Security risk assessment method and system for electric power 5G virtual private network

Abstract

The invention discloses a security risk assessment method and system for an electric power 5G virtual private network. The method comprises the following steps: constructing an assessment index system for security assessment of the electric power 5G virtual private network; performing dimension reduction processing on secondary indexes in the evaluation index system, and determining a target secondary index; respectively calculating the weight of a first-level index in the evaluation index system and the relative weight of a target second-level index; calculating a risk quantized value of each target secondary index, and calculating an overall risk assessment value based on the risk quantized values, the weights of the primary indexes and the relative weights of the target secondary indexes; and performing security risk assessment based on the overall risk assessment value. According to the method, an assessment index system for power 5G virtual private network security assessment is constructed, dimension reduction is carried out on secondary indexes through a principal component analysis method, a traditional fuzzy analytic hierarchy process is improved so as to carry out weight calculation, and overall security risk assessment of the power 5G virtual private network is realized based on the obtained weight.

Description

Technical Field

[0001] The present invention relates to the field of risk assessment technology, and more specifically, to a security risk assessment method and system for a 5G virtual private network for electric power. Background Art

[0002] The power industry is a national infrastructure industry that is related to the national economy and people's livelihood. It includes power grid companies and power generation companies, and the overall requirements for business classification and security management are basically the same. With the emergence of diversified application scenarios of smart grids, 5G networks with network slicing and multi-access edge computing (MEC) as the core have gradually been applied on a large scale in the generation, transmission, transformation, distribution and use of electricity, forming a 5G virtual private network for electricity. The 5G virtual private network for electricity is to virtualize a dedicated network for the power industry in the wireless, bearer, core network and other links in the 5G network of telecom operators based on network slicing, MEC, capability exposure and other technologies, and integrate with the power communication private network across domains to achieve end-to-end power business bearing, high-intensity security isolation and resource management.

[0003] At present, the power 5G virtual private network is in its infancy, and its security system still needs to be further improved, such as refining the security isolation plan for network slicing, the scenarios and processes for terminal secondary authentication, etc.; at the same time, with the continuous emergence of emerging businesses such as energy big data and integrated energy services, the power business environment is more open, the ecology is more complex, and data sharing is more frequent. The network security architecture that carries power business has also diversified. The security of power business and data security are facing severe challenges of mismatch between security protection measures and business systems. There is an urgent need to systematically analyze and evaluate the security risks of the power 5G virtual private network.

[0004] Therefore, a security risk assessment method for power 5G virtual private networks is needed. Summary of the invention

[0005] The present invention proposes a security risk assessment method and system for a 5G virtual private network for electric power, so as to solve the problem of how to efficiently assess the security risks of a 5G virtual private network for electric power.

[0006] In order to solve the above problems, according to one aspect of the present invention, a security risk assessment method for a power 5G virtual private network is provided, the method comprising:

[0007] Construct an evaluation index system for the security assessment of power 5G virtual private networks;

[0008] Performing dimensionality reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators;

[0009] Calculate the weight of the primary indicator and the relative weight of the target secondary indicator in the evaluation indicator system respectively;

[0010] Calculate the risk quantification value of each target secondary indicator, and calculate the overall risk assessment value based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator;

[0011] A security risk assessment is performed based on the overall risk assessment value.

[0012] Preferably, the step of performing dimensionality reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators includes:

[0013] Standardize the indicator data corresponding to each secondary indicator;

[0014] Based on the standardized indicator data, the principal component analysis method PCA is used to reduce the dimension of the secondary indicators to determine the target secondary indicators.

[0015] Preferably, the step of respectively calculating the weights of the primary indicators and the relative weights of the target secondary indicators in the evaluation indicator system comprises:

[0016] Based on the indicator data corresponding to the primary indicators and the secondary indicators, the improved fuzzy analytic hierarchy process is used to calculate respectively to determine the weight of the primary indicator and the relative weight of the target secondary indicator;

[0017] Among them, the improved fuzzy hierarchical analysis method is used for calculation, including:

[0018] The index data is normalized using the ascending trapezoidal distribution function normalization method;

[0019] Based on the normalized index data, the fuzzy judgment matrix B is constructed. ij ) n×n , where 0≤b ij ≤1, b ii = 0.5 and b ij +b ji =1;

[0020] Based on the fuzzy judgment matrix, a fuzzy consistency matrix R is determined. ij ) n×n ,in,

[0021]

[0022] According to the fuzzy consistent matrix, calculate the eigenvector of the judgment matrix in,

[0023] To W iNormalize it to satisfy To obtain the weight corresponding to the indicator;

[0024] Among them, b ij is the element in the i-th row and j-th column of the fuzzy judgment matrix B; n is the number of indicators; b ji is the element in the i-th row and j-th column of the fuzzy judgment matrix; r ij is the element in the i-th row and j-th column of the fuzzy consistent matrix R; r i is the weight coefficient of the i-th indicator; r j is the weight coefficient of the jth indicator; b i is the sum of all elements in the i-th row of the fuzzy judgment matrix B; b ik is the element in the i-th row and k-th column of the fuzzy judgment matrix B; q is the number of indicator elements; α is the eigenvector calculation coefficient.

[0025] Preferably, the step of calculating the risk quantification value of each target secondary indicator includes:

[0026] Based on the weight data of each secondary indicator after fuzzy normalization, the risk matrix method is applied to each target secondary indicator to quantify the risk possibility value and risk severity value corresponding to each target secondary indicator;

[0027] Based on the risk possibility value and risk severity value corresponding to each target secondary indicator, the risk quantification value of each secondary indicator is calculated, including: R j =P j ×S j ; Among them, R j is the risk quantification value of the j-th target secondary indicator; P j and S j They are the risk possibility value and risk severity value corresponding to the j-th target secondary indicator respectively.

[0028] Preferably, the overall risk assessment value is calculated based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator, including:

[0029]

[0030] Where R is the overall risk assessment value; w j is the relative weight W of the j-th secondary index obtained by fuzzy analytic hierarchy process i Multiply by the weight of the primary indicator to which it belongs; R j is the risk quantification value of the j-th secondary indicator.

[0031] According to another aspect of the present invention, a security risk assessment system for a power 5G virtual private network is provided, the system comprising:

[0032] An evaluation index system construction unit, used to construct an evaluation index system for the security evaluation of the power 5G virtual private network;

[0033] A dimensionality reduction processing unit, used to perform dimensionality reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators;

[0034] A weight calculation unit, used to calculate the weight of the primary indicator and the relative weight of the target secondary indicator in the evaluation indicator system respectively;

[0035] An overall risk assessment value calculation unit calculates the risk quantification value of each target secondary indicator, and calculates the overall risk assessment value based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator;

[0036] The risk assessment unit is used to perform a security risk assessment based on the overall risk assessment value.

[0037] Preferably, the dimension reduction processing unit performs dimension reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators, including:

[0038] Standardize the indicator data corresponding to each secondary indicator;

[0039] Based on the standardized indicator data, the principal component analysis method PCA is used to reduce the dimension of the secondary indicators to determine the target secondary indicators.

[0040] Preferably, the weight calculation unit calculates the weight of the primary indicator and the relative weight of the target secondary indicator in the evaluation indicator system respectively, including:

[0041] Based on the indicator data corresponding to the primary indicators and the secondary indicators, the improved fuzzy analytic hierarchy process is used to calculate respectively to determine the weight of the primary indicator and the relative weight of the target secondary indicator;

[0042] Among them, the improved fuzzy hierarchical analysis method is used for calculation, including:

[0043] The index data is normalized using the ascending trapezoidal distribution function normalization method;

[0044] Based on the normalized index data, the fuzzy judgment matrix B is constructed. ij ) n×n , where 0≤b ij ≤1, b ii = 0.5 and b ij +b ji =1;

[0045] Based on the fuzzy judgment matrix, a fuzzy consistency matrix R is determined. ij ) n×n ,in,

[0046]

[0047] According to the fuzzy consistent matrix, calculate the eigenvector of the judgment matrix in,

[0048] To W i Normalize it to satisfy To obtain the weight corresponding to the indicator;

[0049] Among them, b ij is the element in the i-th row and j-th column of the fuzzy judgment matrix B; n is the number of indicators; b ji is the element in the i-th row and j-th column of the fuzzy judgment matrix; r ij is the element in the i-th row and j-th column of the fuzzy consistent matrix R; r i is the weight coefficient of the i-th indicator; r j is the weight coefficient of the jth indicator; b i is the sum of all elements in the i-th row of the fuzzy judgment matrix B; b ik is the element in the i-th row and k-th column of the fuzzy judgment matrix B; q is the number of indicator elements; α is the eigenvector calculation coefficient.

[0050] Preferably, the overall risk assessment value calculation unit calculates the risk quantification value of each target secondary indicator, including:

[0051] Based on the weight data of each secondary indicator after fuzzy normalization, the risk matrix method is applied to each target secondary indicator to quantify the risk possibility value and risk severity value corresponding to each target secondary indicator;

[0052] Based on the risk possibility value and risk severity value corresponding to each target secondary indicator, the risk quantification value of each secondary indicator is calculated, including: R j =P j ×S j ; Among them, R j is the risk quantification value of the j-th target secondary indicator; P j and S j They are the risk possibility value and risk severity value corresponding to the j-th target secondary indicator respectively.

[0053] Preferably, the overall risk assessment value calculation unit calculates the overall risk assessment value based on the risk quantification value, the weight of the first-level indicator and the relative weight of the target second-level indicator, including:

[0054]

[0055] Where R is the overall risk assessment value; w j is the relative weight W of the j-th secondary index obtained by fuzzy analytic hierarchy process i Multiply by the weight of the primary indicator to which it belongs; R j is the risk quantification value of the j-th secondary indicator.

[0056] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any step of a method for security risk assessment of a 5G virtual private network for electric power.

[0057] According to another aspect of the present invention, the present invention provides an electronic device, including:

[0058] The computer-readable storage medium described above; and

[0059] One or more processors are used to execute the program in the computer-readable storage medium.

[0060] The present invention provides a method and system for security risk assessment of electric power 5G virtual private network, including: constructing an evaluation index system for security assessment of electric power 5G virtual private network; performing dimensionality reduction processing on the secondary indicators in the evaluation index system to determine the target secondary indicators; respectively calculating the weight of the primary indicators in the evaluation index system and the relative weight of the target secondary indicators; calculating the risk quantification value of each target secondary indicator, and calculating the overall risk assessment value based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator; and performing security risk assessment based on the overall risk assessment value. The present invention constructs an evaluation index system for security assessment of electric power 5G virtual private network by combing various factors affecting the security of electric power 5G virtual private network, and performs dimensionality reduction on the secondary indicators by principal component analysis, improves the traditional fuzzy hierarchical analysis method to calculate the weight, and realizes the overall security risk assessment of electric power 5G virtual private network based on the obtained weight. The method of the present invention can be used to optimize and adjust the power grid in a targeted manner, further improve the security level of the electric power 5G virtual private network, and provide better communication security guarantee for power grid business carrying. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0062] Figure 1 A flowchart of a security risk assessment method 100 for a power 5G virtual private network according to an embodiment of the present invention;

[0063] Figure 2 It is a structural diagram of a security risk assessment system 200 for a power 5G virtual private network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0065] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0066] Figure 1 FIG. 1 is a flow chart of a security risk assessment method 100 for a power 5G virtual private network according to an embodiment of the present invention. Figure 1 As shown, the security risk assessment method for the electric power 5G virtual private network provided by the embodiment of the present invention constructs an evaluation index system for the security assessment of the electric power 5G virtual private network by combing various factors that affect the security of the electric power 5G virtual private network, and reduces the dimension of the secondary index through the principal component analysis method, improves the traditional fuzzy hierarchical analysis method to calculate the weight, and realizes the overall security risk assessment of the electric power 5G virtual private network based on the obtained weight, which can optimize and adjust the power grid in a targeted manner to continuously improve the safe and stable operation level of the electric power 5G virtual private network. The security risk assessment method 100 for the electric power 5G virtual private network provided by the embodiment of the present invention starts from step 101. In step 101, an evaluation index system for the security assessment of the electric power 5G virtual private network is constructed.

[0067] In this invention, data protection, device attributes, network applications, and operation and maintenance quality are used as the first-level assessment dimensions of the security risk of the power 5G virtual private network. Considering the fine-grained multivariate evaluation indicators under the four dimensions, a security assessment index system for the power 5G virtual private network is constructed. The four first-level dimensions and corresponding second-level indicators are as follows.

[0068] Dimension 1: Data Protection

[0069] With the continuous emergence of emerging businesses such as energy big data and integrated energy services, the power business environment is more open, the ecology is more complex, and data sharing is more frequent. Safe and effective data protection technology is crucial to the security of the power 5G virtual private network. With the advancement of digital transformation, the stable operation of the power 5G virtual private network is inseparable from the comprehensive protection of massive power data, especially sensitive data. This is not only a legal and moral responsibility, but also an important guarantee for the safe, reliable and sustainable development of the energy system. Therefore, the following establishes a multi-indicator set from the perspectives of data transmission, data encryption, etc., to evaluate and analyze the security of the data protection dimension:

[0070] ①Data transmission success rate AAR

[0071]

[0072] Among them, Data Received Indicates the actual amount of data received, Data Expected Indicates the amount of data expected to be received. This indicator reflects the proportion of data successfully received during the transmission process.

[0073] ②Data integrity verification failure rate IVSR

[0074]

[0075] Among them, Data Correct Indicates the amount of data that has passed the integrity check. Verified Indicates the total amount of data that undergoes integrity check. This indicator reflects the success rate of integrity check during data transmission.

[0076] ③Sensitive data protection rate CPL

[0077]

[0078] Among them, Data Encrypted Indicates the amount of sensitive data transmitted in encrypted form. Total Indicates the total amount of sensitive data transmitted. This indicator reflects the proportion of sensitive data that is encrypted during transmission.

[0079] ④Data encryption strength distribution P(k)

[0080] P(k) represents the ratio of data with encryption algorithm strength k to the total transmitted data. It reflects the uniformity of data encryption strength. Using weak encryption algorithms or improper key management may be easily cracked by attackers, leading to sensitive data leakage, affecting the data confidentiality of the system, and reducing the integrity and security of data transmission and storage. A secure key management mechanism should be used to ensure that key generation, distribution, and updates are controlled.

[0081] Dimension 2: Device attributes

[0082] The power 5G virtual private network equipment includes wireless equipment, bearer equipment, core network equipment and operation management platform. Among them, the wireless equipment is mainly responsible for the reception and transmission of signals; the bearer equipment is responsible for the transmission and processing of data; the core network equipment is the core of the entire network, responsible for controlling and managing the entire network; the operation management platform is responsible for monitoring and managing the entire network. Network equipment security is of key significance in the power 5G virtual private network. These devices are the backbone of system communication and data transmission, and their security is directly related to the operational stability and data integrity of the power system. Therefore, the following establishes a multivariate indicator set from the perspectives of equipment status, equipment utilization, etc., to evaluate and analyze the security of equipment attribute dimensions:

[0083] ①Key node failure rate M

[0084]

[0085] In the 5G virtual private network of electric power, a certain type of communication equipment selected by key nodes has a large amount of long-term data. Based on this data, the probability of node failure under a fixed time scale can be estimated. Assume that the network system is in the time [0, t], and the fault distribution obeys P k(t) , then the average number of failures of the system in this interval is

[0086] ②Equipment virus infection rate I

[0087]

[0088] Targeted cyber attacks on infrastructure information systems interfere with the operation of physical systems and can easily cause equipment damage and system paralysis, such as a cyber attack + physical destruction leading to a nationwide power outage.

[0089] ③ Equipment operation life T limit

[0090] Equipment operation life T limit : The operating life of fixed selected equipment is directly provided by the manufacturer.

[0091] ④ Equipment utilization rate L

[0092]

[0093] Number of occupied devices N occupied With the existing number of devices N device The ratio of the existing number of devices refers to the total number of devices in use and available.

[0094] Dimension 3: Web Applications

[0095] The security of the power 5G virtual private network is also reflected in network application levels such as business isolation, identity authentication, malicious traffic interception, and vulnerability repair.

[0096] The power 5G virtual private network usually uses network slicing technology to achieve isolation of different business needs. If the isolation is insufficient, it may cause mutual interference or attacks between different slices. Secure identity authentication technology ensures that only verified users and devices can access the virtual private network, effectively preventing unauthorized access and preventing attackers from invading the system. At the same time, it helps to ensure the integrity of the data, prevent unauthorized users or devices from tampering with system data, and maintain the reliability of the power system. The following establishes a multi-element indicator set from these perspectives to evaluate and analyze the security of the network application dimension:

[0097] ① Identity authentication success rate ASR

[0098]

[0099] Among them, Authenticity Accepted Indicates the number of successful authentications, Attempts indicates the total number of authentication attempts. This metric reflects the success rate of the authentication process.

[0100] ② Unauthorized access attempt interception rate UAAI R

[0101]

[0102] Among them, Access Intercepted Indicates the number of illegal access attempts that were blocked. Total Indicates the total number of illegal access attempts. This indicator reflects the effectiveness of security devices in intercepting illegal access.

[0103] ③Slice transmission success rate STSR

[0104]

[0105] Among them, Slice Received Indicates the number of slice packets successfully transmitted, SL i ce Expected Indicates the total number of slice packets sent. The slice transmission success rate can be used to measure end-to-end integrity.

[0106] ④ Inter-slice fault propagation rate I SFPR

[0107]

[0108] Among them, N faultBIndicates the number of times slice B fails due to slice A failure, and Nfau l tA indicates the total number of failures of slice A. This indicator requires long-term observation and recording of failure events, and reflects the security of business isolation.

[0109] Dimension 4: Operation and Maintenance Quality

[0110] The quality of operation and maintenance is crucial to the security of the power 5G virtual private network. The stable operation of the power 5G virtual private network is inseparable from daily operation and maintenance and event response mechanisms at all levels. Virtual private networks with higher security levels have strict and efficient operation and maintenance response mechanisms. By establishing a powerful monitoring system, potential threats and abnormal activities can be detected in a timely manner, thereby achieving comprehensive monitoring of the power system. At the same time, an efficient response mechanism can quickly respond to security incidents, reduce damage and ensure the stability of the system. The quality of operation and maintenance is directly related to the system's anti-attack capability and agility. Therefore, the following establishes a multi-indicator set from the perspectives of monitoring level, response level, and repair level to evaluate and analyze the security of the operation and maintenance quality dimension:

[0111] ①Mean fault detection time MTTD

[0112]

[0113] Among them, t detect Indicates the time for each fault detection, N fault Indicates the number of power 5G virtual private network failures.

[0114] Mean time to detect is the average time from when a system fails to when the problem is first discovered. It is used to measure the average length of time a problem exists before it is discovered. After a system fails, production events may be quickly identified and automatically alerted by monitoring tools or observation platforms, or they may be discovered by users first. Therefore, the slower the problem is identified, the greater the MTTD, and the longer the user may experience interruptions.

[0115] ②Mean response time MTTR

[0116]

[0117] Among them, t response Indicates the response time for each failure, N fault Indicates the number of power 5G virtual private network failures.

[0118] Average response time refers to the average time from the discovery of a system unavailability to its resolution, reflecting the efficiency of the R&D team in responding to requirements and changes, as well as the maintainability of the system. Average response time does not take into account the latency of event notifications, and is often used in network security to measure the efficiency of a team in mitigating system attacks.

[0119] ③Mean repair time MTTS

[0120]

[0121] Among them, t solve Indicates the repair time for each failure, N fault Indicates the number of power 5G virtual private network failures.

[0122] The mean time to repair measures the efficiency of the R&D team in troubleshooting and repairing faults. It refers to the average time from the start of the repair to the restoration of normal operation of the system by the development team, including repair, testing, deployment and other links. The mean time to repair can be calculated by dividing the total system repair time within a certain period by the total number of events. The smaller the MTTS, the more maintainable the system is and the better the recoverability is.

[0123] ④Historical defect level H

[0124]

[0125] Among them, q i For historical defects, n i Corresponding response level. The number of historical defects and the corresponding defect levels are sorted out based on historical statistical data, and quantitatively weighted.

[0126] ⑤Historical defect frequency S

[0127]

[0128] Among them, h i For historical defects, n i Corresponding defect frequency. Sort out the time frequency of historical defects at all levels based on historical statistical data and quantify and weight them.

[0129] In step 102, the secondary indicators in the evaluation indicator system are subjected to dimensionality reduction processing to determine target secondary indicators.

[0130] Preferably, the step of performing dimensionality reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators includes:

[0131] Standardize the indicator data corresponding to each secondary indicator;

[0132] Based on the standardized indicator data, the principal component analysis method PCA is used to reduce the dimension of the secondary indicators to determine the target secondary indicators.

[0133] In the present invention, PCA is used to reduce the dimension of indicators. Specifically, according to the security assessment indicator system suitable for the power 5G virtual private network established in step 101, PCA is used to reduce the dimension of existing indicators at all levels and extract features. The steps are as follows:

[0134] Step 1: Preprocessing of indicator data

[0135] First, collect a data set containing multiple evaluation indicators. These data may need to be preprocessed, such as missing value filling, standardization or normalization, to ensure the quality and consistency of the data. In the power 5G virtual private network system, sensors for different indicators have different initial recording times and sampling frequencies. When merging data, select the closest collection time point for data merging based on the highest frequency item. Use the random forest filling method to fill in the missing values.

[0136] The data dimensions of different indicators are different, and each indicator value needs to be standardized. Data standardization refers to scaling the original indicator data in proportion, removing the unit restrictions of the data, and converting it into dimensionless pure values, so that indicators of different units or magnitudes can be compared and weighted. The standardization process expression is:

[0137]

[0138] Among them, x i is the original indicator value data, A min and A max Represent the minimum and maximum values ​​of the sample data, y i It is the result of normalization of indicator value data.

[0139] Step 2: Dimensionality reduction of composite indicators for power 5G virtual private network security assessment based on PCA

[0140] The PCA principal component analysis method is suitable for dimensionality reduction and feature extraction of high-dimensional data. The basic principle is to maximize the variance: each principal component is a linear combination of the original features. First, find the first principal component, that is, the direction in which the variance of the data after projection is the largest among all possible directions. Then find the second principal component, that is, the direction in which the variance of the data after projection is the largest among all possible directions orthogonal to the first principal component. And so on, until all principal components are found.

[0141] The present invention uses data transmission success rate, data integrity check failure rate, sensitive data protection rate, data encryption strength distribution, key node failure rate, equipment virus infection rate, equipment operating years, equipment utilization rate, identity authentication success rate, illegal access attempt interception rate, slice transmission success rate, slice fault propagation rate, average fault detection time, average response time, average repair time, historical defect level, and historical defect frequency as secondary indicators of the evaluation system. PCA is used to reduce the dimension of each indicator to form a principal component, eliminate information redundancy interference, and speed up data processing and evaluation efficiency. In order to achieve the goal of maximizing variance, the covariance matrix of the data is first calculated. The covariance matrix is ​​an important tool to reflect the correlation between the dimensions of the data. For each evaluation indicator, the covariance between it and other indicators is calculated, and these covariance values ​​are composed of a covariance matrix. The specific formula for covariance calculation is as follows:

[0142]

[0143] Among them, m represents the number of current indicator values, x i is the data value of the ith indicator, and μ represents the average value of all indicator data.

[0144] The specific formula of the covariance matrix is ​​as follows:

[0145]

[0146] By performing eigendecomposition on the covariance matrix, eigenvalues ​​and eigenvectors are obtained. These eigenvalues ​​and eigenvectors describe the distribution and degree of change of the data in all directions. The vector V multiplied by a matrix X can be regarded as rotating or stretching the vector, and this rotation and stretching are caused by the movement generated by the left multiplication of the matrix X. The eigenvector V indicates the direction of movement of the matrix X, and the eigenvalue λ indicates the amplitude of the movement. The principal component in PCA refers to the eigenvector, and the size of the corresponding eigenvalue indicates the importance of this eigenvector or principal component. The larger the eigenvalue, the higher the importance. By performing eigenvalue decomposition on the covariance matrix, m eigenvalues ​​can be obtained and sorted.

[0147] According to the size of the eigenvalue, the eigenvectors corresponding to the first k largest eigenvalues ​​are selected as principal components. These principal components represent the main direction of change of the data and can retain the information of the original data to the greatest extent. The original data is projected onto the selected principal components to obtain a reduced-dimensional data set. This data set retains the main features of the original data, but the dimension is greatly reduced, which is convenient for subsequent analysis and processing. According to the calculation results, the principal components such as data transmission success rate, sensitive data protection rate, data encryption strength distribution, key node failure rate, equipment operating years, equipment utilization, identity authentication success rate, slice transmission success rate, average fault detection time, average repair time, and historical defect frequency are finally selected as the target secondary indicators of the evaluation system.

[0148] In step 103, the weights of the primary indicators and the relative weights of the target secondary indicators in the evaluation indicator system are calculated respectively.

[0149] Preferably, the step of respectively calculating the weights of the primary indicators and the relative weights of the target secondary indicators in the evaluation indicator system comprises:

[0150] Based on the indicator data corresponding to the primary indicators and the secondary indicators, the improved fuzzy analytic hierarchy process is used to calculate respectively to determine the weight of the primary indicator and the relative weight of the target secondary indicator;

[0151] Among them, the improved fuzzy hierarchical analysis method is used for calculation, including:

[0152] The index data is normalized using the ascending trapezoidal distribution function normalization method;

[0153] Based on the normalized index data, the fuzzy judgment matrix B is constructed. ij ) n×n , where 0≤b ij ≤1, b ii = 0.5 and b ij +b ji =1;

[0154] Based on the fuzzy judgment matrix, a fuzzy consistency matrix R is determined. ij ) n×n ,in,

[0155]

[0156] According to the fuzzy consistent matrix, calculate the eigenvector of the judgment matrix in,

[0157] To W i Normalize it to satisfy To obtain the weight corresponding to the indicator;

[0158] Among them, b ij is the element in the i-th row and j-th column of the fuzzy judgment matrix B; n is the number of indicators; b ji is the element in the i-th row and j-th column of the fuzzy judgment matrix; r ij is the element in the i-th row and j-th column of the fuzzy consistent matrix R; r i is the weight coefficient of the i-th indicator; r j is the weight coefficient of the jth indicator; b i is the sum of all elements in the i-th row of the fuzzy judgment matrix B; b ik is the element in the i-th row and k-th column of the fuzzy judgment matrix B; q is the number of indicator elements; α is the eigenvector calculation coefficient.

[0159] In the present invention, the traditional fuzzy hierarchical analysis method (FAHP) is improved to accurately determine the weight of each indicator for the assessment of the overall security risk of the power 5G virtual private network. The specific process of determining the weight includes:

[0160] Step 1: Normalize the indicator values

[0161] The principle of solving the weight of fuzzy hierarchical analysis method is basically consistent with the hierarchical analysis method. The core work is also to determine the weight of each risk factor in the indicator system. In short, it is necessary to determine the relative importance of each risk factor and other risk factors at the same level.

[0162] Considering that the numerical values ​​of various indicators vary greatly in terms of the quantification of each indicator layer, if the quantitative data of the indicators are directly calculated, the entire evaluation model system may collapse. Therefore, the present invention adopts the normalization method of the ascending ladder distribution function for processing:

[0163]

[0164] The ascending trapezoidal distribution function normalization method makes the fuzzy numbers comparable in different comparisons by dealing with the normalization problem of fuzzy judgment, thereby effectively calculating the fuzzy weights of each factor. This method enhances the ability of the fuzzy hierarchical analysis method to deal with fuzziness and uncertainty, making it more flexible and accurate in decision analysis.

[0165] Step 2: Establish a weight judgment matrix

[0166] The present invention is composed of an expert evaluation team of 30 experts from Tianjin Power Grid Company. They distribute evaluation questionnaires, compare and score the relative importance of each pair of factors of the same level and the same affiliation, and adopt a nine-level scaling method of 0.1-0.9 for dimension segmentation. This method is the maximum number of levels that people can accept for things from a psychological perspective. If the level is finer, people will obviously feel confused and unable to judge.

[0167] When evaluating, the traditional fuzzy hierarchical analysis method only constructs the fuzzy judgment matrix for the criterion layer, and the evaluation indicators at the indicator layer or higher level are only considered as qualitative analysis factors affecting the weight of the criterion layer. The specific importance and overall impact of the evaluation indicators at the indicator layer or higher level cannot be reflected. To overcome this problem, before constructing the fuzzy judgment matrix for the criterion layer, the fuzzy judgment matrix for the evaluation indicators at the indicator layer is first constructed, thereby laying the foundation for a more detailed analysis of how the indicator layer affects the weight results of the criterion layer.

[0168] Establish the fuzzy judgment matrix B = (b ij ) n×n , as shown below:

[0169]

[0170] Where 0≤b ij ≤1, b ii = 0.5 and b ij +b ji =1.

[0171] To check the consistency of the fuzzy judgment matrix, it is necessary to process it into fuzzy consistency. After processing, the fuzzy consistency matrix can be obtained. First, the fuzzy judgment matrix B obtained by scoring is summed up by row. Then, the weight coefficient is obtained by dividing the value of each element in the row matrix obtained in the previous step by the sum of the elements. The calculation formula is as follows:

[0172]

[0173] Then the fuzzy judgment matrix is ​​checked for consistency. It needs to be processed for fuzzy consistency. After the processing, the fuzzy consistency matrix R = (r ij ) n×n , the calculation formula is as follows:

[0174]

[0175] Step 3: Solve the fuzzy consistency matrix

[0176] In the present invention, the eigenvector W of the judgment matrix is ​​calculated based on the fuzzy consistency matrix. i :

[0177]

[0178] in,

[0179] Through the W i Normalize it to satisfy The relative weight of the secondary index with respect to the primary index can be obtained. Similarly, the weight of the primary index can be calculated through the above analysis based on the improved fuzzy hierarchical analysis method.

[0180] In step 104, the risk quantification value of each target secondary indicator is calculated, and the overall risk assessment value is calculated based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator.

[0181] Preferably, the step of calculating the risk quantification value of each target secondary indicator includes:

[0182] Based on the weight data of each secondary indicator after fuzzy normalization, the risk matrix method is applied to each target secondary indicator to quantify the risk possibility value and risk severity value corresponding to each target secondary indicator;

[0183] Based on the risk possibility value and risk severity value corresponding to each target secondary indicator, the risk quantification value of each secondary indicator is calculated, including: R j =P j ×S j ; Among them, R j is the risk quantification value of the j-th target secondary indicator; P j and S j They are the risk possibility value and risk severity value corresponding to the j-th target secondary indicator respectively.

[0184] Preferably, the overall risk assessment value is calculated based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator, including:

[0185]

[0186] Where R is the overall risk assessment value; w j is the relative weight W of the j-th secondary index obtained by fuzzy analytic hierarchy process i Multiply by the weight of the primary indicator to which it belongs; R j is the risk quantification value of the j-th secondary indicator.

[0187] In the present invention, according to the calculated weights of indicators at all levels, for each single risk indicator, the risk matrix method is used for calibration and quantification, and the impact of each factor indicator on the security risk of the power 5G virtual private network is estimated, and the risk valuation of each single indicator is weighted and comprehensively calculated to obtain the overall risk level of the power 5G virtual private network. Specifically, it includes:

[0188] Step 1: Calculate the risk quantification value of each secondary indicator

[0189] In the present invention, the risk is assigned quantitative values ​​from the two aspects of possibility and consequence severity. The larger the risk possibility quantitative value P, the greater the probability of the risk occurring, and the larger the risk severity quantitative value S, the more far-reaching the impact after the risk occurs. The original risk matrix is ​​established, and the probability quantitative value and the severity value are used as matrix dimensions for multiplication calculation. The product R of the numerical probability and severity is used as the comprehensive risk assessment value of the risk. The scores of 1-5 are assigned from the two indicators of possibility and severity.

[0190] Apply the risk matrix method to each secondary indicator to quantify its risk possibility value P i and risk severity value S i , and calculate its risk quantification value R j for:

[0191] R j =P j ×S j ;

[0192] Among them, R j is the risk quantification value of the j-th target secondary indicator; P j and S j They are the risk possibility value and risk severity value corresponding to the j-th target secondary indicator respectively.

[0193] Step 2: Calculate the overall security risk level

[0194] The quantified overall risk assessment value R of the power 5G virtual private network is:

[0195]

[0196] Where R is the overall risk assessment value; w j is the relative weight W of the j-th secondary index obtained by fuzzy analytic hierarchy process i Multiply by the weight of the primary indicator to which it belongs; R j is the risk quantification value of the j-th secondary indicator.

[0197] In step 105, a security risk assessment is performed based on the overall risk assessment value.

[0198] In the present invention, a certain risk assessment criterion can be designed to perform a security risk assessment. In the present invention, the overall risk assessment value is set between 1-25. If the overall risk assessment value is between 1-5 points, it is a low risk. If the overall risk assessment value is between 6-9 points, it is a medium risk. If the overall risk assessment value is between 10-15 points, it is a high risk. If the overall risk assessment value is between 16-25 points, it is an extremely high risk. The security risk level of the power 5G virtual private network can be established according to the overall risk assessment value.

[0199] The overall security risk assessment method applicable to the electric power 5G virtual private network of the present invention, by combing various factors affecting the security of the electric power 5G virtual private network, takes data protection, equipment attributes, network applications, and operation and maintenance quality as four evaluation dimensions, and considers multiple dimensions to summarize the evaluation indicators of each level, and constructs a scientific electric power 5G virtual private network security assessment index system. The existing complex secondary indicators are reduced in dimension by the principal component analysis method, and the traditional fuzzy hierarchical analysis method is improved to propose an overall security risk assessment method for the electric power 5G virtual private network. The risk matrix method is used to determine the risk level of the electric power 5G virtual private network, evaluate the security performance of the electric power 5G virtual private network, and make targeted optimization and adjustments to continuously improve the safe and stable operation level of the electric power 5G virtual private network.

[0200] Figure 2 Schematic diagram of the structure of a security risk assessment system 200 for a power 5G virtual private network according to an embodiment of the present invention. Figure 2 As shown, the security risk assessment system 200 for the power 5G virtual private network provided by the embodiment of the present invention includes: an assessment index system construction unit 201, a dimensionality reduction processing unit 202, a weight calculation unit 203, an overall risk assessment value calculation unit 204 and a risk assessment unit 205.

[0201] Preferably, the evaluation index system construction unit 201 is used to construct an evaluation index system for security assessment of the power 5G virtual private network.

[0202] Preferably, the dimension reduction processing unit 202 is used to perform dimension reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators.

[0203] Preferably, the dimension reduction processing unit 202 performs dimension reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators, including:

[0204] Standardize the indicator data corresponding to each secondary indicator;

[0205] Based on the standardized indicator data, the principal component analysis method PCA is used to reduce the dimension of the secondary indicators to determine the target secondary indicators.

[0206] Preferably, the weight calculation unit 203 is used to respectively calculate the weight of the primary indicator and the relative weight of the target secondary indicator in the evaluation indicator system.

[0207] Preferably, the weight calculation unit 203 calculates the weight of the primary index and the relative weight of the target secondary index in the evaluation index system respectively, including:

[0208] Based on the indicator data corresponding to the primary indicators and the secondary indicators, the improved fuzzy analytic hierarchy process is used to calculate respectively to determine the weight of the primary indicator and the relative weight of the target secondary indicator;

[0209] Among them, the improved fuzzy hierarchical analysis method is used for calculation, including:

[0210] The index data is normalized using the ascending trapezoidal distribution function normalization method;

[0211] Based on the normalized index data, the fuzzy judgment matrix B is constructed. ij ) n×n , where 0≤b ij ≤1, b ii = 0.5 and b ij +b ji =1;

[0212] Based on the fuzzy judgment matrix, a fuzzy consistency matrix R is determined. ij ) n×n ,in,

[0213]

[0214] According to the fuzzy consistent matrix, calculate the eigenvector of the judgment matrix in,

[0215] To W i Normalize it to satisfy To obtain the weight corresponding to the indicator;

[0216] Among them, b ij is the element in the i-th row and j-th column of the fuzzy judgment matrix B; n is the number of indicators; b ji is the element in the i-th row and j-th column of the fuzzy judgment matrix; r ij is the element in the i-th row and j-th column of the fuzzy consistent matrix R; r i is the weight coefficient of the i-th indicator; r jis the weight coefficient of the jth indicator; b i is the sum of all elements in the i-th row of the fuzzy judgment matrix B; b ik is the element in the i-th row and k-th column of the fuzzy judgment matrix B; q is the number of indicator elements; α is the eigenvector calculation coefficient.

[0217] Preferably, the overall risk assessment value calculation unit 204 calculates the risk quantification value of each target secondary indicator, and calculates the overall risk assessment value based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator.

[0218] Preferably, the overall risk assessment value calculation unit 204 calculates the risk quantification value of each target secondary indicator, including:

[0219] Based on the weight data of each secondary indicator after fuzzy normalization, the risk matrix method is applied to each target secondary indicator to quantify the risk possibility value and risk severity value corresponding to each target secondary indicator;

[0220] Based on the risk possibility value and risk severity value corresponding to each target secondary indicator, the risk quantification value of each secondary indicator is calculated, including: R j =P j ×S j ; Among them, R j is the risk quantification value of the j-th target secondary indicator; P j and S j They are the risk possibility value and risk severity value corresponding to the j-th target secondary indicator respectively.

[0221] Preferably, the overall risk assessment value calculation unit 204 calculates the overall risk assessment value based on the risk quantification value, the weight of the first-level indicator and the relative weight of the target second-level indicator, including:

[0222]

[0223] Where R is the overall risk assessment value; w j is the relative weight W of the j-th secondary index obtained by fuzzy analytic hierarchy process i Multiply by the weight of the primary indicator to which it belongs; R j is the risk quantification value of the j-th secondary indicator.

[0224] Preferably, the risk assessment unit 205 is used to perform a security risk assessment based on the overall risk assessment value.

[0225] The security risk assessment system 200 for the power 5G virtual private network of an embodiment of the present invention corresponds to the security risk assessment method 100 for the power 5G virtual private network of another embodiment of the present invention, which will not be repeated here.

[0226] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any step of a security risk assessment method for a power 5G virtual private network.

[0227] According to another aspect of the present invention, the present invention provides an electronic device, including:

[0228] The computer-readable storage medium described above; and

[0229] One or more processors are used to execute the program in the computer-readable storage medium.

[0230] The present invention has been described with reference to a few embodiments. However, it is known to those skilled in the art that other embodiments than the one disclosed above are equally within the scope of the present invention.

[0231] Generally, all terms used in the present invention are interpreted according to their ordinary meaning in the technical field, unless otherwise explicitly defined therein. All references to "a / said / the [device, component, etc.]" are open to interpretation as at least one instance of the device, component, etc., unless otherwise explicitly stated. The steps of any method disclosed herein do not necessarily have to be performed in the exact order disclosed, unless explicitly stated.

[0232] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0233] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0234] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0235] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0236] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A security risk assessment method for power 5G virtual private network, characterized in that: The method comprises: Construct an evaluation index system for the security assessment of power 5G virtual private networks; Performing dimensionality reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators; Calculate the weight of the primary indicator and the relative weight of the target secondary indicator in the evaluation indicator system respectively; Calculate the risk quantification value of each target secondary indicator, and calculate the overall risk assessment value based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator; A security risk assessment is performed based on the overall risk assessment value.

2. The method according to claim 1, characterized in that The step of performing dimensionality reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators includes: Standardize the indicator data corresponding to each secondary indicator; Based on the standardized indicator data, the principal component analysis method PCA is used to reduce the dimension of the secondary indicators to determine the target secondary indicators.

3. The method according to claim 1, characterized in that: The step of respectively calculating the weights of the primary indicators and the relative weights of the target secondary indicators in the evaluation indicator system includes: Based on the indicator data corresponding to the primary indicators and the secondary indicators, the improved fuzzy analytic hierarchy process is used to calculate respectively to determine the weight of the primary indicator and the relative weight of the target secondary indicator; Among them, the improved fuzzy hierarchical analysis method is used for calculation, including: The index data is normalized using the ascending trapezoidal distribution function normalization method; Based on the normalized index data, the fuzzy judgment matrix B is constructed. ij ) n×n , where 0≤b ij ≤1, b ii = 0.5 and b ij +b ji =1; Based on the fuzzy judgment matrix, a fuzzy consistency matrix R is determined. ij ) ×n ,in, According to the fuzzy consistency matrix, calculate the eigenvector of the judgment matrix in, To W i Normalize it to satisfy To obtain the weight corresponding to the indicator; Among them, b ij is the element in the i-th row and j-th column of the fuzzy judgment matrix B; n is the number of indicators; b ji is the element in the i-th row and j-th column of the fuzzy judgment matrix; r ij is the element in the i-th row and j-th column of the fuzzy consistent matrix R; r i is the weight coefficient of the i-th indicator; r j is the weight coefficient of the jth indicator; b i is the sum of all elements in the i-th row of the fuzzy judgment matrix B; b ik is the element in the i-th row and k-th column of the fuzzy judgment matrix B; q is the number of indicator elements; α is the eigenvector calculation coefficient.

4. The method according to claim 1, characterized in that: The calculation of the risk quantification value of each target secondary indicator includes: Based on the weight data of each secondary indicator after fuzzy normalization, the risk matrix method is applied to each target secondary indicator to quantify the risk possibility value and risk severity value corresponding to each target secondary indicator; Based on the risk possibility value and risk severity value corresponding to each target secondary indicator, the risk quantification value of each secondary indicator is calculated, including: R j =P j ×S j ; Among them, R j is the risk quantification value of the j-th target secondary indicator; P j and S j They are the risk possibility value and risk severity value corresponding to the j-th target secondary indicator respectively.

5. The method according to claim 1, characterized in that The overall risk assessment value is calculated based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator, including: Where R is the overall risk assessment value; w j is the relative weight W of the j-th secondary index obtained by fuzzy analytic hierarchy process i Multiply by the weight of the primary indicator to which it belongs; R j is the risk quantification value of the j-th secondary indicator.

6. A security risk assessment system for power 5G virtual private network, characterized in that: The system comprises: An evaluation index system construction unit, used to construct an evaluation index system for the security evaluation of the power 5G virtual private network; A dimensionality reduction processing unit, used to perform dimensionality reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators; A weight calculation unit, used to calculate the weight of the primary index and the relative weight of the target secondary index in the evaluation index system respectively; An overall risk assessment value calculation unit calculates the risk quantification value of each target secondary indicator, and calculates the overall risk assessment value based on the risk quantification value, the weight of the primary indicator and the relative weight of the target secondary indicator; The risk assessment unit is used to perform a security risk assessment based on the overall risk assessment value.

7. The system according to claim 6, characterized in that The dimension reduction processing unit performs dimension reduction processing on the secondary indicators in the evaluation indicator system to determine the target secondary indicators, including: Standardize the indicator data corresponding to each secondary indicator; Based on the standardized indicator data, the principal component analysis method PCA is used to reduce the dimension of the secondary indicators to determine the target secondary indicators.

8. The system according to claim 6, characterized in that The weight calculation unit calculates the weight of the primary index and the relative weight of the target secondary index in the evaluation index system respectively, including: Based on the indicator data corresponding to the primary indicators and the secondary indicators, the improved fuzzy analytic hierarchy process is used to calculate respectively to determine the weight of the primary indicator and the relative weight of the target secondary indicator; Among them, the improved fuzzy hierarchical analysis method is used for calculation, including: The index data is normalized using the ascending trapezoidal distribution function normalization method; Based on the normalized index data, the fuzzy judgment matrix B is constructed. ij ) n×n , where 0≤b ij ≤1, b ii = 0.5 and b ij +b ji =1; Based on the fuzzy judgment matrix, a fuzzy consistency matrix R is determined. ij ) n×n ,in, According to the fuzzy consistency matrix, calculate the eigenvector of the judgment matrix in, To W i Normalize it to satisfy To obtain the weight corresponding to the indicator; Among them, b ij is the element in the i-th row and j-th column of the fuzzy judgment matrix B; n is the number of indicators; b ji is the element in the i-th row and j-th column of the fuzzy judgment matrix; r ij is the element in the i-th row and j-th column of the fuzzy consistent matrix R; r i is the weight coefficient of the i-th indicator; r j is the weight coefficient of the jth indicator; b i is the sum of all elements in the i-th row of the fuzzy judgment matrix B; b ik is the element in the i-th row and k-th column of the fuzzy judgment matrix B; q is the number of indicator elements; α is the eigenvector calculation coefficient.

9. The system according to claim 6, characterized in that The overall risk assessment value calculation unit calculates the risk quantification value of each target secondary indicator, including: Based on the weight data of each secondary indicator after fuzzy normalization, the risk matrix method is applied to each target secondary indicator to quantify the risk possibility value and risk severity value corresponding to each target secondary indicator; Based on the risk possibility value and risk severity value corresponding to each target secondary indicator, the risk quantification value of each secondary indicator is calculated, including: R j =P j ×S j ; Among them, R j is the risk quantification value of the j-th target secondary indicator; P j and S j They are the risk possibility value and risk severity value corresponding to the j-th target secondary indicator respectively.

10. The system according to claim 6, characterized in that The overall risk assessment value calculation unit calculates the overall risk assessment value based on the risk quantification value, the weight of the first-level indicator and the relative weight of the target second-level indicator, including: Where R is the overall risk assessment value; w j is the relative weight W of the j-th secondary index obtained by fuzzy analytic hierarchy process i Multiply by the weight of the primary indicator to which it belongs; R j is the risk quantification value of the j-th secondary indicator.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

12. An electronic device, characterized in that: include: The computer readable storage medium as claimed in claim 11; as well as One or more processors are used to execute the program in the computer-readable storage medium.

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