Real-time monitoring and error calibration method for intelligent electric energy metering equipment

Abstract

The invention relates to the technical field of metering equipment monitoring, and particularly discloses an intelligent electric energy metering equipment real-time monitoring and error calibration method, which comprises the following steps: acquiring equipment state data of electric energy metering equipment in different time periods and equipment operation state data through a sensor group, the equipment state of the electric energy metering equipment can be analyzed by combining the equipment state data of the electric energy metering equipment, then whether the operation state of the electric energy metering equipment is abnormal or not can be monitored in real time by combining the data and the operation state data of the electric energy metering equipment, and on the premise, the operation state of the electric energy metering equipment can be monitored in real time. If the equipment state or the operation state of the electric energy metering equipment is judged to be abnormal, it is indicated that the electricity consumption data collected during operation of the electric energy metering equipment has errors, otherwise, it is indicated that the electricity consumption data is normal, and whether errors exist in the electric energy metering equipment or not can be further judged according to the electricity consumption data; therefore, the accuracy of the judgment result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of metering equipment monitoring, and in particular to a method for real-time monitoring and error calibration of intelligent electric energy metering equipment. Background Art

[0002] As an important part of modern power system, real-time monitoring and error calibration of smart energy metering equipment are crucial to ensure the accuracy and reliability of energy metering.

[0003] Since smart electricity metering devices are usually equipped with corresponding mobile phone applications, users can download, install and log in to the application to view power consumption, current, voltage and other parameters in real time. Therefore, the traditional real-time monitoring method is generally to monitor the user's power consumption in real time, including current, voltage, power and other parameters, to help users discover abnormal power consumption in time, and further judge whether there is any error in the power metering of the power metering device based on the power consumption data, so as to take corresponding measures.

[0004] The real-time monitoring of traditional smart energy metering equipment mostly helps users discover and judge whether there are errors in the energy metering of the energy metering equipment by monitoring the user's electricity consumption data. However, when the energy metering equipment is damaged or its operating condition is poor, the electricity consumption data will be affected, resulting in low data accuracy. On this basis, judging whether there are errors in the energy metering of the energy metering equipment based on the electricity consumption data has low accuracy. Summary of the invention

[0005] The purpose of the present invention is to provide a real-time monitoring and error calibration method for intelligent electric energy metering equipment to solve the following technical problems:

[0006] How to improve the accuracy of the results of judging whether there is an error in the electric energy measurement of electric energy metering equipment.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for real-time monitoring and error calibration of intelligent electric energy metering equipment, the method comprising:

[0009] S1: Collecting the equipment status data of the electric energy metering equipment in different time periods and the equipment operation status data through the sensor group;

[0010] S2: by combining the collected equipment status data of the electric energy metering equipment, calculating the real-time equipment status influence coefficient of the electric energy metering equipment, and analyzing the equipment status of the electric energy metering equipment based on the data;

[0011] S3: Calculating the real-time operation status influence coefficient of the electric energy metering device by combining the collected operation status data of the electric energy metering device with the real-time device status influence coefficient of the electric energy metering device;

[0012] S4: By combining the real-time operating status influence coefficient of the electric energy metering device, analyze whether the operating status of the electric energy metering device is abnormal. If yes, proceed to step S6; otherwise, proceed to step S5:

[0013] S5: By combining the monitored power consumption data of the same time period in the past and the influence coefficient of the operation status of the power metering device, further analyzing whether there is an abnormality in the operation status of the power metering device;

[0014] S6: When it is determined that the operating state of the electric energy metering device is abnormal, calibration is performed through the calibration device according to the requirements of the calibration device.

[0015] Furthermore, the analysis process in S2 includes:

[0016] By formula Calculate the equipment status influence coefficient of the electric energy metering equipment in the i-th time period ;

[0017] Where i is any monitoring time period, is the damaged area of ​​the wire sheath of the electric energy metering equipment in the i-th monitoring period, is the exposed wire area of ​​the electric energy metering equipment in the i-th monitoring period, The total area of ​​wires monitored by the energy metering equipment, is the undamaged area of ​​the wire monitored by the electric energy metering equipment, a is any time point, b is the total number of time points in a monitoring period, is the temperature of the wiring port of the electric energy metering device at the ath time point in the i-th monitoring period, is the preset connection port temperature, is the external temperature influence coefficient, which is set based on empirical fitting. is the connection port resistance of the energy metering device at the ath time point in the i-th monitoring period, is the preset resistance, for The standard value of and is the weight coefficient.

[0018] Furthermore, the analysis process in S2 also includes:

[0019] By calculating the equipment status influence coefficient of the electric energy metering equipment in the i-th time period The preset device status impact coefficient threshold Make a comparison;

[0020] like , determine that the current time period has abnormal state of the electric energy metering equipment, which will cause errors in the operation of the electric energy metering equipment, and issue an early warning to remind maintenance personnel to carry out repairs;

[0021] like , it is judged that there is no abnormality in the state of the electric energy metering device itself in the current time period, which means that there is no error or the error is small in the operation of the electric energy metering device, and no warning is issued.

[0022] Furthermore, the calculation process in S3 includes:

[0023] By formula Calculate the operating status influence coefficient of the electric energy metering equipment in the i-th time period ;

[0024] in, is the current value of the energy metering device at the ath time point in the i-th monitoring period, is the preset current size, for The standard value of is the voltage of the energy metering device at the ath time point in the i-th monitoring period, is the preset voltage size, for The standard value of is the power factor of the electric energy metering device at the ath time point in the i-th monitoring period, For all The average value of .

[0025] Furthermore, the analysis process in S4 includes:

[0026] By using the operating status influence coefficient of the electric energy metering equipment in the i-th time period The preset operating status impact coefficient threshold Make a comparison;

[0027] like , judge that the operating status of the electric energy metering equipment in the current time period is poor, which means that there are errors in the electricity consumption data recorded by it, and issue an early warning to remind the operation and maintenance personnel to calibrate it;

[0028] like , it is judged that the operating status of the electric energy metering equipment in the current time period is good, which means that the electricity consumption data recorded by it has no error or the error is small, and no warning is issued.

[0029] Furthermore, the analysis process in S5 includes:

[0030] Through the electricity consumption data information of different time periods collected by the electric energy metering equipment, the electricity consumption change curve of the i-th time period in the past period is established. ;

[0031] And through the formula Calculate the power consumption dispersion coefficient of the electric energy metering device in the past period of time ;

[0032] Among them, v is any day in the past period of time, t is the total number of days in the past period of time, is the power consumption of the electric energy metering device in the i-th time period of the v-th day in the past period of time, For all The average value of is the starting time period in the past. is the end time period in the past. is the proportionality coefficient, which is set based on empirical fitting.

[0033] Furthermore, the analysis process in S5 also includes:

[0034] By calculating the power consumption dispersion coefficient of the electric energy metering device in the i-th time period in the past period The preset coefficient of dispersion threshold Make a comparison;

[0035] like , judge that the power consumption fluctuation value of the power metering device in the i-th time period in the past period is large, which means that there is an abnormality inside the power metering device, resulting in errors in the collected power consumption data, and issue an early warning;

[0036] like , it is judged that the fluctuation value of the power consumption of the electric energy metering device in the i-th time period in the past period is small, which means that there is no abnormality inside the electric energy metering device, and the collected power consumption data has no error or the error can be ignored.

[0037] Furthermore, the calibration process in S6 includes:

[0038] S61: Turn off the power, unplug all plugs, ensure that the power system is fully discharged, and prepare the calibration power supply and standard energy meter required for calibration;

[0039] S62: Turn on the calibration power supply, ensure effective electrical connection between the calibration source and the calibrated electric energy metering device, and set calibration parameters such as current and voltage;

[0040] S63: calibrate the current channels in sequence, and calibrate according to the standard interface parameters and the measurement results to ensure that the indicator displays a current value that meets the standard;

[0041] S64: calibrate the voltage channels in sequence to match the indicator with the standard and ensure that the measured voltage meets the standard voltage value;

[0042] S65: After the calibration is completed, the obtained calibration parameters are written into a calibration parameter file for storage.

[0043] Beneficial effects of the present invention:

[0044] (1) The present invention can analyze the equipment status of the electric energy metering device by combining the equipment status data of the electric energy metering device, and then combine the data with the equipment operation status data of the electric energy metering device to monitor in real time whether there is any abnormality in the operation status of the electric energy metering device. Under this premise, if it is judged that there is an abnormality in the equipment status or operation status of the electric energy metering device, it means that there is an error in the power consumption data collected by the electric energy metering device during operation. Otherwise, it means that the power consumption data is normal. Then, the power consumption data can be combined to make a further judgment on whether there is an error in the electric energy metering device, thereby improving the accuracy of the judgment result.

[0045] (2) The present invention calculates the device status influence coefficient of the electric energy metering device in the i-th time period by The preset device status impact coefficient threshold Through this comparison method, it is possible to determine whether there is any abnormality in the state of the electric energy metering device itself in the current time period and whether it will cause errors in the operation of the electric energy metering device. Moreover, since the data is obtained through real-time monitoring, after a preliminary judgment is made that there are no errors in the operation of the electric energy metering device, the real-time operating status of the electric energy metering device can be analyzed in combination with the data, thereby further analyzing whether there are errors in the operation of the electric energy metering device.

[0046] (3) The present invention calculates the operating state influence coefficient of the electric energy metering device in the i-th time period by The preset operating status impact coefficient threshold By comparing, it is possible to judge whether the operating status of the electric energy metering device in the current time period is good, and further judge whether there are errors in the electricity consumption data recorded by it. By analyzing the operating status of the electric energy metering device in the current time period based on the fact that the device is in good condition in the current time period, it is possible to judge whether there are working errors from the perspective of the operating status of the electric energy metering device, thereby improving the accuracy of the judgment result.

[0047] (4) The present invention calculates the power consumption dispersion coefficient of the electric energy metering device in the i-th time period in the past period of time. The preset coefficient of dispersion threshold By comparing, the fluctuation value of the electricity consumption of the electric energy metering device in the i-th time period in the past can be judged, and it can be further judged whether there is an abnormality inside the electric energy metering device and whether there is an error in the collected electricity consumption data. Moreover, the data is obtained based on the good equipment status and operation status of the electric energy metering device, so the data quality and accuracy are high, thereby improving the accuracy of error monitoring of the electric energy metering device. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention will be further described below in conjunction with the accompanying drawings.

[0049] Figure 1 It is a flow chart of a method for real-time monitoring and error calibration of intelligent electric energy metering equipment in the present invention;

[0050] Figure 2 It is a flow chart of the calibration process in the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 As shown, in one embodiment, the present application provides a method for real-time monitoring and error calibration of a smart electric energy metering device, the method comprising:

[0053] S1: Collecting the equipment status data of the electric energy metering equipment in different time periods and the equipment operation status data through the sensor group;

[0054] S2: by combining the collected equipment status data of the electric energy metering equipment, calculating the real-time equipment status influence coefficient of the electric energy metering equipment, and analyzing the equipment status of the electric energy metering equipment based on the data;

[0055] S3: Calculating the real-time operation status influence coefficient of the electric energy metering device by combining the collected operation status data of the electric energy metering device with the real-time device status influence coefficient of the electric energy metering device;

[0056] S4: By combining the real-time operating status influence coefficient of the electric energy metering device, analyze whether the operating status of the electric energy metering device is abnormal. If yes, proceed to step S6; otherwise, proceed to step S5:

[0057] S5: By combining the monitored power consumption data of the same time period in the past and the influence coefficient of the operating status of the metering equipment, further analyze whether there is any abnormality in the operating status of the electric energy metering equipment;

[0058] S6: When it is determined that the operating state of the electric energy metering device is abnormal, calibration is performed by the calibration device according to the requirements of the calibration device;

[0059] Through the above technical scheme, this example provides a method for real-time monitoring and error calibration of intelligent electric energy metering equipment. First, the device status data of the electric energy metering equipment in different time periods and the operating status data of the equipment are collected through a sensor group. Then, the real-time device status influence coefficient of the electric energy metering equipment is calculated by combining the collected device status data of the electric energy metering equipment and the real-time device status influence coefficient of the electric energy metering equipment, and the device status of the electric energy metering equipment is analyzed based on the data. Then, the real-time operating status influence coefficient of the electric energy metering equipment is calculated by combining the collected device operating status data of the electric energy metering equipment and the real-time device status influence coefficient of the electric energy metering equipment, and the operating status of the electric energy metering equipment is analyzed by combining the real-time operating status influence coefficient of the electric energy metering equipment. Finally, by combining the monitored electricity consumption data of the same time period in the past period of time with the operating status influence coefficient of the metering equipment, whether the operating status of the electric energy metering equipment is abnormal is further analyzed. When it is judged that the operating status of the electric energy metering equipment is abnormal, calibration is performed according to the requirements of the calibration equipment through the calibration equipment;

[0060] Through such settings, the system can analyze the equipment status of the electric energy metering equipment by combining it with the equipment status data of the electric energy metering equipment, and then combine this data with the equipment operating status data of the electric energy metering equipment to monitor in real time whether there is any abnormality in the operating status of the electric energy metering equipment. Under this premise, if it is judged that there is an abnormality in the equipment status or operating status of the electric energy metering equipment, it means that there is an error in the electricity consumption data collected by the electric energy metering equipment during operation. Otherwise, it means that the electricity consumption data is normal. The electricity consumption data can be combined to make further judgment on whether there is an error in the electric energy metering equipment, thereby improving the accuracy of the judgment result.

[0061] The analysis process in S2 includes:

[0062] By formula Calculate the equipment status influence coefficient of the electric energy metering equipment in the i-th time period ;

[0063] Where i is any monitoring time period, is the damaged area of ​​the wire sheath of the electric energy metering equipment in the i-th monitoring period, is the exposed wire area of ​​the electric energy metering equipment in the i-th monitoring period, The total area of ​​wires monitored by the energy metering device, is the undamaged area of ​​the wire monitored by the electric energy metering equipment, a is any time point, b is the total number of time points in a monitoring period, is the temperature of the wiring port of the electric energy metering device at the ath time point in the i-th monitoring period, is the preset connection port temperature, is the external temperature influence coefficient, which is set based on empirical fitting. is the connection port resistance of the energy metering device at the ath time point in the i-th monitoring period, is the preset resistance, for The standard value can be selected and set according to the allowable error in the empirical data. and is the weight coefficient, which is set according to empirical fitting;

[0064] Through the above technical solution, this example provides the equipment state influence coefficient of the electric energy metering equipment in the i-th time period , can be obtained by formula It is obvious that when the damaged area of ​​the wire skin and the exposed area of ​​the wire of the electric energy metering device in the i-th monitoring period are larger, the average temperature of the wiring port of the electric energy metering device in the i-th monitoring period is higher, and the average resistance of the wiring port of the electric energy metering device in the i-th monitoring period is different from the preset resistance, then the equipment status influence coefficient of the electric energy metering device in the i-th monitoring period is The larger it is, the more damage the electric energy metering device has in the i-th time period, which will affect the use of the electric energy metering device. In this case, the data collected by the electric energy metering device will have errors. On the contrary, when the damaged area of ​​the wire sheath and the exposed area of ​​the wire of the electric energy metering device in the i-th monitoring time period are smaller, the average temperature of the wiring port of the electric energy metering device in the i-th monitoring time period is lower, and the average resistance of the wiring port of the electric energy metering device in the i-th monitoring time period is smaller than the preset resistance, then the equipment status influence coefficient of the electric energy metering device in the i-th time period is The smaller it is, it means that there is no damage to the electric energy metering device itself in the i-th time period, and it will not affect the use of the electric energy metering device. In this case, there will not be too large errors in the data collected. Through this calculation method, accurate data support can be provided for the subsequent judgment of whether there are errors in the work of the electric energy metering device, thereby improving the accuracy of the judgment results.

[0065] The analysis process in S2 further includes:

[0066] By calculating the equipment status influence coefficient of the electric energy metering equipment in the i-th time period The preset device status impact coefficient threshold Make a comparison;

[0067] like , determine that the current time period has abnormal state of the electric energy metering equipment, which will cause errors in the operation of the electric energy metering equipment, and issue an early warning to remind maintenance personnel to carry out repairs;

[0068] like , it is judged that there is no abnormality in the state of the electric energy metering device in the current time period, which means that there is no error or the error is small in the operation of the electric energy metering device, and no warning is issued;

[0069] Through the above technical solution, this example calculates the device status influence coefficient of the electric energy metering device in the i-th time period by The preset device status impact coefficient threshold Through this comparison method, it is possible to determine whether there is any abnormality in the state of the electric energy metering device itself in the current time period and whether it will cause errors in the operation of the electric energy metering device. Moreover, since the data is obtained through real-time monitoring, after a preliminary judgment is made that there are no errors in the operation of the electric energy metering device, the real-time operating status of the electric energy metering device can be analyzed in combination with the data, thereby further analyzing whether there are errors in the operation of the electric energy metering device.

[0070] The calculation process in S3 includes:

[0071] By formula Calculate the operating status influence coefficient of the electric energy metering equipment in the i-th time period ;

[0072] in, is the current value of the energy metering device at the ath time point in the i-th monitoring period, is the preset current size, for The standard value can be selected and set according to the allowable error in the empirical data. is the voltage of the energy metering device at the ath time point in the i-th monitoring period, is the preset voltage size, for The standard value can be selected and set according to the allowable error in the empirical data. is the power factor of the electric energy metering device at the ath time point in the i-th monitoring period, For all The average value of

[0073] Through the above technical solution, this example provides the operating state influence coefficient of the electric energy metering device in the i-th time period , can be obtained by formula Calculated, where the formula The power factor fluctuation value of the electric energy metering device in the i-th monitoring time period can be calculated. Therefore, it is obvious that when the current and voltage of the electric energy metering device at the a-th time point in the i-th monitoring time period are larger, and the power factor fluctuation value of the electric energy metering device in the i-th monitoring time period is larger, then the operating state influence coefficient of the electric energy metering device in the i-th monitoring time period is The larger the value is, the more abnormal the running state of the electric energy metering device is in the current time period, which means that the data collected at this time point is likely to be erroneous. On the contrary, when the current and voltage of the electric energy metering device at the ath time point in the i-th monitoring time period are closer to the preset value, and the power factor fluctuation value of the electric energy metering device in the i-th monitoring time period is smaller, then the running state influence coefficient of the electric energy metering device in the i-th time period is The smaller it is, it means that there is no abnormality in the operating status of the electric energy metering equipment in the current time period, which means that the possibility of errors in the data collected at this time point is small. Through this calculation method, accurate data can be provided for subsequent judgment of the operating status of the electric energy metering equipment and whether there are errors in the data collected during operation, thereby improving the accuracy of the judgment results.

[0074] The analysis process in S4 includes:

[0075] By using the operating status influence coefficient of the electric energy metering equipment in the i-th time period The preset operating status impact coefficient threshold Make a comparison;

[0076] like , judge that the operating status of the electric energy metering equipment in the current time period is poor, which means that there are errors in the electricity consumption data recorded by it, and issue an early warning to remind the operation and maintenance personnel to calibrate it;

[0077] like , it is judged that the operation status of the electric energy metering equipment in the current time period is good, which means that the electricity consumption data recorded by it has no error or the error is small, and no warning is issued;

[0078] Through the above technical solution, this example calculates the operating state influence coefficient of the electric energy metering device in the i-th time period The preset operating status impact coefficient threshold By comparing, it is possible to judge whether the operating state of the electric energy metering device in the current time period is good, and further judge whether there is any error in the electricity consumption data recorded by it. By analyzing the operating state of the electric energy metering device in the current time period based on the fact that the device is in good state in the current time period, it is possible to judge whether there is any working error from the perspective of the operating state of the electric energy metering device, thereby improving the accuracy of the judgment result;

[0079] And since the device status and operating status of the electric energy metering device in the current time period are both good, which means that the power consumption data will not be affected by the electric energy metering device, the power consumption data can be further used to analyze whether there are errors in the operation of the electric energy metering device in the future, and the accuracy of the analysis results can be improved.

[0080] The analysis process in S5 includes:

[0081] Through the electricity consumption data information of different time periods collected by the electric energy metering equipment, the electricity consumption change curve of the i-th time period in the past period is established. ;

[0082] And through the formula Calculate the power consumption dispersion coefficient of the electric energy metering device in the past period of time ;

[0083] Among them, v is any day in the past period of time, t is the total number of days in the past period of time, is the power consumption of the electric energy metering device in the i-th time period of the v-th day in the past period of time, For all The average value of is the starting time period in the past. is the end time period in the past. is the proportionality coefficient, which is set based on empirical fitting;

[0084] Through the above technical solution, this example provides the power consumption dispersion coefficient of the electric energy metering device in the past period of time i , can be obtained by formula It is calculated that through this calculation method, the dispersion coefficient of electricity consumption in the ith time period of the electric energy metering device in the past period of time can be obtained. This data reflects the fluctuation value of electricity consumption in the ith time period of each day in the past period of time. The size of the fluctuation value can explain the fluctuation of the electricity consumption data collected by the electric energy metering device. Since the electricity consumption in the same time period is more similar, if the electricity consumption data collected by the electric energy metering device fluctuates greatly, it means that there may be errors in the data collection of the electric energy metering device. Through such a setting, the accuracy of the result of judging whether there is an error when the electric energy metering device is working can be improved.

[0085] The analysis process in S5 further includes:

[0086] By calculating the power consumption dispersion coefficient of the electric energy metering device in the i-th time period in the past period The preset coefficient of dispersion threshold Make a comparison;

[0087] like , judge that the power consumption fluctuation value of the power metering device in the i-th time period in the past period is large, which means that there is an abnormality inside the power metering device, resulting in errors in the collected power consumption data, and issue an early warning;

[0088] like , it is judged that the fluctuation value of the power consumption of the electric energy metering device in the i-th time period in the past period is small, which means that there is no abnormality inside the electric energy metering device, and the collected power consumption data has no error or the error can be ignored;

[0089] Through the above technical solution, this example calculates the power consumption dispersion coefficient of the electric energy metering device in the i-th time period in the past period of time. The preset coefficient of dispersion threshold By comparing, the fluctuation value of the electricity consumption of the electric energy metering device in the i-th time period in the past can be judged, and it can be further judged whether there is an abnormality inside the electric energy metering device and whether there is an error in the collected electricity consumption data. Moreover, the data is obtained based on the good equipment status and operation status of the electric energy metering device, so the data quality and accuracy are high, thereby improving the accuracy of error monitoring of the electric energy metering device.

[0090] See also Figure 2 As shown, the calibration process in S6 includes:

[0091] S61: Turn off the power, unplug all plugs, ensure that the power system is fully discharged, and prepare the calibration power supply and standard energy meter required for calibration;

[0092] S62: Turn on the calibration power supply, ensure effective electrical connection between the calibration source and the calibrated electric energy metering device, and set calibration parameters such as current and voltage;

[0093] S63: calibrate the current channels in sequence, and calibrate according to the standard interface parameters and the measurement results to ensure that the indicator displays a current value that meets the standard;

[0094] S64: calibrate the voltage channels in sequence to match the indicator with the standard and ensure that the measured voltage meets the standard voltage value;

[0095] S65: After the calibration is completed, the obtained calibration parameters are written into a calibration parameter file for storage;

[0096] Through the above technical solution, this example provides a calibration process. Through such a setting, when it is determined that there is a problem with the electric energy metering device, its current and voltage parameters are calibrated, thereby ensuring the normal operation of the electric energy metering device and reducing the error of its collected data.

[0097] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for real-time monitoring and error calibration of intelligent electric energy metering equipment, characterized in that: The method comprises: S1: Collecting the equipment status data of the electric energy metering equipment in different time periods and the equipment operation status data through the sensor group; S2: by combining the collected equipment status data of the electric energy metering equipment, calculating the real-time equipment status influence coefficient of the electric energy metering equipment, and analyzing the equipment status of the electric energy metering equipment based on the data; S3: Calculating the real-time operation status influence coefficient of the electric energy metering device by combining the collected operation status data of the electric energy metering device with the real-time device status influence coefficient of the electric energy metering device; S4: By combining the real-time operating status influence coefficient of the electric energy metering device, analyze whether the operating status of the electric energy metering device is abnormal. If yes, proceed to step S6; otherwise, proceed to step S5: S5: By combining the monitored power consumption data of the same time period in the past and the influence coefficient of the operation status of the power metering device, further analyzing whether there is an abnormality in the operation status of the power metering device; S6: When it is determined that the operating state of the electric energy metering device is abnormal, calibration is performed through the calibration device according to the requirements of the calibration device.

2. A method for real-time monitoring and error calibration of intelligent electric energy metering equipment according to claim 1, characterized in that: The analysis process in S2 includes: By formula Calculate the equipment status influence coefficient of the electric energy metering equipment in the i-th time period ; Where i is any monitoring time period, is the damaged area of ​​the wire sheath of the electric energy metering equipment in the i-th monitoring period, is the exposed wire area of ​​the electric energy metering equipment in the i-th monitoring period, The total area of ​​wires monitored by the energy metering device, is the undamaged area of ​​the wire monitored by the electric energy metering equipment, a is any time point, b is the total number of time points in a monitoring period, is the temperature of the wiring port of the electric energy metering device at the ath time point in the i-th monitoring period, is the preset connection port temperature, is the external temperature influence coefficient, which is set based on empirical fitting. is the connection port resistance of the energy metering device at the ath time point in the i-th monitoring period, is the preset resistance, for The standard value of and is the weight coefficient.

3. A method for real-time monitoring and error calibration of intelligent electric energy metering equipment according to claim 2, characterized in that: The analysis process in S2 further includes: By calculating the equipment status influence coefficient of the electric energy metering equipment in the i-th time period The preset device status impact coefficient threshold Make a comparison; like , determine that the current time period has abnormal state of the electric energy metering equipment, which will cause errors in the operation of the electric energy metering equipment, and issue an early warning to remind maintenance personnel to carry out repairs; like , it is judged that there is no abnormality in the state of the electric energy metering device itself in the current time period, which means that there is no error or the error is small in the operation of the electric energy metering device, and no warning is issued.

4. A method for real-time monitoring and error calibration of intelligent electric energy metering equipment according to claim 1, characterized in that: The calculation process in S3 includes: By formula Calculate the operating status influence coefficient of the electric energy metering equipment in the i-th time period ; in, is the current value of the energy metering device at the ath time point in the i-th monitoring period, is the preset current size, for The standard value of is the voltage of the energy metering device at the ath time point in the i-th monitoring period, is the preset voltage size, for The standard value of is the power factor of the electric energy metering device at the ath time point in the i-th monitoring period, For all The average value of .

5. The method for real-time monitoring and error calibration of intelligent electric energy metering equipment according to claim 1, characterized in that: The analysis process in S4 includes: By using the operating status influence coefficient of the electric energy metering equipment in the i-th time period The preset operating status impact coefficient threshold Make a comparison; like , judge that the operating status of the electric energy metering equipment in the current time period is poor, which means that there are errors in the electricity consumption data recorded by it, and issue an early warning to remind the operation and maintenance personnel to calibrate it; like , it is judged that the operating status of the electric energy metering equipment in the current time period is good, which means that the electricity consumption data recorded by it has no error or the error is small, and no warning is issued.

6. A method for real-time monitoring and error calibration of intelligent electric energy metering equipment according to claim 1, characterized in that: The analysis process in S5 includes: Through the electricity consumption data information of different time periods collected by the energy metering equipment, the electricity consumption change curve of the i-th time period in the past period is established. ; And through the formula Calculate the power consumption dispersion coefficient of the electric energy metering device in the past period of time ; Among them, v is any day in the past period of time, t is the total number of days in the past period of time, is the power consumption of the electric energy metering device in the i-th time period of the v-th day in the past period of time, For all The average value of is the starting time period in the past. is the end time period in the past. is the proportionality coefficient, which is set based on empirical fitting.

7. A method for real-time monitoring and error calibration of intelligent electric energy metering equipment according to claim 6, characterized in that: The analysis process in S5 further includes: By calculating the power consumption dispersion coefficient of the electric energy metering device in the i-th time period in the past period The preset coefficient of dispersion threshold Make a comparison; like , judge that the power consumption fluctuation value of the power metering device in the i-th time period in the past period is large, which means that there is an abnormality inside the power metering device, resulting in errors in the collected power consumption data, and issue an early warning; like , it is judged that the fluctuation value of the power consumption of the electric energy metering device in the i-th time period in the past period is small, which means that there is no abnormality inside the electric energy metering device, and the collected power consumption data has no error or the error can be ignored.

8. A method for real-time monitoring and error calibration of intelligent electric energy metering equipment according to claim 1, characterized in that: The calibration process in S6 includes: S61: Turn off the power, unplug all plugs, ensure that the power system is fully discharged, and prepare the calibration power supply and standard energy meter required for calibration; S62: Turn on the calibration power supply, ensure effective electrical connection between the calibration source and the calibrated electric energy metering device, and set calibration parameters such as current and voltage; S63: calibrate the current channels in sequence, and calibrate according to the standard interface parameters and the measurement results to ensure that the indicator displays a current value that meets the standard; S64: calibrate the voltage channels in sequence to match the indicator with the standard and ensure that the measured voltage meets the standard voltage value; S65: After the calibration is completed, the obtained calibration parameters are written into a calibration parameter file for storage.

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