Shooting action real-time evaluation and simulation method and system based on dynamic adjustment

Abstract

The invention provides a shooting action real-time evaluation and simulation method and system based on dynamic adjustment, and relates to the field of intelligent shooting training and evaluation. The method comprises the steps that various sensors are used for collecting data in the shooting process, such as acceleration, angular velocity, gun barrel pressure and attitude data; based on the data, a dynamic model of shooting actions is established, and human body actions, firearm recoil and air resistance are comprehensively considered; evaluating and updating the dynamic model in real time through a Kalman filter; using a self-adaptive control algorithm to correct action errors and optimize a control strategy; real-time simulation of shooting actions is carried out on the basis of dynamic adjustment; feedback optimization is carried out according to the simulation result, model parameters are adjusted, input is controlled, and the accuracy and stability of the shooting action are improved. According to the method, the precision and efficiency of shooting training can be improved, and the good self-adaptive adjusting capacity is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent shooting training and evaluation systems, and in particular to a real-time evaluation and simulation method and system for shooting actions based on dynamic adjustment. Background Art

[0002] Shooting training plays a vital role in the military, law enforcement, sports and other fields. With the continuous advancement of technology, traditional shooting training methods are gradually being replaced by intelligent and digital training systems. These systems can more accurately simulate shooting scenes, evaluate shooting actions, and provide timely feedback, thereby effectively improving the skill level of shooters.

[0003] Existing intelligent shooting training systems usually rely on sensor networks to capture various physical parameters during the shooting process, such as acceleration, angular velocity, posture angle, etc. By analyzing these data, the quality of the shooter's movements can be evaluated, errors in the movements can be identified, and corresponding adjustments can be made. However, these systems still have certain limitations in terms of dynamic adjustment and real-time simulation.

[0004] First, current shooting evaluation systems often use simple linear models for motion analysis, which makes it difficult to fully consider nonlinear dynamic factors in shooting actions, such as changes in human posture, gun recoil, and air resistance. These nonlinear factors have a significant impact on shooting accuracy during actual shooting, and ignoring them may lead to inaccurate evaluation results.

[0005] Secondly, many existing systems lack sufficient adaptability in action adjustment. Shooting actions may show different characteristics in different environments and conditions, and a single control strategy cannot effectively cope with changing shooting conditions. Therefore, the shooting training system requires a more flexible dynamic adjustment mechanism that can continuously optimize the shooting action based on real-time evaluation results and improve the training effect.

[0006] Finally, the existing shooting simulation system still has room for improvement in terms of accuracy and real-time performance. Shooting simulation is usually based on pre-set conditions and assumptions, rather than actual dynamic adjustment results, which may cause the simulation results to be inconsistent with the actual shooting situation. Therefore, a shooting simulation system that combines real-time evaluation and dynamic adjustment will be more practical and can provide shooters with a more accurate and realistic training experience.

[0007] In order to solve these problems, an intelligent shooting training system is needed that can comprehensively consider nonlinear dynamic factors, has adaptive dynamic adjustment capabilities, and can simulate shooting actions in real time to meet the needs of high-precision training in military, law enforcement, and competitive shooting fields. Summary of the invention

[0008] In order to solve the technical problems in the prior art that the nonlinear dynamic factors in the shooting action cannot be fully considered, the lack of sufficient adaptability and dynamic adjustment capabilities, and the insufficient accuracy and real-time performance of the shooting simulation, the present invention provides a real-time evaluation and simulation method and system for shooting actions based on dynamic adjustment.

[0009] The technical solution provided by the present invention is as follows:

[0010] First aspect:

[0011] The present invention provides a shooting action real-time evaluation and simulation method based on dynamic adjustment, comprising:

[0012] S1. Data collection: using a variety of sensor systems to collect data during the shooting process. The multiple sensors include an inertial measurement unit (IMU), an accelerometer, a gyroscope, and a pressure sensor. The data include acceleration, angular velocity, handheld device posture, barrel vibration, and environmental factors.

[0013] S2. Action model construction: a dynamic model of the shooting action is established based on the collected data. The dynamic model comprehensively considers the complex dynamic behaviors of the action during the shooting process, including human body movements, gun recoil and air resistance, and describes the dynamic characteristics of the shooting action.

[0014] S3, real-time dynamic evaluation, through real-time update and adjustment of the shooting action model, evaluate the effect of the shooting action, use the Kalman filter for state estimation, to correct and optimize the dynamic model of the shooting action, including real-time feedback adjustment of action errors and external disturbances;

[0015] S4, dynamic adjustment of action, according to the implementation evaluation results, the shooting action is dynamically adjusted, the error in the shooting action is corrected using the adaptive control algorithm, and the action control strategy is optimized. The dynamic adjustment includes not only the correction of the posture, but also the adjustment of the control parameters of the firearm;

[0016] S5. Real-time simulation: Based on dynamic adjustment, the shooting action simulation is carried out. The simulation system combines nonlinear dynamics models to simulate the action performance under different shooting conditions and output accurate simulation results.

[0017] S6. Feedback mechanism and optimization: Feedback optimization of shooting action is performed based on real-time simulation results. The accuracy and stability of the action are improved by adjusting model parameters and control inputs. The feedback mechanism includes re-evaluation and optimization of the effect of the action after adjustment to ensure the system's adaptive adjustment capability.

[0018] Second aspect:

[0019] The present invention provides a shooting action real-time evaluation and simulation system based on dynamic adjustment, comprising:

[0020] processor;

[0021] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the real-time evaluation and simulation method for shooting action based on dynamic adjustment as described in the first aspect is implemented.

[0022] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0023] (1) In the present invention, by introducing a complex nonlinear dynamic model and an adaptive control algorithm, the errors in the shooting action can be adjusted in real time, and the shooting control strategy can be dynamically optimized. Compared with the traditional shooting evaluation system, the present invention can more accurately reflect the actual operation of the shooter, thereby greatly improving the shooting accuracy and the stability of the action;

[0024] (2) In the present invention, the control parameters and model parameters can be automatically adjusted according to the real-time evaluation results under different shooting conditions to ensure that the best action guidance can be provided in a changing shooting environment. By introducing a dynamic adjustment and feedback mechanism, the system can effectively cope with various complex shooting scenarios and improve the practical application effect of training;

[0025] (3) In the present invention, by combining the nonlinear dynamic model with the real-time evaluation results, it is possible to perform high-precision simulation of action performance under different shooting conditions. The output of the shooting simulation system not only takes into account factors such as the ammunition flight trajectory and gun vibration, but also can reflect the effects of dynamic adjustments in real time, providing shooters with a more realistic training experience and feedback, which helps to comprehensively improve shooting skills. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A schematic flow chart of a method for real-time evaluation and simulation of shooting actions based on dynamic adjustment provided by an embodiment of the present invention;

[0028] Figure 2 A schematic structural diagram of a shooting action real-time evaluation and simulation system based on dynamic adjustment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0030] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0031] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0032] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0033] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0034] Reference Manual Attached Figure 1 , showing a flow chart of a method for real-time evaluation and simulation of shooting actions based on dynamic adjustment provided by an embodiment of the present invention.

[0035] The embodiment of the present invention provides a method for real-time evaluation and simulation of shooting action based on dynamic adjustment. The method can be implemented by a real-time evaluation and simulation device for shooting action based on dynamic adjustment. The real-time evaluation and simulation device for shooting action based on dynamic adjustment can be a terminal or a server. The processing flow of the method for real-time evaluation and simulation of shooting action based on dynamic adjustment can include the following steps:

[0036] S1. Data collection: A variety of sensor systems are used to collect data during the shooting process. The sensors include inertial measurement units (IMUs), accelerometers, gyroscopes, and pressure sensors. The data include acceleration, angular velocity, handheld device posture, barrel vibration, and environmental factors.

[0037] It should be noted that shooting action involves multiple dynamic factors, such as human posture, gun operation, environmental influence, etc. These factors will directly affect the accuracy and effect of shooting. In order to comprehensively evaluate shooting action, it is necessary to collect multi-dimensional data.

[0038] It is understandable that by using multiple sensors such as inertial measurement units (IMUs), accelerometers, gyroscopes, and pressure sensors, the system can obtain detailed data during the shooting process. These data include acceleration, angular velocity, handheld device posture, barrel vibration, and environmental factors, providing the necessary basic information for subsequent action modeling and evaluation. The collection of these data ensures a comprehensive monitoring of the shooting action, making the evaluation and optimization process more accurate.

[0039] S2. Action model construction: A dynamic model of the shooting action is established based on the collected data. The dynamic model comprehensively considers the complex dynamic behaviors of the action during the shooting process, including human body movements, gun recoil and air resistance, and describes the dynamic characteristics of the shooting action.

[0040] It should be noted that shooting is a complex dynamic process involving multiple factors such as human body movements, gun recoil, air resistance, etc. Simple models cannot accurately describe these complex behaviors, making it difficult to effectively evaluate and optimize shooting actions.

[0041] It is understandable that by building a dynamic model of the shooting action based on the collected data, the system can comprehensively consider the complex dynamic behavior of the action during the shooting process. The model can accurately describe the dynamic characteristics of the shooting action, such as the posture changes of the human body, the recoil of the firearm, and the influence of air resistance. This step provides a theoretical basis for subsequent real-time evaluation and adjustment, ensuring that the system can conduct a scientific and comprehensive analysis of the shooting action.

[0042] S3. Real-time dynamic evaluation: evaluates the effect of the shooting action by real-time updating and adjusting the shooting action model. The Kalman filter is used for state estimation to correct and optimize the dynamic model of the shooting action. The process includes real-time feedback adjustment of action errors and external disturbances.

[0043] It should be noted that during the shooting process, the effect of the action will be affected by many factors, such as operating errors, external disturbances, etc. These factors need to be evaluated in real time during the shooting process so that the action can be corrected in time to ensure the accuracy of the shooting.

[0044] It is understandable that by updating and adjusting the shooting action model in real time, the system is able to evaluate the effect of the shooting action. Using the Kalman filter for state estimation can provide accurate action estimation under noise and uncertainty conditions, and correct and optimize the model. Real-time dynamic evaluation can not only detect errors in the action, but also provide timely feedback adjustments based on external disturbances, thereby improving the stability and accuracy of shooting.

[0045] S4. Dynamic adjustment of actions. According to the implementation evaluation results, the shooting action is dynamically adjusted. The adaptive control algorithm is used to correct the errors in the shooting action and optimize the action control strategy. The dynamic adjustment includes not only the correction of posture, but also the adjustment of the control parameters of the firearm.

[0046] It should be noted that various errors may occur during the shooting process, such as posture deviation, improper control, etc. If these errors are not adjusted in time, the shooting accuracy will decrease. Therefore, it is necessary to dynamically adjust the shooting action.

[0047] It is understandable that, based on the real-time evaluation results, the system uses an adaptive control algorithm to correct errors in the shooting action and optimize the action control strategy. This dynamic adjustment not only includes posture correction, but also covers the adjustment of the control parameters of the firearm, making the shooting action more accurate and stable. Dynamic adjustment ensures that the shooter can maintain the best shooting state in different environments and conditions, thereby improving the overall shooting effect.

[0048] S5. Real-time simulation: Based on dynamic adjustment, the shooting action simulation is implemented. The simulation system combines nonlinear dynamic models to simulate the action performance under different shooting conditions and output accurate simulation results.

[0049] It should be noted that in shooting training, simulated shooting actions can help shooters understand and improve their operations. However, traditional shooting simulations are often based on preset conditions and lack consideration of dynamic changes, resulting in a gap between simulation results and actual conditions.

[0050] It is understandable that on the basis of dynamic adjustment, the system simulates the shooting action in real time by combining nonlinear dynamic models. This simulation not only takes into account the dynamic adjustment during the shooting process, but also simulates the action performance under different shooting conditions. By outputting accurate simulation results, the system can provide shooters with a more realistic training experience and help improve shooting skills.

[0051] S6. Feedback mechanism and optimization: Feedback optimization of shooting action is performed based on real-time simulation results. The accuracy and stability of the action are improved by adjusting model parameters and control inputs. The feedback mechanism includes re-evaluation and optimization of the effect of the action after adjustment to ensure the system's adaptive adjustment capability.

[0052] It should be noted that even after dynamic adjustment and real-time simulation, there may still be room for improvement in the shooting action. Therefore, it is necessary to further optimize the shooting action through a feedback mechanism to ensure the best shooting effect.

[0053] It is understandable that, based on the real-time simulation results, the system further optimizes the accuracy and stability of the shooting action by adjusting the model parameters and control inputs. The feedback mechanism not only re-evaluates the effect of the adjustment, but also ensures that the system can adapt to different shooting conditions, thereby continuously improving the accuracy of the shooting and the system's adaptability. This step ensures that the system can continuously optimize the shooting action and improve the training effect of the shooter.

[0054] In a possible implementation, S1 specifically includes:

[0055] Use a variety of sensor systems to collect various data during the design process, including:

[0056] Acceleration (a x (t),a y (t),a z (t)): acceleration in the x, y and z directions;

[0057] Angular velocity (ω x (t),ω y (t),ω z (t)): angular velocity in the x, y and z directions;

[0058] Pressure data (p(t)): readings from the muzzle pressure sensor, used to assess changes in barrel pressure during firing;

[0059] Posture data (θ x (t),θ y (t),θ z (t)): gun and shooter posture angle data;

[0060] The data vector is defined as:

[0061] Specifically, before each shooting training, the shooter wears smart gloves and gun accessories that integrate IMU, accelerometer, gyroscope and pressure sensor. During the shooting process, these sensors record the shooter's handheld device posture, barrel vibration, acceleration, angular velocity and muzzle pressure in real time. All data is transmitted to the central processing system via a wireless network. Real-time acquisition of comprehensive shooting data provides a detailed understanding of all aspects of the shooting action. The comprehensiveness of the data lays a solid foundation for subsequent model construction and analysis, ensuring the accuracy and reliability of the model.

[0062] In a possible implementation, S2 specifically includes:

[0063] Construct an action model that comprehensively considers the dynamic factors in the shooting process. The model includes the following equations:

[0064] Kinetic equation:

[0065]

[0066] Among them, q(t) is the posture vector, M is the mass matrix, which describes the inertial characteristics of the system, C is the Coriolis force matrix, which reflects the inertial force caused by the speed change, K is the spring matrix, which represents the elastic response of the system, and T(t) is the external force, including the recoil force and air resistance generated by the shooting;

[0067] Backlash Model:

[0068]

[0069] Where m is the mass of the ammunition, v(t) is the velocity of the ammunition in the barrel, and d is the length of the barrel;

[0070] Air resistance model:

[0071]

[0072] Where ρ is the air density, C d is the drag coefficient and A is the cross-sectional area of ​​the ammunition.

[0073] Specifically, based on the collected data, a dynamic model including human body motion, gun recoil and air resistance is constructed. The dynamic equations are used to describe the inertial characteristics and external forces in the shooting action, such as recoil and air resistance. The recoil model and air resistance model consider factors such as ammunition mass, speed, barrel length, air density, and drag coefficient respectively. The motion model can comprehensively consider various dynamic factors in the shooting process and accurately describe the dynamic characteristics of the shooting action. Through accurate modeling, the complex behavior of the shooting action can be better understood and predicted, providing support for real-time evaluation and optimization.

[0074] In a possible implementation, S3 specifically includes:

[0075] Implement the evaluation shooting action and estimate the state of the dynamic model through the Kalman filter. The update formula is:

[0076] Prediction steps:

[0077] x k|k-1 =Ax k-1 +Bu k

[0078] P k|k-1 =AP k-1 A T +Q

[0079] Update steps:

[0080] K k =Pk|k-1 H T (HP k|k-1 H T +R) -1

[0081] x k =x k|k-1 +K k (z k -Hx k|k-1 )

[0082] Among them, x k is the state estimation, K k is the Kalman gain, P k is the covariance matrix, z k are the observed values, Q and R are the covariance matrices of process noise and observation noise.

[0083] Specifically, the shooting action model is updated in real time, and the Kalman filter is used to estimate the state of the dynamic model. Through the prediction and update steps of the Kalman filter, the dynamic model of the shooting action is corrected and optimized to handle action errors and external disturbances. The real-time effect of the shooting action is accurately evaluated, and problems in the action are identified in time. Through state estimation and filter adjustment, the dynamic model of the shooting action is optimized to improve the real-time accuracy and reliability of the model.

[0084] In a possible implementation, S4 specifically includes:

[0085] According to the real-time evaluation results, the adaptive control algorithm is used to correct the error in the shooting action. The adjustment formula is:

[0086] u k =K d e k

[0087] e k =q target -q k

[0088] Among them, u k is the control input, K d To adjust the gain, e k is the error between the target pose and the current pose, q target is the target pose.

[0089] Specifically, based on the real-time evaluation results, the adaptive control algorithm is applied to correct the errors in the shooting action. The shooting posture and gun control parameters are adjusted, and the control strategy is optimized using a dynamic gain matrix. The shooting action is precisely adjusted and optimized to reduce the posture error. The adaptive adjustment algorithm ensures that the shooting action can maintain the best state under various conditions, improving the shooting accuracy and stability.

[0090] In a possible implementation, S5 specifically includes:

[0091] Based on dynamic adjustment, real-time simulation of shooting action is performed, and the output of the simulation system is based on the following equation:

[0092] S(t)=f(q(t),u(t))

[0093] Among them, S(t) is the simulation result, and f is the simulation function, which takes into account the nonlinear factors in the shooting action, including the ammunition flight trajectory and barrel vibration.

[0094] Specifically, based on the adjustment, the system performs real-time simulation of shooting actions. It uses nonlinear dynamic models to simulate the performance of actions under different shooting conditions and outputs accurate simulation results. The simulation results help trainers understand the shooting performance under various conditions and optimize shooting strategies. Through real-time simulation, potential problems can be foreseen and solved, further improving training results.

[0095] In a possible implementation, S6 specifically includes:

[0096] Feedback optimization is performed on the shooting action based on the real-time simulation results, and the model parameters are updated to improve the simulation accuracy. The adjustment formula is:

[0097] ΔM=α(M true -M est )

[0098] ΔC=β(C true -C est )

[0099] ΔK=γ(K true -K est )

[0100] Among them, α, β and γ are adjustment coefficients, M true , C true and K true is the real model parameter, M est , C est and K est To estimate the value, continuous optimization and adjustment are carried out to ensure the system's adaptability and accuracy to different shooting conditions.

[0101] Specifically, feedback optimization is performed on the shooting action based on the real-time simulation results. Model parameters and control inputs are adjusted, and Bayesian inference is used for nonlinear optimization to reduce the risk of overfitting. The accuracy and stability of the model are improved through feedback mechanisms and optimization adjustments. Feedback optimization ensures that the system can adaptively adjust under different shooting conditions to provide the best shooting action performance.

[0102] Furthermore, S4 may also include:

[0103] Based on the real-time calculation of the multivariable gain matrix K(t) of the nonlinear system, the multivariable gain matrix is ​​dynamically updated according to the following formula:

[0104]

[0105] Among them, K0 is the initial gain matrix, L(t) is the time-related adjustment coefficient matrix, e(t) is the error vector between the current posture and the target posture, and the formula adjusts the control gain in real time according to the nonlinear factors in the shooting action, thereby improving the adaptive adjustment ability and accuracy of the system.

[0106] It is understandable that the adaptive adjustment capability is improved: the real-time calculation of the multivariable gain matrix ensures that the system can dynamically adjust the control gain according to the actual shooting action error, thereby adapting to various shooting conditions. Improved accuracy: By accurately adjusting the control gain, the execution of the shooting action is optimized, the error is reduced, and the shooting accuracy and stability are improved.

[0107] Furthermore, S6 may also include:

[0108] The model parameters are updated by solving the nonlinear optimization problem based on Bayesian inference. The objective function is defined as:

[0109]

[0110] Among them, θ is the model parameter vector, y i is the i-th observation value, f(x i ,θ) is the model prediction value, W is the weight matrix, λ is the regularization parameter, and R(θ) is the regularization term. Through the above optimization process, the overfitting risk can be effectively reduced and the generalization ability of the model for shooting actions can be improved.

[0111] It is understandable that reducing the risk of overfitting: through the optimization process of Bayesian inference, the model can effectively reduce the overfitting of training data and improve the prediction ability of new data. Improving generalization ability: the introduction of regularization parameters enables the model to better adapt to different shooting conditions, ensuring that the model performs stably in various actual shooting scenarios and providing reliable prediction and optimization results.

[0112] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0113] (1) In the present invention, by introducing a complex nonlinear dynamic model and an adaptive control algorithm, the errors in the shooting action can be adjusted in real time, and the shooting control strategy can be dynamically optimized. Compared with the traditional shooting evaluation system, the present invention can more accurately reflect the actual operation of the shooter, thereby greatly improving the shooting accuracy and the stability of the action;

[0114] (2) In the present invention, the control parameters and model parameters can be automatically adjusted according to the real-time evaluation results under different shooting conditions to ensure that the best action guidance can be provided in a changing shooting environment. By introducing a dynamic adjustment and feedback mechanism, the system can effectively cope with various complex shooting scenarios and improve the practical application effect of training;

[0115] (3) In the present invention, by combining the nonlinear dynamic model with the real-time evaluation results, it is possible to perform high-precision simulation of action performance under different shooting conditions. The output of the shooting simulation system not only takes into account factors such as the ammunition flight trajectory and gun vibration, but also can reflect the effects of dynamic adjustments in real time, providing shooters with a more realistic training experience and feedback, which helps to comprehensively improve shooting skills.

[0116] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a shooting action real-time evaluation and simulation method and system based on dynamic adjustment provided by an embodiment of the present invention.

[0117] The present invention also provides a shooting action real-time evaluation and simulation system 20 based on dynamic adjustment, which is applied to the above-mentioned shooting action real-time evaluation and simulation method based on dynamic adjustment, comprising:

[0118] Processor 201.

[0119] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, a real-time evaluation and simulation method of shooting action based on dynamic adjustment as in the method embodiment is implemented.

[0120] The real-time evaluation and simulation system 20 for shooting action based on dynamic adjustment provided by the present invention can execute the above-mentioned real-time evaluation and simulation method for shooting action based on dynamic adjustment, and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0121] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0122] (1) In the present invention, by introducing a complex nonlinear dynamic model and an adaptive control algorithm, the errors in the shooting action can be adjusted in real time, and the shooting control strategy can be dynamically optimized. Compared with the traditional shooting evaluation system, the present invention can more accurately reflect the actual operation of the shooter, thereby greatly improving the shooting accuracy and the stability of the action;

[0123] (2) In the present invention, the control parameters and model parameters can be automatically adjusted according to the real-time evaluation results under different shooting conditions to ensure that the best action guidance can be provided in a changing shooting environment. By introducing a dynamic adjustment and feedback mechanism, the system can effectively cope with various complex shooting scenarios and improve the practical application effect of training;

[0124] (3) In the present invention, by combining the nonlinear dynamic model with the real-time evaluation results, it is possible to perform high-precision simulation of action performance under different shooting conditions. The output of the shooting simulation system not only takes into account factors such as the ammunition flight trajectory and gun vibration, but also can reflect the effects of dynamic adjustments in real time, providing shooters with a more realistic training experience and feedback, which helps to comprehensively improve shooting skills.

[0125] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the above claims.

[0126] There are a few points to note:

[0127] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0128] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0129] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0130] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A real-time evaluation and simulation method for shooting action based on dynamic adjustment, characterized in that: include: S1. Data collection: using a variety of sensor systems to collect data during the shooting process. The multiple sensors include an inertial measurement unit (IMU), an accelerometer, a gyroscope, and a pressure sensor. The data include acceleration, angular velocity, handheld device posture, barrel vibration, and environmental factors. S2. Action model construction: a dynamic model of the shooting action is established based on the collected data. The dynamic model comprehensively considers the complex dynamic behaviors of the action during the shooting process, including human body movements, gun recoil and air resistance, and describes the dynamic characteristics of the shooting action. S3, real-time dynamic evaluation, through real-time update and adjustment of the shooting action model, evaluate the effect of the shooting action, use the Kalman filter for state estimation, to correct and optimize the dynamic model of the shooting action, including real-time feedback adjustment of action errors and external disturbances; S4, dynamic adjustment of action, according to the implementation evaluation results, the shooting action is dynamically adjusted, the error in the shooting action is corrected using the adaptive control algorithm, and the action control strategy is optimized. The dynamic adjustment includes not only the correction of the posture, but also the adjustment of the control parameters of the firearm; S5. Real-time simulation: Based on dynamic adjustment, the shooting action simulation is carried out. The simulation system combines nonlinear dynamics models to simulate the action performance under different shooting conditions and output accurate simulation results. S6. Feedback mechanism and optimization: Feedback optimization of shooting action is performed based on real-time simulation results. The accuracy and stability of the action are improved by adjusting model parameters and control inputs. The feedback mechanism includes re-evaluation and optimization of the effect of the action after adjustment to ensure the system's adaptive adjustment capability.

2. A method for real-time evaluation and simulation of shooting action based on dynamic adjustment according to claim 1, characterized in that: The S1 specifically includes: Use a variety of sensor systems to collect various data during the design process, including: Acceleration (a x (t),a y (t),a z (t)): acceleration in the x, y and z directions; Angular velocity (ω x (t),ω y (t),ω z (t)): angular velocity in the x, y and z directions; Pressure data (p(t)): readings from the muzzle pressure sensor, used to assess changes in barrel pressure during firing; Posture data (θ x (t),θ y (t),θ z (t)): gun and shooter posture angle data; The data vector is defined as:

3. The method for real-time evaluation and simulation of shooting action based on dynamic adjustment according to claim 1, characterized in that: The S2 specifically includes: Construct an action model that comprehensively considers the dynamic factors in the shooting process. The model includes the following equations: Kinetic equation: Among them, q(t) is the posture vector, M is the mass matrix, which describes the inertial characteristics of the system, C is the Coriolis force matrix, which reflects the inertial force caused by the speed change, K is the spring matrix, which represents the elastic response of the system, and T(t) is the external force, including the recoil force and air resistance generated by the shooting; Backlash Model: Where m is the mass of the ammunition, v(t) is the velocity of the ammunition in the barrel, and d is the length of the barrel; Air resistance model: Where ρ is the air density, C d is the drag coefficient and A is the cross-sectional area of ​​the ammunition.

4. The method for real-time evaluation and simulation of shooting action based on dynamic adjustment according to claim 1, characterized in that: The S3 specifically includes: Implement the evaluation shooting action and estimate the state of the dynamic model through the Kalman filter. The update formula is: Prediction steps: x k|k-1 =Ax k-1 +Bu k P k|k-1 =AP k-1 From T +Q Update steps: K k =P k|k-1 H T (HP k|k-1 H T +R) -1 x k =x k|k-1 +K k (z k -Hx k|k-1 ) Among them, x k is the state estimation, K k is the Kalman gain, P k is the covariance matrix, z k are the observed values, Q and R are the covariance matrices of process noise and observation noise.

5. The method for real-time evaluation and simulation of shooting action based on dynamic adjustment according to claim 1, characterized in that: The S4 specifically includes: According to the real-time evaluation results, the adaptive control algorithm is used to correct the error in the shooting action. The adjustment formula is: you k =K d and k e k =q target -q k Among them, u k is the control input, K d To adjust the gain, e k is the error between the target pose and the current pose, q target is the target pose.

6. The method for real-time evaluation and simulation of shooting action based on dynamic adjustment according to claim 1, characterized in that: The S5 specifically includes: Based on dynamic adjustment, real-time simulation of shooting action is performed, and the output of the simulation system is based on the following equation: S(t)=f(q(t),u(t)) Among them, S(t) is the simulation result, and f is the simulation function, which takes into account the nonlinear factors in the shooting action, including the ammunition flight trajectory and barrel vibration.

7. The method for real-time evaluation and simulation of shooting action based on dynamic adjustment according to claim 1, characterized in that: The S6 specifically includes: Feedback optimization is performed on the shooting action based on the real-time simulation results, and the model parameters are updated to improve the simulation accuracy. The adjustment formula is: ΔM=α(M true -M est ) ΔC=β(C true -C est ) ΔK=γ(K true -K est ) Among them, α, β and γ are adjustment coefficients, M true , C true and K true is the real model parameter, M est , C est and K est To estimate the value, continuous optimization and adjustment are carried out to ensure the system's adaptability and accuracy to different shooting conditions.

8. The method for real-time evaluation and simulation of shooting action based on dynamic adjustment according to claim 5, characterized in that: The S4 further comprises: Based on the real-time calculation of the multivariable gain matrix K(t) of the nonlinear system, the multivariable gain matrix is ​​dynamically updated according to the following formula: Among them, K0 is the initial gain matrix, L(t) is the time-related adjustment coefficient matrix, e(t) is the error vector between the current posture and the target posture, and the formula adjusts the control gain in real time according to the nonlinear factors in the shooting action, thereby improving the adaptive adjustment ability and accuracy of the system.

9. The method for real-time evaluation and simulation of shooting action based on dynamic adjustment according to claim 7, characterized in that: The S6 further comprises: The model parameters are updated by solving the nonlinear optimization problem based on Bayesian inference. The objective function is defined as: Among them, θ is the model parameter vector, y i is the i-th observation value, f(x i ,θ) is the model prediction value, W is the weight matrix, λ is the regularization parameter, and R(θ) is the regularization term. Through the above optimization process, the overfitting risk can be effectively reduced and the generalization ability of the model for shooting actions can be improved.

10. A real-time evaluation and simulation system for shooting action based on dynamic adjustment, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the real-time evaluation and simulation method for shooting action based on dynamic adjustment as described in any one of claims 1 to 9 is implemented.

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