Automatic calibration system and method for live firing image target

Abstract

The invention relates to the technical field of military training, in particular to an image target automatic calibration system and method for live firing, and the system comprises a physical attribute analysis module, a rendering control module, a target dynamic adjustment module, a response speed adjustment module and an effect evaluation module. According to the method, the position and the speed of the bullet in the future time are calculated by collecting the tracking data and the speed information of the bullet flight, so that the image target can be dynamically adjusted according to the shooting data of a shooter, the actual trajectory of the bullet is more accurately reflected, and the prediction and adjustment mode is more accurate. The target is allowed to respond in real time in a changeable environment and under different shooting distances, the response speed and hit precision of a shooter are improved, the response speed of the target is optimized according to real-time environment changes, training synchronism and effectiveness are improved, the training effect is evaluated in real time, the skill of the shooter can be improved, and the training efficiency is improved. The training difficulty can be adjusted according to the performance of a shooter, and the pertinence and effect of training are enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of military training, and in particular to an automatic calibration system and method for an image target of live-fire shooting. Background Art

[0002] The field of military training technology includes everything from basic physical training, tactical operations to advanced weapons use and maintenance, which are used to improve the combat and operational skills of military personnel. In this field, the development of technology is particularly critical, such as the use of simulators, the application of virtual reality and augmented reality technology, and the development of automated systems, which aim to improve the safety, efficiency and realism of training. Technology can simulate real battlefield environments, allowing soldiers to train in various complex scenarios under controlled and safe conditions.

[0003] Among them, the automatic calibration system of image targets for live-fire shooting refers to a high-tech device used in military shooting training, which is designed to automatically adjust the position and direction of the target to suit the specific needs of the shooter. The main purpose of this system is to improve the accuracy and effectiveness of shooting training and make the training more in line with actual combat needs. By automatically calibrating the target, shooters can train in changing environments and different shooting distances, thereby improving their reaction speed, accuracy and tactical capabilities.

[0004] Although existing military training technologies have made many advances in simulating real battlefield environments, they still have insufficient technical problems in providing highly personalized and dynamically adjusted training scenarios. Existing shooting training systems rely on preset target behaviors, which make it difficult to reflect environmental changes and the complex interactions required by shooters in real time. This static training model cannot effectively simulate the changing conditions on the battlefield, causing shooters to encounter situations they have not trained for in actual combat, affecting combat effectiveness and safety. For example, shooters need to deal with rapidly moving targets or complex environmental factors in actual combat, but existing technologies fail to provide sufficient training support to simulate scenarios, resulting in shooters not reacting quickly or accurately enough when dealing with unknown or unexpected situations. Summary of the invention

[0005] In order to solve the problem that the prior art is insufficient in providing highly personalized and dynamically adjusted training scenes, the embodiment of the present invention provides a system and method for automatic calibration of image targets for live-fire shooting. The technical solution is as follows: On the one hand, a system for automatic calibration of an image target for live-fire shooting is provided, the system comprising: The physical property analysis module collects tracking data and velocity information of live-fire shooting, calculates the position and velocity of the bullet trajectory at multiple time points in the future time period through the motion equation, and obtains motion prediction data; The rendering control module implements texture mapping and light and shadow processing actions based on the motion prediction data, performs visual effect rendering on the dynamic obstacles fed back by live ammunition shooting, and obtains visual effect data; The target dynamic adjustment module uses the visual effect data to evaluate the current state of the image target, uses the tracking data of the live ammunition shooting to adjust the position of the image target, and refers to the changes in the shooting rate and shooting angle to obtain the adjusted target position; The response speed adjustment module verifies the synchronization between the automatic calibration of the image target and the shooter's behavior according to the adjusted target position and in combination with the real-time environment simulation feedback, and obtains the optimized response speed configuration; The effect evaluation module uses the optimized response speed configuration to evaluate the changes in training effects before and after optimization, compares the correlation between the synchronization of image target response and the shooter's hit rate, analyzes the performance differences of the shooter under differentiated response speeds, calibrates the difficulty of shooting training, and obtains a training optimization plan.

[0006] As a further solution of the present invention, the motion prediction data includes predicted bullet position records, bullet speed records, and predicted time points; the visual effect data includes texture mapping data, light and shadow effect data, and visual rendering output; the adjusted target position includes optimized image target position, adjusted target angle, and target state evaluation data; the optimized response speed configuration includes adjusted response speed settings, environmental simulation data, and synchronization verification; the training optimization plan includes shooting training effect comparison data, response synchronization analysis results, and shooter performance difference information.

[0007] As a further solution of the present invention, the physical property analysis module includes: The data tracking submodule collects the tracking data and speed information of the live-fire shooting, performs speed monitoring and trajectory tracking of the live-fire shooting, and organizes the data format to obtain a formatted data set; The trajectory calculation submodule calculates the motion equation of the live-fire shooting position and speed through the trajectory data set, updates the position and speed information according to the preset time point, and arranges the calculation results in time sequence to obtain a position and speed table; The prediction and analysis submodule performs dynamic simulation analysis based on the position and speed table, identifies trend and pattern changes by comparing the position and speed data at different time points, predicts the live-fire shooting trajectory in the future time period, and obtains motion prediction data.

[0008] As a further solution of the present invention, the rendering control module includes: The texture material adjustment submodule performs texture mapping on the surface of the dynamic obstacle based on the motion prediction data, selects and adjusts the texture material, verifies the consistency with the live-fire shooting scene, and obtains the texture mapping result; The light and shadow processing submodule uses the texture mapping result to perform light simulation and shadow effect processing, adjusts light and shadow parameters according to differentiated lighting angles and light source intensities, simulates the influence of natural light on dynamic obstacles, and obtains light and shadow effect data; The visual rendering submodule performs visual effect rendering of the live-fire shooting scene through the light and shadow effect data, optimizes the realism and dynamic performance of the visual effect, and obtains visual effect data.

[0009] As a further solution of the present invention, the target dynamic adjustment module includes: The image evaluation submodule evaluates the real-time visual state and environmental adaptability of the image target based on the visual effect data, calculates the visual difference between the target and the surrounding environment, and obtains the target state evaluation result; The position analysis submodule uses the target state evaluation results, combined with the tracking data of live ammunition shooting, to analyze the optimal position of the target according to the changes in the firing rate and firing angle, performs real-time position update and automatic calibration, and obtains a target position adjustment plan; The synchronization degree verification submodule implements the target position adjustment plan, verifies the synchronization degree between the position of the image target and the shooting parameter changes, performs rendering update of the image target position, and obtains the adjusted target position.

[0010] As a further solution of the present invention, the formula for calculating the visual difference between the target and the surrounding environment is: ; Among them, V is the visual difference between the target and the surrounding environment. and is the weight, C is the visual contrast, S is the color difference, Represents the time-varying sensitivity coefficient, and T is the time factor.

[0011] As a further solution of the present invention, the response speed adjustment module includes: The influencing factor analysis submodule simulates the analysis of the impact of real-time environmental changes on live-fire shooting based on the adjusted target position, including wind speed, humidity and temperature, performs environmental influencing factor analysis, and obtains environmental simulation data; The speed adjustment submodule uses the environmental simulation data to adjust the response speed setting, automatically calibrate the image target, match the environmental changes and the shooter's operating speed, perform dynamic speed adjustment, and generate a speed adjustment record; The real-time test submodule performs a real-time test of the synchronization between the image target and the shooter's behavior through the speed adjustment record, verifies the coordination between the adjusted response speed and the shooter's action, checks the efficiency of the shooting training, and obtains the optimized response speed configuration.

[0012] As a further solution of the present invention, the effect evaluation module includes: The effect identification submodule performs effect difference analysis based on the optimized response speed configuration by comparing the training data before and after the optimization to obtain a training effect comparison result; The correlation analysis submodule uses the training effect comparison results to analyze the correlation between the image target response synchronization and the shooter's hit rate, verify the impact of the change in response speed on the hit rate, perform correlation statistical analysis, and generate response synchronization analysis results; The performance difference analysis submodule analyzes the shooter's performance under differentiated response speed settings through the response synchronization analysis results, identifies key performance differences, and adjusts the shooting training difficulty in real time to obtain a training optimization plan.

[0013] As a further solution of the present invention, the formula for performing effect difference analysis is: ; Among them, E is the difference in the effect of the training data before and after optimization. Represents the optimized data weighting coefficient, Represents the weighting coefficient of data before optimization, Represents the optimized performance index, Represents the performance index before optimization, represents the normalization coefficient, Represents the sample size.

[0014] A method for automatic calibration of an image target for live-fire shooting, which is performed based on the above-mentioned automatic calibration system for an image target for live-fire shooting, comprises the following steps: S1: Collect the velocity and position data of live-fire shooting, use the equation of motion to calculate the expected position and velocity of the bullet at different time points in the future time period, and obtain motion prediction data; S2: Using the motion prediction data, performing texture mapping and light and shadow processing to optimize the visual feedback effect of live-fire shooting and obtain visual effect data; S3: using the visual effect data to evaluate the current state of the image target, adjusting the image target according to the tracking data of the live ammunition shooting and the changes in the firing rate and firing angle, to obtain an adjusted target position; S4: combining the real-time environment simulation and the adjusted target position, automatically calibrating the image target according to the shooter's behavior to obtain an optimized response speed configuration; S5: using the optimized response speed configuration, evaluating the performance of the shooter under the differentiated response speeds, analyzing the correlation between the performance difference and the hit rate, and obtaining the shooter performance analysis result; S6: Based on the shooter performance analysis results, evaluate the changes in training effects before and after optimization, calibrate the difficulty of shooting training, and obtain a training optimization plan.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: By collecting the tracking data and speed information of the bullet's flight, the position and speed of the bullet in the future are calculated, so that the image target can be dynamically adjusted according to the shooter's shooting data to more accurately reflect the actual trajectory of the bullet. This prediction and adjustment method allows the target to respond in real time under changing environments and different shooting distances, enhancing the realism and challenge of training, and significantly improving the shooter's reaction speed and hit accuracy. The response speed of the target is optimized according to real-time environmental changes, further improving the synchronization and effectiveness of training. By evaluating the training effect in real time, it can not only improve the shooter's skills, but also adjust the training difficulty according to the shooter's performance, enhancing the pertinence and effectiveness of training. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the system flow of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 It is a flow chart of the physical property analysis module of the present invention; Figure 4 It is a flow chart of the rendering control module of the present invention; Figure 5 This is a flow chart of the target dynamic regulation module of the present invention; Figure 6 It is a flow chart of the response speed adjustment module of the present invention; Figure 7 It is a flow chart of the effect evaluation module of the present invention; Figure 8 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0018] 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.

[0019] 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.

[0020] See also Figure 1 , 2 The embodiment of the present invention provides an automatic calibration system for an image target of a live-fire shooting, the system comprising: The physical property analysis module collects tracking data and velocity information of live-fire shooting, calculates the position and velocity of the bullet trajectory at multiple time points in the future time period through the motion equation, and obtains motion prediction data; The rendering control module implements texture mapping and light and shadow processing actions based on the motion prediction data, performs visual effect rendering on the dynamic obstacles fed back by live-fire shooting, and obtains visual effect data; The target dynamic adjustment module uses visual effect data to evaluate the current state of the image target, uses the tracking data of live-fire shooting to adjust the image target position, and refers to the changes in firing rate and shooting angle to obtain the adjusted target position; The response speed adjustment module optimizes the image target response speed according to the adjusted target position and combines the real-time environment simulation feedback, verifies the synchronization between the image target automatic calibration and the shooter's behavior, and obtains the optimized response speed configuration; The effect evaluation module uses the optimized response speed configuration to evaluate the changes in training effects before and after optimization, compares the correlation between the synchronization of image target response and the shooter's hit rate, analyzes the performance differences of shooters under differentiated response speeds, calibrates the difficulty of shooting training, and obtains a training optimization plan.

[0021] Motion prediction data includes predicted bullet position records, bullet speed records, and predicted time points. Visual effects data includes texture mapping data, light and shadow effects data, and visual rendering output. The adjusted target position includes optimized image target position, adjusted target angle, and target status evaluation data. The optimized response speed configuration includes adjusted response speed settings, environmental simulation data, and synchronization verification. The training optimization plan includes shooting training effect comparison data, response synchronization analysis results, and shooter performance difference information.

[0022] See also Figure 2 , 3 , the physical property analysis module includes: The data tracking submodule collects the tracking data and speed information of live-fire shooting, performs speed monitoring and trajectory tracking of live-fire shooting, and organizes the data format to obtain the execution process of the formatted data set as follows; Responsible for collecting tracking data and speed information of live-fire shooting. During this process, the acquisition equipment records the dynamic data of the shooting process in real time through high-precision positioning and speed sensors. The tracking data includes information such as speed, direction and timestamp. Speed ​​monitoring involves measuring the instantaneous speed of the shooting object. Trajectory tracking tracks the movement trajectory of the shooting object by analyzing the speed and direction data, and formats the collected raw data, converting the data into a unified data format for subsequent processing. Through data correction, the accuracy and availability of the data set are guaranteed, which affects the subsequent processing effect and analysis depth, and obtains a formatted data set.

[0023] The trajectory calculation submodule calculates the motion equation of the live-fire shooting position and speed through the trajectory data set, updates the position and speed information according to the preset time point, and arranges the calculation results in time sequence to obtain the execution process of the position and speed table as follows; The equation of motion for calculating the position and velocity of live ammunition shooting is as follows: ; Calculate the updated position and velocity information, where: represents displacement, u represents initial velocity, a represents acceleration, and t represents time; The basic physical equation of motion is used to calculate the position and speed of the live-fire shooting at the preset time point. The initial speed u is set to the measured starting speed of the live-fire, the acceleration a is the average acceleration calculated due to factors such as gravity and air resistance, and the time t is set according to each preset time point. Set the initial velocity u=500 m / s, acceleration a=-9.8 m / s2 (considering the influence of gravity), and time t=10 seconds; Substituting into the formula, we can calculate the displacement: This calculation process not only verifies the correctness of the formula, but also provides a practical example of live-fire shooting trajectory prediction.

[0024] The prediction and analysis submodule performs dynamic simulation analysis based on the position and velocity table. By comparing the position and velocity data at different time points, it identifies trend and pattern changes and predicts the live-fire shooting trajectory in the future time period. The execution process of obtaining motion prediction data is as follows; Model building and simulation analysis of historical position and speed data involve the use of statistical methods and machine learning techniques to perform trend analysis and pattern recognition on the data. By identifying the changes in position and speed data at different time points, the dynamic characteristics of shooting behavior are revealed, and the live-fire shooting trajectory in future time periods is predicted. This process relies on advanced data processing technology and complex mathematical modeling. By comparing data at differentiated time points, the system can predict future state changes and adjust real-time response strategies based on the prediction results to obtain motion prediction data.

[0025] See also Figure 2 , 4 , the rendering control module includes: The texture material adjustment submodule performs texture mapping on the surface of dynamic obstacles based on motion prediction data, selects and adjusts texture materials, verifies consistency with the live-fire shooting scene, and obtains the execution process of the texture mapping result as follows; Texture mapping is performed for dynamic obstacles that appear in live-fire shooting scenes. This process involves the selection and adjustment of a variety of texture materials. During the adjustment process, the texture patterns in the material library are screened to ensure that the selected texture matches the surface characteristics of the obstacles in the actual scene. The texture is resized, color corrected, and pattern synthesized through software tools to adapt to the surface shape and lighting conditions of the obstacles. After the adjusted texture is mapped to the surface of the dynamic obstacle, a series of verification processes are performed to check the consistency of the texture with the live-fire shooting scene. The texture details are adjusted through a real-time feedback mechanism to ensure the high consistency of the mapping results and the authenticity of the visual effects, and the texture mapping results are obtained.

[0026] The light and shadow processing submodule uses texture mapping results to simulate light and shadow effects, adjusts light and shadow parameters according to differentiated lighting angles and light source intensities, simulates the impact of natural light on dynamic obstacles, and obtains the execution process of light and shadow effect data as follows; Use advanced rendering engines to simulate the lighting conditions in the scene, analyze the effects of light reflection and shadows on texture mapping results, adjust light and shadow parameters according to the lighting angle and light intensity at different times and locations, achieve natural interaction between light and objects, simulate multiple light source effects including sunlight and artificial light sources to improve the visual realism of the scene, adjust light intensity and angle, optimize shadow depth and edge blur, enhance the three-dimensional sense and texture detail of dynamic obstacles, and obtain light and shadow effect data.

[0027] The visual rendering submodule uses the light and shadow effect data to render the visual effects of the live-fire shooting scene, optimizing the realism and dynamic performance of the visual effects. The execution process of obtaining the visual effect data is as follows; Rendering of visual effects for live-fire shooting scenes. During this process, advanced image processing technology and rendering algorithms are used to adjust the color balance, contrast, and light distribution of the scene according to the light and shadow effect data, so as to enhance the realism and visual impact of the live-fire shooting scene, optimize the visual performance of dynamic and static elements, and ensure that every detail in the scene can accurately present the expected visual effects. Through real-time rendering technology, it can quickly respond to scene changes and light and shadow adjustments to obtain visual effects data.

[0028] See also Figure 2 , 5 , the target dynamic regulation module includes: The image evaluation submodule evaluates the real-time visual state and environmental adaptability of the image target based on the visual effect data, calculates the visual difference between the target and the surrounding environment, and obtains the target state evaluation result. The execution process is as follows; The formula for calculating the visual difference between the target and the surrounding environment is: ; Among them, V is the visual difference between the target and the surrounding environment. and is the weight, C is the visual contrast, S is the color difference, Represents the time-varying sensitivity coefficient, and T is the time factor.

[0029] Parameter meaning and setting value: is the visual contrast weight, which is set to 0.5. This value is derived from the empirical data of the average contrast difference between the target and the background; is the color difference weight, which is set to 0.3. This value is based on the statistical analysis of the color difference between the target and the background in the color space; C is the contrast factor, which is calculated by image analysis software, and the contrast index between the target and the background is set to 20; S is the color difference factor, which is also obtained through image analysis software and is set to 15; is the time change sensitivity coefficient, which is set to 0.2, reflecting the impact of time changes on the target status assessment. This coefficient is adjusted according to the time sensitivity observed in the laboratory environment; T is the time factor, assuming that the observation has been going on for 10 minutes, which is converted to hours as 0.167; Substitute the parameters into the formula for calculation: The result 14.26 indicates the degree of visual difference between the target and the surrounding environment. A higher value reflects that the target is visually distinguishable from the environment, which helps the accuracy of state classification and recognition. This result is further used in the deep learning model to adjust and optimize the recognition algorithm to ensure more accurate assessment of the target's real-time visual state.

[0030] The position analysis submodule uses the target state evaluation results, combined with the tracking data of live-fire shooting, to analyze the optimal position of the target according to the changes in firing rate and firing angle, and performs real-time position update and automatic calibration to obtain the execution process of the target position adjustment plan as follows; The changes in firing rate and shooting angle are analyzed to determine the optimal position of the target. This process involves real-time monitoring of shooting data and calculating the impact of shooting dynamics on the target position. The automatic calibration system adjusts the target position according to the analysis results to ensure the accuracy and real-time nature of shooting training. Real-time position updates and automatic calibration operations are achieved through high-precision sensors and control systems to ensure perfect alignment of the target position with the shooter's shooting parameters, optimize training results, directly affect the accuracy and efficiency of subsequent shooting, and obtain a target position adjustment plan.

[0031] The synchronization degree verification submodule implements the target position adjustment plan, verifies the synchronization degree between the position of the image target and the change of the shooting parameters, performs the rendering update of the image target position, and obtains the execution process of the adjusted target position as follows; Verify the degree of synchronization between the position of the image target and the shooting parameters. This process includes rendering and updating the image target position and continuous monitoring of the shooting data. By comparing the data before and after adjustment, ensure that the adjustment of the target position is synchronized with the shooter's operation. Optimize the target position adjustment strategy through real-time data analysis and feedback mechanism, improve the target's reaction speed and position accuracy, more accurately reflect the changes in shooting parameters, provide the shooter with a more accurate training environment, and obtain the adjusted target position.

[0032] See also Figure 2 , 6 , the response speed adjustment module includes: The influencing factor analysis submodule simulates the impact of real-time environmental changes on live-fire shooting based on the adjusted target position, including wind speed, humidity and temperature, and performs environmental influencing factor analysis. The execution process of obtaining environmental simulation data is as follows; Comprehensively consider environmental factors such as wind speed, humidity and temperature, collect relevant data through environmental sensors, use mathematical models and algorithms to analyze how environmental factors affect shooting accuracy, and simulate shooting scenes under different environmental conditions through statistical analysis of actual measurement data and construction of prediction models. It can predict the specific impact of environmental changes on shooting results, provide a scientific basis for shooting training, ensure that training can be carried out under various environmental conditions, and obtain environmental simulation data.

[0033] The speed adjustment submodule uses environmental simulation data to adjust the response speed setting, automatically calibrate the image target, match environmental changes and the shooter's operating speed, and perform dynamic speed adjustment. The execution process of generating speed adjustment records is as follows; The response speed is adjusted according to environmental changes and the shooter's operating speed. This process includes automatically calibrating the speed setting of the image target through the control system to match real-time environmental changes and the shooter's reaction speed. The response time of the image target is optimized through dynamic speed adjustment technology to ensure that the target can respond to the shooter's operation in a timely and accurate manner. The speed adjustment results are iterated and optimized many times, which affects the efficiency of shooting training and the shooter's performance, and forms a speed adjustment record.

[0034] The real-time test submodule performs real-time test of the synchronization between the image target and the shooter's behavior through speed adjustment records, verifies the coordination between the adjusted response speed and the shooter's action, checks the efficiency of shooting training, and obtains the execution process of the optimized response speed configuration as follows; The testing process includes a systematic examination of the coordination between the adjusted response speed and the shooter's movements. The time difference between the shooter's operation and the image target's response is captured through high-speed photography and sensor technology. The data is analyzed to evaluate the actual efficiency of shooting training. This can significantly improve the dynamic adaptability of shooting training and the shooter's operation accuracy. The data collection and analysis during the verification process provides an important basis for further system tuning, thereby obtaining an optimized response speed configuration.

[0035] See also Figure 2 , 7 , the effect evaluation module includes: The effect identification submodule is based on the optimized response speed configuration. By comparing the training data before and after optimization, the effect difference analysis is performed to obtain the training effect comparison results. The execution process is as follows; The formula for effect difference analysis is: ; Among them, E is the difference in the effect of the training data before and after optimization. Represents the optimized data weighting coefficient, Represents the weighting coefficient of data before optimization, Represents the optimized performance index, Represents the performance index before optimization, represents the normalization coefficient, Represents the sample size.

[0036] Parameter meaning and setting value: The weighting coefficient of the optimized data reflects the importance attached to the new data and is set to 1.2. This coefficient is set based on the average performance of data improvement in previous optimization projects and is slightly higher than the baseline value of 1.0 to emphasize the importance of the optimized data. is the weighting coefficient of the data before optimization, indicating the focus on the original data, and is set to 1.0 as the baseline comparison coefficient; and They are the performance indicators before and after optimization. The data are obtained through comparative experiments. is 85, is 80, which represents the performance score obtained by the experiment; is the normalization coefficient, which is used to adjust the scale of the difference value and is set to 0.5. This value is determined based on the standard deviation and coefficient of variation in the historical analysis data; is the sample size, which affects the stability and reliability of the difference analysis and is set to 100, representing the total number of data points involved in the comparison; Substitute the parameters into the formula for calculation: 1. Calculate the weighted performance difference: ; 2. Calculate the weighted baseline performance: ; Calculate the difference between the two: ; 4. Calculate the impact of adjusting normalization and sample size: ; 5. Calculate the effect difference value: ; Result 4.4 shows that the performance of the optimized training data is improved by 4.4 units compared with that before optimization, which shows the effectiveness of the optimization measures and provides data support for further optimization.

[0037] The correlation analysis submodule uses the training effect comparison results to analyze the correlation between the image target response synchronization and the shooter's hit rate, verify the impact of the change in response speed on the hit rate, perform correlation statistical analysis, and generate the execution process of the response synchronization analysis results as follows; We deeply explore the relationship between the synchronization of image target response and the shooter's hit rate, perform correlation statistical analysis, use regression analysis and correlation testing methods to evaluate the impact of changes in response speed on the shooter's hit rate, collect batches of training data, build statistical models, analyze the causal relationship between response speed and hit rate, and verify that the statistical results clearly show how the adjustment of response speed affects the shooter's hit rate, generating response synchronization analysis results.

[0038] The performance difference analysis submodule analyzes the shooter's performance under differentiated response speed settings through the response synchronization analysis results, identifies the key differences in performance, and adjusts the shooting training difficulty in real time. The execution process of the training optimization plan is as follows; Conduct a systematic analysis of the shooter's performance under different response speed settings to identify key performance differences. This process includes a detailed assessment of the shooter's shooting accuracy, reaction time, and adaptability. Through data analysis, the changing trends and key factors of the shooter's performance are revealed. The analysis results are used to adjust the difficulty and complexity of shooting training in real time to better adapt to the shooter's ability and improve the effectiveness of training. This directly affects future training content and methods, ensures that the training effect is maximized, and obtains a training optimization plan.

[0039] See also Figure 8 A method for automatic calibration of an image target for live-fire shooting is provided. The method for automatic calibration of an image target for live-fire shooting is performed based on the above-mentioned automatic calibration system for an image target for live-fire shooting, and comprises the following steps: S1: Collect the velocity and position data of live-fire shooting, use the equation of motion to calculate the expected position and velocity of the bullet at different time points in the future time period, and obtain motion prediction data; S2: Use motion prediction data to perform texture mapping and light and shadow processing to optimize the visual feedback effect of live-fire shooting and obtain visual effect data; S3: using visual effect data to evaluate the current state of the image target, adjusting the image target according to the tracking data of live ammunition shooting and the changes in firing rate and firing angle, and obtaining the adjusted target position; S4: Combined with real-time environment simulation and adjusted target position, the image target is automatically calibrated according to the shooter's behavior to obtain the optimized response speed configuration; S5: Using the optimized response speed configuration, evaluate the shooter's performance under differentiated response speeds, analyze the correlation between performance differences and hit rates, and obtain the shooter performance analysis results; S6: Based on the results of the shooter performance analysis, evaluate the changes in training effects before and after optimization, calibrate the difficulty of shooting training, and obtain a training optimization plan.

[0040] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present 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 claims.

Claims

1. An automatic calibration system for image targets for live-fire shooting, characterized in that: The system comprises: The physical property analysis module collects tracking data and velocity information of live-fire shooting, calculates the position and velocity of the bullet trajectory at multiple time points in the future time period through the motion equation, and obtains motion prediction data; The rendering control module implements texture mapping and light and shadow processing actions based on the motion prediction data, performs visual effect rendering on the dynamic obstacles fed back by live ammunition shooting, and obtains visual effect data; The target dynamic adjustment module uses the visual effect data to evaluate the current state of the image target, uses the tracking data of the live ammunition shooting to adjust the position of the image target, and refers to the changes in the shooting rate and shooting angle to obtain the adjusted target position; The response speed adjustment module verifies the synchronization between the automatic calibration of the image target and the shooter's behavior according to the adjusted target position and combines the real-time environment simulation feedback to obtain the optimized response speed configuration; The effect evaluation module uses the optimized response speed configuration to evaluate the changes in training effects before and after optimization, compares the correlation between the synchronization of image target response and the shooter's hit rate, analyzes the performance differences of the shooter under differentiated response speeds, calibrates the difficulty of shooting training, and obtains a training optimization plan.

2. The automatic calibration system for image targets for live-fire shooting according to claim 1, characterized in that: The motion prediction data includes predicted bullet position records, bullet speed records, and predicted time points; the visual effect data includes texture mapping data, light and shadow effect data, and visual rendering output; the adjusted target position includes optimized image target position, adjusted target angle, and target state evaluation data; the optimized response speed configuration includes adjusted response speed settings, environmental simulation data, and synchronization verification; the training optimization plan includes shooting training effect comparison data, response synchronization analysis results, and shooter performance difference information.

3. The automatic calibration system for live-fire shooting image targets according to claim 1, characterized in that: The physical property analysis module includes: The data tracking submodule collects the tracking data and speed information of the live-fire shooting, performs speed monitoring and trajectory tracking of the live-fire shooting, and organizes the data format to obtain a formatted data set; The trajectory calculation submodule calculates the motion equation of the live-fire shooting position and speed through the trajectory data set, updates the position and speed information according to the preset time point, and arranges the calculation results in time sequence to obtain a position and speed table; The prediction and analysis submodule performs dynamic simulation analysis based on the position and speed table, identifies trend and pattern changes by comparing the position and speed data at different time points, predicts the live-fire shooting trajectory in the future time period, and obtains motion prediction data.

4. The automatic calibration system for live-fire shooting image targets according to claim 1, characterized in that: The rendering control module includes: The texture material adjustment submodule performs texture mapping on the surface of the dynamic obstacle based on the motion prediction data, selects and adjusts the texture material, verifies the consistency with the live-fire shooting scene, and obtains the texture mapping result; The light and shadow processing submodule uses the texture mapping result to perform light simulation and shadow effect processing, adjusts light and shadow parameters according to differentiated lighting angles and light source intensities, simulates the influence of natural light on dynamic obstacles, and obtains light and shadow effect data; The visual rendering submodule performs visual effect rendering of the live-fire shooting scene through the light and shadow effect data, optimizes the realism and dynamic performance of the visual effect, and obtains visual effect data.

5. The automatic calibration system for live-fire shooting image targets according to claim 1, characterized in that: The target dynamic adjustment module comprises: The image evaluation submodule evaluates the real-time visual state and environmental adaptability of the image target based on the visual effect data, calculates the visual difference between the target and the surrounding environment, and obtains the target state evaluation result; The position analysis submodule uses the target state evaluation results, combined with the tracking data of live ammunition shooting, to analyze the optimal position of the target according to the changes in the firing rate and firing angle, performs real-time position update and automatic calibration, and obtains a target position adjustment plan; The synchronization degree verification submodule implements the target position adjustment plan, verifies the synchronization degree between the position of the image target and the shooting parameter changes, performs rendering update of the image target position, and obtains the adjusted target position.

6. The automatic calibration system for image targets for live-fire shooting according to claim 5, characterized in that: The formula for calculating the visual difference between the target and the surrounding environment is: ； Among them, V is the visual difference between the target and the surrounding environment. and is the weight, C is the visual contrast, S is the color difference, Represents the time-varying sensitivity coefficient, and T is the time factor.

7. The automatic calibration system for image targets for live-fire shooting according to claim 1, characterized in that: The response speed adjustment module comprises: The influencing factor analysis submodule simulates the analysis of the impact of real-time environmental changes on live-fire shooting based on the adjusted target position, including wind speed, humidity and temperature, performs environmental influencing factor analysis, and obtains environmental simulation data; The speed adjustment submodule uses the environmental simulation data to adjust the response speed setting, automatically calibrate the image target, match the environmental changes and the shooter's operating speed, perform dynamic speed adjustment, and generate a speed adjustment record; The real-time test submodule performs a real-time test of the synchronization between the image target and the shooter's behavior through the speed adjustment record, verifies the coordination between the adjusted response speed and the shooter's action, checks the efficiency of the shooting training, and obtains the optimized response speed configuration.

8. The automatic calibration system for image targets for live-fire shooting according to claim 1, characterized in that: The effect evaluation module includes: The effect identification submodule performs effect difference analysis based on the optimized response speed configuration by comparing the training data before and after the optimization to obtain a training effect comparison result; The correlation analysis submodule uses the training effect comparison results to analyze the correlation between the image target response synchronization and the shooter's hit rate, verify the impact of the change in response speed on the hit rate, perform correlation statistical analysis, and generate response synchronization analysis results; The performance difference analysis submodule analyzes the shooter's performance under differentiated response speed settings through the response synchronization analysis results, identifies key performance differences, and adjusts the shooting training difficulty in real time to obtain a training optimization plan.

9. The automatic calibration system for image targets for live-fire shooting according to claim 8, characterized in that: The formula for effect difference analysis is: ; Among them, E is the difference in the effect of the training data before and after optimization. Represents the optimized data weighting coefficient, Represents the weighting coefficient of data before optimization, Represents the optimized performance index, Represents the performance index before optimization, represents the normalization coefficient, Represents the sample size.

10. A method for automatic calibration of an image target for live ammunition shooting, characterized in that: The automatic calibration system for image targets for live-fire shooting according to any one of claims 1 to 9 comprises the following steps: Collect velocity and position data of live-fire shooting, use the equation of motion to calculate the expected position and velocity of the bullet at different time points in the future, and obtain motion prediction data; Using the motion prediction data, texture mapping and light and shadow processing are performed to optimize the visual feedback effect of live ammunition shooting and obtain visual effect data; Using the visual effect data to evaluate the current state of the image target, adjusting the image target according to the tracking data of the live-fire shooting and the changes in the firing rate and firing angle, to obtain an adjusted target position; Combining the real-time environment simulation and the adjusted target position, the image target is automatically calibrated according to the shooter's behavior to obtain an optimized response speed configuration; Using the optimized response speed configuration, evaluating the performance of the shooter under the differentiated response speeds, analyzing the correlation between the performance difference and the hit rate, and obtaining the shooter performance analysis results; Based on the shooter performance analysis results, the changes in training effects before and after optimization are evaluated, the difficulty of shooting training is calibrated, and a training optimization plan is obtained.

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