Electric energy meter calibration method and system based on Internet of Things

Abstract

The invention relates to the field of data processing, in particular to an electric energy meter calibration method and system based on the Internet of Things, and the method comprises the steps: firstly obtaining the current electric quantity data, the use time and the reference operation temperature of each electric energy meter in a target Internet of Things, and obtaining the ambient temperature mean value of each electric energy meter in a previous preset time period; and then the current electric quantity data, the use time, the reference operation temperature and the ambient temperature mean value are put into a preset electric quantity calibration model, an electric quantity calibration value is obtained through calculation, then whether the ratio of the electric quantity calibration value to the current electric quantity data is in a calibration interval is judged, and if yes, the electric quantity calibration value is replaced with the current electric quantity data and reported to a monitoring end. Compared with the prior art, the method is higher in calibration efficiency and higher in reliability.

Description

Technical Field

[0001] The present invention relates to the field of data processing. More specifically, the present invention relates to a method and system for calibrating an electric energy meter based on the Internet of Things. Background Art

[0002] An intelligent electric meter is a new generation of electric meter with functions such as electric energy monitoring, remote meter reading, and power consumption data analysis, and it is applied to the circuit network of a large-scale Internet of Things. The intelligent electric meter not only undertakes the task of basic electric energy measurement, but also acts as a data acquisition node, and can upload the power consumption data of users to the cloud or data center in real time. This characteristic enables power companies to efficiently manage the power grid load, optimize energy distribution, and predict power consumption trends through big data analysis, providing strong support for the construction of the smart grid.

[0003] In a large-scale Internet of Things, the accuracy of the electric energy record of the intelligent electric meter is directly related to the effectiveness of energy management. However, affected by various factors, the intelligent electric meter may have a deviation in the electric energy record. Among them, the temperature drift of the electronic components in the electric meter and the aging of the electronic components are the two main reasons for the deviation of the electric energy record. Temperature drift refers to the drift phenomenon of the performance of the electronic components inside the electric meter with the change of temperature. Under abnormal temperature conditions, the parameters of components such as resistors, capacitors, or semiconductor devices inside the electric meter may change, resulting in an offset of the real-time measured voltage or current, causing a deviation in the real-time measured power, and further leading to inaccurate measured electric energy. Affected by environmental factors (such as humidity, temperature, light, corrosive substances, or floating dust), the intelligent electric meter will age to varying degrees over time. This aging phenomenon will gradually reduce the measurement accuracy of the intelligent electric meter, thereby affecting the actual measurement of electric energy.

[0004] To address the above problems, the intelligent electric meter usually needs to be regularly calibrated for electric energy by professional personnel, which requires more in-depth data analysis and calculation relying on the professional accumulation of personnel. This method is time-consuming and laborious, and has high professional requirements for personnel. There are often thousands of electric meters in a large-scale Internet of Things, and the demand for professional personnel is extremely large. Chinese Patent Application No. CN119471547A discloses a method and system for self-calibrating a DC electric energy meter, which mainly corrects the reference voltage signal according to the obtained temperature compensation gain, and then realizes the temperature compensation for electric energy. This method lacks consideration of the influence of the aging of the electric energy meter itself, and the calibration accuracy is low. Summary of the Invention

[0005] To solve the above technical problem of low calibration accuracy, the present invention discloses a method and system for calibrating an electric energy meter based on the Internet of Things.

[0006] In the first aspect, the present invention discloses a method for calibrating an electric energy meter based on the Internet of Things, including: Obtain the current power consumption data, the time of being put into use, and the reference operating temperature of each electricity meter in the target Internet of Things, and at the same time obtain the average environmental temperature of each electricity meter in the previous preset time period; Put the current power consumption data, the time of being put into use, the reference operating temperature, and the average environmental temperature into a preset power calibration model to calculate the power calibration value; the power calibration model is a power calibration model based on temperature compensation; Judge whether the ratio of the power calibration value to the current power consumption data is within the calibration interval. If so, replace the power calibration value with the current power consumption data and report it to the monitoring end.

[0007] Beneficial effects: When calibrating the power consumption of the electricity meters in the Internet of Things, the method of the present invention utilizes a power calibration model, which can calibrate the power consumption data of all electricity meters in the target Internet of Things at the same time, without the intervention of professionals to participate in the evaluation and calculation, overcoming the problem of low power calibration efficiency of electricity meters in the prior art. After obtaining the power calibration values of each electricity meter, the method of the present invention also makes an interval judgment on the power calibration values. When the power calibration value is within a relatively reasonable calibration interval, the power calibration value will be used as the actual power display value of the corresponding electricity meter and reported to the monitoring end. This method is more reliable and can effectively reduce the occurrence of incorrect calibration. When calculating the power calibration value, the method of the present invention takes the time of being put into use of the electricity meter into account, realizing the comprehensive operation and analysis of the aging factor and improving the calibration accuracy. Compared with the prior art, the calibration method of the present invention has higher calibration efficiency, stronger reliability and higher accuracy.

[0008] Preferably, the power calibration model is specifically:

[0009] In the formula, represents the power calibration value of a certain electricity meter at the moment, represents the current power consumption data of a certain electricity meter at the moment, represents the temperature drift direction coefficient of a certain electricity meter at the moment, is the temperature compensation gain value based on the environmental temperature , represents the time of being put into use of a certain electricity meter aging mapping value relative to the current aging compensation coefficient.

[0010] Beneficial effects: The calibration directions of the power calibration model are mainly divided into two major directions: temperature compensation and aging compensation. For temperature compensation, a comprehensive evaluation of the temperature drift direction and temperature difference change is incorporated into the power calibration model, and this compensation method is more convenient and reliable. For aging compensation, the power calibration model mainly evaluates in combination with the service time of the electric energy meter. This method requires less data analysis sources and can obtain calibration results quickly. Compared with the prior art, the power calibration model adopted by the method of the present invention is more convenient and reliable.

[0011] Preferably, the mapping expression of the service time relative to the current aging compensation coefficient is:

[0012] In the formula, represents the service time of a certain electric energy meter the aging mapping value relative to the current aging compensation coefficient, represents the first fitting coefficient of the electric energy meter obtained by fitting with a fully connected neural network or a Bayesian neural network, represents the second fitting coefficient of the electric energy meter obtained by fitting with a fully connected neural network or a Bayesian neural network, represents the third fitting coefficient of the electric energy meter obtained by fitting with a fully connected neural network or a Bayesian neural network.

[0013] Beneficial effects: When constructing the mapping expression of the current aging compensation coefficient, corresponding fitting coefficients need to be set according to the corresponding type of electric energy meter to adapt to the internal circuit structure of the corresponding electric energy meter. Through fitting with a fully connected neural network or a Bayesian neural network, more accurate fitting parameters can be obtained, improving the reliability of the method of the present invention.

[0014] Preferably, if the service time of any electric energy meter is less than the predetermined calibration period, the aging mapping value is set to 0.

[0015] Preferably, the calculation expression of the temperature drift direction coefficient is:

[0016] In the formula, represents the temperature drift direction coefficient of a certain electric energy meter at the moment, represents the temperature drift estimate of a certain electric energy meter from to the moment.

[0017] Beneficial effects: Compared with the prior art, the method of the present invention can calculate the corresponding temperature drift direction coefficient according to the temperature environment where the corresponding electric energy meter is located, realize the positive compensation or negative compensation of the electric energy meter power measurement, and is closer to the actual application scenario.

[0018] Further, the calculation expression of the temperature drift estimation value is as follows:

[0019] In the formula, represents the average voltage of a certain electricity meter from to time; represents the voltage of the to th sampling point of a certain electricity meter during the time period from ; represents the mains voltage; represents the average ambient temperature of a certain electricity meter from to time; represents the temperature of the to th sampling point of a certain electricity meter during the time period from ; represents the starting item serial number of the sampling point; represents the number of samplings from to time; represents the reference operating temperature of a certain electricity meter.

[0020] Preferably, if the ratio of the electricity calibration value to the current electricity data is less than the lower limit of the calibration interval, the electricity calibration value is cleared, and the current electricity data is uploaded to the monitoring end.

[0021] Preferably, if the ratio of the electricity calibration value to the current electricity data is greater than the upper limit of the calibration interval, the electricity calibration value and the current electricity data are uploaded to the monitoring end, and a review request is sent to the monitoring end at the same time.

[0022] Preferably, after obtaining the commissioning time of each electricity meter in the target Internet of Things, the method of the present invention further includes: judging whether there is an electricity meter whose commissioning time exceeds the preset service life; If so, the positioning information of the electricity meter exceeding the service life is uploaded to the monitoring end.

[0023] Beneficial effects: The method of the present invention can locate the electricity meters that exceed the service life and send the positioning information to the monitoring end to prompt the corresponding professionals at the monitoring end to replace the electricity meters that exceed the service life in time.

[0024] In a second aspect, the present invention also discloses an electricity meter calibration system based on the Internet of Things, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the electricity meter calibration method based on the Internet of Things described in the first aspect is implemented.

[0025] The beneficial effects of the present invention are as follows: (1) Compared with the prior art, the calibration method of the present invention has higher calibration efficiency, stronger reliability and higher precision.

[0026] (2) Compared with the prior art, the power calibration model adopted by the method of the present invention is simpler and more reliable.

[0027] (3) Compared with the prior art, the method of the present invention can calculate the corresponding temperature drift direction coefficient according to the temperature environment where the corresponding electric energy meter is located, and realize the positive compensation or negative compensation of the electric energy meter power measurement, which is closer to the actual application scenario. Description of the Drawings

[0028] Figure 1 is a flowchart of the electric energy meter calibration method based on the Internet of Things in the first embodiment of the present invention; Figure 2 is an aging mapping curve of a certain electric energy meter in the first embodiment of the present invention; Figure 3 is a schematic structural diagram of the electric energy meter calibration system based on the Internet of Things in the second embodiment of the present invention. Detailed Embodiments

[0029] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments.

[0030] Next, the detailed embodiments of the present invention will be described in detail in conjunction with the accompanying drawings.

[0031] Embodiment 1 As Figure 1 shown, this embodiment discloses an electric energy meter calibration method based on the Internet of Things, including: S10: Obtain the current power data, the time of being put into use, and the reference operating temperature of each electric energy meter in the target Internet of Things, and at the same time obtain the average environmental temperature of each electric energy meter in the previous preset time period.

[0032] In this embodiment, the target Internet of Things is usually deployed in application environments such as smart factories, smart communities, or smart medical communities. The above-mentioned previous preset time period usually refers to the previous hour relative to the current time. In the previous preset time period, usually 5-12 data sampling points are taken. In other embodiments, in order to improve the reliability of the data source, the previous preset time period can be shortened and the number of sampling points can be increased.

[0033] S20: Put the current power data, the time of being put into use, the reference operating temperature, and the average environmental temperature into a preset power calibration model to calculate the power calibration value.

[0034] Among them, the power calibration model is a power calibration model based on temperature compensation, and the power calibration model is specifically:

[0035] In the formula, represents the power calibration value of a certain electricity meter at the th moment, represents the current power data of a certain electricity meter at the th moment, represents the temperature drift direction coefficient of a certain electricity meter at the th moment, is the temperature compensation gain value based on the ambient temperature , represents the commissioning time of a certain electricity meter aging mapping value relative to the current aging compensation coefficient. Existing gain compensation coefficients can be adopted.

[0036] S30: Determine whether the ratio of the power calibration value to the current power data is within the calibration interval. If so, replace the power calibration value with the current power data and report it to the monitoring terminal.

[0037] Through the above steps S10 - S30, the method of the present invention uses the power calibration model to calibrate the power data of all electricity meters in the target Internet of Things. The whole process does not require professional personnel to intervene in the evaluation and calculation, thus overcoming the problem of low power calibration efficiency of electricity meters in the prior art. Compared with the prior art, the power calibration model of the present invention mainly involves calibration improvements in two dimensions: temperature compensation and aging compensation. For temperature compensation, a comprehensive evaluation of the temperature drift direction and temperature difference change is incorporated into the power calibration model, making the compensation result more reliable and reasonable. For aging compensation, the power calibration model mainly evaluates in combination with the commissioning time of the electricity meter. This method requires less data analysis source, can obtain the calibration result faster and has higher calibration accuracy.

[0038] It should be added that, without special exceptions, in principle, electricity meters are not allowed to reverse for billing. The calibration interval in the above step S30 generally takes 1 - 1.2.

[0039] Optionally, each electricity meter is numbered to accurately calibrate and locate each electricity meter. The above power calibration model can be converted to:

[0040] In the formula, represents the th electricity meter's power calibration value at the th moment, Indicates the current power consumption data of the th electricity meter at the Indicates the th electricity meter's temperature drift direction coefficient at the moment, Indicates the temperature compensation basic coefficient, Indicates the th electricity meter's commissioning time mapping value relative to the current aging compensation coefficient, Indicates the th electricity meter's average environmental temperature from to moment, Indicates the th electricity meter's temperature at the rd sampling point from to moment, Indicates the starting item number of the sampling point, Indicates the number of samplings from to moment, Indicates the reference operating temperature of the

[0041] It should be noted that in the actual application environment, the battery life of the smart meter is generally about 4 years. Based on the aging test of the smart meter, it is known that the impact of the smart meter's aging for about 1 year on its power measurement is negligible and can be ignored. Therefore, the calibration period set by the method of the present invention is the 50th week - 200th week after the smart meter is put into use. Based on this, if the th electricity meter's commissioning time is less than the predetermined calibration period, the mapping value is set to 0, thereby reducing unnecessary compensation operations and further improving the operation efficiency. The temperature compensation basic coefficient is generally the basic compensation coefficient carried by the electricity meter after leaving the factory, and this coefficient is generally stored in the MCU module of the electricity meter. In this embodiment, the range of this basic compensation coefficient generally takes 0.01% - 0.05%, and is specifically set according to the accuracy requirements of the electricity meter.

[0042] It should be noted that due to the different internal circuit structures, wiring structures or production processes of different types of electricity meters, when evaluating their aging performance, it is often necessary to perform complex arithmetic analyses such as impedance, capacitive reactance, active power or reactive power according to their internal circuit structures. Such data analysis and evaluation methods are too complex, and for an Internet of Things system with thousands or tens of thousands of smart electricity meters, the computing load is extremely large. Depending on the aging test data of various types of electricity meters, it can be known that the degree of influence of their aging on electricity metering shows a non-linear trend of "increasing and the increasing rate continuously decreasing". Based on this, a mapping expression with a non-linear law can be used to describe this law. In order to improve the accuracy of aging description, the following method can be used to configure the best fitting coefficient (i.e., aging compensation coefficient) for each type of electricity meter. Specifically: First, obtain the aging test data of the target electricity meter. Among them, the aging test data at least includes historical electricity metering values and aging time. Then, use the aging test data as a training set and place it in a fully connected neural network or a Bayesian neural network for training, and finally obtain the best fitting coefficient.

[0043] It should be further supplemented and explained that during the aging process of the electricity meter, it usually causes the metering value to be smaller than the actual value, which is mainly caused by the following major factors: 1. Open circuit of current transformer (CT): Phenomenon: The secondary winding of the CT ages or the insulation is damaged, resulting in the ineffective transfer of the primary side current to the secondary side.

[0044] Result: The electricity meter undercounts the electricity, and the actual electricity consumption of the user is greater than the value recorded by the meter.

[0045] 2. Deterioration of metering chip performance: Phenomenon: Chip aging causes the sampling frequency to decrease or the ADC conversion error to increase.

[0046] Result: When using low power (such as standby state), the meter may miss part of the electricity.

[0047] 3. Insufficient battery power supply (for smart electricity meters): Phenomenon: Aging of the built-in battery causes parameter loss or clock failure, and it needs to rely on the backup power supply to maintain operation.

[0048] Result: The backup power supply may cause additional power consumption, indirectly resulting in a smaller metering value.

[0049] Based on the above explanatory notes, combined with Figure 2 as shown, the mapping expression of the above commissioning time with respect to the current aging compensation coefficient is:

[0050] In the formula, Indicates the commissioning time of a certain electricity meter The aging mapping value relative to the current aging compensation coefficient Indicates the first fitting coefficient of the electricity meter obtained by fitting with a fully connected neural network or a Bayesian neural network Indicates the second fitting coefficient of the electricity meter obtained by fitting with a fully connected neural network or a Bayesian neural network Indicates the third fitting coefficient of the electricity meter obtained by fitting with a fully connected neural network or a Bayesian neural network. The above aging compensation coefficient includes the first fitting coefficient, the second fitting coefficient, and the third fitting coefficient

[0051] Compared with the prior art, the fitting coefficients calculated by the above method are more accurate, and the aging compensation coefficients of different types of electricity meters can be obtained quickly

[0052] Optionally, if traversal calculation is performed in the Internet of Things system, the above mapping expression can also be:

[0053] In the formula Indicates the first fitting coefficient of the th electricity meter obtained by fitting with a fully connected neural network or a Bayesian neural network Indicates the second fitting coefficient of the th electricity meter obtained by fitting with a fully connected neural network or a Bayesian neural network Indicates the third fitting coefficient of the th electricity meter obtained by fitting with a fully connected neural network or a Bayesian neural network

[0054] Furthermore, the specific calculation process of the temperature drift direction coefficient of the above power calibration model is:

[0055] In the formula Indicates the temperature drift direction coefficient of a certain electricity meter at the th moment Indicates the temperature drift estimate of a certain electricity meter from to th moment Among them, the calculation expression of the temperature drift estimate is:

[0056] In the formula Indicates the average voltage of a certain electricity meter from to th moment Indicates the to th moment in a certain electricity meter The voltage of a sampling point represents the mains voltage represents that a certain electricity meter from to the average ambient temperature at a certain moment represents that a certain electricity meter from to the th temperature of the sampling point in a certain moment represents the starting item number of the sampling point represents from to the number of samplings at a certain moment represents the reference operating temperature of a certain electricity meter

[0057] It should be explained that an electronic electricity meter generally obtains the sampling voltage and sampling current through a sampling circuit. Considering the weak current protection of the electricity meter itself, its sampling current is generated by induction of a current transformer. When the temperature rises to a certain level, the resistance value of the voltage-dividing resistor increases, and the temperature coefficient of the current transformer is positive, which will cause the sampling voltage and sampling current to be too high, resulting in too large a metering power. As time goes by, the metering value of the electricity will eventually be too large. On the contrary, if the temperature drops to a certain level, the resistance value of the voltage-dividing resistor decreases, and the temperature coefficient of the current transformer is negative, which will cause the sampling voltage and sampling current to be too low, resulting in too small a metering power. As time goes by, the metering value of the electricity will eventually be too small

[0058] Through the above technical solution, the method of the present invention can automatically determine the corresponding temperature drift direction coefficient according to the temperature environment of the corresponding electricity meter. If the overall temperature drift estimate value is greater than 0, it indicates that the electricity meter has a positive temperature drift and the electricity has a situation of too fast growth rate in the previous period, and negative compensation needs to be given. If the overall temperature drift estimate value is less than 0, it indicates that the electricity has a situation of too slow growth rate in the previous period, and positive compensation needs to be given. By performing positive compensation or negative compensation on the electricity metering of the electricity meter, the abnormal situation of electricity metering caused by temperature factors can be overcome

[0059] It should be added that for the above calibration process, it is necessary to meet the condition that there is a sampling current in the corresponding electricity meter in the previous preset period (that is, there is an electrical appliance connected and running). If there is no sampling current, there is no need to calibrate the electricity meter

[0060] Preferably, when the ratio of the electricity calibration value to the current electricity data is not within the calibration interval, the method of the present invention mainly uses the following two methods for data processing, specifically Method 1: If the ratio of the electricity calibration value to the current electricity data is less than the lower limit value of the calibration interval, clear the electricity calibration value and upload the current electricity data to the monitoring end

[0061] Method 2: If the ratio of the power calibration value to the current power data is greater than the upper limit of the calibration range, upload the power calibration value and the current power data to the monitoring terminal, and at the same time send a review request to the monitoring terminal.

[0062] Specifically, when the ratio of the power calibration value to the current power data is less than the lower limit of the calibration range, based on the principle of not allowing billing reversal, the power calibration value can be selected to be ignored. When the ratio of the power calibration value to the current power data is greater than the upper limit of the calibration range, it indicates that the difference between the power calibration value and the current power data is extremely large, and there may be situations such as power leakage, power theft, or serious aging of the electric meter. A review request needs to be sent to the monitoring terminal so that professionals can respond in a timely manner.

[0063] Furthermore, in order to ensure the stable operation of each electric meter and improve the user's power consumption experience, after obtaining the commissioning time of each electric meter in the target Internet of Things, the method of the present invention further includes: Determine whether there is an electric meter whose commissioning time exceeds the preset service life; If so, upload the location information of the electric meter that has exceeded the service life to the monitoring terminal.

[0064] It should be added that the electric meter mentioned in the present invention refers to an electronic smart meter. The designed service life of such meters is theoretically 10 to 20 years, and the battery life built into the electric meter is about 4 - 8 years. Therefore, the battery of the electric meter needs to be replaced during its entire life cycle. Based on this, in this embodiment, in order to ensure the normal operation of the electric meter during a service life cycle, the above service life can be set to 4 - 5 years.

[0065] Embodiment 2 As Figure 3 shown, this embodiment discloses an electric meter calibration system based on the Internet of Things, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the electric meter calibration method based on the Internet of Things described in Embodiment 1 is implemented.

[0066] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and scope of the present invention.

Claims

1. A method for calibrating an electric energy meter based on the Internet of Things, characterized in that: include: Obtain the current power data, commissioning time and benchmark operating temperature of each power meter in the target Internet of Things, and at the same time obtain the average ambient temperature of each power meter in the previous preset period; The current power data, the time of use, the reference operating temperature and the average value of the ambient temperature are placed into a preset power calibration model to calculate a power calibration value; the power calibration model is a power calibration model based on temperature compensation; It is determined whether the ratio of the power calibration value to the current power data is within a calibration interval. If so, the power calibration value is replaced with the current power data and reported to the monitoring end.

2. The method for calibrating electric energy meters based on the Internet of Things according to claim 1, characterized in that: The power calibration model is specifically: In the formula, Indicates that a certain energy meter is The power calibration value at the moment, Indicates that a certain energy meter is Current power data at the moment, Indicates that a certain energy meter is The temperature drift directional coefficient at the moment, Based on ambient temperature The temperature compensation gain value, Indicates the time a certain energy meter has been put into use Aging map value relative to the current aging compensation factor.

3. The method for calibrating electric energy meters based on the Internet of Things according to claim 1, characterized in that: The mapping expression of the time of use relative to the current aging compensation coefficient is: In the formula, Indicates the time a certain energy meter has been put into use The aging mapping value relative to the current aging compensation coefficient, represents the first fitting coefficient of the electric energy meter obtained by fitting the fully connected neural network or the Bayesian neural network, represents the second fitting coefficient of the electric energy meter obtained by fitting the fully connected neural network or the Bayesian neural network, It represents the third fitting coefficient of the electric energy meter obtained by fitting the fully connected neural network or the Bayesian neural network.

4. The method for calibrating electric energy meters based on the Internet of Things according to claim 2, characterized in that: If the use time of any electric energy meter is less than the predetermined calibration period, the aging mapping value is set to 0.

5. The electric energy meter calibration method according to claim 2, characterized in that: The calculation expression of the temperature drift direction coefficient is: In the formula, Indicates that a certain energy meter is The temperature drift directional coefficient at the moment, Indicates that a certain energy meter arrive Estimated temperature drift at time.

6. The electric energy meter calibration method according to claim 5, characterized in that: The calculation expression of the temperature drift estimation value is: In the formula, Indicates that a certain energy meter arrive The mean voltage at the time, Indicates that a certain energy meter arrive The moment The voltage at each sampling point, Indicates the mains voltage. Indicates that a certain energy meter arrive The average ambient temperature at the time, Indicates that a certain energy meter arrive The first moment The temperature of the sampling point, Indicates the starting number of the sampling point. Indicates from arrive The number of samples at a time, Indicates the reference operating temperature of a certain electric energy meter.

7. The electric energy meter calibration method according to claim 1, characterized in that: If the ratio of the power calibration value to the current power data is less than the lower limit of the calibration interval, the power calibration value is cleared and the current power data is uploaded to the monitoring end.

8. The electric energy meter calibration method according to claim 1, characterized in that: If the ratio of the power calibration value to the current power data is greater than the upper limit of the calibration interval, the power calibration value and the current power data are uploaded to the monitoring end, and a review request is sent to the monitoring end.

9. The electric energy meter calibration method according to claim 1, characterized in that: After obtaining the commissioning time of each electric energy meter in the target Internet of Things, the method further includes: Determine whether there is an electric energy meter whose service life exceeds the preset service life; If so, the location information of the electric energy meter that has exceeded its service life is uploaded to the monitoring terminal.

10. An electric energy meter calibration system based on the Internet of Things, characterized in that: It comprises a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the electric energy meter calibration method based on the Internet of Things as described in any one of claims 1 to 9 is implemented.

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