A simulation model flexible customization method for dynamic game confrontation scenarios

Abstract

The application provides a simulation model flexible customization method for a dynamic game confrontation scene, comprising the following steps: S1: constructing a combat entity model that can be flexibly customized; and S2: constructing a game deduction scene that can be flexibly customized.The simulation model flexible customization method for the dynamic game confrontation scene is constructed by constructing the combat entity model that can be flexibly customized and constructing the game deduction scene that can be flexibly customized, on the combat entity model construction, first, an equipment parameter library is constructed, then, a mechanical domain model and an electromagnetic domain model are constructed by taking the parameter library as a guide, on the basis, a detachable equipment model component is generated to adapt to the requirements of model combination in different scenes, finally, the model flexible assembly is realized through the combat entity model combination that can be flexibly customized, meanwhile, on the scene and rule level, an environment resource library is constructed based on geographic space and environment data as a deduction environment. Based on an extensible rule design method, a rule model, a command model and a decision model are constructed.

Description

Technical Field

[0001] This invention relates to the field of military simulation, and in particular to a method for flexibly customizing simulation models for dynamic game-based adversarial scenarios. Background Technology

[0002] Military game competition under new technological conditions utilizes computer simulation technology to simulate the deployment of forces, tactical choices, and resource allocation of both sides in a specific battlefield environment. Through simulation, the advantages and disadvantages of each side's strategies are presented, thereby achieving the purpose of evaluating strategies and tactics and optimizing the decision-making process. Dynamic game competition refers to a situation where the parties involved do not make decisions simultaneously, but rather choose strategies sequentially. Later parties can observe the decision-making behavior of earlier parties and adjust their own strategies accordingly. The decisions of both sides change dynamically as the game progresses.

[0003] Military game confrontation requires simulation modeling of various entity models, environment models, and rule models. As the scope of the battlefield environment in dynamic game confrontation continues to expand, the scenarios become more numerous and detailed, and the subjects participating in the confrontation become more flexible and changeable, the modeling objects have evolved into large systems containing numerous subsystems and modules, with complex structures and behaviors that are constantly evolving. Traditional simulation systems are difficult to support such modeling needs due to limitations in their technical architecture. Therefore, a composable, assemblable, and flexibly customizable modeling method is needed. By flexibly customizing existing models and rules, simulation systems that meet the changing needs of different application scenarios can be quickly constructed, and the system can respond to its evolution in a timely manner.

[0004] There are generally two approaches to composable simulation modeling: one is to decompose the system's functions according to the requirements of the simulation system, forming functional modules at different levels and their input / output interfaces, and then develop corresponding simulation models according to the functions and interfaces of the modules. Finally, the developed simulation models are combined to form a simulation application system. The other approach is to use the system's basic requirements as constraints, retrieve reusable simulation models from the model library, and then combine these models to form a simulation application system. However, both of these approaches have shortcomings such as pre-defined interfaces, limited model components, and insufficient customization intelligence.

[0005] Therefore, it is necessary to provide a flexible customization method for simulation models in dynamic game-based adversarial scenarios to solve the above-mentioned technical problems. Summary of the Invention

[0006] This invention provides a flexible customization method for simulation models in dynamic game-based adversarial scenarios, which solves the problems of current simulation modeling interfaces being pre-defined, model components being limited, and customization not being intelligent enough.

[0007] To address the aforementioned technical problems, this invention provides a method for flexibly customizing simulation models for dynamic game-theoretic scenarios, comprising the following steps: S1: Constructing a flexible and customizable combat entity model: S101: Equipment Parameter Database Construction: Establish a comprehensive equipment parameter resource, covering the performance parameters of various infrastructures, weapon platforms, platform components, and other equipment. S102: Constructing a mechanical domain model: Constructing entity models of combat units covering the "space, air, land, sea, and underwater" mechanical domains; S103: Construct electromagnetic domain models: Construct electronic reconnaissance models, electronic jamming models, radar detection models, friend-or-foe identification models, communication models, etc., to form an electromagnetic domain entity model library that covers electromagnetic elements of reconnaissance, jamming, detection, identification, and communication. S104: Component-based weapon and equipment modeling: Based on the mechanical and electromagnetic domain models, component-based modeling technology is used to decompose the entity model into multiple relatively independent functional modules and establish relatively independent interaction interfaces. S105: Constructing a flexible and customizable combat entity model: Based on the constructed mechanical domain entity model and electromagnetic domain entity model, a combined model simulating combat entities is created. S2: Constructing flexibly customizable game simulation scenarios: S201: Constructing an environmental resource database: The construction of an environmental resource database includes basic base map image data, topographic data, and high-precision models of military bases, etc. S202: Constructing flexibly customizable game simulation scenarios: Quickly responding to game simulation needs and flexibly customizing diversified game simulation scenarios from tactical-level individual combat to strategic-level system-wide game combat. S203: Building a rule model: The rule base construction is responsible for building comprehensive simulation rules to ensure that the interactive behavior during the simulation process conforms to the actual combat specifications and supports the simulation of multi-service joint operations; S204: Constructing a strategy model: The strategy library is built on top of the rule library and is used to solidify practical tactics and methods, and support intelligent decision-making and scheme selection during the simulation process; S205: Constructing a command model: Through modular design, it realizes the hierarchical management of the command of combat units, the control of command transmission and the interaction of situational information, and supports the efficient transmission of command decisions and real-time perception of the battlefield situation during the simulation process. S206: Co-simulation Scalable Rule Framework: Combining parametric modeling technology, rules are instantiated and customized by configuring rule parameters, breaking down rules into finer-grained elements, and constructing specific rules through combination methods.

[0008] Preferably, the method for constructing the equipment parameter database in step 1 includes authoritative data extraction, measured data supplementation, simulation data verification, and historical simulation data accumulation. The authoritative data extraction is used to extract basic parameters from authoritative documents such as equipment design manuals, military standards and specifications, and official technical manuals. The measured data supplementation is used to collect accurate data on core equipment components using methods such as 3D scanning and performance testing. The simulation data verification is used to simulate and calculate the equipment's motion parameters, load-bearing capacity, and damage resistance using professional simulation tools, and then cross-verify them with the measured data. The historical simulation data accumulation is used to collect effective parameters that have been verified in actual combat during past military simulations and add them to the parameter database.

[0009] Preferably, the electromagnetic domain model in step 1 includes electronic reconnaissance models, electronic jamming models, radar detection models, friend-or-foe identification models, and communication models.

[0010] Preferably, the construction of the environmental resource database in step 2 includes geospatial data acquisition and environmental parameter acquisition technologies. The geospatial data acquisition uses satellite remote sensing, UAV aerial surveying, topographic mapping radar and other means to obtain topographic elevation data, land feature type data, road network and water system distribution data of the combat area. The environmental parameter acquisition technology uses meteorological stations, electromagnetic monitoring equipment, hydrological sensors and other means to collect real-time or historical meteorological data, electromagnetic environment data and hydrological data.

[0011] Preferably, the rule model in step 2 includes basic interaction rules, combat action rules, environmental impact rules, collaborative linkage rules, and victory / defeat determination rules.

[0012] Preferably, the strategy model construction process in step 2 includes building a scenario-based strategy system, realizing the modularization and parameterization of strategies, connecting with the rule base and parameter base, and supporting intelligent inference decision-making.

[0013] Preferably, the command model in step 2 includes a combat platform, a command and control module, and combat units. The combat platform is used for platforms with command capabilities such as early warning aircraft, ships, and ground command centers. The command and control module is used for command and control relationship management units, command sending and receiving units, situation display units, and information collection units. The combat units are used for subordinate execution units such as fighter jets, missile launchers, and radar stations.

[0014] Preferably, the co-simulation extensible rule framework in step 2 includes a rule metadata management module, a basic rule template module, a rule configuration and editing module, a rule parsing and execution module, and a dynamic rule adaptation module.

[0015] Preferably, the co-simulation scalable rule framework is connected to a switching module, which is used to switch between the intelligent battle module, the human-machine collaboration module, the human-machine game module, and the battle game module.

[0016] Preferably, the intelligent battle module is used for two-way self-play by selecting scenarios, modes, tactical style customization, and tactical level; the human-machine collaboration module is used for human-machine collaborative combat by providing options; the human-machine game module is used for human-machine game by selecting tactical level; and the battle game module is used for battle game between people.

[0017] Compared with related technologies, the method for flexibly customizing simulation models for dynamic game-based adversarial scenarios provided by this invention has the following beneficial effects: This invention provides a method for flexibly customizing simulation models for dynamic game-theoretic scenarios. It involves constructing flexibly customizable combat entity models and game-theoretic scenarios. In constructing the combat entity models, an equipment parameter library is first built. Then, guided by this library, mechanical and electromagnetic domain models are constructed. Based on this, detachable equipment model components are generated to adapt to the model combination requirements of different scenarios. Finally, flexible assembly of the models is achieved through the combination of flexibly customizable combat entity models. Simultaneously, at the scenario and rule level, an environmental resource library is constructed based on geospatial and environmental data, containing scenario data for different applications and simulation levels. This enables the construction of flexibly customizable game-theoretic scenarios, serving as the simulation environment. This makes the decomposition and construction patterns of models, resources, and rules more rational, improving the scalability and adaptability of the game-theoretic simulation system. The training method requires less manual operation in model combination, significantly reducing model design and construction time. It allows game-theoretic simulations to quickly extract and reconstruct new application scenarios from existing components and resource libraries. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the structure of the first embodiment of a method for flexibly customizing simulation models for dynamic game adversarial scenarios provided by the present invention; Figure 2 for Figure 1 The diagram shown illustrates the flexible customization of a component-based combat entity model. Figure 3 This is a schematic diagram of the second embodiment of the method for flexibly customizing simulation models for dynamic game adversarial scenarios provided by the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] First Embodiment Please refer to the following: Figure 1 and Figure 2 ,in, Figure 1 This is a schematic diagram of the structure of the first embodiment of a method for flexibly customizing simulation models for dynamic game adversarial scenarios provided by the present invention; Figure 2 for Figure 1 The diagram illustrates the flexible customization of a component-based combat entity model. A method for flexibly customizing a simulation model for dynamic game-theoretic scenarios includes the following steps: S1: Constructing a flexible and customizable combat entity model: S101: Equipment Parameter Library Construction: Establish detailed equipment parameter resources, covering the performance parameters of various infrastructures, weapon platforms, platform components, etc., to ensure that the model can accurately reflect the actual effectiveness of the equipment during the simulation process, provide accurate physical and design parameters for the component decomposition and independent modeling of mechanical domain, electromagnetic domain and other models, provide a unified reference standard for docking parameters, and directly provide combat-related attribute data for the model; S102: Constructing Mechanical Domain Models: Constructing physical models of combat units covering the "air, land, sea, and underwater" mechanical domains, which can be deployed in batches on simulation platforms. Specifically, it is for the construction of detailed 3D physical models of red and blue combat units, providing realistic visual effects and accurate physical behavior through high-precision modeling technology. 1. Construction Method: The principle of modular construction of mechanical domain entity models is to determine whether an entity can be deployed, disassembled, functioned, or destroyed independently, based on the needs of the simulation. For example, a tank as a single combat platform is not further subdivided, while a Patriot air defense and anti-missile system can be subdivided into components such as launchers, tractors, and radar vehicles. 2. Assembly Basis: The assembly of the model is mainly based on physical design specifications (i.e., drawings, computer graphics, etc.), kit configuration schemes, and operation manuals. The assembly interfaces between components and different platforms are designed, and automatic matching is performed based on standardized interface descriptions. 3. Level of detail: Using LOD technology and integrated mapping technology, different models are matched according to configuration files at the strategic, campaign, and tactical levels. For example, at the campaign level and in a wide-range view, detailed models of individual equipment are not displayed, only units above the company level and key equipment are displayed. S103: Constructing Electromagnetic Domain Models: Constructing electronic reconnaissance models, electronic jamming models, radar detection models, IFF models, communication models, etc., forming an electromagnetic domain entity model library that covers electromagnetic elements of reconnaissance, jamming, detection, identification, and communication. The electromagnetic domain entity models are deployed on various mechanical domain models (such as fighter jets, ships, ground armored vehicles, and command vehicles) based on the principles of "modular mounting, on-demand deployment, and interface adaptation," achieving flexible configuration of "one machine with multiple electronic systems." S104: Component-based weapon and equipment modeling: Based on mechanical and electromagnetic domain models, component-based modeling technology is used to decompose the physical model into multiple relatively independent functional modules, establish relatively independent interaction interfaces, and realize the construction of different functional models through flexible assembly, which facilitates model interchangeability, upgrading and division of labor in research and development. 1. Platform Model Components: Combat platforms such as aircraft, ships, drones, and satellites serve as carriers for electromagnetic domain models and other combat components, providing the basis for the physical integration and coordinated operation of these components.

[0021] 2. Sensor Model Components: Radar, optical, electronic and other detection and reconnaissance components to realize the detection, identification and location of battlefield targets (electromagnetic signals, physical targets), and provide raw perception data for command and decision-making, fire strikes and electronic warfare.

[0022] 3. Data Processing Model Components: These include modules for receiving data, parsing data, filtering data, fusing data, and outputting results. They serve as the core hub for data flow, processing raw, perceived data and interactive data to enhance data usability and value, supporting accurate decision-making.

[0023] 4. Weapon System Model Components: Various missiles, artillery, torpedoes, and other weapons receive combat commands; acquire target data from sensor model components and data processing model components; are constrained by the engagement rule base (such as strike permissions and target priority); and obtain ammunition status by associating with ammunition model components.

[0024] 5. Jamming Equipment Model Components: Various electronic jamming and electronic warfare systems acquire enemy radiation source information from sensor model components; receive jamming commands from the command and control module; and coordinate with the strategy library to obtain the optimal jamming strategy.

[0025] 6. Communication Equipment Model Component: Constructs information transmission links between combat units to realize command and control instructions, situational data sharing, and combat status feedback. It works in conjunction with the command and control module to achieve bidirectional transmission of instructions and data. It obtains parameters affecting electromagnetic interference, terrain, and weather from the environmental resource database and transmits the received external data to the data processing model component.

[0026] 7. Ammunition Model Components: Missiles, shells, and other lethal components provide ammunition support for weapon system model components, clarify the usability and combat effectiveness of ammunition, and ensure the implementation of combat operations such as fire strikes and electronic jamming (chaff). S105: Constructing a Flexible and Customizable Combat Entity Model: Based on the constructed mechanical domain entity model and electromagnetic domain entity model, a combined model simulating combat entities is constructed. This combat entity model acts as a container, using a component manager to organize and schedule components within the container. Externally, it presents itself as a combined combat entity model, equipped with behavioral components, giving it behavioral capabilities. The component-based combat entity model is assembled as follows: Figure 2 As shown, the early warning aircraft simulation model consists of platform components, radar components, communication components, and electronic reconnaissance components. By configuring different attribute parameters for the components, different types of combat entity simulation models of the same type can be assembled. For example, for missile simulation models, by configuring different parameters for the platform components and weapon components, weapon types with different uses and capabilities can be constructed. A weapon system entity model can only have one platform model component, and other model components can be arbitrarily combined and expanded according to research needs. S2: Constructing flexibly customizable game simulation scenarios: S201: Constructing an environmental resource database: The environmental resource database includes basic base map image data, terrain data, and high-precision models of military bases, providing rich geographic and environmental information, supporting high-fidelity rendering and dynamic updates of the battlefield environment, and enhancing the realism and immersion of the simulation. S202: Constructing Flexible and Customizable Game Theory Scenarios: Rapidly responding to game theory simulation needs, flexibly customizing diverse game theory simulation scenarios ranging from tactical-level individual combat to strategic-level system-wide game confrontation. The main process is as follows: 1. Relying on technologies such as 3D scene clipping, rapid rendering, rapid reconstruction of battlefield environment data, online construction of simulation scenarios, situation display, and command and control, the system enables rapid customization and generation of battlefield environment data for game simulation scenarios.

[0027] 2. It supports users to quickly customize models, images, terrain, situations, etc. corresponding to the game simulation scenario through a visual configuration interface such as layer management and simulation model construction, and generate the game simulation scenario required by the user.

[0028] 3. Employing technologies such as multi-scale spatial data visualization based on spatiotemporal data models and simulation for joint operations, it supports users in achieving seamless integration from strategic-level operational situation to tactical-level individual soldier behavior.

[0029] 4. Based on the configurability and flexibility of the entity model attributes, it supports the construction of game simulation scenarios that can be flexibly customized. By adjusting parameters such as the type, quantity, and combat effectiveness of the platform, weapons, ammunition, and sensors, it can adapt to the game simulation needs of different adversarial intensities and mission types. S203: Building a rule model: The rule base construction is responsible for building comprehensive simulation rules to ensure that the interactive behavior during the simulation process conforms to the actual combat specifications and supports the simulation of multi-service joint operations; S204: Constructing a strategy model: The strategy library is built on the rule library and is used to solidify combat tactics and methods, support intelligent decision-making and scheme selection in the simulation process, such as air defense and anti-missile strategies, formation air combat strategies, and ship formation interception strategies. S205: Constructing a command model: Through modular design, it realizes hierarchical management of command of combat units, control of command transmission and situational information interaction, supports the efficient transmission of command decisions and real-time perception of battlefield situation during simulation. The command and control model adopts a three-level architecture of "platform-module-unit". Each combat platform (such as early warning aircraft, destroyer, command vehicle) is equipped with an independent command and control module. The command and control module is associated with subordinate combat units through the command and control relationship table to form a tree-like command system. S206: Co-simulation Scalable Rule Framework: Combining parametric modeling technology, rules are instantiated and customized by configuring rule parameters, breaking down rules into finer-grained elements, and constructing specific rules through combination methods.

[0030] The method for constructing the equipment parameter database in step 1 includes authoritative data extraction, measured data supplementation, simulation data verification, and historical simulation data accumulation. The authoritative data extraction is used to extract basic parameters from authoritative documents such as equipment design manuals, military standards and specifications, and official technical manuals. The measured data supplementation is used to collect accurate data on core equipment components using methods such as 3D scanning and performance testing. The simulation data verification is used to simulate and calculate the equipment's motion parameters, load-bearing capacity, and damage resistance using professional simulation tools, and then cross-verify them with the measured data. The historical simulation data accumulation is used to collect effective parameters that have been verified in actual combat from past military simulations and add them to the parameter database.

[0031] The electromagnetic domain model mentioned in step 1 includes electronic reconnaissance models, electronic jamming models, radar detection models, friend-or-foe identification models, and communication models.

[0032] Electronic reconnaissance models: frequency coverage, reconnaissance performance, positioning accuracy, etc.; Electronic jamming models include: jamming coverage, jamming performance, jamming modes, and self-constraints. Radar detection models: operating parameters, detection performance, scanning parameters, environmental impact; Friend or Foe identification models: identification parameters, identification performance, security parameters; Communication-related models: communication parameters, communication performance, and environmental adaptation.

[0033] The construction of the environmental resource database in step 2 includes geospatial data acquisition and environmental parameter acquisition technologies. The geospatial data acquisition uses satellite remote sensing, UAV aerial surveying, topographic mapping radar and other means to obtain topographic elevation data, land feature type data, road network and water system distribution data of the combat area. The environmental parameter acquisition technology uses meteorological stations, electromagnetic monitoring equipment, hydrological sensors and other means to collect real-time or historical meteorological data, electromagnetic environment data and hydrological data.

[0034] The rule model in step 2 includes basic interaction rules, combat action rules, environmental impact rules, collaborative linkage rules, and victory / defeat determination rules.

[0035] Basic interaction rules: target recognition rules, friend-or-foe distinction rules, distance determination rules; Operational rules: Maneuver rules, firepower strike rules, protection rules, and supply rules; Environmental impact rules: meteorological impact rules, electromagnetic interference rules, and topographic constraint rules; Coordination and linkage rules: formation coordination rules, unit cooperation rules, command and control rules; Rules for determining victory or defeat: mission completion rules, damage assessment rules, and battle situation simulation rules.

[0036] The strategy model construction process in step 2 includes building a scenario-based strategy system, realizing the modularization and parameterization of strategies, connecting with the rule base and parameter base, and supporting intelligent inference decision-making.

[0037] Construct a scenario-based strategy system: covering typical combat scenarios such as air defense and missile defense, formation air combat, and ship formation interception, forming a hierarchical strategy library of "basic tactics - advanced tactics - collaborative solutions" for each scenario.

[0038] Modularization and parameterization of strategies: Complex strategies are broken down into reusable tactical modules. Triggering conditions and execution thresholds for tactical actions are defined by parameters, supporting flexible combination and dynamic adjustment.

[0039] Establish linkage with the rule base and parameter base: Strategy execution relies on the constraint logic of the combat rule base and the performance data of the equipment parameter base to ensure the feasibility and accuracy of the strategy.

[0040] Intelligent support for simulation decision-making: Provides the simulation system with a basis for strategy recommendation, scheme comparison and effectiveness evaluation, and assists simulation personnel in making operational decisions quickly.

[0041] The command model in step 2 includes a combat platform, a command and control module, and combat units. The combat platform is used for platforms with command capabilities such as early warning aircraft, ships, and ground command centers. The command and control module is used for command and control relationship management units, command transmission and reception units, situation display units, and information collection units. The combat units are used for subordinate execution units such as fighter jets, missile launchers, and radar stations.

[0042] The co-simulation extensible rule framework mentioned in step 2 includes a rule metadata management module, a basic rule template module, a rule configuration and editing module, a rule parsing and execution module, and a dynamic rule adaptation module.

[0043] Rule metadata management module: Responsible for defining and standardizing the basic information of rules, providing a unified metadata specification for all rules. Basic rule template module: Construct a basic rule template library covering core combat aspects such as "electromagnetic countermeasures, firepower strikes, communication interaction, and friend-or-foe identification". Each template contains a standardized rule logic framework and configurable parameter items, and users can quickly adjust parameters based on the template to generate custom rules.

[0044] Rule configuration and editing module: Provides a visual rule configuration interface and flexible rule editing tools, allowing users to build custom rules based on basic rule templates or from scratch. Supports graphical drag-and-drop configuration of rule logic (such as building "IF-THEN-ELSE" logic links) and provides precise editing functions for rule parameters (such as configuring numerical parameters, enumeration parameters, and logical parameters).

[0045] Rule parsing and execution module: responsible for converting the configured rules into executable code that the simulation system can recognize, receiving battlefield situation data in real time (from the data processing model component), and triggering the condition judgment and execution logic of the rules.

[0046] Dynamic rule adaptation module: Enables dynamic matching and adaptive adjustment of rules and simulation scenarios. Based on changes in battlefield situation, equipment status, and mission objectives during the simulation process, it automatically adjusts the effective status and parameter configuration of the rules.

[0047] Compared with related technologies, the method for flexibly customizing simulation models for dynamic game-based adversarial scenarios provided by this invention has the following beneficial effects: This invention provides a method for flexibly customizing simulation models for dynamic game-theoretic scenarios. It involves constructing flexibly customizable combat entity models and game-theoretic scenarios. In constructing the combat entity models, an equipment parameter library is first built. Then, guided by this library, mechanical and electromagnetic domain models are constructed. Based on this, detachable equipment model components are generated to adapt to the model combination requirements of different scenarios. Finally, flexible assembly of the models is achieved through the combination of flexibly customizable combat entity models. Simultaneously, at the scenario and rule level, an environmental resource library is constructed based on geospatial and environmental data, containing scenario data for different applications and simulation levels. This enables the construction of flexibly customizable game-theoretic scenarios, serving as the simulation environment. This makes the decomposition and construction patterns of models, resources, and rules more rational, improving the scalability and adaptability of the game-theoretic simulation system. The training method requires less manual operation in model combination, significantly reducing model design and construction time. It allows game-theoretic simulations to quickly extract and reconstruct new application scenarios from existing components and resource libraries.

[0048] Second Embodiment Please refer to the following: Figure 3 Based on the first embodiment of this application, which provides a method for flexibly customizing simulation models for dynamic game-based adversarial scenarios, the second embodiment of this application proposes another method for flexibly customizing simulation models for dynamic game-based adversarial scenarios. The second embodiment is merely a preferred embodiment of the first embodiment, and the implementation of the second embodiment will not affect the separate implementation of the first embodiment.

[0049] Specifically, the second embodiment of this application provides a method for flexibly customizing simulation models for dynamic game-based adversarial scenarios, which differs in that it also includes a switching module. The switching module is connected to the joint simulation scalable rule framework and is used to switch between the intelligent battle module, the human-machine collaboration module, the human-machine game module, and the battle game module.

[0050] The intelligent battle module is used for two-player self-play by selecting scenarios, modes, tactical styles, and tactical levels. The human-machine collaboration module is used for human-machine collaborative combat by providing options. The human-machine game module is used for human-machine game by selecting tactical levels. The battle game module is used for battle game between people.

[0051] The intelligent battle module provides standardized configuration options through the interface. Users complete the selection of basic information in sequence. All configurations support quick selection / custom adjustment, specifically: Scene selection: Retrieve customized tactical / campaign-level scenes from the scene library (such as air formation combat, naval interception, and ground armored assault), or quickly create simple scenes (select basic terrain and environmental parameters). Combat mode selection: Configure according to combat type (such as attack and defense mode, confrontation mode, escort mode, interception mode), and define mode rules (such as in attack and defense mode, the attacking side's task is to destroy the target and the defending side's task is to protect the target, and clarify the core indicators for determining victory or defeat). Participant Model Configuration: Configure combat entity models for the red and blue AI, support quick retrieval of preset formations from the model library (such as red fighter formations and blue destroyer formations), and also allow manual combination of models (select platform + mount components + parameter adjustment), and set the initial deployment positions, troop size, and initial equipment status of both sides. Parameter configuration for simulation: Set the simulation time, step size, situation refresh frequency, and environmental dynamic parameters (such as meteorological changes and random triggering of electromagnetic interference).

[0052] The human-machine collaboration module first completes the core configuration of the collaboration mode and participating parties, clarifying the roles of humans and AI and the adversaries, specifically: Collaboration Mode Selection: Three core collaboration modes are provided, with support for custom expansion: ① Offensive Collaboration: Humans command the attack, while AI is responsible for fire support and flank cover; ② Defensive Collaboration: Humans command the defense, while AI is responsible for situational reconnaissance, logistical support, and emergency support; ③ Global Collaboration: Humans are responsible for core decisions (such as target allocation and tactical selection), while AI is responsible for the execution of all tactical actions. Combatant configuration: Clearly define our side (human + AI) and the enemy side (AI / human). Configure combat entity models for our side (core formations commanded by humans + formations / components assisted by AI), and configure models and tactical levels for the enemy side. At the same time, select simulation scenarios and adversarial rules, which are consistent with the scenario configuration logic of the intelligent combat module. Collaborative permission configuration: Define the decision-making permissions of humans and the execution permissions of AI. For example, humans can select collaborative actions of AI and adjust the tactical intensity of AI. AI can only execute actions specified by humans and has no autonomous decision-making permissions.

[0053] The human-machine game module clarifies the basic information of the human player and the AI ​​opponent by completing the core configuration of game difficulty, scenario, and participating parties, specifically: Game difficulty selection: It is tied to the AI's tactical level and offers four difficulty levels: beginner, intermediate, advanced, and custom. The difficulty corresponds to the AI's tactical style, strategy pool, and situational analysis ability. Beginner AI has simple tactics, while advanced AI supports complex tactical combinations and dynamic adaptation. Scenario and Mode Selection: Select a simulation scenario from the scenario library (such as tactical-level air combat, maritime interception), select the combat mode (attack / defense / combat / interception), and define the victory / defeat criteria (such as the human side winning by destroying the target, and the AI ​​side winning by completing the defense). Participant model configuration: 1. Human side: Humans can freely combine combat entity models from the model library, configure troop size, equipment parameters, and initial deployment positions, and support multiple adjustments before the battle; 2. AI enemy side: The module automatically configures the corresponding model formation for the AI ​​according to the difficulty of the game, and the AI's model and tactical style can also be manually adjusted by the human to achieve personalized confrontation.

[0054] The battle and gaming module allows users to create or join rooms through its room management function. The module provides a complete workflow for room creation, retrieval, joining, and disbanding, specifically: Create a room: A user can create a battle room as the room owner and set the room name, password (optional), and battle type (two-player battle / multiplayer team battle). Room core configuration: The host completes the basic configuration for the battle: 1. Select the simulation scenario, combat mode, and victory / defeat rules; 2. Set simulation parameters (simulation time, troop loss threshold, situation refresh frequency); 3. Set team rules (when playing in multiplayer teams, define the number of teams and the number of players per team). Joining a room: Other users can search for a room by name / number, enter a password (if any) to join, and the system will automatically assign opponents / teams. The room owner can adjust the team affiliation.

[0055] Compared with related technologies, the method for flexibly customizing simulation models for dynamic game-based adversarial scenarios provided by this invention has the following beneficial effects: This invention provides a method for flexibly customizing simulation models for dynamic game-based combat scenarios. By using a switching module, it allows switching between intelligent combat modules, human-machine collaboration modules, human-machine game modules, and combat game modules, thereby increasing the functionality of military game-based combat and enabling better coordination for personnel training.

[0056] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for flexibly customizing simulation models for dynamic game-theoretic scenarios, characterized in that, Includes the following steps: S1: Constructing a flexible and customizable combat entity model: S101: Equipment Parameter Database Construction: Establish a comprehensive equipment parameter resource, covering the performance parameters of various infrastructures, weapon platforms, platform components, and other equipment. S102: Constructing a mechanical domain model: Constructing entity models of combat units covering the "space, air, land, sea, and underwater" mechanical domains; S103: Construct electromagnetic domain models: Construct electronic reconnaissance models, electronic jamming models, radar detection models, friend-or-foe identification models, communication models, etc., to form an electromagnetic domain entity model library that covers electromagnetic elements of reconnaissance, jamming, detection, identification, and communication. S104: Component-based weapon and equipment modeling: Based on the mechanical and electromagnetic domain models, component-based modeling technology is used to decompose the entity model into multiple relatively independent functional modules and establish relatively independent interaction interfaces. S105: Constructing a flexible and customizable combat entity model: Based on the constructed mechanical domain entity model and electromagnetic domain entity model, a combined model simulating combat entities is created. S2: Constructing flexibly customizable game simulation scenarios: S201: Constructing an environmental resource database: The construction of an environmental resource database includes basic base map image data, topographic data, and high-precision models of military bases, etc. S202: Constructing flexibly customizable game simulation scenarios: Quickly responding to game simulation needs and flexibly customizing diversified game simulation scenarios from tactical-level individual combat to strategic-level system-wide game combat. S203: Building a rule model: The rule base construction is responsible for building comprehensive simulation rules to ensure that the interactive behavior during the simulation process conforms to the actual combat specifications and supports the simulation of multi-service joint operations; S204: Constructing a strategy model: The strategy library is built on top of the rule library and is used to solidify practical tactics and methods, and support intelligent decision-making and scheme selection during the simulation process; S205: Constructing a command model: Through modular design, it realizes the hierarchical management of the command of combat units, the control of command transmission and the interaction of situational information, and supports the efficient transmission of command decisions and real-time perception of the battlefield situation during the simulation process. S206: Co-simulation Scalable Rule Framework: Combining parametric modeling technology, rules are instantiated and customized by configuring rule parameters, breaking down rules into finer-grained elements, and constructing specific rules through combination methods.

2. The method for flexibly customizing simulation models for dynamic game-based adversarial scenarios according to claim 1, characterized in that, The method for constructing the equipment parameter database in step 1 includes authoritative data extraction, measured data supplementation, simulation data verification, and historical simulation data accumulation. The authoritative data extraction is used to extract basic parameters from authoritative documents such as equipment design manuals, military standards and specifications, and official technical manuals. The measured data supplementation is used to collect accurate data on core equipment components using methods such as 3D scanning and performance testing. The simulation data verification is used to simulate and calculate the equipment's motion parameters, load-bearing capacity, and damage resistance using professional simulation tools, and then cross-verify them with the measured data. The historical simulation data accumulation is used to collect effective parameters that have been verified in actual combat from past military simulations and add them to the parameter database.

3. The method for flexibly customizing simulation models for dynamic game-based adversarial scenarios according to claim 1, characterized in that, The electromagnetic domain model mentioned in step 1 includes electronic reconnaissance models, electronic jamming models, radar detection models, friend-or-foe identification models, and communication models.

4. The method for flexibly customizing simulation models for dynamic game-based adversarial scenarios according to claim 1, characterized in that, The construction of the environmental resource database in step 2 includes geospatial data acquisition and environmental parameter acquisition technologies. The geospatial data acquisition uses satellite remote sensing, UAV aerial surveying, topographic mapping radar and other means to obtain topographic elevation data, land feature type data, road network and water system distribution data of the combat area. The environmental parameter acquisition technology uses meteorological stations, electromagnetic monitoring equipment, hydrological sensors and other means to collect real-time or historical meteorological data, electromagnetic environment data and hydrological data.

5. The method for flexibly customizing simulation models for dynamic game-based adversarial scenarios according to claim 1, characterized in that, The rule model in step 2 includes basic interaction rules, combat action rules, environmental impact rules, collaborative linkage rules, and victory / defeat determination rules.

6. The method for flexibly customizing simulation models for dynamic game-based adversarial scenarios according to claim 1, characterized in that, The strategy model construction process in step 2 includes building a scenario-based strategy system, realizing the modularization and parameterization of strategies, connecting with the rule base and parameter base, and supporting intelligent inference decision-making.

7. The method for flexibly customizing simulation models for dynamic game-based adversarial scenarios according to claim 1, characterized in that, The command model in step 2 includes a combat platform, a command and control module, and combat units. The combat platform is used for platforms with command capabilities such as early warning aircraft, ships, and ground command centers. The command and control module is used for command and control relationship management units, command transmission and reception units, situation display units, and information collection units. The combat units are used for subordinate execution units such as fighter jets, missile launchers, and radar stations.

8. The method for flexibly customizing simulation models for dynamic game-based adversarial scenarios according to claim 1, characterized in that, The co-simulation extensible rule framework mentioned in step 2 includes a rule metadata management module, a basic rule template module, a rule configuration and editing module, a rule parsing and execution module, and a dynamic rule adaptation module.

9. The method for flexibly customizing simulation models for dynamic game-based adversarial scenarios according to claim 1, characterized in that, The co-simulation scalable rule framework is connected to a switching module, which is used to switch between the intelligent battle module, the human-machine collaboration module, the human-machine game module, and the battle game module.

10. The method for flexibly customizing simulation models for dynamic game-based adversarial scenarios according to claim 9, characterized in that, The intelligent battle module is used for two-player self-play by selecting scenarios, modes, tactical styles, and tactical levels. The human-machine collaboration module is used for human-machine collaborative combat by providing options. The human-machine game module is used for human-machine game by selecting tactical levels. The battle game module is used for battle game between people.

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