A method, device and medium for comprehensive analysis of encryption decision for power transmission line

Abstract

The application discloses a kind of encryption decision comprehensive analysis method, equipment and medium for power transmission line, belong to electric power system information security field, including: obtaining line data, environmental data and data center information, constructs line sensitive coefficient;Extract the center position coordinates corresponding to data center information and the line ID corresponding line position coordinates in line data;Calculate the position distance between center position coordinates and line position coordinates;Based on line sensitive coefficient and position distance, dynamically select encryption algorithm.The application generates line sensitive coefficient based on line data and environmental data;Based on line sensitive coefficient and position distance, dynamically select encryption algorithm, by quantifying the importance of different lines for encryption algorithm selection, and combining the position distance between line ID and data center, select the optimal encryption method between data importance and encryption cost, improve the protection degree of high-sensitive data, improve the efficiency of encryption decision analysis method.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system information security, in particular to an encryption decision comprehensive analysis method for a power transmission line, equipment and medium. BACKGROUND

[0002] With the continuous expansion of the power grid scale in China and the continuous improvement of the intelligent level of the power system, as the main artery of power transmission, the operation safety and information security of the power transmission line have become the key link to ensure the national energy security and the stable operation of the economic society. The encryption decision of the power transmission line refers to the confidentiality, integrity and availability of the key information in the whole process of collection, transmission, storage and application through encryption means, and at the same time, based on the encryption data, multi-dimensional and multi-level comprehensive analysis is carried out to support the key decisions such as line operation and maintenance, fault early warning and emergency dispatch. Promote the transformation of the power system from passive response to active defense and intelligent decision-making, help to build a safe, reliable, green and efficient modern energy system, and provide solid support for the construction of new-type infrastructure and energy digital transformation of the country.

[0003] The prior art often uses a fixed encryption algorithm for encryption when selecting an encryption method for a power transmission line, which cannot dynamically adjust the encryption strength according to the actual operation state of the line and the importance of the data, resulting in insufficient protection of high-sensitive data, and thus the efficiency of the encryption decision analysis method is low; therefore, the encryption decision comprehensive analysis method for the power transmission line still needs to be further improved. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is: how to dynamically adjust the encryption strength according to the different operation states of the power transmission line and the importance of the data, comprehensively consider the spatial relationship between the line and the data center, and realize a decision analysis method capable of dynamically adjusting the encryption strength, so as to improve the security protection level of high-sensitive data and the utilization efficiency of encryption resources.

[0006] To solve the above technical problems, the present application provides the following technical scheme: an encryption decision comprehensive analysis method for a power transmission line, comprising,

[0007] obtaining line data, environmental data and data center information;

[0008] generating a line sensitivity coefficient based on the line data and the environmental data;

[0009] extracting the center position coordinates corresponding to the data center information and the line position coordinates corresponding to the line ID in the line data;

[0010] calculating the position distance between the center position coordinates and the line position coordinates;

[0011] The encryption algorithm is dynamically selected based on the line sensitivity coefficient and the position distance.

[0012] As a preferred scheme of the encryption decision comprehensive analysis method for the power transmission line, in the scheme, the line sensitivity coefficient is generated based on the line data and the environment data, and the line sensitivity coefficient comprises a line characteristic factor.

[0013] The line characteristic factor is generated based on the line data and the environment data.

[0014] The line characteristic factor comprises a line position factor, a line service factor and a line environment factor.

[0015] A sensitivity calculation function is constructed to represent the linear relationship between the line characteristic factor and the line sensitivity coefficient.

[0016] The sensitivity calculation function satisfies the following formula:

[0017] ;

[0018] wherein, indicates the number corresponding to the line characteristic factor, indicates the total number of the line characteristic factors, indicates the factor weight coefficient, , indicates the i-th line characteristic factor, indicates the sensitivity calculation function.

[0019] The line characteristic factors are substituted into the sensitivity calculation function to obtain the line sensitivity coefficient corresponding to the line ID.

[0020] In the scheme, the line data and the environment data are introduced, and the line sensitivity coefficient is obtained by calculation. The sensitivity coefficient can accurately reflect the data sensitivity degree of different lines in different operation states, so that the encryption process is no longer limited to a fixed algorithm, but can dynamically adjust the encryption strength according to the sensitivity, thereby improving the utilization efficiency of the encryption resources while ensuring the security of the high-sensitivity data.

[0021] As a preferred scheme of the encryption decision comprehensive analysis method for the power transmission line, in the scheme, the line characteristic factor is generated based on the line data and the environment data, and the line characteristic factor comprises a line position factor, a line service factor and a line environment factor.

[0022] The line ID in the line data and the corresponding line area type and historical line parameters are extracted.

[0023] The historical line parameters refer to the data generated by the current line ID in the last N months, and comprise historical service parameters, historical channel parameters and historical average attack frequency, wherein N is an integer, and N>0. ​

[0024] Extract the surrounding population density in the environment data, calculate the line location factor, the expression is:

[0025] ;

[0026] Wherein, is the line location factor, A is a constant and A>0, is the minimum value operation, is the corresponding regional location base under the line area type, ZRM is the surrounding population density in the environment data, and DRM represents the unit population density;

[0027] Extract the power instruction type and the corresponding average instruction number in the historical service parameter;

[0028] The power instruction type includes fault positioning, video inspection and data collection;

[0029] When the average instruction number corresponding to the fault positioning is not 0, the service sensitive score corresponding to the power instruction type is set to the service sensitive level corresponding to the fault positioning;

[0030] Otherwise, when the average instruction number corresponding to the video inspection is not 0, the service sensitive score corresponding to the power instruction type is set to the service sensitive level corresponding to the data collection; otherwise, the line service factor is 0;

[0031] Calculate the line service factor, the expression is:

[0032] ;

[0033] Wherein, is the line service factor, is the service sensitive score corresponding to the power instruction type, B is a constant, B>0, is the minimum value operation, is the average instruction number, is the standard number of the power instruction type corresponding to the service sensitive score;

[0034] Extract the historical average channel signal-to-noise ratio in the historical channel parameter;

[0035] Compare the historical average channel signal-to-noise ratio with the set channel threshold value to classify and set the corresponding channel quality score;

[0036] Calculate the line environment factor, the expression is:

[0037] ;

[0038] Wherein, is the line environment factor, min() represents a minimum value operation, C is a constant and C>0; represents a standard attack frequency, is a historical average attack frequency.

[0039] The application further refines the formation process of the sensitivity coefficient into a plurality of characteristic factors, including a line location factor, a line service factor and a line environment factor, and establishes a calculation relationship with the sensitivity. Through this structured processing, the factor weight is flexibly adjusted when the operating conditions change or new indicators are added, ensuring the accuracy and adaptability of the sensitivity evaluation.

[0040] As a preferred scheme of the encryption decision comprehensive analysis method for the power transmission line provided by the application, wherein: the factor weight coefficient includes a line location factor weight coefficient, a line service factor weight coefficient and a line environment factor weight coefficient;

[0041] Extract the attack frequency standard deviation corresponding to the historical attack frequency in the historical line parameter;

[0042] Extract the channel signal-to-noise ratio standard deviation in the historical channel parameter;

[0043] Extract the high instruction proportion standard deviation in the historical service parameter;

[0044] Add the attack frequency standard deviation, the channel signal-to-noise ratio standard deviation and the high instruction proportion standard deviation to define the safety weight range of the initial line location factor weight coefficient, the initial line service factor weight coefficient and the initial line environment factor weight coefficient;

[0045] The safety weight range includes the maximum and minimum values of the initial line location factor weight coefficient, the maximum and minimum values of the initial line service factor weight coefficient and the maximum and minimum values of the initial line environment factor weight coefficient;

[0046] Determine the initial line location factor weight coefficient, the initial line service factor weight coefficient and the initial line environment factor weight coefficient respectively by calculation;

[0047] Based on the initial weight coefficient, the final line location factor weight coefficient, the line service factor weight coefficient and the line environment factor weight coefficient are obtained by normalization operation respectively.

[0048] As a preferred scheme of the encryption decision comprehensive analysis method for the power transmission line provided by the application, wherein: the dynamic selection of the encryption algorithm based on the line sensitivity coefficient and the position distance includes,

[0049] The line ID of the line whose sensitivity coefficient is greater than or equal to the first sensitivity threshold is regarded as a high-level line ID.

[0050] The line encryption method of the several high-level line IDs is set to high-level data encryption;

[0051] Extract several line IDs with line sensitivity coefficients less than the first sensitivity threshold as several low-level line IDs;

[0052] Calculate the device-line distance between the line position coordinates of the several low-level line IDs and the device deployment coordinates;

[0053] Generate a line encryption method corresponding to the low-level line ID based on the device-line distance.

[0054] As a preferred scheme of the encryption decision comprehensive analysis method for power transmission lines, the line encryption method corresponding to the low-level line ID based on the device-line distance includes,

[0055] Extract several low-level line IDs with device-line distances less than the device effective distance as several candidate low-level line IDs;

[0056] When the line sensitivity coefficient corresponding to the candidate low-level line ID is greater than or equal to the current corresponding second sensitivity threshold, set the line encryption method of the candidate low-level line ID to high-level data encryption;

[0057] Otherwise, set the line encryption method of the candidate low-level line ID to low-level data encryption.

[0058] As a preferred scheme of the encryption decision comprehensive analysis method for power transmission lines, the first sensitivity threshold is calculated by extracting the historical line sensitivity coefficients of several high-level line IDs for several times;

[0059] The historical line sensitivity coefficients of the several high-level line IDs for M times are used to calculate the historical sensitivity mean QLMJ of the entire line and the historical sensitivity standard deviation of the entire line, and the expression is:

[0060]

[0061] wherein, QLMJ is the historical sensitivity mean of the entire line, k is a safety coefficient, and k>0, is the historical sensitivity standard deviation of the entire line, is the first sensitivity threshold.

[0062] As a preferred scheme of the encryption decision comprehensive analysis method for power transmission lines, the second sensitivity threshold is calculated by extracting the device-line distances of the several candidate low-level line IDs, and the expression for calculating the second sensitivity threshold is: ​

[0063] ;

[0064] wherein, is a second sensitivity threshold, is a device line distance, is a distance sensitivity coefficient, , is a proportional coefficient, , is a unit distance.

[0065] The application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for comprehensive analysis of encryption decision of a power transmission line when executing the computer program.

[0066] The application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method for comprehensive analysis of encryption decision of a power transmission line when executed by a processor.

[0067] The application solves the technical problem that the prior art often uses a fixed encryption algorithm for encryption, cannot dynamically adjust the encryption strength according to the actual operation state of the line and the importance of data, and makes the protection of high-sensitivity data insufficient, thereby reducing the efficiency of the encryption decision analysis method. BRIEF DESCRIPTION OF DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0069] Figure 1 The application provides a method for comprehensive analysis of encryption decision of a power transmission line. DETAILED DESCRIPTION

[0070] In order to make the above objectives, characteristics and advantages of the present application more apparent, comprehensible and easier to be understood, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should belong to the protection scope of the present application.

[0071] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides an encryption decision comprehensive analysis method for a power transmission line, comprising:

[0072] The existing data encryption method for the power transmission line generally adopts a fixed encryption algorithm or a preset encryption strategy, and usually does not consider the differences between the line operation state, the environmental condition and the importance of the data. This way has two main problems:

[0073] The high-sensitive data is not sufficiently protected, while the low-sensitive data consumes unnecessary encryption resources, and the overall security and efficiency are affected.

[0074] The decision comparison is single, and dynamic factors such as position distance and business characteristics are not introduced, so the encryption strategy cannot be adjusted according to the geographical distribution of the line and the data center or the environmental change, which is easy to cause insufficient encryption strength or high cost.

[0075] The core idea of the present application is to obtain and process multi-dimensional data layer by layer to establish an encryption analysis mechanism capable of dynamic decision.

[0076] S1, obtaining line data, environmental data and data center information.

[0077] S2, generating a line sensitivity coefficient based on the line data and the environmental data.

[0078] S3, extracting the center position coordinates corresponding to the data center information and the line position coordinates corresponding to the line ID in the line data.

[0079] S4, calculating the position distance between the center position coordinates and the line position coordinates.

[0080] S5, dynamically selecting an encryption algorithm based on the line sensitivity coefficient and the position distance.

[0081] The application generates a line sensitivity coefficient based on line data and environment data; extracts a center position coordinate corresponding to data center information and a line position coordinate corresponding to a line ID in the line data; calculates a position distance between the center position coordinate and the line position coordinate; dynamically selects an encryption algorithm based on the line sensitivity coefficient and the position distance, selects an optimal encryption method between data importance and encryption cost by quantifying the importance of different lines for encryption algorithm selection and combining the position distance between the line ID and the data center, improves the protection degree of high-sensitive data, and further improves the efficiency of the encryption decision analysis method.

[0082] Embodiment 2 is an embodiment of the application, which provides a comprehensive encryption decision analysis method for a power transmission line based on the previous embodiment, comprising:

[0083] The line sensitivity coefficient in the embodiment is generated based on line data and environment data, comprising:

[0084] The line characteristic factor is generated based on the line data and the environment data; the line characteristic factor comprises a line position factor, a line service factor and a line environment factor.

[0085] A plurality of line characteristic factors are constructed to represent the line position factor, the line service factor and the line environment factor. A sensitivity calculation function respectively linearly related to the line sensitivity coefficient .

[0086] The sensitivity calculation function satisfies the following formula:

[0087] ;

[0088] Wherein, represents the number corresponding to the line characteristic factor, represents the total number of line characteristic factors, represents the factor weight coefficient, , represents the first line characteristic factor, is the sensitivity calculation function.

[0089] The line characteristic factor in the embodiment considers the line position factor, the line service factor and the line environment factor, so in the embodiment is 3.

[0090] The line characteristic factor is substituted into the sensitivity calculation function to obtain the line sensitivity coefficient corresponding to the line ID.

[0091] The line characteristic factor in the embodiment is generated based on the line data and the environment data, comprising:

[0092] Extract the line ID in the line data and its corresponding line area type XQL and historical line parameters; the historical line parameters refer to the data generated by the line ID in the last N months, including historical service parameters, historical channel parameters and historical average attack frequency LPGC; wherein N is an integer, N>0; the specific value is set according to experience.

[0093] It should be further explained that in the present embodiment, N is simulated and tested, and finally N is set to 6. The simulation experiment results are shown in Table 1:

[0094] Table 1 Simulation test table

[0095]

[0096] In Table 1, the trend of encryption decision accuracy rate with N is shown, and when the time window N=6, the accuracy rate of encryption decision reaches the peak.

[0097] This shows that using half a year of historical data can achieve the best balance between data sufficiency and timeliness; too short window, i.e. N=3, leads to insufficient data; too long window, i.e. N=12, introduces too much lag and redundant historical information. Therefore, the parameter N is determined as 6.

[0098] Extract the surrounding population density ZRM in the environment data.

[0099] Calculate the line location factor by the formula The formula satisfies:

[0100] ;

[0101] Wherein, is the line location factor, A is a constant and A>0, the specific value is set according to experience, and in the present embodiment, A is set to 0.25, is the minimum value operation, is the corresponding regional location basic score under the line area type, is the surrounding population density in the environment data, is the unit population density.

[0102] It should be further explained that the corresponding regional location basic score under the line area type is the basic score set for calculating the line location factor under the corresponding line area type, and the specific value is set according to experience, and in the present embodiment, the value range is (0, 1).

[0103] It is further explained that the value of A in the embodiment depends on the regional position base score. When the regional position base score is determined, the maximum value of the line position factor is set to 1. That is, when the regional position base score takes the maximum value and the line position factor is 1, the value of A is determined.

[0104] Extract the power instruction type DZL in the historical service parameter and the corresponding average instruction number The power instruction type includes fault positioning, video inspection and data acquisition.

[0105] When the average instruction number corresponding to the fault positioning is not 0, the service sensitive score corresponding to the power instruction type is set to the service sensitive level corresponding to the fault positioning.

[0106] Otherwise, when the average instruction number corresponding to the video inspection is not 0, the service sensitive score corresponding to the power instruction type is set to the service sensitive level corresponding to the data acquisition; otherwise, the line service factor is 0.

[0107] The line service factor is calculated by the formula ; the formula satisfies:

[0108] ;

[0109] Wherein, is the line service factor, B is a constant, B>0; the specific value is set according to experience, and B is set to 0.3 in the embodiment, is the minimum value operation, is the standard number of the power instruction type corresponding to the service sensitive score, is the service sensitive score corresponding to the power instruction type.

[0110] It is further explained that the service sensitive score corresponding to the power instruction type is the service sensitive score set for calculating the line service factor under the corresponding power instruction type, and the specific value is set according to experience, and the value range is (0, 1) in the application.

[0111] It is further explained that the value of B in the embodiment depends on the service sensitive score. When the service sensitive score is determined, the maximum value of the line service factor is set to 1. That is, when the service sensitive score takes the maximum value and the line service factor is 1, the value of B is determined.

[0112] It is further explained that the standard number of the power instruction type corresponding to the service sensitive score is the standard number set for calculating the line service factor under the corresponding power instruction type, and the standard number is set according to experience, and is a natural integer.

[0113] extracting a historical average channel signal-to-noise ratio LSNR in the historical channel parameter.

[0114] When LSNR is greater than or equal to a channel threshold one, a channel quality score XZF is set to 0.8.

[0115] When LSNR is less than the channel threshold one and LSNR is greater than or equal to a channel threshold two, the channel quality score XZF is set to 0.5.

[0116] When LSNR is less than the channel threshold two, the channel quality score XZF is set to 0.2.

[0117] It is further explained that the channel threshold one is greater than the channel threshold two, and the specific values are set according to experience, and in the embodiment, the channel threshold one and the channel threshold two are respectively set to 15 dB and 8 dB.

[0118] In the embodiment, the values of the channel quality score and the channel threshold are determined through simulation experiments, and the channel simulation results are shown in Table 2.

[0119] Table 2 Channel simulation result table

[0120]

[0121] In the embodiment, two values of the channel quality score and the channel threshold are selected, and a cross combination is used to further form four comparison results. As can be seen from Table 2, when the channel quality score is selected as 0.8, 0.5 and 0.2, and the channel threshold is selected as 15 dB and 8 dB, the encryption decision accuracy is optimal, so in the finally determined channel quality score and channel threshold, the channel quality score is selected as 0.8, 0.5 and 0.2, and the channel threshold is selected as 15 dB and 8 dB.

[0122] The line environment factor is calculated by the formula The formula satisfies:

[0123] ;

[0124] Wherein, is a channel quality score, is a minimum value operation, C is a constant, C>0, and the specific value is set according to experience, and in the embodiment, C is set to 0.2, and BGC represents a standard attack frequency, is a historical average attack frequency.

[0125] It is further explained that the value of C in the embodiment depends on the channel quality score, and when the channel quality score is confirmed, the maximum value in the line environment factor is set to 1; that is, when the channel quality score takes the maximum value, and the line environment factor is 1, the value of C is determined.

[0126] It is further explained that the specific value of the standard attack frequency BGC is set according to experience and is a natural integer.

[0127] The embodiment comprehensively analyzes the power transmission line from multiple dimensions, fully considers multiple key factors such as the geographical position, data state and communication state of the line, extracts and quantifies a series of characteristic factors for calculating the line sensitivity coefficient, realizes adaptive adjustment of the influence degree of different characteristic factors by introducing a dynamic factor weight coefficient mechanism, and thus accurately calculates the line sensitivity coefficient corresponding to each line ID, and improves the calculation accuracy of the sensitivity coefficient.

[0128] The factor weight coefficient in the embodiment is obtained in the following manner, and the factor weight coefficient includes a line position factor weight coefficient, a line service factor weight coefficient and a line environment factor weight coefficient, and includes:

[0129] The attack frequency standard deviation GPBC corresponding to the historical attack frequency in the historical line parameter is extracted.

[0130] The channel signal-to-noise ratio standard deviation XBBC in the historical channel parameter is extracted.

[0131] The high instruction proportion standard deviation GZBC in the historical service parameter is extracted; the high instruction proportion is represented as the ratio between the number of instructions corresponding to fault positioning in the power instruction type and the sum of the number of instructions corresponding to all instructions in the power instruction type.

[0132] Let S = GPBC + XBBC + GZBC. Wherein, S is the safety weight range.

[0133] The safety weight range of the initial factor weight coefficient corresponding to the line position factor, the line service factor and the line environment factor is defined.

[0134] The safety weight range includes the maximum and minimum values of the initial line position factor weight coefficient, the maximum and minimum values of the initial line service factor weight coefficient, and the maximum and minimum values of the initial line environment factor weight coefficient, and is respectively represented as:

[0135] ;

[0136] ;

[0137] ;

[0138] Wherein, are the initial line position factor weight coefficient, the initial line service factor weight coefficient and the initial line environment factor weight coefficient, respectively.

[0139] In the embodiment, the maximum value of the initial line position factor weight coefficient is and minimum value are set to 0.5 and 0.3 respectively, the maximum value of the initial line service factor weight coefficient and minimum value are set to 0.45 and 0.25 respectively, the maximum value of the initial line environment factor weight coefficient and minimum value are set to 0.35 and 0.15 respectively.

[0140] In determining the safe weight range of the initial factor weight coefficient corresponding to the line location factor, the line service factor and the line environment factor, and the basic factor weight coefficient, the embodiment is determined by simulation experiment, and the factor weight simulation experiment is shown in Table 3:

[0141] Table 3 Factor weight simulation experiment table

[0142]

[0143] In the embodiment, four groups of basic factor weight coefficients and their corresponding safe ranges are selected for comparison. As can be seen from the table, under different values and ranges, the final encryption decision accuracy rate is different. When the basic factor weight coefficient and its corresponding safe range are 0.4, 0.35, 0.25, (0.5, 0.3), (0.45, 0.25), (0.35, 0.15), the encryption decision accuracy rate is the highest.

[0144] Therefore, in the embodiment, the basic factor weight coefficient is set to 0.4, 0.35, 0.25; the safe weight range of the initial factor weight coefficient corresponding to the line location factor, the line service factor and the line environment factor is set to (0.5, 0.3), (0.45, 0.25), (0.35, 0.15) respectively.

[0145] The initial line location factor weight coefficient is calculated by the formula ; the formula satisfies:

[0146] ;

[0147] wherein, is the initial line location factor weight coefficient, S is the safe weight range, and respectively represent the minimum value operation and the maximum value operation; set and in order to avoid the situation that the initial line location factor weight coefficient is 0 when calculating the initial line location factor weight coefficient by the attack frequency standard deviation.

[0148] The initial line service factor weight coefficient is calculated by a formula ; the formula satisfies:

[0149] ;

[0150] wherein, is the initial line service factor weight coefficient, S is a safety weight range, and S is set as and are to avoid the initial line service factor weight coefficient being 0 when the initial line service factor weight coefficient is calculated by the high instruction proportion standard deviation.

[0151] The initial line environment factor weight coefficient is calculated by a formula ; the formula satisfies:

[0152] ;

[0153] wherein, is the initial line environment factor weight coefficient, S is a safety weight range, and S is set as and are to avoid the initial line environment factor weight coefficient being 0 when the initial line environment factor weight coefficient is calculated by the channel signal-to-noise ratio standard deviation.

[0154] The initial line position factor weight coefficient, the initial line service factor weight coefficient and the initial line environment factor weight coefficient are normalized to obtain the final line position factor weight coefficient, the line service factor weight coefficient and the line environment factor weight coefficient.

[0155] In another embodiment, the factor weight coefficient is obtained by the following way, comprising:

[0156] obtaining a basic factor weight coefficient; the basic factor weight coefficient is set by experts according to experience, and in the embodiment, the basic factor weight coefficients corresponding to the line position factor, the line service factor and the line environment factor are respectively set as 0.4, 0.35 and 0.25.

[0157] extracting a factor weight influence parameter; the factor weight influence parameter is a plurality of influence parameters which affect the basic factor weight coefficient and thus determine the final factor weight coefficient; in the embodiment, the factor weight influence parameter comprises a historical attack frequency standard deviation, a historical channel signal-to-noise ratio standard deviation, a historical high instruction proportion standard deviation, a current attack frequency, a current channel signal-to-noise ratio and a current high instruction proportion, etc.

[0158] inputting the basic factor weight coefficient and the factor weight influence parameter into a weight adjustment model to obtain the factor weight coefficient; the weight adjustment model is constructed by an artificial intelligence model and is used to generate the optimal factor weight coefficient.

[0159] In another embodiment, the weight adjustment model is constructed by an artificial intelligence model, including:

[0160] A plurality of historical basic factor weight coefficients and historical factor weight influence parameters, and historical factor weight coefficients corresponding to the historical basic factor weight coefficients and the historical factor weight influence parameters are obtained.

[0161] The plurality of historical basic factor weight coefficients and historical factor weight influence parameters, and the historical factor weight coefficients corresponding to the historical basic factor weight coefficients and the historical factor weight influence parameters are divided into training data, validation data and test data; the training data, the validation data and the test data are preprocessed to obtain a training set, a validation set and a test set; the ratio between the training set, the test set and the validation set is 7:2:1.

[0162] An artificial intelligence model is selected as a basic model; in this embodiment, the artificial intelligence model is a BP network model.

[0163] The basic model is trained by the training set, and the learning rate and the hyperparameters are adjusted on the validation set to obtain a pre-trained model.

[0164] The pre-trained model is verified on the test set, and finally a weight adjustment model with the basic factor weight coefficient and the factor weight influence parameter as input and the factor weight coefficient as output is obtained.

[0165] The weight adjustment model is constructed by an artificial intelligence model, including:

[0166] A plurality of historical basic factor weight coefficients and historical factor weight influence parameters, and historical factor weight coefficients corresponding to the historical basic factor weight coefficients and the historical factor weight influence parameters are obtained; the plurality of historical basic factor weight coefficients and historical factor weight influence parameters, and the historical factor weight coefficients corresponding to the historical basic factor weight coefficients and the historical factor weight influence parameters construct a training data source required for training the weight adjustment model.

[0167] The plurality of historical basic factor weight coefficients and historical factor weight influence parameters, and historical factor weight coefficients corresponding to the historical basic factor weight coefficients and the historical factor weight influence parameters are divided into training data, validation data and test data; the training data, the validation data and the test data are preprocessed to obtain a training set, a validation set and a test set; the ratio between the training set, the test set and the validation set is determined as 7:2:1 after simulation experiment. The simulation experiment results are shown in Table 4 of simulation experiment:

[0168] Table 4 Simulation experiment table

[0169]

[0170] In this embodiment, three different division ratios are selected for the training data source, and after simulation experiments, the data ratio of 7:2:1 can obtain the best weight adjustment model with the highest accuracy.

[0171] An artificial intelligence model is selected as the base model; in this embodiment, the artificial intelligence model is a BP network model; when constructing the weight adjustment model, the BP neural network realizes dynamic optimization of the weight through the following steps: forward propagation and error calculation, error back propagation, weight update, iterative optimization and convergence; the process of forward propagation, error calculation, back propagation and weight update is repeated for each training sample until the network output error reaches the maximum iteration number.

[0172] The base model is trained through the training set, and the learning rate and hyperparameters are adjusted on the validation set to obtain a pre-trained model.

[0173] The pre-trained model is verified on the test set to obtain the weight adjustment model; the input data of the weight adjustment model is the base factor weight coefficient and the factor weight influence parameter, and the output result is the factor weight coefficient; the factor weight influence parameter has an impact on the change of the base factor weight coefficient, and the weight adjustment model learns the impact of the factor weight influence parameter on the change of the base factor weight coefficient through the training process, so that when the base factor weight coefficient and the factor weight influence parameter are known, the weight adjustment model can obtain the most suitable factor weight coefficient.

[0174] In this embodiment, when calculating the line sensitivity coefficient, multiple key line feature factors are comprehensively considered, and a dynamic feedback mechanism based on various parameters in historical line operation data is innovatively introduced to adjust the weight coefficient of each feature factor in real time; the dynamic self-adaptive optimization of the factor weight coefficient is realized through two different methods, which improves the scientificity and accuracy of the line sensitivity coefficient calculation, and makes the evaluation result more truly reflect the actual risk level of the line under different operating environments.

[0175] In this embodiment, the dynamic selection of the encryption algorithm based on the line sensitivity coefficient and the position distance includes:

[0176] The line ID of the line with a line sensitivity coefficient greater than or equal to a first sensitivity threshold is set as a high-level line ID.

[0177] The line encryption method of the high-level line ID is set as a high-level data encryption; in this embodiment, the high-level data encryption method is a quantum encryption method.

[0178] Extract the position distance between the data center and the line position coordinates of the high-level line ID.

[0179] The high-level line ID, the corresponding position distance, the line position coordinate, the center position coordinate of the data center information, and the device effective distance are spliced into device deployment analysis data; the device effective distance is determined by the performance of the required device for high-level data encryption.

[0180] The device deployment analysis data is input into a device deployment recommendation model to obtain device deployment coordinates; the device deployment recommendation model is constructed by an artificial intelligence model and is used to generate device deployment coordinates required by the high-level data encryption method.

[0181] Extract several line IDs with a line sensitivity coefficient less than a first sensitivity threshold as several low-level line IDs.

[0182] Calculate the device line distance between the line position coordinates and the device deployment coordinates of the several low-level line IDs.

[0183] Generate a line encryption method corresponding to the low-level line ID based on the device line distance.

[0184] The device deployment recommendation model in this embodiment is constructed by an artificial intelligence model, including:

[0185] Obtain several historical device deployment analysis data and corresponding historical device deployment coordinates; the several historical device deployment analysis data and the corresponding historical device deployment coordinates construct deployment training data sources required for training the device deployment recommendation model.

[0186] Divide the several historical device deployment analysis data and the corresponding historical device deployment coordinates into training data, validation data, and test data; perform data preprocessing on the training data, validation data, and test data to obtain a training set, a validation set, and a test set; the ratio between the training set, the test set, and the validation set is determined to be 7:2:1 after simulation experiments. The simulation experiment results are shown in Table 5:

[0187] Table 5 Simulation experiment table

[0188]

[0189] In this embodiment, three different division ratios of the training data source are selected, and the device deployment recommendation model with the best accuracy can be obtained after selecting a data ratio of 7:2:1 through simulation experiments.

[0190] The artificial intelligence model is selected as the base model; in this embodiment, the artificial intelligence model selects the DeepSeek model; when constructing the device deployment recommendation model, the DeepSeek model realizes the dynamic optimization of the device deployment coordinates through the following steps, including: the device deployment recommendation model outputs the probability distribution of the device deployment coordinates through forward propagation, filters invalid coordinates combined with the constraint conditions, and generates a candidate position set; the constraint conditions include the lower limit of the device distance and the range of the safety area; the model internally balances the conflict targets of the time delay, the cost and the safety, and selects the optimal device deployment coordinates to realize the optimization of the device deployment coordinates.

[0191] The base model is trained through the training set, and the learning rate and hyperparameters are adjusted on the validation set to obtain the pre-trained model.

[0192] The device deployment recommendation model is finally obtained by verifying the pre-trained model on the test set; the input data of the device deployment recommendation model is the device deployment analysis data, and the output result is the device deployment coordinates; the device deployment analysis data will affect the finally selected device deployment coordinates, and the device deployment recommendation model learns the relationship between the device deployment analysis data and the device deployment coordinates through the training process, so that the device deployment recommendation model can obtain the most suitable device deployment coordinates when the device deployment analysis data is known.

[0193] The low-level line ID corresponding to the line encryption method in the embodiment based on the device line distance is generated, including:

[0194] Extracting a plurality of low-level line IDs with device line distances less than the device effective distance as a plurality of candidate low-level line IDs.

[0195] When the line sensitivity coefficient corresponding to the candidate low-level line ID is greater than or equal to the corresponding second sensitivity threshold, the line encryption method of the candidate low-level line ID is set to high-level data encryption.

[0196] Otherwise, the line encryption method of the candidate low-level line ID is set to low-level data encryption; in this embodiment, the method of low-level data encryption is AES-256 algorithm, etc.

[0197] The first sensitivity threshold and the second sensitivity threshold in this embodiment are obtained by the following method, including:

[0198] Extracting the historical line sensitivity coefficients of a plurality of line IDs for M times of history; wherein M is an integer, and the specific value is set according to experience, and in this embodiment, M is set to 100.

[0199] The full-line historical sensitivity mean QLMJ and the full-line historical sensitivity standard deviation QLMBC are calculated through the historical line sensitivity coefficients of a plurality of line IDs for M times of history, and the expression is:

[0200] .

[0201] calculating the first sensitive threshold ; wherein k represents a safety factor, k>0; the specific value is set according to experience, and k is set to 1.5 in the embodiment.

[0202] The device line distance SXJ of the plurality of candidate low-level line IDs is extracted, and the expression is:

[0203] .

[0204] calculating the second sensitive threshold corresponding to the candidate low-level line ID .

[0205] wherein, is a proportional coefficient, ∈(0,1); the specific value is set according to experience, and is set to 0.7 in the embodiment; t is a distance sensitive coefficient, t∈(0,1); the specific value is set according to experience.

[0206] t is set to 0.2 in the embodiment; DJ represents a unit distance, and the specific value is set according to experience, and DJ is set to 10km in the embodiment. Considering that the closer the line ID distance is to the device deployed with high-level data encryption, the lower the quantum key sharing cost is, therefore may be appropriately reduced.

[0207] The values of M, k, , t and DJ appearing in the embodiment are determined through simulation experiments, and the specific simulation experiment results refer to the parameter simulation experiment table, and part of the simulation experiment results are shown in Table 6 as follows:

[0208] Table 6 Parameter simulation experiment table

[0209]

[0210] As can be seen from the parameter simulation experiment table 6, for different values of M, k, , t and DJ, the final encryption decision accuracy is different, when selecting specific parameter values, the embodiment selects the parameter combination corresponding to the optimal encryption decision accuracy to determine the values of the related parameters, and in the embodiment, the values of M, k, , t and DJ are 100, 1.5, 0.7, 0.2 and 10km, and the encryption decision accuracy is the highest value, which is 96.9%; therefore, the embodiment selects M, k, The values of t, and DJ are finally selected as 100, 1.5, 0.7, 0.2 and 10 km.

[0211] The embodiment introduces the change trend of the historical line sensitivity coefficient, dynamically corrects the current first sensitivity threshold, can adapt to the time-varying characteristics of the power grid operation environment, improves the scientificity and foresight of the threshold setting, further combines the first sensitivity threshold and the spatial distance between each line ID and the high-level encryption device deployment coordinates, cooperatively adjusts the second sensitivity threshold, and builds a double-threshold linkage adaptive adjustment mechanism, overcomes the one-size-fits-all problem caused by the traditional fixed threshold, avoids the misjudgment or omission of the encryption strategy caused by the rigid threshold setting, and improves the accuracy of the encryption method matching.

[0212] When the encryption method is selected, the line sensitivity coefficient of each line ID is compared with the first sensitivity threshold, so that the line ID that must be subjected to high-level encryption is determined first, the related device coordinates required for high-level encryption are obtained through the pre-trained device deployment recommendation model, and then the distance between the line ID and the device coordinates and the relationship between the line sensitivity coefficient and the second sensitivity threshold are used to dynamically select the appropriate line ID for high-level encryption, further improve the security of part of the line data on the basis of ensuring the cost, so that the line data is optimally protected, and the efficiency of the encryption decision analysis method is improved.

[0213] Embodiment 3 also provides an electronic device suitable for the case of the encryption decision comprehensive analysis method for the power transmission line, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the encryption decision comprehensive analysis method for the power transmission line proposed in the above embodiment.

[0214] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the encryption decision comprehensive analysis method for the power transmission line proposed in the above embodiment.

[0215] The storage medium proposed in the embodiment and the encryption decision comprehensive analysis method for the power transmission line proposed in the above embodiment belong to the same inventive concept, and the technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0216] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and necessary universal hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0217] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A comprehensive analysis method for encryption decision-making in transmission lines, characterized in that: include, Acquire line data, environmental data, and data center information; Line sensitivity coefficients are generated based on line data and environmental data; Extract the center location coordinates corresponding to the data center information and the line location coordinates corresponding to the line ID in the line data; Calculate the positional distance between the center position coordinates and the line position coordinates; Encryption algorithms are dynamically selected based on line sensitivity coefficients and location distance; The generation of the line sensitivity coefficient based on line data and environmental data includes, Generate route characteristic factors based on route data and environmental data; Line characteristic factors include line location factors, line service factors, and line environment factors; Construct a sensitivity calculation function that characterizes the linear relationship between several line characteristic factors and the line sensitivity coefficient; The sensitivity calculation function satisfies the following formula: Where j represents the number corresponding to the line characteristic factor, J represents the total number of line characteristic factors, and α j Represented as factor weight coefficient, α j ∈(0,1),XTY j Let MJF(XTY) be the j-th line characteristic factor. j ) is a sensitive computation function; Substitute several line characteristic factors into the sensitivity calculation function to obtain the line sensitivity coefficient corresponding to the line ID. The encryption algorithm based on line sensitivity coefficient and location distance includes, Several line IDs with a line sensitivity coefficient greater than or equal to the first sensitivity threshold are designated as several high-level line IDs. Set the encryption method for several high-level line IDs to high-level data encryption; Extract several line IDs whose line sensitivity coefficient is less than the first sensitivity threshold and use them as several low-level line IDs; Calculate the device-line distance between the line location coordinates and the device deployment coordinates of several low-level line IDs; A line encryption method that generates low-level line IDs based on device line distance.

2. The comprehensive analysis method for encryption decision-making in transmission lines as described in claim 1, characterized in that: The generation of line characteristic factors based on line data and environmental data includes, Extract the line ID, corresponding line area type, and historical line parameters from the line data; The historical line parameters refer to the data generated by the current line ID within the past N months, including historical service parameters, historical channel parameters, and historical average attack frequency, where N is an integer and N>0; Extract the surrounding population density from the environmental data and calculate the route location factor, expressed as: Where XTY1 is the line location factor, A is a constant and A>0, min() is the minimum value operation, and QWF XQL ZRM represents the basic score of the area location corresponding to the line area type, ZRM represents the surrounding population density in the environmental data, and DRM represents the population density per unit area. Extract the power command type and the corresponding average command count from historical business parameters; The types of power commands include fault location, video inspection, and data collection. When the average number of instructions corresponding to fault location is not 0, the business sensitivity score corresponding to the power instruction type is set as the business sensitivity level corresponding to fault location. Otherwise, when the average number of commands corresponding to video inspection is not 0, the business sensitivity score corresponding to the power command type is set to the business sensitivity level corresponding to data acquisition; otherwise, the line business factor XTY2 is 0. The line service factor is calculated using the following expression: Where XTY2 is the line service factor, and YMD DZL For the business sensitivity score corresponding to the power instruction type, B is a constant, B>0, min() is the minimum value operation, ZC DZL BC is the average number of instructions. DZL Standard number of power command types corresponding to business-sensitive categories; Extract the historical average channel signal-to-noise ratio from historical channel parameters; The historical average channel signal-to-noise ratio is compared with the set channel threshold to classify the channels and set corresponding channel quality scores. The line environmental factor is calculated using the following expression: Where XTY3 is the line environment factor, XZF is the channel quality score, min() represents the minimum value operation, C is a constant and C>0; BGC represents the standard attack frequency, and LPGC represents the historical average attack frequency.

3. The comprehensive analysis method for encryption decision-making in transmission lines as described in claim 2, characterized in that: The factor weighting coefficients include the line location factor weighting coefficient, the line service factor weighting coefficient, and the line environment factor weighting coefficient. Extract the standard deviation of attack frequency corresponding to the historical attack frequency in the historical line parameters; Extract the standard deviation of channel signal-to-noise ratio from historical channel parameters; Extract the standard deviation of the high-instruction percentage from historical business parameters; The security weight range of the initial line location factor weight coefficient, the initial line service factor weight coefficient, and the initial line environment factor weight coefficient is defined by adding the standard deviation of attack frequency, the standard deviation of channel signal-to-noise ratio, and the standard deviation of high instruction proportion. The safety weight range includes the maximum and minimum values ​​of the line location factor weight coefficient, the maximum and minimum values ​​of the line service factor weight coefficient, and the maximum and minimum values ​​of the line environment factor weight coefficient. The initial route location factor weight coefficient, the initial route business factor weight coefficient, and the initial route environmental factor weight coefficient are determined by calculation. Based on the initial weight coefficients, normalization operations are performed to obtain the final weight coefficients for the line location factor, line service factor, and line environment factor.

4. The comprehensive analysis method for encryption decision-making in transmission lines as described in claim 3, characterized in that: The line encryption method for generating low-level line IDs based on device line distance includes: Extract several low-level line IDs whose equipment line distance is less than the equipment effective distance as several candidate low-level line IDs; When the line sensitivity coefficient corresponding to the candidate low-level line ID is greater than or equal to the current corresponding second sensitivity threshold, the line encryption method of the candidate low-level line ID is set to high-level data encryption. Otherwise, set the line encryption method for the candidate low-level line ID to low-level data encryption.

5. The comprehensive analysis method for encryption decision-making in transmission lines as described in claim 4, characterized in that: The first sensitivity threshold is calculated by extracting the historical sensitivity coefficients of several high-level line IDs corresponding to several historical times. The historical sensitivity mean QLMJ and the historical sensitivity standard deviation of the entire line are calculated using the historical line sensitivity coefficients of several high-level line IDs over M historical periods. The expressions are as follows: MY1 = QLMJ + k × QLMBC Where QLMJ is the historical sensitivity mean of the entire line, k represents the safety factor (k>0), QLMBC is the historical sensitivity standard deviation of the entire line, and MY1 is the first sensitivity threshold.

6. The comprehensive analysis method for encryption decision-making in transmission lines as described in claim 5, characterized in that: The calculation method of the second sensitivity threshold Extract the device line distance from several candidate low-level line IDs, and calculate the second sensitivity threshold, expressed as: Where MY2 is the second sensitivity threshold, SXJ is the equipment line distance, t is the distance sensitivity coefficient, t∈(0,1), g is the proportional coefficient g∈(0,1), and DJ is the unit distance.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the encryption decision-making comprehensive analysis method for power transmission lines as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the encryption decision-making comprehensive analysis method for power transmission lines as described in any one of claims 1 to 6.

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