and the Euclidean distance is recorded as the spatial distance; the minimum Euclidean distance between the target point and all data points in each cluster is taken as the minimum spatial distance between the target point and each cluster.

[0045] 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.

[0046] Then, based on the cluster center spacing and the minimum spatial distance, the similarity factor is obtained.

[0047] Specifically, when the target point is the first In the cluster, the th When there are 10 data points, the similarity factor is: ; 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.

[0048] 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.

[0049] 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.

[0050] Step S212: Obtain the density reference factors of the target point and each data point in each cluster.

[0051] The process of obtaining the density reference factor in this embodiment is as follows: First, obtain the spatial neighborhood points of the target point and each data point in each cluster.

[0052] 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.

[0053] The radius set above is the radius of the empirical neighborhood range, and its value can be... .

[0054] Secondly, calculate the spatial neighborhood similarity between the target point and any data point in any cluster.

[0055] 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: ; 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; 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; This is the normalization function.

[0056] The above normalization function can be used for maximum and minimum value normalization.

[0057] In the formula, Indicates 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 data points of the i-th cluster and the i-th cluster 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 pairwise combinations of data points in a cluster; the higher the total number of pairwise combinations of data points from clusters with high similarity factors in the spatial neighborhood of two data points (the more neighboring data points come from similar terrain), the higher the spatial neighborhood consistency.

[0058] For example, suppose there are a total of 6 clusters. In the first cluster, the spatial neighborhood of data point A includes numbers 1, 2, 3, and 4. Within this cluster, there are two spatial neighborhoods belonging to the third cluster (numbers 1 and 2), and two spatial neighborhoods belonging to the fifth cluster (numbers 3 and 4). In the second cluster, the spatial neighborhood of data point B includes numbers 1', 2', 3', 4', and 5'. Within this cluster, there are two spatial neighborhoods belonging to the fourth cluster (numbers 1' and 2'), one spatial neighborhood belonging to the third cluster (number 3'), and one spatial neighborhood belonging to the sixth cluster (number 5').

[0059] At this point, when k=1 or 2, When the sequence number is any, the collocation pattern is 0; When k=3, When the sequence numbers are 3, 4, 5, or 6, the corresponding combinations are 2, 4, 0, 2; when k=4, When k=5, the corresponding collocation is 0; When the sequence numbers are 3, 4, 5, or 6, the corresponding combinations are 2, 4, 0, 2; when k=6, When the sequence number is any, the combination is 0.

[0060] At this point, the spatial neighborhood consistency between data point A and data point B can be obtained.

[0061] Then, obtain the density reference factor of the target point and any data point in any cluster.

[0062] Specifically, when the target point is the first In the cluster, the th When there are 100 data points, the formula for calculating the density reference factor is as follows: ; In the formula, For the first In the cluster, the th The data point and the first In the cluster, the th Density reference factor for each data point; For the first The cluster and the first Proximity factor between clusters; For the first In the cluster, the th The data point and the first In the cluster, the th Spatial neighborhood consistency of data points.

[0063] In this embodiment, for data points located in clusters with low proximity factors to the cluster where the target point is located, when the difference between the cluster to which the spatial neighbor points of the data point belong and the cluster to which the spatial neighbor points of the target point belong is high, the measured density at the data point has low reference value for subsequent calculation of the weighted density at the target point. Therefore, it is necessary to evaluate the density reference factor between the target point and any data point in any cluster to determine the reference value of any data point.

[0064] Step S213: Obtain the weighted density of the target point.

[0065] Specifically, when the target point is the first In the cluster, the th When there are 10 data points, the weighted density of the target point The calculation formula is as follows: the weighted density for: ; for In the cluster, the th The data point and the first The first density of all data points in a cluster.

[0066] The first density is: ; For the first In the cluster, the th The data point and the first In the cluster, the th Density reference factor for each data point; , 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; This represents the total number of clusters.

[0067] The measured density is the reciprocal of the sum of the spatial distances from each data point to the other remaining data points.

[0068] Since the measured density at data points with similar terrain categories and locations is more likely to be referenced when calculating the weighted density at the target point, the measured density at data points with similar categories and locations is also introduced when calculating the weighted density.

[0069] Meanwhile, since data points with high density reference factors are more representative of the overall density level of similar terrain, the measured density at each data point can be calibrated by the density reference factors of each data point and other data points to reduce the influence of complex terrain on each data point (such as data point A and data point B actually belonging to the same terrain, but the terrain near data point A and data point B are different, resulting in a difference in density between data point A and data point B), which could lead to over- or under-segmentation when building the octree later.

[0070] 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 within a node), a corresponding segmentation strategy is set based on the density of each node to construct an octree of geographic information mapping data for the area to be measured. This allows for adjustment of the segmentation fineness according to the actual situation of the data points, making the segmentation more closely reflect the true characteristics of the data. For complex terrain areas, the weighted density is usually higher, and a finer segmentation strategy can better capture terrain details. For example, in areas with large terrain undulations such as mountains and canyons, it can accurately reflect their unique geomorphic features and avoid losing important geographic information. Conversely, for relatively flat or simple areas, the weighted density is lower, and a coarser segmentation strategy can avoid unnecessary over-segmentation, improve processing efficiency, and reduce the waste of storage resources.

[0071] Step S22: If the normalized value of the density of each node is greater than or equal to the set threshold and condition two is met, then continue to segment the single node; otherwise, do not segment; and obtain the new nodes after segmentation of each node.

[0072] The above conditions are: the recursion depth of the second node is less than the maximum recursion depth, or the number of data points in each node is greater than the set number.

[0073] For example, during any number of recursive steps in building the octree, the density of each node is linearly normalized. If 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, then the segmentation continues; otherwise, no segmentation is performed, thereby obtaining the octree of geographic information mapping data recursively.

[0074] The above setting value is taken as follows: The above-mentioned number of values ​​is... .

[0075] In this embodiment, the method for linearly normalizing the node density is the maximum-minimum value normalization method and the Sigmoid function normalization.

[0076] Step S3: Store the geographic information mapping data using an improved octree and complete the data query.

[0077] Specifically, the query process is as follows: First, define the query range, such as finding all points located within a specific cube or sphere. Second, starting from the root node of the octree, check if the space represented by the root node intersects with the query range. If they do not intersect, terminate the query immediately, as the entire octree does not contain any data points that meet the criteria. If the root node is completely covered by the query range, further check if the data points stored within that node satisfy the query conditions and add these data points to the result set. If the root node partially intersects with the query range, recursively perform the same check on each of its eight child nodes. Then, by traversing these operations, the query operation for the target data points can be achieved, thereby realizing intelligent management of geographic information mapping data.

[0078] In constructing an octree for geographic information mapping data, the present invention calculates the weighted density of data points within each node in each recursive process to obtain the density of the corresponding node. Then, based on the density of each node, a corresponding segmentation strategy is set to segment all data points within each node. In other words, the present invention can adjust the fineness of the segmentation according to the actual situation of the data points, making the segmentation more closely match the real characteristics of the data, thereby obtaining an accurate octree.

[0079] This invention also provides an intelligent management system for geographic information mapping data. For example... Figure 2 As shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the above-described intelligent management method for geographic information mapping data according to the present invention.

[0080] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and therefore will not be described in detail here.

[0081] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., 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 to or connected to a device. Any application or module described in this invention can be implemented by computer-readable / executable instructions stored or otherwise maintained on such a computer-readable medium.

[0082] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A method for intelligent management of geographic information mapping data, characterized in that, include: Obtain geographic information mapping data of the area to be measured; Geographic information mapping data includes multiple data points, each of which includes three-dimensional coordinate information and echo intensity. An improved octree is constructed based on multiple data points; An improved octree is used to store and retrieve geographic information mapping data; The improved octree's subdivision criteria for cubes include that the normalized density of each node is greater than or equal to a set value; the density is the ratio of the weighted density sum of data points within each node to the cube volume of the corresponding node; the weighted density is the mean of all first densities, where the first density is the weighted sum of the measured density of the target point in any cluster and the measured density of each data point in any remaining cluster; the weights used in the weighting are density reference factors for the target point in any cluster and each data point in any remaining cluster, where the target point is any data point in any cluster; the clusters are obtained by clustering the echo intensity of multiple data points. The measured density is the reciprocal of the sum of the spatial distances between each data point and other data points within each node; The density reference factor is the product of the spatial neighborhood similarity and the corresponding proximity factor; the proximity factor represents the similarity between any two clusters; the spatial neighborhood similarity represents the similarity between the spatial neighborhood of any data point and the spatial neighborhood of all data points in all clusters.

2. The intelligent management method for geographic information mapping data according to claim 1, characterized in that, First density for: ； in, For the first In the cluster, the th The data point and the first In the cluster, the th Density reference factor for each data point; , 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.

3. The intelligent management method for geographic information mapping data according to claim 2, characterized in that, The spatial neighborhood consistency for: ; 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.

4. The intelligent management method for geographic information mapping data according to claim 3, characterized in that, The similarity factor is the difference between 1 and the 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 the first distance and the 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.

5. A method for intelligent management of geographic information mapping data according to claim 1 or 3, characterized in that, The spatial distance is the Euclidean distance between any data point and the three-dimensional coordinate information of all other data points.

6. The intelligent management method for geographic information mapping data according to claim 1, characterized in that, The segmentation condition also includes condition two; condition two is that the recursion depth of a node is less than the maximum recursion depth or the number of data points in each node is greater than a set number.

7. The intelligent management method for geographic information mapping data according to claim 6, characterized in that, The specific process of subdividing the cube in the improved octree is as follows: Get the normalized value of the density of each node for any number of recursions; If the normalized density value of each node is greater than or equal to the set threshold and condition two is met, then the node is further segmented; otherwise, no segmentation is performed; new nodes are obtained after each node is segmented; the normalized density value of each node is obtained by using the maximum-minimum normalization method.

8. The intelligent management method for geographic information mapping data according to claim 1, characterized in that, The clusters were obtained by using the DBSCAN algorithm to cluster the echo intensity of multiple data points in the geographic information mapping data.

9. The intelligent management method for geographic information mapping data according to claim 1, characterized in that, The query process is as follows: Define the query range; Starting from the root node of the octree, check whether the space represented by the root node intersects with the query range; If the nodes do not intersect, the query is terminated immediately. If the root node partially intersects with the query range, the eight child nodes of the root node are queried one by one recursively. If the root node is completely covered by the query range, the data stored in the root node is checked to see if it meets the query conditions, and the data points that meet the query conditions are added to the result set. The root node is traversed in turn to perform the query operation.

10. A geographic information mapping data intelligent management system, characterized in that, include: processor; A memory storing computer instructions for intelligent management of geographic information mapping data, wherein when the computer instructions are executed by the processor, the system performs an intelligent management method for geographic information mapping data according to any one of claims 1-9.

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