A Defense Scheduling Method Based on Security Protection Mapping Adaptation in an Active Distribution Network

Abstract

The present invention discloses a defense scheduling method based on security protection mapping adaptation in an active distribution network, belonging to the field of active distribution network security protection technology. The method comprises: using an entropy weight method to calculate the importance of each data in different business types; using a Monte Carlo method to calculate the performance parameters of different security protection strategies; constructing a multi-objective optimization function for different business types based on the importance and the performance parameters; optimizing and solving the multi-objective optimization function to obtain a defense scheduling scheme for the corresponding business type. The present invention can effectively solve the technical problems that existing security protection strategies lack dynamic response capabilities and have limitations, specifically meet the personalized needs of various types of data in the power grid, improve the adaptability and reliability of the security defense process, and enhance the overall security protection capabilities of the active distribution network information system.

Description

Technical Field

[0001] The present invention relates to the technical field of active power distribution network security protection, and in particular to a defense scheduling method based on security protection mapping adaptation in an active power distribution network. Background Art

[0002] The active distribution network (ADN) is the core hub connecting the main grid, distributed resources, and the user side. Its safe and stable operation is crucial for ensuring secure and reliable power supply to users. With the massive access of distributed resources in the distribution network, the boundaries of its information space are continuously expanding, and the information network landscape is becoming increasingly open and interconnected. In this context, the paths for the spread and intrusion of security risks are increasing, making the effective mitigation of security risks particularly important.

[0003] Although the current distribution network information system has deployed basic protection measures such as firewalls, intrusion detection systems (IDS), and system security isolation technology to deal with network risks such as network congestion and data tampering, its security protection policy configuration is fixed and relies on a static rule base. It lacks dynamic response capabilities and is difficult to dynamically adjust policies to meet different business needs under multiple types of security risk intrusions. As a result, the security protection policy is not well matched with actual business needs and has certain limitations. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a defense scheduling method based on security protection mapping adaptation in an active distribution network to solve the technical problems that the existing security protection strategies lack dynamic response capabilities and have limitations.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] The present invention provides a defense scheduling method based on security protection mapping adaptation in an active distribution network, comprising:

[0007] The entropy weight method is used to calculate the importance of each data in different business types;

[0008] Monte Carlo method is used to calculate the performance parameters of different security protection strategies;

[0009] Constructing a multi-objective optimization function for different service types based on the importance and the performance parameters;

[0010] The multi-objective optimization function is optimized and solved to obtain a defense scheduling solution for the corresponding business type.

[0011] Optionally, the using the entropy weight method to calculate the importance of each data in different business types includes:

[0012] Determine the index value of the demand index of each data in the target business type;

[0013] The index values are normalized to obtain normalized results:

[0014]

[0015] Where, Before and after standardization The first data The indicator value of a demand indicator, is the number of data in the target business type;

[0016] Calculate the index value proportion of each data demand index based on the standardization result:

[0017]

[0018] Where, For the The first data The proportion of the indicator values of the demand indicators;

[0019] Calculate the information entropy of each demand indicator according to the proportion of the indicator value:

[0020]

[0021] Where, For the The information entropy of the demand indicators, , m is the number of demand indicators;

[0022] The entropy weight of each demand indicator is calculated based on the information entropy:

[0023]

[0024]

[0025] Where, For the The information utility value of a demand indicator, For the The entropy weight of a demand indicator;

[0026] Calculate the importance of each data under the target business type based on the entropy weight and normalization results:

[0027]

[0028] Where, For the The importance of the data in the target business type.

[0029] Optionally, the demand indicators include an extremely small indicator and an extremely large indicator, the extremely small indicator includes a delay demand indicator, an error rate demand indicator, and a security level demand indicator, and the extremely large indicator includes a sensitivity demand indicator.

[0030] Optionally, the standardization of the indicator value further includes pre-adopting a maximum-minimum conversion method to convert the indicator value of the extremely small indicator into the indicator value of the extremely large indicator.

[0031] Optionally, the security protection steps include encryption, abnormal data detection, abnormal data recovery, DoS attack detection, routing scheduling and decryption; the security protection corresponding to the security protection includes an encryption algorithm, an abnormal data detection algorithm, an abnormal data recovery algorithm, a DoS attack detection algorithm, a routing scheduling algorithm and a decryption algorithm, the encryption algorithm and the decryption algorithm are in a one-to-one correspondence, and the performance parameters of the security protection strategy include the running time of the encryption algorithm, abnormal data detection algorithm, abnormal data recovery algorithm, DoS attack detection algorithm, routing scheduling algorithm and decryption algorithm, the error rate of the abnormal data detection algorithm and the abnormal data recovery algorithm, and the security level of the encryption algorithm and the decryption algorithm.

[0032] Optionally, constructing a multi-objective optimization function for different business types includes:

[0033] Determine the target business type, use the demand index value of the most important data in the target business type as a constraint condition, use the security protection strategy selected in each step of security protection as a variable, and calculate the total time of defense scheduling , the total error rate of the abnormal data detection algorithm and the abnormal data recovery algorithm , the overall security level of the encryption algorithm and decryption algorithm ;

[0034] According to the total time , total error rate and overall safety level Multi-objective optimization function fitting target business type :

[0035]

[0036] Where, are all weight parameters.

[0037] Optionally, the total time of the defense scheduling for:

[0038]

[0039] Where, is the selection matrix and running time matrix of the security protection strategy, * / is the operator, is the selection matrix and transmission time matrix of the routing scheduling algorithm, Solve time for historical multi-objective optimization functions;

[0040]

[0041] Where, For the Step 1 The selection coefficient of a security protection strategy, if Step 1 Select A security protection strategy, ,otherwise, ;

[0042]

[0043] Where, For the Step 1 The running time of each security protection strategy;

[0044]

[0045] Where, For the The number of security protection strategies that can be selected in each step;

[0046] Set up the first The security protection strategy for each step is the routing scheduling algorithm, then:

[0047]

[0048]

[0049] Where, For the Step 1 The selection coefficient of the routing scheduling algorithm, if Step 1 Select A routing scheduling algorithm, ,otherwise, ; Select the target business type for the data The transmission time of each routing scheduling algorithm transmission.

[0050] Optionally, the total error rate of the abnormal data detection algorithm and the abnormal data recovery algorithm is for:

[0051]

[0052] Where, is the selection matrix and error rate matrix of the abnormal data detection and abnormal data recovery algorithm, Multiply the matrix element by element and then sum it up;

[0053]

[0054] Where, For the Anomaly data detection algorithm and The selection coefficient of the abnormal data recovery algorithm, if the first After the abnormal data detection algorithm is An abnormal data recovery algorithm, ,otherwise ;

[0055]

[0056] Where, For the Anomaly data detection algorithm and The error rate of an abnormal data recovery algorithm.

[0057] Optionally, the overall security level of the encryption algorithm and decryption algorithm for:

[0058]

[0059] Where, Selection matrix and security level for encryption algorithms;

[0060] Set up the first The security protection strategy for each step is an encryption algorithm, then:

[0061]

[0062]

[0063] Where, For the Step 1 The selection coefficient of the encryption algorithm, if Step 1 Select encryption algorithms, ,otherwise, ; For the Step 1 The security level of an encryption algorithm and its decryption algorithm.

[0064] Optionally, the constraints of the multi-objective optimization function are:

[0065]

[0066]

[0067]

[0068] Where, These are the indicator values of the delay requirement index, error rate requirement index, and security level requirement index of the most important data in the target business type.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] This invention provides a defense scheduling method based on security protection mapping adaptation in active power distribution networks. This method employs multi-objective optimization theory to construct a real-time mapping model between business needs and protection strategies. By balancing core metrics such as data real-time performance and accuracy, it specifically addresses the personalized needs of various types of data in the power grid, improving the adaptability and reliability of security protection processes. Compared to traditional methods that typically use fixed protection strategies to handle all types of data, this invention efficiently meets various business needs, effectively addresses data security risks and network congestion, and enhances the overall security protection capabilities of active power distribution network information systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flow chart of a defense scheduling method based on security protection mapping adaptation in an active distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0073] Example 1:

[0074] like Figure 1 As shown, an embodiment of the present invention provides a defense scheduling method based on security protection mapping adaptation in an active distribution network, comprising the following steps:

[0075] Step S1: Calculate the importance of each data in different business types using the entropy weight method.

[0076] Considering the existence of multiple types of power services (such as optimized scheduling services, voltage regulation and auxiliary frequency regulation services, and security control services, etc.), in order to optimize and adapt multiple security protection strategies according to the needs of each type of business, the present invention divides the data into importance levels according to different types of business, obtains the importance of data in different business types and sorts them from high to low, and adapts the optimal data protection strategy for this business type based on the business needs of the most important data (the one with the highest importance score).

[0077] Specifically in this embodiment, the entropy weight method is used to calculate the importance of each data in different business types, including:

[0078] Determine the index value of the demand index of each data in the target business type;

[0079] Standardize the indicator values to obtain the standardized results:

[0080]

[0081] Where, Before and after standardization The first data The indicator value of a demand indicator, is the number of data in the target business type;

[0082] Calculate the index value proportion of each data demand index based on the standardization results:

[0083]

[0084] Where, For the The first data The proportion of the indicator values of the demand indicators;

[0085] Calculate the information entropy of each demand indicator according to the proportion of indicator values:

[0086]

[0087] Where, For the The information entropy of the demand indicators, , m is the number of demand indicators;

[0088] Calculate the entropy weight of each demand indicator based on information entropy:

[0089]

[0090]

[0091] Where, For the The information utility value of a demand indicator, For the The entropy weight of a demand indicator;

[0092] Calculate the importance of each data under the target business type based on the entropy weight and normalization results:

[0093]

[0094] Where, For the The importance of the data in the target business type.

[0095] Demand indicators include minimal indicators and maximal indicators. Minimal indicators include latency, error rate, and security level requirements, while maximal indicators include sensitivity. Minimal indicators, meaning smaller values, indicate greater importance, while maximal indicators, meaning larger values, indicate greater importance. Therefore, it is necessary to normalize the values of minimal indicators and convert them uniformly to maximal values to eliminate data discrepancies, ensure the accuracy of importance calculations, and ensure the rationality of evaluation results.

[0096] Use the maximum-minimum conversion method to convert the indicator value of the extremely small indicator into the indicator value of the extremely large indicator:

[0097]

[0098] Where, are the index values before and after conversion, It is the maximum value of the indicator before conversion.

[0099] Step S2: Calculate the performance parameters of different security protection strategies using the Monte Carlo method.

[0100] Specifically in this embodiment, the security protection steps include encryption, abnormal data detection, abnormal data recovery, DoS attack detection, routing scheduling and decryption; the security protection corresponding to the security protection strategy includes encryption algorithm, abnormal data detection algorithm, abnormal data recovery algorithm, DoS attack detection algorithm, routing scheduling algorithm and decryption algorithm, the encryption algorithm and the decryption algorithm are in a one-to-one correspondence, and the performance parameters of the security protection strategy include the running time of the encryption algorithm, abnormal data detection algorithm, abnormal data recovery algorithm, DoS attack detection algorithm, routing scheduling algorithm and decryption algorithm, the error rate of the abnormal data detection algorithm and the abnormal data recovery algorithm, and the security level of the encryption algorithm and the decryption algorithm.

[0101] The Monte Carlo method specifically includes:

[0102] ① Problem modeling:

[0103] The performance parameters of the security protection strategy are estimated through a large number of independent and repeated random experiments (simulating the operation of the security protection strategy), and the statistical mean is used to approximate the actual performance parameters.

[0104] ② Random sampling:

[0105] Run the existing security protection strategy codes multiple times. Assuming that the probability of the performance indicators generated by each run is equal, the total number of runs is recorded as .

[0106] ③Simulation calculation:

[0107] The results of each run are calculated to obtain estimated values of performance parameters.

[0108] ④Statistical analysis:

[0109] According to the law of large numbers, when the sample size is large enough, the frequency of an event can be used as its probability of occurrence. times, and get its total running time , then the running time .

[0110] ⑤Error estimation:

[0111] According to the sample size and variance (a), estimate the error range of the calculation results. According to the central limit theorem, when When large enough, the running time It approximately obeys the normal distribution, and its standard error (i.e., estimation error) is:

[0112] Standard error = Generally, the larger the sample size, the more precise the results.

[0113] Step S3: Based on the importance and performance parameters, construct a multi-objective optimization function for different business types.

[0114] Specifically in this embodiment, constructing a multi-objective optimization function for different business types includes:

[0115] Determine the target business type, use the demand index value of the most important data in the target business type as a constraint condition, use the security protection strategy selected in each step of security protection as a variable, and calculate the total time of defense scheduling , the total error rate of the abnormal data detection algorithm and the abnormal data recovery algorithm , the overall security level of the encryption algorithm and decryption algorithm ;

[0116] According to the total time , total error rate and overall safety level Multi-objective optimization function fitting target business type :

[0117]

[0118] Where, are all weight parameters.

[0119] Since the quantities of different indicators cannot be directly added together, the three parameters here (total time , total error rate and overall safety level ) Normalized data are used in calculations.

[0120] (1), total time of defense scheduling for:

[0121]

[0122] Where, is the selection matrix and running time matrix of the security protection strategy, * / is the operator, is the selection matrix and transmission time matrix of the routing scheduling algorithm, Solve time for historical multi-objective optimization functions;

[0123]

[0124] Where, For the Step 1 The selection coefficient of a security protection strategy, if Step 1 Select A security protection strategy, ,otherwise, ;

[0125]

[0126] Where, For the Step 1 The running time of each security protection strategy;

[0127]

[0128] Where, For the The number of security protection strategies that can be selected in each step;

[0129] Set up the first The security protection strategy for each step is the routing scheduling algorithm, then:

[0130]

[0131]

[0132] Where, For the Step 1 The selection coefficient of the routing scheduling algorithm, if Step 1 Select A routing scheduling algorithm, ,otherwise, ; Select the target business type for the data The transmission time of each routing scheduling algorithm transmission.

[0133] Since the number of selectable security protection strategies in each step is not necessarily the same, it may be necessary to fill in zeros to expand it into a matrix form.

[0134] (2) Total error rate of abnormal data detection algorithm and abnormal data recovery algorithm for:

[0135]

[0136] Where, is the selection matrix and error rate matrix of the abnormal data detection and abnormal data recovery algorithm, Multiply the matrix element by element and then sum it up;

[0137]

[0138] Where, For the Anomaly data detection algorithm and The selection coefficient of the abnormal data recovery algorithm, if the first After the abnormal data detection algorithm is An abnormal data recovery algorithm, ,otherwise ;

[0139]

[0140] Where, For the Anomaly data detection algorithm and The error rate of an abnormal data recovery algorithm.

[0141] (3) The overall security level of the encryption algorithm and decryption algorithm for:

[0142]

[0143] Where, Selection matrix and security level for encryption algorithms;

[0144] Set up the first The security protection strategy for each step is an encryption algorithm, then:

[0145]

[0146]

[0147] Where, For the Step 1 The selection coefficient of the encryption algorithm, if Step 1 Select encryption algorithms, ,otherwise, ; For the Step 1 The security level of an encryption algorithm and its decryption algorithm.

[0148] Only one security protection strategy can be selected for each step, that is, in the steps of encryption, abnormal data detection, abnormal data recovery, DoS attack detection, routing scheduling and decryption, one encryption algorithm, abnormal data detection algorithm, abnormal data recovery algorithm, DoS attack detection algorithm, routing scheduling algorithm and decryption algorithm are selected respectively. Since the encryption algorithm and the decryption algorithm are in a one-to-one correspondence, once the encryption algorithm is selected, the decryption algorithm is also determined.

[0149] Based on the importance obtained in step S1, the most important data in each business type is obtained. When the most important data meets the business requirements of the corresponding business type, it can be considered that the remaining data can meet the requirements. Therefore, the business requirements of the most important data can be used as the common business requirements of the data of this business type to adapt the optimal protection strategy. Therefore, the constraints of the constructed multi-objective optimization function are:

[0150]

[0151]

[0152]

[0153] Where, These are the indicator values of the delay requirement index, error rate requirement index, and security level requirement index of the most important data in the target business type.

[0154] Step S4: Optimize and solve the multi-objective optimization function to obtain a defense scheduling plan for the corresponding business type.

[0155] After integrating the multi-objective optimization function, the optimal security protection strategy was adapted for each business type's data. This means the optimal security protection strategy was selected from multiple security protection strategies at each step, meeting the personalized transmission needs of different businesses and enabling real-time, reliable transmission of diverse data across different business types. Furthermore, the effectiveness of this method can be verified by setting up experimental scenarios.

[0156] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 produce 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 flowcharts and / or block diagrams. 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.

[0158] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0160] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A defense scheduling method based on security protection mapping adaptation in an active distribution network, characterized in that: include: The entropy weight method is used to calculate the importance of each data in different business types; Monte Carlo method is used to calculate the performance parameters of different security protection strategies; Constructing a multi-objective optimization function for different service types based on the importance and the performance parameters; Optimizing and solving the multi-objective optimization function to obtain a defense scheduling plan for the corresponding business type; The security protection steps include encryption, abnormal data detection, abnormal data recovery, DoS attack detection, routing scheduling and decryption; the security protection corresponding to the security protection strategy includes an encryption algorithm, an abnormal data detection algorithm, an abnormal data recovery algorithm, a DoS attack detection algorithm, a routing scheduling algorithm and a decryption algorithm, the encryption algorithm and the decryption algorithm are in a one-to-one correspondence, and the performance parameters of the security protection strategy include the running time of the encryption algorithm, the abnormal data detection algorithm, the abnormal data recovery algorithm, the DoS attack detection algorithm, the routing scheduling algorithm and the decryption algorithm, the error rate of the abnormal data detection algorithm and the abnormal data recovery algorithm, and the security level of the encryption algorithm and the decryption algorithm; The multi-objective optimization function for different business types is constructed as follows: Determine the target business type, use the demand index value of the most important data in the target business type as a constraint condition, use the security protection strategy selected in each step of security protection as a variable, and calculate the total time of defense scheduling , the total error rate of the abnormal data detection algorithm and the abnormal data recovery algorithm , the overall security level of the encryption algorithm and decryption algorithm ; According to the total time , total error rate and overall safety level Multi-objective optimization function fitting target business type : ； Where, are all weight parameters.

2. The defense scheduling method based on security protection mapping adaptation in the active distribution network according to claim 1 is characterized in that: The entropy weight method is used to calculate the importance of each data in different business types, including: Determine the index value of the demand index of each data in the target business type; The index values are normalized to obtain normalized results: ； Where, Before and after standardization The first data The indicator value of a demand indicator, is the number of data in the target business type; Calculate the index value proportion of each data demand index based on the standardization result: ； Where, For the The first data The proportion of the indicator values of the demand indicators; Calculate the information entropy of each demand indicator according to the proportion of the indicator value: ； Where, For the The information entropy of the demand indicators, , m is the number of demand indicators; The entropy weight of each demand indicator is calculated based on the information entropy: ； ； Where, For the The information utility value of a demand indicator, For the The entropy weight of a demand indicator; Calculate the importance of each data under the target business type based on the entropy weight and normalization results: ； Where, For the The importance of the data in the target business type.

3. The defense scheduling method based on security protection mapping adaptation in the active distribution network according to claim 2 is characterized in that: The demand indicators include an extremely small indicator and an extremely large indicator. The extremely small indicator includes a delay demand indicator, an error rate demand indicator, and a security level demand indicator. The extremely large indicator includes a sensitivity demand indicator.

4. The defense scheduling method based on security protection mapping adaptation in the active distribution network according to claim 3 is characterized in that: The standardization of the index value further includes pre-adopting a maximum-minimum conversion method to convert the index value of the extremely small index into the index value of the extremely large index.

5. The defense scheduling method based on security protection mapping adaptation in the active distribution network according to claim 1 is characterized in that: The total time of the defense schedule for: ； Where, is the selection matrix and running time matrix of the security protection strategy, * / is the operator, is the selection matrix and transmission time matrix of the routing scheduling algorithm, Solve time for historical multi-objective optimization functions; ； Where, For the Step 1 The selection coefficient of a security protection strategy, if Step 1 Select A security protection strategy, ,otherwise, ; ； Where, For the Step 1 The running time of each security protection strategy; ； Where, For the The number of security protection strategies that can be selected in each step; Set up the first The security protection strategy for each step is the routing scheduling algorithm, then: ； ； Where, For the Step 1 The selection coefficient of the routing scheduling algorithm, if Step 1 Select A routing scheduling algorithm, ,otherwise, ; Select the target business type for the data The transmission time of each routing scheduling algorithm transmission.

6. The defense scheduling method based on security protection mapping adaptation in the active distribution network according to claim 1 is characterized in that: The total error rate of the abnormal data detection algorithm and the abnormal data recovery algorithm for: ； Where, is the selection matrix and error rate matrix of the abnormal data detection and abnormal data recovery algorithm, Multiply the matrix element by element and then sum it up; ； Where, For the Anomaly data detection algorithm and The selection coefficient of the abnormal data recovery algorithm, if the first After the abnormal data detection algorithm is An abnormal data recovery algorithm, ,otherwise ; ； Where, For the Anomaly data detection algorithm and The error rate of an abnormal data recovery algorithm.

7. The defense scheduling method based on security protection mapping adaptation in an active power distribution network according to claim 1, characterized in that: The overall security level of the encryption algorithm and decryption algorithm for: ； Where, Selection matrix and security level for encryption algorithms; Set up the first The security protection strategy for each step is an encryption algorithm, then: ； ； Where, For the Step 1 The selection coefficient of the encryption algorithm, if Step 1 Select encryption algorithms, ,otherwise, ; For the Step 1 The security level of an encryption algorithm and its decryption algorithm.

8. The defense scheduling method based on security protection mapping adaptation in an active power distribution network according to claim 1, characterized in that: The constraints of the multi-objective optimization function are: ； ； ； Where, These are the indicator values of the delay requirement index, error rate requirement index, and security level requirement index of the most important data in the target business type.

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