High-precision Hexagonal Grid Long-range Pathfinding Method Based on Dynamic Weight Assignment in Wargames

Abstract

The present invention discloses a high-precision hexagon grid long-range pathfinding method based on dynamic weighting in war games, specifically relating to the technical field of war game deduction, and comprising the following steps: matching a regular hexagon grid by using a Cube coordinate system, and obtaining battlefield dynamic change data of each regular hexagon grid, respectively evaluating the threat level and obstacle level of the regular hexagon grid, and dividing the nodes corresponding to the regular hexagon grid into normal nodes and obstacle nodes based on the evaluation results; adjusting the path weight of the path formed by two adjacent nodes, and finding the optimal path from the starting point to the ending point according to a preset long-range pathfinding algorithm; by collecting battlefield dynamic change data in real time and dynamically adjusting the threat index and terrain accessibility index of the nodes, the present invention enables the path weight to timely reflect the changes in the battlefield environment, improves the real-time performance and dynamic adaptability of path decision-making, and effectively deals with complex and changeable battlefield environments.

Description

Technical Field

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

[0002] With the development of information-based warfare and intelligent combat systems, wargaming systems, as important tools for assisting command decision-making and campaign planning, have been widely applied in military training and strategy formulation. Existing wargame systems usually rely on rule-based terrain modeling and fixed path planning algorithms, making it difficult to meet the high-precision path calculation requirements in dynamic battlefield environments. Most existing path planning methods perform path search based on static weights. Such methods have the following deficiencies:

[0003] Path calculation lacks real-time dynamics, making it difficult to adjust weights according to the rapid changes in the confrontation situation and battlefield environment simulated in the wargame, resulting in lagging 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 the threat and terrain obstacles of the opposing side in the wargame simulation environment, and path planning cannot accurately reflect the actual passage difficulty and confrontation risks; there is a lack of a refined resource consumption model, and it is impossible to comprehensively evaluate resource consumption during path planning, affecting the breakthrough and advancement strategies of simulated combat units;

[0004] The existing path planning methods of wargame systems can no longer meet the requirements for high-precision and dynamic path decision-making in modern warfare. Therefore, there is an urgent need to propose a dynamic path optimization method that comprehensively considers battlefield situations, 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

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in wargames, comprising the following steps:

[0007] Match the regular hexagonal grid using the Cube coordinate system, establish an accurate mapping between world coordinates and grid coordinates, and obtain the battlefield dynamic change data of each regular hexagonal grid;

[0008] Evaluate the threat level and obstacle level of the regular hexagonal grid respectively according to the battlefield dynamic change data, and divide the nodes corresponding to the regular hexagonal grid into normal nodes and obstacle nodes based on the evaluation results;

[0009] Based on the partitioning result, adjust the path weight of the path formed by two adjacent nodes, place the adjusted path weight in the Cube coordinate system, and find the optimal path from the starting point to the ending point according to the preset long-range pathfinding algorithm.

[0010] In a preferred embodiment, 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 time of the previous adjustment. Convert the battlefield dynamic change data into the latest vector, and convert the historical data at the time of the previous 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 these two nodes, and the path weight formed by these two nodes remains the result of the previous adjustment. If the comparison and analysis results of the two adjacent nodes are not both 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 these two nodes.

[0011] In a preferred embodiment, the comparison and analysis refers to calculating the similarity or Euclidean distance between the latest vector and the historical vector.

[0012] In a preferred embodiment, the evaluation result refers to:

[0013] Evaluate the threat level of the regular hexagonal grid to obtain the enemy threat index for measuring the threat level of the current node by the enemy;

[0014] Evaluate the obstacle level of the regular hexagonal grid to obtain the terrain accessibility index for measuring the degree of obstacle to passage by the terrain itself.

[0015] In a preferred embodiment, the acquisition logic of the enemy threat index is:

[0016] Obtain the threat heat map and topographic map from the battlefield dynamic change data, and calculate the threat heat value according to the threat heat map:

[0017] ; represents the basic threat intensity value of enemy unit i, represents the state coefficient of enemy unit i, represents 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 enemy units, represents the threat heat value;

[0018] Calculate the exposure probability value according to the topographic map:

[0019] ; represents the coverage rate of terrain type represents the basic exposure coefficient of terrain type represents the maximum value of the corresponding detection ability efficiency coefficient among all enemy units, m represents the total number of terrain types represents the preset influence coefficient corresponding to the current weather represents the exposure probability value;

[0020] The calculation formula for the enemy threat index is:

[0021] ; represents the strike coefficient, which is used to measure the enthusiasm of the enemy to strike at the exposed target represents the enemy threat index.

[0022] In a preferred embodiment, the acquisition logic of the strike coefficient is:

[0023] Divide the optimal path length from the current starting point to the end point of the latest time 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 firepower ability value:

[0024] ; represents the weapon power value carried by enemy unit i represents the weapon range carried by enemy unit i represents the weapon accuracy carried by enemy unit i represents the maximum standard value of the weapon range represents the enemy firepower ability value;

[0025] Calculate the product of the campaign advancement stage value and the enemy firepower ability value to obtain the strike coefficient.

[0026] In a preferred embodiment, the acquisition logic of the terrain accessibility index is:

[0027] Obtain the current terrain vector of the node corresponding to the regular hexagonal grid, and then calculate the Euclidean distance between the current terrain vector and the preset standard passing vector to obtain the terrain accessibility index.

[0028] In a preferred embodiment, dividing the nodes corresponding to the regular hexagonal grid into normal nodes and obstacle nodes means:

[0029] First, perform a weighted sum of the terrain accessibility index and the enemy threat index to obtain a comprehensive risk value , and then the comprehensive risk value Compare with the preset risk threshold If the comprehensive risk value is greater than the preset risk threshold , then it is classified as an obstacle node. If the comprehensive risk value is less than or equal to the preset risk threshold , then it is classified as a normal node.

[0030] In a preferred embodiment, when the node division type result corresponding to the regular hexagon grid is an obstacle node, calculate the breakthrough resource consumption:

[0031] ; represents the preset resource consumption per unit, represents the breakthrough resource consumption;

[0032] When the node division type result corresponding to the regular hexagon grid is a normal node, the breakthrough resource consumption is defaulted to zero.

[0033] In a preferred embodiment, adjusting the path weight of the path formed by two adjacent nodes refers to:

[0034] Obtain the comprehensive risk values of two adjacent nodes respectively and the breakthrough resource consumption , then sum them up and divide by the preset path normalization value to obtain the path weight of the path formed by two adjacent nodes.

[0035] The technical effects and advantages of the present invention:

[0036] By collecting real-time battlefield dynamic change data, the present invention dynamically adjusts the threat index and terrain accessibility index of nodes, enabling the path weight to timely reflect the changes in the battlefield environment and the enemy situation. Intelligently judge whether re-evaluation is needed according to the change amplitude of the node state, avoid redundant calculations, improve the real-time performance and dynamic adaptability of path decision-making, and can effectively cope with complex and changeable battlefield environments.

[0037] 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 the enemy's strike ability, the enemy situation and the target exposure probability, and dynamically reflects the threat intensity received by the node; the terrain accessibility index is calculated by the Euclidean distance between the terrain feature vector and the standard passing vector, and finely measures the passing difficulty of the node. This multi-dimensional evaluation mechanism improves the scientificity and accuracy of path weight evaluation.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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

[0042] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0043] 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

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] Referring to Figure 1 the following embodiments are obtained:

[0046] Embodiment

[0047] With the continuous development of informationized warfare and intelligent operations, the wargaming system has become an important technical support tool for military command decision-making, campaign planning, and tactical drills. The wargame system realizes the dynamic simulation and intelligent evaluation of complex battlefield situations by modeling the battlefield environment, the situation of the enemy and ourselves, and the action plans of troops, and is a key platform for auxiliary decision-making, training drills, and tactical research. However, the path planning modules of most current wargaming systems generally adopt a grid model with simplified rules or traditional path algorithms (such as the A* algorithm, Dijkstra algorithm, etc.), and there are the following technical bottlenecks:

[0048] Slow response to dynamic situations: Traditional path planning algorithms are difficult to timely perceive the changes in the enemy situation and the evolution of the environment in a dynamic battlefield environment, lack the dynamic adaptation ability to the real-time battlefield situation, and affect the intelligent decision-making ability of the wargame system.

[0049] Rough evaluation of threat and obstacle factors: The existing models mostly set static weights for the evaluation of enemy threats and terrain obstacles, lack dynamic and refined comprehensive evaluation methods, and cannot accurately reflect the real impact of the battlefield environment on path selection.

[0050] The present invention takes dynamic weight assignment, 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 terms of dynamic environment response, threat perception, and path calculation efficiency, and provides a more intelligent, refined, and real-time path decision support means for the wargame system, which is specifically reflected in the following aspects:

[0051] Improve the battlefield situation perception ability of the wargame system: By obtaining real-time battlefield dynamic change data, realize the comprehensive perception and dynamic analysis of key tactical elements such as enemy threats, terrain obstacles, and campaign advancement in path planning.

[0052] Enhance the intelligence and flexibility of path planning: Based on dynamic threat assessment and obstacle degree calculation, realize the real-time dynamic classification and weight adjustment of path nodes, effectively respond to the rapid changes in a complex battlefield environment, and improve the intelligent level of wargaming.

[0053] Improve the computational efficiency of long-range path planning: Adopt a mechanism of dynamic comparison of node vectors and deviation threshold judgment to avoid unnecessary repeated calculations of path weights, greatly reduce the computational load of long-range pathfinding, and improve the system operation efficiency and response speed.

[0054] Realize the unified optimization of tactical-level path planning: Support the dynamic parameter adjustment of the strike coefficient for the campaign advancement stage and the enemy's firepower capabilities, realize the adaptive optimization of tactical assault paths and detour paths, and meet the requirements of multi-level wargaming.

[0055] Enhance the authenticity of system combat simulation: High-precision hexagonal grids 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.

[0056] The present invention aims to solve the problem that it is difficult for the path planning algorithm in the existing wargaming system to achieve high precision, high efficiency, and dynamic adaptability, and provides a high-precision hexagonal grid long-range pathfinding method based on dynamic weighting. The specific objectives are as follows:

[0057] Realize high-precision path modeling: Establish an accurate mapping between world coordinates and high-precision hexagonal grids through the Cube coordinate system, realize the division of tactical-level (meter-level) path nodes, and improve the accuracy of path data modeling.

[0058] Dynamically perceive the battlefield situation and adaptively adjust path weights: Based on the dynamic change data of the battlefield, evaluate the threat and obstacle for each node, dynamically divide normal nodes and obstacle nodes, and realize the dynamic real-time adjustment of path weights.

[0059] Optimize the computational logic of long-range path planning; Avoid frequent repeated calculations through the comparison of node historical data and deviation threshold mechanism, optimize the update logic of long-range path nodes, and significantly improve the path planning efficiency.

[0060] Realize a comprehensive evaluation model of enemy threats and obstacles: Introduce elements such as threat heat maps, exposure probabilities, and strike coefficients to dynamically calculate the enemy threat index. At the same time, combine the Euclidean distance between the terrain vector and the standard passing vector to accurately measure the terrain accessibility index and realize comprehensive risk calculation.

[0061] Take into account tactical resource consumption and risk assessment: The node breakthrough resource consumption model combines 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 wargaming system to make differentiated path selections under different tactical tasks.

[0062] The long-range pathfinding method based on dynamic weighting of high-precision hexagonal grids in wargaming includes the following steps:

[0063] 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.

[0064] 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.

[0065] Based on the partitioning result, adjust the path weights of the paths formed by two adjacent nodes, and project the adjusted path weights into the Cube coordinate system. Then, according to the preset long-range pathfinding algorithm, find the optimal path from the starting point to the ending point. Based on the node classification results (normal / obstacle) and dynamic indicators such as the comprehensive risk value of the node and the breakthrough resource consumption, adjust the path weights in real time, making the path selection process more flexible and dynamic, and conforming to the changes in the battlefield situation. Consider the threat index, accessibility index, and breakthrough resource consumption of the nodes comprehensively, and convert them into path weight values, so that the path decision-making comprehensively considers "passage safety" and "resource economy", meeting the diverse and complex requirements of the combat tasks in the wargame system. The dynamic weights are projected into the Cube coordinate system to ensure the structural rigor and pathfinding efficiency of the path calculation process, especially suitable for the rapid planning of long-range paths in complex terrains and multi-threat environments. Support the flexible application of the long-range pathfinding algorithm in different tactical objectives and tasks, and can achieve the comprehensive optimal path planning of large ranges, multiple nodes, and multiple paths. Through real-time adjustment of path weights and dynamic pathfinding, realize the path optimization decision-making in different stages of the campaign, and enhance the depth of campaign deduction and tactical simulation in the wargame system.

[0066] Divide the length of the optimal path from the current starting point to the ending point in the latest time by the preset campaign range value corresponding to the total number of hexagonal grids to obtain the campaign progress stage value. Through the calculated campaign progress stage value (a continuous value between 0 and 1), different stages of the campaign progress can be clearly divided. For example, 0.00 - 0.2 is the initial stage (opening exploration), the campaign has just started, the enemy and friendly situation is not yet clear, mainly for reconnaissance and exploration; 0.21 - 0.40 is the middle stage (stable advancement), the offensive and defensive postures are gradually clear, and the enemy and friendly forces launch the main offensive or defensive postures; 0.41 - 0.60 is the stalemate period (intense confrontation), the main forces of both sides enter high-intensity combat, the situation is complex, and the front line changes frequently; 0.61 - 0.80 is the late stage (advantage accumulation), one side gradually gains the initiative or a breakthrough appears, and the outcome of the campaign begins to emerge; 0.81 - 1.00 is the decisive battle period (ending stage), launching a general attack or organizing the final defense, and the campaign process enters the final decisive moment.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] Measure the change of node situation through cosine similarity or Euclidean distance, simulate the perception and reaction mechanism of combat units or command systems to battlefield information, and conform to the rhythm of information assessment and action reaction in the command decision-making process. That is, tactical decisions will not be adjusted immediately due to minor changes, but will react based on "major battlefield changes". By adjusting the deviation threshold, control the response sensitivity of the system to battlefield changes. Thresholds can be set separately according to the battle stage (such as initial exploration, stalemate advance, decisive battle sprint). Use a sensitive (low threshold) in the initial stage to emphasize detailed perception, and a loose (high threshold) in the decisive battle to prioritize efficiency and quick response. The node state vector mechanism has high scalability. The dynamic change data of each node is uniformly modeled through a standardized vector, supporting flexible expansion in different dimensions (terrain, threat, obstacle, weather, fire coverage, etc.), and providing a unified evaluation interface for the subsequent introduction of more dynamic tactical factors (such as psychological warfare, electronic warfare, AI reconnaissance situation).

[0071] Vector comparison algorithms (such as cosine similarity, Euclidean distance, Manhattan distance, etc.) can be replaced. Different algorithms are optimized for different arms and tactical scenarios to improve the modularity and componentization of the algorithms. The pre-screening mechanism for node weight adjustment = resource scheduling control valve, which controls the frequency and scope of node state evaluation and path weight adjustment, saves system resources for "more meaningful battlefield dynamic responses", and improves the processing ability of high-priority tasks such as battle progress, tactical breakthrough, and resource allocation. The node information determination mechanism ensures path stability and change rationality, avoiding the mobility chaos caused by frequent path changes of troops, and meeting the "decision stability" requirements in actual combat.

[0072] The evaluation result refers to:

[0073] Evaluate the threat level of the regular hexagonal grid to obtain the enemy threat index used to measure the threat degree of the current node by the enemy; it can measure the risk level of the node in the enemy's firepower system, and reflect whether the current node is within the effective strike range, reconnaissance range or control range of the enemy. The higher the index, the more likely it is to be exposed to the threat of high-intensity enemy strikes. The lower the index, the less able the enemy is to effectively strike or affect the node. Nodes with high threat indexes are given priority to avoid in path planning, and may also affect the decision of whether to "take risks to pass". Update the enemy threat index of the node in real time to ensure that the wargame system has the ability of dynamic situation perception. The movement of the enemy, the adjustment of firepower deployment, the impact of electronic warfare, etc. can all be directly reflected in the enemy threat index.

[0074] 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.

[0075] The logic for obtaining the enemy threat index is as follows:

[0076] 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:

[0077] ; 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.

[0078] Calculate the exposure probability value based on the topographic map:

[0079] ; 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. Represents the maximum value of the corresponding reconnaissance ability efficiency coefficient among all enemy units, indicating the strength of the enemy's reconnaissance ability in this area. Radar, drones, etc. can increase the exposure risk, reflecting the dynamic control of the enemy situation on the node risk. For example, the visual value is 1, and the drone value is 2. m represents the total number of terrain types, Represents the preset influence coefficient corresponding to the current weather. Different weathers have significant impacts on reconnaissance and strikes. Foggy and rainy days reduce the exposure probability, while sunny days increase the risk. For example, the value for rainy days is the base value of 1, and the value for foggy days is the base value of 0.8. In fact, the preset influence coefficient corresponding to the current weather is obtained by multiplying the base value corresponding to the weather by the visibility in the current environment . Represents the exposure probability value, which measures the possibility of a unit at the current node being discovered by the enemy and is an important basis for the enemy's strike decision. It comprehensively considers the terrain exposure rate, the enemy's reconnaissance ability, and weather factors, dynamically reflecting the concealment or exposure degree of the node, and simulating the risk level of the node under different terrains, enemy reconnaissance levels, and environmental conditions.

[0080] The calculation formula for the enemy threat index is:

[0081] ; Represents the strike coefficient, which is used to measure the enemy's enthusiasm for striking an exposed target. Represents the enemy threat index, which comprehensively considers the enemy's fire threat (THM) and the node exposure probability (EP), reflecting the actual threat level of the current node in the battlefield environment. The strike coefficient of the exposure probability adjusts the enemy's "enthusiasm" for striking the target, and logarithmic function smoothing is used to ensure the balance of the system output data range and prevent the threat index from being too large and affecting the stability of the pathfinding algorithm.

[0082] The acquisition logic of the strike coefficient is as follows:

[0083] Divide the optimal path length from the current starting point to the end point of the latest time by the preset campaign range value corresponding to the total number of all hexagonal grids to obtain the campaign advancement stage value , and then calculate the enemy firepower ability value:

[0084] ; Represents the weapon power value carried by enemy unit i, Represents the weapon range carried by enemy unit i, Represents the weapon accuracy carried by enemy unit i, Represents the maximum standard value of the weapon range, Represents the enemy firepower ability value;

[0085] Calculate the product of the campaign advancement stage value and the enemy's firepower capability value to obtain the strike coefficient. The strike coefficient is a dynamic adjustment factor used to measure the "enthusiasm" or "intensity" of the enemy's strike on exposed targets at different campaign advancement stages, and plays a role in amplifying or suppressing the enemy's strike force 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 has been advanced to), and the enemy's firepower capability value FA reflects the overall combat effectiveness level of the enemy's current force structure and weapon system. The campaign advancement stage value evaluates the campaign advancement stage through the ratio of the length of the "starting point to ending point" path in the latest time to the proportion of the entire campaign grid, reflecting the dynamic progress of the campaign from the initial stage → middle stage → stalemate stage → decisive battle stage. The deeper the campaign advances, the higher the enemy's strike enthusiasm and intensity theoretically. Dynamically control the strike intensity as the campaign stage changes, reflecting the enemy's combat behavior models in different stages such as "ending" and "decisive battle" of the campaign, and adjusting the influence intensity of the enemy threat index on path planning and tactical behavior. The enemy's firepower capability value is the average weighted result of the weapon performances of all enemy units, 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 force (units with a long range can threaten more areas), and accuracy determines the strike efficiency (high accuracy means more reliable threats). Comprehensive calculation reflects the overall combat strike ability (overall firepower level). The strike coefficient simulates the enemy's tactical thinking at different stages, integrates the enemy's current force firepower and the overall campaign situation, and ensures that the threat index is adjusted in real time and dynamically with the battlefield situation.

[0086] The acquisition logic of the terrain accessibility index is as follows: First, collect the current terrain feature data of the regular hexagon 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 body situation, etc. Preset a standard passing vector, which represents the terrain parameter values under ideal passing conditions. Ideal passing conditions usually refer to the best passing environment without slope, without obstacles, firm ground, and without water bodies. Finally, calculate the Euclidean distance between the terrain feature vector of the current node and the standard passing vector. This distance value is the terrain accessibility index , the larger the distance value, the more unfavorable the terrain conditions of the node for passing; the smaller the distance value, the better the terrain passability.

[0087] The terrain accessibility index objectively reflects the difficulty of passing through a node. The terrain accessibility index comprehensively considers the obstructive effects of terrain features on passage, including slope influence, surface stability, obstacle density, etc. Through a mathematical quantification method, the system can intuitively understand the passage state 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 path planning and flexibly respond to battlefield environment changes. Support the comprehensive risk assessment of nodes. The terrain accessibility index, as an important part of the comprehensive risk value of nodes, together with the enemy threat index, determines whether a node is classified as an obstacle node, thus affecting path selection and tactical decisions.

[0088] For example: There is a regular hexagonal grid node on the battlefield map. This node contains the following terrain information: medium slope, muddy soil on the surface, sparse obstacles, and no water body coverage. 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-fourth, and water body interference is zero. The preset standard represents the ideal terrain conditions through a vector, and all indicators are zero. Calculate the Euclidean distance between the terrain feature vector of this node and the standard vector. Suppose the result is 0.68. Therefore, the terrain accessibility index of this node is 0.68.

[0089] Dividing the node corresponding to the regular hexagonal grid into normal nodes and obstacle nodes means:

[0090] First, perform a weighted sum of the terrain accessibility index and the enemy threat index. For example, the formula:

[0091] ; obtain the comprehensive risk value , and are both preset non-zero proportionality coefficients, respectively adjusting the influence of the two indices on the comprehensive risk. Then compare the comprehensive risk value with the preset risk threshold . If the comprehensive risk value is greater than the preset risk threshold , it is classified as an obstacle node. If the comprehensive risk value is less than or equal to the preset risk threshold , it is classified as a normal node.

[0092] The comprehensive risk value provides a comprehensive node risk assessment. By combining the two core factors of enemy threat and terrain obstacles, it accurately describes the comprehensive passage risk of nodes. The higher the comprehensive risk value, the greater the tactical risk and resource cost of passing through this node. Node classification directly affects path planning, troop dispatching, and tactical behavior decision-making, and can flexibly adapt 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 towards the target.

[0093] For example: In the initial stage, the enemy threat index is 0.7 (high-threat area), and the terrain accessibility index is 0.3 (good terrain). The proportional coefficient is set with priority given to 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. Since the comprehensive risk value is higher than the preset risk threshold of 0.5, the node is determined to be an obstacle node.

[0094] In the decisive battle sprint stage, for the same node, but the current battle has entered the decisive battle period, ignoring the threat factor and focusing on accessibility, the weight coefficient is adjusted to 0.3 (threat) to 0.7 (terrain). Calculate the comprehensive risk value: Comprehensive risk value = 0.3×0.7 + 0.7×0.3 = 0.21 + 0.21 = 0.42. Since the comprehensive risk value is lower than the risk threshold of 0.5, the node is determined to be a normal node, allowing for rapid passage.

[0095] When the node division type result corresponding to the regular hexagon grid is an obstacle node, calculate the breakthrough resource consumption:

[0096] ; represents the preset resource consumption per unit, represents the breakthrough resource consumption;

[0097] 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. The breakthrough resource consumption represents the resource input required to break through one obstacle node per unit. The comprehensive risk value represents 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 benchmark value of the single resource consumption set by the system. The higher the comprehensive risk value of the node, the greater the threat of the enemy, the more complex the terrain, and the stronger the obstacle to the breakthrough. After the risk value exceeds the threshold set by the system, the breakthrough is no longer "easily passing through", but a large amount of resources (troops, equipment, time, supplies, engineering forces, etc.) need to be invested. The resource consumption increases proportionally with the amplitude of the risk value exceeding the threshold, simulating the law of the increase in the input cost with the improvement of the combat difficulty 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 the breakthrough need to be calculated based on the amplitude of the risk value exceeding the threshold, reflecting the complexity and costliness of the breakthrough in the obstacle and difficult sections of the wargame system. By adjusting the resource consumption coefficient, the proportional relationship between the resource consumption and the degree of the risk value exceeding can be controlled: a large consumption coefficient reflects a shortage of resources or an extremely high combat intensity, and the breakthrough is more difficult; a small consumption coefficient reflects a lower breakthrough cost and a stronger tactical passage ability, supporting the numerical settings of different campaign stages and tactical tasks.

[0098] Adjusting the path weight for the path formed by two adjacent nodes means:

[0099] Obtain the comprehensive risk values of the two adjacent nodes respectively And the breakthrough resource consumption , then sum them up and divide by the preset path normalization value to obtain the path weight of the path formed by the two adjacent nodes. Through the sum of the comprehensive risk values and the breakthrough resource consumption of the two adjacent nodes, the path passage cost from one node to another is comprehensively quantified. The path weight directly determines the pathfinding strategy and result of the path planning algorithm. Dynamically adjusting the path weight enables the system to reflect the battlefield situation and resource consumption in real time, ensuring that the path selection is more intelligent, flexible, and close to the tactical reality. The path weight reflects the "passage cost". The higher the weight, the greater the cost of passing through this path. The pathfinding algorithm will comprehensively consider all path weights within the global scope and select the path with the lowest cost for combat operations such as marching, infiltration, and breakthrough.

[0100] The long-range pathfinding algorithm is to plan a path from the starting point to the ending point on a large-scale battlefield map to minimize the total weight of the path. It comprehensively considers factors such as path length, passing risk, resource consumption, etc., and seeks the optimal solution for tactical efficiency and security. Common long-range pathfinding algorithms (optional): Heuristic search algorithms (such as the A* algorithm): Efficiently calculate the optimal path and are suitable for large-scale node maps; Dijkstra's algorithm: A classic shortest path algorithm with stable weight processing; Dynamic replanning algorithms (such as D*Lite): Update the path in real time to cope with the dynamic battlefield environment; Adaptive search (such as A* variants): Can adjust the heuristic factor in combination with the tactical behavior of the wargame.

[0101] Taking A* as an example: Step 1: Initialization. Add the starting point to the open list, initialize the path weight values of all nodes to infinity, and the path cost from the starting point to the starting point is zero.

[0102] Step 2: Loop search. Select the node with the lowest total cost (known path cost + estimated cost) in the open list, traverse all neighbor nodes of this node, calculate the adjacent path weight (known path weight + adjacent node path weight). If the cost is more optimal, update the path and record the parent node, and put the inspected node into the closed list. Repeat until the ending point is added to the closed list.

[0103] Step 3: Path backtracking. After finding the ending point, backtrack the path from the starting point to the ending point. Generate the final path sequence and execute movement or tactical decisions one by one according to the path nodes.

[0104] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0105] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0106] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0107] 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 foregoing method embodiments, and will not be elaborated herein.

[0108] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to 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 put into the Cube coordinate system, and the optimal path from the starting point to the end point is found according to the preset long-range path finding algorithm; 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 used to measure the degree of obstruction of the terrain itself to traffic; 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; 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.

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 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.

5. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 4, 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.

6. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 5, 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.

7. The high-precision hexagonal grid long-range pathfinding method based on dynamic weighting in war games according to claim 6, 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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