Intelligent shooting range training system based on real-time data acquisition

Abstract

The invention discloses an intelligent shooting range training system based on real-time data acquisition, relates to the technical field of intelligent shooting range training based on real-time data acquisition, and is used for solving the problem that an existing intelligent shooting range training system based on real-time data acquisition only pays attention to a single aspect of a training effect. The system is difficult to provide personalized training suggestions according to specific conditions of trainees; and a traditional training method may lack scientific verification. Comprising a real-time information acquisition module, an information analysis module, a scene analysis module and an optimization processing module. The real-time information is acquired through the real-time information acquisition module, and the behavior and physiological data are analyzed by the information analysis module to obtain comprehensive and deep analysis, so that the pertinence and effectiveness of training are improved; the method comprises the following steps: identifying different scene mode factors and setting corresponding evaluation weights; personalized improvement suggestions are provided for trainees, the best physiological state can be kept, the training effect is comprehensively improved, and the training experience is optimized.

Description

Technical Field

[0001] The invention relates to the technical field of real-time data acquisition intelligent shooting range training, and in particular to an intelligent shooting range training system based on real-time data acquisition. Background Art

[0002] The existing intelligent shooting range training system based on real-time data collection aims to improve the accuracy and effectiveness of shooting training through efficient data collection and analysis technology. The system usually uses advanced sensors, cameras and data analysis tools to monitor the training process in real time and automatically record the results and related data of each shooting. These data can be used for subsequent analysis to help coaches and students identify strengths and weaknesses, so as to develop more targeted training plans.

[0003] However, there are some defects in such systems at present. They only focus on a single aspect of training effect. It is difficult for the system to provide personalized training suggestions based on the specific situation of the trainees. Traditional training methods may lack scientific verification and other defects. Summary of the invention

[0004] The purpose of the present invention is to solve the problems that the existing system only focuses on a single aspect of training effect, the system is difficult to provide personalized training suggestions according to the specific circumstances of the trainees, and the traditional training methods may lack scientific verification, and propose an intelligent shooting range training system based on real-time data acquisition.

[0005] The purpose of the present invention can be achieved through the following technical solutions: an intelligent shooting range training system based on real-time data acquisition:

[0006] The information analysis module is used to analyze the behavioral data and physiological data of different trainees in different scenarios. The specific process is as follows:

[0007] Analysis of behavioral data: Obtain the posture of the trainee, identify the actions at different stages, segment them into several different actions, and obtain the corresponding postures; evaluate each posture in the postures corresponding to several different actions to obtain the spatial deviation between the key points of the trainee and the standard position;

[0008] Then, the change rates of acceleration and angular velocity of key joints of trainees during shooting actions are analyzed; by detecting the pressure distribution in each area of ​​the trainees' soles, the real-time change of the trainees' center of gravity during standing or moving is obtained, and the coordinates of the trainees' pressure center on the sole plane are calculated; then, according to the human body model, the pressure center data of the sole is converted into the center of gravity position of the whole body, and the offset of the current trainees' center of gravity relative to the ideal balance position is calculated;

[0009] Then, by calculating the smoothness of the speed and acceleration curves and analyzing the rhythm of the movements, the speed and acceleration curves are calculated, and the speed and acceleration of the trainee at each time point are calculated. The smoothness of the speed and acceleration is evaluated to generate the score of the trainee's movement smoothness. Finally, each posture angle, movement stability and smoothness and other indicators are weighted and calculated to generate a behavior evaluation value.

[0010] Analyze physiological data: extract heart rate characteristics to obtain the average heart rate within time ti; then calculate heart rate variability to obtain heart rate variability value; then extract skin electrical response characteristics to obtain skin conductivity change value;

[0011] Finally, the stress level and fatigue degree of the trainees are determined through a comprehensive analysis of heart rate, heart rate variability, respiratory rate and skin conductivity change values; the behavioral assessment and physiological assessment are then calculated according to the corresponding scenario mode factors to obtain the final shooting range training value.

[0012] As a preferred embodiment of the present invention, it also includes a real-time information acquisition module, a scene analysis module and an optimization processing module;

[0013] The real-time information acquisition module collects behavioral data and physiological data of the trainees, specifically:

[0014] The process of acquiring behavioral data of trainees is as follows: inertial sensors are installed at key parts of trainees, and the accelerometers and gyroscopes inside the inertial sensors collect the trainees' posture, motion trajectory and turning movements; then, through the indoor positioning system and the equipment embedded in the trainees, the trainees are provided with accurate positioning and movement trajectory in the shooting range, and their steps, standing positions and azimuth adjustments are tracked; the posture, shooting angle and aiming accuracy of trainees are captured by cameras, which are specifically installed at different positions and angles in the shooting range; the process of acquiring physiological data of trainees is as follows: the heart rate sensor is used to monitor the heart rate in real time through wearable devices; the breathing frequency and depth are monitored through the breathing monitoring sensor, which is embedded in the trainees' chest strap or attached to the chest area; the skin conductance sensor is placed on the wrist or finger to measure the skin conductivity and monitor the sweating of the trainees; the blood oxygen monitoring sensor is then installed on the finger, wrist or earlobe; the pressure sensor is installed on the ground of the shooting range or in the sole of the trainees' shoes, and detects the pressure distribution and pace of the trainees when standing, walking or running;

[0015] The scenario analysis module is used to obtain real-time data in different scenarios; the scenarios include single-person training, team training, environmental scenario simulation and special scenarios; different scenarios are set with their corresponding scenario mode factors CJN, N = 1, 2, 3, 4, ..., H;

[0016] The optimization processing module obtains the final shooting range training value ZPZ of the trainee and generates the corresponding training evaluation report. If the final shooting range training value ZPZ of the trainee is less than the average of the final shooting range training values ​​of all trainees, a prompt label is generated to suggest improving aiming technique, adjusting breathing rhythm, increasing rest time and performing physical recovery training; if the final shooting range training value ZPZ of the trainee is greater than the average of the final shooting range training values ​​of all trainees, the trainee deficiency is obtained and a personalized suggestion prompt label corresponding to the deficiency is generated.

[0017] As a preferred embodiment of the present invention, the specific process of evaluating each posture in the postures corresponding to the plurality of different actions to obtain the spatial deviation between the key point of the trainee and the standard position is: using a camera or a positioning system to calculate the spatial deviation between the key point of the trainee and the standard position; and then calculating by the Euclidean distance formula of the three-dimensional coordinates: Output spatial deviation D, where x1, y1, z1 are the actual joint positions of the trainee, and x2, y2, z2 are the joint positions of the standard model.

[0018] As a preferred embodiment of the present invention, the specific process of obtaining the real-time change of the center of gravity of the trainee during standing or moving and calculating the coordinates of the pressure center of the trainee on the sole plane is:

[0019] Calculate using the formula: Among them, P i represents the pressure value of the i-th sensor on the foot, n represents the total number of sensors, x i and i Respectively represent the position coordinates of the sensor, CoP x and CoP y It is the coordinate of the trainee's pressure center on the sole plane.

[0020] As a preferred embodiment of the present invention, the pressure center data of the sole is converted into the center of gravity position of the whole body according to the human body model, and the specific process of calculating the offset of the center of gravity of the current trainee relative to the ideal balance position is as follows: if the center of gravity offset is too large, it indicates that the posture is unstable; calculate by the Euclidean distance formula: Output the center of gravity offset ΔCoM, where CoM x and CoM y is the coordinate of the center of gravity; if the deviation exceeds the set threshold, it indicates that the posture is unstable, and the rate of change of the center of gravity is calculated: Δt is the time interval. If the rate of change is large, it indicates that the center of gravity is unstable. Then set the change threshold. If the rate of change is greater than the change threshold, it will be marked and recorded.

[0021] As a preferred embodiment of the present invention, the heart rate characteristics are obtained by time t j The specific process of calculating the heart rate variability to obtain the heart rate variability value is as follows: Calculate the average heart rate over a period of time, using the formula: Output time t j Average heart rate HR avg ; Count the peak and minimum heart rate values ​​during training, and set the normal heart rate peak and low interval; Calculate the heart rate variability: Heart rate variability is the standard deviation of the R1-R2 interval, which is Output heart rate variability HRV, where R j is the time interval between adjacent heartbeats, is the mean of these time intervals.

[0022] As a preferred embodiment of the present invention, the specific process of extracting the skin electrical response characteristics to obtain the skin conductivity change value is as follows: by extracting the skin electrical response characteristics, calculating the skin conductivity change, ΔGSR = GSR (t j+1 )-GSR(t j ) outputs the skin conductivity change value ΔGSR, where GSR(t j ) is the time t j Skin conductivity value at the moment.

[0023] As a preferred embodiment of the present invention, the specific process of judging the stress level and fatigue degree of the trainees through comprehensive analysis of heart rate, HRV, respiratory rate and skin electrical response is as follows: for stress level analysis, the stress level value YLSP is output by YLSP=f(HR,HRV,ΔGSR); for fatigue analysis, fatigue is usually manifested as a significant decrease in HRV, a continuous increase in heart rate and a disordered respiratory rate; and then the fatigue value PLZ is output by PLZ=f(HR,HRV,HX), where HX is the respiratory rate.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. By acquiring the behavioral and physiological data of trainees in real time, the system can accurately analyze the movements, postures and physiological reactions during the training process, and then provide personalized feedback and optimization suggestions; the combination of multiple sensors and positioning devices ensures high-precision data collection, and the simulation of multiple scene modes makes the training closer to the real combat environment, which helps to improve personal skills, teamwork ability and coping ability in complex environments; it not only improves the pertinence and effectiveness of training, but also improves the overall efficiency and accuracy of tactical actions.

[0026] 2. The present invention enables a comprehensive and in-depth analysis of the behavioral and physiological data of trainees through the design of an information analysis module, greatly improving the pertinence and effectiveness of training; through the identification of different scenario mode factors and the setting of corresponding evaluation weights, the system can accurately evaluate the performance of trainees in various scenarios; detailed analysis of behavioral data, including posture recognition, movement stability and fluency evaluation, enables trainees to understand the deviations and improvement directions of their own movements in real time; at the same time, the monitoring and analysis of physiological data can timely evaluate the stress and fatigue status of trainees, ensuring the safety and effectiveness of the training process; finally, the feedback mechanism based on comprehensive scoring not only provides trainees with personalized improvement suggestions, but also helps to maintain the best physiological state, thereby comprehensively improving the training effect and optimizing the training experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0028] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0031] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0032] See also Figure 1 As shown, an intelligent shooting range training system based on real-time data acquisition includes a real-time information acquisition module, an information analysis module, a scene analysis module and an optimization processing module;

[0033] The real-time information acquisition module collects behavioral and physiological data of trainees, specifically:

[0034] The process of acquiring behavioral data of trainees is as follows: inertial sensors are installed on key parts of trainees, such as head, shoulders, wrists, waist, knees and ankles, and the accelerometers and gyroscopes inside the inertial sensors collect the trainees' posture, motion trajectory and turning movements, such as aiming, turning, moving and other key movements;

[0035] Then, through the indoor positioning system, by embedding the training personnel's equipment, such as chest vests, belts or boots, or installing positioning beacons at fixed points in the training ground; provide the training personnel with accurate positioning and movement trajectory in the shooting range, and track their steps, positions and azimuth adjustments;

[0036] Cameras are also used to capture the trainees' postures, shooting angles and aiming accuracy. They are installed at different positions and angles in the shooting range, such as in front of the shooting target, next to the target and above the training ground, to capture the trainees' movements in all directions.

[0037] The process of obtaining the trainees' physiological data is as follows: the heart rate sensor is used to monitor the heart rate in real time through a wearable device; the breathing rate and depth are monitored through a respiratory monitoring sensor, which is embedded in the trainee's chest strap or attached to the chest area. Some smart vests also have a respiratory monitoring function; the skin conductance sensor is placed on the wrist or finger to measure the conductivity of the skin and monitor the sweating of the trainees; the blood oxygen monitoring sensor is then installed on the finger, wrist or earlobe (through a finger-clip oximeter or a wearable device such as a smart watch); the pressure sensor is installed on the ground of the shooting range or in the sole of the trainee's shoe, and detects the pressure distribution and pace of the trainee when standing, walking or running;

[0038] The scenario analysis module is used to obtain real-time data in different scenarios; the scenarios include single-person training, team training, environmental scenario simulation and special scenarios; among them, single-person training is aimed at improving personal shooting skills, mainly including basic training such as aiming, shooting posture, and gun control; the target may be fixed, moving or appear randomly to test the shooter's reaction speed and accuracy; team training is aimed at team collaboration training, simulating the tactical actions of the combat team in a complex battlefield environment; including coordination operations such as communication, cover, attack and retreat between teams to improve the overall combat efficiency; environmental scenario simulation includes urban battlefield simulation, jungle and mountain battlefield simulation, night and bad weather simulation, etc.; special scenarios simulate the scene of vehicles driving at high speed or being attacked to train shooters how to accurately hit the target while protecting the safety of their own vehicles; and set the corresponding scenario mode factor CJN for different scenarios, N = 1, 2, 3, 4, ..., H; H is a positive integer.

[0039] The information analysis module is used to analyze the behavioral data and physiological data of different trainees in different scenarios. The specific process is as follows:

[0040] By identifying the scene mode factor CJN of the corresponding scene, and according to the preset evaluation weights of the corresponding scene mode physiological data and behavioral data; it should be noted that the evaluation weights of the preset scene mode physiological data and behavioral data corresponding to different scenes are different;

[0041] Analyze the behavioral data, obtain the posture of the trainee for identification, identify the actions at different stages through inertial sensor data, and segment these actions into several different actions, and obtain the postures corresponding to several different actions based on camera capture, such as "aim", "shoot", "back off" and other actions; and evaluate each posture within the postures corresponding to several different actions, specifically: the raw data provided by the inertial sensor and camera usually includes acceleration, angular velocity and position coordinates; substitute these data into three-dimensional space and express them in coordinates (usually need to be converted into the world coordinate system or standard reference coordinate system to ensure that the data of different sensors can be calculated uniformly); then use the accelerometer and gyroscope to calculate the posture angle of the trainee, which includes pitch angle, roll angle and yaw angle;

[0042] Then use the standard shooting posture model (the model is usually generated by professional shooting coaches or historical excellent training data, including parameters such as ideal posture angle, limb position, head posture, etc.) as a benchmark, and compare the angle difference between the trainee's current posture and the standard posture. If the difference between the current trainee's pitch angle and the standard posture is greater than a certain threshold (such as 3 degrees), it is considered that the posture has a deviation; then calculate the position deviation: use a camera or positioning system to calculate the spatial deviation between the trainee's key points (such as shoulders, elbows, knees, etc.) and the standard position; then calculate using the Euclidean distance formula of three-dimensional coordinates: Output spatial deviation D, where x1, y1, z1 are the actual joint positions of the trainee, and x2, y2, z2 are the joint positions of the standard model;

[0043] The commonly used indicators include: angular velocity change rate (reflecting whether the trainee's rotation speed is stable. If the change is too fast, it means the posture is unstable), acceleration change rate (reflecting whether the trainee's movement is stable. A sharp change in acceleration may indicate that the posture adjustment is too abrupt); Among them, θ is the angular velocity, α is the acceleration, and t is the time. If the angular velocity changes too quickly, it means that the rotation is unstable and the posture may deviate from the standard. Similarly, if the acceleration changes dramatically, it may mean that the posture is adjusted too quickly or too violently. Then, by detecting the pressure distribution in various areas of the trainer's sole (such as the forefoot, the heel, the inner and outer sides), the real-time changes in the center of gravity of the trainer during standing or moving are obtained and calculated: Among them, P i represents the pressure value of the i-th sensor on the foot, i = 1, 2, ..., n; n is the total number of sensors; x i and i Respectively represent the position coordinates of the sensor, CoP x and CoP y It is the coordinate of the pressure center of the trainee on the sole plane; then according to the human body model, the pressure center data of the sole is converted into the center of gravity position CoM of the whole body, and the offset of the center of gravity of the current trainee relative to the ideal balance position is calculated; if the center of gravity offset is too large, it may indicate an unstable posture; calculated by the Euclidean distance formula: Output ΔCoM center of gravity offset, where CoM x and CoM y is the coordinate of the center of gravity; if the deviation exceeds the set threshold, it indicates that the posture is unstable, and the rate of change of the center of gravity is calculated: Δt is the time interval. If the rate of change is large, it indicates that the center of gravity is unstable. Then set the change threshold. If the rate of change is greater than the change threshold, it will be marked and recorded.

[0044] Then, by calculating the smoothness of the speed and acceleration curves and analyzing the action rhythm, we can find the following:

[0045] For the calculation of the smoothness of the velocity and acceleration curves, the motion trajectory data of the trainee is obtained through sensors, which usually includes the change of displacement x(t) with time t. The velocity v(t) is the first-order derivative of the displacement with respect to time, which is: x(t) is the displacement of the trainee at a certain point in time, and t is the time; then use discrete data representation and the finite difference method for approximate calculation: Among them, t k and t k+1 are adjacent time points, x(t k ) and x(t k+1 ) is the displacement value at the corresponding time point;

[0046] Calculation of acceleration: Acceleration a(t) is the rate of change of velocity with respect to time, that is, the first-order derivative of velocity or the second-order derivative of displacement, which is: In discrete form, the acceleration is calculated by the finite difference method of velocity: Therefore, by calculating the speed and acceleration of the trainee at each time point, a speed and acceleration change curve is generated;

[0047] Then analyze it through the variance formula; Output speed variance Var(v), where, is the average value of the speed, and n1 is the number of data points; if the variance of the speed change Var(v) is less than the preset variance threshold of the speed change, the speed change is stable and the action is smooth; the variance formula of the acceleration is analyzed, and Var(a) is obtained by the same method as the speed. If the variance of the acceleration change Var(a) is greater than the preset variance threshold of the acceleration change, it indicates that the acceleration is not stable and the action may be unstable or sudden;

[0048] Then, by evaluating the smoothness of speed and acceleration, the training personnel's movement smoothness score is generated; if the changes in speed and acceleration are stable, it means that the movement is smooth and the score is high; if the fluctuation is large, the movement score will be reduced; finally, each posture angle, movement stability and smoothness and other indicators are weighted and calculated to generate a behavior evaluation value XWZ; for example, the posture of a certain shooting action may be 85 points out of 100 points, based on the weight distribution of different parameters: posture angle correctness (40%), stability (30%), movement smoothness (30%); and the optimization processing module will display the analysis results to the training personnel in the form of charts or animations, mark which joints or movements have problems, and give adjustment suggestions;

[0049] Analyze the physiological data, extract the heart rate characteristics, and calculate the average heart rate over a period of time. The formula is: Output time t j Average heart rate HR avg ; Then count the peak and minimum heart rate values ​​during training, j is the time number, j = 1, 2, ..., m, m is the total number of time numbers, HR (t j ) is time t j The corresponding heart rate; whether there is an abnormality, and set the normal heart rate peak and low interval; then calculate the heart rate variability (HRV, HRV reflects the activity of the autonomic nervous system, often used to assess stress and recovery status): heart rate variability is the standard deviation of the R1-R2 interval (i.e. the time interval between adjacent heartbeats), which is calculated by Output heart rate variability HRV, where R j is the time interval between adjacent heartbeats, is the mean of these time intervals; if the peak heart rate obtained during the training period does not have a normal heart rate peak-low interval, and the change trend of the heart rate variability value HRV is decreasing, and the heart rate is within the normal heart rate peak-low interval within a fixed time, it is normal; if the lowest heart rate during the training period does not have a normal heart rate peak-low interval, and the change trend of the heart rate variability value HRV is decreasing, and the heart rate is within the normal heart rate peak-low interval within a fixed time, it is normal; otherwise it is abnormal;

[0050] Then, the skin conductivity change is calculated by extracting the skin galvanic response (GSR) feature (when the trainee is under stress or tension, the skin conductivity will increase), and ΔGSR = GSR (t j+1 )-GSR(t j ) outputs the skin conductivity change value ΔGSR, where GSR(t j ) is the time t j Skin conductivity value at the moment,

[0051] Finally, the stress level and fatigue degree of the trainees are judged through comprehensive analysis of heart rate, heart rate variability and skin conductivity change. The stress level is analyzed by the formula:

[0052] YLSP=f(HR,HRV,ΔGSR)=HR×ω HR +(1-HRV)×ω HRV +ΔGSR×ω GSR Output pressure level value YLSP, ω HR ,ω HRV ,ω GSR They are the weights corresponding to heart rate, heart rate variability, and skin conductivity change, and their sizes are custom settings, such as 0.2, 0.37, and 0.43. If the heart rate increases, HRV decreases, and GSR increases, it is identified as a high-stress state, and then based on the preset model or threshold, it is determined whether the stress level exceeds the expected range; if the stress level exceeds the expected range, the training is stopped and an overpressure alarm is generated; for fatigue analysis, fatigue is usually manifested as a significant decrease in HRV, a continuous increase in heart rate, and a disordered breathing rate; then through the formula:

[0053] PLZ=f(HR,HRV,HX)=HR×ξ HR +(1-HRV)×ξ HRV +HX×ξ HX Output fatigue value PLZ, where HX is the breathing rate, ξ HR , HRV , HXThey are the weights corresponding to the heart rate, heart rate variability and respiratory rate, and their sizes are custom settings, such as 0.2, 0.3, and 0.5. If the fatigue value PLZ is greater than the expected fatigue threshold, the trainee is evaluated to see if he needs to rest; if the fatigue value PLZ is greater than the fixed S (S>1) times the expected fatigue threshold, the system will give a fatigue alarm;

[0054] Then, the final range training value is obtained by calculating the behavioral evaluation and physiological evaluation according to the corresponding scene mode factor CJN. Specifically, the final range training value ZPZ is output by ZPZ=XWZ×κ1+SLZ×κ2+CJN×κ3, where XWZ is the behavioral evaluation value; SLZ is the physiological evaluation value (based on physiological data such as heart rate, HRV, GSR, etc., reflecting the physiological state of the trainee, specifically: Output physiological assessment values ​​SLZ, HRV L is the standard heart rate variability value, τ1, τ2, τ3 and τ4 are the corresponding weight coefficients), κ1, κ2, κ3 are the corresponding weight coefficients;

[0055] The optimization processing module obtains the trainee's final shooting range training value ZPZ and generates the corresponding training evaluation report (to help trainees improve their performance and maintain the best physiological state). If the trainee's final shooting range training value ZPZ is less than the average of all trainees' final shooting range training values, it is recommended to improve aiming techniques, adjust breathing rhythm, increase rest time, and conduct physical recovery training; if the trainee's final shooting range training value ZPZ is greater than the average of all trainees' final shooting range training values, it is obtained that the trainee is insufficient and personalized suggestions are given.

[0056] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent shooting range training system based on real-time data acquisition, characterized in that: include: The information analysis module is used to analyze the behavioral data and physiological data of different trainees in different scenarios. The specific process is as follows: Analysis of behavioral data: Obtain the posture of the trainee, identify the actions at different stages, segment them into several different actions, and obtain the corresponding postures; evaluate each posture in the postures corresponding to several different actions to obtain the spatial deviation between the key points of the trainee and the standard position; Then, the change rates of acceleration and angular velocity of key joints of trainees during shooting actions are analyzed; by detecting the pressure distribution in each area of ​​the trainees' soles, the real-time change of the trainees' center of gravity during standing or moving is obtained, and the coordinates of the trainees' pressure center on the sole plane are calculated; then, according to the human body model, the pressure center data of the sole is converted into the center of gravity position of the whole body, and the offset of the current trainees' center of gravity relative to the ideal balance position is calculated; Then, by calculating the smoothness of the speed and acceleration curves and analyzing the rhythm of the movements, the speed and acceleration curves are calculated, and the speed and acceleration of the trainee at each time point are calculated to generate the speed and acceleration change curves; the smoothness of the speed and acceleration is evaluated to generate the score of the trainee's movement smoothness; finally, the indicators corresponding to each posture angle, movement stability and smoothness are weighted and calculated to generate a behavior evaluation value; Analyze physiological data: extract heart rate features to obtain the average heart rate over time; Then calculate the heart rate variability to obtain the heart rate variability value; Then, the skin conductivity change value is obtained by extracting the skin electrical response characteristics; Finally, the stress level and fatigue degree of the trainees are determined through a comprehensive analysis of heart rate, heart rate variability, respiratory rate and skin conductivity change values; the behavioral assessment and physiological assessment are then calculated according to the corresponding scenario mode factors to obtain the final shooting range training value.

2. The intelligent shooting range training system based on real-time data acquisition according to claim 1 is characterized in that: It also includes a real-time information acquisition module, a scene analysis module and an optimization processing module; The real-time information acquisition module collects behavioral data and physiological data of the trainees, specifically: The process of acquiring behavioral data of trainees is as follows: inertial sensors are installed at key parts of trainees, and the accelerometers and gyroscopes inside the inertial sensors collect the trainees' posture, motion trajectory and turning movements; then, through the indoor positioning system and the equipment embedded in the trainees, the trainees are provided with accurate positioning and movement trajectory in the shooting range, and their steps, standing positions and azimuth adjustments are tracked; the posture, shooting angle and aiming accuracy of the trainees are captured by cameras, which are specifically installed at different positions and angles in the shooting range; the process of acquiring physiological data of trainees is as follows: the heart rate sensor is used to monitor the heart rate in real time through wearable devices; the breathing frequency and depth are monitored through the breathing monitoring sensor, which is embedded in the trainees' chest strap or attached to the chest area; the skin conductance sensor is placed on the wrist or finger to measure the skin conductivity and monitor the sweating of the trainees; the blood oxygen monitoring sensor is then installed on the finger, wrist or earlobe; the pressure sensor is installed on the ground of the shooting range or in the sole of the trainees' shoes, and detects the pressure distribution and pace of the trainees when standing, walking or running; The scenario analysis module is used to obtain real-time data in different scenarios; the scenarios include single-person training, team training, environmental scenario simulation and special scenarios; different scenarios are set with their own corresponding scenario mode factors; The optimization processing module obtains the final shooting range training value of the trainee and generates a corresponding training evaluation report. If the final shooting range training value of the trainee is less than the average of the final shooting range training values ​​of all trainees, a prompt label is generated to suggest improving aiming technique, adjusting breathing rhythm, increasing rest time and performing physical recovery training; if the final shooting range training value of the trainee is greater than the average of the final shooting range training values ​​of all trainees, the trainee's deficiencies are obtained and a personalized suggestion prompt label corresponding to the deficiencies is generated.

3. The intelligent shooting range training system based on real-time data acquisition according to claim 1 is characterized in that: The specific process of evaluating each posture within the postures corresponding to several different actions to obtain the spatial deviation between the key points of the trainee and the standard position is: using a camera or a positioning system to calculate the spatial deviation between the key points of the trainee and the standard position; and then calculating the output spatial deviation using the Euclidean distance formula of the three-dimensional coordinates.

4. The intelligent shooting range training system based on real-time data acquisition according to claim 3 is characterized in that: The specific process of obtaining the real-time change of the center of gravity of the trainee during standing or moving and calculating the coordinates of the pressure center of the trainee on the sole plane is as follows: Calculate using the formula: Among them, P i represents the pressure value of the i-th sensor on the foot, n represents the total number of sensors, x i and i Respectively represent the position coordinates of the sensor, CoP x and CoP y It is the coordinate of the trainee's pressure center on the sole plane.

5. The intelligent shooting range training system based on real-time data acquisition according to claim 4 is characterized in that: The pressure center data of the sole is converted into the center of gravity position of the whole body according to the human body model, and the specific process of calculating the offset of the center of gravity of the current trainee relative to the ideal balance position is as follows: if the center of gravity offset is too large, it indicates that the posture is unstable; calculated by the Euclidean distance formula: Output the center of gravity offset ΔCoM, where CoM x and CoM y is the coordinate of the center of gravity; if the deviation exceeds the set threshold, it indicates that the posture is unstable, and the rate of change of the center of gravity is calculated: Δt is the time interval. If the rate of change is large, it indicates that the center of gravity is unstable. Then set the change threshold. If the rate of change is greater than the change threshold, it will be marked and recorded.

6. The intelligent shooting range training system based on real-time data acquisition according to claim 1, characterized in that: The heart rate characteristics are obtained by time t j Average heart rate within The specific process of calculating the heart rate variability and obtaining the heart rate variability value is as follows: calculate the average heart rate over a period of time, then count the peak and lowest heart rate values ​​during training, and set a normal heart rate peak-low range; then calculate the heart rate variability and obtain the heart rate variability value.

7. The intelligent shooting range training system based on real-time data acquisition according to claim 6, characterized in that: The specific process of extracting the skin electrical response characteristics to obtain the skin conductivity change value is: extracting the skin electrical response characteristics, calculating the skin conductivity change to obtain the skin conductivity change value.

8. The intelligent shooting range training system based on real-time data acquisition according to claim 7, characterized in that: The specific process of judging the stress level and fatigue degree of the trainees through comprehensive analysis of heart rate, heart rate variability, respiratory rate and skin conductivity change value is as follows: for stress level analysis, the stress level value YLSP is output by YLSP=f(HR,HRV,ΔGSR); for fatigue analysis, fatigue is usually manifested as a significant decrease in HRV, a continuous increase in heart rate and a disordered respiratory rate; and then the fatigue value PLZ is output by PLZ=f(HR,HRV,HX), where HX is the respiratory rate.

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