Electric energy meter burning loss hidden danger monitoring method and system

Abstract

The invention discloses an electric energy meter burning loss hidden danger monitoring method and system, and the method comprises the steps: receiving the first curve data of the temperature and electric quantity of a micro-control unit corresponding to a first collection task, sent by an electric energy meter, and carrying out the judgment of the electric energy meter as a suspected abnormal electric energy meter based on the first curve data; if yes, sending a second acquisition task to all electric energy meters in a multi-meter-position metering box to which the suspected abnormal electric energy meter belongs, and calculating a temperature difference mean value between the suspected abnormal electric energy meter in the multi-meter-position metering box and other electric energy meters based on the received second curve data; if yes, it is determined that the suspected abnormal electric energy meter has a burning loss hidden danger, the monitoring period in the second acquisition task is smaller than the monitoring period in the first acquisition task, the suspected abnormal electric energy meter is initially locked through the MCU temperature and the electric quantity, and then the suspected abnormal electric energy meter is further monitored in combination with other electric energy meters in the same metering box. Therefore, timely, efficient and accurate monitoring of the burning loss hidden danger of the electric energy meter is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter monitoring, and particularly 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. Electric energy meter burnout faults occur from time to time, 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 on-line 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: (1) Manual on-site inspection: Conduct manual on-site inspections for users with large power consumption.

[0003] (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.

[0004] 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

[0005] 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, so as to realize timely, efficient and accurate monitoring of potential hazards of electric energy meter burnout.

[0006] To solve the above technical problem, a technical solution adopted by the present invention is: A method for monitoring potential hazards of electric energy meter burnout, comprising the steps of: Receiving first curve data of the microcontroller unit temperature and power consumption corresponding to the first acquisition 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; Determining the multi-metering box to which the suspected abnormal electric energy meter belongs, and sending a second acquisition task to all the electric energy meters in the multi-metering box, wherein 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 electric energy meters, and calculating the temperature difference average value between the suspected abnormal electric energy meter and other electric energy meters in the multi-metering box based on the second curve data; Determine whether the average temperature difference is greater than a first preset threshold. If so, determine that there is a risk of burning damage to the suspected abnormal electric energy meter; if not, determine that there is no risk of burning damage to the suspected abnormal electric energy meter.

[0007] To solve the above technical problems, another technical solution adopted by the present invention is: An electric energy meter burning damage 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: Receive the first curve data of the microcontroller unit temperature and the electricity quantity corresponding to the first acquisition task sent by the electric energy meter, and based on the first curve data, determine whether the electric energy meter is a suspected abnormal electric energy meter. If so, determine the electric energy meter as a suspected abnormal electric energy meter; if not, determine the electric energy meter as a normal electric energy meter; Determine the multi-meter position metering box to which the suspected abnormal electric energy meter belongs, and send a second acquisition task to all the electric energy 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; Receive the second curve data of the microcontroller unit temperature and the electricity quantity corresponding to the second acquisition task sent by all the electric energy meters, and calculate the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter position metering box based on the second curve data; Determine whether the average temperature difference is greater than a first preset threshold. If so, determine that there is a risk of burning damage to the suspected abnormal electric energy meter; if not, determine that there is no risk of burning damage to the suspected abnormal electric energy meter.

[0008] The beneficial effects of the present invention are as follows: Receive the first curve data of the microcontroller unit (MCU) temperature and the electricity quantity corresponding to the first acquisition task sent by the electric energy meter. If it is determined based on the first curve data that the electric energy meter is a suspected abnormal electric energy meter, then send a second acquisition task to all the electric energy meters in the multi-meter position metering box to which the suspected abnormal electric energy meter belongs. Calculate the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter position metering box based on the received second curve data. If the average temperature difference is greater than a first preset threshold, determine that there is a risk of burning damage to the suspected abnormal electric energy meter. The monitoring period in the second acquisition task is less than the monitoring period in the first acquisition task. In this way, by adopting a method of full-scale monitoring combined with fine analysis, the acquisition task is flexibly configured. First, the suspected abnormal electric energy meter is initially locked through the MCU temperature and the electricity quantity, and then the suspected abnormal electric energy meter is further monitored in combination with other electric energy meters in the same metering box. Different from the prior art that uses on-site manual inspection or data analysis algorithms, it can improve the monitoring efficiency and accuracy, thereby realizing timely, efficient, and accurate monitoring of the risk of burning damage to the electric energy meter. Description of the Drawings

[0009] Figure 1 It is a flowchart of the steps of a method for monitoring potential hazards of electricity meter burnout according to an embodiment of the present invention; Figure 2 It is a schematic structural diagram of a system for monitoring potential hazards of electricity meter burnout according to an embodiment of the present invention. Specific implementation manners

[0010] To describe in detail the technical content, achieved objectives and effects of the present invention, the following is described in conjunction with the implementation manners and with reference to the accompanying drawings.

[0011] Please refer to Figure 1 , a method for monitoring potential hazards of electricity meter burnout, including the steps: Receiving first curve data of the microcontroller unit temperature and electricity quantity corresponding to a 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; Determining the multi-meter position metering box to which the suspected abnormal electricity meter belongs, and sending a second collection task to all the electricity meters in the multi-meter position metering box, where the monitoring period in the second collection task is less than the monitoring period in the first collection task; Receiving second curve data of the microcontroller unit temperature and electricity quantity corresponding to the second collection task sent by all the electricity meters, and calculating the temperature difference average value 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 temperature difference average value is greater than a first preset threshold. If so, determining that the suspected abnormal electricity meter has a potential hazard of burnout; if not, determining that the suspected abnormal electricity meter does not have a potential hazard of burnout.

[0012] As can be seen from the above description, 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 acquisition 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 sending a second acquisition task to all the electricity meters in the multi-meter position 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-meter position metering box based on the received second curve data, if the average temperature difference is greater than the first preset threshold, then determining that the suspected abnormal electricity meter has a potential burning hazard, and the monitoring period in the second acquisition task is less than the monitoring period in the first acquisition task. In this way, by adopting the method of full-scale monitoring combined with fine analysis, the acquisition 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 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 potential burning hazards of electricity meters.

[0013] Further, 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 the zero-power data from the first curve data to obtain the processed first curve data; Calculating the correlation coefficient of the microcontroller unit temperature and the power consumption according to the processed first curve data; Judging whether the correlation coefficient exceeds the 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.

[0014] As can be seen from the above description, after eliminating the zero-power data from the first curve data, calculating the correlation coefficient of the microcontroller unit temperature and the power consumption according to the processed first curve data ensures the accuracy of the correlation coefficient calculation, and uses the correlation analysis of the microcontroller unit temperature and the power consumption to preliminarily screen out the suspected abnormal electricity meters, which is convenient for more quickly and accurately locating the electricity meters with potential burning hazards in the subsequent process.

[0015] Further, 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 the 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.

[0016] As can be seen from the above description, calculating the temperature compensation values of all 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 meters and other watt-hour meters in the multi-position metering box based on 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 burnout hazard warning.

[0017] Further, the calculating the temperature compensation values of all 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: Calculating the first average temperature of each watt-hour meter in all watt-hour meters within a preset time interval according to the microcontroller unit temperature in the second curve data; Determining the watt-hour meter with the smallest first average temperature as the reference watt-hour meter in the multi-position metering box; Determining the target watt-hour meters with the electricity consumption less than the third preset threshold among all watt-hour meters according to the electricity consumption in the second curve data; Calculating 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; Calculating the third average temperature corresponding to the second average temperature of each target watt-hour meter in the reference watt-hour meter; Calculating the difference between the third average temperature and the second average temperature to obtain the temperature compensation value; Calibrating the microcontroller unit temperature in the second curve data using the temperature compensation value to obtain the temperature calibration value; Obtaining the calibrated second curve data according to the temperature calibration value.

[0018] As can be seen from the above description, calibrating the microcontroller unit temperature of all watt-hour meters using the reference watt-hour meter effectively guarantees the reliability of the data to ensure the accuracy of subsequent warnings.

[0019] Further, the calculating the average temperature difference between the suspected abnormal watt-hour meters and other watt-hour meters in the multi-position metering box according to the temperature calibration values in the calibrated second curve data includes: ; In the formula, represents the average temperature difference between the suspected abnormal watt-hour meters and other watt-hour meters in the multi-position metering box, represents the temperature calibration value of the sample point j in the suspected abnormal watt-hour meters in the multi-position metering box, represents other watt-hour metersi Medium sample points j Temperature calibration value of n represents the total number of other electricity meters.

[0020] As can be seen from the above description, 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 warning.

[0021] Please refer to Figure 2 , an electricity meter burnout hazard 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: Receiving first curve data of the microcontroller 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; 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; Receiving second curve data of the microcontroller 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; Judging whether the average temperature difference is greater than a first preset threshold. If so, determining that the suspected abnormal electricity meter has a burnout hazard; if not, determining that the suspected abnormal electricity meter does not have a burnout hazard.

[0022] As can be seen from the above description, the beneficial effects of the present invention are as follows: receiving the first curve data of the microcontroller unit temperature (MCU) 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, then sending a second acquisition task to all the electricity meters in the multi-meter position 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-meter position metering box based on the received second curve data, if the average temperature difference is greater than the first preset threshold, then determining that the suspected abnormal electricity meter has a potential risk of burnout. The monitoring period in the second acquisition task is less than the monitoring period in the first acquisition task. In this way, by adopting the method of full-scale monitoring combined with fine analysis, the acquisition 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 which uses on-site manual inspections or data analysis algorithms, it can improve the monitoring efficiency and accuracy, thus realizing timely, efficient, and accurate monitoring of the potential risk of electricity meter burnout.

[0023] Further, 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 the zero-electricity-quantity data from the first curve data to obtain the processed first curve data; Calculating the correlation coefficient of the microcontroller unit temperature and the electricity quantity according to the processed first curve data; Judging whether the correlation coefficient exceeds the 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.

[0024] As can be seen from the above description, after eliminating the zero-electricity-quantity data from the first curve data, calculating the correlation coefficient of the microcontroller unit temperature and the electricity quantity according to the processed first curve data ensures the accuracy of the correlation coefficient calculation, and uses the correlation analysis of the microcontroller unit temperature and the electricity quantity to preliminarily screen out the suspected abnormal electricity meters, which is convenient for more quickly and accurately locating the electricity meters with potential burnout risks in the subsequent process.

[0025] Further, 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 the 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.

[0026] As can be seen from the above description, calculating the temperature compensation values of all 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 meters and other watt-hour meters in the multi-position metering box based on 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 burnout hazard warning.

[0027] Further, 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: Calculating 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; Determining the watt-hour meter with the smallest first average temperature as the reference watt-hour meter in the multi-position metering box; Determining 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; Calculating 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; Calculating the third average temperature corresponding to the second average temperature of each target watt-hour meter in the reference watt-hour meter; Calculating the difference between the third average temperature and the second average temperature to obtain the temperature compensation value; Calibrating the microcontroller unit temperature in the second-curve data using the temperature compensation value to obtain the temperature calibration value; Obtaining the calibrated second-curve data according to the temperature calibration value.

[0028] As can be seen from the above description, calibrating the microcontroller unit temperature of all the watt-hour meters using the reference watt-hour meter effectively guarantees the reliability of the data to ensure the accuracy of subsequent warnings.

[0029] Further, calculating the average temperature difference between the suspected abnormal watt-hour meters and other watt-hour meters in the multi-position metering box according to the temperature calibration values in the calibrated second-curve data includes: ; In the formula, represents the average temperature difference between the suspected abnormal watt-hour meters and other watt-hour meters in the multi-position metering box, represents the temperature calibration value of the sample point j in the suspected abnormal watt-hour meters in the multi-position metering box, represents other watt-hour metersi Medium sample points j The temperature calibration value of n represents the total number of other electricity meters.

[0030] As can be seen from the above description, calculating the average temperature difference between the suspected abnormal electricity meter and other electricity meters in the multi - epitope 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 warning.

[0031] 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: Please refer to Figure 1 For Embodiment 1 of the present invention: A method for monitoring potential hazards of electricity meter burnout, including the steps: S1. Receive 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 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, it includes S11 - S14: S11. Receive the first curve data of the micro - control unit temperature and electricity quantity corresponding to the first acquisition task sent by the electricity meter.

[0032] In an optional implementation manner, the concentrator receives 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 after verifying the first curve data, uploads it to the main station for collecting electricity consumption information.

[0033] In an optional implementation manner, before S11, it may further include: The main station for collecting electricity consumption information configures the first acquisition task and issues the first acquisition task to the concentrator; the concentrator issues the first acquisition task to the electricity meter.

[0034] Among them, the acquisition task includes a monitoring period, and the monitoring period can be 1 hour, 15 minutes, 1 minute, etc. In an optional implementation manner, the acquisition 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, and by flexibly configuring the number of data storage times, the urgency of the acquisition task and the occupation of server resources can be balanced.

[0035] The first curve data includes the micro - control unit temperature and electricity quantity in one - to - one correspondence.

[0036] S12: Eliminate zero-power data from the first curve data to obtain processed first curve data.

[0037] Specifically, the zero electric quantity and the corresponding micro-control unit in the first curve data are eliminated to obtain the processed first curve data.

[0038] S13, calculating the correlation coefficient between the temperature and the electric quantity of the micro control unit according to the processed first curve data.

[0039] In an optional implementation, the correlation coefficient between the temperature and the electric quantity of the micro control unit is calculated based on the processed first curve data using a Pearson correlation coefficient method.

[0040] S14. Determine whether the correlation coefficient exceeds a second preset threshold value. If so, determine the electric energy meter as a suspected abnormal electric energy meter. If not, determine the electric energy meter as a normal electric energy meter.

[0041] S2. Determine the multi-metering box to which the suspected abnormal electric energy meter belongs, and send a second collection task to all electric energy meters in the multi-metering box, wherein the monitoring period in the second collection task is shorter than the monitoring period in the first collection task.

[0042] For example, if the monitoring period in the first collection task is 1 hour, then the monitoring period in the second collection task may be 1 minute.

[0043] S3, receiving the second curve data of the temperature and power of the microcontroller unit corresponding to the second acquisition task sent by all the electric energy meters, and calculating the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter meter box based on the second curve data, specifically including S31-S33: S31, receiving the second curve data of the temperature and the electric quantity of the microcontroller unit corresponding to the second acquisition task sent by all the electric energy meters.

[0044] S32, calculating the temperature compensation values ​​of all the electric energy meters based on the second curve data, and calibrating the second curve data according to the temperature compensation values ​​to obtain calibrated second curve data, specifically including S321-S328: S321, calculating a first temperature average value of each electric energy meter in all the electric energy meters within a preset time interval according to the temperature of the micro control unit in the second curve data.

[0045] The preset time interval may be set according to actual conditions.

[0046] Assume that the preset time interval is 1 hour and the monitoring period in the second acquisition task is 1 minute. Then, there are 60 sampling points, that is, 60 pieces of data, within 1 hour. Calculate the first temperature average value of the microcontroller unit temperatures of each electricity meter within 60 in 1 hour for all electricity meters.

[0047] S322. Determine the electricity meter with the smallest first temperature average value as the reference electricity meter in the multi-meter position metering box.

[0048] S323. Determine the target electricity meters with electricity consumption less than the third preset threshold among all the electricity meters according to the electricity consumption in the second curve data.

[0049] S324. Calculate the second temperature average value of each target electricity meter according to the microcontroller unit temperatures at the same time as the electricity consumption less than the third preset threshold in the second curve data.

[0050] For example, if the moments of the electricity consumption less than the third preset threshold in a target electricity meter are 10:01, 10:02, and 10:03, then calculate the second temperature average value of this target electricity meter according to the microcontroller unit temperatures corresponding to 10:01, 10:02, and 10:03.

[0051] S325. Calculate the third temperature average value corresponding to the second temperature average value of each target electricity meter in the reference electricity meter.

[0052] For example, if a target electricity meter a calculates the second temperature average value of this target electricity meter according to the microcontroller unit temperatures corresponding to 10:01, 10:02, and 10:03, and another target electricity meter b calculates the second temperature average value of this target electricity meter according to the microcontroller unit temperatures corresponding to 11:12, 11:13, and 11:14, then the reference electricity meter calculates the third temperature average value corresponding to the target electricity meter a according to the microcontroller unit temperatures corresponding to 10:01, 10:02, and 10:03, and calculates the third temperature average value corresponding to the target electricity meter b according to the microcontroller unit temperatures corresponding to 11:12, 11:13, and 11:14.

[0053] S326. Calculate the difference between the third temperature average value and the second temperature average value to obtain the temperature compensation value, specifically: T i,cp = T i,refer,mean - T i,mean ; In the formula, T i,cp represents the temperature compensation value of the electricity meter i , T i,refer,meanRepresents the third temperature average value of the reference electricity meter, T i,mean Represents the electricity meter i 's second temperature average value.

[0054] S327. Use the temperature compensation value to calibrate the temperature of the microcontroller unit in the second curve data to obtain a temperature calibration value, specifically: T ij,cal = T ij - T i,cp ; In the formula, T ij Represents the electricity meter i 's sample point j 's microcontroller unit temperature.

[0055] S328. Obtain the calibrated second curve data according to the temperature calibration value.

[0056] 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: ; 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 's temperature calibration value, n Represents the total number of other electricity meters.

[0057] S4. Determine whether the average temperature difference is greater than the first preset threshold. If so, it is determined that the suspected abnormal electricity meter has a potential risk of burnout. If not, it is determined that the suspected abnormal electricity meter does not have a potential risk of burnout.

[0058] Specifically, determine whether any of the average temperature differences is greater than the first preset threshold. If so, it is determined that the suspected abnormal electricity meter has a potential risk of burnout. If not, it is determined that the suspected abnormal electricity meter does not have a potential risk of burnout.

[0059] After determining that the suspected abnormal electricity meter has a potential risk of burnout, immediately issue a work order to go to the site for verification.

[0060] Through flexible configuration of the acquisition tasks, the above-mentioned method for monitoring potential hazards of power meter burnout in the present invention can achieve temperature acquisition of tens of millions of power meter MCUs, and can monitor the operating temperature of power meters with a period of 1 minute. It adopts a full-scale monitoring combined with fine analysis mode, configures hourly temperature acquisition tasks for all power meters, initially locks the suspected abnormal power meters through the correlation analysis of temperature and power consumption, and then configures minute-level temperature acquisition tasks for all power meters in the metering box to which the suspected abnormal power meter belongs, realizing the monitoring of potential hazards of power meter burnout at low cost. In addition, by temperature calibration, the deviation of temperature measurement performance of power meters from different manufacturers is avoided, and a calculation model for the temperature difference between adjacent meters in a multi-meter position metering box is established. If the average temperature difference between a suspected abnormal power meter and other power meters in its metering box is greater than the set threshold, it is determined that the power meter has potential hazards of burnout, thus realizing timely, efficient, and accurate monitoring of potential hazards of power meter burnout.

[0061] Please refer to Figure 2 , Embodiment 2 of the present invention is as follows: A system for monitoring potential hazards of power meter burnout includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements each step in the method for monitoring potential hazards of power meter burnout in Embodiment 1.

[0062] In summary, the present invention provides a method and system for monitoring potential hazards of power meter burnout, which receives the first curve data of the microcontroller unit temperature and power consumption corresponding to the first acquisition task sent by the power meter. If it is determined based on the first curve data that the power meter is a suspected abnormal power meter, a second acquisition task is sent to all power meters in the multi-meter position metering box to which the suspected abnormal power meter belongs. Based on the received second curve data, the average temperature difference between the suspected abnormal power meter and other power 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 power meter has potential hazards of burnout. The monitoring period in the second acquisition task is less than the monitoring period in the first acquisition task. In this way, by adopting a full-scale monitoring combined with fine analysis method, the acquisition tasks are flexibly configured. First, the suspected abnormal power meters are initially locked through the MCU temperature and power consumption, and then the suspected abnormal power meters are further monitored in combination with other power 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, thus realizing timely, efficient, and accurate monitoring of potential hazards of power meter burnout. In addition, based on the second curve data, the temperature compensation values of all power meters are calculated, and the second curve data is calibrated according to the temperature compensation values. The average temperature difference between the suspected abnormal power meter and other power meters in the multi-meter position metering box is calculated according to the temperature calibration values in the calibrated second curve data, which can effectively eliminate the influence of other factors on temperature in other links and ensure the reliability of burnout hazard warning.

[0063] The above are only embodiments of the present invention, and thus do not limit the patent scope of the present invention. Any equivalent transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in related technical fields, shall similarly be included within the patent protection scope of the present invention.

Claims

1. A method for monitoring hidden dangers of electric energy meter burning, characterized in that: Includes steps: receiving first curve data of the temperature and power of the microcontroller unit corresponding to the first acquisition 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, and if so, determining the electric energy meter as a suspected abnormal electric energy meter, and if not, determining the electric energy meter as a normal electric energy meter; Determine the multi-meter meter box to which the suspected abnormal electric energy meter belongs, and send a second collection task to all electric energy meters in the multi-meter meter box, wherein the monitoring period in the second collection task is shorter than the monitoring period in the first collection task; receiving second curve data of the temperature and power of the microcontroller unit corresponding to the second acquisition task sent by all the electric energy meters, and calculating the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter metering box based on the second curve data; It is determined whether the temperature difference average is greater than a first preset threshold value. If so, it is determined that the suspected abnormal electric energy meter has a hidden danger of burning. If not, it is determined that the suspected abnormal electric energy meter does not have a hidden danger of burning.

2. The method for monitoring the hidden danger of burning of electric energy meter according to claim 1 is characterized in that: The step of judging whether the electric energy meter is a suspected abnormal electric energy meter based on the first curve data, and if so, determining the electric energy meter as a suspected abnormal electric energy meter, and if not, determining the electric energy meter as a normal electric energy meter comprises: Eliminating zero-electricity data from the first curve data to obtain processed first curve data; Calculating the correlation coefficient between the temperature and the electric quantity of the microcontroller unit according to the processed first curve data; It is determined whether the correlation coefficient exceeds a second preset threshold value. If so, the electric energy meter is determined to be a suspected abnormal electric energy meter. If not, the electric energy meter is determined to be a normal electric energy meter.

3. The method for monitoring the hidden danger of burning of electric energy meter according to claim 1, characterized in that: The calculating the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter meter box based on the second curve data includes: Calculating the temperature compensation values ​​of all the electric energy meters based on the second curve data, and calibrating the second curve data according to the temperature compensation values ​​to obtain calibrated second curve data; The average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter meter box is calculated according to the temperature calibration value in the calibrated second curve data.

4. The method for monitoring the hidden danger of burning of electric energy meter according to claim 3 is characterized in that: The step of calculating the temperature compensation values ​​of all the electric energy 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 comprises: Calculate the first temperature average value of each electric energy meter in all the electric energy meters within a preset time interval according to the temperature of the micro control unit in the second curve data; Determine the electric energy meter with the smallest first temperature average value as the reference electric energy meter in the multi-meter meter box; Determine, according to the electric quantity in the second curve data, a target electric energy meter whose electric quantity among all the electric energy meters is less than a third preset threshold value; Calculate a second temperature average value of each target electric energy meter according to the temperature of the micro control unit at the same time as the electric quantity less than the third preset threshold value in the second curve data; Calculate a third temperature average value in the reference electric energy meter corresponding to the second temperature average value of each target electric energy meter; Calculating a difference between the third temperature average value and the second temperature average value to obtain a temperature compensation value; Using the temperature compensation value to calibrate the temperature of the microcontroller unit in the second curve data to obtain a temperature calibration value; The calibrated second curve data is obtained according to the temperature calibration value.

5. The method for monitoring the hidden danger of burning of electric energy meter according to claim 3 is characterized in that: The calculating the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter meter box according to the temperature calibration value in the calibrated second curve data comprises: ； In the formula, represents the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter meter box, Indicates the sample point of the suspected abnormal electric energy meter in the multi-meter meter box j The temperature calibration value, Indicates other energy meters i Middle sample point j The temperature calibration value, n Indicates the total number of other electric energy meters.

6. A system for monitoring the hidden danger of burning out an electric energy meter, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: receiving first curve data of the temperature and power of the microcontroller unit corresponding to the first acquisition 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, and if so, determining the electric energy meter as a suspected abnormal electric energy meter, and if not, determining the electric energy meter as a normal electric energy meter; Determine the multi-meter meter box to which the suspected abnormal electric energy meter belongs, and send a second collection task to all electric energy meters in the multi-meter meter box, wherein the monitoring period in the second collection task is shorter than the monitoring period in the first collection task; receiving second curve data of the temperature and power of the microcontroller unit corresponding to the second acquisition task sent by all the electric energy meters, and calculating the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter metering box based on the second curve data; It is determined whether the temperature difference average is greater than a first preset threshold value. If so, it is determined that the suspected abnormal electric energy meter has a hidden danger of burning. If not, it is determined that the suspected abnormal electric energy meter does not have a hidden danger of burning.

7. The electric energy meter burning hidden danger monitoring system according to claim 6 is characterized in that: The step of judging whether the electric energy meter is a suspected abnormal electric energy meter based on the first curve data, and if so, determining the electric energy meter as a suspected abnormal electric energy meter, and if not, determining the electric energy meter as a normal electric energy meter comprises: Eliminating zero-electricity data from the first curve data to obtain processed first curve data; Calculating the correlation coefficient between the temperature and the electric quantity of the microcontroller unit according to the processed first curve data; It is determined whether the correlation coefficient exceeds a second preset threshold value. If so, the electric energy meter is determined to be a suspected abnormal electric energy meter. If not, the electric energy meter is determined to be a normal electric energy meter.

8. The electric energy meter burning hidden danger monitoring system according to claim 6 is characterized in that: The calculating the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter meter box based on the second curve data includes: Calculating the temperature compensation values ​​of all the electric energy meters based on the second curve data, and calibrating the second curve data according to the temperature compensation values ​​to obtain calibrated second curve data; The average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter meter box is calculated according to the temperature calibration value in the calibrated second curve data.

9. The electric energy meter burning hidden danger monitoring system according to claim 8 is characterized in that: The step of calculating the temperature compensation values ​​of all the electric energy 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 comprises: Calculate the first temperature average value of each electric energy meter in all the electric energy meters within a preset time interval according to the temperature of the micro control unit in the second curve data; Determine the electric energy meter with the smallest first temperature average value as the reference electric energy meter in the multi-meter meter box; Determine, according to the electric quantity in the second curve data, a target electric energy meter whose electric quantity among all the electric energy meters is less than a third preset threshold value; Calculate a second temperature average value of each target electric energy meter according to the temperature of the micro control unit at the same time as the electric quantity less than the third preset threshold value in the second curve data; Calculate a third temperature average value in the reference electric energy meter corresponding to the second temperature average value of each target electric energy meter; Calculating a difference between the third temperature average value and the second temperature average value to obtain a temperature compensation value; Using the temperature compensation value to calibrate the temperature of the microcontroller unit in the second curve data to obtain a temperature calibration value; The calibrated second curve data is obtained according to the temperature calibration value.

10. The electric energy meter burning hidden danger monitoring system according to claim 8, characterized in that: The calculating the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter meter box according to the temperature calibration value in the calibrated second curve data comprises: ； In the formula, represents the average temperature difference between the suspected abnormal electric energy meter and other electric energy meters in the multi-meter meter box, Indicates the sample point of the suspected abnormal electric energy meter in the multi-meter meter box j The temperature calibration value, Indicates other energy meters i Middle sample point j The temperature calibration value, n Indicates the total number of other electric energy meters.

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