Multi-dimensional data transmission method of power equipment based on blockchain

Abstract

The present application relates to the technical field of data encoding and transmission, and specifically to a method for multi-dimensional data transmission of power equipment based on blockchain, the method comprising: collecting various parameter data at various moments when the power equipment is running; determining the uniqueness of the change trend of various parameters; analyzing the difference between the data of any two types of parameters at the same moment, and determining the character difference between the two types of parameters; analyzing the difference in the total number of change intervals of the two types of parameters, the difference in the length of the change intervals of the same bit sequence, and the difference between the parameter data at the same moment, and determining the time series change difference between the two types of parameters; determining the merge distance between the two types of parameters; dividing all types of parameters into groups based on the merge distance, and encoding and transmitting all types of parameters in each group. The present application improves the encoding efficiency of multi-dimensional data of power equipment.

Description

Technical Field

[0001] The present application relates to the technical field of data coding and transmission, and specifically to a multi-dimensional data transmission method for power equipment based on blockchain. Background Art

[0002] Power equipment generates a large amount of data during operation, including real-time current, voltage, frequency, temperature, humidity, etc. These data are of great value for equipment monitoring, fault diagnosis, energy management, etc. Traditional data transmission methods may face problems such as data security, lack of transparency, and data tampering, while blockchain technology can provide a decentralized, tamper-proof and relatively transparent data management method.

[0003] However, due to the large amount of data generated by power equipment during operation, it is costly to directly upload the power equipment data to the blockchain, and the transmission time is long, which may pose a potential threat to the temporal integrity of the blockchain data. Therefore, the data usually needs to be losslessly encoded when uploading to the blockchain; and multidimensional data often has a high degree of intrinsic dependencies. If these dependencies are ignored during encoding, it is easy to lead to poor compression effects, which in turn affects the transmission of multidimensional data. Summary of the invention

[0004] In order to solve the above technical problems, this application provides a blockchain-based multi-dimensional data transmission method for power equipment to solve existing problems.

[0005] The blockchain-based multi-dimensional data transmission method for power equipment in this application adopts the following technical solutions:

[0006] An embodiment of the present application provides a blockchain-based multi-dimensional data transmission method for power equipment, the method comprising the following steps:

[0007] Collect the data to be transmitted of various parameters at each moment of the operation of the power equipment; the process of transmitting the data includes:

[0008] (1) Calculating the change rate of each parameter at each time; forming a multidimensional array of each time with the change rates of all parameters of each type at each time, and dividing all time into categories based on the correlation between the multidimensional arrays;

[0009] (2) obtaining fitting curves of all the time data of each parameter, and dividing all the time of each parameter into various change intervals based on the distribution of the first-order and second-order derivatives of the fitting curves of each parameter; analyzing the distribution of the change rate of each change interval of each parameter, and the number of categories divided into each change interval, to determine the uniqueness of the change trend of each parameter;

[0010] (3) Analyze the difference between the data of any two types of parameters at the same time to determine the character difference between the two types of parameters; analyze the difference in the total number of change intervals of the two types of parameters, the difference in the length of the change intervals of the same bit sequence, and the difference between the parameter data at the same time to determine the time series change difference between the two types of parameters;

[0011] (4) Determine a merge distance between the two types of parameters based on the uniqueness of the change trend, the character difference, and the time series change difference of the two types of parameters; divide all types of parameters into groups based on the merge distance, and encode and transmit all type parameters in each group.

[0012] In one embodiment, all moments are divided into categories based on the correlation between the multidimensional arrays, including:

[0013] Based on the multidimensional array of all moments, a clustering algorithm is used to obtain the categories divided at all moments.

[0014] In one embodiment, the process of determining the change interval is:

[0015] The time corresponding to the zero point of the first-order and second-order derivatives is used as a segmentation point to obtain various variation intervals of various parameters.

[0016] In one embodiment, the uniqueness of the change trend is calculated as follows:

[0017] ; In the formula, is the uniqueness of the change trend of the i-th type parameter, is the mean of the rate of change of all the data at the moment in the rth change interval of the i-th type parameter, norm() is the normalization function, is the number of categories divided at all moments of the rth variation interval of the i-th parameter, and Z is the number of all variation intervals of the i-th parameter.

[0018] In one embodiment, the character difference is calculated as follows:

[0019] ; In the formula, is the character difference between the i-th parameter and the k-th parameter, is the minimum absolute value of the difference between the vth character in the jth data of the i-th parameter and all the characters in the jth data of the k-th parameter. is the number of characters in the jth data of the i-th type parameter, is the number of data collected for the i-th parameter as of the current moment.

[0020] In one embodiment, the process of determining the timing variation difference is as follows:

[0021] Calculate the mean of the differences between all the lengths of any two types of parameters, recorded as the first mean, calculate the mean of the differences between all the data at the same time of any two types of parameters, recorded as the second mean, and the time series change difference is the fusion result of the difference between the first mean, the second mean, and the total number of change intervals.

[0022] In one embodiment, the time series change difference is the product of the difference between the first mean value, the second mean value, and the total number of change intervals.

[0023] In one embodiment, the process of determining the merge distance is:

[0024] The mean of the uniqueness of the change trends of the arbitrary two types of parameters is calculated and recorded as the third mean. The product of the third mean, the character difference and the time series change difference of the arbitrary two types of parameters is used as the merged distance between the arbitrary two types of parameters.

[0025] In one embodiment, dividing all class parameters into groups based on the merge distance includes:

[0026] All class parameters are divided into groups using a clustering algorithm, wherein the merge distance is used as a metric distance between two class parameters.

[0027] In one embodiment, encoding and transmitting all class parameters in each group includes:

[0028] All data of all class parameters in each group are encoded using the Huffman coding algorithm, hashed after encoding, and data is transmitted based on the blockchain.

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

[0030] The present application collects various parameter data at various moments when the power equipment is running; calculates the change rate of the data of various parameters at various moments; organizes the change rates of all types of parameters at various moments into a multidimensional array at each moment, and divides all moments into categories based on the correlation between the multidimensional arrays; by dividing all moments into categories, the moments with similar parameter data changes are obtained, so as to facilitate the analysis of subsequent parameter data changes; obtains the fitting curve of the data of all types of parameters at all moments, and divides all moments of various types of parameters into various change intervals based on the distribution of the first-order and second-order derivatives of the fitting curve of various types of parameters; analyzes the distribution of the change rate of each change interval of each type of parameter, and the number of categories divided into which the moments of each change interval are divided, to determine the uniqueness of the change trend of each type of parameter; the uniqueness of the change trend reflects the specificity of the data change of each type of parameter, and the degree of difference from the data change of other types of parameters, thereby reflecting the appropriateness of separately encoding the data of each type of parameter; further, analyzes the difference between the data of any two types of parameters at the same moment, and determines the distribution of the change rate of each type of parameter at each change interval, and the number of categories divided into which the moments of each change interval are divided. The character difference between the two types of parameter data; the character difference reflects the difference between the single characters of the two types of parameter data, and reflects the possibility of merging the two types of parameters into a unified code; the difference in the total number of change intervals of the arbitrary two types of parameters, the difference in the length of the change intervals of the same bit sequence, and the difference between the parameter data at the same time are analyzed to determine the time series change difference between the arbitrary two types of parameters; the time series change difference reflects the time series correlation between the two types of parameter data. The greater the time series change difference, the weaker the time series correlation between the two types of parameters, thereby reflecting the adaptability of merging the two types of parameter data into a unified code and improving the reliability of merging the parameter data; combining the uniqueness of the change trend of the arbitrary two types of parameters, the character difference, and the time series change difference, determine the merging distance between the arbitrary two types of parameters; based on the merging distance, all types of parameters are divided into groups, which improves the accuracy of merging parameters, and all types of parameters in each group are encoded and transmitted, which improves the efficiency of encoding multi-dimensional data of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A flowchart of the steps of the blockchain-based multi-dimensional data transmission method for power equipment provided in this application;

[0033] Figure 2Determine the flow chart for the merge distance. DETAILED DESCRIPTION

[0034] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of the multi-dimensional data transmission method of power equipment based on blockchain proposed in this application 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.

[0035] 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.

[0036] The specific scheme of the multi-dimensional data transmission method for power equipment based on blockchain provided by this application is described in detail below with reference to the accompanying drawings.

[0037] An embodiment of the present application provides a method for multi-dimensional data transmission of power equipment based on blockchain. Specifically, the following method for multi-dimensional data transmission of power equipment based on blockchain is provided. Figure 1 , the method comprises the following steps:

[0038] Step S1, collecting various parameter data at various times when the power equipment is running, and performing preprocessing.

[0039] This embodiment uses a smart meter to collect the current and voltage at each moment when the power equipment is running, uses a temperature sensor to collect the surface temperature at each moment when the power equipment is running, uses a humidity sensor to collect the humidity at each moment in the environment where the power equipment is located, and uses a vibration sensor to collect the vibration data of the surface of the power equipment at each moment. The multidimensional data analyzed in this embodiment includes current, voltage, temperature, humidity and vibration data. The power equipment is a generator. The implementer can select the power equipment and data type to be collected according to the actual situation. This embodiment does not limit this. Among them, the collection interval of all data in this embodiment is 0.1s, which can be set by the implementer.

[0040] Current, voltage, temperature, humidity and vibration data are recorded as various parameters, and all data collected for various parameters are cleaned, including missing value filling and outlier processing. Data cleaning is an existing well-known technology and will not be described in detail in this embodiment.

[0041] Step S2, calculating the change rate of each type of parameter data at each moment; forming the change rates of all types of parameters at each moment into a multidimensional array at each moment, and dividing all moments into categories based on the correlation between the multidimensional arrays.

[0042] Massive amounts of power equipment data are directly stored in the server. The regularity of power equipment operation leads to high redundancy of the time series data generated. If the generated data is not processed and directly stored, it will cause a waste of server storage resources. Therefore, it is necessary to compress and encode the power equipment data. Since the blockchain is tamper-proof, the compression encoding method should choose lossless compression.

[0043] Huffman Coding is a data compression coding algorithm based on the frequency of character occurrence, which can reduce the size of data without losing information. However, when encoding multidimensional data of power equipment, since the changes between data of different dimensions may have a certain correlation, Huffman coding directly analyzes the character frequency of the data as a whole, which may increase the complexity of frequency statistics and the construction of the Huffman tree, affecting the efficiency of encoding and subsequent decoding. Therefore, it is necessary to optimize the encoding process in combination with the data change characteristics of power equipment.

[0044] Usually, the monitoring data of power equipment changes mainly with the change of power demand at the power consumption end. Therefore, the data change of power equipment usually has a certain periodic law. In order to analyze the characteristics of power data changes, it is necessary to classify the power data according to its performance at different times.

[0045] Based on the above analysis, for each type of parameter, the change rate of the data of each type of parameter at each moment is calculated, and the change rates of all class parameters at each moment are composed into a multidimensional array. The multidimensional array of all moments before the current moment is used as the K-means clustering algorithm, and the output is the clustering clusters divided at all moments before the current moment, which are recorded as categories. Among them, the clustering parameter K of the K-means clustering algorithm is obtained by the elbow method, and the Euclidean distance is used as the measurement distance. The calculation of the change rate, the elbow method and the K-means clustering algorithm are all existing well-known technologies, and this embodiment will not be described in detail here.

[0046] Step S3, obtaining the fitting curve of all the moment data of each type of parameter, and dividing all the moments of each type of parameter into various change intervals based on the distribution of the first-order and second-order derivatives of the fitting curve of each type of parameter; analyzing the distribution of the change rate of each change interval of each type of parameter, and the number of categories divided into which the moments of each change interval are divided, to determine the uniqueness of the change trend of each type of parameter.

[0047] For each type of parameter, the least squares method is used to fit the data of all moments before the current moment to obtain the fitting curves corresponding to each type of parameter. The zero points of the first-order derivative and the second-order derivative of the fitting curves of each type of parameter are obtained, and the moments corresponding to all zero points are used as dividing points. All moments of each type of parameter are divided into various change intervals. There are differences in the data change trends and change rates in different change intervals.

[0048] It should be noted that the least square method is an existing well-known technology, and the implementer can choose other existing feasible curve fitting algorithms according to the actual situation, and this implementation does not limit it here.

[0049] The purpose of optimizing the encoding based on the changing characteristics of power data is to reduce the computational cost of the encoding and decoding process. Since the changes between the multi-dimensional data of power equipment have a certain correlation, if the dimensions with similar changes are compressed separately, it may not be possible to effectively capture redundant information, thereby affecting the data compression rate; therefore, it is necessary to compare data of different dimensions and optimize the data compression encoding process.

[0050] For each variation interval of each type of parameter, if the number of categories divided into all moments in the variation interval is greater, it indicates that the data distribution of this type of parameter in this variation interval is more chaotic, and the difference may be greater compared with other types of parameters. Therefore, this type of parameter has a certain uniqueness and is more suitable for separate encoding during the encoding process.

[0051] In addition, if the change rate of the data at all moments in the change interval is greater, it means that the change of this type of parameter is more unstable, and the possibility of greater difference with other types of parameters is higher. When encoding, this type of parameter is also more suitable for separate encoding.

[0052] Based on the above analysis, the uniqueness of the changing trends of various parameters is calculated. The specific calculation method is:

[0053] ; In the formula, is the uniqueness of the change trend of the i-th type parameter, is the mean of the rate of change of all the data at the moment in the rth change interval of the i-th type parameter, norm() is the normalization function, is the number of categories divided at all moments of the rth variation interval of the i-th parameter, and Z is the number of all variation intervals of the i-th parameter.

[0054] It should be understood that the greater the uniqueness of the change trend of the i-th type parameter is, the greater the difference between the i-th type parameter and other types of parameters is, the greater the amount of information contained therein is, and the more it needs to be encoded separately.

[0055] Step S4, analyzing the difference between the data of the two types of parameters at the same moment to determine the character difference between the two types of parameters; analyzing the difference in the total number of change intervals of the two types of parameters, the difference in the length of the change intervals of the same bit sequence, and the difference between the parameter data at the same moment to determine the difference in the timing changes between the two types of parameters.

[0056] In Huffman coding, the length of the code depends on the character probability distribution of the number of characters in the data: the way to represent the character code in the Huffman tree is the combination of path symbols of the leaf nodes. For data dimensions with large differences in character distribution, in the process of establishing different Huffman trees, the number of characters represented by the leaf nodes with longer paths is also larger, the coding compression rate is low, and the ideal compression result cannot be achieved.

[0057] In order to improve the encoding efficiency, for data dimensions with small differences in character distribution, their data can be combined and uniformly encoded. In order to improve the compression rate of the combined encoding, it is necessary to select data dimensions with higher similarity for combination.

[0058] Based on the above analysis, the character difference between any two types of parameters is calculated. The specific calculation process is:

[0059] ; In the formula, is the character difference between the i-th parameter and the k-th parameter, is the minimum absolute value of the difference between the vth character in the jth data of the i-th parameter and all the characters in the jth data of the k-th parameter. is the number of characters in the jth data of the i-th type parameter, is the number of data collected for the i-th parameter as of the current moment.

[0060] It should be noted that the characters involved in this embodiment are individual numbers. For example, if the j-th data of the i-th parameter is 120, the j-th data of the i-th parameter contains a total of 3 characters, namely 1, 2, and 0. This embodiment analyzes the differences between characters in the data of the two types of parameters at the same time. The character difference reflects the distribution difference between the data collected by the i-th parameter and the k-th parameter. If the more identical characters are in the collected data, the smaller the character difference between the i-th parameter and the k-th parameter, the more suitable the i-th parameter and the k-th parameter are for merging and unified encoding to improve encoding efficiency.

[0061] However, it is difficult to reflect the time series changes of data of different dimensions only through the character differences between the data. Further analysis is required in combination with the change characteristics of different dimensions of the data. In different data dimensions, the data change trends in different change intervals are different. When the change intervals of two-dimensional data are close, the time series change difference is relatively low.

[0062] For data of different dimensions, if there is a certain correlation between the time series changes, the number and length of the change intervals have similar characteristics. For example, for current and temperature data: when the current increases, the heat generated by the power equipment increases over time, and the temperature also rises; when the current decreases, the heat generated by the power equipment decreases, and the temperature also gradually decreases.

[0063] Therefore, the difference in the temporal changes of any two types of parameters is calculated. The specific calculation method is: taking the i-th parameter and the k-th parameter as an example, the difference in the length of the change interval of the same position sequence in the i-th parameter and the k-th parameter is calculated, and the mean of the differences in all the lengths between the i-th parameter and the k-th parameter is recorded as the first mean. Secondly, the mean of the difference in all simultaneous data between the i-th parameter and the k-th parameter is calculated, recorded as the second mean, and the fusion result of the difference in the first mean, the second mean, and the total number of change intervals between the i-th parameter and the k-th parameter is used as the difference in the temporal changes between the i-th parameter and the k-th parameter.

[0064] It should be noted that the difference indicates the degree of difference between two variables, which can be calculated by the absolute value of the difference, the square of the difference, etc. This embodiment uses the absolute value of the difference as the calculation method of the difference; fusion means combining multiple variables, which can be calculated by multiplication, addition, mixed addition and multiplication, etc. This embodiment uses multiplication as the calculation method of fusion; the same position sequence mentioned in this embodiment represents data in the same arrangement order position. For example, when calculating the first mean, the change intervals of the same position sequence are the first change interval of the i-th parameter and the first change interval of the k-th parameter, the second change interval of the i-th parameter and the second change interval of the k-th parameter, and so on, to obtain the change intervals of each same position sequence.

[0065] In this embodiment, the calculation method of the time series variation difference between the i-th type parameter and the k-th type parameter is:

[0066] ; In the formula, is the difference in temporal variation between the i-th parameter and the k-th parameter, is the absolute value of the difference between the total number of change intervals of the i-th parameter and the k-th parameter, is the mean of the absolute values ​​of the differences between the lengths of the change intervals of the same position order in the i-th type parameter and the k-th type parameter, that is, the first mean, It is the mean of the absolute values ​​of the differences between all simultaneous data of the i-th parameter and the k-th parameter, which is the second mean.

[0067] The same calculation method for the time series variation difference between the i-th type parameter and the k-th type parameter can be used to obtain the time series variation difference between any two types of parameters.

[0068] It should be understood that the greater the difference in the time series changes between the two types of parameters, the weaker the correlation between the data changes of the two types of parameters, and the less suitable the two types of parameters are for combined unified coding.

[0069] Step S5, combining the uniqueness of the change trends of the two types of parameters, the character differences, and the time series change differences, to determine the merge distance between the two types of parameters; based on the merge distance, all types of parameters are divided into groups, and all type parameters in each group are encoded and transmitted.

[0070] In summary, the combined distance between the two types of parameters is determined by combining the time series change difference, character difference, and uniqueness of the change trends of the two types of parameters. The specific calculation method is:

[0071] ; In the formula, is the combined distance between the i-th type parameter and the k-th type parameter, is the mean of the uniqueness of the change trend of the i-th parameter and the uniqueness of the change trend of the k-th parameter, recorded as the third mean, is the difference in temporal variation between the i-th parameter and the k-th parameter, is the character difference between the i-th parameter and the k-th parameter. The flow chart for determining the merge distance is as follows: Figure 2 shown.

[0072] The merged distance between different class parameters is used as the metric distance to cluster all class parameters using the K-means clustering algorithm, wherein the clustering parameter K is obtained by the elbow method; and the multi-dimensional data of the power equipment is divided into clusters.

[0073] It should be noted that, in this embodiment, the minimum data unit for clustering based on the combined distance is each type of parameter, not each data under each type of parameter.

[0074] The Huffman coding algorithm is used to uniformly encode all data of all class parameters under each cluster, and then the encoded data is hashed using the hash algorithm to obtain a unique hash value. In the Ethereum platform, the data is transmitted through a smart contract. Among them, the Huffman coding algorithm, the hash algorithm, and data transmission based on the Ethereum platform are all existing well-known technologies, and this embodiment will not be described in detail here.

[0075] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0076] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0077] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A multi-dimensional data transmission method for power equipment based on blockchain, characterized in that: The method comprises the following steps: Collect the data to be transmitted of various parameters at each moment of the operation of the power equipment; the process of transmitting the data includes: (1) Calculating the change rate of each parameter at each time; forming a multidimensional array of each time with the change rates of all parameters of each type at each time, and dividing all time into categories based on the correlation between the multidimensional arrays; (2) obtaining fitting curves of all the time data of each parameter, and dividing all the time of each parameter into various change intervals based on the distribution of the first-order and second-order derivatives of the fitting curves of each parameter; analyzing the distribution of the change rate of each change interval of each parameter, and the number of categories divided into each change interval, to determine the uniqueness of the change trend of each parameter; (3) Analyze the difference between the data of any two types of parameters at the same time to determine the character difference between the two types of parameters; analyze the difference in the total number of change intervals of the two types of parameters, the difference in the length of the change intervals of the same bit sequence, and the difference between the parameter data at the same time to determine the time series change difference between the two types of parameters; (4) Determining a combined distance between the two types of parameters based on the uniqueness of the change trend, the character difference, and the time series change difference of the two types of parameters; dividing all types of parameters into groups based on the combined distance, and encoding and transmitting all type parameters in each group; The various parameters are specifically: using smart meters to collect the current and voltage of the power equipment at various times when it is running, using temperature sensors to collect the surface temperature of the power equipment at various times when it is running, using humidity sensors to collect the humidity of the environment in which the power equipment is located at various times, and using vibration sensors to collect vibration data on the surface of the power equipment at various times; the current, voltage, temperature, humidity and vibration data are recorded as various parameters.

2. The blockchain-based multi-dimensional data transmission method for power equipment according to claim 1, characterized in that: The method divides all moments into categories based on the correlation between multidimensional arrays, including: Based on the multidimensional array of all moments, a clustering algorithm is used to obtain the categories divided at all moments.

3. The blockchain-based multi-dimensional data transmission method for power equipment according to claim 1, characterized in that: The process of determining the variation interval is as follows: The time corresponding to the zero point of the first-order and second-order derivatives is used as a segmentation point to obtain various variation intervals of various parameters.

4. The blockchain-based multi-dimensional data transmission method for power equipment according to claim 1, characterized in that: The uniqueness of the change trend is calculated as follows: ; In the formula, is the uniqueness of the change trend of the i-th type parameter, is the mean of the rate of change of all the data at the moment in the rth change interval of the i-th type parameter, norm() is the normalization function, is the number of categories divided at all moments of the rth change interval of the i-th parameter, and Z is the number of all change intervals of the i-th parameter.

5. The blockchain-based multi-dimensional data transmission method for power equipment according to claim 1, characterized in that: The character difference is calculated as follows: ; In the formula, is the character difference between the i-th parameter and the k-th parameter, is the minimum absolute value of the difference between the vth character in the jth data of the i-th parameter and all the characters in the jth data of the k-th parameter. is the number of characters in the jth data of the i-th type parameter, is the number of data collected for the i-th parameter as of the current moment.

6. The blockchain-based multi-dimensional data transmission method for power equipment according to claim 1, characterized in that: The process of determining the time series variation difference is as follows: Calculate the mean of the differences between all the lengths of any two types of parameters, recorded as the first mean, calculate the mean of the differences between all the data at the same time of any two types of parameters, recorded as the second mean, and the time series change difference is the fusion result of the difference between the first mean, the second mean, and the total number of change intervals.

7. The blockchain-based multi-dimensional data transmission method for power equipment according to claim 6, characterized in that: The time series change difference is the product of the difference between the first mean, the second mean, and the total number of change intervals.

8. The blockchain-based multi-dimensional data transmission method for power equipment according to claim 1, characterized in that: The process of determining the merge distance is as follows: The mean of the uniqueness of the change trends of the arbitrary two types of parameters is calculated and recorded as the third mean. The product of the third mean, the character difference and the time series change difference of the arbitrary two types of parameters is used as the merged distance between the arbitrary two types of parameters.

9. The blockchain-based multi-dimensional data transmission method for power equipment according to claim 1, characterized in that: The dividing all class parameters into groups based on the merge distance includes: All class parameters are divided into groups using a clustering algorithm, wherein the merge distance is used as a metric distance between two class parameters.

10. The blockchain-based multi-dimensional data transmission method for electric power equipment according to claim 1, characterized in that: The encoding and transmission of all class parameters in each group includes: All data of all class parameters in each group are encoded using the Huffman coding algorithm, hashed after encoding, and data is transmitted based on the blockchain.

Images