CN115438081A - Multi-stage aggregation and real-time updating method for massive ship position point clouds - Google Patents
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Abstract
The invention discloses a multi-stage polymerization and real-time updating method for massive ship position point clouds, which is used for drawing a map of the massive ship point clouds in real time and comprises the following steps: constructing a multilevel spatial grid: performing multi-level spatial grid division on the massive ship position point cloud based on the GeoHash code to form a multi-level spatial index; calculating a point set in each grid in the multilevel spatial grid to obtain an aggregation position; in the real-time updating process of the data of the massive ship position point clouds, real-time updating calculation is carried out on the aggregation positions on the basis of the multilevel space grids; determining an aggregation level according to the current map browsing position and a map scale, and inquiring the aggregation position of the corresponding level from the multi-level space grid; performing neighborhood merging operation on the aggregation position obtained by query to obtain a final aggregation result; and drawing the final aggregation result on a map in a symbolic form to finish the multi-stage aggregation and real-time updating of the massive ship position point clouds.
Description
Technical Field
The invention relates to a ship position aggregation and real-time updating method, in particular to a multi-stage aggregation and real-time updating method for massive ship position point clouds.
Background
The demand of China for monitoring marine targets is gradually expanding, and information of global marine targets needs to be accessed, processed, analyzed and displayed. According to incomplete statistics, the total number of civil ships (including fishing ships, merchant ships and cargo ships) and international sailing ships with more than 300 total tons currently exceeds 100 thousands, wherein the number of ships with maritime activities reaches 40 to 50 thousands, and the position data is updated every several seconds. The real-time ship information can assist in mastering accurate battlefield environment, leakage detection and defect filling are achieved, misjudgment is reduced, and the method has practical significance in offshore defense and open sea defense.
The point cloud data representing the position of the ship has the characteristics of large data volume and high updating frequency, which puts higher requirements on space inquiry, needs efficient space index and data structure to organize and manage the point cloud data of the ship, and supports frequent updating of the point location of the ship. Meanwhile, in order to improve the target drawing efficiency under the global scale, on the basis of a spatial index structure, point clouds need to be aggregated to reduce the number of target points, and ship information display with different detail degrees under different map scales is realized.
The ship position is stored in latitude and longitude values, and is usually organized in a two-dimensional space index mode. The current commonly used two-dimensional point cloud space indexing method mainly comprises a regular grid, a quadtree, an R tree, an improved method thereof and a KD tree. The regular grid method divides a space into a plurality of same grids according to a certain rule, indexes point data by taking the grids as units, and is simple and efficient in algorithm implementation, but under the condition that point cloud data are not uniformly distributed, the grid data quantity difference is large, and the problem of data redundancy exists. The quadtree is usually used for storing two-dimensional space points and is easy to realize, but the construction speed is slow, and the quadtree is too deep in a data-dense area, so that the query efficiency is influenced. The R tree is a space index which is widely applied at present and has the characteristic of high balance, but the minimum enclosing rectangle of the middle node of the R tree is allowed to be overlapped, so that the problem of excessive invalid query is caused when the data volume is increased, and the query efficiency is influenced. A clustering algorithm based on division is often adopted in spatial clustering, typical methods comprise a K-means algorithm and a K-medoids algorithm, the number of clusters and an initial clustering center need to be preset, different settings have large influence on results, time complexity is high, and the requirement of real-time updating is difficult to meet.
The GeoHash algorithm is a geographic data coding technology based on grid division, and can convert the longitude and latitude coordinates of a target into character string codes. The character string represents not one point, but actually represents a certain rectangular area, and all points (longitude and latitude coordinates) in the rectangular area share the same GeoHash character string. The longer the length of the character string code is, the smaller the rectangular area represented by the character string code is, and the higher the indexing accuracy of the spatial data is. The GeoHash code is widely applied to indexing of spatial data, the efficiency of spatial indexing can be improved under the condition of ensuring the precision of a target position, meanwhile, complex neighborhood search calculation can be converted into relatively simple character string comparison, and the speed of spatial query is effectively improved.
Disclosure of Invention
The invention aims to: the invention aims to solve the technical problem of providing a multistage polymerization and real-time updating method for massive ship position point clouds aiming at the defects of the prior art.
In order to solve the technical problem, the invention discloses a multi-stage aggregation and real-time updating method for massive ship position point clouds, which is used for drawing a map of the massive ship point clouds in real time and comprises the following steps:
step 1, constructing a multilevel spatial grid: performing multi-level spatial grid division on the mass ship position point cloud based on a GeoHash code (refer to GeoHash, online, available: http:// www.geoHash.org.), so as to obtain a multi-level spatial grid and a corresponding aggregation level, and forming a multi-level spatial index for the mass ship position point cloud; calculating a point set in each grid in the multilevel space grid to obtain an aggregation position;
step 2, updating and calculating the aggregation position in real time based on the multilevel space grid in the real-time updating process of the data of the massive ship position point clouds;
step 3, determining a polymerization level according to the current browsing position of the map and the scale of the map, and inquiring the polymerization position of the corresponding polymerization level from the multi-level space grid;
step 4, performing neighborhood merging operation on the aggregation positions obtained by inquiring in the step 3 to obtain a final aggregation result; and drawing the final aggregation result on a map in a symbol form, and completing the multi-stage aggregation and real-time updating of the massive ship position point clouds.
The step 1 of the invention comprises:
step 1-1, multi-stage space grid division: calculating GeoHash codes of 6 aggregation levels with the code length of 1-6 for the initial longitude and latitude position of a certain ship, wherein the code of each level represents a space grid in a set range, and a prefix representing the code length is added in front of each code to serve as a unique identifier of the space grid to obtain a space grid identifier;
step 1-2, calculating to obtain a polymerization position: if a certain spatial grid appears for the first time, recording the number of point sets contained in the spatial grid as 1, and setting the aggregation position corresponding to the point sets as the calculated position of the current ship; otherwise, adding 1 to the number of the point sets of the space grid, and calculating a new polymerization position after the current ship position is added;
and step 1-3, performing the operations of the step 1-1 and the step 1-2 on each ship in the massive ship position point cloud, storing the space grid identification, the point set number of the space grid and the aggregation position in a key value pair mode, and completing the construction of the multilevel space grid.
The step 2 of the invention comprises:
step 2-1, removing the original position from the 1 st to 6 th spatial grids of a certain ship with the updated position, subtracting 1 from the number of point sets contained in each spatial grid, and calculating the aggregation position after the original position is removed;
and 2-2, performing the operations of the step 1-1 and the step 1-2 on the new position of the ship to finish the quick updating of the polymerization position.
The position point cloud is mass data needing to be managed, the multilevel spatial grid is a spatial index of the point cloud, and the method is an effective management method, the final aggregation and updating result can be displayed on a map, and the map is a visual carrier of the calculation result.
The step 3 of the invention comprises:
step 3-1, determining an aggregation level through a current map scale according to a corresponding relation between a preset map scale and the aggregation level;
step 3-2, inquiring a space grid set taking the determined aggregation level as a prefix in the constructed multilevel space grid to obtain a result set;
and 3-3, screening according to the result set obtained by querying in the step 3-2, if the spatial grid is in the current map range, reserving the spatial grid, and otherwise, removing the spatial grid from the result set.
The step 4 of the invention comprises the following steps:
step 4-1, aiming at the result set obtained in the step 3, calculating the distance between the aggregation position of each spatial grid and the aggregation position of the adjacent spatial grid in the result set, if the distance is smaller than a set threshold value, merging the spatial grid and the adjacent spatial grid, and calculating a new aggregation position to obtain a final aggregation position;
and 4-2, drawing the result of the multi-stage aggregation and real-time updating of the massive ship position point clouds on a map, and drawing an aggregation point symbol at the final aggregation position.
The threshold value in step 4-1 of the invention is: and the distance between the aggregation position of the spatial grid and the aggregation position of the adjacent spatial grid is proportional to the length of the spatial grid at the level.
The method for obtaining the polymerization position through calculation in the step 1-2 comprises the following steps:
wherein, lng NewAggr And Lat NewAggr Respectively representing the longitude and latitude, lng, of the new aggregated location OldAggr And Lat OldAggr Respectively representing the latitude and longitude of the original aggregated location, lng Point And Lat Point Respectively representing the longitude and the latitude of the position of the ship calculated currently, n representing the number of the original point sets in the space grid, and the calculated aggregation position being the centroid position of the point sets obtained through weighted addition.
In the invention, the method for calculating the polymerization position after removing the in-situ position in the step 2-1 comprises the following steps:
wherein, lng RemovedAggr And Lat RemovedAggr Respectively representing the longitude and latitude, lng, of the aggregate location after removal of the home position Aggr And Lat Aggr Respectively representing the latitude and longitude of the original aggregated location, lng OldPoint And Lat OldPoint Respectively representing the longitude and latitude of the current computed ship's home position, and n represents the number of original point sets in the spatial grid.
In the invention, the new aggregation position is calculated in the step 4-1, and the method comprises the following steps:
wherein, lng MergedAggr And Lat MergedAggr Respectively representing the longitude and latitude, lng, of the merged aggregate location 1 And Lng 2 Longitude, lat, representing two aggregated locations being merged 1 And Lat 2 Denotes the latitude, n, of two aggregate positions being merged 1 And n 2 Respectively represent two polymerizationsThe number of point sets represented by a location.
The aggregation point symbol in the step 4-2 of the invention is used for reflecting the number of the aggregation point set; the aggregate point symbol size reflects how many sets of aggregated points are, with larger aggregate point symbols indicating more points are aggregated there.
Has the beneficial effects that:
(1) The characteristic of GeoHash coding is utilized to realize multi-level spatial grid index based on character strings, the method has more advantages in the spatial index efficiency of point cloud, and the problem of node depth possibly caused by singly using a quadtree or a KD tree is avoided.
(2) Compared with multiple iteration methods such as a K-means algorithm or a K-medoids algorithm and the like, the aggregation method based on the multi-level spatial grid index has linear time complexity, can meet the requirement of rapid real-time updating of point cloud data, does not need to perform aggregation calculation on all point clouds again when the position of a certain point changes, and only needs to update aggregation points in a local range.
(3) Compared with the traditional single-level aggregation algorithm, the method has the advantages that space division of different grid sizes can be realized through one-time construction of space indexes by means of the characteristic of multiple levels of GeoHash codes, so that the multi-level aggregation effect is realized, and the browsing of the ship target is smoother.
Drawings
The foregoing and/or other advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
FIG. 1 is a schematic of the work flow of the present invention.
FIG. 2 is a schematic diagram of the merging of neighboring aggregation points in the present invention.
Detailed Description
The invention relates to a multistage aggregation and real-time updating method for mass ship position point clouds in the field of computer graphics, which comprises the following steps: providing a multistage spatial index based on GeoHash coding, and performing spatial grid division on massive ship position point cloud data by using the method; after the division is finished, the point cloud data in each level of spatial grid are aggregated, and aggregation points and attributes thereof are obtained through calculation; and in the real-time updating process of the point cloud data, updating and calculating the aggregation point in real time based on the proposed multi-level spatial index. When mass point clouds are drawn, only the aggregation level corresponding to the scale needs to be inquired according to the current map position and the scale, and the qualified grid information and the aggregation position thereof are inquired by taking the aggregation level as a prefix, so that the aggregation result in the current map range can be obtained. The multistage aggregation method based on the GeoHash coding has high data indexing efficiency, can simultaneously meet real-time aggregation point calculation and high-frequency data point updating aiming at massive point cloud data, and then uses aggregation points to replace point clouds to render in a map scene, so that the drawing amount of the massive data is effectively reduced, and the map drawing efficiency is improved. In summary, for massive ship position point cloud data, the multilevel spatial grid is a spatial index structure capable of efficiently managing point clouds, and a final aggregation result is displayed on a map serving as a visualization carrier.
The embodiment of the invention discloses a method for multistage aggregation and real-time update of mass ship position point clouds, and provides a strategy capable of calculating and updating aggregation points in real time aiming at mass ship point cloud position data in a global ocean range.
As shown in fig. 1, the method for multistage aggregation and real-time update of a cloud of a large number of ship position points in this embodiment includes the following steps:
step 1, performing multi-stage space grid division on massive ship position point clouds based on GeoHash codes and calculating aggregation positions of point sets in each grid;
step 2, in the real-time updating process of point cloud data, performing real-time updating calculation on the aggregation position based on the multilevel spatial grid;
step 3, determining an aggregation level according to the current position and a map scale, and inquiring the aggregation position of the corresponding level from the multi-level space grid;
and 4, performing neighborhood merging operation on the inquired aggregation positions, and drawing the final aggregation result in a symbolic form.
In the method for multistage aggregation and real-time update of a cloud of massive ship position points, step 1 includes:
step 1-1, calculating GeoHash codes of 6 levels with the code length of 1-6 for the initial longitude and latitude position of a certain ship, wherein the code of each level represents a space grid in a certain range. The prefix of the GeoHash code indicates a larger range, namely, the coding range of the 1 st level is the largest, the coding range of the 6 th level is the smallest, and the 6 levels are in a spatial sequential relationship. The longer the GeoHash code is, the smaller the represented grid range is, and the higher the representation precision is, and the GeoHash code with the length of 6 is used in consideration of the actual driving distance between ships on the route, namely the grid size of 1.2km multiplied by 0.6km can meet the space index of the ship position. Prefixes representing coding lengths are added before each code to serve as unique identification of the grid, and the 6 levels of codes are 1_w, 2_wx, 3_wx4, 4_wx4g, 5 _wx4g0and 6 _wx4g0frespectively by taking longitude and latitude coordinates (116.404, 39.915) as an example;
step 1-2, if a certain grid appears for the first time, recording the number of point sets contained in the grid as 1, and the aggregation position corresponding to the point sets is the calculated position of the current ship; otherwise, adding 1 to the number of the point sets of the grid, and calculating a new aggregation position after adding the new aggregation position to the current ship position, wherein the calculation method is as follows:
wherein, lng NewAggr And Lat NewAggr Longitude and latitude, lng, respectively, representing the location of the new aggregation point OldAggr And Lat OldAggr Respectively representing the longitude and latitude, lng, of the original aggregate location Point And Lat Point Respectively representing the longitude and the latitude of the current calculated ship point location, n representing the number of original point sets in the grid, and the calculated aggregation position actually being the centroid position of the point sets obtained through weighted addition;
and 1-3, performing the operations of the two steps on each ship, storing the grid identification and the attributes thereof in a key value pair mode, and completing the construction of the multilevel space grid.
In the method for multistage aggregation and real-time update of a cloud of massive ship position points described in this embodiment, the step 2 includes:
step 2-1, removing the original position from the grids from the 1 st to the 6 th level, subtracting 1 from the point set number contained in each grid, and calculating the aggregation position after removing the original position for a certain ship with the updated position, wherein the calculation method is as follows:
wherein, lng RemovedAggr And Lat RemovedAggr Respectively representing the longitude and latitude, lng, of the location of the aggregation point Aggr And Lat Aggr Respectively representing the longitude and latitude, lng, of the original aggregate location OldPoint And Lat OldPoint Respectively representing the longitude and latitude of the current calculated ship point location, and n represents the number of original point sets in the grid;
and 2-2, performing the operations of the step 1-1 and the step 1-2 on the new position of the ship, and finishing the quick updating of the polymerization position.
In the method for multistage aggregation and real-time update of a cloud of massive ship position points, step 3 includes:
and 3-1, determining the aggregation level through the current map scale according to the corresponding relation between the preset map scale and the aggregation level. The following correspondence is determined by practical experience: the better map visual effect can be achieved under the preset condition by using the aggregation result of the 1 st level below 1 to 1000 ten thousand scales, the aggregation result of the 2 nd level below 1 to 250 ten thousand scales, the aggregation result of the 3 rd level below 1 to 100 ten thousand scales, the aggregation result of the 4 th level below 1 to 50 ten thousand scales, the aggregation result of the 5 th level below 1 to 25 ten thousand scales and the aggregation result of the 6 th level below 1 to 10 ten thousand scales;
step 3-2, querying a spatial grid set with the determined aggregation level as a prefix in the constructed multilevel spatial grid, for example, determining to use the aggregation result of the level 2, and only querying the spatial grid with the grid identifier having the prefix of "2 _";
and 3-3, further screening the grid set inquired in the last step, judging whether the rectangular range of the grid falls within the current map range, if so, reserving the grid, and otherwise, removing the grid from the result set.
In the method for multistage aggregation and real-time update of a cloud of massive ship position points, step 4 includes:
step 4-1, calculating the distance between the aggregation position of each grid and the aggregation position of the adjacent grid with respect to the grid set queried in step 3, if the distance is smaller than a certain proportion of the length of the hierarchical grid, determining that the two aggregation positions are too close, merging the two aggregation positions, and calculating a new aggregation position, as shown in fig. 2. The calculation method is as follows:
wherein, lng MergedAggr And Lat MergedAggr Respectively representing the longitude and latitude, lng, of the location of the merged aggregate point 1 And Lng 2 Indicating the longitude, lat, of the two aggregation points being merged 1 And Lat 2 Denotes the latitude, n, of two aggregation points being merged 1 And n 2 Respectively representing the number of point sets represented by the two aggregation points. Through the merging operation of adjacent points, the condition of symbol overlapping of aggregation points caused by too close distance can be avoided, and the final aggregation result is more reasonable. The proportion is a preset empirical value, and the proportion is set to be 0.2 through practical experience, so that the overlapping of the symbols of the aggregation points can be effectively avoided;
and 4-2, drawing an aggregation point symbol at the final aggregation position, wherein the size of the symbol reflects the number of the aggregated point sets, and the larger the drawn symbol is, the more the aggregated points are.
In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, where the computer storage medium can store a computer program, and the computer program can run the inventive content of the massive ship position point cloud multistage aggregation and real-time update method provided by the present invention and some or all of the steps in each embodiment when executed by the data processing unit. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like.
It is obvious to those skilled in the art that the technical solutions in the embodiments of the present invention can be implemented by means of a computer program and its corresponding general-purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a computer program, that is, a software product, which may be stored in a storage medium and include several instructions for enabling a device (which may be a personal computer, a server, a single chip microcomputer, an MUU, or a network device, etc.) including a data processing unit to execute the method according to each embodiment or some portions of the embodiments of the present invention.
The invention provides a thought and a method for a multistage aggregation and real-time update method of a mass ship position point cloud, and a plurality of methods and ways for realizing the technical scheme are provided. All the components not specified in the present embodiment can be realized by the prior art.
Claims (10)
1. A multi-stage polymerization and real-time updating method for massive ship position point clouds is characterized in that the method is used for drawing a map of the massive ship point clouds in real time and comprises the following steps:
step 1, constructing a multilevel spatial grid: performing multi-level spatial grid division on the massive ship position point cloud based on the GeoHash codes to obtain multi-level spatial grids and corresponding aggregation levels, and forming a multi-level spatial index on the massive ship position point cloud; calculating a point set in each grid in the multilevel space grid to obtain an aggregation position;
step 2, in the real-time updating process of the data of the massive ship position point clouds, the aggregation positions are updated and calculated in real time on the basis of the multilevel space grids;
step 3, determining a polymerization level according to the current browsing position of the map and the scale of the map, and inquiring the polymerization position of the corresponding polymerization level from the multi-level space grid;
step 4, performing neighborhood merging operation on the aggregation position obtained by inquiring in the step 3 to obtain a final aggregation result; and drawing the final aggregation result on a map in a symbolic form to finish the multi-stage aggregation and real-time updating of the massive ship position point clouds.
2. The method for multistage aggregation and real-time update of the cloud of mass ship position points according to claim 1, wherein the step 1 comprises:
step 1-1, multi-level spatial grid division: calculating GeoHash codes of 6 aggregation levels with the code length of 1-6 for the initial longitude and latitude position of a certain ship, wherein the code of each level represents a space grid in a set range, and a prefix representing the code length is added in front of each code to serve as a unique identifier of the space grid to obtain a space grid identifier;
step 1-2, calculating to obtain a polymerization position: if a certain spatial grid appears for the first time, recording that the number of point sets contained in the spatial grid is 1, and the aggregation position corresponding to the point sets is the calculated position of the current ship; otherwise, adding 1 to the number of the point sets of the space grid, and calculating a new polymerization position after adding the new polymerization position to the current ship position;
and step 1-3, performing the operations of the step 1-1 and the step 1-2 on each ship in the massive ship position point cloud, storing the space grid identification, the point set number of the space grid and the aggregation position in a key value pair mode, and completing the construction of the multilevel space grid.
3. The method for multistage aggregation and real-time update of the cloud of mass ship position points according to claim 2, wherein the step 2 comprises:
step 2-1, removing the original position from the 1 st-6 th spatial grids of a certain ship with the updated position, subtracting 1 from the point set number contained in each spatial grid, and calculating the aggregation position after the original position is removed;
and 2-2, performing the operations of the step 1-1 and the step 1-2 on the new position of the ship to finish the quick updating of the polymerization position.
4. The method for multistage aggregation and real-time update of the mass point clouds of ship positions according to claim 3, wherein the step 3 comprises the following steps:
step 3-1, determining a polymerization level through a current map scale according to a corresponding relation between a preset map scale and the polymerization level;
step 3-2, inquiring a spatial grid set taking the determined aggregation level as a prefix in the constructed multi-level spatial grid to obtain a result set;
and 3-3, screening according to the result set obtained by inquiring in the step 3-2, if the space grid is in the current map range, reserving the space grid, and if not, removing the space grid from the result set.
5. The method for multistage aggregation and real-time update of the cloud of mass ship position points according to claim 4, wherein the step 4 comprises:
step 4-1, aiming at the result set obtained in the step 3, calculating the distance between the aggregation position of each spatial grid and the aggregation position of the adjacent spatial grid in the result set, if the distance is smaller than a set threshold value, merging the spatial grid and the adjacent spatial grid, and calculating a new aggregation position to obtain a final aggregation position;
and 4-2, drawing the result of the multi-stage aggregation and real-time updating of the massive ship position point clouds on a map, and drawing an aggregation point symbol at the final aggregation position.
6. The method for multistage aggregation and real-time update of the cloud of mass ship position points according to claim 5, wherein the threshold in step 4-1 is as follows: and the distance between the aggregation position of the spatial grid and the aggregation position of the adjacent spatial grid is proportional to the length of the spatial grid at the level.
7. The method for multistage aggregation and real-time update of the point clouds of the mass ship positions according to claim 6, wherein the method for obtaining the aggregation positions through calculation in the step 1-2 comprises the following steps:
wherein, lng NewAggr And Lat NewAggr Respectively representing the longitude and latitude, lng, of the new aggregated location OldAggr And Lat OldAggr Respectively representing the longitude and latitude, lng, of the original aggregate location Point And Lat Point Respectively representing the longitude and the latitude of the position of the ship calculated currently, n representing the number of the original point sets in the space grid, and the calculated aggregation position being the centroid position of the point sets obtained through weighted addition.
8. The method for multistage aggregation and real-time update of the point clouds of the mass ship positions according to claim 7, wherein the aggregation positions after the original positions are removed in the step 2-1 are calculated, and the method comprises the following steps:
wherein, lng RemovedAgg And Lat RemovedAggr Respectively representing the longitude and latitude, lng, of the aggregate location after removal of the home position Aggr And Lat Aggr Respectively representing the longitude and latitude, lng, of the original aggregate location OldPoint And Lat OldPoint Respectively representing the current calculated shipLongitude and latitude of the original position of the ship, and n represents the number of the original point sets in the space grid.
9. The method for multistage aggregation and real-time update of the cloud of mass ship position points according to claim 8, wherein the step 4-1 of calculating a new aggregation position comprises:
wherein, lng MergedAggr And Lat MergedAggr Respectively representing the longitude and latitude, lng, of the merged aggregate location 1 And Lng 2 Longitude, lat, representing two aggregated locations being merged 1 And Lat 2 Denotes the latitude, n, of two aggregate positions being merged 1 And n 2 Respectively representing the number of point sets represented by the two aggregation positions.
10. The method for multistage aggregation and real-time update of the point clouds of the mass ship positions according to claim 9, wherein the aggregation point symbols in the step 4-2 are used for reflecting the number of the aggregation point sets; the aggregate point symbol size reflects how many sets of aggregated points are, with larger aggregate point symbols indicating more points are aggregated there.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116109470A (en) * | 2023-04-13 | 2023-05-12 | 深圳市其域创新科技有限公司 | Real-time point cloud data rendering method, device, terminal and storage medium |
| CN117312471A (en) * | 2023-09-26 | 2023-12-29 | 中国人民解放军91977 部队 | Sea-land attribute judging method and device for massive position points |
-
2022
- 2022-07-28 CN CN202210897208.2A patent/CN115438081A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116109470A (en) * | 2023-04-13 | 2023-05-12 | 深圳市其域创新科技有限公司 | Real-time point cloud data rendering method, device, terminal and storage medium |
| CN116109470B (en) * | 2023-04-13 | 2023-06-20 | 深圳市其域创新科技有限公司 | Real-time point cloud data rendering method, device, terminal and storage medium |
| CN117312471A (en) * | 2023-09-26 | 2023-12-29 | 中国人民解放军91977 部队 | Sea-land attribute judging method and device for massive position points |
| CN117312471B (en) * | 2023-09-26 | 2024-05-28 | 中国人民解放军91977部队 | Sea-land attribute judging method and device for massive position points |
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