Electric energy metering window adaptive adjustment method and device based on signal change rate

Abstract

The invention discloses an electric energy metering window adaptive adjustment method and device based on a signal change rate, and the method comprises the steps: firstly calculating the comprehensive change rate of an electric energy signal after the electric energy signal is obtained; then, according to the comprehensive change rate, identifying the state of the electric energy signal, namely identifying whether the electric energy signal is in a stable state, a rapid fluctuation state or a slow change state; and finally, the length and the sampling frequency of the metering time window of the electric energy signal can be adjusted according to the state of the electric energy signal, so that the self-adaptive adjustment of the electric energy metering window is realized. Therefore, the sampling window and the sampling frequency can be automatically adjusted according to the change rate of the electric energy signal, the self-adaptive scaling of the metering window and the dynamic switching of the sampling frequency are realized, so that the metering real-time performance and the metering precision in a photovoltaic power generation scene can be improved, and the method is very suitable for large-scale application and popularization.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy metering sampling, and in particular to a method and device for adaptively adjusting an electric energy metering window based on a signal change rate. Background Art

[0002] With the widespread access to new energy, especially photovoltaic power generation systems, the volatility and non-stationary characteristics of photovoltaic output are becoming increasingly prominent. Especially under low inertia conditions, the system is more sensitive to disturbances in power signals such as voltage, current, and frequency, which can easily cause frequent signal mutations or dynamic changes.

[0003] Among them, the existing electric energy metering methods generally adopt fixed sampling frequency and fixed integration window, which can maintain high accuracy under stable working conditions. However, when the signal undergoes sudden changes or strong fluctuations, there are the following technical defects: (1) The sampling window is fixed and too large to reflect the rapid changes in power in a short period of time, resulting in poor timeliness; (2) The sampling frequency is fixed, resulting in an inability to ensure sufficient sampling density when the signal fluctuates violently; (3) The fixed window length and frequency will lead to insufficient real-time performance, which will cause the metering results to be delayed or lose accuracy; Therefore, how to provide an electric energy metering window adjustment method that can adaptively adjust the sampling window and sampling frequency according to the signal change rate has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention aims to solve the problem of electric energy metering sampling. Its purpose is to provide a method and device for adaptively adjusting the electric energy metering window based on the signal change rate, which solves the problems of low accuracy and insufficient real-time performance in traditional technologies that use fixed sampling frequencies and fixed windows for electric energy metering.

[0005] The present invention is achieved through the following technical solutions: In a first aspect, a method for adaptively adjusting an electric energy metering window based on a signal change rate is provided, comprising: Acquiring an electric energy signal and calculating a comprehensive rate of change of the electric energy signal; Based on the comprehensive change rate, identifying the state of the electric energy signal, wherein the state of the electric energy signal is a stable state, a rapidly fluctuating state, or a slowly changing state; According to the state of the electric energy signal, the length of the metering time window and the sampling frequency of the electric energy signal are adjusted, so as to perform metering sampling of the electric energy signal based on the adjusted window length and sampling frequency.

[0006] Based on the above-disclosed content, after acquiring the electric energy signal, the present invention first calculates its comprehensive change rate; then, based on the comprehensive change rate, identifies the state of the electric energy signal, that is, identifies whether the electric energy signal is in a stable state, a rapidly fluctuating state, or a slowly changing state; finally, the length of the metering time window and the sampling frequency of the electric energy signal can be adjusted according to the state of the electric energy signal, thereby realizing adaptive adjustment of the electric energy metering window; thus, the present invention can automatically adjust the sampling window and sampling frequency according to the change rate of the electric energy signal, realizing adaptive scaling of the metering window and dynamic switching of the sampling frequency, thereby improving the real-time measurement and metering accuracy in photovoltaic power generation scenarios, making it very suitable for large-scale application and promotion.

[0007] In one possible design, the electric energy signal includes a voltage signal and a current signal, wherein calculating the comprehensive rate of change of the electric energy signal includes: Normalizing the voltage signal and the current signal to obtain current time series data and voltage time series data; Calculating the voltage change rate at different times based on the voltage time series data; Calculating a first current change rate and a second current change rate at different times based on the current time series data; The comprehensive change rate of the electric energy signal at different moments is obtained according to the voltage change rate at different moments, and the first current change rate and the second current change rate at different moments.

[0008] In one possible design, calculating the voltage change rate at different times based on the voltage time series data includes: According to the following formula (1), the voltage change rate at different times is calculated; (1) In the above formula (1), represents the voltage change rate at the kth moment, represents the voltage value at the kth moment in the voltage time series data, represents the rated voltage, where k=1, 2, ..., K, and K represents the length of the voltage time series data.

[0009] In one possible design, calculating the first current change rate and the second current change rate at different times based on the current time series data includes: Obtaining the maximum harmonic frequency of the current at different times in the current time series data; The following formula (2) is used to calculate the first current change rate at different times, and the maximum harmonic frequency of the current at different times, and the following formula (3) is used to calculate the second current change rate at different times; (2) (3) In the above formula (2), represents the first current change rate at the kth moment, represents the current value at the kth moment in the current time series data, Indicates the first The current value at time t, where represents the number of cycles of the fundamental wave of the current signal, and is the fundamental period of the current signal; In the above formula (3), represents the second current change rate at the kth moment, represents the maximum harmonic frequency of the current at the kth moment, where k=1, 2, ..., K, and K represents the length of the current time series data.

[0010] In one possible design, the comprehensive change rate of the electric energy signal includes comprehensive change rates at different moments; The step of identifying the state of the electric energy signal based on the comprehensive rate of change includes: According to the comprehensive change rate of the electric energy signal at different times, the state of the electric energy signal at different times is determined; If the comprehensive change rate of the electric energy signal at any moment is less than or equal to a first threshold, it is determined that the electric energy signal is in a stable state at any moment; If the comprehensive change rate of the electric energy signal at any moment is greater than the first threshold and less than or equal to the second threshold, it is determined that the electric energy signal is in a slowly changing state at any moment; If the comprehensive change rate of the electric energy signal at any moment is greater than the second threshold, it is determined that the electric energy signal is in a rapid fluctuation state at any moment.

[0011] In one possible design, adjusting the length of the metering time window and the sampling frequency of the electric energy signal according to the state of the electric energy signal includes: If the power signal is at least continuously If the state at the moment is a stable state, the length of the metering time window of the electric energy signal is adjusted to , and adjusting the sampling frequency to ; If the power signal is at least continuously If the state at the moment is a slowly changing state, the length of the metering time window of the electric energy signal is adjusted to , and adjusting the sampling frequency to ; If the power signal is at least continuously If the state at the moment is a slowly changing state, the length of the metering time window of the electric energy signal is adjusted to , and adjusting the sampling frequency to ; in, , ,and 、 、 、 、 、 、 、 、 All are positive numbers.

[0012] In a possible design, the optimization parameters are obtained by optimizing through a machine learning algorithm, wherein the optimization parameters include 、 、 、 、 、 、 、 and , and the optimization objective function of the machine learning algorithm is: (4) In formula (4), Indicates the sum of window switching response delay times, represents the measurement error rate, represents the data processing cost, is the switching frequency, and Represents weight.

[0013] In a second aspect, a device for adaptively adjusting an electric energy metering window based on a signal change rate is provided, comprising: a comprehensive change rate calculation unit, configured to obtain an electric energy signal and calculate a comprehensive change rate of the electric energy signal; a state identification unit, configured to identify a state of the electric energy signal based on the comprehensive change rate, wherein the state of the electric energy signal is a stable state, a rapidly fluctuating state, or a slowly changing state; The sampling adjustment unit is used to adjust the length of the metering time window and the sampling frequency of the electric energy signal according to the state of the electric energy signal, so as to perform metering sampling of the electric energy signal based on the adjusted window length and sampling frequency.

[0014] In a third aspect, another device for adaptively adjusting the electric energy metering window based on the signal change rate is provided. Taking the device as an electronic device as an example, the device includes a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the method for adaptively adjusting the electric energy metering window based on the signal change rate as described in the first aspect or any possible design of the first aspect.

[0015] In a fourth aspect, a storage medium is provided, on which instructions are stored. When the instructions are executed on a computer, the method for adaptively adjusting the electric energy metering window based on the signal change rate as described in the first aspect or any possible design of the first aspect is executed.

[0016] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer, causes the computer to execute the method for adaptively adjusting the electric energy metering window based on the signal change rate as described in the first aspect or any possible design of the first aspect.

[0017] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention can automatically adjust the sampling window and sampling frequency according to the rate of change of the electric energy signal, realizing adaptive scaling of the metering window and dynamic switching of the sampling frequency. This can improve the real-time performance and metering accuracy in photovoltaic power generation scenarios, making it very suitable for large-scale application and promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings: Figure 1 A schematic flow chart of the steps of a method for adaptively adjusting an electric energy metering window based on a signal change rate provided by an embodiment of the present invention; Figure 2 A structural diagram of an electric energy metering window adaptive adjustment device based on signal change rate provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the following examples and accompanying drawings. The exemplary embodiments of the present invention and their descriptions are intended only to explain the present invention and are not intended to limit the present invention. It should be understood that although the terms "first," "second," and so on may be used herein to describe various elements, these elements should not be limited by these terms. These terms are merely used to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element without departing from the scope of the exemplary embodiments of the present invention.

[0020] Example: See also Figure 1 As shown, the method for adaptively adjusting the electric energy metering window based on the signal change rate provided in this embodiment first calculates the comprehensive change rate of the electric energy signal; then, based on the comprehensive change rate, identifies the state of the electric energy signal, that is, identifies whether the electric energy signal is in a stable state, a rapidly fluctuating state, or a slowly changing state; finally, the length of the metering time window and the sampling frequency of the electric energy signal can be adjusted according to the state of the electric energy signal. Thus, the method can automatically adjust the sampling window and the sampling frequency according to the change rate of the electric energy signal, thereby realizing adaptive scaling of the metering window and dynamic switching of the sampling frequency. In this way, the real-time metering and metering accuracy in the photovoltaic power generation scenario can be improved; among them, for example, the method can be, but is not limited to, running on the electric energy metering end side. It can be understood that the aforementioned execution subject does not constitute a limitation on the embodiments of the present application. Accordingly, the operation steps of the method can be, but are not limited to, as shown in the following steps S1 to S3.

[0021] S1. Acquire an electric energy signal and calculate a comprehensive rate of change of the electric energy signal. In a specific application, the electric energy signal may include, but is not limited to, a voltage signal and a current signal. Thus, in this embodiment, the rates of change of the voltage signal and the current signal are calculated, and then the comprehensive rate of change of the electric energy signal is calculated based on the rates of change of the voltage signal and the current signal. Optionally, the calculation process of the aforementioned comprehensive rate of change may be, but is not limited to, as shown in the following steps S11 to S14.

[0022] S11. Normalize the voltage signal and the current signal to obtain current time series data and voltage time series data. In this embodiment, data normalization is a common method of data preprocessing, and its principle is not repeated here. After the normalization of the voltage signal and the current signal is completed, the voltage change rate and the current change rate can be calculated. The process is shown in the following steps S12 and S13.

[0023] S12. Calculate the voltage change rate at different times based on the voltage time series data. In a specific implementation, the voltage change rate at different times can be calculated by, but not limited to, the following formula (1).

[0024] (1) In the above formula (1), represents the voltage change rate at the kth moment, represents the voltage value at the kth moment in the voltage time series data, represents the rated voltage, where k=1, 2, ..., K, and K represents the length of the voltage time series data.

[0025] Thus, based on the aforementioned formula (1), the voltage change rate at different times can be calculated; then, the current change rate can be calculated; in this embodiment, for the current time series data, this embodiment needs to calculate two change rates, one for the change rate of the current itself, and the other for the change rate of the current harmonics; wherein, the calculation process of the aforementioned two current change rates is shown in the following step S13.

[0026] S13. Based on the current time series data, calculate the first current change rate and the second current change rate at different times; in a specific implementation, for example, but not limited to, first obtain the maximum harmonic frequency of the current at different times in the current time series data; then, calculate the first current change rate at different times based on the current time series data, and calculate the second current change rate at different times based on the maximum harmonic frequency of the current at different times.

[0027] Optionally, one of the calculation methods of the first current change rate is disclosed below, as shown in the following formula (2).

[0028] (2) In the above formula (2), represents the first current change rate at the kth moment, represents the current value at the kth moment in the current time series data, Indicates the first The current value at time t, where represents the number of cycles of the fundamental wave of the current signal, and is the fundamental period of the current signal; in this embodiment, The value is 20ms; of course, it can be set specifically according to actual use and is not limited to the above example.

[0029] After calculating the first current change rate at different times, the second current change rate can be calculated, wherein the calculation formula is shown in the following formula (3).

[0030] (3) In the above formula (3), represents the second current change rate at the kth moment, represents the maximum harmonic frequency of the current at the kth moment, where k=1, 2, ..., K, and K represents the length of the current time series data.

[0031] Based on the above formula (2) and formula (3), after calculating the first current change rate and the second current change rate at different times, the above voltage change rate can be combined to obtain the comprehensive change rate of the electric energy signal at different times. The calculation process is shown in the following step S14.

[0032] S14. Based on the voltage change rates at different times, and the first and second current change rates at different times, a comprehensive change rate of the power signal at different times is obtained. In this embodiment, the voltage change rates at different times, and the first and second current change rates at different times are weighted and summed to obtain the comprehensive change rate of the power signal at different times, namely: , where represents the weight, where , which can be adjusted according to the grid scenario (such as load sensitivity).

[0033] After calculating the comprehensive rate of change of the electric energy signal at different times through the aforementioned steps S11 to S14, state identification can be performed based on this, so that the length of the metering window and the sampling frequency can be adjusted based on the state of the electric energy signal. The state identification process can be, but is not limited to, as shown in the following step S2.

[0034] S2. Based on the comprehensive change rate, the state of the electric energy signal is identified, wherein the state of the electric energy signal is a stable state, a rapidly fluctuating state or a slowly changing state; in this embodiment, the state of the electric energy signal at different moments is determined based on the comprehensive change rate of the electric energy signal at different moments; wherein, two thresholds are set, and then the state of the electric energy signal at different moments is identified based on the relationship between the comprehensive change rate and the two thresholds.

[0035] Specifically, if the comprehensive change rate of the electric energy signal at any moment is less than or equal to a first threshold, it is determined that the electric energy signal is in a stable state at any moment; similarly, if the comprehensive change rate of the electric energy signal at any moment is greater than the first threshold and less than or equal to a second threshold, it is determined that the electric energy signal is in a slowly changing state at any moment; and if the comprehensive change rate of the electric energy signal at any moment is greater than the second threshold, it is determined that the electric energy signal is in a rapidly fluctuating state at any moment.

[0036] In this way, after determining the state of the electric energy signal at different times based on the aforementioned step S2, the length of the metering time window and the sampling frequency can be adaptively adjusted based on the state, and the process is shown in the following step S3.

[0037] S3. According to the state of the electric energy signal, adjust the length of the metering time window and the sampling frequency of the electric energy signal, so as to perform metering sampling of the electric energy signal based on the adjusted window length and sampling frequency.

[0038] In specific implementation, if the electric energy signal is at least continuously If the state at the moment is a stable state, the length of the metering time window of the electric energy signal is adjusted to , and adjusting the sampling frequency to ; Otherwise, the length of the measurement time window and the sampling frequency remain unchanged.

[0039] Similarly, if the power signal is at least continuously If the state at the moment is a slowly changing state, the length of the metering time window of the electric energy signal is adjusted to , and adjusting the sampling frequency to ; Otherwise, the length of the measurement time window and the sampling frequency remain unchanged.

[0040] Finally, if the power signal is at least continuously If the state at the moment is a slowly changing state, the length of the metering time window of the electric energy signal is adjusted to , and adjusting the sampling frequency to Of course, if the power signal is at least continuous If the states at each moment are not all slowly changing states, the length of the measurement time window and the sampling frequency are kept unchanged.

[0041] In this embodiment, For long windows, For the middle window, For short windows, is the low-frequency sampling frequency, is the intermediate frequency sampling frequency, and is the high-frequency sampling frequency, that is ,and ;at the same time, 、 、 、 、 、 、 、 、 All are positive numbers.

[0042] In this way, after adjusting the metering time window length and sampling frequency based on the state of the electric energy signal, the adjusted metering time window and sampling frequency can be used to perform metering sampling of the electric energy signal, thereby outputting the metering result.

[0043] Therefore, through the method for adaptively adjusting the electric energy metering window based on the signal change rate described in detail in the aforementioned steps S1 to S3, the present invention can automatically adjust the sampling window and sampling frequency according to the electric energy signal change rate, thereby realizing adaptive scaling of the metering window and dynamic switching of the sampling frequency. In this way, the real-time measurement and metering accuracy in photovoltaic power generation scenarios can be improved, making it very suitable for large-scale application and promotion.

[0044] In a possible design, the second aspect of this embodiment provides the optimization parameters (i.e. 、 、 、 、 、 、 、 and ) is one of the optimization processes.

[0045] In a specific application, for example, the aforementioned optimization parameters can be obtained by, but are not limited to, optimizing through a machine learning algorithm based on historical data, and this embodiment discloses one of the optimization objective functions of the aforementioned machine learning algorithm, as shown in the following formula (4).

[0046] (4) In formula (4), Indicates the sum of window switching response delay times, represents the measurement error rate, represents the data processing cost, is the switching frequency (which represents the number of times the sampling frequency or metering time window switches when the window is adjusted with a set of optimization parameters), and Represents weight.

[0047] In specific implementation, for example, but not limited to, the following formula (5) can be used to calculate .

[0048] (5) In this embodiment, each time a set of optimization parameters is obtained during optimization, the corresponding optimization parameters are substituted into the aforementioned formula (5) to obtain the sum of the window switching response delay times corresponding to the set of optimization parameters.

[0049] Similarly, the measurement error rate can be calculated by, for example but not limited to, the following formula (6).

[0050] (6) In formula (6), Indicates the calculated electric energy metering value obtained after adjusting the length of the metering time window and the sampling frequency based on the current optimization parameters during an optimization process; Indicates the electric energy metering reference value.

[0051] In this way, based on the above formula (6), the error rate of electric energy metering (i.e., the relative error between the calculated electric energy metering value and the electric energy metering reference value under the current parameters) can be calculated after the sampling frequency and metering time window are adaptively adjusted with different optimization parameters.

[0052] Finally, the data processing cost can be calculated using, but not limited to, the following formula (7).

[0053] (7) In the above formula (7), Indicates that in the current optimization parameters, The time at which the frequency is sampled, Indicates The time at which the frequency is sampled, It means Frequency is the time at which sampling is performed.

[0054] In addition, in this embodiment, the aforementioned machine learning algorithm may be, but is not limited to, the beetle swarm optimization algorithm. Of course, a specific optimization algorithm may be selected according to actual use, and no specific limitation is made here.

[0055] Therefore, by finding the maximum value of the aforementioned objective function, the optimal optimization parameters can be obtained; then, based on the optimal optimization parameters, the metering time window and sampling frequency can be adaptively adjusted. The adjustment process can be referred to the first aspect of the aforementioned embodiment and will not be repeated here.

[0056] like Figure 2As shown, the third aspect of this embodiment provides a hardware device for implementing the method for adaptively adjusting the electric energy metering window based on the signal change rate described in the first aspect of the embodiment, including: The comprehensive change rate calculation unit is used to obtain the electric energy signal and calculate the comprehensive change rate of the electric energy signal.

[0057] A state identification unit is used to identify the state of the electric energy signal based on the comprehensive change rate, wherein the state of the electric energy signal is a stable state, a rapidly fluctuating state or a slowly changing state.

[0058] The sampling adjustment unit is used to adjust the length of the metering time window and the sampling frequency of the electric energy signal according to the state of the electric energy signal, so as to perform metering sampling of the electric energy signal based on the adjusted window length and sampling frequency.

[0059] The working process, working details and technical effects of the device provided in this embodiment can be found in the first aspect of the embodiment and will not be described in detail here.

[0060] like Figure 3 As shown, the fourth aspect of this embodiment provides another electric energy metering window adaptive adjustment device based on the signal change rate. Taking the device as an electronic device as an example, it includes: a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the electric energy metering window adaptive adjustment method based on the signal change rate as described in the first and second aspects of the embodiments.

[0061] For example, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in first-out memory (FIFO), and / or first-in last-out memory (FILO). Specifically, the processor may include one or more processing cores, such as a quad-core processor or an octal-core processor. The processor may be implemented in at least one of the following hardware forms: a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), or a PLA (Programmable Logic Array). Furthermore, the processor may include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state.

[0062] In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. For example, the processor may be, but is not limited to, a microprocessor of the STM32F105 series, a reduced instruction set computer (RISC) microprocessor, an X86 architecture processor, or a processor with an integrated embedded neural network processing unit (NPU). The transceiver may be, but is not limited to, a Wireless Fidelity (WIFI) wireless transceiver, a Bluetooth wireless transceiver, a General Packet Radio Service (GPRS) wireless transceiver, a ZigBee protocol (a low-power local area network protocol based on the IEEE802.15.4 standard, ZigBee) wireless transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. In addition, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0063] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment and will not be described in detail here.

[0064] In a fifth aspect, this embodiment provides a storage medium storing instructions for the method for adaptively adjusting the electric energy metering window based on the signal change rate as described in the first and second aspects of the embodiments, that is, the storage medium stores instructions that, when executed on a computer, execute the method for adaptively adjusting the electric energy metering window based on the signal change rate as described in the first and second aspects of the embodiments.

[0065] The storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive and / or a memory stick, and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0066] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment and will not be described in detail here.

[0067] A sixth aspect of this embodiment provides a computer program product comprising instructions. When the instructions are executed on a computer, the computer is caused to execute the method for adaptively adjusting the electric energy metering window based on the signal change rate as described in the first and second aspects of the embodiment. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0068] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for adaptively adjusting an electric energy metering window based on a signal change rate, characterized in that: include: Acquiring an electric energy signal and calculating a comprehensive rate of change of the electric energy signal; Based on the comprehensive change rate, identifying the state of the electric energy signal, wherein the state of the electric energy signal is a stable state, a rapidly fluctuating state, or a slowly changing state; According to the state of the electric energy signal, the length of the metering time window and the sampling frequency of the electric energy signal are adjusted, so as to perform metering sampling of the electric energy signal based on the adjusted window length and sampling frequency.

2. The method according to claim 1, characterized in that The electric energy signal includes: a voltage signal and a current signal, wherein calculating the comprehensive change rate of the electric energy signal includes: Normalizing the voltage signal and the current signal to obtain current time series data and voltage time series data; Calculating the voltage change rate at different times based on the voltage time series data; Calculating a first current change rate and a second current change rate at different times based on the current time series data; The comprehensive change rate of the electric energy signal at different moments is obtained according to the voltage change rate at different moments, and the first current change rate and the second current change rate at different moments.

3. The method according to claim 2, characterized in that Calculate the voltage change rate at different times based on the voltage time series data, including: According to the following formula (1), the voltage change rate at different times is calculated; （1） In the above formula (1), represents the voltage change rate at the kth moment, represents the voltage value at the kth moment in the voltage time series data, represents the rated voltage, where k=1, 2, ..., K, and K represents the length of the voltage time series data.

4. The method according to claim 2, characterized in that Calculating a first current change rate and a second current change rate at different times based on the current time series data includes: Obtaining the maximum harmonic frequency of the current at different times in the current time series data; The following formula (2) is used to calculate the first current change rate at different times, and the maximum harmonic frequency of the current at different times, and the following formula (3) is used to calculate the second current change rate at different times; （2） （3） In the above formula (2), represents the first current change rate at the kth moment, represents the current value at the kth moment in the current time series data, Indicates the first The current value at time t, where represents the number of cycles of the fundamental wave of the current signal, and is the fundamental period of the current signal; In the above formula (3), represents the second current change rate at the kth moment, represents the maximum harmonic frequency of the current at the kth moment, where k=1, 2, ..., K, and K represents the length of the current time series data.

5. The method according to claim 1, characterized in that The comprehensive change rate of the electric energy signal includes the comprehensive change rate at different moments; The step of identifying the state of the electric energy signal based on the comprehensive rate of change includes: According to the comprehensive change rate of the electric energy signal at different times, the state of the electric energy signal at different times is determined; If the comprehensive change rate of the electric energy signal at any moment is less than or equal to a first threshold, it is determined that the electric energy signal is in a stable state at any moment; If the comprehensive change rate of the electric energy signal at any moment is greater than the first threshold and less than or equal to the second threshold, it is determined that the electric energy signal is in a slowly changing state at any moment; If the comprehensive change rate of the electric energy signal at any moment is greater than the second threshold, it is determined that the electric energy signal is in a rapid fluctuation state at any moment.

6. The method according to claim 1, characterized in that Adjusting the length of the metering time window and the sampling frequency of the electric energy signal according to the state of the electric energy signal includes: If the power signal is at least continuously If the state at the moment is a stable state, the length of the metering time window of the electric energy signal is adjusted to , and adjusting the sampling frequency to ; If the power signal is at least continuously If the state at the moment is a slowly changing state, the length of the metering time window of the electric energy signal is adjusted to , and adjusting the sampling frequency to ; If the power signal is at least continuously If the state at the moment is a slowly changing state, the length of the metering time window of the electric energy signal is adjusted to , and adjusting the sampling frequency to ; in, , ,and 、 、 、 、 、 、 、 、 All are positive numbers.

7. The method according to claim 6, characterized in that The optimization parameters are obtained by machine learning algorithm, wherein the optimization parameters include 、 、 、 、 、 、 、 and , and the optimization objective function of the machine learning algorithm is: （4） In formula (4), Indicates the sum of window switching response delay times, represents the measurement error rate, represents the data processing cost, is the switching frequency, and Represents weight.

8. An adaptive adjustment device for electric energy metering window based on signal change rate, characterized in that: include: a comprehensive change rate calculation unit, configured to obtain an electric energy signal and calculate a comprehensive change rate of the electric energy signal; a state identification unit, configured to identify a state of the electric energy signal based on the comprehensive change rate, wherein the state of the electric energy signal is a stable state, a rapidly fluctuating state, or a slowly changing state; The sampling adjustment unit is used to adjust the length of the metering time window and the sampling frequency of the electric energy signal according to the state of the electric energy signal, so as to perform metering sampling of the electric energy signal based on the adjusted window length and sampling frequency.

9. An electronic device, characterized in that: include: A memory, a processor, and a transceiver that are sequentially communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program, and execute the method for adaptively adjusting the electric energy metering window based on the signal change rate according to any one of claims 1 to 7.

10. A computer program product comprising instructions, characterized in that When the instructions are executed on a computer, the computer is caused to execute the method for adaptively adjusting the electric energy metering window based on the signal change rate according to any one of claims 1 to 7.

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