Evaluation method and device, computer equipment and storage medium

Abstract

The invention provides an evaluation method and device, computer equipment and a storage medium, and the method comprises the steps: constructing an index directed graph according to a plurality of evaluation indexes of to-be-evaluated equipment and an influence relation between any two evaluation indexes in the plurality of evaluation indexes; the plurality of indexes respectively indicate different evaluation dimensions; calculating a first weight of each evaluation index based on the index directed graph; calculating a second weight of each evaluation index based on a preset relative importance matrix; for any evaluation index in the plurality of evaluation indexes, calculating a third weight of the evaluation index based on the first weight and the second weight of the evaluation index; and generating an evaluation result based on the plurality of evaluation indexes and the third weights in one-to-one correspondence with the plurality of evaluation indexes. According to the embodiment of the invention, the accuracy of equipment combat effectiveness evaluation can be greatly improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to an evaluation method, device, computer equipment and storage medium. Background Art

[0002] The Analytic Hierarchy Process (AHP) is an evaluation method that subjectively assigns indicator weights. It allows decision makers to give indicator weights based on their personal subjective preferences and experience. It can better reflect the decision makers' experience and decision intentions. It has a simple principle and is easy to calculate, and is widely used in the field of evaluation.

[0003] When applying the analytic hierarchy process to evaluate the combat effectiveness of equipment, different decision makers may have different subjective weights for the same evaluation problem, which makes the analytic hierarchy process have the disadvantages of strong subjectivity, poor reliability and low confidence, which can easily lead to the problem of low accuracy in the evaluation of the combat effectiveness of equipment.

[0004] Therefore, an evaluation method is urgently needed to solve the above technical problems. Summary of the invention

[0005] In view of this, the present disclosure proposes an evaluation method, apparatus, computer device and storage medium to solve the problem in related technologies that, due to the influence of subjective factors, the confidence and reliability of indicator weight determination are low, which in turn causes low accuracy in equipment combat effectiveness evaluation.

[0006] The first aspect of the present disclosure provides an evaluation method, the method comprising:

[0007] According to multiple evaluation indicators of the equipment to be evaluated and the influence relationship between any two evaluation indicators among the multiple evaluation indicators, an indicator directed graph is constructed; the multiple indicators respectively indicate different evaluation dimensions of the equipment to be evaluated in terms of combat effectiveness;

[0008] Based on the indicator directed graph, calculating a first weight of each evaluation indicator;

[0009] Based on a preset relative importance matrix, a second weight of each evaluation indicator is calculated; any parameter in the relative importance matrix represents the importance comparison result between any two evaluation indicators;

[0010] For any one of the plurality of evaluation indicators, based on the first weight and the second weight of the evaluation indicator, calculating a third weight of the evaluation indicator;

[0011] Based on the multiple evaluation indicators and the third weights corresponding to the multiple evaluation indicators one by one, a combat effectiveness evaluation result of the equipment to be evaluated is generated.

[0012] The disclosed embodiment calculates the third weight of the evaluation index based on the first weight and the second weight, which can greatly improve the accuracy of equipment combat effectiveness evaluation by integrating subjective factors with objective factors and combining quantitative with qualitative factors.

[0013] In the embodiment of the present disclosure, according to multiple evaluation indicators of the equipment to be evaluated and the influence relationship between any two evaluation indicators among the multiple evaluation indicators, an indicator directed graph is constructed, including:

[0014] Constructing a first relationship matrix according to a plurality of evaluation indicators of the equipment to be evaluated and an influence relationship between any two evaluation indicators among the plurality of evaluation indicators;

[0015] Based on the first relationship matrix, the indicator directed graph is constructed.

[0016] The disclosed embodiment constructs an indicator directed graph by presetting multiple evaluation indicators and the influence relationship between any two evaluation indicators among the multiple evaluation indicators, which can fully evaluate the relationship between the indicators and thus help understand the importance of each indicator in the evaluation.

[0017] In an embodiment of the present disclosure, the method further includes:

[0018] Converting the first relationship matrix into a second relationship matrix; the second relationship matrix is ​​used to characterize the mutual influence relationship between the multiple evaluation indicators;

[0019] The second relationship matrix is ​​decomposed to obtain a plurality of hierarchical features, wherein each hierarchical feature includes at least one evaluation index.

[0020] By converting the first relationship matrix into the second relationship matrix, the disclosed embodiment can not only consider the relationship between every two indicators, but also consider the comprehensive impact of multiple other indicators on these two indicators when multiple evaluation indicators are involved and the relationship between the evaluation indicators is more complex. In this way, the calculation accuracy of the indicator weights can be improved, thereby improving the accuracy of equipment combat effectiveness evaluation.

[0021] In the embodiment of the present disclosure, converting the first relationship matrix into a second relationship matrix includes:

[0022] Adding the first relation matrix and the identity matrix to obtain a first submatrix;

[0023] The first submatrix is ​​subjected to multiple power operations until no new element position appears in the first submatrix after the power operation, and the first submatrix after the power operation is used as the second relational matrix.

[0024] In the embodiment of the present disclosure, the second relationship matrix is ​​decomposed to obtain multiple hierarchical features, including:

[0025] Determine a target evaluation indicator from an evaluation indicator matrix to be decomposed, decompose the evaluation indicator matrix to be decomposed, and obtain a first set and a second set; the first set includes a plurality of other evaluation indicators affected by the target evaluation indicator; the second set includes a plurality of other evaluation indicators affecting the target evaluation indicator; the target evaluation indicator is any one of a plurality of evaluation indicators of the second relationship matrix, and the first evaluation indicator matrix to be decomposed is the second relationship matrix;

[0026] If the first set and the second set meet a preset condition, determining the evaluation index to be the first index; the preset condition includes that the intersection result of the first set and the second set is the first set or the second set;

[0027] Traversing the plurality of evaluation indicators, and dividing the determined plurality of first indicators into a hierarchical feature;

[0028] The multiple first indicators are eliminated from the evaluation indicator matrix to be decomposed, and the matrix obtained after the elimination is used as a new evaluation indicator matrix to be decomposed, and the steps are repeated: determine the target evaluation indicator from the evaluation indicator matrix to be decomposed, decompose the evaluation indicator matrix to be decomposed to obtain a first set and a second set; if the first set and the second set meet preset conditions, determine that the evaluation indicator is the first indicator; traverse the multiple evaluation indicators, and divide the determined multiple first indicators into a hierarchical feature.

[0029] In the embodiment of the present disclosure, based on the indicator directed graph, the first weight of each evaluation indicator is calculated, including:

[0030] Calculate the in-degree value and out-degree value of each evaluation indicator based on the indicator directed graph;

[0031] A first weight of each evaluation indicator is calculated according to a plurality of out-degree values ​​corresponding one-to-one to the plurality of evaluation indicators and a plurality of in-degree values ​​corresponding one-to-one to the plurality of evaluation indicators.

[0032] The first weight of the evaluation indicator in the embodiment of the present disclosure is calculated based on the in-degree value and out-degree value of the evaluation indicator, which can reflect the relative weight of the evaluation indicator in the entire indicator system, and can also more accurately understand the importance and influence of each evaluation indicator in the comprehensive evaluation.

[0033] In the embodiment of the present disclosure, based on the preset relative importance matrix, the second weight of each evaluation indicator is calculated, including:

[0034] Normalizing the relative importance matrix to obtain a normalized matrix;

[0035] Calculating multiple eigenvectors and a maximum eigenvalue of the normalized matrix;

[0036] Performing a consistency check on the relative importance matrix based on the maximum eigenvalue;

[0037] If the consistency check of the relative importance matrix passes, the multiple eigenvectors are determined as multiple second weights; the multiple second weights correspond one-to-one to the multiple evaluation indicators.

[0038] In the embodiment of the present disclosure, calculating the third weight of the evaluation indicator based on the first weight and the second weight of the evaluation indicator includes:

[0039] Calculating a first coefficient of the first weight and a second coefficient of the second weight; the first coefficient and the second coefficient are used to reduce the difference between the first weight and the second weight;

[0040] Calculating a first target weight based on the first weight and the first coefficient, and calculating a second target weight based on the second weight and the second coefficient;

[0041] The third weight is calculated based on the first target weight and the second target weight.

[0042] The disclosed embodiment sets corresponding weight coefficients for the subjective weight (second weight) and the objective weight (first weight) by means of comprehensive weighting, which can more comprehensively consider the advantages and disadvantages of different weights. In this way, the results of expert experience and data analysis can be comprehensively utilized to improve the accuracy and credibility of the comprehensive evaluation of equipment combat effectiveness. In addition, the subjective and objective weights are gamed against each other, with the goal of minimizing the sum of the deviations between the combined indicator weights. This method can achieve a balance between different weights, reduce the differences in weights, and improve the stability and consistency of the equipment combat effectiveness evaluation results.

[0043] In an embodiment of the present disclosure, the method further includes:

[0044] The fourth weights of the plurality of second evaluation indicators are calculated according to the plurality of third weights corresponding one-to-one to the plurality of evaluation indicators; wherein each second evaluation indicator corresponds to at least one evaluation indicator.

[0045] An embodiment of a second aspect of the present disclosure provides an evaluation device, the device comprising:

[0046] A directed graph construction module is used to construct an indicator directed graph according to multiple evaluation indicators of the equipment to be evaluated and the influence relationship between any two evaluation indicators among the multiple evaluation indicators; the multiple indicators respectively indicate different evaluation dimensions of the equipment to be evaluated in terms of combat effectiveness;

[0047] A first weight calculation module, used to calculate a first weight of each evaluation indicator based on the indicator directed graph;

[0048] A second weight calculation module is used to calculate a second weight of each evaluation indicator based on a preset relative importance matrix; any parameter in the relative importance matrix represents the importance comparison result between any two evaluation indicators;

[0049] A third weight calculation module, configured to calculate, for any one of the plurality of evaluation indicators, a third weight of the evaluation indicator based on the first weight and the second weight of the evaluation indicator;

[0050] An evaluation module is used to generate a combat effectiveness evaluation result of the equipment to be evaluated based on the multiple evaluation indicators and a third weight corresponding to the multiple evaluation indicators.

[0051] An embodiment of the third aspect of the present disclosure provides a computer device, which includes a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the evaluation method described in the first aspect by executing the computer instructions.

[0052] An embodiment of the fourth aspect of the present disclosure provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the evaluation method described in the first aspect.

[0053] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will become apparent from the following description or be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] By reading the detailed description of the preferred embodiment below, various other advantages and benefits will become clear to those of ordinary skill in the art. The accompanying drawings are only used for the purpose of illustrating the preferred embodiment and are not considered to be limitations of the present disclosure. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings.

[0055] In the attached picture:

[0056] Figure 1 A schematic diagram showing an improved idea of ​​an evaluation method provided by an embodiment of the present disclosure is shown;

[0057] Figure 2 A schematic diagram of a flow chart of an evaluation method provided by an embodiment of the present disclosure is shown;

[0058] Figure 3 A schematic diagram showing a hierarchical relationship of indicators provided by an embodiment of the present disclosure;

[0059] Figure 4 A schematic diagram of an indicator directed graph provided by an embodiment of the present disclosure is shown;

[0060] Figure 5 A schematic diagram showing a flow chart of another evaluation method provided by an embodiment of the present disclosure;

[0061] Figure 6 A schematic diagram showing a specific scenario of decomposing the second relationship matrix to obtain multiple hierarchical features in one embodiment of the present disclosure;

[0062] Figure 7 A schematic diagram showing a specific scenario of decomposing the second relationship matrix to obtain multiple hierarchical features in one embodiment of the present disclosure;

[0063] Figure 8 A schematic diagram of the structure of an evaluation device provided by an embodiment of the present disclosure is shown;

[0064] Fig. 9 A schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure is shown;

[0065] Fig.10 A schematic diagram of a storage medium provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0066] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0067] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present disclosure should have the common meanings understood by those skilled in the art to which the present disclosure belongs.

[0068] The following describes the technical scenarios involved in the embodiments of the present disclosure.

[0069] As the basis of evaluation, the indicator system is a whole composed of a series of interrelated indicators, and is the link between evaluation experts and evaluation objects. The indicator weight refers to the contribution of the indicator to the evaluation target. When the evaluation object, indicator system, and evaluation method are determined, the evaluation result depends on the indicator weight. However, the indicator system is a complex system with many interrelated factors. Each indicator only reflects a certain function or feature of the system. When establishing an indicator system, it is inevitable that indicators overlap and influence each other. The rationality of indicator weight determination is directly related to the credibility of the evaluation results.

[0070] According to the different sources of original data when calculating the indicator weights, the indicator weighting methods can be divided into subjective weighting method, objective weighting method and combined weighting method. Subjective weighting method refers to the method in which decision makers give weight values ​​based on their personal subjective preferences and their own experience, which can better reflect the decision makers' knowledge, experience and decision-making intentions, such as Delphi method, extreme value iteration method, binomial coefficient method, hierarchical analysis method, etc.; objective weighting method refers to the method of weighting according to the objective difference degree reflected by each indicator in the evaluation indicator sample set and the degree of influence on other indicators. Commonly used methods include mean square error method, entropy method, principal component analysis method, coefficient of variation method, etc.; combined weighting method combines the characteristics of subjective weighting method and objective weighting method, first use subjective weighting method and objective weighting method to determine their respective indicator weights, and then determine the proportion of weight coefficients of subjective weighting method and objective weighting method based on certain criteria to form the final indicator weight.

[0071] The analytic hierarchy process is a subjective weighting method for indicator weights. It allows decision makers to give indicator weights based on their personal subjective preferences and their own experience. It can better reflect the decision makers' experience and knowledge and decision-making intentions. It is simple in principle and easy to calculate, and is widely used in the field of evaluation. The explanatory structural model can decompose the complex indicator system into a hierarchical structure, and can be effectively combined with the analytic hierarchy process to further analyze the relationship between indicators and their impact on the evaluation problem. However, different decision makers may have different subjective weights for the same evaluation problem, which makes the analytic hierarchy process have the disadvantages of strong subjectivity and poor reliability. At the same time, the analytic hierarchy process is only applicable to simple hierarchical models. For complex network structures with mutual constraints between indicators, the analytic hierarchy process is no longer applicable.

[0072] The disclosed embodiment uses the analytic hierarchy process and the explanatory structure model to construct an index system and determine the subjective weights of the indexes, determines the objective weights of the indexes based on social network analysis, and then integrates the weights of the two methods to form a new comprehensive weight based on the game confrontation idea, so as to achieve the combination of subjective and objective, qualitative and quantitative, and improve the confidence of the evaluation. Figure 1As shown in the figure, for specific evaluation problems, the hierarchical decomposition idea is used to decompose the evaluation target into several criteria, and an indicator system that can reflect the system function or characteristics is established; the mutual influence relationship of each indicator at the bottom solution layer is analyzed, the adjacency matrix is ​​constructed, and the ISM model is used to hierarchically interpret the indicators; the hierarchical interpretation results are used as the SNA network input, and the indicator network is quantitatively analyzed using the network centrality parameter to determine the objective weight of the indicator; based on the game confrontation idea, the subjective weights obtained by the AHP method and the objective weights obtained by the SNA method are comprehensively weighted to determine the optimal weights of the solution layer indicators; finally, the solution layer indicator weights are aggregated upward layer by layer to obtain new criterion layer weights.

[0073] According to an embodiment of the present disclosure, an evaluation method embodiment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0074] In this embodiment, an evaluation method is provided. Figure 2 is a flow chart of an evaluation method according to an embodiment of the present disclosure, such as Figure 2 As shown, the process includes the following steps:

[0075] Step S101, constructing an indicator directed graph according to multiple evaluation indicators of the equipment to be evaluated and the influence relationship between any two evaluation indicators among the multiple evaluation indicators, wherein the multiple indicators respectively indicate different evaluation dimensions of the combat effectiveness of the equipment to be evaluated.

[0076] Specifically, multiple evaluation indicators for indicating different evaluation dimensions can be determined according to the problem to be evaluated, such as Figure 3 As shown in the figure: when the problem to be evaluated is to evaluate the combat effectiveness of equipment, the first-level indicators can be determined to include command and control effectiveness, survivability and protection effectiveness, firepower strike effectiveness, and comprehensive support effectiveness; among them, command and control effectiveness includes the following three second-level evaluation indicators, namely command and control capability S1, command and communication capability S2, and launch control capability S3; survivability and protection effectiveness includes the following two second-level evaluation indicators, namely, probability of being discovered S4 and probability of being identified S5; firepower strike effectiveness includes the following five second-level evaluation indicators, namely, target locking capability S6, rapid response capability S7, anti-interference capability S8, penetration capability S9, and comprehensive damage capability S10; comprehensive support effectiveness includes the following four second-level evaluation indicators, namely, mission planning capability S11, ballistic support capability S12, meteorological support capability S13, and equipment support capability S14.

[0077] In some embodiments, the influence relationship between any two evaluation indicators among the multiple evaluation indicators may be preset, as shown in the following formula:

[0078]

[0079] Among them, a ij =1 means that the i-th evaluation index has an impact on the j-th evaluation index; a ij =0 means that the i-th evaluation index has no effect on the j-th evaluation index.

[0080] In some embodiments, the above step S101 includes steps S1011-S1012:

[0081] Step S1011: construct a first relationship matrix according to a plurality of evaluation indicators of the equipment to be evaluated and an influence relationship between any two evaluation indicators among the plurality of evaluation indicators.

[0082] As shown in Table 1: Each parameter a in the first relationship matrix ij Represents the influence relationship between evaluation index i and evaluation index j;

[0083] Table 1

[0084] index <![CDATA[S1]]> <![CDATA[S2]]> <![CDATA[S3]]> <![CDATA[S4]]> <![CDATA[S5]]> <![CDATA[S6]]> <![CDATA[S7]]> <![CDATA[S8]]> <![CDATA[S9]]> <![CDATA[S 10 ]]> <![CDATA[S 11 ]]> <![CDATA[S 12 ]]> <![CDATA[S 13 ]]> <![CDATA[S 14 ]]> <![CDATA[S1]]> 0 0 1 0 0 0 1 0 0 0 1 0 0 0 <![CDATA[S2]]> 1 0 1 0 0 0 1 0 0 0 1 0 0 0 <![CDATA[S3]]> 0 0 0 0 0 0 0 0 0 0 1 0 0 0 <![CDATA[S4]]> 1 0 0 0 1 0 0 1 0 0 0 0 0 0 <![CDATA[S5]]> 1 0 0 0 0 0 0 1 0 0 0 0 0 0 <![CDATA[S6]]> 0 0 0 0 0 0 0 0 0 0 1 0 0 0 <![CDATA[S7]]> 0 0 0 0 0 0 0 0 1 0 1 1 1 1 <![CDATA[S8]]> 0 0 0 0 0 0 0 0 1 1 0 0 0 0 <![CDATA[S9]]> 0 0 0 0 0 0 0 0 0 1 1 0 0 0 <![CDATA[S 10 ]]> 1 0 1 0 0 0 1 0 0 0 1 0 0 0 <![CDATA[S 11 ]]> 1 0 0 0 0 0 1 0 0 1 0 1 1 1 <![CDATA[S 12 ]]> 0 0 0 0 0 0 1 0 0 0 1 0 0 0 <![CDATA[S 13 ]]> 0 0 0 0 0 0 1 0 0 0 0 0 0 0 <![CDATA[S 14 ]]> 0 0 0 0 0 0 1 0 0 0 0 0 0 0

[0085] Among them, a ij =0 means that the i-th evaluation index has no effect on the j-th evaluation index, a ij =1 means that the i-th evaluation indicator has an impact on the j-th evaluation indicator.

[0086] Step S1012: construct the indicator directed graph based on the first relationship matrix.

[0087] Specifically, each parameter in the first relationship matrix can be used to determine whether there is a directed line segment between every two nodes in the index directed graph, and the direction of the directed line segment, thereby constructing the index directed graph. For example: the parameter a in Table 1 above 45 =1 means that S4 has an impact on S5, which means that there is a directed line segment between S4 and S5, and the direction of the directed line segment is: S4→S5; where parameter a 45 The "4" represents the vertical axis and the "5" represents the horizontal axis.

[0088] More specifically, the index directed graph is Figure 4 As shown, the multiple nodes in the indicator directed graph correspond one-to-one to the multiple evaluation indicators, and the directed line segments between the nodes represent the influence relationship between the evaluation indicators. For example, "probability of being discovered" → "probability of being recognized" means that the evaluation indicator "probability of being discovered" has an influence on the evaluation indicator "probability of being recognized".

[0089] In some embodiments, Figure 5 As shown, the method further includes steps S501 and S502:

[0090] Step S501: convert the first relationship matrix into a second relationship matrix.

[0091] The parameters in the first relationship matrix are used to characterize the influence relationship between any two evaluation indicators; and the second relationship matrix is ​​used to characterize the mutual influence relationship between the multiple evaluation indicators.

[0092] In the first relationship matrix, the direct impact relationship between two evaluation indicators is considered. For example, if there are two evaluation indicators A and B, the relationship between them can be determined by observing the changes between them.

[0093] In the second relationship matrix, when multiple evaluation indicators are involved and the relationship between the evaluation indicators is more complicated, not only the relationship between each two indicators can be considered, but also the comprehensive impact of multiple other indicators on the two indicators can be considered.

[0094] In some embodiments, the above step S501 includes steps S5011 and S5012:

[0095] Step S5011: Add the first relationship matrix and the identity matrix to obtain a first sub-matrix.

[0096] Step S5012: Perform multiple power operations on the first sub-matrix until no new element positions appear in the first sub-matrix after the power operation, and use the first sub-matrix after the power operation as the second relation matrix.

[0097] In the above steps S5011 and S5012, the first relationship matrix A and the identity matrix I may be added to obtain the A+I matrix; then the A+I matrix is ​​subjected to a power operation until the matrix is ​​stable and no new elements appear. The iteration condition is:

[0098] A+I≠(A+I) 2 ≠...≠(A+I) k-1 ≠(A+I) k =(A+I) k+1

[0099] After calculation, the final second relationship matrix T is:

[0100]

[0101] Among them, the horizontal axis in the second relationship matrix T represents the serial number of the evaluation index, corresponding to "S1-S 14"; the vertical axis represents the serial number of the evaluation index, corresponding to "S1-S 14 "; Parameter b in the second relationship matrix T ij Indicates whether the i-th evaluation indicator has an impact on the j-th evaluation indicator. Parameter b ij = 0 means that the i-th evaluation index has no effect on the j-th evaluation index, and the parameter b ij =0 means that the i-th evaluation index has an impact on the j-th evaluation index.

[0102] Step S502: decompose the second relationship matrix to obtain multiple hierarchical features.

[0103] Among them, each level feature includes at least one evaluation indicator, such as Figure 4 As shown, the 4th layer includes the probability of being discovered; the 3rd layer includes the probability of being identified; and the 2nd layer includes the anti-interference capability, target locking capability, and command and communication capability.

[0104] In some embodiments, the above step S502 includes steps S5021 to S5024:

[0105] Step S5021, determining a target evaluation indicator from the evaluation indicator matrix to be decomposed, and decomposing the evaluation indicator matrix to be decomposed to obtain a first set and a second set.

[0106] Among them, the first set includes multiple other evaluation indicators affected by the target evaluation indicator; the second set includes multiple other evaluation indicators that affect the target evaluation indicator; the target evaluation indicator is any one of the multiple evaluation indicators of the second relationship matrix, and the first evaluation indicator matrix to be decomposed is the second relationship matrix.

[0107] Step S5022: If the first set and the second set meet a preset condition, determine that the evaluation indicator is a first indicator.

[0108] The preset condition includes that the intersection result of the first set and the second set is the first set or the second set.

[0109] Step S5023, traversing the multiple evaluation indicators, and dividing the determined multiple first indicators into a hierarchical feature;

[0110] Step S5024, remove the multiple first indicators from the evaluation indicator matrix to be decomposed, and use the matrix obtained after the removal as a new evaluation indicator matrix to be decomposed, and repeat the steps: determine the target evaluation indicator from the evaluation indicator matrix to be decomposed, decompose the evaluation indicator matrix to be decomposed to obtain a first set and a second set; if the first set and the second set meet the preset conditions, determine that the evaluation indicator is the first indicator; traverse the multiple evaluation indicators, and divide the determined multiple first indicators into a hierarchical feature.

[0111] In the above steps S5021 to S5024, for each evaluation indicator, the corresponding first set and second set can be determined from the evaluation indicator matrix to be decomposed, and the first evaluation indicator matrix to be decomposed is the second relationship matrix T; Figure 6 As shown: for the evaluation index S1, it is possible to determine the first set a1 and the second set a2; then determine whether the first set a1 and the second set a2 meet the following conditions:

[0112] a1∩a2=a1 or a2

[0113] The above condition indicates that the first set a1 and the second set a2 are intersected. If the set obtained after the intersection is the first set or the second set, the evaluation index S1 corresponding to the first set and the second set is extracted as the evaluation index in the first level;

[0114] Similarly, determine whether other evaluation indicators can be extracted as evaluation indicators in the first level according to the above method;

[0115] After traversing all evaluation indicators according to the above method, the extracted multiple indicators can be used as the first level, for example Figure 4 There are 9 evaluation indicators in the first layer.

[0116] Then, multiple indicators of the first level are removed from the evaluation indicator matrix to be decomposed, and the matrix obtained after the removal is used as a new evaluation indicator matrix to be decomposed, such as Figure 7 As shown. The above method is used to reprocess the new evaluation indicator matrix to be decomposed, and multiple indicators of the second level are proposed;

[0117] Indicators are extracted layer by layer in the above manner, and so on, until all indicator element levels are extracted. After the above steps, the obtained indicator hierarchical relationship is shown in Table 2:

[0118] Table 2

[0119]

[0120] The missile combat effectiveness test index system is divided into four layers, among which the first layer includes nine indicators, including command and control capability, launch control capability, rapid response capability, penetration capability, comprehensive damage capability, mission planning capability, ballistic support capability, meteorological support capability, and equipment support capability. These indicators are rich in connotation and have a great impact, and have a direct effect on the missile combat effectiveness; the second layer includes three indicators, including command and communication capability, target locking capability, and anti-interference capability, which have an indirect effect on the missile combat effectiveness compared with the first layer indicators; the third and fourth layer indicators have clear connotations and only affect certain functions of the system. The probability of being discovered is below the probability of being identified, which can be interpreted as the missile being discovered is a prerequisite for being identified.

[0121] The disclosed embodiment uses the above method to decompose the second relationship matrix to obtain multiple hierarchical features, that is, to determine the hierarchical relationship of indicators, thereby determining which evaluation indicators have a greater impact on the intended evaluation problem (evaluation of the combat effectiveness of equipment).

[0122] Step S102: Calculate a first weight of each evaluation indicator based on the indicator directed graph.

[0123] In some embodiments, the above step S102 includes steps S1021 and S1022:

[0124] Step S1021, calculating the in-degree value and out-degree value of each evaluation indicator based on the indicator directed graph.

[0125] Specifically, the out-degree and in-degree of each evaluation indicator can be determined through the indicator directed graph, as shown in Table 3:

[0126] Table 3

[0127]

[0128] The out-degree refers to the number of edges from a target evaluation indicator to other evaluation indicators, and the in-degree refers to the number of edges pointing to the target evaluation indicator, for example Figure 4 The out-degree of "probability of being recognized" is 2 and the in-degree is 1.

[0129] Step S1022: Calculate a first weight of each evaluation indicator according to the multiple out-degree values ​​corresponding one-to-one to the multiple evaluation indicators and the multiple in-degree values ​​corresponding one-to-one to the multiple evaluation indicators.

[0130] Specifically, when determining the first weight of the target evaluation indicator, the out-degree value and the in-degree value of the target evaluation indicator can be added to obtain the absolute degree centrality; then the absolute degree centrality of the target evaluation indicator is divided by the sum of the absolute degree centralities of all evaluation indicators to obtain the first weight of the target evaluation indicator. For example, as shown in Table 3 above; when the target evaluation indicator is S1, the absolute degree centrality of S1 is 3+5=8; the sum of the absolute degree centralities of all evaluation indicators is (3+4+1+3+2+1+5+2+2+4+6+2+1+1)*2=74; the first weight of S1 is 8 / 74=0.108.

[0131] In the embodiment of the present disclosure, the degree centrality is used to measure the centrality of each evaluation indicator. The degree centrality indicates the number of evaluation indicators that are directly related to the evaluation indicator, thereby reflecting the central position of the evaluation indicator in the network and the weight of the evaluation indicator in the network.

[0132] Step S103: Calculate the second weight of each evaluation indicator based on a preset relative importance matrix.

[0133] Among them, any parameter in the relative importance matrix represents the importance comparison result between any two evaluation indicators.

[0134] When setting up the relative importance matrix, you can first determine the overall goal, first-level indicators, and second-level indicators, and then establish a hierarchical structure, such as Figure 3 As shown: the overall goal can be to evaluate the combat effectiveness of missiles. The first-level indicators can include command and control effectiveness, survivability protection effectiveness, firepower strike effectiveness and comprehensive support effectiveness; among them, each first-level indicator has at least one second indicator, for example: the second-level indicators under the first-level indicator - survivability protection effectiveness - detection probability and identification probability. Then, any two indicators in the same level can be compared, using the 1-9 scale method, where 1 means that the two indicators are equally important; 9 means that one indicator is much more important than another indicator; finally, fill in the relative importance matrix according to the comparison results; among them, the parameter c in the relative importance matrix ij It represents the comparison result of the i-th indicator relative to the j-th indicator.

[0135] In some embodiments, the above step S103 includes steps S1031 to S1034:

[0136] Step S1031, normalizing the relative importance matrix to obtain a normalized matrix.

[0137] Specifically, the relative importance matrix is ​​normalized to ensure that the comparison results of each evaluation indicator are between 0 and 1, and the sum of each row is 1. The normalized matrix can more clearly show the relative importance of each indicator, so that the sum of the weights is 1, which is convenient for subsequent weight calculation and decision analysis.

[0138] Step S1032, calculating multiple eigenvectors and a maximum eigenvalue of the normalized matrix.

[0139] Specifically, numerical computing software (such as MATLAB, NumPy library in Python, etc.) can be used to calculate the eigenvalues ​​and eigenvectors of the normalized matrix.

[0140] Step S1033: performing a consistency check on the relative importance matrix based on the maximum eigenvalue.

[0141] Step S1034: if the consistency check of the relative importance matrix passes, the multiple eigenvectors are determined as multiple second weights; the multiple second weights correspond one-to-one to the multiple evaluation indicators.

[0142] In the above steps S1032-S1033, in the process of consistency check of the relative importance matrix, the weight vector of each column can be calculated first, that is, the elements of each column are summed and averaged; then the normalized eigenvector is calculated, that is, the elements of each column are divided by the corresponding weight; finally, the consistency index CI and the consistency ratio CR are calculated to determine whether the consistency ratio CR is less than the preset threshold. If it is less than, the consistency check of the relative importance matrix passes; otherwise, it fails. After the consistency check passes, multiple eigenvectors can be determined as multiple second weights, such as the weights of each evaluation indicator in the solution layer in Table 4;

[0143] In some embodiments, the weights of the evaluation indicators in the corresponding criterion layer can also be determined by the weights of the evaluation indicators in the solution layer, for example, in Table 4, command and control capability (0.038) + command and communication capability (0.012) + launch control capability (0.006) = command and control effectiveness (0.056). Thus, the weights of the indicators in each layer are determined from bottom to top, and the total weight of the indicator hierarchy is shown in Table 4:

[0144] Table 4

[0145]

[0146] Step S104: for any one of the multiple evaluation indicators, a third weight of the evaluation indicator is calculated based on the first weight and the second weight of the evaluation indicator.

[0147] In some embodiments, the above step S104 includes steps S1041 to S1043:

[0148] Step S1041, calculating a first coefficient of the first weight and a second coefficient of the second weight; the first coefficient and the second coefficient are used to reduce the difference between the first weight and the second weight.

[0149] Step S1042, calculating a first target weight based on the first weight and the first coefficient, and calculating a second target weight based on the second weight and the second coefficient;

[0150] Step S1043: Calculate the third weight based on the first target weight and the second target weight.

[0151] In the above steps S1041 to S1043, the game confrontation idea is adopted, and the first weight and the second weight are regarded as the two sides of the game confrontation. The optimal result of comprehensive weighting should minimize the sum of the deviations between the combined indicator weights, as follows:

[0152] The weight coefficient of the second weight W1 is λ1, the weight coefficient of the first weight W2 is λ2, and the third weight W=λ1W1+λ2W2; wherein λ1W1 represents the second target weight, and λ2W2 represents the first target weight.

[0153] The objective function and constraints are:

[0154]

[0155] Differentiate the objective function, and its first-order derivative condition is:

[0156]

[0157] Solving the above equations to obtain λ1 and λ2, and normalizing λ1 and λ2 to obtain the optimal combination coefficient:

[0158]

[0159] Substituting the first weight and the second weight into the above solution process, we can get λ1=0.877, λ2=0.210;

[0160] The balanced comprehensive weight (i.e. the third weight)

[0161]

[0162] For example, the weights of each indicator in the solution layer in Table 5.

[0163] Finally, the weights of the program-level indicators are aggregated upward to obtain the new criterion-level and program-level indicator weights as shown in Table 5:

[0164] Table 5

[0165]

[0166] Step S105: generating a combat effectiveness evaluation result of the equipment to be evaluated based on the multiple evaluation indicators and third weights corresponding one-to-one to the multiple evaluation indicators.

[0167] Specifically, after calculating the third weight of each evaluation indicator, the evaluation accuracy of the proposed evaluation problem can be effectively improved. Through multiple evaluation indicators related to the proposed evaluation problem and multiple third weights corresponding to the multiple evaluation indicators, the evaluation result of the proposed problem can be calculated.

[0168] The disclosed embodiment adopts a network analysis method to improve the indicator weights of the AHP-ISM method, effectively integrating the hierarchical knowledge information of experts on the evaluation problem and the hierarchical interpretation information output by the ISM model, overcoming the shortcoming that the AHP-ISM method cannot solve the indicator weights of the network hierarchical model, and comprehensively balancing the weights of the indicator system based on the game confrontation idea, which not only retains the empirical knowledge of the criterion layer, but also avoids the subjective arbitrariness of indicator weighting. The case analysis process proves the feasibility and effectiveness of the method. The social network analysis parameter selected in this paper is the point degree centrality. In the future, it can be combined with other network characteristic parameters such as cohesive subgroups to analyze the network structure model of ISM and further determine the network attributes and their indicator weights.

[0169] Corresponding to the implementation of the above evaluation method, the embodiment of the present disclosure also provides an evaluation device for executing the above Figures 1 to 7 The evaluation method described in any of the illustrated embodiments. Figure 8 As shown, the evaluation device includes:

[0170] A directed graph construction module is used to construct an indicator directed graph according to multiple evaluation indicators of the equipment to be evaluated and the influence relationship between any two evaluation indicators among the multiple evaluation indicators; the multiple indicators respectively indicate different evaluation dimensions of the equipment to be evaluated in terms of combat effectiveness;

[0171] A first weight calculation module, used to calculate a first weight of each evaluation indicator based on the indicator directed graph;

[0172] A second weight calculation module is used to calculate a second weight of each evaluation indicator based on a preset relative importance matrix; any parameter in the relative importance matrix represents the importance comparison result between any two evaluation indicators;

[0173] A third weight calculation module, configured to calculate, for any one of the plurality of evaluation indicators, a third weight of the evaluation indicator based on the first weight and the second weight of the evaluation indicator;

[0174] An evaluation module is used to generate a combat effectiveness evaluation result of the equipment to be evaluated based on the multiple evaluation indicators and a third weight corresponding to the multiple evaluation indicators.

[0175] Optionally, the directed graph construction module is further used to: construct a first relationship matrix according to multiple evaluation indicators of the equipment to be evaluated and the influence relationship between any two evaluation indicators among the multiple evaluation indicators; and construct the indicator directed graph based on the first relationship matrix.

[0176] Optionally, the device further comprises:

[0177] A matrix conversion module, used to convert the first relationship matrix into a second relationship matrix; the second relationship matrix is ​​used to characterize the mutual influence relationship between the multiple evaluation indicators;

[0178] A matrix decomposition module is used to decompose the second relationship matrix to obtain multiple hierarchical features; wherein each hierarchical feature includes at least one evaluation indicator.

[0179] Optionally, the matrix conversion module is also used to: add the first relationship matrix and the unit matrix to obtain a first sub-matrix; perform multiple power operations on the first sub-matrix until no new element positions appear in the first sub-matrix after the power operation, and use the first sub-matrix after the power operation as the second relationship matrix.

[0180] Optionally, the matrix decomposition module is also used to: determine the target evaluation indicator from the evaluation indicator matrix to be decomposed, decompose the evaluation indicator matrix to be decomposed, and obtain a first set and a second set; the first set includes multiple other evaluation indicators affected by the target evaluation indicator; the second set includes multiple other evaluation indicators that affect the target evaluation indicator; the target evaluation indicator is any one of the multiple evaluation indicators of the second relationship matrix, and the first evaluation indicator matrix to be decomposed is the second relationship matrix; if the first set and the second set meet the preset conditions, then determine that the evaluation indicator is the first indicator; the preset conditions include the first set and the second set The intersection result is the first set or the second set; traverse the multiple evaluation indicators and divide the determined multiple first indicators into a hierarchical feature; eliminate the multiple first indicators from the evaluation indicator matrix to be decomposed, and use the matrix obtained after elimination as a new evaluation indicator matrix to be decomposed, and repeat the steps: determine the target evaluation indicator from the evaluation indicator matrix to be decomposed, decompose the evaluation indicator matrix to be decomposed, and obtain the first set and the second set; if the first set and the second set meet the preset conditions, determine the evaluation indicator to be the first indicator; traverse the multiple evaluation indicators and divide the determined multiple first indicators into a hierarchical feature.

[0181] Optionally, the first weight calculation module is also used to: calculate the in-degree value and out-degree value of each evaluation indicator based on the indicator directed graph; calculate the first weight of each evaluation indicator according to the multiple out-degree values ​​corresponding one-to-one to the multiple evaluation indicators, and the multiple in-degree values ​​corresponding one-to-one to the multiple evaluation indicators.

[0182] Optionally, the second weight calculation module is also used to: normalize the relative importance matrix to obtain a normalized matrix; calculate multiple eigenvectors and a maximum eigenvalue of the normalized matrix; perform a consistency check on the relative importance matrix based on the maximum eigenvalue; if the consistency check of the relative importance matrix passes, determine the multiple eigenvectors as multiple second weights; and the multiple second weights correspond one-to-one to the multiple evaluation indicators.

[0183] Optionally, the third weight calculation module is also used to: calculate the first coefficient of the first weight, and the second coefficient of the second weight; the first coefficient and the second coefficient are used to reduce the difference between the first weight and the second weight; calculate the first target weight based on the first weight and the first coefficient, and calculate the second target weight based on the second weight and the second coefficient; calculate the third weight based on the first target weight and the second target weight.

[0184] The evaluation device provided in the above-mentioned embodiment of the present disclosure and the evaluation method provided in the embodiment of the present disclosure are based on the same inventive concept, and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0185] The present disclosure also provides a computer device to execute the above evaluation method. Fig. 9 , which shows a schematic diagram of a computer device provided by some embodiments of the present disclosure. Fig. 9 As shown, the computer device 9 includes: a processor 900, a memory 901, a bus 902 and a communication interface 903, wherein the processor 900, the communication interface 903 and the memory 901 are connected via the bus 902; the memory 901 stores a computer program that can be run on the processor 900, and the processor 900 executes the aforementioned Figures 1 to 7 The evaluation method provided by any of the illustrated embodiments.

[0186] The memory 901 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 903 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0187] The bus 902 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 901 is used to store programs. After receiving the execution instruction, the processor 900 executes the program. Figures 1 to 7 The evaluation method disclosed in any of the illustrated embodiments may be applied to the processor 900 or implemented by the processor 900 .

[0188] The processor 900 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 900 or the instruction in the form of software. The above processor 900 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 901, and the processor 900 reads the information in the memory 901 and completes the steps of the above method in combination with its hardware.

[0189] The computer device provided by the embodiment of the present disclosure and the evaluation method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by them.

[0190] The present disclosure also provides a computer-readable storage medium corresponding to the evaluation method provided in the above embodiment. Fig.10 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the evaluation method provided by any of the aforementioned embodiments will be executed.

[0191] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0192] The computer-readable storage medium provided by the above-mentioned embodiments of the present disclosure and the evaluation method provided by the embodiments of the present disclosure are based on the same inventive concept, and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0193] It should be noted that:

[0194] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present disclosure can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0195] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following schematic diagram: the claimed disclosure requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present disclosure.

[0196] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of this disclosure and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0197] The above is only a preferred specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. An evaluation method, characterized in that: The method comprises: According to multiple evaluation indicators of the equipment to be evaluated and the influence relationship between any two evaluation indicators among the multiple evaluation indicators, an indicator directed graph is constructed; the multiple indicators respectively indicate different evaluation dimensions of the equipment to be evaluated in terms of combat effectiveness; Based on the indicator directed graph, calculating a first weight of each evaluation indicator; Based on a preset relative importance matrix, a second weight of each evaluation indicator is calculated; any parameter in the relative importance matrix represents the importance comparison result between any two evaluation indicators; For any one of the plurality of evaluation indicators, based on the first weight and the second weight of the evaluation indicator, calculating a third weight of the evaluation indicator; Based on the multiple evaluation indicators and the third weights corresponding to the multiple evaluation indicators one by one, a combat effectiveness evaluation result of the equipment to be evaluated is generated.

2. The method according to claim 1, characterized in that According to multiple evaluation indicators of the equipment to be evaluated and the influence relationship between any two evaluation indicators among the multiple evaluation indicators, an indicator directed graph is constructed, including: Constructing a first relationship matrix according to a plurality of evaluation indicators of the equipment to be evaluated and an influence relationship between any two evaluation indicators among the plurality of evaluation indicators; Based on the first relationship matrix, the indicator directed graph is constructed.

3. The method according to claim 2, characterized in that The method further comprises: Converting the first relationship matrix into a second relationship matrix; the second relationship matrix is ​​used to characterize the mutual influence relationship between the multiple evaluation indicators; The second relationship matrix is ​​decomposed to obtain a plurality of hierarchical features, wherein each hierarchical feature includes at least one evaluation index.

4. The method according to claim 3, characterized in that Converting the first relationship matrix into a second relationship matrix comprises: Adding the first relation matrix and the identity matrix to obtain a first submatrix; The first submatrix is ​​subjected to multiple power operations until no new element position appears in the first submatrix after the power operation, and the first submatrix after the power operation is used as the second relational matrix.

5. The method according to claim 3 or 4, characterized in that: The second relationship matrix is ​​decomposed to obtain multiple hierarchical features, including: Determine a target evaluation indicator from an evaluation indicator matrix to be decomposed, decompose the evaluation indicator matrix to be decomposed, and obtain a first set and a second set; the first set includes a plurality of other evaluation indicators affected by the target evaluation indicator; the second set includes a plurality of other evaluation indicators affecting the target evaluation indicator; the target evaluation indicator is any one of a plurality of evaluation indicators of the second relationship matrix, and the first evaluation indicator matrix to be decomposed is the second relationship matrix; If the first set and the second set meet a preset condition, determining the evaluation index to be the first index; the preset condition includes that the intersection result of the first set and the second set is the first set or the second set; Traversing the plurality of evaluation indicators, and dividing the determined plurality of first indicators into a hierarchical feature; The multiple first indicators are eliminated from the evaluation indicator matrix to be decomposed, and the matrix obtained after the elimination is used as a new evaluation indicator matrix to be decomposed, and the steps are repeated: determine the target evaluation indicator from the evaluation indicator matrix to be decomposed, decompose the evaluation indicator matrix to be decomposed to obtain a first set and a second set; if the first set and the second set meet preset conditions, determine that the evaluation indicator is the first indicator; traverse the multiple evaluation indicators, and divide the determined multiple first indicators into a hierarchical feature.

6. The method according to claim 1 or 2, characterized in that: Based on the indicator directed graph, a first weight of each evaluation indicator is calculated, including: Calculate the in-degree value and out-degree value of each evaluation indicator based on the indicator directed graph; A first weight of each evaluation indicator is calculated according to a plurality of out-degree values ​​corresponding one-to-one to the plurality of evaluation indicators and a plurality of in-degree values ​​corresponding one-to-one to the plurality of evaluation indicators.

7. The method according to claim 1 or 2, characterized in that: Based on the preset relative importance matrix, the second weight of each evaluation indicator is calculated, including: Normalizing the relative importance matrix to obtain a normalized matrix; Calculating multiple eigenvectors and a maximum eigenvalue of the normalized matrix; Performing a consistency check on the relative importance matrix based on the maximum eigenvalue; If the consistency check of the relative importance matrix passes, the multiple eigenvectors are determined as multiple second weights; the multiple second weights correspond one-to-one to the multiple evaluation indicators.

8. The method according to claim 1 or 2, characterized in that: Calculating a third weight of the evaluation indicator based on the first weight and the second weight of the evaluation indicator includes: Calculating a first coefficient of the first weight and a second coefficient of the second weight; the first coefficient and the second coefficient are used to reduce the difference between the first weight and the second weight; Calculating a first target weight based on the first weight and the first coefficient, and calculating a second target weight based on the second weight and the second coefficient; The third weight is calculated based on the first target weight and the second target weight.

9. The method according to claim 1 or 2, characterized in that: The method further comprises: The fourth weights of the plurality of second evaluation indicators are calculated according to the plurality of third weights corresponding one-to-one to the plurality of evaluation indicators; wherein each second evaluation indicator corresponds to at least one evaluation indicator.

10. An evaluation device, characterized in that The device comprises: A directed graph construction module is used to construct an indicator directed graph according to multiple evaluation indicators of the equipment to be evaluated and the influence relationship between any two evaluation indicators among the multiple evaluation indicators; the multiple indicators respectively indicate different evaluation dimensions of the equipment to be evaluated in terms of combat effectiveness; A first weight calculation module, used to calculate a first weight of each evaluation indicator based on the indicator directed graph; A second weight calculation module is used to calculate a second weight of each evaluation indicator based on a preset relative importance matrix; any parameter in the relative importance matrix represents the importance comparison result between any two evaluation indicators; A third weight calculation module, configured to calculate, for any one of the plurality of evaluation indicators, a third weight of the evaluation indicator based on the first weight and the second weight of the evaluation indicator; An evaluation module is used to generate a combat effectiveness evaluation result of the equipment to be evaluated based on the multiple evaluation indicators and a third weight corresponding to the multiple evaluation indicators.

11. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the evaluation method according to any one of claims 1 to 9 by executing the computer instructions.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the evaluation method according to any one of claims 1 to 9.

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