Aircraft capability modeling method under resource constraints

Abstract

The invention relates to a method for modeling aircraft capabilities under resource constraints, which belongs to the field of system engineering. The invention aims to solve the problem that existing rotorcraft do not fully consider factors and are inefficient. The invention comprises: S1, establishing a mapping relationship between the control quantity and the software and hardware resource parameters in the rotorcraft u = f u (x); S2. Establish a mapping relationship c = Ax between the control variables and various comprehensive capabilities in the rotorcraft; S3. When the rotorcraft performs a mission, determine the capability level based on the mission type, and then obtain the maximum value of each comprehensive capability. Design the required capability value within the maximum value range, then obtain software and hardware resource parameters by solving the optimization problem in S2, and then obtain the control variables according to the mapping relationship in S1. This invention is used to optimize the efficiency of aircraft mission execution.

Description

Technical Field

[0001] The invention belongs to the field of system engineering. Background Art

[0002] A rotorcraft has limited resources to carry. To conserve resources when performing tasks, existing technologies typically minimize software and hardware resource parameters to optimize the output of its control quantity. While this approach uses fewer resources, optimizing resource parameters based on a single optimal indicator cannot solve all problems at once. The resource requirements for a rotorcraft vary when it is in different operating conditions or completing different tasks. These methods clearly do not take everything into account and are therefore inefficient.

[0003] The control variables of a rotorcraft include propeller thrust and torque, and the hardware and software resource parameters include ESC input current, ESC input voltage, ESC throttle command, camera attitude, communication bandwidth, and communication latency. For example, when performing large-scale, rapid inspections, a rotorcraft requires superior maneuverability and less demanding communication performance. This means that the ESC input current, ESC input voltage, and ESC throttle command parameters occupy a larger share of resources, while other parameters occupy a smaller share, rather than minimizing all parameters. For another example, if an anomaly is discovered during a large-scale inspection, target recognition performance is more demanding. In this case, increasing the resource share of the camera attitude parameter will optimize task execution. In another scenario, if an information transmission anomaly occurs, communication bandwidth performance must be optimized to ensure efficient task completion, increasing the weight of the communication bandwidth resource parameter. Therefore, appropriately adjusting the weights of different parameters based on the task type can achieve more efficient task completion while optimizing resources. However, existing rotorcraft cannot implement this flexible scheduling approach, resulting in increased resource consumption to complete a given task. Summary of the Invention

[0004] In view of the problems that existing rotorcraft do not fully consider factors and are inefficient, the present invention provides an aircraft capability modeling method under resource constraints for optimizing rotorcraft scheduling.

[0005] The method for modeling aircraft capability under resource constraints of the present invention comprises:

[0006] S1. Establish the control quantity u=[u1,u2,u3,…,u v ] 1×v and hardware and software resource parameters x=[x1,x2,x3,…,x m ] m×1 The mapping relationship is as follows, where v is the number of control quantities, m is the number of software and hardware parameters,

[0007] The mapping relationship between the two is:

[0008] u=f u (x)

[0009] When the software and hardware resource parameters x are known, the control quantity u is determined according to the mapping relationship between the two;

[0010] When the control quantity u is known, the optimization problem is solved to determine the hardware and software parameters through the control quantity:

[0011]

[0012] ||x|| p is the p-norm of x; and establish the constraints:

[0013]

[0014] S2. Establish various comprehensive capabilities of the rotorcraft c=[c1,c2,c3,…,c n ] n×1 and hardware and software resource parameters x=[x1,x2,x3,…,x m ] m×1 The mapping relationship where n is the number of capabilities;

[0015] c=Ax

[0016] Among them A n×m A mapping matrix from hardware and software resources to comprehensive capabilities;

[0017] When the software and hardware resource parameters x are known, the comprehensive capability value c is calculated according to the mapping relationship;

[0018] When the comprehensive capability values ​​are known, the optimization problem is solved to determine the hardware and software parameters through the comprehensive capability values:

[0019]

[0020] ||x|| p is the p-norm of x; and establish the constraints:

[0021]

[0022] S3. When the rotorcraft performs a mission, the capability level is determined according to the mission type, and then the maximum value of each comprehensive capability is obtained. The required capability value is designed within the maximum value range, and then the software and hardware resource parameters are obtained according to the optimization problem solved in S2, and the control quantity is obtained according to the mapping relationship in S1.

[0023] Preferably, the control quantity in the rotorcraft includes propeller thrust T and propeller torque M, and the software and hardware resource parameters related to the control quantity include the electric control input voltage U e, ESC input current I e The mapping relationship between the control quantity and the software and hardware resource parameters in the rotorcraft is: [T,M]=f u ([U e ,I e ,σ])

[0024] The mapping relationship between the two is described as:

[0025]

[0026] Where: D p is the propeller diameter, C T and C M are the dimensionless tension coefficient and torque coefficient, ρ is the flight air density, R m is the armature internal resistance, k1 and k2 are constant coefficients, n r is the number of ESCs, R e is the armature internal resistance, R b is the internal resistance of the battery.

[0027] Preferably, when S1 is aware of the control quantity u, the optimization goal is to maximize the endurance when determining the hardware and software parameters through the control quantity by solving the optimization problem, specifically:

[0028]

[0029] Among them, I b is the battery current, I eMax is the maximum continuous current of the ESC, K b It is the maximum discharge rate of the battery.

[0030] Preferably, the mapping matrix A from software and hardware resources to comprehensive capabilities in S2 n×m for:

[0031]

[0032] Matrix element {a ij The solution process is:

[0033] S21. Conduct 1 test or simulation on the rotorcraft and record the value of each software and hardware parameter x in each experiment. 0 =[x j ] m×1 , and the experts give the ability values ​​c of various abilities in the experiment in the form of scores 0 =[c i ] n×1 ,i=1,2,...,n;

[0034] S22. Obtain the i-th capability value sequence according to S21 and the m software and hardware parameter sequences associated with the i-th capability Then the correlation coefficient of the jth software and hardware resource parameter to the ith capability on the kth group of data can be expressed as

[0035]

[0036] Where ρ∈[0,1] is the resolution coefficient, which is 0.5; the correlation between the jth software and hardware resource parameter and the ith capability is expressed as

[0037]

[0038] Calculate {ζ ij} sequence, according to c 0 =Ax 0 Scaling it to convert it into the weight of the jth parameter relative to the i-th ability {a ij}sequence, determine the mapping matrix A.

[0039] Preferably, when the rotorcraft performs a mission, S3 determines the capability level according to the mission type, and then obtains the maximum value of each comprehensive capability, designs the required capability value within the maximum value range, and then obtains the software and hardware resource parameters according to the optimization problem solved in S2, and then obtains the control quantity according to the mapping relationship in S1, specifically:

[0040] The n capabilities of the rotorcraft are determined by their importance in different types of tasks. q , q≤n, and successively obtain all capability levels c1,c2,…,c q Maximum capacity:

[0041] The general formula for capability level is:

[0042] c i =f i (x)

[0043] f i (x) = a i1 x1+a i2 x2+…+a im x m

[0044] First, find the most important capability with f1(x) as the objective function while the constraints in the original model remain unchanged:

[0045] min-c1=-f1(x)=-(a 11 x1+a 12 x2+…+a 1m x m )

[0046]

[0047] Among them, there is an upper limit on the battery capacity that can be carried on the rotorcraft, which is allocated to different underlying software and hardware resources; the optimal solution is x 1* , the optimal value is x imin Indicates the minimum value of the jth hardware and software parameter, x jmax Indicates the maximum value that the j-th hardware and software parameter can take. Indicates the upper limit of battery capacity;

[0048] Now let’s solve the second most important ability:

[0049] min-c2=-f2(x)=-(a 21 x1+a 22 x2+…+a 2m x m )

[0050]

[0051] Among them, ε1 is a small positive number; The optimal solution is x 2* , the optimal value is

[0052] Now solve for the third most important ability: ε i ,i=1,2,…,q-1

[0053] min-c3=-f3(x)=-(a 31 x1+a 32 x2+…+a 3m x m )

[0054]

[0055] Among them, ε2 is a small positive number; The optimal solution is x 3* , the optimal value is

[0056] And so on, finally solve the ability of the qth importance:

[0057] min-c q =-f q (x)

[0058]

[0059] Among them, ε1,ε2,...,ε q-1 are all appropriately small positive numbers; The optimal solution is The optimal value is

[0060] The optimal value of each level of ability is the maximum value of the ability, and the maximum value of all levels of ability is Corresponding optimal software and hardware resource parameters

[0061] Design the ability values ​​of each level in this task within the maximum value range, that is, the ability value of each level is less than or equal to the maximum value of the ability of this level;

[0062] Then, solve the optimization problem in S2 to obtain the software and hardware resource parameters;

[0063] Then, according to the mapping relationship or linear relationship of S1, the control quantity u=f is obtained. u (x).

[0064] Preferably, the capabilities of the rotorcraft include maneuverability, target recognition capability and communication capability. When performing a mission mainly based on flight, the maneuverability level is the highest, the communication capability level is the second, and the target recognition capability level is the lowest; when performing a mission mainly based on recognition, the target recognition capability level is the highest, the communication capability level is the second, and the maneuverability level is the lowest; when performing a mission under abnormal communication conditions, the communication capability level is the highest, the target recognition capability level is the second, and the maneuverability level is the lowest.

[0065] Preferably, the hardware and software parameters related to the comprehensive capability include the ESC input voltage U e , ESC input current I e And the ESC input throttle command σ, camera attitude, communication bandwidth and communication delay.

[0066] Beneficial effects of the present invention: The rotorcraft described in the present invention serves as a controlled object, and its control quantity takes into account the actual resource consumption. In certain scenarios, when the resources that the rotorcraft itself can carry are limited, in order to make the rotorcraft work longer and complete tasks more efficiently, the rotorcraft evaluates the control quantity designed in the control system: whether it can be realized and through what kind of software and hardware resource scheduling. To this end, the present invention introduces the indicator of comprehensive capability. The various comprehensive capabilities of the rotorcraft describe the inherent software and hardware resources of the rotorcraft as a numerical value, which is used to evaluate whether the designed control quantity can be physically realized and how to solve the problem of how the control quantity can be realized with optimal resource scheduling. Compared with the control quantity and software and hardware parameters, the comprehensive capability value is more intuitive and easy to understand, and is convenient for direct design according to actual needs.

[0067] The present invention introduces comprehensive capability assessment, enabling more efficient resource optimization for different mission types. For example, when a rotorcraft conducts rapid inspections over a large area, maneuverability is prioritized, followed by communication capability, and lastly by target recognition capability. Parameters related to maneuverability, such as the ESC input current, ESC input voltage, and ESC throttle, are increased, while parameters related to communication capability, such as communication bandwidth and communication latency, are reduced. Camera attitude parameters, which are related to target recognition, occupy the least resources. If an anomaly is detected during a large-scale inspection, the aircraft can operate at a lower maneuverability level, prioritizing target recognition capability, communication capability, and maneuverability last. When transmitting information after an anomaly is detected, the maximum possible communication bandwidth is required to ensure high-speed, clear image transmission, placing communication capability first. Thus, the present invention's method adaptively adjusts software and hardware parameters based on the system's designed capability values ​​when performing different mission types, optimizing resource allocation and enabling efficient mission completion while minimizing resource usage. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a model principle diagram of the aircraft capability modeling method under resource constraints of the present invention;

[0069] Figure 2 It is a relationship diagram between rotorcraft capabilities and software and hardware resource parameters. DETAILED DESCRIPTION

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0071] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0072] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0073] Specific implementation method 1: Figure 1 and Figure 2 This embodiment describes a method for modeling aircraft capabilities under resource constraints, which includes:

[0074] S1. Establish the control quantity u=[u1,u2,u3,…,u v ] 1×vand hardware and software resource parameters x=[x1,x2,x3,…,x m ] m×1 The mapping relationship is as follows, where v is the number of control quantities, m is the number of software and hardware parameters,

[0075] The mapping relationship between the two is:

[0076] u=f u (x)

[0077] When the software and hardware resource parameters x are known, the control quantity u is determined according to the mapping relationship between the two;

[0078] When the control quantity u is known, the optimization problem is solved to determine the hardware and software parameters through the control quantity:

[0079]

[0080] ||x|| p is the p-norm of x; and establish the constraints:

[0081]

[0082] When the solution energy is optimal, take p = 2.

[0083] The control quantity u in the rotorcraft includes propeller thrust T and propeller torque M, where v = 2. The hardware and software parameters of the rotorcraft include the ESC input voltage U e , ESC input current I e And the ESC input throttle command σ, camera attitude, communication bandwidth and communication delay, at this time m=5, where the software and hardware resource parameters related to the control quantity include the ESC input voltage U e , ESC input current I e The mapping relationship between the control quantity and the software and hardware resource parameters in the rotorcraft is: [T,M]=f u ([U e ,I e ,σ])

[0084] The mapping relationship between the two is described as:

[0085]

[0086] Where: D p is the propeller diameter, C T and C M are the dimensionless tension coefficient and torque coefficient, ρ is the flight air density, R m is the armature internal resistance, k1 and k2 are constant coefficients, n r is the number of ESCs, R e is the armature internal resistance, Rb is the internal resistance of the battery.

[0087] When the control quantity u is known, the optimization problem is used to determine the software and hardware parameters through the control quantity, with the longest endurance as the optimization goal, specifically:

[0088]

[0089] Among them, I b is the battery current, I eMax is the maximum continuous current of the ESC, K b It is the maximum discharge rate of the battery.

[0090] Battery current I b To obtain according to the formula:

[0091] Among them, T b is the flight time of the rotorcraft, C min To consider the minimum remaining capacity of the battery discharge protection setting, C b is the battery capacity.

[0092] S2. Establish various comprehensive capabilities of the rotorcraft c=[c1,c2,c3,…,c n ] n×1 and hardware and software resource parameters x=[x1,x2,x3,…,x m ] m×1 The mapping relationship where n is the number of capabilities;

[0093] c=Ax

[0094] Where A is the mapping matrix from software and hardware resources to comprehensive capabilities;

[0095] When the software and hardware resource parameters x are known, the comprehensive capability value c is calculated according to the mapping relationship;

[0096] When the comprehensive capability values ​​are known, the optimization problem is solved to determine the hardware and software parameters through the comprehensive capability values:

[0097]

[0098] ||x|| p is the p-norm of x; and establish the constraints:

[0099]

[0100] The present invention introduces comprehensive capabilities. The capability types of rotorcraft include maneuverability, communication capability, and target recognition capability. The software and hardware parameters related to the comprehensive capabilities include the electric adjustment input voltage U e , ESC input current Ie And the ESC input throttle command σ, camera attitude, communication bandwidth and communication delay. Among them, the parameters related to maneuverability include the ESC input voltage U e , ESC input current I e The parameters related to communication capability include communication bandwidth and communication delay, and the parameter related to target recognition capability is camera attitude.

[0101] Among them, the mapping matrix A from software and hardware resources to comprehensive capabilities is:

[0102]

[0103] Matrix element {a ij The solution process is:

[0104] S21. Conduct 1 test or simulation on the rotorcraft and record the value of each software and hardware parameter x in each experiment. 0 =[x j ] m×1 ,j=1,2,...,m, such as current value, voltage value, etc.; and the experts give the ability value c of various abilities in the experiment in the form of scores 0 =[c i ] n×1 , i=1,2,...,n; For example, in the first test, the score of mobility is 8 points, the score of communication ability is 9 points, and the score of target recognition ability is 7 points. Each test is scored and recorded. j ,j=1,2,...,m and c i ,i=1,2,...,n is obtained by calculating the average value of l test or simulation records.

[0105] S22. Obtain the i-th capability value sequence according to S21 and the m software and hardware parameter sequences associated with the i-th capability Then the correlation coefficient of the jth software and hardware resource parameter to the ith capability on the kth group of data can be expressed as

[0106]

[0107] The i-th ability value sequence It is used to present the ability value data set of a certain ability in l trials, such as the score value of the first maneuverability l trial, the maneuverability score value of the first trial Maneuverability score value of the second test Until the first test, the maneuverability sequence is formed Similarly, Voltage and current values ​​related to maneuverability in the first test Voltage and current values ​​related to maneuverability in the second test Until the first test, a sequence of hardware and software parameters related to maneuverability is formed.

[0108] Where ρ∈[0,1] is the resolution coefficient, which is 0.5; the correlation between the jth software and hardware resource parameter and the ith capability is expressed as

[0109]

[0110] Calculate {ζ ij} sequence, according to c 0 =Ax 0 Scaling it to convert it into the weight of the jth parameter relative to the i-th ability {a ij}sequence, determine the mapping matrix A.

[0111] S3. When the rotorcraft performs a mission, the capability level is determined according to the mission type, and then the maximum value of each comprehensive capability is obtained. The required capability value is designed within the maximum value range, and then the software and hardware resource parameters are obtained according to the optimization problem solved in S2, and the control quantity is obtained according to the mapping relationship in S1.

[0112] This step is used to show how to use the mapping relationship determined by S1 and S2 to complete flight control.

[0113] The capabilities of a rotorcraft include maneuverability, target recognition capability, and communication capability. When performing missions primarily focused on flight, the maneuverability level is the highest, the communication capability level is second, and the target recognition capability level is the lowest. When performing missions primarily focused on recognition, the target recognition capability level is the highest, the communication capability level is second, and the maneuverability level is the lowest. When performing missions under abnormal communication conditions, the communication capability level is the highest, the target recognition capability level is second, and the maneuverability level is the lowest.

[0114] Specifically:

[0115] The n capabilities of the rotorcraft are determined by their importance in different types of tasks. q , q≤n, in this step q=n=3, and all capability levels c1, c2,…, c q Maximum capacity:

[0116] The general formula for capability level is:

[0117] c i =f i (x)

[0118] f i (x) = a i1 x1+a i2x2+…+a im x m

[0119] First, find the most important capability with f1(x) as the objective function while the constraints in the original model remain unchanged:

[0120] min-c1=-f1(x)=-(a 11 x1+a 12 x2+…+a 1m x m )

[0121]

[0122] a 11 ,a 12 ,…,a 1m Taken from the mapping matrix A.

[0123] Among them, there is an upper limit on the battery capacity that can be carried on the rotorcraft, which is allocated to different underlying software and hardware resources; the optimal solution is x 1* , the optimal value is x imin Indicates the minimum value of the jth hardware and software parameter, x jmax Indicates the maximum value that the j-th hardware and software parameter can take. Indicates the upper limit of battery capacity;

[0124] Now let’s solve the second most important ability:

[0125] min-c2=-f2(x)=-(a 21 x1+a 22 x2+…+a 2m x m )

[0126]

[0127] Among them, ε1 is a small positive number; The optimal solution is x 2* , the optimal value is Now solve for the third most important ability: ε i ,i=1,2,…,q-1

[0128] min-c3=-f3(x)=-(a 31 x1+a 32 x2+…+a 3m x m )

[0129]

[0130] Among them, ε2 is a small positive number; The optimal solution is x 3* , the optimal value is

[0131] And so on, finally solve the ability of the qth importance:

[0132] min-c q =-f q (x)

[0133]

[0134] Among them, ε1,ε2,...,ε q-1 are all appropriately small positive numbers; The optimal solution is The optimal value is

[0135] The optimal value of each level of ability is the maximum value of the ability, and the maximum value of all levels of ability is

[0136] The ability values ​​of each level in this task are designed within the maximum value range, that is, the ability value of each level is less than or equal to the maximum value of the ability of this level; according to the task requirements, the ability value of each level is You can select the optimal value (maximum value) within the range, or you can select a value less than the optimal value and adjust it as needed. If you select the optimal value, the corresponding optimal software and hardware resource parameters

[0137] Then, solve the optimization problem in S2 to obtain the software and hardware resource parameters;

[0138] Then, according to the mapping relationship or linear relationship of S1, the control quantity u=f is obtained. u (x).

[0139] The present invention introduces capability values ​​to clearly determine whether a specific type of task can be completed, and facilitates efficient completion of the task with minimal resource usage.

[0140] This paper leverages the concepts of performance evaluation and optimization problem solving, combines the intelligent rotorcraft model and application scenarios, and integrates control variables with underlying hardware and software resources to design an optimal hardware and software resource scheduling method for achieving control variables. It establishes a relationship between objective hardware and software resources and the subjective comprehensive capabilities of the intelligent agent, and provides evaluation methods for each comprehensive capability, facilitating intuitive decision-making by decision makers. It also designs an optimal hardware and software resource scheduling method for achieving comprehensive capability values.

[0141] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the invention. It should be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in conjunction with other described embodiments.

Claims

1. The aircraft capability modeling method under resource constraints is characterized by: The method includes: S1. Establish the control quantity u=[u1,u2,u3,…,u v ] 1×v and hardware and software resource parameters x=[x1,x2,x3,…,x m ] m×1 The mapping relationship is as follows, where v is the number of control quantities, m is the number of software and hardware parameters, The mapping relationship between the two is: u=f u (x) When the software and hardware resource parameters x are known, the control quantity u is determined according to the mapping relationship between the two; When the control quantity u is known, the optimization problem is solved to determine the hardware and software parameters through the control quantity: ||x|| p is the p-norm of x; and establish the constraints: S2. Establish various comprehensive capabilities of the rotorcraft c=[c1,c2,c3,…,c n ] n×1 and hardware and software resource parameters x=[x1,x2,x3,…,x m ] m×1 The mapping relationship where n is the number of capabilities; c=Ax Among them A n×m A mapping matrix from hardware and software resources to comprehensive capabilities; When the software and hardware resource parameters x are known, the comprehensive capability value c is calculated according to the mapping relationship; When the comprehensive capability values ​​are known, the optimization problem is solved to determine the hardware and software parameters through the comprehensive capability values: ||x|| p is the p-norm of x; and establish the constraints: S3. When the rotorcraft performs a mission, the capability level is determined according to the mission type, and then the maximum value of each comprehensive capability is obtained. The required capability value is designed within the maximum value range, and then the software and hardware resource parameters are obtained according to the optimization problem solved in S2, and the control quantity is obtained according to the mapping relationship in S1.

2. The method for modeling aircraft capability under resource constraints according to claim 1, characterized in that: The control quantities in a rotorcraft include propeller thrust T and propeller torque M. The software and hardware resource parameters related to the control quantities include the ESC input voltage U e , ESC input current I e The mapping relationship between the control quantity and the software and hardware resource parameters in the rotorcraft is: [T,M]=f u ([U e ,I e ,σ]) The mapping relationship between the two is described as: Where: D p is the propeller diameter, C T and C M are the dimensionless tension coefficient and torque coefficient, ρ is the flight air density, R m is the armature internal resistance, k1 and k2 are constant coefficients, n r is the number of ESCs, R e is the armature internal resistance, R b is the internal resistance of the battery.

3. The method for modeling aircraft capability under resource constraints according to claim 2, characterized in that: When the control quantity u is known, S1 uses the optimization problem to determine the hardware and software parameters through the control quantity, with the longest endurance as the optimization goal, specifically: Among them, I b is the battery current, I eMax is the maximum continuous current of the ESC, K b It is the maximum discharge rate of the battery.

4. The method for modeling aircraft capability under resource constraints according to claim 1, characterized in that: The mapping matrix A from software and hardware resources to comprehensive capabilities in S2 n×m for: Matrix element {a ij The solution process is: S21. Conduct 1 test or simulation on the rotorcraft and record the value of each software and hardware parameter x in each experiment. 0 =[x j ] m×1 , and the experts give the ability values ​​c of various abilities in the experiment in the form of scores 0 =[c i ] n×1 ,i=1,2,...,n; S22. Obtain the i-th capability value sequence according to S21 and the m software and hardware parameter sequences associated with the i-th capability Then the correlation coefficient of the jth software and hardware resource parameter to the ith capability on the kth group of data can be expressed as Where ρ∈[0,1] is the resolution coefficient, which is 0.5; the correlation between the jth software and hardware resource parameter and the ith capability is expressed as Calculate {ζ ij } sequence, according to c 0 =Ax 0 Scaling it to convert it into the weight of the jth parameter relative to the i-th ability {a ij }sequence, determine the mapping matrix A.

5. The method for modeling aircraft capability under resource constraints according to claim 4, characterized in that: When the rotorcraft performs a mission, S3 determines the capability level according to the mission type, and then obtains the maximum value of each comprehensive capability. The required capability value is designed within the maximum value range. Then, the software and hardware resource parameters are obtained according to the optimization problem solved in S2, and the control quantity is obtained according to the mapping relationship in S1. Specifically, The n capabilities of the rotorcraft are determined by their importance in different types of tasks. q , q≤n, and successively obtain all capability levels c1,c2,…,c q Maximum capacity: The general formula for capability level is: c i =f i (x) f i (x)=a i1 x1+a i2 x2+…+a im x m First, find the most important capability with f1(x) as the objective function while the constraints in the original model remain unchanged: min-c1=-f1(x)=-(a 11 x1+a 12 x2+…+a 1m x m ) Among them, there is an upper limit on the battery capacity that can be carried on the rotorcraft, which is allocated to different underlying software and hardware resources; the optimal solution is x 1* , the optimal value is x imin Indicates the minimum value of the jth hardware and software parameter, x jmax Indicates the maximum value that the j-th hardware and software parameter can take. Indicates the upper limit of battery capacity; Now let’s solve the second most important ability: min-c2=-f2(x)=-(a 21 x1+a 22 x2+…+a 2m x m ) Among them, ε1 is a small positive number; The optimal solution is x 2* , the optimal value is Now solve for the third most important ability: ε i ,i=1,2,…,q-1 min-c3=-f3(x)=-(a 31 x1+a 32 x2+…+a 3m x m ) Among them, ε2 is a small positive number; The optimal solution is x 3* , the optimal value is And so on, finally solve the ability of the qth importance: min-c q =-f q (x) Among them, ε1,ε2,...,ε q-1 are all appropriately small positive numbers; The optimal solution is x q* , the optimal value is The optimal value of each level of ability is the maximum value of the ability, and the maximum value of all levels of ability is Corresponding optimal software and hardware resource parameters x * =x q* ; Design the ability values ​​of each level in this task within the maximum value range, that is, the ability value of each level is less than or equal to the maximum value of the ability of this level; Then, solve the optimization problem in S2 to obtain the software and hardware resource parameters; Then, according to the mapping relationship or linear relationship of S1, the control quantity u=f is obtained. u (x).

6. The method for modeling aircraft capability under resource constraints according to claim 5, characterized in that: The capabilities of a rotorcraft include maneuverability, target recognition, and communication capabilities. When performing missions primarily focused on flight, maneuverability is ranked highest, communication is ranked second, and target recognition is ranked lowest. When performing identification-based tasks, the target identification capability level is the highest, the communication capability level is second, and the mobility capability level is the lowest; when performing tasks under abnormal communication conditions, the communication capability level is the highest, the target identification capability level is second, and the mobility capability level is the lowest.

7. The method for modeling aircraft capability under resource constraints according to claim 6, characterized in that: The hardware and software parameters related to comprehensive capabilities include the ESC input voltage U e , ESC input current I e And the ESC input throttle command σ, camera attitude, communication bandwidth and communication delay.

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