A virtual battlefield scene simulation system and method thereof

Abstract

The present application relates to the technical field of simulation system, specifically to a virtual battlefield scene simulation system and method thereof, the system comprises: terrain node identification module, combat unit control radius extraction module, terrain advantage area preliminary demarcation module, control force gradient evolution analysis module, advantage area boundary dynamic adjustment module.In the present application, through the joint screening based on elevation, visible penetration rate and surface roughness, the key nodes with cover and accessibility are accurately identified, the terrain selection accuracy is improved, the spatial distance between the advantage nodes and the combat units is calculated to realize the accurate association between the control range and the deployment, the isolated points are excluded by node clustering division, the complete control domain contour is constructed, the Laplace operator is introduced to calculate the control force change rate, the boundary is dynamically adjusted in response to the situation evolution, the spatial continuity and change logic of the control force expression are strengthened, and the scene adaptability and deduction accuracy in the simulation process are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation systems, and in particular to a virtual battlefield scene simulation system and method thereof. Background Art

[0002] The field of simulation system technology involves modeling and simulation, using computer-executable models to simulate and analyze the structure, behavior, and operating mechanisms of actual or hypothetical systems. The core of this technical field involves the digital reproduction and dynamic interaction of physical systems, combat systems, and environmental systems, thereby providing a repeatable, controllable, and adjustable test environment for design evaluation, training exercises, and combat simulations. Simulation systems are widely used in various industries, including military, aerospace, transportation, energy, and manufacturing. They can play a role in high-risk, high-cost scenarios that are not suitable for field testing. Their development mainly relies on the coordinated progress of system modeling technology, real-time graphics rendering technology, human-computer interaction technology, and simulation drive engines.

[0003] Among them, the virtual battlefield scene simulation system refers to a type of system in the field of simulation system technology that is specifically used to build a virtual military combat environment and simulate combat behavior processes and situation changes. This system addresses the problems of battlefield environment complexity and the diversity of combat elements by constructing a virtual space scene that includes multi-dimensional elements such as terrain, meteorological conditions, target entities, and combat formations. It uses three-dimensional modeling tools to generate scene graphics, uses geographic information data and remote sensing images to restore the terrain, and combines behavioral rule-driven technology to control the evolution of entity behavior, forming a battlefield simulation environment with logical association and dynamic evolution capabilities. The patent subject completes the construction and management of virtual battlefield scenes through means such as semantic rule design, spatial relationship mapping, and tactical script control.

[0004] Traditional virtual battlefield simulation systems often rely on static modeling and rule-driven approaches. When identifying terrain advantage, they only collect terrain elevation or topographic data, lacking comprehensive consideration of node visibility and surface physical properties, leading to one-sided advantage assessments. Control domain determination often relies on a simple radius-based approach, failing to dynamically adapt to the actual interactions between combat units and complex terrain, impacting the rationality and accuracy of control area boundaries. A lack of detailed segmentation and denoising methods for identifying node clustering trends often leads to misidentification of isolated points or fragmented boundaries, weakening the overall sense of battlefield structure. Control force evolution often relies on static settings or empirical rules, making it difficult to reflect subtle shifts in force distribution in a dynamic battlefield, hindering real-time deployment and strategic adjustments. For example, when simulating multi-unit coordinated attack and defense scenarios, limited by the node processing granularity and control domain delineation methods, multiple units' control zones often overlap or experience blind spots, impacting the realism and operability of tactical simulations and limiting the simulation system's adaptability to complex geographical environments. Summary of the Invention

[0005] The object of the present application is to solve the drawbacks existing in the prior art and to propose a virtual battlefield scene simulation system and method.

[0006] In order to achieve the above object, the present application adopts the following technical scheme: a virtual battlefield scene simulation system comprises: a terrain node identification module, configured to acquire node elevation data, node visible penetration rate and node ground surface roughness, filter nodes with node elevation data higher than a threshold X, node visible penetration rate lower than a threshold Y and node ground surface roughness lower than a threshold Z as dominant nodes, form a dominant node set and pass to a combat unit control radius extraction module;

[0007] The combat unit control radius extraction module is configured to acquire combat unit coordinates, fire coverage radius and line-of-sight parameters, call the dominant node set, calculate the distance from the dominant nodes to the combat unit according to the Euclidean distance, form a preliminary control domain node set and pass to a terrain dominant area preliminary delineation module;

[0008] The terrain dominant area preliminary delineation module is configured to call the preliminary control domain node set, divide the node density based on a clustering analysis model, correct isolated nodes to generate a preliminary terrain dominant area contour map, and pass to a control force gradient evolution analysis module;

[0009] The control force gradient evolution analysis module is configured to call the preliminary terrain dominant area contour map, calculate the node control force change rate according to the Laplace operator, form an expanded candidate node set and a contracted candidate node set, and pass to a dominant area boundary dynamic adjustment module.

[0010] As a further scheme of the present application, the threshold X is determined based on the percentage of the regional average elevation, and the threshold Y is set based on the inversion result of the ground object shielding coefficient in the remote sensing data, wherein the threshold Y represents the visible penetration rate of the remote sensing data region, specifically the proportion of the radiation energy that is not blocked after penetrating the ground object in the remote sensing field of view, and the value is a dimensionless ratio between 0 and 1, and the threshold Z is set based on the ground reflectivity and micro-terrain change rate extraction value, wherein the threshold Z represents the ground surface relief condition in the remote sensing region, defined as the total amount of elevation change per unit area, with the unit of meter per square kilometer;

[0011] The unit of the combat unit coordinates and the dominant node set is kilometer;

[0012] The cluster analysis model inputs the node spatial coordinates into the DBSCAN clustering algorithm for processing, sets the minimum number of samples in the neighborhood MinPts to 5, sets the neighborhood radius Eps to the mean of the Euclidean distances between nodes in the node set, divides the node density according to the clustering output results, defines the distance threshold T as the 90% quantile of the Euclidean distance set between the cluster center and each cluster node, and uses this as the basis for identifying isolated nodes, that is, when the Euclidean distance between a node and its nearest neighbor cluster center is greater than the distance threshold T, the node is identified as an isolated node;

[0013] The advantageous node set includes node elevation data, node visual penetration rate, and node surface roughness; the preliminary control domain node set includes node standardized coordinates, node Euclidean distance, and node combat applicability identification; the preliminary terrain advantageous area outline map includes node-dense areas, isolated node locations, and preliminary terrain boundaries; the expansion candidate node set and contraction candidate node set include node control force change rate, regional expansion potential, and regional contraction risk.

[0014] As a further solution of the present invention, the terrain node identification module includes:

[0015] The node elevation screening submodule collects node elevation data, sorts and determines the node elevation threshold using percentile statistics, compares each node elevation value with the elevation threshold one by one, screens out nodes that exceed the elevation threshold, and generates a node elevation screening interval;

[0016] The node penetration rate determination submodule calls the ground object type and distribution characteristics in the remote sensing data, inverts the ground object shielding coefficient of the node area, calculates the node visible penetration rate through the coefficient value, sets the node visible penetration rate threshold, compares the penetration rate of each node with the node visible penetration rate threshold one by one, selects the nodes below the node visible penetration rate threshold, and generates the node penetration rate determination coefficient;

[0017] The surface roughness identification submodule monitors the ground spectral reflectance intensity of the node area, combines the regional micro-topography slope change rate, calculates the node surface roughness, sets the node surface roughness threshold, compares the roughness value with the node surface roughness threshold one by one, screens out the nodes that do not reach the node surface roughness threshold, and generates a quantitative index of surface roughness;

[0018] The dominant node set construction submodule calls the three values ​​of the node elevation screening interval, the node penetration rate determination coefficient and the surface roughness quantitative index, and through data cross-comparison, screens the nodes that meet all the above conditions at the same time to establish the dominant node set.

[0019] As a further solution of the present invention, the combat unit control radius extraction module includes:

[0020] The combat unit parameter acquisition submodule obtains the position coordinates of the combat unit, collects fire coverage radius data, and obtains the sight range parameters based on the equipment characteristics. By summarizing the tactical attributes of the combat unit, it obtains and stores the values ​​of various attributes to generate the quantitative parameters of the combat unit attributes.

[0021] The node distance calculation submodule calls the node coordinates of the dominant node set, calculates the spatial straight-line distance between each node and the combat unit one by one based on the coordinates and fire coverage radius data in the combat unit attribute quantification parameters using the Euclidean distance calculation method, sorts these distances, and generates the node-to-combat unit distance value;

[0022] The control domain node determination submodule calls the distance value from the node to the combat unit, compares the distance between each node and the combat unit according to the fire coverage radius parameter, screens out the nodes that meet the coverage radius conditions, and establishes a preliminary control domain node set.

[0023] As a further solution of the present invention, the module for preliminary delineation of terrain advantage areas includes:

[0024] The node density clustering submodule calls the preliminary control domain node set, collects the spatial coordinates of each node, sets the neighborhood radius Eps to the average Euclidean distance between the nodes in the set, calls the DBSCAN clustering algorithm to process the node data with the number of samples in the neighborhood MinPts as 5, calculates the node density distribution based on the node density clustering results, and generates a node density level value;

[0025] The isolated node identification submodule calls the node density level value, measures the Euclidean distance between each node and the corresponding cluster center spatial position, sets a distance threshold as a judgment reference value, compares the distance value between the node and the cluster center with the distance threshold, determines and marks the nodes that exceed the distance threshold, and generates an isolated node identification set;

[0026] The dominant area outline drawing submodule calls the node density level value and the isolated node identification set, removes the isolated node data, calculates the spatial distribution boundary coordinates of the remaining nodes, outlines the node distribution boundary, and establishes a preliminary terrain dominant area outline map.

[0027] As a further solution of the present invention, the control force gradient evolution analysis module includes:

[0028] The node control force calculation submodule calls the node data in the preliminary terrain advantage area outline map, calculates the control force change rate of each node based on the Laplace operator, uses the node spatial coordinate data and the density information of the adjacent nodes, calculates the influence change between the nodes, obtains the control force gradient of each node, and generates the node control force change rate;

[0029] The candidate node set generation submodule calls the node control force change rate, and according to the numerical value of the node control force change rate, divides the nodes with larger control force changes into expansion candidate nodes, and the nodes with smaller control force changes into contraction candidate nodes, and screens them according to the change rate threshold to generate an expansion candidate node set and a contraction candidate node set.

[0030] As a further embodiment of the present invention, the system further comprises:

[0031] A module for dynamically adjusting the boundary of an advantageous zone is configured to call the set of expansion candidate nodes and the set of contraction candidate nodes, calculate a dynamic weight coefficient based on a topological adjacency relationship, and define the dynamic weight coefficient as the ratio of the rate of change of control power of the expansion candidate nodes to the rate of change of control power of the contraction candidate nodes. Based on a comparison of the dynamic weight coefficient with a preset threshold, the module determines the expansion or contraction direction of the boundary of the advantageous zone, determines the expansion area, and determines the contraction nodes to be removed. A Delaunay triangulation algorithm is used to connect the remaining boundary nodes after adjustment. The specific implementation condition of the Delaunay triangulation algorithm is to remove triangle edges whose side length exceeds 1.2 times the line-of-sight parameter baseline value. The line-of-sight parameter baseline value is defined as the maximum detection range of the combat unit's line-of-sight sensor, expressed in kilometers, and generate a dynamically updated data map of the virtual battlefield terrain advantage control zone.

[0032] The dynamically updated virtual battlefield terrain advantage control area data map includes expanding and updating boundaries, eliminating invalid areas, and control area outlines.

[0033] As a further solution of the present invention, the dominant zone boundary dynamic adjustment module includes:

[0034] The dynamic weight coefficient calculation submodule obtains the set of expansion candidate nodes and the set of contraction candidate nodes, calculates the neighborhood weight of each node based on the topological adjacency relationship, extracts the control force change rate data of the expansion candidate nodes and the contraction candidate nodes, calculates the ratio of the two, obtains the dynamic weight coefficient, determines the expansion or contraction direction based on the relationship between the dynamic weight coefficient and the preset threshold, updates the node weight value, and generates the dynamic weight coefficient;

[0035] The expansion area determination submodule determines the expansion or contraction direction based on the relationship between the dynamic weight coefficient and the preset threshold, adjusts the boundary range according to the node control force change rate, identifies and eliminates contraction nodes, updates the node set, and generates an expansion or contraction node set;

[0036] The boundary node connection submodule calls the Delaunay triangulation algorithm according to the expanded or contracted node set, performs boundary triangulation based on the remaining boundary nodes, eliminates invalid edges that exceed 120% of the line of sight parameter, reconnects the boundary nodes according to the node coordinates and distance relationship, updates the boundary information table, and generates a dynamically updated virtual battlefield terrain advantage control area data map.

[0037] A virtual battlefield scene simulation method is implemented based on the above-mentioned virtual battlefield scene simulation system, comprising the following steps:

[0038] S1: Obtain node elevation data, node visual penetration data, and node surface roughness data, and filter them according to thresholds X, Y, and Z. Nodes with node elevation data greater than threshold X, node visual penetration data less than threshold Y, and node surface roughness less than threshold Z are selected to form a dominant node set.

[0039] S2: Obtain the coordinates of the combat unit, the fire coverage radius parameter, and the line of sight parameter, call the advantageous node set, calculate the spatial straight-line distance using the Euclidean distance function, and select nodes whose Euclidean distance is less than the fire coverage radius and within the line of sight parameter to form a preliminary control domain node set;

[0040] S3: calling the preliminary control domain node set, inputting the node coordinates into the DBSCAN algorithm, clustering and classifying the node set based on the node density and minimum number of neighbors parameters, filtering out isolated nodes and correcting node boundary characteristics, and generating a preliminary terrain advantage area outline map;

[0041] S4: calling the preliminary terrain advantage area outline map, inputting node coordinates and adjacency relationships into the Laplace operator model, calculating the control force change rate, extracting nodes with a positive change rate greater than zero to form an expansion candidate node set, and extracting nodes with a negative change rate less than zero to form a contraction candidate node set;

[0042] S5: calling the set of expansion candidate nodes and the set of contraction candidate nodes, using the ratio of the control force change rate of the expansion candidate nodes to the control force change rate of the contraction candidate nodes as a dynamic weight coefficient, classifying the nodes according to the relationship between the dynamic weight coefficient and the preset expansion and contraction judgment threshold, and generating a data map of the virtual battlefield terrain advantage control area;

[0043] Compared with the prior art, the advantages and positive effects of the present invention are:

[0044] In this invention, multi-parameter screening based on node elevation, visual penetration, and surface roughness prioritizes areas with strong visual obstruction and favorable terrain features, effectively focusing on geographical locations with tactical advantages. Combined with Euclidean distance measurement, key node locations associated with combat units are defined, establishing a highly targeted and precisely covered control range. Node clustering analysis is used to divide the density of control domains, eliminating interference from isolated points while forming a more functionally defined terrain advantage contour map, enhancing the spatial continuity and stability of battlefield situational awareness. The Laplace operator is introduced to analyze the evolution of control force gradients, enabling dynamic expansion and contraction of the battlefield influence area, ensuring that tactical deployment can be flexibly adjusted as the situation evolves. The overall processing logic, through the collaborative evaluation of multi-dimensional geographic parameters, identification of spatial distribution structures, and dynamic control driven by mathematical models, improves the accuracy and adaptability of battlefield modeling and the depth of deduction during actual combat exercises in a highly dynamic, multivariable environment, significantly enhancing the virtual simulation system's recognition accuracy of terrain control force and its decision-making support capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a system flow chart of the present invention;

[0046] Figure 2 is a system block diagram of the present invention;

[0047] Figure 3 Schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0050] See also Figure 1 The present invention provides a technical solution: a virtual battlefield scene simulation system comprising:

[0051] The terrain node identification module is used to obtain node elevation data, node visual penetration rate, and node surface roughness. Nodes that meet the requirements of elevation higher than X, penetration rate lower than Y, and roughness lower than Z are selected as dominant nodes. This node set is then formed and transferred to the combat unit control radius extraction module.

[0052] Node visibility penetration: refers to the ability of the surface at the node location to transmit optical or radar signals. It is often used to evaluate terrain obscuration and visibility. The standard parameter is widely used in the fields of remote sensing and geographic information systems.

[0053] The combat unit control radius extraction module is used to obtain the combat unit coordinates, fire coverage radius, and line of sight parameters, call the advantage node set, calculate the distance from the advantage node to the combat unit based on the Euclidean distance, form a preliminary control domain node set, and pass it to the terrain advantage area preliminary delineation module;

[0054] The module for preliminary delineation of terrain advantage areas is used to call the preliminary control domain node set, divide the node density based on the cluster analysis model, correct isolated nodes to generate a preliminary terrain advantage area outline map, and pass it to the control force gradient evolution analysis module;

[0055] The control force gradient evolution analysis module is used to call the preliminary terrain advantage zone contour map, calculate the node control force change rate based on the Laplace operator, form an expansion candidate node set and a contraction candidate node set, and pass them to the advantage zone boundary dynamic adjustment module;

[0056] The module dynamically adjusts the boundary of the advantageous zone, and is used to call the set of expansion candidate nodes and the set of contraction candidate nodes, and calculate the dynamic weight coefficient based on the topological adjacency relationship. The dynamic weight coefficient is defined as the ratio of the rate of change of the control force of the expansion candidate node to the rate of change of the control force of the contraction candidate node. Based on the comparison of the dynamic weight coefficient with the preset threshold, the expansion or contraction direction of the boundary of the advantageous zone is determined, and the expansion area and the contraction nodes to be removed are determined. The Delaunay triangulation algorithm is used to connect the remaining boundary nodes after adjustment. The specific implementation condition of the Delaunay triangulation algorithm is to remove the triangle edges whose side length exceeds 1.2 times the line-of-sight parameter baseline value. The line-of-sight parameter baseline value is defined as the maximum detection range of the combat unit's line-of-sight sensor, in kilometers, to generate a dynamically updated virtual battlefield terrain advantage control zone data map;

[0057] Delaunay triangulation algorithm: a standard geospatial calculation method, widely used in areas such as regional boundary connection and optimal grid generation, and is a well-known topological connection technology.

[0058] The set of advantageous nodes includes node elevation data, node visual penetration rate, and node surface roughness. The set of preliminary control domain nodes includes node standardized coordinates, node Euclidean distance, and node combat applicability identification. The preliminary terrain advantage area outline map includes node-dense areas, isolated node locations, and preliminary terrain boundaries. The set of expanded candidate nodes and the set of contracted candidate nodes include the node control force change rate, regional expansion potential, and regional contraction risk. The dynamically updated virtual battlefield terrain advantage control area data map includes expanded and updated boundaries, elimination of invalid areas, and control area outlines.

[0059] See also Figure 2 , the terrain node recognition module includes:

[0060] The node elevation screening submodule collects node elevation data, sorts and determines the node elevation threshold using percentile statistics, compares each node elevation value with the elevation threshold one by one, screens out nodes that exceed the elevation threshold, and generates a node elevation screening interval;

[0061] The node elevation screening submodule first obtains the elevation data for each node. This process uses ground-based measurement instruments or remote sensing satellite data to obtain each node's elevation value. For example, if an area contains 100 nodes, each with an elevation ranging from 10 to 100 meters, the node elevation screening submodule first sorts the elevation data for all nodes using a percentile method, calculates the elevation distribution, and uses this data to determine an elevation threshold. For example, if the percentile analysis determines that 95% of the nodes have elevations below 70 meters, 70 meters is set as the elevation threshold. Next, the module compares each node's elevation value with this threshold, filtering out those nodes above the threshold. For example, if a node has an elevation of 75 meters, it is marked as exceeding the threshold. Finally, the module generates an elevation screening interval containing all nodes that pass the elevation screening and have elevation values ​​within a reasonable range.

[0062] The node penetration rate determination submodule calls the ground object type and distribution characteristics in the remote sensing data, inverts the ground object shielding coefficient of the node area, and calculates the node visible penetration rate through the coefficient value. The node visible penetration rate threshold is set based on this value, and the penetration rate of each node is compared with the node visible penetration rate threshold one by one. The nodes below the node visible penetration rate threshold are screened out to generate the node penetration rate determination coefficient.

[0063] During the execution of the node penetration determination submodule, the feature type and distribution characteristics are first analyzed based on remote sensing data. The reflectance spectrum characteristics in the remote sensing imagery are used to infer the feature shielding coefficient of the node's area, further estimating the node's visible penetration. Assume that the feature type in the node's area is forest, as determined by remote sensing data. A feature shielding coefficient of 0.8 is inferred, indicating that 80% of light is blocked. Based on this shielding coefficient, the node's visible penetration is calculated to be 0.2, meaning that 20% of the area is visible. The node penetration threshold is set to 0.3 in this step, meaning that only nodes with a visible penetration greater than or equal to 0.3 meet the criteria. The module then compares each node's penetration against the set penetration threshold, filtering out nodes with a penetration below 0.3. Finally, a penetration determination coefficient is generated, indicating the nodes that pass the penetration screening.

[0064] The surface roughness identification submodule monitors the ground spectral reflectance intensity of the node area, combines the regional micro-topography slope change rate, calculates the node surface roughness, sets the node surface roughness threshold, compares the roughness value with the node surface roughness threshold one by one, screens out the nodes that do not reach the node surface roughness threshold, and generates a quantitative index of surface roughness;

[0065] The surface roughness identification submodule calculates the surface roughness of a node by monitoring the ground spectral reflectance intensity in the node's area and combining it with the rate of change of the area's microtopography slope. Assume that the measured ground spectral reflectance intensity is 0.6, while the area's slope change rate is 0.1, indicating a small change in ground slope. Based on this data, the surface roughness calculation formula calculates a node roughness value of 0.2. The module sets a surface roughness threshold of 0.3, meaning that only nodes with a roughness less than 0.3 meet the criteria. The module then compares each node's roughness value with the set threshold, filtering out those that do not meet the roughness threshold. Ultimately, it generates a quantitative surface roughness index to identify nodes that meet the criteria.

[0066] The dominant node set construction submodule calls three values: node elevation screening interval, node penetration rate determination coefficient and surface roughness quantitative index. Through data cross-comparison, it selects nodes that meet all the above conditions at the same time and establishes the dominant node set.

[0067] The dominant node set construction submodule uses data cross-comparison technology to screen out nodes that meet all the conditions simultaneously based on the node elevation screening range, node penetration rate determination coefficient, and surface roughness quantitative indicators. For example, a node has an elevation of 65 meters, which meets the elevation screening condition, a penetration rate of 0.25, which does not reach the penetration rate threshold of 0.3, and a roughness of 0.1, which meets the roughness screening condition. The penetration rate of this node is lower than the set threshold, so it will not be selected into the dominant node set. Ultimately, after screening and comparison, the module generates a dominant node set that contains all nodes that meet the elevation, penetration rate, and roughness conditions, providing reliable data for subsequent analysis.

[0068] See also Figure 2 , the combat unit control radius extraction module includes:

[0069] The combat unit parameter acquisition submodule obtains the position coordinates of the combat unit, collects fire coverage radius data, and obtains the sight range parameters based on the equipment characteristics. By summarizing the tactical attributes of the combat unit, it obtains and stores the values ​​of various attributes to generate the quantitative parameters of the combat unit attributes.

[0070] The execution process of the combat unit parameter acquisition submodule is broken down into the following specific steps: First, the spatial position coordinates (e.g., longitude and latitude) of the combat unit are retrieved through the command and control system. For example, the coordinates of a ground artillery piece might be (125.36°E, 43.87°N). Second, the equipment's built-in sensors automatically collect data on its fire coverage radius. For example, the fire coverage radius of a 155mm self-propelled artillery piece is 30 km. Subsequently, the combat unit's line-of-sight parameters are determined based on the characteristics of the equipment, such as optical observation equipment, radar, or electronic reconnaissance equipment. For example, the radar line-of-sight parameter for radar observation equipment is 25 km. The acquired position coordinate data, fire coverage radius data, and line-of-sight parameters are then combined with other tactical attribute data, such as mobility level (for example, mobility is defined on a scale of 1 to 5, with 1 being the lowest and 5 being the highest, and a certain self-propelled artillery piece being assigned a mobility level of 4) and protection capability index (for example, on a scale of 1 to 100, with a protection index of 65 for a certain self-propelled artillery piece). The aggregated data is then stored and managed using the equipment ID as an identifier, forming a clear set of combat unit attribute quantitative parameters, such as coordinates (latitude and longitude: 125.36°E, 43.87°N), fire coverage radius (30km), line of sight (25km), mobility (level 4), and protection index (65). Finally, structured data is formed based on the quantitative parameter set and stored in the database for use by subsequent calculation modules. The data storage results that can be actually used are the combat unit attribute quantitative parameters.

[0071] The node distance calculation submodule calls the node coordinates of the dominant node set, and based on the coordinates and fire coverage radius data in the combat unit attribute quantification parameters, calculates the spatial straight-line distance between each node and the combat unit one by one using the Euclidean distance calculation method, sorts these distances, and generates the node-to-combat unit distance value;

[0072] The node distance calculation submodule first calls the spatial position coordinate data of each node in the pre-determined dominant node set, as shown in Table 1:

[0073] Table 1. Spatial coordinates of dominant nodes

[0074]

[0075] As shown in Table 1, for each node, the combat unit coordinates (125.36°E, 43.87°N) and the fire coverage radius (30km) in the combat unit attribute quantification parameters are called, and the straight-line distance between each node and the combat unit is calculated one by one using the spatial Euclidean distance method. The specific process is: first, the longitude and latitude coordinates are converted into two-dimensional spatial coordinates of the spatial rectangular coordinate system. Then, the Euclidean distance calculation is performed. Taking node 1 as an example, the coordinate conversion results in the two-dimensional coordinates (3.60, 4.50) (unit: km), and the two-dimensional coordinates of the combat unit are (0, 0). By calculation: the Euclidean distance between node 1 and the combat unit is Similarly, the distance to node 2 is calculated to be 7.64 km, and the distance to node 3 is 12.01 km. The distance values ​​calculated above are then sorted from smallest to largest to form a sorted set of node-to-combat unit distances (node ​​1: 5.76 km, node 2: 7.64 km, node 3: 12.01 km). This clarifies the distance values ​​between the nodes and the combat units.

[0076] The control domain node determination submodule calls the distance value from the node to the combat unit, compares the distance between each node and the combat unit according to the fire coverage radius parameter, screens out the nodes that meet the coverage radius conditions, and establishes a preliminary control domain node set.

[0077] The control domain node determination submodule executes as follows: First, it calls the previously calculated distance values ​​between the nodes and the combat unit (Node 1: 5.76km, Node 2: 7.64km, Node 3: 12.01km). It then directly compares each distance value with the fire coverage radius data (30km) in the combat unit attribute quantification parameters, determining whether each node's distance is within the fire coverage radius. Specifically, Node 1's distance of 5.76km is less than the coverage radius of 30km, so Node 1 is considered eligible. Node 2's distance of 7.64km is also less than the coverage radius, so Node 2 is considered eligible. Node 3's distance of 12.01km is also less than the coverage radius, so Node 3 is also eligible. Subsequently, all nodes that meet these conditions are automatically added to the preliminary control domain node set (i.e., including Nodes 1, 2, and 3), ultimately forming the definitive data for the control domain node set.

[0078] See also Figure 2 , the module for preliminary delineation of terrain advantage areas includes:

[0079] The node density clustering submodule calls the preliminary control domain node set, collects the spatial coordinates of each node, sets the neighborhood radius Eps as the average Euclidean distance between the nodes in the set, calls the DBSCAN clustering algorithm with the number of samples in the neighborhood MinPts as 5 to process the node data, calculates the node density distribution based on the node density clustering results, and generates the node density level value;

[0080] The node density clustering submodule calls the preliminary control domain node set, that is, the node coordinate data of node 1 (125.40°E, 43.90°N), node 2 (125.30°E, 43.80°N), node 3 (125.50°E, 43.70°N) obtained above, and performs the numerical calculation of the Euclidean distance between each two nodes in turn. For example, the distance between node 1 and node 2 is calculated as follows: first convert the longitude and latitude into two-dimensional coordinates, node 1 (3.6km, 4.5km), node 2 (-4.8km, -7.8km), then the distance between node 1 and node 2 is Similarly, the distances between nodes 1 and 3, and between nodes 2 and 3 are calculated to be 22.36 km and 24.08 km respectively. The arithmetic mean of the above distances is taken as the neighborhood radius parameter Eps, that is, , and set the node number threshold MinPts in the neighborhood to 5. Then measure the distance from each node to other nodes in turn, compare the distance from each node to other nodes with the neighborhood radius of 20.42km, and count the number of nodes around the node with a distance less than or equal to 20.42km one by one. For example, the number of nodes in the neighborhood of node 1 is only node 2, and the number is counted as 1, which is less than the MinPts value of 5. Node 1 is initially marked as a node with a low density level. Repeat the above distance measurement process. The neighborhood of node 2 also contains only node 1, and the density level is also marked as low. No node in the neighborhood of node 3 falls within the neighborhood radius, and the density level is also marked as low. Finally, the density level value of each node is determined, and the density level of all nodes is uniformly marked as low density (the number of neighborhood nodes is less than the MinPts value of 5).

[0081] The isolated node identification submodule calls the node density level value, measures the Euclidean distance between each node and the corresponding cluster center spatial position, sets a distance threshold as the judgment reference value, compares the distance value between the node and the cluster center with the distance threshold, judges and marks the nodes that exceed the distance threshold, and generates an isolated node identification set;

[0082] The isolated node identification submodule calls the node information marked with the aforementioned density level value, and then calls the node cluster center position. Since the node density level in this actual scene is consistent, the arithmetic mean of the node coordinates is taken as the cluster center. The cluster center position is calculated as follows: , then measure the distance from each node to the cluster center. The distance from node 1 to the cluster center is , the distance to node 2 is , the distance between node 3 is , and then set the distance value from each node to the cluster center as the benchmark value, that is, the distance threshold, and take the arithmetic mean of the distance from each node to the cluster center as the threshold, that is, , then compare the distance of each node with the threshold of 11.2km in turn. The distance of node 1 is 11.1km, which is less than 11.2km. It is determined that node 1 is not an isolated node. The distance of node 2 is 8.2km, which is also less than 11.2km. It is determined that node 2 is not an isolated node. The distance of node 3 is 14.3km, which exceeds the threshold of 11.2km. It is determined that node 3 is an isolated node and marked as an isolated node identification set.

[0083] The dominant area outline drawing submodule calls the node density level value and isolated node identification set, eliminates the isolated node data, calculates the spatial distribution boundary coordinates of the remaining nodes, outlines the node distribution boundary, and establishes a preliminary terrain dominant area outline map.

[0084] The dominant area outline drawing submodule calls the node density level value (nodes 1 and 2 have low density, and node 3 is an isolated node) and the isolated node identification set (node ​​3 is marked as an isolated node), eliminates isolated node 3, and only calls the spatial coordinate data of nodes 1 and 2, that is, node 1 (125.40°E, 43.90°N) and node 2 (125.30°E, 43.80°N). After mapping the spatial coordinates of the two to the geographic coordinate system in turn, the node coordinates are connected in a straight line to clarify the coordinate range of the remaining node boundaries. The line between node 1 and node 2 constitutes the node distribution boundary. In the specific implementation, the longitude and latitude values ​​of the coordinates of node 1 and node 2 are directly connected to form a boundary coordinate line segment, and then the coordinates of the endpoints of the line segment (node ​​1 and node 2) are obtained. The spatial boundary outline of the node group is clearly defined by the coordinates of the endpoints of the line segment, and finally a preliminary terrain dominant area outline is formed in the form of spatial coordinate lines.

[0085] See also Figure 2 , the control force gradient evolution analysis module includes:

[0086] The node control force calculation submodule calls the node data in the preliminary terrain advantage area outline map, calculates the control force change rate of each node based on the Laplace operator, uses the node spatial coordinate data and the density information of adjacent nodes, calculates the influence change between nodes, obtains the control force gradient of each node, and generates the node control force change rate;

[0087] The node control force calculation submodule calls the node position data determined by the preliminary terrain advantage area outline map. In this embodiment, the nodes called are node 1 (125.40°E, 43.90°N) and node 2 (125.30°E, 43.80°N). The node space coordinates are converted into local two-dimensional plane coordinates. The node 1 position is (3.6km, 4.5km), the node 2 position is (-4.8km, -7.8km), and the node grid interval parameters are Let it be the distance between two nodes, which is calculated as: , and then measure the nodes through real-time monitoring of on-site equipment, and perform normalization to obtain the control force value (dimensionless), as shown in Table 2:

[0088] Table 2 Node control force monitoring value table

[0089]

[0090] As shown in Table 2, the control force value of node 1 is recorded as , the control force value of node 2 is recorded as Then, the node control force change rate is calculated according to the Laplace operator discrete calculation formula. Taking node 1 as an example, the calling formula is: ;

[0091] Since the number of nodes is small and they are boundary nodes, the boundary condition is set to zero, that is, the control force gradient is: ,

[0092] in, Represents the spatial distribution change rate of the control force of the node at the i-th row and j-th column in the two-dimensional node grid, indicating the spatial variation of the difference between the control force value of this node and the control force values ​​of the adjacent nodes; represents the node control force value at the node position of the i-th row and j-th column, which is obtained through real-time monitoring of the node by field equipment; Represents the node The control force value of the node adjacent to the right (i.e., the adjacent position in the positive x-direction) is obtained by collecting the control force data of the adjacent nodes in real-time monitoring. If the node is at the boundary with no adjacent nodes, the gradient is set to zero according to the Neumann boundary condition. Represents the node The control force value of the node adjacent to the left (i.e., the adjacent position in the negative x direction) is also obtained by monitoring the real-time control force data of the adjacent nodes. If there is no adjacent node, it is handled according to the boundary conditions. Represents the node The control force value of the node located above (i.e., adjacent to the positive y direction) is obtained through data collection of the node real-time monitoring device. If there is no adjacent node at the boundary, the gradient is set to zero. Represents the node The control force value of the node located below (i.e., adjacent to the negative y direction) is obtained by real-time monitoring of the control force data of the node. If the node is located at the boundary and has no adjacent nodes, the gradient is set to zero. It represents the spatial grid distance between adjacent nodes, that is, the spatial distance between a node and its adjacent nodes. The spatial Euclidean distance between the actual nodes is obtained by converting the node position coordinate data into two-dimensional plane coordinates and calculating them.

[0093] The above conditions correspond to discrete nodes, which means that there are no other nodes around the node. Then the node control force gradient is zero, so the control force change rate of node 1 is 0. Similarly, node 2 is also a boundary node, and its control force change rate is also calculated to be 0. The stored node control force change rate values ​​are 0 for both node 1 and node 2.

[0094] The candidate node set generation submodule calls the node control force change rate. According to the numerical value of the node control force change rate, the nodes with larger control force changes are divided into expansion candidate nodes, and the nodes with smaller control force changes are divided into contraction candidate nodes. The nodes are screened according to the change rate threshold to generate the expansion candidate node set and the contraction candidate node set.

[0095] The candidate node set generation submodule calls the above node control force change rate data. The control force change rates of node 1 and node 2 are both 0. The change rate threshold is set by referring to the statistical analysis of the actual control force change rate collection values. The average value of the node control force change rate is calculated as the threshold. The average value calculated by the actual data is , then directly compare the control force change rate of each node with the threshold value 0. The control force change rate value of node 1 is equal to the threshold value 0, and it is determined to be an expansion candidate node. The control force change rate of node 2 is also 0, which is also equal to the threshold value, and is also determined to be an expansion candidate node. No node is demarcated as a contraction candidate node, and the expansion candidate node set (node ​​1, node 2) is generated. The contraction candidate node set is empty, and the node set data is passed to the advantage zone boundary dynamic adjustment module.

[0096] See also Figure 2 ,The dominant zone boundary dynamic adjustment module includes

[0097] The dynamic weight coefficient calculation submodule obtains the set of expansion candidate nodes and the set of contraction candidate nodes, calculates the neighborhood weight of each node based on the topological adjacency relationship, extracts the control force change rate data of the expansion candidate nodes and the contraction candidate nodes, calculates the ratio of the two, and obtains the dynamic weight coefficient. Based on the relationship between the dynamic weight coefficient and the preset threshold, it determines the expansion or contraction direction, updates the node weight value, and generates the dynamic weight coefficient;

[0098] The dynamic weight coefficient calculation submodule calls the expanded candidate node set (node ​​1, node 2) and the contracted candidate node set (empty set). According to the topological adjacency relationship between the nodes, the spatial coordinate data of the nodes is first called. For example, the coordinates of node 1 are (3.6 km, 4.5 km) and the coordinates of node 2 are (-4.8 km, -7.8 km). Then, the neighborhood weight is calculated for each node. The specific calculation process is to measure the spatial distance between each node and its topologically adjacent nodes. The spatial distance between node 1 and node 2 is calculated as: , take the inverse of the distance value as the neighborhood weight, that is, the neighborhood weight between node 1 and node 2 is , then call the control force change rate data of the expansion and contraction candidate nodes (node ​​1 is 0, node 2 is 0). Since the contraction node set is empty, the contraction node change rate takes the minimum positive real number and is set to 0.001 to avoid the denominator being zero. Then calculate the ratio of the control force change rate of the expansion candidate node to the contraction candidate node, that is, The value is recorded as a dynamic weight coefficient, and the preset threshold is determined by historical data statistics. The threshold is set to 1.0. The dynamic weight coefficient value 0 is compared with the threshold 1.0. Since 0 is less than the threshold 1.0, it is determined that the node set needs to be updated in the contraction direction. The dynamic node weight value is updated to the original neighborhood weight multiplied by the dynamic weight coefficient, that is, 0.067x0=0, and the dynamic weight coefficient 0 is generated.

[0099] The expansion area determination submodule determines the expansion or contraction direction according to the relationship between the dynamic weight coefficient and the preset threshold, adjusts the boundary range according to the node control force change rate, identifies and removes the contraction nodes, updates the node set, and generates the expansion or contraction node set.

[0100] The expansion area determination submodule calls the dynamic weight coefficient value 0 and the preset threshold 1.0, directly compares the size relationship between the dynamic weight coefficient and the threshold, and determines that the node set needs to be contracted when the dynamic weight coefficient 0 is less than the threshold 1.0. The node control force change rate data (node 1 and node 2 are both 0) is called, and then the node control force change rate data is compared with the dynamic weight coefficient one by one. The node 1 control force change rate 0 is equal to the dynamic weight coefficient 0, and the node 2 control force change rate 0 is equal to the dynamic weight coefficient 0. According to the rule that the control force change rate is less than or equal to the dynamic weight coefficient, the contraction node is identified. Therefore, node 1 and node 2 are identified as contraction nodes. Then the contraction nodes are deleted from the original set, and the node set is updated to be empty. Finally, the empty expansion node set and the contraction node set are generated.

[0101] The boundary node connection submodule calls the Delaunay triangulation algorithm according to the expansion or contraction node set, performs boundary division based on the remaining boundary nodes, removes invalid edges that exceed the line-of-sight parameter 120%, reconnects the boundary nodes according to the node coordinates and distance relationship, updates the boundary information table, and generates the dynamic updated virtual battlefield terrain advantage control area data graph.

[0102] The boundary node connection submodule calls the empty expansion or contraction node set obtained above. Since the node set is empty, the initial boundary node set is called for processing. The initial node set calls node 1 and node 2, which are (125.40°E, 43.90°N) and (125.30°E, 43.80°N) respectively. The coordinates are converted into two-dimensional plane coordinate data (3.6km, 4.5km) and (-4.8km, -7.8km). Then, based on the node coordinate data, a preliminary triangulation network is established by connecting the position coordinates between the nodes. Since the number of nodes is less than 3, a triangle cannot be formed. Only node 1 and node 2 are connected. For a straight line segment, the distance between node 1 and node 2 is calculated to be 14.9 km. The equipment sight range parameter value is then called as 25 km, and the value of 120% of the sight range is calculated as 25 km × 120% = 30 km. A direct numerical comparison is then performed between the node distances of 14.9 km and 30 km. 14.9 km is less than 30 km, so it is determined to be a valid edge and is not removed. The node coordinate set is updated and a boundary information table is formed. Node 1 (125.40°E, 43.90°N) and node 2 (125.30E, 43.80°N) are used as boundary information. Finally, a dynamically updated data map of the virtual battlefield terrain advantage control area is generated.

[0103] See also Figure 3 A virtual battlefield scene simulation method is provided. The virtual battlefield scene simulation method is performed based on the above-mentioned virtual battlefield scene simulation system and includes the following steps:

[0104] S1: Obtain node elevation data, node visual penetration data, and node surface roughness data, and filter them according to thresholds X, Y, and Z. Nodes with node elevation data greater than threshold X, node visual penetration data less than threshold Y, and node surface roughness less than threshold Z are selected to form a dominant node set.

[0105] S2: Obtain the combat unit coordinates, fire coverage radius parameters, and line of sight parameters, call the dominant node set, use the Euclidean distance function to calculate the spatial straight-line distance, and select nodes whose Euclidean distance is less than the fire coverage radius and within the line of sight parameters to form a preliminary control domain node set;

[0106] S3: Call the preliminary control domain node set, input the node coordinates to the DBSCAN algorithm, cluster and classify the node set based on the node density and minimum number of neighbors parameters, filter out isolated nodes and correct the node boundary characteristics to generate a preliminary terrain advantage area outline map;

[0107] S4: Call the preliminary terrain advantage area outline map, input the node coordinates and adjacency relationship into the Laplace operator model, calculate the control force change rate, extract the nodes with a positive change rate greater than zero to form an expansion candidate node set, and extract the nodes with a negative change rate less than zero to form a contraction candidate node set;

[0108] S5: calling the set of expansion candidate nodes and the set of contraction candidate nodes, using the ratio of the change rate of the control force of the expansion candidate nodes to the change rate of the control force of the contraction candidate nodes as a dynamic weight coefficient, classifying the nodes according to the relationship between the dynamic weight coefficient and the preset expansion and contraction judgment threshold, and generating a data map of the virtual battlefield terrain advantage control area;

[0109] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A virtual battlefield scene simulation system, characterized in that: The system comprises: The terrain node identification module is used to obtain node elevation data, node visual penetration rate, and node surface roughness. Nodes with node elevation data above threshold X, node visual penetration rate below threshold Y, and node surface roughness below threshold Z are selected as dominant nodes. This is then formed into a dominant node set and transmitted to the combat unit control radius extraction module. Node visual penetration rate refers to the ability of the node's surface to transmit optical or radar signals. A combat unit control radius extraction module is used to obtain the combat unit coordinates, fire coverage radius, and line of sight parameters, call the advantageous node set, calculate the distance from the advantageous node to the combat unit based on the Euclidean distance, form a preliminary control domain node set, and pass it to the terrain advantageous area preliminary delineation module; A preliminary delineation module for terrain advantage areas is used to call the preliminary control domain node set, input the node coordinates into the DBSCAN algorithm, cluster and classify the node set based on the node density and minimum number of neighbors parameters, filter out isolated nodes and correct node boundary characteristics, generate a preliminary terrain advantage area outline map, and pass it to the control force gradient evolution analysis module; The control force gradient evolution analysis module is used to call the preliminary terrain advantage area contour map to form an expansion candidate node set and a contraction candidate node set, and transmit them to the advantage area boundary dynamic adjustment module, including: The node control force calculation submodule calls the node data in the preliminary terrain advantage area outline map, calculates the control force change rate of each node based on the Laplace operator, uses the node spatial coordinate data and the density information of the adjacent nodes, calculates the influence change between the nodes, obtains the control force gradient of each node, and generates the node control force change rate; The candidate node set generation submodule calls the node control force change rate, and according to the numerical value of the node control force change rate, divides the nodes whose control force change is greater than or equal to a preset threshold into expansion candidate nodes, and the nodes whose control force change is less than the preset threshold into contraction candidate nodes, and screens them according to the change rate threshold to generate an expansion candidate node set and a contraction candidate node set; A module for dynamically adjusting the boundary of an advantageous zone is configured to call the set of expansion candidate nodes and the set of contraction candidate nodes, calculate a dynamic weight coefficient based on a topological adjacency relationship, and define the dynamic weight coefficient as the ratio of the rate of change of control power of the expansion candidate nodes to the rate of change of control power of the contraction candidate nodes. Based on a comparison of the dynamic weight coefficient with a preset threshold, the module determines the expansion or contraction direction of the boundary of the advantageous zone, determines the expansion area, and determines the contraction nodes to be removed. A Delaunay triangulation algorithm is used to connect the remaining boundary nodes after adjustment. The specific implementation condition of the Delaunay triangulation algorithm is to remove triangle edges whose side length exceeds 1.2 times the line-of-sight parameter baseline value. The line-of-sight parameter baseline value is defined as the maximum detection range of the combat unit's line-of-sight sensor, expressed in kilometers, and generate a dynamically updated data map of the virtual battlefield terrain advantage control zone. The dynamically updated virtual battlefield terrain advantage control area data map includes expanding and updating boundaries, eliminating invalid areas, and control area outlines.

2. The virtual battlefield scene simulation system according to claim 1, characterized in that: The threshold value X is determined based on the percentile calculation of the regional average elevation, and the threshold value Y is set based on the inversion result of the ground object shielding coefficient in the remote sensing data, wherein the threshold value Y represents the visible penetration rate of the remote sensing data area, specifically the proportion of radiation energy that is not blocked after the remote sensing field of view penetrates the ground object, and the value is a dimensionless ratio between 0 and 1. The threshold value Z is set based on the ground reflectivity and the micro-topography change rate extraction value, wherein the threshold value Z represents the surface undulation condition in the remote sensing area, and is defined as the total amount of elevation change within a unit area, and the unit is meter / kilometer squared; The units of the combat unit coordinates and the advantageous node set are both kilometers; The DBSCAN algorithm inputs the node spatial coordinates into the DBSCAN clustering algorithm for processing, sets the minimum number of samples in the neighborhood MinPts to 5, sets the neighborhood radius Eps to the mean of the Euclidean distances between nodes in the node set, divides the node density according to the clustering output results, and defines the distance threshold T as the 90% quantile of the Euclidean distance set between the cluster center and each cluster node to which it belongs, and uses this as the basis for identifying isolated nodes, that is, when the Euclidean distance between a node and its nearest neighbor cluster center is greater than the distance threshold T, the node is identified as an isolated node; The advantageous node set includes node elevation data, node visual penetration rate, and node surface roughness; the preliminary control domain node set includes node standardized coordinates, node Euclidean distance, and node combat applicability identification; the preliminary terrain advantageous area outline map includes node-dense areas, isolated node locations, and preliminary terrain boundaries; the expansion candidate node set and contraction candidate node set include node control force change rate, regional expansion potential, and regional contraction risk.

3. The virtual battlefield scene simulation system according to claim 2, characterized in that: The terrain node identification module includes: The node elevation screening submodule collects node elevation data, sorts and determines the node elevation threshold using percentile statistics, compares each node elevation value with the elevation threshold one by one, screens out nodes that exceed the elevation threshold, and generates a node elevation screening interval; The node penetration rate determination submodule calls the ground object type and distribution characteristics in the remote sensing data, inverts the ground object shielding coefficient of the node area, calculates the node visible penetration rate through the coefficient value, sets the node visible penetration rate threshold, compares the penetration rate of each node with the node visible penetration rate threshold one by one, selects the nodes below the node visible penetration rate threshold, and generates the node penetration rate determination coefficient; The surface roughness identification submodule monitors the ground spectral reflectance intensity of the node area, combines the regional micro-topography slope change rate, calculates the node surface roughness, sets the node surface roughness threshold, compares the roughness value with the node surface roughness threshold one by one, screens out the nodes that do not reach the node surface roughness threshold, and generates a quantitative index of surface roughness; The dominant node set construction submodule calls the three values ​​of the node elevation screening interval, the node penetration rate determination coefficient and the surface roughness quantitative index, and through data cross-comparison, screens the nodes that meet all the above conditions at the same time to establish the dominant node set.

4. The virtual battlefield scene simulation system according to claim 3, characterized in that: The combat unit control radius extraction module includes: The combat unit parameter acquisition submodule obtains the position coordinates of the combat unit, collects fire coverage radius data, and obtains the sight range parameters based on the equipment characteristics. By summarizing the tactical attributes of the combat unit, it obtains and stores the values ​​of various attributes to generate the quantitative parameters of the combat unit attributes. The node distance calculation submodule calls the node coordinates of the dominant node set, calculates the spatial straight-line distance between each node and the combat unit one by one based on the coordinates and fire coverage radius data in the combat unit attribute quantification parameters using the Euclidean distance calculation method, sorts these distances, and generates the node-to-combat unit distance value; The control domain node determination submodule calls the distance value from the node to the combat unit, compares the distance between each node and the combat unit according to the fire coverage radius parameter, screens out the nodes that meet the coverage radius conditions, and establishes a preliminary control domain node set.

5. The virtual battlefield scene simulation system according to claim 4, characterized in that: The module for preliminary delineation of terrain advantage areas includes: The node density clustering submodule calls the preliminary control domain node set, collects the spatial coordinates of each node, sets the neighborhood radius Eps to the average Euclidean distance between the nodes in the set, calls the DBSCAN clustering algorithm to process the node data with the number of samples in the neighborhood MinPts as 5, calculates the node density distribution based on the node density clustering results, and generates a node density level value; The isolated node identification submodule calls the node density level value, measures the Euclidean distance between each node and the corresponding cluster center spatial position, sets a distance threshold as a judgment reference value, compares the distance value between the node and the cluster center with the distance threshold, determines and marks the nodes that exceed the distance threshold, and generates an isolated node identification set; The dominant area outline drawing submodule calls the node density level value and the isolated node identification set, removes the isolated node data, calculates the spatial distribution boundary coordinates of the remaining nodes, outlines the node distribution boundary, and establishes a preliminary terrain dominant area outline map.

6. The virtual battlefield scene simulation system according to claim 1, characterized in that: The dominant zone boundary dynamic adjustment module includes: The dynamic weight coefficient calculation submodule obtains the set of expansion candidate nodes and the set of contraction candidate nodes, calculates the neighborhood weight of each node based on the topological adjacency relationship, extracts the control force change rate data of the expansion candidate nodes and the contraction candidate nodes, calculates the ratio of the two, obtains the dynamic weight coefficient, determines the expansion or contraction direction based on the relationship between the dynamic weight coefficient and the preset threshold, updates the node weight value, and generates the dynamic weight coefficient; The expansion area determination submodule determines the expansion or contraction direction based on the relationship between the dynamic weight coefficient and the preset threshold, adjusts the boundary range according to the node control force change rate, identifies and eliminates contraction nodes, updates the node set, and generates an expansion or contraction node set; The boundary node connection submodule calls the Delaunay triangulation algorithm according to the expanded or contracted node set, performs boundary triangulation based on the remaining boundary nodes, eliminates invalid edges that exceed 120% of the line of sight parameter, reconnects the boundary nodes according to the node coordinates and distance relationship, updates the boundary information table, and generates a dynamically updated virtual battlefield terrain advantage control area data map.

7. A virtual battlefield scene simulation method, characterized in that: The method is used to implement the virtual battlefield scene simulation system according to any one of claims 1 to 6, comprising the following steps: S1: Obtain node elevation data, node visual penetration data, and node surface roughness data, and filter them according to thresholds X, Y, and Z. Nodes with node elevation data greater than threshold X, node visual penetration data less than threshold Y, and node surface roughness less than threshold Z are selected to form a dominant node set. S2: Obtain the coordinates of the combat unit, the fire coverage radius parameter, and the line of sight parameter, call the advantageous node set, calculate the spatial straight-line distance using the Euclidean distance function, and select nodes whose Euclidean distance is less than the fire coverage radius and within the line of sight parameter to form a preliminary control domain node set; S3: calling the preliminary control domain node set, inputting the node coordinates into the DBSCAN algorithm, clustering and classifying the node set based on the node density and minimum number of neighbors parameters, filtering out isolated nodes and correcting node boundary characteristics, and generating a preliminary terrain advantage area outline map; S4: calling the preliminary terrain advantage area outline map, inputting node coordinates and adjacency relationships into the Laplace operator model, calculating the control force change rate, extracting nodes with a positive change rate greater than or equal to zero to form an expansion candidate node set, and extracting nodes with a negative change rate less than zero to form a contraction candidate node set; S5: Call the expansion candidate node set and the contraction candidate node set, use the ratio of the expansion candidate node control force change rate to the contraction candidate node control force change rate as the dynamic weight coefficient, classify the nodes according to the relationship between the dynamic weight coefficient and the preset expansion and contraction judgment threshold, and generate a virtual battlefield terrain advantage control area data map.

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