Ground vehicle configuration number optimization method, device and module based on air-ground cooperation

Abstract

The invention relates to a ground vehicle configuration number optimization method, device and module based on air-ground cooperation, and the method comprises the steps: obtaining task and environment related parameters, including a task target, duration, a coverage range and a precision requirement; and real-time environment data (such as weather conditions, enemy threats and terrain complexity), the number of aircrafts, task loads and other information are obtained. Based on the parameters, a task type and demand analysis sub-item, a real-time environment feedback sub-item and a ground vehicle task completion degree and demand balance sub-item are calculated step by step by adopting nonlinear weighting, a threshold effect and an interaction model. And finally, the required number of the ground vehicles is obtained in a weighted summation mode, so that the optimal configuration of the aircrafts and the ground vehicles in the air-ground cooperative combat is realized. The method can improve combat effectiveness and enhance task adaptability, and is widely applied to unmanned system collaborative operation of military reconnaissance, attack, supply and other tasks.

Description

Technical Field

[0001] The present invention relates to a method, device and module for optimizing the number of ground vehicle configurations based on air-ground collaboration, and belongs to the technical field of unmanned system collaborative control. Background Art

[0002] In recent years, with the rapid development of unmanned system technology, air-ground coordinated operations have become an important way to improve combat efficiency. Especially in complex battlefield environments, how to optimize the coordinated configuration of aerial vehicles and ground vehicles to meet the needs of different mission types and environmental conditions has become the key to improving mission completion and combat efficiency.

[0003] However, traditional air-ground collaborative configuration methods rely too much on experience or single factor considerations, fail to fully consider the dynamic changes of complex factors such as mission objectives, environmental feedback, and ground vehicle loads, and are difficult to provide optimal collaborative configuration in complex situations. Summary of the invention

[0004] Purpose of the invention: The present invention provides a method for optimizing the number of ground vehicle configurations based on air-ground collaboration. By acquiring mission and environment-related parameters and combining multiple factors such as mission requirements, environmental feedback, and mission load of ground vehicles, the configuration number of aerial vehicles and ground vehicles is optimized, thereby improving combat effectiveness and mission success rate. The method can be widely used in the collaborative operation of unmanned systems for military reconnaissance, attack, supply and other tasks, as well as the vehicle configuration optimization when unmanned aerial vehicle formations and ground vehicles jointly perform tasks.

[0005] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is: In a first aspect, a method for optimizing the number of ground vehicle configurations based on air-ground collaboration is provided. The method optimizes the number of ground vehicle configurations according to mission requirements, real-time environmental feedback, and a balance between the mission completion degree and requirements of ground vehicles, and obtains the real-time number of ground vehicle configurations: N UGV =α1•A1+α2•A2+α3•A3 Among them, A1, A2, and A3 represent task type and demand analysis, real-time environmental feedback, and unmanned vehicle task completion and demand balance, respectively; α1, α2, and α3 are the weight coefficients of A1, A2, and A3, respectively.

[0006] Preferably, before optimizing the number of ground vehicles, the method further includes obtaining basic data of the air-ground collaborative mission, including mission type T, mission duration D, mission coverage R, mission accuracy requirement P, weather conditions W, enemy threat level E, terrain complexity T, etc. c , the number of aircraft in the air N UAV , ground vehicle mission load L UGV.

[0007] Preferably, the calculation method of the task requirement is: A1=β1·T+β2·D+β3·R+β4·P n Where β1, β2, β3, β4 are T, D, R, P respectively. n The weight coefficient is , T is the task type, D is the task duration, R is the task coverage, and P is the task accuracy requirement.

[0008] Preferably, the calculation method of the real-time environmental feedback is: A2=γ1•W+γ2•E• I(E≤E th )+γ2'•E •I(E>E th )+γ3•T c +γ4•W•E Where γ1, γ2, γ2', γ3, and γ4 are W, E, E• I (E≤E th ), E •I(E>E th ), T c , the weight coefficient of W•E, I(E≤E th ) is the enemy threat level E is less than or equal to the threshold E th The coefficient when I(E>E th ) is the enemy threat level E greater than the threshold E th The coefficient of time, W is the weather condition, T c The complexity of the terrain.

[0009] Preferably, the calculation method for balancing the ground vehicle mission completion and demand is: A3=δ1•N UAV +δ2•L UGV +δ3•(N UAV •L UGV ) Where δ1, δ2, δ3 are N UAV , L UGV , N UAV •L UGV The weight coefficient, N UAV is the number of aircraft in the air, L UGV For ground vehicle mission loads.

[0010] In a second aspect, a computer-readable storage medium storing one or more programs is also provided. The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method as described above.

[0011] According to a third aspect, an electronic device is provided, comprising one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method described above.

[0012] In a fourth aspect, a module for optimizing the number of air vehicle configurations based on air-ground collaboration is also provided, including: Basic data acquisition unit, used to obtain basic data of air-ground collaborative missions, including mission type T, mission duration D, mission coverage R, mission accuracy requirement P; weather conditions W, enemy threat level E, terrain complexity T c , the number of aircraft in the air N UAV , ground vehicle mission load L UGV ; A task requirement calculation unit, used to calculate task requirements based on basic data; A real-time environment feedback calculation unit, used for calculating real-time environment feedback based on basic data; An unmanned vehicle task completion and demand balance calculation unit, used to calculate the unmanned vehicle task completion and demand balance based on basic data; The real-time configuration quantity calculation unit of the ground vehicle is used to optimize the configuration quantity of the ground vehicle and obtain the real-time configuration quantity of the ground vehicle according to the mission requirements, real-time environmental feedback, and the balance between the unmanned vehicle mission completion and demand.

[0013] Beneficial effects: The method of the present invention can optimize the number of ground vehicles configured, obtain the real-time number of ground vehicles configured, and achieve efficient use of air-ground collaborative resources based on mission requirements, real-time environmental feedback, and the balance between mission completion and demand of ground vehicles. The method has the following advantages: 1. Improve combat effectiveness: It can dynamically optimize the air-ground coordination configuration according to the changes in mission requirements, environmental conditions and ground vehicle mission load, and improve the flexibility and efficiency of mission execution.

[0014] 2. Enhance mission adaptability: By introducing complex models such as nonlinear factors, threshold effects, and interactions, it is possible to more accurately predict the configuration of air vehicles and ground vehicles required for the mission and adapt to complex and changing battlefield environments; 3. Realize intelligent configuration: It can realize real-time optimization configuration during task execution without manual intervention, improve the intelligence level of the system, and reduce errors during task execution; 4. Wide applicability: This device is suitable for various air-ground coordinated combat missions, including military reconnaissance, attack, supply, etc. It can also be extended to the coordinated operations of air vehicle formations and ground vehicles in other fields, such as emergency rescue, logistics distribution, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a method flow chart of an embodiment of the present invention DETAILED DESCRIPTION

[0016] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0017] like Figure 1 As shown, the present invention provides a method for optimizing the number of aerial vehicle configurations based on air-ground collaboration, comprising the following steps: (1) Obtaining mission and environment related parameters, including but not limited to obtaining basic data of air-ground collaborative missions, including mission type T, mission duration D, mission coverage R, mission accuracy requirement P; weather conditions W, enemy threat level E, terrain complexity T c , the number of aircraft in the air N UAV , ground vehicle mission load L UGV .

[0018] (2) Calculate the sub-items of the task requirements; (3) Calculate the sub-items of real-time environmental feedback; (4) Calculate the sub-items of ground vehicle mission completion and demand balance; (5) Calculate the real-time configuration quantity of real-time ground vehicles.

[0019] Specifically, in step (1), the relevant parameters of the task and environment are: Mission Objectives (T) Definition: The primary objective type of the mission, such as reconnaissance, attack, supply, etc.

[0020] Unit: No unit.

[0021] · Acquisition method: Obtained from the mission requirement document or mission planning system.

[0022] Task duration (D) Definition: The time required for a task to go from start to finish.

[0023] Unit: hour (h).

[0024] · Acquisition method: Obtain from the task scheduling system or task plan file.

[0025] Mission coverage (R) Definition: The area that the mission needs to cover.

[0026] Unit: square kilometer (km 2 ). Acquisition method: Through mission planning tools or geographic information systems (GIS).

[0027] Mission accuracy requirement (P) Definition: The accuracy requirements for task execution, including low, medium, and high accuracy requirements.

[0028] Unit: No unit.

[0029] · Acquisition method: Obtained from the task requirements document.

[0030] Weather conditions (W) Definition: Weather conditions during mission execution affect the flight stability and mission execution capability of the aircraft.

[0031] Unit: No unit.

[0032] · Acquisition method: Get real-time weather data through the meteorological monitoring system.

[0033] Enemy Threat (E) · Definition: Threat level of enemy air defense or attack systems.

[0034] Unit: No unit.

[0035] · Acquisition method: Through intelligence acquisition, reconnaissance data or enemy equipment monitoring systems.

[0036] Terrain complexity (T c ) Definition: The complexity of the terrain in the mission execution area affects the mission execution capability of the aircraft.

[0037] Unit: No unit.

[0038] Acquisition method: Acquired through GIS data or geographic mapping system.

[0039] Specifically, in step (2), the calculation method of the task requirement is: A1=β1·T+β2·D+β3·R+β4·P n Where β1, β2, β3, β4 are T, D, R, P respectively. n The weight coefficient is , T is the task type, D is the task duration, R is the task coverage, and P is the task accuracy requirement. The n-th power form is used to represent the accelerated growth of task requirements when the task accuracy requirement increases.

[0040] Specifically, in step (3), the calculation method of the real-time environmental feedback is: A2=γ1•W+γ2•E• I(E≤E th)+γ2'•E •I(E>E th )+γ3•T c +γ4•W•E Where γ1, γ2, γ2', γ3, and γ4 are W, E, E• I (E≤E th ), E •I(E>E th ), T c , the weight coefficient of W•E, I(E≤E th ) is the enemy threat level E is less than or equal to the threshold E th The coefficient when I(E>E th ) is the enemy threat level E greater than the threshold E th The coefficient of time, W is the weather condition, T c The complexity of the terrain.

[0041] Specifically, in step (4), the calculation method of the balance between the ground vehicle mission completion and demand is: A3=δ1•N UAV +δ2•L UGV +δ3•(N UAV •L UGV ) Where δ1, δ2, δ3 are N UAV , L UGV , N UAV •L UGV The weight coefficient of , where δ3 measures the importance of the air-ground synergy effect in the overall balance. UAV is the number of aerial vehicles, indicating the number of drones participating in the mission in the system, and is used to measure the intensity of airspace resource deployment. UGV is the ground vehicle mission load, which is used to measure the mission carrying capacity of ground resources. UAV •L UGV is the interaction term between the number of aerial vehicles and the mission load of ground vehicles, representing the air-ground synergy effect, that is, the joint contribution of aerial vehicles and ground vehicles in mission execution.

[0042] Specifically, in step (5), the real-time configuration quantity of the ground vehicle is: N UGV =α1•A1+α2•A2+α3•A3 Among them, A1, A2, and A3 represent task type and demand analysis, real-time environmental feedback, and unmanned vehicle task completion and demand balance, respectively; α1, α2, and α3 are the weight coefficients of A1, A2, and A3, respectively, indicating the relative importance of each item in the calculation.

[0043] Example: Assume that a mission requires air-ground coordinated operations, the mission objective is reconnaissance, the mission duration is 5 hours, the mission coverage is 100 square kilometers, and the mission accuracy requirement is high precision; the real-time environmental data are as follows: the weather conditions are medium, the enemy threat is low, and the terrain complexity is medium; the number of aerial drones is 3, and the unmanned vehicle mission load is medium. Find the real-time configuration number of ground unmanned vehicles.

[0044] (1) Obtain mission and environment related parameters, and obtain information related to the number of ground vehicle configurations, including mission type T = 1, mission duration D = 5, mission coverage R = 100, mission accuracy requirement P = 3; weather conditions W = 2, enemy threat level E = 1, terrain complexity T c =2, number of aerial drones = 3, and the unmanned vehicle mission load is medium.

[0045] (2) Calculation of task requirements: Assuming the weight coefficients β1=2, β2=0.5, β3=0.1, β4=1, n=2, then: A1=2·1+0.5·5+0.1·100+1·3 2 =23.5.

[0046] (3) Itemized calculation of real-time environmental feedback: Assuming the weight coefficients γ1=1, γ2=1, γ2'=2, γ3=0.5, γ4=0.8, then: A2=1•2+1•1• 1+2•1 •0+0.5•2+0.8 •2•1=5.6.

[0047] (4) Calculation of unmanned vehicle mission completion and demand balance: Assuming the weight coefficients δ1=0.5, δ2=1, δ3=0.2, then: A3=0.5•3+1•1+0.2•(3•1) =3.1.

[0048] (6) Calculation of real-time configuration quantity of ground unmanned vehicles: Assuming the weight coefficients α1=0.4, α2=0.3, α3=0.3, then: N UGV =0.4•23.5+0.3•5.6+0.3•3.1=12.01.

[0049] Through the above steps, we started from the task and environment parameters and gradually calculated the task requirement item (A1), the real-time environment feedback item (A2), and the unmanned vehicle task completion and demand balance item (A3). Finally, the real-time configuration number of ground unmanned vehicles was 12, which is the optimal configuration required to ensure the successful completion of the task.

[0050] The present invention also provides an air vehicle configuration quantity optimization module based on air-ground collaboration, comprising: Basic data acquisition unit, used to obtain basic data of air-ground collaborative missions, including mission type T, mission duration D, mission coverage R, mission accuracy requirement P; weather conditions W, enemy threat level E, terrain complexity T c , the number of drones in the air N UAV , ground vehicle mission load L UGV ; A task requirement calculation unit, used to calculate task requirements based on basic data; A real-time environment feedback calculation unit, used for calculating real-time environment feedback based on basic data; An unmanned vehicle task completion and demand balance calculation unit, used to calculate the unmanned vehicle task completion and demand balance based on basic data; The real-time configuration quantity calculation unit of the ground vehicle is used to optimize the configuration quantity of the ground vehicle and obtain the real-time configuration quantity of the ground vehicle according to the mission requirements, real-time environmental feedback, and the balance between the unmanned vehicle mission completion and demand.

[0051] The technical solution of the above-mentioned optimization module is consistent with the technical solution of the aforementioned optimization method, and will not be repeated here.

[0052] Obviously, the above embodiments are only examples for the purpose of clear explanation and are not intended to be exhaustive. For ordinary technicians in the field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from this are still within the protection scope of the invention.

[0053] Based on the same technical solution, the present invention also discloses a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the above-mentioned method for optimizing the number of aerial vehicle configurations based on air-ground collaboration.

[0054] Based on the same technical solution, the present invention also discloses a computing device, including one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the above-mentioned method for optimizing the number of aerial vehicle configurations based on air-ground collaboration.

[0055] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A method for optimizing the number of ground vehicle configurations based on air-ground collaboration, characterized in that: The method optimizes the configuration quantity of ground vehicles according to task requirements, real-time environmental feedback, and the balance between the task completion degree and demand of ground vehicles, and obtains the real-time configuration quantity of ground vehicles: N UGV =α1•A1+α2•A2+α3•A3 Among them, A1, A2, and A3 represent task type and demand analysis, real-time environmental feedback, and unmanned vehicle task completion and demand balance, respectively; α1, α2, and α3 are the weight coefficients of A1, A2, and A3, respectively.

2. The method according to claim 1, characterized in that: Before optimizing the number of ground vehicles, the basic data of the air-ground collaborative mission is obtained, including the mission type T, mission duration D, mission coverage R, mission accuracy requirement P; weather conditions W, enemy threat level E, terrain complexity T c , the number of aircraft in the air N UAV , ground vehicle mission load L UGV .

3. The method according to claim 1, characterized in that The calculation method of the task requirements is: A1=β1•T+β2•D+β3•R+β4•P n Where β1, β2, β3, β4 are T, D, R, P respectively. n The weight coefficient is , T is the task type, D is the task duration, R is the task coverage, and P is the task accuracy requirement.

4. The method according to claim 1, characterized in that: The calculation method of the real-time environmental feedback is: A2=γ1•W+γ2•E• I(E≤E th )+γ2'•E •I(E>E th )+γ3•T c +γ4•W•E Where γ1, γ2, γ2', γ3, and γ4 are W, E, E• I (E≤E th ), E •I(E>E th ), T c , the weight coefficient of W•E, I(E≤E th ) is the enemy threat level E is less than or equal to the threshold E th The coefficient when I(E>E th ) is the enemy threat level E greater than the threshold E th The coefficient of time, W is the weather condition, T c The complexity of the terrain.

5. The method according to claim 1, characterized in that The calculation method of the ground vehicle task completion and demand balance is: <h2 style=";text-align:left;direction:ltr">A3=δ1•N<h2 style=";text-align:left;direction:ltr"> UAV <h2 style=";text-align:left;direction:ltr"> +δ2•L<h2 style=";text-align:left;direction:ltr"> UGV <h2 style=";text-align:left;direction:ltr"> +δ3•(N<h2 style=";text-align:left;direction:ltr"> UAV <h2 style=";text-align:left;direction:ltr"> •L<h2 style=";text-align:left;direction:ltr"> UGV <h2 style=";text-align:left;direction:ltr"> ) Where δ1, δ2, δ3 are N UAV , L UGV , N UAV •L UGV The weight coefficient, N UAV is the number of aircraft in the air, L UGV For ground vehicle mission loads.

6. A computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, characterized in that: When the instructions are executed by a computing device, the computing device is caused to perform the method according to any one of claims 1 to 5.

7. An electronic device, characterized in that: The method comprises one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method as claimed in any one of claims 1 to 5.

8. The air vehicle configuration quantity optimization module based on air-ground collaboration is characterized by: include: Basic data acquisition unit, used to obtain basic data of air-ground collaborative missions, including mission type T, mission duration D, mission coverage R, mission accuracy requirement P; weather conditions W, enemy threat level E, terrain complexity T c , the number of aircraft in the air N UAV , ground vehicle mission load L UGV ; A task requirement calculation unit, used to calculate task requirements based on basic data; A real-time environment feedback calculation unit, used for calculating real-time environment feedback based on basic data; An unmanned vehicle task completion and demand balance calculation unit, used to calculate the unmanned vehicle task completion and demand balance based on basic data; The real-time configuration quantity calculation unit of the ground vehicle is used to optimize the configuration quantity of the ground vehicle and obtain the real-time configuration quantity of the ground vehicle according to the mission requirements, real-time environmental feedback, and the balance between the unmanned vehicle mission completion and demand.

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