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CN-121994073-A - Shooting training method and system

CN121994073ACN 121994073 ACN121994073 ACN 121994073ACN-121994073-A

Abstract

The invention discloses a shooting training method and a shooting training system, which belong to the technical field of intelligent training. The sensing unit synchronously collects electromyographic signals, motion postures, holding pressure and non-contact ballistic early information and environmental parameters through a sensor array worn on the body of a shooter and a firearm. The analysis unit processes the information in a related manner through time synchronization and data fusion, establishes a real-time mapping model of a shooter action mode and an impact point prediction result, and generates a specific correction instruction and a self-adaptive training parameter according to the real-time mapping model. The feedback unit superimposes aiming deviation guide in the shooter field of view through the augmented reality display and provides real-time prompt through the tactile feedback device integrated in the firearm and the wearing equipment. The method forms a closed-loop training process from motion execution and trajectory prediction to instant correction, and realizes real-time quantitative diagnosis and intervention of key technical links in the shooting process.

Inventors

  • JIANG XIAOYU

Assignees

  • 永续成长(北京)科技有限公司

Dates

Publication Date
20260508
Application Date
20260201

Claims (8)

  1. 1. A shooting training system is characterized by comprising: The multi-mode sensing unit is used for synchronously collecting biomechanical data, weapon posture data, trajectory characteristic data and environmental parameter data of a shooter in the shooting process in real time; The data processing and analyzing unit is in communication connection with the multi-mode sensing unit and is used for receiving and fusing the multi-mode data, analyzing and evaluating shooting actions, ballistic performance and comprehensive performances of a shooter based on an artificial intelligent model, and generating personalized real-time instruction and a self-adaptive training scheme; and the real-time feedback and interaction unit is in communication connection with the data processing and analysis unit and is used for providing multisensory feedback based on the real-time instruction to the shooter and presenting the self-adaptive training scheme.
  2. 2. The shooting training system in accordance with claim 1, wherein said multi-modal sensing unit comprises: The wearable biomechanical sensing module comprises a myoelectric sensor, an inertia measurement unit and a pressure distribution sensor which are arranged on key parts of a shooter body and a firearm and are used for collecting muscle activation signals, limb and weapon motion postures and holding force data; The non-contact type trajectory analysis module comprises a laser array and a micro-pressure sensor network, wherein the laser array and the micro-pressure sensor network are arranged near a shooting axis, and are used for detecting the spatial position and air disturbance of a projectile passing through a virtual monitoring surface before the projectile hits a physical target so as to perform trajectory prediction and early deviation analysis; and the environment sensing module is used for collecting temperature, humidity, wind speed, wind direction, illumination and sound data of the training field.
  3. 3. The shooting training system as claimed in claim 2, wherein the data processing and analyzing unit comprises: the data fusion engine is used for carrying out time synchronization and space registration on multi-source asynchronous data from different sensors within microsecond precision to form a unified time-space correlation data stream; The skill assessment model is used for constructing a personalized capability benchmark of a shooter based on transfer learning and the space-time associated data stream, and dynamically assessing from multiple dimensions of stability, consistency and environment adaptability; And the self-adaptive planning engine dynamically adjusts the difficulty, the scene complexity and the feedback strength of the training task by using a reinforcement learning algorithm based on the output of the skill evaluation model and a preset training target, and generates a personalized training path.
  4. 4. The shooting training system in accordance with claim 1, wherein the real-time feedback and interaction unit comprises: The augmented reality display device is used for displaying real-time aiming deviation indication, action correction guide, virtual aiming auxiliary line and key performance indexes in a superimposed manner in the view field of a shooter; The tactile feedback device is integrated in the firearm kit and/or shooter wearing equipment and comprises a micro-vibration motor and a variable resistance trigger mechanism, and is used for providing tactile prompts and simulation related to trigger control, breathing rhythm and posture adjustment; The immersive environment simulation system is used for generating and projecting a virtual training scene comprising dynamic targets, variable illumination, weather effects and sound field simulation.
  5. 5. A shooting training method based on the system as claimed in any one of claims 1 to 4, comprising the steps of: s1, synchronously collecting multidimensional data of a shooter in the whole process of shooting preparation, firing and follow-up actions in real time through the multi-mode sensing unit; S2, fusing and analyzing the multidimensional data in the data processing and analyzing unit, identifying the technical deviation in shooting action in real time, and predicting the impact point trend before the projectile hits the entity target; s3, providing immediate correction feedback in visual, auditory and/or tactile forms for shooters instantly after shooting through the real-time feedback and interaction unit based on the analysis result of the step S2; And S4, accumulating the data of multiple shots, evaluating the comprehensive skill level and progress track of the shooter through the data processing and analyzing unit, and automatically generating an adaptive training plan in a subsequent stage.
  6. 6. The method of claim 5, wherein the predicting the impact point trend before the projectile hits the solid target in step S2 comprises: capturing an early flight state of the projectile after the projectile leaves a muzzle and before the projectile reaches a target by using the non-contact trajectory analysis module; combining the weapon attitude data and the environmental parameter data, and calculating a trajectory track in real time through an aerodynamic model; The deviation of the projectile from the expected aiming point is continuously predicted during its flight and used to generate real-time feedback in step S3.
  7. 7. The method according to claim 5, further comprising the step S5 of constructing a digital twin model of the shooter; The digital twin model is built based on historical training data, and can simulate expected performances of a shooter in different virtual training scenes; before the self-adaptive training program is applied to actual training, simulation previewing and effect evaluation are performed on the digital twin model, and parameter optimization is performed according to the simulation previewing and effect evaluation.
  8. 8. The method of claim 5, wherein the logic for generating the adaptive training program comprises: monitoring the real-time performance success rate, physiological fatigue index and psychological load level of a shooter in training; when the power is continuously higher than a first threshold value, automatically improving the difficulty of the training task or introducing interference factors; when the physiological fatigue index or the psychological load level exceeds a second threshold value, automatically reducing the training intensity or switching to the restorative training content; The training process is dynamically maintained within a preset skill challenge zone.

Description

Shooting training method and system Technical Field The invention relates to the technical field of intelligent training, in particular to a shooting training method and a shooting training system. Background Gun shooting training, particularly compression shooting and scatter shooting training, has the central goal of enabling a shooter to master the ability to effectively control impact points within a specific area (i.e., a "shot gate") during successive shots. At present, the training method and the evaluation system in the field have obvious defects. The prior art relies mainly on preset fixed area targets or simple ring targets, where the shooter evaluates the spreading effect afterwards by observing the bullet hole distribution. The method has the following fundamental defects that firstly, training feedback is seriously delayed, a shooter cannot obtain guidance about real-time distribution trend of impact points in the shooting process, the result can be checked only after the whole group of shooting is finished, and instant causal relation between shooting actions and scattering forms is difficult to establish. Secondly, the existing method is highly dependent on visual observation and experience judgment of a coach, lacks objective and quantitative data support, cannot accurately evaluate key indexes such as uniformity, density and center deviation of scattering, and cannot perform correlation analysis on scattering effects and specific operations (such as gun stability, breathing rhythm and spot firing rhythm control) of a shooter. Furthermore, the training mode is stiff, the shooting gate (i.e. the target area) is usually fixed, and cannot be dynamically adjusted according to the capability level of the shooter and the training progress, and also cannot simulate the complex scene of the movement or the size change of the target area in actual combat. Disclosure of Invention The invention mainly aims to provide a shooting training method and a shooting training system, which can effectively solve the problems in the background technology. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a shooting training system comprising: The multi-mode sensing unit is used for synchronously collecting biomechanical data, weapon posture data, trajectory characteristic data and environmental parameter data of a shooter in the shooting process in real time; The data processing and analyzing unit is in communication connection with the multi-mode sensing unit and is used for receiving and fusing the multi-mode data, analyzing and evaluating shooting actions, ballistic performance and comprehensive performances of a shooter based on an artificial intelligent model, and generating personalized real-time instruction and a self-adaptive training scheme; and the real-time feedback and interaction unit is in communication connection with the data processing and analysis unit and is used for providing multisensory feedback based on the real-time instruction to the shooter and presenting the self-adaptive training scheme. Preferably, the multi-mode sensing unit includes: The wearable biomechanical sensing module comprises a myoelectric sensor, an inertia measurement unit and a pressure distribution sensor which are arranged on key parts of a shooter body and a firearm and are used for collecting muscle activation signals, limb and weapon motion postures and holding force data; The non-contact type trajectory analysis module comprises a laser array and a micro-pressure sensor network, wherein the laser array and the micro-pressure sensor network are arranged near a shooting axis, and are used for detecting the spatial position and air disturbance of a projectile passing through a virtual monitoring surface before the projectile hits a physical target so as to perform trajectory prediction and early deviation analysis; and the environment sensing module is used for collecting temperature, humidity, wind speed, wind direction, illumination and sound data of the training field. Wherein a non-contact ballistic analysis module is critical. The method realizes the early flight state capture and deviation prediction of the projectile by arranging the virtual monitoring surface on the trajectory, thereby advancing the feedback opportunity from 'hit back' to 'in flight', and providing a data basis for a core training method. Preferably, the data processing and analyzing unit includes: the data fusion engine is used for carrying out time synchronization and space registration on multi-source asynchronous data from different sensors within microsecond precision to form a unified time-space correlation data stream; The skill assessment model is used for constructing a personalized capability benchmark of a shooter based on transfer learning and the space-time associated data stream, and dynamically assessing from multiple dimensions of stability, consistency and environment adaptabil