Search

CN-121994089-A - Intelligent decision method based on multi-mode projectile velocity measurement data

CN121994089ACN 121994089 ACN121994089 ACN 121994089ACN-121994089-A

Abstract

The invention discloses an intelligent decision method based on multi-mode projectile velocity measurement data, which relates to the technical field of projectile velocity measurement and comprises the steps of module deployment initialization, environment acquisition, synchronous triggering, calculation adaptation degree, weighted fusion, error correction and decision storage, and has the advantages that: the environment sensing submodule integrating the multi-parameter detection function and the multi-mode speed measurement module is deployed, a pre-trained environment-mode adaptation degree model is called to calculate the adaptation degree of each module and generate a dynamic priority list, data fusion weights are dynamically distributed according to priorities, abnormal module weights are adjusted by combining environment interference conditions, and errors are compensated by relying on similar scene historical data, so that the defects that in the prior art, a single speed measurement module is difficult to adapt to complex environments and shots with different attributes, the data fusion weights are fixed, scene pertinence is lacking, and speed measurement precision is low and decision is unreliable due to no accurate basis of error correction are effectively solved.

Inventors

  • SUN CHAO
  • GE YIBO

Assignees

  • 上海恩太设备技术有限公司

Dates

Publication Date
20260508
Application Date
20260119

Claims (8)

  1. 1. An intelligent decision method based on multi-mode projectile velocity measurement data comprises an intelligent decision method and is characterized by comprising the following steps: Step one, a laser speed measuring module, an electromagnetic induction speed measuring module, a high-speed camera speed measuring module and a synchronous clock module are deployed and initialized, and the error of the synchronous clock is less than 1 mu s; deploying an environment sensing sub-module, setting an environment parameter acquisition threshold value, and starting real-time acquisition; thirdly, inputting the property of the projectile, capturing an emission trigger signal, and synchronously triggering speed measurement and environmental parameter acquisition; Calling an environment-mode adaptation degree model, inputting the adaptation degree of an environment and projectile data calculation module, and generating a dynamic priority list; Step five, weighting and fusing speed measurement data according to priority distribution weights; Step six, calculating data deviation, triggering conflict correction and error compensation, and checking the final speed error < +/-1.5%; and step seven, inputting the final speed to an intelligent decision model output result, and storing the full data.
  2. 2. The intelligent decision method based on the multi-mode projectile velocity measurement data according to claim 1, wherein in the first step, the measurement precision of the laser velocity measurement module is preset to +/-0.1%, the velocity measurement range is set to 1.2 Mach-5 Mach, the working environment parameters of the electromagnetic induction velocity measurement module are preset to-40 ℃ to 85 ℃ temperature interval and 0-90% humidity interval, the velocity measurement range is set to 0.3 Mach-3 Mach, the frame rate of the high-speed camera velocity measurement module is preset to 20000fps, the image resolution is set to 1920×1080, and the velocity measurement range is set to 0.5 Mach-2 Mach.
  3. 3. The intelligent decision-making method based on the multi-mode projectile velocity measurement data according to claim 1, wherein in the second step, the deployed environment sensing submodule integrates a temperature and humidity sensor, an electromagnetic interference detector and a March number calculator, integrates a beam attenuation detector, collects the beam attenuation rate of the laser velocity measurement module in real time, the collection frequency is consistent with the environment parameters, the March number calculator calculates the estimated Mach number of the projectile by receiving preset parameters before the projectile is emitted and the real-time air sound velocity, then sets an environment parameter collection threshold, namely, sets the humidity of >70% as a high humidity environment threshold, the humidity of <30% as a low humidity environment threshold, sets the electromagnetic interference intensity of >50dB as a strong electromagnetic interference environment threshold, sets the electromagnetic interference intensity of <20dB as a weak electromagnetic interference environment threshold, sets the Mach number of <0.8 as a low speed interval threshold, sets the Mach number of 0.8-1.2 as a near speed interval threshold, sets the Mach number of < 1.2 as a high speed interval threshold, sets the environment sensing threshold after the setting of the threshold is completed, sets the environment sensing submodule to enter the real-time data collection frequency of 100Hz.
  4. 4. The intelligent decision-making method based on multi-mode projectile velocity measurement data according to claim 1, wherein in the third step, the projectile attribute information comprises projectile material, projectile diameter and projectile mass, the projectile attribute information is stored in a local database for subsequent mode adaptation degree calculation, when a projectile transmitting device transmits a transmitting signal, the transmitting trigger signal is captured through a signal sensor and synchronously transmitted to the multi-mode velocity measurement module and the environment perception sub-module in the first step, the multi-mode velocity measurement module is triggered to start to acquire velocity data in the projectile flight process, the environment perception sub-module is triggered to record the triggering moment and real-time environment parameters within 500ms, and the acquired velocity measurement data and the acquired environment parameters all carry the time stamp of the synchronous clock module.
  5. 5. The intelligent decision-making method based on multi-modal projectile velocity measurement data as set forth in claim 1, wherein in step four, a pre-trained environment-modal fitness model in a local database is called, the model is generated through training of 10 ten thousand sets of environment parameter-modal error historical data, the model parameters are optimized by a gradient descent algorithm in the training process, the model is output as the fitness value of each velocity measurement module, and first, the real-time environment parameters captured in step three are extracted and recorded as Including humidity Strength of electromagnetic interference And Mach number Shot attribute information, noted as Comprises a material Diameter of Will be And (3) with Inputting the measured data to an environment-mode adaptation degree model, and calculating an adaptation degree value of each speed measuring module: laser speed measuring module adaptation value : ; Wherein, the The value of the humidity influence weight is 0.4, For the electromagnetic interference influence weight, the value is 0.3, The Mach number influence weight is 0.3, and the pellet material is Is applicable to both magnetic metal and nonmetal when >70%、 When any of the values of >50% is satisfied, 、 The weights are respectively adjusted to 0.6 and 0.4; adaptation value of electromagnetic induction speed measuring module : ; Wherein, the The value of the temperature influence weight is 0.5, The weight of electromagnetic interference is 0.5, and the weight is only the material of the projectile Is a magnetic metal and is effective at a temperature of-40 ℃ to 85 ℃ when When non-metallic The setting is automatically made to be 0, Is the real-time ambient temperature; Adaptation value of speed measuring module for high-speed camera : ; Wherein, the The value of the humidity influence weight is 0.3, The Mach number influence weight is 0.7, and the pellet material is Is applicable to both magnetic metal and nonmetal when At mach 0.8-1.2, The weight is adjusted to 0.8; From the calculation 、 And And (3) carrying out priority sorting on the three types of speed measuring modules according to the numerical values, wherein the sorting rule is that the higher the adaptation degree value is, the higher the priority is, and when the adaptation degree value of a certain module is 0, the module is excluded from the priority sorting, so that a dynamic priority list of the multi-mode speed measuring module in the test scene is finally generated.
  6. 6. The intelligent decision-making method based on multi-modal projectile velocity measurement data as set forth in claim 1 wherein in step five, data fusion weights are assigned to velocity measurement modules corresponding to each priority according to a dynamic priority list, wherein the velocity measurement module weight is priority 1 The weight of the speed measuring module with the priority of 2 is set to be 0.6-0.8 The weight of the speed measuring module is set to be 0.1-0.3 and the priority is 3 Is set to 0-0.2 and is required to satisfy And then, extracting speed measurement data of each module acquired in the third step and laser module data Electromagnetic module data And high speed camera module data And carrying out weighted fusion calculation on the effective data according to the following formula: ; Storing the fusion speed value after fusion is completed And a corresponding weight distribution record.
  7. 7. The intelligent decision method based on multi-modal projectile velocity measurement data as set forth in claim 1, wherein in said step six, a fusion velocity value is extracted And (3) calling the original data of each module, calculating the deviation value of any two effective module data, namely when the deviation is +/-2%, triggering conflict correction, firstly combining the environmental parameters to judge interference, then calling 5 ten thousand groups of similar scene historical effective data, calculating an error compensation value, superposing the error compensation value on the current fusion value to obtain a final speed value, and checking the error of the final speed value, wherein the error is < +/-1.5%, and repeating correction until reaching the standard if the error does not pass.
  8. 8. The intelligent decision method based on the multi-mode projectile velocity measurement data according to claim 1, wherein in the step seven, the final velocity value is input into an intelligent decision model, and the result is output according to the application scene: Detecting the performance of the projectile, namely judging whether the final speed value is within a preset standard range and the difference value between the final speeds of 3 continuous tests is < +/-3 m/s, otherwise, judging whether the projectile is qualified; Optimizing the launching parameters, wherein the final speed value is more than 10% lower than the target speed, the loading quantity of the launching medicine is recommended to be increased, more than 5% higher than the target speed, and the loading quantity is recommended to be reduced; fault diagnosis, namely, the final speed value suddenly drops by more than 10% relative to the final speed of the previous test, and no environment interference exists, and the gun barrel abrasion/shot gravity center shift is prompted; and the decision result is displayed through a human-computer interface, and all test data are stored to a local database and a cloud in a JSON format, so that traceability is ensured.

Description

Intelligent decision method based on multi-mode projectile velocity measurement data Technical Field The invention relates to the technical field of projectile velocity measurement, in particular to an intelligent decision method based on multi-mode projectile velocity measurement data. Background The projectile speed measurement technology is a core support technology for military weapon system performance verification, civil catapulting equipment safety calibration (such as aviation lifesaving catapulting device and industrial catapulting test) and ballistic mechanics research, and the speed measurement precision and decision reliability directly determine the accuracy of equipment performance evaluation, the use safety and the effectiveness of subsequent parameter optimization; The existing intelligent decision method based on the multi-mode projectile speed measurement data mostly adopts a single speed measurement module, is difficult to adapt to complex environments and projectiles with different attributes, has fixed data fusion weight and no accurate basis for error correction, and has low speed measurement precision and unreliable decision, and secondly, the speed measurement data and the environment data are not synchronously acquired, the decision result lacks traceable support, history data are scattered and difficult to reuse, so that the problem investigation is low-efficiency, and the scheme iteration is not basis. Disclosure of Invention The invention aims to provide an intelligent decision method based on multi-mode projectile velocity measurement data. In order to achieve the purpose, the invention provides the following technical scheme that the intelligent decision method based on the multi-mode projectile velocity measurement data comprises the following steps: Step one, a laser speed measuring module, an electromagnetic induction speed measuring module, a high-speed camera speed measuring module and a synchronous clock module are deployed and initialized, and the error of the synchronous clock is less than 1 mu s; deploying an environment sensing sub-module, setting an environment parameter acquisition threshold value, and starting real-time acquisition; thirdly, inputting the property of the projectile, capturing an emission trigger signal, and synchronously triggering speed measurement and environmental parameter acquisition; Calling an environment-mode adaptation degree model, inputting the adaptation degree of an environment and projectile data calculation module, and generating a dynamic priority list; Step five, weighting and fusing speed measurement data according to priority distribution weights; Step six, calculating data deviation, triggering conflict correction and error compensation, and checking the final speed error < +/-1.5%; and step seven, inputting the final speed to an intelligent decision model output result, and storing the full data. In the first step, the measurement precision of the laser speed measuring module is preset to be +/-0.1%, the speed measuring range is set to be 1.2 Mach-5 Mach for adapting to the speed measurement of the high-speed projectile in a clear scene without interference, the working environment parameters of the electromagnetic induction speed measuring module are preset to be-40-85 ℃ temperature interval and 0-90% humidity interval, the speed measuring range is set to be 0.3 Mach-3 Mach for adapting to the speed measurement of the magnetic projectile and a severe temperature and humidity scene, the frame rate of the high-speed camera speed measuring module is preset to be 20000fps, the image resolution is set to be 1920 x 1080, the speed measuring range is set to be 0.5 Mach-2 Mach for adapting to the speed measurement of the nonmetallic projectile and the near-sonic scene, after deployment is completed, all modules are initialized, and the modules are subjected to power supply self-detection and signal transmission link testing, hardware faults are guaranteed, signal transmission is ensured to be normal, and a standby state is entered after initialization is completed, and a projectile is waited for signal triggering is carried out. In the second step, the deployed environment sensing submodule integrates a temperature and humidity sensor, an electromagnetic interference detector and a doherty counter, integrates a light beam attenuation detector, collects the light beam attenuation rate of the laser speed measuring module in real time, the collection frequency is consistent with the environment parameters, the doherty counter calculates the estimated Mach number of the projectile by receiving the preset parameters before the projectile is emitted and the real-time air sound velocity, then sets an environment parameter collection threshold, namely sets humidity of 70% as a high-humidity environment threshold, humidity of 30% as a low-humidity environment threshold, electromagnetic interference intensity of 50dB as a strong electromagnetic