A method and system for monitoring potential hazards of electricity meter burnout

Abstract

The present invention discloses a method and system for monitoring potential hazards of electricity meter burnout. The method receives first curve data of the temperature of the microcontroller unit and the electricity quantity corresponding to the first acquisition task sent by the electricity meter. If it is determined based on the first curve data that the electricity meter is a suspected abnormal electricity meter, a second acquisition task is sent to all the electricity meters in the multi-meter position metering box to which the suspected abnormal electricity meter belongs. Based on the received second curve data, the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box is calculated. If the average temperature difference is greater than the first preset threshold, it is determined that the suspected abnormal electricity meter has a potential hazard of burnout. The monitoring period in the second acquisition task is less than the monitoring period in the first acquisition task. By using the MCU temperature and electricity quantity, the suspected abnormal electricity meter is initially locked, and then the suspected abnormal electricity meter is further monitored in combination with other electricity meters in the same metering box, so as to realize timely, efficient and accurate monitoring of potential hazards of electricity meter burnout.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter monitoring, and particularly relates to a method and system for monitoring potential hazards of electric energy meter burnout. Background Art

[0002] As an important device for power supply and consumption of low-voltage users, the safe and stable operation of the electric energy meter is crucial. The burnout faults of electric energy meters occur frequently, which not only affect the normal power consumption of users, but may even cause fire accidents, threatening the life and property safety of the people. Therefore, it is of great practical significance to realize online monitoring of potential hazards of electric energy meter burnout. At present, the main methods for monitoring potential hazards of electric energy meter burnout are as follows:

[0003] (1) Manual on-site inspection: Conduct manual on-site inspections for users with large power consumption.

[0004] (2) Data analysis algorithm: Collect the voltage, zero-fire line current and power consumption of the electric energy meter for analysis, and extract the historical data characteristics of the burned-out electric energy meter, so as to realize the monitoring of potential hazards of the in-service electric energy meter burnout.

[0005] The above two methods for monitoring potential hazards of electric energy meter burnout have the disadvantages of poor timeliness, low detection rate and low accuracy, and it is difficult to realize timely, efficient and accurate monitoring of potential hazards of electric energy meter burnout. Summary of the Invention

[0006] The technical problem to be solved by the present invention is: to provide a method and system for monitoring potential hazards of electric energy meter burnout, and realize timely, efficient and accurate monitoring of potential hazards of electric energy meter burnout.

[0007] To solve the above technical problem, a technical solution adopted by the present invention is:

[0008] A method for monitoring potential hazards of electric energy meter burnout, comprising the steps of:

[0009] Receiving first curve data of the temperature and power consumption of the microcontroller unit corresponding to the first collection task sent by the electric energy meter, and judging whether the electric energy meter is a suspected abnormal electric energy meter based on the first curve data. If so, determining the electric energy meter as a suspected abnormal electric energy meter; if not, determining the electric energy meter as a normal electric energy meter;

[0010] Determining the multi-meter position metering box to which the suspected abnormal electric energy meter belongs, and sending a second collection task to all the electric energy meters in the multi-meter position metering box, wherein the monitoring period in the second collection task is less than the monitoring period in the first collection task;

[0011] Receive the second curve data of the microcontroller unit temperature and power consumption corresponding to the second acquisition task sent by all the electricity meters, and calculate the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box based on the second curve data;

[0012] Determine whether the average temperature difference is greater than a first preset threshold. If so, determine that there is a potential risk of burnout for the suspected abnormal electricity meter; if not, determine that there is no potential risk of burnout for the suspected abnormal electricity meter.

[0013] To solve the above technical problems, another technical solution adopted by the present invention is:

[0014] An electricity meter burnout risk monitoring system, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0015] Receive the first curve data of the microcontroller unit temperature and power consumption corresponding to the first acquisition task sent by the electricity meter, and determine whether the electricity meter is a suspected abnormal electricity meter based on the first curve data. If so, determine the electricity meter as a suspected abnormal electricity meter; if not, determine the electricity meter as a normal electricity meter;

[0016] Determine the multi-meter position metering box to which the suspected abnormal electricity meter belongs, and send a second acquisition task to all the electricity meters in the multi-meter position metering box. The monitoring period in the second acquisition task is less than the monitoring period in the first acquisition task;

[0017] Receive the second curve data of the microcontroller unit temperature and power consumption corresponding to the second acquisition task sent by all the electricity meters, and calculate the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box based on the second curve data;

[0018] Determine whether the average temperature difference is greater than a first preset threshold. If so, determine that there is a potential risk of burnout for the suspected abnormal electricity meter; if not, determine that there is no potential risk of burnout for the suspected abnormal electricity meter.

[0019] The beneficial effects of the present invention are as follows: receiving the first curve data of the microcontroller unit temperature (MCU) and the power consumption corresponding to the first collection task sent by the electricity meter, if it is determined based on the first curve data that the electricity meter is a suspected abnormal electricity meter, then sending a second collection task to all the electricity meters in the multi-metering box to which the suspected abnormal electricity meter belongs, calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-metering box based on the received second curve data, if the average temperature difference is greater than the first preset threshold, then determining that there is a risk of burnout for the suspected abnormal electricity meter, and the monitoring period in the second collection task is less than the monitoring period in the first collection task. In this way, by adopting the method of full-scale monitoring combined with fine analysis, the collection tasks are flexibly configured. First, the suspected abnormal electricity meter is initially locked through the MCU temperature and power consumption, and then the suspected abnormal electricity meter is further monitored in combination with other electricity meters in the same metering box. Different from the prior art which uses on-site manual inspections or data analysis algorithms, it can improve the monitoring efficiency and accuracy, thereby realizing timely, efficient, and accurate monitoring of the risk of electricity meter burnout. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of the steps of a method for monitoring the risk of electricity meter burnout according to an embodiment of the present invention;

[0021] Figure 2 is a schematic structural diagram of a system for monitoring the risk of electricity meter burnout according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To describe in detail the technical content, the achieved objectives, and the effects of the present invention, the following is described in conjunction with the embodiments and with reference to the accompanying drawings.

[0023] Please refer to Figure 1 , a method for monitoring the risk of electricity meter burnout, comprising the steps of:

[0024] Receiving the first curve data of the microcontroller unit temperature and the power consumption corresponding to the first collection task sent by the electricity meter, and determining whether the electricity meter is a suspected abnormal electricity meter based on the first curve data. If so, determining the electricity meter as a suspected abnormal electricity meter; if not, determining the electricity meter as a normal electricity meter;

[0025] Determining the multi-metering box to which the suspected abnormal electricity meter belongs, and sending a second collection task to all the electricity meters in the multi-metering box, wherein the monitoring period in the second collection task is less than the monitoring period in the first collection task;

[0026] Receiving the second curve data of the microcontroller unit temperature and the power consumption corresponding to the second collection task sent by all the electricity meters, and calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-metering box based on the second curve data;

[0027] Determine whether the average temperature difference is greater than a first preset threshold. If so, determine that there is a risk of burnout for the suspected abnormal electricity meter. If not, determine that there is no risk of burnout for the suspected abnormal electricity meter.

[0028] As can be seen from the above description, the beneficial effects of the present invention are as follows: receiving first curve data of the microcontroller unit temperature (MCU) and electricity quantity corresponding to the first collection task sent by the electricity meter. If it is determined that the electricity meter is a suspected abnormal electricity meter based on the first curve data, then send a second collection task to all the electricity meters in the multi-metering box to which the suspected abnormal electricity meter belongs. Calculate the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-metering box based on the received second curve data. If the average temperature difference is greater than the first preset threshold, determine that there is a risk of burnout for the suspected abnormal electricity meter. The monitoring period in the second collection task is less than the monitoring period in the first collection task. In this way, by adopting the method of full-scale monitoring combined with fine analysis, the collection tasks are flexibly configured. First, the suspected abnormal electricity meter is initially locked through the MCU temperature and electricity quantity, and then the suspected abnormal electricity meter is further monitored in combination with other electricity meters in the same metering box. Different from the prior art that uses on-site manual inspections or data analysis algorithms, it can improve the monitoring efficiency and accuracy, thereby realizing timely, efficient, and accurate monitoring of the risk of burnout of electricity meters.

[0029] Further, determining whether the electricity meter is a suspected abnormal electricity meter based on the first curve data. If so, determine the electricity meter as a suspected abnormal electricity meter. If not, determine the electricity meter as a normal electricity meter includes:

[0030] Eliminate the zero-electricity data from the first curve data to obtain the processed first curve data;

[0031] Calculate the correlation coefficient of the microcontroller unit temperature and electricity quantity according to the processed first curve data;

[0032] Determine whether the correlation coefficient exceeds a second preset threshold. If so, determine the electricity meter as a suspected abnormal electricity meter. If not, determine the electricity meter as a normal electricity meter.

[0033] As can be seen from the above description, after eliminating the zero-electricity data from the first curve data, calculate the correlation coefficient of the microcontroller unit temperature and electricity quantity according to the processed first curve data to ensure the accuracy of the correlation coefficient calculation. Use the correlation analysis of the microcontroller unit temperature and electricity quantity to initially screen out the suspected abnormal electricity meters, which is convenient for more quickly and accurately locating the electricity meters with the risk of burnout subsequently.

[0034] Further, calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-metering box based on the second curve data includes:

[0035] Calculate the temperature compensation values of all the watt-hour meters based on the second curve data, and calibrate the second curve data according to the temperature compensation values to obtain the calibrated second curve data;

[0036] Calculate the average temperature difference between the suspected abnormal watt-hour meter and other watt-hour meters in the multi-position metering box according to the temperature calibration values in the calibrated second curve data.

[0037] As can be seen from the above description, calculating the temperature compensation values of all the watt-hour meters based on the second curve data, calibrating the second curve data according to the temperature compensation values, and calculating the average temperature difference between the suspected abnormal watt-hour meter and other watt-hour meters in the multi-position metering box according to the temperature calibration values in the calibrated second curve data can effectively eliminate the influence of other link factors on the temperature and ensure the reliability of the warning of burnout hidden dangers.

[0038] Further, the calculating the temperature compensation values of all the watt-hour meters based on the second curve data, and calibrating the second curve data according to the temperature compensation values to obtain the calibrated second curve data includes:

[0039] Calculate the first average temperature of each watt-hour meter in all the watt-hour meters within a preset time interval according to the microcontroller unit temperature in the second curve data;

[0040] Determine the watt-hour meter with the minimum first average temperature as the reference watt-hour meter in the multi-position metering box;

[0041] Determine the target watt-hour meters with the electricity consumption less than the third preset threshold among all the watt-hour meters according to the electricity consumption in the second curve data;

[0042] Calculate the second average temperature of each target watt-hour meter according to the microcontroller unit temperature at the same moment as the electricity consumption less than the third preset threshold in the second curve data;

[0043] Calculate the third average temperature corresponding to the second average temperature of each target watt-hour meter in the reference watt-hour meter;

[0044] Calculate the difference between the third average temperature and the second average temperature to obtain the temperature compensation value;

[0045] Use the temperature compensation value to calibrate the microcontroller unit temperature in the second curve data to obtain the temperature calibration value;

[0046] Obtain the calibrated second curve data according to the temperature calibration value.

[0047] As described above, using the reference electricity meter to calibrate the temperature of the micro-control units of all electricity meters effectively ensures the reliability of the data, so as to ensure the accuracy of subsequent early warnings.

[0048] Further, calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-position metering box according to the temperature calibration value in the calibrated second curve data includes:

[0049] ;

[0050] In the formula, represents the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-position metering box, represents the temperature calibration value of the sample point j in the suspected abnormal electricity meter in the multi-position metering box, represents other electricity meters i in the sample point j of the temperature calibration value, n represents the total number of other electricity meters.

[0051] As described above, calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-position metering box can effectively eliminate the temperature fluctuations caused by accidental factors of individual electricity meters, can more accurately judge the true state of the suspected abnormal electricity meter, effectively reduce misjudgment, and ensure the accuracy of early warnings.

[0052] Please refer to Figure 2 , an electricity meter burnout hidden danger monitoring system, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0053] Receiving the first curve data of the micro-control unit temperature and electricity quantity corresponding to the first acquisition task sent by the electricity meter, and judging whether the electricity meter is a suspected abnormal electricity meter based on the first curve data. If so, determining the electricity meter as a suspected abnormal electricity meter; if not, determining the electricity meter as a normal electricity meter;

[0054] Determining the multi-position metering box to which the suspected abnormal electricity meter belongs, and sending a second acquisition task to all the electricity meters in the multi-position metering box, where the monitoring period in the second acquisition task is less than the monitoring period in the first acquisition task;

[0055] Receiving the second curve data of the micro-control unit temperature and electricity quantity corresponding to the second acquisition task sent by all the electricity meters, and calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-position metering box based on the second curve data;

[0056] Determine whether the average temperature difference is greater than a first preset threshold. If so, determine that there is a potential risk of burning damage for the suspected abnormal electricity meter. If not, determine that there is no potential risk of burning damage for the suspected abnormal electricity meter.

[0057] As can be seen from the above description, the beneficial effects of the present invention are as follows: Receive the first curve data of the microcontroller unit temperature (MCU) and the electricity quantity corresponding to the first collection task sent by the electricity meter. If it is determined based on the first curve data that the electricity meter is a suspected abnormal electricity meter, then send a second collection task to all the electricity meters in the multi-meter position metering box to which the suspected abnormal electricity meter belongs. Calculate the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box based on the received second curve data. If the average temperature difference is greater than the first preset threshold, determine that there is a potential risk of burning damage for the suspected abnormal electricity meter. The monitoring period in the second collection task is less than the monitoring period in the first collection task. In this way, by adopting the method of full-scale monitoring combined with fine analysis, the collection tasks are flexibly configured. First, the suspected abnormal electricity meter is initially locked through the MCU temperature and the electricity quantity, and then the suspected abnormal electricity meter is further monitored in combination with other electricity meters in the same metering box. Different from the prior art that uses on-site manual inspections or data analysis algorithms, it can improve the monitoring efficiency and accuracy, thereby realizing timely, efficient, and accurate monitoring of the potential risk of burning damage of the electricity meter.

[0058] Further, determining whether the electricity meter is a suspected abnormal electricity meter based on the first curve data. If so, determining the electricity meter as a suspected abnormal electricity meter. If not, determining the electricity meter as a normal electricity meter includes:

[0059] Eliminate the zero-electricity-quantity data from the first curve data to obtain the processed first curve data;

[0060] Calculate the correlation coefficient of the microcontroller unit temperature and the electricity quantity according to the processed first curve data;

[0061] Determine whether the correlation coefficient exceeds a second preset threshold. If so, determine the electricity meter as a suspected abnormal electricity meter. If not, determine the electricity meter as a normal electricity meter.

[0062] As can be seen from the above description, after eliminating the zero-electricity-quantity data from the first curve data, calculate the correlation coefficient of the microcontroller unit temperature and the electricity quantity according to the processed first curve data to ensure the accuracy of the correlation coefficient calculation. Use the correlation analysis of the microcontroller unit temperature and the electricity quantity to initially screen out the suspected abnormal electricity meters, which is convenient for more quickly and accurately locating the electricity meters with potential burning damage risks in the subsequent process.

[0063] Further, calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box based on the second curve data includes:

[0064] Calculate the temperature compensation values of all the watt-hour meters based on the second curve data, and calibrate the second curve data according to the temperature compensation values to obtain the calibrated second curve data;

[0065] Calculate the average temperature difference between the suspected abnormal watt-hour meter and other watt-hour meters in the multi-position metering box according to the temperature calibration values in the calibrated second curve data.

[0066] As can be seen from the above description, calculating the temperature compensation values of all the watt-hour meters based on the second curve data, calibrating the second curve data according to the temperature compensation values, and calculating the average temperature difference between the suspected abnormal watt-hour meter and other watt-hour meters in the multi-position metering box according to the temperature calibration values in the calibrated second curve data can effectively eliminate the influence of other link factors on the temperature and ensure the reliability of the warning of burnout hidden dangers.

[0067] Further, the calculating the temperature compensation values of all the watt-hour meters based on the second curve data, and calibrating the second curve data according to the temperature compensation values to obtain the calibrated second curve data includes:

[0068] Calculate the first average temperature of each watt-hour meter in all the watt-hour meters within a preset time interval according to the microcontroller unit temperature in the second curve data;

[0069] Determine the watt-hour meter with the minimum first average temperature as the reference watt-hour meter in the multi-position metering box;

[0070] Determine the target watt-hour meters with the electricity consumption less than the third preset threshold among all the watt-hour meters according to the electricity consumption in the second curve data;

[0071] Calculate the second average temperature of each target watt-hour meter according to the microcontroller unit temperature at the same time as the electricity consumption less than the third preset threshold in the second curve data;

[0072] Calculate the third average temperature corresponding to the second average temperature of each target watt-hour meter in the reference watt-hour meter;

[0073] Calculate the difference between the third average temperature and the second average temperature to obtain the temperature compensation value;

[0074] Use the temperature compensation value to calibrate the microcontroller unit temperature in the second curve data to obtain the temperature calibration value;

[0075] Obtain the calibrated second curve data according to the temperature calibration value.

[0076] As described above, using the reference electricity meter to calibrate the microcontroller unit temperature of all electricity meters effectively ensures the reliability of the data to ensure the accuracy of subsequent early warnings.

[0077] Further, calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-position metering box according to the temperature calibration value in the calibrated second curve data includes:

[0078] ;

[0079] In the formula, represents the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-position metering box, represents the temperature calibration value of the sample point j in the suspected abnormal electricity meter in the multi-position metering box, represents other electricity meters i in the sample point j the temperature calibration value of, n represents the total number of other electricity meters.

[0080] As described above, calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-position metering box can effectively eliminate the temperature fluctuations caused by accidental factors of individual electricity meters, can more accurately judge the true state of the suspected abnormal electricity meter, effectively reduce misjudgment, and ensure the accuracy of early warnings.

[0081] The above-mentioned method and system for monitoring potential hazards of electricity meter burnout of the present invention can be applied to the electricity meter monitoring scenario, which will be described below through specific embodiments:

[0082] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0083] A method for monitoring potential hazards of electricity meter burnout, including the steps of:

[0084] S1. Receive the first curve data of the microcontroller unit temperature and electricity quantity corresponding to the first collection task sent by the electricity meter, and based on the first curve data, judge whether the electricity meter is a suspected abnormal electricity meter. If so, determine the electricity meter as a suspected abnormal electricity meter. If not, determine the electricity meter as a normal electricity meter, specifically including S11-S14:

[0085] S11. Receive the first curve data of the microcontroller unit temperature and electricity quantity corresponding to the first collection task sent by the electricity meter.

[0086] In an alternative embodiment, the concentrator receives the first curve data of the microcontroller unit temperature and the electricity consumption corresponding to the first collection task sent by the electricity meter. After verifying the first curve data, it uploads the data to the main station for collecting electricity consumption information.

[0087] In an alternative embodiment, before S11, it may further include:

[0088] The main station for collecting electricity consumption information configures the first collection task and sends the first collection task to the concentrator; the concentrator sends the first collection task to the electricity meter.

[0089] Among them, the collection task includes a monitoring period, and the monitoring period can be 1 hour, 15 minutes, 1 minute, etc. In an alternative embodiment, the collection task may further include the number of data storage times. The monitoring period and the number of data storage times can be flexibly set according to the actual situation. The concentrator packs and uploads multiple curve data of the electricity meter stored to the main station for collecting electricity consumption information. By flexibly configuring the number of data storage times, the urgency of the collection task and the occupation of server resources can be balanced.

[0090] The first curve data includes the microcontroller unit temperature and the electricity consumption that correspond one by one.

[0091] S12. Eliminate the zero-electricity consumption data from the first curve data to obtain the processed first curve data.

[0092] Specifically, eliminate the zero-electricity consumption and its corresponding microcontroller unit from the first curve data to obtain the processed first curve data.

[0093] S13. Calculate the correlation coefficient of the microcontroller unit temperature and the electricity consumption according to the processed first curve data.

[0094] In an alternative embodiment, use the Pearson correlation coefficient method to calculate the correlation coefficient of the microcontroller unit temperature and the electricity consumption according to the processed first curve data.

[0095] S14. Determine whether the correlation coefficient exceeds a second preset threshold. If so, determine the electricity meter as a suspected abnormal electricity meter; if not, determine the electricity meter as a normal electricity meter.

[0096] S2. Determine the multi-metering box to which the suspected abnormal electricity meter belongs, and send a second collection task to all the electricity meters in the multi-metering box. The monitoring period in the second collection task is less than the monitoring period in the first collection task.

[0097] For example, if the monitoring period in the first collection task is 1 hour, then the monitoring period in the second collection task can be 1 minute.

[0098] S3. Receive the second curve data of the microcontroller unit temperature and the electricity quantity corresponding to the second collection task sent by all the electricity meters, and calculate the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-metering box based on the second curve data, specifically including S31 - S33:

[0099] S31. Receive the second curve data of the microcontroller unit temperature and the electricity quantity corresponding to the second collection task sent by all the electricity meters.

[0100] S32. Calculate the temperature compensation values of all the electricity meters based on the second curve data, and calibrate the second curve data according to the temperature compensation values to obtain the calibrated second curve data, specifically including S321 - S328:

[0101] S321. Calculate the first average temperature of each electricity meter in all the electricity meters within a preset time interval according to the microcontroller unit temperature in the second curve data.

[0102] Among them, the preset time interval can be set according to the actual situation.

[0103] Assume that the preset time interval is 1 hour and the monitoring period in the second collection task is 1 minute. Then there are 60 sampling points within 1 hour, that is, 60 pieces of data. Calculate the first average temperature of the microcontroller unit temperature of each electricity meter in all the electricity meters within 60 in 1 hour.

[0104] S322. Determine the reference electricity meter in the multi-metering box as the electricity meter with the minimum first average temperature.

[0105] S323. Determine the target electricity meters with the electricity quantity less than the third preset threshold among all the electricity meters according to the electricity quantity in the second curve data.

[0106] S324. Calculate the second average temperature of each target electricity meter according to the microcontroller unit temperature at the same time as the electricity quantity less than the third preset threshold in the second curve data.

[0107] For example, if the moments of the electricity quantity less than the third preset threshold in a target electricity meter are 10:01, 10:02, and 10:03, then calculate the second average temperature of this target electricity meter according to the microcontroller unit temperature corresponding to 10:01, 10:02, and 10:03.

[0108] S325. Calculate the third average temperature corresponding to the second average temperature of each target electricity meter in the reference electricity meter.

[0109] For example, a target electricity meter a calculates the second average temperature of the target electricity meter based on the microcontroller unit temperatures corresponding to 10:01, 10:02, and 10:03, and another target electricity meter b calculates the second average temperature of the target electricity meter based on the microcontroller unit temperatures corresponding to 11:12, 11:13, and 11:14. Then, the reference electricity meter calculates the third average temperature corresponding to the target electricity meter a based on the microcontroller unit temperatures corresponding to 10:01, 10:02, and 10:03, and calculates the third average temperature corresponding to the target electricity meter b based on the microcontroller unit temperatures corresponding to 11:12, 11:13, and 11:14.

[0110] S326. Calculate the difference between the third average temperature and the second average temperature to obtain a temperature compensation value, specifically:

[0111] T i,cp = T i,refer,mean - T i,mean ;

[0112] In the formula, T i,cp represents the temperature compensation value of the electricity meter i of, T i,refer,mean represents the third average temperature of the reference electricity meter, T i,mean represents the electricity meter i of the second average temperature.

[0113] S327. Use the temperature compensation value to calibrate the microcontroller unit temperature in the second curve data to obtain a temperature calibration value, specifically:

[0114] T ij,cal = T ij - T i,cp ;

[0115] In the formula, T ij represents the sample point of the electricity meter i of j the microcontroller unit temperature.

[0116] S328. Obtain the calibrated second curve data according to the temperature calibration value.

[0117] S33. Calculate the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-position metering box according to the temperature calibration value in the calibrated second curve data, specifically:

[0118] ;

[0119] Wherein, represents the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-position metering box; represents the temperature calibration value of the sample point j in the suspected abnormal electric energy meter in the multi-position metering box; represents other electric energy meters i in which the sample point j is the temperature calibration value; n represents the total number of other electric energy meters.

[0120] S4. Determine whether the average temperature difference is greater than a first preset threshold. If so, it is determined that there is a potential risk of burnout for the suspected abnormal electric energy meter. If not, it is determined that there is no potential risk of burnout for the suspected abnormal electric energy meter.

[0121] Specifically, determine whether any of the average temperature differences is greater than the first preset threshold. If so, it is determined that there is a potential risk of burnout for the suspected abnormal electric energy meter. If not, it is determined that there is no potential risk of burnout for the suspected abnormal electric energy meter.

[0122] After determining that there is a potential risk of burnout for the suspected abnormal electric energy meter, a work order is immediately issued to conduct an on-site verification.

[0123] The above-mentioned method for monitoring potential risks of burnout of electric energy meters in the present invention realizes the temperature acquisition of tens of millions of electric energy meter MCUs through flexible configuration of acquisition tasks, can realize the monitoring of the operating temperature of electric energy meters with a period of 1 minute, adopts a full-scale monitoring combined with fine analysis mode, configures an hourly temperature acquisition task for all electric energy meters, initially locks the suspected abnormal electric energy meters through the correlation analysis of temperature and electricity consumption, and then configures a minute-level temperature acquisition task for all electric energy meters in the metering box to which the suspected abnormal electric energy meter belongs, realizing the monitoring of potential risks of burnout of electric energy meters at low cost; in addition, through temperature calibration, the deviation of temperature measurement performance of electric energy meters from different manufacturers is avoided, and an adjacent meter temperature difference calculation model for multi-position metering boxes is established. If the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in its metering box is greater than the set threshold, it is determined that there is a potential risk of burnout for the electric energy meter, thus realizing timely, efficient and accurate monitoring of potential risks of burnout of electric energy meters.

[0124] Please refer to Figure 2 , and the second embodiment of the present invention is as follows:

[0125] A system for monitoring potential risks of burnout of electric energy meters, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, each step in the method for monitoring potential risks of burnout of electric energy meters in Embodiment 1 is realized.

[0126] In summary, the present invention provides a method and system for monitoring potential hazards of electricity meter burnout. The method receives first curve data of the temperature of the microcontroller unit and the electricity consumption corresponding to the first collection task sent by the electricity meter. If it is determined based on the first curve data that the electricity meter is a suspected abnormal electricity meter, a second collection task is sent to all the electricity meters in the multi-meter position metering box to which the suspected abnormal electricity meter belongs. The average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box is calculated based on the received second curve data. If the average temperature difference is greater than the first preset threshold, it is determined that the suspected abnormal electricity meter has a potential hazard of burnout. The monitoring period in the second collection task is less than the monitoring period in the first collection task. In this way, by adopting a method of full-scale monitoring combined with fine analysis, the collection tasks are flexibly configured. First, the suspected abnormal electricity meter is initially locked through the MCU temperature and electricity consumption, and then the suspected abnormal electricity meter is further monitored in combination with other electricity meters in the same metering box. Different from the prior art that uses on-site manual inspections or data analysis algorithms, the monitoring efficiency and accuracy can be improved, thereby realizing timely, efficient, and accurate monitoring of potential hazards of electricity meter burnout. In addition, the temperature compensation values of all the electricity meters are calculated based on the second curve data, and the second curve data is calibrated according to the temperature compensation values. The average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box is calculated based on the temperature calibration values in the calibrated second curve data, which can effectively eliminate the influence of other factors on the temperature in other links and ensure the reliability of the burnout hazard warning.

[0127] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in the relevant technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for monitoring potential hazards of electric energy meter burnout, characterized in that, Including the steps: Receiving first curve data of the microcontroller unit temperature and power consumption corresponding to a first acquisition task sent by an electricity meter, and determining whether the electricity meter is a suspected abnormal electricity meter based on the first curve data. If so, determining the electricity meter as a suspected abnormal electricity meter; if not, determining the electricity meter as a normal electricity meter; Determining the multi-meter position metering box to which the suspected abnormal electricity meter belongs, and sending a second acquisition task to all the electricity meters in the multi-meter position metering box, where the monitoring period in the second acquisition task is less than the monitoring period in the first acquisition task; Receiving second curve data of the microcontroller unit temperature and power consumption corresponding to the second acquisition task sent by all the electricity meters, and calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box based on the second curve data; Judging whether the average temperature difference is greater than a first preset threshold. If so, determining that the suspected abnormal electricity meter has a potential risk of burnout; if not, determining that the suspected abnormal electricity meter has no potential risk of burnout; The calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box based on the second curve data includes: Calculating the temperature compensation value of all the electricity meters based on the second curve data, and calibrating the second curve data according to the temperature compensation value to obtain calibrated second curve data; Calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-meter position metering box according to the temperature calibration value in the calibrated second curve data, including: ； Wherein, represents the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-position metering box; represents the sample point j in the suspected abnormal electric energy meter in the multi-position metering box; represents the temperature calibration value of other electric energy meters i for the sample point j therein; n represents the total number of other electric energy meters.

2. The method for monitoring potential hazards of electricity meter burnout according to claim 1, wherein The determining whether the electricity meter is a suspected abnormal electricity meter based on the first curve data. If so, determining the electricity meter as a suspected abnormal electricity meter; if not, determining the electricity meter as a normal electricity meter includes: Eliminating zero-power data from the first curve data to obtain processed first curve data; Calculating the correlation coefficient of the microcontroller unit temperature and power consumption according to the processed first curve data; Judging whether the correlation coefficient exceeds a second preset threshold. If so, determining the electricity meter as a suspected abnormal electricity meter; if not, determining the electricity meter as a normal electricity meter.

3. A method for monitoring potential hazards of electricity meter burnout according to claim 1, characterized in that, The calculating the temperature compensation value of all the electricity meters based on the second curve data, and calibrating the second curve data according to the temperature compensation value to obtain calibrated second curve data includes: Calculating the first average temperature of each electricity meter in all the electricity meters within a preset time interval according to the microcontroller unit temperature in the second curve data; Determining the electricity meter with the smallest first average temperature as the reference electricity meter in the multi-meter position metering box; Determining the target electricity meters with power consumption less than a third preset threshold among all the electricity meters according to the power consumption in the second curve data; Calculating the second average temperature of each target electricity meter according to the microcontroller unit temperature at the same moment as the power consumption less than the third preset threshold in the second curve data; Calculating the third average temperature corresponding to the second average temperature of each target electricity meter in the reference electricity meter; Calculate the difference between the third average temperature and the second average temperature to obtain a temperature compensation value; Use the temperature compensation value to calibrate the microcontroller unit temperature in the second curve data to obtain a temperature calibration value; Obtain the calibrated second curve data according to the temperature calibration value.

4. A hidden trouble monitoring system for electric energy meter burnout, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the following steps are implemented: Receive the first curve data of the microcontroller unit temperature and the electricity quantity corresponding to the first collection task sent by the electricity meter, and based on the first curve data, determine whether the electricity meter is a suspected abnormal electricity meter. If so, determine the electricity meter as a suspected abnormal electricity meter. If not, determine the electricity meter as a normal electricity meter; Determine the multi-metering box to which the suspected abnormal electricity meter belongs, and send a second collection task to all the electricity meters in the multi-metering box. The monitoring period in the second collection task is less than the monitoring period in the first collection task; Receive the second curve data of the microcontroller unit temperature and the electricity quantity corresponding to the second collection task sent by all the electricity meters, and calculate the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-metering box based on the second curve data; Judge whether the average temperature difference is greater than a first preset threshold. If so, determine that the suspected abnormal electricity meter has a burnout risk. If not, determine that the suspected abnormal electricity meter does not have a burnout risk; The calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-metering box based on the second curve data includes: Calculate the temperature compensation values of all the electricity meters based on the second curve data, and calibrate the second curve data according to the temperature compensation values to obtain the calibrated second curve data; Calculate the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi-metering box according to the temperature calibration values in the calibrated second curve data, including: ； In the formula, represents the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-position metering box, represents the sample point j in the suspected abnormal electric energy meter in the multi-position metering box, represents the temperature calibration value of other electric energy meters i in the sample point j in the multi-position metering box, n represents the total number of other electric energy meters.

5. The power meter burnout hazard monitoring system according to claim 4, characterized in that The determining whether the electricity meter is a suspected abnormal electricity meter based on the first curve data. If so, determine the electricity meter as a suspected abnormal electricity meter. If not, determine the electricity meter as a normal electricity meter includes: Remove the zero-electricity-quantity data from the first curve data to obtain the processed first curve data; Calculate the correlation coefficient of the microcontroller unit temperature and the electricity quantity according to the processed first curve data; Judge whether the correlation coefficient exceeds a second preset threshold. If so, determine the electricity meter as a suspected abnormal electricity meter. If not, determine the electricity meter as a normal electricity meter.

6. The power meter burn - out hidden danger monitoring system according to claim 4, characterized in that, The calculating the temperature compensation values of all the electricity meters based on the second curve data, and calibrating the second curve data according to the temperature compensation values to obtain the calibrated second curve data includes: Calculate the first average temperature of each electricity meter in all the electricity meters within a preset time interval according to the microcontroller unit temperature in the second curve data; Determine the electricity meter with the smallest first average temperature as the reference electricity meter in the multi-metering box; Determine the target electric energy meters with the electricity quantity less than the third preset threshold among all the electric energy meters according to the electricity quantity in the second curve data; Calculate the second temperature average value of each of the target electric energy meters according to the micro - control unit temperature at the same moment as the electricity quantity less than the third preset threshold in the second curve data; Calculate the third temperature average value corresponding to the second temperature average value of each of the target electric energy meters in the reference electric energy meter; Calculate the difference between the third temperature average value and the second temperature average value to obtain the temperature compensation value; Use the temperature compensation value to calibrate the micro - control unit temperature in the second curve data to obtain the temperature calibration value; Obtain the calibrated second curve data according to the temperature calibration value.

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