Identity authentication method and system for electric power inspection terminal

Abstract

The invention discloses an identity authentication method for an electric power inspection terminal. The identity authentication method comprises the steps of obtaining historical inspection data information of an electric power system and performing data preprocessing to obtain a data set; setting an electric power inspection terminal fingerprint parameter probability model, and representing a relationship between an equipment state and data based on the obtained data set; performing posterior distribution updating on the obtained model based on Bayesian inference; generating fingerprint data of the electric power inspection terminal according to the obtained posterior distribution; and completing the actual identity authentication of the electric power inspection terminal according to the obtained fingerprint data. The invention also discloses a system for realizing the identity authentication method for the electric power inspection terminal. According to the invention, through processing, modeling and fingerprint generation of the historical inspection data of the electric power system, identification of the electric power inspection terminal is carried out according to the generated fingerprint of the electric power inspection equipment; therefore, the identity authentication of the electric power inspection terminal can be realized, the reliability is higher, and the safety is better.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and in particular relates to an identity authentication method and system for a power inspection terminal. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Power system inspection is an important measure to ensure the stable and reliable operation of the power system. At present, with the rapid development of intelligent technology, more and more inspection terminals are being used in the inspection of power systems, such as inspection robots and inspection drones. The power system inspection terminal provides great convenience for power system staff; at the same time, the data security issues of the power inspection terminal have also attracted people's attention.

[0004] The data security of power inspection terminals is directly related to the data security of the power system. In the process of ensuring the data security of power inspection terminals, the identity authentication of power inspection terminals is of utmost importance. At present, the commonly used terminal identity authentication scheme in the power system is an interactive authentication scheme based on the communication process. However, this type of scheme may be at risk of being attacked and decrypted during the communication process, and its reliability and security are not high. Summary of the invention

[0005] One of the purposes of the present invention is to provide an identity authentication method for a power inspection terminal with high reliability and good security.

[0006] A second object of the present invention is to provide a system for implementing the identity authentication method for a power inspection terminal.

[0007] The identity authentication method for the power inspection terminal provided by the present invention comprises the following steps:

[0008] S1. Obtain historical inspection data information of the power system;

[0009] S2. Preprocess the data information obtained in step S1 to obtain a data set;

[0010] S3. Set the fingerprint parameter probability model of the power inspection terminal, and based on the data set obtained in step S2, represent the relationship between the device status and the data;

[0011] S4. Based on Bayesian inference, update the posterior distribution of the model obtained in step S3;

[0012] S5. Generate fingerprint data of the power inspection terminal according to the posterior distribution obtained in step S4;

[0013] S6. According to the fingerprint data obtained in step S5, the actual identity authentication of the power inspection terminal is completed.

[0014] The acquisition of historical inspection data information of the power system described in step S1 specifically includes the following steps:

[0015] Obtain historical inspection data information of the power system;

[0016] The inspection data information includes static data and dynamic data;

[0017] The static data includes the main control chip ID of the power inspection terminal, the sensor serial number of the power inspection terminal, the operating system version of the power inspection terminal, the designated driver list of the power inspection terminal, and the designated application list of the power inspection terminal;

[0018] The dynamic data includes network characteristic data and inspection characteristic data; wherein the network characteristic data includes transmission rate and transmission delay; the inspection characteristic data includes PID control parameters, inspection mode and GPS positioning data.

[0019] The preprocessing described in step S2 specifically includes the following steps:

[0020] Preprocessing includes missing data processing, abnormal data processing and data unification;

[0021] Missing data processing: If the amount of missing data is less than or equal to the set value, interpolation is used to fill the data; if the amount of missing data is greater than the set value, the corresponding data is directly discarded;

[0022] Abnormal data processing: abnormal data is detected by a detection method and directly deleted; the detection method includes Z-Score method or IQR method;

[0023] Data unification: The data obtained after missing data processing and abnormal data processing are unified into a standard time axis to achieve synchronization of the time corresponding to the data; for the dynamic inspection data in the data, the data is divided into several segments and stored separately; the data segmentation specifically includes sliding window segmentation.

[0024] The setting of the fingerprint parameter probability model of the power inspection terminal described in step S3, and the relationship between the device status and the data based on the data set obtained in step S2, specifically includes the following steps:

[0025] The following normal distribution model is used as the prior distribution, and the initial parameter model is constructed:

[0026] p(μ)=N(μ1|μ0,(δ0) 2 )

[0027] Where p(μ) is the normal distribution of parameter μ; N() is the symbol for normal distribution; μ1 is a random variable; μ0 is the expected value of normal distribution; (δ0) 2 is the variance of the normal distribution;

[0028] According to the prior distribution, the following formula is used as the likelihood function:

[0029]

[0030] Where p(X|μ) is the likelihood function; X is the observed quantity; μ is the expected value of the prior normal distribution; δ is the standard deviation of the prior normal distribution; N is the total number of observations; x n is the nth observation in the data set.

[0031] The posterior distribution of the model obtained in step S3 is updated based on Bayesian inference in step S4, which specifically includes the following steps:

[0032] Based on Bayesian inference, the following formula is used as the posterior distribution:

[0033] p(μ|X)=N(μ|μ N ,(δ N ) 2 )

[0034]

[0035]

[0036] Where p(μ|X) is the posterior distribution; μ N is the expected value of the posterior distribution; (δ N ) 2 is the variance of the posterior distribution;

[0037] According to the obtained posterior distribution, the root mean square error of the parameters in the Bayesian model is calculated and the root mean square error is normalized; finally, the normalized value is used as the credibility of the parameter.

[0038] The step S5 described in which the fingerprint data of the power inspection terminal is generated according to the posterior distribution obtained in step S4 specifically includes the following steps:

[0039] According to the credibility of the parameters in the Bayesian model obtained in step S4, several parameters with the lowest credibility parameters are selected as input features for generating fingerprints;

[0040] The obtained input features are input into the transformer model to generate fingerprint data of the power inspection terminal.

[0041] Step S6, according to the fingerprint data obtained in step S5, completes the actual identity authentication of the power inspection terminal, which specifically includes the following steps:

[0042] Obtain inspection data information of target power inspection terminals in real time;

[0043] Generate a fingerprint sequence of the target power inspection terminal based on the acquired inspection data information;

[0044] Match the fingerprint sequence of the target power inspection terminal with the fingerprint data of the power inspection terminal generated in step S5:

[0045] If the match is successful, the target power inspection terminal is determined to be a legitimate terminal;

[0046] If the match fails, the target power inspection terminal is determined to be an illegal terminal.

[0047] The present invention also provides a system for implementing the identity authentication method for power inspection terminals, comprising a data acquisition module, a data processing module, a model construction module, a distribution update module, a fingerprint generation module and an identity authentication module; the data acquisition module, the data processing module, the model construction module, the distribution update module, the fingerprint generation module and the identity authentication module are connected in series in sequence; the data acquisition module is used to acquire historical inspection data information of the power system and upload the data information to the data processing module; the data processing module is used to perform data preprocessing on the acquired data information according to the received data information to obtain a data set, and upload the data information to the model construction module; the model construction module is used to obtain the historical inspection data information of the power system and upload the data information to the data processing module; the model construction module is used to obtain the historical inspection data information of the power system and upload the data information to the model construction ... According to the received data information, a fingerprint parameter probability model of the power inspection terminal is set, and based on the obtained data set, the relationship between the equipment status and the data is represented, and the data information is uploaded to the distribution update module; the distribution update module is used to perform a posterior distribution update on the obtained model based on the received data information and Bayesian inference, and upload the data information to the fingerprint generation module; the fingerprint generation module is used to generate fingerprint data of the power inspection terminal according to the received data information and the obtained posterior distribution, and upload the data information to the identity authentication module; the identity authentication module is used to complete the actual identity authentication of the power inspection terminal according to the received data information and the obtained fingerprint data.

[0048] The identity authentication method and system for electric power inspection terminals provided by the present invention process, model and generate fingerprints of historical inspection data of the power system, and identify the electric power inspection terminal based on the generated fingerprint of the electric power inspection equipment; therefore, the present invention can not only realize the identity authentication of the electric power inspection terminal, but also has higher reliability and better security. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The figure is a schematic diagram of the method flow of the present invention.

[0050] Figure 2 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0051] like Figure 1 The method flow chart of the method of the present invention is shown as follows: the identity authentication method for the power inspection terminal disclosed in the present invention comprises the following steps:

[0052] S1. Obtain historical inspection data information of the power system; specifically includes the following steps:

[0053] Obtain historical inspection data information of the power system;

[0054] The inspection data information includes static data and dynamic data;

[0055] The static data includes the main control chip ID of the power inspection terminal, the sensor serial number of the power inspection terminal, the operating system version of the power inspection terminal, the designated driver list of the power inspection terminal, and the designated application list of the power inspection terminal;

[0056] Dynamic data includes network characteristic data and inspection characteristic data; among which, network characteristic data includes transmission rate and transmission delay; inspection characteristic data includes PID control parameters, inspection mode and GPS positioning data;

[0057] S2. Perform data preprocessing on the data information obtained in step S1 to obtain a data set; specifically, the steps include:

[0058] Preprocessing includes missing data processing, abnormal data processing and data unification;

[0059] Missing data processing: If the amount of missing data is less than or equal to the set value, interpolation is used to fill the data; if the amount of missing data is greater than the set value, the corresponding data is directly discarded;

[0060] Abnormal data processing: abnormal data is detected by a detection method and directly deleted; the detection method includes Z-Score method or IQR method;

[0061] Data unification: The data obtained after missing data processing and abnormal data processing are unified to the standard time axis to achieve synchronization of the time corresponding to the data; for the dynamic inspection data in the data, the data is divided into several segments and stored separately; the data segmentation specifically includes sliding window segmentation;

[0062] S3. Setting the fingerprint parameter probability model of the power inspection terminal, and based on the data set obtained in step S2, representing the relationship between the device status and the data; specifically comprising the following steps:

[0063] The following normal distribution model is used as the prior distribution, and the initial parameter model is constructed:

[0064] p(μ)=N(μ1|μ0,(δ0) 2 )

[0065] Where p(μ) is the normal distribution of parameter μ; N() is the symbol for normal distribution; μ1 is a random variable; μ0 is the expected value of normal distribution; (δ0) 2 is the variance of the normal distribution; choosing such a weak information prior provides a certain degree of flexibility while avoiding introducing too much prior bias into the parameters of the model;

[0066] According to the prior distribution, the following formula is used as the likelihood function:

[0067]

[0068] Where p(X|μ) is the likelihood function; X is the observed quantity; μ is the expected value of the prior normal distribution; δ is the standard deviation of the prior normal distribution; N is the total number of observations; x n is the nth observation in the data set;

[0069] S4. Based on Bayesian inference, the posterior distribution of the model obtained in step S3 is updated; specifically, the steps include:

[0070] Based on Bayesian inference, the following formula is used as the posterior distribution:

[0071] p(μ|X)=N(μ|μ N ,(δ N ) 2 )

[0072]

[0073] Where p(μ|X) is the posterior distribution; μ N is the expected value of the posterior distribution; (δ N ) 2 is the variance of the posterior distribution;

[0074] According to the obtained posterior distribution, the root mean square error of the parameters in the Bayesian model is calculated and the root mean square error is normalized; finally, the normalized value is used as the credibility of the parameter;

[0075] In specific implementation, the reliability of parameters with large errors is low, and the reliability of parameters with small errors is high. The Z-score method can be used for normalization. In this case, the smaller the reliability value after normalization, the higher the reliability of the parameter.

[0076] S5. Generate fingerprint data of the power inspection terminal according to the posterior distribution obtained in step S4; specifically comprising the following steps:

[0077] According to the credibility of the parameters in the Bayesian model obtained in step S4, several parameters with the lowest credibility parameters are selected as input features for generating fingerprints;

[0078] The obtained input features are input into the transformer model to generate fingerprint data of the power inspection terminal;

[0079] In specific implementation, the input features are converted into high-dimensional vectors through the embedding layer, and the position encoding is set so that the model can capture the time sequence information in the input data; then the input data is passed through the Transformer model to gradually generate the fingerprint sequence. The decoder generates characters at each position until the fingerprint sequence is generated; during the training process, the cross entropy loss function is used to measure the difference between the actual sequence and the predicted sequence;

[0080] S6. According to the fingerprint data obtained in step S5, the actual identity authentication of the power inspection terminal is completed; specifically, the following steps are included:

[0081] Obtain inspection data information of target power inspection terminals in real time;

[0082] Generate a fingerprint sequence of the target power inspection terminal based on the acquired inspection data information; in specific implementation, a classification method such as machine learning can be used to classify the fingerprint sequence, and then match it based on the classification results;

[0083] Match the fingerprint sequence of the target power inspection terminal with the fingerprint data of the power inspection terminal generated in step S5:

[0084] If the match is successful, the target power inspection terminal is determined to be a legitimate terminal;

[0085] If the match fails, the target power inspection terminal is determined to be an illegal terminal.

[0086] The scheme of the present invention applies Bayesian inference + transformer identity recognition technology to the identity authentication of the power inspection terminal, and performs identity authentication based on general hardware data and inspection interaction data exclusive to the power inspection terminal. During the verification process, only the data of the power inspection terminal needs to be acquired through communication, and the transmission of fingerprints is not involved, thereby ensuring the security of fingerprint data. The scheme of the present invention constructs a parameter credibility model through Bayesian inference technology, and automatically selects the first n highly credible feature parameters as the basis for constructing the fingerprint sequence when generating the sequence, thereby reducing labor costs and reducing the risk of manually adjusting model parameters. Compared with the scheme of verifying the identity of the device based on fixed hardware data, the present invention integrates dynamic data into the fingerprint generation and verification scheme, so the scheme of the present invention can adapt to dynamic changes such as system upgrades, application program changes, and inspection task changes of the power inspection terminal equipment, and dynamically updates the credibility of the feature parameters using Bayesian inference technology, and selects highly credible parameters to generate fingerprint sequences. Finally, the fingerprint sequence generated by the scheme of the present invention for the power inspection terminal is dynamically variable, and fingerprint recognition is realized based on classification technology. Even if a malicious attacker obtains the hardware sequence of the fingerprint, it is impossible to forge the power inspection terminal equipment based on the existing sequence to obtain important business data.

[0087] like Figure 2 The figure shows a schematic diagram of the functional modules of the system of the present invention: the system disclosed in the present invention for realizing the identity authentication method for the power inspection terminal comprises a data acquisition module, a data processing module, a model construction module, a distribution update module, a fingerprint generation module and an identity authentication module; the data acquisition module, the data processing module, the model construction module, the distribution update module, the fingerprint generation module and the identity authentication module are connected in series in sequence; the data acquisition module is used to acquire the historical inspection data information of the power system and upload the data information to the data processing module; the data processing module is used to perform data preprocessing on the acquired data information according to the received data information to obtain a data set, and upload the data information to the model construction module; the model The model construction module is used to set the fingerprint parameter probability model of the power inspection terminal according to the received data information, and based on the obtained data set, it represents the relationship between the equipment status and the data, and uploads the data information to the distribution update module; the distribution update module is used to perform posterior distribution update on the obtained model based on the received data information and Bayesian inference, and upload the data information to the fingerprint generation module; the fingerprint generation module is used to generate the fingerprint data of the power inspection terminal according to the received data information and the obtained posterior distribution, and upload the data information to the identity authentication module; the identity authentication module is used to complete the actual identity authentication of the power inspection terminal according to the received data information and the obtained fingerprint data.

Claims

1. An identity authentication method for a power inspection terminal comprises the following steps: S1. Obtain historical inspection data information of the power system; S2. Preprocess the data information obtained in step S1 to obtain a data set; S3. Set the fingerprint parameter probability model of the power inspection terminal, and based on the data set obtained in step S2, represent the relationship between the device status and the data; S4. Based on Bayesian inference, update the posterior distribution of the model obtained in step S3; S5. Generate fingerprint data of the power inspection terminal according to the posterior distribution obtained in step S4; S6. According to the fingerprint data obtained in step S5, the actual identity authentication of the power inspection terminal is completed.

2. The identity authentication method for a power inspection terminal according to claim 1 is characterized in that The acquisition of historical inspection data information of the power system described in step S1 specifically includes the following steps: Obtain historical inspection data information of the power system; The inspection data information includes static data and dynamic data; The static data includes the main control chip ID of the power inspection terminal, the sensor serial number of the power inspection terminal, the operating system version of the power inspection terminal, the designated driver list of the power inspection terminal, and the designated application list of the power inspection terminal; The dynamic data includes network characteristic data and inspection characteristic data; wherein the network characteristic data includes transmission rate and transmission delay; the inspection characteristic data includes PID control parameters, inspection mode and GPS positioning data.

3. The identity authentication method for a power inspection terminal according to claim 2 is characterized in that The preprocessing described in step S2 specifically includes the following steps: Preprocessing includes missing data processing, abnormal data processing and data unification; Missing data processing: If the amount of missing data is less than or equal to the set value, interpolation is used to fill the data; if the amount of missing data is greater than the set value, the corresponding data is directly discarded; Abnormal data processing: abnormal data is detected by a detection method and directly deleted; the detection method includes Z-Score method or IQR method; Data unification: The data obtained after missing data processing and abnormal data processing are unified into a standard time axis to achieve synchronization of the time corresponding to the data; for the dynamic inspection data in the data, the data is divided into several segments and stored separately; the data segmentation specifically includes sliding window segmentation.

4. The identity authentication method for a power inspection terminal according to claim 3 is characterized in that The setting of the fingerprint parameter probability model of the power inspection terminal described in step S3, and the relationship between the device status and the data based on the data set obtained in step S2, specifically includes the following steps: The following normal distribution model is used as the prior distribution, and the initial parameter model is constructed: p(μ)=N(μ1|μ0,(δ0) 2 ) Where p(μ) is the normal distribution of parameter μ; N() is the symbol for normal distribution; μ1 is a random variable; μ0 is the expected value of normal distribution; (δ0) 2 is the variance of the normal distribution; According to the prior distribution, the following formula is used as the likelihood function: Where p(X|μ) is the likelihood function; X is the observed quantity; μ is the expected value of the prior normal distribution; δ is the standard deviation of the prior normal distribution; N is the total number of observations; x n is the nth observation in the data set.

5. The identity authentication method for a power inspection terminal according to claim 4 is characterized in that The posterior distribution of the model obtained in step S3 is updated based on Bayesian inference in step S4, which specifically includes the following steps: Based on Bayesian inference, the following formula is used as the posterior distribution: p(μ|X)=N(μ|μ N ,(d N ) 2 ) Where p(μ|X) is the posterior distribution; μ N is the expected value of the posterior distribution; (δ N ) 2 is the variance of the posterior distribution; According to the obtained posterior distribution, the root mean square error of the parameters in the Bayesian model is calculated and the root mean square error is normalized; Finally, the normalized value is used as the credibility of the parameter.

6. The identity authentication method for a power inspection terminal according to claim 5, characterized in that The step S5 described in which the fingerprint data of the power inspection terminal is generated according to the posterior distribution obtained in step S4 specifically includes the following steps: According to the credibility of the parameters in the Bayesian model obtained in step S4, several parameters with the lowest credibility parameters are selected as input features for generating fingerprints; The obtained input features are input into the transformer model to generate fingerprint data of the power inspection terminal.

7. The identity authentication method for a power inspection terminal according to claim 6 is characterized in that Step S6, according to the fingerprint data obtained in step S5, completes the actual identity authentication of the power inspection terminal, which specifically includes the following steps: Obtain inspection data information of target power inspection terminals in real time; Generate a fingerprint sequence of the target power inspection terminal based on the acquired inspection data information; Match the fingerprint sequence of the target power inspection terminal with the fingerprint data of the power inspection terminal generated in step S5: If the match is successful, the target power inspection terminal is determined to be a legitimate terminal; If the match fails, the target power inspection terminal is determined to be an illegal terminal.

8. A system for implementing the identity authentication method for a power inspection terminal according to any one of claims 1 to 7, characterized in that It includes a data acquisition module, a data processing module, a model building module, a distribution update module, a fingerprint generation module and an identity authentication module; the data acquisition module, the data processing module, the model building module, the distribution update module, the fingerprint generation module and the identity authentication module are connected in series in sequence; the data acquisition module is used to obtain the historical inspection data information of the power system and upload the data information to the data processing module; The data processing module is used to perform data preprocessing on the acquired data information according to the received data information to obtain a data set, and upload the data information to the model building module; The model building module is used to set the fingerprint parameter probability model of the power inspection terminal according to the received data information, and based on the obtained data set, represent the relationship between the device status and the data, and upload the data information to the distribution update module; The distribution update module is used to update the posterior distribution of the obtained model based on the received data information and Bayesian inference, and upload the data information to the fingerprint generation module; The fingerprint generation module is used to generate fingerprint data of the power inspection terminal according to the received data information and the obtained posterior distribution, and upload the data information to the identity authentication module; The identity authentication module is used to complete the identity authentication of the actual power inspection terminal according to the received data information and the obtained fingerprint data.

Images