Smart Storage Method for Electricity Meter Data Based on Cloud Platform

Abstract

This invention relates to the field of data analysis technology, specifically to a smart storage method for electricity meter data based on a cloud platform. The method involves: obtaining a suspected anomaly level based on the data distribution characteristics of electricity consumption time-series data; obtaining local volatility based on the variation and intensity characteristics of data fluctuations in the electricity consumption time-series data; obtaining the degree of variation difference based on the data differences between the electricity consumption time-series data and historical periods of the same cycle; obtaining anomaly confidence based on local volatility and variation difference; obtaining a proportional adjustment coefficient based on the anomaly confidence and suspected anomaly level; and adjusting the compression ratio based on the proportional adjustment coefficient to obtain an adaptive compression ratio. This invention compresses and stores electricity consumption time-series data using an adaptive compression ratio, thereby simultaneously meeting the requirements of storage efficiency and data detail preservation.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and specifically to a smart storage method for electricity meter data based on a cloud platform. Background Technology

[0002] Electricity meters are fundamental metering devices in the power industry, used to measure and record electricity consumption. With advancements in intelligence, smart meters have achieved remote data acquisition, transmission, and intelligent analysis, improving the efficiency of power data management and power system dispatch. However, due to the continuous increase in electricity data, efficiently storing massive amounts of electricity consumption data has become a pressing issue. After the electricity meter completes the collection of daily electricity consumption data, traditional algorithms use a fixed compression ratio to compress the data. While a higher compression ratio can significantly reduce the storage burden, it leads to the loss of details in data reflecting various abnormal electricity consumption behaviors, affecting the accuracy of electricity consumption analysis. A lower compression ratio, while preserving data details, results in large storage space consumption and high data redundancy. Therefore, a fixed compression ratio for electricity data cannot simultaneously meet the requirements of storage efficiency and data detail preservation. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide a smart storage method for electricity meter data based on a cloud platform. The specific technical solution adopted is as follows:

[0004] Obtain the time series of electricity consumption from the electricity meter;

[0005] The degree of suspected anomaly is obtained based on the data distribution characteristics in the electricity consumption time series; the degree of local volatility is obtained based on the change and intensity characteristics of the data fluctuations in the electricity consumption time series; and the degree of change difference is obtained based on the data difference characteristics between the electricity consumption time series and historical periods of the same period.

[0006] An anomaly confidence level is obtained based on the local volatility and the degree of change difference; a proportional adjustment coefficient is obtained based on the anomaly confidence level and the degree of suspected anomaly; the compression ratio is adjusted based on the proportional adjustment coefficient to obtain an adaptive compression ratio;

[0007] The power consumption time series is compressed and stored according to the adaptive compression ratio.

[0008] Furthermore, the step of obtaining the degree of suspected anomaly based on the data distribution characteristics in the electricity consumption time series includes:

[0009] Calculate the product of the average electricity consumption of the electricity consumption time period and a preset constant to obtain the boundary value; take the number of electricity consumption values ​​in the electricity consumption time period that exceed the boundary value as the target value; calculate the sum of the differences between all target values ​​and the boundary value to obtain the degree of deviation; calculate the product of the number of values ​​with a constant value of 0 in the electricity consumption time period and the degree of deviation and normalize it to obtain the degree of suspected anomaly.

[0010] Furthermore, the step of obtaining the local volatility based on the variation characteristics and intensity characteristics of the data fluctuations in the electricity consumption time series includes:

[0011] Calculate the difference between any data point in the electricity consumption time series and the preceding adjacent data point to obtain a first difference; calculate the difference between the next adjacent data point and the arbitrary data point to obtain a second difference; calculate the average of the first difference and the second difference to obtain the local rate of change of the arbitrary data point; calculate the product of the standard deviation of the electricity consumption time series and the information entropy of the local rate of change to obtain a complex fluctuation feature value; obtain a dense fluctuation feature value based on the fluctuation characteristics of the electricity consumption time series; calculate the product of the complex fluctuation feature value and the dense fluctuation feature value and normalize it to obtain the local volatility.

[0012] Furthermore, the step of obtaining the fluctuation density feature value based on the fluctuation characteristics of the electricity consumption time series includes:

[0013] The data between the peak and the adjacent trough in the electricity consumption time series is taken as a fluctuation segment; the position of the median of the peak and the adjacent trough in the fluctuation segment is taken as a marker point; the time interval between two marker points in the fluctuation segment is calculated to obtain the fluctuation length; the reciprocal of the average fluctuation length of all fluctuation segments is calculated to obtain the fluctuation density feature value.

[0014] Furthermore, the step of obtaining the degree of change difference based on the data difference characteristics between the electricity consumption time series and the historical time series of the same period includes:

[0015] In the formula, L represents the degree of variation, N represents the number of historical periods with the same cycle in the electricity consumption time series, and F represents the electricity consumption range of the electricity consumption time series. This represents the range of electricity consumption during the nth historical period of the same time. This represents the difference in electricity consumption range, where T represents the amount of data in the electricity consumption time series. This represents the t-th electricity consumption in the electricity consumption time series. This represents the electricity consumption at the t-th time period within the nth historical period. This represents the average difference in electricity consumption. This represents the Pearson correlation coefficient between the electricity consumption time series and the nth historical period with the same cycle, where 'a' represents a preset minimum positive number. This indicates that the change deviates from the characteristic value. This indicates normalization.

[0016] Further, the step of obtaining the anomaly confidence level based on the local volatility and the degree of change includes:

[0017] The average of the local volatility and the degree of change difference is calculated to obtain the anomaly confidence level.

[0018] Further, the step of obtaining the proportional adjustment coefficient based on the anomaly confidence level and the suspected anomaly degree includes:

[0019] The product of the anomaly confidence level and the suspected anomaly level is calculated and normalized to obtain the proportional adjustment coefficient.

[0020] Further, the step of adjusting the compression ratio according to the proportional adjustment coefficient to obtain an adaptive compression ratio includes:

[0021] Calculate the difference between the preset maximum compression ratio and the preset minimum compression ratio to obtain the adjustment benchmark; calculate the product of the adjustment benchmark and the proportional adjustment coefficient to obtain the adjustment amount; calculate the difference between the preset maximum compression ratio and the adjustment amount to obtain the adaptive compression ratio.

[0022] Further, the step of compressing and storing the power consumption time series according to the adaptive compression ratio includes:

[0023] The power consumption time series is compressed according to the Zstd algorithm and the adaptive compression ratio.

[0024] The present invention has the following beneficial effects:

[0025] In this invention, obtaining the suspected anomaly level allows for a preliminary assessment of abnormal electricity consumption based on electricity consumption data. Since the fluctuation characteristics of abnormal electricity consumption data differ from those of normal electricity consumption data, obtaining the local volatility level can reflect the probability of anomalies in the electricity consumption time series based on the fluctuation characteristics of the abnormal electricity consumption data. Because electricity consumption data exhibits regularity in historical cycles, obtaining the change difference level can reflect the probability of anomalies in the electricity consumption time series based on the differences in electricity consumption during the same period. Obtaining the anomaly confidence level can reflect the likelihood of the electricity consumption time series representing abnormal electricity consumption behavior from multiple dimensions. Obtaining the proportional adjustment coefficient can accurately adjust the compression ratio based on the abnormal characteristics of the electricity consumption time series, improving the accuracy of the compression ratio acquisition. Obtaining the adaptive compression ratio can adaptively compress based on the abnormal characteristics of the electricity consumption time series, thereby simultaneously meeting the requirements of storage efficiency and data detail preservation. Attached Figure Description

[0026] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart of a cloud-based smart storage method for electricity meter data, provided as an embodiment of the present invention. Detailed Implementation

[0028] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a cloud-based smart storage method for electricity meter data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, 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 invention pertains.

[0030] The following description, in conjunction with the accompanying drawings, details a specific scheme for a cloud-based intelligent storage method for electricity meter data provided by the present invention.

[0031] Please see Figure 1 The diagram illustrates a flowchart of a cloud-based smart storage method for electricity meter data according to an embodiment of the present invention. The method includes the following steps:

[0032] Step S1: Obtain the electricity consumption time series of the electricity meter.

[0033] First, user electricity consumption data monitored by the electricity meter is acquired. In this embodiment of the invention, the user's electricity consumption within that minute is acquired once per minute, thus obtaining the user's daily electricity consumption data. The implementer can determine the acquisition frequency according to the implementation scenario. To facilitate compression, the electricity consumption data is first processed into blocks. In this embodiment of the invention, the daily electricity consumption data is divided into 32KB data blocks, thereby obtaining different electricity consumption time series segments. At the same time, a time-to-electricity consumption change curve is constructed based on the electricity consumption time series segments. The implementer can determine the size of the electricity consumption time series segments according to the implementation scenario.

[0034] Step S2: Obtain the degree of suspected anomaly based on the data distribution characteristics in the electricity consumption time series; obtain the local volatility based on the change and intensity characteristics of the data fluctuations in the electricity consumption time series; obtain the degree of change difference based on the data difference characteristics between the electricity consumption time series and the historical same period.

[0035] The more representative the data in a power consumption time series is of various abnormal power consumption behaviors, the lower the compression level should be to avoid loss of detail; conversely, the more representative the data is of common power consumption behaviors, the higher the compression level should be to improve storage efficiency. Therefore, it is necessary to analyze the abnormal characteristics in the power consumption time series. When power consumption is significantly higher than common power consumption, it indicates a greater likelihood of abnormal power consumption behavior; similarly, a prolonged absence of power consumption also suggests abnormal power consumption behavior. Therefore, the degree of suspected anomaly is obtained based on the data distribution characteristics in the power consumption time series. Preferably, in this embodiment, the step of obtaining the degree of suspected anomaly includes: calculating the product of the average power consumption of the power consumption time series and a preset constant to obtain a boundary value; in this embodiment, the preset constant is 3, and the boundary value is three times the average power consumption, which can be determined by the implementer according to the implementation scenario. The value of power consumption exceeding the boundary value in the power consumption time series is taken as the target value; the target value indicates excessively high power consumption and a greater likelihood of abnormal power consumption behavior. The sum of the differences between all target values ​​and boundary values ​​is calculated to obtain the degree of deviation. The larger the target value is than the boundary value, the greater the degree of deviation, meaning that the electricity consumption is greater than the normal level, and the more likely this electricity consumption time series is to represent abnormal electricity consumption behavior. The product of the number of constant zero values ​​in the electricity consumption time series and the degree of deviation is calculated and normalized to obtain the suspected anomaly level; this normalization method is Z-score standardization. Since various appliances in a household electricity consumption scenario also consume a small amount of standby power when not in use, it is difficult for there to be a moment when the electricity consumption is 0. Therefore, the longer the time when the electricity consumption is 0, the more likely this electricity consumption time series is to represent abnormal electricity consumption behavior. The higher the suspected anomaly level, the more likely the electricity consumption time series is to represent abnormal electricity consumption behavior.

[0036] Furthermore, since the suspected anomaly level only reflects the magnitude of the electricity consumption anomaly, if the electricity consumption data is abnormal, its local data stability will also change, exhibiting characteristics of increased local data fluctuation and non-periodic fluctuation; at the same time, the fluctuation appears and disappears relatively quickly, exhibiting pulse-like fluctuation characteristics. Therefore, the local volatility is obtained based on the change characteristics and intensity characteristics of the data fluctuation in the electricity consumption time series; preferably, in this embodiment of the invention, the step of obtaining the local volatility includes: calculating the difference between any data in the electricity consumption time series and the adjacent previous data to obtain a first difference; calculating the difference between the adjacent next data and the arbitrary data to obtain a second difference; calculating the average of the first difference and the second difference to obtain the local change rate of the arbitrary data; the local change rate reflects the local fluctuation degree of the electricity consumption. The product of the standard deviation of the electricity consumption time series and the information entropy of the local rate of change is calculated to obtain the fluctuation complexity feature value. It should be noted that information entropy is existing technology, and the specific calculation steps will not be elaborated here. The larger the information entropy of the local rate of change, the more types of local rate of change there are, and the more complex the fluctuation characteristics of the electricity consumption. The larger the standard deviation of the electricity consumption time series, the greater the degree of fluctuation. Therefore, the larger the fluctuation complexity feature value, the stronger and more complex the fluctuation of the electricity consumption time series. The fluctuation density feature value is obtained based on the fluctuation characteristics of the electricity consumption time series, specifically including: taking the data between adjacent troughs before and after the peak point in the electricity consumption time series as a fluctuation segment; taking the location of the median of the peak point and the adjacent trough point in the fluctuation segment as a marker point; calculating the time interval between two marker points in the fluctuation segment to obtain the fluctuation length. The shorter the fluctuation length, the more obvious the pulse fluctuation characteristics. Selecting marker points for time interval calculation is to avoid the bottom of the trough being relatively smooth, exhibiting a long-tail characteristic, which would lead to inaccurate acquisition of the time interval of obvious fluctuation areas. The reciprocal of the average fluctuation length of all fluctuation segments is calculated to obtain the fluctuation density feature value. The smaller the fluctuation length, the larger the fluctuation density feature value, and the more unusual the fluctuation characteristics are. It should be noted that when there are no fluctuation segments in the electricity consumption time series, the fluctuation density feature value is taken as a preset minimum positive number. In this embodiment of the invention, the preset minimum positive number is 0.01. The product of the fluctuation complexity feature value and the fluctuation density feature value is calculated and normalized to obtain the local volatility. The larger the local volatility, the more unusual the fluctuation characteristics are in the electricity consumption time series, and the more likely the electricity consumption data is to be abnormal. The normalization method is the Z-score standardization method.

[0037] When electricity consumption data is abnormal, it will deviate significantly from historical data for the same period. For example, if electricity consumption suddenly decreases or becomes zero on a certain day during a normal nighttime electricity consumption period, the user may have been stealing electricity. Therefore, the degree of change is obtained based on the differences between the electricity consumption time series and historical data for the same period. Preferably, in this embodiment of the invention, the step of obtaining the degree of change includes:

[0038]

[0039] In the formula, L represents the degree of variation, N represents the number of historical periods with the same cycle in the electricity consumption time series. Since the amount of electricity consumption is affected by weekends and weekdays, the historical periods with the same cycle need to be of the same date type as the current electricity consumption time series. For example, if the current electricity consumption time series is on a weekday, then the range of historical periods with the same cycle is weekdays. F represents the range of electricity consumption in the current electricity consumption time series, which is the difference between the maximum and minimum values. This represents the range of electricity consumption during the nth historical period of the same time. This represents the difference in electricity consumption range; the greater the difference in electricity consumption range between two identical time periods, the greater the likelihood of abnormal electricity consumption data. T represents the amount of data in the electricity consumption time series. This represents the t-th electricity consumption in the electricity consumption time series. This represents the electricity consumption at the t-th time period within the nth historical period. This represents the average difference in electricity consumption. The larger the average difference in electricity consumption, the greater the deviation in electricity consumption, and the more likely there is to be abnormal electricity consumption data. This represents the Pearson correlation coefficient between the electricity consumption time series and the nth historical period of the same cycle. 'a' represents a preset minimum positive number. It should be noted that the Pearson correlation coefficient is a current technology. The stronger the positive correlation between the changes in the two series, the larger the Pearson correlation coefficient, and vice versa. This indicates that the change deviates from the characteristic value. The weaker the positive correlation between the electricity consumption trends of two identical time periods, the greater the deviation from the characteristic value, and the more likely an anomaly is to occur in the electricity consumption time series. 'a' is to avoid this item taking a value of 0. This indicates normalization, and the normalization method used is Z-score standardization. The greater the degree of variation, the greater the difference between the electricity consumption characteristics of this time period and the historical electricity consumption characteristics of the same period, and the greater the likelihood that this electricity consumption time period represents abnormal electricity consumption behavior.

[0040] Step S3: Obtain the anomaly confidence level based on local volatility and variation; obtain the proportional adjustment coefficient based on the anomaly confidence level and the degree of suspected anomaly; adjust the compression ratio based on the proportional adjustment coefficient to obtain the adaptive compression ratio.

[0041] Both local volatility and variation variability can reflect the abnormal characteristics of a power consumption time series. Therefore, an anomaly confidence level is obtained based on local volatility and variation variability. Preferably, in this embodiment of the invention, the step of obtaining the anomaly confidence level includes: calculating the average value of local volatility and variation variability to obtain the anomaly confidence level; the higher the anomaly confidence level, the more the power consumption time series reflects abnormal power consumption behavior, and the more it needs to be retained during compression, thereby improving the accuracy of power consumption analysis. Furthermore, a proportional adjustment coefficient can be obtained based on the anomaly confidence level and the degree of suspected anomaly. The higher the anomaly confidence level and the degree of suspected anomaly of a power consumption time series, the greater the possibility that the power consumption time series represents abnormal power consumption behavior, and the smaller the compression ratio should be, and the larger the proportional adjustment coefficient should be; therefore, the proportional adjustment coefficient is obtained by calculating the product of the anomaly confidence level and the degree of suspected anomaly and normalizing it; the normalization method is the maximum-minimum method.

[0042] Further, after obtaining the proportional adjustment coefficient for the electricity consumption time series, the compression ratio can be adjusted according to the proportional adjustment coefficient to obtain an adaptive compression ratio. Preferably, in this embodiment of the invention, the step of obtaining the adaptive compression ratio includes: calculating the difference between a preset maximum compression ratio and a preset minimum compression ratio to obtain an adjustment benchmark; in this embodiment of the invention, the preset maximum compression ratio is 5, and the preset minimum compression ratio is 2, which can be determined by the implementer according to the implementation scenario. The product of the adjustment benchmark and the proportional adjustment coefficient is calculated to obtain the adjustment amount; the larger the proportional adjustment coefficient, the larger the adjustment amount. The difference between the preset maximum compression ratio and the adjustment amount is calculated to obtain the adaptive compression ratio; the more abnormal the electricity consumption data of the electricity consumption time series, the larger the proportional adjustment coefficient, the smaller the adaptive compression ratio, the smaller the compression degree, and the more data details are retained. The formula for obtaining the adaptive compression ratio includes:

[0043]

[0044] In the formula, B represents the adaptive compression ratio. H represents the preset maximum compression ratio, H represents the adjustment baseline, and K represents the proportional adjustment coefficient. Indicates the adjustment amount.

[0045] Step S4: Compress and store the power consumption time series according to the adaptive compression ratio.

[0046] After obtaining the adaptive compression ratio of the electricity consumption time series, the time series can be compressed and stored according to the adaptive compression ratio. Specifically, this includes compressing the electricity consumption time series according to the Zstd algorithm and the adaptive compression ratio. It should be noted that the Zstd algorithm is an existing technology that can compress data according to a set compression ratio; the specific steps are not elaborated here. When the electricity consumption time series can better represent various abnormal electricity consumption behaviors, the smaller the adaptive compression ratio, the more data details are retained, thereby improving the accuracy of electricity consumption behavior analysis. When the electricity consumption time series is more normal, the larger the adaptive compression ratio and the greater the compression degree, thereby improving storage efficiency. Therefore, the adaptive compression ratio can simultaneously meet the requirements of storage efficiency and data detail preservation.

[0047] In summary, this invention provides a cloud-based intelligent storage method for electricity meter data. It obtains the degree of suspected anomaly based on the data distribution characteristics in electricity consumption time-series segments; obtains the local volatility based on the variation and intensity characteristics of data fluctuations in the electricity consumption time-series segments; obtains the degree of variation difference based on the data differences between the electricity consumption time-series segments and historical periods of the same cycle; obtains the anomaly confidence level based on the local volatility and the degree of variation difference; obtains a proportional adjustment coefficient based on the anomaly confidence level and the degree of suspected anomaly; and adjusts the compression ratio based on the proportional adjustment coefficient to obtain an adaptive compression ratio. This invention compresses and stores electricity consumption time-series segments using an adaptive compression ratio, thereby simultaneously meeting the requirements of storage efficiency and data detail preservation.

[0048] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0049] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for intelligent storage of electricity meter data based on a cloud platform, characterized in that, The method includes the following steps: Obtain the time series of electricity consumption from the electricity meter; The degree of suspected anomaly is obtained based on the data distribution characteristics in the electricity consumption time series; the degree of local volatility is obtained based on the change and intensity characteristics of the data fluctuations in the electricity consumption time series; and the degree of change difference is obtained based on the data difference characteristics between the electricity consumption time series and historical periods of the same period. An anomaly confidence level is obtained based on the local volatility and the degree of change difference; a proportional adjustment coefficient is obtained based on the anomaly confidence level and the degree of suspected anomaly; the compression ratio is adjusted based on the proportional adjustment coefficient to obtain an adaptive compression ratio; The power consumption time series is compressed and stored according to the adaptive compression ratio; The step of obtaining the local volatility based on the variation characteristics and intensity characteristics of data fluctuations in the electricity consumption time series includes: Calculate the difference between any data point in the electricity consumption time series and its preceding adjacent data point to obtain a first difference; calculate the difference between the next adjacent data point and the arbitrary data point to obtain a second difference; calculate the average of the first difference and the second difference to obtain the local rate of change of the arbitrary data point; calculate the product of the standard deviation of the electricity consumption time series and the information entropy of the local rate of change to obtain a complex fluctuation feature value; obtain a dense fluctuation feature value based on the fluctuation characteristics of the electricity consumption time series; calculate the product of the complex fluctuation feature value and the dense fluctuation feature value and normalize it to obtain the local volatility. The step of obtaining the fluctuation density feature value based on the fluctuation characteristics of the electricity consumption time series includes: The data between the peak and the adjacent trough in the electricity consumption time series is taken as a fluctuation segment; the position of the median of the peak and the adjacent trough in the fluctuation segment is taken as a marker point; the time interval between two marker points in the fluctuation segment is calculated to obtain the fluctuation length; the reciprocal of the average fluctuation length of all fluctuation segments is calculated to obtain the fluctuation density feature value. The step of obtaining the degree of suspected anomaly based on the data distribution characteristics in the electricity consumption time series includes: Calculate the product of the average electricity consumption of the electricity consumption time period and a preset constant to obtain the boundary value; take the value of electricity consumption in the electricity consumption time period that exceeds the boundary value as the target value; calculate the sum of the differences between all target values ​​and the boundary value to obtain the degree of deviation; calculate the product of the number of values ​​with a constant value of 0 in the electricity consumption time period and the degree of deviation and normalize it to obtain the degree of suspected anomaly. The step of obtaining the proportional adjustment coefficient based on the anomaly confidence level and the suspected anomaly degree includes: The product of the anomaly confidence level and the suspected anomaly level is calculated and normalized to obtain the proportional adjustment coefficient.

2. The intelligent storage method for electricity meter data based on a cloud platform according to claim 1, characterized in that, The step of obtaining the degree of change difference based on the data difference characteristics between the electricity consumption time period and the historical time period of the same cycle includes: In the formula, L represents the degree of variation, N represents the number of historical periods with the same cycle in the electricity consumption time series, and F represents the electricity consumption range of the electricity consumption time series. This represents the range of electricity consumption during the nth historical period of the same time. This represents the difference in electricity consumption range, where T represents the amount of data in the electricity consumption time series. This represents the t-th electricity consumption in the electricity consumption time series. This represents the electricity consumption at the t-th time period within the nth historical period. This represents the average difference in electricity consumption. This represents the Pearson correlation coefficient between the electricity consumption time series and the nth historical period with the same cycle, where 'a' represents a preset minimum positive number. This indicates that the change deviates from the characteristic value. This indicates normalization.

3. The intelligent storage method for electricity meter data based on a cloud platform according to claim 1, characterized in that, The step of obtaining the anomaly confidence level based on the local volatility and the degree of change includes: The average of the local volatility and the degree of change difference is calculated to obtain the anomaly confidence level.

4. The intelligent storage method for electricity meter data based on a cloud platform according to claim 1, characterized in that, The step of adjusting the compression ratio according to the proportional adjustment coefficient to obtain an adaptive compression ratio includes: Calculate the difference between the preset maximum compression ratio and the preset minimum compression ratio to obtain the adjustment benchmark; calculate the product of the adjustment benchmark and the proportional adjustment coefficient to obtain the adjustment amount; calculate the difference between the preset maximum compression ratio and the adjustment amount to obtain the adaptive compression ratio.

5. The intelligent storage method for electricity meter data based on a cloud platform according to claim 1, characterized in that, The step of compressing and storing the power consumption time segment according to the adaptive compression ratio includes: The power consumption time series is compressed according to the Zstd algorithm and the adaptive compression ratio.

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