Network sampling data secure communication method and system based on blockchain

Abstract

The present application relates to the field of data security communication technology, and specifically to a network sampling data security communication method and system based on blockchain, which specifically includes: sampling the voltage, current, temperature and power data of the power grid, constructing a sequence of various types of data at the monitoring point, clustering the data in each sequence, and constructing the significant characteristic value of the cluster based on the fluctuation trend of the data in the cluster, the data chaos and the data prominence; constructing the event prominence weight by merging the clusters and the difference between the overall data in each cluster after the merger and the overall data in the sequence, combined with the significant characteristic value; calibrating the length of each cluster based on the event prominence weight of the clusters with the same sequence number in different sequences, and dividing the data into blocks; signing the data blocks using a signature algorithm to improve the processing efficiency and query efficiency of large-scale data blocks in the smart grid, and avoiding the problem of too many or too few data blocks leading to poor signature efficiency or algorithm utilization.

Description

Technical Field

[0001] The present application relates to the field of data security communication technology, and specifically to a blockchain-based network sampling data security communication method and system. Background Art

[0002] With the development of smart grids, real-time monitoring and analysis of data have become particularly important. Smart grid data has the characteristics of large data volume and multiple data types. Traditional centralized data storage methods have security risks such as single point failure and data tampering. The encryption mechanism and decentralized characteristics of blockchain can enhance the security of data in smart grids and ensure the secure communication of sampled data in smart grids. By building a secure communication system for network sampled data based on blockchain, data transparency, traceability and tamper-proof can be achieved, thereby enhancing the security and reliability of smart grids.

[0003] Digital signature mechanism is a key technology in blockchain technology to ensure data integrity and non-repudiation. Batch signature reduces the number of signature operations and improves efficiency by signing multiple data blocks of the transmitted data at one time. However, when dividing the sampled data into data blocks, if the division is too fine, the computational complexity and storage requirements of the signature will increase; if the division is too coarse, the efficiency advantage of batch signature may not be fully utilized. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a blockchain-based network sampling data security communication method and system. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for secure communication of network sampling data based on blockchain, the method comprising the following steps:

[0006] The voltage, current, temperature and power data of each monitoring point in the power grid are sampled to construct the voltage sequence, current sequence, temperature sequence and power sequence of each monitoring point;

[0007] Clustering the data in the voltage sequence, current sequence, temperature sequence and power sequence to obtain clusters; constructing the significant characteristic values ​​of each cluster based on the fluctuation trend, data confusion and data prominence of the data in each cluster;

[0008] Based on the difference in the number of clusters in different sequences, combined with the number of elements in each cluster and the significant characteristic value, the cluster merging operation of each sequence cluster is performed; for each sequence after the cluster merging operation, based on the difference between the overall data in each cluster in the sequence and the overall data in the sequence, combined with the significant characteristic value, the event prominence weight of each cluster in the sequence is constructed; based on the event prominence weight of the clusters with the same sequence number in different sequences, the length of each cluster is calibrated;

[0009] Each sequence is segmented into data blocks based on the final calibration length of all clusters of each sequence; and data security communication is performed on each data block of each sequence through a digital signature algorithm.

[0010] In one embodiment, the process of obtaining the significant feature value of each cluster is as follows:

[0011] Fit all the data in each cluster with a straight line, and calculate the absolute value of the slope of the fitted straight line of each cluster; calculate the standard deviation of all the data in each cluster; use the sequence composed of all the data in each cluster as the input of the peak detection algorithm, and output the number of peaks in each cluster;

[0012] The fusion value of the standard deviation, the number of peaks and the absolute value of the slope of all data in each cluster is used as the significant characteristic value of each cluster.

[0013] In one embodiment, the acquisition process of the cluster merging operation is:

[0014] Get the minimum number of clusters of all sequences at any monitoring point; perform cluster merging operations on sequences whose number of clusters is greater than the minimum value:

[0015] Step 1: Obtain the cluster with the smallest significant eigenvalue in each sequence, denoted as X;

[0016] Step 2: among the two clusters adjacent to cluster X, obtain the cluster with the least number of data, which is recorded as Y.

[0017] Step 3: merge cluster X and cluster Y, and calculate the significant feature value of the merged cluster;

[0018] Step 4: repeat steps 1 to 3 until the number of clusters in each sequence reaches the minimum value.

[0019] In one embodiment, the process of obtaining the event prominence weight of each cluster is as follows:

[0020] For each sequence of any monitoring point, calculate the ratio of the mean of all data in each cluster of the sequence to the mean of all data in the sequence, which is recorded as the first ratio;

[0021] The event prominence weight of each cluster of the sequence is calculated based on the first ratio and the significant feature value.

[0022] In one embodiment, the expression of the event prominence weight is:

[0023] , where is the event highlight weight of the jth cluster in the sequence, is the first ratio of the jth cluster in the sequence, is the significant feature value of the jth cluster in the sequence, is the normalization function.

[0024] In one embodiment, the process of calibrating the length of each cluster is:

[0025] Obtain clusters with the same sequence number in all sequences of any monitoring point, and normalize the event prominence weights of the clusters with the same sequence number;

[0026] The calibrated length of each cluster is constructed based on the event prominence weight of each cluster and the number of data in the cluster;

[0027] The final calibration length of the nth cluster of any sequence is recorded as , The expression is: , where is the number of clusters in any sequence after cluster merging, is the length of any sequence, A function that performs rounding.

[0028] In one embodiment, the process of obtaining the calibrated length of each cluster is as follows:

[0029] The calibrated length of the nth cluster of any sequence of any monitoring point is recorded as , The expression is:

[0030] , where is the number of sequences of any monitoring point; is the Softmax function; is the event highlight weight of the nth cluster in the mth sequence of any monitoring point; is the number of data in the nth cluster in the mth sequence of any monitoring point; is the ceiling function.

[0031] In one embodiment, the process of dividing each sequence into data blocks is as follows:

[0032] For each sequence, the calibrated length of the nth cluster in the sequence is used as the length of the nth data block in the sequence, and the sequence is divided into data blocks.

[0033] In one embodiment, the data security communication of each data block of each sequence is performed by using the Schnorr signature algorithm, specifically:

[0034] Use a digital signature algorithm for batch signing, generate multiple random numbers, calculate multiple challenge hash values, check whether the batch verification equation holds, and sign each data block once.

[0035] In a second aspect, an embodiment of the present application also provides a blockchain-based network sampling data secure communication system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0036] The embodiments of the present application have at least the following beneficial effects:

[0037] The present application proposes a blockchain-based network sampling data security communication method and system, which samples the voltage, current, temperature and power data of each monitoring point in the power grid, constructs a sequence of various types of data for each monitoring point, clusters the data in each sequence, and constructs the significant characteristic values ​​of each cluster based on the fluctuation trend, data chaos and data prominence of the data in each cluster cluster, reflecting the characteristics of the voltage stability and change trend of the power grid in a specific time period, as well as the frequency of voltage fluctuations and abnormal events in the power grid in a specific time period, solving the problem of insufficient capture of event-driven characteristics and key change points in power grid data; avoiding the impact of uneven data distribution and insufficient time-dependency analysis; and merging clusters and comparing the overall data in each cluster cluster after merging with the overall data in the sequence. The event prominence weight of each cluster is constructed based on the difference in data and the significant eigenvalue; the length of each cluster is calibrated based on the event prominence weight of the clusters with the same sequence number in different sequences, which simplifies the data analysis process between different sequences and improves the data processing efficiency and quality; each sequence is divided into data blocks based on the calibration length to improve the efficiency of power grid data management and the centralization and efficiency of analysis; the Schnorr signature algorithm is used to sign the data block to ensure the integrity and confidentiality of the data, realize the security of smart grid data management and the dynamic nature of the data integrity verification scheme, improve the processing efficiency and query efficiency of large-scale data blocks in the smart grid, and avoid the problem of too many or too few data blocks leading to poor Schnorr signature efficiency or algorithm utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] 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.

[0039] Figure 1 A flowchart of a method for secure communication of network sampling data based on blockchain provided in one embodiment of the present application;

[0040] Figure 2 A schematic diagram of voltage sequence clustering results provided by an embodiment of the present application;

[0041] Figure 3 A schematic diagram of a fitted straight line for all data in each cluster provided in one embodiment of the present application;

[0042] Figure 4 A flowchart of a significant feature value acquisition process provided by one embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the specific implementation method, structure, features and effects of the network sampling data security communication method and system based on blockchain proposed by this application are described in detail below in combination with the accompanying drawings and preferred embodiments. 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.

[0044] 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.

[0045] The specific scheme of the blockchain-based network sampling data secure communication method and system provided by the present application is described in detail below with reference to the accompanying drawings.

[0046] See also Figure 1 , which shows a flowchart of a method for secure communication of network sampling data based on blockchain provided by an embodiment of the present application, the method comprising the following steps:

[0047] Step S001, sampling the voltage, current, temperature and power data of each monitoring point in the power grid, and constructing a voltage sequence, a current sequence, a temperature sequence and a power sequence of each monitoring point.

[0048] The amount of data in smart grids has risen to the PB level, and the performance of traditional centralized data networks in data storage and communication is obviously insufficient. The distributed storage architecture of blockchain maintains data copies through multiple nodes, disperses data processing and storage pressure, and improves processing efficiency through sharding technology, which can be well applied to smart grids. First, through regional division, the data at the monitoring points in each area of ​​the smart grid are sampled, and the sampled data is analyzed through the smart contract of blockchain. Based on the analysis results, the digital signature technology in blockchain is used to encrypt and ensure the secure communication of data in the power grid.

[0049] Specifically, for any area divided by the blockchain, voltage transformers, current transformers, temperature sensors and smart meters are used to sample the voltage, current, temperature and power data of the power grid from various monitoring points in the area.

[0050] Preferably, in the embodiment of the present application, for the sampling of the above four types of data, the data sampling frequency is set to 50 Hz and the sampling period is set to 1 minute. As other embodiments of the present application, the implementer can set the data sampling frequency and sampling period according to the actual situation.

[0051] All sampled data are normalized by a Z-score normalization algorithm to eliminate the dimension effect. The Z-score normalization algorithm is a well-known technology, and the specific process will not be described in detail.

[0052] Furthermore, all sampled voltage data, current data, temperature data and power data of each monitoring point in the power grid in each area divided by the blockchain are arranged by time to obtain voltage sequence, current sequence, temperature sequence and power sequence.

[0053] Step S002, clustering the data in the voltage sequence, current sequence, temperature sequence and power sequence to obtain clusters; constructing the significant characteristic value of each cluster based on the fluctuation trend, data confusion and data prominence of the data in each cluster.

[0054] After the blockchain divides the smart grid into regions, the data sampled from the power grid in each region will be further segmented using sharding technology, and the power grid data in different regions will be shared or encrypted based on the segmentation results. At present, the data segmentation methods of sharding technology include simple random segmentation and time series segmentation. However, when processing large-scale sampled power grid data, simple random segmentation may not guarantee the timeliness and uniformity of data distribution. Although time series segmentation takes into account the time dependence of data, it has high computational complexity and low efficiency when the data volume is large. In addition, these methods may not be able to effectively capture the event-driven characteristics and key change points in power grid data, thus affecting the accuracy and real-time performance of the analysis.

[0055] Furthermore, for the power grid in each area divided by the blockchain, large-scale power grid sampling data is segmented based on event-driven and data feature methods, which can respond to specific events in the power grid in real time (such as power supply failures in the regional power grid, maintenance of power equipment, etc.) and capture key change points of the data. It is suitable for the power grid, a data environment with obvious event characteristics and dynamic changes, and can better adapt to the complexity and dynamics of power grid data, and improve the efficiency and quality of data processing.

[0056] In order to analyze the stability of the power grid, the voltage sequence of any monitoring point in the power grid in each region is taken as an example. Based on the data fluctuation trend, data confusion and data prominence in each sequence of each monitoring point, the significant characteristic values ​​of each cluster in each sequence of each monitoring point are constructed. The flowchart of the significant characteristic value acquisition process is shown in the figure. Figure 4 As shown:

[0057] (1) The voltage sequence is used as the input of the DBSCAN density clustering algorithm. In the embodiment of the present application, MinPts is set to 50 and Eps is set to 30. The output is the clusters in the voltage sequence. The clustering result diagram is shown in FIG. Figure 2 As shown. As other embodiments of the present application, the value of MinPts and the value of Eps can be set by the implementer. Among them, the DBSCAN density clustering algorithm is a common technology in the field of data processing, and the specific process is not allowed to be repeated.

[0058] It should be noted that for the clustering of data in each sequence, this application only provides one clustering method. There are many existing clustering algorithms, and implementers can also use other clustering algorithms to cluster the data in each sequence. This application does not make specific restrictions.

[0059] Each cluster represents a specific time period in the grid voltage data. In each specific time period, the data points have similar characteristics, reflecting the operating status of the grid under certain conditions, such as load peak, equipment failure, maintenance status, etc.

[0060] (2) All the data in each cluster are used as the input of the least squares method to perform straight line fitting, and the output is the fitting straight line of all the data in each cluster, such as Figure 3 As shown, calculate the absolute value of the slope of the fitting straight line;

[0061] Calculate the standard deviation of all data in each cluster;

[0062] The time series of all data in each cluster is used as the input of the peak detection algorithm, and the number of peaks in each cluster is output.

[0063] (3) The fusion value of the standard deviation, the number of peaks and the absolute value of the slope of all the data in each cluster is used as the significant feature value of each cluster.

[0064] It should be noted that the fusion described in this application is to combine multiple variables. The specific fusion method can be determined according to actual conditions during the application process, and this application does not impose any special restrictions.

[0065] Preferably, in an embodiment of the present application, the significant feature value of each cluster may be the product of the standard deviation of all data in each cluster, the number of peaks and the absolute value of the slope.

[0066] As another embodiment of the present application, the significant feature value of each cluster may be the sum of the normalized values ​​of the standard deviation, the number of peaks and the absolute value of the slope of all data in each cluster.

[0067] The slope of the fitting straight line reflects the trend of the grid voltage changing over time. The larger its absolute value is, the greater the trend of the voltage change is, the more drastic the characteristic of the data in the cluster is, and the larger the significant characteristic value of the cluster is. The standard deviation of the data in the cluster reflects the degree of fluctuation of the voltage data around its average value, and the number of peaks reflects the number of sudden increases or decreases in the voltage in the cluster. The larger the values ​​of these two, the more fluctuations and abnormal events of the voltage data are, the stronger the characteristic is, and the larger the significant characteristic value is.

[0068] The significant eigenvalue can measure the stability of the voltage of the power grid in a specific time period. The larger the significant eigenvalue is, the more obvious the potential characteristics of the power grid's operating status in this period are.

[0069] (4) Based on the current series, temperature series and power series of each monitoring point, the significant characteristic values ​​of each cluster in the current series, temperature series and power series are obtained by using the same calculation method as the significant characteristic values ​​of each cluster in the voltage series.

[0070] Step S003, based on the difference in the number of clusters in different sequences, combined with the number of elements in each cluster and the significant characteristic value, the cluster merging operation of each sequence cluster is performed; for each sequence after the cluster merging operation, based on the difference between the overall data in each cluster in the sequence and the overall data in the sequence, combined with the significant characteristic value, the event prominence weight of each cluster in the sequence is constructed; based on the event prominence weight of the clusters with the same sequence number in different sequences, the length of each cluster is calibrated.

[0071] (1) Since the number of clusters in the four sequences of each monitoring point may not be uniform, the clusters of the four sequences of any monitoring point are processed as follows:

[0072] The minimum number of clusters of the four sequences of any monitoring point is obtained, which is recorded as k; the cluster merging operation is performed on the sequences whose number of clusters is greater than k. Taking the voltage sequence as an example, if the number of clusters in the voltage sequence is greater than k, the cluster merging process is:

[0073] Step 1: Obtain the cluster with the smallest significant eigenvalue in the voltage sequence, denoted as X;

[0074] Step 2: among the two clusters adjacent to cluster X, obtain the cluster with the least number of data, which is recorded as Y.

[0075] Step 3: merge cluster X and cluster Y, and recalculate the significant feature value of the merged cluster;

[0076] Step 4: Repeat steps 1 to 3 until the number of clusters in the voltage sequence is k.

[0077] Furthermore, cluster merging operations are performed on the current sequence, the temperature sequence, and the power sequence respectively, so that the number of clustering clusters in the current sequence, the temperature sequence, and the power sequence is k.

[0078] By merging clusters, each cluster contains more data points, which helps to improve the representativeness of each cluster, making each cluster better reflect the operating status of the power grid under specific conditions and simplifying the subsequent data analysis process. Especially when making cross-sequence comparisons, the same number of clusters can be more easily matched and analyzed, making the analysis more focused and efficient.

[0079] (2) For each sequence of any monitoring point after the cluster merging operation, calculate the event prominence weight of each cluster in the sequence:

[0080] Calculate the mean of all data in the sequence, recorded as the sequence mean;

[0081] Calculate the mean of all data in each cluster of the sequence, recorded as the cluster mean;

[0082] Calculate the ratio of the cluster mean to the sequence mean, and record it as the first ratio;

[0083] The expression of the event prominence weight of each cluster of the sequence is:

[0084] , where is the event highlight weight of the jth cluster in the sequence, is the first ratio of the jth cluster in the sequence, is the significant feature value of the jth cluster in the sequence, is the normalization function.

[0085] It is used to measure the degree of deviation of cluster data from the average level of data in the entire sequence. The larger the value, the higher the data of the cluster is than the average level of the data in the entire sequence. At this time, the event characteristics of the cluster are more significant. The bigger; It reflects the degree of data stability of the power grid in a specific period of time. The larger the value, the more obvious the data change trend is during this time period, the more drastic the fluctuation is, and the more obvious the potential characteristics of the power grid operation status during this period are. It then increases.

[0086] Used to measure the importance of clusters in the overall power grid data. The larger the value, the more obvious the power consumption characteristics of the power grid during this time period.

[0087] (3) Calibrate the length of each cluster based on the event prominence weight of clusters with the same sequence number in different sequences:

[0088] In smart grids, there are usually a large number of monitoring points in each regional power grid after the blockchain is divided into regions, so the sampled network data is massive. In order to accurately detect power events at each monitoring point, the blockchain's block technology should be able to analyze the value of the data from the sampled data and block the data according to the information value. The event prominence weight can not only capture the key change points of the power grid data, but also evaluate the importance of each cluster in the overall power grid data, identify the key features and potential abnormal events in the power grid operation, and divide the power grid data according to the event prominence weight. The regular characteristics and abnormal patterns of the power grid operation can be discovered, and combined with the smart contracts in the blockchain, such as the preset voltage fluctuation threshold, it provides a scientific basis for the monitoring, maintenance and optimization of the power grid.

[0089] However, since the voltage, current, temperature and power data in the power grid are affected by multiple factors such as the dynamic changes in the grid load, equipment aging or failure, the characteristic changes are inconsistent. For example, an increase in load may cause a sharp increase in current and power data, while the voltage may drop due to line loss. Equipment failure may cause abnormal fluctuations in voltage and current, while temperature changes affect equipment performance and grid stability. The complex interaction of these factors makes the grid data show diverse and nonlinear characteristic changes.

[0090] Get the clusters with the same sequence number in the four sequences of any monitoring point. Assume that the nth cluster in the sequence is obtained for all four sequences. Perform Softmax normalization on the event prominence weights of the nth cluster in the four sequences. The Softmax function is used for normalization so that the sum of the normalized values ​​of the event prominence weights of the four clusters can be 1. The Softmax function is a well-known function and the specific calculation process will not be repeated here.

[0091] Then the cluster length of each cluster in the four sequences is calibrated, and the expression of the calibrated length of each cluster is:

[0092] , where is the calibrated length of the nth cluster of any sequence at any monitoring point; is the number of sequences of any monitoring point, in the embodiment of the present application The value of is 4; is the Softmax function; is the event highlight weight of the nth cluster in the mth sequence of any monitoring point; is the number of data in the nth cluster in the mth sequence of any monitoring point; is the ceiling function.

[0093] Since the sum of the calibrated lengths of all clusters of the same sequence is inconsistent with the length of the sequence, the calibrated lengths of each cluster are calculated by the formula Further calibration is performed, where is the final calibration length of the nth cluster of any sequence, is the calibrated length of the nth cluster of any sequence, is the number of clusters in any sequence after cluster merging, is the length of any sequence, that is, the number of elements in any sequence, This is a function that rounds the number in brackets to the nearest integer.

[0094] The clusters of power grid data are corrected. In order to enhance the representativeness of each cluster and simplify the analysis process, by merging clusters, the data characteristics can be maintained while reducing the amount of data to be processed, thereby improving the analysis efficiency. During the correction process, the Softmax normalization and calibration methods are used to ensure the consistency and comparability of the data between different sequences obtained when the blockchain is segmented using the above method in each regional power grid, so that the clustering results can better reflect the actual operating status of the power grid, such as load peaks or equipment failures, and also help to identify key features and potential abnormal events in power grid operation in combination with smart contracts in the blockchain.

[0095] Step S004, dividing each sequence into data blocks based on the final calibration length of all clusters of each sequence; and performing data security communication on each data block of each sequence through a digital signature algorithm.

[0096] (1) Each sequence is segmented according to the calibrated length of each cluster of each sequence. For example, if the calibrated length of the first cluster in any sequence is 200, then the data from 1 to 200 in the sequence are merged into the first data block. If the calibrated length of the second cluster is 400, then the data from 201 to 600 in all sequences are merged into the second data block, and so on, to achieve data segmentation processing for the sequence.

[0097] (2) Finally, the Schnorr signature algorithm is used for batch signing. By generating multiple random numbers, calculating multiple challenge hash values, and then checking whether the batch verification equation is established, each data block is signed once. If there are K data blocks in the four sequences of any monitoring point, only K signatures are required. Avoid the problem of too many or too few data blocks leading to poor Schnorr signature efficiency or algorithm utilization. In the application of smart grids, the Schnorr signature algorithm can provide a dynamic data integrity verification solution to ensure the confidentiality and integrity of data, while improving the query efficiency of updating stored data through the fast retrieval method of location-sensitive hashing. The Schnorr signature algorithm is a common technology in the field of blockchain technology, and the specific process will not be repeated.

[0098] (3) After the above process, the encryption result of the sampled data is obtained in each regional network. Secondly, when the terminal layer communicates with the regional network, the terminal layer and the regional network negotiate the session key, the terminal layer generates a temporary key pair, and the regional network starts encrypted communication by generating a session key through the public key; secondly, the blockchain transmits the signature results of all data blocks through encrypted communication. After the terminal layer receives the signature result, it performs identity authentication, data decryption and signature verification in turn, aggregates the sampled data in each regional power grid into a data set, and assists in completing the decision-making analysis of the regional power grid.

[0099] Based on the same inventive concept as the above method, an embodiment of the present application also provides a blockchain-based network sampling data secure communication system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, it implements the steps of any one of the above-mentioned blockchain-based network sampling data secure communication methods.

[0100] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. 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.

[0101] The various embodiments in the present application 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.

[0102] 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 network sampling data security communication method based on blockchain, characterized in that: The method comprises the following steps: The voltage, current, temperature and power data of each monitoring point in the power grid are sampled to construct the voltage sequence, current sequence, temperature sequence and power sequence of each monitoring point; Clustering the data in the voltage sequence, current sequence, temperature sequence and power sequence to obtain clusters; constructing the significant characteristic values ​​of each cluster based on the fluctuation trend, data confusion and data prominence of the data in each cluster; Based on the difference in the number of clusters in different sequences, combined with the number of elements in each cluster and the significant characteristic value, the cluster merging operation of each sequence cluster is performed; for each sequence after the cluster merging operation, based on the difference between the overall data in each cluster in the sequence and the overall data in the sequence, combined with the significant characteristic value, the event prominence weight of each cluster in the sequence is constructed; based on the event prominence weight of the clusters with the same sequence number in different sequences, the length of each cluster is calibrated; Each sequence is segmented into data blocks based on the final calibration length of all clusters of each sequence; each data block of each sequence is securely communicated through a digital signature algorithm; The process of calibrating the length of each cluster is as follows: Obtain clusters with the same sequence number in all sequences of any monitoring point, and normalize the event prominence weights of the clusters with the same sequence number; The calibrated length of each cluster is constructed based on the event prominence weight of each cluster and the number of data in the cluster; The final calibration length of the nth cluster of any sequence is recorded as , The expression is: , where is the number of clusters in any sequence after cluster merging, is the length of any sequence, A function for rounding; The process of obtaining the calibrated length of each cluster is as follows: The calibrated length of the nth cluster of any sequence of any monitoring point is recorded as , The expression is: , where is the number of sequences of any monitoring point; is the Softmax function; is the event highlight weight of the nth cluster in the mth sequence of any monitoring point; is the number of data in the nth cluster in the mth sequence of any monitoring point; is the ceiling function.

2. The method for secure communication of network sampling data based on blockchain according to claim 1, characterized in that: The process of obtaining the significant feature values ​​of each cluster is as follows: Fit all the data in each cluster with a straight line, and calculate the absolute value of the slope of the fitted straight line of each cluster; calculate the standard deviation of all the data in each cluster; use the sequence composed of all the data in each cluster as the input of the peak detection algorithm, and output the number of peaks in each cluster; The fusion value of the standard deviation, the number of peaks and the absolute value of the slope of all data in each cluster is used as the significant characteristic value of each cluster.

3. The method for secure communication of network sampling data based on blockchain as claimed in claim 1, characterized in that: The acquisition process of the cluster merging operation is: Get the minimum number of clusters of all sequences at any monitoring point; perform cluster merging operations on sequences whose number of clusters is greater than the minimum value: Step 1: Obtain the cluster with the smallest significant eigenvalue in each sequence, denoted as X; Step 2: among the two clusters adjacent to cluster X, obtain the cluster with the least number of data, which is recorded as Y. Step 3: merge cluster X and cluster Y, and calculate the significant feature value of the merged cluster; Step 4: repeat steps 1 to 3 until the number of clusters in each sequence reaches the minimum value.

4. The method for secure communication of network sampling data based on blockchain as claimed in claim 1, characterized in that: The process of obtaining the event prominence weight of each cluster is as follows: For each sequence of any monitoring point, calculate the ratio of the mean of all data in each cluster of the sequence to the mean of all data in the sequence, which is recorded as the first ratio; The event prominence weight of each cluster of the sequence is calculated based on the first ratio and the significant feature value.

5. The method for secure communication of network sampling data based on blockchain as claimed in claim 4, characterized in that: The expression of the event prominence weight is: , where is the event highlight weight of the jth cluster in the sequence, is the first ratio of the jth cluster in the sequence, is the significant feature value of the jth cluster in the sequence, is the normalization function.

6. The method for secure communication of network sampling data based on blockchain according to claim 1, characterized in that: The process of dividing each sequence into data blocks is as follows: For each sequence, the calibrated length of the nth cluster in the sequence is used as the length of the nth data block in the sequence, and the sequence is divided into data blocks.

7. The method for secure communication of network sampling data based on blockchain as claimed in claim 1, characterized in that: The digital signature algorithm is used to perform data security communication on each data block of each sequence, specifically: Use a digital signature algorithm for batch signing, generate multiple random numbers, calculate multiple challenge hash values, check whether the batch verification equation holds, and sign each data block once.

8. A network sampling data secure communication system based on blockchain, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the blockchain-based network sampling data secure communication method as described in any one of claims 1 to 7 are implemented.

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