Intelligent virtual simulation and situation system based on serious game

Abstract

This invention relates to the field of virtual simulation technology, specifically an intelligent virtual simulation and situational awareness system based on serious games. It comprises, from top to bottom, a serious game interaction layer, a situational analysis and decision support layer, and an intelligent virtual simulation layer. The serious game interaction layer provides gamified interaction entry points and incentive mechanisms for users such as trainers and decision-makers, and is responsible for collecting user operations, displaying feedback information, and outputting incentive elements. The situational analysis and decision support layer is responsible for collecting real-time data during the simulation process, performing intelligent analysis and evaluation, and generating situational views and decision suggestions. The intelligent virtual simulation layer constructs a virtual environment and intelligent agents based on artificial intelligence technology, realizing dynamic scene adaptation, entity behavior simulation, and user operation response.

Description

Technical Field

[0001] This invention relates to the field of virtual simulation technology, specifically to an intelligent virtual simulation and situational awareness system based on serious games. Background Technology

[0002] Serious games are interactive application systems that integrate gamification design elements and are designed for non-entertainment purposes such as training and decision support. They differ from purely entertainment games and are used in military training and immersive learning. Intelligent virtual simulation is based on intelligent decision-making algorithms to construct a high-fidelity, interactive, and adaptive virtual environment that simulates the physical rules, system behaviors, and personnel operations in real-world scenarios, enabling the digital replication and dynamic simulation of complex objects or processes.

[0003] With the acceleration of digital transformation, virtual simulation technology has been widely applied in various fields such as military training and education, becoming an important means of replacing real-world scenarios for skills training and decision-making simulations. Meanwhile, the rapid development of artificial intelligence technology provides technical support for the intelligent upgrading of virtual simulation, while gamification design concepts offer advantages in enhancing user interaction and engagement. Against this backdrop, the integration of serious games, virtual simulation, and intelligence has become a technological development trend, and the demand is increasingly urgent.

[0004] The military field needs high-fidelity, adaptive virtual training systems to simulate complex battlefield environments, which can help improve soldiers' tactical decision-making capabilities and collaborative combat skills.

[0005] The education and training sector needs to enhance the practical skills of students and professionals through interactive and immersive virtual scenarios to compensate for the shortcomings of traditional theoretical teaching.

[0006] Currently, virtual simulation technology has upgraded from static visualization to dynamic interaction, but it still has shortcomings in areas such as intelligence, adaptability, and user experience optimization. Existing technologies have seen some solutions attempt to introduce gamification elements to enhance user engagement, but a complete closed loop of intelligent simulation – situation analysis – gamified incentives has not yet been formed.

[0007] In terms of intelligence, most systems still rely on fixed rules and lack dynamic optimization capabilities based on machine learning, making it impossible to continuously iterate scenario logic based on user behavior data;

[0008] In terms of situational awareness methods, most approaches remain at the level of data display, lacking in-depth analysis of situational data and intelligent decision support.

[0009] In terms of gamification design, most of them are simply stacking up incentive elements, without being deeply integrated with the simulation task objectives and situation analysis results, resulting in limited incentive effects.

[0010] Existing virtual simulation technologies are mostly script-driven, resulting in poor scenario adaptability: Traditional virtual simulation systems rely on pre-set scripts for scenario logic and task flows, failing to dynamically adjust based on user behavior and external environmental changes. This limits their effectiveness in simulating complex and ever-changing real military training scenarios, making it difficult to meet personalized and differentiated application needs. There is a disconnect between situational awareness and decision support: Existing technologies focus primarily on the visualization of virtual scenarios, lacking intelligent analysis capabilities for real-time situational data. They cannot automatically identify key information or predict potential risks, forcing decision-makers / trainers to manually process large amounts of data, leading to low decision-making efficiency and difficulty in ensuring accuracy. User engagement and training efficiency are imbalanced: Traditional virtual simulation systems have cumbersome operation processes and limited interaction methods, lacking effective incentive and guidance mechanisms. This easily leads to user fatigue and insufficient engagement, impacting training effectiveness or work progress efficiency, especially in complex task simulation scenarios. Simulation results do not closely match real-world scenarios: Existing technologies rely heavily on simplified models for simulating the behavior of complex systems, lacking dynamic optimization capabilities combined with artificial intelligence. This results in discrepancies between simulated entity behavior and environmental feedback and real-world scenarios, limiting the reference value and guidance significance of simulation results. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an intelligent virtual simulation and situational awareness system based on serious games.

[0012] An intelligent virtual simulation and situational awareness system based on serious games includes, from top to bottom, a serious game interaction layer, a situational analysis and decision support layer, and an intelligent virtual simulation layer. The serious game interaction layer provides gamified interaction entry points and incentive mechanisms for users such as trainers and decision-makers, and is responsible for collecting user operation data, displaying feedback information, and outputting incentive elements. The situational analysis and decision support layer is responsible for collecting real-time data during the simulation process, performing intelligent analysis and evaluation, and generating situational views and decision suggestions. The intelligent virtual simulation layer constructs a virtual environment and intelligent agents based on artificial intelligence technology to achieve dynamic scene adaptation, entity behavior simulation, and user operation response.

[0013] Furthermore, the serious game interaction layer includes a 3D immersive interactive interface, a task system management interface, and an incentive mechanism management interface. The 3D immersive interactive interface supports multi-terminal operation, and its layout integrates various gamification elements, simplifying the operation process and enhancing immersion. The task system management interface breaks down training objectives and decision-making task objectives into main tasks and side tasks. Main tasks correspond to core requirements, while side tasks correspond to auxiliary requirements. The difficulty of the tasks is dynamically adjusted according to the user's proficiency. The incentive mechanism management interface sets up diverse incentive elements, and the incentive element data is correlated with the situation analysis results. Incentive information is fed back in real time, strengthening the user's motivation to participate.

[0014] Furthermore, the situation analysis and decision support layer includes a data acquisition module, an intelligent analysis module, a situation visualization module, and a decision support module. The data acquisition module collects environmental data, behavioral data, and task data in real time through sensor simulation interfaces, intelligent agent state interfaces, and user operation interfaces. The intelligent analysis module uses a decision tree-based machine learning algorithm to quickly process the collected real-time data, identify key information, and conduct threat assessments. The situation visualization module adopts a multi-dimensional visualization display method to facilitate users to quickly obtain key information. The decision support module generates targeted decision suggestions based on real-time analysis results and provides suggestion priority ranking to assist users in making reasonable decisions quickly.

[0015] Furthermore, the intelligent virtual simulation layer includes virtual environment modeling, agent design, and adaptive adjustment. The virtual environment modeling adopts a modeling method that combines a basic model with dynamic plugins. The agent design embeds multiple types of agents and adopts a hybrid driving mode of rule base and machine learning algorithm. The adaptive adjustment introduces a behavior adaptive algorithm, collects user operation data and simulation status data in real time, and dynamically adjusts the agent behavior by analyzing the user's skill level, operation habits, and current task progress through the algorithm.

[0016] In summary, the present invention has the following beneficial effects:

[0017] 1. Deep integration of situational analysis and decision support: Through intelligent analysis algorithms, real-time data can be processed and deeply mined. It can not only visualize the situational status, but also proactively predict threats and generate targeted decision suggestions, thereby improving the efficiency and accuracy of user decision-making and solving the pain points of large amounts of data and difficult decision-making.

[0018] 2. Enhanced User Engagement and Goal Achievement: By introducing serious gamification design, reasonable task breakdown, diverse incentive mechanisms, and immersive interactive experience, the system effectively enhances user engagement and focus, reduces operational fatigue, and significantly improves training effectiveness or work progress efficiency, especially in long-term training and complex task simulation scenarios.

[0019] 3. High simulation realism and intelligence level: The intelligent agent design is driven by a hybrid rule base and machine learning, combined with high-fidelity virtual environment modeling, which makes the environmental changes and entity behaviors in the simulation process closer to the real scene. At the same time, the model is continuously improved by iteratively optimizing the model through historical data. Attached Figure Description

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0021] Figure 1This is a structural block diagram of an intelligent virtual simulation and situational awareness system based on serious games according to the present invention;

[0022] Figure 2 This is a flowchart of the workflow of an intelligent virtual simulation and situational awareness system based on serious games according to the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The present invention will be further described below with reference to the accompanying drawings:

[0025] like Figure 1 As shown, an intelligent virtual simulation and situational awareness system based on serious games includes, from top to bottom, a serious game interaction layer, a situational analysis and decision support layer, and an intelligent virtual simulation layer.

[0026] The serious game interaction layer provides gamified interaction entry points and incentive mechanisms for trainers, decision-makers, and other users. It is responsible for collecting user actions, displaying feedback information, and outputting incentive elements. This layer includes a 3D immersive interactive interface, a task system management interface, and an incentive mechanism management interface. The 3D immersive interactive interface supports multi-terminal operation, and its layout integrates various gamified elements, simplifying the operation process and enhancing immersion. The task system management interface breaks down training and decision-making tasks into main tasks and side tasks. Main tasks correspond to core requirements, while side tasks correspond to auxiliary requirements. The difficulty of the tasks is dynamically adjusted according to the user's proficiency. The incentive mechanism management interface sets up diverse incentive elements, with incentive element data linked to situational analysis results. Incentive information is fed back in real time, strengthening user motivation.

[0027] In this embodiment, the serious game interaction layer provides a gamified interactive interface and incentive mechanism to enhance user engagement, while collecting user operation data and providing feedback on simulation results and situation information.

[0028] The 3D immersive interactive interface supports operation on multiple terminals such as keyboard, mouse, and VR devices. The interface layout incorporates gamified elements such as task panels, status indicators, and shortcut operation buttons to simplify the operation process and enhance the immersive experience.

[0029] The task management interface breaks down training objectives and decision-making task objectives into main tasks and branch tasks. Main tasks correspond to core requirements, while branch tasks correspond to auxiliary requirements. The difficulty of the tasks is dynamically adjusted according to the user's proficiency.

[0030] The incentive mechanism management interface features diverse incentive elements, including points earned for completing tasks and achieving operational standards, levels gained through accumulated points, badges awarded for completing special tasks and achieving outstanding results, and leaderboards. Incentive data is linked to situational analysis results; for example, high operational efficiency and high decision-making accuracy can earn extra points. Incentive information is also provided in real time through pop-ups, sound effects, and other means to enhance user engagement.

[0031] The situation analysis and decision support layer is responsible for collecting real-time data during the simulation process, performing intelligent analysis and evaluation, and generating situation views and decision suggestions. This layer includes a data acquisition module, an intelligent analysis module, a situation visualization module, and a decision support module. The data acquisition module collects environmental, behavioral, and task data in real time through sensor simulation interfaces, agent state interfaces, and user operation interfaces. The intelligent analysis module uses a decision tree-based machine learning algorithm to quickly process the collected real-time data, identify key information, and conduct threat assessments. The situation visualization module uses a multi-dimensional visualization method to facilitate users' quick access to key information. The decision support module generates targeted decision suggestions based on the real-time analysis results and provides suggestion priority ranking to assist users in making reasonable decisions quickly.

[0032] In this embodiment, the situation analysis and decision support layer collects real-time data during the simulation process, performs intelligent analysis and evaluation, and generates a visualized situation view and decision suggestions to provide users with precise support.

[0033] The data acquisition module collects three types of core data in real time through sensor simulation interfaces, agent status interfaces, and user operation interfaces, specifically as follows: Environmental data: parameters such as temperature, humidity, and smoke concentration in the virtual environment; Behavioral data: user operation data such as operation instructions, operation time, and operation path, as well as agent behavior data such as movement trajectory, interactive behavior, and task completion status; Task data: task progress such as the number of completed sub-tasks, remaining time, operation accuracy, and resource consumption.

[0034] The intelligent analysis module uses a decision tree-based machine learning algorithm to quickly process the collected real-time data, identify key information, and conduct threat assessments.

[0035] The situational awareness visualization module employs a multi-dimensional visualization approach, including a global situational map, detailed local views, and a data dashboard. The global situational map displays the real-time status of the entire virtual scenario, such as using different colors to mark friendly areas, enemy areas, and support routes. The detailed local views focus on key areas or entities, such as equipment operational status details and user operation trajectories. The data dashboard displays core metrics, such as mission completion rate, threat level, and remaining resources. The visualization interface supports interactive operations such as zooming, dragging, and filtering, allowing users to quickly obtain key information.

[0036] The decision support module generates targeted decision suggestions based on real-time analysis results. These suggestions are presented in the form of pop-ups, voice prompts, and annotations. The module also prioritizes suggestions based on factors such as threat level and task weight to help users make reasonable decisions quickly.

[0037] The intelligent virtual simulation layer constructs a virtual environment and intelligent agents based on artificial intelligence technology, realizing dynamic scene adaptation, entity behavior simulation, and user operation response. The intelligent virtual simulation layer includes virtual environment modeling, intelligent agent design, and adaptive adjustment. The virtual environment modeling adopts a modeling method of basic model and dynamic plug-in; the intelligent agent design embeds multiple types of intelligent agents and adopts a hybrid driving mode of rule base and machine learning algorithm; the adaptive adjustment introduces behavior adaptive algorithm, collects user operation data and simulation status data in real time, and dynamically adjusts the intelligent agent behavior by analyzing the user's skill level, operation habits, and current task progress through the algorithm.

[0038] In this embodiment, the intelligent virtual simulation layer constructs a high-fidelity, adaptive virtual environment to simulate the dynamic changes and entity behaviors of real scenes, respond to user operations, and output real-time simulation data.

[0039] Virtual environment modeling adopts a modeling approach that combines a basic model with dynamic plugins. The basic model is built based on the physical parameters, geographic information, and equipment data of the real scene, while the dynamic plugins are used to achieve real-time adjustment of environmental parameters.

[0040] The intelligent agent design incorporates multiple types of intelligent agents, including environmental intelligent agents, role intelligent agents, and equipment intelligent agents. The intelligent agents adopt a hybrid driving mode of rule base and machine learning algorithm.

[0041] a. Rule base: Preset basic behavior rules, such as moving, firing, getting in a vehicle, driving, getting out of a vehicle, etc.;

[0042] b. Machine learning algorithm: Based on historical simulation data and user operation data, the behavior of the intelligent agent is continuously optimized through reinforcement learning algorithm. Through repeated training, the intelligent agent learns better collaborative action strategies.

[0043] Adaptive Adjustment: An adaptive behavior algorithm is introduced to collect user operation data and simulation state data in real time. The user operation data includes operation speed and decision selection, while the simulation state data includes environmental changes and agent behavior state. The algorithm analyzes the user's skill level, operation habits, and current task progress to dynamically adjust the agent's behavior.

[0044] The complete workflow of an intelligent virtual simulation and situational awareness system based on serious games according to the present invention is as follows: Figure 2 As shown, a closed loop of user operation-simulation response-situation analysis-decision support-stimulus feedback is achieved through the initialization phase, interaction and simulation phase, situation analysis and decision support phase, stimulus feedback phase, and iterative optimization phase. The content is as follows:

[0045] 1. Initialization phase: After logging into the system, the user selects the application scenario and target task. The system loads the basic model, intelligent agent rule base, task system and incentive rules for the corresponding scenario to complete the initialization of the virtual simulation environment.

[0046] 2. Interaction and Simulation Phase: Users operate through the serious game interaction layer. The system collects user operation data in real time and transmits it to the intelligent virtual simulation layer. The simulation layer dynamically adjusts the behavior of the intelligent agent based on the user operation data and the intelligent agent driving logic, and outputs simulation data such as environmental data, behavioral data, and task data in real time.

[0047] 3. Situation Analysis and Decision Support Stage: The situation analysis layer receives real-time data output from the simulation layer, processes it through the intelligent analysis module, identifies key information, predicts threats, generates a visual situation view and decision suggestions, and feeds them back to the serious game interaction layer for display to the user.

[0048] 4. Incentive and Feedback Phase: The system calculates corresponding points and level upgrades based on the user's task completion, decision accuracy, operational efficiency, and situational analysis results, and provides real-time feedback to the user through the interaction layer to enhance user motivation.

[0049] 5. Iterative optimization phase: The system records user operation data, simulation data, analysis results, and stimulus data during this simulation process and stores them in the database as historical data; the agent optimization module continuously optimizes the agent's behavioral logic and decision-making model based on the historical data to improve the system's intelligence level.

[0050] In summary, this invention is not limited to the specific embodiments described above. Those skilled in the art can make various modifications and alterations without departing from the spirit and scope of this invention. The scope of protection of this invention should be determined by the claims of this invention.

[0051] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions claimed by the present invention.

Claims

1. An intelligent virtual simulation and situational awareness system based on serious games, characterized in that, The system comprises, from top to bottom, a serious game interaction layer, a situation analysis and decision support layer, and an intelligent virtual simulation layer. The serious game interaction layer provides gamified interaction entry points and incentive mechanisms for users such as trainers and decision-makers, and is responsible for collecting user operation data, displaying feedback information, and outputting incentive elements. The situation analysis and decision support layer is responsible for collecting real-time data during the simulation process, performing intelligent analysis and evaluation, and generating situation views and decision suggestions. The intelligent virtual simulation layer constructs virtual environments and intelligent agents based on artificial intelligence technology, realizing dynamic scene adaptation, entity behavior simulation, and user operation response.

2. The intelligent virtual simulation and situational awareness system based on serious games as described in claim 1, characterized in that, The serious game interaction layer includes a 3D immersive interactive interface, a task system management interface, and an incentive mechanism management interface. The 3D immersive interactive interface supports multi-terminal operation, and the interface layout integrates various gamification elements, simplifies the operation process, and enhances the sense of immersion. The task system management interface breaks down training objectives and decision-making task objectives into main tasks and branch tasks. Main tasks correspond to core requirements, while branch tasks correspond to auxiliary requirements. The difficulty of tasks is dynamically adjusted according to the user's proficiency. The incentive mechanism management interface sets up diverse incentive elements, and the incentive element data is linked to the situation analysis results. Incentive information is fed back in real time, which enhances the user's motivation to participate.

3. The intelligent virtual simulation and situational awareness system based on serious games as described in claim 2, characterized in that, The situation analysis and decision support layer includes a data acquisition module, an intelligent analysis module, a situation visualization module, and a decision support module. The data acquisition module collects environmental data, behavioral data, and task data in real time through sensor simulation interfaces, intelligent agent state interfaces, and user operation interfaces. The intelligent analysis module uses a decision tree-based machine learning algorithm to quickly process the collected real-time data, identify key information, and perform threat assessment. The situation visualization module adopts a multi-dimensional visualization display method, which makes it convenient for users to quickly obtain key information; the decision support module generates targeted decision suggestions based on real-time analysis results, and provides suggestion priority ranking to help users make reasonable decisions quickly.

4. The intelligent virtual simulation and situational awareness system based on serious games as described in claim 3, characterized in that, The intelligent virtual simulation layer includes virtual environment modeling, agent design, and adaptive adjustment. The virtual environment modeling adopts a modeling method of basic model and dynamic plug-in. The agent design embeds multiple types of agents and adopts a hybrid driving mode of rule base and machine learning algorithm. The adaptive adjustment introduces behavior adaptive algorithm, collects user operation data and simulation status data in real time, and dynamically adjusts the agent behavior by analyzing the user's skill level, operation habits and current task progress through the algorithm.

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