Training system based on holographic projection

Abstract

The invention relates to a training system based on holographic projection. The training system comprises a physiological data acquisition device; the holographic projection shooting simulation system comprises a performance data acquisition device and a holographic projection device, the holographic projection device is used for projecting holographic images, and the performance data acquisition device is used for collecting performance data of a shooter; an environmental data acquisition means; an environment simulation device; the adjusting system is used for obtaining training state data according to the physiological data, the shooting data and the environment data and outputting an optimized training strategy of a shooter according to the training state data; and dynamically adjusting the training rhythm of the holographic projection shooting simulation system and / or adjusting the shooting environment of the environment simulation device according to the optimized training strategy.

Description

Technical Field

[0001] The present invention relates to the field of training systems, and in particular to a training system based on holographic projection. Background Art

[0002] With the growing demand for military, police and competitive shooting training, traditional shooting training methods face many technical bottlenecks and challenges. Traditional training mainly relies on fixed shooting ranges or actual combat simulation sites. Although these methods can provide basic shooting training, they have obvious deficiencies in dynamic adaptability, environmental diversity and personalized training. In training scenarios, shooting training is often limited to fixed conditions (such as indoor shooting ranges or single weather environments), simulating environmental factors (such as wind speed, light changes, noise interference, etc.) and the dynamic movement of targets, thereby providing training simulations for shooters.

[0003] In simulation training, existing technologies only provide dynamic movement of the environment and targets. However, in real shooting environments, the situation is often very complicated. In this situation, shooters need to have good quick decision-making ability and the ability to remain calm under pressure, which places high demands on the shooters' physical and mental qualities. During training, when the shooters' performance declines, it is difficult for existing technologies to analyze whether the reason is caused by the shooters' psychological or physical qualities, or whether the training scene is too complicated or the pressure is too great, and it is impossible to dynamically adjust the training process for the shooters.

[0004] The purpose of the present invention is to design a training system based on holographic projection in view of the above-mentioned problems in the prior art. Summary of the invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention provides a training system based on holographic projection, which can effectively solve at least one problem existing in the above-mentioned prior art.

[0006] The technical solution of the present invention is:

[0007] A training system based on holographic projection, comprising:

[0008] A physiological data acquisition device, used to collect physiological data of the shooter;

[0009] A holographic projection shooting simulation system, comprising a performance data acquisition device and a holographic projection device, wherein the holographic projection device is used to project a holographic image, and the performance data acquisition device is used to collect performance data of a shooter;

[0010] An environmental data acquisition device, used to collect environmental data of the training site;

[0011] Environmental simulation devices are arranged at training sites to simulate corresponding shooting environments;

[0012] The adjustment system is used to obtain training status data according to the physiological data, the shooting data and the environmental data, output an optimized training strategy for the shooter according to the training status data, and dynamically adjust the training rhythm of the holographic projection shooting simulation system and / or adjust the shooting environment of the environmental simulation device according to the optimized training strategy.

[0013] Further, the performance data includes shooting accuracy, reaction time, and aiming deviation;

[0014] The physiological data includes the shooter's heart rate and breathing rate;

[0015] The environmental data includes light intensity, wind speed, noise, and weather.

[0016] Furthermore, obtaining the training status data according to the physiological data, the shooting data, and the environmental data includes:

[0017] According to the shooter's performance score combined with the shooter's physiological data and shooting environment data, the influence degree between the performance data, physiological data and environmental data is analyzed, and the performance score is calculated as follows: S = w1·hit rate-w2·reaction time-w3·aiming deviation, where w1, w2, w3 are weight coefficients, and S is the performance score;

[0018] The performance data, physiological data and environmental data are fused according to the degree of influence between them to obtain training status data.

[0019] Furthermore, the analyzing the influence degree between the performance data, the physiological data, and the environmental data according to the performance score of the shooter in combination with the physiological data of the shooter and the environmental data of the shooting includes:

[0020] If the performance score is satisfactory, the advanced training weight will be obtained;

[0021] If the performance score is unsatisfactory, determine whether the physiological data affects the performance data. If the physiological data does not affect the performance data, obtain the action training weight. Otherwise, determine whether the environmental data affects the physiological data.

[0022] If the environmental data affects the physiological data, the environmental training weight is obtained, otherwise the psychological training weight is obtained.

[0023] Further, the determining whether the physiological data is related to the performance-affecting data includes:

[0024] A multivariate linear regression model was constructed based on physiological data and performance scores. The model formula is as follows:

[0025] Y performance = β0 + β1·heart rate + β2·respiratory rate + ∈;

[0026] Among them, Y represents the performance score, β1 and β2 are regression coefficients, and ∈ is the error term;

[0027] If any regression coefficient of β1 or β2 is greater than the significance threshold, the physiological data affects the performance data; otherwise, the physiological data does not affect the performance data.

[0028] Further, the determining whether the environmental data affects the physiological data includes:

[0029] A random forest regression model was constructed based on environmental data and physiological data. The model formula is as follows:

[0030] Y heart rate, Y breathing rate = f (light intensity, wind speed, noise),

[0031] Among them, Y heart rate is the target heart rate, Y respiratory rate is the target respiratory rate, and f is the random forest model;

[0032] The environmental data is used as the input of the random forest regression model, and the physiological data is used as the output of the random forest regression model. The random forest regression model is trained to obtain the feature importance parameters of the random forest regression model.

[0033] If any of the feature importance parameters of the random forest regression model is greater than the feature importance threshold, the environmental data affects the physiological data, otherwise the environmental data does not affect the physiological data.

[0034] Furthermore, the optimization training strategy is obtained through the following steps:

[0035] The performance data, physiological data, and environmental data are used as state variables, and the adjustment of simulation parameters of the training environment, training rhythm adjustment, and training content adjustment are used as action variables to build a training strategy model;

[0036] Construct a reward function based on shooting hit rate, shooting reaction time, heart rate, and aiming deviation to optimize the training strategy model;

[0037] The current physiological data, shooting data, and environmental data are input into the optimization training strategy model to obtain the optimization training strategy.

[0038] Furthermore, the calculation formula for constructing the reward function based on the shooting hit rate, shooting reaction time, heart rate, and aiming trajectory is as follows:

[0039] R = α1 hit rate - α2 reaction time - α3 heart rate deviation + α4 aiming deviation,

[0040] Among them, α1, α2, α3, and α4 are weight parameters.

[0041] The step of inputting the training state data into the training strategy model to obtain the shooter's optimized training strategy includes:

[0042] The training status data is input into the training strategy model to obtain environmental simulation parameters, training rhythm parameters, and training content parameters. The environmental simulation parameters include: light intensity, wind speed, noise, and weather. The training rhythm parameters include: number of shootings, shooting intervals, and shooting duration. The training content parameters include: static shooting, dynamic shooting, and static and dynamic combined shooting.

[0043] Furthermore, the environmental simulation device includes one or more of a fan, a lamp, a sprinkler, and a speaker.

[0044] Therefore, the present invention provides the following effects and / or advantages:

[0045] The present application obtains training status data based on the physiological data, the shooting data, and the environmental data, outputs an optimized training strategy for the shooter based on the training status data, and dynamically adjusts the training rhythm of the holographic projection shooting simulation system and / or adjusts the shooting environment of the environmental simulation device based on the optimized training strategy, thereby being able to appropriately adjust the training rhythm of the holographic projection shooting simulation system and / or adjust the shooting environment of the environmental simulation device based on the shooter's physiological data and the shooting data, thereby providing the shooter with more suitable training scenarios under different human body conditions and improving the training effect.

[0046] This application uses performance data as the core indicator to reflect the shooter's shooting ability. The shooting hit rate reflects the shooter's shooting accuracy. Reaction time reflects the shooter's reaction speed. Aiming deviation reflects the shooter's precision and stability. Physiological data reflects the shooter's physical state when shooting, including heart rate and breathing rate. Environmental data reflects the external conditions of the shooting site, including light intensity, wind speed and noise. These data can be used to analyze the impact of the external environment on the shooter's performance and physiological state. So as to provide the shooter with the most suitable training mode for the current situation.

[0047] This application uses performance scores to provide feedback on the direction that the shooter needs to improve. If the performance score is qualified, the shooter will obtain advanced training weights, and by adjusting the training content, the training will be more focused on improving difficult skills. If the performance score is unsatisfactory, the performance influencing factors (physiological state and environmental conditions) will be further analyzed to develop training weights with different focuses for the shooter (such as action training or environmental adaptation training).

[0048] The contribution of different types of data to the training strategy in this application is obviously different. If these data are simply mixed and the weights are ignored, the model may overfit some data and ignore other more critical information. The training weights reflect the areas where the current training needs to be strengthened or adjusted. The specific training direction can be determined by the training weights obtained based on performance scores, physiological data, and environmental data.

[0049] The fused data (training status data) of this application is the result of performance data, physiological data, and environmental data after weight adjustment, which can comprehensively reflect the current training status of the shooter. The training strategy model is based on this comprehensive status to analyze the shooter's current ability level, adaptability, and weak links, and formulate an optimized training strategy.

[0050] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description.

[0051] It is to be understood that both the foregoing general description and the following detailed description of the present invention are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. DETAILED DESCRIPTION

[0052] In order to facilitate understanding by those skilled in the art, the present invention is now described in further detail by way of examples:

[0053] A training system based on holographic projection, comprising:

[0054] A physiological data acquisition device, used to collect physiological data of the shooter;

[0055] A holographic projection shooting simulation system, comprising a performance data acquisition device and a holographic projection device, wherein the holographic projection device is used to project a holographic image, and the performance data acquisition device is used to collect performance data of a shooter;

[0056] An environmental data acquisition device, used to collect environmental data of the training site;

[0057] Environmental simulation devices are arranged at training sites to simulate corresponding shooting environments;

[0058] The adjustment system is used to obtain training status data according to the physiological data, the shooting data and the environmental data, output an optimized training strategy for the shooter according to the training status data, and dynamically adjust the training rhythm of the holographic projection shooting simulation system and / or adjust the shooting environment of the environmental simulation device according to the optimized training strategy.

[0059] In this embodiment, the physiological data acquisition device may be a smart watch or smart bracelet for collecting the shooter's heart rate, breathing rate, etc. The performance data acquisition device may be set on an electronic target or an electronic gun, etc., and acquire the time required for the shooter to aim and shoot, as well as the shooting position, etc., through the infrared dots projected by the electronic gun on the electronic target. The environmental data acquisition device may be a fan, a lamp, a sprinkler, a speaker, etc., which is used to generate interference such as wind, light, water spray, and noise in the designed environment.

[0060] Among them, the holographic projection shooting simulation system can be a direct adoption of existing technology, which projects the corresponding image on a screen or medium through holographic projection, thereby displaying the image used for training, and then captures the shooter's shooting position through electronic targets and electronic guns to evaluate it.

[0061] At the same time, when the shooter is conducting simulation training, the shooter's heart rate, breathing rate and other physiological data need to be at a low point to reduce the interference of slight shaking of human breathing, heartbeat, etc. on the aiming of the gun during the shooting process. When the human body is tired, stressed, the external environment is too strong, and other complex situations, the human body's breathing, heartbeat, etc. will increase or fluctuate, thereby affecting the shooter's performance data. During the training process, it is necessary to appropriately adjust the training rhythm of the holographic projection shooting simulation system and / or adjust the shooting environment of the environmental simulation device, so as to reduce the training structure when the human body is tired, give the human body proper rest and recovery, and reduce the interference components in the shooting environment when the human body is under too much pressure and the external environment is too strong, so that the shooter can shoot under the best conditions for his training.

[0062] Further, the performance data includes shooting accuracy, reaction time, and aiming deviation;

[0063] The physiological data includes the shooter's heart rate and breathing rate;

[0064] The environmental data includes light intensity, wind speed, noise, and weather.

[0065] However, the prior art is unable to distinguish the proportion of the impact of various complex situations such as human fatigue, excessive stress, and excessive external environmental interference on human performance data. Therefore, this embodiment proposes the following specific steps to obtain training status data based on the physiological data, the shooting data, and the environmental data, output the shooter's optimized training strategy based on the training status data, and dynamically adjust the training rhythm of the holographic projection shooting simulation system and / or adjust the shooting environment of the environmental simulation device based on the optimized training strategy.

[0066] Furthermore, obtaining the training status data according to the physiological data, the shooting data, and the environmental data includes:

[0067] According to the shooter's performance score combined with the shooter's physiological data and shooting environment data, the influence degree between the performance data, physiological data and environmental data is analyzed, and the performance score is calculated as follows: S = w1·hit rate-w2·reaction time-w3·aiming deviation, where w1, w2, w3 are weight coefficients, and S is the performance score;

[0068] The performance data, physiological data and environmental data are fused according to the degree of influence between them to obtain training status data.

[0069] Furthermore, the analyzing the influence degree between the performance data, the physiological data, and the environmental data according to the performance score of the shooter in combination with the physiological data of the shooter and the environmental data of the shooting includes:

[0070] If the performance score is satisfactory, the advanced training weight will be obtained;

[0071] If the performance score is unsatisfactory, determine whether the physiological data affects the performance data. If the physiological data does not affect the performance data, obtain the action training weight. Otherwise, determine whether the environmental data affects the physiological data.

[0072] If the environmental data affects the physiological data, the environmental training weight is obtained, otherwise the psychological training weight is obtained.

[0073] If the performance score is satisfactory, the advanced training weight will be obtained;

[0074] In this step, the training weight reflects the aspects that need to be strengthened or adjusted in the current training. The training weights obtained by judging the performance score, physiological data and environmental data can determine the specific training direction. If the performance score is qualified, the advanced training weights are obtained, and the training content is adjusted to make the training more focused on improving difficult skills. For example, adding more complex dynamic targets or more stringent time limits can improve the accuracy and quick response ability of the shooter. The advanced training weights distinguish between more skilled shooters and beginners, and focus on advanced skills rather than repeated improvement of basic abilities. If the performance score is unsatisfactory, further analyze the factors affecting performance (physiological state and environmental conditions), so as to formulate training weights with different focuses for the shooter (such as action training or environmental adaptation training). If the physiological data affects the performance score, it is necessary to coordinate the relationship between the action and the physiological state. It is recommended to obtain training weights with action control and breathing rhythm as the core (such as shooting stability training to control breathing). If the environmental data affects the physiological state, the training conditions are adjusted to help the shooter adapt to the environment, such as reducing light or noise interference, and obtaining training weights that are more suitable for environmental adaptation.

[0075] If the performance score is unsatisfactory, determine whether the physiological data affects the performance data. If the physiological data does not affect the performance data, obtain the action training weight. Otherwise, determine whether the environmental data affects the physiological data.

[0076] A multivariate linear regression model was constructed based on physiological data and performance scores. The model formula is as follows:

[0077] Y performance = β0 + β1·heart rate + β2·respiratory rate + ∈

[0078] Among them, Y represents the performance score, β1 and β2 are regression coefficients, and ∈ is the error term;

[0079] If any regression coefficient of β1 or β2 is greater than the significance threshold, the physiological data affects the performance data; otherwise, the physiological data does not affect the performance data.

[0080] In this step, there is usually a relatively simple linear relationship between physiological data (such as heart rate, respiratory rate) and performance scores. For example, a higher heart rate may indicate a high stress level, which may reduce performance scores. Multiple linear regression can directly quantify this linear relationship (represented by the regression coefficient β). Multiple linear regression provides a significance test result of the regression coefficient, which can determine the independent impact of each physiological feature on the performance data. In this application, the significance threshold can be set to 1. For example, if β1=2.5, it means that for every 1 unit increase in heart rate, the performance score will increase by 2.5 units. If β1 is greater than the significance threshold, the physiological data affects the performance data.

[0081] If the environmental data affects the physiological data, the environmental training weight is obtained, otherwise the psychological training weight is obtained.

[0082] A random forest regression model was constructed based on environmental data and physiological data. The model formula is as follows:

[0083] Y heart rate, Y breathing rate = f (light intensity, wind speed, noise),

[0084] Among them, Y heart rate is the target heart rate, Y respiratory rate is the target respiratory rate, and f is the random forest model;

[0085] The environmental data is used as the input of the random forest regression model, and the physiological data is used as the output of the random forest regression model. The random forest regression model is trained to obtain the feature importance parameters of the random forest regression model.

[0086] If any of the feature importance parameters of the random forest regression model is greater than the feature importance threshold, the environmental data affects the physiological data, otherwise the environmental data does not affect the physiological data.

[0087] In this step, the random forest regression model is a non-parametric model that does not have strict assumptions about the distribution of data and can handle complex nonlinear relationships and interactions between variables. The relationship between environmental data (such as light intensity, noise level, wind speed) and physiological data (such as heart rate, breathing rate) is usually a complex nonlinear relationship. For example: the noise level may only significantly affect the heart rate after exceeding a certain threshold. The effect of wind speed on heart rate may be nonlinear and increasing. The random forest regression model is trained with training samples (environmental data and physiological data). In this process, the random forest regression model can reflect the association between physiological data and environmental data. For the multi-output random forest regression model, the feature importance of each training sample can be viewed separately.

[0088] The calculation formula for calculating the shooter's performance score based on the shooter's performance data is as follows:

[0089] S = w1·hit rate-w2·reaction time-w3·aiming deviation,

[0090] Among them, w1, w2, w3 are weight coefficients, and S is the performance score.

[0091] In this step, the performance data directly reflects the core indicators of the shooter's shooting ability. The shooting hit rate is the ratio of the number of times the target is hit to the total number of shots, which reflects the shooter's shooting accuracy. The reaction time is the time interval from the target appearing to the shooter firing, which reflects the shooter's reaction speed. The aiming deviation is the deviation distance between the shooting point and the target point, which reflects the shooter's accuracy and stability.

[0092] Physiological data reflects the shooter's physical state during shooting, including heart rate and breathing rate. Heart rate is the number of heart beats per unit time (usually expressed in bpm), which reflects the shooter's tension and physical load. Breathing rate is the number of breaths per unit time, which reflects the shooter's breathing control and physical state. These physiological data can be recorded using a wristband.

[0093] Environmental data reflects the external conditions of the shooting range, including light intensity, wind speed and noise. These data can be used to analyze the impact of the external environment on the shooter's performance and physiological state. Light intensity is the light level of the shooting range (usually expressed in lux), reflecting the visual conditions of shooting. Wind speed is the wind speed of the shooting range (usually expressed in m / s), reflecting the air flow during shooting. Noise is the noise level of the shooting range (usually expressed in dB), reflecting the sound interference during shooting.

[0094] Since the hit rate, reaction time, and aiming deviation have different dimensions, adding them together directly will cause the indicator with a larger dimension to have too much influence on the score. Therefore, it is necessary to normalize the reaction time and aiming deviation. The weight setting needs to be based on the actual scenario and task requirements: the higher the performance score, the higher the shooter's level.

[0095] The calculation formula for calculating the shooter's performance score based on the shooter's performance data is as follows:

[0096] S = w1·hit rate-w2·reaction time-w3·aiming deviation,

[0097] Among them, w1, w2, w3 are weight coefficients, and S is the performance score.

[0098] In this step, the performance data directly reflects the core indicators of the shooter's shooting ability. The shooting hit rate is the ratio of the number of times the target is hit to the total number of shots, which reflects the shooter's shooting accuracy. The reaction time is the time interval from the target appearing to the shooter firing, which reflects the shooter's reaction speed. The aiming deviation is the deviation distance between the shooting point and the target point, which reflects the shooter's accuracy and stability.

[0099] Physiological data reflects the shooter's physical state during shooting, including heart rate and breathing rate. Heart rate is the number of heart beats per unit time (usually expressed in bpm), which reflects the shooter's tension and physical load. Breathing rate is the number of breaths per unit time, which reflects the shooter's breathing control and physical state. These physiological data can be recorded using a wristband.

[0100] Environmental data reflects the external conditions of the shooting range, including light intensity, wind speed and noise. These data can be used to analyze the impact of the external environment on the shooter's performance and physiological state. Light intensity is the light level of the shooting range (usually expressed in lux), reflecting the visual conditions of shooting. Wind speed is the wind speed of the shooting range (usually expressed in m / s), reflecting the air flow during shooting. Noise is the noise level of the shooting range (usually expressed in dB), reflecting the sound interference during shooting.

[0101] Since the hit rate, reaction time, and aiming deviation have different dimensions, adding them together directly will cause the indicator with a larger dimension to have too much influence on the score. Therefore, it is necessary to normalize the reaction time and aiming deviation. The weight setting needs to be based on the actual scenario and task requirements: the higher the performance score, the higher the shooter's level.

[0102] Further, the determining whether the physiological data is related to the performance-affecting data includes:

[0103] A multivariate linear regression model was constructed based on physiological data and performance scores. The model formula is as follows:

[0104] Y performance = β0 + β1·heart rate + β2·respiratory rate + ∈;

[0105] Among them, Y represents the performance score, β1 and β2 are regression coefficients, and ∈ is the error term;

[0106] If any regression coefficient of β1 or β2 is greater than the significance threshold, the physiological data affects the performance data; otherwise, the physiological data does not affect the performance data.

[0107] Further, the determining whether the environmental data affects the physiological data includes:

[0108] A random forest regression model was constructed based on environmental data and physiological data. The model formula is as follows:

[0109] Heart rate, Y breathing rate = f(light intensity, wind speed, noise),

[0110] Among them, Y heart rate is the target heart rate, Y respiratory rate is the target respiratory rate, and f is the random forest model;

[0111] The environmental data is used as the input of the random forest regression model, and the physiological data is used as the output of the random forest regression model. The random forest regression model is trained to obtain the feature importance parameters of the random forest regression model.

[0112] If any of the feature importance parameters of the random forest regression model is greater than the feature importance threshold, the environmental data affects the physiological data, otherwise the environmental data does not affect the physiological data.

[0113] Specifically, the performance data, physiological data, and environmental data are fused according to the obtained different types of weights to obtain the training state data.

[0114] In this step, the contribution of different types of data to the training strategy is obviously different. If these data are simply mixed and the weights are ignored, the model may overfit some data and ignore other more critical information. It is necessary to determine which are the main factors affecting the shooter's performance based on the various types of data in step S2. For example, if environmental data is the main factor affecting the shooter's performance, it is necessary to increase the weight of environmental data. According to the weights of other data, training status data = k1 performance data + k2 physiological data + k3 environmental data, k3> k1, k2.

[0115] Furthermore, the optimization training strategy is obtained through the following steps:

[0116] The performance data, physiological data, and environmental data are used as state variables, and the adjustment of simulation parameters of the training environment, training rhythm adjustment, and training content adjustment are used as action variables to build a training strategy model;

[0117] Construct a reward function based on shooting hit rate, shooting reaction time, heart rate, and aiming deviation to optimize the training strategy model;

[0118] The current physiological data, shooting data, and environmental data are input into the optimization training strategy model to obtain the optimization training strategy.

[0119] The training strategy model is obtained through the following steps:

[0120] The performance data, physiological data, and environmental data are used as state variables, and the adjustment of simulation parameters of the training environment, training rhythm adjustment, and training content adjustment are used as action variables to build a training strategy model;

[0121] Construct a reward function based on shooting hit rate, shooting reaction time, heart rate, and aiming deviation to optimize the training strategy model.

[0122] Furthermore, the calculation formula for constructing the reward function based on the shooting hit rate, shooting reaction time, heart rate, and aiming trajectory is as follows:

[0123] R = α1 hit rate - α2 reaction time - α3 heart rate deviation + α4 aiming deviation,

[0124] Among them, α1, α2, α3, and α4 are weight parameters.

[0125] Furthermore, the step of inputting the training state data into the training strategy model to obtain the shooter's optimized training strategy includes:

[0126] The training status data is input into the training strategy model to obtain environmental simulation parameters, training rhythm parameters, and training content parameters. The environmental simulation parameters include: light intensity, wind speed, noise, and weather. The training rhythm parameters include: number of shootings, shooting intervals, and shooting duration. The training content parameters include: static shooting, dynamic shooting, and static and dynamic combined shooting.

[0127] In this step, the fused data (training state data) is the result of performance data, physiological data, and environmental data after weight adjustment, which can comprehensively reflect the shooter's current training state. The training strategy model is based on this comprehensive state to analyze the shooter's current ability level, adaptability, and weak links, and formulate an optimized training strategy. Data fusion only integrates multiple data types into an input vector according to weights, but it cannot directly guide the optimization of the training strategy. The training strategy model not only requires the current training state data, but also needs to combine historical data (such as training progress) and training goals to formulate a dynamic adjustment strategy. Therefore, the reinforcement learning model is used to construct the training strategy model, and the performance data, physiological data, and environmental data are the current state variables. The reward function is the core of the reinforcement learning model and determines the direction of model optimization. Positive indicators (such as hit rate) have a positive contribution to the training effect, so they are represented by a positive sign in the reward function, and negative indicators (such as reaction time and heart rate deviation) have a negative impact on the training effect, so they are represented by a negative sign. Action variables are simulation parameter adjustment variables of the training environment, such as adjustment of light intensity; training rhythm adjustment, such as adjustment of the number of shootings; training content adjustment, such as changing static shooting to dynamic shooting.

[0128] Specifically, the training status data is input into the training strategy model to obtain environmental simulation parameters, training rhythm parameters, and training content parameters. The environmental simulation parameters include: light intensity, wind speed, and noise. The training rhythm parameters include: number of shootings, shooting intervals, and shooting duration. The training content parameters include: static shooting, dynamic shooting, and static and dynamic combined shooting.

[0129] In this step, environmental simulation parameters (such as light intensity, wind speed, and noise) directly affect the training difficulty and adaptability of the shooter. The model senses the impact of the current environment on the shooter by inputting the shooter's training status data. For example, when the noise level is too high and the hit rate drops, the model can identify the adverse effects of noise on the shooter. The model adjusts parameters such as light intensity, wind speed, and noise level through output actions to optimize the training environment. For example, when the shooter performs well in a quiet environment, the model can increase the noise level to train the shooter's anti-interference ability. The reward function evaluates whether the adjustment of environmental parameters helps to improve the training effect through indicators such as hit rate and reaction time. The model finds the optimal combination of environmental parameters for the shooter through multiple trial and error learning.

[0130] The training rhythm parameters (such as the number of shots, the interval between shots, and the duration of shots) determine the intensity and rhythm of the training. A reasonable training rhythm can improve the efficiency of training while preventing the shooter from becoming fatigued due to overtraining. The model senses whether the shooter is in the best condition by inputting the shooter's physiological data (such as heart rate deviation) and performance data (such as hit rate and reaction time). For example, if the shooter's heart rate is high, the model will identify that the shooter may be in a state of fatigue. The model dynamically adjusts the training rhythm by outputting actions, for example, increasing the training interval to help the shooter recover from fatigue. The reward function comprehensively evaluates the effect of adjusting the training rhythm. For example, if the hit rate is significantly improved after increasing the training intensity, the model will strengthen this adjustment strategy.

[0131] The training content parameters (such as static shooting, dynamic shooting, and static and dynamic combined shooting) determine the type and complexity of the training task. By dynamically adjusting the training content, the shooter's weak links can be optimized while improving the shooter's overall ability. The model determines whether the current training content is suitable by inputting the shooter's performance data (such as hit rate, aiming deviation) and training goals. For example: If the shooter performs stably in static shooting, but the hit rate decreases in dynamic shooting, the model will identify dynamic shooting as the shooter's weak link. The model dynamically adjusts the training content by outputting actions. For example: Increase the proportion of dynamic shooting to train the shooter's dynamic aiming ability. The reward function evaluates the effect of the adjustment of the training content. For example: If the hit rate gradually increases after adding dynamic shooting, the model will strengthen this adjustment strategy.

[0132] Furthermore, the environmental simulation device includes one or more of a fan, a lamp, a sprinkler, and a speaker.

[0133] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0134] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0135] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

Claims

1. A training system based on holographic projection, characterized in that: include: A physiological data acquisition device, used to collect physiological data of the shooter; A holographic projection shooting simulation system, comprising a performance data acquisition device and a holographic projection device, wherein the holographic projection device is used to project a holographic image, and the performance data acquisition device is used to collect performance data of a shooter; An environmental data acquisition device, used to collect environmental data of the training site; Environmental simulation devices are arranged at training sites to simulate corresponding shooting environments; The adjustment system is used to obtain training status data according to the physiological data, the shooting data and the environmental data, output an optimized training strategy for the shooter according to the training status data, and dynamically adjust the training rhythm of the holographic projection shooting simulation system and / or adjust the shooting environment of the environmental simulation device according to the optimized training strategy.

2. A training system based on holographic projection according to claim 1, characterized in that: The performance data include shooting accuracy, reaction time, and aiming deviation; The physiological data includes the shooter's heart rate and breathing rate; The environmental data includes light intensity, wind speed, noise, and weather.

3. A training system based on holographic projection according to claim 1, characterized in that: The obtaining of training status data according to the physiological data, the shooting data, and the environmental data comprises: According to the shooter's performance score combined with the shooter's physiological data and shooting environment data, the influence degree between the performance data, physiological data and environmental data is analyzed, and the performance score is calculated as follows: S = w1·hit rate-w2·reaction time-w3·aiming deviation, where w1, w2, w3 are weight coefficients, and S is the performance score; The performance data, physiological data and environmental data are fused according to the degree of influence between them to obtain training status data.

4. A training system based on holographic projection according to claim 3, characterized in that: The analyzing the influence degree between the performance data, the physiological data, and the environmental data according to the performance score of the shooter combined with the physiological data of the shooter and the environmental data of the shooting includes: If the performance score is satisfactory, the advanced training weight will be obtained; If the performance score is unsatisfactory, determine whether the physiological data affects the performance data. If the physiological data does not affect the performance data, obtain the action training weight. Otherwise, determine whether the environmental data affects the physiological data. If the environmental data affects the physiological data, the environmental training weight is obtained, otherwise the psychological training weight is obtained.

5. A training system based on holographic projection according to claim 4, characterized in that: The determining whether the physiological data is related to the performance-affecting data includes: A multivariate linear regression model was constructed based on physiological data and performance scores. The model formula is as follows: Y performance = β0 + β1·heart rate + β2·respiratory rate + ∈; Among them, Y represents the performance score, β1 and β2 are regression coefficients, and ∈ is the error term; If any regression coefficient of β1 or β2 is greater than the significance threshold, the physiological data affects the performance data; otherwise, the physiological data does not affect the performance data.

6. A training system based on holographic projection according to claim 4, characterized in that: The determining whether the environmental data affects the physiological data includes: A random forest regression model was constructed based on environmental data and physiological data. The model formula is as follows: Y heart rate, Y breathing rate = f (light intensity, wind speed, noise), Among them, Y heart rate is the target heart rate, Y respiratory rate is the target respiratory rate, and f is the random forest model; The environmental data is used as the input of the random forest regression model, and the physiological data is used as the output of the random forest regression model. The random forest regression model is trained to obtain the feature importance parameters of the random forest regression model. If any of the feature importance parameters of the random forest regression model is greater than the feature importance threshold, the environmental data affects the physiological data, otherwise the environmental data does not affect the physiological data.

7. The training system based on holographic projection according to claim 1, characterized in that: The optimization training strategy is obtained through the following steps: The performance data, physiological data, and environmental data are used as state variables, and the adjustment of simulation parameters of the training environment, training rhythm adjustment, and training content adjustment are used as action variables to build a training strategy model; Construct a reward function based on shooting hit rate, shooting reaction time, heart rate, and aiming deviation to optimize the training strategy model; The current physiological data, shooting data, and environmental data are input into the optimization training strategy model to obtain the optimization training strategy.

8. A training system based on holographic projection according to claim 7, characterized in that: The calculation formula for constructing the reward function based on the shooting hit rate, shooting reaction time, heart rate, and aiming trajectory is as follows: R = α1 hit rate - α2 reaction time - α3 heart rate deviation + α4 aiming deviation, Among them, α1, α2, α3, and α4 are weight parameters.

9. The training system based on holographic projection according to claim 7, characterized in that: The step of inputting the training state data into the training strategy model to obtain the shooter's optimized training strategy includes: The training status data is input into the training strategy model to obtain environmental simulation parameters, training rhythm parameters, and training content parameters. The environmental simulation parameters include: light intensity, wind speed, noise, and weather. The training rhythm parameters include: number of shootings, shooting intervals, and shooting duration. The training content parameters include: static shooting, dynamic shooting, and static and dynamic combined shooting.

10. A training system based on holographic projection according to claim 9, characterized in that: The environmental simulation device includes one or more of a fan, a lamp, a sprinkler, and a speaker.