Energy big data secure storage method, storage medium and system

Abstract

The present application relates to the field of big data processing technology, and specifically to a method, storage medium, and system for secure storage of energy big data. The method comprises: obtaining energy data of any type of energy, which includes multiple data categories, arranging any group of data in any data category in time sequence to form a data sequence; determining the correlation value and data regularity of each type of data; and then obtaining the encryption strength coefficient of each type of data; determining the local feature approximation of each type of data based on the difference in mutation time and distribution range between each type of data and all other types of data corresponding to the subsequence; determining the periodic approximation of each type of data; and then obtaining the key length coefficient of each type of data; and when storing the energy data, encrypting each type of data using the AES encryption algorithm based on the key length coefficient. The present application aims to improve encryption strength while ensuring encryption efficiency.

Description

Technical Field

[0001] The present application relates to the field of big data processing technology, and specifically to a method, storage medium and system for secure storage of energy big data. Background Art

[0002] The secure storage of energy big data is a key factor in ensuring the success of the energy industry's digital transformation. As the digital transformation of the energy industry advances, data security issues are becoming increasingly prominent. This is especially true in new power systems dominated by renewable energy sources, where data security risks exist in every stage of collection, storage, use, processing, transmission, provision, and disclosure.

[0003] In the process of storing energy big data, to prevent data leakage or theft, the stored data is usually encrypted to ensure its security in static state. Among existing encryption algorithms, the AES encryption algorithm is widely used for big data encryption due to its advantages such as high efficiency, high security, and easy implementation.

[0004] The AES encryption algorithm usually uses a fixed-size key for encryption, but the amount of data contained in energy big data is large and the content involved is relatively wide. Not all categories of data need to be highly encrypted. For example, the charging pile location data in electric vehicle charging pile data usually needs to be announced to the public, while the charging user data in electric vehicle charging pile data needs to be highly confidential. If a larger key is used to encrypt all categories of data, a lot of encryption time and resources will be consumed, resulting in a significant reduction in encryption efficiency. However, if shorter keys are used for encryption, the more important data in energy big data may be easily stolen due to the lower encryption strength, resulting in losses. Summary of the Invention

[0005] In view of the above, it is necessary to provide a method, storage medium and system for secure storage of energy big data, which can improve encryption strength while ensuring encryption efficiency compared with traditional energy big data secure storage methods:

[0006] In a first aspect, an embodiment of the present application provides a method for securely storing energy big data, the method comprising the following steps:

[0007] Obtain energy data of any type of energy, which includes multiple data categories, each data category includes multiple groups of data, and arrange any group of data in any data category in time sequence to form a data sequence;

[0008] Determine the correlation value of each category of data based on the difference in the changing trends of all corresponding data series between each category of data and all other categories of data, as well as the difference in the time of extreme values ​​in all corresponding data series;

[0009] Determine the data regularity of each type of data based on the similarity between any two data sequences and the degree of disorder of all data sequences;

[0010] Based on the correlation value and the data regularity, obtaining encryption strength coefficients of various types of data;

[0011] Divide each data sequence into subsequences of preset lengths, and determine the local feature similarity of each data type based on the differences in mutation times and distribution ranges between each data type and all other data types corresponding to the subsequences;

[0012] Determine the periodic approximation of each data type based on the similarity of the periodic characteristics between any two subsequences of each data sequence and the similarity of the data fluctuations between them;

[0013] Combining the local feature approximation, the periodic approximation and the encryption strength coefficient to obtain a key length coefficient for each type of data;

[0014] When storing energy data, various types of data are encrypted using the AES encryption algorithm based on the key length coefficient.

[0015] In one embodiment, the expression of the associated value is:

[0016] Where A i is the correlation value of the i-th category data; B i,j is the mean of the trend intensity difference between the i-th category data and all other categories of data in the j-th data series; C i,j is the mean of the differences between the i-th category data and all other categories of data at the time of the corresponding extreme values ​​in the j-th data sequence; J i is the minimum value of the total number of data sequences in all categories of data; α is a first value preset to be greater than 0.

[0017] In one embodiment, the process of determining the data regularity is as follows:

[0018] Calculate the approximate mean entropy of all data sequences in each type of data;

[0019] The mean of the distances between any two data sequences in each type of data is recorded as the mean distance;

[0020] The data regularity is inversely proportional to the approximate entropy mean and the distance mean, respectively.

[0021] In one embodiment, the process of determining the local feature similarity is as follows:

[0022] Let any data category be denoted as i, and calculate the difference in the time of each corresponding mutation point of the x-th subsequence of the j-th data sequence between the i-th data and the rest of the categories of data, and record it as the mutation time difference;

[0023] The ratio of the range of each subsequence to the sequence length is recorded as the first ratio. The difference in the first ratio of the x-th subsequence of the j-th data sequence between the i-th category data and the rest of the categories of data is calculated and recorded as the range difference.

[0024] The expression of the local feature approximation of the i-th type of data is:

[0025] Where G i is the local feature approximation of the i-th type of data; H i,j K is the mean of all the differences in the mutation moments corresponding to the j-th data sequence in the i-th category of data; i,j J is the mean of all the range differences corresponding to the j-th data sequence in the i-th category of data; i is the minimum value of the total number of data sequences in all categories of data; γ is a third value preset to be greater than 0.

[0026] In one embodiment, the expression of the periodic approximation is:

[0027] Where M i is the periodic approximation of the i-th type of data; N i,j 、P i,j are respectively the mean of the phase locking values ​​between all arbitrary two subsequences of the j-th data sequence in the i-th category of data and the sum of the differences in the degree of dispersion between them; I is the total number of data sequences in the i-th category of data; ε is a fourth value preset to be greater than 0.

[0028] In one embodiment, the key length coefficient is a fusion result of the local feature approximation, the periodic approximation and the encryption strength coefficient.

[0029] In one embodiment, the process of encrypting various types of data is as follows:

[0030] Using the threshold segmentation algorithm to obtain the segmentation thresholds of all key length coefficients;

[0031] The data of the category whose key length coefficient is greater than or equal to the segmentation threshold is marked as high confidentiality data, otherwise, it is marked as normal confidentiality data;

[0032] The AES encryption algorithm is used to encrypt high-confidentiality data and ordinary confidentiality data using keys of different lengths.

[0033] In one embodiment, the key length used for encrypting high-confidentiality data is greater than the key length used for encrypting normal-confidentiality data.

[0034] In a second aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for secure storage of energy big data as described in the first aspect is implemented.

[0035] In a third aspect, an embodiment of the present application also provides an energy big data security storage system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned energy big data security storage methods are implemented.

[0036] This application has at least the following beneficial effects:

[0037] When storing energy data, this application analyzes the mutual influence between energy data and the regularity of the energy data itself to obtain an encryption strength coefficient, which is used to characterize the importance of the stored energy data and the required encryption strength. The more important the energy data, the greater the encryption strength.

[0038] Furthermore, when the energy data is missing and the data characteristics are affected, the approximation of the local characteristics between the energy data and the periodic characteristics of the energy data itself are analyzed to obtain the encryption compensation coefficient. In the case of data missing, the degree of correlation between the energy data and the regularity of the energy data itself can be characterized, thereby avoiding the misjudgment of the encryption strength required for the data due to data missing. The encryption strength coefficient and the encryption compensation coefficient are combined to obtain the key length coefficient to characterize the encryption strength required for the energy data. When the AES encryption algorithm is used to encrypt and store the energy data, the key length can be adaptively adjusted to improve the encryption strength of important energy data while ensuring the encryption efficiency. This solves the problem of low encryption efficiency or low encryption strength when encrypting and storing energy data with a fixed key length, which may lead to the leakage of important energy data. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1A flowchart of a method for securely storing energy big data provided in accordance with one embodiment of the present application;

[0041] Figure 2 This is a schematic diagram of the STL algorithm decomposition of the social electricity consumption data series;

[0042] Figure 3 This is a schematic diagram of extreme point detection in the social electricity consumption data series;

[0043] Figure 4 Schematic diagram of the process for determining data regularity;

[0044] Figure 5 Schematic diagram of the process of obtaining the key length coefficient. DETAILED DESCRIPTION

[0045] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application relates. The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. It should be understood that, unless otherwise indicated, " / " represents or.

[0047] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0048] The following describes in detail a method, storage medium, and system for secure storage of energy big data provided by this application in conjunction with the accompanying drawings.

[0049] See also Figure 1 , which shows a flowchart of a method for securely storing energy big data provided by an embodiment of the present application, the method comprising the following steps:

[0050] Step 1: Obtain energy data of any type of energy, which includes multiple data categories, each data category includes multiple groups of data, and arrange any group of data in any data category in time sequence to form a data sequence.

[0051] The Energy Big Data Center retrieves the latest energy data stored there. Due to the large number of energy categories, each energy category contains multiple data categories, and each data category contains multiple sets of data. For example, in the case of electric energy, data categories include power consumption, power generation, and grid operation data. Power generation includes thermal power generation, wind power generation, and other data.

[0052] In this application, electric energy data is used as an example, and any group of data in any data category of the electric energy data is arranged in time sequence to form a data sequence. For example, the electricity consumption data of any enterprise is arranged in time sequence to form the electricity consumption data sequence of the enterprise.

[0053] At this point, the data sequences of all group data in all data categories in the electric power data can be obtained.

[0054] Step 2: Determine the correlation value of each category of data based on the difference in the changing trends of all corresponding data series between each category of data and all other categories of data, as well as the difference in the time of extreme values ​​in all corresponding data series.

[0055] In electric power data, different types of data have a certain degree of mutual influence. The greater the influence of a certain type of data on the rest of the data, the more important it is within the electric power data. Therefore, it requires a longer encryption key to protect its security during storage. For example, for the i-th type of data, each group of data in the i-th type has similar data variation characteristics to the other groups of data in the rest of the data, meaning that peaks and valleys occur at similar times and with similar magnitudes of variation.

[0056] Based on the above analysis, the correlation value of each type of data is determined based on the difference in the change trend of all corresponding data series between each type of data and all other types of data, as well as the difference in the time of the extreme values ​​in all corresponding data series. The expression is:

[0057] Where A i is the correlation value of the i-th category data; B i,j is the mean of the trend intensity difference between the i-th category data and all other categories of data in the j-th data series; C i,j is the mean of the differences between the i-th category data and all other categories of data at the time of the corresponding extreme values ​​in the j-th data sequence; J iis the minimum value of the total number of data sequences in all categories of data; α is a first value preset to be greater than 0, the purpose of which is to prevent the denominator from being 0. The value of α is preset manually and can be set by the implementer. In this embodiment, the value of α is 0.001; wherein, the method for obtaining the trend strength of the data sequence is: first, the trend term and residual term of the data sequence are obtained by using the STL (Seasonal and Trend decomposition using Loess) algorithm, and then the trend strength calculation formula is used to obtain the trend strength of the data sequence. The trend strength calculation formula is a well-known technology and will not be described in detail in this application. Taking the social electricity consumption data sequence in the electric energy data as an example, the STL algorithm decomposition diagram of the social electricity consumption data sequence is shown as follows: Figure 2 As shown; the extreme value in the data sequence can be obtained by the extreme point detection algorithm, wherein the extreme point detection algorithm is a well-known technology and will not be described in detail in this application. Taking the social power consumption data sequence in the electric energy data as an example, the extreme point detection diagram of the social power consumption data sequence is shown as follows Figure 3 shown.

[0058] In this embodiment, the difference between trend strengths is the absolute value of the difference. As other implementation methods, on the basis of being able to measure the difference between trend strengths, the implementer may use other calculation methods to measure the difference between trend strengths, such as ratio relationship, square of difference, etc. The implementer may limit it according to actual conditions, and this application does not impose any special restrictions.

[0059] In this embodiment, the difference between the moments where the extreme values ​​are located is the absolute value of the difference. As other implementation methods, on the basis of being able to measure the difference between the moments where the extreme values ​​are located, the implementer may use other calculation methods to measure the difference between the moments where the extreme values ​​are located, such as a ratio relationship, the square of the difference, etc. The implementer may limit it according to the actual situation, and this application does not impose any special restrictions.

[0060] It should be noted that: the more similar the changing trends of the data sequences between the i-th category data and all other categories of data are, and the closer the corresponding extreme values ​​between the data sequences are, the greater the influence of the i-th category data on the other categories of data, and the greater the correlation value.

[0061] Step 3: Determine the data regularity of each type of data based on the similarity between any two data sequences in each type of data and the degree of disorder of all data sequences.

[0062] Many types of power energy data exhibit certain regularities. For example, residential electricity consumption is typically higher in the evening and lower in the early morning, while corporate electricity consumption is higher during the peak season and lower during the off-season. The more regular the data, the greater the likelihood of being cracked and stolen after encryption. Therefore, when data exhibits high regularity, the required encryption key length should also be increased. Regularity in a particular data category is specifically manifested as a high degree of regularity in the data sequences within that category, as well as a high degree of similarity between the data sequences.

[0063] Based on the above analysis, based on the similarity between any two data sequences in each type of data, and the degree of disorder of all data sequences, the data regularity of each type of data is determined to characterize the regular characteristics of each type of data. The expression is:

[0064] D i is the data regularity of the i-th type of data; E i is the approximate entropy mean of all data sequences in the i-th category; F i is the mean of the distances between all arbitrary two data sequences in the i-th category of data; β is a second value preset to be greater than 0, the purpose of which is to prevent the denominator from being 0. The value of β is preset manually and can be set by the implementer. In this embodiment, the value of β is 0.001; wherein, the calculation of approximate entropy is a well-known technology and will not be described in detail in this application.

[0065] In this embodiment, the distance between the data sequences is the DTW (Dynamic Time Warping) distance. As other implementation methods, based on the measurable distance between the two data sequences, the implementer may adopt other existing technologies to obtain the distance between the two data sequences, such as Euclidean distance, Manhattan distance, etc. This application does not impose any special restrictions.

[0066] It should be noted that: if the regularity of the data sequence in the i-th category of data is greater and the similarity between the data sequences is stronger, the mean of the approximate entropy is smaller and the mean of the distance is smaller; that is, the greater the data regularity, the stronger the regularity of the i-th category of data. The flow chart for determining the data regularity is as follows: Figure 4 shown.

[0067] Step 4: Based on the association value and the data regularity, the encryption strength coefficient of each type of data is obtained.

[0068] The fusion result of the correlation value of each type of data and the data regularity is used as the encryption strength coefficient of each type of data.

[0069] It should be noted that fusion refers to combining multiple independent variables in a way that enhances the overall effect, such as additive relationship, multiplicative relationship, etc. The implementer can set it according to the actual situation, and this application does not impose any special restrictions.

[0070] In this embodiment, the sum of the association value of each type of data and the data regularity is used as the encryption strength coefficient of each type of data.

[0071] In another embodiment, the product of the association value of each type of data and the data regularity is used as the encryption strength coefficient of each type of data.

[0072] It should be noted that: taking the i-th category data as an example, if the i-th category data has a stronger impact on the other categories of data among all electric energy data, the greater the degree of correlation between the i-th category data and the other categories of data, and the stronger the data regularity contained in the i-th category data, it means that the i-th category data is more important among all electric energy data, and the greater the possibility of being cracked and stolen after encryption, the longer the key is needed for encryption to improve the encryption effect.

[0073] Step 5: Divide each data sequence into subsequences of preset lengths, and determine the local feature similarity of each type of data based on the differences in mutation moments and distribution ranges between each type of data and all other types of data corresponding to the subsequences.

[0074] Due to the large amount of electric energy data, different data collection methods and sources, the quality of the collected data may be low. Specifically, there are missing data in the data stored in the energy big data center, resulting in null values ​​in the data sequence, which affects the overall data characteristics of some categories of data in the electric energy data.

[0075] For example, a data sequence that originally had strong regularity may have lost some of its regularity due to the loss of some data; or the correlation between a certain category of data and the rest of the category of data may have been strong, but due to the loss of some data, the correlation has weakened, and the encryption strength coefficient finally calculated is low, so a shorter key is used to encrypt the data of the said category, resulting in poor encryption effect. Although there are data missing in this part of the data, there may still be complete data. These complete data may be more important content in the power energy data, such as the address and identity information of the residential users. If the encryption effect of this part of the data is poor, it will make the data easy to be cracked and stolen, resulting in privacy leakage. Therefore, the selection of key length based solely on the encryption strength coefficient is not comprehensive and requires further analysis.

[0076] If any category of data is missing, some of its features will be lost, but the remaining data will retain their features, and the features of the remaining data and its associated data will remain similar. For example, in the case of category i, the data mutation and magnitude of a certain interval in the jth data sequence within category i will be similar to those of the remaining categories.

[0077] To characterize the local characteristics of a data sequence, we divide each data sequence within the i-th category into a subsequence, taking the i-th category as an example. We then use a mutation point detection algorithm to identify mutation points within each subsequence. It should be noted that if the data volume is insufficient, no further processing will be performed.

[0078] In this embodiment, the value of the preset number is 30. The value of the preset number is preset manually and the implementer can set it according to actual conditions. This application does not impose any special restrictions.

[0079] In this embodiment, the Pettitt mutation point detection algorithm is used to obtain the mutation points in the subsequence. As other implementation methods, based on the ability to obtain the mutation points in the subsequence, the implementer may use other existing technologies to obtain the mutation points in the subsequence, such as the Bayesian mutation point detection algorithm, the Mann-Kendall mutation point detection algorithm, etc., and this application does not impose any special restrictions.

[0080] Based on the differences in mutation time and distribution range of the subsequences between each category of data and all other categories of data, the local feature similarity of each category of data is determined. The specific process is as follows:

[0081] Calculate the difference in the time of each corresponding mutation point of the x-th subsequence of the j-th data sequence between the i-th category data and the rest of the categories of data, and record it as the mutation time difference;

[0082] The ratio of the range of each subsequence to the sequence length is recorded as the first ratio. The difference in the first ratio of the x-th subsequence of the j-th data sequence between the i-th category data and the rest of the categories of data is calculated and recorded as the range difference.

[0083] The expression of the local feature approximation of the i-th type of data is:

[0084] Where G i is the local feature approximation of the i-th type of data; H i,j K is the mean of all the differences in the mutation moments corresponding to the j-th data sequence in the i-th category of data; i,j J is the mean of all the range differences corresponding to the j-th data sequence in the i-th category of data; iis the minimum value of the total number of data sequences in all categories of data; γ is a third value preset to be greater than 0, the purpose of which is to prevent the denominator from being 0. The value of γ is preset manually and can be set by the implementer. In this embodiment, the value of γ is 0.001.

[0085] In this embodiment, the difference between the moments at which the mutation points are located is the absolute value of the difference. On the basis of the measurable difference between the moments at which the mutation points are located, the implementer may use other calculation methods for measurement, such as a ratio relationship, the square of the difference, etc. The implementer may limit it according to the actual situation, and this application does not impose any special restrictions.

[0086] In this embodiment, the difference between the first ratios is the absolute value of the difference. On the basis of the measurable difference between the first ratios, the implementer may use other calculation methods for measurement, such as ratio relationship, square of difference, etc. The implementer may limit it according to actual conditions, and this application does not impose any special restrictions.

[0087] It should be noted that: if the difference in the time of the corresponding mutation point of the subsequence between the i-th category data and the rest of the category data is smaller, and the difference in the distribution range of the subsequence is smaller, it means that the local features of the data sequence between the i-th category data and the rest of the category data are more similar, and the value of the local feature approximation is larger.

[0088] Step 6: Determine the periodic similarity of each data type based on the similarity of the periodic characteristics between any two subsequences of each data sequence in each data type and the similarity of the data fluctuations between them.

[0089] If the original data of the i-th category of data has a large periodicity, then even if there is a certain amount of data missing in the i-th category of data, the periodic characteristics between its subsequences are still relatively similar. Specifically, the periodic characteristics and data fluctuation levels of the subsequences of the data sequence in the i-th category of data are similar.

[0090] Based on the similarity of the periodic characteristics between any two subsequences of each data sequence in each data type and the similarity of the data fluctuations between them, the periodic approximation of each data type is determined. The expression is:

[0091] Where M i is the periodic approximation of the i-th type of data; N i,j 、P i,jwhere ∈ ...

[0092] In this embodiment, the degree of dispersion is variance. As other implementation methods, based on the degree of uneven distribution of data in the descriptive subsequence, the implementer may use other existing statistical quantities for measurement, such as standard deviation, coefficient of variation, etc., and this application does not impose any special restrictions.

[0093] In this embodiment, the difference between the degrees of discreteness is the absolute value of the difference. On the basis of the difference between the measurable degrees of discreteness, the implementer may use other calculation methods for measurement, such as ratio relationship, square of difference, etc. The implementer may limit it according to the actual situation, and this application does not impose any special restrictions.

[0094] It should be noted that: if the periodicity of the i-th type of data is stronger, the similarity of the periodic characteristics between the subsequences in the i-th type of data is higher, the phase locking value between the subsequences is larger and the difference in discreteness is smaller; that is, the larger the value of the periodic approximation, the more obvious the periodicity of the i-th type of data is.

[0095] Step 7: Combining the local feature approximation, the periodic approximation and the encryption strength coefficient to obtain the key length coefficient of each type of data.

[0096] Based on the local feature approximation and periodic approximation of each type of data, the encryption compensation coefficient of each type of data is determined.

[0097] The fusion results of the local feature approximation and the periodic approximation of each type of data are used as the encryption compensation coefficient of each type of data.

[0098] In this embodiment, the sum of the local feature approximation and the period approximation of each type of data is used as the encryption compensation coefficient of each type of data.

[0099] In another embodiment, the product of the local feature approximation and the periodic approximation of each type of data is used as the encryption compensation coefficient of each type of data.

[0100] It should be noted that: if the local features between the i-th category data and the rest of the category data are more similar, and the periodic approximation of the i-th category data is greater, the feature correlation between the i-th category data and the rest of the category data is greater, the importance of the i-th category data is greater, and the more regular the i-th category data is, the greater the encryption compensation coefficient of the i-th category data is, and the longer the key length required for encryption.

[0101] Furthermore, based on the encryption strength coefficient and encryption compensation coefficient of each type of data, the key length coefficient of each type of data is determined.

[0102] The fusion result of the encryption strength coefficient and encryption compensation coefficient of each type of data is used as the key length coefficient of each type of data.

[0103] In this embodiment, the sum of the encryption strength coefficient and the encryption compensation coefficient of each type of data is used as the key length coefficient of each type of data.

[0104] In another embodiment, the product of the encryption strength coefficient of each type of data and the encryption compensation coefficient is used as the key length coefficient of each type of data.

[0105] It should be noted that if the i-th category of data has a greater impact on the rest of the category data and has a higher degree of regularity, it means that the i-th category of data is more important in the power energy data and requires a longer key for encryption to ensure its security during storage. Figure 5 shown.

[0106] Step 8: When storing energy data, encrypt various types of data using the AES encryption algorithm based on the key length coefficient.

[0107] The threshold segmentation algorithm is used to obtain the segmentation threshold of all key length coefficients, and the category data with key length coefficient greater than or equal to the segmentation threshold is marked as high confidentiality data, and the category data with key length coefficient less than the segmentation threshold is marked as ordinary confidentiality data.

[0108] In this embodiment, the Otsu threshold segmentation algorithm is used to obtain the segmentation threshold. As other implementation methods, on the basis of being able to obtain the segmentation threshold, the implementer can use other existing technologies to obtain the segmentation threshold, such as global threshold segmentation, iterative threshold segmentation, etc. This application does not impose any special restrictions.

[0109] When the Energy Big Data Center stores energy data, it first marks the energy data as either highly confidential or generally confidential. It then uses the AES encryption algorithm to encrypt all marked energy data, generating encrypted ciphertext. Finally, the resulting ciphertext is stored in the Energy Big Data Center's database. When encrypting energy data, a 192-bit key is used to encrypt data marked as highly confidential, and a 128-bit key is used to encrypt data marked as generally confidential. The AES encryption algorithm is well known and will not be described in detail in this application.

[0110] Based on the same inventive concept as the above-mentioned method, a computer-readable storage medium is proposed, which stores a computer program. When the computer program is executed by a processor, it implements a method for secure storage of energy big data as described in the first aspect. Its specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0111] Based on the same inventive concept as the above method, an embodiment of the present application also provides an energy big data security storage system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned energy big data security storage methods are implemented.

[0112] To sum up, when storing energy data, this application analyzes the mutual influence between energy data and the regularity of the energy data itself to obtain an encryption strength coefficient, which is used to characterize the importance of the stored energy data and the strength required for encryption. The more important the energy data, the greater the encryption strength.

[0113] Furthermore, when the energy data is missing and the data characteristics are affected, the approximation of the local characteristics between the energy data and the periodic characteristics of the energy data itself are analyzed to obtain the encryption compensation coefficient. In the case of data missing, the degree of correlation between the energy data and the regularity of the energy data itself can be characterized, thereby avoiding the misjudgment of the encryption strength required for the data due to data missing. The encryption strength coefficient and the encryption compensation coefficient are combined to obtain the key length coefficient to characterize the encryption strength required for the energy data. When the AES encryption algorithm is used to encrypt and store the energy data, the key length can be adaptively adjusted to improve the encryption strength of important energy data while ensuring the encryption efficiency. This solves the problem of low encryption efficiency or low encryption strength when encrypting and storing energy data with a fixed key length, which may lead to the leakage of important energy data.

[0114] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0115] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from all perspectives, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. A method for secure storage of energy big data, characterized in that: The method comprises the following steps: Obtain energy data of any type of energy, which includes multiple data categories, each data category includes multiple groups of data, and arrange any group of data in any data category in time sequence to form a data sequence; Determine the correlation value of each category of data based on the difference in the changing trends of all corresponding data series between each category of data and all other categories of data, as well as the difference in the time of extreme values ​​in all corresponding data series; Determine the data regularity of each type of data based on the similarity between any two data sequences and the degree of disorder of all data sequences; Based on the correlation value and the data regularity, obtaining encryption strength coefficients of various types of data; Divide each data sequence into subsequences of preset lengths, and determine the local feature similarity of each data type based on the differences in mutation times and distribution ranges between each data type and all other data types corresponding to the subsequences; Determine the periodic approximation of each data type based on the similarity of the periodic characteristics between any two subsequences of each data sequence and the similarity of the data fluctuations between them; Combining the local feature approximation, the periodic approximation, and the encryption strength coefficient, a key length coefficient for each type of data is obtained; the key length coefficient is the fusion result of the encryption strength coefficient and the encryption compensation coefficient, and the encryption compensation coefficient is the fusion result of the local feature approximation and the periodic approximation; the enhanced density coefficient is used to characterize the importance of the stored energy data and the required encryption strength, and the encryption compensation coefficient is used to characterize the degree of correlation between the energy data and the regularity of the energy data itself; When storing energy data, various types of data are encrypted using the AES encryption algorithm based on the key length coefficient.

2. A method for secure storage of energy big data according to claim 1, characterized in that: The expression of the associated value is: Where A i is the associated value of the i-th category data; B i,j is the mean of the trend intensity difference between the i-th category data and all other categories of data in the j-th data series; C i,j is the mean of the differences between the i-th category data and all other categories of data at the time of the corresponding extreme values ​​in the j-th data sequence; J i is the minimum value of the total number of data sequences in all categories of data; α is a first value preset to be greater than 0.

3. The method for secure storage of energy big data according to claim 1, wherein: The process of determining the data regularity is as follows: Calculate the approximate mean entropy of all data sequences in each type of data; The mean of the distances between any two data sequences in each type of data is recorded as the mean distance; The data regularity is inversely proportional to the approximate entropy mean and the distance mean, respectively.

4. The method for secure storage of energy big data according to claim 1, wherein: The process of determining the local feature approximation is as follows: Let any data category be denoted as i, and calculate the difference in the time of each corresponding mutation point of the x-th subsequence of the j-th data sequence between the i-th data and the rest of the categories of data, and record it as the mutation time difference; The ratio of the range of each subsequence to the sequence length is recorded as the first ratio. The difference in the first ratio of the x-th subsequence of the j-th data sequence between the i-th category data and the rest of the categories of data is calculated and recorded as the range difference. The expression of the local feature approximation of the i-th type of data is: Where G i is the local feature approximation of the i-th type of data; H i,j K is the mean of all the differences in the mutation moments corresponding to the j-th data sequence in the i-th category of data; i,j J is the mean of all the range differences corresponding to the j-th data sequence in the i-th category of data; i It is the minimum value of the total number of data sequences in all categories of data; γ is a third value that is preset to be greater than 0.

5. The method for secure storage of energy big data according to claim 1, wherein: The expression of the period approximation is: Where M i is the periodic approximation of the i-th type of data; N i,j 、P i,j are respectively the mean of the phase locking values ​​between all arbitrary two subsequences of the j-th data sequence in the i-th category of data and the sum of the differences in the degree of dispersion between them; I is the total number of data sequences in the i-th category of data; ε is a fourth value preset to be greater than 0.

6. The method for secure storage of energy big data according to claim 1, wherein: The process of encrypting various types of data is as follows: Using the threshold segmentation algorithm to obtain the segmentation thresholds of all key length coefficients; The data of the category whose key length coefficient is greater than or equal to the segmentation threshold is marked as high confidentiality data, otherwise, it is marked as normal confidentiality data; The AES encryption algorithm is used to encrypt high-confidentiality data and ordinary confidentiality data using keys of different lengths.

7. A method for secure storage of energy big data according to claim 6, characterized in that: The key length for encrypting high-confidentiality data is greater than the key length for encrypting ordinary-confidentiality data.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for securely storing energy big data as described in any one of claims 1 to 7 is implemented.

9. An energy big data security storage system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for secure storage of energy big data as described in any one of claims 1 to 7 are implemented.

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