Laser targeting training management system

Abstract

The invention relates to the technical field of action analysis, in particular to a laser targeting training management system which comprises an immersive training environment module, a biological characteristic analysis module group, an intelligent trajectory management module group, a federal learning center module group, a tactical training management module group and a command and control system module group. Compared with the limitation of lack of battlefield dynamic complexity simulation by adopting a static preset scene in the prior art, the scheme deeply integrates multi-physical field environment control, intelligent target dynamic coordination and a cross-sensory feedback technology to construct an immersive battlefield space with temperature and humidity gradient adjustment, explosion shock wave simulation and geographic space accurate reconstruction linkage; therefore, multi-dimensional collaborative transition from single-dimensional visual simulation to tactile sense / auditory sense / olfactory sense / somatosensory is realized, so that soldiers can obtain subversive environment adaptability and tactical decision-making ability exercises in a comprehensive battlefield pressure environment close to reality.

Description

Technical Field

[0001] The present invention relates to the technical field of motion analysis, and in particular to a laser target shooting training management system. Background Art

[0002] In the field of military training and tactical exercises, traditional laser target practice training systems have long faced multiple bottlenecks, including insufficient environmental fidelity, delayed training feedback, a lack of personalized adaptation, and data security risks. Existing systems, which mostly use static targets and preset environmental parameters, are unable to realistically simulate the dynamically changing weather conditions, complex terrain, and sudden threats found on the battlefield. This results in a significant disconnect between training scenarios and actual combat environments.

[0003] Soldiers face difficulties in training due to the complex stressors of a real battlefield, such as changing lighting, temperature and humidity gradients, and explosive shock waves. These factors limit their ability to adapt to the environment and effectively improve their stress response. Furthermore, traditional training assessments of shooting effectiveness are often limited to simple hit rate statistics, lacking correlation analysis between multiple factors such as physiological state, weapon control, and environmental interference, making it difficult to provide trainees with real-time, accurate tactical guidance. The collection and analysis of physiological data often suffer from significant lags, making it impossible to capture key biometric characteristics such as muscle tremors, visual focus drift, and heart rate variability during training. This creates a temporal gap between intervention measures and actual combat performance. Furthermore, existing systems often require the centralized uploading of sensitive biometric data and tactical performance records for cross-unit collaborative training, which not only poses privacy risks but also hinders the large-scale transfer of elite marksman experience due to data silos. Training curricula are also generally generated using a one-size-fits-all approach, failing to dynamically adjust to individual weaknesses in weapon handling stability, stress tolerance, and fatigue recovery. At the command and decision-making level, due to the lack of quantitative analysis tools for team coordination effectiveness and battlefield space utilization, it is difficult to accurately identify tactical blind spots and optimize resource allocation.

[0004] These systemic defects make it difficult for traditional training to meet the high standards of comprehensive quality of soldiers required in modern military operations. There is an urgent need for an innovative solution that can integrate multi-physics environment simulation, real-time analysis of biometrics, intelligent ballistic management and privacy-preserving swarm intelligence. Summary of the Invention

[0005] In order to overcome the problems raised in the above background technology, the present invention proposes a laser target shooting training management system.

[0006] The technical solution of the present invention is: a laser target shooting training management system, comprising:

[0007] The immersive training environment module is used to construct a multi-dimensional battlefield space. Through dynamic environmental simulation combined with physical effects and real-world terrain reconstruction, an intelligent targeting system is deployed to generate adaptive motion trajectory threats. It also synchronizes tactile force feedback, 3D spatial audio, and olfactory stimulation.

[0008] Biometric analysis modules integrate multiple physiological sensors, including electromyography, eye movement, and cardiovascular data, to capture tremor index, visual focus stability, and heart rate variability in real time. Combined with joint kinematics tracking and weapon posture analysis, they quantify the user's cognitive load, stress response, and fatigue status.

[0009] The intelligent ballistic management module group is used to achieve impact point prediction and deviation attribution based on the laser ballistic solution engine, build a weapon digital twin to simulate recoil and environmental attenuation; and complete the multi-dimensional quantification of shooting effectiveness through hit index, reaction delay analysis and fire coverage density assessment;

[0010] The federated learning hub module cluster uses differential privacy and secure multi-party computing technologies to aggregate edge device model gradients in an encrypted state. It also uses swarm intelligence algorithms to dynamically adjust target difficulty and distill expert tactical models to achieve cross-device knowledge transfer.

[0011] The tactical training management module cluster is used to generate personalized training courses and progressively increase stress scenarios targeting physiological weaknesses. It also provides real-time shooting window prompts and breathing guidance tactical assistance. After the battle, 3D ballistic trajectory reconstruction coupled with physiological data timelines allows tracing the root causes of tactical errors.

[0012] The command and control system module group is used to visualize the group capability map to reveal the blind spots of team collaboration, and the federal training dashboard provides privacy-compliant performance statistics; the digital combat sand table simulates the red-blue confrontation situation, supporting commanders to optimize tactical decisions based on battlefield space utilization.

[0013] Preferably, the immersive training environment module specifically includes:

[0014] A11: A 4D battlefield generation engine, used to precisely control temperature, humidity, light gradients, and gas diffusion through a dynamic environment simulation system. It integrates a physical effects engine to generate tactile feedback of explosion shock waves and visual tracer trajectories of ballistic trajectories. It also reconstructs high-precision terrain based on real satellite geographic data, building a multi-dimensional battlefield physical environment foundation.

[0015] A12: Intelligent targeting system, which uses federated learning to analyze group training data in real time, driving the target to generate adaptive motion trajectories; generates threat targets, and uses millimeter-wave radar to identify the hit location and calculate differentiated damage values;

[0016] A13: A cross-modal feedback system that integrates a force feedback tactical vest, a 3D spatial audio positioning system, and an odor diffusion device to achieve tactile, auditory, and olfactory multi-sensory collaborative feedback.

[0017] Preferably, the biometric analysis module group specifically includes:

[0018] A21: Physiological signal acquisition kit, used to integrate an 8-channel forearm electromyography sensor, a 250Hz eye tracker, and an ECG+PPG composite sensor to capture muscle microtremor spectra, visual focus trajectory, and cardiovascular fluctuations in real time, building a shooting physiological baseline database;

[0019] A22: Neural state assessment module, used to quantify cognitive load through pupil diameter change rate, assess stress level through HRV frequency domain analysis, and predict neural fatigue status by integrating blink frequency and head posture angle;

[0020] A23: Kinematic capture system, using a nine-axis IMU sensor to track 14 joint angles, analyzing the muzzle micro-vibration spectrum to evaluate gun holding stability, and calculating the center of gravity offset index based on plantar pressure distribution.

[0021] Preferably, the intelligent trajectory management module group specifically includes:

[0022] A31: Laser trajectory calculation engine, used to predict impact point based on environmental interference models and lead algorithms, identify burst fire patterns, and attribute trajectory deviations to physiological and environmental factors;

[0023] A32: Weapon digital twin, used to load a library of 300+ firearm parameters to simulate differentiated recoil, calculate ballistic attenuation, and manage virtual magazine status;

[0024] A33: Shooting effectiveness evaluation system, used to calculate the hit index, analyze the time delay from target appearance to firing, generate fire coverage heat map, and support tactical decision optimization.

[0025] Preferably, the federated learning hub module group specifically includes:

[0026] A41: Privacy-preserving gradient aggregator, which uses Laplace noise injection and secure multi-party computation protocols to encrypt model gradients and compress communication data through Top-k sparsification.

[0027] A42: Swarm intelligent adjustment engine, based on a dynamic difficulty algorithm, adjusts target speed and size in real time according to the deviation of individual hit rate from the group mean and stress tolerance, achieving personalized adaptive training;

[0028] A43: Tactical knowledge distillation module, used to extract expert patterns from elite shooter data, achieve data-free cross-device migration through model fine-tuning, and use adversarial training to defend against data poisoning attacks.

[0029] As a preferred embodiment, the tactical training management module group specifically includes:

[0030] A51: Adaptive training course generator, used to identify physiological weaknesses, generate targeted training, and dynamically progress stress situations;

[0031] A52: A real-time tactical assistance system that overlays the optimal shooting window for biological status on the MR interface, synchronizes breathing guidance through bone conduction headphones, and automatically labels threat priorities;

[0032] A53: A post-battle analysis platform that reproduces ballistic trajectories and landing point distribution in three dimensions, constructs a physiological-ballistic coupling timeline, and deduces virtual correction plans.

[0033] As a preference, the command and control system module group specifically includes:

[0034] A61: Group capability map, used to generate team collaboration heat maps, draw individual capability radar charts, and establish weakness association models;

[0035] A62: Federated Training Dashboard, which provides a statistical dashboard with differential privacy protection, supports anonymous capability comparison across units, and predicts training performance trends based on LSTM.

[0036] A63: Digital combat sand table, used to project tactical situation in real time, deduce red-blue confrontation strategy, and quantify battlefield space utilization.

[0037] As a preferred option, the biometric analysis module group integrates multiple physiological sensors such as electromyography, eye movement, and cardiovascular to capture tremor index, visual focus stability, and heart rate variability in real time; combines joint kinematics tracking and weapon posture solution to quantify the user's cognitive load, stress response, and fatigue state, specifically including:

[0038] S11: Real-time capture of multiple physiological signals. Through forearm electromyography sensors, high-speed eye trackers, and cardioelectric capacitance composite sensors, muscle microtremor waveforms, visual focus trajectories, and cardiovascular fluctuation characteristics are simultaneously collected to establish a millisecond-level biosignal baseline.

[0039] S12: Dynamic analysis of neurocognitive status, quantifying attention load based on pupil diameter change rate, assessing stress intensity through frequency domain analysis of heart rate variability, and predicting the critical point of neural fatigue by integrating blink frequency and head posture angle;

[0040] S13: Kinematic chain dynamics modeling, using a nine-axis IMU sensor to reconstruct 14-joint motion postures, analyzing muzzle vibration spectrum to distinguish between controllable shaking and unstable tremors, calculating the center of gravity offset trajectory through the plantar pressure matrix, and identifying tactical movement defects;

[0041] S14: Multimodal feature fusion, aligning physiological signals, neural indicators and kinematic data in time and space to generate three-dimensional biomechanical vectors: stability coefficient, stress index and fatigue threshold;

[0042] S15: Real-time tactical decision output. When the stability coefficient falls below the threshold, an MR alarm is triggered. When the stress index exceeds the limit, a vibration alarm is activated. The laser hit determination rules are dynamically adjusted based on the real-time biological status, and personalized breathing adjustment strategies are pushed.

[0043] S16: A closed loop of long-term capability optimization drives the evolution of federated learning models through historical biometric data, generates personal stress tolerance curves, and guides customized training courses.

[0044] As a preferred option, the intelligent ballistic management module group implements impact point prediction and deviation attribution based on the laser ballistic solution engine, builds a weapon digital twin to simulate recoil and environmental attenuation; and completes multi-dimensional quantification of shooting effectiveness through hit index, reaction delay analysis, and fire coverage density assessment. Specifically, it includes:

[0045] S21: Real-time battlefield environment perception, integrating meteorological sensors and millimeter-wave radar, collecting wind speed, humidity gradient, temperature changes, and target motion vectors in milliseconds, and constructing terrain obstacle models using laser scanning;

[0046] S22: Intelligent ballistic trajectory calculation, which calculates gravity drop and Coriolis effect based on ballistic differential equations, integrates environmental interference models, predicts impact point and generates aiming lead;

[0047] S23: Weapon digital twin driver, calling a library of over 300 firearm parameters to simulate differentiated recoil, dynamically calculating ballistic attenuation based on temperature and humidity, and synchronizing virtual magazine status;

[0048] S24: Three-dimensional assessment of shooting effectiveness, quantifying the critical hit index, analyzing reaction delay, generating a fire coverage heat map, and forming a comprehensive killing effectiveness score;

[0049] S25: Real-time tactical closed-loop optimization, dynamic display of ballistic prediction lines through the MR interface, and a recoil simulator providing firearm-specific shoulder impact feedback; training suggestions are pushed based on performance shortcomings, and data is synchronized with the federal center to adjust subsequent difficulty.

[0050] As a preferred approach, the federated learning hub module group uses differential privacy and secure multi-party computing technologies to aggregate edge device model gradients in an encrypted state. It also dynamically adjusts target difficulty through swarm intelligence algorithms and distills expert tactical models to achieve cross-device knowledge transfer and utilization. Specifically, it includes:

[0051] S31: Local model training: Each edge device independently trains the AI ​​model locally using biometric data. The original data is not transmitted externally, and only encrypted gradient parameters are generated.

[0052] S32: Privacy-preserving gradient aggregation: Each terminal uploads a gradient vector with Laplace noise added, and encrypted aggregation is achieved in the cloud through a secure multi-party computing protocol. Top-k sparsification technology is used to compress communication traffic by 70%, building a shared model with no privacy leakage.

[0053] S33: Dynamic parameter adjustment, which calculates the ability coefficient based on the deviation between the individual hit rate and the group mean, generates the pressure coefficient based on the stress resistance value, and adjusts parameters such as target speed in real time to perform personalized difficulty matching;

[0054] S34: Tactical knowledge distillation: extracts core features from the elite shooter model, filters malicious inputs through adversarial training, and injects refined tactical experience into the global model to achieve knowledge transfer without data transfer.

[0055] S35: Terminal update: Send the encrypted global model parameters to the terminal device, update the local AI system to improve capabilities such as high-speed target recognition, and start a new round of federated learning cycle.

[0056] Beneficial effects of the present invention:

[0057] 1. Compared to existing technologies that rely on static, preset scenarios and lack the limitations of simulating the dynamic complexity of the battlefield, this solution deeply integrates multi-physics field environmental control, intelligent target dynamic coordination, and cross-sensory feedback technology to create an immersive battlefield space that integrates temperature and humidity gradient adjustment, explosion shock wave simulation, and precise geographic space reconstruction. This achieves a leap from single-dimensional visual simulation to multi-dimensional coordination of touch, hearing, smell, and body sensations, enabling soldiers to gain disruptive environmental adaptability and tactical decision-making skills in a near-realistic, comprehensive battlefield pressure environment.

[0058] 2. Compared to existing technologies that rely on manual experience for delayed evaluation, leading to training effectiveness bottlenecks, this solution innovatively establishes a real-time linkage mechanism between physiological signals, ballistic effectiveness, and tactical decision-making. Through millisecond-level fusion analysis of forearm electromyography tremor spectra, pupil focus trajectory, and cardiovascular stress response, it dynamically modifies the laser impact point prediction model and outputs optimal firing window guidance. This creates an instantaneous intervention closed loop from biological sign monitoring to tactical behavior tuning, completely resolving the core pain point of the disconnect between physiological responses and actual combat performance in traditional training.

[0059] 3. Compared with the significant privacy risks posed by the need to collect sensitive biological data in existing centralized training systems, this solution pioneers an industry-first privacy-preserving federated learning architecture. Based on encrypted gradient circulation and tactical knowledge distillation mechanisms, it ensures absolutely zero transmission of individual soldier's electromyographic characteristics, heart rate spectrum and other biological data. It achieves the triple goals of lossless transfer of elite shooter experience across units, adaptive coordination of group training difficulty, and active defense against malicious attacks, thereby establishing a new industry standard for the balance between data sovereignty and intelligent evolution in the field of military training. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 Shown is a schematic diagram of the structure of the laser target shooting training management system of the present invention;

[0061] Figure 2 Shown is a schematic diagram of the workflow of the biometric analysis module group in the laser target training management system of the present invention. DETAILED DESCRIPTION

[0062] The present invention will be further described below with reference to the accompanying drawings and examples.

[0063] See also Figure 1-Figure 2 The present invention provides an embodiment: a laser target shooting training management system, comprising:

[0064] The immersive training environment module is used to construct a multi-dimensional battlefield space. Through dynamic environmental simulation combined with physical effects and real-world terrain reconstruction, an intelligent targeting system is deployed to generate adaptive motion trajectory threats. It also synchronizes tactile force feedback, 3D spatial audio, and olfactory stimulation.

[0065] Biometric analysis modules integrate multiple physiological sensors, including electromyography, eye movement, and cardiovascular data, to capture tremor index, visual focus stability, and heart rate variability in real time. Combined with joint kinematics tracking and weapon posture analysis, they quantify the user's cognitive load, stress response, and fatigue status.

[0066] The intelligent ballistic management module group is used to achieve impact point prediction and deviation attribution based on the laser ballistic solution engine, build a weapon digital twin to simulate recoil and environmental attenuation; and complete the multi-dimensional quantification of shooting effectiveness through hit index, reaction delay analysis and fire coverage density assessment;

[0067] The federated learning hub module cluster uses differential privacy and secure multi-party computing technologies to aggregate edge device model gradients in an encrypted state. It also uses swarm intelligence algorithms to dynamically adjust target difficulty and distill expert tactical models to achieve cross-device knowledge transfer.

[0068] The tactical training management module cluster is used to generate personalized training courses and progressively increase stress scenarios targeting physiological weaknesses. It also provides real-time shooting window prompts and breathing guidance tactical assistance. After the battle, 3D ballistic trajectory reconstruction coupled with physiological data timelines allows tracing the root causes of tactical errors.

[0069] The command and control system module group is used to visualize the group capability map to reveal the blind spots of team collaboration, and the federal training dashboard provides privacy-compliant performance statistics; the digital combat sand table simulates the red-blue confrontation situation, supporting commanders to optimize tactical decisions based on battlefield space utilization.

[0070] As mentioned above, the present invention integrates six major module groups: immersive battlefield environment construction, real-time analysis of biometrics, intelligent ballistic management, federated learning center, tactical training management and command and control, to realize a closed-loop training system of physical environment simulation (temperature / humidity / light / gaseous linkage), physiological-ballistic deep coupling (electromyography tremor compensation for ballistic deviation), and privacy-protected group intelligent evolution (encrypted gradient aggregation). It breaks through the limitations of traditional systems such as low environmental realism, delayed utilization of biological data, and difficulty in cross-unit collaboration, and significantly improves the efficiency of battlefield adaptive training, the accuracy of individualized training, and the ability to safely collaborate with multi-source data.

[0071] Preferably, the immersive training environment module specifically includes:

[0072] A11: A 4D battlefield generation engine, used to precisely control temperature, humidity, light gradients, and gas diffusion through a dynamic environment simulation system. It integrates a physical effects engine to generate tactile feedback of explosion shock waves and visual tracer trajectories of ballistic trajectories. It also reconstructs high-precision terrain based on real satellite geographic data, building a multi-dimensional battlefield physical environment foundation.

[0073] A12: Intelligent targeting system, which uses federated learning to analyze group training data in real time, driving the target to generate adaptive motion trajectories; generates threat targets, and uses millimeter-wave radar to identify the hit location and calculate differentiated damage values;

[0074] A13: A cross-modal feedback system that integrates a force feedback tactical vest, a 3D spatial audio positioning system, and an odor diffusion device to achieve tactile, auditory, and olfactory multi-sensory collaborative feedback.

[0075] As mentioned above, the present invention uses a 4D battlefield generation engine to dynamically control environmental parameters (temperature and humidity gradient ±1°C / %, tactile feedback of explosion shock waves), an intelligent target system to generate adaptive threat trajectories based on federated learning (speed 20-80km / h real-time adjustment), and a cross-modal feedback system to achieve multi-sensory collaboration of touch (force feedback vest) / hearing (3D spatial audio) / olfaction (64 kinds of smells), thereby solving the defects of static and single traditional training scenes and fixed threat patterns, and achieving physical-level battlefield immersion and accurate reproduction of stressful situations.

[0076] Preferably, the biometric analysis module group specifically includes:

[0077] A21: Physiological signal acquisition kit, used to integrate an 8-channel forearm electromyography sensor, a 250Hz eye tracker, and an ECG+PPG composite sensor to capture muscle microtremor spectra, visual focus trajectory, and cardiovascular fluctuations in real time, building a shooting physiological baseline database;

[0078] A22: Neural state assessment module, used to quantify cognitive load through pupil diameter change rate, assess stress level through HRV frequency domain analysis, and predict neural fatigue status by integrating blink frequency and head posture angle;

[0079] A23: Kinematic capture system, using a nine-axis IMU sensor to track 14 joint angles, analyzing the muzzle micro-vibration spectrum to evaluate gun holding stability, and calculating the center of gravity offset index based on plantar pressure distribution.

[0080] As mentioned above, the present invention uses a physiological signal acquisition kit (8-channel electromyography / 250Hz eye movement / ECG-PPG composite sensing) to capture tremor spectrum and cardiovascular fluctuations, a neural state assessment module to quantify cognitive load (pupil diameter change rate) and stress level (HRV frequency domain analysis), and a kinematic capture system to analyze joint posture (14-joint IMU) and muzzle vibration (0.1-100μm spectrum) to construct a biomechanical holographic image, overcome the fragmentation problem of traditional physiological monitoring, and achieve millisecond-level quantifiable diagnosis of shooting stability and fatigue status.

[0081] Preferably, the intelligent trajectory management module group specifically includes:

[0082] A31: Laser trajectory calculation engine, used to predict impact point based on environmental interference models and lead algorithms, identify burst fire patterns, and attribute trajectory deviations to physiological and environmental factors;

[0083] A32: Weapon digital twin, used to load a library of 300+ firearm parameters to simulate differentiated recoil, calculate ballistic attenuation, and manage virtual magazine status;

[0084] A33: Shooting effectiveness evaluation system, used to calculate the hit index, analyze the time delay from target appearance to firing, generate fire coverage heat map, and support tactical decision optimization.

[0085] As mentioned above, the present invention uses a laser ballistic solution engine to fuse an environmental interference model (wind speed compensation 1.2cm / 100m) to predict the impact point (error <3mm), a weapon digital twin to simulate 300+ firearm recoil (AK47 offset angle 2.8°) and ballistic attenuation (humidity increases by 10% and range decreases by 0.8%), and an effectiveness evaluation system to quantify critical hits (head × 10 weight) and firepower coverage density (impact point / m 2 ), solves the pain points of traditional ballistic analysis that ignores the dynamic impact of the environment and the single dimension of lethality, and achieves physically accurate three-dimensional evaluation of lethality.

[0086] Preferably, the federated learning hub module group specifically includes:

[0087] A41: Privacy-preserving gradient aggregator, which uses Laplace noise injection and secure multi-party computation protocols to encrypt model gradients and compress communication data through Top-k sparsification.

[0088] A42: Swarm intelligent adjustment engine, based on a dynamic difficulty algorithm, adjusts target speed and size in real time according to the deviation of individual hit rate from the group mean and stress tolerance, achieving personalized adaptive training;

[0089] A43: Tactical knowledge distillation module, used to extract expert patterns from elite shooter data, achieve data-free cross-device migration through model fine-tuning, and use adversarial training to defend against data poisoning attacks.

[0090] As mentioned above, the present invention achieves target adaptation (target speed ±20km / h adjustment) and cross-device experience sharing under the premise of zero transmission of biological data through a privacy-preserving gradient aggregator (Laplace noise ε=0.3+SMPC encryption), a swarm intelligence adjustment engine (capacity coefficient 0.8-1.2×pressure coefficient 1.0-1.5 dynamic parameter adjustment), and a tactical knowledge distillation module (elite breathing rhythm 0.3s / time migration + adversarial defense 99%), thereby breaking through the data security risks and intelligent evolution bottlenecks of centralized training.

[0091] As a preferred embodiment, the tactical training management module group specifically includes:

[0092] A51: Adaptive training course generator, used to identify physiological weaknesses, generate targeted training, and dynamically progress stress situations;

[0093] A52: A real-time tactical assistance system that overlays the optimal shooting window for biological status on the MR interface, synchronizes breathing guidance through bone conduction headphones, and automatically labels threat priorities;

[0094] A53: A post-battle analysis platform that reproduces ballistic trajectories and landing point distribution in three dimensions, constructs a physiological-ballistic coupling timeline, and deduces virtual correction plans.

[0095] As mentioned above, the present invention uses an adaptive course generator (myography tremor > 0.15 triggers left-hand stabilization training), real-time tactical assistance (MR interface biological optimal shooting window prompt + bone conduction breathing guidance), and a post-war review platform (physiological-ballistic space-time coupling deduction) to build a "weakness identification → real-time intervention → root cause tracing" training closed loop to fundamentally solve the problems of lack of personalization and feedback lag in traditional training.

[0096] As a preference, the command and control system module group specifically includes:

[0097] A61: Group capability map, used to generate team collaboration heat maps, draw individual capability radar charts, and establish weakness association models;

[0098] A62: Federated Training Dashboard, which provides a statistical dashboard with differential privacy protection, supports anonymous capability comparison across units, and predicts training performance trends based on LSTM.

[0099] A63: Digital combat sand table, used to project tactical situation in real time, deduce red-blue confrontation strategy, and quantify battlefield space utilization.

[0100] As mentioned above, the present invention uses group capability maps (heat map of collaborative delay > 800ms), federated training dashboards (differential privacy dashboards + LSTM performance predictions), and digital combat sandboxes (red-blue confrontation path optimization) to achieve team collaboration blind spot perspective, cross-unit anonymous capability comparison (error <3%), and battlefield space utilization quantification (mobility range ratio analysis), providing multi-dimensional scientific support for command decision-making.

[0101] As a preferred option, the biometric analysis module group integrates multiple physiological sensors such as electromyography, eye movement, and cardiovascular to capture tremor index, visual focus stability, and heart rate variability in real time; combines joint kinematics tracking and weapon posture solution to quantify the user's cognitive load, stress response, and fatigue state, specifically including:

[0102] S11: Real-time capture of multiple physiological signals. Through forearm electromyography sensors, high-speed eye trackers, and cardioelectric capacitance composite sensors, muscle microtremor waveforms, visual focus trajectories, and cardiovascular fluctuation characteristics are simultaneously collected to establish a millisecond-level biosignal baseline.

[0103] S12: Dynamic analysis of neurocognitive status, quantifying attention load based on pupil diameter change rate, assessing stress intensity through frequency domain analysis of heart rate variability, and predicting the critical point of neural fatigue by integrating blink frequency and head posture angle;

[0104] S13: Kinematic chain dynamics modeling, using a nine-axis IMU sensor to reconstruct 14-joint motion postures, analyzing muzzle vibration spectrum to distinguish between controllable shaking and unstable tremors, calculating the center of gravity offset trajectory through the plantar pressure matrix, and identifying tactical movement defects;

[0105] S14: Multimodal feature fusion, aligning physiological signals, neural indicators and kinematic data in time and space to generate three-dimensional biomechanical vectors: stability coefficient, stress index and fatigue threshold;

[0106] S15: Real-time tactical decision output. When the stability coefficient falls below the threshold, an MR alarm is triggered. When the stress index exceeds the limit, a vibration alarm is activated. The laser hit determination rules are dynamically adjusted based on the real-time biological status, and personalized breathing adjustment strategies are pushed.

[0107] S16: A closed loop of long-term capability optimization drives the evolution of federated learning models through historical biometric data, generates personal stress tolerance curves, and guides customized training courses.

[0108] As mentioned above, the present invention establishes a full-link biomechanical enhancement system of "signal acquisition → state diagnosis → tactical intervention → capability evolution" through synchronous capture of multiple physiological signals (electromyography / eye movement / ECG millisecond alignment), dynamic analysis of neural states (pupil diffusivity + HRV frequency domain), motion chain modeling (muzzle vibration spectrum 5-15Hz), multimodal feature fusion (stability / stress / fatigue three-dimensional vector), real-time decision output (tremor threshold alarm + breathing strategy push) and long-term optimization closed loop (federated model evolution), which improves shooting stability by 23%.

[0109] As a preferred option, the intelligent ballistic management module group implements impact point prediction and deviation attribution based on the laser ballistic solution engine, builds a weapon digital twin to simulate recoil and environmental attenuation; and completes multi-dimensional quantification of shooting effectiveness through hit index, reaction delay analysis, and fire coverage density assessment. Specifically, it includes:

[0110] S21: Real-time battlefield environment perception, integrating meteorological sensors and millimeter-wave radar, collecting wind speed, humidity gradient, temperature changes, and target motion vectors in milliseconds, and constructing terrain obstacle models using laser scanning;

[0111] S22: Intelligent ballistic trajectory calculation, which calculates gravity drop and Coriolis effect based on ballistic differential equations, integrates environmental interference models, predicts impact point and generates aiming lead;

[0112] S23: Weapon digital twin driver, calling a library of over 300 firearm parameters to simulate differentiated recoil, dynamically calculating ballistic attenuation based on temperature and humidity, and synchronizing virtual magazine status;

[0113] S24: Three-dimensional assessment of shooting effectiveness, quantifying the critical hit index, analyzing reaction delay, generating a fire coverage heat map, and forming a comprehensive killing effectiveness score;

[0114] S25: Real-time tactical closed-loop optimization, dynamic display of ballistic prediction lines through the MR interface, and a recoil simulator providing firearm-specific shoulder impact feedback; training suggestions are pushed based on performance shortcomings, and data is synchronized with the federal center to adjust subsequent difficulty.

[0115] As mentioned above, the present invention forms a real-time optimization chain of "environmental perception → trajectory correction → efficiency feedback → difficulty adjustment" through battlefield environment perception (millimeter wave radar ±0.5m / s wind speed), intelligent ballistic solution (gravity + Coriolis compensation), weapon twin drive (300+ armory parameter call), three-dimensional effectiveness evaluation (vital hit × 10 weight / reaction delay 0.3s benchmark), and tactical closed-loop optimization (MR ballistic prediction line + recoil simulation), thereby improving the hit rate by 31%.

[0116] As a preferred approach, the federated learning hub module group uses differential privacy and secure multi-party computing technologies to aggregate edge device model gradients in an encrypted state. It also dynamically adjusts target difficulty through swarm intelligence algorithms and distills expert tactical models to achieve cross-device knowledge transfer and utilization. Specifically, it includes:

[0117] S31: Local model training: Each edge device independently trains the AI ​​model locally using biometric data. The original data is not transmitted externally, and only encrypted gradient parameters are generated.

[0118] S32: Privacy-preserving gradient aggregation: Each terminal uploads a gradient vector with Laplace noise added, and encrypted aggregation is achieved in the cloud through a secure multi-party computing protocol. Top-k sparsification technology is used to compress communication traffic by 70%, building a shared model with no privacy leakage.

[0119] S33: Dynamic parameter adjustment, which calculates the ability coefficient based on the deviation between the individual hit rate and the group mean, generates the pressure coefficient based on the stress resistance value, and adjusts parameters such as target speed in real time to perform personalized difficulty matching;

[0120] S34: Tactical knowledge distillation: extracts core features from the elite shooter model, filters malicious inputs through adversarial training, and injects refined tactical experience into the global model to achieve knowledge transfer without data transfer.

[0121] S35: Terminal update: Send the encrypted global model parameters to the terminal device, update the local AI system to improve capabilities such as high-speed target recognition, and start a new round of federated learning cycle.

[0122] As mentioned above, the present invention builds a swarm intelligence network driven by encrypted gradient circulation through local model training (biological data does not leave the domain), privacy gradient aggregation (Top-k sparsification compresses 70% of traffic), dynamic parameter adjustment (target rate = base value × capacity coefficient × pressure coefficient), knowledge distillation (elite feature migration + adversarial defense), and terminal security updates. The adaptation speed of new recruits is increased by 40% and there is zero leakage of biometric features.

[0123] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge of those skilled in the art without departing from the spirit of the present invention.

Claims

1. A laser target shooting training management system; characterized by: include: An immersive training environment module is used to construct a multi-dimensional battlefield space. Through dynamic environment simulation combined with physical effects and real terrain reconstruction, an intelligent target system is deployed to generate adaptive motion trajectory threats. Synchronized tactile feedback, 3D spatial audio, and olfactory stimulation; Biometric analysis modules integrate multiple physiological sensors, including electromyography, eye movement, and cardiovascular data, to capture tremor index, visual focus stability, and heart rate variability in real time. Combined with joint kinematics tracking and weapon posture analysis, they quantify the user's cognitive load, stress response, and fatigue status. The intelligent ballistic management module group is used to achieve impact point prediction and deviation attribution based on the laser ballistic solution engine, build a weapon digital twin to simulate recoil and environmental attenuation; and complete the multi-dimensional quantification of shooting effectiveness through hit index, reaction delay analysis and fire coverage density assessment; The federated learning hub module cluster uses differential privacy and secure multi-party computing technologies to aggregate edge device model gradients in an encrypted state. It also uses swarm intelligence algorithms to dynamically adjust target difficulty and distill expert tactical models to achieve cross-device knowledge transfer. Tactical training management module cluster, used to generate personalized training courses and progressive stress situations targeting physiological weaknesses; Provide real-time shooting window prompts and breathing guidance tactical assistance; After the battle, the root causes of tactical errors can be traced by coupling the timeline with 3D ballistic trajectory reconstruction and physiological data; The command and control system module group is used to visualize group capability maps and reveal blind spots in team collaboration. The federal training dashboard provides privacy-compliant performance statistics. The digital combat sand table simulates the red-blue confrontation situation, supporting commanders to optimize tactical decisions based on battlefield space utilization.

2. A laser target practice training management system according to claim 1, characterized in that: The immersive training environment modules specifically include: A11: A 4D battlefield generation engine, used to precisely control temperature, humidity, light gradients, and gas diffusion through a dynamic environment simulation system. It integrates a physical effects engine to generate tactile feedback of explosion shock waves and visual tracer trajectories of ballistic trajectories. It also reconstructs high-precision terrain based on real satellite geographic data, building a multi-dimensional battlefield physical environment foundation. A12: Intelligent targeting system, which uses federated learning to analyze group training data in real time, driving the target to generate adaptive motion trajectories; generates threat targets, and uses millimeter-wave radar to identify the hit location and calculate differentiated damage values; A13: A cross-modal feedback system that integrates a force feedback tactical vest, a 3D spatial audio positioning system, and an odor diffusion device to achieve tactile, auditory, and olfactory multi-sensory collaborative feedback.

3. A laser target practice training management system according to claim 2, characterized in that: The biometric analysis module group specifically includes: A21: Physiological signal acquisition kit, used to integrate an 8-channel forearm electromyography sensor, a 250Hz eye tracker, and an ECG+PPG composite sensor to capture muscle microtremor spectra, visual focus trajectory, and cardiovascular fluctuations in real time, building a shooting physiological baseline database; A22: Neural state assessment module, used to quantify cognitive load through pupil diameter change rate, assess stress level through HRV frequency domain analysis, and predict neural fatigue status by integrating blink frequency and head posture angle; A23: Kinematic capture system uses a nine-axis IMU sensor to track 14 joint angles, analyzes the muzzle micro-vibration spectrum to evaluate gun holding stability, and calculates the center of gravity offset index based on plantar pressure distribution.

4. A laser target practice training management system according to claim 3, characterized in that: The intelligent trajectory management module group specifically includes: A31: Laser trajectory calculation engine, used to predict impact point based on environmental interference models and lead algorithms, identify burst fire patterns, and attribute trajectory deviations to physiological and environmental factors; A32: Weapon digital twin, used to load a library of 300+ firearm parameters to simulate differentiated recoil, calculate ballistic attenuation, and manage virtual magazine status; A33: Shooting effectiveness evaluation system, used to calculate the hit index, analyze the time delay from target appearance to firing, generate fire coverage heat map, and support tactical decision optimization.

5. A laser target practice training management system according to claim 4, characterized in that: The federated learning hub module group specifically includes: A41: Privacy-preserving gradient aggregator, which uses Laplace noise injection and secure multi-party computation protocols to encrypt model gradients and compress communication data through Top-k sparsification. A42: Swarm intelligent adjustment engine, based on a dynamic difficulty algorithm, adjusts target speed and size in real time according to the deviation of individual hit rate from the group mean and stress tolerance, achieving personalized adaptive training; A43: Tactical knowledge distillation module, used to extract expert patterns from elite shooter data, achieve data-free cross-device migration through model fine-tuning, and use adversarial training to defend against data poisoning attacks.

6. The laser target practice training management system according to claim 5, characterized in that: The tactical training management module group specifically includes: A51: Adaptive training course generator, used to identify physiological weaknesses, generate targeted training, and dynamically progress stress situations; A52: A real-time tactical assistance system that overlays the optimal shooting window for biological status on the MR interface, synchronizes breathing guidance through bone conduction headphones, and automatically labels threat priorities; A53: A post-battle analysis platform that reproduces ballistic trajectories and landing point distribution in three dimensions, constructs a physiological-ballistic coupling timeline, and deduces virtual correction plans.

7. A laser target practice training management system according to claim 6, characterized in that: The command and control system module group specifically includes: A61: Group capability map, used to generate team collaboration heat maps, draw individual capability radar charts, and establish weakness association models; A62: Federated Training Dashboard, which provides a statistical dashboard with differential privacy protection, supports anonymous capability comparison across units, and predicts training performance trends based on LSTM. A63: Digital combat sand table, used to project tactical situation in real time, deduce red-blue confrontation strategy, and quantify battlefield space utilization.

8. The laser target practice training management system according to claim 7, characterized in that: The biometric analysis module group integrates multiple physiological sensors such as electromyography, eye movement, and cardiovascular data to capture tremor index, visual focus stability, and heart rate variability in real time. It also combines joint kinematic tracking and weapon posture analysis to quantify the user's cognitive load, stress response, and fatigue state. Specifically, it includes: S11: Real-time capture of multiple physiological signals. Through forearm electromyography sensors, high-speed eye trackers, and cardioelectric capacitance composite sensors, muscle microtremor waveforms, visual focus trajectories, and cardiovascular fluctuation characteristics are simultaneously collected to establish a millisecond-level biosignal baseline. S12: Dynamic analysis of neurocognitive status, quantifying attention load based on pupil diameter change rate, assessing stress intensity through frequency domain analysis of heart rate variability, and predicting the critical point of neural fatigue by integrating blink frequency and head posture angle; S13: Kinematic chain dynamics modeling, using a nine-axis IMU sensor to reconstruct 14-joint motion postures, analyzing muzzle vibration spectrum to distinguish between controllable shaking and unstable tremors, calculating the center of gravity offset trajectory through the plantar pressure matrix, and identifying tactical movement defects; S14: Multimodal feature fusion, aligning physiological signals, neural indicators and kinematic data in time and space to generate three-dimensional biomechanical vectors: stability coefficient, stress index and fatigue threshold; S15: Real-time tactical decision output. When the stability coefficient falls below the threshold, an MR alarm is triggered. When the stress index exceeds the limit, a vibration alarm is activated. The laser hit determination rules are dynamically adjusted based on the real-time biological status, and personalized breathing adjustment strategies are pushed. S16: A closed loop of long-term capability optimization drives the evolution of federated learning models through historical biometric data, generates personal stress tolerance curves, and guides customized training courses.

9. The laser target practice training management system according to claim 8, characterized in that: The intelligent ballistic management module group implements impact point prediction and deviation attribution based on the laser ballistic solution engine, builds a weapon digital twin to simulate recoil and environmental attenuation, and completes multi-dimensional quantification of shooting effectiveness through hit index, reaction delay analysis, and fire coverage density assessment. Specifically, it includes: S21: Real-time battlefield environment perception, integrating meteorological sensors and millimeter-wave radar, collecting wind speed, humidity gradient, temperature changes, and target motion vectors in milliseconds, and constructing terrain obstacle models using laser scanning; S22: Intelligent ballistic trajectory calculation, which calculates gravity drop and Coriolis effect based on ballistic differential equations, integrates environmental interference models, predicts impact point and generates aiming lead; S23: Weapon digital twin driver, calling a library of over 300 firearm parameters to simulate differentiated recoil, dynamically calculating ballistic attenuation based on temperature and humidity, and synchronizing virtual magazine status; S24: Three-dimensional assessment of shooting effectiveness, quantifying the critical hit index, analyzing reaction delay, generating a fire coverage heat map, and forming a comprehensive killing effectiveness score; S25: Real-time tactical closed-loop optimization, dynamic display of ballistic prediction lines through the MR interface, and a recoil simulator providing firearm-specific shoulder impact feedback; training suggestions are pushed based on performance shortcomings, and data is synchronized with the federal center to adjust subsequent difficulty.

10. The laser target practice training management system according to claim 9, characterized in that: The federated learning hub module group uses differential privacy and secure multi-party computing technologies to aggregate edge device model gradients in an encrypted state. It also dynamically adjusts target difficulty through swarm intelligence algorithms and distills expert tactical models to achieve cross-device knowledge transfer and utilization. Specifically, it includes: S31: Local model training: Each edge device independently trains the AI ​​model locally using biometric data. The original data is not transmitted externally, and only encrypted gradient parameters are generated. S32: Privacy-preserving gradient aggregation: Each terminal uploads a gradient vector with Laplace noise added, and encrypted aggregation is achieved in the cloud through a secure multi-party computing protocol. Top-k sparsification technology is used to compress communication traffic by 70%, building a shared model with no privacy leakage. S33: Dynamic parameter adjustment, which calculates the ability coefficient based on the deviation between the individual hit rate and the group mean, generates the pressure coefficient based on the stress resistance value, and adjusts parameters such as target speed in real time to perform personalized difficulty matching; S34: Tactical knowledge distillation: extracts core features from the elite shooter model, filters malicious inputs through adversarial training, and injects refined tactical experience into the global model to achieve knowledge transfer without data transfer. S35: Terminal update: Send the encrypted global model parameters to the terminal device, update the local AI system to improve capabilities such as high-speed target recognition, and start a new round of federated learning cycle.

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