A method and apparatus for generating data fingerprints for new energy detection data

Abstract

This invention proposes a method and apparatus for generating data fingerprints for new energy detection data, comprising the following steps: mapping voltage and current data sequences into RGB triples according to mapping rules to generate corresponding color image data; extracting key feature sets from the color image data using image recognition technology, the key feature sets including three-phase balance, color fluctuation rate, oversaturation risk coefficient, etc.; performing a hash operation on the concatenation of the color image data and a timestamp to generate an image-timestamp joint hash value; concatenating the key feature set with the image-timestamp joint hash value and encrypting it using a private key to generate a data fingerprint; storing the generated data fingerprint in a database or storage system for future verification. This invention aims to solve the problems in existing technologies, such as difficulty in ensuring data uniqueness, susceptibility to tampering, and low traceability efficiency caused by the large volume of detection data, frequent version iterations, and high similarity of multi-source heterogeneous data.

Description

Technical Field

[0001] This invention relates to the field of new energy detection, specifically to a method and apparatus for generating data fingerprints for new energy detection data. Background Technology

[0002] Data fingerprinting is a technique used to generate unique identifiers (i.e., "fingerprints") for data, which helps in the rapid identification and comparison of datasets or data items. This concept is similar to human fingerprints, each of which is unique and can be used to uniquely identify a person. In the fields of data management, cybersecurity, and information technology, data fingerprinting is widely used to ensure the integrity, security, and consistency of data. Data integrity verification checks whether data has been tampered with or corrupted during transmission or storage by comparing the current fingerprint of the data with previously generated fingerprints.

[0003] With the increasing proportion of renewable energy, both domestic and international authorities are placing greater emphasis on the grid connection standard test results of renewable energy equipment, including low-voltage ride-through tests, high-voltage ride-through tests, and grid adaptability tests. Based on this data, further electromechanical and electromagnetic modeling work will be carried out on converters and renewable energy power plants. The aforementioned test data exhibits characteristics of multiple sets and high sampling rates, with the total data volume reaching several gigabytes or 10 GB. Furthermore, test results are similar between sets of test data for the same type of inverter and test data for different types of inverters. As the number of inverters tested increases, software versions iterate, and tests are re-conducted after standard revisions, the management of test data has become a priority. Existing methods only roughly determine whether data belongs to the same test based on the test time range. Therefore, a new method is needed that can guarantee the uniqueness and integrity of test data, thereby laying the foundation for rapid data traceability, anti-counterfeiting, and unified management. Summary of the Invention

[0004] This invention proposes a method and apparatus for generating data fingerprints for new energy detection data, aiming to solve the problems in existing technologies, such as difficulty in ensuring data uniqueness, susceptibility to tampering, and low traceability efficiency caused by the large volume of detection data, frequent version iterations, and high similarity of multi-source heterogeneous data. The technical solution provided by this invention is as follows:

[0005] In a first aspect, the present invention provides a method for generating data fingerprints for new energy detection data, comprising the following steps:

[0006] Step 1: Map the voltage and current data sequences into RGB triples according to the mapping rules to generate the corresponding color image data;

[0007] Step 2: Extract feature indicators from the color image data using image processing and recognition techniques, convert the feature indicators into fixed-precision formatted strings, and connect them with delimiters to form a key feature set;

[0008] Step 3: Perform a hash operation on the concatenated color image data and timestamp to generate a joint image-timestamp hash value;

[0009] Step 4: Concatenate the key feature set with the image-timestamp joint hash value, and encrypt it using a private key to generate a data fingerprint; the generated data fingerprint is stored in a database or storage system for future verification.

[0010] Preferably, the mapping rule for the voltage and current data sequences to RGB triples is as follows: after normalizing the three-phase voltage and current values ​​corresponding to each time series, they are mapped to R channel values, G channel values ​​and B channel values ​​respectively; each RGB pixel corresponds to voltage or current sampling data within a time window.

[0011] The preferred formula for normalization is:

[0012] Voltage RGB channel value = [255 * U / 1.5 U] n Round down;

[0013] Current RGB channel value = [255*I / 1.5I] n Round down;

[0014] Where U and I are the three-phase voltage and current data, U n I n These are the rated voltage and rated current of the inverter.

[0015] Preferably, the color image data is concatenated with the timestamp, and the feature vectors of each RGB triplet are arranged sequentially according to the file generation order and time order.

[0016] Preferably, the key feature set includes three-phase balance, color volatility, synergy coefficient, oversaturation risk, and color mutation density, specifically defined as follows:

[0017] Three-phase balance reflects the consistency of the average values ​​of the three channels. The closer the value is to 0, the more balanced it is. The calculation formula is as follows:

[0018]

[0019] mean represents the mean of the corresponding color channel, std() is the standard deviation of the mean, and max() is the maximum value of the mean;

[0020] Color volatility reflects the mean of the three-channel variance, and the calculation formula is as follows:

[0021]

[0022] Var() represents the variance of a single channel;

[0023] The synergy coefficient reflects the minimum pairwise correlation coefficient of the three channels, and the calculation formula is as follows:

[0024]

[0025] Corr() is the correlation coefficient between two values;

[0026] Oversaturation risk reflects the percentage of pixels close to 255 in any channel, calculated using the following formula:

[0027]

[0028] N is the total number of pixels in any channel;

[0029] Color mutation density reflects the proportion of color mutation regions extracted by Canny edge detection, and the calculation formula is as follows:

[0030]

[0031] The above indicators are converted into fixed-precision formatted strings and connected with delimiters to form a key feature set.

[0032] Preferably, the key feature set is concatenated with the image-timestamp joint data hash value, and a timestamp digest is generated through hash operation. The digest and the original timestamp are then encrypted together.

[0033] Preferably, the encryption method involves signing the concatenated data with a private key, followed by publishing the public key and signature, allowing anyone to verify the authenticity of the data. The public and private key pair employs an elliptic curve asymmetric encryption algorithm.

[0034] Secondly, a data fingerprint generation device for new energy detection data, used to implement the above method, characterized in that it includes:

[0035] Data mapping module: Converts voltage and current sequences into RGB images;

[0036] Feature extraction module: Extracts key feature sets from RGB images;

[0037] Hash generation module: Generates a corresponding hash value by concatenating the timestamp with the color image data;

[0038] Fingerprint generation module: Concatenates key feature sets, timestamps, and color image data hash values, then encrypts the data with a private key;

[0039] Storage module: Stores data fingerprints and associated public key metadata.

[0040] Preferably, the data mapping module supports configurable rules: dynamically selects mapping channels based on the input data type, and adapts to the generalized mapping interface of non-power data sources.

[0041] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0042] First, by binding and encrypting timestamps, device characteristic parameters, and key indicators of the testing process, a unique data fingerprint is generated, avoiding confusion and duplicate storage of test data of the same type / version.

[0043] Second, based on the direct extraction of key features and the hierarchical hash verification mechanism, it supports fast integrity verification of massive high-sampling-rate data (GB level) and reduces computation and storage overhead.

[0044] Third, by utilizing asymmetric encryption and spatiotemporal correlation design, we ensure that data fingerprints are unforgeable and can quickly locate the source of data through feature matching after decryption, thus achieving anti-counterfeiting and accurate traceability.

[0045] Fourth, through dynamic feature weight configuration and mapping rule scalability, it adapts to the updates and iterations of new energy grid connection standards, avoids the invalidation of historical data due to standard revisions, and can achieve cross-version and cross-standard compatibility. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0047] Figure 1 This is the overall flowchart of the method of the present invention.

[0048] Figure 2 This is a flowchart of the data conversion RGB value of the present invention.

[0049] Figure 3 This is a flowchart of the process for obtaining key features in this invention.

[0050] Figure 4 This is a flowchart of various data splicing methods according to the present invention.

[0051] Figure 5 This is a flowchart of the asymmetric encryption of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 The present invention provides the following technical solution:

[0054] Example 1:

[0055] A method for generating data fingerprints for new energy detection data, such as Figure 1 As shown, it includes the following steps:

[0056] Step 1: Map the voltage and current data sequences into RGB triples according to a mapping rule to generate corresponding color image data. The mapping rule is as follows: normalize the three-phase voltage and current values ​​corresponding to each time series and map them into R-channel, G-channel, and B-channel values ​​respectively; each RGB pixel corresponds to voltage or current sampling data within a time window. This method defines the mathematical relationship between voltage / current and RGB triples, ensuring data reversibility and unique visualization.

[0057] Converting data to RGB images and extracting key features from them offers several advantages over directly extracting features from the raw data: First, visualization and intuitive understanding: Images can visually display patterns, distributions, and trends in data, facilitating rapid observation of anomalies and changes. This conversion transforms the temporal information of time-series data into spatial distribution, presenting complex temporal relationships in an image structure for easier analysis. Second, leveraging mature image processing techniques: Images as input can utilize numerous pre-trained models and image recognition technologies to improve the accuracy and robustness of key feature extraction, providing a suitable dataset for subsequent deep learning. Third, data structuring and information integration... By mapping different types of data (such as voltage and current) to RGB channels, not only is their respective numerical information preserved, but the interrelationships between them (such as balance and coordination) can also be reflected. This mapping can reduce multidimensional data to a unified color image format, which facilitates subsequent unified processing and comparison. Finally, image data has the advantage of anti-tampering. Image data generally has strong redundancy and structured characteristics, making it relatively difficult to hide unauthorized tampering. Therefore, in some application scenarios, image data is less likely to be maliciously modified or forged. In addition, images can be combined with technologies such as digital watermarking and signatures to further enhance the integrity verification of data, thereby improving security.

[0058] In this embodiment, the process of converting data to RGB values ​​is as follows: Figure 2As shown, FFT calculations were performed on the three-phase voltage and three-phase current data to obtain the effective value sequence of the fundamental wave. The data are the time series t and the three-phase voltage data U. arms U brms U crms and three-phase current data I arms I brms I crms ,in The color corresponding to pixel 1 , The color corresponding to pixel 2 .

[0059] To ensure all values ​​are between 0 and 255, normalization is performed:

[0060]

[0061]

[0062] Among them, U n I n These represent the inverter's rated voltage and rated current; other data follow the same pattern. t represents the time series, starting with 1.

[0063] Step 2: Extract the feature set of the image from the color image data using image processing and recognition techniques, and use the corresponding feature indicators as the key feature set. The key feature set includes three-phase balance, color volatility, synergy coefficient, oversaturation risk, and color mutation density, such as... Figure 3 As shown.

[0064] The three-phase balance index (PBI) reflects the consistency of the average values ​​of the three channels (R / G / B). The closer the value is to 0, the more balanced it is. Physically, it represents the balance of the three-phase voltage / current. The calculation is shown in formula (3):

[0065]

[0066] mean represents the mean value of the corresponding color channel, std() is the standard deviation of the mean, and max() is the maximum value of the mean. The normal range should be less than 0.05.

[0067] Color Fluctuation Index (CFI) reflects the mean of the variance of the three channels and measures the fluctuation range. Its physical meaning is the degree of voltage / current fluctuation. The calculation formula (4) is as follows:

[0068]

[0069] Var() represents the variance of a single channel. Under normal circumstances, CFI should be less than 100. 100-300 is considered to be slightly fluctuating, and above 300 is considered to be abnormal.

[0070] The cross-channel correlation (CCC) coefficient reflects the minimum value of the pairwise correlation coefficients of the three channels, and its physical meaning is the synergy of the three-phase changes (the closer the value is to 1, the more stable it is). The calculation formula (5) is as follows:

[0071]

[0072] Corr() is the correlation coefficient between two values. Under normal circumstances, CCC should be greater than 0.8.

[0073] Over-saturation risk (OSR) reflects the percentage of pixels close to 255 in any channel, and its physical meaning is overvoltage or overcurrent risk. The calculation formula (6) is as follows:

[0074]

[0075] N is the total number of pixels in any channel. The OSR should be less than 0.01, otherwise there is a risk of overload.

[0076] Edge Density (ED) reflects the proportion of color-change regions extracted by Canny edge detection, and its physical meaning is transient events (such as load changes and arc faults). The calculation formula (7) is as follows:

[0077]

[0078] Transient events that may occur during startup or shutdown should be kept within a certain range.

[0079] Channel Discrepancy (CD) reflects the maximum difference ratio of the three channel averages, and its physical meaning is a single-phase anomaly (such as phase loss or short circuit). The calculation formula (8) is as follows:

[0080]

[0081] CD should be less than 1.2, otherwise there may be phase loss abnormalities.

[0082] The above indicators are converted into fixed-precision formatted strings and connected with delimiters to form a key feature set.

[0083] Step 3: Perform a hash operation on the concatenated color image data and timestamp to generate a joint image-timestamp hash value. The hash function used may be SHA-256, such as... Figure 4As shown.

[0084] The RGB values ​​of color image data generated by segmenting voltage and current data sequences according to time windows are concatenated. Each RGB triplet is then arranged sequentially according to the file generation order and time recursion to generate a fixed-length feature vector. Feature extraction adapts to the characteristics of time-series data, while the RGB triplet values ​​reduce storage overhead.

[0085] Specifically, all data are arranged in a three-dimensional array (t, 2*m, color) according to the order of voltage first and current second, where t is the data length, which is related to the time length, m is the number of test data points (since one test data point corresponds to two pixels of color for voltage and current at a certain moment, the total number is equal to 2m), and color is the RGB expression for the corresponding pixel, as follows:

[0086] Test data for a device

[0087]

[0088] For test data from multiple devices, the data is stacked column by column.

[0089]

[0090] The hash generation process is as follows: as in formula (11), the timestamp is concatenated with the sequence generated by the triple; as in formula (12), the concatenated sequence is hashed, where T is the timestamp sequence, S is the concatenated string, and H is the hash value.

[0091]

[0092]

[0093] Step 4: Concatenate the key feature set with the image-timestamp joint hash value, generate a timestamp digest through hash operation, and encrypt the digest and the original timestamp together to prevent timestamp forgery, enhance resistance to replay attacks, and prevent hash collisions caused by data similarity. Figure 5 As shown.

[0094] The encryption process is as follows: the concatenated data is signed using the private key of the testing company, and then the public key and signature are released, allowing anyone to verify the authenticity of the data. The public and private key pair uses an elliptic curve asymmetric encryption algorithm, such as the x25519 curve.

[0095] The generated data fingerprint is stored in a database or storage system for future comparison and verification.

[0096] Example 2:

[0097] A data fingerprint verification method for new energy detection data, when verifying the data fingerprint generated in Example 1, uses a public key to decrypt the data fingerprint, separates the key feature set and image hash value, and verifies the data integrity and authenticity by comparing the key feature set and image hash value of the original data. The verification rules are as follows:

[0098] If both the key feature set and the image hash value match the original data, then the data is determined to be unaltered.

[0099] If the image hash value does not match but the key feature set matches, the tampered area is located as the pixel block in the RGB image with the hash collision.

[0100] Example 3:

[0101] A data fingerprint generation device for new energy detection data, used to implement the method described in Embodiment 1, includes:

[0102] Data mapping module: Converts voltage and current sequences into RGB images; The data mapping module supports configurable rules: dynamically selects the mapping channel based on the input data type, and adapts to the generalized mapping interface of non-power data sources;

[0103] Feature extraction module: Extracts key feature sets from color image data;

[0104] Hash generation module: Generates a corresponding hash value by concatenating the timestamp with the color image data;

[0105] Fingerprint generation module: Concatenates key feature sets, timestamps, and color image data hash values, then encrypts the data with a private key;

[0106] Storage module: Stores data fingerprints and associated public key metadata.

[0107] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for generating data fingerprints for new energy detection data, characterized in that, Includes the following steps: Step 1: Map the voltage and current data sequences into RGB triples according to the mapping rules to generate the corresponding color image data; Step 2: Extract feature indicators from the color image data using image processing and recognition techniques, convert the feature indicators into fixed-precision formatted strings, and connect them with delimiters to form a key feature set; Step 3: Perform a hash operation on the concatenated color image data and timestamp to generate a joint image-timestamp hash value; Step 4: Concatenate the key feature set with the image-timestamp joint hash value, and encrypt it using a private key to generate a data fingerprint; the generated data fingerprint is stored in a database or storage system for future verification.

2. The method for generating data fingerprints for new energy detection data according to claim 1, characterized in that, The mapping rule for mapping the voltage and current data sequences to RGB triples in step 1 is as follows: after normalizing the three-phase voltage and current values ​​corresponding to each time series, they are mapped to R channel values, G channel values ​​and B channel values ​​respectively; each RGB pixel corresponds to voltage or current sampling data within a time window.

3. The method for generating data fingerprints for new energy detection data according to claim 2, characterized in that, The formula for normalization is: Voltage RGB channel value = [255 * U / 1.5 U] n Round down; Current RGB channel value = [255*I / 1.5I] n Round down; Where U and I are the three-phase voltage and current data, U n I n These are the rated voltage and rated current of the inverter.

4. The method for generating data fingerprints for new energy detection data according to claim 3, characterized in that, Step 3 concatenates the color image data with the timestamp, and arranges the feature vectors of each RGB triplet in the order of file generation and time.

5. The method for generating data fingerprints for new energy detection data according to claim 1, characterized in that, The key feature set includes three-phase balance, color volatility, synergy coefficient, oversaturation risk, and color mutation density, specifically defined as follows: Three-phase balance reflects the consistency of the average values ​​of the three channels. The closer the value is to 0, the more balanced it is. The calculation formula is as follows: ； mean represents the mean of the corresponding color channel, std() is the standard deviation of the mean, and max() is the maximum value of the mean; Color volatility reflects the mean of the three-channel variance, and the calculation formula is as follows: ； Var() represents the variance of a single channel; The synergy coefficient reflects the minimum pairwise correlation coefficient of the three channels, and the calculation formula is as follows: ； Corr() is the correlation coefficient between two values; Oversaturation risk reflects the percentage of pixels close to 255 in any channel, calculated using the following formula: ； N is the total number of pixels in any channel; Color mutation density reflects the proportion of color mutation regions extracted by Canny edge detection, and the calculation formula is as follows: ； The above indicators are converted into fixed-precision formatted strings and connected with delimiters to form a key feature set.

6. The method for generating data fingerprints for new energy detection data according to claim 5, characterized in that, Step 4 involves concatenating the key feature set with the image-timestamp joint data hash value, generating a timestamp digest through hash operation, and then encrypting the digest together with the original timestamp.

7. The method for generating data fingerprints for new energy detection data according to claim 6, characterized in that, The encryption method involves signing the concatenated data using a private key pair, followed by the release of the public key and signature, allowing anyone to verify the authenticity of the data. The public and private key pair employs an elliptic curve asymmetric encryption algorithm.

8. A data fingerprint generation device for new energy detection data, used to implement the method described in any one of claims 1-6, characterized in that, include: Data mapping module: Converts voltage and current sequences into RGB images; Feature extraction module: Extracts key feature sets from RGB images; Hash generation module: Generates a corresponding hash value by concatenating the timestamp with the color image data; Fingerprint generation module: Concatenates key feature sets, timestamps, and color image data hash values, then encrypts the data with a private key; Storage module: Stores data fingerprints and associated public key metadata.

9. A data fingerprint generation device for new energy detection data according to claim 8, characterized in that, The data mapping module supports configurable rules: dynamically selects mapping channels based on the input data type, and adapts to a generalized mapping interface for non-power data sources.

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