A virtual-real cluster simulation confrontation system

Abstract

The present application provides a virtual-real combined cluster simulation confrontation system, including: a scenario design module, used to construct the number of simulated UAVs and simulated targets in the scenario and the simulated UAV scene; a task planning module, used to receive the parameters set by the scenario design module and the analysis results of the situation fusion module to complete the UAV force allocation and UAV task allocation; a simulation execution confrontation module, used to fuse the target information found by the simulated UAV when performing the task and the damage information of each simulated UAV into a new situation; a physical execution module, used to execute control instructions on the physical UAV through a ground control station; a confrontation result display module, used to display and output the confrontation execution results of the virtual domain according to the simulation execution confrontation module to the screen; because the computer simulates the UAV scene and confrontation target and adopts the software algorithm to perform confrontation simulation, the UAV cluster confrontation and the difficulty in realizing some scenes are solved.

Description

Technical Field

[0001] The present invention belongs to the fields of unmanned aerial vehicle (UAV) simulation and computer, and particularly relates to a cluster simulation confrontation system combining virtual and real scenarios. Background Art

[0002] Light and small UAVs have good concealment and are not easily detected during combat, and can perform tasks such as detection, strike, and damage assessment. Therefore, UAVs have become one of the important combat units in modern warfare. Studying the confrontation effect of UAV clusters has become a hot topic. However, actual combat drills have problems such as high costs and difficulties in realizing some scenarios. In addition, due to their own load and flight characteristics, light and small UAVs are limited in assembling too many or too heavy loads. Moreover, cluster UAVs using technologies such as swarm intelligence, artificial intelligence, and wireless ad hoc networks have problems of long research cycles and high costs. To solve the above problems, a method for realizing cluster simulation confrontation combining virtual and real scenarios is proposed, creating a virtual mapping model of real UAVs, and conducting confrontation research by simulating the sensors and confrontation targets of UAVs through computer and using software algorithms to solve the above problems. Summary of the Invention

[0003] The purpose of this embodiment is to provide a cluster simulation confrontation system combining virtual and real scenarios, which is used to solve the problems of high costs and difficulties in realizing some scenarios in UAV cluster confrontation and actual combat drills.

[0004] A cluster simulation confrontation system combining virtual and real scenarios includes:

[0005] A scenario design module, which is used to construct the number of simulated UAVs and simulated targets and the simulated UAV scenario in this scenario;

[0006] A mission planning module, which is used to receive the parameters set by the scenario design module and the analysis results of the situation fusion module, complete the UAV force allocation, UAV mission allocation, and route planning, divide them into different cluster formations, set formation information, model the mission allocation for the cluster formations, perform dynamic optimization, set problems for "detection -> attack -> damage -> assessment", establish an objective cost function, and solve the objective cost function;

[0007] A mission analysis module, which is used to analyze the flight routes and mission coordination parameters of the UAV cluster formation, and generate a mission execution queue according to the set routes, formations, and mission coordination processes;

[0008] A simulation execution confrontation module, which is used to fuse messages such as the target information found by the simulated UAVs during mission execution and the damage information of each simulated UAV into a new situation, and send it to the mission planning module. The mission planning module re-plans the mission and changes the route according to the latest situation;

[0009] The entity execution module is used to execute control instructions on the entity UAV through the ground control station, look up the corresponding entity UAV number, address information, affiliated airspace, radio frequency point, etc. according to the virtual mapping table, and execute flight control instructions;

[0010] The confrontation result display module is used to display the confrontation execution result in the virtual domain output to the screen according to the simulation execution confrontation module, and the display includes the position of the simulation UAV, the damage state of the simulation target, the evaluation result of the damaged target, and the map airspace calibration.

[0011] Furthermore, the cluster simulation confrontation system further includes a virtual domain and an entity domain. The virtual domain consists of servers with local area network functions, and the entity domain consists of a ground control station with a wireless network and UAVs.

[0012] Furthermore, before solving the objective cost function, determine the decision variables of the objective cost function and determine the optimization objective of the objective cost function.

[0013] Furthermore, the decision variables include UAV: v ∈ V, target: n ∈ N, task: k ∈ K, execution task time: t, where V = {micro UAV, light and small UAV, medium and small UAV}, N = {building facilities, weaponry, radiation target}, K = {being dropped, searching, attacking, jamming, evaluating}.

[0014] Furthermore, the optimization objectives include the maximum overall effectiveness of the UAV after completing the task and the minimum total flight time of the UAV to complete the task.

[0015] Furthermore, the standard cost function is where is the path threat loss for UAV v to fly from i to n to complete k, is the benefit of completing task K, and J is the solution result.

[0016] Furthermore, before solving the objective cost function, it also includes determining the constraint conditions, where the constraint conditions are that each target and each task can only be completed once, and each UAV can only execute 1 attack task.

[0017] Furthermore, the task allocation is a random tree search method.

[0018] Furthermore, the simulation execution confrontation module also includes that the communication between the virtual domain and the entity domain is realized by a wired local area network.

[0019] Furthermore, the dynamic optimization is to solve the optimization of mixed integer nonlinear programming (MINLP).

[0020] The present invention provides a virtual-reality combined cluster simulation and confrontation system, which uses computer simulation of the scenarios and confrontation targets of unmanned aerial vehicles (UAVs) and adopts software algorithms for confrontation simulation, solving problems such as UAV cluster confrontation and difficulties in realizing some scenarios.

[0021] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0023] Figure 1 : is the step diagram of the first embodiment of the virtual-reality combined cluster simulation and confrontation system provided by the embodiment of the present application.

[0024] Figure 2 : is the step diagram of the second embodiment of the virtual-reality combined cluster simulation and confrontation system provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The following will describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application.

[0026] The system framework diagram of the present invention is shown in Figure 1 , which is divided into a virtual domain part and a physical domain part. The virtual domain consists of servers with local area network functions, and the physical domain consists of a ground control station with a wireless network and UAVs. The communication between the virtual domain and the physical domain adopts a wired local area network solution, and the communication between the ground control station and the physical UAVs in the physical domain part adopts a wireless network solution. The virtual domain simulates UAVs, confrontation targets, and geographical information through a computer and displays the UAV confrontation results on a display screen.

[0027] P1100: Scenario design module, used to construct the number of simulated UAVs and simulated targets, the coordinates of simulated targets, the initial positions of simulated UAVs, the number of loaded bombs, attack parameters, etc. in this scenario.

[0028] P1200: The mission planning module is used to receive the parameters set by the scenario design module and the analysis results of the situation fusion module to complete the UAV force allocation, UAV mission allocation, and route planning. It is divided into different cluster formations, and the formation information is set to construct a mapping relationship table between the simulated UAVs and the physical UAVs. Model the mission allocation for each cluster formation, set problems for "detection -> attack -> damage -> assessment", establish an optimized objective cost function, and dynamically solve the mixed-integer nonlinear programming (MINLP) optimization under relevant constraints. Finally, generate an evasive threat mission route according to the mission optimization results.

[0029] In this embodiment, the optimization of the mixed-integer nonlinear programming (MINLP) is dynamically solved under relevant constraints for the optimized objective cost function established for the mission allocation:

[0030] Determine the decision variables of the objective cost function: UAV: v ∈ V {micro UAV, light and small UAV, medium and small UAV}, target: n ∈ N {building facilities, weaponry, radiation targets}, mission: k ∈ K {be dropped, search, attack, interference, assessment}, execution mission time: t.

[0031] Table 1 MILP decision variables

[0032]

[0033]

[0034] Determine the optimization objective of the objective cost function:

[0035] The maximum overall effectiveness of the UAV after completing the mission (completing the most missions with the least loss);

[0036] The minimum total flight time of the UAV to complete the mission.

[0037] Execute the objective optimization function:

[0038]

[0039] Among them They are respectively the path threat loss of the UAV v flying from i to n to complete k and the benefit of completing the mission K, and J is the solution result.

[0040] The constraint conditions are:

[0041] For each target, each mission can only be completed once;

[0042] Each UAV can only execute at most 1 attack mission.

[0043] In this implementation, the high-dimensional MINLP cannot be solved in real time, and even when using heuristic algorithms, there will be cases where not all constraints can be satisfied. Therefore, the above problem is split into two parts: task allocation (0-1 programming part) and timing optimization (continuous variable part) for step-by-step solution. Although the optimal solution cannot be achieved, a sub-optimal solution that satisfies the constraints can be guaranteed within the specified time.

[0044] Random tree search method is used for task allocation to strictly satisfy the task sequence constraints, UAV payload capacity constraints, and range constraints. In the case of extremely short optimization time, the optimization effect of random tree search can meet the requirements.

[0045] After task allocation is completed, continue to optimize the allocation result for timing to minimize the combat time while satisfying the timing constraints.

[0046] In this embodiment, the objective cost function is implemented in C language to ensure the timeliness of data calculation.

[0047] P1300: Task analysis module, which is used to analyze the flight routes and task coordination parameters of the UAV cluster formation, including the simulated UAV flight route and the real UAV flight route. Generate a task execution queue according to the set route, formation, and task coordination process.

[0048] P1400: Simulation execution and confrontation module, which is used to fuse messages such as the target information discovered by the simulated UAVs during task execution and the damage information of each simulated UAV into a new situation and feedback it to the task planning module. The task planning module re-plans the task and changes the route according to the latest situation. This module also has the ability to control the cluster flight and execute tasks in the virtual domain. According to the set route, formation, and task flow, control the simulated UAVs to fly according to the formation information and execute tasks at the corresponding route points, and send control commands to the ground station through the local area network to control the real UAVs to fly. The real UAVs execute task actions at the task route points, and at the same time receive the real UAV position information through the ground control station to update the data, and correct the position information of the simulated UAVs in the virtual domain according to the real UAV position information. In this module, there are commands sent to the simulated UAVs in the virtual domain and commands sent to the real UAVs. The two command targets are different. The method of distinguishing the two sending targets is adopted in this embodiment as a mapping table method. Create a mapping table in the memory, including data packets containing command data sent from the virtual domain and rally point position data, etc. The command data packet contains damage command information, strike task commands, etc. Before the command is forwarded, the ground control station adds two fields {target address, flag bit} in front of the data. The flag bit is used to distinguish command data and position data, and the target address is used to inform the ground station which real UAV the current data is sent to.

[0049] P1500: Physical Execution Module, which is used to execute control instructions on physical UAVs through a ground control station, find the corresponding physical UAV numbers, address information, affiliated airspace, radio frequencies, etc. according to a virtual mapping table, and execute flight control instructions. And according to the results of the instructions executed by the physical UAVs, find the corresponding simulation UAV numbers and address information through the virtual mapping table and send them to the simulation execution confrontation module. The simulation execution confrontation module updates the physical domain simulation UAV position information according to the physical UAV position information.

[0050] P1600: Confrontation Result Display Module, which is used to display the confrontation results in the virtual domain output to the screen by the simulation execution confrontation module, and the display includes the positions of simulation UAVs, the damage states of simulation targets, the evaluation results of damaged targets, and the calibration of map airspace.

[0051] The connection relationships between the various modules are shown in Figure 2 。

[0052] The present invention provides a virtual-real combined cluster simulation confrontation system, which adopts computer simulation of UAV scenarios, confrontation targets and uses software algorithms for confrontation simulation, solves the problems of UAV cluster confrontation and difficulties in realizing some scenarios.

[0053] The above are only embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A virtual-real combined cluster simulation confrontation system, characterized in that: include: The scenario design module is used to construct the number of simulated drones and simulated targets and simulated drone scenarios in this scenario, wherein the drone scenarios include the initial position of the simulated drone, the number of missiles carried, and the attack parameters; The task planning module is used to receive the parameters set by the assumption design module and the analysis results of the situation fusion module to complete the UAV force allocation, UAV task allocation and route planning, divide into different cluster formations, and set formation information, model the task allocation of the cluster formation, perform dynamic optimization, set problems for "detection->attack->damage->evaluation", establish a target cost function, and complete the solution of the target cost function; and also includes, before solving the target cost function, determining the decision variables of the target cost function and determining the optimization target of the target cost function; The decision variables include: drone: v∈V, target: n∈N, task: k∈K, task execution time: t, where V={micro drone, light small drone, medium and small drone}, N={building facilities, weapons and equipment, radiation target}, K={being launched, searching, attacking, interfering, evaluating}; the optimization objectives include the maximum overall efficiency of the drone after completing the task and the shortest total flight time of the drone to complete the task; The label cost function is, ,in To complete the path threat loss of k for v drones to fly from i to n, is the benefit of completing task K, and J is the solution result; The task analysis module is used to analyze the flight routes and task coordination parameters of the drone cluster formation, and generate a task execution queue according to the set route, formation, and task coordination process; The simulation execution confrontation module is used to fuse the target information found by the simulated UAV when performing the mission and the damage information of each simulated UAV into a new situation, and send it to the mission planning module, and the mission planning module replans the mission and route change according to the latest situation; The physical execution module is used to execute control instructions on the physical drone through the ground control station, and to search the corresponding physical drone number, address information, airspace, and wireless frequency according to the virtual mapping table to execute flight control instructions; The confrontation result display module is used to display the confrontation execution results of the virtual domain according to the simulation execution confrontation module and output them to the screen, including the simulated UAV position, simulated target damage status, damaged target assessment results, and map airspace calibration.

2. The virtual-real combined cluster simulation confrontation system according to claim 1 is characterized in that: The cluster simulation confrontation system also includes a virtual domain and a physical domain. The virtual domain is composed of servers with local area network functions, and the physical domain is composed of a ground control station and a drone with a wireless network.

3. The virtual-real combined cluster simulation confrontation system according to claim 1, characterized in that: Before solving the target cost function, it also includes determining constraint conditions, wherein the constraint conditions are that each target and each task can only be completed once, and each drone can only perform one attack task.

4. The virtual-real combined cluster simulation confrontation system according to claim 1, characterized in that: The task allocation is a random tree search method.

5. The virtual-real combined cluster simulation confrontation system according to claim 2 is characterized in that: The simulation execution confrontation module also includes that the communication between the virtual domain and the physical domain is realized by a wired local area network.

6. The virtual-real combined cluster simulation confrontation system according to claim 1 is characterized in that: The dynamic optimization is optimization for solving mixed integer nonlinear programming.

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