CN116090757A - Method for evaluating capability demand satisfaction of information guarantee system - Google Patents

Method for evaluating capability demand satisfaction of information guarantee system Download PDF

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CN116090757A
CN116090757A CN202211662306.4A CN202211662306A CN116090757A CN 116090757 A CN116090757 A CN 116090757A CN 202211662306 A CN202211662306 A CN 202211662306A CN 116090757 A CN116090757 A CN 116090757A
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孙文
葛萌萌
吴超蓉
董海
宋丹
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Abstract

The invention discloses a method for evaluating the capability demand satisfaction of an information guarantee system, which comprises the following steps of constructing a capability index set: setting a capability index set, wherein the set comprises a primary comprehensive index, a secondary capability index and a tertiary performance index; and (3) capability index weight determination: performing importance comparison on the indexes based on the capability index set and the Saath scale, determining the importance scale among the indexes, constructing a pairwise comparison judgment matrix, converting the pairwise comparison judgment matrix into an attribute judgment matrix to calculate relative attribute weights, and synthesizing absolute weights of the indexes based on the hierarchical relationship of the capability index set; demand satisfaction evaluation: constructing quantized sample values of all indexes into matrixes, and carrying out cloud model conversion on each index; based on the qualitative concept Euclidean distance between the cloud model and the index, the positive and negative direction distances of the system capacity are calculated, so that the information guarantee system capacity requirement satisfaction is calculated. The invention can intuitively and scientifically calculate the satisfaction degree of the capacity requirement of the information guarantee system.

Description

Method for evaluating capability demand satisfaction of information guarantee system
Technical Field
The invention relates to the crossing field of uncertainty artificial intelligence and efficiency evaluation, in particular to a method for evaluating the satisfaction of the capability requirement of an information guarantee system.
Background
Along with the continuous development of informatization combat, information data presents characteristics such as multisource, multiclass, mass, and the like, information guarantee faces great challenges, the guarantee system structure and the mode change greatly, and it is particularly important to pay attention to whether the guarantee system meets the current task demands in real time. The satisfaction degree of the judgment and guarantee system on the current task can be called as demand satisfaction degree, and the capability state of the current system can be intuitively displayed by quantifying the demand satisfaction degree through the efficiency evaluation method.
At present, the research capability demand satisfaction evaluation method is less, the domestic research is in a starting stage in the field, most of researches realize the evaluation of capability demand satisfaction under deterministic evaluation data, and the research is mainly divided into a mode of constructing an index system-defining an index satisfaction quantization formula-evaluating algorithm and a mode of constructing a demand satisfaction calculation model from the working principle of an evaluation object and tasks, but the problem that evaluation results are inaccurate due to randomness and ambiguity of evaluation data is often encountered in the evaluation process.
To solve this problem, some scholars have done the following work: the uncertain data are converted into a credibility structure by constructing a credibility rule base, and an evidence reasoning method is introduced to solve the capability requirement satisfaction; adopting a fuzzy comprehensive evaluation idea to complete the capability requirement satisfaction evaluation; solving the problem of data uncertainty based on QFD and a rough set; and a cloud model theory is introduced, and the cloud center of gravity distance is adopted to judge that the requirements meet the grade. However, these methods have different defects, so that the evaluated result is not scientific and objective enough and cannot meet the evaluated requirement, so how to scientifically, reasonably and objectively evaluate whether the capability of the information guarantee system meets the requirement is an important problem to be solved.
Disclosure of Invention
In order to solve the problems, the invention provides a method for evaluating the capability requirement satisfaction of an information guarantee system, which can intuitively and scientifically calculate the capability requirement satisfaction of the guarantee system, and adopts the following technical scheme:
a method for evaluating the satisfaction of the capacity requirement of an information guarantee system comprises the following steps:
s1, constructing a capability index set: setting a capability index set, wherein the set comprises a first-level comprehensive index, a second-level capability index and a third-level performance index, the first-level comprehensive index corresponds to a plurality of second-level capability indexes, and each second-level capability index corresponds to a plurality of third-level performance indexes;
s2, determining the capability index weight: performing importance comparison on the two-level capability indexes and the three-level performance indexes based on the capability index set and the Saath scale, determining importance scales among the indexes, constructing a pairwise comparison judgment matrix, converting the pairwise comparison judgment matrix into an attribute judgment matrix, calculating relative attribute weights according to the attribute judgment matrix, and synthesizing absolute weights of the indexes based on the layering and grading relationship of the capability index set;
s3, demand satisfaction evaluation: constructing quantized sample values of all indexes as matrixes based on the capability index set, and performing cloud model conversion on each index, wherein the cloud model comprises a weighted evaluation object performance decision cloud matrix, a weighted positive ideal cloud and a weighted negative ideal cloud; based on the cloud model, calculating the positive and negative distances of the system capacity by combining the expected characteristics, entropy characteristics and super entropy characteristics of the cloud model through qualitative concept Euclidean distances among indexes; and finally, calculating information to ensure the satisfaction of the system capacity requirement by utilizing the positive and negative distances of the system capacity.
Further, the two-by-two comparison judgment matrix between the two-level capability indexes is as follows:
B＝(b i,j ) n×n
wherein ,bi,j An importance scale between the ith and j secondary capability indexes, wherein n is the number of the secondary capability indexes;
the comparison judgment matrix between the three-level performance indexes is as follows:
Figure BDA0004014530580000031
wherein ,
Figure BDA0004014530580000032
an importance scale between the ith and jth tertiary performance indexes corresponding to the qth secondary performance index, m q The number of the three-level performance indexes corresponding to the q-th two-level performance index.
Further, element d of the attribute judgment matrix i,j The conversion formula of (2) is:
Figure BDA0004014530580000033
wherein ,ci,j Comparing the values of the ith row and the jth column of the judging matrix for every two indexes, wherein k is a scale value;
and transforming the pairwise comparison judgment matrix into an attribute judgment matrix according to the conversion formula:
Figure BDA0004014530580000034
further, the formula for calculating the relative attribute weight according to the attribute judgment matrix is as follows:
Figure BDA0004014530580000035
wherein ,
Figure BDA0004014530580000036
weight of relative attribute for the ith secondary capability index, +.>
Figure BDA0004014530580000037
For the relative attribute weight of the three-level performance index corresponding to the ith two-level capacity index, the corresponding vector representation form is as follows:
Figure BDA0004014530580000041
further, the absolute weights of the indexes synthesized based on the hierarchical relation of the capability index set are as follows:
Figure BDA0004014530580000042
wherein ,WU (i) Is the relative weight of the ith secondary capability index.
Further, the matrix constructed by quantized sample values of all the indicators based on the capability indicator set is:
Figure BDA0004014530580000043
wherein ,xi,j Normalized quantized sample value of the jth index i, which has a value ranging from 0 to 1, N s The number of samples is M, and M is the number of indexes;
desired Ex for each index in quantized sample value matrix D j Variance of
Figure BDA0004014530580000044
Entropy En j Super entropy He j The method comprises the following steps of:
Figure BDA0004014530580000045
Figure BDA0004014530580000046
Figure BDA0004014530580000047
Figure BDA0004014530580000048
further, the performance decision cloud matrix, the weighted positive ideal cloud and the weighted negative ideal cloud of the weight evaluation object constructed according to each index are respectively:
Figure BDA0004014530580000051
Figure BDA0004014530580000052
Figure BDA0004014530580000053
wherein W (M) is the weight of the Mth index.
Further, based on Euclidean distance, reflecting weighted ideal cloud C + And weighted negative ideal cloud C - The calculation formula of the system capacity forward distance is as follows:
Figure BDA0004014530580000054
wherein ,
Figure BDA00040145305800000513
is vector 1-norm, i.e. Euclidean distance,>
Figure BDA0004014530580000055
for weighting the forward difference matrix->
Figure BDA0004014530580000056
M column vector of>
Figure BDA0004014530580000057
For the forward distance of the mth index, the weighted forward difference matrix is:
Figure BDA0004014530580000058
expected difference between the evaluation sample and the forward demand sample
Figure BDA0004014530580000059
Entropy difference->
Figure BDA00040145305800000510
And super entropy difference->
Figure BDA00040145305800000511
The method comprises the following steps of:
Figure BDA00040145305800000512
further, the formula for calculating the negative distance of the system capacity is as follows:
Figure BDA0004014530580000061
wherein ,
Figure BDA0004014530580000062
for weighting the negative difference matrix->
Figure BDA0004014530580000063
M column vector of>
Figure BDA0004014530580000064
For the negative distance of the mth performance index, the weighted negative difference matrix is:
Figure BDA0004014530580000065
expected difference between the evaluation sample and the negative demand sample in the above
Figure BDA0004014530580000066
Entropy difference->
Figure BDA0004014530580000067
And super entropy difference->
Figure BDA0004014530580000068
The method comprises the following steps of: />
Figure BDA0004014530580000069
Further, the calculation formula for calculating the satisfaction of the system capacity requirement by utilizing the positive and negative distances of the system capacity is as follows:
Figure BDA00040145305800000610
wherein, the larger the satisfaction degree p value is, the more the requirement is met, and when p=1, the system capacity is completely met; when p=0, this indicates that the demand is not satisfied at all;
similarly, the calculation formula of each performance requirement satisfaction is as follows:
Figure BDA00040145305800000611
the invention has the beneficial effects that:
(1) The invention introduces the attribute hierarchy model, can fully exert the advantages of expert knowledge, and is simpler and faster in calculation compared with the traditional AHP method.
(2) According to the method, the problem of data uncertainty in demand satisfaction evaluation is solved through the cloud model, so that the original fuzzy evaluation becomes more accurate.
(3) Compared with the existing processing flow in which the accurate numerical satisfaction degree evaluation needs to be normalized based on the requirement and capability evaluation data, the method directly performs the calculation of the positive and negative distances by weighting the evaluation object performance decision cloud matrix and the positive and negative ideal cloud without the need of performing normalization processing in advance through the approach to ideal solution ordering (TOPSIS) method, and saves the calculation resources to a certain extent.
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FIG. 1 is a schematic diagram of a capability index set of an information security system according to an embodiment of the present invention.
Detailed Description
Specific embodiments of the present invention will now be described in order to provide a clearer understanding of the technical features, objects and effects of the present invention. It should be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
The embodiment provides a method for evaluating the satisfaction of the capacity requirement of an information guarantee system, which comprises the following steps:
s1, constructing a capability index set: setting a capability index set, wherein the set comprises a first-level comprehensive index, a second-level capability index and a third-level performance index, the first-level comprehensive index corresponds to a plurality of second-level capability indexes, and each second-level capability index corresponds to a plurality of third-level performance indexes;
s2, determining the capability index weight: performing importance comparison on the two-level capability indexes and the three-level performance indexes based on the capability index set and the Saath scale, determining importance scales among the indexes, constructing a pairwise comparison judgment matrix, converting the pairwise comparison judgment matrix into an attribute judgment matrix, calculating relative attribute weights according to the attribute judgment matrix, and synthesizing absolute weights of the indexes based on the layering and grading relationship of the capability index set;
s3, demand satisfaction evaluation: constructing quantized sample values of all indexes as matrixes based on the capability index set, and performing cloud model conversion on each index, wherein the cloud model comprises a weighted evaluation object performance decision cloud matrix, a weighted positive ideal cloud and a weighted negative ideal cloud; based on the cloud model, calculating the positive and negative distances of the system capacity by combining the expected characteristics, entropy characteristics and super entropy characteristics of the cloud model through qualitative concept Euclidean distances among indexes; and finally, calculating information to ensure the satisfaction of the system capacity requirement by utilizing the positive and negative distances of the system capacity.
Preferably, in the step S1, when the capability index set is constructed, based on the index system architecture of hierarchical hierarchy, 1 comprehensive security capability, 4 seed capability and 16 performance index information security system comprehensive capability assessment index set is constructed from 4 aspects of the project organization capability, the data discovery capability, the intelligent analysis capability and the data application capability. In the step S2, when the weight of the capability index is determined, the relative weight and the absolute weight of each index are quickly converted by using expert scoring data through an attribute hierarchy model (Attribute Hierarchy Model, AHM). And step S3, when the demand satisfaction is evaluated, based on digital characteristics of a cloud model conversion qualitative concept, defining a positive distance and a negative distance by using an improved Euclidean distance, and calculating the system capacity demand satisfaction and each performance demand satisfaction by approaching an ideal solution ordering (TOPSIS) method.
More preferably, the method for evaluating the satisfaction of the capability requirement of the information security system according to the embodiment specifically includes the following steps.
Step S1: and (5) constructing a capability index set.
Based on daily duty tasks of the information guarantee system, in order to emphasize the capability of the information guarantee system in the aspects of intelligence, planning, service, reconnaissance and the like, the comprehensive capability of the information guarantee system is divided into 4 seed capability such as planning organization capability, data discovery capability, intelligent analysis capability and data application capability, 16 performance indexes such as timeliness of node planning organization, coverage rate of an action range, timeliness of intelligent analysis and the like, and the corresponding relation between each performance index and the capability index is shown in figure 1.
Step S2: and (5) determining the capability index weight.
Step S201: and combining expert scoring to construct a pairwise comparison judgment matrix.
And (3) based on the capability index set constructed in the step (S1), carrying out importance comparison on the secondary indexes and the tertiary indexes according to the Saath scale, determining the importance scale among the indexes, and constructing a pairwise comparison judgment matrix. The Satty scale table is specifically as follows:
table 1Satty scale table
Figure BDA0004014530580000091
Two-by-two comparison judgment matrix between two-level indexes is B= (B) i,j ) n×n N is the number of secondary indexes, namely n=4, and the three-level indexes under the capability of planning organization are compared with each other to judge the matrix as
Figure BDA0004014530580000101
Three-level index pair-by-pair comparison judgment matrix under data discovery capability is +.>
Figure BDA0004014530580000102
The three-level index under the intelligent analysis capability is compared with each other to judge that the matrix is
Figure BDA0004014530580000103
Three-level index pair-by-pair comparison judgment matrix under data application capability is +.>
Figure BDA0004014530580000104
wherein m1 、m 2 、m 3 and m4 The number of the corresponding tertiary performance indexes under each secondary capability is respectively.
Step S202: and constructing an attribute judgment matrix based on the pairwise comparison judgment matrix.
Suppose c i,j Judging the matrix element d for the value of the ith row and the jth column of the matrix by comparing every two indexes i,j The conversion formula of (2) is as follows:
Figure BDA0004014530580000105
according to the conversion formula, the pairwise comparison judgment matrix is converted into an attribute judgment matrix, and the specific form is as follows:
Figure BDA0004014530580000106
step S203: and calculating relative attribute weights according to the attribute judgment matrix.
Based on the attribute judgment matrix obtained by the conversion formula, the relative weight calculation method between the three-level indexes under each sub-capability is as follows:
Figure BDA0004014530580000111
wherein, the ith secondary index weight of the first behavior
Figure BDA0004014530580000112
The three-level index weight calculation formula under each sub-capability of other behaviors.
Its vector representation is:
Figure BDA0004014530580000113
step S204: synthesizing absolute weights of performance indexes based on hierarchical relation of index sets:
Figure BDA0004014530580000114
wherein ,WU (i) Is the relative weight of the ith secondary index.
Step S3: and (5) evaluating the satisfaction degree of the demand.
Step S301: a matrix of quantized sample values is constructed.
Based on the capability index set of the information guarantee system, the quantized sample values of all the performance indexes are constructed as a matrix D, and the matrix can be expressed as
Figure BDA0004014530580000121
wherein ,xi,j Normalized quantized sample value of the jth index i, which has a value ranging from 0 to 1, N s For the number of samples, M is the number of performance indicators, which is 16.
Step S302: and carrying out cloud model conversion on each index.
By using the quantized sample value matrix D, cloud model conversion can be performed on each evaluation index, and accurate data is converted into qualitative concepts with digital characteristics according to cloud model theory, and the method is specifically as follows:
(1) Calculating expected Ex of each index according to the normalized sample value of each index j
Figure BDA0004014530580000122
(2) Calculating variance of each index by using expected and normalized sample values of each index
Figure BDA0004014530580000123
Figure BDA0004014530580000124
(3) Calculating entropy En of each index by using expected and normalized sample values of each index j
Figure BDA0004014530580000125
(4) By variance of each index
Figure BDA0004014530580000126
Sum entropy En j The super-entropy He of each index is calculated together j
Figure BDA0004014530580000127
Aiming at the evaluation object and the index set, constructing a weighted evaluation object performance decision cloud matrix C and a weighted positive ideal cloud C + And weighted negative ideal cloud C - The concrete form is as follows:
(5) Weighted evaluation object performance decision cloud matrix C
Based on system capacity evaluation index sample data, calculating expected entropy and super entropy of each index cloud model by using a reverse cloud generator, namely C j (Ex j ,En j ,He j ) And introducing each index weight to construct a weighted evaluation object performance decision cloud matrix C, wherein the specific form is as follows:
Figure BDA0004014530580000131
wherein W (j) is the weight of the jth performance index.
(6) Weighted ideal cloud C +
Based on system capacity demand index sample data, calculating expected entropy and super entropy of each index demand cloud model by using a reverse cloud generator, namely
Figure BDA0004014530580000132
Introducing each index weight to construct weighted ideal cloud C + The concrete form is as follows: />
Figure BDA0004014530580000133
(7) Weighted negative ideal cloud C -
The weighted negative ideal cloud corresponds to the lower limit of the demand, so that the expected, entropy and super entropy of the cloud model of the lower limit of each index demand are set to be 0, 0.005 and 0.005, and the representation form of the weighted negative ideal cloud is as follows:
Figure BDA0004014530580000134
step S303: and calculating the positive and negative distances of the system capacity based on the cloud model.
Based on C, C + and C- By combining the expected, entropy and super entropy characteristics in the cloud model through qualitative concept Euclidean distance between indexes, the positive and negative distances of the system capacity are calculated, and the specific calculation formula is as follows:
(1) System capacity forward distance L +
Reflecting C and C by Euclidean distance + The absolute difference of the index cloud model is specifically expressed as follows:
Figure BDA0004014530580000135
wherein ,
Figure BDA00040145305800001417
is vector 1-norm, i.e. Euclidean distance,>
Figure BDA0004014530580000141
for weighting the forward difference matrix->
Figure BDA0004014530580000142
Is selected from the group consisting of the (j) th column vector,
Figure BDA0004014530580000143
for the forward distance of the j-th performance index, weighting the forward difference matrix +.>
Figure BDA0004014530580000144
The specific form is as follows:
Figure BDA0004014530580000145
since the expectations are the mean value of the descriptive sample data, the entropy and the super entropy are the ambiguity and the randomness of the descriptive sample data, in the demand satisfaction evaluation, when the evaluation sample expectations are higher than the forward demand sample expectations or the evaluation samplesWhen the entropy and the super entropy are lower than the forward demand sample entropy and the super entropy, the average value, the ambiguity and the randomness are respectively considered to meet the demands, namely the difference between the evaluation sample and the forward demand sample is zero. Based on the above principle, the expected difference between the evaluation sample and the forward demand sample is defined
Figure BDA0004014530580000146
Entropy difference->
Figure BDA0004014530580000147
And super entropy difference->
Figure BDA0004014530580000148
The method comprises the following steps:
Figure BDA0004014530580000149
Figure BDA00040145305800001410
Figure BDA00040145305800001411
/>
(2) Negative system capacity distance L -
The calculation formula of the negative distance of the system capacity is as follows, and the positive distance is as follows:
Figure BDA00040145305800001412
wherein ,
Figure BDA00040145305800001413
for weighting the negative difference matrix->
Figure BDA00040145305800001414
Is j-th column vector,/>
Figure BDA00040145305800001415
For the negative distance of the j-th performance index, the negative difference matrix is weighted +.>
Figure BDA00040145305800001416
The specific form is as follows:
Figure BDA0004014530580000151
expected difference between the evaluation sample and the negative demand sample in the above
Figure BDA0004014530580000152
Entropy difference->
Figure BDA0004014530580000153
And super entropy difference->
Figure BDA0004014530580000154
The method comprises the following steps:
Figure BDA0004014530580000155
Figure BDA0004014530580000156
Figure BDA0004014530580000157
step S304: and calculating the satisfaction degree of the system capacity requirement and the satisfaction degree of the performance requirement.
The system demand satisfaction P is calculated as follows:
Figure BDA0004014530580000158
the larger the value, the more the demand is met, and when the value is equal to 1, the system capacity is fully met; conversely, a smaller satisfaction P value indicates that the demand is not satisfied, and when the demand is not satisfied at all, the satisfaction P value is 0.
Similarly, the demand satisfaction degree P of each performance index j The calculation formula is as follows:
Figure BDA0004014530580000159
it should be noted that, for the sake of simplicity of description, the foregoing method embodiments are expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously according to the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.

Claims (10)

1. The method for evaluating the satisfaction degree of the capacity requirement of the information guarantee system is characterized by comprising the following steps of:
s1, constructing a capability index set: setting a capability index set, wherein the set comprises a first-level comprehensive index, a second-level capability index and a third-level performance index, the first-level comprehensive index corresponds to a plurality of second-level capability indexes, and each second-level capability index corresponds to a plurality of third-level performance indexes;
s2, determining the capability index weight: performing importance comparison on the two-level capability indexes and the three-level performance indexes based on the capability index set and the Saath scale, determining importance scales among the indexes, constructing a pairwise comparison judgment matrix, converting the pairwise comparison judgment matrix into an attribute judgment matrix, calculating relative attribute weights according to the attribute judgment matrix, and synthesizing absolute weights of the indexes based on the layering and grading relationship of the capability index set;
s3, demand satisfaction evaluation: constructing quantized sample values of all indexes as matrixes based on the capability index set, and performing cloud model conversion on each index, wherein the cloud model comprises a weighted evaluation object performance decision cloud matrix, a weighted positive ideal cloud and a weighted negative ideal cloud; based on the cloud model, calculating the positive and negative distances of the system capacity by combining the expected characteristics, entropy characteristics and super entropy characteristics of the cloud model through qualitative concept Euclidean distances among indexes; and finally, calculating information to ensure the satisfaction of the system capacity requirement by utilizing the positive and negative distances of the system capacity.
2. The method for evaluating the capability requirement satisfaction of an intelligence guarantee system according to claim 1, wherein the two-by-two comparison judgment matrix between the two-level capability indexes is:
B＝(b i,j ) n×n
wherein ,bi,j An importance scale between the ith and j secondary capability indexes, wherein n is the number of the secondary capability indexes;
the comparison judgment matrix between the three-level performance indexes is as follows:
Figure FDA0004014530570000011
wherein ,
Figure FDA0004014530570000021
an importance scale between the ith and jth tertiary performance indexes corresponding to the qth secondary performance index, m q The number of the three-level performance indexes corresponding to the q-th two-level performance index.
3. The method for evaluating the capability requirement satisfaction of an intelligence guarantee system according to claim 2, wherein the element d of the attribute judgment matrix i,j The conversion formula of (2) is:
Figure FDA0004014530570000022
wherein ,ci,j Judging matrix for comparing N indexes in pairsThe value of the ith row and the jth column of (a), k is a scale value;
and transforming the pairwise comparison judgment matrix into an attribute judgment matrix according to the conversion formula:
Figure FDA0004014530570000023
4. the method for evaluating the capability requirement satisfaction of an information security system according to claim 3, wherein the formula for calculating the relative attribute weight according to the attribute judgment matrix is as follows:
Figure FDA0004014530570000024
wherein ,
Figure FDA0004014530570000025
weight of relative attribute for the ith secondary capability index, +.>
Figure FDA0004014530570000026
For the relative attribute weight of the three-level performance index corresponding to the ith two-level capacity index, the corresponding vector representation form is as follows:
Figure FDA0004014530570000031
5. the method for evaluating the capability requirement satisfaction of an intelligence guarantee system according to claim 4, wherein the absolute weight of each index synthesized based on the hierarchical relationship of the capability index set is:
Figure FDA0004014530570000032
wherein ,WU (i) Is the relative weight of the ith secondary capability index.
6. The intelligence guarantee system capability requirement satisfaction evaluation method according to claim 5, wherein the matrix constructed by the quantized sample values of all the indexes based on the capability index set is:
Figure FDA0004014530570000033
wherein ,xi,j Normalized quantized sample value of the jth index i, which has a value ranging from 0 to 1, N s The number of samples is M, and M is the number of indexes;
desired Ex for each index in quantized sample value matrix D j Variance of
Figure FDA0004014530570000034
Entropy En j Super entropy He j The method comprises the following steps of:
Figure FDA0004014530570000035
Figure FDA0004014530570000036
Figure FDA0004014530570000037
Figure FDA0004014530570000038
7. the method for evaluating the satisfaction of the capacity requirement of the intelligence guarantee system according to claim 6, wherein the performance decision cloud matrix, the weighted positive ideal cloud and the weighted negative ideal cloud of the weight evaluation object constructed according to each index are respectively:
Figure FDA0004014530570000041
Figure FDA0004014530570000042
Figure FDA0004014530570000043
wherein W (M) is the weight of the Mth index.
8. The information security system capability demand satisfaction evaluation method according to claim 7, wherein the weighted ideal cloud C is reflected based on the euclidean distance + And weighted negative ideal cloud C - The calculation formula of the system capacity forward distance is as follows:
Figure FDA0004014530570000044
wherein ,
Figure FDA0004014530570000045
is vector 1-norm, i.e. Euclidean distance,>
Figure FDA0004014530570000046
for weighting the forward difference matrix->
Figure FDA0004014530570000047
M column vector of>
Figure FDA0004014530570000048
For the forward distance of the mth index, the weighted forward difference matrix is:
Figure FDA0004014530570000049
expected difference between the evaluation sample and the forward demand sample
Figure FDA00040145305700000410
Entropy difference->
Figure FDA00040145305700000411
And super entropy difference->
Figure FDA00040145305700000412
The method comprises the following steps of:
Figure FDA0004014530570000051
Figure FDA0004014530570000052
Figure FDA0004014530570000053
9. the intelligence guarantee system capability requirement satisfaction evaluation method according to claim 8, wherein a calculation formula of a system capability negative distance is as follows:
Figure FDA0004014530570000054
wherein ,
Figure FDA0004014530570000055
for weighting the negative difference matrix->
Figure FDA0004014530570000056
M column vector of>
Figure FDA0004014530570000057
For the negative distance of the mth performance index, the weighted negative difference matrix is:
Figure FDA0004014530570000058
expected difference between the evaluation sample and the negative demand sample in the above
Figure FDA0004014530570000059
Entropy difference->
Figure FDA00040145305700000510
And super entropy difference->
Figure FDA00040145305700000511
The method comprises the following steps of:
Figure FDA00040145305700000512
Figure FDA00040145305700000513
Figure FDA00040145305700000514
10. the method for evaluating the satisfaction of the capability requirement of the information guarantee system according to claim 9, wherein the calculation formula for calculating the satisfaction of the capability requirement of the information guarantee system by utilizing the positive and negative distances of the capability of the system is as follows:
Figure FDA00040145305700000515
wherein, the larger the satisfaction degree p value is, the more the requirement is met, and when p=1, the system capacity is completely met; when p=0, this indicates that the demand is not satisfied at all;
similarly, the calculation formula of each performance requirement satisfaction is as follows:
Figure FDA0004014530570000061
/>
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116883414A (en) * 2023-09-08 2023-10-13 国网上海市电力公司 Multi-system data selection method and system suitable for operation and maintenance of power transmission line
CN117909200A (en) * 2024-03-19 2024-04-19 中国电子科技集团公司第十研究所 Method, equipment and system for incremental comparison and evaluation of capability of information guarantee system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116883414A (en) * 2023-09-08 2023-10-13 国网上海市电力公司 Multi-system data selection method and system suitable for operation and maintenance of power transmission line
CN116883414B (en) * 2023-09-08 2024-01-26 国网上海市电力公司 Multi-system data selection method and system suitable for operation and maintenance of power transmission line
CN117909200A (en) * 2024-03-19 2024-04-19 中国电子科技集团公司第十研究所 Method, equipment and system for incremental comparison and evaluation of capability of information guarantee system
CN117909200B (en) * 2024-03-19 2024-06-11 中国电子科技集团公司第十研究所 Method, equipment and system for incremental comparison and evaluation of capability of information guarantee system

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