CN-121998459-A - Intelligent referee decision system in red and blue countermeasure training

CN121998459ACN 121998459 ACN121998459 ACN 121998459ACN-121998459-A

Abstract

The invention discloses an intelligent judge decision system in red and blue countermeasure training, which comprises a multi-source battlefield data acquisition module, an intelligent behavior simulation and countermeasure decision module, a multi-dimensional battlefield evaluation and compound disc analysis module, a closed-loop feedback and guide intervention pre-module and a closed-loop regulation pre-module, wherein the multi-source battlefield data acquisition module is used for acquiring the weapon force state and the fight event of both red and blue parties in real time and generating real-time fight situation data, the intelligent behavior simulation and countermeasure decision module is used for driving a blue army target to act based on the real-time fight situation data and outputting a hit position and damage level judgment result, the multi-dimensional battlefield evaluation and compound disc analysis module is used for performing fight damage statistics and capability evaluation and supporting two/three-dimensional situation playback and key event reproduction, and the closed-loop feedback and guide intervention pre-module is used for dynamically regulating the fight strength according to the evaluation result and the training target so as to realize real-time intervention and closed-loop regulation of a training process of a blue army action strategy. By utilizing the embodiment of the invention, objectification, intellectualization and flow of the red and blue countermeasure training arbitration can be realized, and the fidelity, evaluation accuracy and training efficiency of military training are improved.

Inventors

LU YANMIN
JIN RUI
WANG HANHUI

Assignees

杭州富凌科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260410

Claims (10)

1. An intelligent referee decision system in red and blue countermeasure training, the system comprising: the multi-source battlefield data acquisition module is used for acquiring the weapon force state and the fight event of both red and blue in real time, acquiring position information, equipment state and ammunition consumption data through a sensor array deployed on the real-world equipment and a target terminal, and generating real-time fight situation data by combining digital twin environment mapping; The intelligent behavior simulation and countermeasure judging module is used for driving the army target to act and calculating the striking effect based on the real-time fight situation data, simulating the striking process of the army force by integrating the ballistic model, the damage model and the autonomous fight rule, and outputting the judgment result of the hit position and the damage level; The multidimensional battlefield evaluation and duplication analysis module is used for performing battlefield damage statistics and capability evaluation according to the damage level and the battlefield event, generating a battlefield effectiveness analysis report by combining a preset evaluation index system, and supporting two/three-dimensional situation playback and key event duplication based on a time sequence; The closed-loop feedback and guiding and regulating pre-module is used for dynamically adjusting the countermeasure intensity according to the evaluation result and the training target, and realizing real-time intervention of the army action strategy and closed-loop regulation and control of the training process through a human-in-loop control interface and an intelligent algorithm driving mode.
2. The system of claim 1, wherein the multi-source battlefield data collection module is specifically configured to: the method comprises the steps that through a multi-source sensor array deployed on red army individual equipment and a blue army target terminal, raw data comprising position coordinates, attitude orientations, equipment running states and ammunition consumption are collected in real time, and raw sensor data streams are generated; Preprocessing and fusing the original sensor data stream, eliminating noise and compensating data delay by adopting a Kalman filtering algorithm, and generating calibrated multi-source state data; Inputting the calibrated multi-source state data into a digital twin environment, and carrying out one-to-one correspondence on the weapon forces of red Lan Jun in the physical world and the virtual model through an entity mapping algorithm to generate a digital twin entity state mapping table; Based on the digital twin entity state mapping table, the fire striking and movement behaviors are detected by combining the real-time fight event trigger, the virtual battlefield situation is dynamically updated, and finally the real-time fight situation data is generated.
3. The system according to claim 2, wherein the intelligent behavior simulation and countermeasure module is specifically configured to: Analyzing the army force position, red square target distribution and environment information in the real-time fight situation data, inputting an autonomous fight rule model to conduct behavior decision, and generating an army target behavior control instruction; according to the action control instruction of the blue army target, the target is driven to execute the implicit display, maneuvering or striking actions, and meanwhile, the integrated trajectory model is used for calculating the flight trajectory of the digital ammunition to generate simulated trajectory data; Performing hit intersection calculation based on the simulated trajectory data, detecting collision points by combining terrain shielding and target gestures, and outputting hit positions and hit time stamps; And calling a damage model according to the hit position, calculating a damage effect by combining the ammunition type and target protection data, and finally outputting a damage grade judging result.
4. The system of claim 3, wherein the multi-dimensional battlefield evaluation and multi-disk analysis module is specifically configured to: Collecting a damage level judging result and an engagement event record, classifying and aggregating data according to a red and blue army marshalling structure, and generating an engagement loss statistical data set and an ammunition consumption summary table; Inputting the combat loss statistical data set into a preset evaluation index system, calculating combat effectiveness index scores, wherein the combat effectiveness indexes at least comprise survival rate, hit rate and task completion degree, and generating a preliminary evaluation result; based on the preliminary evaluation result and time sequence fight data, automatically generating a fight efficiency analysis report, wherein the report comprises diagrammatical fight loss comparison and key event analysis; and playing back the two/three-dimensional situation by using the time sequence database, reproducing the key battle scene by using the event trigger, and finally outputting the interactive complex disc analysis interface.
5. The system of claim 4, wherein the closed loop feedback and pilot pre-module is configured to: Analyzing an evaluation result in the combat effectiveness analysis report, comparing the evaluation result with a preset training target recognition deviation, and generating an antagonism intensity adjustment requirement; Based on the countermeasure intensity adjustment requirement, designing a dynamic difficulty regulation strategy, wherein the strategy comprises the steps of adjusting Lan Jun hit rate, reconnaissance range or weapon deployment, and generating a regulation parameter set; Allowing a pilot to manually intervene in the army action through a human ring control interface, or automatically issuing a regulation and control parameter set to a blue arms control system terminal through an intelligent algorithm driving mode to generate a real-time control instruction; executing the real-time control instruction and monitoring the training process, and iteratively optimizing the regulation and control parameters according to the real-time feedback data to realize closed-loop regulation and control of the training process.
6. An intelligent referee decision method in red and blue countermeasure training, comprising: Acquiring weapon force states and fight events of both red and blue in real time, acquiring position information, equipment states and ammunition consumption data through a sensor array deployed at an actual equipment and target terminals, and generating real-time fight situation data by combining digital twin environment mapping; Driving the army target to act and calculating the striking effect based on the real-time fight situation data, simulating the striking process of the army force by integrating a ballistic model, a damage model and an autonomous fight rule, and outputting the judgment result of the hit position and the damage level; performing combat damage statistics and capability assessment according to the damage level and the combat event, generating combat effectiveness analysis reports by combining a preset assessment index system, and supporting two/three-dimensional situation playback and key event reproduction based on time sequences; and dynamically adjusting the countermeasure intensity according to the evaluation result and the training target, and realizing real-time intervention of the army action strategy and closed-loop regulation and control of the training process through a human ring control interface and an intelligent algorithm driving mode.
7. The method of claim 6, wherein the acquiring the weapon status and the engagement event of both the red and blue in real time, acquiring the position information, the equipment status and the ammunition consumption data through the sensor array deployed at the real equipment and the target terminal, and generating the real-time engagement situation data in combination with the digital twin environment mapping, comprises: the method comprises the steps that through a multi-source sensor array deployed on red army individual equipment and a blue army target terminal, raw data comprising position coordinates, attitude orientations, equipment running states and ammunition consumption are collected in real time, and raw sensor data streams are generated; Preprocessing and fusing the original sensor data stream, eliminating noise and compensating data delay by adopting a Kalman filtering algorithm, and generating calibrated multi-source state data; Inputting the calibrated multi-source state data into a digital twin environment, and carrying out one-to-one correspondence on the weapon forces of red Lan Jun in the physical world and the virtual model through an entity mapping algorithm to generate a digital twin entity state mapping table; Based on the digital twin entity state mapping table, the fire striking and movement behaviors are detected by combining the real-time fight event trigger, the virtual battlefield situation is dynamically updated, and finally the real-time fight situation data is generated.
8. The method of claim 7, wherein driving the army target to act and calculate the striking effect based on the real-time engagement situation data, simulating the army force striking process by integrating a ballistic model, a damage model and an autonomous engagement rule, and outputting a hit portion and a damage level determination result, comprises: Analyzing the army force position, red square target distribution and environment information in the real-time fight situation data, inputting an autonomous fight rule model to conduct behavior decision, and generating an army target behavior control instruction; according to the action control instruction of the blue army target, the target is driven to execute the implicit display, maneuvering or striking actions, and meanwhile, the integrated trajectory model is used for calculating the flight trajectory of the digital ammunition to generate simulated trajectory data; Performing hit intersection calculation based on the simulated trajectory data, detecting collision points by combining terrain shielding and target gestures, and outputting hit positions and hit time stamps; And calling a damage model according to the hit position, calculating a damage effect by combining the ammunition type and target protection data, and finally outputting a damage grade judging result.
9. A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of claims 6-8 when run.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of claims 6-8.

Description

Intelligent referee decision system in red and blue countermeasure training Technical Field The invention belongs to the technical field of simulation decision making, and particularly relates to an intelligent referee decision making system in red and blue countermeasure training. Background In modern military training systems, red-blue antagonism is a key means to improve army combat capability. The traditional countermeasure training decision depends on manual decision and fixed rules, and has the problems of strong subjectivity, low efficiency and the like. With the development of simulation technology, although a computer system is adopted for carrying out partial judgment, the conventional system generally has the following limitations that firstly, battlefield data acquisition dimension is single, a real-time battlefield situation with high fidelity is difficult to construct, secondly, behavior simulation and judgment models are solidified, the bluing target is not intelligent enough, complex and changeable countermeasure environments cannot be truly simulated, thirdly, training evaluation is concentrated on a post-recovery disk, and dynamic regulation and closed loop feedback capacity based on real-time data in the training process is lacking. This results in limited training results, and difficulty in accurately assessing and improving the combat effectiveness and strain capacity of the troops under approximate combat conditions. ck (ck) Disclosure of Invention The invention aims to provide an intelligent judge decision system in red and blue countermeasure training, which solves the defects in the prior art, can realize objectification, intellectualization and flow of red and blue countermeasure training judgment, and improves the fidelity, evaluation accuracy and training efficiency of military training. One embodiment of the present application provides an intelligent referee decision system in red and blue countermeasure training, the system comprising: the multi-source battlefield data acquisition module is used for acquiring the weapon force state and the fight event of both red and blue in real time, acquiring position information, equipment state and ammunition consumption data through a sensor array deployed on the real-world equipment and a target terminal, and generating real-time fight situation data by combining digital twin environment mapping; The intelligent behavior simulation and countermeasure judging module is used for driving the army target to act and calculating the striking effect based on the real-time fight situation data, simulating the striking process of the army force by integrating the ballistic model, the damage model and the autonomous fight rule, and outputting the judgment result of the hit position and the damage level; The multidimensional battlefield evaluation and duplication analysis module is used for performing battlefield damage statistics and capability evaluation according to the damage level and the battlefield event, generating a battlefield effectiveness analysis report by combining a preset evaluation index system, and supporting two/three-dimensional situation playback and key event duplication based on a time sequence; The closed-loop feedback and guiding and regulating pre-module is used for dynamically adjusting the countermeasure intensity according to the evaluation result and the training target, and realizing real-time intervention of the army action strategy and closed-loop regulation and control of the training process through a human-in-loop control interface and an intelligent algorithm driving mode. Optionally, the multi-source battlefield data acquisition module is specifically configured to: the method comprises the steps that through a multi-source sensor array deployed on red army individual equipment and a blue army target terminal, raw data comprising position coordinates, attitude orientations, equipment running states and ammunition consumption are collected in real time, and raw sensor data streams are generated; Preprocessing and fusing the original sensor data stream, eliminating noise and compensating data delay by adopting a Kalman filtering algorithm, and generating calibrated multi-source state data; Inputting the calibrated multi-source state data into a digital twin environment, and carrying out one-to-one correspondence on the weapon forces of red Lan Jun in the physical world and the virtual model through an entity mapping algorithm to generate a digital twin entity state mapping table; Based on the digital twin entity state mapping table, the fire striking and movement behaviors are detected by combining the real-time fight event trigger, the virtual battlefield situation is dynamically updated, and finally the real-time fight situation data is generated. Optionally, the intelligent behavior simulation and countermeasure module is specifically configured to: Analyzing the army force position, red square target distrib