A mass ship position point cloud multi-level aggregation and real-time updating method

Abstract

The application discloses a kind of massive ship position point cloud multistage aggregation and real-time updating method, the method is used for the real-time drawing of massive ship point cloud map, comprising: constructing multistage space grid: massive ship position point cloud is divided into multistage space grid based on GeoHash coding, and multistage space index is formed;The point set in each grid in multistage space grid is calculated, and aggregated position is obtained;In the data real-time updating process of massive ship position point cloud, based on multistage space grid, real-time updating calculation is carried out to aggregated position;According to current map browsing position and map scale, determine aggregation level, and query the aggregated position of corresponding level from multistage space grid;The aggregated position obtained by query is carried out neighborhood merging operation, and the final aggregation result is obtained;The final aggregation result is drawn in symbolic form on map, and the multistage aggregation and real-time updating of massive ship position point cloud are completed.

Description

Technical Field

[0001] This invention relates to a method for ship position aggregation and real-time updating, and more particularly to a method for multi-level aggregation and real-time updating of massive ship position point clouds. Background Technology

[0002] my country's need for surveillance of maritime targets is gradually expanding, requiring the access, processing, analysis, and display of intelligence information on global maritime targets. According to incomplete statistics, the total number of Chinese civilian vessels (including fishing boats, merchant ships, and cargo ships) and international vessels with a gross tonnage of over 300 tons has exceeded one million, of which 400,000 to 500,000 are active at sea, with their position data updated every few seconds. Real-time vessel information can help to accurately grasp the battlefield environment, identify gaps, and reduce misjudgments, which is of practical significance in both near-shore and far-sea defense.

[0003] Point cloud data representing ship positions is characterized by its large volume and high update frequency, which places higher demands on spatial querying. It requires efficient spatial indexing and data structures to organize and manage ship point cloud data and support frequent updates to ship positions. Simultaneously, to improve the efficiency of target rendering at a global scale, point clouds need to be aggregated based on the spatial indexing structure to reduce the number of target points and enable the display of ship information at different levels of detail across different map scales.

[0004] Ship positions are stored using latitude and longitude values, typically organized using a two-dimensional spatial index. Commonly used two-dimensional point cloud spatial indexing methods include regular grids, quadtrees, R-trees and their improvements, and KD-trees. Regular grids divide space into several identical grids according to certain rules, indexing point data on a grid-by-grid basis. The algorithm is simple and efficient, but when point cloud data is unevenly distributed, the amount of data in each grid varies significantly, leading to data redundancy. Quadtrees are commonly used to store two-dimensional spatial points and are easy to implement, but their construction speed is relatively slow, and in densely populated areas, the quadtree depth can become excessive, affecting query efficiency. R-trees are a widely used spatial index, characterized by high balance. However, the minimum bounding rectangles of intermediate nodes in R-trees can overlap, leading to excessive invalid queries as the data volume increases, impacting query efficiency. Spatial clustering often uses partition-based clustering algorithms, such as K-means and K-medoids. These require pre-setting the number of clusters and initial cluster centers; different settings significantly affect the results, and the time complexity is high, making it difficult to meet the needs of real-time updates.

[0005] GeoHash is a grid-based geographic data encoding technique that converts target latitude and longitude coordinates into string codes. The string does not represent a single point, but rather a rectangular region. All points (latitude and longitude coordinates) within this region share the same GeoHash string. The longer the string, the smaller the rectangular region it represents, resulting in higher spatial data indexing accuracy. GeoHash encoding is widely used in spatial data indexing, improving efficiency while maintaining target location accuracy. It also transforms complex neighborhood search calculations into relatively simple string comparisons, effectively increasing the speed of spatial queries. Summary of the Invention

[0006] Purpose of the invention: The technical problem to be solved by the present invention is to provide a method for multi-level aggregation and real-time updating of massive ship location point clouds, which addresses the shortcomings of the existing technology.

[0007] To address the aforementioned technical problems, this invention discloses a method for multi-level aggregation and real-time updating of massive ship location point clouds. This method is used for real-time map rendering of massive ship point clouds and includes the following steps:

[0008] Step 1, Construct a multi-level spatial grid: Based on GeoHash encoding (reference: "Geohash", [online] Available: http: / / www.geohash.org.), divide the massive ship location point cloud into a multi-level spatial grid to obtain a multi-level spatial grid and corresponding aggregation levels, forming a multi-level spatial index for the massive ship location point cloud; calculate the aggregated position for the point set within each grid in the multi-level spatial grid;

[0009] Step 2: During the real-time update of the massive ship location point cloud data, the aggregated location is updated and calculated in real time based on the multi-level spatial grid;

[0010] Step 3: Determine the aggregation level based on the current browsing location of the map and the map scale, and query the aggregation position of the corresponding aggregation level from the multi-level spatial grid;

[0011] Step 4: Perform neighborhood merging on the aggregated locations obtained in Step 3 to obtain the final aggregation result; plot the final aggregation result on the map in symbolic form to complete the multi-level aggregation and real-time update of massive ship location point clouds.

[0012] Step 1 in this invention includes:

[0013] Step 1-1, Multi-level spatial grid division: Calculate the GeoHash code of 6 aggregation levels with a coding length of 1 to 6 for the initial latitude and longitude position of a certain ship. Each level of code represents a spatial grid within a set range. Add a prefix representing the coding length to each code as a unique identifier for the spatial grid to obtain the spatial grid identifier.

[0014] Steps 1-2: Calculate the aggregation position: If a spatial grid appears for the first time, record the number of points contained in the spatial grid as 1, and the aggregation position corresponding to the point set is the calculated position of the current ship; otherwise, increment the number of points in the spatial grid by 1, and calculate the new aggregation position after adding the current ship's position.

[0015] Steps 1-3 involve performing steps 1-1 and 1-2 on each ship in the massive ship location point cloud, storing the spatial grid identifier and the number of points and aggregation positions of the spatial grid in key-value pairs, thus completing the construction of a multi-level spatial grid.

[0016] Step 2 in this invention includes:

[0017] Step 2-1: For a ship whose position has been updated, remove the original position from the spatial grid of level 1 to level 6, reduce the number of points in each spatial grid by 1, and calculate the aggregated position after removing the original position.

[0018] Step 2-2: Perform steps 1-1 and 1-2 on the new position of the vessel to complete the rapid update of the aggregated position.

[0019] Location point clouds are massive amounts of data that need to be managed. Multi-level spatial grids are spatial indexes of point clouds and are an effective management method. The final aggregation and update results will be displayed on a map, which is a visualization carrier of the calculation results.

[0020] Step 3 in this invention includes:

[0021] Step 3-1: Determine the aggregation level based on the current map scale according to the preset correspondence between map scale and aggregation level.

[0022] Step 3-2: Query the set of spatial grids prefixed with the determined aggregation level in the constructed multi-level spatial grid to obtain the result set;

[0023] Step 3-3: Filter the results obtained in Step 3-2. If the spatial grid is within the current map range, keep it; otherwise, remove it from the results set.

[0024] Step 4 in this invention includes:

[0025] Step 4-1: For the result set obtained in step 3, calculate the distance between the aggregation position of each spatial grid in the result set and the aggregation position of the adjacent spatial grid. If the distance is less than the set threshold, merge the spatial grid with the adjacent spatial grid and calculate the new aggregation position to obtain the final aggregation position.

[0026] Step 4-2: Draw the results of multi-level aggregation and real-time updating of massive ship location point clouds on the map, and draw aggregation point symbols at the final aggregation location.

[0027] The threshold mentioned in step 4-1 of this invention is the ratio of the distance between the aggregation position of the spatial grid and the aggregation position of the adjacent spatial grid to the length of the spatial grid at the same level.

[0028] The method for calculating the aggregation position in steps 1-2 of this invention includes:

[0029]

[0030] Among them, Lng NewAggr and Lat NewAggr Lng represents the longitude and latitude of the new aggregation location, respectively. OldAggr and Lat OldAggr Lng represents the longitude and latitude of the original aggregation location, respectively. Point and Lat Point represents the longitude and latitude of the currently calculated ship position, respectively, n represents the original number of points in the spatial grid, and the calculated aggregate position is the centroid position of the point set obtained by weighted summation.

[0031] The method for calculating the aggregation position after removing the original position in step 2-1 of this invention includes:

[0032]

[0033] Among them, Lng RemovedAggr and Lat RemovedAggr Lng represents the longitude and latitude of the convergence position after removing the original position, respectively. Aggr and Lat Aggr Lng represents the longitude and latitude of the original aggregation location, respectively. OldPoint and Lat OldPoint represents the longitude and latitude of the original position of the ship being calculated, respectively, and n represents the original number of points in the spatial grid.

[0034] The method for calculating the new aggregation position in step 4-1 of this invention includes:

[0035]

[0036] Among them, Lng MergedAggr and Lat MergedAggr Lng1 and Lng2 represent the longitude and latitude of the merged aggregation positions, respectively; Lng1 and Lng2 represent the longitude of the two merged aggregation positions; Lat1 and Lat2 represent the latitude of the two merged aggregation positions; and n1 and n2 represent the number of point sets represented by the two aggregation positions, respectively.

[0037] The aggregation point symbol mentioned in step 4-2 of this invention is used to reflect the number of aggregated point sets; the size of the aggregation point symbol reflects the number of aggregated point sets, and the larger the aggregation point symbol, the more points are aggregated at that point.

[0038] Beneficial effects:

[0039] (1) The feature of GeoHash encoding is used to implement a multi-level spatial grid index based on strings, which has a greater advantage in the spatial index efficiency of point clouds and avoids the problem of excessive node depth that may be caused by using a single quadtree or KD tree.

[0040] (2) Compared with multiple iteration methods such as K-means algorithm or K-medoids algorithm, the aggregation method based on multi-level spatial grid index has linear time complexity and can meet the fast real-time update of point cloud data. When the position of a certain point changes, it is not necessary to re-aggregate the entire point cloud. It is only necessary to update the aggregation point in a local range.

[0041] (3) Compared with the traditional single-level aggregation algorithm, this method takes advantage of the multi-level characteristics of GeoHash encoding, and can realize the spatial division of different grid sizes through the construction of a single spatial index, thereby achieving a multi-level aggregation effect, making the browsing of ship targets smoother. Attached Figure Description

[0042] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0043] Figure 1 This is a schematic diagram of the workflow of the present invention.

[0044] Figure 2 This is a schematic diagram of merging adjacent aggregation points in this invention. Detailed Implementation

[0045] This invention relates to a method for multi-level aggregation and real-time updating of massive ship location point clouds in the field of computer graphics. The method includes: proposing a multi-level spatial index based on GeoHash encoding; using this method to divide massive ship location point cloud data into spatial grids; after division, aggregating the point cloud data within each level of spatial grid to calculate aggregation points and their attributes; and during real-time updating of the point cloud data, performing real-time update calculations on the aggregation points based on the proposed multi-level spatial index. When drawing massive point clouds, it is only necessary to query the aggregation level corresponding to the current map location and scale, and then query the grid information and aggregation positions that meet the conditions using the aggregation level as a prefix to obtain the aggregation results within the current map range. The proposed multi-level aggregation method based on GeoHash encoding has high data indexing efficiency. For massive point cloud data, it can simultaneously satisfy real-time aggregation point calculation and high-frequency data point updates. Therefore, aggregation points are used instead of point clouds for rendering in the map scene, effectively reducing the amount of drawing massive data and improving map drawing efficiency. In summary, for massive amounts of ship location point cloud data, multi-level spatial grids are a spatial indexing structure that can efficiently manage point clouds, and the final aggregation results will be displayed on a map as a visualization carrier.

[0046] This invention discloses a method for multi-level aggregation and real-time updating of massive ship location point clouds. This method proposes a strategy for real-time calculation and updating of aggregated points for massive ship point cloud location data in the global ocean.

[0047] like Figure 1 As shown in this embodiment, a method for multi-level aggregation and real-time updating of massive ship location point clouds includes the following steps:

[0048] Step 1: Divide the massive ship location point cloud into multi-level spatial grids based on GeoHash encoding and calculate the aggregated position of the point set within each grid.

[0049] Step 2: During the real-time update of point cloud data, the aggregation position is updated and calculated in real time based on the multi-level spatial grid;

[0050] Step 3: Determine the aggregation level based on the current location and map scale, and query the aggregation location of the corresponding level from the multi-level spatial grid;

[0051] Step 4: Perform a neighborhood merging operation on the queried aggregation locations and draw the final aggregation result in symbolic form.

[0052] In the method for multi-level aggregation and real-time updating of massive ship location point clouds described in this embodiment, step 1 includes:

[0053] Step 1-1: Calculate a GeoHash code with six levels of length 1 to 6 for the initial latitude and longitude position of a ship. Each level of code represents a certain range of spatial grid. The prefix of the GeoHash code indicates a larger range, i.e., the first level has the largest range, and the sixth level has the smallest range. The six levels are spatially contained within each other. The longer the GeoHash code, the smaller the grid range it represents, and the higher the accuracy. Considering the actual travel distance between ships on the route, a GeoHash code of length 6, i.e., a grid size of 1.2km × 0.6km, is sufficient for the spatial indexing of the ship's position. Add a prefix representing the code length to each code as a unique identifier for that grid. Taking the latitude and longitude coordinates (116.404, 39.915) as an example, the six levels of codes are 1_w, 2_wx, 3_wx4, 4_wx4g, 5_wx4g0, and 6_wx4g0f.

[0054] Steps 1-2: If a grid appears for the first time, record the number of points contained in that grid as 1, and the corresponding aggregation position is the calculated position of the current ship; otherwise, increment the number of points in that grid by 1, and calculate the new aggregation position after adding the current ship's position, as shown below:

[0055]

[0056] Among them, Lng NewAggr and Lat NewAggr Lng represents the longitude and latitude of the new aggregation point, respectively. OldAggr and Lat OldAggr Lng represents the longitude and latitude of the original aggregation location, respectively. Point and Lat Point These represent the longitude and latitude of the currently calculated ship position, respectively, and n represents the original number of points in the grid. The calculated aggregation position is actually the centroid position of the point set obtained by weighted summation.

[0057] Steps 1-3 involve performing the above two steps for each ship to store the grid identifier and its attributes in key-value pairs, thus completing the construction of a multi-level spatial grid.

[0058] In the method for multi-level aggregation and real-time updating of massive ship location point clouds described in this embodiment, step 2 includes:

[0059] Step 2-1: For a ship whose position has been updated, remove its original position from the grid at levels 1 to 6, reduce the number of points in each grid by 1, and calculate the aggregated position after removing the original position. The calculation method is as follows:

[0060]

[0061] Among them, Lng RemovedAggr and Lat RemovedAggr Lng represents the longitude and latitude of the aggregation point, respectively. Aggr and Lat Aggr Lng represents the longitude and latitude of the original aggregation location, respectively. OldPoint and Lat OldPoint These represent the longitude and latitude of the currently calculated ship location, respectively, and n represents the original number of points in the grid.

[0062] Step 2-2: Perform steps 1-1 and 1-2 on the new position of the ship to complete the rapid update of the aggregate position.

[0063] In the method for multi-level aggregation and real-time updating of massive ship location point clouds described in this embodiment, step 3 includes:

[0064] Step 3-1: Based on the preset correspondence between map scales and aggregation levels, determine the aggregation level using the current map scale. Practical experience has established the following correspondence: Level 1 aggregation results are used for scales below 1:10 million; Level 2 aggregation results are used for scales below 1:2.5 million; Level 3 aggregation results are used for scales below 1:1 million; Level 4 aggregation results are used for scales below 1:500,000; Level 5 aggregation results are used for scales below 1:250,000; and Level 6 aggregation results are used for scales below 1:100,000. Under these preset conditions, a good map visual effect can be achieved.

[0065] Step 3-2: In the constructed multi-level spatial grid, query the set of spatial grids prefixed with the determined aggregation level. For example, if you determine to use the aggregation result of the 2nd level, you only need to query the spatial grids whose grid identifiers are prefixed with "2_".

[0066] Step 3-3: Further filter the grid set retrieved in the previous step, and determine whether the rectangular range of the grid falls within the current map range. If the grid is within the current map range, keep it; otherwise, remove it from the result set.

[0067] In the method for multi-level aggregation and real-time updating of massive ship location point clouds described in this embodiment, step 4 includes:

[0068] Step 4-1: For the grid set retrieved in Step 3, calculate the distance between the aggregation position of each grid and the aggregation positions of its adjacent grids. If this distance is less than a certain proportion of the grid length at the same level, then the two aggregation positions are considered too close, and they are merged and a new aggregation position is calculated. Figure 2 As shown. The calculation method is as follows:

[0069]

[0070] Among them, Lng MergedAggr and Lat MergedAggr Lng1 and Lng2 represent the longitude and latitude of the merged aggregation point, respectively; Lat1 and Lat2 represent the longitude of the two merged aggregation points; and n1 and n2 represent the number of points represented by the two aggregation points. Merging adjacent points avoids overlapping aggregation point symbols caused by excessive proximity, resulting in a more reasonable final aggregation result. The above ratio is a preset empirical value; practical experience shows that setting this ratio to 0.2 effectively avoids overlapping aggregation point symbols.

[0071] Step 4-2: Draw an aggregation point symbol at the final aggregation location. The size of the symbol reflects the number of points aggregated. The larger the symbol, the more points are aggregated at that location.

[0072] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding a method for multi-level aggregation and real-time updating of massive ship location point clouds, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0073] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MUU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0074] This invention provides a method for multi-level aggregation and real-time updating of massive ship location point clouds. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for multi-level aggregation and real-time updating of massive ship location point clouds, characterized in that, The method is used for real-time map rendering of massive ship point clouds, and includes the following steps: Step 1, Construct a multi-level spatial grid: Divide the massive ship location point cloud into a multi-level spatial grid based on GeoHash encoding to obtain a multi-level spatial grid and corresponding aggregation levels, forming a multi-level spatial index for the massive ship location point cloud; Calculate the point set in each grid in the multi-level spatial grid to obtain the aggregated position; Step 2: During the real-time update of the massive ship location point cloud data, the aggregated location is updated and calculated in real time based on the multi-level spatial grid; Step 3: Determine the aggregation level based on the current browsing location of the map and the map scale, and query the aggregation position of the corresponding aggregation level from the multi-level spatial grid; Step 4: Perform a neighborhood merging operation on the aggregated positions obtained in Step 3 to obtain the final aggregation result; The final aggregation result is plotted on the map in symbolic form, completing the multi-level aggregation and real-time update of massive ship location point clouds; Step 1 includes: Step 1-1, Multi-level spatial grid division: Calculate the GeoHash code of 6 aggregation levels with a coding length of 1 to 6 for the initial latitude and longitude position of a certain ship. Each level of code represents a spatial grid within a set range. Add a prefix representing the coding length to each code as a unique identifier for the spatial grid to obtain the spatial grid identifier. Steps 1-2: Calculate the aggregation position: If a spatial grid appears for the first time, record the number of points contained in the spatial grid as 1, and the aggregation position corresponding to the point set is the calculated position of the current ship; otherwise, increment the number of points in the spatial grid by 1, and calculate the new aggregation position after adding the current ship's position. Steps 1-3: Perform steps 1-1 and 1-2 on each ship in the massive ship location point cloud, and store the spatial grid identifier, the number of points in the spatial grid, and the aggregation position in the form of key-value pairs to complete the construction of a multi-level spatial grid. The method for calculating the aggregation position described in steps 1-2 includes: Among them, Lng NewAggr and Lat NewAggr Lng represents the longitude and latitude of the new aggregation location, respectively. OldAggr and Lat OldAggr Lng represents the longitude and latitude of the original aggregation location, respectively. Point and Lat Point represents the longitude and latitude of the currently calculated ship position, respectively, n represents the original number of points in the spatial grid, and the calculated aggregate position is the centroid position of the point set obtained by weighted summation.

2. The method for multi-level aggregation and real-time updating of massive ship location point clouds according to claim 1, characterized in that, Step 2 includes: Step 2-1: For a ship whose position has been updated, remove the original position from the spatial grid of level 1 to level 6, reduce the number of points in each spatial grid by 1, and calculate the aggregated position after removing the original position. Step 2-2: Perform steps 1-1 and 1-2 on the new position of the vessel to complete the rapid update of the aggregated position.

3. The method for multi-level aggregation and real-time updating of massive ship location point clouds according to claim 2, characterized in that, Step 3 includes: Step 3-1: Determine the aggregation level based on the current map scale according to the preset correspondence between map scale and aggregation level. Step 3-2: Query the set of spatial grids prefixed with the determined aggregation level in the constructed multi-level spatial grid to obtain the result set; Step 3-3: Filter the results obtained in Step 3-2. If the spatial grid is within the current map range, keep it; otherwise, remove it from the results set.

4. The method for multi-level aggregation and real-time updating of massive ship location point clouds according to claim 3, characterized in that, Step 4 includes: Step 4-1: For the result set obtained in step 3, calculate the distance between the aggregation position of each spatial grid in the result set and the aggregation position of the adjacent spatial grid. If the distance is less than the set threshold, merge the spatial grid with the adjacent spatial grid and calculate the new aggregation position to obtain the final aggregation position. Step 4-2: Draw the results of multi-level aggregation and real-time updating of massive ship location point clouds on the map, and draw aggregation point symbols at the final aggregation location.

5. The method for multi-level aggregation and real-time updating of massive ship location point clouds according to claim 4, characterized in that, The threshold mentioned in step 4-1 is the ratio of the distance between the aggregation position of the spatial grid and the aggregation position of the adjacent spatial grid to the length of the spatial grid at the same level.

6. The method for multi-level aggregation and real-time updating of massive ship location point clouds according to claim 5, characterized in that, The method for calculating the aggregation position after removing the original position as described in step 2-1 includes: Among them, Lng RemovedAgg and Lat RemovedAggr Lng represents the longitude and latitude of the convergence position after removing the original position, respectively. Aggr and Lat Aggr Lng represents the longitude and latitude of the original aggregation location, respectively. oldPoint and Lat OldPoint represents the longitude and latitude of the original position of the ship being calculated, respectively, and n represents the original number of points in the spatial grid.

7. The method for multi-level aggregation and real-time updating of massive ship location point clouds according to claim 6, characterized in that, The method for calculating the new aggregation position as described in step 4-1 includes: Among them, Lng MergedAggr and Lat MergedAggr Lng1 and Lng2 represent the longitude and latitude of the merged aggregation positions, respectively; Lng1 and Lng2 represent the longitude of the two merged aggregation positions; Lat1 and Lat2 represent the latitude of the two merged aggregation positions; and n1 and n2 represent the number of point sets represented by the two aggregation positions, respectively.

8. The method for multi-level aggregation and real-time updating of massive ship location point clouds according to claim 7, characterized in that, The aggregation point symbol mentioned in step 4-2 is used to reflect the number of aggregated point sets; the size of the aggregation point symbol reflects the number of aggregated point sets, and the larger the aggregation point symbol, the more points are aggregated at that point.

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