Machine gun shooting door design method and system based on shooting dispersion

Abstract

The invention discloses a machine gun shooting door design method and system based on shooting dispersion, belongs to the technical field of automatic weapon firepower control, and provides a dynamic optimization method for solving the technical problems of an existing shooting door. The core of the method is that a machine gun continuous shooting probability distribution model is established, and an initial shooting gate is determined based on a preset hit probability; in the shooting process, impact point data, environmental parameters and weapon states are collected in real time, a distribution model is dynamically updated, and the shape, size and spatial orientation of a shooting door are continuously adjusted through an adaptive optimization algorithm, so that the shooting door is always attached to a high-probability distribution area of an impact point. Correspondingly, the system comprises a sensing module, a data processing and algorithm module, a firepower control interface module and a man-machine interaction module. According to the method, scattering is converted from interference factors to design basis, fundamental conversion of the shooting door from static presetting to dynamic intelligent optimization is achieved, and the hit efficiency and the ammunition utilization rate are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of automatic weapon fire control technology, and in particular to a design method and system for machine gun firing gates based on firing dispersion. Background Technology

[0002] The machine gun firing gate (or fire control domain) is a key concept in automated and semi-automatic machine gun fire control systems. It is used to define the effective firing or aiming area of ​​the machine gun during continuous fire to optimize fire coverage efficiency. Existing firing gate design methods are mainly divided into two categories: one is a static preset based on a simple geometric model (such as a fixed-angle sector or rectangle), whose parameters (such as gate width and height) mostly depend on theoretical projectile dispersion data or shooter experience settings, and usually remain unchanged during firing once set; the other type introduces simple feedback, but is mostly limited to translation or scaling of the overall area according to projectile deviation, and its underlying model is still a fixed geometric framework. These existing technologies generally suffer from the following inherent defects: First, they fail to deeply model and utilize the inherent probability dispersion patterns of burst fire, resulting in a low degree of matching between the design results and the actual bullet impact distribution. This leads to a fire domain that does not "fit" the actual dispersion, resulting in low efficiency. Second, they employ static or semi-static designs, making it impossible to dynamically optimize and adjust based on real-time bullet impact data generated during firing, changing environmental conditions (such as crosswinds and temperature), and the weapon's own condition (such as barrel temperature rise and wear), resulting in poor adaptability. Third, they heavily rely on preset parameters based on human experience and lack the ability to autonomously learn and optimize based on effectiveness objectives. Therefore, existing technologies struggle to intelligently achieve the optimal balance between fire density, hit probability, and ammunition consumption in complex and ever-changing combat environments, thus hindering further improvements in the effectiveness of machine gun automatic fire control systems. Summary of the Invention

[0003] The main objective of this invention is to provide a machine gun firing gate design method and system based on firing dispersion, which can effectively solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A machine gun firing gate design method based on firing dispersion includes the following steps: S1: Establish a two-dimensional probability distribution model of bullet impact points in machine gun burst fire; S2: Based on the preset expected hit probability Based on the probability scattering model, the initial shooting gate region Ω that satisfies the probability conditions is determined, and its boundary is defined by the following probability integral constraints:

[0005] in, Let be the joint probability density function of the point of impact; S3: During firing, real-time acquisition and processing of bullet impact data, environmental parameters, and weapon status parameters are performed. S4: Based on the real-time data collected in step S3, dynamically update the parameters of the probability scattering model, and adaptively adjust the shape, size, and spatial orientation of the shooting gate area Ω accordingly; S5: Outputs the optimized firing gate parameters to the fire control unit for auxiliary or automatic control of machine gun firing.

[0006] By introducing probability and statistics theory into the design of the firing gate, the firing gate is no longer a fixed geometric shape, but a "high-hit-probability zone" based on the dynamic evolution of the actual bullet dispersion probability. Integral constraints ensure that the design of the firing gate always aims to achieve the preset hit probability, realizing a fundamental shift from "fixed parameter control" to "probabilistic performance control".

[0007] Preferably, the probability scattering model in step S1 is a two-dimensional Gaussian distribution model; the real-time acquisition in step S3 is achieved through a bullet impact detection system, environmental sensors, and weapon status sensors; the dynamic update and adaptive adjustment in step S4 are achieved through an embedded machine learning algorithm. This algorithm takes historical and real-time bullet impact data, environmental data, and weapon status data as input, and uses shooting effectiveness indicators as optimization targets for iterative learning. Using a two-dimensional Gaussian model can effectively describe the scattering ellipse characteristics of continuous fire with the simplest parameters (mean, covariance), facilitating real-time calculation. The introduction of machine learning algorithms, especially reinforcement learning, enables the system not only to adjust based on current data but also to learn the optimal adjustment strategy through historical interaction experience, achieving autonomous dynamic optimization of the firing gate parameters, surpassing traditional rule-based or simple feedback-based adjustment mechanisms.

[0008] Preferably, the adaptive adjustment in step S4 specifically includes the following sub-steps: S41: Calculate the statistical characteristics of the current shot dispersion using the bullet impact data within the sliding time window; S42: Based on environmental parameters, the statistical characteristics are corrected in real time to generate the current effective dispersion parameters; S43: Based on the effective dispersion parameters and weapon state parameters, calculate the adjustment strategy for the firing gate area Ω using an optimized decision model. The adjustment strategy includes adjustments to the shape, area, and orientation of the area. S44: Implement the adjustment strategy, evaluate the adjusted shooting effectiveness, and feed the evaluation results back to the optimization decision model for continuous improvement.

[0009] By constructing a complete closed loop of "perception-correction-decision-execution-evaluation," the system ensures the timeliness of statistical features through a sliding window, improves the physical realism of the model through environmental and state corrections, and feeds the evaluation results back to the decision model, forming an online learning cycle. This allows the system to continuously adapt to changes in weapon performance and complex external environments, thereby continuously improving control accuracy.

[0010] Preferably, the environmental parameters include wind speed, wind direction, temperature, and air pressure, and the weapon state parameters include barrel temperature and vibration characteristics. The optimization decision model employs a deep reinforcement learning algorithm, whose state space includes effective dispersion parameters, target motion information, and weapon state, while the action space represents the adjustment amount of the firing gate parameters. The reward function is constructed based on a comprehensive performance index of hit rate and ammunition consumption rate. By incorporating specific environmental and state parameters into the decision-making process, the model can precisely characterize the impact of external interference and weapon degradation on dispersion. The use of deep reinforcement learning enables the system to learn complex nonlinear mappings in a high-dimensional, continuous state and action space. The design of its reward function directly guides the system to balance the two key operational indicators of "accuracy" and "ammunition conservation," achieving intelligent multi-objective optimization.

[0011] Preferably, in step S41, the length of the sliding time window is dynamically adjusted according to the firing mode: a shorter time window is used in continuous firing or rapid firing modes to quickly respond to dispersion changes; a longer time window is used after burst firing or paused firing to ensure statistical stability. Dynamically adjusting the sliding window length is an important engineering optimization measure. It enables the system to maintain high agility when the rate of fire is high and the dispersion may change rapidly, while at slower rates of fire or during firing intervals, it obtains more stable and reliable statistical estimates through longer data accumulation, thus ensuring the timeliness and accuracy of firing gate parameter adjustments in different tactical scenarios.

[0012] Preferably, step S6 is also included: providing a human-machine interface that displays the current shooting gate area Ω, historical and real-time bullet impact point distribution, and key parameters in real time, and allows operators to manually set and intervene in the expected hit probability P_target and optimization weights. This clarifies the design concept of "human in the loop." The visual interface makes the abstract algorithmic decision-making process transparent, greatly enhancing the operator's understanding and trust in the system status. At the same time, allowing manual intervention in key parameters enables the system to leverage its autonomous optimization advantages while fully respecting and incorporating the shooter's tactical intentions and experience judgments, achieving efficient human-machine intelligent collaboration.

[0013] An intelligent control system for implementing the aforementioned method includes: The sensing module includes a multimodal sensor array for detecting the point of impact, an environmental sensor group for measuring environmental parameters, and a weapon status sensor for monitoring the machine gun's own status. The data processing and algorithm module includes: Scattering analysis unit, used to build and update probabilistic scattering models; The gate parameter generation unit is used to calculate the shooting gate parameters based on probability constraints; An adaptive optimization unit, with an embedded machine learning algorithm, is used to make dynamic optimization decisions for the shooting gate parameters; The fire control interface module is used to convert the optimized firing gate parameters into control commands and send them to the machine gun's fire control system. The human-computer interaction module is used for parameter input, status display, and manual intervention.

[0014] The four modules have clearly defined functions, forming a complete technology chain from data acquisition, intelligent processing, command generation to human-computer interaction. Its modular design not only clearly defines the system's composition but also facilitates the flexible integration of this technical solution into various existing machine gun fire control platforms in the form of independent external devices or embedded subsystems.

[0015] Preferably, the multimodal sensor array is a fusion system of an acoustic detection array and a millimeter-wave radar; the adaptive optimization unit integrates an online learning function, which can continuously update its internal decision-making model using data generated during missions. The fusion design of acoustics and radar improves the reliability and anti-jamming capability of impact point detection, ensuring the quality of core input data. The online learning function of the adaptive optimization unit is the key to the "growth potential" of this system, meaning that after deployment, its performance can continuously evolve as the number of missions executed increases, better adapting to the usage habits and operational environments of specific weapons and units.

[0016] Preferably, the system further includes a collaborative control unit, used to collaboratively plan and resolve conflicts in the fire coverage area based on the real-time firing gate parameters and dispersion model of each machine gun when multiple machine guns are operating in a network, avoiding fire overlap or blind spots. Through collaborative planning, intelligent allocation and complementarity of firepower among multiple machine guns are achieved, organizing discrete "intelligent fire points" into an organic "intelligent fire network," thereby maximizing fire suppression effects and area control efficiency at the company and platoon tactical levels.

[0017] Preferably, the data processing and algorithm module further includes an anomaly handling unit for identifying abnormal dispersion patterns caused by sensor malfunctions, extreme interference, or weapon failures, and triggering preset robust control strategies. These strategies include switching to a safe firing gate based on a historical average dispersion model or prompting the shooter for manual takeover. The anomaly handling unit is a crucial design element for ensuring the system's operational robustness and security. It enables the system to detect faults and degrade safely, automatically switching to a conservative but reliable backup mode when encountering unreliable anomalies. This ensures that partial failure of the algorithm or sensors does not lead to a complete loss of combat capability or a security incident, thus improving the system's battlefield survivability and reliability.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention overcomes the limitations of traditional machine gun firing gates that rely on fixed geometry and shooter experience, proposing a dynamic optimization method and system with a firing dispersion probability model as the core design. Its core lies in transforming firing dispersion from a "disruptive factor" to be overcome into a "design basis" for constructing the fire domain. This is achieved by real-time fusion of bullet impact data, environmental information, and weapon status, and by using adaptive algorithms to dynamically adjust the shape, size, and direction of the firing gate. This method and system significantly improve the probability of hitting moving targets with the same ammunition consumption, or significantly save ammunition while achieving the same hit requirements. Simultaneously, it endows the machine gun with adaptive fire control capabilities in various complex environments and weapon states, achieving a leap from "rough coverage" to "intelligent precision control." Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the component architecture of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] like Figures 1-2 The system architecture and methodology shown are illustrated below, and will be further explained with reference to a detailed implementation example.

[0022] I. System Hardware Composition and Function Implementation The intelligent control system of this invention is integrated into the machine gun fire control unit in the form of physical hardware and software modules, or as an independent external module. Its hardware implementation mainly includes: Implementation of the sensing module: The bullet impact detection unit preferably employs a fusion scheme of an acoustic detection array and a low-power millimeter-wave radar. The acoustic array consists of multiple high-precision microphones arranged in a specific geometric layout, mounted to the side and rear of the muzzle. By measuring the time difference between the arrival times of the shock waves generated by the supersonic flight of the bullet at different microphones, the planar coordinates of the bullet impact point in the geodetic coordinate system are calculated. The millimeter-wave radar beam covers a fan-shaped area directly in front of the machine gun, using ground splash echoes to assist in localization and verify the acoustic detection results. The data fusion processor performs time synchronization, coordinate alignment, and probabilistic correlation on the raw data from both types of sensors, ultimately outputting a series of bullet impact point coordinate data with timestamps and confidence levels.

[0023] Environmental sensing unit: Integrates a miniature weather sensor to collect real-time data on lateral and longitudinal wind speeds, ambient temperature, atmospheric pressure, and relative humidity along the firing axis. These parameters are transmitted to the main processor at a fixed frequency via a communication bus.

[0024] Weapon status sensing unit: includes a temperature sensor installed on a key part of the barrel to monitor the barrel temperature change curve; and a microelectromechanical system accelerometer installed on the receiver or cradle to measure the recoil vibration spectrum during firing, the spectral characteristics of which are related to the weapon's fixed state and firing consistency.

[0025] Implementation of the data processing and algorithm module: This module uses an embedded industrial computer or a high-performance processor as its core and runs the following software units: Scatter Analysis Unit: Receives real-time bullet impact data stream. First, it segments the bullet impact points of continuous firing. For each set of bullet impact points, it calculates the average landing position and analyzes its scatter characteristics. The core task is to calculate the standard deviations characterizing the lateral and longitudinal dispersion of bullet impact points, and the correlation between them. This unit maintains a rolling buffer containing scatter characteristic parameters from multiple recent shots.

[0026] Gate Parameter Generation Unit: Based on the latest dispersion feature parameters and a preset expected hit probability, the core task of this unit is to determine a region where the probability of the bullet impact point landing is not lower than a preset value. For a typical elliptical dispersion, this region is an ellipse. This unit determines the length of the major and minor axes, the azimuth angle, and the center position of this ellipse based on the calculated lateral and longitudinal standard deviations and their correlation. This elliptical region is defined as the current theoretically optimal shooting gate.

[0027] Adaptive Optimization Unit: This unit implements an adaptive adjustment process.

[0028] Statistical feature calculation: Extract the scatter parameters of the most recent shots from the rolling buffer, calculate their weighted average as the current statistical feature, and give higher weight to the data of the most recent shots.

[0029] Environment and Weapon Condition Correction: Based on real-time environmental sensor data, a pre-stored ballistic correction model is applied. For example, crosswinds can cause a systematic lateral shift in the dispersion center and may increase the degree of lateral dispersion; barrel heating may also affect dispersion. This step outputs the "effective dispersion parameters" after environmental and weapon condition corrections.

[0030] Optimization Decision: The optimization decision model is implemented as a deep reinforcement learning-based agent. The agent observes state information including effective dispersion parameters, target motion information, barrel temperature, and vibration characteristics. The agent's output action is an adjustment to the parameters of the current firing gate ellipse (such as major and minor axes, center position, and azimuth angle). The training objective of the agent is to maximize a comprehensive reward function that simultaneously considers the target hit situation, the size of the firing gate area (related to ammunition consumption efficiency), and the smoothness of the adjustment. The agent is trained offline through numerous simulated scenarios, and online by optimizing and adjusting actions based on real-time state output.

[0031] Execution and Feedback: Execute the adjustment actions given by the agent to obtain new shooting gate parameters. At the end of the next evaluation cycle, calculate the hit efficiency based on the actual proportion of new bullet impact points landing in the new shooting gate, and feed this result back to the agent as a reward signal to achieve online strategy fine-tuning and continuous improvement.

[0032] Anomaly Handling Unit: Continuously monitors data quality and dispersion patterns. For example, if the dispersion characteristics suddenly deteriorate significantly beyond the normal range, accompanied by abnormal weapon vibration, a malfunction is suspected. In this case, the unit will trigger a preset robust control strategy: for example, ignoring the abnormal real-time data, switching to a conservative dispersion model based on the weapon's historical safety data, generating a safe firing threshold with higher fault tolerance, and simultaneously issuing an alarm to the operator.

[0033] Cooperative Control Unit (for multi-gun networking): This unit is activated when the system is in networked combat mode. It receives and processes the firing gate parameters and dispersion center positions of neighboring fire units, and uses geometric calculations to determine whether there is excessive overlap or gaps in the overall fire coverage. Through a distributed negotiation algorithm, it dynamically adjusts the boundaries or orientations of each firing gate to achieve coordinated optimization of regional firepower, avoiding resource waste or coverage blind spots.

[0034] Implementation of the fire control interface module: This module contains a protocol conversion chip and drive circuitry. It converts the firing gate parameters, described in a coordinate system and output by the algorithm module, into commands that the servo control system can directly execute, combined with the machine gun's current attitude angles. For example, it maps the firing gate boundary to a set of coordinated motion trajectory points for the azimuth and pitch servo motors, or converts it into an "electronic boundary" parameter that allows the servo system to autonomously scan within that area. The converted commands are then sent to the machine gun's servo driver and firing controller via a standard communication interface.

[0035] Implementation of the human-computer interaction module: It includes a ruggedized display terminal and related driver software. The interface is mainly divided into: Situation display area: Dynamically overlays the battlefield background, the currently optimized firing gate area, historical and real-time bullet impact points, and key system parameter values ​​in a graphical manner.

[0036] Parameter Control Area: Provides input controls, allowing shooters to adjust core parameters in real time, including expected hit probability, ammo saving weight, firing mode, etc. All manual adjustments are fed into the algorithm module in real time, affecting subsequent optimization decisions.

[0037] II. A complete and coherent description of the implementation process The entire system operates as a closed-loop automatic control process, and its sequential flow is as follows: Initialization Phase: The system powers on and performs a self-test. The operator sets initial tactical parameters. The system loads the baseline dispersion model and generates the initial firing gate based on it, which is displayed on the interactive interface.

[0038] Operation phase: The operator authorized firing, and the machine gun fired its first burst within the area defined by the initial firing gate.

[0039] The sensing module simultaneously collects the impact point data, environmental data, and weapon status data of the first burst.

[0040] The dispersion analysis unit calculates the actual dispersion characteristics of the initial burst. The gate parameter generation unit, combined with environmental correction, generates a corrected firing gate that better reflects the actual situation of the initial burst.

[0041] Adaptive optimization loop startup: a. The agent in the adaptive optimization unit calculates and outputs fine-tuning instructions for the shooting gate based on the current comprehensive state (corrected shooting gate parameters, gun temperature, wind speed, etc.).

[0042] b. The fire control interface module converts the optimized and adjusted new firing gate parameters into servo control commands.

[0043] c. The servo system drives the muzzle, so that the second burst is fired within the new optimized firing gate area.

[0044] d. After the second burst is completed, the system collects data again, evaluates the actual effect of the bullet impact point landing in the new firing gate, and generates an effectiveness feedback signal.

[0045] e. This feedback signal is used to fine-tune the agent's decision-making strategy online. Subsequently, based on all the information to date (cumulative firing results, latest environmental state), the system generates the next round of optimization adjustments for the firing gate.

[0046] Continuous Iteration: The above steps constitute a continuous "shoot-evaluate-learn-adjust" closed loop. As firing continues, the system continuously learns the optimal fire control law under the current specific conditions and dynamically adjusts the firing gate to always approach the highest probability distribution area of ​​the bullet impact point. Operators can adjust parameters such as the desired hit probability through the interface, and the system will respond in real time, dynamically balancing hit rate and firepower efficiency.

[0047] Anomaly Handling: Throughout the process, the anomaly handling unit monitors in parallel. Once an anomaly pattern is detected, the system will perform degradation processing according to preset rules to ensure safety and alert the operator.

[0048] Mission completion: After ceasing firing or changing targets, the system can save the currently learned optimized model parameters as initial knowledge for subsequent similar mission conditions, thus accumulating experience.

[0049] Through the above implementation methods, the present invention achieves a deep integration of probability statistics, real-time sensing, machine learning and automatic control technologies, transforming the machine gun firing gate from a static, experience-based preset area into a dynamic, adaptive intelligent fire control domain that closely matches the real-time firing dispersion, thereby significantly improving the intelligence level and overall combat effectiveness of the machine gun automatic fire control system.

[0050] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A machine gun firing gate design method based on firing dispersion, characterized in that: Includes the following steps: S1: Establish a two-dimensional probability scattering model of bullet impact points in machine gun burst fire; S2: Based on the preset expected hit probability Based on the probability scattering model, the initial shooting gate region Ω that satisfies the probability conditions is determined, and its boundary is defined by the following probability integral constraints:

2. Among them, Let be the joint probability density function of the point of impact; S3: During firing, real-time acquisition and processing of bullet impact data, environmental parameters, and weapon status parameters; S4: Based on the real-time data collected in step S3, dynamically update the parameters of the probability scattering model, and adaptively adjust the shape, size, and spatial orientation of the shooting gate area Ω accordingly; S5: Outputs the optimized firing gate parameters to the fire control unit for auxiliary or automatic control of machine gun firing.

3. The machine gun firing gate design method based on firing dispersion according to claim 1, characterized in that: The probability distribution model in step S1 is a two-dimensional Gaussian distribution model. The real-time acquisition in step S3 is achieved through a bullet impact detection system, environmental sensors, and weapon status sensors. The dynamic update and adaptive adjustment in step S4 are achieved through an embedded machine learning algorithm. This algorithm takes historical and real-time bullet impact data, environmental data, and weapon status data as inputs and uses shooting effectiveness indicators as optimization targets for iterative learning.

4. A machine gun firing gate design method based on firing dispersion according to claim 1 or 2, characterized in that: The adaptive adjustment in step S4 specifically includes the following sub-steps: S41: Calculate the statistical characteristics of the current shot dispersion using the bullet impact data within the sliding time window; S42: Based on environmental parameters, the statistical characteristics are corrected in real time to generate the current effective dispersion parameters; S43: Based on the effective dispersion parameters and weapon status parameters, calculate the adjustment strategy for the firing gate area Ω using an optimized decision model. The adjustment strategy includes adjustments to the shape, area, and orientation of the area. S44: Implement the adjustment strategy, evaluate the adjusted shooting effectiveness, and feed the evaluation results back to the optimization decision model for continuous improvement.

5. The machine gun firing gate design method based on firing dispersion according to claim 3, characterized in that: The environmental parameters include wind speed, wind direction, temperature, and air pressure; the weapon status parameters include barrel temperature and vibration characteristics. The optimized decision-making model employs a deep reinforcement learning algorithm. Its state space includes effective dispersion parameters, target motion information, and weapon state. The action space consists of the adjustment amount of the firing gate parameters. The reward function is constructed based on the comprehensive performance index of hit rate and ammunition consumption rate.

6. The machine gun firing gate design method based on firing dispersion according to claim 3, characterized in that: In step S41, the length of the sliding time window is dynamically adjusted according to the firing mode: a shorter time window is used in continuous firing or rapid firing modes to quickly respond to changes in dispersion; a longer time window is used after burst firing or paused firing to ensure statistical stability.

7. The machine gun firing gate design method based on firing dispersion according to claim 1, characterized in that: This also includes step S6: providing a human-machine interface that displays the current shooting gate area Ω, historical and real-time bullet impact point distribution, and key parameters in real time, and allows operators to adjust the expected hit probability. And optimize weights by manually setting and intervening.

8. An intelligent control system for implementing the method according to any one of claims 1-6, characterized in that: include: The sensing module includes a multimodal sensor array for detecting the point of impact, an environmental sensor group for measuring environmental parameters, and a weapon status sensor for monitoring the machine gun's own status. The data processing and algorithm module includes: Scattering analysis unit, used to build and update probabilistic scattering models; The gate parameter generation unit is used to calculate the shooting gate parameters based on probability constraints; An adaptive optimization unit, with an embedded machine learning algorithm, is used to make dynamic optimization decisions for the shooting gate parameters; The fire control interface module is used to convert the optimized firing gate parameters into control commands and send them to the machine gun's fire control system. The human-computer interaction module is used for parameter input, status display, and manual intervention.

9. The system according to claim 7, characterized in that: The multimodal sensor array is a fusion system of acoustic detection array and millimeter-wave radar; the adaptive optimization unit integrates online learning function, which can continuously update its internal decision model using data generated during the task.

10. The system according to claim 7 or 8, characterized in that: The system also includes a collaborative control unit, which, when multiple machine guns are networked together, performs collaborative planning and conflict resolution of the fire coverage area based on the real-time firing gate parameters and dispersion model of each machine gun, so as to avoid fire overlap or coverage blind spots.

11. The system according to claim 7 or 8, characterized in that: The data processing and algorithm module also includes an anomaly handling unit, which is used to identify abnormal dispersion patterns caused by sensor failure, extreme interference or weapon failure, and trigger a preset robust control strategy. The strategy includes switching to a safe firing gate based on a historical average dispersion model or prompting the shooter to take over manually.

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