Power distribution network data transmission method and device based on big data analysis

Abstract

The invention discloses a big data analysis-based power distribution network data transmission method and equipment, and the method comprises the steps: collecting to-be-encrypted power distribution network data of each node in a power distribution network, constructing each power distribution network data time sequence of each node, obtaining an indifference following factor and a decryption key factor of each power distribution network data time sequence, and obtaining a decryption key factor of each power distribution network data time sequence; the method comprises the following steps: calculating a rule-following cracking factor of each node, further obtaining a joint cracking factor of each node, obtaining the comprehensive encryption strength of each node, and carrying out primary encryption processing on all to-be-encrypted power distribution network data of each node to obtain a power distribution network primary encryption matrix; and performing second-level encryption processing on all the first-level encryption matrixes of the power distribution network by using an RSA encryption algorithm to obtain second-level encryption matrixes of the power distribution network, and transmitting all the second-level encryption matrixes of the power distribution network through a wireless communication network. According to the big data analysis-based power distribution network data transmission method and device provided by the embodiment of the invention, the security and real-time performance of power distribution network data transmission are considered.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network data time series transmission, and in particular to a distribution network data transmission method and device based on big data analysis. Background Art

[0002] The distribution network is the most critical component in the overall architecture of the smart grid and an important bridge connecting the distribution main station and distribution terminals to achieve distribution automation services. With the integration of informatization and industrialization, the automation level of distribution network construction and operation management has been greatly improved, and new technologies such as wireless communication have developed rapidly.

[0003] By combining wireless communication technology with smart grid technology, the intelligent development of the distribution network has been promoted, facilitating a more accurate understanding of energy usage and thus enabling more efficient energy distribution and management.

[0004] However, due to the complex structure, large scale, and wide data sources of the distribution network, it is difficult for existing technologies to balance the security and real-time performance of distribution network data transmission. Summary of the Invention

[0005] The present invention provides a distribution network data transmission method and device based on big data analysis to solve the technical problem that existing technologies are difficult to balance the security and real-time performance of distribution network data transmission.

[0006] To solve the above technical problem, an embodiment of the present invention provides a distribution network data transmission method based on big data analysis.

[0007] Collect various types of encrypted distribution network data for each node in the distribution network at each collection moment;

[0008] Perform binary conversion serialization processing on all the encrypted distribution network data of each node to obtain each distribution network data time series of each node; perform predictive autocorrelation analysis on each distribution network data time series to obtain the non-differential cyclic factor of each distribution network data time series;

[0009] Perform keyword analysis on all the distribution network data time series to obtain the decryption key factor of each distribution network data time series; based on all the decryption key factors of each node, perform weighted summation on all the non-differential cyclic factors of each node to calculate the cyclic crackable factor of each node;

[0010] Calculate the joint crackable factor of each node according to the correlation characteristics between all the distribution network data time series of each node and the corresponding cyclic crackable factor; perform value range mapping processing on all the joint crackable factors to obtain the comprehensive encryption strength of each node;

[0011] Perform first-level encryption processing on all the to-be-encrypted distribution network data of each node based on the comprehensive encryption strength of each node to obtain the first-level encryption matrices of the distribution network for each node; perform second-level encryption processing on all the first-level encryption matrices of the distribution network using the RSA encryption algorithm to obtain the second-level encryption matrices of the distribution network for each node, and transmit all the second-level encryption matrices of the distribution network through a wireless communication network.

[0012] As one of the preferred solutions, the performing predictive autocorrelation analysis on each time series of the distribution network data to obtain the undifferentiated regular factor of each time series of the distribution network data includes:

[0013] Use a prediction algorithm to perform predictive analysis on each element in each time series of the distribution network data, and replace each element value in each time series of the distribution network data with the result of the corresponding predictive analysis to obtain the distribution network prediction sequence of each time series of the distribution network data;

[0014] Take the absolute value of the difference between each time series of the distribution network data and the corresponding distribution network prediction sequence as the prediction deviation sequence of each time series of the distribution network data, and take the sum of all element values in the prediction deviation sequence of each time series of the distribution network data as the prediction deviation index of each time series of the distribution network data;

[0015] Calculate the Hurst exponent of each time series of the distribution network data, and calculate the undifferentiated regular factor of each time series of the distribution network data based on the linear relationship between the Hurst exponent of each time series of the distribution network data and the corresponding prediction deviation index.

[0016] As one of the preferred solutions, the first-level encryption processing is designed as:

[0017] Perform matrix conversion processing on the joint cracking factor of each node and all the to-be-encrypted distribution network data corresponding thereto to obtain the distribution network matrix data of each node;

[0018] Perform scrambling processing on each distribution network matrix data several times to obtain the distribution network scrambling matrix for each scrambling processing;

[0019] Take the distribution network scrambling matrix obtained from each scrambling processing as the input for the next scrambling processing, and take the final distribution network scrambling matrix as the first-level encryption matrix of the distribution network for each node;

[0020] Wherein, the number of times of the scrambling processing is equal to the comprehensive encryption strength of the corresponding node.

[0021] As one of the preferred solutions, the matrix conversion process for the combined cracking factors of each of the nodes and all the corresponding encrypted distribution network data to obtain the distribution network matrix data for each node includes:

[0022] Taking each of the encrypted distribution network data of each node as a row vector of a matrix to obtain a combined matrix of the distribution network data for each node;

[0023] Concatenating the combined cracking factor of each node and the corresponding combined matrix of the distribution network data to obtain a concatenated matrix of the distribution network data for each node;

[0024] Performing missing value processing on the concatenated matrices of the distribution network data for all the nodes to obtain the distribution network matrix data for each node.

[0025] As one of the preferred solutions, the scrambling process is designed as:

[0026] Using a random number generator to generate a random number sequence, where the length of the random number sequence is equal to the number of columns of each of the distribution network matrix data; concatenating the random number sequence and the corresponding distribution network matrix data to obtain a corresponding randomly concatenated matrix of the distribution network;

[0027] Swapping each odd column of each randomly concatenated matrix of the distribution network with the corresponding next odd column, and swapping each even column of each randomly concatenated matrix of the distribution network with the corresponding next even column to obtain a scrambled matrix of the distribution network for each scrambling process.

[0028] As one of the preferred solutions, the radix conversion serialization process for all the encrypted distribution network data of each node to obtain the time series of each distribution network data of each node includes:

[0029] Processing each of the encrypted distribution network data using ASCII encoding to obtain corresponding converted distribution network encoded data;

[0030] Sorting all the converted distribution network encoded data of each type of each node in ascending order according to the obtained time sequence to obtain the time series of each distribution network data of each node.

[0031] As one of the preferred solutions, the keyword analysis of all the time series of the distribution network data to obtain the decryption key factors of each time series of the distribution network data includes:

[0032] Performing word frequency statistics on all the time series of the distribution network data to obtain the word frequency of each distribution network keyword;

[0033] Count the number of occurrences of each of the distribution network keywords in each of the distribution network data time series, and input the number of occurrences of each of the distribution network keywords in each of the distribution network data time series and the corresponding word frequency into the key sequence evaluation expression to calculate the decryption key factor for each of the distribution network data time series;

[0034] The key sequence evaluation expression is:

[0035]

[0036] where Kve i is the decryption key factor for the i-th distribution network data time series, Norm() is the normalization function, n is the number of distribution network keywords, Ocur i j is the number of occurrences of the j-th distribution network keyword in the i-th distribution network data time series, is the word frequency of the j-th distribution network keyword, and Len is the length of each distribution network data time series.

[0037] As one preferred solution, calculating the combined cracking factor for each node according to the correlation characteristics between all the distribution network data time series of each node and the corresponding rule-following crackable factor includes:

[0038] Pairwise combine all the distribution network data time series of each node, and use the Pearson correlation coefficient between the two distribution network data time series in each combination as the power grid correlation factor for each combination;

[0039] Take the average value of the power grid correlation factors of all the combinations of each node as the correlation cracking index of each node, and take the sum of the correlation cracking index of each node and the corresponding rule-following crackable factor as the combined cracking factor of each node.

[0040] As one preferred solution, performing a value range mapping process on all the combined cracking factors to obtain the comprehensive encryption strength of each node includes:

[0041] Perform a normalization process on all the combined cracking factors to obtain the combined cracking normalization coefficient of each node;

[0042] Take the product of the preset maximum encryption times and the combined cracking normalization coefficient of each node as the adjusted encryption strength of each node;

[0043] Take the integer value of the adjusted encryption strength of each node as the comprehensive encryption strength of each node.

[0044] Another embodiment of the present invention provides a distribution network data transmission device based on big data analysis, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above-mentioned distribution network data transmission method based on big data analysis is implemented.

[0045] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0046] (1) Collect various types of to-be-encrypted distribution network data of each node in the distribution network at each collection moment. Considering the complex structure and wide data sources of the distribution network, for the convenience of subsequent unified analysis, perform radix conversion serialization processing on all to-be-encrypted distribution network data of each node to obtain the distribution network data time series of each node, convert all to-be-encrypted distribution network data into a unified format, and improve the efficiency of subsequent analysis of distribution network data;

[0047] (2) Before encrypting the distribution network data, first perform predictive autocorrelation analysis on each distribution network data time series to obtain the non-difference regular factor of each distribution network data time series; then perform keyword analysis on all distribution network data time series to obtain the decryption key factor of each distribution network data time series, which is used to evaluate the importance of each distribution network data time series. Based on all the decryption key factors of each node, perform weighted summation on all the non-difference regular factors of each node to calculate the regular crackable factor of each node, and improve the reliability of evaluating the regular feature inside the to-be-encrypted distribution network data of each node;

[0048] (3) Analyze the correlation characteristics between all distribution network data time series of each node, calculate the joint crackable factor of each node in combination with the corresponding regular crackable factor, perform value range mapping processing on all joint crackable factors to obtain the comprehensive encryption strength of each node. For the to-be-encrypted distribution network data with more obvious regular characteristics and correlation characteristics, perform higher-strength encryption to prevent crackers from decrypting by using the regular characteristics inside the to-be-encrypted distribution network data or the correlation characteristics between the to-be-encrypted distribution network data, and improve the security of the transmission of the to-be-encrypted distribution network data. For the to-be-encrypted distribution network data with lower regular characteristics and correlation characteristics, perform lower-strength encryption to improve the real-time performance of the transmission of the to-be-encrypted distribution network data;

[0049] (4) Perform first-level encryption processing on all the power distribution network data to be encrypted for each node based on the comprehensive encryption strength of each node, obtaining the first-level encryption matrices of the power distribution network for each node, which improves the real-time performance of the transmission of the power distribution network data to be encrypted while ensuring the security of the transmission of the power distribution network data to be encrypted; perform second-level encryption processing on all the first-level encryption matrices of the power distribution network using the RSA encryption algorithm, obtaining the second-level encryption matrices of the power distribution network for each node, and transmit all the second-level encryption matrices of the power distribution network through a wireless communication network, further improving the security of the transmission of the power distribution network data to be encrypted. Brief Description of the Drawings

[0050] Figure 1 It is a flowchart of the power distribution network data transmission method based on big data analysis in one embodiment of the present invention;

[0051] Figure 2 It is a schematic diagram for obtaining the comprehensive encryption strength in one embodiment of the present invention. Detailed Embodiments

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0054] In the description of the present application, it should be noted that, unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0055] In the description of the present application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0056] An embodiment of the present invention provides a method for transmitting distribution network data based on big data analysis. Specifically, please refer to Figures 1 to 2 , Figure 1 which shows the flowchart of the method for transmitting distribution network data based on big data analysis in one of the embodiments of the present invention. Figure 2 which shows the schematic diagram of obtaining the comprehensive encryption strength in one of the embodiments of the present invention.

[0057] The flowchart of the method for transmitting distribution network data based on big data analysis in one of the embodiments of the present invention includes the following steps S1 to S5, specifically as follows:

[0058] Step S1: Collect various types of distribution network data to be encrypted at each node in the distribution network at each collection moment.

[0059] Specifically, collect various types of distribution network data to be encrypted at each node in the distribution network every t seconds, and perform data cleaning on the distribution network data to be encrypted.

[0060] It should be noted that the acquisition time interval t is a value preset by the user. In this embodiment, the acquisition time interval t is set to 5. For the setting of the acquisition time interval t, in other implementation manners, the implementer can select it by himself / herself, and this application does not make special restrictions thereon. The data types of the data to be encrypted in the distribution network include voltage, current, power factor, active power, reactive power, electric energy, and frequency.

[0061] Step S2: Perform binary conversion serialization processing on all the data to be encrypted in the distribution network for each node to obtain the time series of each distribution network data for each node; perform predictive autocorrelation analysis on each time series of distribution network data to obtain the non-difference regular factor of each time series of distribution network data.

[0062] It should be noted that since the distribution network structure is complex and the data sources are extensive, in order to facilitate subsequent unified analysis, it is necessary to convert all the data to be encrypted in the distribution network into a unified format.

[0063] Specifically, use ASCII encoding to process each data to be encrypted in the distribution network to obtain the corresponding converted distribution network code data; arrange all the converted distribution network code data of each type for each node in ascending order according to the obtained time sequence to obtain the time series of each distribution network data for each node.

[0064] It should be noted that ASCII encoding is a character encoding scheme that uses 7-bit binary numbers to represent each character. ASCII encoding is a well-known technology and will not be elaborated in this embodiment.

[0065] Furthermore, it should be noted that since there are many duplicate data in the data to be encrypted in the distribution network and they have a certain regularity, it is very likely that the cracker will use the repetitive characteristics and regular characteristics of the data to be encrypted in the distribution network to crack the already encrypted distribution network data, resulting in the leakage of the data to be encrypted in the distribution network. Therefore, it is necessary to perform predictive autocorrelation analysis on each time series of distribution network data to obtain the non-difference regular factor of each time series of distribution network data, so as to evaluate the repetitive characteristics and regular characteristics within each time series of distribution network data.

[0066] Specifically, use a prediction algorithm to perform prediction analysis on each element in each time series of distribution network data, and replace each element value in each time series of distribution network data with the result of the corresponding prediction analysis to obtain the distribution network prediction sequence of each time series of distribution network data.

[0067] It should be noted that the prediction algorithm establishes a model by analyzing historical data, features, and patterns, and uses this model to predict the results of unknown data or future events. The prediction algorithm is a well-known technology and will not be elaborated in this embodiment. As an implementation manner of this application, a long short-term memory neural network model is used to perform prediction analysis on each element in each distribution network data time series. For the selection of the prediction algorithm, as other implementation manners, the implementer can select it by himself / herself, and this application does not impose special restrictions on this.

[0068] Further, the absolute value of the difference between each distribution network data time series and the corresponding distribution network prediction series is used as the prediction deviation series of each distribution network data time series, and the sum of all element values in the prediction deviation series of each distribution network data time series is used as the prediction deviation index of each distribution network data time series.

[0069] Calculate the Hurst exponent of each distribution network data time series, and based on the linear relationship between the Hurst exponent of each distribution network data time series and the corresponding prediction deviation index, calculate the undifferentiated compliance factor of each distribution network data time series.

[0070] It should be noted that the Hurst exponent can be used to reveal whether the time series data has long-term correlation. The calculation of the Hurst exponent is a well-known technology and will not be elaborated in this embodiment. When the Hurst exponent of the distribution network data time series is larger and the prediction deviation index is smaller, it indicates that the repetitive and regular characteristics of the internal data of the distribution network data time series are more obvious, the undifferentiated compliance factor value is larger, and it is more likely that crackers will use these characteristics to crack the encrypted distribution network data during the transmission process, and it is more necessary to perform a higher-intensity encryption on this distribution network data time series.

[0071] As an embodiment of this application, the calculation result of the exponential function with the natural constant as the base and the opposite number of the prediction deviation index of each distribution network data time series as the exponent is used as the predictable factor of each distribution network data time series, and the product of the predictable factor and the Hurst exponent of the corresponding distribution network data time series is used as the undifferentiated compliance factor of the corresponding distribution network data time series.

[0072] Step S3: Perform keyword analysis on all distribution network data time series to obtain the decryption key factor of each distribution network data time series; based on all the decryption key factors of each node, perform weighted summation on all the undifferentiated compliance factors of each node to calculate the compliance crackable factor of each node.

[0073] It should be noted that each node has multiple time series of distribution network data. However, the importance degrees of different time series of distribution network data are different. When determining the encryption intensity of the distribution network data for each node, in order to improve the reliability of determining the encryption intensity of the distribution network data for each node, keyword analysis is performed on all time series of distribution network data, and a higher weight is set for the relatively important time series of distribution network data.

[0074] Specifically, word frequency statistics are performed on all time series of distribution network data to obtain the word frequency of each distribution network keyword; the number of occurrences of each distribution network keyword in each time series of distribution network data is counted, and the number of occurrences of each distribution network keyword in each time series of distribution network data and the corresponding word frequency are input into the key sequence evaluation expression to calculate the decryption key factor for each time series of distribution network data.

[0075] In this step, the key sequence evaluation expression is:

[0076]

[0077] Among them, Kve i is the decryption key factor for the i-th time series of distribution network data, Norm() is the normalization function, n is the number of distribution network keywords, Ocur i j is the number of occurrences of the j-th distribution network keyword in the i-th time series of distribution network data, is the word frequency of the j-th distribution network keyword, and Len is the length of each time series of distribution network data.

[0078] It should be noted that when the word frequency of the distribution network keyword is higher, the time series of distribution network data is shorter, and the number of occurrences of the distribution network keyword in the time series of distribution network data is more, it indicates that the proportion of the distribution network keyword in the time series of distribution network data is larger, and the corresponding time series of distribution network data is more likely to be the key for the cracker to crack the encrypted distribution network data, the importance degree is higher, and the decryption key factor value is larger.

[0079] Step S4: Calculate the joint cracking factor for each node according to the correlation characteristics between all time series of distribution network data of each node and the corresponding rule-based crackable factor; perform value range mapping processing on all joint cracking factors to obtain the comprehensive encryption intensity of each node.

[0080] It should be noted that in a complex distribution network system, there are often correlations and mutual influences among different types of data at each node. By analyzing the correlation characteristics, the cracker can identify which data sequences are closely related, and then crack the encrypted distribution network data during the transmission process. When encrypting the distribution network data to be encrypted with obvious correlation characteristics, higher-strength encryption should be performed.

[0081] Specifically, all the time series of distribution network data at each node are combined in pairs, and the Pearson correlation coefficient between the two time series of distribution network data in each combination is used as the grid correlation factor for each combination. The average value of the grid correlation factors of all combinations at each node is used as the correlation cracking index for each node. The sum of the correlation cracking index of each node and the corresponding rule-based cracking factor is used as the combined cracking factor for each node.

[0082] It should be noted that the Pearson correlation coefficient is a statistical index used to measure the linear correlation degree between two variables. The calculation of the Pearson correlation coefficient is a well-known technology and will not be elaborated in this embodiment. When the Pearson correlation coefficient between the time series of distribution network data at a node is larger, it indicates that the correlation degree between the time series of distribution network data at the node is higher, and the correlation cracking index is larger.

[0083] Furthermore, normalize all the combined cracking factors to obtain the combined cracking normalization coefficient for each node. Multiply the preset maximum number of encryption times by the combined cracking normalization coefficient for each node to obtain the adjusted encryption intensity for each node. Take the integer value of the adjusted encryption intensity for each node as the comprehensive encryption intensity.

[0084] It should be noted that the preset maximum number of encryption times is a value preset by humans. In this embodiment, the preset maximum number of encryption times is set to 10. For the value of the preset maximum number of encryption times, as other implementation manners, the implementer can select it by himself / herself, and this application does not make special restrictions on it. The comprehensive encryption intensity is used to evaluate the encryption intensity for the distribution network data to be encrypted. For the distribution network data to be encrypted with more obvious regularity characteristics and correlation characteristics, higher-strength encryption should be performed to prevent crackers from using the regularity characteristics inside the distribution network data to be encrypted or the correlation characteristics between the distribution network data to be encrypted for decryption, and improve the security of the transmission of the distribution network data to be encrypted; for the distribution network data to be encrypted with lower regularity characteristics and correlation characteristics, lower-strength encryption should be performed to reduce the encryption processing process and improve the real-time performance of the transmission of the distribution network data to be encrypted.

[0085] Step S5: Based on the comprehensive encryption strength of each node, perform primary encryption processing on all the to-be-encrypted distribution network data of each node to obtain the primary encryption matrices of the distribution network for each node; perform secondary encryption processing on all the primary encryption matrices of the distribution network using the RSA encryption algorithm to obtain the secondary encryption matrices of the distribution network for each node, and transmit all the secondary encryption matrices of the distribution network through a wireless communication network.

[0086] It should be noted that the RSA encryption algorithm is an asymmetric encryption algorithm, and different keys are used for encryption and decryption. The RSA encryption algorithm is a well-known technology, and thus will not be elaborated in this embodiment.

[0087] Specifically, the primary encryption processing is designed as follows: perform matrix transformation processing on the combined cracking factor of each node and all the corresponding to-be-encrypted distribution network data to obtain the distribution network matrix data of each node; perform scrambling processing on each distribution network matrix data for several times to obtain the scrambled matrices of the distribution network for each scrambling process; use the scrambled matrix of the distribution network obtained from each scrambling process as the input for the next scrambling process, and use the final scrambled matrix of the distribution network as the primary encryption matrix of the distribution network for each node.

[0088] It should be noted that the number of scrambling processes is equal to the comprehensive encryption strength of the corresponding node.

[0089] Determine the number of scrambling processes for the to-be-encrypted distribution network data of each node based on the comprehensive encryption strength, which improves the real-time performance of the transmission of the to-be-encrypted distribution network data while ensuring the security of the transmission of the to-be-encrypted distribution network data.

[0090] In this step, performing matrix transformation processing on the combined cracking factor of each node and all the corresponding to-be-encrypted distribution network data to obtain the distribution network matrix data of each node specifically includes:

[0091] Take each to-be-encrypted distribution network data of each node as a row vector of a matrix to obtain the combined matrix of the distribution network data of each node; splice the combined cracking factor of each node and the combined matrix of the distribution network data to obtain the spliced matrix of the distribution network data of each node; perform missing value processing on the spliced matrices of the distribution network data of all nodes to obtain the distribution network matrix data of each node.

[0092] It should be noted that missing value processing is a well-known technology, and thus will not be elaborated in this embodiment.

[0093] In this step, the scrambling process is designed as follows: a random number sequence is generated by a random number generator, where the length of the random number sequence is equal to the number of columns of each distribution network matrix data; the random number sequence and the corresponding distribution network matrix data are spliced to obtain the corresponding distribution network random splicing matrix; each odd column of each distribution network random splicing matrix is swapped with the corresponding next odd column, and each even column of each distribution network random splicing matrix is swapped with the corresponding next even column to obtain the distribution network scrambling matrix for each scrambling process.

[0094] It should be noted that through the first-level encryption process and the second-level encryption process, the distribution network second-level encryption matrix of each node is obtained, which ensures the security of the data transmission of the distribution network to be encrypted and improves the real-time performance of the data transmission of the distribution network to be encrypted.

[0095] Correspondingly, another embodiment of the present invention provides a distribution network data transmission device based on big data analysis, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the distribution network data transmission method based on big data analysis as described above is implemented.

[0096] The above embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent should be subject to the appended claims.

Claims

1. A distribution network data transmission method based on big data analysis, characterized in that: The method comprises: Collect various types of distribution network data to be encrypted at each node in the distribution network at each collection moment; Performing base-to-base conversion and serialization processing on all the to-be-encrypted distribution network data of each node to obtain the distribution network data time series of each node; performing predictive autocorrelation analysis on each distribution network data time series to obtain the indifference compliance factor of each distribution network data time series; Perform keyword analysis on all the distribution network data time series to obtain the decryption key factor of each distribution network data time series; perform weighted summation on all the indifference compliance factors of each node based on all the decryption key factors of each node to calculate the compliance crackable factor of each node; Calculate the joint cracking factor of each node according to the correlation characteristics between all the distribution network data time series of each node and the corresponding rule-compliant cracking factor; perform range mapping processing on all the joint cracking factors to obtain the comprehensive encryption strength of each node; Based on the comprehensive encryption strength of each node, all the distribution network data to be encrypted of each node are subjected to primary encryption processing to obtain the primary encryption matrix of each distribution network of each node; the RSA encryption algorithm is used to perform secondary encryption processing on all the primary encryption matrices of the distribution network to obtain the secondary encryption matrix of each distribution network of each node, and all the secondary encryption matrices of the distribution network are transmitted through the wireless communication network.

2. The method for data transmission in a distribution network based on big data analysis according to claim 1, characterized in that: The step of performing a predictive autocorrelation analysis on each of the distribution network data time series to obtain a uniform regularity factor for each of the distribution network data time series includes: Using a prediction algorithm to perform prediction analysis on each element in each of the distribution network data time series, replacing each element value in each of the distribution network data time series with the corresponding result of the prediction analysis, and obtaining a distribution network prediction sequence of each of the distribution network data time series; The absolute value of the difference between each of the distribution network data time series and the corresponding distribution network prediction sequence is used as the prediction deviation sequence of each of the distribution network data time series, and the sum of all element values ​​in the prediction deviation sequence of each of the distribution network data time series is used as the prediction deviation index of each of the distribution network data time series; The Hurst index of each of the distribution network data time series is calculated, and based on the linear relationship between the Hurst index of each of the distribution network data time series and the corresponding prediction deviation index, the indifference compliance factor of each of the distribution network data time series is calculated.

3. The distribution network data transmission method based on big data analysis according to claim 1 is characterized in that: The first level encryption process is designed to: Performing matrix conversion processing on the joint cracking factor of each node and all corresponding distribution network data to be encrypted to obtain distribution network matrix data of each node; Performing scrambling processing on each of the distribution network matrix data for several times to obtain a distribution network scrambling matrix after each scrambling processing; The distribution network scrambling matrix obtained by each scrambling process is used as the input of the next scrambling process, and the final distribution network scrambling matrix is ​​used as the first-level encryption matrix of the distribution network of each node; The number of scrambling processes is equal to the comprehensive encryption strength of the corresponding node.

4. The method for data transmission in a distribution network based on big data analysis according to claim 3, characterized in that: The matrix conversion process is performed on the joint cracking factor of each node and all the corresponding distribution network data to be encrypted to obtain the distribution network matrix data of each node, including: Taking each of the to-be-encrypted distribution network data of each of the nodes as a row vector of a matrix, obtaining a distribution network data merging matrix of each of the nodes; The joint cracking factor of each node and the corresponding distribution network data merging matrix are spliced ​​to obtain a distribution network data splicing matrix of each node; The distribution network data concatenation matrix of all the nodes is processed for missing values ​​to obtain the distribution network matrix data of each node.

5. The method for data transmission in distribution network based on big data analysis according to claim 3, characterized in that: The scrambling process is designed to: A random number generator is used to generate a random number sequence, wherein the length of the random number sequence is equal to the number of columns of each of the distribution network matrix data; the random number sequence and the corresponding distribution network matrix data are spliced ​​to obtain a corresponding distribution network random splicing matrix; Each odd column of each random splicing matrix of the distribution network is swapped with the corresponding next odd column, and each even column of each random splicing matrix of the distribution network is swapped with the corresponding next even column to obtain a scrambled matrix of the distribution network for each scrambling process.

6. The method for data transmission in a distribution network based on big data analysis according to claim 1, characterized in that: The step of performing base conversion and serialization processing on all the to-be-encrypted power distribution network data of each node to obtain a time series of each power distribution network data of each node includes: Each of the distribution network data to be encrypted is processed using ASCII encoding to obtain corresponding distribution network encoding conversion data; All the power distribution network encoding conversion data of each type of each node are arranged in ascending order according to the time sequence of acquisition, so as to obtain the time series of each power distribution network data of each node.

7. The method for data transmission in a distribution network based on big data analysis according to claim 1, characterized in that: The keyword analysis is performed on all the distribution network data time series to obtain the decryption key factor of each distribution network data time series, including: Performing word frequency statistics on all the distribution network data time series to obtain the word frequency of each distribution network keyword; Counting the number of occurrences of each of the distribution network keywords in each of the distribution network data time series, inputting the number of occurrences of each of the distribution network keywords in each of the distribution network data time series and the corresponding word frequency into a key sequence evaluation expression, and calculating the decryption key factor of each of the distribution network data time series; The key sequence evaluation expression is: Among them, Kve i is the decryption key factor of the distribution network data time series i, Norm() is the normalization function, n is the number of distribution network keywords, is the number of occurrences of the jth distribution network keyword in the distribution network data time series i, is the frequency of the jth distribution network keyword, and Len is the length of each distribution network data time series.

8. The method for data transmission in a distribution network based on big data analysis according to claim 1, characterized in that: The calculating of the joint cracking factor of each node according to the correlation characteristics between all the distribution network data time series of each node and the corresponding compliance cracking factor includes: Combining all the distribution network data time series of each node in pairs, and taking the Pearson correlation coefficient between the two distribution network data time series in each combination as the power grid correlation factor of each combination; The average value of the grid association factors of all the combinations of each node is used as the association cracking index of each node, and the sum of the association cracking index of each node and the corresponding compliance crackable factor is used as the joint cracking factor of each node.

9. The method for data transmission in a distribution network based on big data analysis according to claim 1, characterized in that: The performing of range mapping processing on all the joint cracking factors to obtain the comprehensive encryption strength of each node includes: Normalizing all the joint cracking factors to obtain a joint cracking normalization coefficient for each node; The product of the preset maximum number of encryption times and the joint cracking normalization coefficient of each node is used as the adjusted encryption strength of each node; The rounded value of the adjusted encryption strength of each of the nodes is used as the comprehensive encryption strength of each of the nodes.

10. Distribution network data transmission equipment based on big data analysis, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the distribution network data transmission method based on big data analysis as described in any one of claims 1 to 9.

Images