A method and system for modeling geoid grid data

Abstract

This invention relates to the field of grid data modeling technology and discloses a geoid-like grid data modeling method and system to solve the problem of uneven distribution of control points during grid data modeling. The method includes: calculating spatial clustering characteristic parameters corresponding to each control point based on the neighborhood association relationship and the distance between control points; calculating the node distance between each grid node and each control point based on the spatial coordinate data of the grid node and the spatial coordinate data of each control point for each grid node in the regular grid structure; determining the candidate control point set corresponding to each grid node based on the node distance; and performing spatial distribution balancing processing on the candidate control point set based on the spatial clustering characteristic parameters corresponding to each candidate control point to obtain the associated control point set corresponding to each grid node, effectively improving the stability and reliability of the geoid-like grid data model.

Description

Technical Field

[0001] This invention relates to the field of grid data modeling technology, and more specifically to a geoid-like grid data modeling method and system. Background Technology

[0002] With the continuous development of surveying and mapping technology and spatial data processing technology, the control point data involved in regional elevation information modeling exhibits characteristics such as uneven spatial distribution, diverse data sources, and complex spatial variation features. In the process of geoid modeling, it is usually necessary to express the regional elevation distribution based on the spatial location and corresponding elevation information of control points, and construct a corresponding grid data model accordingly. Therefore, how to effectively process control point data and improve the accuracy of grid modeling results in complex spatial data environments has become an important technical direction of continuous focus in the field of spatial data processing.

[0003] In existing technologies, geoid grid modeling has been widely applied in surveying engineering and geographic information system (GIS) construction. This type of method typically acquires the spatial coordinates and elevation information of control points within a target area, organizes and processes the control point data, and then performs data calculations on the grid nodes to generate a grid data model covering the target area. With the development of data processing technology, some modeling methods have begun to incorporate multi-source data processing techniques. By analyzing spatial data, the system can automatically complete the grid modeling process based on the input control point data, thereby improving modeling efficiency and automation.

[0004] In actual grid modeling, once the system determines the set of control points involved in the grid node data calculation, it typically needs to perform point-by-point calculations on each grid node based on the control point data to obtain the corresponding elevation difference data. In existing modeling mechanisms, the aforementioned set of control points is generally determined based on spatial proximity or preset rules, thereby completing the grid node data calculation and constructing the grid data model.

[0005] However, the above-mentioned technologies have at least the following technical problems: In practical geoid grid modeling, when control points exhibit local clustering or sparse distribution in spatial distribution, if the distribution characteristics of control points are not differentiated during grid node data calculation, the control point data in local areas may be significantly affected during the calculation, thereby altering the spatial distribution results of the grid node data. In this case, if control points are selected for calculation solely based on spatial proximity or fixed rules, local bias may be introduced during grid modeling, affecting the overall spatial consistency of the grid data model and thus reducing the stability and reliability of the geoid grid data model. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a geoid grid data modeling method and system to solve the problems existing in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for modeling geoid grid data includes the following steps: Step 1: Acquire spatial extent data of the target area and construct a regular grid structure based on the spatial extent data. The regular grid structure includes multiple grid nodes and spatial coordinate data of each grid node. Simultaneously, acquire spatial datasets corresponding to multiple control points within the target area. The spatial datasets include spatial coordinate data and reference elevation data of each control point. Step 2: Based on the spatial datasets, calculate the correspondence between the reference elevation data of each control point to obtain the elevation difference data of each control point, and construct a control point elevation difference dataset based on the elevation difference data of each control point. Step 3: Based on the spatial coordinate data of each control point, calculate the distance between control points, determine the neighborhood range based on the distance between points, and construct the neighborhood association relationship corresponding to each control point based on the neighborhood range. Step 4: Based on the neighborhood association relationship corresponding to each control point... Step 5: For each grid node in the regular grid structure, based on the spatial coordinate data of the grid nodes and the spatial coordinate data of each control point, calculate the node distance between each grid node and each control point, and determine the candidate control point set corresponding to each grid node based on the node distance; Step 6: Based on the spatial clustering characteristic parameters corresponding to each candidate control point, perform spatial distribution equalization processing on each candidate control point set to obtain the associated control point set corresponding to each grid node; Step 7: Based on the elevation difference data between each associated control point set and the control point elevation difference data, calculate the elevation difference data corresponding to each grid node, and collect the elevation difference data corresponding to each grid node to form a grid node dataset. Then, based on the grid node dataset and the spatial coordinate data of the grid nodes, construct a geoid-like grid data model.

[0008] Preferably, the construction steps of the regular grid structure are as follows: Based on spatial range data, obtain a set of boundary point coordinates to characterize the target area range, wherein the boundary point coordinate set includes spatial coordinate data of multiple boundary points; Based on the boundary point coordinate set, determine the maximum and minimum coordinate values ​​of the target area in the horizontal direction and the maximum and minimum coordinate values ​​in the vertical direction, thereby obtaining the coordinate range of the target area; Based on the coordinate range of the target area, calculate the coordinate span in the horizontal direction and the coordinate span in the vertical direction, and determine the grid division spacing based on the coordinate span; Starting from the minimum coordinate value in the horizontal direction, divide the grid according to the grid division spacing... Multiple horizontal coordinate values ​​are generated incrementally. Starting from the minimum vertical coordinate value, multiple vertical coordinate values ​​are generated incrementally according to the grid division interval. Based on the combination of horizontal and vertical coordinate values, spatial coordinate data of multiple grid nodes are generated, forming a grid node coordinate set. Each spatial coordinate data in the grid node coordinate set is identified as a grid node, and each grid node is marked, forming a grid node set. Based on the spatial coordinate data of each grid node in the grid node set, the adjacency relationship between grid nodes is determined according to the relationship of the same horizontal or vertical coordinate value, and a regular connection relationship between grid nodes is established based on the adjacency relationship, thereby forming a regular grid structure.

[0009] Preferably, the step of obtaining the neighborhood range is as follows: based on the spatial coordinate data of each control point, calculate the distance between any two control points, and collect the distances between each point to form a set of distances between control points; sort the distances between each point in the set of distances between control points according to the numerical size to form an ordered distance sequence; select the distance between the points located in the middle position from the ordered distance sequence as the neighborhood range.

[0010] Preferably, the steps for obtaining the neighborhood association relationship corresponding to each control point are as follows: based on the set of distances between control points and the neighborhood range, for any two control points, determine whether the distance between them is not greater than the neighborhood range; when the distance between two control points is not greater than the neighborhood range, determine that there is a neighborhood relationship between the two control points; when the distance between two control points is greater than the neighborhood range, determine that there is no neighborhood relationship between the two control points; for any control point, gather other control points that have a neighborhood relationship with the control point to form a set of neighborhood control points corresponding to the control point; based on the set of neighborhood control points corresponding to each control point, establish the neighborhood association relationship between control points.

[0011] Preferably, the steps for obtaining the spatial aggregation feature parameters are as follows: based on the neighborhood association relationship corresponding to each control point, count the number of neighboring control points that have a neighborhood association relationship with each control point to obtain the number of neighboring control points corresponding to each control point; for any control point, extract the point distances between the control point and each of its neighboring control points from the control point distance set, and accumulate the extracted point distances to obtain the cumulative neighborhood distance value corresponding to the control point; for each control point, when the number of neighboring control points, the cumulative neighborhood distance value, and the neighborhood range of the control point are all large... When the value is 0, calculate the local clustering degree corresponding to each control point; when any value of the number of neighboring control points, the cumulative value of the neighboring distance, or the neighboring range of a control point is 0, record the local clustering degree as 0; compare the local clustering degrees corresponding to each control point and determine the maximum value as the maximum local clustering degree; for each control point, when the maximum local clustering degree is greater than 0, divide the local clustering degree corresponding to the control point by the maximum local clustering degree to calculate the spatial clustering characteristic parameter corresponding to each control point; when the maximum local clustering degree is equal to 0, record the spatial clustering characteristic parameter as 0.

[0012] Preferably, the steps for obtaining the candidate control point set are as follows: For any grid node in the regular grid structure, based on the spatial coordinate data of the grid node and the spatial coordinate data of each control point, calculate the node distance between the grid node and each control point to form a node distance set corresponding to the grid node; sort the node distances in the node distance set according to the numerical size to form an ordered sequence of node distances; obtain the median and maximum values ​​from the ordered sequence of node distances, and determine the candidate distance threshold based on the average of the median and maximum values; determine the control points whose node distance is not greater than the candidate distance threshold as initial candidate control points, and form an initial candidate control point set; when the number of control points in the initial candidate control point set is greater than 1, use it as the candidate control point set; when the number of control points in the initial candidate control point set is not greater than 1, select the two control points with the smallest node distance from the ordered sequence of node distances to construct the candidate control point set.

[0013] Preferably, the steps for obtaining the associated control point set are as follows: For any grid node, obtain the spatial coordinate data of each candidate control point in the candidate control point set corresponding to that grid node, and calculate the horizontal and vertical coordinate offsets of each candidate control point relative to that grid node; based on the horizontal and vertical coordinate offsets of each candidate control point, divide each candidate control point into different directional partitions to distinguish candidate control points located in different directions; for each directional partition, aggregate the candidate control points assigned to the same directional partition to form a subset of candidate control points for that directional partition; for each non-empty subset of candidate control points, compare the spatial aggregation feature parameters corresponding to each candidate control point in the subset, and select the candidate control point with the smallest spatial aggregation feature parameter as the representative control point of that directional partition; when multiple candidate control points in the same directional partition have the same spatial aggregation feature parameter, select the candidate control point with the smallest node distance to that grid node. The control point is used as the representative control point for the directional partition. The representative control points corresponding to each directional partition are collected to form the initial associated control point set corresponding to the grid node. The number of representative control points in the initial associated control point set is counted. When the number of representative control points in the initial associated control point set is greater than or equal to 2, the initial associated control point set is determined as the associated control point set corresponding to the grid node. When the number of representative control points in the initial associated control point set is less than 2, the candidate control points already selected as representative control points are removed from the candidate control point set corresponding to the grid node, resulting in the remaining candidate control point set. The candidate control points in the remaining candidate control point set are sorted in ascending order of spatial aggregation feature parameters. When the spatial aggregation feature parameters are the same, they are sorted in ascending order of node distance from the grid node. The remaining candidate control points are selected sequentially according to the sorting order to supplement the initial associated control point set until the number of supplemented control points reaches 2, thus obtaining the associated control point set corresponding to the grid node.

[0014] Preferably, the step of dividing each candidate control point into different directional partitions is as follows: For any grid node, obtain the horizontal and vertical coordinate offsets of each candidate control point in the candidate control point set corresponding to that grid node relative to that grid node; For any candidate control point, compare the absolute values ​​of the horizontal and vertical coordinate offsets corresponding to that candidate control point; When the absolute value of the horizontal coordinate offset is greater than or equal to the absolute value of the vertical coordinate offset, and the horizontal coordinate offset is greater than or equal to 0, divide the candidate control point into the first directional partition; When the absolute value of the horizontal coordinate offset is greater than or equal to the absolute value of the vertical coordinate offset, and the horizontal coordinate offset is less than 0, divide the candidate control point into the second directional partition; When the absolute value of the horizontal coordinate offset is less than the absolute value of the vertical coordinate offset, and the vertical coordinate offset is greater than or equal to 0, divide the candidate control point into the third directional partition; When the absolute value of the horizontal coordinate offset is less than the absolute value of the vertical coordinate offset, and the vertical coordinate offset is less than 0, divide the candidate control point into the fourth directional partition.

[0015] Preferably, a geoid-like grid data modeling system includes: a grid construction module for acquiring spatial extent data of a target area and constructing a regular grid structure based on the spatial extent data, the regular grid structure including multiple grid nodes and spatial coordinate data of each grid node; simultaneously acquiring spatial datasets corresponding to multiple control points within the target area, the spatial datasets including spatial coordinate data and reference elevation data of each control point; a difference calculation module for calculating the correspondence between the reference elevation data of each control point based on the spatial dataset, obtaining elevation difference data of each control point, and constructing a control point elevation difference dataset based on the elevation difference data of each control point; a neighborhood construction module for calculating the distance between control points based on the spatial coordinate data of each control point, determining the neighborhood range based on the distance between points, and constructing the neighborhood association relationship corresponding to each control point based on the neighborhood range; and an aggregation calculation module for calculating the neighborhood association relationship corresponding to each control point based on the spatial coordinate data of each control point. The system comprises the following modules: a domain association module and a control point distance module; a candidate selection module; a candidate control point set module; a balancing module; and a grid generation module. The grid generation module calculates the elevation difference data of each control point based on the associated control point set and the control point elevation difference data, and aggregates the elevation difference data of each grid node to form a grid node dataset. Finally, a geoid-like grid data model is constructed based on the grid node dataset and the spatial coordinate data of the grid nodes.

[0016] The technical effects and advantages of this invention are as follows: Based on the neighborhood relationships of each control point and the distance between control points, the spatial clustering characteristic parameters of each control point are calculated. For each grid node in the regular grid structure, the node distance between each grid node and each control point is calculated based on the spatial coordinate data of the grid node and the spatial coordinate data of each control point. The candidate control point set corresponding to each grid node is determined according to the node distance. Based on the spatial clustering characteristic parameters of each candidate control point, the spatial distribution balance of each candidate control point set is processed to obtain the associated control point set corresponding to each grid node, which effectively improves the stability and reliability of the geoid grid data model. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a method for modeling quasi-geoid grid data provided in this application embodiment.

[0018] Figure 2 A structural diagram of a geoid grid data modeling system provided in this application embodiment. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The cloud computing-based neurosurgical nursing management system involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention provides a method for modeling geoid grid data, such as... Figure 1 As shown, it includes the following steps: Step 1: Obtain the spatial extent data of the target area, and construct a regular grid structure based on the spatial extent data. The regular grid structure includes multiple grid nodes and the spatial coordinate data of each grid node. At the same time, obtain the spatial datasets corresponding to multiple control points within the target area. The spatial datasets include the spatial coordinate data and reference elevation data of each control point. Spatial extent data is used to characterize the spatial coverage of a target area, including spatial coordinate information describing the location of the target area's boundaries. Specifically, spatial extent data can be the spatial coordinates of multiple boundary points constituting the target area's boundary, or it can be the minimum and maximum coordinate values ​​that determine the target area's boundary range. Through spatial extent data, the coverage of the target area in a spatial coordinate system can be determined, thus providing a spatial extent basis for the subsequent construction of a regular grid structure.

[0021] The target area refers to the spatial region where geoid grid data modeling is to be performed, corresponding to the actual geographic area where elevation distribution modeling is required. The target area can be determined according to specific application needs, such as a surveying operation area, an engineering construction area, or a geographic information processing area, and its boundaries are defined by the spatial range data.

[0022] It should be noted that control points are discrete spatial points with known elevation difference data, used to provide the basic data source; grid nodes are spatial nodes obtained by dividing a regular grid structure, and they do not have elevation difference data themselves, which needs to be calculated based on the data of surrounding control points. By constructing the spatial relationship between control points and grid nodes, the data can be expanded from discrete control point data to continuous grid data, thereby forming a complete spatial data model.

[0023] In this embodiment, it should be specifically explained that the construction steps of the regular grid structure are as follows: Based on spatial range data, a set of boundary point coordinates is obtained to characterize the range of the target area. The set of boundary point coordinates includes spatial coordinate data of multiple boundary points, which is used to determine the spatial coverage of the target area. Based on the set of boundary point coordinates, the maximum and minimum coordinate values ​​of the target area in the horizontal direction and the maximum and minimum coordinate values ​​in the vertical direction are determined respectively, thereby obtaining the coordinate range of the target area; Based on the coordinate range of the target area, the coordinate span in the horizontal direction and the coordinate span in the vertical direction are calculated, and the grid division spacing is determined based on the coordinate span, which is used to divide the target area in a regular manner. It should be noted that, in this embodiment, the process of determining the grid spacing based on the coordinate span can be implemented using existing spatial data partitioning methods. Specifically, the spatial data can be uniformly discretized according to the coordinate range of the target area to determine the grid spacing. This processing method is a conventional data processing technique in the field, and this embodiment does not limit it.

[0024] Starting from the minimum coordinate value in the horizontal direction, multiple horizontal coordinate values ​​are generated sequentially according to the grid division spacing; starting from the minimum coordinate value in the vertical direction, multiple vertical coordinate values ​​are generated sequentially according to the grid division spacing; and based on the combination of horizontal and vertical coordinate values, spatial coordinate data of multiple grid nodes are generated to form a grid node coordinate set. Each spatial coordinate data in the grid node coordinate set is identified as a grid node, and each grid node is labeled to form a grid node set; Based on the spatial coordinate data of each grid node in the grid node set, the adjacency relationship between grid nodes is determined according to the relationship that the horizontal coordinate values ​​are the same or the vertical coordinate values ​​are the same. Based on the adjacency relationship, a regular connection relationship between grid nodes is established, thereby forming a regular grid structure.

[0025] Step 2: Based on the spatial dataset, calculate the correspondence between the reference elevation data of each control point to obtain the elevation difference data of each control point, which is used to characterize the elevation offset characteristics of each control point in the target area, and construct the control point elevation difference dataset based on the elevation difference data of each control point. In this embodiment, the elevation difference data of each control point can be obtained using existing elevation difference calculation methods. Specifically, the elevation difference data can be calculated based on the difference relationship between reference elevation data corresponding to the same control point to obtain elevation difference data characterizing the elevation offset of the control point. This calculation method is a conventional technique in the art, and this embodiment does not limit it.

[0026] Step 3: Based on the spatial coordinate data of each control point, calculate the distance between control points, determine the neighborhood range based on the distance between points, and construct the neighborhood association relationship corresponding to each control point based on the neighborhood range, which is used to characterize the distribution of neighboring control points of each control point within a certain spatial range; In this embodiment, it should be specifically explained that the step of obtaining the neighborhood range is as follows: Based on the spatial coordinate data of each control point, the distance between any two control points is calculated, and the distances between each point are aggregated to form a set of distances between control points, which is used to characterize the spatial distance distribution between control points. The distances between control points are sorted according to their numerical values ​​to form an ordered distance sequence, which is used to determine the neighborhood range later. The distance between points located in the middle position of the ordered distance sequence is selected as the neighborhood range to characterize the typical spatial proximity distance between control points.

[0027] In this embodiment, it should be specifically explained that the steps for obtaining the neighborhood association relationship corresponding to each control point are as follows: Based on the set of distances between control points and the neighborhood range, for any two control points, determine whether the distance between their corresponding points is not greater than the neighborhood range. When the distance between two control points is not greater than the neighborhood range, it is determined that there is a neighborhood relationship between the two control points. When the distance between two control points is greater than the neighborhood range, it is determined that there is no neighborhood relationship between the two control points. For any given control point, other control points that have a neighborhood relationship with that control point are aggregated to form a set of neighborhood control points corresponding to that control point, which is used to characterize the distribution of neighboring control points within the neighborhood range of that control point; Based on the set of neighboring control points corresponding to each control point, a neighborhood association relationship is established between control points. Each control point has a neighborhood association relationship with control points in its neighboring control point set.

[0028] Step 4: Based on the neighborhood association relationship of each control point and the distance between control points, calculate the spatial clustering characteristic parameters of each control point to characterize the spatial distribution density of the area where each control point is located. In this embodiment, it should be specifically explained that the steps for obtaining the spatial clustering feature parameters are as follows: Based on the neighborhood association relationship corresponding to each control point, the number of neighborhood control points that have a neighborhood association relationship with each control point is counted to obtain the number of neighborhood control points corresponding to each control point, which is used to characterize the number of neighboring control points of each control point within the neighborhood range. For any control point, extract the corresponding inter-point distances between the control point and each of its neighboring control points from the set of inter-point distances, and accumulate the extracted inter-point distances to obtain the cumulative value of the neighborhood distances corresponding to the control point, which is used to characterize the overall distance between the control point and its neighboring control points. For each control point, when the number of neighboring control points, the cumulative neighbor distance, and the neighborhood range of the corresponding control point are all greater than 0, the local clustering degree of each control point is calculated. The specific steps are as follows: ; In the formula, Represented as local clustering degree, it is used to characterize the degree of concentration of control points within the neighborhood of that control point. This is expressed as the number of neighboring control points. Represented as the neighborhood range, It is represented as the cumulative neighborhood distance. The more neighborhood control points there are and the smaller the cumulative neighborhood distance, the greater the corresponding local clustering. When any of the following values ​​are 0: the number of neighboring control points, the cumulative neighboring distance, and the neighboring range, the local clustering degree is recorded as 0. The local clustering of each control point is compared, and the maximum value is determined as the maximum local clustering, which is used to unify the local clustering of each control point. For each control point, when the maximum local clustering degree is greater than 0, the local clustering degree corresponding to the control point is divided by the maximum local clustering degree to calculate the spatial clustering characteristic parameters corresponding to each control point, which are used to uniformly characterize the spatial distribution density of the area where each control point is located. When the maximum local clustering degree is equal to 0, the spatial clustering characteristic parameter is recorded as 0.

[0029] Step 5: For each grid node in the regular grid structure, calculate the node distance between each grid node and each control point based on the spatial coordinate data of the grid node and the spatial coordinate data of each control point, and determine the set of candidate control points corresponding to each grid node based on the node distance. In this embodiment, it should be specifically explained that the steps for obtaining the candidate control point set are as follows: For any grid node in a regular grid structure, based on the spatial coordinate data of the grid node and the spatial coordinate data of each control point, the node distance between the grid node and each control point is calculated to form a set of node distances corresponding to the grid node, which is used to characterize the spatial proximity of each control point relative to the grid node. The distances of each node in the node distance set are sorted according to their numerical values ​​to form an ordered sequence of node distances, which is used to characterize the order of distances of each control point relative to the nodes of the grid. Obtain the median and maximum values ​​from the ordered sequence of node distances, and determine the candidate distance threshold based on the average of the median and maximum values; Control points whose node distance is not greater than the candidate distance threshold are identified as initial candidate control points, and an initial candidate control point set is formed. If the number of control points in the initial candidate control point set is greater than 1, it is used as the candidate control point set; if the number of control points in the initial candidate control point set is not greater than 1, the two control points with the smallest node distance are selected from the ordered sequence of node distances to construct the candidate control point set.

[0030] Step 6: Based on the spatial clustering characteristic parameters corresponding to each candidate control point, perform spatial distribution balancing processing on each candidate control point set to reduce the concentrated influence of control points with large spatial clustering characteristic parameters on the calculation of grid node data, and obtain the associated control point set corresponding to each grid node. In this embodiment, it should be specifically explained that the steps for obtaining the associated control point set are as follows: For any grid node, obtain the spatial coordinate data of each candidate control point in the candidate control point set corresponding to the grid node, and calculate the horizontal and vertical coordinate offsets of each candidate control point relative to the grid node to characterize the spatial distribution of each candidate control point relative to the grid node. Based on the horizontal and vertical offsets of each candidate control point, each candidate control point is divided into different directional partitions to distinguish candidate control points located in different directional positions for subsequent equalization selection. For each directional partition, the candidate control points assigned to the same directional partition are aggregated to form a partition candidate control point subset for the corresponding directional partition, which is used to characterize the distribution of candidate control points that can participate in the subsequent equilibrium selection within that directional partition. For each non-empty subset of candidate control points for a partition, the spatial clustering feature parameters corresponding to each candidate control point in the subset are compared, and the candidate control point with the smallest spatial clustering feature parameter is selected as the representative control point of that directional partition. When multiple candidate control points in the same directional partition have the same spatial clustering feature parameter, the candidate control point with the smallest node distance to the grid node is selected as the representative control point of that directional partition. Through the above processing, each directional partition prioritizes retaining candidate control points located in low-cluster areas and closer to the grid nodes. Representative control points corresponding to each directional partition are aggregated to form the initial associated control point set corresponding to the grid node, which is used to characterize the combination of candidate control points that are retained in different directional positions. Count the number of control points represented in the initial set of associated control points; when the number of control points represented in the initial set of associated control points is greater than or equal to 2, the initial set of associated control points is determined as the set of associated control points corresponding to the grid node. When the number of representative control points in the initial associated control point set is less than 2, the candidate control points that have been selected as representative control points are removed from the candidate control point set corresponding to the grid node, resulting in a remaining candidate control point set. The candidate control points in the remaining candidate control point set are sorted in ascending order according to their spatial clustering feature parameters. When the spatial clustering feature parameters are the same, they are sorted in ascending order according to their node distance from the grid node. The remaining candidate control points are selected in the sorting order to be added to the initial associated control point set until the number of added control points reaches 2, thus obtaining the associated control point set corresponding to the grid node.

[0031] In this embodiment, it should be specifically explained that the step of dividing each candidate control point into different directional partitions is as follows: For any grid node, obtain the horizontal and vertical coordinate offsets of each candidate control point in the candidate control point set corresponding to the grid node relative to the grid node, which are used to characterize the spatial position and orientation of each candidate control point relative to the grid node. For any candidate control point, compare the absolute value of the horizontal coordinate offset with the absolute value of the vertical coordinate offset to determine the dominant offset direction of the candidate control point relative to the grid node. When the absolute value of the horizontal coordinate offset is greater than or equal to the absolute value of the vertical coordinate offset, and the horizontal coordinate offset is greater than or equal to 0, the candidate control point is assigned to the first direction partition. When the absolute value of the horizontal coordinate offset is greater than or equal to the absolute value of the vertical coordinate offset, and the horizontal coordinate offset is less than 0, the candidate control point is assigned to the second direction partition. When the absolute value of the horizontal axis offset is less than the absolute value of the vertical axis offset, and the vertical axis offset is greater than or equal to 0, the candidate control point is assigned to the third-party directional partition. When the absolute value of the horizontal coordinate offset is less than the absolute value of the vertical coordinate offset, and the vertical coordinate offset is less than 0, the candidate control point is assigned to the fourth direction partition.

[0032] It should be noted that the first directional partition is the candidate control point area that is biased towards the positive direction of the horizontal coordinate relative to the grid node; the second directional partition is the candidate control point area that is biased towards the negative direction of the horizontal coordinate relative to the grid node; the third directional partition is the candidate control point area that is biased towards the positive direction of the vertical coordinate relative to the grid node; and the fourth directional partition is the candidate control point area that is biased towards the negative direction of the vertical coordinate relative to the grid node.

[0033] Step 7: Based on the set of associated control points and the elevation difference data of control points, calculate the elevation difference data corresponding to each grid node, and collect the elevation difference data corresponding to each grid node to form a grid node dataset. Then, construct a geoid grid data model based on the grid node dataset and the spatial coordinate data of the grid nodes.

[0034] It should be noted that, in this embodiment, the process of calculating the elevation difference data corresponding to each grid node based on the set of associated control points and the elevation difference data of the control points can be implemented using existing spatial data interpolation methods. Specifically, the grid nodes can be extrapolated based on the spatial positional relationship of the associated control points and their corresponding elevation difference data to obtain the elevation difference data corresponding to the grid node. This type of calculation method is a conventional technique in the field, and this embodiment does not limit it.

[0035] It should be noted that, in this embodiment, the process of constructing a geoid-like grid data model based on the grid node dataset and the spatial coordinate data of the grid nodes can be implemented using existing spatial data organization and modeling methods. Specifically, the spatial coordinate data of the grid nodes and the corresponding elevation difference data can be correlated and structured to form a grid data model used to characterize the spatial distribution features of the target area. This processing method is a conventional data processing technique in the field, and this embodiment does not limit it.

[0036] It should be noted that the control point elevation difference data obtained in step 2 is the result of calculation based on the original data of the known control point locations, and belongs to discrete spatial data; The grid node elevation difference data calculated in step 7 is the result of extrapolating the grid nodes without control points based on the control point elevation difference data and through spatial correlation. It is an extended calculation result of a continuous spatial area. The two are fundamentally different in terms of data source and target: the former is used to provide basic data, while the latter is used to build continuous spatial data models.

[0037] In this embodiment, it should be specifically explained that, as Figure 2 As shown, a geoid-like grid data modeling system is provided, comprising: The grid construction module is used to acquire spatial extent data of the target area and construct a regular grid structure based on the spatial extent data. The regular grid structure includes multiple grid nodes and spatial coordinate data of each grid node. At the same time, it acquires spatial datasets corresponding to multiple control points within the target area. The spatial datasets include spatial coordinate data and reference elevation data of each control point. The difference calculation module is used to calculate the correspondence between the reference elevation data of each control point based on the spatial dataset, obtain the elevation difference data of each control point, and construct the control point elevation difference dataset based on the elevation difference data of each control point. The neighborhood construction module is used to calculate the distance between control points based on the spatial coordinate data of each control point, determine the neighborhood range based on the distance between the points, and construct the neighborhood association relationship corresponding to each control point based on the neighborhood range. The aggregation calculation module is used to calculate the spatial aggregation feature parameters of each control point based on the neighborhood association relationship of each control point and the distance between control points. The candidate filtering module is used to calculate the node distance between each grid node and each control point based on the spatial coordinate data of the grid node and the spatial coordinate data of each control point in the regular grid structure, and determine the set of candidate control points corresponding to each grid node according to the node distance. The equalization processing module is used to perform spatial distribution equalization processing on the set of candidate control points based on the spatial aggregation feature parameters corresponding to each candidate control point, so as to obtain the set of associated control points corresponding to each grid node. The grid generation module is used to calculate the elevation difference data corresponding to each grid node based on the set of associated control points and the elevation difference data of control points, and to collect the elevation difference data corresponding to each grid node to form a grid node dataset. Then, based on the grid node dataset and the spatial coordinate data of the grid nodes, a geoid-like grid data model is constructed.

[0038] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0039] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A quasi-geoid grid data modeling method, characterized in that, Includes the following steps: Step 1: Obtain the spatial extent data of the target area, and construct a regular grid structure based on the spatial extent data. The regular grid structure includes multiple grid nodes and the spatial coordinate data of each grid node. At the same time, obtain the spatial datasets corresponding to multiple control points within the target area. The spatial datasets include the spatial coordinate data and reference elevation data of each control point. Step 2: Based on the spatial dataset, calculate the correspondence between the reference elevation data of each control point to obtain the elevation difference data of each control point, and construct the control point elevation difference dataset based on the elevation difference data of each control point. Step 3: Based on the spatial coordinate data of each control point, calculate the distance between control points, determine the neighborhood range based on the distance between points, and construct the neighborhood association relationship corresponding to each control point based on the neighborhood range; Step 4: Based on the neighborhood association relationship of each control point and the distance between control points, calculate the spatial clustering feature parameters of each control point; Step 5: For each grid node in the regular grid structure, calculate the node distance between each grid node and each control point based on the spatial coordinate data of the grid node and the spatial coordinate data of each control point, and determine the set of candidate control points corresponding to each grid node based on the node distance. Step 6: Based on the spatial clustering feature parameters corresponding to each candidate control point, perform spatial distribution balancing processing on each candidate control point set to obtain the associated control point set corresponding to each grid node; Step 7: Based on the set of associated control points and the elevation difference data of control points, calculate the elevation difference data corresponding to each grid node, and collect the elevation difference data corresponding to each grid node to form a grid node dataset. Then, construct a geoid grid data model based on the grid node dataset and the spatial coordinate data of the grid nodes.

2. The quasi-geoid grid data modeling method according to claim 1, wherein: The steps for constructing the regular grid structure are as follows: Based on spatial range data, obtain a set of boundary point coordinates to characterize the target area range, wherein the set of boundary point coordinates includes spatial coordinate data of multiple boundary points; Based on the set of boundary point coordinates, the maximum and minimum coordinate values ​​of the target area in the horizontal direction and the maximum and minimum coordinate values ​​in the vertical direction are determined respectively, thereby obtaining the coordinate range of the target area; Based on the coordinate range of the target area, calculate the coordinate span in the horizontal direction and the coordinate span in the vertical direction, and determine the grid division spacing based on the coordinate span. Starting from the minimum coordinate value in the horizontal direction, multiple horizontal coordinate values ​​are generated by sequentially increasing the grid spacing. Starting from the minimum coordinate value in the vertical direction, multiple vertical coordinate values ​​are generated sequentially according to the grid division spacing; and based on the combination of horizontal and vertical coordinate values, spatial coordinate data of multiple grid nodes are generated to form a grid node coordinate set. Each spatial coordinate data in the set of grid node coordinates is identified as a grid node, and each grid node is labeled to form a set of grid nodes; Based on the spatial coordinate data of each grid node in the grid node set, the adjacency relationship between grid nodes is determined according to the relationship that the horizontal coordinate values ​​are the same or the vertical coordinate values ​​are the same. Based on the adjacency relationship, a regular connection relationship between grid nodes is established, thereby forming a regular grid structure.

3. The quasi-geoid grid data modeling method according to claim 1, wherein, The steps for obtaining the neighborhood range are as follows: Based on the spatial coordinate data of each control point, the distance between any two control points is calculated, and the distances between each control point are aggregated to form a set of distances between control points. The distances between control points are sorted according to their numerical values ​​to form an ordered distance sequence. The distance between points located in the middle position of the ordered distance sequence is selected as the neighborhood range.

4. The quasi-geoid grid data modeling method according to claim 1, wherein, The steps for obtaining the neighborhood association relationship corresponding to each control point are as follows: Based on the set of distances between control points and the neighborhood range, for any two control points, determine whether the distance between their corresponding points is not greater than the neighborhood range. When the distance between two control points is not greater than the neighborhood range, it is determined that there is a neighborhood relationship between the two control points. When the distance between two control points is greater than the neighborhood range, it is determined that there is no neighborhood relationship between the two control points. For any given control point, other control points that have a neighborhood relationship with that control point are aggregated to form a set of neighborhood control points corresponding to that control point. Based on the set of neighboring control points corresponding to each control point, establish the neighborhood association relationship between control points.

5. The quasi-geoid gridding data modeling method of claim 1, wherein: The steps for obtaining the spatial clustering feature parameters are as follows: Based on the neighborhood association relationship corresponding to each control point, the number of neighborhood control points that have a neighborhood association relationship with each control point is counted, and the number of neighborhood control points corresponding to each control point is obtained. For any control point, extract the corresponding inter-point distances between the control point and each of its neighboring control points from the set of inter-point distances, and sum up the extracted inter-point distances to obtain the cumulative value of the neighborhood distances corresponding to the control point. For each control point, when the number of neighboring control points, the cumulative value of the neighboring distance, and the neighborhood range of the corresponding control point are all greater than 0, the local clustering degree of each control point is calculated. When any of the following values ​​are 0: the number of neighboring control points, the cumulative neighboring distance, and the neighboring range, the local clustering degree is recorded as 0. The local clustering of each control point is compared, and the maximum value is determined as the maximum local clustering. For each control point, when the maximum local clustering degree is greater than 0, the local clustering degree corresponding to the control point is divided by the maximum local clustering degree to calculate the spatial clustering characteristic parameters corresponding to each control point. When the maximum local clustering degree is equal to 0, the spatial clustering characteristic parameter is recorded as 0.

6. The method for modeling quasi-geoid grid data according to claim 1, characterized in that: The steps for obtaining the candidate control point set are as follows: For any grid node in a regular grid structure, based on the spatial coordinate data of the grid node and the spatial coordinate data of each control point, the node distance between the grid node and each control point is calculated to form a set of node distances corresponding to the grid node. Sort the distances of each node in the node distance set according to their numerical values ​​to form an ordered sequence of node distances; Obtain the median and maximum values ​​from the ordered sequence of node distances, and determine the candidate distance threshold based on the average of the median and maximum values; Control points whose node distance is not greater than the candidate distance threshold are identified as initial candidate control points, and an initial candidate control point set is formed. If the number of control points in the initial candidate control point set is greater than 1, it is used as the candidate control point set; if the number of control points in the initial candidate control point set is not greater than 1, the two control points with the smallest node distance are selected from the ordered sequence of node distances to construct the candidate control point set.

7. The method for modeling quasi-geoid grid data according to claim 1, characterized in that: The steps for obtaining the set of associated control points are as follows: For any grid node, obtain the spatial coordinate data of each candidate control point in the candidate control point set corresponding to that grid node, and calculate the horizontal and vertical coordinate offsets of each candidate control point relative to that grid node. Based on the horizontal and vertical offsets of each candidate control point, each candidate control point is divided into different directional partitions to distinguish candidate control points located in different directional positions. For each directional partition, the candidate control points assigned to the same directional partition are aggregated to form a partition candidate control point subset for the corresponding directional partition; For each non-empty subset of candidate control points for a partition, compare the spatial clustering feature parameters corresponding to each candidate control point in the subset, and select the candidate control point with the smallest spatial clustering feature parameter as the representative control point of that directional partition. When multiple candidate control points in the same directional partition have the same spatial clustering feature parameters, the candidate control point with the smallest node distance to the grid node is selected as the representative control point of the directional partition. The representative control points corresponding to each directional partition are aggregated to form the initial set of associated control points corresponding to the grid node. Count the number of control points in the initial set of associated control points; When the number of control points represented in the initial set of associated control points is greater than or equal to 2, the initial set of associated control points is determined as the set of associated control points corresponding to the grid node. When the number of representative control points in the initial associated control point set is less than 2, the candidate control points that have been selected as representative control points are removed from the candidate control point set corresponding to the grid node to obtain the remaining candidate control point set; the candidate control points in the remaining candidate control point set are sorted in ascending order according to the spatial clustering feature parameters. When the spatial clustering feature parameters are the same, the nodes are sorted in ascending order of distance from the grid node; the remaining candidate control points are selected in the sorting order to be added to the initial associated control point set until the number of added control points reaches 2, thus obtaining the associated control point set corresponding to the grid node.

8. A method for modeling quasi-geoid grid data according to claim 7, characterized in that: The step of dividing each candidate control point into different directional partitions is as follows: For any grid node, obtain the horizontal and vertical coordinate offsets of each candidate control point in the candidate control point set corresponding to that grid node relative to that grid node. For any candidate control point, compare the absolute value of the x-axis offset with the absolute value of the y-axis offset corresponding to the candidate control point; When the absolute value of the horizontal coordinate offset is greater than or equal to the absolute value of the vertical coordinate offset, and the horizontal coordinate offset is greater than or equal to 0, the candidate control point is assigned to the first direction partition. When the absolute value of the horizontal coordinate offset is greater than or equal to the absolute value of the vertical coordinate offset, and the horizontal coordinate offset is less than 0, the candidate control point is assigned to the second direction partition. When the absolute value of the horizontal axis offset is less than the absolute value of the vertical axis offset, and the vertical axis offset is greater than or equal to 0, the candidate control point is assigned to the third-party directional partition. When the absolute value of the horizontal coordinate offset is less than the absolute value of the vertical coordinate offset, and the vertical coordinate offset is less than 0, the candidate control point is assigned to the fourth direction partition.

9. A geoid grid data modeling system, used to implement the geoid grid data modeling method according to any one of claims 1-8, characterized in that: The system includes: The grid construction module is used to acquire spatial extent data of the target area and construct a regular grid structure based on the spatial extent data. The regular grid structure includes multiple grid nodes and spatial coordinate data of each grid node. At the same time, it acquires spatial datasets corresponding to multiple control points within the target area. The spatial datasets include spatial coordinate data and reference elevation data of each control point. The difference calculation module is used to calculate the correspondence between the reference elevation data of each control point based on the spatial dataset, obtain the elevation difference data of each control point, and construct the control point elevation difference dataset based on the elevation difference data of each control point. The neighborhood construction module is used to calculate the distance between control points based on the spatial coordinate data of each control point, determine the neighborhood range based on the distance between the points, and construct the neighborhood association relationship corresponding to each control point based on the neighborhood range. The aggregation calculation module is used to calculate the spatial aggregation feature parameters of each control point based on the neighborhood association relationship of each control point and the distance between control points. The candidate filtering module is used to calculate the node distance between each grid node and each control point based on the spatial coordinate data of the grid node and the spatial coordinate data of each control point in the regular grid structure, and determine the set of candidate control points corresponding to each grid node according to the node distance. The equalization processing module is used to perform spatial distribution equalization processing on the set of candidate control points based on the spatial aggregation feature parameters corresponding to each candidate control point, so as to obtain the set of associated control points corresponding to each grid node. The grid generation module is used to calculate the elevation difference data corresponding to each grid node based on the set of associated control points and the elevation difference data of control points, and to collect the elevation difference data corresponding to each grid node to form a grid node dataset. Then, based on the grid node dataset and the spatial coordinate data of the grid nodes, a geoid-like grid data model is constructed.

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