Grey target comprehensive bull's-eye evaluation method and equipment for equipment combat effectiveness

Abstract

The present invention discloses a gray target comprehensive bull's-eye evaluation method and device for equipment combat effectiveness, the gray target comprehensive bull's-eye evaluation method comprises the following steps: constructing a positive bull's-eye based on gray target theory according to high-quality performance reference index information; generating a combat effectiveness index matrix corresponding to actual performance index information; using the gray correlation between the combat effectiveness index matrix and the positive bull's-eye as the positive bull's-eye degree of gray target theory; constructing a virtual negative bull's-eye according to poor performance reference index information, and using the gray correlation between the combat effectiveness index matrix and the virtual negative bull's-eye as the virtual negative bull's-eye degree; calculating the comprehensive bull's-eye degree to evaluate equipment combat effectiveness based on the positive bull's-eye degree and the virtual negative bull's-eye degree. The present invention also discloses a system, a device and a computer-readable storage medium. The present invention improves the reliability and objectivity of equipment combat effectiveness evaluation.

Description

Technical Field

[0001] The invention relates to the field of equipment combat effectiveness evaluation, and in particular to a grey target comprehensive bull's-eye degree evaluation method and device for equipment combat effectiveness. Background Art

[0002] The combat effectiveness of weapons and equipment refers to the functional performance and combat capability of the equipment to meet the requirements of a set of expected combat missions in a specified combat phase and combat environment. The evaluation of combat effectiveness is essentially to measure and evaluate the degree to which the combat effectiveness index data sequence meets the combat mission requirement data sequence, which is usually achieved through the description, statistics, and analysis of multi-dimensional data. Due to the small amount and missing characteristics of evaluation data, the gray target theory has a place in the field of equipment combat effectiveness evaluation.

[0003] The model of the grey target theory is based on the grey correlation analysis method. The basic idea of ​​the combat effectiveness evaluation based on the grey target theory is to target a certain index value sequence in the equipment combat effectiveness or comprehensive capability evaluation index system, and after the index value is processed without quantity tempering, a reference data pattern corresponding to each equipment is formed, that is, the standard pattern. The grey target theory believes that the standard pattern is the bull's eye of the grey target, and the pattern far away from the bull's eye is called the target edge, the one far away upward is called the upper target edge, and the one far away downward is called the lower target edge; the grey correlation degree between the pattern in each grey correlation difference information space and the bull's eye is called the bull's eye proximity, or bull's eye degree for short. Finally, based on the bull's eye degree, the combat effectiveness of each evaluation object can be evaluated and ranked. Obviously, the closer the data pattern representing the combat effectiveness of each equipment is to the standard pattern, the better the combat effectiveness of the equipment represented by the pattern. Therefore, as long as the bull's eye degree of each pattern is obtained, the combat effectiveness of the equipment represented by each pattern can be evaluated and ranked.

[0004] At present, when applying the grey target theory, the data sequence of the positive demand of the combat mission is generally selected as the reference sequence. However, this method cannot directly reflect the real contribution of various functional performance and combat capabilities to combat effectiveness. Because in the actual combat application environment of weapons and equipment, the closer the various functional performance and combat capabilities are to the standard mode or the bull's eye, the better the combat effect of the equipment can be achieved. With the changes in the battlefield environment, in order to achieve better combat application effects, it is also hoped that the functional performance and combat capabilities of weapons and equipment will be far away from those unfavorable factors. In other words, in order to better guide the combat application of weapons and equipment, in addition to evaluating the degree to which the equipment meets the requirements of the combat mission, in most cases we also need to understand the degree to which the combat mission requirements are not met, that is, the degree to which the negative requirements of the combat mission are met, so as to improve the reliability and objectivity of the evaluation of the combat effectiveness of the equipment.

[0005] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of the present invention is to provide a grey target comprehensive bull's-eye degree evaluation method and device for equipment combat effectiveness, aiming to improve the reliability and objectivity of equipment combat effectiveness evaluation.

[0007] To achieve the above object, the present invention provides a gray target comprehensive bull's-eye evaluation method for equipment combat effectiveness, the gray target comprehensive bull's-eye evaluation method comprising the following steps:

[0008] Acquire high-quality performance reference indicator information of the equipment to be evaluated, and construct a positive bull's eye based on the grey target theory according to the high-quality performance reference indicator information;

[0009] Acquire actual performance index information of the equipment to be evaluated, convert the actual performance index information according to a preset combat effectiveness index system corresponding to the equipment to be evaluated, and generate a combat effectiveness index matrix corresponding to the actual performance index information;

[0010] The grey correlation degree between the operational effectiveness index matrix and the positive bull's eye is used as the positive bull's eye degree of the grey target theory;

[0011] Acquire poor performance reference index information of the equipment to be evaluated, construct a virtual negative bull's eye according to the poor performance reference index information, and use the grey correlation degree between the combat effectiveness index matrix and the virtual negative bull's eye as the virtual negative bull's eye degree;

[0012] A comprehensive bull's-eye degree is calculated based on the positive bull's-eye degree and the virtual negative bull's-eye degree, and the combat effectiveness of the equipment is evaluated based on the comprehensive bull's-eye degree.

[0013] Optionally, the actual performance index information is a multi-attribute index matrix of the equipment to be evaluated, and the step of converting the actual performance index information according to a preset combat effectiveness index system corresponding to the equipment to be evaluated to generate a combat effectiveness index matrix corresponding to the actual performance index information includes:

[0014] According to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, the multi-attribute indicator matrix is ​​subjected to isopolarization processing to obtain an isopolarized matrix;

[0015] The equal polarity matrix is ​​dimensionlessly processed to obtain a combat effectiveness index matrix corresponding to the equal polarity matrix.

[0016] Optionally, the step of performing isopolarization processing on the multi-attribute indicator matrix according to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated to obtain the isopolarization matrix includes:

[0017] According to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, determining the inherent attributes of the behavior performance parameter values ​​in the multi-attribute indicator matrix, and obtaining the maximum value and the minimum value in the multi-attribute indicator matrix;

[0018] If the inherent attribute of the behavior performance parameter value is a maximum value attribute, the quotient between the behavior performance parameter value and the maximum value is taken as the equal polarity value corresponding to the behavior performance parameter value;

[0019] If the inherent attribute of the behavior performance parameter value is a minimum value attribute, the quotient between the behavior performance parameter value and the minimum value is taken as the equal polarity value corresponding to the behavior performance parameter value;

[0020] If the inherent attribute of the behavior performance parameter value is a moderate value attribute, obtaining the moderate value corresponding to the behavior performance parameter value, and calculating the equal polarity value corresponding to the behavior performance parameter value according to the moderate value;

[0021] An isopolarization matrix is ​​constructed based on the isopolarity values.

[0022] Optionally, the step of performing dimensionless processing on the isopolarization matrix to obtain a combat effectiveness index matrix corresponding to the isopolarization matrix comprises:

[0023] Calculate the sample mean and sample variance corresponding to the equipolarization matrix;

[0024] The equal polarization matrix is ​​converted into a combat effectiveness index matrix corresponding to the equal polarization matrix according to the sample mean and the sample variance.

[0025] Optionally, the step of using the grey correlation degree between the operational effectiveness index matrix and the positive bull's eye as the positive bull's eye degree of the grey target theory comprises:

[0026] Determine a first reference index value matrix corresponding to the positive bull's eye;

[0027] Performing difference processing on the combat effectiveness index matrix and the first reference index value matrix to obtain a first index value difference matrix;

[0028] A positive combat effectiveness evaluation value is calculated according to the first indicator value difference matrix, and the positive combat effectiveness evaluation value is used as the positive bull's-eye degree of the grey target theory.

[0029] Optionally, before the step of calculating the positive combat effectiveness evaluation value according to the first indicator value difference matrix and using the positive combat effectiveness evaluation value as the positive bull's-eye degree of the grey target theory, the step includes:

[0030] Calculate the weighted sum of squares of deviations according to the sample mean and sample variance of the first indicator value difference matrix;

[0031] Calculating the performance indicator data weight based on the weighted sum of squared deviations;

[0032] The step of calculating the positive combat effectiveness evaluation value according to the first indicator value difference matrix and using the positive combat effectiveness evaluation value as the positive bull's-eye degree of the grey target theory comprises:

[0033] A forward combat effectiveness evaluation value is calculated based on the first indicator value difference matrix and the performance indicator data weight, and the forward combat effectiveness evaluation value is used as the forward bull's-eye degree of the grey target theory.

[0034] Optionally, the step of using the grey correlation degree between the combat effectiveness index matrix and the virtual negative bull's eye as the virtual negative bull's eye degree comprises:

[0035] Determine a second reference index value matrix corresponding to the virtual negative bull's eye;

[0036] Performing differentiation processing on the combat effectiveness index matrix and the second reference index value matrix to obtain a second index value difference matrix;

[0037] A negative combat effectiveness evaluation value is calculated according to the second indicator value difference matrix and the performance indicator data weight, and the negative combat effectiveness evaluation value is used as a virtual negative bull's eye degree.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also provides a gray target comprehensive bull's-eye degree evaluation device for equipment combat effectiveness, and the gray target comprehensive bull's-eye degree evaluation device for equipment combat effectiveness includes: a memory, a processor, and a gray target comprehensive bull's-eye degree evaluation program stored in the memory and executable on the processor, and when the gray target comprehensive bull's-eye degree evaluation program is executed by the processor, the steps of the gray target comprehensive bull's-eye degree evaluation method as described in any one of the above items are implemented.

[0039] The conventional standard mode or positive bullseye selected for evaluating the combat effectiveness of weapons and equipment cannot directly reflect the true contribution of various functional performances and combat capabilities to combat effectiveness. Because in the actual combat application environment of weapons and equipment, the closer the various functional performances and combat capabilities are to the conventional standard mode or positive bullseye, the better the combat effect of the equipment can be achieved. With the changes in the battlefield environment, in order to achieve better combat application effects, it is also hoped that the functional performances and combat capabilities of weapons and equipment will be far away from those unfavorable factors. Based on the above reasons and the concept of TOPSIS method, if we can simultaneously determine the best and worst standard modes or bullseyes, and respectively obtain the best bullseye degree (i.e., positive bullseye degree) and the worst bullseye degree (i.e., virtual negative bullseye degree) for the two bullseyes, and aggregate the two bullseye degrees to obtain the comprehensive bullseye degree based on the grey target theory, then the combat effectiveness of different weapons and equipment can be evaluated and ranked based on the comprehensive bullseye degree, thereby providing more reference information for the combat application of weapons and equipment and improving the reliability and objectivity of the evaluation of the combat effectiveness of equipment. The present invention comprehensively utilizes the optimal and worst information of the original evaluation data parameters when constructing the standard model. The evaluation mechanism considers both the reference value of the positive demand for equipment to complete combat missions and the reference value of the negative demand for equipment to complete combat missions, thereby improving the reliability and objectivity of the equipment combat effectiveness evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0041] Figure 1 It is a flow chart of the first embodiment of the grey target comprehensive bull's-center degree evaluation method of the present invention;

[0042] Figure 2 Schematic diagram of the improvement of the bull's eye of the grey target theory based on the TOPSIS method in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of a model flow chart of an improvement of the grey target theory in an embodiment of the present invention;

[0044] Figure 4 It is an example diagram of a preset combat effectiveness index system in the gray target comprehensive bull's-eye evaluation method of the present invention;

[0045] Figure 5 It is a flow chart of the second embodiment of the grey target comprehensive bull's-center degree evaluation method of the present invention;

[0046] Figure 6 The figure is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0047] The realization of the purpose, functional features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The above-mentioned drawings have shown clear embodiments of this application, which will be described in more detail later. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0048] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0049] It should be noted that, in the present embodiment, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context in the specific embodiment.

[0050] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this embodiment, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this embodiment, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determination". Furthermore, as used in this embodiment, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising" and "including" indicate the presence of the described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the presence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or", "and / or", "including at least one of the following" etc. used in this application may be interpreted as inclusive, or mean any one or any combination. For example, “comprising at least one of the following: A, B, C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”, and for another example, “A, B or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”. An exception to this definition will only occur when a combination of elements, functions, steps or operations are inherently mutually exclusive in some manner.

[0051] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless there is a clear description in the present embodiment, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0052] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0053] It should be noted that in this embodiment, step codes such as S100, S200, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing the step codes, those skilled in the art may execute S200 first and then S100, etc., but these should all be within the scope of protection of this application.

[0054] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0056] At present, when applying the grey target theory, the data sequence of the positive demand of the combat mission is generally selected as the reference sequence. However, this method cannot directly reflect the real contribution of various functional performance and combat capabilities to combat effectiveness. Because in the actual combat application environment of weapons and equipment, the closer the various functional performance and combat capabilities are to the standard mode or the bull's eye, the better the combat effect of the equipment can be achieved. With the changes in the battlefield environment, in order to achieve better combat application effects, it is also hoped that the functional performance and combat capabilities of weapons and equipment will be far away from those unfavorable factors. In other words, in order to better guide the combat application of weapons and equipment, in addition to evaluating the degree to which the equipment meets the requirements of the combat mission, in most cases we also need to understand the degree to which the combat mission requirements are not met, that is, the degree to which the negative requirements of the combat mission are met, so as to improve the reliability and objectivity of the evaluation of the combat effectiveness of the equipment.

[0057] Based on this, the first embodiment of the present invention provides a gray target comprehensive bull's-eye evaluation method for equipment combat effectiveness, please refer to Figure 1 The gray target comprehensive bull's-eye evaluation method comprises the following steps:

[0058] Step S100, obtaining high-quality performance reference indicator information of the equipment to be evaluated, and constructing a positive bull's eye based on the grey target theory according to the high-quality performance reference indicator information;

[0059] First, the grey target theory is introduced. It can be understood by those skilled in the art that the model of the grey target theory is based on the grey correlation analysis method. The basic idea of ​​the combat effectiveness evaluation based on the grey target theory is to target a certain index value sequence in the equipment combat effectiveness or comprehensive capability evaluation index system, and after dimensionless processing of the index value, to form a reference data mode or standard mode (i.e., the positive bull's eye referred to in this embodiment) corresponding to each equipment. Each mode and the standard mode together constitute a grey target, and then the analysis is performed. That is, firstly, the positive bull's eye based on the grey target theory is set (i.e., the reference data column in the grey correlation analysis. Since the optimal value of the evaluation index in the effectiveness evaluation problem is usually difficult to directly give, the conventional grey target theory generally constructs the bull's eye based on the maximum value of the index of all evaluation objects), and then the grey correlation coefficient and grey bull's eye degree between the index value sequence of each evaluation object and the bull's eye are calculated.

[0060] The grey target theory believes that the standard mode is the bull's eye of the grey target (i.e., the positive bull's eye in this embodiment), and the mode far from the bull's eye is called the target edge, the one far away upward is called the upper target edge, and the one far away downward is called the lower target edge; the grey correlation degree between the mode and the bull's eye in each grey correlation difference information space is called the bull's eye proximity, or the positive bull's eye degree for short. Finally, based on the positive bull's eye degree, the positive combat effectiveness of each evaluation object can be evaluated and ranked. Obviously, the closer the data mode representing the combat effectiveness of each equipment is to the standard mode, the better the combat effectiveness of the equipment represented by the mode. Therefore, as long as the positive bull's eye degree of each mode is obtained, the equipment represented by each mode can be evaluated and ranked for positive combat effectiveness.

[0061] Specifically, the high-quality performance reference index information refers to the information on the degree to which the equipment meets the high-quality index requirements of a series of expected combat missions under a specified combat environment. As the most critical and important comprehensive index for evaluating the quality of equipment, its evaluation problem is commonly found in the fields of equipment demonstration, equipment testing, equipment application, etc. In this embodiment, the high-quality performance reference index information may include various high-quality performance index information of the equipment to be evaluated.

[0062] In this embodiment, a positive bull's eye based on the grey target theory is constructed according to the high-quality performance reference indicator information, that is, a standard model based on the grey target theory is established, specifically:

[0063] Assume that xi is the combat effectiveness evaluation data model of the i-th weapon equipment, x(k) is the k-th combat effectiveness evaluation index sequence of the weapon equipment, for x i =(x i (1),x i (2),…,x i (n)), Then k∈K={1,2,…,n},i∈I={1,2,…,m}; for x(k)=(x1(k),x2(k),…,x n (k)), Then i∈I={1,2,…,m}.

[0064] Before performing gray target calculation, it is necessary to select combat effectiveness evaluation indicators of different polarities and determine the reference data column of positive combat effectiveness. There are three types of evaluation indicators: maximum polarity, minimum polarity and moderate polarity. They are usually selected according to the following method.

[0065] 1. Maximum polarity selection algorithm

[0066] Maximum polarity refers to the indicator characteristic that the larger the target value, the better, that is, POLx(k) = POLx(max). It generally focuses on the degree to which the indicator effect deviates from the maximum value. The selection algorithm is:

[0067]

[0068] 2. Minimum polarity selection algorithm

[0069] Minimum polarity refers to the indicator characteristic that the smaller the target value, the better, that is, POLx(k) = POLx(min). It generally focuses on the degree to which the indicator effect deviates from the minimum value. The selection algorithm is:

[0070]

[0071] 3. Moderate polarity selection algorithm

[0072] Moderate value polarity refers to the indicator characteristic that the target value requires a moderate value, that is, POLx(k) = POLx(mem). It generally focuses on the degree to which the indicator effect deviates from the moderate value or the specified value. The selection algorithm is:

[0073]

[0074] The sequence x0=(x0(1),x0(2),…,x0(n)) is called the standard model or positive bull’s eye for evaluating the combat effectiveness of weapons and equipment.

[0075] Step S200, obtaining actual performance index information of the equipment to be evaluated, converting the actual performance index information according to a preset combat effectiveness index system corresponding to the equipment to be evaluated, and generating a combat effectiveness index matrix corresponding to the actual performance index information;

[0076] In this embodiment, in order to improve the flexibility of equipment evaluation, equipment usually needs to have a series of combat capabilities to complete the specified combat mission, and the combat capability is usually reflected by a number of equipment functions or performance attributes. Therefore, the tree analysis method can be referred to to decompose the mission phase and key combat issues of the equipment to be evaluated layer by layer, so as to construct a hierarchical preset combat effectiveness indicator system corresponding to the equipment to be evaluated, so that the preset combat effectiveness indicator system can be characterized from top to bottom through combat effectiveness indicators, mission capabilities, functions or performance indicators, and the bottom-level function or performance indicator data can be aggregated from bottom to top to obtain the combat effectiveness information of the equipment to be evaluated.

[0077] It is understandable that a corresponding preset combat effectiveness indicator system can be established for each combat stage of the entire combat process of the equipment, so that one or more preset combat effectiveness indicator systems can be selected and combined to construct a preset combat effectiveness system that meets the needs of different users. Assume that the entire combat process of the equipment can be decomposed into t1, t2...t M The combination of the underlying functions or performance indicator data of all mission stages constitutes the data of the entire combat mission of the equipment; therefore, the aggregation calculation of the underlying functions or performance indicator data can be performed for all mission stages respectively, and then the combat effectiveness evaluation results of all mission stages can be aggregated to obtain the combat effectiveness of the entire combat mission of the equipment to be evaluated.

[0078] In this embodiment, the actual performance index information includes a multi-attribute index matrix, and the actual performance index information of the equipment to be evaluated can be obtained. The actual performance index information may include but is not limited to the evaluation values ​​of various performance indicators in the preset combat effectiveness index system corresponding to the equipment to be evaluated. Then, the actual performance index information can be qualitatively and quantitatively converted and polarity converted according to the preset combat effectiveness index system corresponding to the equipment to be evaluated, and then a combat effectiveness index matrix corresponding to the actual performance index information is generated. Assuming that there are M types of combat effectiveness of equipment to be evaluated and compared, the i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th (i-th Then the multi-attribute index matrix of the qth task capability of all equipment in the jth task stage (i.e., the actual performance index information in this embodiment) is:

[0079]

[0080] Based on the above performance matrix, the combat effectiveness index matrix of multi-stage and multi-capability data for equipment combat effectiveness evaluation (i.e., the combat effectiveness index matrix corresponding to the performance index information of the equipment to be evaluated) can be constructed as follows:

[0081]

[0082] The combat effectiveness index matrix expresses the evaluation index values ​​of the multi-attribute functions or performances of each evaluation object under the qth task capability in the jth task stage. Each row in the combat effectiveness index matrix corresponds to the performance parameter values ​​of all functions or performance indicators of a certain equipment, and each column corresponds to the performance parameter values ​​of all equipment with a certain function or performance indicator. The combat effectiveness index matrix is ​​usually referred to as the decision matrix for equipment combat effectiveness evaluation and optimization under conventional combat mission profiles, and is also the preferred combat effectiveness index matrix in this application. The above-mentioned combat effectiveness index matrix in this embodiment It covers the performance indicators of all equipment to be evaluated, multiple combat mission stages, and the behavioral manifestations of multi-attribute evaluation indicators, which is conducive to the comprehensive evaluation of the mission stages and the entire mission process of combat effectiveness from the perspectives of the degree of difference and change in the evaluation indicator values, thereby overcoming the one-sidedness and information omissions of equipment combat effectiveness evaluation and improving the rationality and credibility of effectiveness evaluation results.

[0083] like Figure 4 As shown, Figure 4 This is an example diagram of the preset combat effectiveness index system in the gray target comprehensive bull's-eye evaluation method of the present invention. Taking a certain type of ultra-short wave ground communication confrontation system as an example, the combat mission of a certain type of ultra-short wave ground communication confrontation system is to carry out electromagnetic deterrence and electromagnetic blockade on the enemy's ultra-short wave communication system in a certain combat area, and to support and interfere with the enemy's ultra-short wave voice and data communications. The entire combat mission process of the ultra-short wave ground communication confrontation system can be divided into multiple mission stages such as pre-war reconnaissance and pre-war key reconnaissance. Then, according to the needs of selecting a certain mission stage, a preset combat effectiveness index system corresponding to the certain type of ultra-short wave ground communication confrontation system can be constructed, wherein the combat effectiveness of the ultra-short wave ground communication confrontation system can include four capabilities: reconnaissance capability, direction finding capability, interference capability, and command and control capability. Then the four capabilities are divided separately. The reconnaissance capability can include six performance indicators: reconnaissance target coverage, maximum reconnaissance distance, reconnaissance probability, reconnaissance timeliness, and parameter measurement capability; the direction finding capability can include four performance indicators: direction finding distance, direction finding time, direction finding error, and positioning accuracy; the interference capability can include four performance indicators: interference coverage airspace, equivalent radiation power, interference response time, and target interference probability; the command and control capability can include five performance indicators: information transmission accuracy, information transmission timeliness, intelligence processing capability, command and control capability, and intelligence receiving and sending capability. For example, the actual performance indicator information corresponding to six ultra-short wave ground communication countermeasure systems of the same type (hereinafter referred to as System I, System II, System III, System IV, System V, System VI) can be selected and the corresponding combat effectiveness indicator matrix can be obtained after qualitative and quantitative conversion and polarity conversion processing as follows:

[0084]

[0085] Each row of the above matrix corresponds to 6 ultra-short wave ground communication countermeasure systems of the same type to be evaluated, and each column represents the evaluation value corresponding to each performance indicator in different combat capabilities.

[0086] Step S300, taking the grey correlation degree between the combat effectiveness index matrix and the positive bull's eye as the positive bull's eye degree of the grey target theory;

[0087] Step S400, obtaining poor performance reference index information of the equipment to be evaluated, constructing a virtual negative bull's eye according to the poor performance reference index information, and using the grey correlation degree between the combat effectiveness index matrix and the virtual negative bull's eye as the virtual negative bull's eye degree;

[0088] Specifically, the inferior performance reference index information refers to the information on the degree to which the equipment meets the inferior performance index requirements of a series of expected combat missions under a specified combat environment. In this embodiment, the inferior performance reference index information may include various inferior performance index information of the equipment to be evaluated.

[0089] Step S500, calculating a comprehensive bull's-eye degree according to the positive bull's-eye degree and the virtual negative bull's-eye degree, and evaluating the equipment combat effectiveness according to the comprehensive bull's-eye degree.

[0090] The conventional standard mode or positive bullseye selected for evaluating the combat effectiveness of weapons and equipment cannot directly reflect the true contribution of various functional performances and combat capabilities to combat effectiveness. Because in the actual combat application environment of weapons and equipment, the closer the various functional performances and combat capabilities are to the conventional standard mode or positive bullseye, the better the combat effect of the equipment can be achieved. With the changes in the battlefield environment, in order to achieve better combat application effects, it is also hoped that the functional performances and combat capabilities of weapons and equipment will be far away from those unfavorable factors. Based on the above reasons and the concept of TOPSIS method, if we can simultaneously determine the best and worst standard modes or bullseyes, and respectively obtain the best bullseye degree (i.e., positive bullseye degree) and the worst bullseye degree (i.e., virtual negative bullseye degree) for the two bullseyes, and aggregate the two bullseye degrees to obtain the comprehensive bullseye degree based on the grey target theory, then the combat effectiveness of different weapons and equipment can be evaluated and ranked based on the comprehensive bullseye degree, thereby providing more reference information for the combat application of weapons and equipment and improving the reliability and objectivity of the evaluation of the combat effectiveness of equipment. This embodiment comprehensively utilizes the optimal and worst information of the original evaluation data parameters when constructing the standard model. The evaluation mechanism considers both the reference value of the positive requirements of the equipment to complete the combat mission and the reference value of the negative requirements of the equipment to complete the combat mission, thereby improving the reliability and objectivity of the equipment combat effectiveness evaluation.

[0091] In order to help understand this application, a further introduction is given. Due to the core defect of the TOPSIS method, that is, when the performance index of the equipment to be evaluated is located on the median of the positive and negative ideal solution lines, the TOPSIS method cannot distinguish the combat effectiveness of the equipment to be evaluated based on the evaluation results. The application of the gray target theory based on the TOPSIS method will also have this problem, such as Figure 3 As shown, especially when the performance index of the equipment to be evaluated is located on a vertical line connecting the positive and negative ideal solutions, it will also lead to the wrong distinction between the good and bad combat effectiveness of the equipment to be evaluated. This application constructs a virtual worst standard mode or a negative bull's eye, obtains the conventional positive gray target bull's eye degree, and the virtual negative gray target bull's eye degree, and then aggregates to obtain the final gray target comprehensive bull's eye degree.

[0092] This embodiment proposes a gray target comprehensive bull's-eye evaluation method based on comprehensive bull's-eye. The basic idea is that in addition to considering the conventional positive gray target bull's-eye calculation, the TOPSIS concept is also used to consider the degree of dissatisfaction of combat application requirements, and the core defects of the TOPSIS method are overcome to construct a virtual negative bull's-eye, and the corresponding negative gray bull's-eye is calculated. The positive gray bull's-eye and the negative gray bull's-eye are integrated to obtain the comprehensive bull's-eye, so as to evaluate and rank the combat effectiveness of each evaluation object based on the comprehensive bull's-eye, thereby improving the reliability and objectivity of the equipment combat effectiveness evaluation.

[0093] For further information, please refer to Figure 5 The actual performance index information is a multi-attribute index matrix of the equipment to be evaluated. The step S200 of acquiring the actual performance index information of the equipment to be evaluated, converting the actual performance index information according to a preset combat effectiveness index system corresponding to the equipment to be evaluated, and generating a combat effectiveness index matrix corresponding to the actual performance index information includes:

[0094] Step S210, obtaining a multi-attribute index matrix of the equipment to be evaluated;

[0095] Step S220, performing isopolarization processing on the multi-attribute indicator matrix according to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, to obtain an isopolarization matrix;

[0096] Step S230, performing dimensionless processing on the isopolarization matrix to obtain a combat effectiveness index matrix corresponding to the isopolarization matrix.

[0097] In this embodiment, after evaluating various functions or performance indicators of various equipment for different task capabilities at different task stages to obtain different behavioral performance parameter values, a number of multi-attribute indicator matrices representing different functions or performance indicators of different equipment for the same task capability at the same task stage are constructed based on these different behavioral performance parameter values. After generating multiple multi-attribute indicator matrices based on each behavioral performance parameter value, since the polarities and dimensions of these multi-attribute indicator matrices are different, these multi-attribute indicator matrices cannot be directly compared. In order to unify the polarities and dimensions of these multi-attribute indicator matrices, first, according to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, these multi-attribute indicator matrices are subjected to isopolarization processing to obtain an isopolarization matrix corresponding to the multi-attribute indicator matrix, and then the isopolarization matrix is ​​dimensionless processed to obtain a combat effectiveness indicator matrix corresponding to the multi-attribute indicator matrix.

[0098] In a possible implementation, step S220, performing isopolarization processing on the multi-attribute indicator matrix according to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, and the step of obtaining the isopolarization matrix includes:

[0099] Step A10, determining the inherent attributes of the behavior performance parameter values ​​in the multi-attribute indicator matrix according to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, and obtaining the maximum value and the minimum value in the multi-attribute indicator matrix;

[0100] Step A20, if the inherent attribute of the behavior performance parameter value is a maximum value attribute, then the quotient between the behavior performance parameter value and the maximum value is used as the equal polarity value corresponding to the behavior performance parameter value;

[0101] Step A30, if the inherent attribute of the behavior performance parameter value is a minimum value attribute, then the quotient between the behavior performance parameter value and the minimum value is used as the equal polarity value corresponding to the behavior performance parameter value;

[0102] Step A40, if the inherent attribute of the behavior performance parameter value is a moderate value attribute, obtaining the moderate value corresponding to the behavior performance parameter value, and calculating the equal polarity value corresponding to the behavior performance parameter value according to the moderate value;

[0103] Step A50: constructing an isopolarization matrix based on the isopolarity values.

[0104] In this embodiment, since the polarity of the evaluation index of each behavior performance parameter value in the multi-attribute index matrix is ​​different, in order to unify the polarity of the evaluation index, it is necessary to perform equal polarity processing on each element in the multi-attribute index matrix. In this embodiment, the gray polarity effect processing method is used to perform equal polarity processing on each multi-attribute index matrix to obtain an equal polarity matrix. Specifically, taking the isopolarization processing process of a multi-attribute indicator matrix as an example, the gray polarity effect processing method is to first calculate the maximum and minimum values ​​in the multi-attribute indicator matrix, and determine the inherent attributes of the behavior performance parameter value in the multi-attribute indicator matrix in turn according to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated; if the behavior performance parameter value has a maximum value attribute, then the quotient of the behavior performance parameter value and the maximum value is calculated as the isopolarity value corresponding to the behavior performance parameter value; if the behavior performance parameter value has a minimum value attribute, then the quotient of the minimum value and the behavior performance parameter value is calculated as the isopolarity value corresponding to the behavior performance parameter value; if the behavior performance parameter value has a moderate value attribute, then the preset preset value is set as the moderate value corresponding to the behavior performance parameter value, or the average value of all behavior performance parameter values ​​in the multi-attribute indicator matrix is ​​calculated, and the average value is set as the moderate value corresponding to the behavior performance parameter value, and then the isopolarity value corresponding to the behavior performance parameter value is calculated according to the moderate value.

[0105] After all the behavior performance parameter values ​​in the multi-attribute indicator matrix are converted into corresponding isopolarity values, an isopolarization matrix corresponding to the multi-attribute indicator matrix can be obtained. The preset values ​​are set in advance by those skilled in the art according to specific needs and can be adjusted in real time.

[0106] In a possible implementation manner, the step S230 of performing dimensionless processing on the isopolarization matrix to obtain a combat effectiveness index matrix corresponding to the isopolarization matrix includes:

[0107] Step B10, calculating the sample mean and sample variance corresponding to the equipolarization matrix;

[0108] Step B20: converting the equal polarization matrix into a combat effectiveness index matrix corresponding to the equal polarization matrix according to the sample mean and the sample variance.

[0109] In this embodiment, after the multi-attribute matrix is ​​converted into the corresponding equipolarization matrix, the polarity of the equipolarization matrix is ​​consistent compared to the multi-attribute matrix, but its dimension is not consistent. Therefore, it is also necessary to perform dimensionless processing on the equipolarization matrix. The dimensionless processing method mainly includes a standardized processing method, an extreme value processing method, a linear proportional method, a vector standard method, and an efficiency coefficient method. In this embodiment, a standardized processing method is used to process the equipolarization matrix. Specifically, the average value of all equipolarity values ​​in an equipolarization matrix is ​​first calculated as the sample mean, and the sample variance in the equipolarization matrix is ​​calculated based on the sample mean. Then, the equipolarity values ​​in the equipolarization matrix are converted into corresponding standard values ​​based on the sample mean and the sample variance, and the corresponding equipolarization matrix is ​​converted into a combat effectiveness index matrix.

[0110] In a possible implementation, the step S300 of using the grey correlation between the operational effectiveness index matrix and the positive bull's eye as the positive bull's eye degree of the grey target theory includes:

[0111] Step C10, determining a first reference index value matrix corresponding to the positive bull's eye;

[0112] Step C20, performing difference processing on the combat effectiveness index matrix and the first reference index value matrix to obtain a first index value difference matrix;

[0113] Step C30, calculating a positive combat effectiveness evaluation value according to the first indicator value difference matrix, and using the positive combat effectiveness evaluation value as a positive bull's-eye degree of the grey target theory.

[0114] In one embodiment, regarding the algorithm of the positive bull's-eye degree, specifically, the absolute value of the difference between the index element of the i-th (i=1, 2, ..., m)-th equipment and the corresponding bull's-eye is calculated, that is,

[0115] Δ 0i (k)=|x0(k)-x i (k)|;

[0116] And the maximum distance environmental parameter Δ max and minimum distance environmental parameter Δ min Denoted as:

[0117]

[0118] Then calculate the bull's eye coefficient of the kth performance indicator of the i-th equipment to be evaluated, that is:

[0119]

[0120] In the above formula, ξ∈(0,1) is the resolution coefficient, usually ξ=0.5. i) is the bull's-eye degree of xi, satisfying:

[0121]

[0122] r(x0,x i ) represents the degree to which the combat effectiveness mode of the ith weapon equipment is close to the bull's eye (standard mode), that is, the positive bull's eye degree of the grey target theory.

[0123] It should be noted that the combat effectiveness of different weapons and equipment is evaluated and ranked based on the bull's-eye degree. i ) to rank the combat effectiveness, r(x0,x i ) is larger, the higher the combat effectiveness of the equipment represented by this mode, and vice versa.

[0124] In a possible implementation manner, the step C30, before the step of calculating the positive combat effectiveness evaluation value according to the first indicator value difference matrix and using the positive combat effectiveness evaluation value as the positive bull's-eye degree of the grey target theory, includes:

[0125] Step D10, calculating a weighted sum of squared deviations according to the sample mean and sample variance of the first indicator value difference matrix;

[0126] Step D20, calculating the performance indicator data weight based on the weighted sum of squared deviations;

[0127] The step C30 of calculating the positive combat effectiveness evaluation value according to the first indicator value difference matrix and using the positive combat effectiveness evaluation value as the positive bull's-eye degree of the grey target theory comprises:

[0128] Step D30, calculating a positive combat effectiveness evaluation value according to the first indicator value difference matrix and the performance indicator data weight, and using the positive combat effectiveness evaluation value as the positive bull's-eye degree of the grey target theory.

[0129] In this embodiment, the calculation process of the performance indicator data weight is to calculate the weighted sum of squared deviations according to the sample mean and sample variance of the combat effectiveness indicator matrix, and then calculate the performance indicator data weight based on the weighted sum of squared deviations.

[0130] In this embodiment, specifically, after calculating the performance indicator data weight corresponding to each performance indicator value in the first indicator value difference matrix, the sum of the products of each performance indicator value and its corresponding performance indicator data weight is calculated to obtain the forward combat effectiveness evaluation value. Among them, the performance indicator data weight is used to influence the evaluation result of the ability of a certain equipment to perform a certain task at a certain specific task stage, and the forward combat effectiveness evaluation value is used to characterize the forward combat effectiveness of a certain equipment in performing a certain task at a certain specific task stage.

[0131] In the combat application of weapons and equipment, when calculating the comprehensive bull's-eye degree of combat effectiveness based on the gray target, because the importance of different functional performance and combat capabilities to the combat application of equipment is different, it is extremely important to examine the relative importance of various functional performance and combat capabilities to combat effectiveness. Therefore, this embodiment introduces the performance indicator data weight corresponding to each performance indicator value in the first indicator value difference matrix, and obtains the forward combat effectiveness evaluation value by calculating the sum of the products of each performance indicator value and its corresponding performance indicator data weight, thereby improving the reliability and objectivity of the evaluation of the forward combat effectiveness.

[0132] In a possible implementation, in step S400, the step of using the grey correlation between the operational effectiveness index matrix and the virtual negative bull's eye as the virtual negative bull's eye degree includes:

[0133] Step E10, determining a second reference index value matrix corresponding to the virtual negative bull's eye;

[0134] Step E20, performing differentiation processing on the combat effectiveness index matrix and the second reference index value matrix to obtain a second index value difference matrix;

[0135] Step E30, calculating a negative combat effectiveness evaluation value according to the second indicator value difference matrix and the performance indicator data weight, and using the negative combat effectiveness evaluation value as a virtual negative bull's eye degree.

[0136] In this embodiment, specifically, after calculating the performance indicator data weight corresponding to each performance indicator value in the second indicator value difference matrix, the sum of the products of each performance indicator value and its corresponding performance indicator data weight is calculated to obtain the negative combat effectiveness evaluation value. Among them, the performance indicator data weight is used to influence the evaluation result of the ability of a certain equipment to perform a certain task at a certain specific task stage, and the negative combat effectiveness evaluation value is used to characterize the negative combat effectiveness of the ability of a certain equipment to perform a certain task at a certain specific task stage.

[0137] In the combat application of weapons and equipment, when calculating the comprehensive bull's-eye degree of combat effectiveness based on the gray target, because the importance of different functional performance and combat capabilities to the combat application of equipment is different, it is extremely important to examine the relative importance of various functional performance and combat capabilities to combat effectiveness. Therefore, this embodiment introduces the performance indicator data weight corresponding to each performance indicator value in the second indicator value difference matrix, and calculates the sum of the products of each performance indicator value and its corresponding performance indicator data weight to obtain the negative combat effectiveness evaluation value, thereby improving the reliability and objectivity of the evaluation of negative combat effectiveness.

[0138] In one embodiment, a virtual negative standard mode or negative bull's eye is constructed on the basis of constructing a positive standard mode or positive bull's eye. Similar to the construction method of the conventional standard mode or positive bull's eye, for the three polarity characteristics of the maximum value, minimum value and moderate value of the evaluation index, it is necessary to select the combat effectiveness evaluation index of different polarities and determine the reference data column of negative combat effectiveness before performing the gray target calculation, and the negative bull's eye is often selected according to the following method.

[0139] For the maximum polarity index, the selection algorithm is:

[0140]

[0141] For the minimum polarity index, the selection algorithm is:

[0142]

[0143] For moderate polarity indicators, assume that the moderate value is xmem = u0 or The selection algorithm is:

[0144]

[0145] The sequence x′0=(x′0(1), x′0(2),…, x′0(n)) is called the negative standard model or negative bull’s eye for the combat effectiveness evaluation of weapons and equipment.

[0146] Finally construct the virtual negative standard pattern or bullseye:

[0147]

[0148] In a possible implementation, a method for determining the index weight based on a grey autocorrelation matrix is ​​used to directly use the combat effectiveness evaluation data information of weapons and equipment. i =(x i (1),x i (2),…,x i (n)), by calculating the gray relational degree γ(x i ,x j ) and then construct the grey autocorrelation matrix R n×n for:

[0149]

[0150] Then the matrix R is calculated by n×n The characteristic values ​​of:

[0151] R n×n ·X=λ·X;

[0152] Where λ is the eigenvalue and X is the eigenvector corresponding to λ.

[0153] Then calculate the eigenvector corresponding to the maximum eigenvalue as follows:

[0154]

[0155] Finally, the performance index data weight is calculated. Perform normalization processing to obtain the performance indicator data weight ω i :

[0156]

[0157] In a possible implementation, the algorithm for calculating the comprehensive bull's-eye degree based on the positive bull's-eye degree and the virtual negative bull's-eye degree is as follows:

[0158] For the regular bullseye x0 = (x0(1), x0(2), …, x0(n)) and the virtual negative bullseye Calculate the positive gray bull's-eye degree r(x0,x i ) and virtual negative gray bullseye They are:

[0159]

[0160]

[0161] Then the comprehensive bull's-eye degree of combat effectiveness of the i-th weapon equipment based on the gray target can be calculated as:

[0162]

[0163] Due to the comprehensive bullseye The reference bullseye takes into account both the optimal bullseye value (i.e., positive bullseye degree) and the worst bullseye value (i.e., negative bullseye degree). Therefore, the combat effectiveness evaluation and ranking of weapons and equipment based on the comprehensive bullseye degree provides more comprehensive decision-making information for the combat use of equipment, thereby improving the reliability and objectivity of the combat effectiveness evaluation of equipment. The size of the combat effectiveness is sorted. The larger the value is, the greater the combat effectiveness of the equipment represented by the mode, and vice versa.

[0164] In order to further help understand the present application, in one embodiment, a combat effectiveness evaluation example based on comprehensive bullseye is proposed. Figure 4Here, the combat effectiveness evaluation index system of four similar ultra-short wave ground communication countermeasure systems (assuming system I, system II, system III, and system IV) is established. The combat effectiveness of the four equipment is evaluated and ranked using the comprehensive bull's-eye model based on the gray target to verify the rationality and effectiveness of the method of this embodiment. Before verifying the algorithm of this embodiment, qualitative and quantitative conversion and polarity conversion processing are first performed to obtain the evaluation data matrix of the four systems.

[0165] First, the reconnaissance capability, direction finding capability, jamming capability and command capability evaluation data matrices A1, A2, A3 and A4 (i.e., the above-mentioned multi-attribute indicator matrix) are constructed based on the evaluation data matrix A. The reconnaissance capability evaluation data matrix A1 is

[0166]

[0167] Its conventional bull's eye (i.e. positive bull's eye) is

[0168] Negative bullseye Then the virtual negative bull's eye can be constructed as Construct its grey autocorrelation matrix according to matrix A1 (i.e., the operational effectiveness indicator matrix of reconnaissance capability) is:

[0169]

[0170] Thus, the weight vectors of the five indicators under the reconnaissance capability (i.e., the performance indicator data weights) are obtained as follows:

[0171] W1=(0.212,0.270,0.133,0.298,0.087);

[0172] Then the positive grey bullseye degree vectors and virtual negative grey bullseye degree vectors of the four systems are calculated as follows:

[0173] r1(x0,x i )=(0.730,0.680,0.687,0.577);

[0174]

[0175] Thus, the comprehensive bullseye vector of the gray target-based reconnaissance capability of the four systems is calculated as:

[0176]

[0177] Similarly the data matrix can be evaluated for direction finding capability Interference Capability Assessment Data Matrix and Control Capability Assessment Data Matrix The relevant bullseye construction and weight, bullseye degree calculations are performed, and the comprehensive bullseye degree vectors of the direction finding capability, jamming capability and command and control capability of the four systems based on the gray target are obtained as follows:

[0178]

[0179] Assuming that the weight vector (i.e., performance index data weight) between reconnaissance capability, direction finding capability, jamming capability, and command and control capability is W = (0.3, 0.2, 0.3, 0.2), the comprehensive bullseye vector of the combat effectiveness of the four systems based on the gray target is:

[0180]

[0181] Based on the above calculation results, we can draw the following conclusions, including but not limited to:

[0182] Conclusion 1: Based on the comprehensive bullseye vector Its third component is the largest, indicating that System III has the highest combat effectiveness.

[0183] Conclusion 2: Based on the comprehensive bullseye vector The third component is the largest, indicating that system III has the strongest reconnaissance capability. Similarly, which system has the strongest analytical direction finding capability, jamming capability, and command and control capability?

[0184] Conclusion 3: Based on the above calculations, the comprehensive bullseye vector of system I's reconnaissance capability, direction finding capability, jamming capability, and command and control capability can be constructed as follows:

[0185]

[0186] In the combat effectiveness evaluation example of this embodiment, according to the size of the components, the order of contribution to the combat effectiveness of System I is jamming capability, command and control capability, reconnaissance capability, and direction finding capability. This is what System I must pay attention to during combat use, and its unfavorable factors must be noted. Similarly, the same analysis can be performed on System II, System III, and System IV.

[0187] like Figure 6 As shown, Figure 6 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0188] like Figure 6As shown, the gray target comprehensive bull's-eye evaluation device for equipment combat effectiveness may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) as a display unit, and may also include a keyboard (Keyboard) as an input unit or a touch screen (Touch Panel) that can be used as an input unit in addition to a display unit. The optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface (such as a USB interface) and a wireless interface (such as a Bluetooth interface and a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0189] Optionally, the device may also include RF (Radio Frequency) circuits, sensors, audio circuits, WiFi modules, Bluetooth modules, etc. Those skilled in the art will appreciate that Figure 6 The equipment structure shown in the figure does not constitute a limitation on the gray target comprehensive bull's-eye evaluation equipment for the combat effectiveness of the equipment, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0190] like Figure 6 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an equipment combat effectiveness evaluation application.

[0191] exist Figure 6 In the device shown, the processor 1001 can be used to call the gray target comprehensive bull's-eye evaluation program stored in the memory 1005, and perform the following operations:

[0192] Determine a second reference index value matrix corresponding to the virtual negative bull's eye;

[0193] Performing differentiation processing on the combat effectiveness index matrix and the second reference index value matrix to obtain a second index value difference matrix;

[0194] A negative combat effectiveness evaluation value is calculated according to the second indicator value difference matrix and the performance indicator data weight, and the negative combat effectiveness evaluation value is used as a virtual negative bull's eye degree.

[0195] Furthermore, the actual performance index information is a multi-attribute index matrix of the equipment to be evaluated, and the processor 1001 can also be used to call the gray target comprehensive bull's-eye evaluation program stored in the memory 1005, and perform the following operations:

[0196] According to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, the multi-attribute indicator matrix is ​​subjected to isopolarization processing to obtain an isopolarized matrix;

[0197] The equal polarity matrix is ​​dimensionlessly processed to obtain a combat effectiveness index matrix corresponding to the equal polarity matrix.

[0198] Furthermore, the processor 1001 may also be used to call the gray target comprehensive bull's-eye evaluation program stored in the memory 1005, and perform the following operations:

[0199] If there is a rank correlation coefficient exceeding the preset critical value, it is determined that the correlation information of the preselected evaluation method corresponding to the rank correlation coefficient exceeding the preset critical value is significantly correlated.

[0200] Furthermore, the processor 1001 may also be used to call the gray target comprehensive bull's-eye evaluation program stored in the memory 1005, and perform the following operations:

[0201] According to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, determining the inherent attributes of the behavior performance parameter values ​​in the multi-attribute indicator matrix, and obtaining the maximum value and the minimum value in the multi-attribute indicator matrix;

[0202] If the inherent attribute of the behavior performance parameter value is a maximum value attribute, the quotient between the behavior performance parameter value and the maximum value is taken as the equal polarity value corresponding to the behavior performance parameter value;

[0203] If the inherent attribute of the behavior performance parameter value is a minimum value attribute, the quotient between the behavior performance parameter value and the minimum value is taken as the equal polarity value corresponding to the behavior performance parameter value;

[0204] If the inherent attribute of the behavior performance parameter value is a moderate value attribute, obtaining the moderate value corresponding to the behavior performance parameter value, and calculating the equal polarity value corresponding to the behavior performance parameter value according to the moderate value;

[0205] An isopolarization matrix is ​​constructed based on the isopolarity values.

[0206] Furthermore, the processor 1001 may also be used to call the gray target comprehensive bull's-eye evaluation program stored in the memory 1005, and perform the following operations:

[0207] Calculate the sample mean and sample variance corresponding to the equipolarization matrix;

[0208] The equal polarization matrix is ​​converted into a combat effectiveness index matrix corresponding to the equal polarization matrix according to the sample mean and the sample variance.

[0209] Furthermore, the processor 1001 may also be used to call the gray target comprehensive bull's-eye evaluation program stored in the memory 1005, and perform the following operations:

[0210] Determine a first reference index value matrix corresponding to the positive bull's eye;

[0211] Performing difference processing on the combat effectiveness index matrix and the first reference index value matrix to obtain a first index value difference matrix;

[0212] A positive combat effectiveness evaluation value is calculated according to the first indicator value difference matrix, and the positive combat effectiveness evaluation value is used as the positive bull's-eye degree of the grey target theory.

[0213] Furthermore, the processor 1001 may also be used to call the gray target comprehensive bull's-eye evaluation program stored in the memory 1005, and perform the following operations:

[0214] Calculate the weighted sum of squares of deviations according to the sample mean and sample variance of the first indicator value difference matrix;

[0215] Calculating the performance indicator data weight based on the weighted sum of squared deviations;

[0216] A forward combat effectiveness evaluation value is calculated based on the first indicator value difference matrix and the performance indicator data weight, and the forward combat effectiveness evaluation value is used as the forward bull's-eye degree of the grey target theory.

[0217] Furthermore, the processor 1001 may also be used to call the gray target comprehensive bull's-eye evaluation program stored in the memory 1005, and perform the following operations:

[0218] Determine a second reference index value matrix corresponding to the virtual negative bull's eye;

[0219] Performing differentiation processing on the combat effectiveness index matrix and the second reference index value matrix to obtain a second index value difference matrix;

[0220] A negative combat effectiveness evaluation value is calculated according to the second indicator value difference matrix and the performance indicator data weight, and the negative combat effectiveness evaluation value is used as a virtual negative bull's eye degree.

[0221] In addition, an embodiment of the present invention further provides a computer storage medium.

[0222] The computer storage medium stores a computer program, which, when executed by the processor, implements the operations in the gray target comprehensive bull's center evaluation method provided in the above embodiment. The specific implementation steps can be referred to the above embodiment and will not be described in detail here.

[0223] It is understood that the above scenarios are only examples and do not constitute a limitation on the application scenarios of the technical solutions provided in the embodiments of the present application. The technical solutions of the present application can also be applied to other scenarios. For example, it is known to those skilled in the art that with the evolution of the system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0224] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0225] The steps in the method of the embodiment of the present application can be adjusted in order, combined and deleted according to actual needs.

[0226] The units in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0227] In the present application, the same or similar terminology concepts, technical solutions and / or application scenario descriptions are generally described in detail only the first time they appear. When they appear again later, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of the present application, for the same or similar terminology concepts, technical solutions and / or application scenario descriptions that are not described in detail later, reference can be made to the previous related detailed descriptions.

[0228] In the present application, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0229] The various technical features of the technical solution of the present application can be arbitrarily combined. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present application.

[0230] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, a controlled terminal, or a network device, etc.) to execute the method of each embodiment of the present application.

[0231] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integrated. The available medium can be a magnetic medium, (e.g., a floppy disk, a storage disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state storage disk Solid State Disk (SSD)), etc.

[0232] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A grey target comprehensive bull's-eye evaluation method for equipment combat effectiveness, characterized in that: The grey target comprehensive bull's-eye evaluation method comprises the following steps: Acquire high-quality performance reference indicator information of the equipment to be evaluated, and construct a positive bull's eye based on the grey target theory according to the high-quality performance reference indicator information; Acquire actual performance index information of the equipment to be evaluated, convert the actual performance index information according to a preset combat effectiveness index system corresponding to the equipment to be evaluated, and generate a combat effectiveness index matrix corresponding to the actual performance index information; The grey correlation degree between the operational effectiveness index matrix and the positive bull's eye is used as the positive bull's eye degree of the grey target theory; Acquire poor performance reference index information of the equipment to be evaluated, construct a virtual negative bull's eye according to the poor performance reference index information, and use the grey correlation degree between the combat effectiveness index matrix and the virtual negative bull's eye as the virtual negative bull's eye degree; Calculating a comprehensive bull's-eye degree according to the positive bull's-eye degree and the virtual negative bull's-eye degree, and evaluating the combat effectiveness of the equipment according to the comprehensive bull's-eye degree; The step of using the grey correlation degree between the operational effectiveness index matrix and the positive bull's eye as the positive bull's eye degree of the grey target theory comprises: Determine a first reference index value matrix corresponding to the positive bull's eye; Performing difference processing on the combat effectiveness index matrix and the first reference index value matrix to obtain a first index value difference matrix; Calculating a positive combat effectiveness evaluation value according to the first indicator value difference matrix, and using the positive combat effectiveness evaluation value as a positive bull's-eye degree of the grey target theory; The step of calculating the positive combat effectiveness evaluation value according to the first indicator value difference matrix and using the positive combat effectiveness evaluation value as the positive bull's-eye degree of the grey target theory includes: Calculate the weighted sum of squares of deviations according to the sample mean and sample variance of the first indicator value difference matrix; Calculating the performance indicator data weight based on the weighted sum of squared deviations; The step of calculating the positive combat effectiveness evaluation value according to the first indicator value difference matrix and using the positive combat effectiveness evaluation value as the positive bull's-eye degree of the grey target theory comprises: According to the first indicator value difference matrix and the performance indicator data weight, a positive combat effectiveness evaluation value is calculated, and the positive combat effectiveness evaluation value is used as a positive bull's-eye degree of the grey target theory; The step of using the grey correlation degree between the combat effectiveness index matrix and the virtual negative bull's eye as the virtual negative bull's eye degree comprises: Determine a second reference index value matrix corresponding to the virtual negative bull's eye; Performing differentiation processing on the combat effectiveness index matrix and the second reference index value matrix to obtain a second index value difference matrix; A negative combat effectiveness evaluation value is calculated according to the second indicator value difference matrix and the performance indicator data weight, and the negative combat effectiveness evaluation value is used as a virtual negative bull's eye degree.

2. The grey target comprehensive bull's-eye evaluation method according to claim 1, characterized in that: The actual performance index information is a multi-attribute index matrix of the equipment to be evaluated, and the step of converting the actual performance index information according to the preset combat effectiveness index system corresponding to the equipment to be evaluated to generate a combat effectiveness index matrix corresponding to the actual performance index information includes: According to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, the multi-attribute indicator matrix is ​​subjected to isopolarization processing to obtain an isopolarized matrix; The equal polarity matrix is ​​dimensionlessly processed to obtain a combat effectiveness index matrix corresponding to the equal polarity matrix.

3. The gray target comprehensive bull's-eye evaluation method according to claim 2, characterized in that: The step of performing isopolarization processing on the multi-attribute indicator matrix according to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated to obtain an isopolarization matrix comprises: According to the preset combat effectiveness indicator system corresponding to the equipment to be evaluated, determining the inherent attributes of the behavior performance parameter values ​​in the multi-attribute indicator matrix, and obtaining the maximum value and the minimum value in the multi-attribute indicator matrix; If the inherent attribute of the behavior performance parameter value is a maximum value attribute, the quotient between the behavior performance parameter value and the maximum value is taken as the equal polarity value corresponding to the behavior performance parameter value; If the inherent attribute of the behavior performance parameter value is a minimum value attribute, the quotient between the behavior performance parameter value and the minimum value is taken as the equal polarity value corresponding to the behavior performance parameter value; If the inherent attribute of the behavior performance parameter value is a moderate value attribute, obtaining the moderate value corresponding to the behavior performance parameter value, and calculating the equal polarity value corresponding to the behavior performance parameter value according to the moderate value; An isopolarization matrix is ​​constructed based on the isopolarity values.

4. The gray target comprehensive bull's-eye evaluation method according to claim 2, characterized in that: The step of performing dimensionless processing on the isopolarization matrix to obtain a combat effectiveness index matrix corresponding to the isopolarization matrix comprises: Calculate the sample mean and sample variance corresponding to the equipolarization matrix; The equal polarization matrix is ​​converted into a combat effectiveness index matrix corresponding to the equal polarization matrix according to the sample mean and the sample variance.

5. A gray target comprehensive bull's-eye evaluation device for equipment combat effectiveness, characterized in that: The gray target comprehensive bull's-center degree evaluation device includes: a memory, a processor, and a gray target comprehensive bull's-center degree evaluation program stored in the memory and executable on the processor. When the gray target comprehensive bull's-center degree evaluation program is executed by the processor, the steps of the gray target comprehensive bull's-center degree evaluation method as described in any one of claims 1 to 4 are implemented.

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