CN114548637A - AHP-RST-based power communication backbone data network security comprehensive evaluation method - Google Patents
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Abstract
The invention discloses a comprehensive evaluation method for the safety of a power communication backbone data network based on AHP-RST. The method aims to solve the problems that the evaluation index of the existing network security evaluation method is not fine enough, is relatively unilateral and lacks quantitative measurement, and the evaluation content is relatively subjective and the like; the method comprises the steps of respectively calculating the weight of each index of an index layer and a standard layer by adopting an analytic hierarchy process AHP and a rough set RST, solving the optimal combination weight, combining a fuzzy relation matrix with the weight of a network safety index to calculate a total fuzzy evaluation result, and finally converting the fuzzy comprehensive evaluation result into a corresponding state grade according to a maximum membership principle and a power communication backbone data network safety grade evaluation set. The method has the advantages that risk management and control can be performed on the power communication network, the problems that factors are uncertain and the method depends too much on subjective experience can be effectively solved, and accordingly comprehensive evaluation of subjective and objective combination is performed on the safe operation state of the power communication backbone network data.
Description
Technical Field
The invention relates to the technical field of power communication and data network security, in particular to a comprehensive assessment method for power communication backbone data network security based on AHP-RST.
Background
At present, the research on the whole evaluation of the safety performance of the communication network at home and abroad is less, and the research on the safety evaluation system of the power communication transmission network is mainly researched from the aspects of the reliability, the vulnerability, the transmission quality and the like of the power communication network. In part of research, a plurality of factors influencing the safety degree of the communication network, such as reliability, survivability, vulnerability and the like, are considered, but systematic and global analysis of the safety identification level of the operation condition of the whole communication network is lacked, and needs to be promoted. Based on the research of ISO/IEC1335 (the standard gives a general step of power communication network safety judgment), high-risk factors on the aspects of economy, engineering and the like are fully considered, the main aspects of communication network safety are obviously influenced by fuzzy judgment, but the subjectivity is large. The method for estimating and modeling the safety of the power communication network by means of fuzzy comprehensive evaluation can be used as a beneficial reference for research of the subject, but the fuzzy mathematics is based on the membership function and has certain limitation when being pushed to the actual engineering (the membership function is not easy to determine). In addition, the domestic power industry also carries out key research on a safety evaluation method of the power communication network. The security evaluation is included in the risk management and belongs to a policy of risk assessment.
Generally, the evaluation work of the power communication network is mainly performed to evaluate the security of the power communication network from several aspects such as communication structure configuration and operation management. In the aspect of power communication safety, a power communication safety risk assessment method based on a system safety engineering (SSE-CMM) and based on an ISO/IEC13335 standard is proposed according to risk assessment strategies and standards. In actual engineering, the national power grid evaluates the power communication network mainly according to qualitative analysis, namely, relevant experts of the national power communication network score all evaluation indexes and evaluation parts of a certain power communication network, artificial factors are large, evaluation results are greatly influenced, and the manpower, material resources and financial resources which are input each time are large. In addition, as the structure of the electric power communication network system is more and more complex, the risk events are more and more disordered and have more varieties, the accumulated field data are very few, the probability of event formation is inaccurate, and the determination of certain evaluation indexes is not represented by data and is difficult to form the probability. In order to meet the higher requirements of the ubiquitous power internet of things on the power communication network, the risk prediction capability and the accurate and reliable safety risk assessment capability of the power communication network should be enhanced.
In summary, in recent years, research on the safety of the power communication network has been vigorously developed, and a large number of safety suggestions and evaluation methods have been proposed, and certain research results have been achieved. However, when the network security evaluation is performed as a requirement of the power internet of things for power communication, not only the survivability of a network topology structure is considered, but also various factors such as environments and management of various communication devices and devices in the network need to be considered, and the like, the existing research results do not achieve comprehensive security evaluation, and in addition, the existing evaluation indexes are not fine enough, are relatively one-sided and lack of quantitative measure, and the evaluation content is relatively subjective, so that the security evaluation technology in the field also has a great space for improvement.
For example, a chinese patent document discloses "a method and a system for predicting a security situation of a power communication network", which is published under the publication number CN107124394B, 3/20/2020, and includes: extracting the power grid data from an original storage position by an extraction conversion loading technology, and loading the predefined power grid data related to the evaluation of the safety situation of the power communication network to a data warehouse after data cleaning; preprocessing the power grid data in the data warehouse; calculating to obtain related indexes of the safety potential state according to the preprocessed power grid data based on a Spark platform; and calculating a safety situation value of the power communication network based on a particle swarm optimization neural network model, classifying the danger level according to the relevant indexes of the safety situation corresponding to the safety situation value, and early warning. The invention has the advantages of one-sided prediction of network security and insufficient detail of prediction data.
Disclosure of Invention
The invention mainly solves the problems that the evaluation index of the existing network security evaluation method is not fine enough, is comparatively unilateral and lacks quantitative measure, and the evaluation content is comparatively subjective and the like; the comprehensive evaluation method for the safety of the power communication backbone data network based on the AHP-RST is provided.
The technical problem of the invention is mainly solved by the following technical scheme:
the invention comprises the following steps:
1) constructing a power communication backbone data network security assessment index system;
2) collecting related data of a power communication backbone network, statistical data of operation maintenance of a power communication department, fault reports and the like, and classifying the types of the data;
3) processing the acquired data of the power communication backbone network by using a fuzzy comprehensive evaluation theory to obtain a fuzzy relation matrix R;
4) determining subjective weight of a power communication backbone data network security index;
5) determining objective weight of the network security index of the power communication backbone data;
6) determining the optimal combination weight of the safety indexes of the power communication backbone data network;
7) synthesizing the fuzzy relation matrix R and each safety index weight w to obtain a safety fuzzy comprehensive evaluation result B of the power communication backbone network;
8) and converting the fuzzy comprehensive evaluation result B into a corresponding state grade according to a maximum membership principle and the electric power communication backbone data network safety state comment set V.
The assessment method is beneficial to risk management and control of the power communication network, and can effectively solve the problems of possible factor uncertainty and excessive dependence on subjective experience, so that comprehensive assessment of subjective and objective combination is performed on the safe operation state of the power communication backbone network data.
Preferably, in the step 1), a data network security assessment index system is constructed from equipment, environment, network, management and control levels by analyzing influence factors of data security situation of a power communication backbone network, and the index level is divided into three levels.
By adopting the method, the safety of the electric network can be comprehensively evaluated, and the method is more scientific.
Preferably, in the step 3), a fuzzy relation matrix R is obtained by using a fuzzy comprehensive evaluation theory in processing the collected power communication backbone network data, and the specific steps are as follows:
3.1) establishing a safety factor set u of the power communication backbone network;
the method comprises the following steps that a power communication network backbone network safety factor set u is obtained, factors influencing the power communication backbone network safety are divided into 3 layers by a fuzzy evaluation method, the first layer is a target layer, and an evaluation target is u; the second layer is a content layer with an evaluation content of ui(ii) a The third layer is a factor layer uijEach evaluation has a respective evaluation factor;
3.2) determining a security level comment set of the power communication backbone data network;
the safety state of the power communication backbone data network is represented by 5 levels, and corresponding treatment measures are taken:
the safety state is safe operation, and the processing measures are normal monitoring;
the safety state is controlled safety, and the treatment measures are regulatory measures;
the safety state is alert operation, and the processing measures are to take preventive measures in time, strengthen monitoring and send early warning signals;
the safety state is dangerous operation, and the treatment measures are that related counter measures must be implemented and monitoring is strengthened;
the safety state is an emergency state, and the processing measures are various correction, stabilization and recovery control, so that the power communication network is recovered to a normal state as much as possible, and the monitoring is strengthened to avoid cascading failures;
and describing the security risk evaluation level of the power communication backbone data network, wherein the risk level corresponds to the security state of the power communication backbone data network one by one and is expressed as follows:
the risk grades are D1/very low, D2/low, D3/medium, D4/high and D5/very high respectively, the corresponding risk description is gradually aggravated along with the risk events caused by the improvement of the risk grades, and the economic loss caused by the gradual aggravation is more and more serious;
the safety state of the power communication backbone network and the corresponding risk level are divided into 5 levels: the emergency state/risk is very high, the dangerous operation/risk is high, the warning operation/intermediate risk, the controlled safety/risk is low, the safe operation/risk is very low, the emergency state/risk corresponds to V1, V2, V3, V4 and V5 respectively, and 5 grades respectively describe the difference of the safety of each power communication backbone data network;
3.3) constructing a membership function model;
fuzzifying a finite set u of evaluation factors by a membership function, changing the finite set u into a membership degree on [0,1], representing the degree of the fuzzy relation of the set, selecting and using small-bias type and large-bias type expressions, and constructing a membership function corresponding to an evaluation set V which is { VV1, V2, V3V4 and V5} according to an evaluation grade quantization standard;
the small-sized indexes comprise unreasonable optical fiber use, access equipment fault rate, network equipment fault rate and unreasonable time slot distribution, and the small-sized membership function is as follows:
wherein, a and b are preset reference values of each safety index;
the large-scale indexes comprise management rule completeness, personnel allocation completeness and line protection channel dualization rate; the partial membership function is:
3.4) establishing a fuzzy relation matrix;
determining the degree of association of the evaluation object to the evaluation grade set from an evaluation factor, and gradually selecting each index u for the evaluated objecti(i-1, 2, …, p) and determining the degree of membership (R | u) of the evaluated object to the rank-fuzzy subset from a single indexi) And further obtaining a fuzzy relation matrix:
the ith row and jth column element R in the matrix RijAnd the matrix R is a single-factor fuzzy evaluation set. Substituting data monitored by the power communication backbone network safety index for multiple times into each corresponding membership function formula to obtain membership degrees, wherein each membership degree corresponds to one power communication backbone network safety grade, and the proportion of each grade is taken as rijA value of (d); the relationship between the network security level and the degree of membership is as follows:
the safety level is V1, and the belonging range of the membership degree is [0,0.2 ];
the safety level is V2, and the membership degree belongs to the interval of [0.2,0.4 ];
the safety level is V3, and the belonging interval of the membership degree is [0.4,0.6 ];
the safety level is V4, and the belonging interval of the membership degree is [0.6,0.8 ];
the safety level is V5, and the membership degree belongs to the interval of [0.8,1.0 ].
The scheme can comprehensively reflect a plurality of key safety factors of the data network by utilizing a fuzzy comprehensive evaluation theory.
Preferably, in the step 4), the subjective weight ω of each index is obtained by an analytic hierarchy process AHPaiThe analytic hierarchy process comprises the following specific steps:
4.1) constructing a hierarchical architecture model of an index system;
decomposing the provided typical scene adjustment demand index system into a target layer, a first-level index and a second-level index, wherein the index system is divided into three layers from top to bottom based on the idea of an analytic hierarchy process, and the three layers are sequentially as follows:
1. target layer/zero level refers to: overall indexes of power communication backbone data network safety evaluation;
2. criterion layer/level one indicator: determining an integral framework and an evaluation angle of an evaluation index system;
3. index layer/secondary index: the specific evaluation indexes corresponding to the planning scheme can be directly applied to the calculation of the scheme layer;
4.2) constructing a judgment matrix of each level index;
in a power communication backbone data network safety index system, according to the comparison of the index of an index layer to the importance degree of a target layer, a pairwise comparison method and a nine-level scale method are adopted to assign values to elements of a judgment matrix, and the obtained judgment matrix is as follows:
by comparing the importance of the ith element and the jth element, the relative importance degree is quantified as aijIf the number of elements to be compared is n, the evaluation matrix can be expressed as a ═ a (a)ij)n×nWherein
The values are assigned according to a 1-9 scale method, the elements in each layer are compared pairwise, and the relatively important elements and the important degrees thereof are judged, and the important degrees can be represented by corresponding numerical values as follows:
aij1, 3, 5, 7 and 9, namely the ith index and the jth index are more and more important as the number increases;
aij2, 4, 6 and 8, which are intermediate values of the adjacent judgment;
aijis reciprocal and is expressed as
4.3) judging the consistency check of the matrix;
because the judgment matrix reflects the subjective judgment value and has a certain difference with the importance degree of the objective value of the index, the judgment matrix needs to be subjected to consistency check, and the calculation formulas of the consistency index CI and the consistency ratio CR of the judgment matrix are respectively as follows:
CI＝(λmax-n)/(n-1)
CR＝CI/RI
in the formula: lambda [ alpha ]maxJudging the maximum eigenvalue of the matrix; RI is an average random consistency index, RI is only related to n, the larger the value of CI is, the worse the consistency of the judgment matrix is, when CR is less than 0.1, the judgment matrix can be considered to meet the consistency requirement, otherwise, the mutual importance degree of each index is adjusted until the consistency passes the consistency inspection;
4.4) determining subjective weight;
normalizing the judgment matrix, and calculating the sum of each row of elements
Calculating each element in the current column SjTo obtain normalized new elements
Further obtaining a judgment moment after normalization processing;
calculating the sum of each line element
Weight of each element
By the weight number WiThe constituent factor weight sets W are fuzzy subsets on the factor set UExpressed as W ═ W using fuzzy vectors1，w2，w3，…，wn]；
Providing a layer A comprising A1，A2，…，AmM factors, the total hierarchical ranking weight is a1，a2，…，amThe next layer B includes n factors B1，B2，…，BnThen with respect to AjThe hierarchical order of the order is b1j，b2j，…，bnjWhen B is presentiAnd AjWhen not relevant, bIjWhen the total target weight is 0, the weight of each factor in the B layer is calculated
Shown;
the consistency check is also carried out on the total sequence of the layers, and the layer B is set to be consistent with the layer AjThe pair-wise comparison judgment matrix of related factors is subjected to consistency check in the single-rank order to obtain single-rank order consistency indexes of CI (j), (j is 1, …, m), and corresponding average random consistency indexes of RI (j), CI (j), and RI (j) which are obtained when the single-rank order is carried out, so that the proportion of the total sorting random consistency of the B layer is
When CR is less than 0.10, the total ranking result is considered to have accurate consistency and the analysis result is accepted.
According to the scheme, the subjective weight of each index is obtained through an Analytic Hierarchy Process (AHP), and quantitative basis and basic data are provided for planning, construction and optimization of the power communication network.
Preferably, in the step 5), the objective weight ω of each index is obtained from the rough set RSTαiThe rough set comprises the following specific steps:
5.1) discretizing continuous data;
selecting an equal-width discretization method, wherein the value interval after condition index discretization is as follows:
wherein
Indicates the length of the interval, max (l)xy) Indicating index cxyMaximum value of (d), min (l)xy) Indicating index cxyAnd m represents the set number of discretization intervals;
5.2) constructing a decision table;
after a large amount of evaluation target data is collected, discretization is performed on each attribute value to form a two-dimensional knowledge expression system, and the result after data discretization is expressed by using a decision table S { (U, A, V, f). U is a non-empty finite set of objects called a discourse field, with U ═ U1，U1，…UmRepresents a set of evaluation objects, a is an attribute totality, a ═ C ≡ D, and
c is a conditional attribute, D is a decision attribute, with C ═ C1，C1，…CmRepresents a condition attribute evaluation index set, wherein Cx(x is 1,2, …, z) as the primary index attribute, and if the primary index contains a secondary index attribute, C may be usedxy＝{Cx1，Cx2，…，CxyDescription is carried out, and index y for evaluating the security level is a decision attribute, the decision attribute set is D ═ y }, and V ═ U { }, thena∈AVaIs a value range, VaRepresenting a set of objects with the same attribute a in the domain of discourse U; setting each element in V ═ 1,2,3,4 and 5 as 5 state levels of emergency state, dangerous operation, alert operation, controlled safety and safe operation respectively by referring to a grading mode for the safety of the power communication data backbone network; f: UxAa→VaIs a function of information, then
5.3) calculating attribute dependency;
the expression is as follows:
posp(Q) is a set composed of elements in U that is judged to definitely belong to Q from knowledge P, γp(Q) is the dependency level of knowledge Q on knowledge P, also called the approximate quality, support or classification quality of Q with respect to P, and has a value range of [0, 1%]When γ is 1, it means that knowledge Q completely depends on knowledge P, and when γ is 0, that is, it means
When, knowledge Q is completely independent of knowledge P;
the degree of dependence of the decision index D on the condition index C is as follows:
wherein y isiThe method is characterized in that the method is used for evaluating the safety state and comprises the steps of emergency state, dangerous operation, warning operation, controlled safety and safe operation;
after a certain index is eliminated, the decision index D is used for judging the condition indexes C-CiDegree of dependence of (c):
importance of the ith index to the decision index:
σCD(ci)＝γc-γC-ci(D)，i＝1，2，…m；
5.4) normalizing to obtain attribute weight;
weighting factor of i-th condition index:
according to the scheme, the objective weight of each index is obtained through the rough set RST, and quantitative basis and basic data are provided for planning, construction and optimization of the power communication network.
Preferably, in the step 6), the AHP method and the rough set method are effectively combined, and the ratio of the host weight to the guest weight is optimized, so that the error of the decision system in the state evaluation process is minimized, and the specific process is as follows:
6.1) establishing an optimization model;
setting omega in decision system SαiFor subjective weighting by AHP, ωαiIs an objective weight, omega, obtained by a rough set theory methodiIs an optimized combination of the two weights, wherein
Build an optimization model namely
In the formula (I), the compound is shown in the specification,
6.2) determining the coefficient mu;
the optimization model has a unique solution in the feasible domain Ω:
ωi＝μωai+(1-μ)ωαi，i＝1，2，…，m
by applying the optimization weight combination theory, the subjective and objective weights are optimized according to the above formula, and when μ is solved to be 0.382, the above formula can be changed to:
ωi＝0.382×ωai+(1-0.382)×ωαi。
according to the scheme, an AHP method and a rough set method are effectively combined, an optimization model is built, and a key coefficient is solved by applying an optimization weight combination theory.
Preferably, in the step 7), the specific step of obtaining the power communication backbone network safety ambiguity comprehensive evaluation result B is as follows:
7.1) synthesizing w with R of each evaluated object, and using the formula in determining fuzzy comprehensive evaluation results of each subsystem:
Bi＝WiRi＝(bi1，bi2，bi3，bi4，bi5)
in the formula: b isiIs the evaluation result of the subsystem i; wiThe fuzzy weight vector of the subsystem i is formed by the secondary index weight of the subsystem i; riAn evaluation matrix of the subsystem i is determined by membership functions of all indexes;
7.2) after the subsystem results are known, the safety condition of the power communication backbone network can be comprehensively evaluated, and the formula of the safety evaluation result B of the power communication backbone network is obtained as follows:
in the formula: the comprehensive evaluation vector B is an overall evaluation result of the quality of a certain power communication network, and the comprehensive evaluation matrix can be converted into a power communication network quality score; a fuzzy weight vector W, which is composed of the weights of 4 primary indexes; the comprehensive fuzzy evaluation matrix is composed of evaluation results Bi of all subsystems.
According to the scheme, the fuzzy relation matrix and the safety index weights are synthesized to obtain a comprehensive evaluation result, so that the evaluation result is more accurate and scientific.
Preferably, in the step 8), the column with the largest numerical value is found according to the maximum membership criterion from the comprehensive evaluation result of the power communication backbone network security obtained in the step 7), and the network security level of the column is determined, so as to obtain the evaluation level of the power communication backbone data network security.
According to the scheme, the network security comprehensive evaluation result can be used for obtaining the evaluation level of the network security according to the maximum membership criterion, and the method has theoretical research value and engineering guidance significance.
The invention has the beneficial effects that: the method is favorable for risk management and control of the power communication network, and can effectively solve the problems of possible factor uncertainty and excessive dependence on subjective experience, thereby carrying out comprehensive evaluation of subjective and objective combination on the safe operation state of the power communication backbone network data; in addition, the index system provided by the invention comprehensively reflects a plurality of key safety factors of the data network, makes a preliminary attempt on paying attention to artificial safety and exploring a new idea of electric power safety evaluation, can provide quantitative basis and basic data for planning, building and optimizing the electric power communication network, and has theoretical research value and engineering guidance significance.
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FIG. 1 is a flow chart of the steps of the present invention.
FIG. 2 is a block diagram of the evaluation architecture of the present invention.
Fig. 3 is a flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b):
in this embodiment, a method for comprehensively evaluating the security of a power communication backbone data network based on AHP-RST is shown in fig. 3, and includes
Step 1) the method comprises the steps of constructing a backbone data network security assessment index system from multi-dimensional levels of equipment, environment, network, management, control and the like by analyzing influence factors of the data security situation of the power communication backbone network. The index hierarchy is divided into three levels, including 4 first-level indexes and 19 second-level indexes. Specifically, as shown in fig. 2, in the first-level indexes of the power communication data backbone network index system, the first-level indexes of the network level and the equipment level are focused on researching the inherent safety and reliability of the power communication network and reflect the inherent factors of the network safety and reliability; the first-level indexes of the environment level and the management and control level focus on researching the influence of site environment and human factors on the network safety and reliability and reflect the external factors of the network safety and reliability. According to the relevance and membership of the influence factors, the lower indexes of the 4 primary indexes are respectively defined as follows:
1. network plane C1. Mainly embodies the wind risk resistance of the electric power communication network and the service. The network level indexes are divided into an optical equipment ring forming rate C11, an optical routing configuration unreasonable C12, a safety automatic device channel doubling rate C13, a time slot allocation unreasonable C14 and a line protection channel doubling rate C15.
2. Device level C2. The influence of main equipment of power communication on safety and reliability is mainly reflected, including the fault condition, redundancy and the like of each equipment. The equipment level indexes are divided into unreasonable optical fiber utilization C21, access equipment failure rate C22, network equipment failure rate C23, switching equipment redundancy C24 and optical equipment failure rate C25.
3. Environmental level C3. The method mainly aims at the influence of the environment of the network on the network safety from the macroscopic and microscopic angles, and comprises a communication machine room environment, a site geographical environment and the like. The environmental level indexes are divided into geological factors and climate factors C31, natural disaster occurrence frequency C32, standard rate of fire and theft prevention of the machine room C33 and standard rate of lightning protection and grounding of the machine room C34.
4. Management and control plane C4. The management and control layer respectively evaluates the influence on the network safety and reliability from the aspects of the timeliness and the effect of the network defect elimination, the overhaul condition, the personnel post, the training and the like. The management and control level indexes are divided into equipment deficiency elimination timeliness rate and completion rate C41, overhaul plan completion rate C42, personnel training condition C43, personnel allocation completeness C44 and management procedure completeness C45.
And 2) collecting and collecting certain power communication backbone data network operation data, statistical data of power communication department operation maintenance, fault reports and the like as sample data through intelligent measurement equipment and an information communication technology. And then, carrying out hierarchical structure division on the operation data of the power communication data network by referring to a power communication backbone data network safety index system.
And 3) obtaining a fuzzy relation matrix (evaluation matrix) R by using a fuzzy comprehensive evaluation theory. The method comprises the following specific steps:
3.1) establishing a safety factor set u of the power communication backbone network;
the fuzzy evaluation method divides factors influencing the safety of the power communication backbone network into 3 layers according to a certain rule, wherein the first layer is a target layer, and an evaluation target is u; the second layer is a content layer with an evaluation content of ui(ii) a The third layer is a factor layer uijEach evaluation has various evaluation factors, and the step 1) can know that the primary evaluation factor u is the safety factor of the power communication backbone data networki(i-1, 2) corresponds to 4 primary indexes, a secondary evaluation factor uij(j ═ 1,2, …, 5) corresponds to 5, 4, 3 secondary indices, respectively;
3.2) determining a security level comment set of the power communication backbone data network;
the embodiment expresses the safety state of the power communication backbone data network by the following 5 grades and provides corresponding treatment measures
TABLE 1 Power communication backbone data network Security State description
And describes the security risk evaluation levels of the power communication backbone data network, wherein the risk levels correspond to the security states of the power communication backbone data network one by one, as shown in table 2,
table 2 power communication backbone data network risk level description
In this embodiment, the security status of the power communication backbone network and the corresponding risk level thereof are classified into 5 levels: emergency/risk is high, critical operation/risk is high, armed operation/medium risk, controlled safety/risk is low, safe operation/risk is low. Respectively corresponding to V1, V2, V3, V4 and V5. The 5 levels respectively describe the difference of the safety of each power communication backbone data network.
3.3) constructing a membership function model;
fuzzifying a finite set u of evaluation factors by a membership function to obtain membership degrees on [0,1], representing the degree of the set belonging to the fuzzy relation, selecting and using small-bias type and large-bias type expressions, and constructing the membership function corresponding to the evaluation set according to an evaluation grade quantization standard;
the small-sized indexes comprise unreasonable optical fiber use, access equipment fault rate, network equipment fault rate and unreasonable time slot distribution, and the small-sized membership function is as follows:
in the formula, a and b are reference values preset for each safety index when the safety index is measured, for example, a small-scale index indicates that the smaller the index is, the better the index is, so when the index is smaller than a, the membership degree of the index is 1, and the safety index is safe; when the index is too large and is larger than b, the membership degree is 0, which indicates that the index is unsafe; the following larger scale indicators are also the same, but conversely, the larger the measurement indicator, the better;
the large-scale indexes comprise management rule completeness, personnel allocation completeness and line protection channel dualization rate; the partial membership function is:
3.4) establishing a fuzzy relation matrix (judgment matrix);
from one to anotherDetermining the degree of association of the evaluation object to the evaluation grade set based on the evaluation factors, and selecting each index u for the object to be evaluatedi(i-1, 2, …, p), i.e. determining the degree of membership (R | u) of the evaluated object to the rank-fuzzy subset from a single indexi) And further obtaining a fuzzy relation matrix:
the ith row and jth column element R in the matrix RijThe expression "R" represents the result of the evaluation of the ith security index, and is referred to as a single-factor fuzzy evaluation set. Substituting data monitored by the power communication backbone network safety index for multiple times into each corresponding membership function formula to obtain membership degrees, wherein each membership degree corresponds to one power communication backbone network safety grade, and the proportion of each grade is taken as rijA value of (d); the relationship between the network security level and the degree of membership is as follows:
TABLE 3 corresponding relationship between network security level and membership range
Five-index fuzzy relation matrix R of criterion layer C11As follows:
similarly, the fuzzy relation matrix R of the layers C2, C3 and C4 is aligned2、R3、R4Comprises the following steps:
and 4) determining the security index attribute weight of the power communication backbone data network by using the AHP. Firstly, a judgment matrix of index elements at the same level is established, and 4 primary indexes and 19 secondary indexes are subjected to level single sequencing according to the importance degree according to the influence of each index on the safety of the power communication backbone data network. Comparing every two of the two, respectively representing the same importance, more importance, obvious importance, strong importance and extreme importance by adopting 5 scales of 1, 3, 5, 7, 9 and the like according to the importance degree, 2, 4, 6 and 8 representing the intermediate values between two adjacent judgment values, constructing the judgment matrix of each layer weight, and calculating the maximum eigenvalue lambdamaxAnd carrying out consistency check, and determining subjective weight parameter distribution after passing the check.
The analytic hierarchy process includes the following steps:
4.1) constructing a hierarchical architecture model of an index system;
decomposing the provided typical scene adjustment demand index system into a target layer, a first-level index and a second-level index, wherein the index system is divided into three layers from top to bottom based on the idea of an analytic hierarchy process, and the three layers are sequentially as follows:
1. target layer/zero level refers to: overall indexes of power communication backbone data network safety evaluation;
2. criterion layer/level one indicator: determining an integral framework and an evaluation angle of an evaluation index system;
3. index layer/secondary index: the specific evaluation indexes corresponding to the planning scheme can be directly applied to the calculation of the scheme layer;
4.2) constructing a judgment matrix of each level index;
in a power communication backbone data network safety index system, according to the comparison of the index of an index layer to the importance degree of a target layer, a pairwise comparison method and a nine-level scale method are adopted to assign values to elements of a judgment matrix, and the obtained judgment matrix is as follows:
by comparing the importance of the ith element and the jth element, the relative importance degree is quantified as aijIf the number of elements to be compared is n, the evaluation matrix can be expressed as a ═ a (a)ij)n×nWherein
aijThe values are assigned according to a 1-9 scale method, the elements in each layer are compared pairwise, and the relatively important elements and the important degrees thereof are judged, and the important degrees can be represented by corresponding numerical values, as shown in table 4.
TABLE 4 meanings of the scales in the analytic hierarchy Process
4.3) judging the consistency check of the matrix;
because the judgment matrix reflects the subjective judgment value and has a certain difference with the importance degree of the objective value of the index, the judgment matrix needs to be subjected to consistency check, and the calculation formulas of the consistency index CI and the consistency ratio CR of the judgment matrix are respectively as follows:
CI＝(λmax-n)/(n-1) (9)
CR＝CI/RI (10)
in the formula: lambda [ alpha ]maxJudging the maximum eigenvalue of the matrix; RI is an average random consistency index, RI is only related to n, the larger the value of CI is, the worse the consistency of the judgment matrix is, when CR is less than 0.1, the judgment matrix can be considered to meet the consistency requirement, otherwise, the mutual importance degree of each index is adjusted until the consistency passes the consistency inspection;
4.4) determining subjective weight;
the judgment matrix is processedNormalization processing, calculating the sum of each column element
Calculating each element in the current column SjTo obtain normalized new elements
Further obtaining a judgment moment after normalization processing;
calculating the sum of each line element
Weight of each element
By the weight number WiThe constituent factor weight set W is a fuzzy subset on the factor set U, and may be represented as W ═ W with a fuzzy vector1，w2，w3，…，wn]；
Providing a layer A comprising A1，A2，…，AmM factors, the total hierarchical ranking weight is a1，a2，…，amThe next layer B includes n factors B1，B2，…，BnThen with respect to AjThe hierarchical order of the order is b1j，b2j，…，bnjWhen B is presentiAnd AjWhen not relevant, bIjWhen the total target weight is 0, the weight of each factor in the B layer is obtained, namely the total hierarchical ranking weight B of each factor in the B layer is obtained1，b2，…，bnThe calculation is as shown in Table 5, i.e.
Table 5 Total order composite Table
The consistency check is also carried out on the total sequence of the layers, and the layer B is set to be consistent with the layer AjThe pair-wise comparison judgment matrix of related factors is subjected to consistency check in the single-rank order to obtain single-rank order consistency indexes of CI (j), (j is 1, …, m), and corresponding average random consistency indexes of RI (j), CI (j), and RI (j) which are obtained when the single-rank order is carried out, so that the proportion of the total sorting random consistency of the B layer is
When CR <0.10, the overall ranking result is considered to have more satisfactory consistency and the analysis result is accepted.
The primary index decision matrix is shown in table 6.
TABLE 6 determination matrix of first-level index Ci for target layer C
The consistency was checked to obtain a CR <0.1, and the consistency check passed, further weighing the primary index, as shown in table 7:
TABLE 7 first-level index weight values
And respectively calculating the weighted values of the secondary indexes according to the calculation steps of the weighted values of the primary indexes in sequence, and obtaining that all the secondary indexes pass consistency check. The subjective weights and ranks of the indices of each level are shown in table 8:
TABLE 8 subjective weighting and ranking of the hierarchy indices
Step 5) Objective weighting ω according to the definition of the Rough Set (RST)αiThe determination steps are as follows:
5.1) discretizing continuous data;
selecting an equal-width discretization method, wherein the value interval after condition index discretization is as follows:
wherein
Indicates the length of the interval, max (l)xy) Indicating index cxyMaximum value of (1), minxy) Indicating index cxyAnd m represents the set number of discretization intervals;
5.2) constructing a decision table;
after a large amount of evaluation target data is collected, discretization is performed on each attribute value to form a two-dimensional knowledge expression system, and the result after data discretization is expressed by using a decision table S { (U, A, V, f). U is a non-empty finite set of objects called a discourse field, with U ═ U1，U1，…UmRepresents a set of evaluation objects, a is an attribute totality, a ═ C ≡ D, and
c is a conditional attribute, D is a decision attribute, with C ═ C1，C1，…CmRepresents a condition attribute evaluation index set, wherein Cx(x is 1,2, …, z) as the primary index attribute, and if the primary index contains a secondary index attribute, C may be usedxy＝{Cx1，Cx2，…，CxyDescription is carried out, and index y for evaluating the security level is a decision attribute, the decision attribute set is D ═ y }, and V ═ U { }, thena∈AVaIs a value range, VaRepresenting a set of objects with the same attribute a in the domain of discourse U; setting each element in V ═ 1,2,3,4 and 5 as 5 state levels of emergency state, dangerous operation, alert operation, controlled safety and safe operation respectively by referring to a grading mode for the safety of the power communication data backbone network; f: UxAa→VaIs an information function, then
5.3) normalizing to obtain attribute weight;
knowledge dependence:
posp(Q) is a set composed of elements in U that is judged to definitely belong to Q from knowledge P, γp(Q) is the dependency level of knowledge Q on knowledge P, also called the approximate quality, support or classification quality of Q with respect to P, and has a value range of [0, 1%]When γ is 1, it means that knowledge Q completely depends on knowledge P, and when γ is 0, that is, it means
When, knowledge Q is completely independent of knowledge P;
the degree of dependence of the decision index D on the condition index C is as follows:
wherein y isiThe method is characterized in that the method is used for evaluating the safety state and comprises the steps of emergency state, dangerous operation, warning operation, controlled safety and safe operation;
after a certain index is eliminated, the decision index D is used for judging the condition indexes C-CiDegree of dependence of (c):
importance of the ith index to the decision index:
5.4) normalizing to obtain attribute weight;
weighting factor of i-th condition index:
dividing the universe of discourse set U into different equivalence classes by using a rough set, and solving ciSigma ofCD(ci) And is to σCD(ci) Normalization processing to obtain objective weight ωαi. Similarly, a decision system of each index value relative to the previous level is determined, and the rough set theory is utilized to solve, so that the objective weight value of each index value at the same level is obtained.
TABLE 9 Objective weight and ranking of the hierarchy indices
Step 6) the AHP method and the rough set method are effectively combined, the proportion of the host weight and the guest weight is optimized, and the error of the decision system in the state evaluation process is minimized, and the specific process is as follows:
6.1) establishing an optimization model;
setting omega in decision system SαiFor subjective weighting by AHP, ωαiIs an objective weight, omega, obtained by a rough set theory methodiIs an optimized combination of the two weights, wherein
Build an optimization model namely
In the formula (I), the compound is shown in the specification,
6.2) determining the coefficient mu;
the optimization model has a unique solution in the feasible domain Ω:
ωi＝μωai+(1-μ)ωαi，i＝1，2，…，m (19)
by applying the optimization weight combination theory, the subjective and objective weights are optimized according to the above formula, and when μ is solved to be 0.382, the above formula can be changed to:
ωi＝0.382×ωai+(1-0.382)×ωαi (20)
by the formula omegai＝0.382×ωai+(1-0.382)×ωαiObtaining the optimized weight omega of each safety index of the power communication backbone networki。
Table 10 subjective/objective combination weights and rankings of hierarchical indices
And 7) synthesizing the fuzzy relation matrix R and each safety index weight w to obtain a safety fuzzy comprehensive evaluation result B of the power communication backbone network.
From Bi＝WiRi＝(bi1，bi2，bi3，bi4，bi5) In the formula: b isiIs the evaluation result of the subsystem i; wiThe fuzzy weight vector of the subsystem i is formed by the secondary index weight of the subsystem i; riAn evaluation matrix of the subsystem i is determined by membership functions of all indexes; we obtained the evaluation results of the index layer C1, as shown in the formula:
from B2＝W2R2We obtained the evaluation results of the index layer C2 as shown in the formula:
from B3＝W3R3We obtained the evaluation results of the index layer C3, as shown in formula:
from B4＝W 44, we obtained the evaluation result of the index layer C4, as shown in the formula:
b is to be1、B2、B3、B4The matrices of (a) are combined to form an overall ambiguity relationship matrix, as shown in equation 9:
by
Obtaining:
and step 8) converting the fuzzy comprehensive evaluation result B into a corresponding safety state grade according to a maximum membership rule and a safety grade evaluation set V of the power communication backbone data network, wherein the maximum element 0.372 in the B belongs to the grade of V4, so that the safety risk evaluation grade of the power communication data network A is judged to be low and is in a controlled safety state. This indicates that the network may be controlled and cause less economic loss, although there is a possibility of risk occurrence, and regulatory measures are required for prevention and monitoring. The result accords with the actual safety condition of the power communication backbone data network in the city A, and further proves the scientificity of the AHP-RST-based power communication backbone data network safety comprehensive evaluation method.
The embodiment establishes a comprehensive evaluation method for the safety of the power communication backbone data network based on the AHP-RST, the evaluation method is beneficial to carrying out risk management and control on the power communication network, and can effectively solve the problems of possible factor uncertainty and excessive dependence on subjective experience, thereby carrying out comprehensive evaluation of subjective and objective combination on the safe operation state of the power communication backbone data network; in addition, the index system provided by the invention comprehensively reflects a plurality of key safety factors of the data network, makes a preliminary attempt on paying attention to artificial safety and exploring a new idea of electric power safety evaluation, can provide quantitative basis and basic data for planning, building and optimizing the electric power communication network, and has theoretical research value and engineering guidance significance.
It should be understood that the examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
Claims (8)
1. A safety comprehensive evaluation method for a power communication backbone data network based on an AHP-RST is characterized by comprising the following steps:
1) constructing a power communication backbone data network security assessment index system;
2) collecting relevant data of the power communication backbone network, statistical data of operation maintenance of a power communication department, fault reports and the like, and classifying the data according to the types of the data;
3) processing the acquired data of the power communication backbone network by using a fuzzy comprehensive evaluation theory to obtain a fuzzy relation matrix R;
4) determining subjective weight of a power communication backbone data network security index;
5) determining objective weight of the network security index of the power communication backbone data;
6) determining the optimal combination weight of the safety indexes of the power communication backbone data network;
7) synthesizing the fuzzy relation matrix R and each safety index weight w to obtain a safety fuzzy comprehensive evaluation result B of the power communication backbone network;
8) and converting the fuzzy comprehensive evaluation result B into a corresponding state grade according to a maximum membership principle and the electric power communication backbone data network safety state comment set V.
2. The AHP-RST-based power communication backbone data network security comprehensive assessment method as claimed in claim 1, wherein in step 1), by analyzing power communication backbone network data security situation influence factors, a data network security assessment index system is constructed from equipment, environment, network, management and control, and the index hierarchy is divided into three levels.
3. The method for comprehensively evaluating the safety of the AHP-RST-based power communication backbone data network according to claim 1, wherein in the step 3), a fuzzy relation matrix R is obtained by using a fuzzy comprehensive evaluation theory on the processing of the collected power communication backbone network data, and the specific steps are as follows:
3.1) establishing a safety factor set u of the power communication backbone network;
dividing factors influencing the safety of a backbone network of the power communication network into 3 layers by a fuzzy evaluation method of a safety factor set u of the backbone network of the power communication network, wherein the first layer is a target layer and an evaluation target is u; the second layer is a content layer with an evaluation content of ui(ii) a The third layer is a factor layer uijEach evaluation has a respective evaluation factor;
3.2) determining a security level comment set of the power communication backbone data network;
the safety state of the power communication backbone data network is represented by 5 levels, and corresponding treatment measures are taken:
the safety state is safe operation, and the processing measures are normal monitoring;
the safety state is controlled safety, and the treatment measures are regulatory measures;
the safety state is alert operation, and the processing measures are to take preventive measures in time, strengthen monitoring and send early warning signals;
the safety state is dangerous operation, and the treatment measures are that related counter measures must be implemented and monitoring is strengthened;
the safety state is an emergency state, and the processing measures are various correction, stabilization and recovery control, so that the power communication network is recovered to a normal state as far as possible, and the monitoring is enhanced to avoid cascading failures;
and describing the security risk evaluation level of the power communication backbone data network, wherein the risk level corresponds to the security state of the power communication backbone data network one by one and is expressed as follows:
the risk grades are D1/very low, D2/low, D3/medium, D4/high and D5/very high respectively, the corresponding risk description is gradually aggravated along with the risk events caused by the improvement of the risk grades, and the economic loss caused by the gradual aggravation is more and more serious;
the safety state of the power communication backbone network and the corresponding risk level are divided into 5 levels: the emergency state/risk is very high, the dangerous operation/risk is high, the warning operation/intermediate risk, the controlled safety/risk is low, the safe operation/risk is very low, the emergency state/risk corresponds to V1, V2, V3, V4 and V5 respectively, and 5 grades respectively describe the difference of the safety of each power communication backbone data network;
3.3) constructing a membership function model;
fuzzifying a finite set u of evaluation factors by a membership function, changing the finite set u into a membership degree on [0,1], representing the degree of the fuzzy relation of the set, selecting and using small-bias type and large-bias type expressions, and constructing a membership function corresponding to an evaluation set V which is { VV1, V2, V3V4 and V5} according to an evaluation grade quantization standard;
the small-sized indexes comprise unreasonable optical fiber use, access equipment fault rate, network equipment fault rate and unreasonable time slot distribution, and the small-sized membership function is as follows:
wherein, a and b are preset reference values of each safety index;
the large-scale indexes comprise management rule completeness, personnel allocation completeness and line protection channel dualization rate; the partial membership function is:
3.4) establishing a fuzzy relation matrix;
determining the degree of association of the evaluation object to the evaluation grade set from an evaluation factor, and gradually selecting each index u for the evaluated objecti(i-1, 2, …, p) and determining the degree of membership (R | u) of the evaluated object to the rank-fuzzy subset from a single indexi) And further obtaining a fuzzy relation matrix:
the ith row and jth column element R in the matrix RijExpressing the association degree of the jth grade of the ith safety index, expressing the evaluation result of the ith safety index by R, taking the matrix R as a single-factor fuzzy evaluation set, substituting data monitored for multiple times by the safety index of the power communication backbone network into each corresponding membership function formula to obtain membership degrees, wherein each membership degree corresponds to one safety grade of the power communication backbone network, and taking the proportion of each grade as RijA value of (d); the relationship between the network security level and the degree of membership is as follows:
the safety level is V1, and the belonging range of the membership degree is [0,0.2 ];
the safety level is V2, and the membership degree belongs to the interval of [0.2,0.4 ];
the safety level is V3, and the belonging interval of the membership degree is [0.4,0.6 ];
the safety level is V4, and the belonging interval of the membership degree is [0.6,0.8 ];
the safety level is V5, and the membership degree belongs to the interval of [0.8,1.0 ].
4. The method as claimed in claim 1, wherein in step 4), the subjective weight ω of each index is obtained by an analytic hierarchy process AHPaiDetails of the analytic hierarchy ProcessThe method comprises the following steps:
4.1) constructing a hierarchical architecture model of an index system;
decomposing the provided typical scene adjustment demand index system into a target layer, a first-level index and a second-level index, wherein the index system is divided into three layers from top to bottom based on the idea of an analytic hierarchy process, and the three layers are sequentially as follows:
1. target layer/zero level refers to: overall indexes of power communication backbone data network safety evaluation;
2. criterion layer/level one indicator: determining an integral framework and an evaluation angle of an evaluation index system;
3. index layer/secondary index: the specific evaluation indexes corresponding to the planning scheme can be directly applied to the calculation of the scheme layer;
4.2) constructing a judgment matrix of each level index;
in a power communication backbone data network safety index system, according to the comparison of the index of an index layer to the importance degree of a target layer, a pairwise comparison method and a nine-level scale method are adopted to assign values to elements of a judgment matrix, and the obtained judgment matrix is as follows:
by comparing the importance of the ith element and the jth element, the relative importance degree is quantified as aijAssuming that the number of elements to be compared is n, the evaluation matrix can be expressed as
Wherein
aijThe values are assigned according to a 1-9 scale method, the elements in each layer are compared pairwise, and the relatively important elements and the important degrees thereof are judged, wherein the important degrees can be represented by corresponding numerical values as follows:
aij1, 3, 5, 7, 9, denoted as the ith fingerThe mark and the jth index are more and more important as the number increases;
aij2, 4, 6 and 8, which are intermediate values of the adjacent judgment;
aijis reciprocal and is expressed as
4.3) judging the consistency check of the matrix;
because the judgment matrix reflects the subjective judgment value and has a certain difference with the importance degree of the objective value of the index, the judgment matrix needs to be subjected to consistency check, and the calculation formulas of the consistency index CI and the consistency ratio CR of the judgment matrix are respectively as follows:
CI＝(λmax-n)/(n-1)
CR＝CI/RI
in the formula: lambda [ alpha ]maxJudging the maximum eigenvalue of the matrix; RI is an average random consistency index, RI is only related to n, the larger the value of CI is, the worse the consistency of the judgment matrix is, when CR is<When the time is 0.1, the judgment matrix can be considered to meet the requirement of consistency, otherwise, the mutual importance degree of each index is adjusted until the consistency test is passed;
4.4) determining subjective weight;
normalizing the judgment matrix, and calculating the sum of each row of elements
Calculating each element in current column SjTo obtain normalized new elements
Further obtaining a judgment moment after normalization processing;
calculating the sum of each line element
Weight of each element
By the weight number WiThe component factor weight set W is a fuzzy subset on the factor set U, and is represented as W ═ W by a fuzzy vector1,w2,w3,…,wn]；
Providing a layer A comprising A1,A2,…,AmM factors, the total hierarchical ranking weight is a1,a2,…,amThe next layer B includes n factors B1,B2,…,BnThen with respect to AjThe hierarchical order of the order is b1j,b2j,…,bnjWhen B is presentiAnd AjWhen not relevant, bIjWhen the total target weight is equal to 0, the weight of each factor in the B layer is calculated
Shown;
the consistency check is also carried out on the total sequence of the layers, and the layer B is set to be consistent with the layer AjThe pair-wise comparison judgment matrix of related factors is subjected to consistency check in the single-rank order to obtain single-rank order consistency indexes of CI (j), (j is 1, …, m), and corresponding average random consistency indexes of RI (j), CI (j), and RI (j) which are obtained when the single-rank order is carried out, so that the proportion of the total sorting random consistency of the B layer is
When CR <0.10, the overall ranking result is considered to have accurate consistency and the analysis result is accepted.
5. The method as claimed in claim 1, wherein in step 5), the objective weight ω of each index is obtained from the rough set RSTαiThe rough set comprises the following specific steps:
5.1) discretizing continuous data;
selecting an equal-width discretization method, wherein the value interval after condition index discretization is as follows:
wherein
Indicates the length of the interval, max (l)xy) Indicating index cxyMaximum value of (d), min (l)xy) Indicating index cxyAnd m represents the set number of discretization intervals;
5.2) constructing a decision table;
after a large amount of evaluation target data is collected, discretization is performed on each attribute value to form a two-dimensional knowledge expression system, and the result after data discretization is expressed by using a decision table S { (U, A, V, f). U is a non-empty finite set of objects called a discourse field, with U ═ U1,U1,…UmRepresents a set of evaluation objects, a is an attribute totality, a ═ C ≡ D, and
c is a conditional attribute, D is a decision attribute, with C ═ C1,C1,…CmRepresents a condition attribute evaluation index set, wherein Cx(x is 1,2, …, z) as the primary index attribute, and if the primary index contains a secondary index attribute, C may be usedxy＝{Cx1,Cx2,…,CxyDescription is carried out, and index y for evaluating the security level is a decision attribute, the decision attribute set is D ═ y }, and V ═ U { }, thena∈AVaIs a value range, VaRepresenting a set of objects with the same attribute a in the domain of discourse U; setting each element in V ═ 1,2,3,4 and 5 as 5 state levels of emergency state, dangerous operation, alert operation, controlled safety and safe operation respectively by referring to a grading mode for the safety of the power communication data backbone network; f is UxAa→VaIs an information function, then
5.3) calculating attribute dependency;
the expression is as follows:
posp(Q) is a set composed of elements in U that is judged to definitely belong to Q from knowledge P, γp(Q) is the dependency level of knowledge Q on knowledge P, also called the approximate quality, support or classification quality of Q with respect to P, and has a value range of [0, 1%]When γ is 1, it means that knowledge Q completely depends on knowledge P, and when γ is 0, that is, it means
When, knowledge Q is completely independent of knowledge P;
the degree of dependence of the decision index D on the condition index C is as follows:
wherein y isiThe method is characterized in that the method is used for evaluating the safety state and comprises the steps of emergency state, dangerous operation, warning operation, controlled safety and safe operation;
after a certain index is eliminated, the decision index D is used for judging the condition indexes C-CiDegree of dependence of (c):
importance of the ith index to the decision index:
5.4) normalizing to obtain attribute weight;
weighting factor of i-th condition index:
6. the method as claimed in claim 1, wherein in step 6), the AHP method and the rough set method are effectively combined, and the ratio of the primary and secondary weights is optimized, so that the error of the decision system in the state evaluation process is minimized, and the specific process is as follows:
6.1) establishing an optimization model;
setting omega in decision system SαiFor subjective weighting by AHP, ωαiIs an objective weight, omega, obtained by a rough set theory methodiIs an optimized combination of the two weights, wherein
Build an optimization model namely
In the formula (I), the compound is shown in the specification,
6.2) determining the coefficient mu;
the optimization model has a unique solution in the feasible domain Ω:
ωi＝μωai+(1-μ)ωαi，i＝1,2,…,m
by applying the optimization weight combination theory, the subjective and objective weights are optimized according to the above formula, and when μ is solved to be 0.382, the above formula can be changed to:
ωi＝0.382×ωai+(1-0.382)×ωαi。
7. the method as claimed in claim 1, wherein the step 7) of obtaining the fuzzy comprehensive evaluation result B of the security of the backbone network for power communication is as follows:
7.1) synthesizing w with R of each evaluated object, and determining fuzzy comprehensive evaluation results of each subsystem by using the formula:
Bi＝WiRi＝(bi1,bi2,bi3,bi4,bi5)
in the formula: b isiIs the evaluation result of the subsystem i; wiThe fuzzy weight vector of the subsystem i is formed by the secondary index weight of the subsystem i; riAn evaluation matrix of the subsystem i is determined by membership functions of all indexes;
7.2) after the subsystem results are known, the safety condition of the power communication backbone network can be comprehensively evaluated, and the formula of the safety evaluation result B of the power communication backbone network is obtained as follows:
in the formula: the comprehensive evaluation vector B is an overall evaluation result of the quality of a certain power communication network, and the comprehensive evaluation matrix can be converted into a power communication network quality score; a fuzzy weight vector W, which is composed of the weights of 4 primary indexes; the comprehensive fuzzy evaluation matrix is composed of evaluation results Bi of all subsystems.
8. The AHP-RST-based integrated assessment method for security of power communication backbone data networks according to claim 1, wherein in step 8), the row with the largest value is found according to the maximum membership criterion from the integrated assessment result of security of power communication backbone network obtained in step 7), and the network security level of the row is determined, thereby obtaining the assessment level of security of power communication backbone data networks.
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