CN117909200B - Method, equipment and system for incremental comparison and evaluation of capability of information guarantee system - Google Patents
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Abstract
The invention discloses a method, equipment and a system for carrying out incremental comparison and evaluation on capability of an information guarantee system, belonging to the field of efficiency evaluation, and comprising the following steps: based on a hierarchical index system construction principle, combining with daily use and working principles of an information guarantee system, constructing an evaluation index set from dimensions of information acquisition capacity, intelligent processing capacity, transmission application capacity and system protection capacity; according to the output hierarchical index set, configuring the relative weights of the secondary index and the tertiary index by a QFD method of an intuitional fuzzy function; calculating absolute weights according to the relative weights formed by the weight configuration; and acquiring index quantized values of different guarantee systems, constructing a weighted decision matrix by using a normalized processing method and index absolute weights, and realizing capability increment rate evaluation of the systems and the indexes by calculating negative distances among different systems to finally perform visual display. The invention can intuitively and objectively calculate the capability contrast difference degree between different information guarantee systems.
Description
Technical Field
The invention relates to the field of efficiency evaluation, in particular to a method, equipment and a system for incremental comparison evaluation of capability of an information guarantee system.
Background
Under the promotion of development of emerging technologies such as big data, artificial intelligence and the like, the scene faced by an information guarantee system is more complex, the system architecture is updated and iterated continuously, so that an information acquisition mode, an information processing production mode and an information application service type in the system are changed significantly, each capacity comparison situation between a new information guarantee system and an old information guarantee system is quantitatively analyzed and visually presented through a performance evaluation method, the full grasp of the capacities of each information guarantee system by a user is realized, and an important traction effect is provided for the development of the information guarantee system.
In the prior art, the approach ideal solution ordering (Technique for Order Preference by Similarity to an Ideal Solution, TOPSIS) method solves the problem of relative good and bad ordering of multiple evaluation objects, can be applied to an information guarantee system capacity increment evaluation scene, and can complete the quantification of relative capacity size by constructing positive and negative ideal solutions and calculating positive and negative ideal distances, and perform capacity increment calculation between different systems according to relative capacity quantification results, but if the evaluation objects are increased, the related calculation under the TOPSIS framework needs to be performed again, so that the method has more repeatability and low robustness, and meanwhile, the TOPSIS method is often matched with subjective weight configuration methods such as a analytic hierarchy (ANALYTIC HIERARCHY Process, AHP) and a Delphi method, so that the subjectivity of the evaluation results is high, and therefore, how to realize scientific, comprehensive and objective evaluation of the capacity increment between the systems based on the TOPSIS architecture is an important technical problem to be solved continuously.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method, equipment and a system for carrying out incremental comparison and evaluation on the capability of an information guarantee system, which can intuitively and objectively calculate the capability comparison difference degree between different information guarantee systems.
The invention aims at realizing the following scheme:
An incremental comparison evaluation method for the capability of an information guarantee system comprises the following steps:
S1, constructing an evaluation index set based on a computer from the dimensions of information acquisition capacity, intelligent processing capacity, transmission application capacity and system protection capacity by combining with the daily use and working principle of an information guarantee system based on the index system construction principle of layering and grading;
s2, configuring the relative weights of the secondary index and the tertiary index based on a computer by a QFD method of an intuitional fuzzy function according to the layered grading index set output in the step S1;
S3, forming relative weights according to the weight configuration completed in the step S2, and calculating absolute weights according to the corresponding relation by using a computer;
And S4, obtaining index quantized values of different guarantee systems, constructing a weighted decision matrix by using a normalized processing method and index absolute weights, calculating negative distances among different systems by using a computer, realizing capability increment rate evaluation of the systems and the indexes, and finally performing visual display.
Further, in step S1, the evaluation index set specifically includes an evaluation index set of 15 tertiary indexes, 4 secondary indexes, and 1 primary index; the 15 three-level indexes comprise information interception timeliness C 1, coverage rate C 2 of an action range, information accuracy C 3, information type integrity C 4, intelligent processing timeliness C 5, intelligent processing universe C 6, intelligent processing accuracy C 7, information transmission speed C 8, information transmission error rate C 9, information application service universe C 10, information application service accuracy C 11, Information storage capacity C 12, anti-intrusion capability C 13, system anti-interference capability C 14 and system node survivability C 15; the 4 secondary indexes comprise information acquisition capacity A 1, intelligent processing capacity A 2, transmission application capacity A 3 and embodiment protection capacity A 4, and the 1 primary indexes comprise information guarantee system comprehensive capacity A.
Further, in step S2, the QFD method of the intuitive fuzzy function specifically includes the following sub-steps:
s21, including the evaluation index set Individual capability items, th/>Individual capability items/>The following contains/>Item of performance, thenItem of personal ability/>The individual performance terms are/>; Invitations/>Expert pairs of abilities/>Relative Performance/>Membership degree/>, which is "importantAnd non-membership/>Scoring, membership/>And non-membership/>The constraints of (2) are as follows:
Wherein, Is expert serial number;
S22, quantifying the degree of understanding of an expert on 'importance' through an intuitive fuzzy function, and marking as follows:
Wherein, Intuitive fuzzy vector for expert's "important" degree of awareness, m is the number of experts,/>And/>Scoring upper and lower bounds for intuitive ambiguity of expert awareness of "importance";
s23, quantifying the degree of understanding of each expert' S importance according to the scoring result of each expert on the degree of importance of each index, and calculating the intuitive ambiguity set of the association mapping matrix:
Wherein, And/>For expert on the/>Individual capability item relative to the/>Upper and lower bounds of an intuitive fuzzy set of individual performance terms;
S24, calculating the comprehensive membership degree based on the upper bound and the lower bound of the intuitionistic fuzzy set of each index item, and forming a comprehensive membership degree matrix:
Wherein, For/>The/>, of the individual comprehensive membership matrixAn element;
S25, normalizing to obtain each normalized weight of the performance item:
Wherein, For/>Item of personal ability/>Weights of individual Performance items,/>For/>Number of performance items under individual capability items.
Further, in step S3, relative weights are formed according to the weight configuration completed in step S2, and absolute weights are calculated according to the correspondence and by using a computer, specifically including the following sub-steps:
after the index weight configuration is completed through the steps S21-S25, a secondary index is formed To/>Relative weights/>、、/>And/>Three-level index/>To/>Relative weight/>To/>And calculating the absolute weight of each three-level index according to the corresponding relation and the following formula:
Wherein, Absolute weight vector of three-level index,/>To/>Three-level index/>, respectivelyTo/>Absolute weight of/>、/>、/>And/>Respectively two-level index/>To/>The relative weight vector of the corresponding three-level index is specifically:
。
Further, in step S4, index quantized values of different security systems are obtained, a weighted decision matrix is constructed by using a normalized processing method and an index absolute weight, a capability increment rate evaluation of the system and the index is realized by calculating negative distances between different systems by using a computer, and finally visual display is performed, and the method specifically comprises the following steps:
S41, collecting statistical quantization values of three-level indexes of different information guarantee systems to form an initial index quantization matrix ；
Wherein,For/>First/>, of individual objectQuantitative results of the evaluation index,/>For the number of systems,/>The number of the three-level indexes is;
s42, performing normalization processing according to the normalization formulas of the benefit type index and the cost type index, namely
Normalized benefit indexThe method comprises the following steps:
Normalized cost index The method comprises the following steps:
Wherein, Is normalized/>Personal System No./>The evaluation values of the evaluation indexes are used for constructing a standard evaluation matrix/>, according to the normalized evaluation valuesI.e.
S43, constructing a weighted decision matrix based on the absolute weights of the three-level indexesThe method is characterized by comprising the following steps:
Wherein, For weighted/>Personal System No./>Decision values of the individual evaluation indexes;
S44, constructing a negative ideal solution Wherein/>The negative distances of each system and each index are calculated respectively, and the specific formula is as follows:
S45, calculating the capacity increment rate between the systems/indexes according to the negative distances of each system and each index, wherein the specific formula is as follows:
Wherein, ，/>To evaluate the sequence number of the system standard,/>For the serial number of the evaluated system,/>Represents the/>Personal architecture relative to the/>Capability increment Rate of individual System,/> Represents the/>Personal architecture relative to the/>First/>, of personal systemCapability increment rate of individual index,/>Is a constant;
And S46, visually displaying the evaluation result, and carrying out early warning prompt aiming at a system and a capability index with the capability increment rate result smaller than 1.
Further, in step S4, the obtaining the index quantized values of the different security systems specifically includes obtaining the index quantized values of the different security systems based on a statistical test method.
Further, the visual display of the evaluation result specifically comprises display in a radar chart and histogram form.
Further, the early warning prompt is specifically performed through highlighting.
An information security system capability increment contrast evaluation device, comprising:
the capacity increment index set construction module is used for constructing an evaluation index set from the dimensions of the information acquisition capacity, the intelligent processing capacity, the transmission application capacity and the system protection capacity;
The index weight configuration module is used for constructing a relative weight quantization model of each evaluation index of the capacity increment by utilizing a QFD method based on an intuitive fuzzy function by utilizing the scoring results of experts on important membership degrees of different indexes;
The capacity increment evaluation module is used for constructing a weighted decision matrix and a weighted negative ideal solution by utilizing index quantization values and index weights among information guarantee systems, respectively calculating the negative distances and increment rates of the systems/indexes, realizing capacity increment comparison quantization among the systems and carrying out visual display.
The information security system capability increment comparison and evaluation system comprises the information security system capability increment comparison and evaluation equipment.
The beneficial effects of the invention include:
(1) The invention constructs a new system capacity increment index system, relates to the dimensions of information acquisition, intelligent processing, transmission application, system protection and the like, and lays a foundation for scientific and comprehensive assessment of capacity increment.
(2) The intuitive fuzzy function QFD method introduced by the invention quantifies the random fuzzy characteristics of expert knowledge, weakens subjectivity of weight configuration to a certain extent, and makes capability increment evaluation of a system more scientific.
(3) Compared with the traditional TOPSIS framework, the method for acquiring the capacity increment comparison between different systems by calculating the positive and negative distances cancels the construction of positive ideal solutions and the calculation of the positive and negative distances, improves the calculation formula of the closeness, ensures that the calculation of the capacity increment comparison evaluation model of the proposed information guarantee system is simpler, and saves the calculation resources.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a set of incremental capability assessment indicators for an information security system according to an embodiment of the present invention.
Detailed Description
All of the features disclosed in all of the embodiments of this specification, or all of the steps in any method or process disclosed implicitly, except for the mutually exclusive features and/or steps, may be combined and/or expanded and substituted in any way.
The present invention is directed in particular to a capability increment assessment scheme based on improved quality function deployment (quality function deployment, QFD) and TOPSIS, wherein in particular embodiments it comprises: the capacity increment index set construction module constructs an evaluation index set of 15 tertiary indexes, 4 secondary indexes and 1 comprehensive capacity from 4 dimensions of information acquisition capacity, intelligent processing capacity, transmission application capacity, system protection capacity and the like; the index weight configuration module utilizes the expert to score the important membership degree of different indexes, and builds a relative weight quantization model of each evaluation index of the capacity increment based on the QFD method of the intuitionistic fuzzy function; the capacity increment evaluation module utilizes index quantized values and index weights among information guarantee systems to construct a weighted decision matrix and a weighted negative ideal solution, calculates the negative distances and increment rates of the systems/indexes respectively through Euclidean distances, realizes capacity increment comparison quantization among the systems, and performs visual display in the forms of radar images, bar charts and the like.
In a further embodiment, the specific implementation steps are as follows:
Step 1: the capability increment index set construction module is based on a hierarchical index system construction principle, combines the daily use and working principle of an information guarantee system, constructs an evaluation index set of 15 three-level indexes, 4 two-level indexes and 1 comprehensive capability from 4 dimensions such as information acquisition capability, intelligent processing capability, transmission application capability, system protection capability and the like, and the relation among the capability indexes is shown in figure 1.
Step 2: the index weight configuration module configures the relative weights of the secondary index and the tertiary index through the QFD method of the intuitive fuzzy function according to the layered and hierarchical index set output by the capability increment index set construction module, and the QFD method of the specific intuitive fuzzy function is as follows:
the assumed evaluation index set includes Individual capability items, th/>Individual capability items/>The following contains/>Item of performance, thenItem of personal ability/>The individual performance terms are/>Then
(1) Invitations toExpert pairs of abilities/>Relative Performance/>Membership degree/>, which is "importantAnd non-membership/>Scoring, membership/>And non-membership/>The constraints of (2) are as follows:
Wherein, Is expert serial number.
(2) The expert is quantified by an intuitive fuzzy function for the degree of "important" recognition, noted as:
Wherein, Intuitive fuzzy vector for expert's "important" degree of awareness,/>And/>For/>The individual experts score the upper and lower bounds on the intuitive ambiguity of the degree of "important" awareness.
(3) Quantifying the degree of understanding of each expert's importance according to the scoring result of each expert on the degree of importance of each index, and calculating the intuitive ambiguity set of the association mapping matrix:
Wherein, And/>For expert on the/>Individual capability item relative to the/>The performance terms intuitively blur the upper and lower bounds of the set.
(4) The comprehensive membership degree is calculated based on the upper bound and the lower bound of the intuitionistic fuzzy set of each index item, and a comprehensive membership degree matrix is formed:
Wherein, For/>The/>, of the individual comprehensive membership matrixThe elements.
(5) Normalizing to obtain each normalized weight of the performance item:
Wherein, For/>Item of personal ability/>Weights of individual Performance items,/>For/>Number of performance items under individual capability items.
Step 3: the index weight configuration module completes weight configuration on each level index sequentially through the operations from the step (1) to the step (5) in the step 2 to form a second level indexTo/>Relative weights/>、/>、/>And/>Three-level index/>To/>Relative weight/>To/>The absolute weight of each three-level index is calculated according to the corresponding relation, and the formula is as follows:
Wherein, Absolute weight vector of three-level index,/>To/>Three-level index/>, respectivelyTo/>Absolute weight of/>、/>、/>And/>Respectively two-level index/>To/>The relative weight vector of the corresponding three-level index is specifically:
Step 4: the capacity increment evaluation module acquires three-level index quantized values of different guarantee systems based on a test statistical mode, a weighted decision matrix is constructed by using a normalized processing method and index absolute weights, capacity increment rate evaluation of the systems and the indexes is realized by calculating negative distances among different systems, and finally visual display is carried out in the forms of radar images, bar graphs and the like. The specific method comprises the following steps:
(1) Collecting statistical quantization values of three-level indexes of different information guarantee systems to form an initial index quantization matrix 。
Wherein,For/>First/>, of individual objectQuantitative results of the evaluation index,/>For the number of systems,/>Is the number of three-level indexes
(2) Performing normalization processing according to the normalization formulas of the benefit type index and the cost type index, namely
Normalized benefit indexThe method comprises the following steps:
Normalized cost index The method comprises the following steps:
Wherein, Is normalized/>Personal System No./>The evaluation values of the evaluation indexes are used for constructing a standard evaluation matrix/>, according to the normalized evaluation valuesI.e.
(3) Construction of weighted decision matrix based on absolute weight of three-level indexThe method is characterized by comprising the following steps:
Wherein, For weighted/>Personal System No./>Decision values of the individual evaluation indicators.
(4) Construction of negative ideal solutionsWherein/>The negative distances of each system and each index are calculated respectively, and the specific formula is as follows:
(5) The capacity increment rate between the systems/indexes is calculated according to the negative distances of each system and each index, and the specific form is as follows:
Wherein, ，/>To evaluate the sequence number of the system standard,/>For the serial number of the evaluated system,/>Represents the/>Personal architecture relative to the/>Capability increment Rate of individual System,/> Represents the/>Personal architecture relative to the/>First/>, of personal systemCapability increment rate of individual index,/>Is constant.
(6) The evaluation result is visually displayed in the forms of a radar chart, a histogram and the like, and early warning prompt is carried out through highlighting aiming at a system and a capability index with the capability increment rate result smaller than 1.
It should be noted that, within the scope of protection defined in the claims of the present invention, the following embodiments may be combined and/or expanded, and replaced in any manner that is logical from the above specific embodiments, such as the disclosed technical principles, the disclosed technical features or the implicitly disclosed technical features, etc. In other embodiments, including but not limited to the following examples.
Example 1
An incremental comparison evaluation method for the capability of an information guarantee system comprises the following steps:
S1, constructing an evaluation index set based on a computer from the dimensions of information acquisition capacity, intelligent processing capacity, transmission application capacity and system protection capacity by combining with the daily use and working principle of an information guarantee system based on the index system construction principle of layering and grading;
s2, configuring the relative weights of the secondary index and the tertiary index based on a computer by a QFD method of an intuitional fuzzy function according to the layered grading index set output in the step S1;
S3, forming relative weights according to the weight configuration completed in the step S2, and calculating absolute weights according to the corresponding relation by using a computer;
And S4, obtaining index quantized values of different guarantee systems, constructing a weighted decision matrix by using a normalized processing method and index absolute weights, calculating negative distances among different systems by using a computer, realizing capability increment rate evaluation of the systems and the indexes, and finally performing visual display.
Example 2
On the basis of embodiment 1, in step S1, the evaluation index set specifically includes an evaluation index set of 15 tertiary indexes, 4 secondary indexes, and 1 primary index; the 15 three-level indexes comprise information interception timeliness C 1, coverage rate C 2 of an action range, information accuracy C 3, information type integrity C 4, intelligent processing timeliness C 5, intelligent processing universe C 6, intelligent processing accuracy C 7, information transmission speed C 8, information transmission error rate C 9, information application service universe C 10, information application service accuracy C 11, Information storage capacity C 12, anti-intrusion capability C 13, system anti-interference capability C 14 and system node survivability C 15; the 4 secondary indexes comprise information acquisition capacity A 1, intelligent processing capacity A 2, transmission application capacity A 3 and embodiment protection capacity A 4, and the 1 primary indexes comprise information guarantee system comprehensive capacity A.
Example 3
On the basis of embodiment 1, in step S2, the QFD method of the intuitive fuzzy function specifically includes the following sub-steps:
s21, including the evaluation index set Individual capability items, th/>Individual capability items/>The following contains/>Item of performance, thenItem of personal ability/>The individual performance terms are/>; Invitations/>Expert pairs of abilities/>Relative Performance/>Membership degree/>, which is "importantAnd non-membership/>Scoring, membership/>And non-membership/>The constraints of (2) are as follows:
Wherein, Is expert serial number;
S22, quantifying the degree of understanding of an expert on 'importance' through an intuitive fuzzy function, and marking as follows:
Wherein, Intuitive fuzzy vector for expert's "important" degree of awareness, m is the number of experts,/>And/>Scoring upper and lower bounds for intuitive ambiguity of expert awareness of "importance";
s23, quantifying the degree of understanding of each expert' S importance according to the scoring result of each expert on the degree of importance of each index, and calculating the intuitive ambiguity set of the association mapping matrix:
Wherein, And/>For expert on the/>Individual capability item relative to the/>Upper and lower bounds of an intuitive fuzzy set of individual performance terms;
S24, calculating the comprehensive membership degree based on the upper bound and the lower bound of the intuitionistic fuzzy set of each index item, and forming a comprehensive membership degree matrix:
Wherein, For/>The/>, of the individual comprehensive membership matrixAn element;
S25, normalizing to obtain each normalized weight of the performance item:
Wherein, For/>Item of personal ability/>Weights of individual Performance items,/>For/>Number of performance items under individual capability items.
Example 4
On the basis of embodiment 3, in step S3, relative weights are formed according to the weight configuration completed in step S2, and absolute weights are calculated according to the correspondence and by using a computer, specifically including the following sub-steps:
after the index weight configuration is completed through the steps S21-S25, a secondary index is formed To/>Relative weights/>、、/>And/>Three-level index/>To/>Relative weight/>To/>And calculating the absolute weight of each three-level index according to the corresponding relation and the following formula:
Wherein, Absolute weight vector of three-level index,/>To/>Three-level index/>, respectivelyTo/>Absolute weight of/>、/>、/>And/>Respectively two-level index/>To/>The relative weight vector of the corresponding three-level index is specifically:
。
Example 5
Based on embodiment 1, in step S4, index quantized values of different security systems are obtained, a weighted decision matrix is constructed by using a normalized processing method and index absolute weights, capability increment rate evaluation of the system and the index is realized by calculating negative distances among different systems by using a computer, and finally visual display is performed, and the method specifically comprises the following steps:
S41, collecting statistical quantization values of three-level indexes of different information guarantee systems to form an initial index quantization matrix ；
Wherein,For/>First/>, of individual objectQuantitative results of the evaluation index,/>For the number of systems,/>The number of the three-level indexes is;
s42, performing normalization processing according to the normalization formulas of the benefit type index and the cost type index, namely
Normalized benefit indexThe method comprises the following steps: /(I)
Normalized cost indexThe method comprises the following steps:
Wherein, Is normalized/>Personal System No./>The evaluation values of the evaluation indexes are used for constructing a standard evaluation matrix/>, according to the normalized evaluation valuesI.e.
S43, constructing a weighted decision matrix based on the absolute weights of the three-level indexesThe method is characterized by comprising the following steps:
Wherein, For weighted/>Personal System No./>Decision values of the individual evaluation indexes;
S44, constructing a negative ideal solution Wherein/>The negative distances of each system and each index are calculated respectively, and the specific formula is as follows:
S45, calculating the capacity increment rate between the systems/indexes according to the negative distances of each system and each index, wherein the specific formula is as follows:
/>
Wherein, ，/>To evaluate the sequence number of the system standard,/>For the serial number of the evaluated system,/>Represents the/>Personal architecture relative to the/>Capability increment Rate of individual System,/> Represents the/>Personal architecture relative to the/>First/>, of personal systemCapability increment rate of individual index,/>Is a constant;
And S46, visually displaying the evaluation result, and carrying out early warning prompt aiming at a system and a capability index with the capability increment rate result smaller than 1.
Example 6
Based on embodiment 1, in step S4, the obtaining the index quantized values of the different security systems specifically includes obtaining the index quantized values of the different security systems based on a statistical test method.
Example 7
On the basis of the embodiment 1, the visual display of the evaluation result specifically comprises the display in a radar chart and a bar chart form.
Example 8
Based on embodiment 5, the early warning prompt is specifically performed by highlighting.
Example 9
An information security system capability increment contrast evaluation device, comprising:
the capacity increment index set construction module is used for constructing an evaluation index set from the dimensions of the information acquisition capacity, the intelligent processing capacity, the transmission application capacity and the system protection capacity;
The index weight configuration module is used for constructing a relative weight quantization model of each evaluation index of the capacity increment by utilizing a QFD method based on an intuitive fuzzy function by utilizing the scoring results of experts on important membership degrees of different indexes;
The capacity increment evaluation module is used for constructing a weighted decision matrix and a weighted negative ideal solution by utilizing index quantization values and index weights among information guarantee systems, respectively calculating the negative distances and increment rates of the systems/indexes, realizing capacity increment comparison quantization among the systems and carrying out visual display.
Example 10
An information security system capacity increment comparison and evaluation system, comprising the information security system capacity increment comparison and evaluation device described in embodiment 9.
The units involved in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
According to other aspects of embodiments of the present invention, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from the computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the methods provided in the various alternative implementations described above.
Claims (6)
1. The incremental comparison and evaluation method for the capability of the information guarantee system is characterized by comprising the following steps of:
S1, constructing an evaluation index set based on a computer from the dimensions of information acquisition capacity, intelligent processing capacity, transmission application capacity and system protection capacity by combining with the daily use and working principle of an information guarantee system based on the index system construction principle of layering and grading; in step S1, the evaluation index set specifically includes an evaluation index set of 15 tertiary indexes, 4 secondary indexes, and 1 primary index; the 15 three-level indexes comprise information interception timeliness C 1, coverage rate C 2 of an action range, information accuracy C 3, information type integrity C 4, intelligent processing timeliness C 5, intelligent processing universe C 6, intelligent processing accuracy C 7, information transmission speed C 8, information transmission error rate C 9, information application service universe C 10, information application service accuracy C 11, Information storage capacity C 12, anti-intrusion capability C 13, system anti-interference capability C 14 and system node survivability C 15; the 4 secondary indexes comprise information acquisition capability A 1, intelligent processing capability A 2, transmission application capability A 3 and embodiment protection capability A 4, and the 1 primary indexes comprise information guarantee system comprehensive capability A;
S2, configuring the relative weights of the secondary index and the tertiary index based on a computer by a QFD method of an intuitional fuzzy function according to the layered grading index set output in the step S1; in step S2, the QFD method of the intuitive fuzzy function specifically includes the following sub-steps:
s21, including the evaluation index set Individual capability items, th/>Individual capability items/>The following contains/>Item of performance, thenItem of personal ability/>The individual performance terms are/>; Invitations/>Expert pairs of abilities/>Relative Performance/>Membership degree/>, which is "importantAnd non-membership/>Scoring, membership/>And non-membership/>The constraints of (2) are as follows:
Wherein, Is expert serial number;
S22, quantifying the degree of understanding of an expert on 'importance' through an intuitive fuzzy function, and marking as follows:
Wherein, Intuitive fuzzy vector for expert's "important" degree of awareness, m is the number of experts,/>And/>Scoring upper and lower bounds for intuitive ambiguity of expert awareness of "importance";
s23, quantifying the degree of understanding of each expert' S importance according to the scoring result of each expert on the degree of importance of each index, and calculating the intuitive ambiguity set of the association mapping matrix:
Wherein, And/>For expert on the/>Individual capability item relative to the/>Upper and lower bounds of an intuitive fuzzy set of individual performance terms;
S24, calculating the comprehensive membership degree based on the upper bound and the lower bound of the intuitionistic fuzzy set of each index item, and forming a comprehensive membership degree matrix:
Wherein, For/>The/>, of the individual comprehensive membership matrixAn element;
S25, normalizing to obtain each normalized weight of the performance item:
Wherein, For/>Item of personal ability/>Weights of individual Performance items,/>For/>Number of performance items under individual capability items;
s3, forming relative weights according to the weight configuration completed in the step S2, and calculating absolute weights according to the corresponding relation by using a computer; in step S3, the relative weights are formed according to the weight configuration completed in step S2, and the absolute weights are calculated according to the correspondence and by using a computer, specifically including the following sub-steps:
after the index weight configuration is completed through the steps S21-S25, a secondary index is formed To/>Relative weights/>、/>、/>And/>Three-level index/>To/>Relative weight/>To/>And calculating the absolute weight of each three-level index according to the corresponding relation and the following formula:
Wherein, Absolute weight vector of three-level index,/>To/>Three-level index/>, respectivelyTo/>Is used as a reference to the absolute weight of (a),、/>、/>And/>Respectively two-level index/>To/>The relative weight vector of the corresponding three-level index is specifically:
；
S4, obtaining index quantized values of different guarantee systems, constructing a weighted decision matrix by using a normalized processing method and index absolute weights, calculating negative distances among different systems by using a computer, realizing capability increment rate evaluation of the systems and the indexes, and finally performing visual display; in step S4, index quantized values of different security systems are obtained, a weighted decision matrix is constructed by using a normalized processing method and index absolute weights, capability increment rate evaluation of the system and the index is realized by calculating negative distances among different systems by using a computer, and finally visual display is performed, and the method specifically comprises the following steps:
S41, collecting statistical quantization values of three-level indexes of different information guarantee systems to form an initial index quantization matrix ；
Wherein,For/>First/>, of individual objectQuantitative results of the evaluation index,/>For the number of systems,/>The number of the three-level indexes is;
s42, performing normalization processing according to the normalization formulas of the benefit type index and the cost type index, namely
Normalized benefit indexThe method comprises the following steps:
Normalized cost index The method comprises the following steps:
Wherein, Is normalized/>Personal System No./>The evaluation values of the evaluation indexes are used for constructing a standard evaluation matrix/>, according to the normalized evaluation valuesI.e.
S43, constructing a weighted decision matrix based on the absolute weights of the three-level indexesThe method is characterized by comprising the following steps:
Wherein, For weighted/>Personal System No./>Decision values of the individual evaluation indexes;
S44, constructing a negative ideal solution Wherein/>The negative distances of each system and each index are calculated respectively, and the specific formula is as follows:
S45, calculating the capacity increment rate between the systems/indexes according to the negative distances of each system and each index, wherein the specific formula is as follows:
Wherein, ，/>To evaluate the sequence number of the system standard,/>For the serial number of the evaluated system,/>Represents the/>Personal architecture relative to the/>Capability increment Rate of individual System,/>Represents the/>Personal architecture relative to the/>First/>, of personal systemCapability increment rate of individual index,/>Is a constant;
And S46, visually displaying the evaluation result, and carrying out early warning prompt aiming at a system and a capability index with the capability increment rate result smaller than 1.
2. The method for incremental capacity comparison and evaluation of information security systems according to claim 1, wherein in step S4, the obtaining the index quantized values of the different security systems specifically includes obtaining the index quantized values of the different security systems based on a statistical test method.
3. The method for incremental comparison and evaluation of capability of an information security system according to claim 1, wherein the step of visually displaying the evaluation result specifically comprises displaying the evaluation result in a radar chart or a bar chart.
4. The method for incremental comparison and evaluation of information security system according to claim 1, wherein the early warning prompt is specifically performed by highlighting.
5. An information security system capability increment contrast evaluation device, comprising:
the capacity increment index set construction module is used for constructing an evaluation index set from the dimensions of the information acquisition capacity, the intelligent processing capacity, the transmission application capacity and the system protection capacity;
The index weight configuration module is used for constructing a relative weight quantization model of each evaluation index of the capacity increment by utilizing a QFD method based on an intuitive fuzzy function by utilizing the scoring results of experts on important membership degrees of different indexes;
The capacity increment evaluation module is used for constructing a weighted decision matrix and a weighted negative ideal solution by utilizing index quantization values and index weights among information guarantee systems, respectively calculating the negative distances and increment rates of the systems/indexes, realizing capacity increment comparison quantization among the systems and carrying out visual display; and is used for executing the incremental comparison and evaluation method for the capability of the information security system as claimed in claim 1.
6. An information security system capability increment comparison and evaluation system, which is characterized by comprising the information security system capability increment comparison and evaluation device according to claim 5.
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