CN109117651B - Metering data safety protection method - Google Patents

Abstract

The invention discloses a metering data security protection method, which includes: grading the meters of users, charging piles and bidirectional metering users, and using the user's metering data as a block chain label node alone; The pile contract adopts hierarchical security verification; it is classified according to voltage level, power supply quality requirements and capacity; for users, the security domain method is used for verification; the transaction of direct supply to users and electric vehicle transactions that have passed the security domain assessment adopts blockchain smart contracts Conduct transactions; measure users, and use blockchain to mark and transport measurement data; use FAHP algorithm to analyze influencing factors, and then conduct overall network security assessment. The introduction of smart contracts running on the blockchain reduces the trust cost of electricity market transactions and improves the efficiency of clearing and settlement. At the same time, the security domain method is used to verify the network constraints and compliance of the contract, so as to realize the security assurance and intelligence of measurement and transaction. change.

Description

A kind of measurement data security protection method

technical field

The invention relates to the technical field of electric energy meter measurement, in particular to a measurement data safety protection method.

Background technique

Block Chain (BC) is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. The so-called consensus mechanism is a mathematical algorithm that realizes the establishment of trust between different nodes and the acquisition of rights and interests in the blockchain system.

The Security Region (SR) method is a new method developed on the basis of the point-by-point method. It considers the problem from the point of view of the domain, and describes the overall safe and stable operation area.

Metering is one of the basic functions of the power grid. Metering is to record the amount of electricity (electric energy) used, as the basis for charging, there are high-voltage metering and low-voltage metering, direct meter metering and metering via a transformer. The core device of metering is the watt-hour meter. At present, electricity suppliers generally provide metering services, but with the development and application of technologies such as smart grids, microgrids, and distributed energy, as well as the advancement of power industry reforms, the authority of metering increasingly requires authoritative third parties and new technology and safety mechanisms to ensure.

At present, direct supply users often only rely on grid metering and endorsement through the credit of grid companies, which is not suitable for the security management of metering identity and metering data in the future multi-subject and multi-mode power market.

At present, electric vehicles and social charging piles lack each other, and there is a lack of measurement methods that both parties trust. On the other hand, a large number of electric vehicle loads will threaten and impact the safe and stable operation of the power grid. It is necessary to timely check according to the regulations during the transaction, so as to realize the contract and charge and discharge.

The main disadvantages are:

(1) It is not considered that the data of many electricity meters are collected by power grid companies, lacking third-party certification, and for a large number of users such as electric vehicles, the reliability of the collection is relatively low.

(2) The method of integrating the security domain is not adopted, and only the basic matching is performed for users, and the lack of awareness of the security boundary may cause influence and impact on the stability of the power grid.

The two-way metering of new energy, the energy consumption metering of large users, and the metering of multi-network integration based on the power grid all require a new security mechanism to ensure, and for smart contracts, electric vehicles, and the sharing of charging piles with social forces are not only required. Trusted metering also needs to meet the operational constraints of the power grid. Therefore, a metering security method integrating blockchain and security domain technology is proposed.

SUMMARY OF THE INVENTION

In view of the above-mentioned defects of the prior art, the purpose of the present invention is to provide a measurement data security protection method, which can realize the smart contract directly supplied to the user, the smart contract of the charging pile and the electric vehicle based on the blockchain technology. The electricity trading market at both ends has the characteristics of multi-subject, multi-mode and multi-rule. The security method of electricity market transaction smart contract is designed for users such as large users and electric vehicles. At the same time, the key technical difficulties are analyzed, and targeted solutions are given. Program. It is proposed to introduce smart contracts running on the blockchain to reduce the trust cost of electricity market transactions and improve the efficiency of clearing and settlement. At the same time, the security domain method is used to verify the network constraints and compliance of the contract, so as to realize the security guarantee of measurement and transaction. Intelligent.

The purpose of the present invention is to realize through such technical scheme, a kind of metering data security protection method, it comprises:

S1: Classify the meters of users, charging piles, and two-way metering users, and classify high-level users (electricity metering device for trade settlement of 220kV and above, electric energy metering device for assessment of 500kV and above, and single-unit capacity of 300MW and above to generate electricity The metering data of the electric energy metering device for electromechanical power generation is used as a block chain tag node alone, and the low-level users (electric energy metering device for trade settlement of 10kV-220kV and above, electric energy metering device for assessment of 10kV-500kV level and above, Electric energy metering devices that measure the power generation of generators with a single unit capacity of less than 300MW and the power consumption of power generation enterprises (stations), 380V-10kV energy metering devices, 220V single-phase energy metering devices) The metering data is integrated according to the network topology as a blockchain tag node;

S2: Hierarchical security check is adopted for user power supply contracts and social charging pile contracts; according to voltage level, power supply quality requirements and capacity classification; for high-level users, the security domain method is directly used for verification; for low-level users, it is integrated according to the network topology After being used as a virtual user, check it through the security domain method;

S3: Use blockchain smart contracts for transactions of user-directed supply and electric vehicles that have passed the security domain assessment;

S4: Measure users, and the measurement data is marked and transported by blockchain;

S5: Use FAHP algorithm (Fuzzy Analytic Hierarchy Process) to analyze influencing factors, and then conduct overall network security assessment.

Further, the specific steps of the security domain method in the step S2 are as follows:

S21: Establish a private/hybrid blockchain platform to provide data security and basic capability support for smart contracts;

S22: Mark, securely store and transmit metering data; perform security check on related transactions;

S23: Perform security domain analysis;

S24: Analyze the influencing factors and give a transaction decision transaction.

Further, in the step S5, the specific steps of using the FAHP algorithm to analyze the influencing factors are as follows:

S51: Determine the target of the influencing factor system;

S52: Establish a hierarchy model of influencing factors;

S53: construct the fuzzy complementary judgment matrix R of the influencing factor indexes;

S54: Consistency test and adjustment are performed on the fuzzy judgment matrix R;

S55: Calculate the weight of each layer element in the hierarchical structure model to the upper layer element, and perform hierarchical sorting to determine the importance of each element at the bottom in the hierarchical structure diagram in the overall objective of the influencing factor system.

Further, the specific steps of establishing the influencing factor hierarchy model in the step S52 are as follows:

S521: Suppose the evaluation target G of the influencing factor system has n influencing factors, then the factor set is U={U1, U2, . . . , Un};

S522: The ith subset Ui of the evaluation target system U satisfies the condition: Ui={Ui1, Ui2, . . . , Uin}.

Further, the specific steps of performing consistency check and adjustment on the fuzzy judgment matrix in the step S54 are as follows:

S541: Determine the index ρ of the additive consistency degree of the fuzzy complementary judgment matrix R;

S542: Judging whether the fuzzy complementary matrix R has the additive consistency that meets the target requirements according to the value test of ρ, and the larger the value of ρ is, the worse the additive consistency of the fuzzy complementary judgment matrix R is; wherein:

Further, the specific steps for adjusting consistency are as follows:

S543: Find the additive consistency index value ρ of the fuzzy complementary judgment matrix R, if it is less than the preset threshold σ, then go to step S549, otherwise, go to step S544;

S544: Construct n matrixes of additive consistency according to each row of the fuzzy complementary judgment matrix R respectively

R^(k)表示根据模糊互补判断矩阵R的第k行构造的加性一致性矩阵；in,

R ^(k) represents the additive consistency matrix constructed according to the kth row of the fuzzy complementary judgment matrix R;

S545: Calculate the closeness of the fuzzy complementary judgment matrix R and the additive consistency matrix R ^(k) respectively

S546: Let α ^(l) =min{α ⁽¹⁾ ,α ⁽²⁾ ,...,α ⁽ⁿ⁾ }, determine the neutral and fuzzy complementarity in R ^(k) (k=1,2,3...n) Judgment matrix R is the closest additive consistency fuzzy complementary matrix R ^(l) , and denoted as R ^* ;

找出偏差矩阵C中使c_ij的绝对值达到最大的值i和j，分别记为s和t；S547: Find the deviation matrix C=( _ci j) _n×n of the judgment matrix R and R*, where

Find the values i and j that maximize the absolute value of c _ij in the deviation matrix C, and denote them as s and t respectively;

S548: If c _st >0, let r' _st =r _st -λ, r' _ts =r _ts +λ; if c _st <0, let r' _st =r _st +λ, r' _ts =r _ts - λ; Q where λ is an adjustment coefficient, generally taken as 0.05; denote this matrix as R, and go to step S543;

S549: End.

Further, the specific steps of the hierarchical sorting in the step S55 are as follows:

对矩阵R^*行和归一化得到单层排序向量w＝(w₁,w₂,…,w_n)^T，其中：S551: Set the fuzzy complementary judgment matrix R=(r _ij ) _n×n , sum the matrix R row by row, record

Normalize the matrix R ^* row sum to get the single-level sorting vector w=(w ₁ ,w ₂ ,...,w _n ) ^T , where:

S552: The combined weight vector of the kth layer to the first layer satisfies the following formula:

w ^(k) = W ^(k) w ^(k-1) , k = 3,4,...,s;

Wherein W ^(k) is a matrix composed of the weight vector of the kth layer to the k-1th layer as a column vector;

S553: The combined weight vector of the S-th layer to the top layer is:

w ^(s) = W ^(s) W ^(s-1) ... W ⁽³⁾ w ⁽²⁾ .

Further, the method also includes:

S6: Introduce the variable weight formula:

In the formula, w _i is the initial index weight, _xi is the corresponding index score value, and a ₁ , a ₂ , a ₃ , and a ₄ are empirical variable weight coefficients.

Further, the calculation formula of the state evaluation score S is:

Owing to adopting the above-mentioned technical scheme, the present invention has the following advantages:

(1) The blockchain technology can mark the information obtained by the metering device, and so the information has the characteristics that once it is generated, it cannot be modified or deleted. On the other hand, based on blockchain technology, smart contracts for direct supply to users, smart contracts for charging piles and electric vehicles can be realized.

(2) The smart grid can easily collect various data of electric energy meters at all levels, which provides the possibility to improve the accuracy of grid state estimation and reliability. The energy meter clusters in the grid generally form a tree topology. In the power grid, there are often losses (electric energy meter loss, leakage loss, line resistance loss, etc.). In order to be consistent with the operation of the actual system, a virtual branch is introduced to improve the estimation accuracy, and the branch includes a virtual meter and a virtual load. Using this method, the total loss of the meter cluster can be equivalent to the energy consumption of the virtual load, the general system security domain model can be extended to support the state estimation of the metering network, and the generalized security domain model that allows various losses in the system .

(3) Integrating the above technologies can provide data security guarantee for metering data, and can provide security inspection and compliance inspection for smart contracts to comply with grid operation constraints. In addition, the metering reliability of social charging piles and the sharing of charging piles among users will greatly reduce construction costs and reduce grid construction investment.

(4) In view of the multi-subject, multi-mode and multi-rule characteristics of the power trading market that will be opened at both ends in the future, a smart contract security method for power market transactions is designed for users such as large users and electric vehicles, and the key technical difficulties are analyzed at the same time. Targeted solutions are given. It is proposed to introduce smart contracts running on the blockchain to reduce the trust cost of electricity market transactions and improve the efficiency of clearing and settlement. At the same time, the security domain method is used to verify the network constraints and compliance of the contract, so as to realize the security guarantee of measurement and transaction. Intelligent.

Other advantages, objects, and features of the present invention will be set forth in the description that follows, and will be apparent to those skilled in the art based on a study of the following, to the extent that is taught in the practice of the present invention.

Description of drawings

The accompanying drawings of the present invention are described as follows:

FIG. 1 is a schematic flowchart of a method for security protection of metering data.

Fig. 2 is the principle block diagram of FAHP.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and embodiments.

Embodiment 1: as shown in Figure 1 and Figure 2; a method for measuring data security protection, which includes:

S1: Classify the meters of users, charging piles, and two-way metering users, use the metering data of high-level users as blockchain label nodes alone, and integrate the metering data of low-level users according to the network topology as blockchain label nodes The high-level users refer to: 220kV and above electric energy metering devices for trade settlement, 500kV and above assessment energy metering devices, or electric energy metering devices for measuring the power generation of generators with a single unit capacity of 300MW and above. The low-level users refer to: 10kV-220kV and above power metering devices for trade settlement, 10kV-500kV and above power metering devices for assessment, measuring the power generation of generators with a single unit capacity of less than 300MW, and power generation enterprises (stations) Electric energy metering device, 380V-10kV energy metering device or 220V single-phase energy metering device

S2: Hierarchical security check is adopted for user power supply contracts and social charging pile contracts; according to voltage level, power supply quality requirements and capacity classification; for high-level users, the security domain method is directly used for verification; for low-level users, it is integrated according to the network topology After being used as a virtual user, check it through the security domain method;

S3: Use blockchain smart contracts for transactions of user-directed supply and electric vehicles that have passed the security domain assessment;

S4: Measure users, and the measurement data is marked and transported by blockchain;

S5: Use FAHP algorithm (Fuzzy Analytic Hierarchy Process) to analyze influencing factors, and then conduct overall network security assessment.

The specific steps of the security domain method in the step S2 are as follows:

S21: Establish a private/hybrid blockchain platform to provide data security and basic capability support for smart contracts;

S22: Mark, securely store and transmit metering data; perform security check on related transactions;

S23: Perform security domain analysis;

S24: Analyze the influencing factors and give a transaction decision transaction.

The specific steps of adopting the FAHP algorithm to analyze the influencing factors in the step S5 are as follows:

S51: Determine the target of the influencing factor system;

S52: Establish a hierarchy model of influencing factors;

S53: construct the fuzzy complementary judgment matrix R of the influencing factor indexes;

S54: Consistency test and adjustment are performed on the fuzzy judgment matrix R;

S55: Calculate the weight of each layer element in the hierarchical structure model to the upper layer element, and perform hierarchical sorting to determine the importance of each element at the bottom in the hierarchical structure diagram in the overall objective of the influencing factor system.

The specific steps of establishing the influencing factor hierarchy model in the step S52 are as follows:

S521: Suppose the evaluation target G of the influencing factor system has n influencing factors, then the factor set is U={U1, U2, . . . , Un};

S522: The ith subset Ui of the evaluation target system U satisfies the condition: Ui={Ui1, Ui2, . . . , Uin}.

The specific steps of performing consistency check and adjustment on the fuzzy judgment matrix in step S54 are as follows:

S541: Determine the index ρ of the additive consistency degree of the fuzzy complementary judgment matrix R;

S542: Judging whether the fuzzy complementary matrix R has the additive consistency that meets the target requirements according to the value test of ρ, and the larger the value of ρ is, the worse the additive consistency of the fuzzy complementary judgment matrix R is; wherein:

The specific steps to adjust the consistency are as follows:

S543: Find the additive consistency index value ρ of the fuzzy complementary judgment matrix R, if it is less than the preset threshold σ, then go to step S549, otherwise, go to step S544;

S544: Construct n matrixes of additive consistency according to each row of the fuzzy complementary judgment matrix R respectively

R^(k)表示根据模糊互补判断矩阵R的第k行构造的加性一致性矩阵；in,

R ^(k) represents the additive consistency matrix constructed according to the kth row of the fuzzy complementary judgment matrix R;

S545: Calculate the closeness of the fuzzy complementary judgment matrix R and the additive consistency matrix R ^(k) respectively

S546: Let α ^(l) =min{α ⁽¹⁾ ,α ⁽²⁾ ,...,α ⁽ⁿ⁾ }, determine the neutral and fuzzy complementarity in R ^(k) (k=1,2,3...n) Judgment matrix R is the closest additive consistency fuzzy complementary matrix R ^(l) , and denoted as R ^* ;

找出偏差矩阵C中使c_ij的绝对值达到最大的值i和j，分别记为s和t；S547: Find the deviation matrix C=(c _ij ) _n×n of the judgment matrix R and R*, where

Find the values i and j that maximize the absolute value of c _ij in the deviation matrix C, and denote them as s and t respectively;

S548: If c _st >0, let r' _st =r _st -λ, r' _ts =r _ts +λ; if c _st <0, let r' _st =r _st +λ, r' _ts =r _ts - λ; Q where λ is an adjustment coefficient, generally taken as 0.05; denote this matrix as R, and go to step S543;

S549: End.

The specific steps of the hierarchical sorting in the step S55 are as follows:

对矩阵R^*行和归一化得到单层排序向量w＝(w₁,w₂,…,w_n)^T，其中：S551: Set the fuzzy complementary judgment matrix R=(r _ij ) _n×n , sum the matrix R row by row, record

Normalize the matrix R ^* row sum to get the single-level sorting vector w=(w ₁ ,w ₂ ,...,w _n ) ^T , where:

S552: The combined weight vector of the kth layer to the first layer satisfies the following formula:

w ^(k) = W ^(k) w ^(k-1) , k = 3,4,...,s;

Wherein W ^(k) is a matrix composed of the weight vector of the kth layer to the k-1th layer as a column vector;

S553: The combined weight vector of the S-th layer to the top layer is:

w ^(s) = W ^(s) W ^(s-1) ... W ⁽³⁾ w ⁽²⁾ .

The method also includes:

S6: Introduce the variable weight formula:

In the formula, w _i is the initial index weight, _xi is the corresponding index score value, and a ₁ , a ₂ , a ₃ , and a ₄ are empirical variable weight coefficients.

The calculation formula of the state evaluation score S is:

Example 2: In view of the problems affecting large-scale metering security and transaction verification in the prior art, we analyzed the relevant factors and proposed some targeted technical means to solve the problem of insufficient data, numerous influencing factors, and interrelatedness. unfavorable conditions, and gives the following improved security measurement and transaction process:

As shown in Figure 1 and Figure 2; a measurement data security protection method, which includes:

S1: Classify the meters of users, charging piles, and two-way metering users, use the metering data of high-level users as blockchain label nodes alone, and integrate the metering data of low-level users according to the network topology as blockchain label nodes ; Step S1 grades the measurement data in order to reduce the computational complexity of the blockchain technology. Only specific large users and large-scale charging pile groups that have a great impact on the stability of the power grid are directly used as the object of blockchain processing. Other users, especially the charging pile metering data, aggregate data according to the network topology, and then perform blockchain tagging processing after aggregation. This reduces processing requirements by an order of magnitude.

S2: Hierarchical security check is adopted for user power supply contracts and social charging pile contracts; according to voltage level, power supply quality requirements and capacity classification; for high-level users, the security domain method is directly used for verification; for low-level users, it is integrated according to the network topology After being used as a virtual user, the security domain method is used for verification; in step S2, in order to further ensure system stability and power grid security, and at the same time reduce computing power requirements, a hierarchical method is also used for security verification. Only specific large users and large-scale charging pile groups that have a great impact on the grid stability are directly checked for the safety domain. Other users, especially the charging pile metering data, aggregate data according to the network topology, and then perform security check after aggregation. This reduces processing requirements by an order of magnitude.

Through the above-mentioned 2-level hierarchical processing, the estimated computing power requirements can be reduced by 2 orders of magnitude, so that real-time data protection can be achieved based on blockchain technology, and smart contracts can be processed in a timely manner.

S3: Use blockchain smart contracts for transactions of user-directed supply and electric vehicles that have passed the security domain assessment;

S4: Measure users, and the measurement data is marked and transported by blockchain;

S5: Use FAHP algorithm (Fuzzy Analytic Hierarchy Process) to analyze influencing factors, and then conduct overall network security assessment. In step S5, in order to analyze and obtain the key factors that mainly affect the state, the TOPSIS analysis algorithm establishes the index system required for FAHP to analyze the influencing factors: the index system of the influence state of the metering device itself and the index system of the network structure formed by the metering device.

The ideal metering data collection and social charging pile management should have a relatively sufficient security mechanism, comprehensively consider from the aspects of trusted data sources, smart contracts and grid security checks, and provide metering and new services for flexible and diverse electricity marketization in the future. Security analysis capabilities. For the measurement data security domain transaction assurance method with blockchain infrastructure and integrated security domain method, the following main steps can be used:

The specific steps of the security domain method in the step S2 are as follows:

S21: Establish a private/hybrid blockchain platform to provide data security and basic capability support for smart contracts;

S22: Mark, securely store and transmit metering data; perform security check on related transactions;

S23: Perform security domain analysis;

S24: Analyze the influencing factors and give a transaction decision transaction.

The specific steps of adopting the FAHP algorithm to analyze the influencing factors in the step S5 are as follows:

S51: Determine the target of the influencing factor system;

S52: Establish a hierarchy model of influencing factors;

S53: construct the fuzzy complementary judgment matrix R of the influencing factor indexes;

S54: Consistency test and adjustment are performed on the fuzzy judgment matrix R;

S55: Calculate the weight of each layer element in the hierarchical structure model to the upper layer element, and perform hierarchical sorting to determine the importance of each element at the lowest level in the hierarchical structure diagram in the overall objective of the influencing factor system.

(1) Establish a hierarchical structure model U

The establishment of the hierarchical structure model is the basis of the AHP. As mentioned above, the state management and evaluation system of the electric energy metering device with a five-layer tree structure actually constitutes a hierarchical structure model. The hierarchical structure of the evaluation system also determines that it is more appropriate to use AHP for weight calculation and analysis.

Assuming that the evaluation target G has n influencing factors, the factor set is U={U ₁ , U ₂ , . . . , Un}.

The i-th subset U _i of the evaluation index system U satisfies the condition: U _i ={U _i1 , U _i2 ,...,Uin}.

(2) Constructing the index weight judgment matrix R

The fuzzy analytic hierarchy process requires calculating the relative importance of the influence of the interrelated elements layer by layer, and quantifying them to form a fuzzy complementary judgment matrix as the basis of the analysis.

Taking the weight coefficient analysis of the indicator "operating state of electric energy meter" in the quality evaluation system as an example, this indicator includes three indicators: offline information, online information and important factors. The influence of state” and giving appropriate weights are issues that need to be addressed. In this example, we take the "operating state of the electric energy meter" as the analysis target G, and take the relative importance level between the specific influencing factors as the element r _ij constituting the judgment matrix, and the constructed matrix is called the influence element on the target G The judgment matrix is shown in Table 1.

Table 1 Fuzzy judgment matrix of elements affecting target G

The score x _k (k=1, 2, ... n) represents each influencing factor that constitutes the target G, and ri _ij represents the relative importance of the factor score x _i to the score x _j relative to G. The following table 2 shows the assignment criteria for the elements of the judgment matrix,

Table 2 assigns criteria to the elements of the judgment matrix

(3) Consistency inspection and adjustment

An index ρ is given to determine the degree of additive consistency of the judgment matrix. The value of ρ is used to test whether the judgment matrix has satisfactory additive consistency. The larger the value of ρ, the worse the additive consistency of the judgment matrix R. in,

Specific steps to adjust consistency:

Step 1: Find the additive consistency index value ρ of the matrix R, if ρ is less than the preset threshold σ, go to step 7, otherwise go to the next step;

Step 2: Construct n additively consistent matrices according to each row of R respectively

R^(k)表示根据R的第k行构造的加性一致性矩阵；in

R ^(k) represents the additive consistency matrix constructed according to the kth row of R;

Step 3: Calculate the closeness of R and R ^(k) separately

Step 4: Let α ^(l) =min{α ⁽¹⁾ ,α ⁽²⁾ ,...,α ⁽ⁿ⁾ }, determine R ^(k) (k=1,2,3...n) and neutralize R The nearest additive consistent fuzzy complementary matrix R ^(l) , and denoted as R ^* ;

找出偏差矩阵C中使cij的绝对值达到最大的值i、j，记为s、t；Step 5: Find the deviation matrix C=(c _ij ) _n×n of the judgment matrix R and R*, where

Find the values i and j that maximize the absolute value of cij in the deviation matrix C, denoted as s and t;

Step 6: If c _st >0, let r' _st =r _st -λ, r' _ts =r _ts +λ; if c _st <0, let r' _st =r _st +λ, r' _ts =r _ts -λ; Q where λ is the adjustment coefficient, generally taken as 0.05. Denote this matrix as R, go to step 1;

Step 7: End.

(4) Ranking of indicators

对矩阵R^*行和归一化得到单层排序向量w＝(w₁,w₂,…,w_n)^T，其中：Set the fuzzy complementary judgment matrix R=(r _ij ) _n×n , sum the matrix R row by row, and denote

Normalize the matrix R ^* row sum to get the single-level sorting vector w=(w ₁ ,w ₂ ,...,w _n ) ^T , where:

In the present invention, there are four-layer index systems in total, and the combined weight vector of the kth layer to the first layer satisfies the following formula:

w ^(k) = W ^(k) w ^(k-1) , k = 3,4,...,s;

Among them is a matrix composed of the weight vector of the kth layer to the k-1th layer as the column vector. So the combined weight vector of the lowermost layer (the sth layer) to the uppermost layer is:

w ^(s) = W ^(s) W ^(s-1) ... W ⁽³⁾ w ⁽²⁾ ;

The present invention obtains the weight of the lowest index of different types of metering devices to the target layer by using the fuzzy analytic hierarchy process.

(5) The constant weight is optimized to the variable weight

Fuzzy AHP belongs to constant weight evaluation, and its shortcomings are not only that the weight itself is highly subjective, but more seriously, the constant weight often leads to the injustice of the evaluation. This is because the importance of factors tends to change with the different state values of each factor. That is, in the state evaluation, some factors need to be motivated, that is, their weights should increase with the increase of the state value of the factors; while some factors may need to be punished, that is, their weights should decrease with the increase of the state value of the factors. To this end, the variable weight formula is introduced:

In the formula, _wi is the initial index weight, _xi is the corresponding index score value, and a ₁ , a ₂ , a ₃ , and a ₄ are empirical variable weight coefficients. In the present invention, in order to obtain the appropriate variable weight coefficient, a large number of simulation tests are carried out for the state evaluation of the electric energy metering device, various abnormal conditions are set, the evaluation score is calculated, and the variable weight coefficient is continuously adjusted according to the rationality of the evaluation result. a ₁ =−4.19×10 ⁻⁴ ; a ₂ =9.63×10 ⁻² ; a ₃ =−12.07; a ₄ =666.1.

(6) Calculate the state evaluation score S:

It should be understood that the parts not described in detail in this specification belong to the prior art. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements, without departing from the spirit and scope of the technical solution, should all be included in the scope of the claims of the present invention.

Claims (7)

1. A metering data safety protection method is characterized by comprising the following specific steps:

s1: classifying meters of users, charging piles and bidirectional metering users, taking metering data of high-grade users as block chain label nodes independently, and taking metering data of low-grade users as block chain label nodes after integrating according to network topology;

s2: adopting grading safety check on a user power supply contract and a social charging pile contract; classifying according to voltage grade, power supply quality requirement and capacity; for high-level users, a security domain method is directly adopted for checking; after the low-level users are integrated according to the network topology and then serve as virtual users, checking is carried out through a security domain method;

s3: adopting a block chain intelligent contract to trade the user direct supply and the electric vehicle which are evaluated through the security domain;

s4: metering the user, and marking and transporting metering data by adopting a block chain;

s5: analyzing influence factors by adopting an FAHP algorithm, and then evaluating the overall security of the network;

the security domain method in step S2 includes the following specific steps:

s21: establishing a private/mixed block chain platform to provide data security and intelligent contract basic capability support;

s22: marking, safely storing and transmitting the metering data; performing security check on the related transaction;

s23: performing security domain analysis;

s24: analyzing the influence factors and giving a transaction decision transaction;

the specific steps of analyzing the influence factors by using the FAHP algorithm in the step S5 are as follows:

s51: determining an influence factor system target;

s52: establishing an influence factor hierarchical structure model;

s53: constructing a fuzzy complementary judgment matrix R of the influence factor indexes;

s54: carrying out consistency check and adjustment on the fuzzy judgment matrix R;

s55: and calculating the weight of each layer of element in the hierarchical structure model to the upper layer of element, and performing hierarchical sequencing to determine the importance degree of each element at the bottommost layer in the hierarchical structure diagram in the total target of the influence factor system.

2. The metering data security protection method according to claim 1, wherein the step S52 of establishing the hierarchical model of the influencing factors comprises the following steps:

s521: if the evaluation target G of the influence factor system has n influence factors, the factor set is U ═ U₁，U2，…，Un}；

S522: the ith subset Ui of the evaluation target system U meets the condition: ui ═ Ui1, Ui2, …, Uin }.

3. The metering data security protection method of claim 1, wherein the step S54 of performing consistency check and adjustment on the fuzzy judgment matrix comprises the following specific steps:

s541: determining an index rho of the consistency degree of the additivity of the fuzzy complementary judgment matrix R;

s542; checking and judging whether the fuzzy complementary matrix R has additive consistency meeting the target requirement according to the value of rho, wherein the larger the value of rho is, the worse the additive consistency of the fuzzy complementary judgment matrix R is; wherein:

4. the metering data security protection method of claim 3, wherein the specific steps of adjusting the consistency are as follows:

s543: solving an additive consistency index value rho of the fuzzy complementary judgment matrix R, if the additive consistency index value rho is smaller than a preset threshold value sigma, turning to a step S549, otherwise, turning to the step S544;

s544: n matrixes with additive consistency are constructed according to each row of the fuzzy complementary judgment matrix R respectively

Wherein,

R^(k)the additive consistency matrix constructed according to the k-th row of the fuzzy complementary judgment matrix R is represented;

s545: respectively calculating a fuzzy complementary judgment matrix R and an additive consistency matrix R^(k)Degree of closeness of

S546: let alpha^(l)＝min{α⁽¹⁾,α⁽²⁾,…,α⁽ⁿ⁾Determining R^(k)(k ═ 1,2,3 … … n) and the additive consistency fuzzy complementary matrix R closest to the fuzzy complementary judging matrix R^(l)And is denoted by R^*；

S547: calculating a deviation matrix C ═ (C) of the judgment matrices R and R-_ij)_n×nWherein

Finding the value C in the deviation matrix C_ijThe absolute values of i and j which reach the maximum are respectively marked as s and t;

s548: if c is_st>0, r'_st＝r_st-λ，r′_ts＝r_ts+ lambda; if c is_st<0, r'_st＝r_st+λ，r′_ts＝r_ts- λ; wherein lambda is an adjustment coefficient and is generally 0.05; marking the matrix as R, turning to step S543;

s549: and (6) ending.

5. The metering data security protection method according to claim 4, wherein the specific steps of the hierarchical ordering in the step S55 are as follows:

s551: let fuzzy complementary judging matrix R ═ (R)_ij)_n×nSumming the matrix R by rows and recording

For matrix R^*The row and normalization result in a single-layer ordering vector w ═ (w)₁,w₂,…,w_n)^TWherein:

s552: the combined weight vector of the k-th layer to the first layer satisfies the following formula:

w^(k)＝W^(k)w^(k-1),k＝3,4,…,s；

wherein W^(k)A matrix which is formed by taking the weight vector of the kth layer to the kth-1 layer as a column vector;

s553: the combined weight vector of the S layer to the uppermost layer is as follows:

w^(s)＝W^(s)W^(s-1)…W⁽³⁾w⁽²⁾。

6. the metering data security method of claim 1, further comprising:

s6: introducing a variable weight formula:

in the formula, w_iIs an initial index weight, x_iA score value for the corresponding index, a₁、a₂、a₃、a₄Is an empirical weight coefficient.

7. The metering data security protection method of claim 6, wherein the status is assessedThe calculation formula of the score S is as follows:

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