Secure storage method and system for cross-regional distribution heterogeneous energy data
Through clustering and encryption technology, heterogeneous energy data distributed across regions is processed, which reduces the heterogeneity characteristics of the data, solves the problem of low data storage security and achieves higher data security.
Patent Information
- Application Number
- CN202410429563.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-04-10
- Publication Date
- 2025-04-15
AI Technical Summary
Heterogeneous energy data distributed across regions is easily identified by attackers during storage, resulting in low security of data storage.
Heterogeneous energy data are clustered through the DBSCAN algorithm, the energy variation index in the cluster and the energy similarity index between clusters are calculated, the heterogeneity measurement index is constructed, and the Markov clustering algorithm is used for clustering, the heterogeneous energy data storage matrix is generated, and the RSA data encryption algorithm is used to encrypt and distributed storage is performed.
The heterogeneous characteristics between heterogeneous energy data are reduced, and the attacker avoids identifying heterogeneous characteristics of stored data, thereby improving the security of heterogeneous energy data storage.
Smart Images
Figure CN119830310A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data security technology, and in particular to a method and system for securely storing heterogeneous energy data distributed across regions. Background Art
[0002] With the development and construction of the energy industry, data and information technology continue to penetrate all aspects of energy industry production. The scale of various energy production data in the energy industry continues to grow rapidly, which has led to continuous changes in the production management methods of the energy industry. The ability to quickly and accurately obtain effective energy data and timely mine effective information has become an important competitive factor in the energy industry. Against the background of the continuous growth of the scale of various energy production data in the energy industry, various energy data are usually stored securely in computers. The secure storage of energy data has become a key task that is constantly explored in energy data management in order to achieve the subsequent scheduling, deployment and application of energy data.
[0003] Generally speaking, energy data has the characteristics of huge data volume, high distribution, and data heterogeneity, which is manifested as heterogeneous energy data distributed across regions. Due to the highly distributed characteristics of heterogeneous energy data, the security of heterogeneous energy data is particularly important. When data leakage occurs, it often leads to leakage of multiple distributed energy data. Therefore, how to ensure the security of energy data storage has become an urgent problem to be solved.
[0004] At present, in order to achieve the security of heterogeneous energy data storage, the heterogeneous energy data is often encrypted before data storage to ensure the security of heterogeneous energy storage. However, due to the prominent heterogeneous data characteristics in heterogeneous data, it is often easy for attackers to identify the stored data as heterogeneous data, making heterogeneous data the target of attackers, resulting in low security of data storage. Summary of the invention
[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a method and system for securely storing heterogeneous energy data distributed across regions. The technical solutions adopted are as follows:
[0006] In a first aspect, an embodiment of the present invention provides a method for securely storing heterogeneous energy data distributed across regions, the method comprising the following steps:
[0007] Heterogeneous energy data from multiple regions are collected, and the heterogeneous energy data from each region are clustered using the DBSCAN algorithm; the intra-cluster energy variation index of each cluster of heterogeneous energy data from each region is obtained based on the difference in data within the clusters of heterogeneous energy data from each region; the inter-cluster energy similarity index of each cluster of heterogeneous energy data from each region is obtained based on the relationship between each cluster of heterogeneous energy data from each region; the energy fragmentation distribution index of heterogeneous energy data from each region is obtained based on the intra-cluster energy variation index and the inter-cluster energy similarity index; the energy data mutation sequence of each region is constructed based on the change of heterogeneous energy data from each region; the energy change heterogeneity index of heterogeneous energy data from each region is obtained based on the relationship between energy data mutation sequences from each region and the difference in the distribution of heterogeneous energy data from each region; the energy characteristic difference index of heterogeneous energy data from each region is obtained based on the energy change heterogeneity index of heterogeneous energy data from each region and the difference in heterogeneous energy data from each region to other regions; the heterogeneity measurement index of heterogeneous energy data from each region is obtained based on the energy fragmentation distribution index and energy characteristic difference index of heterogeneous energy data from each region;
[0008] The Markov clustering algorithm is used to construct the heterogeneous energy data storage matrix of each cluster in the Markov clustering results based on the heterogeneity measurement index of heterogeneous energy data in each region; the RSA data encryption algorithm is used to encrypt the data in the heterogeneous energy data storage matrix of each cluster, and the encrypted ciphertext is stored using a distributed storage algorithm.
[0009] Preferably, the step of obtaining the intra-cluster energy variation index of each cluster of heterogeneous energy data in each region according to the difference of intra-cluster data of the cluster of heterogeneous energy data in each region includes:
[0010] For each cluster of heterogeneous energy data in each region, the sum of the absolute values of the differences between all arbitrary two data in the cluster is calculated, and the coefficient of variation of the data in each cluster of the heterogeneous energy data in each region is calculated, and the product of the coefficient of variation and the sum is used as the intra-cluster energy variation index of each cluster of the heterogeneous energy data in each region.
[0011] Preferably, the inter-cluster energy similarity index of each cluster of heterogeneous energy data in each region is obtained according to the relationship between each cluster of heterogeneous energy data in each region, including:
[0012] The mean of the Jaccard similarity coefficient between each cluster of heterogeneous energy data in each region and other clusters is calculated, and the opposite of the mean is used as the exponent of an exponential function with a natural constant as the base to obtain the difference between 1 and the exponential function; the mean of the Euclidean distance between each cluster of heterogeneous energy data in each region and the cluster center points of other clusters is calculated, which is recorded as the mean Euclidean distance; the product of the difference and the mean Euclidean distance is used as the inter-cluster energy similarity index of each cluster of heterogeneous energy data in each region.
[0013] Preferably, the method of obtaining the energy fragmentation distribution index of heterogeneous energy data in each region according to the intra-cluster energy variation index and the inter-cluster energy similarity index includes:
[0014] For each cluster of heterogeneous energy data in each region, the opposite number of the inter-cluster energy similarity index of the cluster is used as the exponent of an exponential function with a natural constant as the base, and the product of the intra-cluster energy variation index of the cluster and the calculation result of the exponential function is obtained. The mean of the product of all clusters of heterogeneous energy data in each region is used as the energy fragmentation distribution index of the heterogeneous energy data in each region.
[0015] Preferably, the step of constructing the energy data mutation sequence of each region according to the change of heterogeneous energy data of each region includes:
[0016] The heterogeneous energy data of each region are arranged in ascending order of values to form an ascending sequence of heterogeneous energy data of each region, and the first-order difference sequence of the ascending sequence of heterogeneous energy data of each region is used as the heterogeneous energy data change sequence of each region;
[0017] The Pettitt mutation point detection algorithm is used to obtain the mutation points in the heterogeneous energy data change sequence of each region, and all the mutation points in the heterogeneous energy data change sequence of each region are sorted in ascending order to form the energy data mutation sequence of each region.
[0018] Preferably, the step of obtaining the energy change heterogeneity index of heterogeneous energy data in each region according to the relationship between the energy data mutation sequences in each region and the distribution difference of heterogeneous energy data in each region includes:
[0019] The mean of the DTW distances between each region and the heterogeneous energy data change sequences of other regions is calculated, recorded as the first distance mean. The mean of the DTW distances between each region and the energy data mutation sequences of other regions is calculated, recorded as the second distance mean. The weighted sum of the first distance mean and the second distance mean is used as the energy change heterogeneity index of the heterogeneous energy data of each region.
[0020] Preferably, the energy characteristic difference index of the heterogeneous energy data in each region includes:
[0021] The mean of all heterogeneous energy data in each region is calculated and recorded as the heterogeneous data mean. The mean of the absolute values of the differences between the means of the heterogeneous data in each region and other regions is obtained and recorded as the first mean. The product of the energy change heterogeneity index of the heterogeneous energy data in each region and the first mean is used as the energy characteristic difference index of the heterogeneous energy data in each region.
[0022] Preferably, the obtaining of the heterogeneity measurement index of the heterogeneous energy data of each region according to the energy fragmentation distribution index and the energy characteristic difference index of the heterogeneous energy data of each region includes:
[0023] Calculate the absolute value of the difference between the energy fragmentation distribution index of heterogeneous energy data of each region and other regions, obtain the sum of all the absolute values of the difference calculated between each region and other regions, and obtain the sum result of the sum and the number 1;
[0024] Obtain the calculation result of an exponential function with a natural constant as the base and the opposite number of the energy characteristic difference index of heterogeneous energy data in various regions as the exponent;
[0025] The ratio of the calculation result to the summation result is obtained, and the difference between the number 1 and the ratio is used as a heterogeneity measurement index of the heterogeneous energy data of each region.
[0026] Preferably, the method of using the Markov clustering algorithm to construct a heterogeneous energy data storage matrix of each cluster in the Markov clustering result based on the heterogeneity measurement index of the heterogeneous energy data in each region includes:
[0027] Each region is taken as a graph node, the absolute value of the difference between the heterogeneity measurement index of the heterogeneous energy data of any two regions is taken as the edge weight of the edge between the graph nodes of the any two regions, a weighted undirected graph composed of all the graph nodes and the edge weights between the graph nodes is taken as a heterogeneity partition graph, the heterogeneity partition graph is taken as the input of the Markov clustering algorithm, and each clustering cluster of the Markov clustering result is obtained;
[0028] For each cluster, all heterogeneous energy data of each region in the cluster are combined into a heterogeneous energy data sequence of each region, and the heterogeneous energy data sequence of each region in the cluster is used as each row of the heterogeneous energy data storage matrix of the cluster.
[0029] In a second aspect, an embodiment of the present invention further provides a secure storage system for cross-regionally distributed heterogeneous energy data, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.
[0030] The present invention has at least the following beneficial effects:
[0031] The present invention obtains the intra-cluster energy variation index and the inter-cluster energy similarity index according to the clustering results of heterogeneous energy data, and calculates the energy fragmentation distribution index based on the index; constructs a heterogeneity measurement index, uses the Markov clustering algorithm to obtain the clustering results of all regions based on the heterogeneity measurement index, and obtains a heterogeneous energy data storage matrix based on the clustering results of all regions; uses the RSA data encryption algorithm to obtain ciphertext based on the heterogeneous energy data storage matrix, so as to store the ciphertext data in a computer and improve the data storage security;
[0032] The present invention combines the fragmented characteristics of energy data distribution and the difference characteristics between energy data to construct a heterogeneity measurement index, clusters all regions based on the heterogeneity measurement index, obtains a heterogeneous energy data storage matrix according to the clustering results, completes the data storage task through the heterogeneous energy data storage matrix, reduces the heterogeneous data characteristics between heterogeneous energy data, avoids attackers identifying the heterogeneous data characteristics of stored data and launching special attacks, and improves the security of heterogeneous energy data storage. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0034] Figure 1 A flowchart of the steps of a method for securely storing heterogeneous energy data distributed across regions provided in one embodiment of the present invention. DETAILED DESCRIPTION
[0035] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method and system for secure storage of heterogeneous energy data distributed across regions proposed by the present invention, in combination with the accompanying drawings and preferred embodiments, and its specific implementation, structure, features and effects are described as follows. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.
[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
[0037] The specific scheme of the secure storage method and system for cross-regionally distributed heterogeneous energy data provided by the present invention is described in detail below with reference to the accompanying drawings.
[0038] See also Figure 1 , which shows a flowchart of a method for securely storing heterogeneous energy data distributed across regions provided by an embodiment of the present invention, the method comprising the following steps:
[0039] Step S001, obtaining heterogeneous energy data in different locations from a heterogeneous energy database, and preprocessing the heterogeneous energy data.
[0040] In order to improve the security of heterogeneous energy data storage, this embodiment focuses on reducing the heterogeneous data characteristics of heterogeneous energy data, and then stores the heterogeneous energy data to ensure the security of heterogeneous energy data storage. This embodiment obtains heterogeneous energy data in data formats of K regions from a distributed heterogeneous energy database. The empirical value of K is 50, where the number of heterogeneous energy data in data formats of each region is n. In this embodiment, the value of n is 500, which can be set by the implementer. This embodiment does not limit this. In order to avoid data loss, data redundancy and other information data during data collection, the heterogeneous energy data in the data format of each region is cleaned using data cleaning technology. After data cleaning, the heterogeneous energy data of each region is obtained. The data cleaning technology is a well-known technology, and the specific process will not be repeated.
[0041] At this point, the heterogeneous energy data of each region is obtained, which is used for subsequent data storage of the heterogeneous energy data.
[0042] Step S002, constructing an energy fragmentation distribution index and an energy characteristic difference index, based on which a heterogeneity measurement index is calculated, using a Markov clustering algorithm to obtain clustering results of all regions based on the heterogeneity measurement index, and obtaining a heterogeneous energy data storage matrix based on the clustering results of all regions.
[0043] Generally, there is a large heterogeneity problem between heterogeneous energy data distributed across regions. In order to ensure the security of data storage, it is often necessary to reduce the heterogeneity between heterogeneous energy data to avoid direct identification and attacks. In order to ensure that the data features of heterogeneous energy data are obfuscated, the security of heterogeneous energy data storage can be improved.
[0044] In order to reduce the heterogeneity between heterogeneous energy data, it is necessary to analyze the characteristics of heterogeneous energy data in each region in order to confuse the characteristics of heterogeneous energy data in different regions and improve the security of data storage.
[0045] Specifically, in order to analyze the distribution characteristics of the heterogeneous energy data in each region, the heterogeneous energy data in each region is used as the input of the DBSCAN density clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN), the minimum points MinPts is set to 4, the neighborhood radius ε is set to 5, and the output of the DBSCAN density clustering algorithm is used as the clustering result of the heterogeneous energy data in each region. The clustering result contains multiple clustering clusters. The DBSCAN density clustering algorithm is a well-known technology, and the specific process will not be repeated here.
[0046] Furthermore, based on the above analysis, the energy fragmentation distribution index of the heterogeneous energy data in each region is calculated:
[0047]
[0048]
[0049]
[0050] In the formula, represents the intra-cluster energy variation index of the jth cluster in the clustering result of heterogeneous energy data in the i-th region, U represents the set of all data in the jth cluster in the clustering result of heterogeneous energy data in the i-th region, and represents the qth and sth data in the jth cluster in the clustering result of heterogeneous energy data in the i-th region, It represents the coefficient of variation of all data in the jth cluster in the clustering result of heterogeneous energy data in the i-th region;
[0051] represents the inter-cluster energy similarity index of the jth cluster in the clustering result of the heterogeneous energy data of the i-th region, M represents the number of clusters in the clustering result of the heterogeneous energy data of the i-th region, dist() is the Euclidean distance function, and represents the cluster centers of the jth and bth clusters in the clustering results of heterogeneous energy data in the i-th region, represents the Euclidean distance between the cluster centers of the jth and bth clusters in the clustering result of heterogeneous energy data in the i-th region, exp() represents an exponential function with a natural constant as the base, jac() is the Jaccard similarity coefficient function, and represents the jth and bth clusters in the clustering result of heterogeneous energy data in the i-th region, It represents the Jaccard similarity coefficient between the jth and bth clusters in the clustering results of heterogeneous energy data in the i-th region;
[0052] D i represents the energy fragmentation distribution index of the heterogeneous energy data of the ith region. The calculation of the coefficient of variation is a well-known technique, and the specific process will not be repeated here.
[0053] The differences between the data within the cluster The larger the coefficient of variation, The larger the value is, the greater the difference between the energy data within the cluster is, that is, the higher the dispersion of the energy data within the cluster is, which reflects to a certain extent that the energy distribution in the region has a fragmented distribution characteristic, resulting in greater differences in energy data. The greater the energy variation index within the cluster is;
[0054] Euclidean distance between cluster centers The larger the Jaccard similarity coefficient is, the The smaller it is, the greater the difference in energy data between different clusters in the clustering results, and the smaller the similarity. To a certain extent, it reflects that the energy distribution in the region is more fragmented, resulting in greater differences in energy data, and the smaller the inter-cluster energy similarity index is.
[0055] Therefore, the intra-cluster energy variation index The larger the cluster energy similarity index is, the The smaller it is, the more discrete the distribution of heterogeneous energy data obtained through cluster analysis is, that is, the more obvious the fragmentation phenomenon is. The heterogeneous energy data in the region is more likely to show that there are more types of energy data and greater differences, then the energy fragmentation distribution index is.
[0056] Furthermore, based on the above analysis, the energy fragmentation distribution index of the heterogeneous energy data in each region is obtained. Since the heterogeneity between heterogeneous energy data is manifested as large differences, it is very easy to be identified. In order to improve the effect of data storage security, the heterogeneity between heterogeneous energy data in different regions is analyzed based on the energy fragmentation distribution index, in order to reduce the heterogeneity between heterogeneous energy data.
[0057] Specifically, the sequence of heterogeneous energy data of each region in ascending numerical order is used as the ascending sequence of heterogeneous energy data of each region, and the first-order difference sequence of the ascending sequence of heterogeneous energy data of each region is used as the heterogeneous energy data change sequence of each region. In order to more fully reflect the heterogeneous characteristics of different regions, the values of all data points in the heterogeneous energy data change sequence of each region are used as the input of the Pettitt mutation point detection algorithm, the output of the Pettitt mutation point detection algorithm is used as all mutation points in the heterogeneous energy data change sequence of each region, and the sequence of all mutation points in the heterogeneous energy data change sequence of each region in ascending numerical order is used as the energy data mutation sequence of each region. The Pettitt mutation point detection algorithm is a well-known technology, and the specific process is not repeated here.
[0058] Furthermore, the heterogeneity metric index of heterogeneous energy data in each region is calculated:
[0059]
[0060]
[0061]
[0062] In the formula, E i represents the energy change heterogeneity index of heterogeneous energy data in the i-th region, K represents the number of regions, dtw() represents the dtw distance function, and f i and f g represent the heterogeneous energy data change sequence of the i-th and g-th regions respectively, and h i and h l represent the energy data mutation sequence of the ith and lth regions respectively, dtw(f i ,f g ) represents the dtw distance between the heterogeneous energy data change sequences of the i-th and g-th regions, dtw(h i ,h l ) represents the dtw distance between the energy data mutation sequences of the ith and lth regions, W1 represents the weight of the similarity between heterogeneous energy data change sequences, and W2 represents the weight of the similarity between energy data mutation sequences. In this embodiment, the value of W1 is 0.3, and the value of W2 is 0.7. It should be noted that since the energy data mutation sequence is composed of mutation data, it can better describe the mutation characteristics of energy changes, so the weight W2 of the similarity between energy data mutation sequences is relatively large. The implementer can set the value of the weight by himself, and this embodiment does not limit this;
[0063] F i represents the energy characteristic difference index of heterogeneous energy data in the ith region, Xi and X p denote the mean of all heterogeneous energy data in the ith and pth regions respectively;
[0064] G i represents the heterogeneity measurement index of heterogeneous energy data in the ith region, exp() represents the exponential function with natural constant as the base, and D i and D r They represent the energy fragmentation distribution index of heterogeneous energy data in the i-th and r-th regions respectively.
[0065] The dtw distance dtw(f i ,f g ) is larger, and the dtw distance dtw(h i ,h l ) is larger, indicating that the similarities between heterogeneous energy data change sequences and energy data mutation sequences are smaller, which to a certain extent reflects that the greater the heterogeneity of energy data changes in different regions, the greater the risk during storage, and the easier it is to identify that the stored data is heterogeneous data, the larger the energy change heterogeneity index;
[0066] In addition, the energy heterogeneity index E i The larger the value, the greater the difference between energy characteristics. That is, the difference between energy characteristics is explained from the perspective of energy change. At the same time, the difference between the means|X i -X p | is larger, that is, there is a greater difference in the average distribution of energy data from different regions, and the greater the energy characteristic difference index;
[0067] Therefore, the energy characteristic difference index F i The larger the value, the greater the difference between the energy data in this region and other regions. To a certain extent, it reflects that the heterogeneity of the energy data in this region is more obvious. At the same time, the difference between the energy fragmentation distribution index |D i -D r The larger the value of |, the greater the difference in scattered distribution between the energy data in this region and that in other regions. To a certain extent, the clearer the heterogeneity characteristics between the energy data in this region and that in other regions, the larger the heterogeneity measurement index.
[0068] Furthermore, based on the above analysis of the heterogeneous characteristics of each region, in order to reduce the heterogeneity between the heterogeneous energy data of different regions, the heterogeneous energy data of all regions are classified based on the heterogeneity measurement index.
[0069] Specifically, each region is taken as a graph node, the absolute value of the difference between the heterogeneity measurement indexes of the heterogeneous energy data of any two regions is taken as the edge weight of the edge between the graph nodes represented by any two regions, the weighted undirected graph composed of all the graph nodes and the edge weights of the edges between the graph nodes is taken as the heterogeneity partition graph, the heterogeneity partition graph is taken as the input of the Markov clustering algorithm, and the output of the Markov clustering algorithm is taken as the clustering result of all regions. The Markov clustering algorithm is a well-known technology, and the specific process will not be repeated here.
[0070] Furthermore, in the clustering results of all regions, the heterogeneous energy data of different regions within each cluster have similar heterogeneity. At this time, storing the heterogeneous energy data of different regions within each cluster can effectively reduce the heterogeneity, improve the security of data storage, and avoid attackers identifying the stored data as heterogeneous data.
[0071] Specifically, for each cluster, the sequence composed of the heterogeneous energy data of each region in the cluster is used as the heterogeneous energy data sequence of each region in the cluster, and the matrix composed of the heterogeneous energy data sequences of all regions in the cluster is used as the heterogeneous energy data storage matrix of the cluster.
[0072] At this point, by reducing the heterogeneity between heterogeneous energy data, the heterogeneous energy data storage matrix of each cluster is obtained.
[0073] Step S003, using the RSA data encryption algorithm to obtain ciphertext based on the heterogeneous energy data storage matrix, and using the distributed storage algorithm to store the ciphertext in the computer, to complete the secure storage of heterogeneous energy data distributed across regions.
[0074] Furthermore, in order to improve the security of data storage, all data in the heterogeneous energy data storage matrix of each cluster cluster is used as the input of the RSA data encryption algorithm, and the output of the RSA data encryption algorithm is used as the ciphertext and key after all data in the heterogeneous energy data storage matrix of each cluster cluster are encrypted. The RSA data encryption algorithm is a well-known technology, and the specific process is not repeated here. It should be noted that the key obtained by the RSA data encryption algorithm is divided into a public key and a private key, the public key is made public, and the private key is kept by the administrator.
[0075] The encrypted ciphertext is stored, and the encrypted ciphertext of all data in the heterogeneous energy data storage matrix of each cluster is used as the input of the distributed storage algorithm. The encrypted ciphertext of all data in the heterogeneous energy data storage matrix of each cluster is stored in the computer using the distributed storage algorithm. The distributed storage algorithm is a well-known technology and the specific process will not be repeated here.
[0076] Based on the same inventive concept as the above method, an embodiment of the present invention also provides a secure storage system for cross-regionally distributed heterogeneous energy data, 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 methods for securely storing heterogeneous energy data distributed across regions are implemented.
[0077] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0078] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.
[0079] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A secure storage method for cross-regionally distributed heterogeneous energy data, characterized in that: The method comprises the following steps: Heterogeneous energy data from multiple regions are collected, and the heterogeneous energy data from each region are clustered using the DBSCAN algorithm; the intra-cluster energy variation index of each cluster of heterogeneous energy data from each region is obtained based on the difference in intra-cluster data of the heterogeneous energy data from each region; the inter-cluster energy similarity index of each cluster of heterogeneous energy data from each region is obtained based on the relationship between each cluster of heterogeneous energy data from each region; the energy fragmentation distribution index of heterogeneous energy data from each region is obtained based on the intra-cluster energy variation index and the inter-cluster energy similarity index; the energy data mutation sequence of each region is constructed based on the change of heterogeneous energy data from each region; the energy change heterogeneity index of heterogeneous energy data from each region is obtained based on the relationship between the energy data mutation sequences of each region and the distribution difference of heterogeneous energy data from each region; the energy characteristic difference index of heterogeneous energy data from each region is obtained based on the energy change heterogeneity index of heterogeneous energy data from each region and the difference between heterogeneous energy data from each region and other regions; the heterogeneity measurement index of heterogeneous energy data from each region is obtained based on the energy fragmentation distribution index and energy characteristic difference index of heterogeneous energy data from each region; The Markov clustering algorithm is used to construct the heterogeneous energy data storage matrix of each cluster in the Markov clustering results based on the heterogeneity measurement index of heterogeneous energy data in each region; the RSA data encryption algorithm is used to encrypt the data in the heterogeneous energy data storage matrix of each cluster, and the encrypted ciphertext is stored using a distributed storage algorithm.
2. The secure storage method for cross-regionally distributed heterogeneous energy data according to claim 1, characterized in that: The method of obtaining the intra-cluster energy variation index of each cluster of heterogeneous energy data in each region according to the difference of the intra-cluster data of the heterogeneous energy data in each region includes: For each cluster of heterogeneous energy data in each region, the sum of the absolute values of the differences between all arbitrary two data in the cluster is calculated, and the coefficient of variation of the data in each cluster of the heterogeneous energy data in each region is calculated, and the product of the coefficient of variation and the sum is used as the intra-cluster energy variation index of each cluster of the heterogeneous energy data in each region.
3. The secure storage method for cross-regionally distributed heterogeneous energy data according to claim 1, characterized in that: The inter-cluster energy similarity index of each cluster of heterogeneous energy data in each region is obtained according to the relationship between each cluster of heterogeneous energy data in each region, including: The mean of the Jaccard similarity coefficient between each cluster of heterogeneous energy data in each region and other clusters is calculated, and the opposite of the mean is used as the exponent of an exponential function with a natural constant as the base to obtain the difference between 1 and the exponential function; the mean of the Euclidean distance between each cluster of heterogeneous energy data in each region and the cluster center points of other clusters is calculated, which is recorded as the mean Euclidean distance; the product of the difference and the mean Euclidean distance is used as the inter-cluster energy similarity index of each cluster of heterogeneous energy data in each region.
4. The secure storage method for cross-regionally distributed heterogeneous energy data according to claim 1, characterized in that: The energy fragmentation distribution index of heterogeneous energy data in each region is obtained according to the intra-cluster energy variation index and the inter-cluster energy similarity index, including: For each cluster of heterogeneous energy data in each region, the opposite number of the inter-cluster energy similarity index of the cluster is used as the exponent of an exponential function with a natural constant as the base, and the product of the intra-cluster energy variation index of the cluster and the calculation result of the exponential function is obtained. The mean of the product of all clusters of heterogeneous energy data in each region is used as the energy fragmentation distribution index of the heterogeneous energy data in each region.
5. The secure storage method for cross-regionally distributed heterogeneous energy data according to claim 4, characterized in that: The energy data mutation sequence of each region is constructed according to the change of heterogeneous energy data in each region, including: The heterogeneous energy data of each region are arranged in ascending order of values to form an ascending sequence of heterogeneous energy data of each region, and the first-order difference sequence of the ascending sequence of heterogeneous energy data of each region is used as the heterogeneous energy data change sequence of each region; The Pettitt mutation point detection algorithm is used to obtain the mutation points in the heterogeneous energy data change sequence of each region, and all the mutation points in the heterogeneous energy data change sequence of each region are sorted in ascending order to form the energy data mutation sequence of each region.
6. The secure storage method for cross-regionally distributed heterogeneous energy data according to claim 5, characterized in that: The energy change heterogeneity index of heterogeneous energy data in each region is obtained according to the relationship between the mutation sequences of energy data in each region and the distribution difference of heterogeneous energy data in each region, including: The mean of the DTW distances between each region and the heterogeneous energy data change sequences of other regions is calculated, recorded as the first distance mean. The mean of the DTW distances between each region and the energy data mutation sequences of other regions is calculated, recorded as the second distance mean. The weighted sum of the first distance mean and the second distance mean is used as the energy change heterogeneity index of the heterogeneous energy data of each region.
7. The secure storage method for cross-regionally distributed heterogeneous energy data according to claim 6, characterized in that: The energy characteristic difference index of the heterogeneous energy data in each region includes: The mean of all heterogeneous energy data in each region is calculated and recorded as the heterogeneous data mean. The mean of the absolute values of the differences between the means of the heterogeneous data in each region and other regions is obtained and recorded as the first mean. The product of the energy change heterogeneity index of the heterogeneous energy data in each region and the first mean is used as the energy characteristic difference index of the heterogeneous energy data in each region.
8. The secure storage method for cross-regionally distributed heterogeneous energy data according to claim 1, characterized in that: The heterogeneity measurement index of heterogeneous energy data in each region is obtained according to the energy fragmentation distribution index and energy characteristic difference index of heterogeneous energy data in each region, including: Calculate the absolute value of the difference between the energy fragmentation distribution index of heterogeneous energy data of each region and other regions, obtain the sum of all the absolute values of the difference calculated between each region and other regions, and obtain the sum result of the sum and the number 1; Obtain the calculation result of an exponential function with a natural constant as the base and the opposite number of the energy characteristic difference index of heterogeneous energy data in various regions as the exponent; The ratio of the calculation result to the summation result is obtained, and the difference between the number 1 and the ratio is used as a heterogeneity measurement index of the heterogeneous energy data of each region.
9. The secure storage method for cross-regionally distributed heterogeneous energy data according to claim 8, characterized in that: The method of using the Markov clustering algorithm to construct a heterogeneous energy data storage matrix of each cluster in the Markov clustering result based on the heterogeneity measurement index of the heterogeneous energy data in each region includes: Each region is taken as a graph node, the absolute value of the difference between the heterogeneity measurement index of the heterogeneous energy data of any two regions is taken as the edge weight of the edge between the graph nodes of the any two regions, a weighted undirected graph composed of all the graph nodes and the edge weights between the graph nodes is taken as a heterogeneity partition graph, the heterogeneity partition graph is taken as the input of the Markov clustering algorithm, and each clustering cluster of the Markov clustering result is obtained; For each cluster, all heterogeneous energy data of each region in the cluster are combined into a heterogeneous energy data sequence of each region, and the heterogeneous energy data sequence of each region in the cluster is used as each row of the heterogeneous energy data storage matrix of the cluster.
10. A secure storage system for cross-regionally distributed heterogeneous energy data, 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 according to any one of claims 1 to 9 are implemented.