A method and system for comparing and evaluating the effectiveness of a multi-scheme system

Abstract

The present invention belongs to the technical field of system performance evaluation, and provides a method and system for comparing and evaluating the performance of a multi-scheme system, the method comprising: semantically evaluating a plurality of schemes to be evaluated, and converting them into intuitive fuzzy numbers; constructing a system performance evaluation matrix of a plurality of schemes to be evaluated; determining the best and worst items of the semantic evaluation values ​​of the evaluation indicators according to the system performance evaluation matrix of the plurality of schemes to be evaluated; establishing a mesh hierarchical structure diagram, constructing a multi-level indicator judgment matrix under the influence of the current associated indicators, further calculating a weighted supermatrix to generate a limit supermatrix, determining the indicator weights of each evaluation indicator, calculating the scheme evaluation parameters of each scheme, performing a ranking analysis, and obtaining a multi-scheme system performance comparison evaluation result. The present invention is helpful for comprehensively and accurately evaluating the performance of a multi-scheme system under the conditions of lack of data, uncertainty and multiple criteria, has higher reliability, and at the same time reduces the number of three-level indicator judgment matrices that need to be calculated, and can effectively alleviate the problem of the explosion of the number of judgment matrices, especially when facing a more complex evaluation indicator system.

Description

Technical Field

[0001] The present invention relates to the technical field of performance evaluation, and is a method and system for comparing and evaluating the performance of a multi-scheme system. Background Art

[0002] Commonly used methods for calculating indicator weights include Analytical Network Process (AHP) and Analytical Network Procedure (ANP). AHP decomposes a complex decision-making problem into multiple levels through stratification, comparison and weighting, and quantitatively analyzes and evaluates the factors at each level. When constructing the judgment matrix in AHP, the indicator is not affected by other indicators, and only the relative importance relationship between the two is considered.

[0003] The existing methods for calculating the weights of evaluation indicators have the following main problems: AHP does not consider the correlation between indicators, but only considers the relative importance relationship between two indicators. It does not consider the correlation between indicators, which will lead to the inability to accurately calculate the weight of each indicator, thus affecting the accuracy of the evaluation results; the ANP network layer reflects the mutual influence relationship between indicator systems in the form of element sets. If there is a correlation between two element sets, it means that all elements in the element sets have a mutual influence relationship. For complex networks with multiple element sets, there will be a problem of judgment matrix explosion. In addition, when planning the effectiveness evaluation of the plan, there are problems such as difficulty in determining probability indicators and lack of relevant data.

[0004] Therefore, it is necessary to provide a new multi-scheme system effectiveness comparison and evaluation method and system to solve the above problems. Summary of the invention

[0005] The present invention aims to provide a multi-scheme system performance comparison evaluation method and system to solve the problem that the correlation of indicators is not considered in the prior art, which causes the accuracy of the evaluation indicator weight to be low and thus affects the evaluation result, and the technical problem that the number of judgment matrices is too large when calculating the indicator weight of a complex network system with correlation, causing the amount of calculation to explode. The technical problem to be solved by the present invention is achieved by the following technical solution.

[0006] The first aspect of the present invention provides a multi-scheme system performance comparison and evaluation method, comprising: based on the determined evaluation criteria, semantically evaluating multiple schemes to be evaluated, and converting the semantic evaluation results of the multiple schemes to be evaluated into intuitive fuzzy numbers; constructing a system performance evaluation matrix of the multiple schemes to be evaluated according to the converted intuitive fuzzy numbers; determining the optimal and worst items of the semantic evaluation values ​​of the evaluation indicators according to the system performance evaluation matrix of the multiple schemes to be evaluated; analyzing the correlation between the evaluation indicators according to the established evaluation indicator system, establishing a network hierarchical structure diagram to construct a multi-level indicator judgment matrix under the influence of the current correlation indicators, further calculating the weighted super matrix to generate a limit super matrix, and then determining the indicator weight of each evaluation indicator; calculating the scheme evaluation parameters of each scheme according to the determined indicator weight of each evaluation indicator and the intuitive fuzzy number of each evaluation indicator, so as to perform a ranking analysis and obtain the multi-scheme system performance comparison and evaluation results.

[0007] The second aspect of the present invention provides a multi-scheme system performance comparison and evaluation system, which uses the multi-scheme system performance comparison and evaluation method described in the first aspect of the present invention, and the multi-scheme system performance comparison and evaluation system includes: a semantic evaluation processing module, which performs semantic evaluation on multiple schemes to be evaluated based on the determined evaluation criteria, and converts the semantic evaluation results of the multiple schemes to be evaluated into intuitive fuzzy numbers; a first construction module, which constructs the system performance evaluation matrix of the multiple schemes to be evaluated according to the converted intuitive fuzzy numbers; a determination processing module, which determines the best and worst items of the semantic evaluation values ​​of the evaluation indicators according to the system performance evaluation matrix of the multiple schemes to be evaluated; a second construction module, which analyzes the correlation relationship between the evaluation indicators according to the established evaluation indicator system, establishes a network hierarchical structure diagram, so as to construct a multi-level indicator judgment matrix under the influence of the current correlation indicator, further calculates the weighted super matrix to generate a limit super matrix, and then determines the indicator weight of each evaluation indicator; a calculation processing module, which calculates the scheme evaluation parameters of each scheme according to the determined indicator weight of each evaluation indicator and the intuitive fuzzy number of each evaluation indicator, so as to perform ranking analysis and obtain the multi-scheme system performance comparison and evaluation results.

[0008] The third aspect of the present invention provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the multi-scheme system performance comparison and evaluation method described in the first aspect of the present invention.

[0009] A fourth aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the multi-scheme system performance comparison and evaluation method described in the first aspect of the present invention is implemented.

[0010] Beneficial effects of the present invention:

[0011] Compared with the prior art, the present invention performs semantic evaluation on multiple schemes to be evaluated based on the determined evaluation criteria, and converts the semantic evaluation results of the multiple schemes to be evaluated into intuitive fuzzy numbers; constructs a system effectiveness evaluation matrix of the multiple schemes to be evaluated based on the converted intuitive fuzzy numbers; determines the optimal and the worst items of the semantic evaluation values ​​of the evaluation indicators based on the system effectiveness evaluation matrix of the multiple schemes to be evaluated; analyzes the correlation between the evaluation indicators based on the established evaluation indicator system, establishes a network hierarchical structure diagram to construct a multi-level indicator judgment matrix under the influence of the current correlation indicators, further calculates the weighted supermatrix to generate the limit supermatrix, and can accurately determine the indicator weight of each evaluation indicator; by calculating the scheme evaluation parameters of each scheme and performing ranking analysis, a comparative evaluation result of the system effectiveness of multiple schemes is obtained, which is helpful to comprehensively and accurately evaluate the system effectiveness of multiple schemes under the conditions of data lack, uncertainty and multiple evaluation indicators, and has higher reliability.

[0012] In addition, a mesh hierarchical structure is used to display the correlation between indicators rather than an element set or a simple hierarchy, which can more accurately reflect the influence relationship between indicators. When constructing a super matrix, the judgment matrix under the influence of the third-level indicators that do not directly affect the relative importance of the third-level indicators is represented by the judgment matrix under the influence of the second-level indicators, which further reduces the number of third-level indicator judgment matrices that need to be calculated. This can effectively alleviate the problem of the explosion of the number of judgment matrices, especially when facing a more complex evaluation indicator system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flowchart of an example of the multi-scheme system performance comparison and evaluation method of the present invention;

[0014] Figure 2 It is a schematic diagram of a specific implementation of the adversarial system effectiveness evaluation index system in the multi-scheme system effectiveness comparison evaluation method of the present invention;

[0015] Figure 3 It is a schematic diagram of an example of a network hierarchical structure diagram of multi-level indicators in the multi-scheme system performance comparison and evaluation method of the present invention;

[0016] Figure 4 It is a schematic diagram of an example of a multi-scheme system effectiveness comparison and evaluation system;

[0017] Figure 5 is a schematic structural diagram of an electronic device embodiment according to the present invention;

[0018] Figure 6 It is a schematic diagram of the structure of a computer-readable medium embodiment according to the present invention. DETAILED DESCRIPTION

[0019] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] In view of the above problems, the present invention provides a multi-scheme system performance comparison and evaluation method. The method performs semantic evaluation on multiple schemes to be evaluated based on the determined evaluation criteria, and converts the semantic evaluation results of the multiple schemes to be evaluated into intuitive fuzzy numbers; constructs a system performance evaluation matrix of the multiple schemes to be evaluated according to the converted intuitive fuzzy numbers; determines the optimal and worst terms of the semantic evaluation values ​​of the evaluation indicators according to the system performance evaluation matrix of the multiple schemes to be evaluated; analyzes the correlation between the evaluation indicators according to the established evaluation indicator system, establishes a network hierarchical structure diagram, so as to construct a multi-level indicator judgment matrix under the influence of the current correlation indicators, further calculates the weighted super matrix to generate the limit super matrix, and can accurately determine the indicator weight of each evaluation indicator; obtains the multi-scheme system performance comparison and evaluation results by calculating the scheme evaluation parameters of each scheme and performing ranking analysis, which is helpful to comprehensively and accurately evaluate the performance of the multi-scheme system under the conditions of data lack, uncertainty and multiple evaluation indicators, and has higher reliability.

[0021] In addition, a mesh hierarchical structure is used to display the correlation between indicators rather than an element set or a simple hierarchy, which can more accurately reflect the influence relationship between indicators. When constructing a super matrix, the judgment matrix under the influence of the third-level indicators that do not directly affect the relative importance of the third-level indicators is represented by the judgment matrix under the influence of the second-level indicators, which further reduces the number of third-level indicator judgment matrices that need to be calculated. This can effectively alleviate the problem of the explosion of the number of judgment matrices, especially when facing a more complex evaluation indicator system.

[0022] It should be noted that in order to achieve the best effect of the confrontation task, multiple schemes are often designed, and the system effectiveness of each scheme is evaluated and compared to select the best scheme. The evaluation indicators of the comparative evaluation of the effectiveness of multiple schemes often have a mutual influence relationship and are not independent. At the same time, it is difficult to determine the specific indicator value. Therefore, a method combining ANP and fuzzy VIKOR is used for comparative evaluation calculation.

[0023] Example 1

[0024] Refer to the following Figure 1 , Figure 2 and Figure 3 , the multi-scheme system performance comparison and evaluation method of the present invention will be described in detail.

[0025] Figure 1 It is a flowchart of an example of the multi-scheme system performance comparison and evaluation method of the present invention.

[0026] First, in step S101, a plurality of schemes to be evaluated are semantically evaluated based on the determined evaluation criteria, and the semantic evaluation results of the plurality of schemes to be evaluated are converted into intuitionistic fuzzy numbers.

[0027] Specifically, determine the evaluation criteria and evaluation objects for system effectiveness comparison. According to the specific effectiveness evaluation objectives, multiple planning schemes are used as evaluation objects Ai (i = 1, 2, ..., n), and the evaluation indicators of the multi-scheme system effectiveness comparison evaluation are used as evaluation criteria Dj (j = 1, 2, ..., m). The modern intelligent confrontation system is an OODA loop (Observe-Orient-Decide-Act link loop) involving observation, judgment, decision-making and action. For the effectiveness evaluation of the confrontation system, it is necessary to start from the effectiveness of each link of the OODA loop and construct a confrontation system effectiveness evaluation index system including reconnaissance detection effectiveness, firepower strike effectiveness, and command and control effectiveness. The composition and meaning of the indicators are specifically shown in Table 1 below:

[0028] Table 1

[0029]

[0030] Table 1 shows a specific example of the countermeasure system effectiveness evaluation index system, wherein the target in Table 1 refers to, for example, enemy weapons or fortifications.

[0031] Furthermore, based on the determined evaluation criteria, a plurality of schemes to be evaluated are semantically evaluated.

[0032] For example, for the same evaluation criteria (specifically the same evaluation index), semantic evaluation is performed on the evaluation schemes A1, A2, A3...An, and the semantic variables of each evaluation scheme are specifically described to obtain the semantic evaluation results of each evaluation scheme.

[0033] Next, the semantic evaluation results of the multiple schemes to be evaluated are converted into intuitionistic fuzzy numbers.

[0034] It should be noted that semantic variables are expressed in the form of natural language phrases, and the evaluator can evaluate the evaluation indicators using simple and familiar language terms. Specifically, the semantic variables are divided into 7 levels: Very Low (VL), Low (Low), Medium Low (ML), Fair (Fair), Medium High (MH), High (H), Very High (VH), and then the semantic variables are converted into intuitive fuzzy numbers for processing. See Table 2 below for details.

[0035] Let X be a non-empty domain (in this example, it is the solution set, including all solutions to be evaluated, also referred to as "solutions"), define the intuitionistic fuzzy set on X and express it using the following expression.

[0036] A={(x,μ A (x),γ A (x))|x∈X}

[0037] Among them, μ A (x) is the membership function of element x (in this example, each scheme to be evaluated) to set A (in this example, the evaluation criterion set, specifically the set of all evaluation indicators, also referred to as the evaluation indicator set), μ A (x):X→[0,1], x∈X→μ A (x)∈[0,1]; γ A (x) is the non-membership function of the evaluation scheme x for the evaluation index set A, γ A (x):X→[0,1], x∈X→γ A (x)∈[0,1]. For any x∈X, 0≤μ A (x)+γ A (x)≤1.π A (x) = 1-μ A (x)-γ A (x) is the intuitionistic fuzzy hesitation of the evaluation index set A, and 0≤π A (x)≤1. It is called α=(μ α ,γ α ,π α ) is the intuitionistic fuzzy number, μ α ∈[0,1],γ α ∈[0,1],μ α +γ α ≤1,π α =1-μ α -γ α , α + =(1,0,0) and α - =(0,1,0) are the maximum and minimum intuitive fuzzy numbers respectively. Intuitive fuzzy set theory describes uncertain information from two aspects: membership and non-membership, which can better describe the uncertainty of the evaluation information of each scheme to be evaluated.

[0038] Table 2

[0039]

[0040]

[0041] Table 2 shows the conversion relationship between semantic variables and intuitionistic fuzzy numbers.

[0042] It should be noted that the above is only described as an optional example and should not be understood as a limitation to the present invention.

[0043] Next, in step S102, a system performance evaluation matrix of the plurality of schemes to be evaluated is constructed according to the converted intuitionistic fuzzy numbers.

[0044] For constructing a multi-scheme system effectiveness evaluation matrix. j (j=1, 2, ..., m) conditions, for example, using experts to evaluate plan A i Semantic variables are evaluated and the evaluation results are expressed as f ij It is then converted into intuitive fuzzy numbers according to Table 2 to form the system performance evaluation matrix F k , specifically expressed as follows.

[0045]

[0046] Among them, f ij It represents the intuitive fuzzy number of the semantic evaluation of the i-th scheme under the j-th evaluation criterion, i is a positive integer, n represents the number of rows of the system effectiveness evaluation matrix, that is, the number of schemes to be evaluated, j is a positive integer, m is the number of columns of the system effectiveness evaluation matrix, that is, the number of evaluation criteria.

[0047] Assume there are 5 planning plans A 1、 A 2、 A 3、 A 4、 A5, perform semantic evaluation on each scheme to be evaluated, as shown in Table 3. It should be noted that the evaluation indicator D in the evaluation criteria 1、 D 2、 D 4、 D 5、 D 6、 D7 is a benefit-based indicator, the larger the evaluation value, the better; evaluation indicator D 3、 D 8、 D 9、 D 10 It is a cost-type indicator, and the smaller the evaluation value, the better. In order to maintain the consistency of the evaluation results, the cost-type indicators are converted into benefit-type indicators. For example, the semantic evaluation result of evaluation criterion D3 is VH, which means that the target recognition time is very short.

[0048] Table 3

[0049]

[0050]

[0051] Table 3 shows the evaluation results of each scheme to be evaluated.

[0052] Specifically, the semantic variables in Table 3 are converted into intuitionistic fuzzy numbers to form a system effectiveness evaluation matrix (also called "decision matrix"), as shown below. Since the intuitionistic fuzzy hesitation can be calculated from the membership and non-membership degrees, only the membership and non-membership values ​​are given in this decision matrix.

[0053]

[0054] It should be noted that the above is only described as an optional example and should not be understood as a limitation to the present invention.

[0055] Next, in step S103, the best item and the worst item of the semantic evaluation value of the evaluation indicator are determined according to the system performance evaluation matrix of the multiple solutions to be evaluated.

[0056] Specifically, according to the system effectiveness evaluation matrix of the scheme to be evaluated obtained above, the optimal and worst items of the semantic evaluation values ​​of each scheme to be evaluated are determined according to the following expression, and the value with the largest membership degree in the intuitive fuzzy number of the evaluation result of each scheme is taken as the optimal item of the semantic evaluation value, and the smallest value is taken as the worst item of the semantic evaluation value.

[0057]

[0058]

[0059] in, For the D m The intuitionistic fuzzy number f of the evaluation result of the i-th evaluation scheme ij Therefore, the best and worst items of the 10 evaluation indicators are as follows.

[0060]

[0061] It should be noted that the above is only described as an optional example and should not be understood as a limitation to the present invention.

[0062] Next, in step S104, according to the established evaluation index system, the correlation between the evaluation indicators is analyzed, and a network hierarchical structure diagram is established to construct a multi-level indicator judgment matrix under the influence of the current correlation indicators, and the weighted super matrix is ​​further calculated to generate the extreme super matrix, thereby determining the indicator weight of each evaluation indicator.

[0063] By adopting the network analysis method, a network hierarchical structure diagram of multi-level indicators is established according to the reconnaissance and detection effectiveness, firepower strike effectiveness, and command and control effectiveness of the confrontation system effectiveness evaluation.

[0064] Based on the adversarial system effectiveness evaluation index system established in step S101, a network evaluation index system is constructed. For details, see Figure 2 Specifically analyze the correlation between the evaluation index systems of the above-mentioned adversarial system effectiveness evaluation index system. First, use the network hierarchy structure to accurately represent the influence relationship between the evaluation indicators, such as Figure 3 The network hierarchical structure diagram of the multi-level indicators shown.

[0065] Based on the target to be evaluated and the object to be evaluated, the evaluation indicators are identified and the correlation between the evaluation indicators is analyzed to establish an evaluation indicator system, wherein the evaluation indicator system includes multi-level indicators.

[0066] Specifically, Figure 2 As shown, the effectiveness evaluation index system of the confrontation system includes a reconnaissance and detection effectiveness index set B1, a firepower strike effectiveness index set B2, and a command and control effectiveness index set B3. The reconnaissance and detection effectiveness index set B1 includes target recognition accuracy C11, target positioning accuracy C12, target recognition time C13, and target discovery rate C14. The firepower strike effectiveness index set B2 includes firepower strike range C21, hit probability C22, damage probability C23, and average ammunition consumption C24. The command and control effectiveness index set B3 includes decision response time C31 and situation fusion processing time C32.

[0067] exist Figure 3 In the example, the target recognition accuracy C11 affects the target discovery rate C14, so the reconnaissance and detection effectiveness index set B1 has a self-influence relationship, which is manifested as an arrow pointing to itself in the network structure of the effectiveness evaluation index set; the target positioning accuracy C12 affects the hit probability C22, so the reconnaissance and detection effectiveness index set B1 affects the firepower strike effectiveness index set B2, which is manifested as an arrow pointing from the reconnaissance and detection effectiveness index set B1 to the firepower strike effectiveness index set B2 in the network structure of the effectiveness evaluation index set. Specifically, the reconnaissance and detection effectiveness index set B1 and the firepower strike effectiveness index set B2 have a self-influence relationship, and at the same time, the reconnaissance and detection effectiveness index set B1 affects the firepower strike effectiveness index set B2, the firepower strike effectiveness index set B2 affects the command and control effectiveness index set B3, and the command and control effectiveness index set B3 affects the reconnaissance and detection effectiveness index set B1 and the firepower strike effectiveness index set B2.

[0068] Next, a multi-level indicator judgment matrix under the influence of the current correlation indicators is constructed.

[0069] The scaling method is used to describe the relative importance of evaluation indicators (including secondary indicators and tertiary indicators).

[0070] Table 4

[0071]

[0072] Table 4 shows the meaning of the relative importance judgment scale between evaluation indicators (including secondary indicators and tertiary indicators).

[0073] According to the correlation analysis of the effectiveness evaluation indicators of the confrontation system, the influence relationship between the reconnaissance and detection effectiveness indicator set B1, the firepower strike effectiveness indicator set B2, and the command and control effectiveness indicator set B3 is shown in Table 5 below.

[0074] Table 5

[0075]

[0076]

[0077] Table 5 shows the influence relationship between the reconnaissance detection effectiveness index set B1, the firepower strike effectiveness index set B2, and the command and control effectiveness index set B3.

[0078] In a specific implementation, the current correlation index is selected, a secondary index judgment matrix under the influence of the current correlation index is constructed, and a secondary index weight matrix is ​​generated. The current correlation index is a secondary index (specifically corresponding to the reconnaissance detection effectiveness index set B1 (hereinafter, also referred to as the effectiveness index set B1), the firepower strike effectiveness index set B2, and the command and control effectiveness index set B3).

[0079] For generating the secondary indicator weight matrix, the influence relationship between the reconnaissance and detection effectiveness index set B1 and the firepower strike effectiveness index set B2 (hereinafter also referred to as "effectiveness index set B2") under the influence of the reconnaissance and detection effectiveness index set B1 (hereinafter also referred to as "effectiveness index set B1"). The influence relationship between effectiveness index set B2 and effectiveness index set B3 under the influence of effectiveness index set B2. The influence relationship between effectiveness index set B1 and effectiveness index set B2 under the influence of effectiveness index set B3. Therefore, three secondary judgment matrices are obtained, and the eigenvectors of the three secondary judgment matrices are calculated respectively. If there is no influence relationship between the indicators, it is expressed as the value of the corresponding element in the eigenvector is 0. The weighted matrix U is obtained according to the pairwise judgment matrix of the effectiveness indicator set, as shown in the following formula. The weighted matrix U clarifies the importance (mutual influence or interaction) between the effectiveness indicator sets.

[0080]

[0081] Further, the following secondary indicator weight matrix U is generated.

[0082]

[0083] Next, the current correlation index is selected, a three-level index judgment matrix under the influence of the current correlation index is constructed, and the judgment matrix is ​​integrated to form a super matrix. The current correlation index is a second-level index or a third-level index.

[0084] By traversing the performance indicator sets B1, B2, and B3 in turn, six judgment matrices of the evaluation indicators under the influence of the secondary indicator sets are generated: the judgment matrix of the evaluation indicators in the performance indicator set B1 under the influence of the performance indicator sets B1 and B3; the judgment matrix of the evaluation indicators in the performance indicator set B2 under the influence of the performance indicator sets B1, B2, and B3; the judgment matrix of the evaluation indicators in the performance indicator set B3 under the influence of the performance indicator set B2.

[0085] Next, observe the connection relationship of the evaluation indicators. The evaluation indicator Ci points to the evaluation indicator Cj, which means that Ci directly affects the relative importance relationship of the evaluation indicators in the performance indicator set where Cj is located. It is necessary to generate a judgment matrix under the influence of the evaluation indicator. There are nine evaluation indicator judgment matrices under the influence of the evaluation indicators that need to be generated: the judgment matrix of the evaluation indicators in the performance indicator set B1 under the influence of the evaluation indicators C11 and the evaluation indicator C32; the judgment matrix of the evaluation indicators in the performance indicator set B2 under the influence of the evaluation indicators C12, C13, C14, C21, C22, and C31; the judgment matrix of the evaluation indicators in the performance indicator set B3 under the influence of the evaluation indicator C23.

[0086] For the judgment matrix under the influence of the third-level indicators that do not directly affect the relative importance relationship of the third-level indicators, the judgment matrix under the influence of the second-level indicators is used to represent it.

[0087] Specifically, there are two third-level indicators that are located in two different performance indicator sets (or in the same performance indicator set) without a direct connection (that is, one of the third-level indicators does not directly affect the other third-level indicator), but because there is a connection between the two performance indicator sets (that is, the second-level indicators) (or the same performance indicator set has an arrow pointing to itself, that is, there is a self-influence relationship), in this case, the influence of the second-level indicator is used instead of the third-level indicator.

[0088] For example, the judgment matrix of the evaluation indicators in the performance indicator set B1 under the influence of the evaluation indicators C12, C13, C14, and C31 is represented by the judgment matrix under the influence of B1(C12, C13, C14) and B3(C31), respectively. The judgment matrix of the evaluation indicators in the performance indicator set B2 under the influence of the evaluation indicators C11, C23, C24, and C32 is represented by the judgment matrix under the influence of B1(C11), B2(C23, C24), and B3(C32), respectively. The judgment matrix of the evaluation indicators in the performance indicator set B3 under the influence of the evaluation indicators C21, C22, and C24 is represented by the judgment matrix under the influence of B2. Obviously, compared with the evaluation indicator judgment matrix that needs to be generated by the traditional indicator element set representation method, the calculation amount of the five judgment matrices is reduced.

[0089] The generated judgment matrix is ​​as follows.

[0090]

[0091]

[0092] The use of a mesh hierarchical structure evaluation index system can accurately reflect the influence relationship between indicators. When constructing a super matrix, the judgment matrix under the influence of evaluation indicators that do not directly affect the relative importance relationship of the evaluation indicators can be represented by the judgment matrix under the influence of the performance indicator set. This can reduce the number of evaluation indicator judgment matrices that need to be calculated, especially when facing a more complex evaluation index system, which can effectively alleviate the problem of the explosion of the number of judgment matrices.

[0093] Specifically, the normalized eigenvectors of the above fifteen judgment matrices are calculated, input into the corresponding columns of the supermatrix, and combined to generate the supermatrix W as shown below. The supermatrix W establishes the importance measurement between the evaluation indicators.

[0094]

[0095] Next, the weighted supermatrix is ​​calculated. From the obtained supermatrix W and weighted matrix U, the weighted supermatrix can be constructed Weighted supermatrix W * It indicates the actual importance of each evaluation index in the above evaluation index system.

[0096] Specifically, based on the generated secondary index weight matrix and the formed supermatrix, the weighted supermatrix construction W is calculated. * .

[0097]

[0098] Next, a limit supermatrix is ​​generated to obtain the weights of each evaluation indicator. When the weighted matrix is ​​raised to an exponential power, the mutual influence between the elements will show continuity. In order to achieve the convergence of the final weights, the weighted supermatrix is ​​usually raised to a limit power, such as 2k+1, where k is an arbitrarily large number. The resulting matrix is ​​called a limit supermatrix, and the limit matrix is ​​in the following form:

[0099]

[0100] in, It is the weight of each evaluation indicator.

[0101] In a specific implementation, the weights of target recognition accuracy, target positioning accuracy, target recognition time, target detection rate, fire strike range, hit probability, damage probability, average ammunition consumption, decision response time, and situation fusion processing time are 0.1477, 0.0783, 0.0294, 0.0587, 0.0646, 0.3136, 0.1736, 0.0372, 0.0751, and 0.0231, respectively.

[0102]

[0103] In an optional implementation, before generating a judgment matrix between three-level indicators, a judgment matrix of relative importance relationships between three-level indicators under the influence of current correlation indicators is generated based on correlation relationships between secondary indicators, where the current correlation indicator is a secondary indicator.

[0104] A judgment matrix of the relative importance relationship between the three-level indicators directly affected by the current associated indicator is further generated, and the current associated indicator is a three-level indicator.

[0105] The weighted supermatrix is ​​further calculated to generate the limit supermatrix, and then the indicator weights of each evaluation indicator are determined.

[0106] Next, in step S105, based on the determined indicator weights of each evaluation indicator and the intuitive fuzzy numbers of each evaluation indicator, the scheme evaluation parameters of each scheme are calculated to perform a ranking analysis and obtain a multi-scheme system performance comparison evaluation result.

[0107] Specifically, the scheme evaluation parameters of each scheme are calculated to perform ranking analysis.

[0108] Use the following expression to calculate the group benefit value of each plan:

[0109]

[0110] Among them, S i represents the group benefit value of the i-th solution, i is a positive integer; d represents the distance between two semantic evaluation values, using the following expression:

[0111]

[0112] ω j represents the weight of the jth evaluation criterion, that is, the weight of the jth evaluation indicator, represents the optimal item of semantic evaluation value under the jth evaluation index, represents the worst item of semantic evaluation value under the jth evaluation index, f ij represents the semantic evaluation value of the i-th solution under the j-th evaluation indicator;

[0113] Use the following expression to calculate the minimum regret value of each solution:

[0114]

[0115] Among them, R i represents the minimum regret value of the i-th solution, i is a positive integer;

[0116] d represents the distance between two semantic evaluation values, and the calculation formula is as follows.

[0117]

[0118] ω j represents the weight of the j-th evaluation criterion, that is, the weight of the j-th evaluation indicator, where j is a positive integer; represents the optimal item of semantic evaluation value under the jth evaluation index (i.e., evaluation criterion), represents the worst item of the semantic evaluation value under the jth evaluation index (i.e., evaluation criterion), f ij represents the semantic evaluation value of the i-th solution under the j-th evaluation indicator (i.e., evaluation criterion);

[0119] The following expressions are used to calculate the distance between each evaluation indicator of each solution and the ideal option:

[0120]

[0121] Among them, Q i represents the distance between the evaluation indicators of the ith option and the ideal option, i is a positive integer; v∈[0,1] represents the decision-making mechanism coefficient between group benefit and minimum regret. When v>0.5, it means that the decision maker tends to maximize the decision-making mechanism of group benefit. When v<0.5, it means that the decision maker tends to minimize the decision-making mechanism of individual regret. When v=0.5, it means that the decision is made according to the decision-making mechanism agreed upon by the decision maker, and the compromise optimal solution of v=0.5 is selected; S ＊ It represents the maximum value of the group benefit value of each plan. S - It represents the minimum value of the group benefit value of each plan. R ＊ represents the maximum value of the minimum regret value of each solution, R - represents the minimum value of the minimum regret value of each solution, R i Represents the minimum regret value of the i-th solution, where i is a positive integer.

[0122] According to the calculated distance between each evaluation index of each scheme and the ideal option, each scheme is ranked and analyzed.

[0123] In one embodiment, in Q i Based on the above, the schemes to be evaluated are ranked and analyzed. i The smaller the evaluation result is and the closer it is to 0, the better the scheme to be evaluated is; the larger the evaluation result is and the closer it is to 1, the worse the scheme is. Therefore, among the five schemes to be evaluated, the scheme with the best system performance evaluation result is A2, the second is A1, and the worst is A4.

[0124] Table 6

[0125] Solutions to be evaluated <![CDATA[S i ]]> <![CDATA[R i ]]> <![CDATA[Q i ]]> i＝1 0.3074 0.0694 0.0413 i＝2 0.2666 0.0784 0.0184 i＝3 0.4060 0.1736 0.3545 i＝4 0.7601 0.3136 1 i＝5 0.3469 0.1960 0.3405

[0126] Table 6 shows the S of each scheme to be evaluated. i , R i and Q i value

[0127] It should be noted that the above is only described as an optional example and should not be understood as a limitation to the present invention.

[0128] Compared with the prior art, the present invention performs semantic evaluation on multiple schemes to be evaluated based on the determined evaluation criteria, and converts the semantic evaluation results of the multiple schemes to be evaluated into intuitive fuzzy numbers; constructs a system effectiveness evaluation matrix of the multiple schemes to be evaluated based on the converted intuitive fuzzy numbers; determines the optimal and the worst items of the semantic evaluation values ​​of the evaluation indicators based on the system effectiveness evaluation matrix of the multiple schemes to be evaluated; analyzes the correlation between the evaluation indicators based on the established evaluation indicator system, establishes a network hierarchical structure diagram to construct a multi-level indicator judgment matrix under the influence of the current correlation indicators, further calculates the weighted supermatrix to generate the limit supermatrix, and can accurately determine the indicator weight of each evaluation indicator; by calculating the scheme evaluation parameters of each scheme and performing ranking analysis, a comparative evaluation result of the system effectiveness of multiple schemes is obtained, which is helpful to comprehensively and accurately evaluate the system effectiveness of multiple schemes under the conditions of data lack, uncertainty and multiple evaluation indicators, and has higher reliability.

[0129] In addition, a mesh hierarchical structure is used to display the correlation between indicators rather than an element set or a simple hierarchy, which can more accurately reflect the influence relationship between indicators. When constructing a super matrix, the judgment matrix under the influence of the third-level indicators that do not directly affect the relative importance of the third-level indicators is represented by the judgment matrix under the influence of the second-level indicators, which further reduces the number of third-level indicator judgment matrices that need to be calculated. This can effectively alleviate the problem of the explosion of the number of judgment matrices, especially when facing a more complex evaluation indicator system.

[0130] Example 2

[0131] The following is a system embodiment of the present invention, which can be used to implement the method embodiment of the present invention. For details not disclosed in the system embodiment of the present invention, please refer to the method embodiment of the present invention.

[0132] Figure 4 4 is a schematic structural diagram of an example of a multi-scheme system performance comparison and evaluation system according to the present invention.

[0133] The following will refer to Figure 4, a multi-scheme system performance comparison and evaluation system is described, which uses the multi-scheme system performance comparison and evaluation method described in the present invention.

[0134] The multi-scheme system effectiveness comparison and evaluation system 400 includes a semantic evaluation processing module 410 , a first construction module 420 , a determination processing module 430 , a second construction module 440 and a calculation processing module 450 .

[0135] Specifically, the semantic evaluation processing module 410 performs semantic evaluation on multiple schemes to be evaluated based on the determined evaluation criteria, and converts the semantic evaluation results of the multiple schemes to be evaluated into intuitive fuzzy numbers. The first construction module 420 constructs the system performance evaluation matrix of the multiple schemes to be evaluated according to the converted intuitive fuzzy numbers. The determination processing module 430 determines the optimal and worst items of the semantic evaluation values ​​of the evaluation indicators according to the system performance evaluation matrix of the multiple schemes to be evaluated. The second construction module 440 analyzes the correlation relationship between the evaluation indicators according to the established evaluation indicator system, establishes a mesh hierarchical structure diagram, constructs a multi-level indicator judgment matrix under the influence of the current correlation indicator, further calculates the weighted super matrix to generate the limit super matrix, and then determines the indicator weight of each evaluation indicator. The calculation processing module 450 calculates the scheme evaluation parameters of each scheme according to the determined indicator weights of each evaluation indicator and the intuitive fuzzy numbers of each evaluation indicator, so as to perform ranking analysis and obtain the system performance comparison evaluation results of multiple schemes.

[0136] According to an optional implementation method, the semantic variables are divided into seven levels; according to the divided semantic variable levels, the schemes under each evaluation criterion are semantically evaluated to form corresponding semantic variables; and the formed semantic variables are converted into intuitive fuzzy numbers.

[0137] According to an optional implementation, the system effectiveness evaluation matrix of the multiple schemes to be evaluated is constructed based on the converted intuitive fuzzy numbers, including: combining the semantic variable intuitive fuzzy numbers of each scheme under all evaluation criteria to generate a system effectiveness evaluation decision matrix, and obtaining the best and worst items of each semantic evaluation value based on the maximum and minimum values ​​of the membership in the semantic variable intuitive fuzzy numbers.

[0138] According to an optional implementation method, a network analysis method is used to establish a multi-level index network hierarchical structure diagram based on the reconnaissance and detection effectiveness, firepower strike effectiveness, and command and control effectiveness of the confrontation system effectiveness evaluation, and the network hierarchical structure diagram includes secondary indicators and tertiary indicators.

[0139] Specifically, a multi-level indicator judgment matrix under the influence of the current correlation indicator is constructed, including: selecting the current correlation indicator, constructing a secondary indicator judgment matrix under the influence of the current correlation indicator, and generating a secondary indicator weight matrix; selecting the current correlation indicator, constructing a tertiary indicator judgment matrix under the influence of the current correlation indicator, and integrating the judgment matrix to form a super matrix; and calculating a weighted super matrix based on the generated secondary indicator weight matrix and the formed super matrix.

[0140] According to an optional implementation method, a scaling method is used to describe the relative importance between the secondary indicators; based on whether there is an influence relationship between the secondary indicators, a judgment matrix of the secondary indicators is constructed to obtain a weighted matrix of the secondary indicators; before generating a judgment matrix between the tertiary indicators, a judgment matrix of the relative importance relationship between the tertiary indicators under the influence of the current associated indicator is generated based on the association relationship between the secondary indicators, and the current associated indicator is a secondary indicator.

[0141] Further generate a judgment matrix of the relative importance relationship between the three-level indicators directly affected by the current correlation indicator, and the current correlation indicator is a three-level indicator;

[0142] For the judgment matrix under the influence of the third-level indicators that do not directly affect the relative importance relationship of the third-level indicators, the judgment matrix under the influence of the second-level indicators is used to represent it.

[0143] According to an optional implementation manner, the scheme evaluation parameters of each scheme are calculated to perform a ranking analysis.

[0144] Use the following expression to calculate the group benefit value of each plan:

[0145]

[0146] Among them, S i represents the group benefit value of the i-th solution, i is a positive integer; d represents the distance between two semantic evaluation values, using the following expression:

[0147]

[0148]

[0149] ω j represents the weight of the jth evaluation criterion, that is, the weight of the jth evaluation indicator, represents the optimal item of semantic evaluation value under the jth evaluation criterion, represents the worst item of semantic evaluation value under the jth evaluation index, f ij It represents the semantic evaluation value of the i-th solution under the j-th evaluation indicator.

[0150] Use the following expression to calculate the minimum regret value of each solution:

[0151]

[0152] Among them, R i represents the minimum regret value of the i-th solution, i is a positive integer; d represents the distance between two semantic evaluation values; ω j represents the weight of the jth evaluation criterion, that is, the weight of the jth evaluation indicator, represents the optimal item of semantic evaluation value under the jth evaluation criterion, represents the worst item of semantic evaluation value under the jth evaluation criterion, f ij It represents the semantic evaluation value of the i-th solution under the j-th evaluation indicator.

[0153] The following expressions are used to calculate the distance between each evaluation indicator of each solution and the ideal option:

[0154]

[0155] Among them, Q i Indicates the distance between each evaluation index of the i-th option and the ideal option, i is a positive integer; v∈[0,1] represents the decision-making mechanism coefficient between group benefit and minimum regret, v>0.5 represents the decision-making mechanism that the decision-maker tends to maximize the group benefit, v<0.5 represents the decision-making mechanism that the decision-maker tends to minimize the individual regret, v=0.5 represents making decisions according to the decision-making mechanism agreed upon by the decision-maker, and selecting the compromise optimal solution of v=0.5;

[0156] S ＊ It represents the maximum value of the group benefit value of each plan.

[0157] S - It represents the minimum value of the group benefit value of each plan.

[0158] R ＊ represents the maximum value of the minimum regret value of each solution,

[0159] R - represents the minimum value of the minimum regret value of each solution, R i represents the minimum regret value of the i-th solution, i is a positive integer;

[0160] According to the calculated distance between each evaluation index of each scheme and the ideal option, each scheme is ranked and analyzed.

[0161] It should be noted that due to Figure 4 The multi-scheme system effectiveness comparison and evaluation method implemented by the multi-scheme system effectiveness comparison and evaluation system is Figure 1The multi-scheme system effectiveness comparison and evaluation methods in the examples are roughly the same, so the description of the same parts is omitted.

[0162] Compared with the prior art, the present invention performs semantic evaluation on multiple schemes to be evaluated based on the determined evaluation criteria, and converts the semantic evaluation results of the multiple schemes to be evaluated into intuitive fuzzy numbers; constructs a system effectiveness evaluation matrix of the multiple schemes to be evaluated based on the converted intuitive fuzzy numbers; determines the optimal and the worst items of the semantic evaluation values ​​of the evaluation indicators based on the system effectiveness evaluation matrix of the multiple schemes to be evaluated; analyzes the correlation between the evaluation indicators based on the established evaluation indicator system, establishes a network hierarchical structure diagram to construct a multi-level indicator judgment matrix under the influence of the current correlation indicators, further calculates the weighted supermatrix to generate the limit supermatrix, and can accurately determine the indicator weight of each evaluation indicator; by calculating the scheme evaluation parameters of each scheme and performing ranking analysis, a comparative evaluation result of the system effectiveness of multiple schemes is obtained, which is helpful to comprehensively and accurately evaluate the system effectiveness of multiple schemes under the conditions of data lack, uncertainty and multiple criteria, and has higher reliability.

[0163] In addition, a mesh hierarchical structure is used to display the correlation between indicators rather than an element set or a simple hierarchy, which can more accurately reflect the influence relationship between indicators. When constructing a super matrix, the judgment matrix under the influence of the third-level indicators that do not directly affect the relative importance of the third-level indicators is represented by the judgment matrix under the influence of the second-level indicators, which further reduces the number of third-level indicator judgment matrices that need to be calculated. This can effectively alleviate the problem of the explosion of the number of judgment matrices, especially when facing a more complex evaluation indicator system.

[0164] Example 3

[0165] Figure 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

[0166] like Figure 5 As shown, the electronic device is presented in the form of a general computing device. The processor may be one or more and work in coordination. The present invention does not exclude distributed processing, that is, the processor may be dispersed in different physical devices. The electronic device of the present invention is not limited to a single entity, but may also be the sum of multiple physical devices.

[0167] The memory stores a computer executable program, which is usually a machine-readable code. The computer-readable program can be executed by the processor to enable the electronic device to perform the method of the present invention, or at least part of the steps in the method.

[0168] The memory includes a volatile memory, such as a random access memory unit (RAM) and / or a cache memory unit, and may also be a non-volatile memory, such as a read-only memory unit (ROM).

[0169] Optionally, in this embodiment, the electronic device further includes an I / O interface, which is used for the electronic device to exchange data with an external device. The I / O interface can represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0170] It should be understood that Figure 5 The electronic device shown is only an example of the present invention, and the electronic device of the present invention may also include elements or components not shown in the above examples. For example, some electronic devices also include display units such as display screens, and some electronic devices also include human-computer interaction elements such as buttons, keyboards, etc. As long as the electronic device can execute the computer-readable program in the memory to implement the method of the present invention or at least part of the steps of the method, it can be considered as an electronic device covered by the present invention.

[0171] Through the above description of the implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by combining software with necessary hardware. Figure 6 As shown, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of commands to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present invention.

[0172] The software product may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0173] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with a command execution system, device, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0174] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0175] The computer-readable medium carries one or more programs. When the one or more programs are executed by a device, the computer-readable medium implements the data interaction method of the present disclosure.

[0176] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be changed accordingly and only used in one or more devices different from the embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.

[0177] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several commands to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.

[0178] It should be noted that the above detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present application belongs.

[0179] In the above detailed description, reference is made to the accompanying drawings, which form a part of this document. In the accompanying drawings, similar symbols typically identify similar components unless the context indicates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein.

[0180] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-scheme system performance comparison and evaluation method, characterized in that: include: Based on the determined evaluation criteria, a plurality of schemes to be evaluated are semantically evaluated, and the semantic evaluation results of the plurality of schemes to be evaluated are converted into intuitionistic fuzzy numbers; Constructing a system effectiveness evaluation matrix of the plurality of schemes to be evaluated according to the converted intuitionistic fuzzy numbers; Determining the best and worst terms of the semantic evaluation values ​​of the evaluation indicators according to the system effectiveness evaluation matrix of the multiple schemes to be evaluated; According to the established evaluation index system, the correlation between the evaluation indicators is analyzed, and a network hierarchical structure diagram is established to construct a multi-level indicator judgment matrix under the influence of the current correlation indicators, and the weighted super matrix is ​​further calculated to generate the limit super matrix, thereby determining the indicator weight of each evaluation indicator; According to the determined indicator weights of each evaluation indicator and the intuitive fuzzy numbers of each evaluation indicator, the scheme evaluation parameters of each scheme are calculated to perform a ranking analysis and obtain the comparative evaluation results of the effectiveness of the multi-scheme system; wherein, the multi-level indicator judgment matrix under the influence of the current correlation indicator is constructed, including: Select the current correlation index, construct the secondary index judgment matrix under the influence of the current correlation index, and generate the secondary index weight matrix; select the current correlation index, construct the tertiary index judgment matrix under the influence of the current correlation index, and integrate the judgment matrix to form a super matrix; calculate the weighted super matrix based on the generated secondary index weight matrix and the formed super matrix; The relative importance between the secondary indicators is described by the scaling method; the judgment matrix of the secondary indicators is constructed according to whether there is an influence relationship between the secondary indicators to obtain the weighted matrix of the secondary indicators; before generating the judgment matrix between the tertiary indicators, the judgment matrix of the relative importance relationship between the tertiary indicators under the influence of the current associated indicators is generated based on the association relationship between the secondary indicators, and the current associated indicators are secondary indicators; further, the judgment matrix of the relative importance relationship between the tertiary indicators under the direct influence of the current associated indicators is generated, and the current associated indicators are tertiary indicators; the judgment matrix under the influence of the tertiary indicators that do not directly affect the relative importance relationship of the tertiary indicators is represented by the judgment matrix under the influence of the secondary indicators; among them, Use the following expression to calculate the group benefit value of each plan: Among them, S i represents the group benefit value of the i-th solution, i is a positive integer; d represents the distance between two semantic evaluation values, using the following expression: ω j represents the weight of the jth evaluation criterion, that is, the weight of the jth evaluation indicator, f j ＊ represents the optimal item of semantic evaluation value under the jth evaluation criterion, represents the worst item of semantic evaluation value under the jth evaluation index, f ij represents the semantic evaluation value of the i-th solution under the j-th evaluation indicator; Use the following expression to calculate the minimum regret value of each solution: Among them, R i represents the minimum regret value of the i-th solution, i is a positive integer; d represents the distance between two semantic evaluation values; ω j represents the weight of the jth evaluation criterion, that is, the weight of the jth evaluation indicator, f j ＊ represents the optimal item of semantic evaluation value under the jth evaluation criterion, represents the worst item of semantic evaluation value under the jth evaluation criterion, f ij It represents the semantic evaluation value of the i-th solution under the j-th evaluation indicator.

2. The multi-scheme system performance comparison and evaluation method according to claim 1, further comprising: Semantic variables are divided into seven levels; According to the semantic variable levels, semantic evaluation is performed on the schemes under each evaluation criterion to form corresponding semantic variables; The formed semantic variables are converted into intuitionistic fuzzy numbers.

3. The multi-scheme system performance comparison and evaluation method according to claim 2 is characterized in that: The step of constructing a system performance evaluation matrix of the plurality of schemes to be evaluated based on the converted intuitionistic fuzzy numbers comprises: The semantic variable intuitionistic fuzzy numbers of each scheme under all evaluation criteria are combined to generate a system effectiveness evaluation decision matrix, and the optimal and worst items of each evaluation value are obtained according to the maximum and minimum values ​​of the membership degree in the semantic variable intuitionistic fuzzy numbers.

4. The multi-scheme system performance comparison and evaluation method according to claim 1, characterized in that: The following expressions are used to calculate the distance between each evaluation indicator of each solution and the ideal option: Among them, Q i represents the distance between each evaluation index of the i-th option and the ideal option, i is a positive integer; v∈[0,1] represents the decision-making mechanism coefficient between group benefit and minimum regret. When v>0.5, it means that the decision maker tends to maximize the decision-making mechanism of group benefit. When v<0.5, it means that the decision maker tends to minimize the decision-making mechanism of individual regret. When v=0.5, it means that the decision is made according to the decision-making mechanism agreed upon by the decision maker, and the compromise optimal solution of v=0.5 is selected. S ＊ It represents the maximum value of the group benefit value of each plan. S - It represents the minimum value of the group benefit value of each plan. R ＊ represents the maximum value of the minimum regret value of each solution, R - represents the minimum value of the minimum regret value of each solution, R i represents the minimum regret value of the i-th solution, i is a positive integer; According to the calculated distance between each evaluation index of each scheme and the ideal option, each scheme is ranked and analyzed.

5. The multi-scheme system performance comparison and evaluation method according to claim 1, characterized in that: By adopting the network analysis method, a network hierarchical structure diagram of multiple levels of indicators is established according to the reconnaissance and detection effectiveness, firepower strike effectiveness, and command and control effectiveness of the confrontation system effectiveness evaluation. The network hierarchical structure diagram includes secondary indicators and tertiary indicators.

6. A multi-scheme system effectiveness comparison and evaluation system based on hierarchical analysis and fuzzy VIKOR method, characterized in that: It uses the multi-scheme system performance comparison and evaluation method according to any one of claims 1 to 5, and the multi-scheme system performance comparison and evaluation system comprises: A semantic evaluation processing module, which performs semantic evaluation on a plurality of schemes to be evaluated based on the determined evaluation criteria, and converts the semantic evaluation results of the plurality of schemes to be evaluated into intuitive fuzzy numbers; A first construction module constructs a system effectiveness evaluation matrix of the plurality of schemes to be evaluated according to the converted intuitionistic fuzzy numbers; A determination processing module determines the best and worst items of the semantic evaluation values ​​of the evaluation criteria according to the system effectiveness evaluation matrix of the plurality of schemes to be evaluated; The second construction module analyzes the correlation between the evaluation indicators according to the established evaluation indicator system, establishes a network hierarchical structure diagram, constructs a multi-level indicator judgment matrix under the influence of the current correlation indicators, further calculates the weighted super matrix to generate the limit super matrix, and then determines the indicator weight of each evaluation indicator; The calculation processing module calculates the scheme evaluation parameters of each scheme according to the determined indicator weights of each evaluation indicator and the intuitive fuzzy numbers of each evaluation indicator to perform ranking analysis and obtain the comparative evaluation results of the effectiveness of the multi-scheme system; wherein, the multi-level indicator judgment matrix under the influence of the current correlation indicator is constructed, including: Select the current correlation index, construct the secondary index judgment matrix under the influence of the current correlation index, and generate the secondary index weight matrix; select the current correlation index, construct the tertiary index judgment matrix under the influence of the current correlation index, and integrate the judgment matrix to form a super matrix; calculate the weighted super matrix based on the generated secondary index weight matrix and the formed super matrix; The relative importance between the secondary indicators is described by the scaling method; the judgment matrix of the secondary indicators is constructed according to whether there is an influence relationship between the secondary indicators to obtain the weighted matrix of the secondary indicators; before generating the judgment matrix between the tertiary indicators, the judgment matrix of the relative importance relationship between the tertiary indicators under the influence of the current associated indicators is generated based on the association relationship between the secondary indicators, and the current associated indicators are secondary indicators; further, the judgment matrix of the relative importance relationship between the tertiary indicators under the direct influence of the current associated indicators is generated, and the current associated indicators are tertiary indicators; the judgment matrix under the influence of the tertiary indicators that do not directly affect the relative importance relationship of the tertiary indicators is represented by the judgment matrix under the influence of the secondary indicators; among them, Use the following expression to calculate the group benefit value of each plan: Among them, S i represents the group benefit value of the i-th solution, i is a positive integer; d represents the distance between two semantic evaluation values, using the following expression: ω j represents the weight of the jth evaluation criterion, that is, the weight of the jth evaluation indicator, f j ＊ represents the optimal item of semantic evaluation value under the jth evaluation criterion, represents the worst item of semantic evaluation value under the jth evaluation index, f ij represents the semantic evaluation value of the i-th solution under the j-th evaluation indicator; Use the following expression to calculate the minimum regret value of each solution: Among them, R i represents the minimum regret value of the i-th solution, i is a positive integer; d represents the distance between two semantic evaluation values; ω j represents the weight of the jth evaluation criterion, that is, the weight of the jth evaluation indicator, f j ＊ represents the optimal item of semantic evaluation value under the jth evaluation criterion, represents the worst item of semantic evaluation value under the jth evaluation criterion, f ij It represents the semantic evaluation value of the i-th solution under the j-th evaluation indicator.

7. The multi-scheme system performance comparison and evaluation system according to claim 6, characterized in that: The calculation of the scheme evaluation parameters of each scheme to perform ranking analysis includes: Using the intuitionistic fuzzy number f ij =(μ ij ,γ ij ,π ij ) represents the evaluation value of each evaluation scheme under each evaluation criterion, and the evaluation decision matrix is ​​as follows: Among them, μ ij represents the degree of membership, γ ij represents the non-membership degree, π ij Indicates intuitive fuzziness and hesitation; The following expressions are used to calculate the distance between each evaluation indicator of each solution and the ideal option: Among them, Q i represents the distance between each evaluation index of the i-th option and the ideal option, i is a positive integer; v∈[0,1] represents the decision-making mechanism coefficient between group benefit and minimum regret. When v>0.5, it means that the decision maker tends to maximize the decision-making mechanism of group benefit. When v<0.5, it means that the decision maker tends to minimize the decision-making mechanism of individual regret. When v=0.5, it means that the decision is made according to the decision-making mechanism agreed upon by the decision maker, and the compromise optimal solution of v=0.5 is selected. S ＊ It represents the maximum value of the group benefit value of each plan. S - It represents the minimum value of the group benefit value of each plan. R ＊ represents the maximum value of the minimum regret value of each solution, R - represents the minimum value of the minimum regret value of each solution, R i Represents the minimum regret value of the i-th solution, where i is a positive integer.

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