Test environment monitoring method and equipment for intelligent electric power measurement laboratory

Abstract

The invention relates to a test environment monitoring method and equipment for an electric power metering intelligent laboratory, and belongs to the technical field of electric power management, and the method comprises the following steps: obtaining and preprocessing electric power data and environment data of the laboratory, obtaining a current and voltage frequency domain sequence through the electric power data, and determining a total harmonic distortion rate; and the apparent power and the power factor are calculated. A metering error and environment sensitivity are obtained according to the environment data and the power factor, and an environment quality index and a metering reliability index are obtained at the same time. Temperature, humidity and power supply purification parameters are obtained based on the metering error, the environmental sensitivity, the environmental quality index and the metering reliability index and serve as input of the multi-layer perceptron, and a control decision vector is generated. And generating temperature, humidity and power supply purification control signals according to the vectors to regulate and control the laboratory. According to the invention, an end-to-end automatic control mode is realized, manual intervention is not needed, and dynamic balance of a complex environment can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to a test environment monitoring method and device for a power metering smart laboratory, and belongs to the technical field of power management. BACKGROUND

[0002] With the rapid development of intelligent power systems, power metering laboratories, as the core places for testing and calibrating power equipment performance, have a crucial role in ensuring power metering accuracy and long-term operation performance. Modern power metering laboratories not only need to accurately measure power parameters such as current, voltage, and power factor, but also need to monitor and regulate laboratory environmental conditions in real time to ensure the accuracy and repeatability of test data. However, traditional laboratory environmental monitoring methods usually rely on single sensors and fixed control strategies, which are difficult to adapt to complex and variable test environment requirements.

[0003] Traditional monitoring systems usually process environmental parameters (such as temperature and humidity) and power parameters (such as power factor and harmonic distortion rate) separately, ignoring the mutual influence between them. For example, the influence of temperature changes on power factor and the indirect effect of humidity fluctuations on harmonic distortion rate are often not fully considered. This fragmented processing approach makes it difficult for the system to comprehensively evaluate the overall quality of the test environment and develop effective control strategies. SUMMARY

[0004] To solve the problems existing in the prior art, the present application provides a test environment monitoring method and device for a power metering smart laboratory.

[0005] The technical solution of the present application is as follows: On the one hand, the present application provides a test environment monitoring method for a power metering smart laboratory, comprising the following steps: Obtaining power data and environmental data of the power metering smart laboratory and preprocessing them; Obtaining current frequency domain sequences and voltage frequency domain sequences of the power data, and determining total harmonic distortion rate based on the current frequency domain sequences and voltage frequency domain sequences; Calculating the apparent power of the power data, and obtaining the power factor of the power data based on the apparent power and total harmonic distortion rate; Obtaining the metering error of the power metering smart laboratory based on the environmental data and power factor, and obtaining the environmental sensitivity based on the metering error; Obtaining the environmental quality index and the metering reliability index of the power metering smart laboratory; Obtaining temperature control parameters, humidity control parameters, and power supply purification parameters based on the power factor, environmental data, metering error, environmental sensitivity, environmental quality index, and metering reliability index; The temperature regulation parameter, humidity regulation parameter and power purification parameter are taken as inputs of a multi-layer perception machine to obtain a control decision vector; Based on the control decision vector, a temperature control signal, a humidity control signal and a power purification control signal are generated to regulate and control the smart power metering laboratory.

[0006] Preferably, the environmental data includes temperature data and humidity data; The power data includes current data and voltage data; The pre-processing of the temperature data is represented by the following formula: ; ; In the formula, represents the pre-processed temperature data, represents the size of the sliding window, represents the th temperature weight, represents the th temperature sample value, represents the average temperature value within the window, represents the temperature variance within the window; The pre-processing of the humidity data is represented by the following formula: ; ; In the formula, represents the pre-processed humidity data, represents the th humidity weight, represents the th humidity sample value, represents the average humidity value within the window, represents the humidity variance within the window; The pre-processing of the current data is represented by the following formula: ; In the formula, represents the pre-processed current data, represents the th current sample signal; The pre-processing of the voltage data is represented by the following formula: ; In the formula, represents the pre-processed voltage data, represents the th voltage sample signal.

[0007] Preferably, the current sampling signal and the voltage sampling signal are processed using a Fast Fourier Transform algorithm to obtain the current frequency domain sequence and the voltage frequency domain sequence, as expressed by the formula: ; ; In the formula, Represents the current frequency domain sequence. Represents the Fast Fourier Transform function. Represents a voltage frequency domain sequence; The total harmonic distortion (THD) of the current in the current frequency domain sequence and the total harmonic distortion (THD) of the voltage in the voltage frequency domain sequence are calculated using the following formulas: ; ; In the formula, This represents the total harmonic distortion rate of the current. Indicates the total harmonic distortion (THD) of the voltage. Indicates the harmonic order Represents the first current in the frequency domain sequence The amplitude of the second harmonic. This represents the amplitude of the first harmonic in the current frequency domain sequence. Represents the voltage frequency domain sequence of the first... The amplitude of the second harmonic. This represents the amplitude of the first harmonic in the voltage frequency domain sequence; The total harmonic distortion (THD) is determined based on the current THD and the voltage THD.

[0008] Preferably, the apparent power of the power data is calculated using the following formula: ; In the formula, Indicates apparent power; The active power of electrical data is calculated using the following formula: ; In the formula, Indicates active power; The power factor, which is derived from the apparent power, active power, and total harmonic distortion (THD), is expressed by the formula: ; In the formula, Indicates the power factor. Represents the harmonic compensation coefficient. This represents the total harmonic distortion rate.

[0009] Preferably, the metering error of the smart power metering laboratory is obtained based on the environmental data and power factor, expressed by the formula: ; In the formula, Indicates measurement error, Indicates the reference error. Temperature error coefficient, Indicates reference temperature. Indicates the humidity error coefficient. Reference humidity, Indicates the current error coefficient. Indicates the rated current. Represents the current nonlinearity coefficient. Indicates the voltage error coefficient. Indicates the rated voltage. Indicates the harmonic influence coefficient. Indicates the power factor error coefficient. This represents the frequency deviation error coefficient. Indicates frequency deviation. Indicates the nominal frequency; The frequency deviation is expressed by the formula: ; In the formula, Indicates the measurement frequency; The environmental sensitivity is obtained based on the aforementioned measurement error, expressed by the formula: ; In the formula, Indicates environmental sensitivity.

[0010] Preferably, the environmental quality index is expressed by the formula: ; In the formula, This represents the environmental quality index. Indicates the ideal temperature. Indicates temperature tolerance. Indicates ideal humidity. Indicates humidity tolerance. Indicates the maximum permissible distortion rate; The measurement reliability index is expressed by the formula: ; In the formula, Indicates the reliability index of measurement. Indicates frequency tolerance.

[0011] Preferably, the temperature control parameters, humidity control parameters, and power purification parameters are obtained based on the power factor, environmental data, metering error, environmental sensitivity, environmental quality index, and metering reliability index. The specific steps are as follows: Temperature control parameters are obtained based on preprocessed temperature data, environmental quality index, and measurement error, expressed by the following formula: ; In the formula, Indicates temperature control parameters. Indicates temperature control gain. Indicates the error threshold. Represents the hyperbolic tangent function; Humidity control parameters are obtained based on preprocessed humidity data, environmental sensitivity, and metering reliability index, expressed by the following formula: ; In the formula, Indicates humidity control parameters. Indicates humidity control gain. Indicates the sensitivity threshold; Power purification parameters are obtained based on total harmonic distortion, power factor, and frequency deviation, and are expressed by the following formula: ; In the formula, Indicates power purification parameters, This indicates the power purification and regulation gain.

[0012] Preferably, the temperature control parameters, humidity control parameters, and power purification parameters are used as inputs to the multilayer sensor. The specific steps are as follows: An input feature vector is constructed based on the metering reliability index, environmental quality index, temperature control parameters, humidity control parameters, and power purification parameters. The input feature vector is used as the input to the multilayer perceptron, as expressed by the formula: ; ; In the formula, This represents the input feature vector. This represents the hidden layer output vector. This represents the sigmoid activation function. This represents the hidden layer weight matrix. This represents the hidden layer bias vector. This represents the output layer weight matrix. This represents the output layer bias vector. Represents the control decision vector; The control decision vector is expressed by the formula: ; In the formula, This indicates the temperature control decision. Indicates humidity control decisions. This indicates a power purification decision.

[0013] Preferably, the temperature control signal is expressed by the formula: ; In the formula, This indicates a temperature control signal. Indicates temperature control gain; The humidity control signal is expressed by the formula: ; In the formula, This indicates a humidity control signal. Indicates humidity control gain; The power purification control signal is expressed by the formula: ; In the formula, This indicates a power purification control signal. This indicates the power purification control gain.

[0014] In another aspect, the present invention also provides an electronic device having a computer program stored thereon, which, when executed by a processor, implements the test environment monitoring method for a smart power metering laboratory as described in any embodiment of the present invention.

[0015] The present invention has the following beneficial effects: 1. This invention constructs a dynamic error compensation mechanism through multi-dimensional analysis of power data. By calculating the correlation between apparent power, active power, and harmonic distortion rate, the power factor, a core indicator, can be accurately derived, providing theoretical support for metering accuracy.

[0016] 2. This invention combines dynamic assessment of environmental sensitivity indicators, enabling real-time perception of the impact of environmental disturbances on measurement results, such as increased thermal noise due to temperature changes and signal attenuation caused by humidity fluctuations. This dynamic perception capability can quickly initiate adaptive adjustment strategies, offsetting external interference through fine-tuning of environmental parameters to ensure that measurement results remain within a high-precision range.

[0017] 3. This invention uses temperature, humidity, and power purification parameters as input variables for a multilayer sensor, and generates the optimal control decision vector through an intelligent algorithm. This end-to-end automated control mode realizes intelligent control throughout the entire process from environmental monitoring to regulation execution, achieving dynamic equilibrium in complex environments without human intervention. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the implementation of the method in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0021] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0023] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0024] Example 1: See Figure 1 This invention provides a method for monitoring the test environment of a smart power metering laboratory, comprising the following steps: Acquire power and environmental data from the smart power metering laboratory and perform preprocessing. Acquire current frequency domain sequence and voltage frequency domain sequence of power data, and determine total harmonic distortion rate based on the current frequency domain sequence and voltage frequency domain sequence; Calculate the apparent power of the power data, and obtain the power factor of the power data based on the apparent power and the total harmonic distortion rate; The metering error of the smart power metering laboratory is obtained based on the environmental data and power factor, and the environmental sensitivity is obtained based on the metering error. Obtain the environmental quality index and metering reliability index of the smart power metering laboratory; Temperature control parameters, humidity control parameters, and power purification parameters are obtained based on the power factor, environmental data, metering error, environmental sensitivity, environmental quality index, and metering reliability index. The temperature control parameters, humidity control parameters, and power purification parameters are used as inputs to the multilayer sensor to obtain the control decision vector. Based on the control decision vector, temperature control signals, humidity control signals, and power purification control signals are generated to regulate the smart power metering laboratory.

[0025] Raw sensor data directly contains noise, spikes, and interference. In metrology labs, even minute signal distortions can be amplified in subsequent calculations, leading to erroneous conclusions. This is especially true for current and voltage signals, whose RMS values ​​and waveform quality are fundamental to metrology. Therefore, employing adaptive weighted sliding filters to process slowly varying temperature and humidity signals can effectively smooth out random noise while preserving the true trend of change.

[0026] Preferably, the environmental data includes temperature data and humidity data; The power data includes current data and voltage data; The preprocessing of the temperature data is expressed by the following formula: ; ; In the formula, This represents the preprocessed temperature data. This represents the size of the sliding window; in this embodiment, it is set to 512. Indicates the first Each temperature weight, Indicates the first Each temperature sample value, This represents the average temperature within the window. This represents the temperature variance within the window; The average temperature within the window is expressed by the formula: ; The temperature variance within the window is expressed by the formula: ; The preprocessing of the humidity data is expressed by the following formula: ; ; In the formula, This represents the preprocessed humidity data. Indicates the first Each humidity weight, Indicates the first One humidity sampling value, This indicates the average humidity level inside the window. Indicates the humidity variance within the window; The average humidity within the window is expressed by the formula: ; The humidity variance within the window is expressed by the formula: ; The preprocessing of the current data is expressed by the following formula: ; In the formula, This represents the preprocessed current data, i.e., the effective value of the current. Indicates the first One current sampling signal; The preprocessing of the voltage data is expressed by the following formula: ; In the formula, This represents the preprocessed voltage data, i.e., the effective voltage value. Indicates the first One voltage sampling signal.

[0027] Preferably, the current sampling signal and the voltage sampling signal are processed using a Fast Fourier Transform algorithm to obtain the current frequency domain sequence and the voltage frequency domain sequence, as expressed by the formula: ; ; In the formula, Represents the current frequency domain sequence. Represents the Fast Fourier Transform function. Represents a voltage frequency domain sequence; The total harmonic distortion (THD) of the current in the current frequency domain sequence and the total harmonic distortion (THD) of the voltage in the voltage frequency domain sequence are calculated using the following formulas: ; ; In the formula, This represents the total harmonic distortion rate of the current. Indicates the total harmonic distortion (THD) of the voltage. Indicates the harmonic order Represents the first current in the frequency domain sequence The amplitude of the second harmonic. This represents the amplitude of the first harmonic in the current frequency domain sequence, corresponding to the amplitude of the fundamental frequency of the current. Represents the voltage frequency domain sequence of the first... The amplitude of the second harmonic. This represents the amplitude of the first harmonic in the voltage frequency domain sequence, corresponding to the amplitude of the fundamental voltage. The total harmonic distortion (THD) is determined based on the current THD and the voltage THD, and is expressed by the following formula: ; In the formula, Represents the total harmonic distortion rate. This represents the maximum value function.

[0028] Preferably, the apparent power of the power data is calculated using the following formula: ; In the formula, Indicates apparent power; The active power of electrical data is calculated using the following formula: ; In the formula, Indicates active power; The power factor, which is derived from the apparent power, active power, and total harmonic distortion (THD), is expressed by the formula: ; In the formula, Indicates the power factor. This represents the harmonic compensation coefficient.

[0029] The traditional definition of power factor is the ratio of active power to apparent power. This definition is perfectly accurate under ideal conditions of a pure sine wave, reflecting the cosine of the phase difference between the fundamental components of voltage and current. However, in modern power grids, especially under the complex load environments encountered in power metering laboratories, current and voltage waveforms are typically not perfect sine waves but distorted waveforms containing numerous harmonic components. In this situation, the traditional definition of power factor reveals its limitations, as it cannot distinguish whether the decrease in power factor stems from phase difference or waveform distortion.

[0030] By introducing compensation terms The Total Harmonic Distortion (THD) corrects for the additional effects of harmonic distortion, quantifying the degree to which a waveform deviates from a pure sine wave. A high THD value indicates a significant amount of harmonic components in the current or voltage. These harmonic currents flow in the system, but the vast majority do not perform work (i.e., do not generate active power); they simply circulate between the grid and the load, increasing the current burden on lines and equipment. This ineffective harmonic current significantly increases apparent power, leading to an overestimation of the power factor calculated using conventional formulas.

[0031] Compensation items are introduced through This is used as the denominator to offset the overestimation. Among them, This reflects the specific contribution weight of harmonic components to the increase in apparent power. When THD is zero (ideal sine wave), the compensation term equals 1, and the formula degenerates into its traditional form. As THD increases, the value of the compensation term decreases from 1, thereby correcting the power factor. This corrected PF value accurately reflects the total energy utilization efficiency, including thermal effects.

[0032] Preferably, the metering error of the smart power metering laboratory is obtained based on the environmental data and power factor, expressed by the formula: ; In the formula, Indicates measurement error, Indicates the reference error. Temperature error coefficient, Indicates reference temperature. Indicates the humidity error coefficient. Reference humidity, Indicates the current error coefficient. Indicates the rated current. Represents the current nonlinearity coefficient. Indicates the voltage error coefficient. Indicates the rated voltage. Indicates the harmonic influence coefficient. Indicates the power factor error coefficient. This represents the frequency deviation error coefficient. Indicates frequency deviation. Indicates the nominal frequency; The frequency deviation is expressed by the formula: ; In the formula, Indicates the measurement frequency; The environmental sensitivity is obtained based on the aforementioned measurement error, expressed by the formula: ; In the formula, Indicates environmental sensitivity.

[0033] Preferably, the environmental quality index is expressed by the formula: ; In the formula, This represents the environmental quality index. Indicates the ideal temperature. Indicates temperature tolerance. Indicates ideal humidity. Indicates humidity tolerance. Indicates the maximum permissible distortion rate; The measurement reliability index is expressed by the formula: ; In the formula, Indicates the reliability index of measurement. Indicates frequency tolerance.

[0034] Preferably, the temperature control parameters, humidity control parameters, and power purification parameters are obtained based on the power factor, environmental data, metering error, environmental sensitivity, environmental quality index, and metering reliability index. The specific steps are as follows: Temperature control parameters are obtained based on preprocessed temperature data, environmental quality index, and measurement error, expressed by the following formula: ; In the formula, Indicates temperature control parameters. Indicates temperature control gain. Indicates the error threshold. Represents the hyperbolic tangent function; The physical meaning of measurement error is the deviation between the current measured value and the true value, directly reflecting the instantaneous accuracy of the system. Measurement error is used in temperature control because the impact of temperature changes on measuring equipment is instantaneous. When the ambient temperature changes, parameters such as the resistance and semiconductor characteristics of electronic components change immediately, and these changes are directly reflected in the measurement results within minutes. Using measurement error as a control parameter allows for rapid feedback control. When an increase in measurement error is detected, it indicates that the temperature change has had a substantial impact on the equipment performance, requiring immediate adjustment to correct the deviation.

[0035] The Environmental Quality Index (EQI) is a comprehensive indicator for assessing the overall environmental condition of a laboratory, encompassing multiple dimensions such as air quality and pollutant concentration. Introducing this parameter into temperature control is based on the coupling effect between environmental factors. When environmental quality is poor, other environmental factors may exacerbate the impact of temperature changes.

[0036] Humidity control parameters are obtained based on preprocessed humidity data, environmental sensitivity, and metering reliability index, expressed by the following formula: ; In the formula, Indicates humidity control parameters. Indicates humidity control gain. Indicates the sensitivity threshold; The physical meaning of environmental sensitivity is the degree of response to changes in environmental parameters, quantifying the potential impact of humidity fluctuations. This parameter is used in humidity control because the effects of humidity have a significant cumulative characteristic. Humidity changes indirectly affect parameters such as the properties of insulating materials and dielectric constants, and these effects can take hours or even days to fully manifest. Using environmental sensitivity allows for proactive control; when the system exhibits high sensitivity to humidity changes, even if the current humidity level is normal, it is necessary to strengthen control in advance to prevent potential measurement deviations.

[0037] The metrological reliability index is an indicator for assessing the long-term stability of a system, reflecting the equipment's ability to maintain accuracy. This parameter is introduced into humidity control based on the hysteresis and irreversibility of humidity's effects. Many types of humidity-induced equipment damage, such as insulation aging and contact corrosion, are irreversible processes. When metrological reliability decreases, it indicates that the overall system condition is beginning to deteriorate, requiring more stringent humidity control to prevent further escalation.

[0038] Power purification parameters are obtained based on total harmonic distortion, power factor, and frequency deviation, and are expressed by the following formula: ; In the formula, Indicates power purification parameters, This indicates the power purification and regulation gain.

[0039] Preferably, the temperature control parameters, humidity control parameters, and power purification parameters are used as inputs to the multilayer sensor. The specific steps are as follows: An input feature vector is constructed based on the metering reliability index, environmental quality index, temperature control parameters, humidity control parameters, and power purification parameters, expressed by the formula: ; In the formula, This represents the input feature vector; The input feature vector is used as the input to the multilayer perceptron, as expressed by the formula: ; ; In the formula, This represents the input feature vector. This represents the hidden layer output vector. This represents the sigmoid activation function. This represents the hidden layer weight matrix. This represents the hidden layer bias vector. This represents the output layer weight matrix. This represents the output layer bias vector. Represents the control decision vector; The control decision vector is expressed by the formula: ; In the formula, This indicates the temperature control decision. Indicates humidity control decisions. This indicates a power purification decision.

[0040] Preferably, the temperature control signal is expressed by the formula: ; In the formula, This indicates a temperature control signal used to set the target temperature value for laboratory environmental control equipment. Indicates temperature control gain; The humidity control signal is expressed by the formula: ; In the formula, This indicates a humidity control signal used to set the target humidity value for a laboratory humidifier or dehumidifier. Indicates humidity control gain; The power purification control signal is expressed by the formula: ; In the formula, This indicates a power purification control signal, used to control the output intensity of power quality treatment equipment. This indicates the power purification control gain.

[0041] for example: A 45A command is sent to the active power filter. The active power filter immediately injects a compensation current into the grid through its power circuitry, equal in magnitude but opposite in direction to the detected harmonic current, with a target effective value of 45 amps for the total compensation current. This action cancels out the harmonics generated by the load in real time, thereby reducing the total harmonic distortion (THD) to the target range.

[0042] Example 2: This embodiment provides an electronic device that stores a computer program. When the computer program is executed by a processor, it implements the test environment monitoring method of the smart power metering laboratory as described in any embodiment of the present invention.

[0043] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0044] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0045] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0046] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0047] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A test environment monitoring method of a power metering smart laboratory, characterized by, The method comprises the following steps: obtaining power data and environment data of a power metering smart laboratory and preprocessing the data; obtaining current frequency domain sequences and voltage frequency domain sequences of the power data, and determining total harmonic distortion based on the current frequency domain sequences and the voltage frequency domain sequences; calculating apparent power of the power data, and obtaining power factor of the power data based on the apparent power and the total harmonic distortion; obtaining metering error of the power metering smart laboratory based on the environment data and the power factor, and obtaining environment sensitivity based on the metering error; obtaining environment quality index and metering reliability index of the power metering smart laboratory; obtaining temperature control parameters, humidity control parameters and power supply purification parameters based on the power factor, the environment data, the metering error, the environment sensitivity, the environment quality index and the metering reliability index; taking the temperature control parameters, the humidity control parameters and the power supply purification parameters as inputs of a multi-layer perception machine to obtain a control decision vector; generating temperature control signals, humidity control signals and power supply purification control signals based on the control decision vector to regulate the power metering smart laboratory.

2. The test environment monitoring method of the power metering smart lab according to claim 1, characterized in that, The environment data comprises temperature data and humidity data; The power data comprises current data and voltage data; The preprocessing of the temperature data is represented by a formula as follows: ； ； In the formula, represents the temperature data after preprocessing, represents the size of the sliding window, represents the temperature weight of the th, represents the temperature sample value of the th, represents the temperature average value within the window, represents the temperature variance within the window; The preprocessing of the humidity data is represented by a formula as follows: ； ； wherein, represents the pre-processed humidity data, represents the first humidity weight, represents the first humidity sample value, represents the humidity average value within the window, represents the humidity variance within the window; The preprocessing of the current data is represented by a formula as follows: ； In the formula, represents the pre-processed current data, represents the first current sampling signal; The preprocessing of the voltage data is represented by a formula as follows: ； In the formula, represents the voltage data after preprocessing, represents the first voltage sampling signal.

3. The test environment monitoring method of the power metering smart lab according to claim 2, characterized in that, The current frequency domain sequences and the voltage frequency domain sequences are obtained by using a fast Fourier transform algorithm on current sampling signals and voltage sampling signals, and are represented by a formula as follows: ； ； wherein denotes a sequence of current frequencies, denotes a fast Fourier transform function, denotes a sequence of voltage frequencies; The current total harmonic distortion of the current frequency domain sequences and the voltage total harmonic distortion of the voltage frequency domain sequences are calculated, and are represented by a formula as follows: ； ； wherein denotes the total harmonic distortion of the current, denotes the total harmonic distortion of the voltage, denotes the harmonic order denotes the amplitude of the harmonic in the current frequency domain sequence, denotes the amplitude of the 1st harmonic in the current frequency domain sequence, denotes the amplitude of the harmonic in the voltage frequency domain sequence, denotes the amplitude of the 1st harmonic in the voltage frequency domain sequence; The total harmonic distortion is determined based on the current total harmonic distortion and the voltage total harmonic distortion.

4. The test environment monitoring method of the power metering smart lab according to claim 3, characterized in that, The apparent power of the power data is calculated, and is represented by a formula as follows: ； wherein denotes the apparent power; The active power of the power data is calculated, and is represented by a formula as follows: ； In the formula, P represents the active power; The power factor of the power data is obtained based on the apparent power, the active power and the total harmonic distortion, and is represented by a formula as follows: ； In the formula, represents the power factor, represents the harmonic compensation coefficient, represents the total harmonic distortion.

5. The method of claim 4, wherein the method further comprises: The metering error of the power metering smart laboratory is obtained based on the environment data and the power factor, and is represented by a formula as follows: ； wherein denotes the metering error, denotes the reference error, temperature error coefficient, denotes the reference temperature, humidity error coefficient, reference humidity, current error coefficient, denotes the rated current, current non-linearity coefficient, voltage error coefficient, denotes the rated voltage, harmonic influence coefficient, power factor error coefficient, frequency deviation error coefficient, denotes the frequency deviation, denotes the nominal frequency; The frequency deviation is represented by a formula as follows: ； In the formula, denotes the measurement frequency; The environment sensitivity is obtained based on the metering error, and is represented by a formula as follows: ； In the formula, represents the environmental sensitivity.

6. The method of claim 5, wherein the method further comprises: The environment quality index is represented by a formula as follows: ； In the formula, represents an environmental quality index, represents an ideal temperature, represents a temperature tolerance, represents an ideal humidity, represents a humidity tolerance, represents a maximum allowable distortion rate; The metering reliability index is represented by a formula as follows: ； In the formula, represents a measurement reliability index, represents a frequency tolerance.

7. The method of claim 6, wherein the method further comprises: The temperature control parameters, the humidity control parameters and the power supply purification parameters are obtained based on the power factor, the environment data, the metering error, the environment sensitivity, the environment quality index and the metering reliability index, and the specific steps are as follows: The temperature control parameters are obtained based on the preprocessed temperature data, the environment quality index and the metering error, and are represented by a formula as follows: ； wherein denotes a temperature regulation parameter, denotes a temperature regulation gain, denotes an error threshold, denotes a hyperbolic tangent function; The humidity control parameters are obtained based on the preprocessed humidity data, the environment sensitivity and the metering reliability index, and are represented by a formula as follows: ； wherein denotes a humidity control parameter, denotes a humidity control gain, denotes a sensitivity threshold; The power supply purification parameters are obtained based on the total harmonic distortion, the power factor and the frequency deviation, and are represented by a formula as follows: ； wherein represents the power purification parameter, represents the power purification regulation gain.

8. The method of claim 7, wherein the method further comprises: The temperature control parameters, the humidity control parameters and the power supply purification parameters are taken as inputs of a multi-layer perception machine, and the specific steps are as follows: An input feature vector is constructed based on a metering reliability index, an environmental quality index, a temperature regulation parameter, a humidity regulation parameter, and a power supply purification parameter; The input feature vector is taken as an input of a multi-layer perception machine, and is expressed by a formula as follows: ； ； wherein, represents an input feature vector, represents a hidden layer output vector, represents a sigmoid activation function, represents a hidden layer weight matrix, represents a hidden layer bias vector, represents an output layer weight matrix, represents an output layer bias vector, represents a control decision vector; A control decision vector is expressed by a formula as follows: ； wherein represents a temperature control decision, represents a humidity control decision, represents a power supply purification decision.

9. The method of claim 8, wherein the method further comprises: A temperature control signal is expressed by a formula as follows: ； In the formula, denotes a temperature control signal, denotes a temperature control gain; A humidity control signal is expressed by a formula as follows: ； In the formula, denotes a humidity control signal, denotes a humidity control gain; A power supply purification control signal is expressed by a formula as follows: ； In the formula, represents a power purification control signal, represents a power purification control gain.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the test environment monitoring method of the power metering smart laboratory according to any one of claims 1 to 9 when executing the program.

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