Block chain-based power utilization control system

Abstract

The invention discloses a power resource allocation method executed by a power utilization control system based on a block chain, and the method comprises the steps: obtaining a dynamic value of renewable energy storage and related data of user power utilization through a block chain module, and constructing a four-dimensional user power utilization data matrix according to the related data of user power utilization through a NumPy library, dimensionality reduction processing is carried out on the basis of the four-dimensional user power consumption data matrix, each dimension is visualized, grey correlation analysis is carried out, the first-level correlation degree and the second-level correlation degree of each sub-sequence and the user power consumption event data are further divided, the influence of each factor on the user power consumption condition is analyzed, and the user power consumption condition is determined. And performing visual analysis on the plurality of one-dimensional user power consumption data matrixes to obtain a user power consumption behavior analysis result, and allocating the power resources according to the user power consumption behavior analysis result, thereby improving the accuracy and stability of balanced allocation of the power resources.

Description

Technical Field

[0001] The present invention relates to the technical field of power consumption control systems, and specifically to a power consumption control system based on blockchain. Background Art

[0002] In today's era, electricity has become the infrastructure that supports our daily lives. It not only promotes the development of families and industries, but is also a key indicator for measuring the progress of modern civilization. In the face of the growing global demand for electricity, the introduction of renewable energy has become crucial. The volatility of renewable energy production has posed a challenge to the existing power control system that relies on centralized data processing processes. This centralized model has a long response time when processing large-scale, high-frequency data, and it is difficult to meet the needs of real-time monitoring and analysis of renewable energy power consumption. In addition, since the centralized system relies on historical data for planning and optimization, it lacks the support of real-time or predictive data. The lack of real-time data makes it difficult for the system to respond quickly to sudden changes in renewable energy, and the lack of predictive data limits the accuracy of the system's real-time prediction of future power supply and demand trends and the accuracy of controlling the balanced distribution of power resources. When the power generation of renewable energy is higher than the demand, it may lead to excess and waste of electricity; on the other hand, when the power generation is insufficient, it may need to rely on traditional fossil energy to supplement it, which increases the risk of carbon emissions and environmental pollution and affects the stability and reliability of the entire power system. Therefore, it is necessary to design a blockchain-based power control system that improves the accuracy and stability of the balanced distribution of power resources. Summary of the invention

[0003] The purpose of the present invention is to provide a blockchain-based electricity consumption control system to solve the problems raised in the above-mentioned background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a blockchain-based power control system, and a power resource allocation method for a blockchain-based power control system, wherein the operation steps of the method include:

[0005] Step S1: Obtain the dynamic value of renewable energy storage and relevant data of user electricity consumption through the blockchain module, and use the NumPy library to construct a four-dimensional user electricity consumption data matrix based on the relevant data of user electricity consumption;

[0006] Step S2: Performing dimensionality reduction processing based on the four-dimensional user power consumption data matrix and visualizing each dimension, respectively performing grey correlation analysis, and obtaining the primary correlation and secondary correlation between the further divisions in each subsequence and the user power consumption event data;

[0007] Step S3: Based on the results of the first-level correlation and the second-level correlation, analyzing the influence of various factors on the user's electricity consumption, performing a visual analysis on the multiple one-dimensional user electricity consumption data matrices, and obtaining the user's electricity consumption behavior analysis results;

[0008] Step S4: Allocate power resources according to the analysis results of the user's power consumption behavior.

[0009] According to the above technical solution, step S1 further includes the following steps:

[0010] Step S11: The acquired user electricity consumption related data is the influencing factors of the user electricity consumption event, including: electricity consumption data, user type data, electricity consumption time data, and climate data;

[0011] The electricity consumption data includes: electric energy data, power data, frequency data, and demand factor data;

[0012] The user type data includes: residential user data, commercial user data, and industrial user data;

[0013] The time data includes: peak time data, normal time data, and valley time data;

[0014] The weather data includes: temperature data, humidity data, and wind speed data;

[0015] Step S12: extract the main components of the user electricity consumption related data through the PCA dimensionality reduction method, the main components can represent the main change trend of the data, and construct multiple one-dimensional user electricity consumption data matrices, the one-dimensional user electricity consumption data matrix X', the one-dimensional user electricity consumption data matrix X' is:

[0016]

[0017] In the matrix, m represents the number of items of user electricity consumption event data, and n represents the number of factors that affect the user's electricity consumption. m*n ;

[0018] Step S13: In the data related to the user's electricity consumption, the electricity consumption data is used as a parent sequence, and the user type, the electricity consumption time data, and the subdivided data in the climate data are used as subsequences associated with the parent sequence, wherein the parent sequence is represented by X0(t)=(x0(1), x0(2),..., x0(n),), and the subsequence is represented by X p (t) = (x p (1),x p (2),...,x p (n),).

[0019] According to the above technical solution, step S2 further includes the following steps:

[0020] Step S21: performing grey correlation analysis based on the parent sequence and the subsequence to obtain a first-level correlation degree between each subsequence and the user's power consumption event;

[0021] Step S22: calculating the association weight between the parent sequence and each of the subsequences based on the primary association degree and the one-dimensional user power consumption data matrix, and calculating the secondary association degree between each of the subsequences and the user power consumption event based on the association weight and the one-dimensional user power consumption data matrix;

[0022] Step S23: Based on the analyzed primary and secondary correlations, the grey correlations of the data are arranged in descending order. The grey correlation at the top has the highest value of the relevant data and has the greatest impact on the user's electricity consumption, which is the optimal correlation factor. Otherwise, it is the worst correlation factor.

[0023] According to the above technical solution, step S21 further includes the following steps:

[0024] Step S211: Standardization is performed based on the constructed one-dimensional user electricity consumption data matrix, using a standardization method in which one subsequence is divided by the average value of the corresponding subsequence data set to process the dimension effects of different data in a dimensionless manner:

[0025]

[0026] Where: X' p (t) represents the original data of the t-th user's electricity consumption event in the p-th subsequence, X p (t) represents the standardized data of the t-th user's electricity consumption event in the p-th subsequence, represents the average value of the original data of all users' electricity consumption events in the pth subsequence;

[0027] Step S212: Based on step S211, the original data of the power consumption data, the power consumption time data, and the climate data are standardized. Based on qualitative data such as user type, the residential user data is coded as 01, the commercial user data is coded as 02, and the industrial user data is coded as 03. The one-dimensional user power consumption data matrix after dimensionless conversion is obtained as follows:

[0028]

[0029] Step S213: Based on the parent sequence and the child sequence in step S13, the formula for calculating the correlation coefficient between each child sequence and the parent sequence is:

[0030]

[0031] Where: t represents the data number of the t-th user electricity consumption event, p represents the p-th subsequence number, X0(t) represents the standardized value of the t-th user electricity consumption event in the parent sequence, represents the absolute value of the difference between the pth subsequence and the parent sequence, ρ represents the resolution coefficient, where ρ = 0.5, represents the minimum difference between the p-th subsequence and the parent sequence. represents the maximum difference between the p-th subsequence and the parent sequence, γ p (t) represents the correlation coefficient of the p-th subsequence with the data number of the t-th user's electricity consumption event;

[0032] Step S214: Based on the correlation coefficient between each subsequence and the parent sequence, the average value of the correlation coefficient between each subsequence and the parent sequence is calculated, and the calculation formula for the first-level correlation is obtained as follows:

[0033]

[0034] In the formula, Y(γ p (t)) represents the first-order correlation coefficient between the p-th subsequence and the parent sequence.

[0035] According to the above technical solution, step S22 further includes the following steps:

[0036] Step S221: Based on the calculation result of the first-level association degree in step S21, the association weight between the parent sequence and each of the child sequences is calculated, that is, the calculation formula of the weight is obtained as follows:

[0037]

[0038] Where W p represents the weight of the p-th subsequence;

[0039] Step S222: performing normalization processing based on the calculation result of the weight value, calculating the sum of the grey relational degrees of all subsequences to determine whether it is 1, and when the result is not 1, calculating an adjustment factor, multiplying each biased weight by the adjustment factor, otherwise, no processing is performed when the result is 1;

[0040] Step S223: Based on the association weight and the association coefficient between the corresponding subsequence and the parent sequence, the calculation formula of the secondary association degree between the subsequence and the user's power consumption event is:

[0041]

[0042] In the formula, Z(γp (t)) represents the secondary correlation degree of the correlation coefficient between the p-th subsequence and the parent sequence.

[0043] According to the above technical solution, step S3 further includes: based on the optimal correlation factor and the worst correlation factor, the analysis results of multiple one-dimensional user electricity consumption data matrices are integrated to identify the influencing factors of the same user electricity consumption event, and a visual analysis is performed based on the calculation results. A heat map is drawn by using the depth of color to represent the level of the secondary correlation. The darker the color, the higher the secondary correlation. Conversely, the lighter the color, the lower the secondary correlation. The analysis results of the user's electricity consumption behavior are obtained.

[0044] According to the above technical scheme, it includes a blockchain module, a grey correlation analysis module, a power consumption analysis module, and a power resource allocation module: the blockchain module is used to obtain the dynamic value of renewable energy storage and relevant data on user power consumption in real time; the grey correlation analysis module is used to analyze the first-level correlation and second-level correlation of each user power consumption factor in the four-dimensional user power consumption data matrix through grey correlation analysis; the power consumption analysis module is used to further visualize the analysis results in the grey analysis module to obtain the analysis results of the user's power consumption behavior; the power resource allocation module is used to predict the allocation of power resources and monitor power resources in real time.

[0045] According to the above technical scheme, it includes a blockchain module, a grey correlation analysis module, a power consumption analysis module, and a power resource allocation module: the blockchain module is used to obtain the dynamic value of renewable energy storage and relevant data on user power consumption in real time; the grey correlation analysis module is used to analyze the first-level correlation and second-level correlation of each user power consumption factor in the four-dimensional user power consumption data matrix through grey correlation analysis; the power consumption analysis module is used to further visualize the analysis results in the grey analysis module to obtain the analysis results of the user's power consumption behavior; the power resource allocation module is used to predict the allocation of power resources and monitor power resources in real time.

[0046] According to the above technical solution, the electricity consumption analysis module includes a visualization analysis module and a user electricity consumption behavior analysis module: the visualization analysis module is used to integrate the secondary correlations of the multiple one-dimensional user electricity consumption data matrices, and perform visualization analysis to obtain a heat map of the secondary correlations of different user electricity consumption factors to the user electricity consumption events, providing an intuitive and efficient tool for in-depth understanding of user electricity consumption habits; the user electricity consumption behavior analysis module is used to capture the behavioral characteristics of the factors affecting user electricity consumption in the heat map of the visualization analysis.

[0047] According to the above technical solution, the power resource allocation module includes a power resource prediction module and a power resource real-time monitoring module: the power resource prediction module is used to predict the user's power demand according to the user's power consumption behavior characteristics, reasonably allocate power resources, and optimize the power energy management strategy; the power resource real-time monitoring module is used to monitor the user's power consumption and the dynamic changes of energy storage in real time, and provide accurate and timely decision-making support to ensure that the allocation of power resources is both effective and timely to meet user needs.

[0048] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention realizes real-time monitoring of the dynamic value of renewable energy storage and relevant data of user electricity consumption through the blockchain module, and provides accurate and timely decision support. Furthermore, a four-dimensional user electricity consumption data matrix is ​​constructed according to the relevant data of the user electricity consumption, and key features are extracted from the four-dimensional electricity consumption data through the PCA dimensionality reduction method to construct multiple one-dimensional data matrices. Through grey correlation analysis, the electricity consumption data that intuitively reflects the user's electricity consumption is used as the parent sequence, and other factors affecting the user's electricity consumption events are subsequences. The system evaluates the primary and secondary correlations between different electricity consumption factors and user electricity consumption events, and visualizes the analysis results in the form of a heat map, intuitively showing the impact of each user electricity consumption factor on the user's electricity consumption behavior. Based on these analyses, the system can predict the user's electricity demand, and rationally allocate electricity resources based on this, optimize energy management strategies, and thereby improve the accuracy and stability of balanced allocation of electricity resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 A schematic diagram of a flow chart of a power resource allocation method performed by a blockchain-based power consumption control system provided in Embodiment 1 of the present invention;

[0051] Figure 2 A schematic diagram of the module composition of a blockchain-based electricity control system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Embodiment 1: This embodiment can be applied to the electricity consumption control scenario of renewable energy. The method can be executed by a blockchain-based electricity consumption control system provided by this embodiment. Figure 1 A flowchart diagram of a power resource allocation method implemented by a power consumption control system based on blockchain provided in Embodiment 1 of the present invention, the method specifically comprises the following steps:

[0054] Step S1: Obtain the dynamic value of renewable energy storage and relevant data of user electricity consumption through the blockchain module, and use the NumPy library to construct a four-dimensional user electricity consumption data matrix based on the relevant data of user electricity consumption;

[0055] Step S2: Performing dimensionality reduction processing based on the four-dimensional user power consumption data matrix and visualizing each dimension, respectively performing grey correlation analysis, and obtaining the primary correlation and secondary correlation between the further divisions in each subsequence and the user power consumption event data;

[0056] Step S3: Based on the results of the first-level correlation and the second-level correlation, analyzing the influence of various factors on the user's electricity consumption, performing a visual analysis on the multiple one-dimensional user electricity consumption data matrices, and obtaining the user's electricity consumption behavior analysis results;

[0057] Step S4: Allocate power resources according to the analysis results of the user's power consumption behavior.

[0058] In an embodiment of the present invention, the step S1 further includes the following steps:

[0059] Step S11: The acquired user electricity consumption related data is the influencing factors of the user electricity consumption event, including: electricity consumption data, user type data, electricity consumption time data, and climate data;

[0060] The electricity consumption data includes: electric energy data, power data, frequency data, and demand factor data;

[0061] The user type data includes: residential user data, commercial user data, and industrial user data;

[0062] The time data includes: peak time data, normal time data, and valley time data;

[0063] The weather data includes: temperature data, humidity data, and wind speed data;

[0064] Step S12: extract the main components of the user electricity consumption related data through the PCA dimensionality reduction method, the main components can represent the main change trend of the data, and construct multiple one-dimensional user electricity consumption data matrices, the one-dimensional user electricity consumption data matrix X', the one-dimensional user electricity consumption data matrix X' is:

[0065]

[0066] In the matrix, m represents the number of items of user electricity consumption event data, and n represents the number of factors that affect the user's electricity consumption. m*n ;

[0067] Step S13: In the data related to the user's electricity consumption, the electricity consumption data is used as a parent sequence, and the user type, the electricity consumption time data, and the subdivided data in the climate data are used as subsequences associated with the parent sequence, wherein the parent sequence is represented by X0(t)=(x0(1), x0(2),..., x0(n),), and the subsequence is represented by X p (t) = (x p (1),x p (2),...,x p (n),).

[0068] In an embodiment of the present invention, the step S2 further includes the following steps:

[0069] Step S21: performing grey correlation analysis based on the parent sequence and the subsequence to obtain a first-level correlation degree between each subsequence and the user's power consumption event;

[0070] Step S22: calculating the association weight between the parent sequence and each of the subsequences based on the primary association degree and the one-dimensional user power consumption data matrix, and calculating the secondary association degree between each of the subsequences and the user power consumption event based on the association weight and the one-dimensional user power consumption data matrix;

[0071] Step S23: Based on the analyzed primary and secondary correlations, the grey correlations of the data are arranged in descending order. The grey correlation at the top has the highest value of the relevant data and has the greatest impact on the user's electricity consumption, which is the optimal correlation factor. Otherwise, it is the worst correlation factor.

[0072] In an embodiment of the present invention, the step S21 further includes the following steps:

[0073] Step S211: Standardization is performed based on the constructed one-dimensional user electricity consumption data matrix, using a standardization method in which one subsequence is divided by the average value of the corresponding subsequence data set to process the dimension effects of different data in a dimensionless manner:

[0074]

[0075] Where: X' p (t) represents the original data of the t-th user's electricity consumption event in the p-th subsequence, Xp (t) represents the standardized data of the t-th user's electricity consumption event in the p-th subsequence, represents the average value of the original data of all users' electricity consumption events in the pth subsequence;

[0076] Step S212: Based on step S211, the original data of the power consumption data, the power consumption time data, and the climate data are standardized. Based on qualitative data such as user type, the residential user data is coded as 01, the commercial user data is coded as 02, and the industrial user data is coded as 03. The one-dimensional user power consumption data matrix after dimensionless conversion is obtained as follows:

[0077]

[0078] Step S213: Based on the parent sequence and the child sequence in step S13, the formula for calculating the correlation coefficient between each child sequence and the parent sequence is:

[0079]

[0080] Where: t represents the data number of the t-th user electricity consumption event, p represents the p-th subsequence number, X0(t) represents the standardized value of the t-th user electricity consumption event in the parent sequence, represents the absolute value of the difference between the pth subsequence and the parent sequence, ρ represents the resolution coefficient, where ρ = 0.5, represents the minimum difference between the p-th subsequence and the parent sequence. represents the maximum difference between the p-th subsequence and the parent sequence, γ p (t) represents the correlation coefficient of the p-th subsequence with the data number of the t-th user's electricity consumption event;

[0081] Step S214: Based on the correlation coefficient between each subsequence and the parent sequence, the average value of the correlation coefficient between each subsequence and the parent sequence is calculated, and the calculation formula for the first-level correlation is obtained as follows:

[0082]

[0083] In the formula, Y(γ p (t)) represents the first-order correlation coefficient between the p-th subsequence and the parent sequence.

[0084] In an embodiment of the present invention, the step S22 further includes the following steps:

[0085] Step S221: Based on the calculation result of the first-level association degree in step S21, the association weight between the parent sequence and each of the child sequences is calculated, that is, the calculation formula of the weight is obtained as follows:

[0086]

[0087] Where W p represents the weight of the p-th subsequence;

[0088] Step S222: performing normalization processing based on the calculation result of the weight value, calculating the sum of the grey relational degrees of all subsequences to determine whether it is 1, and when the result is not 1, calculating an adjustment factor, multiplying each biased weight by the adjustment factor, otherwise, no processing is performed when the result is 1;

[0089] Step S223: Based on the association weight and the association coefficient between the corresponding subsequence and the parent sequence, the calculation formula of the secondary association degree between the subsequence and the user's power consumption event is:

[0090]

[0091] In the formula, Z(γ p (t)) represents the secondary correlation degree of the correlation coefficient between the p-th subsequence and the parent sequence.

[0092] In an embodiment of the present invention, the step S3 further includes: based on the optimal correlation factor and the worst correlation factor, the analysis results of multiple one-dimensional user electricity consumption data matrices are integrated to identify the influencing factors of the same user electricity consumption event, and a visual analysis is performed based on the calculation results. A heat map is drawn by using the depth of color to represent the level of the secondary correlation. The darker the color, the higher the secondary correlation. Conversely, the lighter the color, the lower the secondary correlation. The user electricity consumption behavior analysis results are analyzed.

[0093] In an embodiment of the present invention, the step S4 further includes the following steps:

[0094] Step S41: Based on the analysis results of the power consumption behavior of the users, decision support is provided for the system allocation of power resources. By collecting the dynamic value of the renewable energy storage and the relevant data of the user's power consumption in real time, the unreasonable allocation of power resources is reduced, and the problems caused by emergencies can be handled.

[0095] Embodiment 2: Embodiment 2 of the present invention provides a power consumption control system based on blockchain. Figure 2 A schematic diagram of the module composition of a blockchain-based power consumption control system provided in the second embodiment of the present invention, such as Figure 2 As shown, the system includes a blockchain module, a grey correlation analysis module, a power consumption analysis module, and a power resource allocation module:

[0096] The blockchain module is used to obtain the dynamic value of renewable energy storage and relevant data of user electricity consumption in real time;

[0097] The grey correlation analysis module is used to analyze the primary correlation and secondary correlation of each user power consumption factor in the four-dimensional user power consumption data matrix through grey correlation analysis;

[0098] The power consumption analysis module is used to further visualize the analysis results in the gray analysis module to obtain the analysis results of the user's power consumption behavior;

[0099] The power resource allocation module is used to predict the allocation of power resources and monitor power resources in real time.

[0100] In some embodiments of the present invention, the grey correlation analysis module includes a matrix processing module, a primary correlation module, and a secondary correlation module:

[0101] The matrix processing module is used to reduce the dimension of the four-dimensional user electricity consumption data matrix to obtain a plurality of one-dimensional user electricity consumption data matrices, and select a parent sequence and a child sequence for grey relational analysis;

[0102] The primary correlation module is used to perform grey correlation analysis based on the parent sequence and the subsequence to obtain the primary correlation between each subsequence and the user's power consumption event;

[0103] The secondary correlation module is used to calculate the correlation weight between the parent sequence and each of the subsequences based on the primary correlation and the one-dimensional user electricity consumption data matrix, and calculate the secondary correlation between each of the subsequences and the user electricity consumption event based on the correlation weight and the one-dimensional user electricity consumption data matrix.

[0104] In some embodiments of the present invention, the power consumption analysis module includes a visualization analysis module and a user power consumption behavior analysis module:

[0105] The visualization analysis module is used to integrate the secondary correlations of the multiple one-dimensional user electricity consumption data matrices, and perform visualization analysis to obtain a heat map of the secondary correlations of different user electricity consumption factors to the user electricity consumption events, providing an intuitive and efficient tool for in-depth understanding of user electricity consumption habits;

[0106] The user electricity consumption behavior analysis module is used to capture the behavior characteristics of factors affecting user electricity consumption by visually analyzing the heat map.

[0107] In some embodiments of the present invention, the power resource allocation module includes a power resource prediction module and a power resource real-time monitoring module:

[0108] The power resource prediction module is used to predict the power demand of users according to the behavioral characteristics of their power consumption, reasonably allocate power resources, and optimize the power energy management strategy;

[0109] The power resource real-time monitoring module is used to monitor the user's power consumption and the dynamic changes of energy storage in real time, providing accurate and timely decision support to ensure that the allocation of power resources is both effective and timely to meet user needs.

[0110] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0111] Finally, it should be noted that the above description is only 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 aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A blockchain-based power control system, and a blockchain-based power control system power resource allocation method, characterized in that: The operating steps of the method include: Step S1: Obtain the dynamic value of renewable energy storage and relevant data of user electricity consumption through the blockchain module, and use the NumPy library to construct a four-dimensional user electricity consumption data matrix based on the relevant data of user electricity consumption; Step S2: Performing dimensionality reduction processing based on the four-dimensional user power consumption data matrix and visualizing each dimension, respectively performing grey correlation analysis, and obtaining the primary correlation and secondary correlation between the further divisions in each subsequence and the user power consumption event data; Step S3: Based on the results of the first-level correlation and the second-level correlation, analyzing the influence of various factors on the user's electricity consumption, performing a visual analysis on the multiple one-dimensional user electricity consumption data matrices, and obtaining the user's electricity consumption behavior analysis results; Step S4: Allocate power resources according to the analysis results of the user's power consumption behavior.

2. According to claim 1, a blockchain-based electricity control system is characterized in that: The step S1 further comprises the following steps: Step S11: The acquired user electricity consumption related data is the influencing factors of the user electricity consumption event, including: electricity consumption data, user type data, electricity consumption time data, and climate data; The electricity consumption data includes: electric energy data, power data, frequency data, and demand factor data; The user type data includes: residential user data, commercial user data, and industrial user data; The time data includes: peak time data, normal time data, and valley time data; The weather data includes: temperature data, humidity data, and wind speed data; Step S12: extract the main components of the user electricity consumption related data through the PCA dimensionality reduction method, the main components can represent the main change trend of the data, and construct multiple one-dimensional user electricity consumption data matrices, the one-dimensional user electricity consumption data matrix X', the one-dimensional user electricity consumption data matrix X' is: In the matrix, m represents the number of items of user power consumption event data, and n represents the number of factors that affect the user's power consumption. m*n ; Step S13: In the data related to the user's electricity consumption, the electricity consumption data is used as a parent sequence, and the user type, the electricity consumption time data, and the subdivided data in the climate data are used as subsequences associated with the parent sequence, wherein the parent sequence is represented by X0(t)=(x0(1), x0(2),..., x0(n),), and the subsequence is represented by X p (t) = (x p (1),x p (2),...,x p (n),).

3. According to claim 2, a blockchain-based electricity control system is characterized in that: The step S2 further comprises the following steps: Step S21: performing grey correlation analysis based on the parent sequence and the subsequence to obtain a first-level correlation degree between each subsequence and the user's power consumption event; Step S22: calculating the association weight between the parent sequence and each of the subsequences based on the primary association degree and the one-dimensional user power consumption data matrix, and calculating the secondary association degree between each of the subsequences and the user power consumption event based on the association weight and the one-dimensional user power consumption data matrix; Step S23: Based on the analyzed primary and secondary correlations, the grey correlations of the data are arranged in descending order. The grey correlation at the top has the highest value of the relevant data and has the greatest impact on the user's electricity consumption, which is the optimal correlation factor. Otherwise, it is the worst correlation factor.

4. According to claim 3, a blockchain-based electricity control system is characterized in that: The step S2 further comprises the following steps: The step S21 further comprises the following steps: Step S211: Standardization is performed based on the constructed one-dimensional user electricity consumption data matrix, using a standardization method in which one subsequence is divided by the average value of the corresponding subsequence data set to process the dimension effects of different data in a dimensionless manner: Where: X' p (t) represents the original data of the t-th user's electricity consumption event in the p-th subsequence, X p (t) represents the standardized data of the t-th user's electricity consumption event in the p-th subsequence, represents the average value of the original data of all users' electricity consumption events in the pth subsequence; Step S212: Based on step S211, the original data of the power consumption data, the power consumption time data, and the climate data are standardized. Based on qualitative data such as user type, the residential user data is coded as 01, the commercial user data is coded as 02, and the industrial user data is coded as 03. The one-dimensional user power consumption data matrix after dimensionless conversion is obtained as follows: Step S213: Based on the parent sequence and the child sequence in step S13, the formula for calculating the correlation coefficient between each child sequence and the parent sequence is: Where: t represents the data number of the t-th user electricity consumption event, p represents the p-th subsequence number, X0(t) represents the standardized value of the t-th user electricity consumption event in the parent sequence, represents the absolute value of the difference between the pth subsequence and the parent sequence, ρ represents the resolution coefficient, where ρ = 0.5, represents the minimum difference between the p-th subsequence and the parent sequence. represents the maximum difference between the p-th subsequence and the parent sequence, γ p (t) represents the correlation coefficient of the p-th subsequence with the data number of the t-th user's electricity consumption event; Step S214: Based on the correlation coefficient between each subsequence and the parent sequence, the average value of the correlation coefficient between each subsequence and the parent sequence is calculated, and the calculation formula for the first-level correlation is obtained as follows: In the formula, Y(γ p (t)) represents the first-order correlation coefficient between the p-th subsequence and the parent sequence.

5. According to claim 4, a blockchain-based electricity control system is characterized in that: The step S22 further comprises the following steps: Step S221: Based on the calculation result of the first-level association degree in step S21, the association weight between the parent sequence and each of the child sequences is calculated, that is, the calculation formula of the weight is obtained as follows: Where W p represents the weight of the p-th subsequence; Step S222: performing normalization processing based on the calculation result of the weight value, calculating the sum of the grey relational degrees of all subsequences to determine whether it is 1, and when the result is not 1, calculating an adjustment factor, multiplying each biased weight by the adjustment factor, otherwise, no processing is performed when the result is 1; Step S223: Based on the association weight and the association coefficient between the corresponding subsequence and the parent sequence, the calculation formula of the secondary association degree between the subsequence and the user's power consumption event is: In the formula, Z(γ p (t)) represents the secondary correlation degree of the correlation coefficient between the p-th subsequence and the parent sequence.

6. According to claim 5, a blockchain-based electricity control system is characterized in that: The step S3 further includes: based on the optimal correlation factor and the worst correlation factor, integrating the analysis results of multiple one-dimensional user electricity consumption data matrices, integrating the influencing factors of the same user electricity consumption events, performing visual analysis according to the calculation results, drawing a heat map by using the depth of color to represent the level of the secondary correlation, the darker the color, the higher the secondary correlation, and conversely, the lighter the color, the lower the secondary correlation, and analyzing the user electricity consumption behavior analysis results.

7. According to claim 6, a blockchain-based electricity consumption control system comprises a blockchain module, a grey correlation analysis module, an electricity consumption analysis module, and an electric power resource allocation module: the blockchain module is used to obtain the dynamic value of renewable energy storage and relevant data on user electricity consumption in real time; the grey correlation analysis module is used to analyze the primary and secondary correlations of each user's electricity consumption factors in the four-dimensional user electricity consumption data matrix through grey correlation analysis; the electricity consumption analysis module is used to further visualize the analysis results in the grey analysis module to obtain the analysis results of the user's electricity consumption behavior; the electric power resource allocation module is used to predict the allocation of electric power resources and monitor electric power resources in real time.

8. According to claim 7, a blockchain-based electricity control system is characterized in that: The grey correlation analysis module includes a matrix processing module, a primary correlation module, and a secondary correlation module: the matrix processing module is used to reduce the dimension of the four-dimensional user electricity consumption data matrix to obtain a plurality of one-dimensional user electricity consumption data matrices, and select a parent sequence and a child sequence for grey correlation analysis; The primary correlation module is used to perform grey correlation analysis based on the parent sequence and the subsequence to obtain the primary correlation between each subsequence and the user's power consumption event; The secondary correlation module is used to calculate the correlation weight between the parent sequence and each of the subsequences based on the primary correlation and the one-dimensional user electricity consumption data matrix, and calculate the secondary correlation between each of the subsequences and the user electricity consumption event based on the correlation weight and the one-dimensional user electricity consumption data matrix.

9. The blockchain-based electricity control system according to claim 8, characterized in that: The electricity consumption analysis module includes a visualization analysis module and a user electricity consumption behavior analysis module: the visualization analysis module is used to integrate the secondary correlations of the multiple one-dimensional user electricity consumption data matrices, and perform visualization analysis to obtain a heat map of the secondary correlations of different user electricity consumption factors to the user electricity consumption events, providing an intuitive and efficient tool for in-depth understanding of user electricity consumption habits; the user electricity consumption behavior analysis module is used to capture the behavioral characteristics of the factors affecting user electricity consumption in the heat map of the visualization analysis.

10. The blockchain-based electricity control system according to claim 9, characterized in that: The power resource allocation module includes a power resource prediction module and a power resource real-time monitoring module: the power resource prediction module is used to predict the power demand of users according to the behavioral characteristics of users' power consumption, reasonably allocate power resources, and optimize the power energy management strategy; The power resource real-time monitoring module is used to monitor the user's power consumption and the dynamic changes of energy storage in real time, providing accurate and timely decision support to ensure that the allocation of power resources is both effective and timely to meet user needs.

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