Power equipment safety status assessment method, device and computer equipment

Abstract

The present application relates to a method, device, computer equipment, storage medium and computer program product for assessing the safety status of power equipment. The method comprises: obtaining the expert scoring results of each assessment dimension of the safety status assessment of the power equipment, calling the smart contract on the blockchain according to the expert scoring results, and obtaining the initialization judgment matrix of each assessment dimension; determining the hierarchical single sorting of each assessment dimension according to the initialization judgment matrix of each assessment dimension; obtaining the safety status data of each assessment dimension, calling the smart contract of the data information array of each assessment dimension on the blockchain, and obtaining the data information array of each assessment dimension; obtaining the insecurity rate of the power equipment according to the data information array of each assessment dimension and the hierarchical single sorting of each assessment dimension. The method utilizes the decentralized and tamper-proof characteristics of the blockchain, and is based on the smart contract combined with the hierarchical analysis method to ensure that the safety status of the power equipment is not tampered with and improve the accuracy of the safety assessment.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a method, device, computer equipment, storage medium and computer program product for evaluating the safety status of power equipment. Background Art

[0002] In the smart grid environment, timely detection and assessment of various security risks of the power grid information system is crucial for the design, control and operation of the power information system. Driven by the "dual carbon" goal, the new power system is closely related to digital technology. With the change of the interactive mode of source, grid, load and storage integration, the access subject of the new power is more complex, including not only dispatching centers, power plants, etc., but also integrated energy service companies, microgrids, etc., and a large number of heterogeneous IoT devices are connected to the network.

[0003] The existing power equipment safety status assessment schemes include fuzzy comprehensive evaluation methods and power information system safety status monitoring mechanisms. Among them, the fuzzy comprehensive evaluation method establishes secondary and primary evaluation indicators according to various working conditions in actual operation, realizing the hierarchical assessment of security risks. The power information system safety status monitoring mechanism based on big data analysis comprehensively considers the association analysis, game theory and reinforcement learning of fuzzy clustering, and constructs a big data analysis security situation awareness model to realize the real-time extraction of network security situation factors, network situation assessment and security status prediction of power information system. However, most of the existing power equipment safety status assessment schemes use traditional assessment systems, that is, the security status is stored locally or requires the participation of the server, and there are problems such as single point failure. When the server storing the security status is attacked, the security status information of the power equipment may be tampered with, affecting further evaluation and laying hidden dangers for the safe operation of the power grid. Therefore, the existing power equipment safety status assessment scheme has the problem of security status information being tampered with. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for assessing the safety status of power equipment in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for evaluating the safety status of an electric power device. The method comprises:

[0006] Obtain expert scoring results for each assessment dimension of the safety status assessment of power equipment;

[0007] According to the expert scoring results, the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain is called to obtain the initialized judgment matrix of each evaluation dimension;

[0008] Determine the hierarchical order of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension;

[0009] Obtain security status data for each assessment dimension;

[0010] According to the security status data, the smart contract of the data information array of each evaluation dimension on the blockchain is called to obtain the data information array of each evaluation dimension;

[0011] The unsafe rate of the electric power equipment is obtained by weighting the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension.

[0012] In one embodiment, determining the hierarchical order of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension includes:

[0013] Hierarchical single sorting uses the sum-product method to calculate the maximum eigenvector of the judgment matrix of each evaluation dimension;

[0014] Calculate the maximum eigenvalue of each evaluation dimension according to the maximum eigenvector;

[0015] The judgment matrix of each evaluation dimension is verified according to the maximum eigenvalue root. If the verification passes, the normalized maximum eigenvector is used as the hierarchical single-ranking weight vector of each evaluation dimension.

[0016] In one of the embodiments, the method further includes: if the verification fails, adjusting the initialization judgment matrix of each evaluation dimension, and returning to the step of calculating the maximum eigenvector of the judgment matrix of each evaluation dimension using the sum-product method until the verification passes.

[0017] In one embodiment, the method further comprises:

[0018] When a change in the security status data is monitored, the step of obtaining expert scoring results of each evaluation dimension of the security status evaluation of the power equipment is executed to trigger the security status evaluation of the power equipment;

[0019] According to the unsafe rate of the electric power equipment obtained by multiple safety status evaluations, a dynamic safety status evaluation result of the electric power equipment within a period of time is obtained.

[0020] In one embodiment, the method further comprises:

[0021] Establishing a multi-level hierarchical model of the analytic hierarchy process, wherein the multi-level hierarchical model includes a highest level, a middle level and a lowest level;

[0022] The highest level element is the power equipment unsafe rate;

[0023] The middle layer is the lower layer of the highest layer, and the elements of the middle layer include: the failure rate of the device itself, the rate of network attack on the device terminal, and the failure rate of device communication;

[0024] The lowest level elements of the equipment failure rate include: sensor failure rate and RFID failure rate;

[0025] The lowest level elements of the network attack rate of the device terminal include: DDOS attack rate, data leakage rate, API attack rate and Trojan virus attack rate;

[0026] The elements of the device communication failure rate include the lowest level elements of: communication channel interruption rate, data packet error rate and data packet loss rate.

[0027] In one embodiment, the method further comprises:

[0028] When the unsafe rate of the electric power equipment is higher than a preset value, the electric power equipment is determined to be an unsafe equipment.

[0029] In a second aspect, the present application also provides a device for evaluating the safety status of electric power equipment. The device comprises:

[0030] An expert scoring module is used to obtain expert scoring results of each evaluation dimension of the safety status evaluation of power equipment;

[0031] A judgment matrix acquisition module is used to call the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain according to the expert scoring results, and obtain the initialized judgment matrix of each evaluation dimension;

[0032] A hierarchical single sorting module, used to determine the hierarchical single sorting of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension;

[0033] A safety status data acquisition module is used to acquire safety status data of each evaluation dimension;

[0034] A data information array acquisition module is used to call the smart contract of the data information array of each evaluation dimension on the blockchain according to the security status data to obtain the data information array of each evaluation dimension;

[0035] The unsafe rate determination module is used to perform weighting according to the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension to obtain the unsafe rate of the power equipment.

[0036] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0037] Obtain expert scoring results for each assessment dimension of the safety status assessment of power equipment;

[0038] According to the expert scoring results, the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain is called to obtain the initialized judgment matrix of each evaluation dimension;

[0039] Determine the hierarchical order of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension;

[0040] Obtain security status data for each assessment dimension;

[0041] According to the security status data, the smart contract of the data information array of each evaluation dimension on the blockchain is called to obtain the data information array of each evaluation dimension;

[0042] The unsafe rate of the electric power equipment is obtained by weighting the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0044] Obtain expert scoring results for each assessment dimension of the safety status assessment of power equipment;

[0045] According to the expert scoring results, the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain is called to obtain the initialized judgment matrix of each evaluation dimension;

[0046] Determine the hierarchical order of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension;

[0047] Obtain security status data for each assessment dimension;

[0048] According to the security status data, the smart contract of the data information array of each evaluation dimension on the blockchain is called to obtain the data information array of each evaluation dimension;

[0049] The unsafe rate of the electric power equipment is obtained by weighting the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension.

[0050] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0051] Obtain expert scoring results for each assessment dimension of the safety status assessment of power equipment;

[0052] According to the expert scoring results, the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain is called to obtain the initialized judgment matrix of each evaluation dimension;

[0053] Determine the hierarchical order of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension;

[0054] Obtain security status data for each assessment dimension;

[0055] According to the security status data, the smart contract of the data information array of each evaluation dimension on the blockchain is called to obtain the data information array of each evaluation dimension;

[0056] The unsafe rate of the electric power equipment is obtained by weighting the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension.

[0057] The above-mentioned method, device, computer device, storage medium and computer program product for assessing the safety status of power equipment obtain the expert scoring results of each assessment dimension of the safety status assessment of the power equipment, and call the smart contract initialized by the judgment matrix of each assessment dimension on the blockchain according to the expert scoring results to obtain the initialization judgment matrix of each assessment dimension; determine the hierarchical single sorting of each assessment dimension according to the initialization judgment matrix of each assessment dimension; obtain the safety status data of each assessment dimension; call the smart contract of the data information array of each assessment dimension on the blockchain according to the safety status data to obtain the data information array of each assessment dimension; weight the data information array of each assessment dimension and the hierarchical single sorting of each assessment dimension to obtain the insecurity rate of the power equipment. This method utilizes the decentralized and tamper-proof characteristics of the blockchain, and is based on the smart contract combined with the hierarchical analysis method, which not only ensures that the safety status of the power equipment will not be tampered with, but also improves the accuracy of the safety assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 An application environment diagram of a method for assessing the safety status of electric power equipment in one embodiment;

[0059] Figure 2 A schematic diagram of a flow chart of a method for evaluating a safety status of an electric power device in one embodiment;

[0060] Figure 3 A schematic diagram of a multi-level hierarchical model for evaluating the safety status of power equipment in one embodiment;

[0061] Figure 4 It is a schematic diagram of a flow chart of determining a single sorting of a matrix hierarchy in another embodiment;

[0062] Figure 5 is a structural block diagram of a safety status assessment device for power equipment in one embodiment;

[0063] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] This application provides a safety assessment method for power equipment system based on blockchain, which can be applied to Figure 1 The power equipment system shown. By obtaining the expert scoring results of each evaluation dimension of the safety status evaluation of the power equipment, according to the expert scoring results, the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain is called to obtain the initialization judgment matrix of each evaluation dimension; according to the initialization judgment matrix of each evaluation dimension, the hierarchical single sorting of each evaluation dimension is determined; the safety status data of each evaluation dimension is obtained; according to the safety status data, the smart contract of the data information array of each evaluation dimension on the blockchain is called to obtain the data information array of each evaluation dimension; according to the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension, the insecurity rate of the power equipment is obtained by weighting. Triggers are set to realize the continuous evaluation of the safety status of the power equipment, and the dynamic safety status evaluation results of the power equipment within a period of time are obtained.

[0066] In one embodiment, Figure 2 As shown, a method for evaluating the safety status of electric power equipment is provided. The method is applied to Figure 1 The power equipment system in the embodiment comprises the following steps:

[0067] To conduct a safety status assessment of power equipment based on Ethereum blockchain technology, it is necessary to build a local Ethereum private chain, select Ganache to build the blockchain environment, and open the HTTP-RPC API (default port 8545) for external calls. Smart contracts are a general term for codes running on the Ethereum Virtual Machine (EVM). Smart contracts are developed using the Solidity programming language and are deployed to the EVM for execution after successful compilation.

[0068] Write the security status assessment algorithm into the smart contract of the contract layer and compile the contract. Compilation is a pre-step for deploying and testing contracts. The common method is to use the solc tool for command line compilation, but this process involves large sections of content such as ABI, and copying and pasting bytecode is prone to errors. Therefore, consider introducing the solc module in the project, automatically compiling and reading the results through the solc.compile() function call in the node command line, and further optimize the compilation, that is, write the above solc compilation process into a script and automatically complete the compilation through the node command, which is convenient and less prone to errors.

[0069] Since any transaction on Ethereum requires an account to initiate, there must be enough balance in the account to pay the transaction fee (Transaction Fee). If the balance is 0, the deployment will fail. Using ganache, there are 10 accounts by default, each with 100ETH. In addition, the web3.js dependency package needs to be installed in the project for publishing contracts, etc. The compile script introduces fs-extra, a path gadget, which is used to save the compilation results to the file system, so that the compilation results can be used directly in the subsequent deployment process. Using fs-extra, the previous compilation results are cleared before the compilation operation, and then the latest compilation results are saved, ensuring the consistency of the compilation and deployment process of the smart contract.

[0070] As with the compilation process, the deployment operation is written into the script and automatically completed through the node command. The Ethereum account address0, as the data owner, deploys the contract to the blockchain in the form of sending transactions; each Ethereum account calls the contract function by sending transactions, and finally implements the security assessment.

[0071] Step 202: Obtain expert scoring results for each evaluation dimension of the safety status evaluation of the power equipment.

[0072] Among them, the safety status of power equipment refers to the occurrence of equipment failures, network attacks, and equipment communication failures, which represents the overall safety of power equipment. The safety status of power equipment includes three types: equipment failures, network attacks on equipment terminals, and equipment communication failures. Equipment failures include sensor failures and radio frequency identification failures. Network attacks on equipment terminals include DDOS attacks, data leaks, API attacks, Trojan attacks, and other factors. Equipment communication failures include communication channel interruptions, data packet errors, and data packet losses.

[0073] The above security statuses are stored on the blockchain by calling the security status array initialization function in the smart contract through the Ethereum account to prevent malicious tampering from affecting the evaluation results. The array data structure is shown in Table 1.

[0074] Table 1 Data structure of smart contract array

[0075] Array Name Storage Content criterionMatrix[][] Criteria layer judgment matrix physicalStateFactorMatrix[][] Factor level judgment matrix: physical state cyberAttackFactorMatrix[][] Factor-level judgment matrix: Cyberattack communicationTransportFactorMatrix[][] Factor layer judgment matrix: communication transmission physicalStateData[] Equipment physical status data cyberAttackData[] Device attack status data communicationTransportData[] Device communication transmission status data

[0076] The smart contract functions involved in the power equipment safety status assessment algorithm and hierarchical analysis algorithm are shown in Table 2.

[0077] Table 2 Smart contract functions for power equipment safety status assessment

[0078]

[0079]

[0080] Based on the state assessment idea of ​​analytic hierarchy process (AHP), the following Figure 3 The multi-level hierarchical model of the hierarchical analysis method shown in the figure has three layers, the highest layer is the target layer, the middle layer is the criterion layer, and the lowest layer is the factor layer. According to the hierarchical structure model of power equipment, each layer of factors is based on the adjacent upper layer of factors, and the scale A of the judgment matrix A is constructed according to the scale value method in Table 3. ij ,The importance relative to the target layer is obtained through pairwise comparison, for example, the three elements of equipment failure, network attack situation and equipment communication failure in the criterion layer are obtained with respect to the target layer.,The scale value of the factor indicator in each judgment matrix is ​​determined by senior experts in the power system.

[0081] Table 3 Scale A ij and meaning

[0082]

[0083] Based on the hierarchical analysis algorithm, a multi-level hierarchical model for power equipment safety status assessment is constructed to obtain the scale value of the factor index in each judgment matrix determined by the expert scoring. Since the experts have a comprehensive understanding of all aspects of the power equipment system, the scoring results are authentic and reliable.

[0084] Step 204: Based on the expert scoring results, the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain is called to obtain the initialized judgment matrix of each evaluation dimension.

[0085] Among them, the judgment matrix of each evaluation dimension includes a judgment matrix at the criterion level and a judgment matrix at the factor level.

[0086] The scores of the criterion layer and the factor layer after expert scoring are used as input parameters of the smart contract functions setCriterionMatrix, setPhysicalStateFactorMatrix, setCyberAttackFactorMatrix and setCommunicationTransportFactorMatrix respectively. The functions are called by the data owner of Ethereum (account address0) to obtain the corresponding criterion layer judgment matrix and factor layer judgment matrix.

[0087] Specifically, the smart contract function setCriterionMatrix obtains the initialization criterion layer judgment matrix according to the expert scoring results of the criterion layer, the smart contract function setPhysicalStateFactorMatrix obtains the initialization physical state factor layer judgment matrix according to the expert scoring results of the physical state factor layer, the smart contract function setCyberAttackFactorMatrix obtains the initialization network attack factor layer judgment matrix according to the expert scoring results of the network attack factor layer, and the smart contract function setCommunicationTransportFactorMatrix obtains the initialization communication transmission factor layer judgment matrix according to the expert scoring results of the communication transmission factor layer. The criterion layer judgment matrix is ​​shown in Table 4.

[0088] Table 4 Judgment matrix of criterion layer of hierarchical structure model of power equipment

[0089]

[0090] The factor layer judgment matrix of equipment failure is shown in Table 5.

[0091] Table 5 Judgment matrix of power equipment fault factor layer

[0092] Sensor failure rate RFID failure rate Sensor failure rate 1 2 RFID failure rate 1 / 2 1

[0093] The factor-level judgment matrix of network attack situations is shown in Table 6.

[0094] Table 6 Factor layer judgment matrix of cyber attack situation of power equipment

[0095] DDOS attack rate Data Breach Rate API attack rate Trojan virus attack rate DDOS attack rate 1 7 5 6 Data Breach Rate 1 / 7 1 2 3 API attack rate 1 / 5 1 / 2 1 2 Trojan virus attack rate 1 / 6 1 / 3 1 / 2 1

[0096] The factor layer judgment matrix of device communication failure is shown in Table 7.

[0097] Table 7 Judgment matrix of power equipment communication failure factor layer

[0098] Communication channel interruption rate Packet Error Rate Packet loss rate Communication channel interruption rate 1 7 8 Packet Error Rate 1 / 7 1 2 Packet loss rate 1 / 8 1 / 2 1

[0099] Step 206, determining the hierarchical single ranking of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension.

[0100] Among them, hierarchical single sorting refers to the sorting weight of the relative importance of the same hierarchical element to an element of the previous layer. The hierarchical single sorting of the present invention includes the sorting weight of the relative importance of the factor layer elements to the criterion layer elements, and the sorting weight of the relative importance of the criterion layer to the target layer. The hierarchical single sorting weights of the factor layer elements to the criterion layer elements are subdivided into: the sorting weights of the relative importance of the two factors of sensor failure and radio frequency identification failure to the equipment's own failure criterion, the sorting weights of the relative importance of the factors of DDOS attack, data leakage, API attack and Trojan attack to the equipment terminal's network attack criterion, and the sorting weights of the relative importance of the factors of communication channel interruption, data packet error and data packet loss to the equipment communication failure criterion. The hierarchical single sorting weights of the criterion layer elements to the target layer elements are subdivided into: the sorting weights of the relative importance of the equipment's own failure, network attack situation and equipment communication failure criterion to the safety status assessment of power equipment.

[0101] The safety status assessment algorithm also requires all the weight information of the criterion layer and the factor layer, so the hierarchical analysis is used to obtain reasonable weights. Specifically, the sum-product method is used to calculate the maximum eigenvector of the judgment matrix of the hierarchical structure model of the power equipment, and the maximum eigenvector is normalized to obtain the normalized maximum eigenvector. According to the normalized maximum eigenvector, the hierarchical single sorting of each evaluation dimension is obtained. The above steps are written into the hierarchical analysis analyticHierarchyProcess function, which returns all the weight information of the criterion layer relative to the target layer and the factor layer relative to the criterion layer.

[0102] Step 208: Obtain security status data of each evaluation dimension.

[0103] Among them, security status data includes equipment physical status monitoring data information, equipment network attack monitoring data information and equipment communication transmission monitoring data information.

[0104] Specifically, all security status data to be evaluated in the factor layer are monitored by the power monitoring equipment, that is, the equipment physical status monitoring data information, the equipment network attack monitoring data information and the equipment communication transmission monitoring data information are monitored by the power monitoring equipment, and the above-mentioned security status data equipment physical status monitoring data information, equipment network attack monitoring data information and equipment communication transmission monitoring data information are all stored on the blockchain.

[0105] Step 210: Based on the security status data, call the smart contract of the data information array of each evaluation dimension on the blockchain to obtain the data information array of each evaluation dimension.

[0106] Specifically, according to the device physical state monitoring data, the setPhysicalStateData function of the smart contract is called to obtain the initialized device physical state data array; according to the device network attack monitoring data, the setCyberAttackData function is called to obtain the initialized device network attack data array; according to the device communication transmission monitoring data, the setCommunicationTransportData function is called to obtain the initialized device communication transmission data array.

[0107] The above setPhysicalStateData function, setCyberAttackData function and setCommunicationTransportData function are called by three Ethereum accounts (address1, address2, address3), which represent the power monitoring equipment for monitoring network attack information, the power monitoring equipment for monitoring communication transmission information and the power monitoring equipment for monitoring the physical state of the equipment. The above security status data are all stored on the blockchain, and the above security status data are used in the security status assessment algorithm.

[0108] Step 212, weighting is performed according to the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension to obtain the unsafe rate of the power equipment.

[0109] The security state assessment securityStateAssessment function processes the running results of the hierarchical analysis algorithm, that is, the weighted sum of each weight of the criterion layer and each weight of the factor layer obtained by the hierarchical analysis algorithm and each ratio in the corresponding security state data is used to obtain the insecurity rate.

[0110] The above-mentioned method for assessing the safety status of power equipment obtains the expert scoring results of each assessment dimension of the safety status assessment of the power equipment, and calls the smart contract for initializing the judgment matrix of each assessment dimension on the blockchain according to the expert scoring results to obtain the initialization judgment matrix of each assessment dimension; determines the hierarchical single sorting of each assessment dimension according to the initialization judgment matrix of each assessment dimension; obtains the safety status data of each assessment dimension; calls the smart contract of the data information array of each assessment dimension on the blockchain according to the safety status data to obtain the data information array of each assessment dimension; and weights the data information array of each assessment dimension and the hierarchical single sorting of each assessment dimension to obtain the insecurity rate of the power equipment. This method utilizes the decentralized and tamper-proof characteristics of the blockchain, and is based on the smart contract combined with the hierarchical analysis method, which not only ensures that the safety status of the power equipment will not be tampered with, but also improves the accuracy of the safety assessment.

[0111] In one embodiment, Figure 4 As shown, according to the initialization judgment matrix of each evaluation dimension, the hierarchical single sorting of each evaluation dimension is determined, including:

[0112] In the hierarchical single sorting step 402, the maximum eigenvector of the judgment matrix of each evaluation dimension is calculated using the sum-product method.

[0113] Normalize each column element of the judgment matrix, that is, let the general term of the elements in the matrix be: Add the normalized judgment matrix of each column row by row: Vector W i Perform normalization, that is, get It is the maximum eigenvector of the normalized matrix.

[0114] Step 404, calculating the maximum eigenvalue of each evaluation dimension according to the maximum eigenvector.

[0115] The maximum eigenvalue of the judgment matrix max The calculation formula is Among them, A is the judgment matrix, n is the number of dimensions of the judgment matrix, is the judgment matrix and The product of (n rows and 1 columns) matrices.

[0116] Step 406: Verify the judgment matrix of each evaluation dimension according to the maximum eigenvalue.

[0117] Step 408, determining whether the matrix check has passed, if the check has passed, executing step 410.

[0118] Step 410: Use the normalized maximum eigenvector as the hierarchical single-ranking weight vector of each evaluation dimension.

[0119] The judgment matrix of each evaluation dimension is verified by random consistency ratio. If the verification passes, the normalized maximum eigenvector is used as the hierarchical single-ranking weight vector of each evaluation dimension.

[0120] Specifically, according to the maximum characteristic root λ max Calculate the consistency index of the judgment matrix The random consistency index RI is introduced to measure the size of CI. The random consistency index RI is shown in Table 8.

[0121] Table 8 Random consistency index RI

[0122] n 1 2 3 4 5 6 7 8 9 10 11 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51

[0123] Random Consistency Ratio: When the consistency ratio CR is less than 0.1, it is considered that the inconsistency of the judgment matrix A is within the allowable range, has satisfactory consistency, and passes the consistency test. Its normalized eigenvector can be used As the hierarchical single-rank weight vector for each evaluation dimension.

[0124] In this embodiment, the initialization judgment matrix of each evaluation dimension is calculated by the sum-product method, and the relative importance of the corresponding sub-elements of the criterion layer and the factor layer is detected by the random consistency ratio. If the initialization judgment matrix has satisfactory consistency, the weight vector of the criterion layer relative to the target layer and the weight vectors of the factor layer relative to the criterion layer can be reasonably given.

[0125] In one embodiment, Figure 4 As shown, if the verification fails, step 409 is executed.

[0126] Step 409, adjust the initialization judgment matrix of each evaluation dimension, and return to step 402 of calculating the maximum eigenvector of the judgment matrix of each evaluation dimension using the sum-product method until the verification passes.

[0127] Wherein, failure of the check means that the random consistency ratio CR ≥ 0.1. Adjusting the initialization judgment matrix of each evaluation dimension means reconstructing the judgment matrix A and adjusting the elements in the matrix A.

[0128] Specifically, when the random consistency ratio CR≥0.1, the inconsistency of the judgment matrix A exceeds the allowable range and does not have satisfactory consistency. At this time, it is necessary to reconstruct the judgment matrix A and adjust the elements in the matrix A. The maximum eigenvector of the judgment matrix of each evaluation dimension is calculated using the sum-product method for the adjusted judgment matrix until the verification passes.

[0129] In this embodiment, the initialization judgment matrix that does not have satisfactory consistency is adjusted until the adjusted judgment matrix passes the verification, so that the weight vector of the criterion layer relative to the target layer and the weight vectors of the factor layer relative to the criterion layer can be obtained.

[0130] In one embodiment, the method also includes: when a change in security status data is monitored, executing the step of obtaining expert scoring results for each assessment dimension of the security status assessment of the power equipment to trigger the security status assessment of the power equipment; and obtaining a dynamic security status assessment result of the power equipment over a period of time based on the insecurity rate of the power equipment obtained from multiple security status assessments.

[0131] Among them, the security status data includes equipment physical status monitoring data information, equipment network attack monitoring data information and equipment communication transmission monitoring data information. The security status assessment of power equipment is carried out by calling the security assessment contract function in the smart contract. The security status assessment results are divided according to the insecurity rate. If the assessment result is in the range of 0% to 10%, the power equipment is very safe; if the assessment result is in the range of 10% to 20%, the power equipment is safe; if the assessment result is in the range of 120% to 30%, the power equipment is relatively safe; if the assessment result is in the range of 30% to 40%, the power equipment is in general security; if the assessment result is in the range of 40% to 50%, the power equipment is relatively dangerous; if the assessment result is in the range of 50% to 60%, the power equipment is dangerous; if the assessment result is higher than 60%, the power equipment is at high risk.

[0132] Specifically, a trigger is set on the monitoring device node connected to the blockchain network. When the increase of unsafe data in the security status data is detected, the script is triggered. The script includes: obtaining the expert scoring results of each evaluation dimension of the security status evaluation of the power equipment, calling the security status array initialization function in the smart contract to update the security status information stored in the contract, calling the security assessment contract function to perform security assessment, etc. Thus, the unsafe rate of the power equipment in a period of time is obtained based on multiple security status evaluations, and the security status evaluation result of the power equipment in the period of time is obtained based on the unsafe rate of the power equipment in the period of time. The security status evaluation result can be more intuitively presented through the security situation diagram.

[0133] In this embodiment, the safety status of the power equipment is evaluated by setting a trigger to automatically trigger the script. Within a period of time, the safety status evaluation results of the power equipment are presented through the security situation diagram, thereby realizing dynamic and continuous evaluation of the safety status of the power equipment.

[0134] In one embodiment, the method also includes: establishing a multi-level hierarchical model of the hierarchical analysis method, the multi-level hierarchical model includes a highest level, a middle level and a lowest level; the element of the highest level is the unsafe rate of the power equipment; the middle level is the lower level of the highest level, and the elements of the middle level include: the failure rate of the equipment itself, the rate at which the equipment terminal suffers from a network attack, and the equipment communication failure rate; the lowest level elements included in the equipment failure rate are: sensor failure rate and radio frequency identification failure rate; the lowest level elements included in the rate at which the equipment terminal suffers from a network attack are: DDOS attack rate, data leakage rate, API attack rate and Trojan virus attack rate; the lowest level elements included in the equipment communication failure rate are: communication channel interruption rate, data packet error rate and data packet loss rate.

[0135] The multi-level hierarchical model for the safety status assessment of power equipment is established based on the state assessment concept of hierarchical analysis, and the multi-level hierarchical model is a completely independent structure, that is, each factor in the upper layer has an independent lower-level element. The multi-level hierarchical model for the safety status assessment of power equipment has three layers, namely the highest layer, the middle layer and the lowest layer. The highest layer: the target layer, the purpose of decision-making, the problem to be solved; the middle layer: the criterion layer, the main factors, the criteria for decision-making; the lowest layer: the factor layer, the sub-factors of the middle layer. The middle layer is the lower layer of the highest layer, and the lowest layer is the lower layer of the middle layer.

[0136] Specifically, the target layer of the multi-level hierarchical model for power equipment safety status assessment includes three types: equipment failure, equipment terminal network attack, and equipment communication failure. Among them, equipment failure takes into account two factors: sensor failure and radio frequency identification failure, reflecting the stability of the equipment at the physical level; network attacks on equipment terminals take into account factors such as DDOS attacks, data leakage, API attacks, and Trojan attacks, reflecting the defense capability of equipment network security; equipment communication failure takes into account factors such as communication channel interruption, data packet error, and data packet loss, reflecting the stability of equipment communication.

[0137] In this embodiment, a multi-level hierarchical model for safety status assessment of power equipment is established based on the analytic hierarchy process, which provides conditions for establishing a criterion layer judgment matrix and a factor layer judgment matrix.

[0138] In one embodiment, the method further includes: when the unsafe rate of the electric power equipment is higher than a preset value, determining the electric power equipment as an unsafe equipment.

[0139] Specifically, the evaluation result of the safety status adopts a percentage system, and the preset value of the unsafe rate is set to 60%. When the unsafe rate of the power equipment is higher than the preset value, the power equipment is determined to be an unsafe equipment and recorded.

[0140] In this embodiment, by setting a preset value of the unsafe rate, the evaluation result of the safety status of the power equipment is determined, and the power equipment determined to be unsafe is recorded, so as to facilitate users to understand the safety status of the power equipment.

[0141] The safety status assessment method of power equipment provided in this application is aimed at the multiple participating entities of the new power system, the grid, the load and the storage, and the uneven safety protection capabilities. Based on the characteristics of decentralization, non-tamperability, openness, transparency, traceability, and collective maintenance of blockchain technology, the safety status of the power equipment to be assessed is stored in the blockchain, ensuring that the safety status of the power equipment will not be tampered with, and realizing the continuous assessment of the safety status of the power equipment. First, the power equipment safety status assessment algorithm is written into the smart contract to realize automatic execution, and the automatic triggering of the safety assessment is realized. The safety status assessment algorithm obtains weights based on the hierarchical analysis algorithm, and the expert scoring mechanism is introduced into the safety status assessment based on the hierarchical analysis to make the weight distribution more reasonable. The unsafe rate of the power equipment is obtained by weighted summation of the calculated weights and the corresponding ratios in the safety status data, and the safety status assessment result of the power equipment is obtained by comparing with the threshold. Triggers are set to realize the continuous assessment of the safety status of the power equipment, and the safety situation of the power equipment is reflected through continuous safety status assessment.

[0142] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0143] Based on the same inventive concept, the embodiment of the present application also provides a safety status assessment device for electric equipment for implementing the safety status assessment method for electric equipment involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the safety status assessment device for one or more electric equipment provided below can refer to the limitations of the safety status assessment method for electric equipment above, and will not be repeated here.

[0144] In one embodiment, Figure 5 As shown, a safety status assessment device for power equipment is provided, including: an expert scoring module 502, a judgment matrix acquisition module 504, a hierarchical single sorting module 506, a safety status data acquisition module 508, a data information array acquisition module 510 and an unsafe rate determination module 512, wherein:

[0145] The expert scoring module 502 is used to obtain the expert scoring results of each evaluation dimension of the safety status evaluation of the power equipment.

[0146] The judgment matrix acquisition module 504 is used to call the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain according to the expert scoring results, and obtain the initialized judgment matrix of each evaluation dimension.

[0147] The hierarchical single sorting module 506 is used to determine the hierarchical single sorting of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension.

[0148] The security status data acquisition module 508 is used to acquire the security status data of each evaluation dimension.

[0149] The data information array acquisition module 510 is used to call the smart contract of the data information array of each evaluation dimension on the blockchain according to the security status data to obtain the data information array of each evaluation dimension.

[0150] The unsafe rate determination module 512 is used to perform weighting according to the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension to obtain the unsafe rate of the power equipment.

[0151] In another embodiment, the hierarchical single sorting module is used to calculate the maximum eigenvector of the judgment matrix of each evaluation dimension using the sum-product method; calculate the maximum eigenroot of each evaluation dimension according to the maximum eigenvector;

[0152] The judgment matrix of each evaluation dimension is verified according to the maximum eigenvalue root. If the verification passes, the normalized maximum eigenvector is used as the hierarchical single-ranking weight vector of each evaluation dimension.

[0153] In another embodiment, the hierarchical single sorting module is also used to adjust the hierarchical single sorting of the initialization judgment matrix of each evaluation dimension if the verification fails, and return to the step of calculating the maximum eigenvector of the judgment matrix of each evaluation dimension using the sum-product method until the verification passes.

[0154] In another embodiment, the device also includes a monitoring trigger module, which is used to execute the steps of obtaining expert scoring results of each assessment dimension of the safety status assessment of the power equipment when a change in the safety status data is monitored, so as to trigger the safety status assessment of the power equipment; based on the insecurity rate of the power equipment obtained from multiple safety status assessments, a dynamic safety status assessment result of the power equipment over a period of time is obtained.

[0155] In another embodiment, the device also includes a hierarchical model establishment module, which is used to establish a multi-level hierarchical model of the hierarchical analysis method, and the multi-level hierarchical model includes a highest level, a middle level and a lowest level; the element of the highest level is the unsafe rate of the power equipment; the middle level is the lower level of the highest level, and the elements of the middle level include: the equipment's own failure rate, the rate at which the equipment terminal suffers from a network attack, and the equipment communication failure rate; the lowest level elements included in the equipment's own failure rate are: sensor failure rate and radio frequency identification failure rate; the lowest level elements included in the rate at which the equipment terminal suffers from a network attack are: DDOS attack rate, data leakage rate, API attack rate and Trojan virus attack rate; the lowest level elements included in the equipment communication failure rate are: communication channel interruption rate, data packet error rate and data packet loss rate.

[0156] In another embodiment, the apparatus further comprises an unsafe equipment determination module, which is used to determine the electric equipment as an unsafe equipment when the unsafe rate of the electric equipment is higher than a preset value.

[0157] Each module in the above-mentioned power equipment safety status assessment device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0158] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for evaluating the safety status of an electric power device is implemented.

[0159] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0160] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0161] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0162] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0163] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0164] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0165] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for evaluating the safety status of electric power equipment, characterized in that: The method comprises: Obtain expert scoring results for each assessment dimension of the safety status assessment of power equipment; According to the expert scoring results, the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain is called to obtain the initialized judgment matrix of each evaluation dimension; Determine the hierarchical order of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension; Obtain security status data for each assessment dimension; According to the security status data, the smart contract of the data information array of each evaluation dimension on the blockchain is called to obtain the data information array of each evaluation dimension; The unsafe rate of the electric power equipment is obtained by weighting the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension.

2. The method according to claim 1, characterized in that: Determining the hierarchical order of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension includes: Hierarchical single sorting uses the sum-product method to calculate the maximum eigenvector of the judgment matrix of each evaluation dimension; Calculate the maximum eigenvalue of each evaluation dimension according to the maximum eigenvector; The judgment matrix of each evaluation dimension is verified according to the maximum eigenvalue root. If the verification passes, the normalized maximum eigenvector is used as the hierarchical single-ranking weight vector of each evaluation dimension.

3. The method according to claim 2, characterized in that The method further includes: if the verification fails, adjusting the initialization judgment matrix of each evaluation dimension, and returning to the step of using the sum-product method to calculate the maximum eigenvector of the judgment matrix of each evaluation dimension until the verification passes.

4. The method according to claim 1, characterized in that The method further comprises: When a change in the security status data is monitored, the step of obtaining expert scoring results of each evaluation dimension of the security status evaluation of the power equipment is executed to trigger the security status evaluation of the power equipment; According to the unsafe rate of the electric power equipment obtained by multiple safety status evaluations, a dynamic safety status evaluation result of the electric power equipment within a period of time is obtained.

5. The method according to claim 1, characterized in that The method further comprises: Establishing a multi-level hierarchical model of the analytic hierarchy process, wherein the multi-level hierarchical model includes a highest level, a middle level and a lowest level; The highest level element is the power equipment unsafe rate; The middle layer is the lower layer of the highest layer, and the elements of the middle layer include: the failure rate of the device itself, the rate of network attack on the device terminal, and the failure rate of device communication; The lowest level elements of the equipment failure rate include: sensor failure rate and RFID failure rate; The lowest level elements of the network attack rate of the device terminal include: DDOS attack rate, data leakage rate, API attack rate and Trojan virus attack rate; The lowest level elements of the device communication failure rate include: communication channel interruption rate, data packet error rate and data packet loss rate.

6. The method according to claim 1, characterized in that The method further comprises: When the unsafe rate of the electric power equipment is higher than a preset value, the electric power equipment is determined to be an unsafe equipment.

7. A safety status assessment device for electric power equipment, characterized in that: The device comprises: An expert scoring module is used to obtain expert scoring results of each evaluation dimension of the safety status evaluation of power equipment; A judgment matrix acquisition module is used to call the smart contract for initializing the judgment matrix of each evaluation dimension on the blockchain according to the expert scoring results, and obtain the initialized judgment matrix of each evaluation dimension; A hierarchical single sorting module, used to determine the hierarchical single sorting of each evaluation dimension according to the initialization judgment matrix of each evaluation dimension; A safety status data acquisition module is used to acquire safety status data of each evaluation dimension; A data information array acquisition module is used to call the smart contract of the data information array of each evaluation dimension on the blockchain according to the security status data to obtain the data information array of each evaluation dimension; The unsafe rate determination module is used to perform weighting according to the data information array of each evaluation dimension and the hierarchical single sorting of each evaluation dimension to obtain the unsafe rate of the power equipment.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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