Secure transmission method and system for integrated energy big data platform

Abstract

The present application relates to the technical field of encrypted transmission of digital information, and specifically to a secure transmission method and system for a comprehensive energy big data platform, the method comprising: in each area, according to the discreteness, fluctuation and autocorrelation of all consumption data of each type of energy, determining the energy consumption stability factor of each type of energy in each area; based on the distribution of the energy consumption stability factor, determining the energy consumption characteristic value of each area; based on the energy consumption characteristic value of all nodes in each growth area and the number of connections of all nodes, determining the energy consumption measurement factor of each growth area, and combining the average distribution of all undirected edges in each growth area, determining the importance index of each growth area, and encrypting and transmitting the energy consumption data. The present application optimizes the encryption key by analyzing the energy consumption characteristics of different areas, thereby improving the security of the energy data encryption process.

Description

Technical Field

[0001] The present application relates to the technical field of encrypted transmission of digital information, and specifically to a secure transmission method and system for an integrated energy big data platform. Background Art

[0002] With the development of technologies such as smart grids, renewable energy, and distributed generation, energy systems are becoming more complex and interconnected, requiring a large amount of data exchange and analysis to optimize energy distribution and improve efficiency. However, this also brings challenges to data security and privacy protection. With the widespread deployment of IoT devices and the application of big data technologies, the amount of data generated by energy systems is growing exponentially. These data include not only energy consumption patterns and device status information, but may also involve user privacy and sensitive operation data. Therefore, ensuring that these data are not tampered with or accessed by unauthorized persons during transmission is crucial to maintaining the integrity of the system.

[0003] In the process of data transmission, if the key security is insufficient, the data may be intercepted and decrypted during transmission, thereby leaking sensitive information or being tampered with. For example, factors such as weak keys or insufficient key length may enable attackers to crack encryption and obtain unauthorized data access rights, resulting in economic losses, reputation damage, legal risks, and infringement of user privacy. Although existing technologies such as public key infrastructure (PKI) and key exchange protocols provide a certain degree of key protection, since energy consumption data is usually updated in real time and widely distributed across regions, fixed encryption keys will reduce the security of the data encryption process for complex and changeable energy consumption data. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a secure transmission method and system for an integrated energy big data platform. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a secure transmission method for an integrated energy big data platform, the method comprising the following steps:

[0006] Obtain consumption data of each type of energy at all collection times in each area within a preset time period;

[0007] In each area, the energy consumption fluctuation value of each type of energy in each area is determined according to the degree of dispersion and fluctuation of all consumption data of each type of energy, and the energy consumption stability factor of each type of energy in each area is determined in combination with the autocorrelation of all consumption data of each type of energy; based on the distribution of energy consumption stability factors of all types of energy in each area, the energy consumption characteristic value of each area is determined;

[0008] All the areas are recorded as nodes, and the node with the largest energy consumption characteristic value is taken as the initial central node of the undirected graph. Based on the difference in energy consumption characteristic values ​​between the initial central node and the remaining nodes, and the distance between the initial central node and the remaining nodes, and in combination with the preset characteristic value threshold and the preset position threshold, an undirected graph is constructed, and the regional growing algorithm is used to obtain the various growth areas in the undirected graph; the number of connections of each node in each growth area is counted, and the energy consumption measurement factor of each growth area is determined based on the average distribution of the energy consumption characteristic values ​​of all nodes in each growth area and the average distribution of the number of connections of all nodes; the importance index of each growth area is determined based on the average length of all undirected edges in each growth area and the energy consumption measurement factor;

[0009] Based on the importance index and in combination with an encryption algorithm, the energy consumption data is encrypted and transmitted.

[0010] Preferably, the method for determining the energy consumption fluctuation value of each type of energy in each area is:

[0011] Determine the degree of dispersion of all consumption data for each type of energy in each area;

[0012] All consumption data of each type of energy in each area are used as input of the volatility model, and the conditional variance of each type of energy in each area is output;

[0013] The energy consumption fluctuation value of each type of energy in each area is the result of the fusion of the discrete degree and conditional variance of each type of energy in each area.

[0014] Preferably, the expression of the energy consumption stabilization factor of each type of energy in each area is: ; In the formula, represents the energy consumption stability factor of the j-th energy source in area i; represents the autocorrelation coefficient of all consumption data of the jth energy type in area i; Represents the energy consumption fluctuation value of the j-th energy in area i.

[0015] Preferably, the energy consumption characteristic value of each area is the average level of the energy consumption stability factors of all types of energy in each area.

[0016] Preferably, the method for constructing the undirected graph is:

[0017] Calculate the absolute value of the difference between the energy consumption characteristic value of the initial central node and the energy consumption characteristic value of the remaining nodes;

[0018] When the distance is less than the preset position threshold and the absolute value is less than the preset eigenvalue threshold, an undirected edge is established between the initial central node and the corresponding node, and all nodes connected to the initial central node are iterated as new central nodes to obtain an undirected graph.

[0019] Preferably, the method for determining each growth region in the undirected graph is:

[0020] Get the number of connections of all nodes in the undirected graph, use all nodes as the input of the region growing algorithm, and use a random node in the undirected graph as the initial seed point. If the absolute value of the difference between the number of connections between the remaining nodes and the initial seed point is less than the preset value, the corresponding node will be included in the growth region.

[0021] Preferably, the method for determining the energy consumption measurement factor of each growth area is:

[0022] The mean of the energy consumption factors of all nodes in each growth area is analyzed, and the mean of the number of connections of all nodes in each growth area is determined. The energy consumption measurement factor of each growth area is the result of the fusion of the mean of the energy consumption factor and the mean of the number of connections of each growth area.

[0023] Preferably, the importance index of each growth area is: ; In the formula, represents the importance index of the growth region m; represents the average length of all undirected edges in the growth region m; represents the energy consumption measurement factor of the growth area m; norm( ) represents the normalization function.

[0024] Preferably, the encrypted transmission of energy consumption data includes:

[0025] Taking the preset initial key and the normalized value of the importance index of each growth area as the input of the key derivation function, and outputting the derived key of each growth area;

[0026] Concatenate the derived keys of all growth regions and use them as input to the secure hash algorithm to output the encryption key;

[0027] All energy consumption data of all growth areas are used as input of the encryption algorithm, the encryption key is used as the key of the encryption algorithm, the energy consumption data is encrypted, and the encrypted data is transmitted through a secure channel.

[0028] In the second aspect, an embodiment of the present application also provides a secure transmission system for an integrated energy big data platform, 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 secure transmission methods for an integrated energy big data platform when executing the computer program.

[0029] This application has at least the following beneficial effects:

[0030] This application determines the energy consumption fluctuation value of each type of energy in each area by analyzing the discreteness and fluctuation of all consumption data of each type of energy, and determines the energy consumption stability factor of each type of energy in each area in combination with the autocorrelation of all consumption data of each type of energy. Its beneficial effect is that it can reflect the periodicity and volatility characteristics of energy consumption and improve the accuracy of energy management. This application aims at the flexibility problem of regional energy consumption data, constructs an undirected graph according to the energy consumption characteristic values ​​of all nodes and the distances between different nodes, and uses the regional growing algorithm to obtain each growth area in the undirected graph. By analyzing the average distribution of the energy consumption characteristic values ​​of all nodes in each growth area and the average distribution of the number of connections of all nodes, the energy consumption measurement factor of each growth area is determined, and the importance index of each growth area is determined in combination with the average distribution of all undirected edges in each growth area. Its beneficial effect is that dynamic keys are set to improve the security of data encryption. This application optimizes the encryption key by analyzing the energy consumption characteristics of different areas, thereby improving the security of the energy data encryption process. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A flowchart of the steps of a secure transmission method for an integrated energy big data platform provided in one embodiment of the present application;

[0033] Figure 2 A flow chart of the steps for obtaining the energy consumption stability factor provided in one embodiment of the present application;

[0034] Figure 3 A schematic diagram of the importance index extraction process provided for one embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the specific implementation methods, structures, features and effects of the secure transmission method and system for the integrated energy big data platform proposed in the present application. 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 application belongs.

[0037] The specific scheme of the secure transmission method and system for the integrated energy big data platform provided by the present application is described in detail below with reference to the accompanying drawings.

[0038] See also Figure 1 , which shows a flowchart of a secure transmission method for an integrated energy big data platform provided by an embodiment of the present application, the method comprising the following steps:

[0039] Step S1: Obtain consumption data of each type of energy at all collection times in each area within a preset time period.

[0040] Regional energy consumption data is collected, and the regional energy consumption data includes the energy consumption data of a community, residential area, industrial park or office building. The consumption data of each type of energy at all collection times in each area within a preset time t is obtained from the data source. Each type of energy consumption data includes electricity, thermal energy, and natural gas energy, and the sampling time interval is set to T.

[0041] It should be noted that the values ​​of the preset duration t and the sampling time interval T are both manually set. In this embodiment, the value of the preset duration t is 2 days, and the value of the sampling time interval T is 10 minutes. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0042] Step S2: In each area, the energy consumption fluctuation value of each type of energy in each area is determined according to the degree of discreteness and fluctuation of all consumption data of each type of energy, and the energy consumption stability factor of each type of energy in each area is determined in combination with the autocorrelation of all consumption data of each type of energy; based on the distribution of energy consumption stability factors of all types of energy in each area, the energy consumption characteristic value of each area is determined.

[0043] The usage characteristics of energy data usually include high-frequency real-time updates, wide distribution across regions, and strict requirements for data integrity and accuracy. The usage characteristics of energy data require that the key design must have a high degree of security and flexibility. The real-time update of energy data requires that the key can be generated and updated quickly, which prompts the key design to adopt a dynamic generation mechanism to increase the randomness and unpredictability of the key to ensure the security of data during transmission.

[0044] Therefore, in order to improve the security of energy consumption data encryption, in each area, by analyzing the discreteness, fluctuation and autocorrelation of all consumption data of each type of energy, the energy consumption stability factor of each type of energy in each area is determined to determine the characteristics of energy consumption in different areas, so as to assign different levels of encryption to them to improve the security of encryption, specifically:

[0045] (1) Determine the degree of dispersion of all consumption data for each type of energy in each area.

[0046] It should be noted that there are many methods to measure the degree of discreteness of a set of data. In this embodiment, the degree of discreteness of all consumption data of each type of energy in each area is measured by calculating the standard deviation of all consumption data of each type of energy in each area. In actual application, as other implementation methods, implementers may also adopt variance, dispersion coefficient and other methods to measure the degree of discreteness of data. This embodiment does not impose any special restrictions on the selection of methods for measuring the degree of discreteness of data.

[0047] (2) Furthermore, all consumption data of each type of energy in each area are used as the input of the volatility model, and the conditional variance of each type of energy in each area is output.

[0048] It should be noted that there are many commonly used volatility models. In this embodiment, the generalized autoregressive conditional heteroskedasticity (GARCH) algorithm is used to obtain the conditional variance of each type of energy in each area. The implementer may also use other volatility models such as the exponential GARCH (EGARCH) model. This embodiment does not impose any special restrictions on the selection of volatility models.

[0049] Among them, the generalized autoregressive conditional heteroskedasticity algorithm is a well-known technology, and its specific calculation process will not be repeated here.

[0050] (3) Further, based on the discrete degree and the conditional variance, the energy consumption fluctuation value of each type of energy in each area is determined, and the energy consumption fluctuation value of each type of energy in each area is the result of the fusion of the discrete degree and the conditional variance of each type of energy in each area.

[0051] It should be understood that fusion refers to combining two or more indicators by adding or multiplying them together in order to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. The implementer can choose according to the specific situation, and this embodiment does not impose any special restrictions.

[0052] Preferably, in the present embodiment, the energy consumption fluctuation value of each type of energy in each area is the product of the discreteness and conditional variance of each type of energy in each area; in actual application, as other implementation methods, the energy consumption fluctuation value of each type of energy in each area is an exponential function value with a natural constant as the base and the sum of the discreteness and conditional variance of each type of energy in each area as the independent variable.

[0053] According to the energy consumption fluctuation values ​​of each type of energy in each area, it can be understood that the greater the discreteness of the energy consumption data and the larger the conditional variance, the worse the periodicity of the energy consumption data and the larger the fluctuation range. Therefore, the larger the energy consumption fluctuation value, the more unstable the energy consumption in the corresponding area, and the more necessary it is to encrypt the energy consumption data of the corresponding area more securely. Conversely, the smaller the discreteness of the energy consumption data and the smaller the conditional variance, the better the periodicity of the energy consumption data and the smaller the fluctuation range. Therefore, the smaller the energy consumption fluctuation value.

[0054] (4) Furthermore, by analyzing the autocorrelation of all consumption data of each type of energy in each area and combining the energy consumption fluctuation value, the energy consumption stability factor of each type of energy in each area is determined, specifically:

[0055] The expression of the energy consumption stability factor of the j-th energy in area i is: ; In the formula, represents the autocorrelation coefficient of all consumption data of the jth energy type in area i; Represents the energy consumption fluctuation value of the j-th energy in area i.

[0056] According to the energy consumption stability factor of each type of energy in each area, it can be understood that the larger the autocorrelation coefficient, the higher the periodicity of the consumption data of the corresponding type of energy, that is, the greater the similarity in the consumption of the corresponding type of energy, and the more consistent the energy consumption pattern; the smaller the energy consumption fluctuation value of the energy, the more stable the energy consumption; therefore, if the autocorrelation coefficient of the energy is larger, the smaller the energy consumption fluctuation value is, the more stable the energy use pattern is; conversely, if the autocorrelation coefficient is smaller, the periodicity of the consumption data of the corresponding type of energy is worse, that is, the similarity in the consumption degree of the corresponding type of energy at different times is smaller, and the energy consumption pattern is more inconsistent; the larger the energy consumption fluctuation value of the energy, the more unstable the energy consumption; therefore, if the autocorrelation coefficient of the energy is smaller, the larger the energy consumption fluctuation value is, the more unstable the energy use pattern is.

[0057] Preferably, the energy consumption stability factor acquisition step flow chart provided in this embodiment is as follows: Figure 2 shown.

[0058] Furthermore, the energy consumption characteristic value of each area is determined based on the energy consumption stability factor. The energy consumption characteristic value of each area is the average level of the energy consumption stability factors of all types of energy in each area. The more uniform the distribution of the energy consumption stability factors, the larger the energy consumption characteristic value, indicating that the energy usage pattern in the corresponding area is more stable.

[0059] Step S3: record all regions as nodes, construct an undirected graph according to the energy consumption characteristic values ​​of all nodes and the distances between different nodes, and use the regional growing algorithm to obtain the growth regions in the undirected graph; count the number of connections of each node in each growth region, and determine the energy consumption measurement factor of each growth region based on the average distribution of the energy consumption characteristic values ​​of all nodes in each growth region and the average distribution of the number of connections of all nodes, and determine the importance index of each growth region in combination with the average distribution of all undirected edges in each growth region.

[0060] Different areas have different levels of complexity, so the consumption of various energy sources is also different. For example, the central business district of a city has more dense and diverse infrastructure, and the transportation network, building density, and industrial distribution are all relatively complex. Therefore, the greater the consumption of various types of energy, the more likely it is to involve more private operation data, so a more secure encryption method is needed. Therefore, in order to facilitate analysis, all areas are regarded as nodes of an undirected graph. An undirected graph is constructed based on the energy consumption characteristic values ​​of all nodes and the distances between different nodes. The undirected graph is segmented using the growing region algorithm, and areas with similar complexity are divided into a growing region. Based on the energy consumption of all nodes in the growing region, different encryption levels are dynamically set for different growing regions to improve the security and efficiency of the encryption process. Specifically:

[0061] S301: All the areas are regarded as nodes of an undirected graph, and the undirected graph is constructed according to the energy consumption characteristic values ​​of all the nodes and the distances between different nodes.

[0062] (1) The node with the largest energy consumption characteristic value in each area is taken as the initial central node of the undirected graph, the distance between the initial central node and the remaining nodes is calculated, and the absolute value of the difference between the energy consumption characteristic value of the initial central node and the energy consumption characteristic value of the remaining nodes is calculated;

[0063] It should be noted that there are many methods for measuring the distance between two points on the earth. In this embodiment, Haversine is used to calculate the distance between the initial central data point and the remaining nodes. The implementer can also use the spherical cosine law or the spherical sine law to calculate the distance between two points on the earth. This embodiment does not impose any special restrictions on the selection of the method for calculating the distance between two points on the earth.

[0064] Among them, the calculation process of Haversine is a well-known technology, and the specific calculation process will not be repeated.

[0065] (2) Further, when the distance is less than a preset position threshold and the absolute value is less than a preset characteristic value threshold, an undirected edge is established between the initial central node and the corresponding node, and all nodes connected to the initial central node are iterated as new central nodes until all nodes are traversed to obtain an undirected graph. The establishment of an undirected graph can reveal the intrinsic relationship between energy consumption and spatial distribution.

[0066] It should be noted that the values ​​of the preset location threshold and the preset feature threshold are both manually set. In this embodiment, the value of the preset location threshold is 1km, and the value of the preset feature threshold is 0.3. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0067] S302: Using a region growing algorithm based on the undirected graph to obtain each growth region in the undirected graph.

[0068] Count the number of connections of all nodes in the undirected graph, take a random node as the initial seed point, and use the difference in the number of connections between the initial seed point and the remaining nodes as the growth criterion. Nodes whose absolute value of the difference between the number of connections and the number of connections of the initial seed point is less than a preset value are included in the growth region. Until all nodes are visited or there are no nodes that meet the growth criterion, continue to randomly select any node from the remaining nodes as the initial seed point for iteration, and finally output the growth regions in the undirected graph.

[0069] It should be noted that the value of the preset value is manually set. In this embodiment, the value of the preset value is 3. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0070] S303: Based on the average distribution of energy consumption characteristic values ​​of all nodes in each growth area and the average distribution of the number of connections of all nodes, the energy consumption measurement factor of each growth area is determined, and combined with the average distribution of all undirected edges in each growth area, the energy consumption importance index of each growth area is determined.

[0071] In order to determine the relative importance of different growth areas in the energy network, so as to assign appropriate encryption levels to them and ensure the security of data encryption, the importance index of the growth area is determined by analyzing the energy consumption characteristics of different growth areas. Specifically:

[0072] The mean of the energy consumption factors of all nodes in each growth area is analyzed, and the mean of the number of connections of all nodes in each growth area is determined. The energy consumption measurement factor of each growth area is the result of the fusion of the mean of the energy consumption factor and the mean of the number of connections of each growth area.

[0073] Preferably, the energy consumption measurement factor of each growth area in the present embodiment is the product of the mean energy consumption factor of each growth area and the mean connection number; in actual application, as other implementation methods, the energy consumption measurement factor of each growth area is an exponential function value with a natural constant as the base and the sum of the mean energy consumption factor of each growth area and the mean connection number as the independent variable.

[0074] Further, according to the energy consumption measurement factor and in combination with the distribution of the lengths of all undirected edges in each growth area, the importance index of each growth area is determined, specifically:

[0075] Importance index of growth region m: ; In the formula, represents the average length of all undirected edges in the growth region m; represents the energy consumption measurement factor of the growth area m; norm( ) represents the normalization function.

[0076] According to the importance index of each growth area, it can be understood that the larger the energy consumption measurement factor of the growth area, the higher the similarity of energy consumption in the corresponding growth area, and the higher the importance in the energy network; the average length of all undirected edges in the growth area measures the average distance between nodes in the corresponding growth area, reflecting the distribution uniformity of energy consumption points in the growth area. The smaller the average length, the more concentrated the energy consumption points in the growth area, which means that the layout of energy facilities in the corresponding growth area is more compact; therefore, if the energy consumption measurement factor is larger, the average length is smaller, and the importance index of the corresponding growth area is larger, indicating that the importance and The higher the coverage of energy facilities, the greater their contribution and impact on the overall energy network; conversely, the smaller the energy consumption measurement factor of the growth area, the lower the similarity of energy consumption in the corresponding growth area, and the lower its importance in the energy network; the smaller the average length of all undirected edges in the growth area, the more dispersed the energy consumption points in the growth area, which means that the layout of energy facilities in the corresponding growth area is more sparse; therefore, if the energy consumption measurement factor is smaller, the larger the average length, and the smaller the importance index of the corresponding growth area, it means that the importance of the corresponding growth area in the energy network and the coverage of energy facilities are lower, and the contribution and impact on the overall energy network are smaller.

[0077] Preferably, the importance index extraction process diagram provided in this embodiment is as follows: Figure 3 shown.

[0078] Step S4: Based on the importance index, the energy consumption data is encrypted and transmitted in combination with an encryption algorithm.

[0079] In the process of ensuring the safe transmission of urban energy data, encryption is a key step. Therefore, this embodiment selects the AES encryption algorithm, which supports multiple key lengths and provides flexible security levels. A secure random number generator generates a 256-bit initial key to ensure its randomness and unpredictability. Subsequently, the consumption importance index B of all growth areas is standardized.

[0080] The preset initial key and the normalized value of the importance index of each growth area are used as the input of the key derivation function, and the derived key of each growth area is output. Specifically:

[0081] The expression of the derived key of the growth region m is: , where Indicates the preset initial key; represents the normalized value of the importance index of the growth region m; Represents a key derivation function.

[0082] It should be noted that the preset initial key in this embodiment is a 256-bit initial key generated by a random number generator in the AES encryption algorithm (Advanced Encryption Standard), and the implementer can set it according to the specific situation.

[0083] Furthermore, the derived keys of all growth regions are combined to obtain the encryption key, and the expression of the encryption key is: ; In the formula, It means that the derived keys of M growth areas are concatenated; HMAC-SHA256 is the secure hash algorithm 256.

[0084] Furthermore, the Final key is used as the key of the AES encryption algorithm, and the key is regularly updated with a period of a preset time t to encrypt the consumption data of all types of energy. The encrypted data is transmitted through the established secure channel.

[0085] Among them, the AES encryption algorithm and the secure hash algorithm 256 are both well-known technologies in the encryption field, and their specific encryption processing processes are not repeated here.

[0086] So far, this embodiment aims at the real-time update and cross-regional distribution of urban regional energy consumption data. By analyzing the periodicity and volatility characteristics of energy consumption and the uniformity of distribution of energy facilities, it eliminates the one-sidedness and limitations that may be brought about by single-region analysis, and improves the key to achieve security and flexibility in the data transmission process, enhances the randomness and unpredictability of the encryption process, and allows the use of different derived keys for data in different regions, thereby improving the confidentiality of the data.

[0087] Based on the same inventive concept as the above method, an embodiment of the present application also provides a secure transmission system for an integrated energy big data platform, 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 secure transmission methods for an integrated energy big data platform are implemented.

[0088] It should be noted that the above sequence of the embodiments of the present application is for description only 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.

[0089] 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.

[0090] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A secure transmission method for an integrated energy big data platform, characterized in that: The method comprises the following steps: Obtain consumption data of each type of energy at all collection times in each area within a preset time period; In each area, the energy consumption fluctuation value of each type of energy in each area is determined according to the degree of dispersion and fluctuation of all consumption data of each type of energy, and the energy consumption stability factor of each type of energy in each area is determined in combination with the autocorrelation of all consumption data of each type of energy; based on the distribution of energy consumption stability factors of all types of energy in each area, the energy consumption characteristic value of each area is determined; All the areas are recorded as nodes, and the node with the largest energy consumption characteristic value is taken as the initial central node of the undirected graph. Based on the difference in energy consumption characteristic values ​​between the initial central node and the remaining nodes, and the distance between the initial central node and the remaining nodes, and combined with the preset characteristic value threshold and the preset position threshold, the undirected graph is constructed, and the regional growing algorithm is used to obtain the various growth areas in the undirected graph; the number of connections of each node in each growth area is counted, and the energy consumption measurement factor of each growth area is determined based on the average distribution of the energy consumption characteristic values ​​of all nodes in each growth area and the average distribution of the number of connections of all nodes; the importance index of each growth area is determined based on the average length of all undirected edges in each growth area and the energy consumption measurement factor; Based on the importance index, the energy consumption data is encrypted and transmitted in combination with an encryption algorithm; The method for determining the energy consumption fluctuation value of each type of energy in each area is as follows: Determine the degree of dispersion of all consumption data of each type of energy in each area; All consumption data of each type of energy in each area are used as input of the volatility model, and the conditional variance of each type of energy in each area is output; The energy consumption fluctuation value of each type of energy in each area is the result of the fusion of the discrete degree and conditional variance of each type of energy in each area; The expression of the energy consumption stabilization factor of each type of energy in each area is: ; In the formula, represents the energy consumption stability factor of the j-th energy source in area i; represents the autocorrelation coefficient of all consumption data of the jth energy type in area i; represents the energy consumption fluctuation value of the j-th energy source in area i; The energy consumption characteristic value of each area is the average level of the energy consumption stability factor of all types of energy in each area; The method for determining the energy consumption measurement factor of each growth area is: Analyze the mean value of the energy consumption factor of all nodes in each growth area, determine the mean value of the number of connections of all nodes in each growth area, and the energy consumption measurement factor of each growth area is the result of the fusion of the mean value of the energy consumption factor and the mean value of the number of connections of each growth area; The importance index of each growth area is: ; In the formula, represents the importance index of the growth region m; represents the average length of all undirected edges in the growth region m; represents the energy consumption measurement factor of the growth area m; norm( ) represents the normalization function; The encrypted transmission of energy consumption data includes: Taking the preset initial key and the normalized value of the importance index of each growth area as the input of the key derivation function, and outputting the derived key of each growth area; Concatenate the derived keys of all growth regions and use them as input to the secure hash algorithm to output the encryption key; All energy consumption data of all growth areas are used as input of the encryption algorithm, the encryption key is used as the key of the encryption algorithm, the energy consumption data is encrypted, and the encrypted data is transmitted through a secure channel.

2. The secure transmission method for the integrated energy big data platform according to claim 1, characterized in that: The method for constructing the undirected graph is: Calculate the absolute value of the difference between the energy consumption characteristic value of the initial central node and the energy consumption characteristic value of the remaining nodes; When the distance is less than the preset position threshold and the absolute value is less than the preset eigenvalue threshold, an undirected edge is established between the initial central node and the corresponding node, and all nodes connected to the initial central node are iterated as new central nodes to obtain an undirected graph.

3. The secure transmission method for the integrated energy big data platform according to claim 1, characterized in that: The method for determining each growth region in the undirected graph is: Get the number of connections of all nodes in the undirected graph, use all nodes as the input of the region growing algorithm, and use a random node in the undirected graph as the initial seed point. If the absolute value of the difference between the number of connections between the remaining nodes and the initial seed point is less than the preset value, the corresponding node will be included in the growth region.

4. A secure transmission system for an integrated energy big data platform, 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, it implements the steps of the secure transmission method for the integrated energy big data platform as described in any one of claims 1-3.

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