Operation data privacy protection method for distributed energy storage virtual power plant

Abstract

The invention discloses an operation data privacy protection method for a distributed energy storage virtual power plant, and belongs to the technical field of power plant data protection, and the method comprises the steps: 1, carrying out the safety detection of a login account according to the login habit of the login account, and judging whether the login account is abnormal or not; 2, fragmenting operation data in the virtual power plant, and carrying out dispersed storage; 3, the access information of the logged-in user is analyzed and processed, so that whether the access user is abnormal or not is judged; and 4, in combination with parameter data acquired by the security protection software, judging potential abnormal risks of the virtual power plant. According to the method, the login abnormal coefficient is obtained according to the obtained login address and the login time, so that whether the login account is abnormal during login is judged, abnormal login is prevented, whether the login account is stolen or not can be judged, and safety protection is performed on data privacy of the virtual power plant from a login source.

Description

Technical Field

[0001] The present invention belongs to the technical field of power plant data protection, and specifically relates to a method for protecting the privacy of operating data of a distributed energy storage virtual power plant. Background Art

[0002] Distributed energy storage virtual power plant is a coordinated management system that aggregates and coordinates distributed resources such as distributed power sources, energy storage systems, controllable loads, electric vehicles, etc. through information technology and software systems. It integrates the charging piles, air conditioners, energy storage and other resources scattered at the user end through the "aggregation + communication" method, and realizes coordinated optimization, participating in the operation of the power grid as a special "power plant".

[0003] Since distributed energy storage virtual power plants play an important role in the power system, they can assist the operation of the power system and participate in power market transactions. Therefore, the security of the operating data of distributed energy storage virtual power plants is particularly important. The existing security of the operating data of virtual power plants is to encrypt the operating data, and then only certified engineers or technical support personnel can access the encrypted operating data, and install anti-virus software on the corresponding data storage devices to ensure data security. However, with the advancement of technology, encrypted data may be hacked and cracked, which will still cause data loss. There will also be data theft by misusing the login account of the certified personnel, resulting in some difficult to estimate losses. Therefore, it is more urgent to protect the privacy of the operating data of virtual power plants. Summary of the invention

[0004] The purpose of the present invention is to provide a method for protecting the privacy of operating data of a distributed energy storage virtual power plant, so as to solve the problems faced in the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for protecting privacy of operation data of a distributed energy storage virtual power plant, the protection method comprising:

[0007] Step 1: Perform security checks on the login account based on the daily login habits of the login account to determine whether the login account is abnormal and block the abnormal account from logging in;

[0008] Step 2: fragment the operation data in the virtual power plant and store them in a distributed manner;

[0009] Step 3: Analyze and process the access information of the logged-in user to determine whether the access user is abnormal and interrupt the abnormal access;

[0010] Step 4: Combine the parameter data obtained by the security protection software to judge the potential abnormal risks of the virtual power plant and update the security protection software.

[0011] Furthermore, the method for determining whether the login account is abnormal in step 1 is:

[0012] According to the daily login habits of the login account, obtain the commonly used login address range and login time range of the login account, so as to formulate a standard login address P N And the standard login time T N ;

[0013] Get the login time of the current login account and the login address information at the login time T, so as to use the formula Obtain the login anomaly coefficient A lg ;

[0014] when When the login account is abnormal, the login account is judged to be abnormal and blocked;

[0015] Where m is the login time T+t y The number of login addresses obtained within t y is the preset login time range, P i is the i-th login address obtained within the login time range, and i∈[1,m], is the minimum difference between the obtained login address and the standard login address, is the maximum difference between the acquired login address and the standard login address, ΔP is the comparison value of the login address difference, α1 and α2 are preset coefficients, It is the preset login abnormality coefficient threshold.

[0016] Furthermore, in step 2, the method for fragmenting and distributing the operating data in the virtual power plant is as follows:

[0017] Split the operation data into multiple small fragments, and divide the storage module of the virtual power plant into multiple servers, each of which is connected by a random key;

[0018] Small pieces of data are stored in order in each server. When the login account needs to access the next server, the system randomly generates a key and sends it to the terminal bound to the login account. When the login account enters the generated random key, the login account is allowed to access.

[0019] Furthermore, the validity period of the random key is 90 seconds.

[0020] Furthermore, the method for determining whether the access user is abnormal in step 3 is:

[0021] When a user visits, the click volume change curve function U(t) and the download volume change curve function D(t) of the visiting user are obtained every monitoring period;

[0022] By formula The access anomaly coefficient A is obtained fg ;

[0023] when When the access user is abnormal, the access user is judged to be abnormal and the access is interrupted;

[0024] Among them, t a is the start time of the monitoring cycle, t b is the end time of the monitoring period, U N and D N These are the standard values ​​for click volume and download volume changes. is the maximum change in click volume during the monitoring period. is the maximum value of the download volume change during the monitoring period, ΔU and ΔD are the preset comparison values ​​respectively. is the preset access anomaly coefficient threshold.

[0025] Furthermore, the method for judging the potential abnormal risk of the virtual power plant in step 4 is:

[0026] When the virtual power plant is operating normally, the number of warnings S that appear is obtained from the log of the security protection software. h , the number of unauthorized accesses blocked S g And the number of malicious attacks S n ,

[0027] By formula The update index σ is obtained;

[0028] When σ>1, the security protection software is updated;

[0029] Among them, U CPU is the CPU usage, U M is the memory usage, is the preset standard value of CPU usage. is the preset memory usage standard value, S N The judgment threshold is set.

[0030] Beneficial effects of the present invention:

[0031] This application first performs a security check on the login account based on the daily login habits of the login account, and obtains the login anomaly coefficient by analyzing the obtained login address and login time. It determines whether the login account has an abnormality when logging in based on the size of the login anomaly coefficient. If an abnormality occurs, the login is blocked. In this way, it can be determined whether the login account is likely to be stolen, so that the login can be blocked in time, thereby protecting the data privacy of the virtual power plant. At the same time, the data privacy in the virtual power plant is also dispersedly stored through fragmentation processing. In this way, even if a storage server is hacked and breached, it is difficult for the intruder to reconstruct the complete operating data, which can be minimized. Minimize data loss as much as possible; in addition, analyze and process the access information of users after login, and judge the user's download volume and click volume to analyze whether there is any abnormality in the access user after login. When an abnormality occurs, the access can be interrupted in time to avoid excessive data loss; finally, when the virtual power plant is operating normally, the parameter data obtained by the security protection software is analyzed to generate an update index. According to the size of the update index, it is judged whether the virtual power plant has potential abnormal risks, and the security protection software is updated in time, which can greatly reduce the occurrence of data intrusion and cracking, and better protect the security of data privacy in the virtual power plant.

[0032] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] A method for protecting the privacy of operating data of a distributed energy storage virtual power plant, such as Figure 1 As shown, the protection method includes:

[0037] Step 1: Perform security checks on the login account based on the daily login habits of the login account to determine whether the login account is abnormal and block the abnormal account from logging in;

[0038] Step 2: fragment the operation data in the virtual power plant and store them in a distributed manner;

[0039] Step 3: Analyze and process the access information of the logged-in user to determine whether the access user is abnormal and interrupt the abnormal access;

[0040] Step 4: Combine the parameter data obtained by the security protection software to determine the potential abnormalities of the virtual power plant, thereby updating the security protection software.

[0041] Through the above scheme, the present application first performs security detection on the login account according to the daily login habits of the login account, obtains the login anomaly coefficient by analyzing the obtained login address and login time, and judges whether the login account has an abnormality when logging in according to the size of the login anomaly coefficient. When an abnormality occurs, the login is blocked. In this way, it can be judged whether there is a possibility of the login account being stolen, so as to block the login in time, thereby protecting the data privacy of the virtual power plant. At the same time, the data privacy in the virtual power plant is also dispersedly stored through fragmentation processing, so that even if a storage server is hacked and breached, it is difficult for the intruder to reconstruct the complete operation data. This can minimize data loss. In addition, the access information of logged-in users is analyzed and processed, and the user's download volume and click volume are judged to determine whether there is any abnormality in the access of logged-in users. If an abnormality occurs, the access can be interrupted in time to avoid excessive data loss. Finally, when the virtual power plant is operating normally, the parameter data obtained by the security protection software is analyzed to generate an update index. The potential abnormal risk of the virtual power plant is judged according to the size of the update index, and the security protection software is updated in time, thereby greatly reducing the occurrence of data intrusion and cracking, and better protecting the security of data privacy in the virtual power plant.

[0042] The method for determining whether the login account is abnormal in step 1 is: according to the daily login habits of the login account, obtain the commonly used login address range and login time range of the login account, so as to formulate a standard login address P N And the standard login time T N ;

[0043] Get the login time of the current login account and the login address information at the login time T, so as to use the formula Obtain the login anomaly coefficient A lg ;

[0044] When When the login account is abnormal, the login account is judged to be abnormal and blocked;

[0045] Where m is the login time T+t y The number of login addresses obtained within t y is the preset login time range, P i is the i-th login address obtained within the login time range, and i∈[1,m], is the minimum difference between the obtained login address and the standard login address, is the maximum difference between the acquired login address and the standard login address, ΔP is the comparison value of the login address difference, α1 and α2 are preset coefficients, It is the preset login abnormality coefficient threshold.

[0046] The above scheme analyzes the method of judging whether the login account is abnormal. First, according to the daily login habits of the login account, the commonly used login address range and login time range of the login account are obtained, so as to formulate a standard login address P N And the standard login time T N , and then obtain the login time of the current login account and the login address information at the login time T, so as to use the formula Obtain the login anomaly coefficient A lg , where m is the login time T+t y The number of login addresses obtained within t y is the login time range preset based on empirical data, the formula It shows the change of login address within the current login time range, and the formula shows the difference between the current login time and the standard login time T N The difference between the two values ​​is that when the values ​​are larger, it means that the abnormal coefficient of the current login account is larger than that of the previous login, which means that the possibility of the theft is greater. Therefore, when obtaining the login abnormal coefficient A lg Then compare it with the preset login anomaly coefficient threshold For comparison, when In this way, we can judge whether the login account is likely to be stolen based on the login address and login time changes of the login account. When the risk is high, the login account is blocked from logging in, thereby ensuring the security of data privacy.

[0047] In the above scheme, the comparison value ΔP of the login address difference, the preset coefficients α1 and α2 are, and the preset login abnormality coefficient threshold Both can be determined based on historical data and experience data.

[0048] The method for fragmenting and dispersing the operating data in the virtual power plant in step 2 is: split the operating data into multiple small fragments, and divide the storage module of the virtual power plant into multiple servers, and each server is connected by a random key; the small fragments of data are stored in each server in sequence. When the login account needs to access the next server, the system randomly generates a key and sends it to the terminal bound to the login account. The random key is valid for 90 seconds. When the login account enters the generated random key, the login account is allowed to access.

[0049] In the above scheme, the running data is split into multiple small fragments and stored in each server, and connected by a random key, so as to ensure the security of the data. The system randomly generates a key and sends it to the terminal bound to the login account. When the login account enters the generated random key, the login account is allowed to access it. This ensures that it is the current login person who accesses it, which can ensure data security. The data is split into fragments for storage, so that even if a storage server is hacked and compromised, it is difficult for the intruder to reconstruct the complete running data, which can minimize data loss.

[0050] The method for determining whether the access user is abnormal in step 3 is: when the user accesses, the click volume change curve function U(t) and the download volume change curve function D(t) of the access user are obtained every monitoring period;

[0051] By formula The access anomaly coefficient A is obtained fg ;

[0052] when When the access user is abnormal, the access user is judged to be abnormal and the access is interrupted;

[0053] Among them, t a is the start time of the monitoring cycle, t b is the end time of the monitoring period, U N and D N These are the standard values ​​for click volume and download volume changes. is the maximum change in click volume during the monitoring period. is the maximum value of the download volume change during the monitoring period, ΔU and ΔD are various preset comparison values, is the preset access anomaly coefficient threshold.

[0054] The above solution analyzes the method of judging whether the access user is abnormal. The intruder may still log in to the database through the login account to steal data. Therefore, in order to further ensure data security, after the access user logs in, a secondary protection is performed, that is, the curve function U(t) of the access user's click volume and the curve function D(t) of the download volume over time are obtained every monitoring period, and then the formula is used to calculate the value of the access user's click volume over time. The access anomaly coefficient A is obtained fg ,formula Indicates the change in click volume during the monitoring period, and the formula It shows the changes in download volume during the monitoring period. Generally speaking, when users visit normally, the download volume and click volume fluctuate around a relatively stable standard value. If the download volume and click volume change a lot in a short period of time, the possibility of being stolen is also high. Therefore, the formula The access anomaly coefficient A is obtained fg , compare it with the preset access anomaly coefficient threshold, when When the access user is abnormal, it is judged that the access user is abnormal and the access is interrupted, so as to ensure the security of data in time and reduce the loss of data privacy.

[0055] In the above scheme, the set click volume and download volume change standard value U N and D N , respective preset comparison values ​​ΔU and ΔD, preset access anomaly coefficient threshold All of these can be determined based on the historical operating data and experience data of the power plant, so I will not elaborate on them here.

[0056] The method for judging the potential abnormal risk of the virtual power plant in step 4 is: when the virtual power plant is operating normally, the number of warnings S that appear is obtained from the log of the security protection software. h , the number of unauthorized accesses blocked S g And the number of malicious attacks S n ,

[0057] By formula The update index σ is obtained;

[0058] When σ>1, the security protection software is updated;

[0059] Among them, U CPU is the CPU usage, U M is the memory usage, U CPUth is the preset standard value of CPU usage. is the preset memory usage standard value, S N The judgment threshold is set.

[0060] The above technical solution provides a method for judging the potential abnormal risks of a virtual power plant. First, when the virtual power plant is working normally, the number of warnings S that appear is obtained from the log of the security protection software. h , the number of unauthorized accesses blocked S g And the number of malicious attacks S n , and obtain the CPU usage and memory usage of the security protection software at the same time, and use the formula The update index σ is obtained. From the formula, we can see that when the number of warnings S h , the number of unauthorized accesses blocked S g And the number of malicious attacks S n The more there are, the greater the possibility of being hacked. Similarly, when the ratio between the CPU usage and memory usage and the standard value is larger, it means that the possibility of being hacked is greater. Therefore, a judgment threshold S is set based on historical data. N , and compare it with the preset judgment threshold S N A comparison is made. When the value of the update index is greater than 1, it indicates that there is a high possibility of potential abnormal risk. In order to reduce the occurrence of abnormal risks, the security protection software is updated to ensure the security of data privacy within the virtual power plant.

[0061] It should be noted that the preset CPU usage standard value The default memory usage standard value is Set the judgment threshold S N All can be determined based on historical data.

[0062] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for protecting the privacy of operating data of a distributed energy storage virtual power plant, characterized in that: The protection method comprises: Step 1: Perform security checks on the login account based on the daily login habits of the login account to determine whether the login account is abnormal and block the abnormal account from logging in; Step 2: fragment the operation data in the virtual power plant and store them in a distributed manner; Step 3: Analyze and process the access information of the logged-in user to determine whether the access user is abnormal and interrupt the abnormal access; Step 4: Combine the parameter data obtained by the security protection software to judge the potential abnormal risks of the virtual power plant and update the security protection software.

2. A method for protecting the privacy of operating data of a distributed energy storage virtual power plant according to claim 1, characterized in that: The method for determining whether the login account is abnormal in step 1 is: According to the daily login habits of the login account, obtain the commonly used login address range and login time range of the login account, so as to formulate a standard login address P N And the standard login time T N ; Get the login time of the current login account and the login address information at the login time T, so as to use the formula Obtain the login anomaly coefficient A lg ; when When the login account is abnormal, the login account is judged to be abnormal and blocked; Where m is the login time T+t y The number of login addresses obtained within t y is the preset login time range, P i is the i-th login address obtained within the login time range, and i∈[1,m], is the minimum difference between the obtained login address and the standard login address, is the maximum difference between the acquired login address and the standard login address, ΔP is the comparison value of the login address difference, α1 and α2 are preset coefficients, It is the preset login abnormality coefficient threshold.

3. The method for protecting the privacy of operation data of a distributed energy storage virtual power plant according to claim 1, characterized in that: The method for fragmenting and distributing the operating data in the virtual power plant in step 2 is as follows: Split the operation data into multiple small fragments, and divide the storage module of the virtual power plant into multiple servers, each of which is connected by a random key; Small pieces of data are stored in order in each server. When the login account needs to access the next server, the system randomly generates a key and sends it to the terminal bound to the login account. When the login account enters the generated random key, the login account is allowed to access.

4. A method for protecting privacy of operation data of a distributed energy storage virtual power plant according to claim 3, characterized in that: The validity period of the random key is 90 seconds.

5. The method for protecting the privacy of operation data of a distributed energy storage virtual power plant according to claim 1, characterized in that: The method for determining whether the access user is abnormal in step 3 is: When a user visits, the click volume change curve function U(t) and the download volume change curve function D(t) of the visiting user are obtained every monitoring period; By formula The access anomaly coefficient A is obtained fg ; when When the access user is abnormal, the access user is judged to be abnormal and the access is interrupted; Among them, t a is the start time of the monitoring cycle, t b is the end time of the monitoring period, U N and D N These are the standard values ​​for click volume and download volume changes. is the maximum change in click volume during the monitoring period. is the maximum value of the download volume change during the monitoring period, ΔU and ΔD are the preset comparison values ​​respectively. is the preset access anomaly coefficient threshold.

6. The method for protecting the privacy of operation data of a distributed energy storage virtual power plant according to claim 1, characterized in that: The method for judging the potential abnormal risk of the virtual power plant in step 4 is: When the virtual power plant is operating normally, the number of warnings S that appear is obtained from the log of the security protection software. h , the number of unauthorized accesses blocked S g And the number of malicious attacks S n , By formula The update index σ is obtained; When σ>1, the security protection software is updated; Among them, U CPU is the CPU usage, U M is the memory usage, is the preset standard value of CPU usage. is the preset memory usage standard value, S N The judgment threshold is set.

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