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

Abstract

The invention relates to an air-ground cooperation-based aircraft configuration number optimization method, device and module, and the method comprises the steps: obtaining related parameters such as task demands, ground unmanned vehicle functions, real-time environment changes and air-ground cooperation degrees, and employing a nonlinear relation, a threshold effect and an interaction effect model to dynamically optimize the number of aircrafts. The method comprises the following steps: firstly, obtaining task and environment related parameters; according to the task demand, the ground unmanned vehicle function, the environment change and the influence of air-ground cooperation, calculating the subitem value; and finally, calculating the number of required aircrafts according to a weighted sum formula. Through the method, efficient utilization of space resources can be realized, and the task execution flexibility and response capability are improved. The method is widely applied to an unmanned system for air-ground collaborative operation in the fields of military, emergency rescue, logistics transportation and the like, and is particularly applied to an application scene in which the configuration of an aircraft needs to be dynamically adjusted according to a real-time task and an environment.

Description

Technical Field

[0001] The present invention relates to an aircraft configuration quantity optimization method, device and module based on air-ground collaboration, belonging to the technical field of unmanned system collaborative control. Background Art

[0002] With the rapid development of UAV technology and ground unmanned vehicle (UGV) technology, air-ground collaborative operations play an increasingly important role in military, emergency rescue, logistics and other fields. In these applications, the collaborative work between aerial vehicles (UAVs) and ground unmanned vehicles is essential. However, in actual applications, due to the differences in mission types, environmental factors, and ground unmanned vehicle functions, how to reasonably configure the number of aerial vehicles according to real-time mission requirements and environmental changes is still a technical problem that needs to be solved.

[0003] At present, there are some methods for air-ground collaborative resource allocation, however, most of them are too simple and fail to fully consider the mission requirements, the functions of ground unmanned vehicles, real-time environmental changes and the complex impact of air-ground collaboration. Therefore, how to propose a scientific, accurate and dynamic method to adjust the number of aerial vehicles based on the above factors has become a key issue in improving the effectiveness of air-ground collaboration. Summary of the invention

[0004] Purpose of the invention: The present invention provides a method for optimizing the configuration quantity of aerial vehicles based on air-ground collaboration, which can dynamically optimize the configuration quantity of aerial vehicles according to mission requirements, the functions of ground unmanned vehicles, real-time environmental changes and the degree of air-ground collaboration, so as to improve resource utilization and mission execution efficiency.

[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 aerial vehicle configurations based on air-ground collaboration is provided. The method dynamically optimizes the number of aerial vehicle configurations according to mission requirements, functions of ground unmanned vehicles, real-time environmental changes, and the degree of air-ground collaboration, and obtains the real-time number of aerial vehicle configurations: N UAV = a1• S1+ a2• S2+ a3• S3+ a4• S4 Among them, S1, S2, S3, and S4 represent mission requirements, functions of ground unmanned vehicles, real-time environmental changes, and the degree of air-ground coordination, respectively; a1, a2, a3, and a4 are the weight coefficients of S1, S2, S3, and S4, respectively.

[0006] Preferably, before dynamically optimizing the number of configurations of aerial vehicles, the method further includes obtaining basic data of air-ground collaborative tasks, including task type T type , task scope M scope , task complexity Ccomplexity , task priority P priority ; Number of ground unmanned vehicles G mount , ground unmanned vehicle functional strength F functionality , ground unmanned vehicle state S status , carrying capacity of ground unmanned vehicle C capacity ; Weather conditions condition , ground complexity T terrain , enemy interference E enemy , task execution time D time ; Air-ground coordination level C coordination , task sharing ratio T sharing , communication efficiency Q communication , reaction time R response .

[0007] Preferably, the calculation method of the task requirement is:

[0008] Where α1, α2, α3, and α4 are the weight coefficients of task type, task scope, task complexity, and task priority, respectively. type is the task type, M scope C is the scope of the task; complexity is the task complexity, P priority The priority of the task.

[0009] Preferably, the calculation method of the ground unmanned vehicle function is:

[0010] Where α5, α6, α7 are weight coefficients, G mount is the number of ground unmanned vehicles, F functionality is the functional strength of the ground unmanned vehicle, S status is the state of the ground unmanned vehicle, C capacity The carrying capacity of ground unmanned vehicles.

[0011] Preferably, the calculation method of the real-time environmental change is:

[0012] Where α8, α9, α 10 is the weight coefficient, W condition For weather conditions, T terrain is the ground complexity, E enemy For enemy interference, D time The task execution time. Preferably, the calculation method of the degree of coordination between the air and the ground is:

[0013] Where α 11 , α 12 , α 13 , α 14 is the weight coefficient, C coordination is the air-ground coordination level, T sharing is the task sharing ratio, Q communication is the communication efficiency, R response For reaction time.

[0014] 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.

[0015] 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.

[0016] 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 tasks, including task type T type , task scope M scope , task complexity C complexity , task priority P priority ; Number of ground unmanned vehicles G mount , ground unmanned vehicle functional strength F functionality , ground unmanned vehicle state S status , carrying capacity of ground unmanned vehicle C capacity ; Weather conditions condition , ground complexity T terrain , enemy interference E enemy , task execution time D time ; Air-ground coordination level C coordination , task sharing ratio T sharing , communication efficiency Q communication , reaction time R response ; A task requirement calculation unit, used to calculate task requirements based on basic data; A ground unmanned vehicle function calculation unit, used to calculate the ground unmanned vehicle function based on basic data; A real-time environment change calculation unit, used to calculate real-time environment changes based on basic data; An air-ground coordination degree calculation unit, used for calculating the air-ground coordination degree based on basic data; The real-time configuration quantity calculation unit of the aerial vehicle is used to dynamically optimize the configuration quantity of the aerial vehicle and obtain the real-time configuration quantity of the aerial vehicle according to the mission requirements, the functions of the ground unmanned vehicle, the real-time environmental changes and the degree of air-ground coordination.

[0017] Beneficial effects: The method of the present invention can dynamically optimize the number of aerial vehicles according to mission requirements, ground unmanned vehicle functions, real-time environmental changes, and changes in the degree of air-ground coordination, thereby achieving efficient use of air-ground coordination resources. The method has the following advantages: Efficiency: Ability to dynamically adjust the number of aircraft in the air in real time to avoid waste of resources; Scientificity: Adopt nonlinear relationship, threshold effect and interaction effect models to accurately simulate complex situations in practical applications; Strong adaptability: Ability to flexibly optimize drone configuration according to different mission types and real-time environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a method flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0020] 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, and obtaining information related to the number of aerial drones configured, including mission type T type , task scope M scope , task complexity C complexity , task priority P priority ; Number of ground unmanned vehicles G mount , ground unmanned vehicle functional strength F functionality , ground unmanned vehicle state S status , carrying capacity of ground unmanned vehicle C capacity ; Weather conditions condition , ground complexity T terrain , enemy interference E enemy , task execution time D time ; Air-ground coordination level C coordination , task sharing ratio T sharing , communication efficiency Q communication , reaction time R response .

[0021] (2) Computational task requirements.

[0022] (3) Calculate the functions of ground unmanned vehicles.

[0023] (4) Calculate real-time environmental changes.

[0024] (5) Calculate the degree of coordination between air and ground forces.

[0025] (6) Calculate the real-time configuration quantity of aerial vehicles.

[0026] Specifically, in step (1), the relevant parameters of the task and environment are: Mission parameters: including mission type (such as reconnaissance, strike, supply, etc.), mission scope (coverage area or size of mission area), mission complexity (number of targets and complexity of target types), and mission priority (mission urgency).

[0027] Task Type T type : Identifies the mission category. The unit is dimensionless (e.g., reconnaissance mission is 1, strike mission is 2, etc.).

[0028] Mission Scope scope :Indicates the mission coverage, in square meters (m2). It can be obtained through the area or map size in the mission plan.

[0029] Task complexity C complexity : Indicates the complexity of the task, taking into account factors such as the number of targets and target types. The unit is dimensionless, and the larger the value, the more complex the task.

[0030] Task priority P priority : Indicates the urgency of the task. The unit is dimensionless and ranges from 1 to 10. The larger the value, the more urgent the task.

[0031] Ground unmanned vehicle parameters: including the number of ground unmanned vehicles, functional type, operating status, and carrying capacity.

[0032] Number of ground unmanned vehicles G mount :The unit is vehicle (unit), which is derived from the number of ground unmanned vehicles.

[0033] Ground unmanned vehicle function F functionality : Indicates the functional strength of the ground unmanned vehicle, and the unit is dimensionless. The higher the value, the stronger the function.

[0034] Ground unmanned vehicle status S status : Indicates the working status of the ground unmanned vehicle (such as normal, faulty), and the unit is dimensionless (for example, the normal state is 1 and the faulty state is 0).

[0035] Ground unmanned vehicle carrying capacity C capacity: Indicates the load or mission carrying capacity of the ground unmanned vehicle, in kilograms (kg).

[0036] Environmental change parameters: including weather conditions, terrain complexity, enemy interference, and mission execution time.

[0037] Weather conditions condition : represents the weather impact factor, in dimensionless units. The higher the value, the more obvious the bad weather (for example, high wind speed or high precipitation).

[0038] Terrain complexity T terrain : Indicates the complexity of the terrain. The unit is dimensionless. The higher the value, the more complex the terrain.

[0039] Enemy Interference E enemy : Indicates the degree of enemy interference, and the unit is dimensionless. The higher the value, the more serious the enemy interference.

[0040] Task execution time D time : Indicates the time of task execution in hours (h).

[0041] Air-ground coordination parameters: including air-ground coordination level, task sharing ratio, communication efficiency, and response time.

[0042] Air-ground coordination level C coordination : Indicates the effectiveness of air-ground coordination. The unit is dimensionless. The higher the value, the better the coordination efficiency.

[0043] Task sharing ratio T sharing : Indicates the distribution ratio of tasks between space and ground. The unit is dimensionless. The higher the value, the more tasks the ground system shares.

[0044] Communication efficiency Q communication : Indicates the air-to-ground communication efficiency, the unit is dimensionless, and the higher the value, the better the communication quality.

[0045] Reaction time R response : Indicates the time for the air-ground system to respond, in seconds (s). The smaller the value, the faster the response.

[0046] Specifically, in step (2), the calculation method of the task requirement is:

[0047] Where α1, α2, α3, α4 are weight coefficients, M scope is the task scope, using quadratic form to express the accelerated growth of demand as the scope increases; C complexity A logarithmic form is used to consider the gradual impact of increasing complexity on demand.

[0048] Specifically, in step (3), the method for calculating the ground unmanned vehicle function is:

[0049] Where α5, α6, α7 are weight coefficients, G count and F functionality The interactive effect of represents the joint effect of the number and functional intensity of ground unmanned vehicles; C capacity The square root form is used to reflect the marginal effect of the increase in carrying capacity.

[0050] Specifically, in step (4), the method for calculating the real-time environmental change is:

[0051] Where α8, α9, α 10 is the weight coefficient, W condition When it exceeds 5, it starts to significantly affect demand, reflecting the threshold effect; E enemy ·D time Represents the trade-off effect of enemy interference and mission time.

[0052] Specifically, in step (5), the calculation method of the degree of coordination between the air and the ground is:

[0053] Where α 11 , α 12 , α 13 , α 14 is the weight coefficient, which represents the quadratic effect of the coordination level. When the coordination level is high, the effect of demand reduction will be accelerated. A logarithmic relationship is used to represent the progressive impact of communication efficiency on demand.

[0054] Specifically, in step (6), the method for calculating the real-time configuration quantity of the aerial vehicles is: N UAV = a1• S1+ a2• S2+ a3• S3+ a4• S4 Where a1, a2, a3, and a4 are the weight coefficients of each factor, indicating the relative importance of mission requirements, ground unmanned vehicle functions, environmental changes, and air-ground collaboration in the calculation.

[0055] The present invention is applicable to various air-ground collaborative tasks, especially in the fields of military, emergency rescue, logistics and transportation. In these fields, when aerial vehicles and ground unmanned vehicles need to work together and there are complex situations of mission requirements and environmental changes, the method of the present invention can provide an effective aerial vehicle quantity configuration plan.

[0056] Example: Assume there is an air-ground collaborative mission involving a small reconnaissance and strike operation. The mission execution area is an urban environment, and the mission targets include several enemy air defense facilities. There are 10 ground unmanned vehicles (UGVs) available, and the weather is somewhat bad during the mission execution, and there is a certain amount of enemy electronic interference. Known parameters: the mission range is 5000 square meters, the ground unmanned vehicle carrying capacity is 500 kg, the mission execution time is 10 hours, and the reaction time is 20 seconds. Solve for the real-time configuration number of air vehicles.

[0057] (1) Obtaining mission and environment related parameters, and obtaining information related to the number of aerial drones configured, including mission type T type =1, task scope M scope =5000, task complexity C complexity =8, task priority P priority =7; Number of ground unmanned vehicles G mount =10, ground unmanned vehicle functional strength F functionality =8, ground unmanned vehicle status S status =1, ground unmanned vehicle carrying capacity C capacity =500; Weather conditions W condition =6, ground complexity T terrain =7, enemy interference E enemy =4, task execution time D time =10; Air-ground coordination level C coordination =9, task sharing ratio T sharing =0.6, communication efficiency Q communication =8, reaction time R response =20.

[0058] (2) Calculation of task requirements: Assuming the weight coefficients α1=2, α2=0.0001, α3=1, α4=0.5, then: S1=2·1+0.0001·500 2 +1•log(8)+0.5•7=2507.5794.

[0059] (3) Calculation of ground unmanned vehicle functions: Assuming the weight coefficients α5=1.5, α6=2, α7=0.1, then: S2=1.5·10·8+2·1+0.1·√500=124.236.

[0060] (4) Calculation of real-time environmental changes: Assume that the weight coefficients α8=0.8, α9=1.2, α 10 =0.5, then: S3=0.8·max(0,6-5)+1.2·7+0.5·4·10=29.2.

[0061] (5) Calculation of air-ground coordination: Assuming the weight coefficient α 11 =0.5, α 12 =0.8, α 13 =1, α 14 =0.2, then: S4=0.5·9 2 +0.8·0.6+1·log (8)-0.2·20=38.0594.

[0062] (6) Calculation of real-time configuration quantity of aerial vehicles: Assuming the weight coefficients a1=0.0001, a2=0.01, a3=0.05, a4=0.05, then N UAV =0.0001•2507.5794+0.01•124.236+0.05•29.2+0.05•38.0594=7.1129094.

[0063] Therefore, the real-time configuration number of aircraft in the air is 7.

[0064] 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 tasks, including task type T type , task scope M scope , task complexity C complexity , task priority P priority ; Number of ground unmanned vehicles G mount , ground unmanned vehicle functional strength F functionality , ground unmanned vehicle state S status , carrying capacity of ground unmanned vehicle C capacity ; Weather conditions condition , ground complexity T terrain , enemy interference E enemy , task execution time D time ; Air-ground coordination level C coordination , task sharing ratio Tsharing , communication efficiency Q communication , reaction time R response ; A task requirement calculation unit, used to calculate task requirements based on basic data; A ground unmanned vehicle function calculation unit, used to calculate the ground unmanned vehicle function based on basic data; A real-time environment change calculation unit, used to calculate real-time environment changes based on basic data; An air-ground coordination degree calculation unit, used for calculating the air-ground coordination degree based on basic data; The real-time configuration quantity calculation unit of the aerial vehicle is used to dynamically optimize the configuration quantity of the aerial vehicle and obtain the real-time configuration quantity of the aerial vehicle according to the mission requirements, the functions of the ground unmanned vehicle, the real-time environmental changes and the degree of air-ground coordination.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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 in the computer or other programmable device. 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 aerial vehicle configurations based on air-ground collaboration, characterized in that: The method dynamically optimizes the configuration quantity of the aerial vehicle according to the mission requirements, the functions of the ground unmanned vehicle, the real-time environmental changes and the degree of air-ground coordination, and obtains the real-time configuration quantity of the aerial vehicle: <h2 style=";text-align:left;direction:ltr">N<h2 style=";text-align:left;direction:ltr"> UAV <h2 style=";text-align:left;direction:ltr"> = a1 • S1 + a2 • S2 + a3 • S3 + a4 • S4 Among them, S1, S2, S3, and S4 represent mission requirements, functions of ground unmanned vehicles, real-time environmental changes, and the degree of air-ground coordination, respectively; a1, a2, a3, and a4 are the weight coefficients of S1, S2, S3, and S4, respectively.

2. The method according to claim 1, characterized in that Before dynamically optimizing the number of configurations of aerial vehicles, the basic data of air-ground collaborative tasks is obtained, including the task type T type , task scope M scope , task complexity C complexity , task priority P priority ; Number of ground unmanned vehicles G mount , ground unmanned vehicle functional strength F functionality , ground unmanned vehicle state S status , carrying capacity of ground unmanned vehicle C capacity ; Weather conditions condition , ground complexity T terrain , enemy interference E enemy , task execution time D time ; Air-ground coordination level C coordination , task sharing ratio T sharing , communication efficiency Q communication , reaction time R response .

3. The method according to claim 1, characterized in that The calculation method of the task requirements is: , where α1, α2, α3, and α4 are the weight coefficients of task type, task scope, task complexity, and task priority, respectively. type is the task type, M scope C is the scope of the task; complexity is the task complexity, P priority The priority of the task.

4. The method according to claim 1, characterized in that: The calculation method of the ground unmanned vehicle function is: , where α5, α6, α7 are weight coefficients, G mount is the number of ground unmanned vehicles, F functionality is the functional strength of the ground unmanned vehicle, S status is the state of the ground unmanned vehicle, C capacity The carrying capacity of ground unmanned vehicles.

5. The method according to claim 1, characterized in that The calculation method of the real-time environmental change is: , where α8, α9, α 10 is the weight coefficient, W condition For weather conditions, T terrain is the ground complexity, E enemy For enemy interference, D time The task execution time.

6. The method according to claim 1, characterized in that The calculation method of the degree of coordination between air and ground is: , where α 11 , α 12 , α 13 , α 14 is the weight coefficient, C coordination is the air-ground coordination level, T sharing is the task sharing ratio, Q communication is the communication efficiency, R response For reaction time.

7. 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 6.

8. 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 6.

9. 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 tasks, including task type T type , task scope M scope , task complexity C complexity , task priority P priority ; Number of ground unmanned vehicles G mount , ground unmanned vehicle functional strength F functionality , ground unmanned vehicle state S status , carrying capacity of ground unmanned vehicle C capacity ; Weather conditions condition , ground complexity T terrain , enemy interference E enemy , task execution time D time ; Air-ground coordination level C coordination , task sharing ratio T sharing , communication efficiency Q communication , reaction time R response ; A task requirement calculation unit, used to calculate task requirements based on basic data; A ground unmanned vehicle function calculation unit, used to calculate the ground unmanned vehicle function based on basic data; A real-time environment change calculation unit, used to calculate real-time environment changes based on basic data; An air-ground coordination degree calculation unit, used for calculating the air-ground coordination degree based on basic data; The real-time configuration quantity calculation unit of the aerial vehicle is used to dynamically optimize the configuration quantity of the aerial vehicle and obtain the real-time configuration quantity of the aerial vehicle according to the mission requirements, the functions of the ground unmanned vehicle, the real-time environmental changes and the degree of air-ground coordination.

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