A shooting simulation training method and system based on ballistic prediction and position feedback

Abstract

The application discloses a shooting simulation training method and system based on trajectory prediction and position feedback, and relates to the technical field of shooting training. The method comprises the following steps: acquiring the trajectory parameters of a bullet, the state information of a firearm, and the position information of a moving target; predicting the position relationship between the bullet and the moving target within a preset time in the future based on the state information of the firearm, the trajectory parameters, and the position information of the moving target; judging whether the hit condition is met according to the position relationship; if the hit condition is met, sending a hit instruction to the signal receiving module of the moving target, and the moving target performing a hit action in response to the hit instruction. The application replaces the virtual rendering scene by introducing the spatial posture, motion information of a real firearm and the trajectory parameters of a bullet for trajectory prediction, so that the trajectory law and the hit determination are closer to the physical reality, the dependence on VR / AR equipment can be reduced, the gap between simulation training and live firing is greatly reduced, and the training effect is effectively improved.

Description

Technical Field

[0001] This application relates to the field of shooting training technology, and in particular to a shooting simulation training method and system based on ballistic prediction and position feedback. Background Technology

[0002] With advancements in computer vision, sensors, and simulation technologies, shooting training systems have rapidly evolved from early live-fire target practice to virtualization and intelligentization. Training systems based on Virtual Reality (VR), Augmented Reality (AR), and hardware-in-the-loop simulation are now widely used, capable of simulating complex combat environments and moving targets, significantly improving training safety and scenario diversity.

[0003] However, current technologies rely heavily on VR / AR simulation experiences, resulting in high training costs and significant discrepancies between simulated scene details and real-world environments, leading to poor training outcomes. Therefore, reducing the reliance on VR / AR in shooting simulation training, lowering training costs, and improving training effectiveness have become critical issues that urgently need to be addressed. Summary of the Invention

[0004] In view of this, this application provides a shooting simulation training method and system based on ballistic prediction and position feedback, in order to at least solve the problems of how to reduce the dependence of shooting simulation training on VR / AR, reduce training costs, and improve training effectiveness.

[0005] This application provides a shooting simulation training method based on ballistic prediction and position feedback. The method includes: acquiring the ballistic parameters of a pre-loaded bullet in a firearm, the state information of the firearm, and the position information of a moving target; predicting the positional relationship between the bullet and the moving target within a preset time period based on the state information of the firearm, the ballistic parameters of the pre-loaded bullet, and the positional information continuously fed back by the moving target; determining whether the hit condition is met based on the positional relationship; and if the hit condition is met, sending a hit command to the signal receiving module of the moving target, and the moving target responding to the hit command by performing a hit action.

[0006] This application also provides a moving target shooting simulation training system, comprising: a ballistic prediction system and a moving target equipped with a signal receiving module and a positioning module, wherein: the ballistic prediction system is used to acquire the ballistic parameters of a pre-loaded bullet in a firearm, the state information of the firearm, and the position information of the moving target; the ballistic prediction system is used to predict the positional relationship between the bullet and the moving target within a preset time period based on the state information of the firearm, the ballistic parameters of the pre-loaded bullet, and the positional information continuously fed back by the moving target; the ballistic prediction system is used to determine whether the hit condition is met based on the positional relationship; if the hit condition is met, a hit command is sent to the signal receiving module of the moving target; the moving target is used to receive the hit command through the signal receiving module and respond to the hit command by performing a hit action.

[0007] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described shooting simulation training methods based on ballistic prediction and position feedback.

[0008] This application also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of any of the above-described shooting simulation training methods based on ballistic prediction and position feedback.

[0009] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above-described shooting simulation training methods based on ballistic prediction and position feedback.

[0010] In the shooting simulation training method and system based on ballistic prediction and position feedback described in the above embodiments of this application, ballistic prediction is performed by introducing the spatial attitude, motion information, and bullet trajectory parameters of real firearms, replacing virtual rendering scenes. This makes the ballistic patterns and hit determination closer to physical reality, reducing reliance on VR / AR devices, greatly narrowing the gap between simulated training and live-fire shooting, and effectively improving training results. There is no need to construct complex VR / AR virtual scenes and their supporting display devices, nor is it necessary to deploy dedicated transceiver devices such as lasers / infrared transceivers between the firearm and the target. This can be achieved through general sensors and positioning technology combined with prediction algorithms, significantly reducing the hardware and maintenance costs of the system.

[0011] Furthermore, trainees can operate firearms equipped with realistic position sensors and fire blanks, gaining a fully realistic shooting experience including recoil, sound, and handling. This completely avoids the safety risks and location limitations associated with live ammunition, achieving both training safety and a realistic combat experience. Moving targets receive a hit command and execute a hit action, providing trainees with visual and real-time feedback on the impact, preserving the realistic combat experience of shooting training and helping them quickly perceive the shooting effects, thereby improving training effectiveness. Attached Figure Description

[0012] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1a An exemplary schematic diagram of the architecture of a moving target shooting simulation training system applied to a shooting simulation training method based on ballistic prediction and position feedback provided in an embodiment of this application;

[0014] Figure 1b A flowchart illustrating a shooting simulation training method based on ballistic prediction and position feedback provided in an embodiment of this application;

[0015] Figure 2 An exemplary schematic diagram of the architecture of a moving target shooting simulation training system applied to another shooting simulation training method based on ballistic prediction and position feedback provided in the embodiments of this application;

[0016] Figure 3 This is a schematic diagram of the structure of a moving target shooting simulation training system provided in an embodiment of this application;

[0017] Figure 4 This is a schematic diagram of another moving target shooting simulation training system provided in the embodiments of this application. Detailed Implementation

[0018] Early shooting training simulations were primarily used in basic military and police shooting training, mainly focusing on live-fire shooting and simple simulated firing ranges. Basic skills training was conducted using fixed targets and pre-set scenarios. With technological advancements, computer simulations and basic sensor technologies were gradually introduced, leading to pilot applications in military tactical training and professional shooting competitions. These simulations can simulate simple environmental changes and target movement trajectories. The development of moving target simulation shooting training began with basic flight and shooting maneuver simulations. Initially focused on simulating basic operational procedures, advancements in computer graphics, physics engines, and other technologies have enabled the construction of more realistic virtual battlefield environments. Incorporating principles of ballistics and aerodynamics to improve simulation accuracy, this technology continues to evolve towards greater immersion, realism, and intelligence, becoming an efficient training method for improving UAV operation and shooting skills.

[0019] Among the relevant technologies, the following shooting training systems and methods exist:

[0020] 1. Live-fire shooting training system: The live-fire shooting training system integrates weapons, range facilities and data evaluation functions. It provides real feedback through live-fire shooting, and assists training through automatic target reporting and data recording and analysis, ensuring safety and improving shooting skills.

[0021] 2. Virtual Shooting Training System: This system uses computer technology to construct virtual scenarios, providing trainees with an immersive shooting experience by simulating weapon operation, ballistic trajectories, and target interaction. The system can record shooting data in real time and generate evaluation reports, supports diverse scenario settings, and allows for safe and efficient training without the need for live ammunition.

[0022] 3. Semi-physical shooting training system: The semi-physical shooting training system integrates physical equipment with virtual technology. It uses real weapons or simulated equipment as the operating carrier, combined with virtual scenes, simulated ballistics and target feedback. It retains the feel of real operation, while expanding the training scenario through the virtual environment. It can collect operation data in real time and evaluate the effect.

[0023] However, the aforementioned shooting training systems often have the following problems in their application:

[0024] 1. Live-fire training systems typically require dedicated training grounds, which are costly and pose certain safety risks, making it difficult to conduct large-scale, high-frequency training. Furthermore, live-fire exercises cause significant environmental damage and hinder the diversification of training scenarios.

[0025] 2. Semi-physical shooting training systems simply summarize shooting training as point-to-point strikes, ignoring the actual strike coverage generated by firing at different distances with different ammunition. Furthermore, they often require the installation of a laser or infrared transceiver between the gun and the target. Feedback can only be provided after the receiver receives the emitted beam from the transmitter. This dual-end coordination results in a certain bit error rate and false alarm rate.

[0026] 3. Virtual shooting training systems rely heavily on VR / AR simulation experiences, which are costly, and the details of the simulated scenes differ significantly from the real environment, resulting in some discrepancies between shooting training and actual conditions.

[0027] To address the aforementioned problems, various embodiments of this application provide a shooting simulation training method based on ballistic prediction and position feedback. The method includes: acquiring the ballistic parameters of a pre-loaded bullet in a firearm, the state information of the firearm, and the position information of a moving target; predicting the positional relationship between the bullet and the moving target within a preset time period based on the state information of the firearm, the ballistic parameters of the pre-loaded bullet, and the position information continuously fed back by the moving target; determining whether a hit condition is met based on the positional relationship; and if the hit condition is met, sending a hit command to the signal receiving module of the moving target, and the moving target responding to the hit command by performing a hit action.

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0029] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0030] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Please refer to Figure 1a , Figure 1aThis is an exemplary schematic diagram of the architecture of a moving target shooting simulation training system used in a shooting simulation training method based on ballistic prediction and position feedback, as provided in an embodiment of this application. Figure 1a As shown, the system includes: a ballistic prediction system and a moving target equipped with a signal receiving module and a positioning module.

[0032] Among them, the ballistic prediction system can be a collection of functions used to collect the weapon's attitude, receive target location information, perform ballistic calculations, and determine hits.

[0033] Specifically, the ballistic prediction system's sensors and computing modules can be mounted as independent devices on the firearm. The firearm can be equipped with a reserved universal interface for communication with the ballistic prediction system.

[0034] A moving target equipped with a signal receiving module can serve as a mobile carrier for firing targets, and can be used to receive signals and respond to hit commands to simulate the effect of being shot down.

[0035] The moving target localization module is used to send the actual position of the moving target to the ballistic prediction system.

[0036] For example, moving targets can include, but are not limited to: drones, ground moving targets, aerial simulated targets, underwater moving targets, and land moving targets. When the moving target is a drone, a falling motion can be performed to simulate being shot down; when the moving target is a ground moving target, a tilting motion can be performed to simulate being hit. In this way, the moving target is simulated to be hit, but the actual moving target itself is not damaged, which can extend the lifespan of the moving target while ensuring training effectiveness.

[0037] Furthermore, when the moving target is a drone, it can also be used for combat training or to perform various tactical maneuvers, such as realistic maneuvers, dodging, two-drone cooperation, or even swarming, to further improve the realism of shooting training.

[0038] When the moving target is a rotary-wing drone, its high degree of freedom of flight allows for irregular changes in flight trajectory at any time. The position information continuously returned by the positioning module mounted on the moving target can greatly improve the strike effect of ballistic prediction.

[0039] Furthermore, there can be one-way data interaction between the ballistic prediction system and the moving target. That is, the ballistic prediction system sends data unidirectionally to the signal receiving module of the moving target, and there is usually no reverse data transmission from the moving target to the ballistic prediction system.

[0040] Further reference Figure 1b , Figure 1bThe flowchart of a shooting simulation training method based on ballistic prediction and position feedback provided in this application embodiment includes the following steps:

[0041] Step S101: Obtain the ballistic parameters of the pre-loaded bullets in the firearm, the status information of the firearm, and the position information of the moving target.

[0042] In this embodiment, the firearm is pre-loaded with at least one bullet before the start of the shooting simulation training.

[0043] The firearms can be real firearms, and the bullets can be specially made blanks to achieve a completely realistic shooting experience.

[0044] The ballistic prediction system obtains the ballistic parameters of the pre-loaded bullet through a universal interface or preset input interface on the firearm; obtains its status information through a position sensor installed on the firearm; and obtains its position information through a positioning device mounted on the moving target.

[0045] Here, ballistic parameters can refer to ballistic simulation parameters used to characterize the flight path and impact range of ammunition. The firearm's state information includes its spatial attitude and motion information at the time of firing.

[0046] Step S102: Based on the firearm's status information, the ballistic parameters of the pre-loaded bullet, and the position information continuously fed back by the moving target, predict the positional relationship between the bullet and the moving target within a preset time in the future.

[0047] In this embodiment, the ballistic prediction system calculates the firing delay time from when the firearm is fired to when the bullet theoretically arrives at the area where the moving target is located, based on the firearm's state information, the bullet's ballistic parameters, and the real-time position information of the moving target, and predicts the relative positional relationship between the bullet and the moving target at that moment.

[0048] Here, the firing delay time includes at least the firing delay and the system transmission delay.

[0049] Step S103: Determine whether the hit condition is met based on the positional relationship.

[0050] In this embodiment, the ballistic prediction system determines whether a bullet hits a moving target based on the hit conditions corresponding to different bullet types.

[0051] In step S104, if the hit condition is met, a hit command is sent to the signal receiving module of the moving target, and the moving target responds to the hit command by performing the hit action.

[0052] In this embodiment, if the bullet is determined to have hit a moving target based on the hit conditions, the ballistic prediction system sends a hit command to the signal receiving module of the moving target, causing the moving target to respond to the hit command and execute the pre-stored hit action, thereby making the hit effect visible to the tester and providing the tester with intuitive result feedback.

[0053] In the shooting simulation training method and system based on ballistic prediction and position feedback described in the above embodiments of this application, ballistic prediction is achieved by introducing the spatial attitude, motion information, and bullet trajectory parameters of real firearms, replacing virtual rendering scenes. This makes the ballistic patterns and hit determination closer to physical reality, reducing reliance on VR / AR devices, greatly narrowing the gap between simulated training and live-fire shooting, and effectively improving training results. There is no need to construct complex VR / AR virtual scenes and their supporting display devices, nor is it necessary to deploy dedicated transceiver devices such as lasers / infrared transceivers between the firearm and the target. This can be achieved through general sensors and positioning technology combined with prediction algorithms, significantly reducing the hardware and maintenance costs of the system. Trainees can operate firearms equipped with real position sensors and fire blank rounds, obtaining a fully realistic shooting experience including recoil, sound, and handling, while completely avoiding the safety risks and site limitations associated with live-fire shooting, achieving both training safety and a realistic combat experience. After receiving the hit command, the moving target executes the hit action, providing trainees with visual and real-time feedback on the hit results. This preserves the realistic combat experience of shooting training, helps trainees quickly perceive the shooting effect, and thus improves training effectiveness.

[0054] Please refer to Figure 2 , Figure 2 This is an exemplary schematic diagram of the architecture of a moving target shooting simulation training system applied to another shooting simulation training method based on ballistic prediction and position feedback provided in an embodiment of this application. Figure 2 As shown, the ballistic prediction system includes a position sensor and a data processing module.

[0055] Here, the position sensor is a measuring device installed on the firearm to collect spatial attitude and motion information of the firearm during firing; the position sensor is connected to the data processing module via wired or wireless means.

[0056] The position sensor can be mounted on the firearm via a standard Picatinny rail, with virtually no impact on the user's experience.

[0057] The data processing module is the core control and calculation unit of the ballistic prediction system. It can receive various types of data transmitted by the system, execute algorithms such as ballistic simulation, timing calculation, and hit determination, and generate control commands to link other modules in the system and related components of the training system.

[0058] In one possible implementation of step S101 above, obtaining the ballistic parameters of the pre-loaded bullet in the firearm, the firearm's state information, and the position information of the moving target includes:

[0059] Obtain the preset ballistic parameters of the pre-loaded bullets in the firearm;

[0060] Acquire spatial attitude and motion information of the firearm during firing, collected by a position sensor mounted on the firearm;

[0061] Continuously acquire the real-time location information of the moving target output by the positioning module on the moving target; wherein, the real-time location information includes, but is not limited to: absolute latitude and longitude coordinates, vertical height, and relative position information of the moving target and the firing point of the gun.

[0062] In this embodiment, the ballistic parameters of the pre-loaded bullet are obtained through a general interface or preset input interface on the firearm. The ballistic parameters are inherent attribute parameters that are digitally set in advance according to the type of ammunition, including but not limited to: bullet type, weight, muzzle velocity and bullet-specific parameters.

[0063] Among them, bullet-specific parameters may include, but are not limited to: the number of pellets in a shotgun, the shape of the bullet tip in a slug, etc.; bullet-specific parameters can be dynamically adapted based on the specific type of bullet.

[0064] Furthermore, the ballistic parameters of the bullet, the information collected by the position sensor, and the information output by the positioning device are sent to the data processing module through the internal signal transmission link of the system to ensure the integrity and timeliness of parameter transmission.

[0065] After receiving various parameters, the data processing module calls the preset ballistic simulation algorithm to calculate and generate ballistic prediction data that characterizes the bullet's flight pattern. The ballistic prediction data includes, but is not limited to: the bullet's ideal trajectory, the bullet's velocity decay characteristics, the bullet's projectile trajectory, the shotgun's coverage area, and the ideal impact point of the slug.

[0066] For example, the ideal trajectory of a bullet can be a parabolic model based on initial velocity and gravitational acceleration; the bullet's velocity decay characteristics can be a velocity loss curve of the bullet over flight distance; and the bullet's trajectory after ejection can be a vertical displacement model considering air resistance. When the bullet type is shotgun shells, the shotgun shell's coverage area can refer to the shot's dispersion radius and density distribution. When the bullet type is slugs, the ideal impact point of a slug can be the theoretical hit position of the slug based on a straight trajectory.

[0067] In the shooting simulation training method and system based on ballistic prediction and position feedback described in the above embodiments of this application, ballistic parameters are obtained through a preset method. Combined with data collected in real time by position sensors and positioning devices, the inherent properties, specific parameters, and real-time status information of the bullet can be directly obtained. This avoids simulation deviations caused by manual input errors or missing parameters, thereby achieving accurate acquisition of ammunition parameters and status information and improving the realism of simulation training. The ballistic prediction data generated by the data processing module through a preset ballistic simulation algorithm covers the key physical processes of bullet flight and can also achieve differentiated simulation of different ammunition trajectories, thereby further making the simulation training more closely resemble actual shooting and further improving the training effect.

[0068] In one possible implementation of step S102 above, based on the firearm's state information, the ballistic parameters of the pre-loaded bullet, and the position information continuously fed back by the moving target, the positional relationship between the bullet and the moving target within a preset time period is predicted, including:

[0069] Based on the spatial attitude and motion information of the firearm at the time of firing, as well as the ballistic parameters of the bullet, the theoretical ballistic trajectory of the bullet is calculated, and the bullet position at each refresh time within a preset time after the firing delay is predicted successively according to the refresh time of the target position.

[0070] Based on the positioning module mounted on the moving target, after the firing delay time, the system continuously receives and obtains the actual position of the moving target at each refresh time interval, with the refresh time of the target position as the interval.

[0071] The bullet position at each refresh time is spatially matched with the actual position of the moving target at the same refresh time. Based on the relative relationship between the bullet position and the actual position, the positional relationship between the bullet and the moving target at each refresh time within a preset time after the firing delay is determined.

[0072] In this embodiment, the data processing module, based on the received firearm spatial attitude, motion information, and bullet trajectory parameters, calls the built-in ballistic model to calculate the theoretical flight trajectory of the bullet from firing to hitting the target. Simultaneously, based on the real-time position information of the moving target, a motion prediction algorithm infers its motion patterns, including parameters such as velocity, acceleration, and direction of motion.

[0073] The data processing module calculates the bullet's flight time based on the bullet's muzzle velocity, ballistic coefficient, and the real-time distance to the moving target, and determines the precise delay time by combining the system's inherent firing delay and transmission delay.

[0074] Furthermore, based on the calculated delay time, the data processing module performs the following predictions:

[0075] Based on the bullet trajectory model, predict the precise position of the bullet in three-dimensional space at each refresh time after the firing delay; and receive and process the actual three-dimensional position continuously fed back by the moving target.

[0076] The data processing module calculates the spatial geometric relationship between the predicted bullet position and the actual position of the moving target, which serves as the basis for hit determination.

[0077] In the shooting simulation training method and system based on ballistic prediction and position feedback described in the above embodiments of this application, accurate prediction of the future positions of bullets and moving targets is achieved through precise ballistic models and motion prediction algorithms, ensuring the accuracy and reliability of hit determination. Using mathematical model calculations instead of physical measurements avoids interference from environmental factors, improving the system's adaptability and stability. Simultaneously, the digital prediction method provides a unified computational framework for hit determination of different bullet types and targets, enhancing the system's versatility and scalability.

[0078] In one possible implementation of the above embodiments, the firing delay time is determined at least based on the firing delay and the system transmission delay.

[0079] In this embodiment, the firing delay t c This refers to the time difference between when the trainee pulls the trigger of the firearm and when the system detects the firing action and generates a firing trigger signal. This parameter can be obtained from the preset parameter library of the firearm firing mechanism.

[0080] System transmission delay t t It refers to the time difference in the transmission of electrical signals between modules within the system, including the transmission delay of position sensor data and positioning device data, which is determined by the design of the system communication link.

[0081] T = t c +t t

[0082] Here, total delay (i.e. firing delay time) refers to the total time from the occurrence of the firing action of the firearm to the theoretical departure of the bullet from the barrel.

[0083] Among them, the bullet flight time, without considering velocity decay, can be calculated by dividing the real-time distance by the muzzle velocity to calculate the initial flight time;

[0084] In the shooting simulation training method and system based on ballistic prediction and position feedback according to the above embodiments of this application, by incorporating all inherent system delays such as firing delay and system transmission delay into the calculation, the timing accuracy of the delay time calculation can be ensured. This allows the calculated delay time to accurately correspond to the actual time from firing to the bullet theoretically arriving at the actual position of the moving target, thereby improving the simulation degree of shooting training to real shooting. The bullet's flight coordinate position is calculated based on the ballistics, flight time, and muzzle velocity, and can be dynamically corrected by combining velocity decay characteristics, improving the physical realism of the ballistic simulation, conforming to the actual shooting law, and further enhancing the realism of the simulation training.

[0085] In one possible implementation of step S103 above, determining whether the hit condition is met based on the positional relationship includes:

[0086] If the bullet type is a slug, determine whether the spatial distance between the predicted position of the bullet and the actual position of the moving target is less than a first threshold; if it is less than the first threshold, then determine that the slug has hit the target.

[0087] If the bullet type is shotgun shells, then based on the shotgun shell dispersion model, it is determined whether the probability of being hit by at least one bullet at the actual location of the moving target is greater than a preset probability threshold; if it is greater than the preset probability threshold, then it is determined that the shotgun shells have hit.

[0088] In this embodiment, the data processing module calculates the relative positional relationship between the bullet's predicted position and the moving target's predicted position in three-dimensional space.

[0089] For a slug, the data processing module calculates the Euclidean distance between the predicted position of the bullet and the actual position of the moving target, and compares this distance with a preset first threshold. The first threshold is an empirical value determined based on the ballistic accuracy of the slug and the actual size of the moving target. For example, the first threshold can be 1 / 2 to 1 / 5 of the characteristic size of the moving target.

[0090] For shotgun shells, the data processing module calls a preset shotgun dispersion model, which defines the distribution pattern of projectiles at a specific distance. Based on the actual position of the moving target, it calculates the probability of being hit by at least one projectile at that position. The probability threshold is set based on a combination of training requirements and actual combat results; for example, the probability threshold can be set between 70% and 90%.

[0091] Furthermore, the hit determination output includes information such as hit status, hit probability (for shotguns), and hit location. This information will be used to generate training evaluation reports and drive the hit action of moving targets.

[0092] In a specific example, when a slug is used to shoot a drone target, the data processing module calculates that the spatial distance between the predicted position of the bullet and the actual position of the drone is 0.15 meters, while the preset first threshold is 0.2 meters, so it is determined to be a hit.

[0093] In another example, when the same target is shot with a shotgun, the data processing module calculates based on the shotgun dispersion model that the probability of being hit by at least one projectile at the actual location of the drone is 85%, while the preset probability threshold is 75%, so it is judged as a hit.

[0094] In the shooting simulation training method and system based on ballistic prediction and position feedback described in the above embodiments of this application, mathematical calculations replace image processing, enabling accurate determination of the hit conditions for different types of ammunition. For slugs, a distance threshold is used to ensure accuracy; for shotgun shells, a probability threshold is used to reflect the dispersion characteristics of shotgun fire. This determination method not only improves computational efficiency and reduces system complexity but also avoids the influence of ambient light and obstructions on image recognition accuracy, significantly improving the system's reliability and practicality. Furthermore, the mathematical model-based calculation method provides a unified framework for hit determination of different types of ammunition, enhancing the system's versatility and scalability.

[0095] As a concrete example, a ballistic prediction system is installed on a real shotgun with a reserved universal interface. The parameters of the pre-loaded ammunition are digitized and input into a data processing module. The pre-loaded ammunition is blank cartridges. When the trainee aims at and fires a drone acting as a moving target, the resulting load and recoil are identical to those of actual shooting, but without posing any risk to shooting training.

[0096] At the moment the trigger is fired, the position sensor mounted on the firearm collects the firearm's spatial attitude and motion information, while the positioning device on the drone outputs its real-time position information; this data is transmitted to the data processing center via a wireless communication network.

[0097] Based on the received firearm attitude information, bullet ballistic parameters (including muzzle velocity, ballistic coefficient, and shot dispersion characteristics), and the real-time position information of the UAV, the data processing center calculates the theoretical ballistic trajectory of the bullet and predicts the spatial distribution of the 28 projectiles after the firing delay time; at the same time, it obtains the actual position of the UAV based on the UAV's positioning module.

[0098] Based on the shot dispersion model, the data processing center calculates the probability of being hit by at least one projectile at the actual location of the drone: when the projectile dispersion model of the planned shot is known, the probability of at least one projectile hitting the drone is calculated based on the projectile distribution; when the projectile distribution is not fixed, the dispersion range of the shot at the target distance is determined based on the projectile divergence angle parameter, and the probability of this range coinciding with the actual location of the drone is calculated.

[0099] When the probability of a hit exceeds a preset probability threshold, the shooting feedback result is "shot down," and the ballistic prediction system will send this feedback result to the drone via the signal transmission module. The drone is equipped with a signal receiving module, and upon receiving the "shot down" feedback signal, it will simulate being shot down by falling, providing shooting trainers with intuitive feedback.

[0100] This example demonstrates how to implement simulated training of shotgun combat against drones using ballistic prediction technology. It retains the physical feedback of real shooting while replacing live-fire shooting with mathematical calculations, ensuring the safety and effectiveness of the training.

[0101] In one embodiment, a moving target shooting simulation training system 300 is provided, which corresponds one-to-one with the shooting simulation training method based on ballistic prediction and position feedback in the above embodiments. For example... Figure 3 As shown, the system includes a ballistic prediction system 31 and a moving target 32 ​​equipped with a signal receiving module 321 and a positioning module 322. The functional modules are described in detail below:

[0102] The ballistic prediction system 31 is used to obtain the ballistic parameters of the pre-loaded bullet in the firearm, the status information of the firearm, and the position information of the moving target.

[0103] The ballistic prediction system 31 is used to predict the positional relationship between the bullet and the moving target 32 ​​within a preset time based on the firearm's state information, the ballistic parameters of the pre-loaded bullet, and the position information continuously fed back by the moving target 32.

[0104] The ballistic prediction system 31 is used to determine whether the hit conditions are met based on the positional relationship; if the hit conditions are met, a hit command is sent to the signal receiving module 321 of the moving target.

[0105] The moving target 32 ​​is used to receive a hit command through the signal receiving module 321 and to perform a hit action in response to the hit command.

[0106] In one embodiment, the ballistic prediction system 31 includes a position sensor 311, wherein:

[0107] Position sensor 311 is used to acquire preset ballistic parameters of a pre-loaded bullet in a firearm;

[0108] Position sensor 311 is used to acquire spatial attitude and motion information of the firearm during firing, which is collected by a position sensor mounted on the firearm;

[0109] The position sensor 311 is used to continuously acquire the real-time position information of the moving target output by the positioning module mounted on the moving target; wherein, the real-time position information includes, but is not limited to: absolute latitude and longitude coordinates, vertical height, and relative position information of the moving target and the firing point of the gun.

[0110] In one embodiment, the ballistic parameters include, but are not limited to, bullet type, muzzle velocity and ballistic coefficient, and bullet type includes, but is not limited to, slug and shotgun shell.

[0111] In one embodiment, the ballistic prediction system 31 includes a data processing module 312, wherein:

[0112] The data processing module 312 is used to calculate the theoretical trajectory of the bullet based on the spatial attitude and motion information of the firearm when it is fired and the ballistic parameters of the bullet, and to predict the bullet position at each refresh time within a preset time after the firing delay, according to the refresh time of the target position.

[0113] The data processing module 312 is used to continuously receive and obtain the actual position of the moving target at each refresh time based on the positioning module 322 mounted on the moving target 32 ​​after the firing delay time, with the refresh time of the target position as the interval.

[0114] The data processing module 312 is used to spatially match the bullet position at each refresh time with the actual position of the moving target 32 ​​at the same refresh time, and determine the positional relationship between the bullet and the moving target 32 ​​at each refresh time within a preset time after the firing delay based on the relative relationship between the bullet position and the actual position.

[0115] In one embodiment, the firing delay time is determined based at least on the firing delay and the system transmission delay.

[0116] In one embodiment, the data processing module 312 is used to determine whether the spatial distance between the predicted position of the bullet and the actual position of the moving target is less than a first threshold if the bullet type is a slug; if it is less than the first threshold, the slug is determined to have hit the target.

[0117] If the bullet type is shotgun shells, then based on the shotgun shell dispersion model, it is determined whether the probability of being hit by at least one bullet at the actual location of the moving target is greater than a preset probability threshold; if it is greater than the preset probability threshold, then it is determined that the shotgun shells have hit.

[0118] It should be noted that the moving target shooting simulation training system provided in the above embodiments, when implementing the corresponding shooting simulation training method based on ballistic prediction and position feedback, is only illustrated by the division of the above-described program modules. In practical applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the above system can be divided into different program modules to complete all or part of the processing described above. Furthermore, the system provided in the above embodiments and the corresponding... Figure 1b The embodiments of the methods shown belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0119] This disclosure also provides an electronic device having the above-described features. Figure 3 The moving target shooting simulation training system shown.

[0120] Please see Figure 4 , Figure 4 This is a schematic diagram of another moving target shooting simulation training system provided in an embodiment of this application, as shown below. Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0121] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0122] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0123] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0124] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0125] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0126] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0127] The computer device also includes a communication interface for communicating with other devices or communication networks.

[0128] This disclosure also provides a computer-readable storage medium in which the methods described in this disclosure can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium after being downloaded over a network. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium may be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium may also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0129] A portion of this disclosure can be applied to computer program products, such as computer program instructions, which, when executed by a computer, can invoke or provide methods and / or technical solutions according to this disclosure through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, and installation package files. Accordingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions; the computer compiling the instructions and then executing the corresponding compiled program; the computer reading and executing the instructions; or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0130] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A shooting simulation training method based on ballistic prediction and position feedback, characterized in that, The method includes: The ballistic parameters of the pre-loaded bullets in the firearm, the status information of the firearm, and the position information of the moving target are obtained. Based on the state information of the firearm, the ballistic parameters of the pre-loaded bullet, and the position information continuously fed back by the moving target, the positional relationship between the bullet and the moving target is predicted within a preset time in the future. Determine whether the hit condition is met based on the positional relationship; If the hit condition is met, a hit command is sent to the signal receiving module of the moving target, and the moving target responds to the hit command by performing a hit action.

2. The method according to claim 1, characterized in that, The acquisition of the ballistic parameters of the pre-loaded bullets in the firearm, the state information of the firearm, and the position information of the moving target includes: Obtain the preset ballistic parameters of the pre-loaded bullets in the firearm; Acquire the spatial attitude and motion information of the firearm at the time of firing, collected by a position sensor mounted on the firearm; The system continuously acquires the real-time location information of the moving target, output by the positioning module mounted on the moving target; wherein the real-time location information includes, but is not limited to: absolute latitude and longitude coordinates, vertical height, and relative position information of the moving target and the firing point of the gun.

3. The method according to claim 2, characterized in that, The ballistic parameters include, but are not limited to: bullet type, muzzle velocity and ballistic coefficient. The bullet type includes, but is not limited to: slugs and shotgun shells.

4. The method according to claim 3, characterized in that, The prediction of the positional relationship between the bullet and the moving target within a preset time period based on the firearm's state information, the ballistic parameters of the pre-loaded bullet, and the position information continuously fed back by the moving target includes: Based on the spatial attitude and motion information of the firearm at the time of firing, as well as the ballistic parameters of the bullet, the theoretical ballistic trajectory of the bullet is calculated, and the bullet position at each refresh time within a preset time after the firing delay is predicted successively according to the refresh time of the target position. Based on the positioning module mounted on the moving target, after the firing delay time, the system continuously receives and obtains the actual position of the moving target at each refresh time interval, with the refresh time of the target position as the interval. The bullet position at each refresh time is spatially matched with the actual position of the moving target at the same refresh time. Based on the relative relationship between the bullet position and the actual position, the positional relationship between the bullet and the moving target at each refresh time within a preset time after the firing delay is determined.

5. The method according to claim 4, characterized in that, The firing delay time is determined based on at least the firing delay and the system transmission delay.

6. The method according to claim 5, characterized in that, The step of determining whether the hit condition is met based on the positional relationship includes: If the bullet type is a slug, determine whether the spatial distance between the predicted position of the bullet and the actual position of the moving target is less than a first threshold; if it is less than the first threshold, then determine that the slug has hit the target. If the bullet type is shotgun shells, then based on the shotgun dispersion model, it is determined whether the probability of being hit by at least one bullet at the actual location of the moving target is greater than a preset probability threshold; if it is greater than the preset probability threshold, then it is determined that the shotgun shells have hit.

7. A moving target shooting simulation training system, characterized in that, The system includes: a ballistic prediction system and a moving target equipped with a signal receiving module and a positioning module, wherein: A ballistic prediction system is used to acquire the ballistic parameters of a pre-loaded bullet in a firearm, the state information of the firearm, and the position information of a moving target. The ballistic prediction system is used to predict the positional relationship between the bullet and the moving target within a preset time period based on the state information of the firearm, the ballistic parameters of the pre-loaded bullet, and the position information continuously fed back by the moving target. The ballistic prediction system is used to determine whether the hit conditions are met based on the positional relationship; if the hit conditions are met, a hit command is sent to the signal receiving module of the moving target. The moving target is configured to receive the hit command through the signal receiving module and perform a hit action in response to the hit command.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the shooting simulation training method based on ballistic prediction and position feedback as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the shooting simulation training method based on ballistic prediction and position feedback as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the shooting simulation training method based on ballistic prediction and position feedback as described in any one of claims 1 to 6.

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