Self-adaptive shooting training system and method and storage medium

Abstract

The invention provides a self-adaptive shooting training system and method and a storage medium, and relates to the technical field of simulated shooting training.The system comprises a plan establishing unit used for determining a simulated shooting training plan according to training requirements; the three-dimensional modeling unit is used for building a three-dimensional training scene according to the scene related plan and the action plan and sending the three-dimensional training scene to the display equipment; the dynamic adaptation unit is used for acquiring real-time training data of a shooter; according to the real-time training data of the shooter, an adjustment strategy of scene-related planning and action planning is generated; and the updating unit is used for updating the three-dimensional training scene according to the adjustment strategy of the scene related planning and the action planning to obtain an updated three-dimensional training scene, and sending the updated three-dimensional training scene to the display equipment again. According to the invention, through a self-adaptive mechanism, training content with more challenging and adaptive performance is provided according to individual demands of a shooter, and the authenticity and effectiveness of training are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of simulated shooting training, and in particular to an adaptive shooting training system, method and storage medium. Background Art

[0002] The simulated shooting training system plays an important role in improving the shooter's personal skills. It can ensure that the shooter can conduct shooting training in a large number of different scenarios within a limited space. The simulated shooting training system can build a variety of training scenarios for the shooter. Generally, the shooter shoots according to the targets generated in each training scenario to achieve the purpose of training shooting skills.

[0003] In related technologies, although the simulated shooting training system can build a variety of training scenarios, the training scenarios of each category are usually fixed. After more training, the shooter can master the shooting rules of the training scenario, which restricts the shooter's thinking and easily forms muscle memory and stereotypes. Summary of the invention

[0004] The problem solved by the invention is how to improve the training effect of shooters during simulated shooting.

[0005] To solve the above problems, the present invention provides an adaptive shooting training system, method and storage medium.

[0006] In a first aspect, the present invention provides an adaptive shooting training system, comprising:

[0007] A plan establishment unit, used to determine a training plan for simulated shooting according to training requirements, wherein the training plan includes a scenario-related plan for the shooter to perform training and an action plan for the shooting target;

[0008] A three-dimensional modeling unit, used to build a three-dimensional training scene according to the scene-related plan and the action plan, and send the three-dimensional training scene to a display device;

[0009] A dynamic adaptation unit, configured to obtain real-time training data of the shooter when the shooter performs simulation training in the three-dimensional training scene; and generate adjustment strategies for the scene-related plan and the action plan according to the real-time training data of the shooter;

[0010] An updating unit is used to update the three-dimensional training scene according to the scene-related plan and the adjustment strategy of the action plan, obtain the updated three-dimensional training scene, and send the updated three-dimensional training scene to the display device again.

[0011] Optionally, the plan establishing unit is specifically used for:

[0012] Decomposing the training requirement to obtain at least one training target, wherein each training target corresponds to at least one indicator requirement;

[0013] Perform scene matching according to the indicator requirements of the training target to obtain a scene type, and then set the object distribution in the scene according to the scene type to obtain scene layout data; use the scene type and the scene layout data as the scene-related planning;

[0014] The action plan of the shooting target is set according to the scene arrangement data, wherein the action plan includes the appearance position, movement trajectory and speed change of the shooting target.

[0015] Optionally, the three-dimensional modeling unit is specifically used for:

[0016] According to the scene type, an initial scene model matching the scene type is retrieved from a preset 3D modeling scene library;

[0017] Adjusting the initial scene model according to the scene arrangement data to obtain a final scene model;

[0018] According to the action plan of the shooting target, the three-dimensional model of the shooting target is placed in the final scene model to obtain the three-dimensional training scene.

[0019] Optionally, the dynamic adaptation unit is specifically used for:

[0020] When the shooter performs simulation training in the three-dimensional training scene, acquiring the real-time training data of the shooter, the real-time training data including shooting data and physiological data;

[0021] Evaluating the shooting data and the physiological data respectively to obtain shooting performance data and physiological state data of the shooter;

[0022] An adjustment strategy for the scene-related planning and an adjustment strategy for the action planning are generated according to the shooting performance data and the physiological state data.

[0023] Optionally, the dynamic adaptation unit is further configured to:

[0024] Obtaining adjustment weights of the scene-related plan and the action plan according to the shooting performance data and the physiological state data;

[0025] According to the adjustment weights, an adjustment strategy for the scenario-related plan and an adjustment strategy for the action plan are generated.

[0026] Optionally, the dynamic adaptation unit is further configured to:

[0027] Obtaining the correlation between each of the training targets included in the training requirements and the scene type and the scene distribution data in the scene-related plan, and the correlation between each of the training targets and the appearance position, movement trajectory and speed change of the shooting target in the action plan;

[0028] The sum of the degrees of association between the scene type, the scene arrangement data, and the appearance position, movement trajectory, and speed change of the shooting target and each of the training targets is taken as the total value of the degrees of association between the scene type, the scene arrangement data, and the appearance position, movement trajectory, and speed change of the shooting target;

[0029] Normalization processing is performed based on the total value of the correlation between the scene type, the scene layout data, and the appearance position, movement trajectory and speed change of the shooting target to obtain the adjustment weights of the scene type, the scene layout data, and the appearance position, movement trajectory and speed change of the shooting target.

[0030] Optionally, the dynamic adaptation unit is further configured to:

[0031] Determining, according to the adjustment weights, adjustment coefficients corresponding to the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target;

[0032] Determine the adjustment values ​​corresponding to the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target, respectively, according to the maximum adjustment ranges corresponding to the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target, respectively, in combination with the adjustment coefficients of the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target;

[0033] Prioritizing the scene types, the scene layout data, and the appearance positions, movement trajectories, and speed changes of the shooting targets according to the adjustment weights to obtain an adjustment order of the scene types, the scene layout data, and the appearance positions, movement trajectories, and speed changes of the shooting targets;

[0034] The adjustment values ​​and adjustment order of the scene type and the scene layout data are used as the adjustment strategy of the scene-related planning, and the adjustment values ​​and adjustment order of the appearance position, movement trajectory and speed change of the shooting target are used as the adjustment strategy of the action planning.

[0035] Optionally, the updating unit is specifically used for:

[0036] Adjusting the current three-dimensional training scene according to the adjustment strategy of the scene-related planning and the adjustment strategy of the shooting target;

[0037] Determining whether there is a model error in the adjusted three-dimensional training scene;

[0038] If yes, re-adjust the current three-dimensional training scene until the adjusted three-dimensional training scene has no model errors;

[0039] If not, the adjusted three-dimensional training scene is rendered to obtain the updated three-dimensional training scene.

[0040] In a second aspect, the present invention provides an adaptive shooting training method, comprising:

[0041] Determine a simulated shooting training plan based on training requirements, wherein the training plan includes scenario-related planning for the shooter to conduct training and action planning for the shooting target;

[0042] Building a three-dimensional training scene according to the scene-related plan and the action plan, and sending the three-dimensional training scene to a display device;

[0043] When the shooter performs simulation training in the three-dimensional training scene, obtaining real-time training data of the shooter;

[0044] generating, according to the real-time training data of the shooter, an adjustment strategy for the scenario-related plan and the action plan;

[0045] According to the adjustment strategy of the scene-related plan and the action plan, the three-dimensional training scene is updated to obtain the updated three-dimensional training scene, and the updated three-dimensional training scene is sent to the display device again.

[0046] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned adaptive shooting training method is implemented.

[0047] The adaptive shooting training system, method and storage medium of the present invention realize the personalization and dynamic adjustment of the simulated shooting training scene through the collaborative work of the plan establishment unit, the three-dimensional modeling unit, the dynamic adaptation unit and the update unit, thereby significantly improving the training effect of the shooter. First, a targeted training plan is generated according to the training requirements by the plan establishment unit, covering the scene-related planning and the action planning of the shooting target, and providing the shooter with a variety of training scenes that meet the training goals. Secondly, the shooter's training data is obtained in real time through the dynamic adaptation unit, so as to evaluate the shooter's performance and state in real time, and generate accurate adjustment strategies accordingly. Through the dynamic adjustment capability, the training scene can be optimized in real time according to the shooter's training situation, such as reducing the target difficulty when the shooter performs poorly, or increasing the training intensity when the physiological state is good. In addition, the update unit can quickly feed back the adjusted three-dimensional training scene to the display device to ensure the real-time and coherence of the training scene. The present invention avoids the muscle memory and stereotypes that are easily formed in traditional fixed-scenario training through an adaptive mechanism, and can also provide more challenging and adaptive training content according to the shooter's personalized needs, thereby comprehensively improving the shooter's shooting skills and tactical decision-making ability in complex and changing environments, and enhancing the authenticity and effectiveness of training. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of the structure of an adaptive shooting training system in one embodiment of the present invention;

[0049] Figure 2 It is a structural schematic diagram of a VR shooting training facility in another embodiment of the present invention;

[0050] Figure 3 The figure is a flow chart of an adaptive shooting training method in another embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0052] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0053] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0054] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0055] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0056] Combination Figure 1 As shown, the present invention provides an adaptive shooting training system, comprising:

[0057] The plan establishment unit is used to determine a training plan for simulated shooting according to training requirements, wherein the training plan includes scenario-related planning for the shooter to perform training and action planning for the shooting target.

[0058] Specifically, the plan establishment unit is the core starting point of the adaptive shooting training system, which accurately determines the training plan for simulated shooting according to the training requirements. The plan establishment unit breaks down the complex training requirements into specific scenario-related planning and action planning for shooting targets through in-depth analysis of the training objectives. Scenario-related planning covers detailed designs from environment types (such as cities, jungles, deserts) to scene layouts (such as obstacle distribution, bunker locations), providing shooters with a variety of training backgrounds. The action plan carefully defines the appearance location, movement trajectory, and speed changes of the shooting target to ensure the dynamic and unpredictable nature of the training scene. The refined planning of this application can not only meet the needs of different training stages and goals, but also provide a clear blueprint for subsequent three-dimensional modeling and dynamic adjustment, thereby ensuring the efficient operation of the entire training system and the realization of personalized training.

[0059] The three-dimensional modeling unit is used to build a three-dimensional training scene according to the scene-related plan and the action plan, and send the three-dimensional training scene to a display device.

[0060] Specifically, the 3D modeling unit accurately builds a highly realistic 3D training scene based on the scene-related plans and action plans output by the planning unit. This includes not only the detailed modeling of scene elements such as terrain, buildings, and vegetation, but also the creation of a 3D model of the shooting target, as well as the precise setting of its appearance position, movement trajectory, and speed change. By integrating and sending these complex modeling data to the display device, the 3D modeling unit creates an immersive training environment for the shooter, enabling him to conduct shooting training in a highly simulated scene. This realistic visual experience not only enhances the realism of the training, but also provides the shooter with training conditions close to actual combat, thereby effectively improving his shooting skills and tactical decision-making ability in complex environments.

[0061] The dynamic adaptation unit is used to obtain the real-time training data of the shooter when the shooter performs simulation training in the three-dimensional training scene; and generate the adjustment strategy of the scene-related plan and the action plan according to the real-time training data of the shooter.

[0062] Specifically, the dynamic adaptation unit needs to obtain the shooter's training data in real time during the shooter's three-dimensional training scene training, including key indicators such as shooting accuracy, reaction time, target recognition ability, and physiological state data, such as heart rate, fatigue level, etc. Furthermore, based on real-time data, the dynamic adaptation unit uses data analysis algorithms and models to accurately evaluate the shooter's current performance and state, and generates adjustment strategies for scene-related planning and action planning accordingly. This dynamic adjustment capability enables the training system to optimize the training scene in real time according to the shooter's actual performance and physiological state, such as reducing the difficulty of training when the shooter performs poorly, or increasing the challenge when the physiological state is good. In this way, the dynamic adaptation unit not only avoids the muscle memory and stereotypes that are easily formed in traditional fixed scene training, but also ensures that the training content always matches the shooter's personalized needs, thereby significantly improving the training effect and efficiency.

[0063] In a preferred embodiment of the present invention, a decision tree model is used to quickly determine the scene or target features that need to be adjusted based on the shooter's real-time performance and physiological state. For example, if the hit rate is below a certain threshold and the heart rate is too high, the decision tree can recommend reducing the target movement speed and simplifying the scene complexity.

[0064] An updating unit is used to update the three-dimensional training scene according to the scene-related plan and the adjustment strategy of the action plan, obtain the updated three-dimensional training scene, and send the updated three-dimensional training scene to the display device again.

[0065] Specifically, the update unit accurately and efficiently updates the three-dimensional training scene based on the adjustment strategy generated by the dynamic adaptation unit. This process covers a full range of adjustments from scene-related planning (such as terrain complexity and environmental conditions) to action planning (such as target location, movement trajectory and speed changes). The update unit optimizes the three-dimensional modeling data to ensure that the adjusted scene can quickly and accurately reflect the new training requirements, and sends the updated three-dimensional training scene to the display device again for the shooter to continue training. This real-time update mechanism not only ensures the continuity of the training scene, but also enables the shooter to continuously adapt and improve in a dynamically changing environment, thereby achieving truly adaptive training.

[0066] Combination Figure 2 As shown, in a preferred embodiment of the present invention, the adaptive shooting training system is applied to a VR shooting training facility, which includes a control segment, a server, a transmission base station, and multiple scenes. The control end allows the user to input a training plan according to specific training requirements, which is processed by the plan establishment unit to determine the scene-related plan and the action plan of the shooting target. Subsequently, the three-dimensional modeling unit builds the corresponding three-dimensional training scene according to these plans and sends it to the transmission base station through the server. The transmission base station is responsible for distributing the scene data to different training scenes, such as scene 1, scene 2...scene n, each scene corresponding to different training goals and difficulty levels. When the shooter is training in these three-dimensional training scenes, the dynamic adaptation unit obtains the shooter's training data in real time, including shooting performance and physiological state, and then generates adjustment strategies. These strategies are sent back to the server, and the update unit performs necessary updates to the three-dimensional training scene according to the strategy to adapt to the shooter's performance and state. The updated three-dimensional training scene is sent to the corresponding display device through the transmission base station again for the shooter to continue training.

[0067] The adaptive shooting training system of the present invention realizes the personalization and dynamic adjustment of the simulated shooting training scene through the collaborative work of the plan establishment unit, the three-dimensional modeling unit, the dynamic adaptation unit and the update unit, thereby significantly improving the training effect of the shooter. First, the plan establishment unit generates a targeted training plan according to the training requirements, covering the scene-related planning and the action planning of the shooting target, and provides the shooter with a variety of training scenes that meet the training goals. Secondly, the dynamic adaptation unit obtains the shooter's training data in real time, thereby evaluating the shooter's performance and state in real time, and generating an accurate adjustment strategy accordingly. Through the dynamic adjustment capability, the training scene can be optimized in real time according to the shooter's training situation, such as reducing the target difficulty when the shooter performs poorly, or increasing the training intensity when the physiological state is good. In addition, the update unit can quickly feed back the adjusted three-dimensional training scene to the display device to ensure the real-time and coherence of the training scene. The present invention avoids the muscle memory and mindset that are easily formed in traditional fixed scene training through the adaptive mechanism, and can also provide more challenging and adaptive training content according to the personalized needs of the shooter, thereby comprehensively improving the shooter's shooting skills and tactical decision-making ability in a complex and changeable environment, and enhancing the authenticity and effectiveness of the training.

[0068] Optionally, the plan establishing unit is specifically used for:

[0069] Decomposing the training requirement to obtain at least one training target, wherein each training target corresponds to at least one indicator requirement;

[0070] Perform scene matching according to the indicator requirements of the training target to obtain a scene type, and then set the object distribution in the scene according to the scene type to obtain scene layout data; use the scene type and the scene layout data as the scene-related planning;

[0071] The action plan of the shooting target is set according to the scene arrangement data, wherein the action plan includes the appearance position, movement trajectory and speed change of the shooting target.

[0072] Specifically, the planning unit first decomposes the training requirements in detail and clarifies each training goal and its corresponding index requirements. For example, if the training requirement is to improve the shooting accuracy and reaction speed of the shooter in a complex environment, the planning unit will decompose this into two training goals: one is to improve the shooting accuracy, corresponding to the index requirements, such as the hit rate reaching more than 80%; the other is to improve the reaction speed, corresponding to the index requirements, such as shortening the reaction time to less than 0.5 seconds. Subsequently, the unit matches the scene according to these index requirements and selects the appropriate scene type. For example, for shooting accuracy training, a static urban street scene is matched; and for reaction speed training, a dynamic jungle environment is matched. After determining the scene type, the unit further sets the distribution of objects in the scene and generates scene layout data. For example, in the urban street scene, obstacles such as buildings and vehicles are arranged to form shelters and shooting channels; in the jungle scene, natural elements such as trees and shrubs are arranged to increase the complexity of the environment. Finally, the scene type and scene layout data are integrated into scene-related planning. In addition, the planning unit can also set the action plan of the shooting target according to the scene layout data, including the target's appearance position, movement trajectory and speed change. For example, in a city street scene, the target appears in a fixed position, moves in a straight line, and moves slowly; while in a jungle scene, the target may appear randomly, move in a curved line, and move quickly. Through this series of steps, the planning unit provides an accurate blueprint for subsequent 3D modeling and dynamic adjustment, ensuring that the training scene can accurately meet the needs of the training objectives.

[0073] In this optional embodiment, through systematic decomposition and planning, complex training objectives are clarified and specific, and each training objective corresponds to clear indicator requirements, which provides a clear standard for subsequent training effect evaluation. Secondly, the generation of scene matching and layout data ensures the high personalization and adaptability of training scenes, and can provide a variety of environments and conditions according to different training objectives, thereby avoiding the limitations of single scene training.

[0074] Optionally, the three-dimensional modeling unit is specifically used for:

[0075] According to the scene type, an initial scene model matching the scene type is retrieved from a preset 3D modeling scene library;

[0076] Adjusting the initial scene model according to the scene arrangement data to obtain a final scene model;

[0077] According to the action plan of the shooting target, the three-dimensional model of the shooting target is placed in the final scene model to obtain the three-dimensional training scene.

[0078] Specifically, first, according to the scene type determined by the planning unit, the 3D modeling unit retrieves the initial scene model that matches it from the preset 3D modeling scene library. For example, if the scene type is "city street", the initial model containing elements such as city buildings, roads and bunkers is retrieved from the scene library. Subsequently, the unit adjusts the initial scene model according to the scene layout data to meet specific training needs, including modifying the undulations of the terrain, adjusting the position of obstacles, and adding or reducing interference elements.

[0079] In a preferred embodiment of the present invention, if the scene layout data requires an increase in the number of shelters to increase tactical complexity, the modeling unit will add more buildings or vehicles as shelters based on the initial model. Finally, according to the action plan of the shooting target, the three-dimensional model of the shooting target is placed in the adjusted final scene model. In this step, the target's appearance position, movement trajectory and speed change need to be accurately set; for example, if the action plan requires the target to move in a curved trajectory at a specific position, the modeling unit will place the target model at the corresponding position and set the corresponding animation path and speed parameters for it; through the above operations, the three-dimensional modeling unit generates a highly realistic three-dimensional training scene that meets the training requirements, providing an immersive training environment for the shooter.

[0080] In this optional embodiment, the high fidelity and adaptability of the three-dimensional training scene are ensured through a refined modeling process, thereby significantly improving the realism and effectiveness of the training. It not only provides shooters with a variety of training scenes, but also provides a solid foundation for subsequent dynamic adjustments, thereby comprehensively improving the flexibility and practicality of the training system.

[0081] Optionally, the dynamic adaptation unit is specifically used for:

[0082] When the shooter performs simulation training in the three-dimensional training scene, acquiring the real-time training data of the shooter, the real-time training data including shooting data and physiological data;

[0083] Evaluating the shooting data and the physiological data respectively to obtain shooting performance data and physiological state data of the shooter;

[0084] An adjustment strategy for the scene-related planning and an adjustment strategy for the action planning are generated according to the shooting performance data and the physiological state data.

[0085] Specifically, first, the dynamic adaptation unit obtains the shooter's real-time training data through sensors and data acquisition devices, including shooting data (such as hit rate, reaction time and shooting accuracy) and physiological data (such as heart rate, breathing rate and fatigue level). In a preferred embodiment of the present invention, in terms of shooting data acquisition, the shooter's shooting action and hit point can be captured by a high-precision optical sensor to calculate the hit rate and shooting accuracy; the physiological data is transmitted to the system in real time through a wearable device (such as a heart rate monitor). Next, the dynamic adaptation unit evaluates these data and analyzes the shooter's shooting performance and physiological state using a preset evaluation model. For example, through a machine learning algorithm, the victory data and shooting data are analyzed. If the hit rate is lower than a preset threshold (such as 70%), the shooting performance is judged to be poor; if the heart rate exceeds the normal range (such as more than 120 times / minute), the physiological state is judged to be poor. Based on these evaluation results, the dynamic adaptation unit generates adjustment strategies for scene-related planning and action planning. For example, if the shooting performance is poor but the physiological state is good, the system will reduce the target movement speed to help the shooter improve the hit rate; if the physiological state is not good, the scene complexity will be simplified to reduce the shooter's fatigue.

[0086] In this optional embodiment, through real-time data collection and accurate evaluation, dynamic optimization of training scenarios is achieved, significantly improving the personalization and effectiveness of training. First, the collection of real-time data ensures that the system can respond to the shooter's performance and physiological changes in a timely manner, avoiding the limitations of traditional fixed-scene training. Second, by evaluating shooting performance and physiological status, the system can accurately identify the shooter's strengths and weaknesses in training, thereby generating targeted adjustment strategies.

[0087] Optionally, the dynamic adaptation unit is further configured to:

[0088] Obtaining adjustment weights of the scene-related plan and the action plan according to the shooting performance data and the physiological state data;

[0089] According to the adjustment weights, an adjustment strategy for the scenario-related plan and an adjustment strategy for the action plan are generated.

[0090] Specifically, first, based on the shooter's shooting performance data (such as hit rate, reaction time) and physiological state data (such as heart rate, fatigue level), the preset weight distribution model is used to calculate the adjustment weights of the scene-related planning and action planning. This process is based on the underlying data processing logic. For example, if the hit rate is lower than expected and the heart rate is too high, it means that the shooter may be facing both technical challenges and physiological fatigue in the current scene. At this time, the system will give higher adjustment weights to the target size and scene complexity because these factors have a more direct impact on the shooter's performance. Based on these weights, the unit generates specific adjustment strategies. For example, if the adjustment weight of the target size is 0.4 and the adjustment weight of the scene complexity is 0.3, the system will give priority to increasing the target size to improve the hit rate, and moderately simplify the scene complexity to reduce fatigue. This weight-based adjustment strategy generation mechanism not only takes into account the shooter's real-time performance, but also takes into account the physiological state, ensuring that the adjustment strategy can dynamically adapt to the shooter's personalized needs.

[0091] In this optional embodiment, by introducing adjustment weights, the dynamic adaptation unit can generate adjustment strategies more accurately, thereby significantly improving the personalization and effectiveness of training. First, the introduction of adjustment weights enables the system to quantify the importance of each adjustment factor, avoiding the one-sided influence of a single indicator on the adjustment strategy.

[0092] Optionally, the dynamic adaptation unit is further configured to:

[0093] Obtaining the correlation between each of the training targets included in the training requirements and the scene type and the scene distribution data in the scene-related plan, and the correlation between each of the training targets and the appearance position, movement trajectory and speed change of the shooting target in the action plan;

[0094] The sum of the degrees of association between the scene type, the scene arrangement data, and the appearance position, movement trajectory, and speed change of the shooting target and each of the training targets is taken as the total value of the degrees of association between the scene type, the scene arrangement data, and the appearance position, movement trajectory, and speed change of the shooting target;

[0095] Normalization processing is performed based on the total value of the correlation between the scene type, the scene layout data, and the appearance position, movement trajectory and speed change of the shooting target to obtain the adjustment weights of the scene type, the scene layout data, and the appearance position, movement trajectory and speed change of the shooting target.

[0096] Specifically, the dynamic adaptation unit calculates the correlation between each training target in the training requirements and the scene type and scene distribution data in the scene-related planning. At the same time, for each training target, the dynamic adaptation unit also analyzes the correlation between it and the appearance position, movement trajectory and speed change of the shooting target in the action plan. For example, if the training goal is to improve the shooting hit rate of the intelligent agent on fast-moving targets, then the correlation between the target and the appearance position, movement trajectory and speed change of the shooting target is relatively high. The dynamic adaptation unit further optimizes the generation process of the adjustment strategy by introducing the correlation between the training target and the scene-related planning and action planning. Specifically, the dynamic adaptation unit first obtains the correlation between each training target in the training requirements and the scene-related planning and action planning. For example, if the training goal is to improve the shooting accuracy, then the target size and movement speed have a high correlation with the training target, while the scene environment has a relatively low correlation. Subsequently, the dynamic adaptation unit calculates the total value of the correlation between the scene type, scene layout data, and the appearance position, movement trajectory and speed change of the shooting target and all training targets. For example, the position where the shooting target appears may have a correlation of 0.6 and 0.4 with multiple training targets, such as shooting accuracy and reaction speed, respectively, and the total correlation value is 1.0. By normalizing these total correlation values, the unit obtains the adjustment weight of each adjustment factor; wherein, the normalization process ensures that the sum of the weights is 1, thereby making the generation of the adjustment strategy more scientific and quantifiable. For example, after normalization, the adjustment weight of the target size may be 0.3, the adjustment weight of the target moving speed is 0.25, the adjustment weight of the scene complexity is 0.2, the adjustment weight of the target appearance position is 0.15, and the adjustment weight of the scene environment is 0.1. The weight calculation method based on correlation and normalization processing of this embodiment, starting from the underlying logic of data processing, ensures that the generation of the adjustment strategy not only meets the requirements of the training target, but also can dynamically adapt to the real-time performance and physiological state of the shooter, thereby improving the adaptive ability and personalization level of the training system.

[0097] In this optional embodiment, by introducing correlation and normalization, the dynamic adaptation unit can generate adjustment weights more scientifically, thereby significantly improving the adaptability and personalization level of the training system. The introduction of correlation enables the system to quantify the importance of each adjustment factor to the training goal, avoiding the one-sided influence of a single indicator on the adjustment strategy. In addition, the normalization process ensures that the sum of the adjustment weights is 1, making the generation of the adjustment strategy more scientific and quantifiable; it not only improves the credibility and feasibility of the adjustment strategy, but also enables the training system to dynamically adapt to the real-time status of the shooter.

[0098] Optionally, the dynamic adaptation unit is further configured to:

[0099] Determining, according to the adjustment weights, adjustment coefficients corresponding to the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target;

[0100] Determine the adjustment values ​​corresponding to the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target, respectively, according to the maximum adjustment ranges corresponding to the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target, respectively, in combination with the adjustment coefficients of the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target;

[0101] Prioritizing the scene types, the scene layout data, and the appearance positions, movement trajectories, and speed changes of the shooting targets according to the adjustment weights to obtain an adjustment order of the scene types, the scene layout data, and the appearance positions, movement trajectories, and speed changes of the shooting targets;

[0102] The adjustment values ​​and adjustment order of the scene type and the scene layout data are used as the adjustment strategy of the scene-related planning, and the adjustment values ​​and adjustment order of the appearance position, movement trajectory and speed change of the shooting target are used as the adjustment strategy of the action planning.

[0103] Specifically, according to the adjustment weight, the adjustment coefficient corresponding to the scene type, scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target is determined. The adjustment coefficient is calculated based on the adjustment weight and is used to quantify the adjustment range of each adjustment factor.

[0104] In a preferred embodiment of the present invention, if the training goal is to improve the shooter's reaction speed and target tracking ability, the movement trajectory of the shooting target has a high correlation with these training goals, so its adjustment weight may be assigned a higher value, such as 0.4. Next, the dynamic adaptation unit determines the adjustment coefficient of the movement trajectory according to the adjustment weight. Assuming that the maximum adjustment range of the movement trajectory is ±30% speed change and ±45 degrees direction change, the adjustment coefficient may be set to 0.7, which means that within the maximum adjustment range, the movement trajectory can be adjusted to 70% amplitude. Combining the adjustment coefficient and the maximum adjustment range, the dynamic adaptation unit calculates the specific adjustment value. For example, if the speed of the current movement trajectory is 1 meter per second and the direction is a straight line, according to the adjustment coefficient of 0.7, the speed adjustment value may be ±0.3 meters per second, and the direction adjustment value may be ±31.5 degrees (i.e., 0.7×45 degrees). Finally, the dynamic adaptation unit prioritizes the adjustment values ​​of the movement trajectory according to the adjustment weight. If the adjustment weight of the movement trajectory is higher than other factors (such as target size or scene complexity), its adjustment order will be given priority. Finally, the adjustment value and order of the movement trajectory are integrated into the adjustment strategy of action planning to ensure that during the training process, the movement trajectory of the shooting target can dynamically adapt to the shooter's real-time performance and physiological state, thereby improving the training effect.

[0105] Next, all adjustment factors are prioritized according to the adjustment weights to determine the adjustment order. For example, if the target size has the highest adjustment weight, its adjustment order has the highest priority. Finally, the adjustment values ​​and adjustment order are integrated into adjustment strategies for scenario-related planning and action planning. The underlying logic based on data processing in this embodiment ensures that the generation of adjustment strategies is both scientific and quantifiable. At the same time, through priority sorting, the system can dynamically adjust the factors that have the greatest impact on the training effect according to the shooter's real-time performance and physiological state, thereby improving the adaptability and personalization of training.

[0106] In this optional embodiment, by introducing adjustment coefficients and priority sorting, the dynamic adaptation unit can generate adjustment strategies in a more refined manner, significantly improving the adaptability and personalization level of the training system. In addition, the hierarchical adjustment strategy generation mechanism not only improves the flexibility of the training system, but also enhances the dynamic adaptability of the system, so that the training scene can be dynamically optimized according to the shooter's real-time performance and physiological state, avoiding the limitations of traditional fixed scene training.

[0107] Optionally, the updating unit is specifically used for:

[0108] Adjusting the current three-dimensional training scene according to the adjustment strategy of the scene-related planning and the adjustment strategy of the shooting target;

[0109] Determining whether there is a model error in the adjusted three-dimensional training scene;

[0110] If yes, re-adjust the current three-dimensional training scene until the adjusted three-dimensional training scene has no model errors;

[0111] If not, the adjusted three-dimensional training scene is rendered to obtain the updated three-dimensional training scene.

[0112] Specifically, the update unit first adjusts the current three-dimensional training scene according to the scene-related planning and the adjustment strategy of the shooting target. If the adjustment strategy requires increasing the target's moving speed and changing its moving trajectory, the update unit will modify the animation parameters of the target model so that it moves at the new speed and trajectory in the three-dimensional scene. At the same time, if the scene-related planning requires simplifying the scene complexity, the update unit will remove some obstacles or adjust the terrain undulations. After the adjustment is completed, the update unit enters the model error detection stage. Model errors may include geometric errors of the target model (such as the model penetrating the terrain), texture errors (such as texture map mismatch), or logical errors (such as the target movement trajectory is blocked by the terrain). The update unit checks these errors through automated scripts and collision detection algorithms. If a model error is detected, the update unit will readjust the three-dimensional training scene until all errors are fixed. This process is based on the underlying logic of data processing, and ensures that the adjusted scene meets the training requirements through real-time feedback and iterative optimization. For example, if the target model has an error of penetrating the terrain during movement, the update unit will adjust the target's path planning or terrain model to ensure its logical consistency. Finally, when the adjusted three-dimensional training scene has no model errors, the update unit renders it and generates an updated three-dimensional training scene for the shooter to continue training.

[0113] In this optional embodiment, the update unit ensures the accuracy and reliability of the three-dimensional training scene through strict model error detection and iterative adjustment mechanisms, thereby significantly improving the stability of the training system and the training effect.

[0114] Combination Figure 3 As shown, the present invention also provides an adaptive shooting training method, comprising:

[0115] Determine a simulated shooting training plan based on training requirements, wherein the training plan includes scenario-related planning for the shooter to conduct training and action planning for the shooting target;

[0116] Building a three-dimensional training scene according to the scene-related plan and the action plan, and sending the three-dimensional training scene to a display device;

[0117] When the shooter performs simulation training in the three-dimensional training scene, obtaining real-time training data of the shooter;

[0118] generating, according to the real-time training data of the shooter, an adjustment strategy for the scenario-related plan and the action plan;

[0119] According to the adjustment strategy of the scene-related plan and the action plan, the three-dimensional training scene is updated to obtain the updated three-dimensional training scene, and the updated three-dimensional training scene is sent to the display device again.

[0120] The adaptive shooting training method of the present invention has similar technical effects as the adaptive shooting training system compared with related technologies, and will not be described in detail here.

[0121] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned adaptive shooting training method is implemented.

[0122] The computer-readable storage medium of the present invention has similar technical effects as the adaptive shooting training system compared to related technologies, and will not be described in detail here.

[0123] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. An adaptive shooting training system, characterized in that: include: A plan establishment unit, used to determine a training plan for simulated shooting according to training requirements, wherein the training plan includes a scenario-related plan for the shooter to perform training and an action plan for the shooting target; A three-dimensional modeling unit, used to build a three-dimensional training scene according to the scene-related plan and the action plan, and send the three-dimensional training scene to a display device; A dynamic adaptation unit, configured to obtain real-time training data of the shooter when the shooter performs simulation training in the three-dimensional training scene; and generate adjustment strategies for the scene-related plan and the action plan according to the real-time training data of the shooter; An updating unit is used to update the three-dimensional training scene according to the scene-related plan and the adjustment strategy of the action plan, obtain the updated three-dimensional training scene, and send the updated three-dimensional training scene to the display device again.

2. The adaptive shooting training system according to claim 1, characterized in that: The plan establishment unit is specifically used for: Decomposing the training requirement to obtain at least one training target, wherein each training target corresponds to at least one indicator requirement; Perform scene matching according to the indicator requirements of the training target to obtain a scene type, and then set the object distribution in the scene according to the scene type to obtain scene layout data; use the scene type and the scene layout data as the scene-related planning; The action plan of the shooting target is set according to the scene arrangement data, wherein the action plan includes the appearance position, movement trajectory and speed change of the shooting target.

3. The adaptive shooting training system according to claim 2, characterized in that: The three-dimensional modeling unit is specifically used for: According to the scene type, an initial scene model matching the scene type is retrieved from a preset three-dimensional modeling scene library; Adjusting the initial scene model according to the scene arrangement data to obtain a final scene model; According to the action plan of the shooting target, the three-dimensional model of the shooting target is placed in the final scene model to obtain the three-dimensional training scene.

4. The adaptive shooting training system according to claim 2, characterized in that: The dynamic adaptation unit is specifically used for: When the shooter performs simulation training in the three-dimensional training scene, acquiring the real-time training data of the shooter, the real-time training data including shooting data and physiological data; Evaluating the shooting data and the physiological data respectively to obtain shooting performance data and physiological state data of the shooter; An adjustment strategy for the scene-related planning and an adjustment strategy for the action planning are generated according to the shooting performance data and the physiological state data.

5. The adaptive shooting training system according to claim 4, characterized in that: The dynamic adaptation unit is further specifically used for: According to the shooting performance data and the physiological state data, obtaining adjustment weights of the scene-related plan and the action plan; According to the adjustment weights, an adjustment strategy for the scenario-related plan and an adjustment strategy for the action plan are generated.

6. The adaptive shooting training system according to claim 5, characterized in that: The dynamic adaptation unit is further specifically used for: Obtaining the correlation between each of the training targets included in the training requirements and the scene type and the scene distribution data in the scene-related plan, and the correlation between each of the training targets and the appearance position, movement trajectory and speed change of the shooting target in the action plan; The sum of the degrees of association between the scene type, the scene arrangement data, and the appearance position, movement trajectory, and speed change of the shooting target and each of the training targets is taken as the total value of the degrees of association between the scene type, the scene arrangement data, and the appearance position, movement trajectory, and speed change of the shooting target; Normalization processing is performed based on the total value of the correlation between the scene type, the scene layout data, and the appearance position, movement trajectory and speed change of the shooting target to obtain the adjustment weights of the scene type, the scene layout data, and the appearance position, movement trajectory and speed change of the shooting target.

7. The adaptive shooting training system according to claim 5, characterized in that: The dynamic adaptation unit is further specifically used for: Determining, according to the adjustment weights, adjustment coefficients corresponding to the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target; Determine the adjustment values ​​corresponding to the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target, respectively, according to the maximum adjustment ranges corresponding to the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target, respectively, in combination with the adjustment coefficients of the scene type, the scene layout data, and the appearance position, movement trajectory, and speed change of the shooting target; Prioritizing the scene types, the scene layout data, and the appearance positions, movement trajectories, and speed changes of the shooting targets according to the adjustment weights to obtain an adjustment order of the scene types, the scene layout data, and the appearance positions, movement trajectories, and speed changes of the shooting targets; The adjustment values ​​and adjustment order of the scene type and the scene layout data are used as the adjustment strategy of the scene-related planning, and the adjustment values ​​and adjustment order of the appearance position, movement trajectory and speed change of the shooting target are used as the adjustment strategy of the action planning.

8. The adaptive shooting training system according to claim 1, characterized in that: The updating unit is specifically used for: Adjusting the current three-dimensional training scene according to the adjustment strategy of the scene-related planning and the adjustment strategy of the shooting target; Determining whether there is a model error in the adjusted three-dimensional training scene; If yes, re-adjust the current three-dimensional training scene until the adjusted three-dimensional training scene has no model errors; If not, the adjusted three-dimensional training scene is rendered to obtain the updated three-dimensional training scene.

9. An adaptive shooting training method, characterized in that: include: Determine a simulated shooting training plan based on training requirements, wherein the training plan includes scenario-related planning for the shooter to conduct training and action planning for the shooting target; Building a three-dimensional training scene according to the scene-related plan and the action plan, and sending the three-dimensional training scene to a display device; When the shooter performs simulation training in the three-dimensional training scene, obtaining real-time training data of the shooter; generating, according to the real-time training data of the shooter, an adjustment strategy for the scenario-related plan and the action plan; According to the adjustment strategy of the scene-related plan and the action plan, the three-dimensional training scene is updated to obtain the updated three-dimensional training scene, and the updated three-dimensional training scene is sent to the display device again.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the adaptive shooting training method described in claim 9 is implemented.

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