High-precision hexagonal grid long-range path-finding method based on dynamic empowerment in war chess

Abstract

The invention discloses a dynamic empowerment-based high-precision hexagonal grid long-range way-finding method in war chess, and particularly relates to the technical field of war chess deduction, comprising the following steps: matching regular hexagonal grids by using a Cube coordinate system, and obtaining battlefield dynamic change data of each regular hexagonal grid, respectively evaluating the threat degree and the obstacle degree of the regular hexagonal grid, and dividing nodes corresponding to the regular hexagonal grid into normal nodes and obstacle nodes based on an evaluation result; performing path weight adjustment on a path formed by two adjacent nodes, and searching an optimal path from a starting point to an end point according to a preset long-range path-finding algorithm; according to the method, the dynamic change data of the battlefield is collected in real time, and the threat index and the terrain arrival index of the node are dynamically adjusted, so that the path weight can reflect the change of the environment of the battlefield in time, the real-time performance and the dynamic adaptability of the path decision are improved, and the complicated and changeable battlefield environment is effectively coped with.

Description

Technical Field

[0001] The present invention relates to the technical field of war game simulation, and more specifically, to a high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games. Background Art

[0002] With the development of information warfare and intelligent combat systems, war game simulation systems have been widely used in military training and strategy formulation as an important tool to assist command decision-making and campaign planning. Existing war game systems usually rely on regular terrain modeling and fixed path planning algorithms, which are difficult to meet the needs of high-precision path calculation in dynamic battlefield environments. Most existing path planning methods perform path search based on static weights. Such methods have the following shortcomings: Path calculation lacks real-time dynamics, making it difficult to adjust weights according to the rapid changes in the game situation and battlefield environment under the war game simulation state, resulting in delayed path planning results and affecting decision-making effects; there is a lack of a detailed and comprehensive evaluation mechanism for multi-dimensional factors such as threats from the opposing party and terrain obstacles in the war game simulation environment, and path planning cannot accurately reflect the actual difficulty of passage and game risks; there is a lack of a refined resource consumption model, and it is impossible to comprehensively evaluate resource consumption during the path planning process, affecting the breakthrough and advancement strategies of simulated combat units; The existing path planning methods of wargame systems can no longer meet the needs of high-precision and dynamic path decision-making in modern warfare. Therefore, it is urgent to propose a dynamic path optimization method that comprehensively considers battlefield situation, terrain factors and resource consumption to improve the intelligence level and tactical simulation accuracy of wargame systems. Therefore, the present invention proposes a high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in wargames to solve the above problems. Summary of the invention

[0003] To achieve the above object, the present invention provides the following technical solutions: The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games includes the following steps: Use the Cube coordinate system to match the regular hexagonal grid, establish an accurate mapping between world coordinates and grid coordinates, and obtain battlefield dynamic change data for each regular hexagonal grid; According to the battlefield dynamic change data, the threat level and obstacle level of the regular hexagonal grid are evaluated respectively, and the nodes corresponding to the regular hexagonal grid are divided into normal nodes and obstacle nodes based on the evaluation results; Based on the division results, the path weight of the path formed by two adjacent nodes is adjusted, and the adjusted path weight is placed in the Cube coordinate system. The optimal path from the start point to the end point is found according to the preset long-range pathfinding algorithm.

[0004] In a preferred embodiment, before adjusting the weight of the path formed by two adjacent nodes, the battlefield dynamic change data corresponding to each of the two adjacent nodes is compared with the historical data at the time of the last adjustment, the battlefield dynamic change data is converted into the latest vector, and the historical data at the time of the last adjustment is converted into a historical vector. The latest vector and the historical vector are compared and analyzed. If the comparison and analysis results of the two adjacent nodes are both less than or equal to the preset deviation threshold, there is no need to evaluate the threat level and obstacle level of the two nodes, and the weight of the path formed by the two nodes remains the result of the last adjustment. If the comparison and analysis results of the two adjacent nodes are not less than or equal to the preset deviation threshold, it is necessary to re-evaluate the threat level and obstacle level of the nodes greater than the preset deviation threshold, and the weight of the path formed by the two nodes is adjusted.

[0005] In a preferred embodiment, the comparison analysis refers to calculating the similarity or Euclidean distance between the latest vector and the historical vector.

[0006] In a preferred embodiment, the evaluation result refers to: The threat level of the regular hexagonal grid is evaluated to obtain the enemy threat index used to measure the threat level of the current node to the enemy; The obstacle degree of the regular hexagonal grid is evaluated to obtain the terrain accessibility index, which is used to measure the degree of obstruction of the terrain itself to traffic.

[0007] In a preferred embodiment, the logic for obtaining the enemy threat index is: Obtain the threat heat map and terrain map from the battlefield dynamic change data, and calculate the threat heat value based on the threat heat map: ; Represents the basic threat strength value of enemy unit i, represents the state coefficient of enemy unit i, Indicates the distance between the center point of the current node and enemy unit i, represents the preset non-zero distance attenuation coefficient, n represents the total type value of the enemy unit, Indicates the threat heat value; Calculate the exposure probability value based on the topographic map: ; Indicates terrain type The coverage rate, Indicates terrain type The basic exposure factor, It represents the maximum value of the corresponding reconnaissance efficiency coefficient among all enemy units, and m represents the total number of terrain types. Indicates the preset impact coefficient corresponding to the current weather. represents the exposure probability value; The calculation formula of enemy threat index is: ; Indicates the strike coefficient, which is used to measure the enemy's enthusiasm for striking exposed targets. Indicates the enemy threat index.

[0008] In a preferred embodiment, the logic for obtaining the strike coefficient is: Divide the most recent optimal path length from the current starting point to the end point by the preset campaign range value corresponding to the number of all hexagonal grids to obtain the campaign advancement stage value , and then calculate the enemy's firepower capability value: ; Indicates the power value of the weapon carried by enemy unit i, represents the range of the weapons carried by enemy unit i, represents the accuracy of the weapons carried by enemy unit i, Indicates the maximum standard value of the range of the carried weapon. Indicates the enemy's firepower capability value; Calculate the product of the campaign advancement phase value and the enemy's firepower capability value to obtain the strike coefficient.

[0009] In a preferred embodiment, the logic for obtaining the terrain accessibility index is: The current terrain vector of the corresponding node of the regular hexagonal grid is obtained, and then the Euclidean distance between the current terrain vector and the preset standard through vector is calculated to obtain the terrain accessibility index.

[0010] In a preferred embodiment, dividing the nodes corresponding to the regular hexagonal grid into normal nodes and obstacle nodes means: First, weighted sum of terrain accessibility index and enemy threat index is performed to obtain the comprehensive risk value. , and then the comprehensive risk value Compared with the preset risk threshold For comparison, if the comprehensive risk value Greater than the preset risk threshold , then it is divided into obstacle nodes. If the comprehensive risk value Less than or equal to the preset risk threshold , it is classified as a normal node.

[0011] In a preferred embodiment, when the node division type result corresponding to the regular hexagonal grid is an obstacle node, the breakthrough resource consumption is calculated: ; Indicates the preset resource consumption per unit. Indicates breakthrough resource consumption; When the node division type result corresponding to the regular hexagonal grid is a normal node, the default value of the breakthrough resource consumption is zero.

[0012] In a preferred embodiment, adjusting the path weight of a path formed by two adjacent nodes refers to: Get the comprehensive risk value of two adjacent nodes and breakthrough resource consumption , and then sum them together and divide them by the preset path normalization value to get the path weight of the path composed of two adjacent nodes.

[0013] Technical effects and advantages of the present invention: The present invention collects battlefield dynamic change data in real time, dynamically adjusts the threat index and terrain accessibility index of the node, so that the path weight can timely reflect the changes in the battlefield environment and enemy situation. It intelligently determines whether re-evaluation is needed based on the change amplitude of the node status, avoids redundant calculations, improves the real-time and dynamic adaptability of path decision-making, and can effectively cope with complex and changeable battlefield environments.

[0014] The present invention adopts a dual evaluation model of enemy threat index and terrain accessibility index to comprehensively quantify the risk level of each node. The threat index comprehensively considers factors such as enemy strike capability, enemy situation and target exposure probability, and dynamically reflects the intensity of threat to the node; the terrain accessibility index is calculated by the Euclidean distance between the terrain feature vector and the standard through vector, and accurately measures the difficulty of node passage. This multi-dimensional evaluation mechanism improves the scientificity and accuracy of path weight evaluation.

[0015] By comparing the historical data and the latest data of adjacent nodes, node evaluation and path weight adjustment are triggered only when the node change exceeds the preset deviation threshold, avoiding unnecessary repeated calculations. This greatly reduces the system's computing burden, improves the computing efficiency of the long-range pathfinding algorithm in a large-scale battlefield environment, and ensures that the wargame system still has efficient pathfinding capabilities in a highly dynamic battlefield situation.

[0016] The present invention introduces the campaign advancement stage value and the enemy's firepower capability value, dynamically generates the strike coefficient, and realizes the linkage between path planning and campaign rhythm. In different campaign stages (such as early, mid-term, stalemate, and decisive battle), the intelligentization and phased optimization of tactical actions can be achieved by adjusting the node evaluation criteria and path selection weights. According to the comprehensive risk value and breakthrough resource consumption, the path travel cost is dynamically evaluated to achieve refined management of breakthrough resource scheduling and ensure efficient utilization and reasonable allocation of combat resources.

[0017] The present invention establishes a breakthrough resource consumption model based on comprehensive risk value, and dynamically calculates the breakthrough cost of the obstacle node according to the node risk level and the resource consumption coefficient preset by the system. The breakthrough consumption is combined with the path planning logic, so that the war game system can not only intelligently avoid high-risk areas, but also evaluate the resource cost and tactical benefits of breakthrough actions when necessary, realize the coordinated optimization of path selection and resource management, and enhance the credibility of tactical decision-making and actual combat simulation effect. The present invention solves the problems of low path search efficiency and delayed decision-making in large-scale battlefield environments through a dynamic path weight adjustment mechanism combined with an efficient long-range path-finding algorithm (such as a heuristic search algorithm, a dynamic replanning algorithm, etc.), and can realize real-time path search and adjustment, meeting the tactical action planning requirements in long-distance and large-scale war game simulation scenarios.

[0018] Through the dynamic adjustment of risk value and path weight normalization mechanism, while ensuring the optimality of the path, the tactical action confusion caused by frequent path jitters is avoided, and the path planning maintains continuity and stability, which meets the tactical requirements for continuous actions such as marching, infiltration, and advancement of troops in real combat missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a schematic diagram of the principle of the high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in the war game of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Reference Figure 1 The following embodiments are obtained: Example

[0022] With the continuous development of information warfare and intelligent warfare, war game simulation systems have become an important technical support tool for military command decision-making, campaign planning and tactical exercises. The war game system realizes dynamic simulation and intelligent evaluation of complex battlefield situations by modeling battlefield environment, enemy and friendly situation and troop action plans. It is a key platform for auxiliary decision-making, training exercises and tactical research. However, the path planning modules of most current war game simulation systems generally adopt rule-simplified grid models or traditional path algorithms (such as A* algorithm, Dijkstra algorithm, etc.), which have the following technical bottlenecks: Slow response to dynamic situations: Traditional path planning algorithms are unable to perceive enemy situation changes and environmental evolution in a dynamic battlefield environment in a timely manner, and lack the ability to dynamically adapt to real-time battlefield situations, which affects the intelligent decision-making ability of the war game system.

[0023] Rough assessment of threat and obstacle factors: Existing models mostly use static weight settings to assess enemy threats and terrain obstacles. They lack a dynamic and sophisticated comprehensive assessment method and cannot accurately reflect the true impact of the battlefield environment on path selection.

[0024] The present invention takes dynamic weighting, high-precision hexagonal grid modeling and long-range path optimization algorithm as the core, breaks through the technical bottlenecks of traditional wargame path planning in dynamic environment response, threat perception and path calculation efficiency, and provides a more intelligent, precise and real-time path decision support method for the wargame system, which is specifically reflected in the following aspects: Improve the battlefield situation awareness capability of the wargame system: By acquiring real-time data on battlefield dynamic changes, path planning can be achieved through comprehensive perception and dynamic analysis of key tactical elements such as enemy threats, terrain obstacles, and campaign advancement.

[0025] Enhance the intelligence and flexibility of path planning: Based on dynamic threat assessment and obstacle degree calculation, realize real-time dynamic classification and weight adjustment of path nodes, effectively respond to rapid changes in complex battlefield environments, and improve the intelligence level of war game simulation.

[0026] Improve the computational efficiency of long-range path planning: Adopt node vector dynamic comparison and deviation threshold judgment mechanism to avoid unnecessary repeated calculation of path weights, greatly reduce the computational load of long-range path finding, and improve system operation efficiency and response speed.

[0027] Achieve unified optimization of tactical-level path planning: support the dynamic parameter adjustment of the strike coefficient during the campaign advancement phase and the enemy's firepower capability, achieve adaptive optimization of tactical assault paths and detour paths, and meet the needs of multi-level war game simulations.

[0028] Strengthen the authenticity of system combat simulation: high-precision hexagonal grid and Cube coordinate mapping ensure the rigor of the spatial model, and the comprehensive evaluation model of threat index and accessibility index improves the credibility and scientificity of battlefield simulation.

[0029] The present invention aims to solve the problem that the path planning algorithm in the existing war game simulation system is difficult to achieve high precision, high efficiency and dynamic adaptability, and provide a high-precision hexagonal grid long-range pathfinding method based on dynamic weighting. The specific objectives are as follows: Achieve high-precision path modeling: Establish accurate mapping between world coordinates and high-precision hexagonal grids through the Cube coordinate system, realize tactical-level (meter-level) path node division, and improve the accuracy of path data modeling.

[0030] Dynamically perceive battlefield situation and adaptively adjust path weights: Based on the battlefield dynamic change data, conduct threat and obstacle assessments on each node, dynamically divide normal nodes and obstacle nodes, and realize dynamic real-time adjustment of path weights.

[0031] Optimize the calculation logic of long-range path planning; avoid frequent repeated calculations through node historical data comparison and deviation threshold mechanism, optimize the long-range path node update logic, and significantly improve path planning efficiency.

[0032] Implement a comprehensive assessment model for enemy threats and obstacles: introduce threat heat maps, exposure probability, strike coefficient and other factors to dynamically calculate the enemy threat index. At the same time, combine the Euclidean distance between the terrain vector and the standard through vector to accurately measure the terrain accessibility index and implement comprehensive risk calculation.

[0033] Taking into account both tactical resource consumption and risk assessment: The node breakthrough resource consumption model is combined with the comprehensive risk value of the path to achieve a balance between resource consumption and tactical risk in the path selection process, and assist the war game system in making differentiated path selections under different tactical tasks.

[0034] The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games includes the following steps: The Cube coordinate system is used to match the regular hexagonal grid, establish an accurate mapping between the world coordinates and the grid coordinates, and obtain the battlefield dynamic change data of each regular hexagonal grid; the Cube coordinate system is a coordinate system specially designed for regular hexagonal grid maps, which abstracts the hexagonal grid into three axes of the cube, namely: Q axis, R axis and S axis. These three axes meet a core constraint: Q+R+S=0. The Cube coordinate system can effectively solve the complex adjacency and pathfinding problems of regular hexagonal grids in two-dimensional space, and ensure the stability and consistency of the spatial topological structure. The accurate mapping of world coordinates and grid coordinates realizes the seamless connection from real geographic space to virtual war chess deduction map, so that the model has the ability to simulate the real battlefield environment. Through the division of high-precision hexagonal grids (such as 1 meter and 5 meter levels), the fine path control of tactical units (infantry, tanks, drones, etc.) is realized to meet the needs of high-precision combat simulation. Dynamically collect battlefield change data (enemy position, reconnaissance status, fire strike area, terrain changes, etc.) for each grid node, laying a data foundation and ensuring the real-time and tactical responsiveness of path planning. Through the real-time collection and synchronization of dynamically changing data, the real-time application of battlefield situation information in path decision-making can be realized, thereby improving the actual combat simulation effect of war game simulation.

[0035] According to the battlefield dynamic change data, the threat level and obstacle level of the regular hexagonal grid are evaluated respectively, and the nodes corresponding to the regular hexagonal grid are divided into normal nodes and obstacle nodes based on the evaluation results; through the evaluation of threat thermal value, exposure probability, etc., the enemy's strike capability and enemy situation changes are dynamically reflected, so that the war game system has environmental perception and dynamic decision-making capabilities. The nodes are divided into "normal nodes" and "obstacle nodes", and the pass area and restricted area model under the battlefield environment is constructed to reduce the search space of path planning and improve the calculation efficiency. The node division mechanism is integrated with threat assessment and terrain obstacle analysis, so that the path decision results are more in line with tactical logic and battlefield reality, avoiding the problem of "theoretical path" and "tactical path" being out of touch. After clarifying the obstacle nodes, the resource consumption required to break through the obstacles can be further calculated to assist resource management and tactical priority decision-making, and improve the simulation accuracy of tactical advancement and resource consumption of the war game system.

[0036] Based on the division results, the path weight of the path formed by two adjacent nodes is adjusted, and the adjusted path weight is placed in the Cube coordinate system. The optimal path from the start point to the end point is found according to the preset long-range pathfinding algorithm. Based on the node classification results (normal / obstacle) and dynamic indicators such as the node's comprehensive risk value and breakthrough resource consumption, the path weight is adjusted in real time to make the path selection process more flexible and dynamic, in line with the changes in the battlefield situation. The threat index, accessibility index and breakthrough resource consumption of the node are comprehensively considered and converted into path weight values, so that the path decision comprehensively considers "traffic safety" and "resource economy", which meets the complex and diverse needs of the war game system's combat tasks. The dynamic weight is placed in the Cube coordinate system to ensure the structural rigor and pathfinding efficiency of the path calculation process, which is especially suitable for long-range path rapid planning in complex terrain and multi-threat environments. It supports the flexible application of long-range pathfinding algorithms in different tactical goals and tasks, and can realize comprehensive optimal path planning for large-scale, multi-node and multi-path. By adjusting the path weight and dynamic pathfinding in real time, path optimization decisions at different stages of the campaign are realized, and the depth of campaign deduction and tactical simulation of the war game system is improved.

[0037] The latest optimal path length from the current starting point to the end point is divided by the preset campaign range value corresponding to the number of all hexagonal grids to obtain the campaign advancement stage value. The calculated campaign advancement stage value (continuous value between 0-1) can be used to clearly divide the different stages of campaign advancement. For example, 0.00-0.2 is the initial stage (opening trial), the campaign has just started, the enemy situation is not clear, and reconnaissance and trial are the main ones. 0.21-0.40 is the middle stage (stable advancement), the offensive and defensive situation gradually becomes clear, and the enemy and us launch a main offensive or defensive situation. 0.41-0.60 is the stalemate period (stalemate confrontation), the main forces of both sides enter high-intensity combat, the situation is complicated, and the battle line changes frequently. 0.61-0.80 is the late stage (advantage accumulation), one side gradually takes the initiative or breaks through, and the outcome of the campaign begins to emerge. 0.81-1.00 is the decisive battle period (closing stage), launching a general offensive or organizing the final defense, and the campaign process enters the final decisive moment.

[0038] Based on this, a further specific solution content of the present invention is proposed: the impact of different battle advancement stages on path planning, for example, in the early and middle stages, the long-range pathfinding algorithm strategy is to reduce consumption, while in the decisive battle stage, a "risk-ignoring" pathfinding strategy can be adopted, that is: priority is given to the shortest path (straight charge, quick target), ignoring the comprehensive risk value (the threat index and obstacle index influence tend to be zero), allowing obstacle nodes to be ignored (even obstacle areas are forced to break through), resource consumption and loss are no longer prioritized (breakthrough consumption priority is reduced), normal state: obstacle nodes are not passable or resources are blocked, and finally the route with the least resource consumption is selected, decisive battle stage: all nodes are passable, obstacle nodes do not need to evaluate resource consumption or obstacle breaking costs, and forced passage (war chess logic) is equivalent to the "forced attack / fighting to capture" command.

[0039] Before adjusting the weight of the path formed by two adjacent nodes, first compare the battlefield dynamic change data corresponding to each of the two adjacent nodes with the historical data at the last adjustment, convert the battlefield dynamic change data into the latest vector, convert the historical data at the last adjustment into the historical vector, and compare and analyze the latest vector and the historical vector. If the comparison and analysis results of the two adjacent nodes are both less than or equal to the preset deviation threshold, there is no need to evaluate the threat level and obstacle level of the two nodes, and the weight of the path formed by the two nodes remains the result of the last adjustment. If the comparison and analysis results of the two adjacent nodes are not less than or equal to the preset deviation threshold, it is necessary to re-evaluate the threat level and obstacle level of the nodes greater than the preset deviation threshold, and adjust the weight of the path formed by the two nodes. Comparison analysis refers to calculating the similarity or Euclidean distance between the latest vector and the historical vector. The similarity can be cosine similarity or other similarity calculation methods used to measure the comprehensive change of two vectors.

[0040] Most nodes are in a stable or slowly changing stage in a dynamic battlefield. Through similarity detection or distance comparison, "nodes with minor changes" can be quickly identified. There is no need to repeat complex threat assessment and obstacle assessment calculations. Only high-overhead evaluation logic is executed for "nodes that actually have major changes", saving CPU / GPU resources and improving the iteration efficiency of the path-finding algorithm. This is especially important for large-scale battle maps and long-range path planning, ensuring the real-time and high responsiveness of the system. Through vector comparison, it is possible to more intelligently identify which nodes become "key influencing factors" due to battlefield situation fluctuations, ensuring that path weight adjustments are only performed for "important change nodes", avoiding frequent jitters in path adjustments caused by "noise" or "small fluctuations". Unnecessary and frequent path adjustments may cause troop movement command oscillations, affecting the continuity and tactical credibility of troop behavior in war game simulations. This mechanism ensures that path adjustments occur when nodes with real tactical value change, maintaining the tactical rationality of war game simulation actions.

[0041] The node situation change is measured by cosine similarity or Euclidean distance, simulating the perception and response mechanism of combat units or command systems to battlefield information, which conforms to the rhythm of information evaluation and action response in the command decision-making process, that is, tactical decisions will not be adjusted immediately due to minor changes, but will respond based on "major changes in the battlefield". Through the deviation threshold adjustment, the system's response sensitivity to battlefield changes can be controlled. The threshold can be set according to the campaign stage (such as initial exploration, anxious advancement, and decisive battle sprint). In the early stage, sensitive (low threshold) is used to emphasize detailed perception, and loose (high threshold) is used in decisive battles, giving priority to efficiency and rapid response. The node state vector mechanism is highly scalable. The dynamic change data of each node is uniformly modeled through standardized vectors, supporting flexible expansion in different dimensions (terrain, threats, obstacles, weather, firepower coverage, etc.), and providing a unified evaluation interface for the subsequent introduction of more dynamic tactical factors (such as psychological warfare, electric warfare, and AI reconnaissance situation).

[0042] Vector comparison algorithms (cosine similarity, Euclidean distance, Manhattan distance, etc.) are replaceable, and different algorithms are optimized for different arms and tactical scenarios to improve algorithm modularization and componentization. The node weight pre-adjustment screening mechanism = resource scheduling control valve controls the frequency and range of node status evaluation and path weight adjustment, saving system resources for "more meaningful battlefield dynamic response", improving the processing capabilities of high-priority tasks such as campaign advancement, tactical breakthroughs, and resource allocation. The node information judgment mechanism ensures path stability and rationality of changes, avoiding maneuver confusion caused by frequent path changes, and meets the "decision-making stability" requirements in actual combat.

[0043] The evaluation results refer to: The threat level of the regular hexagonal grid is evaluated to obtain the enemy threat index, which is used to measure the threat level of the current node to the enemy. The risk level of the node in the enemy's firepower system can be measured to reflect whether the current node is within the enemy's effective strike range, reconnaissance range or control range. The higher the index, the easier it is to be exposed to the enemy's high-intensity strike threat. The lower the index, the less likely the enemy is to effectively strike or affect the node. Nodes with high threat indexes are given priority in path planning, which may also affect the decision of whether to "risk passing". The enemy threat index of the node is updated in real time to ensure that the war game system has dynamic situation awareness capabilities. Enemy mobilization, firepower deployment adjustments, electronic warfare impacts, etc. can all be directly fed back to the enemy threat index.

[0044] The obstacle degree of the regular hexagonal grid is evaluated to obtain the terrain accessibility index used to measure the degree of obstruction of the terrain itself to traffic, which reflects the complexity of the node's own terrain, surface conditions and traffic conditions. The higher the terrain accessibility index, the more difficult it is to pass (mountains, swamps, obstacle-intensive areas, etc.), and the lower the terrain accessibility index, the smoother the traffic (plains, roads, etc.), which directly affects the evaluation of the traffic cost of path planning. Nodes with high terrain accessibility index will have their priority reduced in path planning or be treated as obstacle nodes. In addition, it can dynamically reflect the impact of battlefield environment changes on terrain. Dynamic events such as war damage, weather conditions, and tactical fortifications affect terrain accessibility.

[0045] The logic for obtaining the enemy threat index is as follows: Obtain the threat heat map and terrain map from the battlefield dynamic change data, and calculate the threat heat value based on the threat heat map: ; Indicates the basic threat intensity value of enemy unit i, which represents the threat capability of enemy weapon systems or forces to the node. It is usually a preset value. It represents the state coefficient of enemy unit i, reflecting the action state of the enemy unit. It is usually a preset value. For example, the patrol state takes a value of 1, and the transfer state takes a value of 0.8. Indicates the distance between the center point of the current node and enemy unit i. The spatial distance determines the degree of attack threat. The farther the distance, the lower the threat, which conforms to the law of firepower influence range. Represents the preset non-zero distance attenuation coefficient, which determines the speed at which the threat decreases with distance. n represents the total type value of the enemy unit. Represents the threat heat value; THM means that at the current node, the firepower threat of all enemy units to the node is superimposed, reflecting the threat degree of the enemy's force distribution, firepower intensity, and situation behavior to the node. It is calculated using a combination of distance attenuation model + enemy state weight adjustment + basic firepower model.

[0046] Calculate the exposure probability value based on the topographic map: ; Indicates terrain type The coverage rate, the proportion of different terrains in the current node, reflects the impact of mixed terrain environments (such as jungle + swamp and other mixed terrains) on unit concealment. Indicates terrain type The basic exposure coefficient is the shielding effect of different terrains. Plains have high exposure, while forests or trenches have low exposure. These are usually preset values. It indicates the maximum value of the corresponding reconnaissance efficiency coefficient among all enemy units, indicating the strength of the enemy's reconnaissance capability in the area. Radars, drones, etc. can increase the exposure risk, reflecting the dynamic control of the enemy's situation on the node risk. For example, the visual value is 1, and the drone value is 2. m indicates the total number of terrain types. Indicates the preset impact coefficient corresponding to the current weather. Different weather has a significant impact on reconnaissance and strike. Foggy and rainy days reduce the probability of exposure, while clear weather increases the risk. For example, the basic value for rainy days is 1, and the basic value for foggy days is 0.8. In fact, the preset impact coefficient corresponding to the current weather is obtained by multiplying the basic value corresponding to the weather by the visibility in the current environment. . It represents the exposure probability value, which measures the possibility of the unit at the current node being discovered by the enemy. It is an important basis for the enemy's attack decision-making. It combines the terrain exposure rate, enemy reconnaissance capability and weather factors to dynamically reflect the concealment or exposure degree of the node, and simulates the risk level of the node under different terrains, enemy reconnaissance levels and environmental conditions.

[0047] The calculation formula of enemy threat index is: ; Indicates the strike coefficient, which is used to measure the enemy's enthusiasm for striking exposed targets. It represents the enemy threat index, which combines the enemy fire threat (THM) and the node exposure probability (EP) to reflect the actual threat level of the current node in the battlefield environment. The strike coefficient of the exposure probability adjusts the enemy's "incentive" to strike the target. The logarithmic function is smoothed to ensure the balance of the system output data range and prevent the threat index from being too large to affect the stability of the pathfinding algorithm.

[0048] The logic for obtaining the strike coefficient is: Divide the most recent optimal path length from the current starting point to the end point by the preset campaign range value corresponding to the number of all hexagonal grids to obtain the campaign advancement stage value , and then calculate the enemy's firepower capability value: ; Indicates the power value of the weapon carried by enemy unit i, represents the range of the weapons carried by enemy unit i, represents the accuracy of the weapons carried by enemy unit i, Indicates the maximum standard value of the range of the carried weapon. Indicates the enemy's firepower capability value; The product of the campaign advancement stage value and the enemy's firepower capability value is calculated to obtain the strike coefficient. The strike coefficient is a dynamic adjustment factor used to measure the enemy's "incentive" or "intensity" of attacking exposed targets at different campaign advancement stages. It plays a role in amplifying or suppressing the enemy's attack strength in the enemy threat index formula. The campaign advancement stage value ST reflects the overall rhythm of the campaign and the current battlefield situation (what stage the campaign has reached), and the enemy's firepower capability value FA reflects the enemy's current force structure and the overall combat effectiveness level of the weapon system. The campaign advancement stage value evaluates the campaign advancement stage through the proportion of the latest "starting point to end point" path length to the overall grid of the campaign, reflecting the dynamic progress of the campaign from the initial stage → mid-term → anxious period → decisive battle period. The deeper the campaign advances, the higher the enemy's strike enthusiasm and strike intensity in theory. The dynamic control of the strike intensity changes with the campaign stage, reflecting the enemy's combat behavior model at different stages of the campaign such as "closing" and "decisive battle", and adjusting the impact of the enemy threat index on path planning and tactical behavior. The enemy's firepower capability value is the average weighted result of the performance of all enemy unit weapons, reflecting the overall threat capability of the enemy's weapon system, integrating three key factors: power, range and accuracy. Power reflects the lethality of the firepower system, range determines the unit's tactical control (long-range units can threaten more areas), accuracy determines the strike efficiency (high accuracy means more reliable threats), and comprehensive calculations reflect the overall combat strike capability (overall firepower level). The strike coefficient simulates the enemy's tactical thinking at different stages, integrates the enemy's current force and firepower with the overall situation of the campaign, and ensures that the threat index is adjusted dynamically in real time with the battlefield situation.

[0049] The logic for obtaining the terrain accessibility index is as follows: first, collect the current terrain feature data of the regular hexagonal grid nodes to form a terrain feature vector. This terrain feature vector includes multiple terrain parameter information of the current node, such as slope, surface material, obstacle density, water conditions, etc. A standard pass vector is preset, which represents the terrain parameter value under ideal traffic conditions. Ideal traffic conditions usually refer to the best traffic environment such as no slope, no obstacles, solid ground, and no water. Finally, calculate the Euclidean distance between the terrain feature vector of the current node and the standard pass vector. This distance value is the terrain accessibility index. The larger the distance value, the more unfavorable the terrain conditions at the node are; the smaller the distance value, the better the terrain accessibility.

[0050] The terrain accessibility index objectively reflects the difficulty of a node's passage. The terrain accessibility index comprehensively considers the obstruction of terrain characteristics to passage, including the impact of slope, surface stability, and the density of obstacles. Through mathematical quantification, the system can intuitively understand the passage status of each node. Dynamically evaluate the impact of terrain changes on path selection. On the battlefield, the terrain environment often changes due to factors such as weather changes, combat damage, and fortification construction. By calculating the terrain accessibility index in real time, the system can dynamically adjust the path planning and flexibly respond to changes in the battlefield environment. Supporting node comprehensive risk assessment, the terrain accessibility index, as an important component of the node's comprehensive risk value, together with the enemy threat index, determines whether the node is classified as an obstacle node, thereby affecting path selection and tactical decision-making.

[0051] For example: There is a regular hexagonal grid node on the battlefield map. The node contains the following terrain information: the slope is medium, the surface is muddy soil, there are sparse obstacles, and there is no water cover. According to the terrain assessment, it is converted into indicators in the vector. The values ​​of the above four indicators are: slope index is one-fifth, surface stability is one-third, obstacle density is one-quarter, and water interference is zero. The preset standard pass vector represents ideal terrain conditions. All indicators are zero. The Euclidean distance between the terrain feature vector and the standard vector of the node is calculated. Assuming the result is 0.68, the terrain accessibility index of the node is 0.68.

[0052] Dividing the nodes corresponding to the regular hexagonal grid into normal nodes and obstacle nodes means: First, perform weighted summation of the terrain accessibility index and the enemy threat index, for example, the formula: ; Get the comprehensive risk value , and Both are preset non-zero proportional coefficients, which adjust the impact of the two indexes on the comprehensive risk respectively, and then convert the comprehensive risk value Compared with the preset risk threshold For comparison, if the comprehensive risk value Greater than the preset risk threshold , then it is divided into obstacle nodes. If the comprehensive risk value Less than or equal to the preset risk threshold , it is classified as a normal node.

[0053] The comprehensive risk value provides a comprehensive node risk assessment, combining the two core factors of enemy threat and terrain obstacles to accurately describe the comprehensive passage risk of the node; the higher the comprehensive risk value, the greater the tactical risk and resource cost of the node; node classification directly affects path planning, troop dispatch and tactical behavior decision-making, and flexibly adapts to the needs of different battle stages. For example, in the decisive battle stage, the proportional coefficient can be adjusted to ignore terrain accessibility and prioritize sprinting to the target.

[0054] For example: In the initial stage, the enemy threat index is 0.7 (high threat area), the terrain accessibility index is 0.3 (good terrain), and the proportional coefficient is set to prioritize the enemy threat coefficient, with a ratio of 0.7 (threat) to 0.3 (terrain). Calculate the comprehensive risk value: Comprehensive risk value = 0.7×0.7+0.3×0.3=0.49+0.09=0.58. The comprehensive risk value is higher than the preset risk threshold of 0.5, and the node is determined to be an obstacle node.

[0055] In the decisive battle sprint stage, the same node, but the current battle has entered the decisive battle period, ignore the threat factors, focus on accessibility, adjust the weight coefficient to 0.3 (threat) to 0.7 (terrain), and calculate the comprehensive risk value: comprehensive risk value = 0.3×0.7+0.7×0.3=0.21+0.21=0.42. The comprehensive risk value is lower than the risk threshold of 0.5. The node is judged as a normal node and is allowed to pass quickly.

[0056] When the node division type corresponding to the regular hexagonal grid is an obstacle node, calculate the breakthrough resource consumption: ; Indicates the preset resource consumption per unit. Indicates breakthrough resource consumption; When the node division type corresponding to the regular hexagonal grid is a normal node, the default value of breakthrough resource consumption is zero. Breakthrough resource consumption indicates the resource investment required for a unit to break through an obstacle node. The comprehensive risk value indicates the evaluation value of the current comprehensive threat and obstacle degree of the node. The risk threshold is the demarcation standard preset by the system. If the comprehensive risk value is greater than this value, it is an obstacle node. The resource consumption coefficient is the system-set single resource consumption benchmark value. The higher the comprehensive risk value of the node, the greater the enemy threat, the more complex the terrain, and the greater the obstacle to breakthrough. When the risk value exceeds the threshold set by the system, the breakthrough is no longer "easy to cross", but requires a large amount of resources (troops, equipment, time, supplies, engineering forces, etc.). Resource consumption increases proportionally with the extent of the risk value exceeding the threshold, simulating the rule that the investment cost increases with the difficulty of combat in the actual battlefield. If the node is a "normal node", the breakthrough resource consumption is zero, indicating that the unit can pass naturally without consuming additional resources. If the node is an "obstacle node", the resources required for breakthrough need to be calculated based on the extent of the risk value exceeding the threshold, reflecting the complexity and cost of breakthroughs in obstacles and difficult areas in the war game system. By adjusting the resource consumption coefficient, the proportional relationship between resource consumption and the degree of risk value excess can be controlled: a large consumption coefficient reflects resource shortages or extremely high combat intensity, making breakthroughs more difficult; a small consumption coefficient reflects a lower breakthrough cost and stronger tactical accessibility, supporting numerical settings for different campaign stages and tactical tasks.

[0057] Adjusting the path weight of a path formed by two adjacent nodes means: Get the comprehensive risk value of two adjacent nodes and breakthrough resource consumption , and then sum them together and divide them by the preset path normalization value to get the path weight of the path formed by two adjacent nodes. The path travel cost from one node to another is fully quantified by the sum of the comprehensive risk value of the two adjacent nodes and the breakthrough resource consumption. The path weight directly determines the path-finding strategy and results of the path planning algorithm. The path weight is adjusted dynamically, so that the system can reflect the battlefield situation and resource consumption in real time, ensuring that the path selection is more intelligent, flexible and close to tactical reality. The path weight reflects the "passing cost". The higher the weight, the greater the cost of passing through this path. The path-finding algorithm will comprehensively consider all path weights in a global scope and select the path with the lowest cost for marching, infiltration, breakthrough and other combat operations.

[0058] The long-range pathfinding algorithm plans the path from the starting point to the end point on a large-scale battlefield map to minimize the total weight of the path. It comprehensively considers factors such as path length, risk of passing, resource consumption, etc. to seek the optimal solution for tactical efficiency and safety. Commonly used long-range pathfinding algorithms (optional): Heuristic search algorithm (such as A* algorithm): Efficient calculation of the optimal path, suitable for large-scale node maps; Dijkstra algorithm: Classic shortest path algorithm with stable weight processing; Dynamic replanning algorithm (such as D*Lite): Real-time update of the path to cope with dynamic battlefield environments; Adaptive search (such as A* variant): The heuristic factor can be adjusted in combination with the tactical behavior of the war game.

[0059] Take A* as an example: Step 1: Initialization, the starting point is added to the open list, the path weight values ​​of all nodes are initialized to infinity, and the path cost from the starting point to the starting point is zero.

[0060] Step 2: Search in a loop, select the node with the lowest total cost (known path cost + estimated cost) in the open list, traverse all neighboring nodes of the node, calculate the adjacent path weight (known path weight + adjacent node path weight), if the cost is better, update the path and record the parent node, put the checked node into the closed list, and repeat until the end point is added to the closed list.

[0061] Step 3: Path backtracking, after the end point is found, the path from the starting point to the end point is backtracked. The final path sequence is generated, and the movement or tactical decision is executed one by one according to the path nodes.

[0062] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0063] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0064] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0065] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0066] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games, characterized in that: The following steps are involved: Use the Cube coordinate system to match the regular hexagonal grid, establish an accurate mapping between world coordinates and grid coordinates, and obtain battlefield dynamic change data for each regular hexagonal grid; According to the battlefield dynamic change data, the threat level and obstacle level of the regular hexagonal grid are evaluated respectively, and the nodes corresponding to the regular hexagonal grid are divided into normal nodes and obstacle nodes based on the evaluation results; Based on the division results, the path weight of the path formed by two adjacent nodes is adjusted, and the adjusted path weight is placed in the Cube coordinate system. The optimal path from the start point to the end point is found according to the preset long-range pathfinding algorithm.

2. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 1 is characterized in that: Before adjusting the weight of the path formed by two adjacent nodes, first compare the battlefield dynamic change data corresponding to the two adjacent nodes with the historical data at the time of the last adjustment, convert the battlefield dynamic change data into the latest vector, and convert the historical data at the time of the last adjustment into the historical vector. Compare and analyze the latest vector and the historical vector. If the comparison and analysis results of the two adjacent nodes are both less than or equal to the preset deviation threshold, there is no need to evaluate the threat level and obstacle level of the two nodes, and the weight of the path formed by the two nodes remains the result of the last adjustment. If the comparison and analysis results of the two adjacent nodes are not less than or equal to the preset deviation threshold, it is necessary to re-evaluate the threat level and obstacle level of the nodes greater than the preset deviation threshold, and adjust the weight of the path formed by the two nodes.

3. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 2 is characterized in that: Comparative analysis refers to calculating the similarity or Euclidean distance between the latest vector and the historical vector.

4. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 3 is characterized in that: The evaluation results refer to: The threat level of the regular hexagonal grid is evaluated to obtain the enemy threat index used to measure the threat level of the current node to the enemy; The obstacle degree of the regular hexagonal grid is evaluated to obtain the terrain accessibility index, which is used to measure the degree of obstruction of the terrain itself to traffic.

5. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 4, characterized in that: The logic for obtaining the enemy threat index is as follows: Obtain the threat heat map and terrain map from the battlefield dynamic change data, and calculate the threat heat value based on the threat heat map: ; Represents the basic threat strength value of enemy unit i, represents the state coefficient of enemy unit i, Indicates the distance between the center point of the current node and enemy unit i, represents the preset non-zero distance attenuation coefficient, n represents the total type value of the enemy unit, Indicates the threat heat value; Calculate the exposure probability value based on the topographic map: ; Indicates terrain type The coverage rate, Indicates terrain type The basic exposure factor, It represents the maximum value of the corresponding reconnaissance efficiency coefficient among all enemy units, and m represents the total number of terrain types. Indicates the preset impact coefficient corresponding to the current weather. represents the exposure probability value; The calculation formula of enemy threat index is: ; Indicates the strike coefficient, which is used to measure the enemy's enthusiasm for striking exposed targets. Indicates the enemy threat index.

6. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 5, characterized in that: The logic for obtaining the strike coefficient is: Divide the most recent optimal path length from the current starting point to the end point by the preset campaign range value corresponding to the number of all hexagonal grids to obtain the campaign advancement stage value , and then calculate the enemy's firepower capability value: ; Indicates the power value of the weapon carried by enemy unit i, represents the range of the weapons carried by enemy unit i, represents the accuracy of the weapons carried by enemy unit i, Indicates the maximum standard value of the range of the carried weapon. Indicates the enemy's firepower capability value; Calculate the product of the campaign advancement phase value and the enemy's firepower capability value to obtain the strike coefficient.

7. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 6, characterized in that: The logic for obtaining the terrain accessibility index is: The current terrain vector of the corresponding node of the regular hexagonal grid is obtained, and then the Euclidean distance between the current terrain vector and the preset standard through vector is calculated to obtain the terrain accessibility index.

8. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 7, characterized in that: Dividing the nodes corresponding to the regular hexagonal grid into normal nodes and obstacle nodes means: First, weighted sum of terrain accessibility index and enemy threat index is performed to obtain the comprehensive risk value. , and then the comprehensive risk value Compared with the preset risk threshold For comparison, if the comprehensive risk value Greater than the preset risk threshold , then it is divided into obstacle nodes. If the comprehensive risk value Less than or equal to the preset risk threshold , it is classified as a normal node.

9. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 8, characterized in that: When the node division type corresponding to the regular hexagonal grid is an obstacle node, calculate the breakthrough resource consumption: ; Indicates the preset resource consumption per unit. Indicates breakthrough resource consumption; When the node division type result corresponding to the regular hexagonal grid is a normal node, the default value of the breakthrough resource consumption is zero.

10. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 9, characterized in that: Adjusting the path weight of a path formed by two adjacent nodes means: Get the comprehensive risk value of two adjacent nodes and breakthrough resource consumption , and then sum them together and divide them by the preset path normalization value to get the path weight of the path composed of two adjacent nodes.

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