CN114859978A - Unmanned aerial vehicle autonomous target sequencing method and unmanned aerial vehicle control system - Google Patents
Unmanned aerial vehicle autonomous target sequencing method and unmanned aerial vehicle control system Download PDFInfo
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Abstract
The application belongs to the technical field of unmanned aerial vehicle control, and particularly relates to an unmanned aerial vehicle autonomous target sequencing method and an unmanned aerial vehicle control system. The method comprises the steps of S1, obtaining the importance degree between two factors of a target distance, a target off-axis angle, a damage degree, a target importance degree, a target coordinate precision and a target threat degree; s2, constructing an importance matrix; step S3, calculating a normalized feature vector; s4, acquiring a parameter value of each factor of each target, and mapping the parameter value to a range of 1-9; step S5, calculating the ratio of the parameters after the mapping of every two targets respectively, and constructing six quantization matrixes; step S6, calculating the normalized eigenvector of each quantization matrix; step S7, splicing the normalized eigenvectors of the six quantization matrixes into a parameter matrix, and multiplying the parameter matrix by the normalized eigenvectors of the importance matrix to form a sorting matrix; and step S8, sequencing a plurality of targets to be hit. The autonomous decision making capability of the unmanned aerial vehicle control system is improved.
Description
Technical Field
The application belongs to the technical field of unmanned aerial vehicle control, and particularly relates to an unmanned aerial vehicle autonomous target sequencing method and an unmanned aerial vehicle control system.
Background
At present, with the development of unmanned aerial vehicle technology, the requirement of using unmanned aerial vehicles to execute the striking task becomes more urgent. From the view of equipment autonomy and intellectualization, the method for improving the autonomous decision making capability of the unmanned aerial vehicle is a method for effectively improving the striking efficiency, and the autonomous target sequencing is one of the key problems for realizing the autonomous striking task. At present, target selection and sorting of unmanned aerial vehicles mainly come from a ground control system, and the capability of generating a striking list by autonomous target sorting on the unmanned aerial vehicles is lacked, so that the autonomy of striking of the unmanned aerial vehicles is influenced.
Disclosure of Invention
In order to solve the problems, the application provides an unmanned aerial vehicle autonomous target sequencing method and an unmanned aerial vehicle control system, and target sequencing is used as a module of the unmanned aerial vehicle control system to realize autonomous decision of a target striking sequence.
The application provides an unmanned aerial vehicle autonomous target sequencing method in a first aspect, which mainly comprises the following steps:
s1, acquiring the importance of six factors, namely the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance of the target, the coordinate precision of the target and the threat degree of the target;
s2, constructing an importance matrix A based on importance among all factors, wherein the importance matrix A comprises six rows and six columns, each row and each column represent one factor, the value of the jth column of the ith row in the importance matrix A represents the importance of the factor corresponding to the ith row compared with the importance of the factor corresponding to the jth column, all the importance is changed within the range of 1-9, the larger the number is, the higher the importance is, the smaller the number is, and the lower the importance is;
step S3, calculating the normalized feature vector W of the importance matrix A A For characterizing the weight of each factor;
step S4, for each target, acquiring parameter values of six factors including the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance degree of the target, the coordinate precision of the target and the threat degree of the target respectively, and mapping the parameter values of the six factors into a range of 1-9 according to a preset function, wherein the closer the distance between the unmanned aerial vehicle and the target, the smaller the off-axis angle of the target, the greater the damage degree, the greater the importance degree of the target, the more accurate the coordinate precision of the target, the higher the threat degree of the target, the larger the number, and vice versa;
step S5, respectively calculating the ratio of the factor parameter value after mapping between every two targets for each factor, and constructing six quantization matrixes B according to the ratio, wherein each quantization matrix B corresponds to one factor, the quantization matrix B comprises n rows and n columns, n is the number of targets to be sorted, and the value of the jth row and the jth column represents the ratio of the parameter value of a factor of a target corresponding to the ith row after mapping to the parameter value of the factor of the target corresponding to the jth column after mapping;
step S6, calculating the normalized eigenvector of each quantization matrix B;
step S7, splicing the normalized eigenvectors of the six quantization matrixes B into an n x 6 order parameter matrix W B The parameter matrix W B The normalized eigenvector W multiplied by the importance matrix A A Forming a sorting matrix;
and step S8, sequencing the targets to be hit based on the sequencing matrix to obtain a hit list.
Preferably, in step S4, mapping the parameter values of the six factors into a range of 1 to 9 according to a predetermined function includes:
step S41, quantizing the parameter value of each parameter, and determining the parameter value change range of each parameter;
and step S42, constructing a conversion function by respectively corresponding two edge values of the variation range of the parameter value to the number 1 and the number 9, and mapping the parameter value to the range of 1-9 based on the conversion function.
Preferably, the unmanned aerial vehicle autonomous target ranking method further includes consistency check on the importance matrix a, and if the importance matrix a does not pass the consistency check, manual check is performed on values in the importance matrix a.
This application second aspect provides an unmanned aerial vehicle control system, mainly includes:
the importance degree acquisition module is used for acquiring the importance degrees of six factors, namely the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance degree of the target, the coordinate precision of the target and the threat degree of the target;
the importance matrix building module is used for building an importance matrix A based on the importance among all factors, the importance matrix A comprises six rows and six columns, each row and each column represent one factor, the value of the jth column in the ith row in the importance matrix A represents the importance of the factor corresponding to the ith row compared with the importance of the factor corresponding to the jth column, all the importance is changed within the range of 1-9, the larger the number is, the higher the importance is, the smaller the number is, and the lower the importance is;
an importance matrix calculation module for calculating the normalized eigenvector W of the importance matrix A A For characterizing the weight of each factor;
the parameter acquisition module is used for respectively acquiring parameter values of six factors, namely, the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance degree of the target, the coordinate precision of the target and the threat degree of the target, and mapping the parameter values of the six factors into a range of 1-9 according to a preset function, wherein the closer the distance between the unmanned aerial vehicle and the target, the smaller the off-axis angle of the target, the larger the damage degree, the higher the importance degree of the target, the more accurate the coordinate precision of the target and the higher the threat degree of the target are, the larger the number is, and the smaller the number is otherwise;
the device comprises a quantization matrix construction module, a parameter value mapping module and a parameter value sorting module, wherein the quantization matrix construction module is used for calculating the ratio of the factor parameter value after mapping between every two targets, and constructing six quantization matrices B according to the ratio, each quantization matrix B corresponds to one factor, the quantization matrix B comprises n rows and n columns, n is the number of targets to be sorted, and the value of the jth column of the ith row represents the ratio of the parameter value of a certain factor of the target corresponding to the ith row after mapping and the parameter value of the factor of the target corresponding to the jth column after mapping;
the quantization matrix calculation module is used for calculating the normalized eigenvector of each quantization matrix B, and each eigenvector is used for representing the weight of a parameter value under a single factor;
a ranking matrix generation module for splicing the normalized eigenvectors of the six quantization matrices B into an n x 6 order parameter matrix W B The parameter matrix W B The normalized eigenvector W multiplied by the importance matrix A A Forming a sorting matrix;
and the target sorting module is used for sorting a plurality of targets to be hit based on the sorting matrix to obtain a hit list, and controlling the unmanned aerial vehicle to hit the targets based on the hit list.
Preferably, the parameter acquiring module includes:
the parameter value quantization unit is used for quantizing the parameter value of each parameter and determining the parameter value change range of each parameter;
and the conversion unit is used for constructing a conversion function by respectively corresponding two edge values of the variation range of the parameter value to the number 1 and the number 9, and mapping the parameter value to a range of 1-9 based on the conversion function.
Preferably, the system further comprises a consistency check module, configured to perform consistency check on the importance matrix a, and if the importance matrix a does not pass the consistency check, perform manual check on the values in the importance matrix a.
The method and the device can automatically sequence the hit targets, and the autonomous decision making capability of the unmanned aerial vehicle control system is improved.
Drawings
Fig. 1 is a flowchart of a method for autonomous target ranking of an unmanned aerial vehicle according to a preferred embodiment of the present invention.
Fig. 2 is a schematic diagram of an importance matrix a according to the embodiment shown in fig. 1.
Fig. 3 is a diagram of a quantization matrix B according to the embodiment of fig. 1.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all embodiments of the present application. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application, and should not be construed as limiting the present application. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application are within the scope of protection of the present application. Embodiments of the present application will be described in detail below with reference to the drawings.
The application provides an autonomous target sorting method for an unmanned aerial vehicle in a first aspect, as shown in fig. 1, the method mainly includes:
and S1, acquiring the importance of six factors, namely the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance of the target, the coordinate precision of the target and the threat degree of the target, between two factors.
In the step, the target off-axis angle refers to an angle of a target deviating from a horizontal axis of the unmanned aerial vehicle, the damage degree mainly refers to a damage degree expected to be caused to the target based on the current capability level and the field situation of the unmanned aerial vehicle, the target threat degree is related to the action of the target on the unmanned aerial vehicle, for example, the target detection indicates that the threat degree is low, the target tracking indicates that the threat degree is middle in the threat degree, the target guidance indicates that the threat degree is high, and the data can be fed back through an unmanned aerial vehicle control system or various sensors on the unmanned aerial vehicle.
Step S2, constructing an importance matrix A based on importance among the factors, wherein the importance matrix A comprises six rows and six columns, each row and each column represent one factor, the value of the ith row and the jth column in the importance matrix A represents the importance of the factor corresponding to the ith row compared with the importance of the factor corresponding to the jth column, each importance is changed within the range of 1-9, the larger the number is, the higher the importance is, the smaller the number is, and the lower the importance is.
As shown in FIG. 2, the importance matrix A is a decision matrix, for example, x on diagonal corners in the matrix 11 ，x 22 ，x 33 All are 1, x 21 Representing a target off-axis angle compared to a drone and a droneImportance of the factor distance between objects, this value and x 12 Reciprocal to each other; for another example x 25 The importance of the factor representing the off-axis angle of the target compared to the target coordinate accuracy, the value of which is related to x 52 Reciprocal to each other.
The matrix adopts 1-9 to represent the importance, and usually takes integer values, for example, in the matrix, 1 represents that two former and latter factors are equally important, 3 represents that the former factor is slightly more important than the latter factor, 5 represents that the former factor is obviously more important than the latter factor, 7 represents that the former factor is more important than the latter factor, 9 represents that the former factor is extremely more important than the latter factor, and other integers are between the above numbers.
Step S3, calculating the normalized feature vector W of the importance matrix A A And the weights are used for characterizing all factors.
The step calculates the normalized eigenvector of the importance matrix a by the following formula:
AW A =λ max W A ;
in the formula of max Is the largest feature root, W, of the matrix A A To correspond to λ max Normalized feature vector of (1).
Step S4, for each target, obtaining parameter values of six factors including a distance between the unmanned aerial vehicle and the target, a target off-axis angle, a damage degree, a target importance degree, a target coordinate precision and a target threat degree, and mapping the parameter values of the six factors into a range of 1-9 according to a preset function, wherein the closer the distance between the unmanned aerial vehicle and the target, the smaller the target off-axis angle, the larger the damage degree, the higher the target importance degree, the more precise the target coordinate precision, the higher the target threat degree, the larger the number, and vice versa.
In some alternative embodiments, the step S4, the mapping the parameter values of the six factors into the range of 1-9 according to the predetermined function includes:
step S41, quantizing the parameter value of each parameter, and determining the parameter value change range of each parameter;
and step S42, constructing a conversion function by respectively corresponding two edge values of the variation range of the parameter value to the number 1 and the number 9, and mapping the parameter value to the range of 1-9 based on the conversion function.
In the embodiment, conversion is carried out through a preset conversion function, so that the converted data fall into the range of 1-9, a parameter matrix formed later is taken as a judgment matrix, and consistency check can be further carried out. The conversion function may be in the form of a linear function, a quadratic function, or a power exponent function, and the linear function is taken as an example, and assuming that the range of the target distance to the mobile terminal is 100m to 9000m, it is assumed that 100m corresponds to the number 9, 1200m corresponds to the number 8, 2300m corresponds to the number 7, … …, 7800m corresponds to the number 2, and 9000m corresponds to the number 1, after the above proportional calculation. Assuming that the target coordinate precision varies in 220-300 units, the precision 300 corresponds to the number 9, the precision 290 corresponds to the numbers 8, … …, and the precision 220 corresponds to the number 1.
The above linear function can be written as s j+1 =s j + (a-b)/(c-d), where ab represents the upper and lower limits of the actual value of each parameter, e.g., the above-mentioned on-board target distance a-b =8900m, cd represents the end values 9 and 1 of the mapping interval, respectively, with a difference of 8, s j Indicating an actual value corresponding to a certain value of the mapping interval, e.g. the actual value s corresponding to the number 1 1 Is 100, then 2 corresponds to the actual value s 2 100+8900/8 ≈ 1200 m; 3 corresponding to the actual value s 3 Is 1200+8900/8 ≈ 2300 m.
Step S5, for each factor, respectively calculating the ratio of the factor parameter value after mapping between every two targets, and constructing six quantization matrixes B according to the ratio, wherein each quantization matrix B corresponds to one factor, the quantization matrixes B comprise n rows and n columns, n is the number of targets to be sorted, and the value of the jth column of the ith row represents the ratio of the parameter value of a certain factor of the target corresponding to the ith row after mapping to the parameter value of the factor of the target corresponding to the jth column.
FIG. 3 shows one of the quantization matrices B, which is constructed by taking the off-axis angle factor as an example，b 32 Representing the ratio of the off-axis angle parameter value of the third target after mapping to the off-axis angle parameter value of the second target, b 23 Then it is the reciprocal; for another example, in the matrix for the degree of damage quantization, b 54 Representing the ratio of the damage degree parameter value of the fifth target after mapping to the damage degree parameter value of the fourth target after mapping, b 45 It is the reciprocal.
Step S6, calculating normalized eigenvector W of each quantization matrix B m And m is 1-6.
Step S7, splicing the normalized eigenvectors of the six quantization matrixes B into an n x 6-order parameter matrix W B The parameter matrix W B The normalized eigenvector W multiplied by the importance matrix A A And forming a sorting matrix.
In this embodiment, the weight of each factor corresponds to a feature vector of W A The parameter matrix W B =（W 1 W 2 W 3 W 4 W 5 W 6 ) Then the ordering matrix is W = W B W A . It will be appreciated that since each quantization matrix B is of order n x n, the corresponding normalized eigenvector is of order n x 1, and the normalized eigenvectors of these 6 quantization matrices B are combined to form the parameter matrix W of n x 6 B And then multiplied by the 6 x 1 eigenvector W calculated in step S3 A Thus, an n x 1 ranking matrix W is formed, the ranking matrix W comprises the ranking parameter values of n targets, and the larger the parameter value is, the more important the target is.
And step S8, sequencing the targets to be hit based on the sequencing matrix to obtain a hit list.
In some optional embodiments, the unmanned aerial vehicle autonomous target ranking method further includes performing consistency check on the importance matrix a, and if the importance matrix a does not pass the consistency check, performing manual check on values in the importance matrix a.
It is understood that, since the importance matrix a and the quantization matrix B are both judgment matrices, for example, if a1 is in the judgment matrix>B1 (indicating that A1 is more important than B1), B1>C1, which, according to common sense, should be A1>C1, but due to dataLabeling errors, etc. result in the label C1>A1, at this time, the consistency determination is not passed, for example, it is determined that a1 is 3 times more important than B1, B1 is 2 times more important than C1, when a1 is compared with C1, it should be 6 times, but it is marked as 4 times, at this time, it is also possible to determine whether there is a problem in the matrix through consistency check, although incomplete consistency is allowed, it is required to determine that the matrix has substantial consistency, so it is necessary to perform consistency check max -n |/(n-1). Wherein λ is max The characteristic root is the maximum characteristic root of the matrix, n is the order number of the matrix, when CI is smaller than a set value, the consistency is considered to be met, otherwise, manual check is carried out on the numerical value in the matrix.
In a second aspect of the present application, the above method is solidified into an drone control system in a software mode, the drone control system mainly comprising:
the importance degree acquisition module is used for acquiring the importance degrees of six factors, namely the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance degree of the target, the coordinate precision of the target and the threat degree of the target;
the importance matrix building module is used for building an importance matrix A based on the importance among all factors, the importance matrix A comprises six rows and six columns, each row and each column represent one factor, the value of the jth column in the ith row in the importance matrix A represents the importance of the factor corresponding to the ith row compared with the importance of the factor corresponding to the jth column, all the importance is changed within the range of 1-9, the larger the number is, the higher the importance is, the smaller the number is, and the lower the importance is;
an importance matrix calculation module for calculating the normalized eigenvector W of the importance matrix A A For characterizing the weight of each factor;
the parameter acquisition module is used for respectively acquiring parameter values of six factors including the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance degree of the target, the coordinate precision of the target and the threat degree of the target aiming at each target, and mapping the parameter values of the six factors into a range of 1-9 according to a preset function, wherein the closer the distance between the unmanned aerial vehicle and the target, the smaller the off-axis angle of the target, the greater the damage degree, the higher the importance degree of the target, the more accurate the coordinate precision of the target and the higher the threat degree of the target are, the larger the number is, and the smaller the number is vice versa;
the device comprises a quantization matrix construction module, a parameter value mapping module and a parameter value sorting module, wherein the quantization matrix construction module is used for calculating the ratio of the factor parameter value after mapping between every two targets, and constructing six quantization matrices B according to the ratio, each quantization matrix B corresponds to one factor, the quantization matrix B comprises n rows and n columns, n is the number of targets to be sorted, and the value of the jth column of the ith row represents the ratio of the parameter value of a certain factor of the target corresponding to the ith row after mapping and the parameter value of the factor of the target corresponding to the jth column after mapping;
the quantization matrix calculation module is used for calculating the normalized eigenvector of each quantization matrix B, and each eigenvector is used for representing the weight of a parameter value under a single factor;
a ranking matrix generation module for splicing the normalized eigenvectors of the six quantization matrices B into an n x 6 order parameter matrix W B The parameter matrix W B The normalized eigenvector W multiplied by the importance matrix A A Forming a sorting matrix;
and the target sorting module is used for sorting a plurality of targets to be hit based on the sorting matrix to obtain a hit list, and controlling the unmanned aerial vehicle to hit the targets based on the hit list.
In some optional embodiments, the parameter obtaining module comprises:
the parameter value quantization unit is used for quantizing the parameter value of each parameter and determining the parameter value change range of each parameter;
and the conversion unit is used for constructing a conversion function by respectively corresponding two edge values of the variation range of the parameter value to the number 1 and the number 9, and mapping the parameter value to a range of 1-9 based on the conversion function.
In some optional embodiments, the system further includes a consistency check module, configured to perform a consistency check on the importance matrix a, and if the consistency check is not passed, perform a manual check on the values in the importance matrix a.
The method and the device can automatically sequence the hit targets, and the autonomous decision making capability of the unmanned aerial vehicle control system is improved.
Although the present application has been described in detail with respect to the general description and specific embodiments, it will be apparent to those skilled in the art that certain modifications or improvements may be made based on the present application. Accordingly, such modifications and improvements are intended to be within the scope of this invention as claimed.
Claims (6)
1. An unmanned aerial vehicle autonomous target sequencing method is characterized by comprising the following steps:
s1, acquiring the importance of six factors, namely the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance of the target, the coordinate precision of the target and the threat degree of the target;
s2, constructing an importance matrix A based on importance among all factors, wherein the importance matrix A comprises six rows and six columns, each row and each column represent one factor, the value of the jth column of the ith row in the importance matrix A represents the importance of the factor corresponding to the ith row compared with the importance of the factor corresponding to the jth column, all the importance is changed within the range of 1-9, the larger the number is, the higher the importance is, the smaller the number is, and the lower the importance is;
step S3, calculating the normalized feature vector W of the importance matrix A A For characterizing the weight of each factor;
step S4, for each target, acquiring parameter values of six factors including the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance degree of the target, the coordinate precision of the target and the threat degree of the target respectively, and mapping the parameter values of the six factors into a range of 1-9 according to a preset function, wherein the closer the distance between the unmanned aerial vehicle and the target, the smaller the off-axis angle of the target, the greater the damage degree, the greater the importance degree of the target, the more accurate the coordinate precision of the target, the higher the threat degree of the target, the larger the number, and vice versa;
step S5, for each factor, respectively calculating the ratio of the factor parameter value after mapping between every two targets, and constructing six quantization matrixes B according to the ratio, wherein each quantization matrix B corresponds to one factor, the quantization matrixes B comprise n rows and n columns, n is the number of targets to be sorted, and the value of the jth column of the ith row represents the ratio of the parameter value of a certain factor of the target corresponding to the ith row after mapping to the parameter value of the factor of the target corresponding to the jth column;
step S6, calculating the normalized eigenvector of each quantization matrix B;
step S7, splicing the normalized eigenvectors of the six quantization matrixes B into an n x 6 order parameter matrix W B The parameter matrix W is divided into B The normalized eigenvector W multiplied by the importance matrix A A Forming a sorting matrix;
and step S8, sequencing the targets to be hit based on the sequencing matrix to obtain a hit list.
2. The unmanned aerial vehicle autonomous target ranking method of claim 1, wherein in step S4, mapping the parameter values of the six factors into the range of 1-9 according to the predetermined function comprises:
step S41, quantizing the parameter value of each parameter, and determining the parameter value change range of each parameter;
and step S42, constructing a conversion function by respectively corresponding two edge values of the variation range of the parameter value to the number 1 and the number 9, and mapping the parameter value to the range of 1-9 based on the conversion function.
3. The unmanned aerial vehicle autonomous target ranking method of claim 1, further comprising consistency checking the importance matrix a, and if the consistency checking is not passed, manually checking values in the importance matrix a.
4. An unmanned aerial vehicle control system, comprising:
the importance degree acquisition module is used for acquiring the importance degrees of six factors, namely the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance degree of the target, the coordinate precision of the target and the threat degree of the target;
the importance matrix building module is used for building an importance matrix A based on the importance among all factors, the importance matrix A comprises six rows and six columns, each row and each column represent one factor, the value of the jth column in the ith row in the importance matrix A represents the importance of the factor corresponding to the ith row compared with the importance of the factor corresponding to the jth column, all the importance is changed within the range of 1-9, the larger the number is, the higher the importance is, the smaller the number is, and the lower the importance is;
an importance matrix calculation module for calculating the normalized feature vector W of the importance matrix A A For characterizing the weight of each factor;
the parameter acquisition module is used for respectively acquiring parameter values of six factors including the distance between the unmanned aerial vehicle and the target, the off-axis angle of the target, the damage degree, the importance degree of the target, the coordinate precision of the target and the threat degree of the target aiming at each target, and mapping the parameter values of the six factors into a range of 1-9 according to a preset function, wherein the closer the distance between the unmanned aerial vehicle and the target, the smaller the off-axis angle of the target, the greater the damage degree, the higher the importance degree of the target, the more accurate the coordinate precision of the target and the higher the threat degree of the target are, the larger the number is, and the smaller the number is vice versa;
the quantization matrix construction module is used for calculating the ratio of the factor parameter value after mapping between every two targets for each factor, and constructing six quantization matrices B according to the ratio, wherein each quantization matrix B corresponds to one factor, each quantization matrix B comprises n rows and n columns, n is the number of targets to be sorted, and the value of the jth column in the ith row represents the ratio of the parameter value of a factor of a target corresponding to the ith row after mapping to the parameter value of the factor of the target corresponding to the jth column;
the quantization matrix calculation module is used for calculating the normalized eigenvector of each quantization matrix B, and each eigenvector is used for representing the weight of a parameter value under a single factor;
a ranking matrix generation module for splicing the normalized eigenvectors of the six quantization matrices B into an n x 6 order parameter matrix W B The parameter matrix W B The normalized eigenvector W multiplied by the importance matrix A A Forming a sorting matrix;
and the target sorting module is used for sorting a plurality of targets to be hit based on the sorting matrix to obtain a hit list, and controlling the unmanned aerial vehicle to hit the targets based on the hit list.
5. The drone control system of claim 4, wherein the parameter acquisition module includes:
the parameter value quantization unit is used for quantizing the parameter value of each parameter and determining the parameter value change range of each parameter;
and the conversion unit is used for constructing a conversion function by respectively corresponding two edge values of the variation range of the parameter value to the number 1 and the number 9, and mapping the parameter value to a range of 1-9 based on the conversion function.
6. The drone control system of claim 4, further comprising a consistency check module to perform a consistency check on the importance matrix A, if the consistency check is not passed, then to perform a manual check on the values within the importance matrix A.
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