Wargame deduction map generation method based on GIS

Abstract

The invention discloses a method for generating a war game deduction map based on a GIS (Geographic Information System). The method comprises the following steps: acquiring a GIS data set, and performing coordinate unification and data clipping processing on the GIS data set to obtain processed standardized geographic data; based on the standardized geographic data, establishing a hexagonal grid on the two-dimensional projection plane by using a grid division algorithm, and adjusting the side length of a hexagonal grid unit of the hexagonal grid according to the elevation variance of each region to obtain a hexagonal grid division result; for each hexagonal grid unit, calculating an area proportion of each ground feature type in the hexagonal grid unit, and calculating fuzzy probability distribution of the ground feature types based on the area proportion; obtaining feature entropy distribution data according to the fuzzy probability distribution; and based on the feature entropy distribution data, performing adaptive symbol generation and rendering processing on each hexagonal grid unit. The technical problem that a map generated by existing war chess deduction is inaccurate is solved.

Description

Technical Field

[0001] This invention relates to the field of wargaming, and more specifically, to a method for generating wargaming maps based on GIS. Background Technology

[0002] Wargaming systems are widely used in combat simulations, tactical training, and command decision-making. The accuracy of their map data directly affects the reliability of the simulation results. Currently, the generation of simulation maps largely relies on manual or semi-automatic methods, typically using GIS platforms to stitch and render elevation, topographic, and feature data. However, existing methods suffer from low automation, fixed grid divisions, insufficient feature matching accuracy, and limited symbolic representation, making them unsuitable for simulations in complex terrain conditions.

[0003] Especially in hexagonal grid map generation, grids with fixed side lengths struggle to adapt to terrain undulations, leading to geometric distortions in mountainous or high-altitude areas. Furthermore, feature types are often determined by maximizing area, ignoring feature blending boundaries and causing tactical assessment biases. Existing symbol rendering methods are also mostly static templates, unable to automatically adjust symbol density and color levels according to terrain complexity.

[0004] Therefore, there is an urgent need for a map generation method that can combine GIS data features, automatically construct terrain-adaptive hexagonal grids, and achieve symbolic dynamic rendering to improve the accuracy, automation level, and battlefield adaptability of wargaming maps. Summary of the Invention

[0005] This invention provides a GIS-based method for generating wargame maps, which at least solves the technical problem of inaccurate maps generated by existing wargame simulations.

[0006] According to one aspect of the present invention, a GIS-based wargaming map generation method is provided, comprising: acquiring a GIS dataset containing elevation data, topographic data, and feature data; performing coordinate unification and data clipping on the GIS dataset according to a user-defined wargaming area and scale to obtain processed standardized geographic data; establishing a hexagonal grid on a two-dimensional projection plane based on the standardized geographic data using a grid partitioning algorithm, and adjusting the side length of the hexagonal grid cells according to the elevation variance of each region to obtain a terrain-adaptive hexagonal grid partitioning result; calculating the area proportion of each feature type within each hexagonal grid cell for each hexagonal grid cell, and calculating a fuzzy probability distribution of the feature types based on the area proportion; calculating the terrain feature entropy of each hexagonal grid cell according to the fuzzy probability distribution, and mapping the feature entropy to the terrain complexity to obtain feature entropy distribution data; constructing a symbol density adjustment matrix based on the feature entropy distribution data, and performing adaptive symbol generation and rendering processing on each hexagonal grid cell based on the symbol density adjustment matrix to obtain a symbolized map result that meets the requirements of wargaming.

[0007] According to another aspect of the present invention, a GIS-based wargaming map generation system is provided, comprising: a processing module configured to acquire a GIS dataset containing elevation data, topographic data, and feature data, and to perform coordinate unification and data clipping processing on the GIS dataset according to a user-defined wargaming area and scale to obtain processed standardized geographic data; and a partitioning module configured to establish a hexagonal grid on a two-dimensional projection plane based on the standardized geographic data using a grid partitioning algorithm, and to adjust the side length of the hexagonal grid cells according to the elevation variance of each region to obtain terrain-adaptive maps. The hexagonal grid division results; the matrix determination module is configured to calculate the area ratio of each feature type within each hexagonal grid cell, and calculate the fuzzy probability distribution of the feature types based on the area ratio; the map generation module is configured to calculate the terrain feature entropy of each hexagonal grid cell according to the fuzzy probability distribution, and correlate the feature entropy with the terrain complexity to obtain feature entropy distribution data, construct a symbol density adjustment matrix based on the feature entropy distribution data, and perform adaptive symbol generation and rendering processing on each hexagonal grid cell based on the symbol density adjustment matrix to obtain a symbolized map result that meets the requirements of wargaming simulation.

[0008] In this embodiment of the invention, a GIS dataset containing elevation data, topographic data, and feature data is acquired. Based on the user-defined simulation area and scale, the GIS dataset undergoes coordinate unification and data cropping to obtain processed standardized geographic data. Based on this standardized geographic data, a hexagonal grid is established on a two-dimensional projection plane using a grid partitioning algorithm. The side lengths of the hexagonal grid cells are adjusted according to the elevation variance of each region to obtain a terrain-adaptive hexagonal grid partitioning result. For each hexagonal grid cell, the area proportion of each feature type within that cell is calculated, and a fuzzy probability distribution of the feature types is calculated based on the area proportions. According to the fuzzy probability distribution, the terrain feature entropy of each hexagonal grid cell is calculated, and the feature entropy is correlated with the terrain complexity to obtain feature entropy distribution data. Based on the feature entropy distribution data, a symbol density adjustment matrix is ​​constructed. Adaptive symbol generation and rendering processing is performed on each hexagonal grid cell based on the symbol density adjustment matrix to obtain a symbolized map result that meets the requirements of wargaming simulations. This solution addresses the technical problem of inaccurate maps generated by existing wargaming simulations. Attached Figure Description

[0009] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0010] Figure 1 This is a flowchart of an optional GIS-based wargaming map generation method according to an embodiment of the present invention;

[0011] Figure 2 This is a flowchart of another optional GIS-based wargaming map generation method according to an embodiment of the present invention;

[0012] Figure 3 This is a flowchart of an optional spatial mapping method for land feature attributes according to an embodiment of the present invention;

[0013] Figure 4 This is a flowchart of an optional method for generating a map according to an embodiment of the present invention;

[0014] Figure 5 This is an architecture diagram of an optional GIS-based wargaming map generation system according to an embodiment of the present invention;

[0015] Figure 6 This is a schematic diagram of another optional GIS-based wargaming map generation system according to an embodiment of the present invention;

[0016] Figure 7A schematic diagram of the structure of a computer device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] According to an embodiment of the present invention, a method embodiment for generating wargaming maps based on GIS is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0020] Figure 1 This is a GIS-based wargaming map generation method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0021] Step S102: Obtain a GIS dataset containing elevation data, topographic data, and ground feature data. Based on the user-defined projection area and scale, perform coordinate unification and data cropping on the GIS dataset to obtain processed standardized geographic data.

[0022] For example, a Gaussian-Kruger projection transformation is performed on the GIS dataset, and the projection scale factor is dynamically corrected based on the local elevation variance of the corresponding area of ​​the GIS dataset to obtain terrain-corrected projection coordinate data; based on the user-defined range of the projection area, a spatial clipping operation is performed on the terrain-corrected projection coordinate data to extract a subset of geographic data corresponding to the projection area as the standardized geographic data.

[0023] Step S104: Based on the standardized geographic data, a hexagonal grid is established on the two-dimensional projection plane using a grid partitioning algorithm. The side length of the hexagonal grid cells is adjusted according to the elevation variance of each region to obtain a terrain-adaptive hexagonal grid partitioning result.

[0024] For example, based on the spatial range of the standardized geographic data, an initial equilateral hexagonal grid is generated on the two-dimensional projection plane to obtain an initial set of hexagonal grid cells; for each initial hexagonal grid cell, the elevation variance value within the coverage area of ​​the hexagonal grid cell is calculated; according to the elevation variance value and a preset side length adjustment function, the side length of the corresponding hexagonal grid cell is scaled and adjusted to obtain a terrain-adaptive hexagonal grid division result that reflects the terrain undulation characteristics.

[0025] Step S106: For each hexagonal grid cell, calculate the area ratio of each land cover type within the hexagonal grid cell, and calculate the fuzzy probability distribution of the land cover type based on the area ratio.

[0026] For example, the feature data in the standardized geographic data is mapped to the corresponding hexagonal grid cells in the terrain adaptive hexagonal grid division result; for each hexagonal grid cell, the coverage area corresponding to different feature types within the hexagonal grid cell is calculated, and the area ratio of each feature type to the total area of ​​the hexagonal grid cell is calculated; based on the area ratio, a fuzzy membership function for each feature type is constructed, and the area ratio is fuzzified to obtain the fuzzy probability distribution characterizing the uncertainty of the feature composition of the hexagonal grid cell.

[0027] Step S108: Calculate the terrain feature entropy of each hexagonal grid unit according to the fuzzy probability distribution, and correlate the feature entropy with the terrain complexity to obtain feature entropy distribution data.

[0028] For example, for each hexagonal grid cell, the information entropy value of each land cover type is calculated based on the fuzzy probability distribution corresponding to the hexagonal grid cell; the information entropy values ​​of each land cover type are summarized to obtain the terrain feature entropy that characterizes the uncertainty of the land cover distribution in the hexagonal grid cell; the terrain feature entropy is matched with a preset terrain complexity mapping relationship to obtain the feature entropy distribution data used for symbol density adjustment.

[0029] Step S110: Based on the feature entropy distribution data, construct a symbol density adjustment matrix, and perform adaptive symbol generation and rendering processing on each hexagonal grid unit based on the symbol density adjustment matrix to obtain a symbolized map result that meets the requirements of wargaming simulation.

[0030] For example, based on the feature entropy distribution data, the symbol density weight value corresponding to each hexagonal grid cell is determined, and the symbol density adjustment matrix composed of the symbol density weight values ​​is constructed; according to the symbol density adjustment matrix, the number, size and display priority of symbols in each hexagonal grid cell are adaptively adjusted to generate wargaming symbols that match the terrain complexity; the adjusted wargaming symbols are subjected to graphic rendering processing to obtain the symbolized map result that meets the wargaming requirements.

[0031] Figure 2 This is another GIS-based method for generating wargame maps according to an embodiment of the present invention. In this embodiment, a GIS dataset is first loaded onto a computer processor. This dataset may include a topographic elevation model (DEM), geomorphic classification data, land feature vector layers, and a raster base map. Then, based on the user-defined wargame area and map scale, a corresponding geographical subset is extracted from the dataset.

[0032] Specifically, such as Figure 2 As shown, the method includes the following steps:

[0033] Step S202, spatial mapping of land feature attributes.

[0034] like Figure 3 As shown, the spatial mapping of land feature attributes includes the following steps:

[0035] Step S2022: Data preprocessing to obtain standardized geographic data.

[0036] During the data import phase, spatial coordinate transformation is performed on each pixel or vector unit to ensure that all data layers are superimposed and calculated in a unified projection coordinate system. This embodiment uses an improved Gauss-Kruger projection algorithm, and its coordinate transformation formula is as follows:

[0037]

[0038] in, The coordinates are those of the projection plane. These represent longitude and latitude, respectively. The central meridian; The radius of curvature of the ellipsoid; This is a scaling factor correction parameter used to correct errors caused by local elevation. Unlike traditional projection, the scaling correction coefficient in this embodiment... It is not a constant, but dynamically adjusted according to the variance of local elevations in the DEM data, so that the projection results still maintain the geometric consistency of the hexagonal grid shape in mountainous areas.

[0039] Step S2024: Hexagonal grid division.

[0040] An equidistant hexagonal grid is established on a two-dimensional projection plane, with each hexagonal cell representing a basic operational area in the simulation map. To ensure spatial matching between the grid and geographic features, this embodiment employs an adaptive partitioning algorithm based on centroid offset. Specifically, the side length of the reference hexagon is first determined according to a set scale. Then, using the formula:

[0041]

[0042] The side lengths of the hexagons in different regions are adjusted. Among them, For the first The side length of each grid, This represents the standard deviation of the elevation in this area. The global average elevation. This is an adjustable terrain sensitivity coefficient. When the terrain is undulating, the grid side length increases accordingly, resulting in higher accuracy in representing features in complex terrain areas; while in flat terrain, the standard size is maintained to improve rendering efficiency.

[0043] Through the above modifications, the grid generation process considers not only geometric uniformity but also terrain adaptability. This significantly improves the problem in existing technologies where hexagonal grids cannot accurately fit regions of elevation difference.

[0044] Step S2026, Mapping of land feature attributes.

[0045] Each hexagonal grid cell is traversed, and the main land cover types and average elevations within that cell are calculated through spatial overlay analysis. The specific processing includes the following calculations:

[0046] For each unit Its land feature weight (i.e., area ratio) can be expressed as:

[0047]

[0048] in, Indicates land feature type In unit The area inside, For unit The total area. The system determines the main land cover types of this unit based on the maximum weight principle; at the same time, to avoid land cover misclassification caused by boundary pixels, the system introduces a fuzzy normalization function (i.e., a fuzzy probability distribution):

[0049]

[0050] in This is a smoothing coefficient, typically ranging from [1.2, 2.0]. This function enables probabilistic assignment of terrain features, resulting in a more natural transition effect during map rendering. Compared to traditional single-primary-type labeling methods, this method can automatically generate mixed terrain legends at terrain feature boundaries, improving the realism of the map and the accuracy of tactical expression.

[0051] Step S204: Generate a map using an adaptive symbol generation algorithm based on terrain feature entropy.

[0052] like Figure 4 As shown, the method for generating a map includes the following steps:

[0053] Step S2042: Calculate the terrain feature entropy.

[0054] Based on terrain features and tactical applications, hexagonal grid cells are transformed into visual symbols that meet the needs of wargaming simulations. Traditional GIS mapping often relies on static symbol libraries for symbol mapping, while this embodiment introduces an adaptive symbol generation algorithm based on terrain feature entropy. By calculating the terrain complexity index of each hexagonal grid cell, the algorithm dynamically adjusts its symbol style, line density, and color saturation, thereby improving the map's recognition efficiency during simulations.

[0055] In the system's computer execution flow, each hexagonal cell terrain feature entropy The definition is as follows:

[0056]

[0057] in, This represents the total number of land cover types. For the first The first unit The probability distribution values ​​of land features are derived from the fuzzy normalization results of the previous stage. Feature entropy. The range of values ​​is A higher value indicates a more diverse range of land cover types and a more complex terrain. The density coefficient, based on this, automatically selects the appropriate symbol style. The calculation formula is as follows:

[0058]

[0059] in, Based on symbol density, The global average feature entropy, To adjust the coefficients, when the terrain feature entropy is higher than the average, the system automatically increases the symbol density and color saturation, making the map present more obvious layer changes in complex areas; conversely, it simplifies the symbols to reduce visual noise. This adaptive symbolization mechanism effectively improves the readability and information carrying capacity of the map in the simulation scenario.

[0060] Step S2044: Generate a symbol density adjustment matrix and generate a map based on the symbol density adjustment matrix.

[0061] Within the symbol generation module, the computer first calls the symbol template library. This library contains basic legend templates (such as mountains, rivers, woodlands, roads, buildings, etc.) and dynamic color adjustment rules. For each cell, the system generates a corresponding symbol density adjustment matrix based on its main land cover type and local entropy value.

[0062]

[0063] in, These are the red, green, and blue component values ​​of the unit color, respectively; Indicates brightness; For sign density; This is the texture orientation angle. This matrix serves as input to the symbolic rendering engine, enabling the renderer to adjust the texture orientation based on terrain trends. For example, when a local elevation gradient is detected to be primarily oriented northeast-southwest, the system automatically rotates the mountain symbolic texture to enhance visual consistency of the terrain.

[0064] To ensure visual consistency of the simulation map across different resolutions, this embodiment further employs a layered rendering and fusion strategy. First, a basic terrain layer is generated, then a feature layer, a symbol layer, and a text annotation layer are overlaid. The layers are then fused using a weighted transparency function, as shown in the following formula:

[0065]

[0066] in, This represents the final rendered pixel value. For the first The pixel value matrix of the layer, These are the weights for the corresponding layers. Unlike existing map engines that use fixed transparency, the weight parameters in this embodiment are dynamically determined using the following formula:

[0067]

[0068] in, This is an adjustment factor used to control the sensitivity of weight changes. When the feature entropy of a local area is high, the system will automatically increase the weights of the symbol layer and the feature layer, making the terrain information of the map more prominent in tactical key areas.

[0069] Furthermore, this embodiment specifically optimizes the coupling method between elevation data and symbol rendering. Traditional GIS maps often process terrain shadow effects separately, while this invention employs a real-time shadow generation algorithm based on gradient direction weighting. During the rendering process, the system calculates the local elevation gradient of the center point of each cell:

[0070]

[0071] And utilize its direction angle Control the direction of shadows. Shadow intensity. It is then determined by the following formula:

[0072]

[0073] in, As the reference light intensity, For the set incident angle of the light, This represents the elevation attenuation coefficient. The algorithm executes in real-time on a computer GPU, achieving a more three-dimensional terrain visual effect while maintaining computational efficiency. Compared to traditional 2D shadow mapping methods, this algorithm not only reduces memory usage but also dynamically updates lighting effects according to the viewpoint during the simulation process, significantly enhancing the immersive experience of the map.

[0074] Step S2046: Optimize the map.

[0075] To ensure the accuracy and consistency of the generated map, this embodiment sets boundary conditions and anomaly handling rules. When the system detects missing areas or projection anomalies in the input data, it will automatically generate a fill layer and use interpolation to perform terrain compensation. The interpolation formula is as follows:

[0076]

[0077] in, For the elevation of the interpolation point, Given the elevation of the sample points, The distance from the sample point to the interpolation point. This is a power-law attenuation factor (typically 2). This method can avoid abrupt changes in abnormal boundaries while ensuring terrain continuity. The system performs local calculations only on the missing areas during interpolation, thereby reducing the computational burden.

[0078] Step S2048: Output the map.

[0079] In the map generation output stage, the system outputs the aforementioned blended layers as a digital map file usable for simulation. The output format can include GeoTIFF, MBTiles, or a custom binary format. To ensure fast loading and scaling in the tactical simulation system, this embodiment uses a multi-level pyramid structure to store map data, meaning each level's data resolution is [missing information - likely a factor of the previous level]. During scaling operations, the computer can automatically select the optimal resolution layer for rendering based on the view scale, thus balancing performance and accuracy.

[0080] When exporting data, the system attaches a map generation metadata file. This file records key parameters of the generated map, such as projection type, scale, and terrain sensitivity coefficient. Sign adjustment coefficient Rendering weight parameters By saving these parameters, the system can maintain consistency in map generation during subsequent updates and support differentiated regeneration. For example, when battlefield environment data changes, only the local elevation or feature layer needs to be updated, and the system can automatically reconstruct the affected hexagonal grid cells based on the original metadata, thereby significantly shortening the map update cycle.

[0081] This embodiment also performs map accuracy verification after map output. The elevation and feature types of the generated map are compared using standard reference terrain data, and the average error rate is calculated. :

[0082]

[0083] in, To generate the first in the map Elevation values ​​of each unit This is the corresponding value for the reference data. When the average error exceeds the set threshold, a log is automatically generated to locate the error source module, and that part of the task can be selectively re-executed.

[0084] This application also provides a GIS-based wargame map generation system. The core of this embodiment lies in how to efficiently organize, schedule, and cache the aforementioned data operations within a computer system, achieving a dynamic balance between performance and accuracy in the entire map generation process. To this end, this embodiment, for example... Figure 5 The paper proposes a hierarchical parallel execution architecture that establishes an allocatable task chain between the algorithm layer, data layer, and rendering layer to achieve pipelined processing from data loading to image output.

[0085] In terms of computer system architecture, the entire process can be viewed as consisting of three main modules: a data parsing module, an algorithm operation module, and a visualization rendering module. The data parsing module is responsible for reading and standardizing various types of GIS input data; the algorithm operation module performs core operations such as coordinate transformation, grid division, attribute mapping, and feature entropy calculation; and the visualization rendering module generates the final map image based on the algorithm output.

[0086] To avoid resource contention during multi-threaded parallel computation, this embodiment designs a task allocation mechanism based on a grid cell queue in the algorithm operation module. First, all hexagonal grid cells... Divided into multiple subsets according to the spatial block in which it is located. Each subset is processed independently by a single thread. The thread scheduler determines the number of CPU cores. The scheduling function for dynamically allocating computational tasks is as follows:

[0087]

[0088] in, Indicates allocation to thread The number of units, This represents the total number of units. This represents the average terrain complexity index for the region corresponding to this thread. By introducing complexity weights, the computer can more evenly distribute task processing across different threads, avoiding computational bottlenecks caused by terrain differences.

[0089] At the data storage level, this embodiment employs a dual-index data structure: a spatial index and an attribute index. The spatial index utilizes an improved R-Tree structure to achieve fast spatial queries. Instead of using a fixed minimum bounding box for the rectangular boundaries of tree nodes, a terrain curvature factor is introduced. The node range can be dynamically adjusted through the following relationships:

[0090]

[0091] in, This indicates the adjusted boundary dimensions. The average curvature within the nodal region. To adjust the coefficients, this strategy reduces frequent node overlap in high-curvature regions, improving the search efficiency of the spatial index. The attribute index uses a hash mapping structure, with the land feature type and hexagonal grid number as keys, for quick access to the feature parameter set of each unit. The two-level index works in tandem, enabling the map generation algorithm to maintain real-time performance even in large-scale data environments.

[0092] To further improve performance, this embodiment introduces a delayed write caching mechanism in computer memory. When the algorithm processing module generates intermediate data, the system does not immediately write it to disk, but instead stores it in a high-efficiency cache. Cache capacity Based on dynamic adjustments to system memory, the write strategy is as follows:

[0093]

[0094] in, This is a write trigger flag. When the cache occupancy rate reaches 80%, the system performs batch writes to disk, significantly reducing I / O overhead. This mechanism effectively reduces resource lock contention in a multi-threaded environment, shortening map generation time by approximately 40%.

[0095] In terms of algorithm optimization, this embodiment employs a grid adjustment algorithm based on terrain gradient distribution. Traditional hexagonal grid partitioning algorithms often rely on fixed side lengths, while this method adaptively fine-tunes the grid vertex coordinates according to the terrain gradient direction distribution, aligning the hexagonal grid boundaries with the main terrain directions as much as possible, thereby improving the accuracy of terrain representation. Its mathematical model is as follows:

[0096] Let the local gradient direction field be Then each hexagonal vertices Adjusting the offset vector Defined as:

[0097]

[0098] in, The offset magnitude is determined by the control parameters; This represents the local gradient magnitude. By fine-tuning the vertices along the principal gradient direction, the grid boundaries can better match the terrain orientation. For example, in ridge or valley areas, hexagonal grids are automatically stretched along the terrain direction, while in plain areas they maintain a standard shape. This improvement significantly enhances terrain fitting accuracy and provides more precise tactical terrain delineation for subsequent extrapolation models.

[0099] To verify the effectiveness of the algorithm, two sets of comparative tests were designed during the experimental phase: one set used the traditional equidistant hexagonal grid generation algorithm, and the other set used the improved algorithm of this embodiment. The results show that in complex terrain areas, the hexagonal grid terrain fitting error generated by this algorithm was reduced by an average of approximately 27%, and the map symbol misalignment rate was reduced by nearly half. Simultaneously, because the grid fine-tuning amplitude is controlled, its overall geometric uniformity remains within the allowable range. This improved feature has a clear technical effect and can be protected as an independent limiting feature in subsequent claims.

[0100] In addition to geometric adjustments, this implementation also proposes a spatial smoothing filtering strategy to address data noise issues in map generation. During the elevation data preprocessing stage, traditional mean filtering or Gaussian filtering can easily lead to the loss of terrain details. Therefore, this embodiment employs an edge-preserving adaptive smoothing algorithm. For any pixel... Its elevation value It is given by the following formula:

[0101]

[0102] in, For the set of pixel neighborhoods, the weight function Defined as:

[0103]

[0104] in, and These are the adjustment parameters for elevation difference and spatial distance, respectively. This algorithm can effectively remove noise while maintaining the clarity of terrain edges, thereby improving the stability of subsequent hexagonal grid division and symbol rendering.

[0105] In summary, this embodiment, by introducing a hierarchical parallel execution architecture, a dual-index data structure, a delayed write mechanism, and a terrain gradient-based grid fine-tuning algorithm, significantly improves the geometric accuracy and terrain realism of the wargaming map while ensuring computational efficiency. In particular, the gradient-guided grid generation mechanism enables the wargaming map to more accurately represent terrain changes in complex terrains such as mountains or valleys, enhancing the scientific rigor and operability of tactical simulations.

[0106] The GIS-based wargame map generation method of this invention demonstrates high scalability and intelligence during computer execution. Its core innovations are reflected in the following aspects: First, it introduces a symbolic adaptive algorithm based on terrain feature entropy to achieve dynamic matching between terrain complexity and map symbol representation; second, it proposes a grid fine-tuning algorithm based on terrain gradient direction, significantly improving the accuracy of hexagonal grid terrain fitting; third, it employs a layered rendering fusion and multi-level caching mechanism to ensure the map generation process remains efficient and stable even under large-scale data conditions; and fourth, it achieves rapid map updates and high-performance output through local reconstruction and delayed writing strategies.

[0107] In summary, the GIS-based wargaming map generation method provided in this embodiment utilizes multi-dimensional fusion of elevation data, topographic features, and land cover attributes. Through improved grid division and symbol adaptive algorithms, it achieves automated and intelligent generation of wargaming maps. This method can be executed efficiently in computer systems, possessing broad engineering applicability and promising prospects for widespread adoption. Its core algorithm exhibits good scalability, allowing parameters to be adjusted according to different wargaming needs, enabling multi-scale map generation from tactical to strategic levels.

[0108] This application also provides another GIS-based wargame map generation system, such as... Figure 6 As shown, it includes: a processing module 62, configured to acquire a GIS dataset containing elevation data, topographic data, and feature data, and perform coordinate unification and data clipping on the GIS dataset according to the user-defined projection area and scale to obtain processed standardized geographic data; a partitioning module 64, configured to establish a hexagonal grid on a two-dimensional projection plane based on the standardized geographic data using a grid partitioning algorithm, and adjust the side length of the hexagonal grid cells according to the elevation variance of each region to obtain a terrain-adaptive hexagonal grid partitioning result; and a matrix determination module 66. The system is configured to calculate the area ratio of each feature type within each hexagonal grid cell, and calculate the fuzzy probability distribution of the feature types based on the area ratio; the map generation module 68 is configured to calculate the terrain feature entropy of each hexagonal grid cell according to the fuzzy probability distribution, and correlate the feature entropy with the terrain complexity to obtain feature entropy distribution data, construct a symbol density adjustment matrix based on the feature entropy distribution data, and perform adaptive symbol generation and rendering processing on each hexagonal grid cell based on the symbol density adjustment matrix to obtain a symbolized map result that meets the requirements of wargaming simulation.

[0109] It should be noted that the GIS-based wargame map generation system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the GIS-based wargame map generation system and the GIS-based wargame map generation method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0110] Figure 7 A schematic diagram of a computer device suitable for implementing embodiments of the present disclosure is shown. It should be noted that... Figure 7 The computer device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0111] like Figure 7 As shown, the computer device includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0112] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 1010 as needed so that computer programs read from it can be installed into storage section 1008 as needed.

[0113] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for generating wargame maps based on GIS, characterized in that, include: A GIS dataset containing elevation data, topographic data, and ground feature data is acquired. Based on the user-defined projection area and scale, the GIS dataset is subjected to coordinate unification and data cropping to obtain processed standardized geographic data. Based on the standardized geographic data, a hexagonal grid is established on a two-dimensional projection plane using a grid partitioning algorithm. The side length of the hexagonal grid cells is adjusted according to the elevation variance of each region to obtain a terrain-adaptive hexagonal grid partitioning result. For each hexagonal grid cell, the area ratio of each land cover type within that hexagonal grid cell is calculated, and the fuzzy probability distribution of the land cover type is calculated based on the area ratio. Based on the fuzzy probability distribution, the terrain feature entropy of each hexagonal grid cell is calculated, and the feature entropy is correlated with the terrain complexity to obtain the feature entropy distribution data; Based on the feature entropy distribution data, a symbol density adjustment matrix is ​​constructed. Based on the symbol density adjustment matrix, adaptive symbol generation and rendering processing is performed on each hexagonal grid unit to obtain a symbolized map result that meets the requirements of wargaming simulation.

2. The method according to claim 1, characterized in that, Based on the user-defined projection area and scale, the GIS dataset undergoes coordinate unification and data cropping to obtain processed standardized geographic data, including: A Gaussian-Kruger projection transformation is performed on the GIS dataset, and the projection scale factor is dynamically corrected based on the local elevation variance of the corresponding area of ​​the GIS dataset to obtain terrain-corrected projection coordinate data. Based on the user-defined projection area range, a spatial clipping operation is performed on the terrain-corrected projection coordinate data to extract a subset of geographic data corresponding to the projection area range, which is then used as the standardized geographic data.

3. The method according to claim 1, characterized in that, Based on the standardized geographic data, a hexagonal grid is established on a two-dimensional projection plane using a grid partitioning algorithm. The side lengths of the hexagonal grid cells are then adjusted according to the elevation variance of each region to obtain a terrain-adaptive hexagonal grid partitioning result, including: Based on the spatial extent of the standardized geographic data, an initial equilateral hexagonal grid is generated on the two-dimensional projection plane to obtain an initial set of hexagonal grid cells. For each of the initial hexagonal grid cells, calculate the elevation variance value within the area covered by that hexagonal grid cell; Based on the elevation variance value and the preset side length adjustment function, the side length of the corresponding hexagonal grid unit is scaled and adjusted to obtain a terrain adaptive hexagonal grid division result that reflects the terrain undulation characteristics.

4. The method according to claim 1, characterized in that, For each hexagonal grid cell, the area proportion of each land cover type within that hexagonal grid cell is calculated, and the fuzzy probability distribution of the land cover type is calculated based on the area proportion, including: Map the feature data in the standardized geographic data to the corresponding hexagonal grid cells in the terrain adaptive hexagonal grid division result; For each hexagonal grid cell, the coverage area corresponding to different land cover types within the hexagonal grid cell is calculated, and the area ratio of each land cover type to the total area of ​​the hexagonal grid cell is calculated. Based on the area ratio, fuzzy membership functions for each land cover type are constructed, and the area ratio is fuzzified to obtain the fuzzy probability distribution characterizing the uncertainty of the land cover composition of the hexagonal grid unit.

5. The method according to claim 1, characterized in that, Based on the fuzzy probability distribution, the terrain feature entropy of each hexagonal grid cell is calculated, and the feature entropy is correlated with the terrain complexity to obtain feature entropy distribution data, including: For each hexagonal grid cell, the information entropy value of each land cover type is calculated based on the fuzzy probability distribution corresponding to the hexagonal grid cell; The information entropy values ​​of the various land cover types are summarized to obtain the terrain feature entropy that characterizes the uncertainty of the land cover distribution in the hexagonal grid unit; The terrain feature entropy is matched with a preset terrain complexity mapping relationship to obtain the feature entropy distribution data used for symbol density adjustment.

6. The method according to claim 1, characterized in that, Based on the aforementioned feature entropy distribution data, a symbol density adjustment matrix is ​​constructed. Adaptive symbol generation and rendering are then performed on each hexagonal grid cell based on this matrix to obtain a symbolized map result that meets the requirements of wargaming simulations, including: Based on the feature entropy distribution data, the symbol density weight value corresponding to each hexagonal grid cell is determined, and the symbol density adjustment matrix composed of the symbol density weight values ​​is constructed. Based on the symbol density adjustment matrix, the number, size and display priority of symbols in each hexagonal grid cell are adaptively adjusted to generate wargaming symbols that match the terrain complexity. The adjusted wargaming symbols are subjected to graphic rendering processing to obtain the symbolized map result that meets the requirements of wargaming.

7. A GIS-based wargame map generation system, characterized in that, include: The processing module is configured to acquire a GIS dataset containing elevation data, topographic data, and ground feature data, and perform coordinate unification and data cropping on the GIS dataset according to the user-defined projection area and scale to obtain processed standardized geographic data. The partitioning module is configured to establish a hexagonal grid on a two-dimensional projection plane based on the standardized geographic data using a grid partitioning algorithm, and adjust the side length of the hexagonal grid cells according to the elevation variance of each region to obtain a terrain-adaptive hexagonal grid partitioning result. The matrix determination module is configured to calculate the area ratio of each land cover type within each hexagonal grid cell, and calculate the fuzzy probability distribution of the land cover type based on the area ratio; The map generation module is configured to calculate the terrain feature entropy of each hexagonal grid cell according to the fuzzy probability distribution, and correlate the feature entropy with the terrain complexity to obtain feature entropy distribution data. Based on the feature entropy distribution data, a symbol density adjustment matrix is ​​constructed, and adaptive symbol generation and rendering processing is performed on each hexagonal grid cell based on the symbol density adjustment matrix to obtain a symbolized map result that meets the requirements of wargaming simulation.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images