Electric energy data storage method of optical storage and charging system

Abstract

The invention relates to the technical field of optical storage and charging data storage, in particular to an electric energy data storage method for an optical storage and charging system, which comprises the following steps of: acquiring electric energy data of the optical storage and charging system according to a time sequence, setting a mean value window, acquiring a photovoltaic power generation power difference factor according to a photovoltaic power generation power data difference condition in each window, obtaining an optimal window length according to a photovoltaic power generation power difference factor, obtaining a data weight adjustment parameter according to a difference degree between data in the optimal window and a mean value, obtaining a data weight influence factor according to a data difference with adjacent time sequences in the optimal window, adjusting the weight of data in a reference block according to the data weight, calculating the reference block according to the weight, and obtaining a reference block; and compressing the data according to the reference block. According to the invention, the differential coding compression effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical storage and charging data storage, and particularly relates to a method for storing electrical energy data of an optical storage and charging system. Background Art

[0002] The optical storage and charging system generates a large amount of data based on time series and needs to transmit the data to an energy management system or other related systems for analysis and processing. By compressing the data, the bandwidth required for data transmission can be reduced, and the efficiency and speed of data transmission can be improved.

[0003] The traditional method for compressing electrical energy data of optical storage and charging is differential coding compression. The basic idea of differential coding compression is to perform a differential operation on each element in the data sequence with the previous element, and then encode the differential result. However, the electrical energy data of optical storage and charging has large fluctuations, and there may be a large difference between adjacent elements. Then the differential result is also large. When the differential result is greater than the element itself, the length of the compressed data will be greater than the length of the data before compression, resulting in an unsatisfactory compression effect. Summary of the Invention

[0004] The present invention provides a method for storing electrical energy data of an optical storage and charging system to solve the existing problems.

[0005] The method for storing electrical energy data of an optical storage and charging system of the present invention adopts the following technical solutions:

[0006] An embodiment of the present invention provides a method for storing electrical energy data of an optical storage and charging system, and the method includes the following steps:

[0007] Collect the electrical energy data of the optical storage and charging system in time series;

[0008] Set a mean window; obtain a photovoltaic power generation power difference factor according to the difference situation of photovoltaic power generation power data in each window; obtain an optimal window length according to the photovoltaic power generation power difference factor;

[0009] Obtain a data weight adjustment parameter according to the difference degree between the data in the window corresponding to the optimal window length and the mean value; obtain a data weight influence factor according to the difference between adjacent data in time series in the window corresponding to the optimal window length; adjust the weight of the data in the reference block according to the data weight;

[0010] Calculate a reference block according to the weight; compress the data according to the reference block.

[0011] Further, the step of obtaining a photovoltaic power generation power difference factor according to the difference situation of photovoltaic power generation power data in each window includes the following specific steps:

[0012]

[0013] Where Dif(D) represents the photovoltaic power generation power difference factor corresponding to a window length of D, D represents the window length, 1440 represents 1440 minutes per day, T represents the time interval for collecting data as T minutes, i represents the window position sequence subscript, j represents the photovoltaic power generation power data subscript within the window, and x i，j represents the j-th photovoltaic power generation power data within the window when the window is at the i-th position, represents the mean value of all photovoltaic power generation power data within the window when the window is at the i-th position.

[0014] Furthermore, obtaining the data weight adjustment parameter according to the difference degree between the data within the window corresponding to the optimal window length and the mean value includes the following specific steps:

[0015]

[0016] Where P(x I，J ) is the data weight adjustment parameter of the J-th data in the I-th position window under the optimal window length, x I，J is the J-th data in the I-th position window, is the mean value of all data in the I-th position window, x I，m is the weight of the m-th data in the I-th position window, D opt is the optimal window length.

[0017] Furthermore, obtaining the data weight influence factor according to the difference between adjacent data in time series within the window corresponding to the optimal window length includes the following specific steps:

[0018]

[0019] Where Fac(x I，J ) is the data weight influence factor of the J-th data in the I-th window under the optimal window length, x I，J is the J-th data in the I-th window under the optimal window length, x I，m is the data weight influence factor of the m-th data in the I-th window under the optimal window length, D opt is the optimal window length.

[0020] Furthermore, adjusting the weight of the data within the benchmark block according to the data weight includes the following specific steps:

[0021] Wei(x I，J ) = [P(x I，J ) + 0.01] × [Fac(x I，J ) + 0.01]

[0022] Where Wei(x I，J) is the weight of the J-th data in the I-th window under the optimal window length, P(x I，J ) is the weight adjustment parameter of the J-th data in the I-th position window under the optimal window length, Fac(x I，J ) is the weight influence factor of the J-th data in the I-th window under the optimal window length.

[0023] Furthermore, the steps for obtaining the optimal window length according to the photovoltaic power difference factor are as follows:

[0024] Iteratively calculate the photovoltaic power difference factor with the window length ranging from the minimum window length to the maximum window length, and find the window length corresponding to the minimum photovoltaic power difference factor as the optimal window length, denoted as D opt .

[0025] Furthermore, the steps for calculating the reference block according to the weight are as follows:

[0026] Perform weighted averaging on the data in the window as the reference block for the next data adjacent to the window in time series. For the data in the first window, no differential processing is performed, and the original data is retained during the compression process.

[0027] Furthermore, the steps for compressing the data according to the reference block are as follows:

[0028] Perform differential operation on the next data adjacent to the calculated reference block in time series, and store the difference value.

[0029] Furthermore, the steps for setting the mean window reduction are as follows:

[0030] Set a window length D. The first data at the initial position of the window is the first data of the collected data sequence. The window slides one grid each time until the last data of the window is the penultimate data of the collected data sequence.

[0031] Furthermore, the minimum window length is 10 and the maximum window length is 100.

[0032] The beneficial effect of the technical solution of the present invention is that when the existing differential coding compresses the electrical energy data of the photovoltaic energy storage and charging system, the previous data adjacent in time series is used as the reference block to perform differential operation on the current data. Since the electrical energy data fluctuates greatly, the differential result may be greater than the original data, thus affecting the compression effect. The present invention sets a sliding window and sets the reference block according to the data distribution in the window to adapt to the change trend of the data, thereby reducing the differential result between the reference block and the data to be compressed and improving the compression effect. Description of the Drawings

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0034] Figure 1 It is a flowchart of the steps of a method for storing electrical energy data of a photovoltaic energy storage charging system of the present invention. Detailed implementation manners

[0035] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a method for storing electrical energy data of a photovoltaic energy storage charging system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0037] The following specifically describes the specific solution of a method for storing electrical energy data of a photovoltaic energy storage charging system provided by the present invention with reference to the drawings.

[0038] Please refer to Figure 1 , which shows a flowchart of the steps of a method for storing electrical energy data of a photovoltaic energy storage charging system provided by an embodiment of the present invention. The method includes the following steps:

[0039] Step S001: Collect the electrical energy data of the photovoltaic energy storage charging system in time series.

[0040] In a photovoltaic energy storage charging system, the storage of photovoltaic energy storage electrical energy data can record the operating status and performance parameters of the charging equipment. When a device fails, the cause of the failure can be diagnosed by analyzing the stored data, providing accurate repair and maintenance guidance. At the same time, by regularly analyzing the energy data, the lifespan and maintenance cycle of the device can be predicted, and maintenance can be carried out in advance to reduce device failures and downtime. Photovoltaic energy storage electrical energy data is usually recorded in the form of time series. Without compression, the data volume will be very large, occupying a large amount of storage space. Compression can effectively reduce the storage requirements of the data and save storage space.

[0041] It should be noted that the photovoltaic energy storage charging data includes photovoltaic power generation data, charging power data, load power data, etc. Since the amount of photovoltaic power generation data is large, if it is not compressed, it will cause delays and increased bandwidth occupancy during data transmission, reducing the data transmission efficiency.

[0042] Furthermore, it should be noted that the photovoltaic power generation data of the photovoltaic energy storage charging system is collected based on time series. Since the photovoltaic power generation of the photovoltaic energy storage charging system is affected by light, in order to more accurately analyze the light change situation, the data collection time should be relatively long. In this embodiment, the data collection time t = 30 days is taken as an example for description, and this embodiment does not make specific limitations, and the specific data collection time depends on the specific implementation situation.

[0043] Preset a data collection time interval T. In this embodiment, T = 1 minute is taken as an example for description, and this embodiment does not make specific limitations, and T depends on the specific implementation situation.

[0044] Specifically, the photovoltaic power generation data of the photovoltaic energy storage charging system is collected and recorded by the photovoltaic power generation power sensor of the photovoltaic energy storage charging system according to the preset collection time t and collection time interval T.

[0045] Step S002: Set a mean window; obtain a photovoltaic power generation difference factor according to the difference situation of the photovoltaic power generation data in each window; obtain an optimal window length according to the photovoltaic power generation difference factor; obtain a data weight adjustment parameter according to the difference degree between the data in the optimal window and the mean value; obtain a data weight influence factor according to the difference between adjacent data in time series in the optimal window; adjust the weights of the data in the benchmark block according to the data weight adjustment.

[0046] It should be noted that the photovoltaic power generation is affected by complex environments, resulting in large fluctuations in its power generation data. The traditional differential coding is to perform a differential operation on each data in the photovoltaic power generation data sequence with the previous adjacent data in time series, and then encode the differential result. For data with large fluctuations, the difference value between adjacent data may be greater than the original data, resulting in poor compression effect of the differential coding. Therefore, in this embodiment, a sliding window is set, and the data is compressed with the weighted mean value of the data in the window as the benchmark block, which smooths the noise data to a certain extent, makes the benchmark block closer to the change trend of the data, and makes the compression effect better.

[0047] 1. Set a mean window; obtain a photovoltaic power generation difference factor according to the difference situation of the photovoltaic power generation data in each window; obtain an optimal window length according to the photovoltaic power generation difference factor.

[0048] It should be noted that the power generation of photovoltaic power generation is related to the light intensity. The trend of the light intensity changes over time, and the total impact of the light intensity on the power generation power fluctuation of photovoltaic power generation is relatively large, while the impact of other interference factors is relatively small. Therefore, in this step, a mean window is set, and the mean value of all photovoltaic power generation power data within the mean window is used as the reference block for the next data to be compressed adjacent to the window time sequence to perform difference operation on the data to be compressed. At this time, the obtained difference value may be smaller because the change of the light intensity has a certain trend. Therefore, the power generation power data of photovoltaic power generation fluctuates approximately close to the change of the light intensity. Also, because the mean value of the photovoltaic power generation power data for a period of time can smooth the fluctuation of the photovoltaic power generation power affected by other complex factors to a certain extent, using the stationarity of the time series, the random fluctuation is eliminated by averaging the data within a certain period of time, so as to obtain the trend information. Therefore, the degree of closeness between the mean value within the window and the next data to be compressed adjacent to the window time sequence may be higher. Using the mean value of all photovoltaic power generation power data within the mean window as the reference block for the next data to be compressed adjacent to the window time sequence to perform difference operation on the data to be compressed, the obtained difference value may be smaller at this time, and the compression effect is better.

[0049] Specifically, a window length D is set. When the window is at the initial position, the first data is the first data of the collected data sequence. The window slides one grid each time until the last data of the window is the penultimate data of the collected data sequence.

[0050] It should be noted that the window length determines the degree of closeness between the mean value of the photovoltaic power generation power data within the window and the next data to be compressed adjacent to the window time sequence. The larger the window, the more the data within the window is over-smoothed, resulting in the mean value of the data within the window over-responding to the change of the light intensity, which may lead to a lower degree of closeness between the mean value of the data within the window and the data to be compressed, a larger difference result, and a poorer compression effect. While the smaller the window, the lower the degree of data smoothing within the window, and the mean value of the data within the window is more affected by other influencing factors other than the light intensity, resulting in the mean value not conforming to the overall change of the light intensity and affecting the compression ratio. Therefore, in this step, by comparing the difference situation of the photovoltaic power generation power, a suitable window length is selected as the optimal window length to ensure that the degree of influence of all photovoltaic power generation power data within the window by the light intensity is closer. At this time, when the mean value of the data within the window is used as the reference block, the difference value between the photovoltaic power generation power data within the window and the mean value of the photovoltaic power generation power data within the window is smaller. At this time, the obtained mean value conforms more to the change trend of the power generation power data, and the possibility of a higher degree of closeness to the data to be compressed is greater. The differential coding length obtained according to the photovoltaic power generation power data within the window and the photovoltaic power generation power data within the window may be shorter.

[0051] Specifically, when the window length is D, the photovoltaic power generation power difference factor corresponding to the window length can be obtained at the initial position of the window and all positions after each sliding under the current window length. The mean value of the dispersion degree of the photovoltaic power generation power data within the window at all positions is used to obtain the photovoltaic power generation power difference factor. The specific formula is as follows:

[0052]

[0053] In the formula, Dif(D) represents the photovoltaic power generation power difference factor corresponding to the window length of D, D represents the window length, 1440 represents 1440 minutes per day, T represents the time interval for collecting data as T minutes, i represents the window position sequence subscript, j represents the subscript of the photovoltaic power generation power data within the window, and x i，j represents the j-th photovoltaic power generation power data within the window when the window is at the i-th position, represents the mean value of all photovoltaic power generation power data within the window when the window is at the i-th position.

[0054] It should be noted that the minimum window length is 10 and the maximum window length is 100. This embodiment does not make specific limitations, and the minimum and maximum window lengths are determined according to specific implementation situations. For the first D data in the total collected data sequence, no difference processing is performed.

[0055] Furthermore, it should be noted that when the photovoltaic power generation power difference factor corresponding to a certain window length is the smallest, at this time, the mean value of the standard deviations of the photovoltaic power generation power data in all positions during the sliding of all windows is the smallest. Then, when using the mean value of the photovoltaic power generation power data within the window as the differential coding reference block for compression, the sum of the difference values of all photovoltaic power generation power data is the smallest, and the compression effect at this time is also the best, that is, this window length is the optimal window length.

[0056] Specifically, the photovoltaic power generation power difference factor is iteratively calculated from the minimum window length to the maximum window length, and the window length corresponding to the smallest photovoltaic power generation power difference factor is found as the optimal window length, denoted as D opt .

[0057] 2. Obtain the data weight adjustment parameter according to the degree of difference between the data within the window corresponding to the optimal window length and the mean value; obtain the data weight influence factor according to the difference between adjacent data in time series within the window corresponding to the optimal window length; adjust the weights of the data within the data weight adjustment reference block.

[0058] It should be noted that using the above optimal window length can ensure that the mean value of the data within the window better conforms to the data change trend. By this method, the influence of the light intensity changing with time series on the photovoltaic power generation is reduced. However, the photovoltaic power generation data is still affected by complex environmental factors such as weather and temperature. As a result, the data within a certain window fluctuates greatly, and the mean value within the window cannot well respond to the data changes, leading to a large difference between the reference block and the data to be compressed, a large differential result, and affecting the compression effect. Therefore, in this step, the data weight adjustment parameter is obtained according to the difference degree between the data within the optimal window and the mean value; the data weight influence factor is obtained according to the difference distribution of adjacent data in time series within the optimal window; the data weight is obtained according to the weight adjustment parameter and the weight influence factor; and the weight of the data within the reference block is adjusted according to the data weight to make the reference block closer to the data to be compressed and improve the compression ratio.

[0059] It should be further noted that when the difference between the data within the window and the mean value is large, it indicates that the data is greatly affected by other factors besides light, and a larger weight should be assigned to retain the data change trend, reduce the difference between the reference block and the data to be compressed, and improve the compression efficiency.

[0060] Specifically, according to the proportion of the difference between each data within a certain window and the mean value of all data within the window, the data weight adjustment parameter for each data in the window is obtained. The specific calculation formula is as follows:

[0061]

[0062] In the formula, P(x I，J ) is the data weight adjustment parameter of the Jth data in the I-th position window under the optimal window length, x I，J is the Jth data in the I-th position window, is the mean value of all data in the I-th position window, x I，m is the weight of the mth data in the I-th position window, D opt is the optimal window length.

[0063] It should be noted that the larger the data weight adjustment parameter of a certain data in the window, the greater the influence of the data on the data trend, and a larger weight should be given to make the mean value calculated within the window closer to the data trend, which may make the difference between the reference block and the data to be compressed smaller and the data compression effect better.

[0064] Further, it should be noted that there are some data that do not conform to the trend of data change, but the difference between them and the data mean is large. At this time, the probability that the data may be noise data is higher. Therefore, in this step, the data weight influence factor is obtained according to the difference distribution of adjacent data in time series in the window. The higher the degree of difference between adjacent data, the more it does not conform to the trend of data change, and the greater the probability that it belongs to noise data. A lower weight should be assigned to avoid the influence of noise data in the window on the data change trend, make the mean value obtained by the window closer to the data change trend, make the reference block closer to the data to be compressed, and thus improve the compression effect.

[0065] Specifically, the data weight influence factor is obtained according to the proportion of the difference between a certain data in the optimal window and its adjacent data in time series in the difference between all data in the window and their corresponding adjacent data. The specific calculation formula is as follows:

[0066]

[0067] In the formula, Fac(x I，J ) is the weight influence factor of the Jth data in the Ith window under the optimal window length, x I，J is the Jth data in the Ith window under the optimal window length, x I，m is the weight influence factor of the mth data in the Ith window under the optimal window length, D opt is the optimal window length.

[0068] It should be noted that the greater the difference between a certain data and its adjacent data in time series, the greater the probability that the data does not conform to the data change trend. Therefore, a lower weight should be assigned to avoid the influence of noise data in the window on the data change trend, make the mean value obtained by the window closer to the data change trend, make the reference block closer to the data to be compressed, and thus improve the compression effect.

[0069] Further, it should be noted that the weight adjustment parameter in the window and the data weight influence factor restrict each other, which can make the obtained data weight more accurate, the mean value calculated according to the weight adjustment closer to the data to be compressed, and the compression effect better. Therefore, in this step, the data weight is determined according to the adjustment parameter of the data in the window and the data weight influence factor. The specific calculation formula is as follows:

[0070] Wei(x I，J ) = [P(x I，J ) + 0.01] × [Fac(x I，J ) + 0.01]

[0071] In the formula, Wei(x I，J ) is the weight of the Jth data in the Ith window under the optimal window length, P(x I，J) is the weight adjustment parameter for the Jth data in the Ith position window under the optimal window length, Fac(x I，J ) is the weight influence factor for the Jth data in the Ith window under the optimal window length.

[0072] It should be noted that 0.01 is to avoid the situation where the weight adjustment parameter of the data or the weight influence factor of the data is 0, resulting in a data weight of 0 and a calculated weight of 0.

[0073] Step S003: Calculate the reference block according to the weight; compress the data according to the reference block.

[0074] It should be noted that the reference block calculated according to the window mean may be severely affected by the noise data in the window, resulting in the reference block deviating from the data trend, or it may not be able to respond to the influence of different environments on the photovoltaic power generation in a timely manner, leading to the reference block deviating from the data trend and making the compression effect unsatisfactory. Therefore, in this step, the weights of each data in the obtained optimal window are used to calculate the weighted average of the data in the window to obtain the reference block, so as to compress the data.

[0075] Specifically, the weighted average of the data in the window is calculated as the reference block for the next data adjacent to the window in time series. For the data in the first window, no differential processing is performed, and the original data is retained during the compression process.

[0076] It should be noted that calculating the weighted average of the data in the window can make the reference block more conform to the change trend of the data, make the reference block closer to the data to be compressed, obtain a smaller difference value, and make the compression effect better. Since there is no corresponding reference block for the data in the first window, the data in the first window is not processed.

[0077] Specifically, a differential operation is performed on the next data adjacent to the calculated reference block in time series, and the difference value is stored.

[0078] In summary, by collecting the electrical energy data of the photovoltaic-storage-charging system in time series, setting the mean window, obtaining the photovoltaic power generation difference factor according to the difference of the photovoltaic power generation data in each window, obtaining the window corresponding to the optimal window length according to the photovoltaic power generation difference factor, obtaining the data weight adjustment parameter according to the difference degree between the data in the optimal window length and the mean value, obtaining the data weight influence factor according to the difference between the data adjacent in time series in the window corresponding to the optimal window length, adjusting the weight of the data in the reference block according to the data weight, calculating the reference block according to the data weight in the window corresponding to the optimal window length, performing a differential operation on the next data adjacent to the calculated reference block in time series, and storing the difference value, the reference block can be made more conform to the change trend of the data, reduce the difference value, and make the compression effect better.

[0079] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for storing electrical energy data of a photovoltaic energy storage charging system, characterized in that, The method includes the following steps: Collect the electrical energy data of the photovoltaic-storage-charging system in time series; Set a mean window; obtain a photovoltaic power generation power difference factor according to the difference situation of the photovoltaic power generation power data in each window; obtain the optimal window length according to the photovoltaic power generation power difference factor; Obtain a data weight adjustment parameter according to the difference degree between the data in the window corresponding to the optimal window length and the mean value; obtain a data weight influence factor according to the difference between adjacent data in time series in the window corresponding to the optimal window length; adjust the weight of the data in the reference block according to the data weight; Calculate the reference block according to the weight; compress the data according to the reference block.

2. The method for storing electrical energy data of a photovoltaic energy storage and charging system according to claim 1, characterized in that, The specific steps for obtaining the photovoltaic power generation power difference factor according to the difference situation of the photovoltaic power generation power data in each window are as follows: Wherein, Dif(D) represents the photovoltaic power generation power difference factor corresponding to a window length of D, D represents the window length, 1440 represents 1440 minutes per day, T represents the time interval for collecting data is T minutes, i represents the window position sequence subscript, j represents the photovoltaic power generation power data subscript within the window, and x i,j represents the j-th photovoltaic power generation power data within the window when the window is in the i-th position, represents the mean value of all photovoltaic power generation power data within the window when the window is in the i-th position.

3. The method for storing electrical energy data of a photovoltaic energy storage charging system according to claim 1, characterized in that, The specific steps for obtaining the data weight adjustment parameter according to the difference degree between the data in the window corresponding to the optimal window length and the mean value are as follows: Wherein, P(x I,J ) is the weight adjustment parameter of the Jth data in the Ith position window under the optimal window length, x I,J is the Jth data in the Ith position window, is the average value of all data in the Ith position window, x I,m is the weight of the mth data in the Ith position window, D opt is the optimal window length.

4. The method for storing electrical energy data of a photovoltaic energy storage and charging system according to claim 1, wherein, The specific steps for obtaining the data weight influence factor according to the difference between adjacent data in time series in the window corresponding to the optimal window length are as follows: In the formula, Fac(x I,J ) is the weight influence factor of the Jth data in the Ith window under the optimal window length, x I,J is the Jth data in the Ith window under the optimal window length, x I,m is the weight influence factor of the mth data in the Ith window under the optimal window length, D opt is the optimal window length.

5. The method for storing electrical energy data of a photovoltaic energy storage charging system according to claim 1, characterized in that The specific steps for adjusting the weight of the data in the reference block according to the data weight are as follows: Wei(x I,J ) = [P(x I,J ) + 0.01] × [Fac(x I,J ) + 0.01] where Wei(x I,J ) is the weight of the Jth data in the Ith window under the optimal window length, P(x I,J ) is the weight adjustment parameter of the Jth data in the Ith position window under the optimal window length, and Fac(x I,J ) is the weight influence factor of the Jth data in the Ith window under the optimal window length.

6. The method for storing electrical energy data of a photovoltaic energy storage and charging system according to claim 1, wherein, The specific steps for obtaining the optimal window length according to the photovoltaic power generation power difference factor are as follows: Iteratively calculate the photovoltaic power generation power difference factor with the window length ranging from the minimum window length to the maximum window length, and find the window length corresponding to the minimum photovoltaic power generation power difference factor as the optimal window length, denoted as D opt .

7. The method for storing electrical energy data of a photovoltaic-storage-charging system according to claim 1, characterized in that, The specific steps for calculating the reference block according to the weight are as follows: Perform weighted averaging on the data in the window as the reference block for the next data adjacent to the window in time series. For the data in the first window, no differential processing is performed, and the original data is retained during the compression process.

8. The method for storing electrical energy data of a photovoltaic energy storage and charging system according to claim 1, characterized in that The specific steps for compressing the data according to the reference block are as follows: Perform a differential operation on the next data adjacent to the calculated reference block in time series, and store the difference value.

9. The method for storing electrical energy data of a photovoltaic energy storage charging system according to claim 1, characterized in that, The specific steps for setting the mean window reduction are as follows: Set a window length D. When the window is at the initial position, the first data is the first data of the collected data sequence. The window slides one grid each time until the last data of the window is the penultimate data of the collected data sequence.

10. The method for storing electrical energy data of a photovoltaic energy storage and charging system according to claim 2, characterized in that, The minimum window length is 10, and the maximum window length is 100.

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