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CN-122018370-A - Gun shooting door design method and system based on shooting scattering

CN122018370ACN 122018370 ACN122018370 ACN 122018370ACN-122018370-A

Abstract

The invention discloses a gun shooting door design method and system based on shooting scattering, belongs to the technical field of automatic weapon fire control, and provides a dynamic optimization method aiming at the technical problem of the existing shooting door. The method is characterized by comprising the steps of establishing a probability dispersion model of continuous shooting of a machine gun, determining an initial shooting gate based on a preset hit probability, collecting impact point data, environmental parameters and weapon state in real time in the shooting process, dynamically updating the dispersion model, and continuously adjusting the shape, size and spatial orientation of the impact gate by utilizing a self-adaptive optimization algorithm so as to enable the impact gate to be always clung to a high probability distribution area of the 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 invention, the scattering is converted into design basis from interference factors, so that the fundamental transition from static presetting to dynamic intelligent optimization of the shooting door is realized, and the hit efficiency and the ammunition utilization rate are obviously improved.

Inventors

  • JIANG XIAOYU

Assignees

  • 永续成长(北京)科技有限公司

Dates

Publication Date
20260512
Application Date
20260201

Claims (11)

  1. 1. A machine gun shooting door design method based on shooting scattering is characterized by comprising the following steps: s1, establishing a two-dimensional probability dispersion model of an impact point of continuous firing of a machine gun; S2, based on preset expected hit probability Determining an initial firing gate area omega meeting probability conditions according to the probability distribution model, wherein the boundary of the initial firing gate area omega is defined by the following probability integration constraint:
  2. 2. Wherein, the A joint probability density function for the impact point; s3, in the shooting process, acquiring and processing impact point data, environment parameters and weapon state parameters in real time; S4, dynamically updating parameters of the probability dispersion model based on the real-time data acquired in the step S3, and adaptively adjusting the shape, the size and the spatial orientation of the shooting gate region omega according to the parameters; And S5, outputting the optimized shooting door parameters to a fire control unit for assisting or automatically controlling the gun shooting.
  3. 3. The method for designing the firing gate of the machine gun based on firing distribution as set forth in claim 1, wherein the probability distribution model in the step S1 is a two-dimensional Gaussian distribution model, the real-time acquisition in the step S3 is realized by a firing point detection system, an environment sensor and a weapon state sensor, and the dynamic updating and the adaptive adjustment in the step S4 are realized by an embedded machine learning algorithm which takes historical and real-time firing data, environment data and weapon state data as inputs and takes a firing efficiency index as an optimization target for iterative learning.
  4. 4. The method for designing a firing gate of a machine gun based on firing spread according to claim 1 or 2, wherein the adaptive adjustment in the step S4 comprises the following steps: s41, calculating the statistical characteristics of current shooting spread by utilizing the impact point data in the sliding time window; s42, correcting the statistical characteristics in real time based on the environmental parameters to generate current effective scattering parameters; s43, calculating an adjustment strategy of a shooting gate region omega by utilizing an optimization decision model according to the effective spreading parameters and weapon state parameters, wherein the adjustment strategy comprises adjustment amounts of region shape, area and orientation; And S44, executing an adjustment strategy, evaluating the shooting efficiency after adjustment, and feeding back the evaluation result to the optimized decision model to continuously improve.
  5. 5. The method for designing a firing gate of a machine gun based on firing distribution as set forth in claim 3, wherein the environmental parameters include wind speed, wind direction, temperature and air pressure, the weapon state parameters include barrel temperature and vibration characteristics, the optimized decision model employs a deep reinforcement learning algorithm, a state space of the optimized decision model includes effective distribution parameters, target motion information and weapon state, an action space is an adjustment amount of the firing gate parameters, and a reward function is constructed based on a comprehensive efficiency index of hit rate and ammunition consumption rate.
  6. 6. The method of claim 3, wherein in step S41, the sliding time window is dynamically adjusted according to the firing pattern, wherein a shorter time window is used to rapidly respond to the firing variation in the continuous firing or the rapid firing pattern, and a longer time window is used to ensure the statistical stability after the firing is stopped or the firing is stopped.
  7. 7. The method for designing a firing gate of a machine gun based on firing distribution as set forth in claim 1, further comprising the step of providing a man-machine interface for displaying the current firing gate area Ω, the history and the real-time impact point distribution, and the key parameters in real time and allowing the operator to have a probability of expected hits And (5) manually setting and intervening the optimized weight.
  8. 8. An intelligent regulation system for implementing the method of any one of claims 1-6, comprising: the sensing module comprises a multi-mode sensor array for detecting impact points, an environment sensor group for measuring environment parameters and a weapon state sensor for monitoring the state of the gun; a data processing and algorithm module comprising: a dispersion analysis unit for constructing and updating a probability dispersion model; The gate parameter generating unit is used for calculating shooting gate parameters according to probability constraints; the self-adaptive optimization unit is embedded with a machine learning algorithm and is used for executing dynamic optimization decisions of shooting gate parameters; the fire control interface module is used for converting the optimized shooting door parameters into control instructions and sending the control instructions to a fire control system of the machine gun; and the man-machine interaction module is used for parameter input, state display and manual intervention.
  9. 9. The system of claim 7, wherein the multi-modal sensor array is a fusion system of an acoustic detection array and a millimeter wave radar, and the adaptive optimization unit is integrated with an online learning function, and can continuously update an internal decision model by using data generated in a task.
  10. 10. The system of claim 7 or 8, wherein the system further comprises a cooperative control unit for performing cooperative planning and conflict resolution of fire coverage areas according to real-time shooting gate parameters and a dispersion model of each machine gun when the multi-jack machine gun networking is in a war, and avoiding fire overlapping or coverage dead zones.
  11. 11. The system of claim 7 or 8, wherein the data processing and algorithm module further comprises an anomaly processing unit for identifying an anomaly dispersion pattern due to sensor failure, extreme disturbance or weapon failure, and triggering a preset robust control strategy, wherein the strategy comprises switching to a safe shooting gate based on a historical average dispersion model or prompting a shooter to take over manually.

Description

Gun shooting door design method and system based on shooting scattering Technical Field The invention relates to the technical field of automatic weapon fire control, in particular to a gun firing gate design method and system based on firing scattering. Background Gun firing gates (or fire control domains) are key concepts in automated and semi-automated gun fire control systems for defining effective sweep or aiming areas of the gun during successive fires to optimize fire coverage efficiency. The existing shooting gate design methods are mainly divided into two types, one type is static presetting based on a simple geometric model (such as a fixed angle sector and a rectangle), parameters (such as gate width and gate height) are set according to weapon theory projectile dispersion data or shooter experience, once the parameters are set, the parameters are always unchanged in the shooting process, and the other type is simple feedback, but the method is limited to translation or scaling of an integral area according to projectile deviation, and the bottom model is still a fixed geometric framework. The prior art has the following inherent defects that firstly, deep modeling is not achieved, inherent probability distribution rules of continuous shooting are utilized, the matching degree of a design result and real impact distribution is low, the actual distribution of a fire domain is not attached, the efficiency is low, secondly, static or semi-static design is adopted, dynamic optimization adjustment cannot be carried out according to impact point data, changing environmental conditions (such as crosswind and temperature) and weapon self-state (such as barrel temperature rise and abrasion) generated in real time in the shooting process, and the adaptability is poor, thirdly, artificial experience preset parameters are seriously relied, and autonomous learning and optimization capacity based on efficiency targets is lacked. Therefore, in the prior art, the optimal balance among the fire intensity, the hit probability and the ammunition consumption is difficult to intelligently realize under the complex and changeable actual combat environment, and the further improvement of the efficiency of the automatic fire control system of the machine gun is restricted. Disclosure of Invention The invention mainly aims to provide a machine gun shooting door design method and system based on shooting scattering, which can effectively solve the problems in the background art. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a machine gun shooting door design method based on shooting scattering comprises the following steps: s1, establishing a two-dimensional probability dispersion model of an impact point of continuous firing of a machine gun; S2, based on preset expected hit probability Determining an initial firing gate area omega meeting probability conditions according to the probability distribution model, wherein the boundary of the initial firing gate area omega is defined by the following probability integration constraint: Wherein, the A joint probability density function for the impact point; s3, in the shooting process, acquiring and processing impact point data, environment parameters and weapon state parameters in real time; S4, dynamically updating parameters of the probability dispersion model based on the real-time data acquired in the step S3, and adaptively adjusting the shape, the size and the spatial orientation of the shooting gate region omega according to the parameters; And S5, outputting the optimized shooting door parameters to a fire control unit for assisting or automatically controlling the gun shooting. By introducing the probability statistical theory into the shot gate design, the shot gate is no longer a fixed geometry, but a "high hit rate region" that dynamically evolves based on the actual impact dispersion probability. The integral constraint ensures that the design of the shooting door always aims at achieving the preset hit probability, and the fundamental transition from 'fixed parameter control' to 'probability efficiency control' is realized. Preferably, the probability distribution model in the step S1 is a two-dimensional gaussian distribution model, the real-time acquisition in the step S3 is realized by an impact point detection system, an environment sensor and a weapon state sensor, and the dynamic updating and self-adaptive adjustment in the step S4 are realized by an embedded machine learning algorithm, wherein the algorithm takes historical and real-time impact data, environment data and weapon state data as inputs, and takes a shooting efficiency index as an optimization target to carry out iterative learning. The adoption of the two-dimensional Gaussian model can effectively describe the scattered ellipse characteristics of the continuous shooting by the simplest parameters (mean value and covariance), and is conven