Dynamic evaluation method of equipment combat effectiveness based on virtual ideal solution and grey correlation degree

Abstract

The present invention relates to the technical field of equipment testing and identification, and in particular to a method for dynamically evaluating the combat effectiveness of equipment based on a virtual ideal solution and a gray correlation degree. The method solves the technical problem that these methods cannot distinguish the superiority and inferiority of the combat effectiveness of the evaluated object based on the evaluation results. The specific steps are: S1 constructing a hierarchical combat effectiveness index system; S2 constructing multi-stage three-dimensional data for combat effectiveness evaluation; S3 calculating and establishing a combat effectiveness evaluation model based on the difference and change degree of the evaluation index values; S4 a dynamic evaluation model of a typical mission stage and the entire mission process based on the difference and change degree of the comprehensive index. The distance scale reflecting the positional relationship between the evaluation index system data curve and the combat mission requirement data curve, and the correlation scale reflecting the situation change relationship between the evaluation index system data curve and the combat mission requirement data curve are integrated.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment testing and identification, and in particular to a method for dynamically evaluating equipment combat effectiveness based on a virtual ideal solution and grey correlation. Background Art

[0002] Equipment combat effectiveness evaluation is the basic basis for weapon equipment demonstration, development, planning and configuration, and it occupies an extremely important position in weapon equipment construction, management and development activities. The current equipment combat effectiveness evaluation methods are generally divided into expert experience method, data-driven method and combat simulation method, among which data-driven method is a research hotspot in the field of combat and equipment construction at home and abroad. Among them, many literatures have proposed the TOPSIS method for trial training data, which measures the distance relationship between the effectiveness evaluation index sequence curve and the combat mission demand curve from both positive and negative aspects, and solves the combat effectiveness evaluation problem of more equipment. Although these literatures have proposed many TOPSIS improved algorithms, when the evaluated object is located on the median of the connection line of the positive and negative ideal solutions, these methods cannot distinguish the advantages and disadvantages of the combat effectiveness of the evaluated object based on the evaluation results. Summary of the invention

[0003] The purpose of the present invention is to provide a dynamic evaluation method for equipment combat effectiveness based on virtual ideal solution and grey correlation degree, so as to solve the technical problem that these methods cannot distinguish the good from the bad of the combat effectiveness of the evaluated object according to the evaluation results.

[0004] To achieve the above purpose, the specific technical scheme of the equipment combat effectiveness dynamic evaluation method based on virtual ideal solution and grey correlation degree of the present invention is as follows:

[0005] The equipment combat effectiveness dynamic evaluation method based on virtual ideal solution and grey correlation degree, the specific steps are as follows:

[0006] S1 Construction of hierarchical combat effectiveness indicator system;

[0007] S2 combat effectiveness evaluation multi-stage three-dimensional data construction;

[0008] S3 calculates and establishes the combat effectiveness evaluation model based on the difference and change degree of the evaluation index values.

[0009] S4 is a dynamic evaluation model for typical mission stages and the entire mission process based on the differences and degree of change of comprehensive indicators.

[0010] Furthermore, the method for constructing the hierarchical combat effectiveness indicator system is as follows:

[0011] S1.1 Determine the function or performance indicators of the equipment;

[0012] S1.2 Determine the task capability indicators;

[0013] S1.3 Determine the combat effectiveness indicators of the equipment.

[0014] Furthermore, the method for constructing multi-stage three-dimensional data for combat effectiveness evaluation is as follows:

[0015] S2.1 Assume that P equipment of the same type are based on Figure 1 The indicator system is used to evaluate and compare the combat effectiveness;

[0016] S2.2 Determine the qth (q=1,2,…,p)th task capability indicator of the lower layer for the mth (m=1,2,…,P)th equipment;

[0017] S2.3 Assume that the i-th (i=1,2,…,N) (N represents the total number of functions or performance indicators under the q-th task capability) function or performance indicator is in the j-th (j=1,2,…,M) task stage (corresponding to a task cycle t1,t2,…,t M Behavioral performance parameters during the task phase);

[0018] S2.4 Determine the behavior performance parameter value described as

[0019] S2.5 Construct a stage performance matrix of specific equipment for multi-stage three-dimensional data profile of equipment combat effectiveness evaluation

[0020] S2.6 Determine the behavior performance parameter of the i-th (i=1,2,…,N) function or performance indicator of the m-th (m=1,2,…,P) equipment corresponding to the q-th (q=1,2,…,p)-th task capability indicator in the j-th (j=1,2,…,M)-th task stage;

[0021] S2.7 Determine the behavior performance parameter value described as

[0022] S2.8 Construct the function or performance matrix of all equipment for multi-stage three-dimensional data profile of equipment combat effectiveness evaluation:

[0023] Further, the

[0024]

[0025]

[0026] Furthermore, the method for calculating and establishing the combat effectiveness evaluation model based on the degree of difference in evaluation index values ​​is as follows:

[0027] S3.1 The matrix is ​​processed by gray polarity effect method After standardization, the standardized matrix is ​​obtained

[0028] S3.2 For the standardized matrix The relative closeness calculation is based on the degree of difference in the indicator values.

[0029] Further, the gray polarity effect processing method is:

[0030] S3.1.1 If the indicator If it has a maximum value attribute, then the original element is divided by its maximum value as the standardized element, that is,

[0031] S3.1.2 If the indicator If it has a minimum value attribute, then take its minimum value divided by the original element as the standardized element, that is,

[0032] S3.1.3 If the indicator With attributes, its moderate value is taken as the specified value or the average value, that is,

[0033]

[0034] At this time, the standardized element calculation model is:

[0035]

[0036] S3.1.4 Obtaining the standardized matrix

[0037] further,

[0038] The normalization matrix is

[0039] Furthermore, the method for calculating the relative closeness based on the difference in index values ​​is:

[0040] S3.2.1 Determine a weighted standardized matrix of specific combat capabilities for typical mission phases;

[0041] S3.2.2 Determine the ideal solution and virtual negative ideal solution of specific combat capability;

[0042] S3.2.3 Calculate the Euclidean distance between the specific combat capability of each evaluation object and the positive ideal solution and the virtual negative ideal solution;

[0043] S3.2.4 Calculate the relative closeness of the specific combat capability of each evaluation object to the virtual ideal solution;

[0044] S3.2.5 Calculate the grey correlation between each evaluation object and the positive ideal solution of specific combat capability;

[0045] S3.2.6 Calculate the comprehensive closeness between each evaluation object and the specific combat capability based on the difference matrix.

[0046] S3.2.7 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the typical mission stage based on the difference matrix;

[0047] S3.2.8 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the entire combat mission based on the difference matrix solution;

[0048] S3.2.9 Ranking of the combat effectiveness of each assessment object.

[0049] Furthermore, the method for calculating and establishing the combat effectiveness evaluation model based on the degree of change of the evaluation index value is as follows:

[0050] S3.3 Pair Matrix Perform gray polarity effect preprocessing to obtain a standardized matrix

[0051] S3.4 Constructing the evaluation index value change matrix

[0052] S3.5 Evaluation index value change matrix The comprehensive closeness calculation based on the virtual ideal solution and grey correlation degree is used when considering the degree of change of the index value.

[0053] Furthermore, the method for calculating the comprehensive closeness based on the virtual ideal solution and the grey correlation degree when considering the degree of change of the index value is:

[0054] S3.5.1 Determine the weighted normalized variation matrix;

[0055] S3.5.2 Determine the positive ideal solution and virtual negative ideal solution based on the change matrix;

[0056] S3.5.3 Calculate the Euclidean distance between each evaluation object and the positive ideal solution and the virtual negative ideal solution based on the change matrix;

[0057] S3.5.4 Calculate the relative closeness of the specific combat capabilities of each evaluation object based on the change matrix;

[0058] S3.5.5 Calculate the grey correlation between each evaluation object and the positive ideal solution;

[0059] S3.5.6 Aggregate the Euclidean distance and grey correlation between each evaluation object and specific combat capability based on the change matrix;

[0060] S3.5.7 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the typical mission stage based on the change matrix;

[0061] S3.5.8 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the entire combat mission based on the change matrix;

[0062] S3.5.9 Rank the combat effectiveness of each evaluation object based on the degree of change of the indicator.

[0063] The advantages of the present invention are:

[0064] The present invention combines the virtual ideal solution construction method with the grey correlation analysis method, and proposes a new method for dynamic evaluation of equipment combat effectiveness based on virtual ideal distance and grey correlation degree, aiming at the small sample characteristics of weapon equipment combat effectiveness evaluation data. The method simultaneously embodies the characteristics of dual benchmarks, and depicts and reflects the similarity mechanism between the evaluation index sequence curve and the combat mission requirement curve based on the virtual ideal distance, and depicts and reflects the similarity mechanism between the evaluation index sequence curve and the combat mission requirement curve based on the grey correlation degree; at the same time, a new comprehensive closeness is constructed from the two perspectives of the difference degree and change degree of the evaluation index, and the comprehensive evaluation and ranking of the combat effectiveness of the combat mission stage and the whole process of the combat mission are realized at the same time; it not only depicts the similarity and similarity mechanism of the combat effectiveness evaluation through the position relationship and situation change between the data curves, but also overcomes the unreasonable problem of the evaluation and ranking results of the traditional ideal solution. It can realize dynamic evaluation and ranking of equipment combat effectiveness from multiple angles and multiple levels. The evaluation mechanism and physical meaning are reflected in three levels. First, the difference degree and change degree of evaluation indicators are considered separately, and the two can be considered comprehensively. Second, the distance scale reflecting the position relationship between the evaluation indicator system data curve and the combat mission requirement data curve, and the correlation scale reflecting the situation change relationship between the evaluation indicator system data curve and the combat mission requirement data curve are combined. Third, the combat effectiveness evaluation value and its ranking can be targeted at both each task stage and the entire combat mission process. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the basic idea structure of the dynamic evaluation method of equipment combat effectiveness based on virtual ideal solution and grey correlation of the present invention;

[0066] Figure 2 It is a schematic diagram of the hierarchical combat effectiveness evaluation index system structure of the equipment combat effectiveness dynamic evaluation method based on virtual ideal solution and grey correlation degree of the present invention;

[0067] Figure 3 The present invention is a schematic diagram of the combat effectiveness index system structure of the ultra-short wave communication countermeasure system based on the virtual ideal solution and the grey correlation degree of the equipment combat effectiveness dynamic evaluation method. DETAILED DESCRIPTION

[0068] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0070] Example 1

[0071] The equipment combat effectiveness dynamic evaluation method based on virtual ideal solution and grey correlation degree, the specific steps are as follows:

[0072] S1 Construction of hierarchical combat effectiveness indicator system;

[0073] Among them, Figure 1-3 As shown, the method for constructing the hierarchical combat effectiveness indicator system is:

[0074] S1.1 Determine the function or performance index of the equipment;

[0075] S1.2 Determine the mission capability indicators;

[0076] S1.3 Determine the combat effectiveness indicators of the equipment.

[0077] The hierarchical combat effectiveness index system indicates that the functional or performance indicators and mission capability indicators of the equipment can summarize and reflect the behavioral performance of the equipment in the entire combat process from bottom to top. The combat effectiveness indicators of the equipment have behavioral performance in three dimensions from top to bottom, namely, they have attribute values ​​in these three dimensions.

[0078] S2 combat effectiveness evaluation multi-stage three-dimensional data construction;

[0079] Among them, Figure 1-3 As shown, the method for constructing multi-stage three-dimensional data for combat effectiveness evaluation is as follows:

[0080] S2.1 Assume that P equipment of the same type are based on Figure 1 The indicator system is used to evaluate and compare the combat effectiveness;

[0081] S2.2 Determine the qth (q=1,2,…,p)th task capability indicator of the lower layer for the mth (m=1,2,…,P)th equipment;

[0082] S2.3 Assume that the i-th (i=1,2,…,N) (N represents the total number of functions or performance indicators under the q-th task capability) function or performance indicator is in the j-th (j=1,2,…,M) task stage (corresponding to a task cycle t1,t2,…,t M Behavioral performance parameters during the task phase);

[0083] S2.4 Determine the behavior performance parameter value described as

[0084] S2.5 Construct a stage performance matrix of specific equipment for multi-stage three-dimensional data profile of equipment combat effectiveness evaluation

[0085] S2.6 Determine the behavior performance parameter of the i-th (i=1,2,…,N) function or performance indicator of the m-th (m=1,2,…,P) equipment corresponding to the q-th (q=1,2,…,p)-th task capability indicator in the j-th (j=1,2,…,M)-th task stage;

[0086] S2.7 Determine the behavior performance parameter value described as

[0087] S2.8 Construct the function or performance matrix of all equipment for multi-stage three-dimensional data profile of equipment combat effectiveness evaluation:

[0088] The multi-stage 3D data of equipment combat effectiveness evaluation expands the conventional 2D data into 3D data, which can cover the behavioral performance of the evaluated equipment in the whole process of combat mission, and is conducive to the comprehensive evaluation of combat effectiveness from the perspectives of the difference degree of evaluation index values ​​and the degree of change of evaluation index values, thereby overcoming the one-sidedness and information omission of equipment combat effectiveness evaluation and improving the rationality and credibility of effectiveness evaluation results.

[0089] Said

[0090]

[0091] This matrix expresses the evaluation index values ​​of the specific mission capability (i.e., mission capability q) of a specific equipment (i.e., equipment m). Each row corresponds to the performance value of all mission stages of a certain function or performance indicator, and each column corresponds to the performance value of all functions or performance indicators in a certain mission stage.

[0092]

[0093] This matrix expresses the evaluation index values ​​under the specific mission capability (i.e., mission capability q) of the specific mission stage (i.e., stage j). Each row corresponds to the performance value of all functions or performance indicators of a certain equipment, and each column corresponds to the performance value of all equipment of a certain function or performance indicator. It can be seen that this matrix is ​​the decision matrix for equipment combat effectiveness evaluation and optimization under the conventional combat mission profile.

[0094] S3 calculates and establishes the combat effectiveness evaluation model based on the difference and change degree of the evaluation index values.

[0095] Among them, Figure 1-3 As shown, the method for calculating and establishing the combat effectiveness evaluation model based on the degree of difference in evaluation index values ​​is:

[0096] S3.1 The matrix is ​​processed by gray polarity effect method After standardization, the standardized matrix is ​​obtained

[0097] Among them, Figure 1-3 As shown, the gray polarity effect processing method is:

[0098] S3.1.1 If the indicator If it has a maximum value attribute, then the original element is divided by its maximum value as the standardized element, that is,

[0099] S3.1.2 If the indicator If it has a minimum value attribute, then take its minimum value divided by the original element as the standardized element, that is,

[0100] S3.1.3 If the indicator With attributes, its moderate value is taken as the specified value or the average value, that is,

[0101]

[0102] At this time, the standardized element calculation model is:

[0103]

[0104] S3.1.4 Obtaining the standardized matrix

[0105] The normalization matrix is

[0106] S3.2 For the standardized matrix The relative closeness calculation is based on the degree of difference in the indicator values.

[0107] Among them, Figure 1-3 As shown, the method for calculating the relative closeness based on the difference degree of the index values ​​is:

[0108] S3.2.1 Determine a weighted standardized matrix of specific combat capabilities for typical mission phases;

[0109] Assume that the weight vector of N functions or performance indicators is w = (w1, w2, ..., w N ), the standardized matrix F is obtained by weighted calculation (the matrix is ​​defined as the difference matrix), and its algorithm is:

[0110]

[0111] S3.2.2 Determine the ideal solution and virtual negative ideal solution of specific combat capability;

[0112] The positive ideal solution is

[0113]

[0114] The negative ideal solution is

[0115]

[0116] Refer to the method proposed in [9] to construct the virtual negative ideal solution as

[0117]

[0118] S3.2.3 Calculate the Euclidean distance between the specific combat capability of each evaluation object and the positive ideal solution and the virtual negative ideal solution;

[0119] The Euclidean distance from the specific combat capability to the positive ideal solution is

[0120]

[0121] The Euclidean distance from the specific combat capability to the virtual negative ideal solution is

[0122]

[0123] S3.2.4 Calculate the relative closeness of the specific combat capability of each evaluation object to the virtual ideal solution;

[0124]

[0125] S3.2.5 Calculate the grey correlation between each evaluation object and the positive ideal solution of specific combat capability;

[0126] First, calculate the absolute value of the difference between the i-th index element of the m-th (m=1,2,…,P) equipment and the corresponding ideal solution

[0127]

[0128] And the maximum distance environmental parameters and minimum distance environmental parameters Recorded as

[0129]

[0130] Therefore, based on the basic idea of ​​Deng's correlation degree, the grey correlation coefficient of the i-th index element of the m-th equipment can be obtained as

[0131]

[0132] Where ξ∈(0,1) is the resolution coefficient, usually ξ=0.5. Finally, we continue to calculate and get the grey correlation degree between the mth equipment and the positive ideal solution:

[0133]

[0134] S3.2.6 Calculate the comprehensive closeness between each evaluation object and the specific combat capability based on the difference matrix;

[0135] Relative closeness based on virtual ideal solution Grey relational The larger the value of , the closer the qth combat capability of the mth equipment at the jth mission stage is to the ideal solution. Therefore, under the decision preference of the similar mechanism and similar mechanism based on the virtual ideal solution, the preference degree of the decision maker for position and shape is α, so that the relative closeness can be calculated. Grey relational After merging, the comprehensive closeness of the specific combat capability based on the virtual ideal solution is obtained as

[0136]

[0137] Comprehensive closeness based on virtual ideal solution It reflects the closeness between the combat effectiveness of the mth equipment and the specific combat capability task requirements under the similar mechanism and similar mechanism based on the virtual ideal solution. The larger the value, the better the equipment combat effectiveness. The larger the α, the more decision-makers prefer the position relationship that reflects the similar mechanism (ideal solution); the smaller the α, the more decision-makers prefer the shape relationship that reflects the similar mechanism (grey correlation method); the comprehensive closeness of the specific combat capability is based on the Euclidean distance and the grey correlation, and reflects the position relationship between each evaluation object and the ideal task requirements and the similarity shape of the data curve;

[0138] S3.2.7 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the typical mission stage based on the difference matrix;

[0139] Assume that the weight vector of p task capability indicators in a typical task stage is W = (W1, W2, ..., W p ), then the comprehensive closeness of the combat effectiveness of the mth equipment in the typical mission phase based on the virtual ideal solution is

[0140]

[0141] Combat effectiveness based on the comprehensive closeness of virtual ideal solution Based on Euclidean distance and grey correlation, the problem of indistinguishability of some scenarios in conventional ideal solutions is overcome. At the same time, the positional relationship between the combat effectiveness of the evaluation object and the virtual ideal solution of the task requirements and the similarity difference of the data curves are reflected, which better explains the physical meaning of the evaluation method based on similar mechanisms and similar mechanisms.

[0142] S3.2.8 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the entire combat mission based on the difference matrix solution;

[0143] Assume that the weight vector of the M task stages is A = (a1, a2, ..., a M ), then the comprehensive closeness of the combat effectiveness of the mth equipment in the whole combat mission based on the virtual ideal solution is

[0144]

[0145] S3.2.9 Ranking of the combat effectiveness of each assessment object.

[0146] Comprehensive closeness of combat effectiveness based on virtual ideal solution at a certain mission stage Or the overall combat effectiveness of the combat mission is based on the comprehensive closeness of the virtual ideal solution The size of each evaluation object is used to rank the combat effectiveness, and the comprehensive closeness or The larger one has better combat effectiveness, while the smaller one has worse combat effectiveness.

[0147] Among them, Figure 1-3 As shown, the method for calculating and establishing the combat effectiveness evaluation model based on the degree of change of the evaluation index value is:

[0148] S3.3 Pair Matrix Perform gray polarity effect preprocessing to obtain a standardized matrix

[0149] S3.4 Constructing the evaluation index value change matrix

[0150]

[0151] In the formula, m=1,2,…,P;j=1,2,…,M;q=1,2,…,p;i=1,2,…,M。

[0152] S3.5 Evaluation index value change matrix The comprehensive closeness calculation based on the virtual ideal solution and grey correlation degree is used when considering the degree of change of the index value.

[0153] Among them, Figure 1-3 As shown, the method for calculating the comprehensive closeness based on the virtual ideal solution and the grey correlation degree when considering the degree of change of the index value is:

[0154] S3.5.1 Determine the weighted normalized variation matrix;

[0155] Similarly, the weight vector of N functions or performance indicators is w = (w1, w2, ..., w N ), the standardized change matrix G is obtained through weighted calculation, that is,

[0156]

[0157] S3.5.2 Determine the positive ideal solution and virtual negative ideal solution based on the change matrix;

[0158] The positive ideal solution and negative ideal solution of the change matrix are

[0159]

[0160] A similar algorithm is used to construct the virtual negative ideal solution:

[0161]

[0162] S3.5.3 Calculate the Euclidean distance between each evaluation object and the positive ideal solution and the virtual negative ideal solution based on the change matrix;

[0163] The Euclidean distances to the positive ideal solution and the negative ideal solution are

[0164]

[0165] S3.5.4 Calculate the relative closeness of the specific combat capabilities of each evaluation object based on the change matrix;

[0166]

[0167] S3.5.5 Calculate the grey correlation between each evaluation object and the positive ideal solution;

[0168] The absolute value of the difference between the i-th index element of the m-th (m=1,2,…,P) equipment and the corresponding positive ideal solution is calculated as

[0169]

[0170] Thus, the maximum and minimum distance environmental parameters are obtained They are

[0171]

[0172] Then, the grey correlation coefficients of the i-th index element of the m-th equipment for the positive ideal solution can be obtained as follows:

[0173]

[0174] In the formula, ξ∈(0,1) is the resolution coefficient, usually ξ=0.5. Finally, the grey correlation degree between the mth equipment and the positive ideal solution can be calculated as follows:

[0175]

[0176] S3.5.6 Aggregate the Euclidean distance and grey correlation between each evaluation object and specific combat capability based on the change matrix;

[0177] Relative closeness based on virtual ideal solution Grey relational The larger the value of , the closer the qth combat capability of the mth equipment at the jth mission stage is to the ideal solution. Therefore, under the decision preference of the similar mechanism and similar mechanism based on the virtual ideal solution, the decision maker's preference for position and shape is α, so the comprehensive closeness of the specific combat capability based on the virtual ideal solution and the gray correlation degree is obtained as

[0178]

[0179] Comprehensive closeness based on virtual ideal solution and grey relational degree It reflects the degree of closeness between the combat effectiveness of the mth equipment and the specific combat capability task requirements under the similar and similar mechanisms of the virtual ideal solution when considering the degree of change of the index. The larger the value, the better the combat effectiveness of the equipment.

[0180] S3.5.7 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the typical mission stage based on the change matrix;

[0181] Assume that the weight vector of p task capability indicators in a typical task stage is W = (W1, W2, ..., W p ), then the comprehensive closeness of the combat effectiveness of the mth equipment based on the change matrix in the typical mission stage is

[0182]

[0183] Comprehensive closeness The mechanism of action and Similarly, when considering the degree of change of indicators, it also reflects the positional relationship between the changes in the combat effectiveness of the evaluation object in the typical mission stage and the mission requirements under similar mechanisms and similar mechanisms, as well as the similarity and difference of the data curves. The size of each evaluation object in the typical mission phase is used to rank the combat effectiveness of each evaluation object. The larger one has better combat effectiveness, while the smaller one has worse combat effectiveness.

[0184] S3.5.8 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the entire combat mission based on the change matrix;

[0185] Assume that the weight vector of the M task stages is A = (a1, a2, ..., a M ), then the combat effectiveness of the mth equipment in the whole combat mission is based on the comprehensive closeness of the change matrix

[0186]

[0187] S3.5.9 Rank the combat effectiveness of each evaluation object based on the degree of change of the indicator.

[0188] When considering the degree of change of indicators, the comprehensive closeness of combat effectiveness at a certain mission stage Or the comprehensive closeness of combat effectiveness during the entire combat mission The size of each evaluation object is used to rank the combat effectiveness, and the comprehensive closeness or The larger one has better combat effectiveness, while the smaller one has worse combat effectiveness.

[0189] S4 is a dynamic evaluation model for typical mission stages and the entire mission process based on the differences and degree of change of comprehensive indicators.

[0190] For the jth typical mission stage, considering the difference and change of the index values ​​comprehensively, assuming that the relative importance of the difference of the index values ​​is β, the comprehensive closeness of the combat effectiveness of each evaluation object is

[0191]

[0192] When β = 1, it means that only the difference of the index value is considered, and its comprehensive closeness is the comprehensive closeness When β = 0, it means that only the change degree of the index value is considered, and its comprehensive closeness is the comprehensive closeness Rank the operational effectiveness of each evaluation object and comprehensively evaluate the degree of closeness. The bigger one is better, the smaller one is worse.

[0193] For the entire combat mission, assume that the weight vector of the M mission stages is A = (a1, a2, ..., a M), then the comprehensive closeness of the combat effectiveness of each evaluation object when the difference and change of the index values ​​are comprehensively considered is

[0194]

[0195] In the formula, when β = 1, it means the comprehensive closeness of the whole combat process when only the difference of the index value is considered; when β = 0, it means the comprehensive closeness of the whole combat process when only the change of the index value is considered. The combat effectiveness of the whole combat process of each evaluation object is ranked, and the comprehensive closeness The bigger one is better, the smaller one is worse.

[0196] Example 2

[0197] The combat mission process of three similar ultra-short wave ground communication countermeasure systems (assuming they are System I, System II, and System III) is divided into four stages: pre-war reconnaissance stage COR, pre-war key reconnaissance stage CIR, accompanying attack stage CJA, and combat end stage COE. The combat effectiveness evaluation index system is as shown in the attached manual. Figure 3 shown.

[0198] As the instruction manual Figure 3 The 18 underlying indicators shown are generally expressed in qualitative language or quantitative values. Assume that after qualitative and quantitative conversion and polarity conversion, the data matrix A of the three systems for the four combat phases and 18 underlying indicators is obtained. Ⅰ , A II and A Ⅲ .

[0199]

[0200]

[0201]

[0202] Each row of the above three matrices represents the 18 underlying indicator data of the evaluation index system, and each column represents the four stage data of different underlying evaluation indicators.

[0203] Operational effectiveness evaluation calculation based on the degree of difference in index values

[0204] First, we take the reconnaissance capability as an example to carry out relevant calculations. Based on the above three data matrices, assuming that the indicators are equally weighted, the positive ideal solution of the reconnaissance capability is determined according to the polarity of the indicators as F1 + (1,1,1,1,1), the negative ideal solution is F1 - (0.62, 0.62, 0.68, 0.72, 0.78), then the virtual negative ideal solution can be constructed as In the calculation example, it is assumed that the rows of the matrix represent the three evaluated objects, and the columns represent the four task stages of the reconnaissance capability. The distance matrices of the reconnaissance capabilities of the three evaluated objects to the positive ideal solution and the virtual negative ideal solution are calculated as follows:

[0205]

[0206] Further calculation of the relative closeness matrix of the reconnaissance capabilities of the three evaluated objects based on the virtual ideal solution is:

[0207]

[0208] The grey correlation matrix of the reconnaissance capabilities of the three evaluated objects and the positive ideal solution is

[0209]

[0210] Here, the decision maker's preference is to regard position and shape as equally important, that is, α = 0.5, 1-α = 0.5, then the comprehensive closeness of the reconnaissance capabilities of the three evaluated objects based on the difference matrix can be obtained as follows:

[0211]

[0212] Similarly, the comprehensive closeness of the direction finding capability, jamming capability and command capability of the three evaluated objects based on the difference matrix is ​​calculated as follows:

[0213] and

[0214] It is also assumed that the weights between the four capabilities of the system to be evaluated are (0.3, 0.2, 0.3, 0.2), so the comprehensive closeness and ranking of the combat effectiveness of the three systems to be evaluated in four stages based on the difference matrix are aggregated as shown in Table 1.

[0215] Table 1 Combat effectiveness and ranking of the three systems and four stages based on the difference matrix

[0216]

[0217] It can be seen from the data in Table 1 that, considering only the degree of difference in the index values, the combat effectiveness of the three systems to be evaluated in the pre-war reconnaissance stage is ranked as system II, I, and III, the combat effectiveness of the pre-war key reconnaissance stage is ranked as system II, III, and I, the combat effectiveness of the accompanying attack stage is ranked as system III, I, and II, and the combat effectiveness of the battle end stage is ranked as system II, III, and I. The weights of the four stages are (0.2, 0.3, 0.4, and 0.1). According to the comprehensive closeness of the entire combat mission process shown in Table 1, the combat effectiveness of the three systems to be evaluated is ranked as system III, II, and I.

[0218] It can be seen from this example that when considering the degree of difference in index values, the comprehensive evaluation method of TOPSIS and grey correlation degree of virtual ideal solution is used to evaluate the combat effectiveness of equipment. The evaluation mechanism and significance are relatively obvious. Not only can the comprehensive evaluation value and ranking results of each evaluated equipment at different mission stages be obtained, but also the overall evaluation value and ranking results of the entire combat mission process can be obtained.

[0219] Operational effectiveness evaluation calculation based on the degree of change of index values

[0220] The degree of change of the evaluation index value reflects the trend of the combat effectiveness of the three systems. In the example, the data matrix describes four combat stages, so the degree of change of the combat stage is expressed as 3. Here, we continue to use reconnaissance capability as an example for related calculations. Assuming that the columns of the matrix are the five underlying indicators of reconnaissance capability, and the rows of the matrix represent the change values ​​of the combat stage, the change matrix B of the three systems to be evaluated is constructed. Ⅰ , B II and B Ⅲ They are

[0221]

[0222] and

[0223]

[0224] Assuming that the indicators of the above three data matrices are equally weighted, the positive ideal solution of the reconnaissance capability under the degree of change of the evaluation indicator value is determined as F1 + =(0.24, 0.13, 0.20, 0.10, 0.12), the negative ideal solution is F1 - =(-0.17,-0.26,-0.13,-0.18,-0.20), then the virtual negative ideal solution can be constructed as

[0225] Assuming that the rows of the matrix represent the three systems to be evaluated and the columns represent the changes in the three mission stages, the distance matrices of the changes in the reconnaissance capabilities of the three objects to be evaluated to the positive ideal solution and the negative ideal solution are calculated as follows:

[0226]

[0227] Further calculation of the relative closeness matrix of the reconnaissance capabilities of the three evaluated objects based on the virtual ideal solution is:

[0228]

[0229] The grey correlation matrix of the changes in the reconnaissance capabilities of the three evaluated objects and the positive ideal solution is:

[0230]

[0231] Taking the decision maker's weights for position and shape preferences as α = 0.5 and 1-α = 0.5, the comprehensive closeness of the changes in the reconnaissance capabilities of the three evaluated objects based on the virtual ideal solution and the grey correlation degree can be obtained as follows:

[0232]

[0233] Similarly, the comprehensive closeness matrix of the direction finding capability change, interference capability change and command capability change of the three evaluated objects based on the virtual ideal solution and the grey correlation degree can be calculated as follows:

[0234] and

[0235] Also, assuming that the weights between the four are (0.3, 0.2, 0.3, 0.2), the combat effectiveness of the three evaluated objects based on the three stages of change and their ranking are aggregated as shown in Table 2.

[0236] Table 2 The operational effectiveness and ranking of the three systems based on the degree of change (β=0)

[0237]

[0238] It can be seen from the data in Table 2 that, considering only the degree of change of the indicators, the three evaluated objects are ranked in order of combat effectiveness based on the three stages of change; however, judging from the average of the three stages of change, the combat effectiveness of System III ranks first.

[0239] Comprehensive evaluation of combat effectiveness based on weighting

[0240] When β=0.85, that is, considering both the difference and change of each indicator value at the same time, and paying more attention to the difference of each indicator value, the combat effectiveness of the three evaluated objects in the four stages and their ranking are shown in Table 3.

[0241] Table 3 Operational effectiveness and ranking of the three systems in four stages (β=0.85)

[0242]

[0243] It can be seen from the data in Table 3 that, considering the difference and change of each indicator value, the combat effectiveness of the three evaluated objects in the first mission stage is ranked as system II, I, and III, the combat effectiveness of the second mission stage is ranked as system II, III, and I, the combat effectiveness of the third mission stage is ranked as system III, I, and II, and the combat effectiveness of the fourth mission stage is ranked as system III, II, and I. The same weight is taken for the four stages, and based on the comprehensive closeness shown in the table, the combat effectiveness of the three evaluated objects in the entire combat mission process is ranked as system III, II, and I.

[0244] Comparing Table 1 and Table 3, the results of the ranking of the combat effectiveness of the three evaluated objects in the whole combat mission are consistent. Combined with Table 2, the combat effectiveness of System III ranks first. From the results of the ranking of the combat effectiveness of the four combat mission stages, considering the difference and change of each indicator value, the trend of System III having the best combat effectiveness is the most obvious.

[0245] It can also be seen from the above formula that when comprehensively considering the degree of difference and change of the index values, there are two ways to calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the whole combat mission process. The two ways are equivalent. One is to consider the degree of difference and change of the index values ​​in the typical mission stage respectively, calculate the comprehensive closeness of the combat effectiveness of the whole combat mission process under the two degrees, and then obtain the final result based on the weight vector (β, 1-β); the other is for the comprehensive closeness of the combat effectiveness in the typical mission stage. First, based on the weight vector (β, 1-β), the comprehensive closeness of the combat effectiveness under the degree of difference and change of the index values ​​is aggregated, and then the comprehensive closeness of the combat effectiveness of each mission stage is aggregated to obtain the final result.

[0246] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A dynamic evaluation method of equipment combat effectiveness based on virtual ideal solution and grey correlation degree, characterized in that: The specific steps are as follows: S1 Construction of hierarchical combat effectiveness indicator system; S2 combat effectiveness evaluation multi-stage three-dimensional data construction; S3 calculates and establishes the combat effectiveness evaluation model based on the difference and change degree of the evaluation index values; S4 Dynamic evaluation model of typical mission stages and the whole mission process based on the differences and changes of comprehensive indicators; The method for constructing the hierarchical combat effectiveness indicator system is as follows: S1.1 Determine the function or performance indicators of the equipment; S1.2 Determine the mission capability indicators; S1.3 Determine the combat effectiveness indicators of the equipment; The method for constructing multi-stage three-dimensional data for combat effectiveness evaluation is as follows: S2.1 Assume that P pieces of equipment of the same type are evaluated and compared in terms of their combat effectiveness based on the indicator system in Figure 1; S2.2 Determine the qth (q=1,2,…,p)th task capability indicator of the lower layer for the mth (m=1,2,…,P)th equipment; S2.3 Assume that the i-th (i=1,2,…,N) (N represents the total number of functions or performance indicators under the q-th task capability) function or performance indicator is in the j-th (j=1,2,…,M) task stage (corresponding to a task cycle t1,t2,…,t M Behavioral performance parameters during the task phase); S2.4 Determine the behavior performance parameter value described as S2.5 Construct a stage performance matrix of specific equipment for multi-stage three-dimensional data profile of equipment combat effectiveness evaluation S2.6 Determine the behavior performance parameter of the i-th (i=1,2,…,N) function or performance indicator of the m-th (m=1,2,…,P) equipment corresponding to the q-th (q=1,2,…,p)-th task capability indicator in the j-th (j=1,2,…,M)-th task stage; S2.7 Determine the behavior performance parameter value described as S2.8 Construct the function or performance matrix of all equipment for multi-stage three-dimensional data profile of equipment combat effectiveness evaluation: The method for calculating and establishing the combat effectiveness evaluation model based on the degree of difference in evaluation index values ​​is as follows: S3.1 The matrix is ​​processed by gray polarity effect method After standardization, the standardized matrix is ​​obtained S3.2 For the standardized matrix Relative closeness calculation based on the degree of difference in indicator values; The method for calculating and establishing the combat effectiveness evaluation model based on the degree of change of the evaluation index value is as follows: S3.3 Pair Matrix Perform gray polarity effect preprocessing to obtain a standardized matrix S3.4 Constructing the evaluation index value change matrix S3.5 Evaluation index value change matrix The comprehensive closeness calculation based on the virtual ideal solution and grey correlation degree is used when considering the degree of change of the index value.

2. The equipment combat effectiveness dynamic evaluation method based on virtual ideal solution and grey correlation degree according to claim 1 is characterized in that: Said 3. The equipment combat effectiveness dynamic evaluation method based on virtual ideal solution and grey correlation degree according to claim 1 is characterized in that: The gray polarity effect processing method is: S3.1.1 If the indicator If it has a maximum value attribute, then the original element is divided by its maximum value as the standardized element, that is, S3.1.2 If the indicator If it has a minimum value attribute, then take its minimum value divided by the original element as the standardized element, that is, S3.1.3 If the indicator With attributes, its moderate value is taken as the specified value or the average value, that is, At this time, the standardized element calculation model is: S3.1.4 Obtaining the standardized matrix 4. The equipment combat effectiveness dynamic evaluation method based on virtual ideal solution and grey correlation degree according to claim 1 is characterized in that: The normalization matrix is 5. The equipment combat effectiveness dynamic evaluation method based on virtual ideal solution and grey correlation degree according to claim 1 is characterized in that: The method for calculating the relative closeness based on the difference in index values ​​is: S3.2.1 Determine a weighted standardized matrix of specific combat capabilities for typical mission phases; S3.2.2 Determine the ideal solution and virtual negative ideal solution of specific combat capability; S3.2.3 Calculate the Euclidean distance between the specific combat capability of each evaluation object and the positive ideal solution and the virtual negative ideal solution; S3.2.4 Calculate the relative closeness of the specific combat capability of each evaluation object to the virtual ideal solution; S3.2.5 Calculate the grey correlation between each evaluation object and the positive ideal solution of specific combat capability; S3.2.6 Calculate the comprehensive closeness between each evaluation object and specific combat capability based on the difference matrix. S3.2.7 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the typical mission stage based on the difference matrix; S3.2.8 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the entire combat mission based on the difference matrix solution; S3.2.9 Ranking of the combat effectiveness of each assessment object.

6. The equipment combat effectiveness dynamic evaluation method based on virtual ideal solution and grey correlation according to claim 1 is characterized in that: The method for calculating the comprehensive closeness based on the virtual ideal solution and the grey correlation degree when considering the degree of change of the index value is: S3.5.1 Determine the weighted normalized variation matrix; S3.5.2 Determine the positive ideal solution and virtual negative ideal solution based on the change matrix; S3.5.3 Calculate the Euclidean distance between each evaluation object and the positive ideal solution and the virtual negative ideal solution based on the change matrix; S3.5.4 Calculate the relative closeness of the specific combat capabilities of each evaluation object based on the change matrix; S3.5.5 Calculate the grey correlation between each evaluation object and the positive ideal solution; S3.5.6 Aggregate the Euclidean distance and grey correlation between each evaluation object and specific combat capability based on the change matrix; S3.5.7 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the typical mission stage based on the change matrix; S3.5.8 Calculate the comprehensive closeness of the combat effectiveness of each evaluation object in the entire combat mission based on the change matrix; S3.5.9 Rank the combat effectiveness of each evaluation object based on the degree of change of the indicator.

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