Intelligent electric meter data optimization storage method

Abstract

The invention relates to the field of data processing, in particular to an intelligent electric meter data optimization storage method, which comprises the following steps of: performing normalization, smoothing and abnormal value processing on multi-dimensional time series data such as electric energy, current, voltage and power; performing periodic analysis on time sequences of power, voltage, current and the like by using an autocorrelation function, and determining periodic intensity of the data; and finally, performing compression processing on the data through pulse code modulation (PCM), differential pulse code modulation (DPCM) and Huffman coding, thereby effectively improving the data compression rate, reducing redundancy, and ensuring optimization of data transmission efficiency and storage space. According to the invention, the ammeter data is compressed through periodic analysis and Huffman coding, so that the storage efficiency and the transmission efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method for optimizing and storing data of a smart electric meter. Background Art

[0002] A smart meter is an electronic device that can collect, transmit and record electricity usage data in real time. Its main functions include electricity metering, data storage and remote transmission. Smart meters can upload power data to the cloud or server in real time through wireless communication technology, which is convenient for power companies to centrally manage and analyze. With the development of smart grids, the popularity of smart meters continues to increase, and the amount of real-time data collected by meters has also increased dramatically. Due to the huge amount of data generated every second, higher requirements are placed on data storage and transmission in smart meters.

[0003] First, the amount of data collected by the meter is huge, especially at high sampling frequencies. The meter needs to store a large amount of data, which places high demands on storage space and computing power. Secondly, since the meter data is frequently uploaded to the server, how to reduce the amount of uploaded data and reduce the consumption of network bandwidth has also become an urgent problem to be solved. In addition, when the network transmission is interrupted, the meter data will be saved locally, and the pressure on data storage will continue to increase. In order to reduce the pressure of storage and transmission and improve data processing efficiency, smart meters need to adopt efficient compression algorithms to reduce redundant data and optimize data storage.

[0004] At present, Huffman coding, as a classic lossless data compression technology, is applied to various data compression scenarios. Although Huffman coding can effectively compress data, its effect may be limited in the application scenario of smart meter data. For example, the power count may not change much within a sampling cycle, but the value of each data point is not exactly the same. In addition, other parameters in the meter data (such as current and voltage) vary widely, which reduces the efficiency of Huffman coding. Summary of the invention

[0005] In view of the problem of reduced efficiency of Huffman coding, the present invention proposes a method for optimizing and storing data of a smart meter, comprising: obtaining multiple operating parameters of the smart meter, wherein data at multiple continuous collection moments of each operating parameter constitute a corresponding parameter time series; calculating the period of each parameter time series, and recording any data in the parameter time series as target data; the collection moment corresponding to the target data is the target moment, and the collection moment corresponding to every other period of the target moment is recorded as the reference moment; a set number of data before and after the target moment constitute a neighborhood sequence; obtaining the neighborhood sequence of each reference moment and the target moment to obtain multiple neighborhood sequences, and obtaining the periodicity strength of the target data based on the similarity between the multiple neighborhood sequences; obtaining the periodicity strength of all data in any parameter time series, and in response to the periodicity strength being greater than or equal to a set threshold, recording the corresponding data as strong periodic data; in response to the periodicity strength being less than the set threshold, recording the corresponding data as weak periodic data; using pulse code modulation for all strong periodic data and differential pulse code modulation for all weak periodic data; and compressing all modulated data using Huffman coding to reduce the storage space of smart meter data.

[0006] The present invention dynamically detects and quantifies the periodic characteristics of the time series of various parameters of the smart meter, realizes the accurate comparison of the similarity between the target data and its periodic neighborhood, thereby effectively distinguishing strong periodic data from weak periodic data, and respectively adopts pulse code modulation and differential pulse code modulation processing, and then combines Huffman coding compression to significantly reduce the data storage space and upload bandwidth requirements, while ensuring data accuracy and integrity. Compared with the existing method of directly using Huffman coding, the present invention greatly improves the compression efficiency and data restoration rate.

[0007] Furthermore, calculating the period of each parameter time series also includes: obtaining the autocorrelation function corresponding to different time lag values ​​of each parameter time series; and recording the time lag value corresponding to the maximum value of all autocorrelation functions as the period.

[0008] By obtaining the autocorrelation functions corresponding to different time lag values ​​of each parameter time series and selecting the time lag value corresponding to the maximum value in the autocorrelation function as the period, the present invention accurately reflects the intrinsic periodicity of the data. Compared with the traditional method that relies on experience to set the period, the present invention provides an objective and automatic period identification method, ensuring the accuracy and stability of period calculation.

[0009] Furthermore, the calculation method of the periodicity intensity is specifically as follows: ; in Indicates timestamp The corresponding periodic intensity of the power; Indicates the maximum value of the cycle sequence number in the power sequence; Indicates timestamp No. Neighborhood sequence of the control time after cycles; Indicates timestamp No. Neighborhood sequence of the control time after cycles; Represents the calculation function of Dtw distance; Represents the tuning factor.

[0010] The present invention adopts a periodic strength calculation formula based on dynamic time warping (DTW) distance to quantify the similarity between neighborhood sequences in different periods, effectively captures periodic repeatability, and avoids reducing compression efficiency due to inconsistent data continuity. Compared with traditional methods, this formula has higher accuracy and robustness in identifying data repeatability.

[0011] Furthermore, obtaining multiple operating parameters of the smart meter also includes: collecting electric energy readings, power, voltage and current parameters of the smart meter; and setting the frequency range of the smart meter to collect data to [1,15].

[0012] Furthermore, it also includes temporarily storing the time series of each parameter in the electric meter memory and regularly uploading it to the cloud platform through the RS485 interface.

[0013] Furthermore, it also includes not calculating the cycle of the electric energy reading timing sequence, and performing data compression processing on the electric energy reading timing sequence using pulse code modulation and then using Huffman coding.

[0014] In order to solve the problem that the electric energy reading time series lacks periodicity due to its cumulative characteristics, the present invention does not perform periodic calculations on it, but directly adopts pulse code modulation (PCM) processing to achieve data compression and retain key information. Compared with the traditional method of uniformly processing all data, this solution better adapts to the characteristics of electric energy readings and improves compression efficiency and data recovery accuracy.

[0015] Furthermore, it also includes preprocessing operations on the parameter time series, specifically: normalizing the parameter time series; and performing noise reduction on the normalized parameter time series.

[0016] Furthermore, the maximum and minimum value normalization algorithm is used to normalize the time series of each parameter.

[0017] Furthermore, the sliding average method is used to smooth the normalized time series of each parameter to remove noise.

[0018] The sliding average method is used to smooth the normalized parameter time series, effectively eliminating noise and short-term fluctuations, ensuring that the data more accurately reflects the long-term trend. Compared with traditional methods that have not been smoothed, the present invention improves data stability and data quality before compression, thereby optimizing the overall compression effect.

[0019] Furthermore, the set threshold is obtained using a maximum inter-class variance method.

[0020] The technical effects of the present invention are: The present invention pre-processes the multi-dimensional data of smart meters, quantifies the periodicity intensity by using the autocorrelation function and the neighborhood sequence similarity, effectively distinguishes strong periodic and weak periodic data, and then respectively uses pulse code modulation and differential pulse code modulation, and finally combines Huffman coding to achieve data compression. Compared with the existing method that only relies on Huffman coding to directly process raw data, the present invention makes full use of the inherent periodic characteristics of the data, greatly reduces the storage space and upload bandwidth requirements, and ensures data accuracy and restoration rate at the same time. It has automation, objectivity and robustness, and significantly improves the overall performance of smart meter data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 is a flow chart schematically illustrating a method for optimizing and storing data of a smart meter according to an embodiment of the present invention; Figure 2 Schematically shows the electric energy reading and power timing diagram with a cycle of 1 day in an embodiment of the present invention; Figure 3 The figure schematically shows the electric energy reading and power timing diagram with a period of 5 days in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] Smart meter data optimization storage method embodiment: like Figure 1 As shown, the smart meter data optimization storage method of the present invention includes: S1. Obtain relevant data of the smart meter and perform preprocessing.

[0025] Smart meters generate a large amount of high-frequency data records during operation, and these data records not only need to be stored, but also need to be uploaded to the cloud or other devices. Therefore, Huffman coding is used in this embodiment to improve data storage efficiency, reduce storage space occupancy, reduce redundant data and improve upload efficiency.

[0026] In the actual application of smart meters, multiple data items are collected in real time, usually including the following types of information: power reading (Unit: kilowatt-hour, kWh), power (Unit: Watt, W), voltage (unit: volt, V) and current (Unit: Ampere, A). In this embodiment, the frequency of data collection by the smart meter can be set to 1 Hz, and the data record collected each time can be represented as a vector: ; in, Indicates the timestamp of the current record; Indicates the collection time The data vector of Indicates the collection time The energy readings; Indicates the collection time Power; Indicates the collection time Voltage; Indicates the collection time The frequency of the above data collection can be adjusted to 5Hz, 10Hz, 15Hz, etc. by the implementer according to the application scenario of the smart meter, in addition to 1Hz.

[0027] The collected data will be temporarily stored in the meter's memory and will be uploaded to the cloud platform regularly through the RS485 interface. However, since there is no guarantee that the network and communication of the smart meter are always connected, when the smart meter cannot be connected to the external network, the power data is more dependent on the local storage capacity of the meter, which further reflects the necessity of improving data storage efficiency.

[0028] The above use Indicates time All the data recorded include multi-dimensional data such as electric energy, voltage, current, power, etc. Then the data recorded by the electric meter will form a continuous data sequence, including: ; in Indicates total A data set consisting of multidimensional data at each collection moment; Represents a timestamp sequence; It indicates the timestamp Corresponding multidimensional data. After obtaining the raw data, the data needs to be preprocessed to eliminate redundant information in the data, reduce the impact of outliers, standardize data of different dimensions, and provide a consistent data format for subsequent algorithms.

[0029] First, all data are normalized. Here, the maximum and minimum values ​​of each parameter timing can be used for normalization. It should be noted that the above-mentioned timing of each parameter refers to the total value of each parameter. The time sequence of the acquisition moments is shown as an example: The voltage parameter timing at each acquisition moment can be recorded as ; Then, the sliding average method is used to smooth the time series of each parameter to remove noise. The above-mentioned preprocessing related algorithms belong to the well-known technology and will not be described here. The data set composed of the preprocessed multidimensional data is recorded as ,have: ; in Represents a data set consisting of preprocessed multidimensional data; It indicates the timestamp The corresponding preprocessed data vector has: ; in Indicates timestamp The corresponding preprocessed data vector; Indicates timestamp The corresponding pre-processed energy readings; Indicates timestamp The corresponding preprocessed power; Indicates timestamp The corresponding pre-processed voltage; Indicates timestamp The corresponding pre-processed current.

[0030] S2. Perform periodic analysis on power series, voltage series and current series based on autocorrelation function; obtain the neighborhood sequence of data, and calculate the periodic strength of data based on the similarity between neighborhood sequences of different periods; obtain the intensity threshold based on the maximum inter-class variance method to obtain strong periodic data and weak periodic data; complete smart meter data compression based on pulse code modulation, differential pulse code modulation and Huffman coding.

[0031] In step S1, the processed time series data including multiple dimensions such as electric energy, current, voltage and power are obtained. However, although these data are optimized in format and quality, they still occupy a large storage space and require a high bandwidth for uploading. Therefore, to solve this problem, the Huffman coding algorithm can be used as the main compression method in this embodiment.

[0032] S2.1. Perform periodic analysis on power time series, voltage time series and current time series based on autocorrelation function.

[0033] Huffman Coding is an optimal prefix coding algorithm based on the frequency of data symbols. It assigns codes to symbols in the input data by constructing an optimal binary tree (Huffman tree). The higher the frequency of the symbol, the shorter the code assigned; the lower the frequency of the symbol, the longer the code assigned. In this way, Huffman coding can effectively reduce the storage space of data.

[0034] In step S1, a data set consisting of preprocessed multidimensional data is obtained. , which can be expanded to be expressed as: ; Each row of parameters can be further expressed as a parameter time series: ; in Represents the timing of the pre-processed energy readings; represents the power timing after preprocessing; Represents the voltage timing after preprocessing; In this embodiment, if Huffman coding is directly used to encode the data set Possible problems with data compression include: Due to the working principle of smart meters, the energy readings are cumulative, that is, the energy reading time series If there are a large number of non-repeated energy readings, then when the Huffman tree is constructed directly based on the frequencies of different energy readings, a large amount of raw data will be generated. For example, there are 10 energy readings, namely 100, 101, 102, ..., 109. At this time, the scenario for obtaining the energy readings may be a period of heavy electricity consumption by users. There are no identical energy readings, and since the frequency of occurrence of each energy reading is 1, 10 different Huffman codes will be generated when passing through the Huffman tree. At this time, the data cannot be effectively compressed. In addition, in addition to the energy readings, there will also be a large amount of raw data for other current and voltage data collected by the smart meter, which will increase the length of the Huffman code and reduce the compression efficiency.

[0035] Based on the above analysis, first observe Figure 2 The diagram shows the energy reading and power timing for a period of one day, which represents the household electricity consumption in a general scenario. For example, it increases in the morning and evening, and for the power sequence For example, it also changes in the morning, with a surge and a sharp drop in the evening. Figure 3 The diagram shows the energy readings and power sequence for a 5-day period. Although the power sequence varies from day to day, with sudden increases and decreases, the daily power sequence variation curve shows that the user's daily routines are similar. In other words, in the long run, the power sequence changes are cyclical. Similarly, the voltage sequence changes are also cyclical. And the current timing There are similar properties, which will not be elaborated here.

[0036] In this embodiment, the power timing can be first , Voltage Timing And the current timing For periodic analysis, although there is a certain similarity between the data in each cycle, that is, there will be a lot of repeated data between the data in one cycle and the data in another cycle, but given the power timing The data of the parameter sequence in a cycle varies greatly. If Huffman coding is directly applied, a large amount of raw data will still be generated. Therefore, in this embodiment, the data in the cycle can be processed using pulse code modulation (PCM). The overall presentation is incremental, and pulse code modulation (PCM) can also be used for processing first.

[0037] Specifically, based on the power timing As an example, we first use the autocorrelation function to measure its similarity with itself at different time lags, which can be expressed as: ; in The time lag is When the power sequence The autocorrelation function of , which measures the similarity of power data at different lag time intervals; Indicates power timing The total number of data in; Indicates timestamp The power value; Indicates power timing The mean of the data in ; Represents the time lag, that is, the time interval from one time point to the next.

[0038] When acquiring power timing After the autocorrelation function is obtained, the time lag value is further adjusted dynamically In order to find the significant periodicity of the power time series, in this embodiment, the time lag value can be preset based on the user's power consumption habits and the data collection frequency of the smart meter. The optional values ​​are 21600 (6h), 43200 (12h) and 86400 (24h). In addition to the above optional values, implementers can also set the time lag value according to actual conditions. 36000 (10h), 64800 (18h) and 108000 (30h) etc.

[0039] Calculate the autocorrelation function corresponding to each time lag value. As shown in the above example, we can get , as well as It should be noted that: for example, only the autocorrelation functions corresponding to three time lag values ​​are obtained here, but in actual applications, more time lag values ​​may be selected. After obtaining all the autocorrelation functions, the time lag value corresponding to the largest autocorrelation function is used as the power time series The cycle is recorded as .

[0040] S2.2. Obtain the neighborhood sequence of the data, and calculate the periodicity strength of the data based on the similarity between neighborhood sequences of different periods; obtain the strength threshold based on the maximum inter-class variance method to obtain strong periodic data and weak periodic data.

[0041] Next, the periodicity strength of each data in the time series data is quantified. In this embodiment, based on the data and a period And the neighborhood similarity of the corresponding data after multiple cycles is calculated. First, for the power time series Medium timestamp Power value , obtain the first 5 power values ​​and the last 5 power values ​​adjacent to it, and obtain a power data neighborhood sequence containing a total of 11 timestamps, recorded as ; Then get the timestamp The data corresponding to the subsequent cycles can be recorded as: ,in Indicates power timing The maximum value of the periodic sequence number contained in , and the above-mentioned acquisition moments can also be recorded as control moments; then obtain the neighborhood sequence of the above-mentioned data, and the acquisition method is the same as the power value The specific process is the same and will not be described in detail. So far, multiple neighborhood sequences can be obtained, which can be recorded as: , further calculate the timestamp The corresponding power value The periodic strength is: ; in Indicates timestamp Corresponding power The periodic intensity of Indicates power timing The maximum value of the mid-cycle sequence number; Indicates timestamp No. Cycle Rear Power Neighborhood sequence of ; Indicates timestamp No. Cycle Rear Power Neighborhood sequence of ; Represents the calculation function of Dtw distance; It represents the parameter adjustment factor to avoid the situation where the denominator is 0. In this embodiment, the empirical value 5E-6 can be taken.

[0042] when The smaller the time, the greater the similarity between the neighborhood sequences corresponding to the same position in different periods. Corresponding power The more likely it is to be repeated data within a period, the stronger its periodicity is. The bigger; when The larger the value, the smaller the similarity between the neighborhood sequences corresponding to the same position in different periods. Corresponding power The less likely it is to be data within the period, the greater the strength of its periodicity. The smaller.

[0043] Obtain the power timing according to the above method The periodicity intensity of all data in is calculated, and the maximum inter-class variance method is used to obtain the intensity threshold. The power corresponding to the periodicity intensity greater than or equal to the intensity threshold is recorded as the strong periodicity power, and the power corresponding to the periodicity intensity less than the intensity threshold is recorded as the weak periodicity power. Figure 3 The power time series change lines shown in the figure show that the power changes in the morning and evening have a certain periodicity. The power in these time periods is a strong periodic power. Figure 3 As shown, the data is continuous. If a user has an unusual event such as taking a leave on a certain day, the power data generated will no longer meet the requirements. Figure 3 The power data at this time is weak periodic power. At the same time, when the smart meter collects power, certain errors may occur. At this time, the power data does not belong to periodic data, so it can be classified as weak periodic power.

[0044] To sum up, weak periodic power is mainly data generated by some unconventional matters, that is, weak periodic power accounts for a small proportion and may be mutant. If pulse code modulation (PCM) is used for processing, it will not only generate more coded data but also reduce the coding efficiency. Therefore, in order to save these weak periodic powers, differential pulse code modulation (DPCM) can be used for processing in this embodiment.

[0045] So far, the power timing has been obtained The strong cycle power and weak cycle power in the voltage sequence And the current timing The strong periodic voltage, weak periodic voltage, strong periodic current and weak periodic current are obtained in the same manner as above.

[0046] Similarly, the power timing For example, for power timing The strong periodic power in is processed using pulse code modulation (PCM), while for power timing The weak periodic power in the signal is processed using differential pulse code modulation (DPCM), and all the modulated coded data is processed using Huffman coding to complete the power timing. Correspondingly, for the voltage timing And the current timing Usage and Power Sequencing The same method is used to complete data compression, and for the energy reading timing , also uses pulse code modulation (PCM) processing first and then Huffman coding to complete data compression.

[0047] S2.3. Smart meter data compression is completed based on pulse code modulation, differential pulse code modulation and Huffman coding.

[0048] Finally, an example is given to illustrate the data storage process of the smart meter: Assume that there are 15 consecutive powers: 500 (1), 505 (2), 510 (3), 510 (4), 515 (5), 510 (6), 799 (7), 800 (8), 520 (9), 525 (10), 530 (11), 535 (12), 540 (13), 545 (14), 540 (15); it should be noted that for the convenience of illustration, the power value selected here is the actual power value, not the power data pre-processed in step S1.

[0049] After calculating the periodic intensity of each power and comparing it with the intensity threshold, the strong periodic powers are: 500 (1), 505 (2), 510 (3), 510 (4), 515 (5), 510 (6), 505 (9), 505 (10), 530 (11), 535 (12), 540 (13), 545 (14), 540 (15); and the weak periodic powers are: 799 (7), 800 (8). First, pulse code modulation (PCM) is used to process all continuous strong periodic powers to obtain the encoding results: 0 (1), +++++ (2), +++++ (3), 0 (4), +++++ (5), ----- (6), 505 (9), 0 (10), +++++ (11), +++++ (12), +++++ (13), +++++ (14), ----- (15); then differential pulse code modulation (DPCM) is used to process all continuous weak periodic powers to obtain the encoding results: +289 (7), +1 (8). It should be noted that: the ninth power 505 mentioned above does not use pulse code modulation (PCM) because the strong periodic power and the weak periodic power may not be continuous. At this time, if the strong periodic power connected to the weak periodic power is directly subjected to pulse code modulation (PCM), a large number of symbols will be generated, thereby affecting the subsequent coding efficiency. In other words, the first strong periodic power following the weak periodic power retains the original data without pulse code modulation (PCM).

[0050] So far, the result of coding modulation is: 0 (1), +++++ (2), +++++ (3), 0 (4), +++++ (5), ----- (6), +289 (7), +1 (8), 505 (9), 0 (10), +++++ (11), +++++ (12), +++++ (13), +++++ (14), ----- (15), which means that the initial data only includes 6 data as shown below: 0, +++++, -----, +289, +1, 505. The original data without coding modulation includes the following data: 500, 505, 510, 515, 530, 535, 540, 545, 799, 800, that is, a total of 10 data. If the above original data is directly encoded using the Huffman algorithm, the length of the code will be significantly greater than the above data after coding modulation. At the same time, since the data given in the above example is relatively small, when the smart meter generates a large amount of data, the original data without coding modulation will directly use the Huffman algorithm. Will produce a longer code length. The encoding process of the above Huffman algorithm and the subsequent decoding process of the Huffman algorithm belong to the well-known technology, and the specific implementation method will not be repeated here.

Claims

1. A method for optimizing and storing smart meter data, characterized in that: The method comprises: Acquire multiple operating parameters of the smart meter, where data of each operating parameter at multiple continuous acquisition times constitute a corresponding parameter time series; Calculate the period of each parameter time series, record any data in the parameter time series as target data; the collection time corresponding to the target data is the target time, and the collection time corresponding to every other period of the target time is recorded as the control time; the set number of data before and after the target time constitutes a neighborhood sequence; Obtaining the neighborhood sequences of each control moment and the target moment to obtain multiple neighborhood sequences, and obtaining the periodicity strength of the target data based on the similarity between the multiple neighborhood sequences; obtaining the periodicity strength of all data in any parameter time series, and in response to the periodicity strength being greater than or equal to a set threshold, recording the corresponding data as strong periodic data; in response to the periodicity strength being less than the set threshold, recording the corresponding data as weak periodic data; Pulse code modulation is used for all strong periodic data and differential pulse code modulation is used for all weak periodic data; all modulated data are compressed using Huffman coding to reduce the storage space of smart meter data.

2. The method for optimizing and storing smart meter data according to claim 1, characterized in that: Calculate the period of each parameter timing, including: Obtain the autocorrelation function corresponding to different time lag values ​​of each parameter time series; The time lag value corresponding to the maximum value in all autocorrelation functions is recorded as the period.

3. The method for optimizing and storing smart meter data according to claim 2, characterized in that: The calculation method of the periodic intensity is specifically as follows: ； in Indicates timestamp The corresponding periodic intensity of the power; Indicates the maximum value of the cycle sequence number in the power sequence; Indicates timestamp No. Neighborhood sequence of the control time after cycles; Indicates timestamp No. Neighborhood sequence of the control time after cycles; Represents the calculation function of Dtw distance; Represents the tuning factor.

4. The method for optimizing and storing smart meter data according to claim 1, characterized in that: Get multiple operating parameters of smart meters, including: Collect the energy readings, power, voltage and current parameters of smart meters; The frequency range of data collected by smart meters is set to [1,15].

5. The method for optimizing and storing smart meter data according to claim 4, characterized in that: It also includes temporarily storing the time series of the parameters in the memory of the electric meter and regularly uploading them to the cloud platform through the RS485 interface.

6. The method for optimizing and storing smart meter data according to claim 4, characterized in that: It also includes not calculating the period of the electric energy reading timing, and performing data compression processing on the electric energy reading timing by using pulse code modulation and then using Huffman coding.

7. The method for optimizing and storing smart meter data according to claim 4, characterized in that: It also includes preprocessing operations on the time series of each parameter, specifically: Normalize the time series of each parameter; The normalized time series of each parameter are subjected to noise reduction processing.

8. The method for optimizing and storing smart meter data according to claim 7, characterized in that: The maximum and minimum normalization algorithm is used to normalize the timing of each parameter.

9. The method for optimizing and storing smart meter data according to claim 7, characterized in that: The sliding average method is used to smooth the normalized time series of each parameter to remove noise.

10. The method for optimizing and storing smart meter data according to claim 1, characterized in that: The set threshold is obtained using the maximum inter-class variance method.

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