Efficient monitoring and processing method for power failure data of electric energy meter

Abstract

The invention relates to the technical field of data processing, in particular to an efficient monitoring and processing method for power failure data of an electric energy meter, which comprises the following steps: acquiring a real-time voltage sequence of the electric energy meter in a current time period, and if the first real-time voltage data in the real-time voltage sequence is smaller than a rated voltage, judging whether the first real-time voltage data is smaller than a rated voltage; if yes, obtaining a historical voltage sequence of the electric energy meter before the current time period; acquiring a voltage normal fluctuation threshold value of the electric energy meter according to the data fluctuation condition of the historical voltage sequence; according to the difference between the voltage fluctuation condition in the real-time voltage sequence and the voltage normal fluctuation threshold value and the abnormal performance of the real-time voltage sequence, the voltage abnormal coefficient of the electric energy meter is obtained; the power failure condition of the electric energy meter in the current time period is monitored by integrating the data change speed and the data fluctuation condition of other electric power data of the electric energy meter in the current time period and the voltage abnormal coefficient of the electric energy meter, and the accuracy of monitoring the power failure condition of the electric energy meter is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an efficient monitoring and processing method for power meter power-off data. Background Art

[0002] Modern intelligent power meters not only need to have high-precision power metering capabilities, but also undertake important tasks such as data collection, storage, and transmission, providing key data support for power enterprises' electricity bill settlement, load management, fault analysis, etc. During actual operation, the power meter may experience power-off due to various reasons, such as power outages caused by grid failures, or faults in the power meter's own power supply module. During the power-off period of the power meter, the electricity consumption data stored inside and the relevant information at the moment of power-off are of great significance for the operation and management of power enterprises and the electricity consumption analysis of users.

[0003] Currently, the power-off monitoring mechanisms adopted by most power meters are relatively simple. Generally, it is considered that when the voltage fluctuates and is lower than 60% of the reference voltage of the power meter, it indicates a power-off situation. However, this single monitoring method is easily interfered by factors such as grid fluctuations and power supply noise, resulting in misjudgment or missed judgment of power-off events. For example, during a short-term voltage fluctuation in the grid, it may be wrongly considered that the power meter has experienced a power-off, thus recording unnecessary data; or in the case of a real power-off, due to insufficient monitoring sensitivity, the power-off signal cannot be detected in time, resulting in the loss of key data.

[0004] Therefore, how to improve the accuracy of monitoring the power-off situation of power meters has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide an efficient monitoring and processing method for power meter power-off data to solve the problem of how to improve the accuracy of monitoring the power-off situation of power meters.

[0006] An efficient monitoring and processing method for power meter power-off data is provided in an embodiment of the present invention. The method includes the following steps: Obtain the real-time voltage sequence of the power meter within the current period and the rated voltage of the power meter. If the first real-time voltage data in the real-time voltage sequence is less than the rated voltage, obtain the historical voltage data of the power meter before the current period to obtain the historical voltage sequence; According to the data fluctuation situation of the historical voltage sequence, obtain at least three historical normal voltage sequences when the power meter is operating normally, and obtain the normal voltage fluctuation threshold of the power meter according to the voltage fluctuation situation within each historical normal voltage sequence; Based on the difference between the voltage fluctuations in the real-time voltage sequence and the normal voltage fluctuation threshold, as well as the abnormal manifestations of the real-time voltage sequence itself, obtain the voltage abnormality coefficient of the electric energy meter at the current time period; Obtain the real-time data sequence of at least one other electrical data of the electric energy meter within the current time period. According to the data change speed and data fluctuation conditions in the real-time data sequence of each type of the other electrical data, obtain the abnormal manifestation coefficient of each type of the other electrical data within the current time period; Comprehensively consider the voltage abnormality coefficient of the electric energy meter and the abnormal manifestation coefficients of each type of the other electrical data to monitor the power-off situation of the electric energy meter within the current time period.

[0007] Preferably, the obtaining of at least three historical normal voltage sequences of the electric energy meter during normal operation according to the data fluctuation conditions of the historical voltage sequence includes: For any historical voltage data of a non-rated voltage in the historical voltage sequence, record the moment corresponding to the historical voltage data of the non-rated voltage as the fluctuation start moment, and obtain the moment corresponding to the historical voltage data of the rated voltage that is the nearest neighbor to the historical voltage data of the non-rated voltage, and record it as the fluctuation end moment. If the historical voltage data corresponding to every two adjacent moments between the fluctuation start moment and the fluctuation end moment are different, then record the time period composed of the fluctuation start moment and the fluctuation end moment as the voltage fluctuation time period, and the time length between the fluctuation start moment and the fluctuation end moment is the voltage fluctuation duration of the voltage fluctuation time period; Obtain the average duration of the peak electricity consumption within the preset historical time period of the electric energy meter. Among all the voltage fluctuation time periods, record the voltage fluctuation time periods with voltage fluctuation durations less than or equal to the average duration of the peak electricity consumption as the normal voltage fluctuation time periods, and respectively form historical normal voltage sequences with the historical voltage data within each normal voltage fluctuation time period.

[0008] Preferably, the obtaining of the normal voltage fluctuation threshold of the electric energy meter according to the voltage fluctuation conditions within each historical normal voltage sequence includes: For any historical normal voltage sequence, calculate the absolute value of the difference between each historical voltage data in the historical normal voltage sequence and the rated voltage respectively, to obtain the historical voltage normal fluctuation value sequence of the historical normal voltage sequence; Obtain the historical voltage normal fluctuation value sequences of all historical normal voltage sequences, and take the maximum value in the union of all historical voltage normal fluctuation value sequences as the normal voltage fluctuation threshold of the electric energy meter.

[0009] Preferably, obtaining the voltage anomaly coefficient of the watt-hour meter at the current time period according to the difference between the voltage fluctuation condition in the real-time voltage sequence and the normal voltage fluctuation threshold, and the self-abnormal performance of the real-time voltage sequence includes: Calculate the absolute value of the difference between each real-time voltage data in the real-time voltage sequence and the rated voltage respectively to obtain the real-time voltage fluctuation value sequence of the real-time voltage sequence, and calculate the difference between the maximum value in the real-time voltage fluctuation value sequence and the normal voltage fluctuation threshold to obtain the voltage fluctuation characteristic value of the watt-hour meter at the current time period; If the voltage fluctuation characteristic value is greater than 0, calculate 1 minus the reciprocal of the voltage fluctuation characteristic value to obtain the voltage fluctuation difference coefficient of the watt-hour meter at the current time period. If the voltage fluctuation characteristic value is less than or equal to 0, use the preset error value as the voltage fluctuation difference coefficient of the watt-hour meter at the current time period; Calculate the average value of all real-time voltage data in the real-time voltage sequence to obtain the average voltage, obtain the critical voltage of the watt-hour meter, calculate the difference between the average voltage and the critical voltage, select the maximum value between 0 and the difference, calculate the reciprocal of the sum of the maximum value and 1, and subtract the reciprocal from 1 to obtain the power-off characteristic coefficient of the watt-hour meter at the current time period; According to the similarity of the fluctuation conditions between each historical normal voltage sequence and the self-abnormal performance of the real-time voltage sequence, obtain the weights of the voltage fluctuation difference coefficient and the power-off characteristic coefficient of the watt-hour meter at the current time period, and perform weighted summation on the voltage fluctuation difference coefficient and the power-off characteristic coefficient to obtain the first anomaly coefficient of the watt-hour meter at the current time period; Calculate the standard deviation of all real-time voltage data in the real-time voltage sequence, and calculate 1 minus the reciprocal of the standard deviation to obtain the second anomaly coefficient of the watt-hour meter at the current time period; Calculate the average value between the first anomaly coefficient and the second anomaly coefficient to obtain the voltage anomaly coefficient of the watt-hour meter at the current time period.

[0010] Preferably, obtaining the weights of the voltage fluctuation difference coefficient and the power-off characteristic coefficient of the watt-hour meter at the current time period according to the similarity of the fluctuation conditions between each historical normal voltage sequence and the self-abnormal performance of the real-time voltage sequence includes: For any historical normal voltage sequence, calculate the absolute value of the difference between each historical voltage data in the any historical normal voltage sequence and the rated voltage respectively to obtain the historical voltage normal fluctuation value sequence of the any historical normal voltage sequence, obtain the historical voltage normal fluctuation value sequences of all historical normal voltage sequences, and obtain the credibility of the voltage fluctuation difference coefficient of the watt-hour meter at the current time period according to the similarity between each historical voltage normal fluctuation value sequence; Perform a linear fit on the real-time voltage data in the real-time voltage sequence to obtain the fitted line of the real-time voltage sequence, and obtain the credibility of the power-off characteristic coefficient of the electric energy meter in the current period according to the slope of the fitted line; Calculate the sum between the credibility of the voltage fluctuation difference coefficient and the credibility of the power-off characteristic coefficient to obtain the comprehensive credibility, and calculate the proportion of the credibility of the voltage fluctuation difference coefficient in the comprehensive credibility to obtain the weight of the voltage fluctuation difference coefficient of the electric energy meter in the current period; Calculate the proportion of the credibility of the power-off characteristic coefficient in the comprehensive credibility to obtain the weight of the power-off characteristic coefficient of the electric energy meter in the current period.

[0011] Preferably, the obtaining the credibility of the voltage fluctuation difference coefficient of the electric energy meter in the current period according to the similarity between each of the historical voltage normal fluctuation value sequences includes: Calculate the DTW distance between every two of the historical voltage normal fluctuation value sequences, and use the reciprocal of the sum between the average value of all DTW distances and the constant 1 as the credibility of the voltage fluctuation difference coefficient of the electric energy meter in the current period.

[0012] Preferably, the obtaining the credibility of the power-off characteristic coefficient of the electric energy meter in the current period according to the slope of the fitted line includes: Calculate the difference between the constant 0 and the slope of the fitted line, select the maximum value between the constant 0 and the difference, denote it as the downward trend eigenvalue, calculate the sum between the constant 1 and the downward trend eigenvalue to obtain the sum result, and use the reciprocal of 1 minus the sum result to obtain the credibility of the power-off characteristic coefficient of the electric energy meter in the current period.

[0013] Preferably, the obtaining the abnormal performance coefficient of each of the other power data in the current period according to the data change speed and data fluctuation condition in the real-time data sequence of each of the other power data includes: For the real-time data sequence of any one of the other power data, calculate the absolute value of the difference between any two adjacent data in the real-time data sequence of the any one of the other power data to obtain the change amount of the any two adjacent data, obtain the time interval corresponding to the any two adjacent data, and calculate the ratio between the change amount and the time interval to obtain the change speed of the any two adjacent data; Obtain the change speed of every two adjacent data in the real-time data sequence of the any one of the other power data, denote the average value of all change speeds as the average change speed of the any one of the other power data in the current period, and calculate the reciprocal of 1 minus the average change speed to obtain the first abnormal eigenvalue of the any one of the other power data in the current period; Obtain the standard deviation of all data in the real-time data sequence of any one of the other power data, and calculate the reciprocal of the constant 1 minus the standard deviation to obtain the second abnormal characteristic value of any one of the other powers in the current period; Calculate the average value between the first abnormal characteristic value and the second abnormal characteristic value to obtain the abnormal performance coefficient of any one of the other power data in the current period.

[0014] Preferably, monitor the power-off situation of the watt-hour meter in the current period based on the voltage abnormal coefficient of the integrated watt-hour meter and the abnormal performance coefficient of each of the other power data, including: Calculate the average value of the abnormal performance coefficients of all other power data to obtain the comprehensive abnormal coefficient of all other power data of the watt-hour meter in the current period, and perform weighted summation on the voltage abnormal coefficient and the comprehensive abnormal coefficient of the watt-hour meter in the current period to obtain the power-off possibility index of the watt-hour meter in the current period; Monitor the power-off situation of the watt-hour meter in the current period according to the power-off possibility index of the watt-hour meter in the current period.

[0015] Preferably, it is characterized in that the monitoring of the power-off situation of the watt-hour meter in the current period according to the power-off possibility index of the watt-hour meter in the current period includes: If the power-off possibility index of the watt-hour meter in the current period is greater than or equal to the preset power-off possibility index threshold, it is determined that the watt-hour meter has a power-off situation in the current period.

[0016] The beneficial effects of the embodiments of the present invention compared with the prior art are: The present invention obtains the real-time voltage sequence of the electricity meter during the current period and the rated voltage of the electricity meter. If the first real-time voltage data in the real-time voltage sequence is less than the rated voltage, the historical voltage data of the electricity meter before the current period is obtained to obtain a historical voltage sequence; according to the data fluctuation condition of the historical voltage sequence, at least three historical normal voltage sequences when the electricity meter is operating normally are obtained, and according to the voltage fluctuation condition within each historical normal voltage sequence, the normal voltage fluctuation threshold of the electricity meter is obtained; according to the difference between the voltage fluctuation condition in the real-time voltage sequence and the normal voltage fluctuation threshold, and the self-abnormal performance of the real-time voltage sequence, the voltage abnormality coefficient of the electricity meter during the current period is obtained; the real-time data sequence of at least one other electrical data of the electricity meter during the current period is obtained, and according to the data change speed and data fluctuation condition in the real-time data sequence of each other electrical data, the abnormal performance coefficient of each other electrical data during the current period is obtained; the power-off condition of the electricity meter during the current period is monitored by integrating the voltage abnormality coefficient of the electricity meter and the abnormal performance coefficient of each other electrical data. Among them, the normal voltage fluctuation threshold is obtained according to the voltage data (historical normal voltage sequence) when the electricity meter is operating normally in history, so as to reduce the possibility of misjudgment or missed judgment of power-off events caused by a fixed threshold. According to the difference between the voltage fluctuation condition of the real-time voltage sequence and the normal voltage fluctuation threshold, and the self-abnormal performance of the real-time voltage sequence, the voltage abnormality coefficient is obtained. At the same time, according to the abnormal performance of other electrical data of the electricity meter in the same time, the abnormal performance coefficient is obtained to more comprehensively monitor the power-off condition of the electricity meter, improve the accuracy of the monitoring result, and then monitor the power-off condition of the electricity meter by integrating the voltage abnormality coefficient and the abnormal performance coefficient of other electrical data, thereby improving the accuracy of monitoring the power-off condition of the electricity meter. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0018] Figure 1 It is a flowchart of a method for efficiently monitoring and processing power-off data of an electricity meter provided in Embodiment 1 of the present invention. Detailed Embodiments

[0019] The following will describe in detail the embodiments of the present disclosure, and the examples of the embodiments are shown in the drawings. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present disclosure and should not be construed as a limitation of the present disclosure.

[0020] It should be noted that the terms "first", "second", etc. in the description of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0021] In order to illustrate the technical solution of the present invention, the following will be described through specific embodiments.

[0022] See Figure 1 , which is a flowchart of a method for efficiently monitoring and processing power meter power-off data provided in the first embodiment of the present invention. As Figure 1 shown, the method may include: Step S101, obtain the real-time voltage sequence of the power meter during the current period and the rated voltage of the power meter. If the first real-time voltage data in the real-time voltage sequence is less than the rated voltage, obtain the historical voltage data of the power meter before the current period to obtain a historical voltage sequence.

[0023] The voltage monitored by the power meter is recorded once per second to obtain voltage data. There is no limitation here, and the implementer can set the recording frequency according to the specific scenario. In the traditional method, it is considered that when the voltage data at a certain moment is lower than a fixed threshold (i.e., 60% of the reference voltage of the power meter), it means that a power-off situation occurs at that moment. However, under normal circumstances, the voltage of the power meter will have some fluctuations, such as changes in the grid load: the grid load increases during the morning and evening peak periods, which may cause a short-term voltage drop; some loads will also cause voltage fluctuations, such as when motors and transformers start, they may cause a voltage drop. Therefore, the method of using a fixed threshold to monitor the power-off situation of the power meter is too single, which may lead to misjudgment or missed judgment of power-off events. For example, when there is a short-term voltage fluctuation in the grid, it may be wrongly considered that the power meter has a power-off, resulting in the recording of unnecessary data.

[0024] Therefore, in the embodiments of the present invention, in order to reduce the situation of missed judgment, when the voltage data at a certain moment is less than the rated voltage (the rated voltage is usually the same as the reference voltage, and the voltage reference can also be used, which is not limited here, and the implementer can set it according to the specific scenario), it is determined that the electric energy meter may have a power failure. At this time, the voltage data of the electric energy meter within 5 s after this moment is analyzed to determine whether the electric energy meter has a power failure, that is, the time length of the set time period is 5 s, which is not limited here, and the implementer can set it according to the specific scenario. The real-time voltage data of the electric energy meter within the current time period is obtained to obtain a real-time voltage sequence. If the first real-time voltage data in the real-time voltage sequence is less than the rated voltage of the electric energy meter, the real-time voltage sequence is analyzed to accurately determine whether the electric energy meter has a power failure within the current time period.

[0025] Since there are significant differences in the fluctuation duration and fluctuation range between the voltage fluctuations caused by the power failure of the electric energy meter and the normal voltage fluctuations, in the embodiments of the present invention, when it is detected that the first real-time voltage data in the real-time voltage sequence is less than the rated voltage of the electric energy meter, the historical voltage data of the electric energy meter in the previous three weeks before the current time period is obtained, which is not limited here, and the implementer can set it according to the specific scenario, to obtain a historical voltage sequence, so as to analyze the fluctuation situation of the historical voltage data to obtain the normal voltage fluctuation range, and then judge whether the electric energy meter has a power failure in the current time period according to the similarity between the fluctuation situation of the real-time voltage data in the current time period and the normal voltage fluctuation range, so as to reduce the possibility of misjudgment or missed judgment of power failure events caused by a fixed threshold (i.e., 60% of the reference voltage of the electric energy meter) and improve the accuracy of monitoring the power failure situation of the electric energy meter.

[0026] Step S102: According to the data fluctuation situation of the historical voltage sequence, obtain at least three historical normal voltage sequences when the electric energy meter is operating normally, and obtain the normal voltage fluctuation threshold of the electric energy meter according to the voltage fluctuation situation in each historical normal voltage sequence.

[0027] Since the power failure of the electric energy meter is generally due to the failure of power facilities, the voltage fluctuations caused by the power failure often last for a long time, while the normal voltage fluctuations last for a short time. At the same time, the voltage fluctuations caused by the external environment are generally instantaneous or short-term fluctuations, so the normal voltage fluctuations generally do not exceed the peak electricity consumption period. Therefore, in the embodiments of the present invention, the average peak electricity consumption duration of the electric energy meter in the previous week before the current time period is obtained through a third-party voltage monitoring tool (such as the local power APP, the electricity consumption report generated by the voltage recorder, etc.). Obtaining the average peak electricity consumption duration is an existing technology and will not be elaborated here. According to the average peak electricity consumption duration, at least three historical normal voltage sequences are obtained from the historical voltage sequence to represent the voltage condition of the electric energy meter in the normal operating state. Specifically: For any historical voltage data of a non - rated voltage in the historical voltage sequence, the moment corresponding to the historical voltage data of the non - rated voltage is recorded as the starting moment of the fluctuation. Obtain the moment corresponding to the historical voltage data of the rated voltage that is the nearest neighbor to the historical voltage data of the non - rated voltage, and record it as the ending moment of the fluctuation. If the historical voltage data corresponding to every two adjacent moments between the starting moment of the fluctuation and the ending moment of the fluctuation are different, then the time period composed of the starting moment of the fluctuation and the ending moment of the fluctuation is recorded as the voltage fluctuation period, and the time length between the starting moment of the fluctuation and the ending moment of the fluctuation is the voltage fluctuation duration of the voltage fluctuation period; Obtain at least one voltage fluctuation period in the historical voltage sequence. Among all the voltage fluctuation periods, the voltage fluctuation period with a voltage fluctuation duration less than or equal to the average duration of the peak electricity consumption period is recorded as the normal voltage fluctuation period, and the historical voltage data within each normal voltage fluctuation period are respectively composed into a historical normal voltage sequence.

[0028] The historical normal voltage sequence reflects the voltage condition of the electric energy meter in the normal operation state. Therefore, the fluctuation situation of the historical normal voltage sequence can also reflect the normal voltage fluctuation range of the electric energy meter. Thus, in the embodiments of the present invention, according to the voltage fluctuation situation within each historical normal voltage sequence, the normal voltage fluctuation threshold of the electric energy meter is obtained. Specifically: For any historical normal voltage sequence, calculate the absolute value of the difference between each historical voltage data in the historical normal voltage sequence and the rated voltage respectively, which is used to characterize the normal fluctuation value of each historical voltage data, and obtain the historical voltage normal fluctuation value sequence of the historical normal voltage sequence; Obtain the historical voltage normal fluctuation value sequences of all historical normal voltage sequences, and take the maximum value in the union of all historical voltage normal fluctuation value sequences as the normal voltage fluctuation threshold of the electric energy meter, denoted as 。

[0029] So far, the normal voltage fluctuation threshold of the electric energy meter has been obtained, which is used to judge whether the electric energy meter has a power - off in the current period according to the difference between the normal voltage fluctuation threshold of the electric energy meter and the fluctuation situation of the real - time voltage sequence in the current period, so as to reduce the possibility of misjudgment or missed judgment of power - off events caused by a fixed threshold and improve the accuracy of monitoring the power - off situation of the electric energy meter.

[0030] Step S103, according to the difference between the voltage fluctuation situation in the real - time voltage sequence and the normal voltage fluctuation threshold, and the abnormal performance of the real - time voltage sequence itself, obtain the voltage abnormality coefficient of the electric energy meter in the current period.

[0031] When the power supply of the electricity meter is cut off, the voltage data generally shows that the voltage fluctuation exceeds the normal range (i.e., the normal voltage fluctuation threshold) or continuously drops below the critical voltage (i.e., 60% of the reference voltage of the electricity meter), and the voltage decays exponentially. Therefore, in the embodiment of the present invention, according to the difference between the voltage fluctuation of the real-time voltage sequence of the electricity meter in the current period and the normal voltage fluctuation threshold, and the voltage abnormality of the real-time voltage sequence itself, the voltage abnormality coefficient of the electricity meter in the current period is obtained to preliminarily determine whether the electricity meter has a power cut-off in the current period. Specifically: Calculate the absolute value of the difference between each real-time voltage data in the real-time voltage sequence and the rated voltage respectively, which is used to characterize the fluctuation value of each real-time voltage data, and obtain the real-time voltage fluctuation value sequence of the real-time voltage sequence, denoted as Z. Calculate the difference between the maximum value in the real-time voltage fluctuation value sequence and the normal voltage fluctuation threshold, and obtain the voltage fluctuation characteristic value of the electricity meter in the current period, denoted as A, that is , where A represents the voltage fluctuation characteristic value of the electricity meter in the current period, and Z represents the real-time voltage fluctuation value sequence of the real-time voltage sequence, represents the maximum value function, represents the normal voltage fluctuation threshold; the larger A is, the greater the degree of voltage fluctuation of the electricity meter in the current period deviates from the normal voltage fluctuation, and the more likely the electricity meter is to have a power cut-off in the current period; If the voltage fluctuation characteristic value is greater than 0, it means that the voltage fluctuation exceeds the normal range at this time, then calculate the reciprocal of 1 minus the voltage fluctuation characteristic value, and obtain the voltage fluctuation difference coefficient of the electricity meter in the current period, that is ; if the voltage fluctuation characteristic value is less than or equal to 0, it means that the voltage fluctuation does not exceed the normal range at this time, but considering that the normal voltage fluctuation threshold calculated in step S102 may be inaccurate and there is an error, so the preset error value is used as the voltage fluctuation difference coefficient of the electricity meter in the current period. In the embodiment of the present invention, the preset error value is set to 0.1, which is not limited here, and the implementer can set it according to the specific scenario; Calculate the average value of all real-time voltage data in the real-time voltage sequence to obtain the average voltage, obtain the critical voltage of the electricity meter, calculate the difference between the average voltage and the critical voltage, select the maximum value between 0 and the difference, calculate the reciprocal of the sum of the maximum value and 1, and subtract the reciprocal from 1 to obtain the power cut-off characteristic coefficient of the electricity meter in the current period; According to the similarity of the fluctuation conditions between each historical normal voltage sequence and the abnormal performance of the real-time voltage sequence itself, obtain the weights of the voltage fluctuation difference coefficient and the power cut-off characteristic coefficient of the electricity meter in the current period, and perform weighted summation on the voltage fluctuation difference coefficient and the power cut-off characteristic coefficient to obtain the first abnormal coefficient of the electricity meter in the current period; Calculate the standard deviation of all real-time voltage data in the real-time voltage sequence, and calculate the reciprocal of the constant 1 minus the standard deviation to obtain the second anomaly coefficient of the electricity meter in the current period; Calculate the average value between the first anomaly coefficient and the second anomaly coefficient to obtain the voltage anomaly coefficient of the electricity meter in the current period.

[0032] In an embodiment, the calculation formula for the voltage anomaly coefficient of the electricity meter in the current period is:

[0033] Among them, represents the voltage anomaly coefficient of the electricity meter in the current period, represents the weight of the voltage fluctuation difference coefficient of the electricity meter in the current period, R represents the voltage fluctuation difference coefficient of the electricity meter in the current period. When the voltage fluctuation characteristic value A is greater than 0, ; when the voltage fluctuation characteristic value A is less than or equal to 0, , represents the weight of the power-off characteristic coefficient of the electricity meter in the current period, is the power-off characteristic coefficient of the electricity meter in the current period, represents the critical voltage of the electricity meter, represents the average value of all real-time voltage data in the real-time voltage sequence of the electricity meter, that is, the average voltage, represents the standard deviation of all real-time voltage data in the real-time voltage sequence of the electricity meter, and 0.1 represents the preset error value, represents the maximum value function.

[0034] It should be noted that the larger R is, the more the voltage fluctuation of the electricity meter in the current period deviates from the normal fluctuation, and thus the larger it is, the more likely the electricity meter is to have an abnormal power-off situation in the current period; the smaller it is, the larger it is, the lower the average voltage of the electricity meter in the current period is lower than the critical voltage, and the voltage of the electricity meter is low in the current period. Thus the larger it is, the more likely the electricity meter is to have an abnormal power-off situation in the current period; the larger it is, the more unstable the voltage of the electricity meter in the current period is. Thus the larger it is, the more likely the electricity meter is to have an abnormal power-off situation in the current period.

[0035] Among them, considering that the normal voltage fluctuation threshold obtained in step S102 is obtained based on the historical normal voltage sequence of the electricity meter, and the obtained historical normal voltage sequence may be inaccurate, which may lead to errors in the normal voltage fluctuation threshold. The voltage fluctuation difference coefficient (i.e., ) There may be errors; at the same time, it can only determine that the voltage of the electric energy meter is low during the current period, and it cannot reflect the power-off characteristic of the rapid voltage drop. Therefore, in the embodiments of the present invention, according to the similarity between each historical normal voltage sequence, the credibility of the voltage fluctuation difference coefficient of the electric energy meter in the current period is obtained, and according to the abnormal performance of the real-time voltage sequence itself, the credibility of the power-off characteristic coefficient of the electric energy meter in the current period is obtained. Further, calculate the sum between the credibility of the voltage fluctuation difference coefficient and the credibility of the power-off characteristic coefficient to obtain the comprehensive credibility, and use the proportion of the credibility of the voltage fluctuation difference coefficient in the comprehensive credibility as the weight of the voltage fluctuation difference coefficient, that is , and use the proportion of the credibility of the power-off characteristic coefficient in the comprehensive credibility as the weight of the power-off characteristic coefficient, that is .

[0036] Among them, the specific method for obtaining the credibility of the voltage fluctuation difference coefficient according to the similarity between each historical normal voltage sequence is: For any historical normal voltage sequence, calculate the absolute value of the difference between each historical voltage data in the any historical normal voltage sequence and the rated voltage respectively, which is used to characterize the normal fluctuation value of each historical voltage data, and obtain the historical voltage normal fluctuation value sequence of the any historical normal voltage sequence, and obtain the historical voltage normal fluctuation value sequences of all historical normal voltage sequences; Form a group of every two of the historical voltage normal fluctuation value sequences, calculate the DTW distance between the two historical voltage normal fluctuation value sequences in each group, and use the reciprocal of the sum of the average value of all DTW distances and the constant 1 as the credibility of the voltage fluctuation difference coefficient of the electric energy meter in the current period.

[0037] In an embodiment, the calculation formula for the credibility of the voltage fluctuation difference coefficient of the electric energy meter in the current period is:

[0038] Among them, represents the credibility of the voltage fluctuation difference coefficient of the electric energy meter in the current period, N represents the number of combinations of pairwise combinations of all historical voltage normal fluctuation value sequences, that is, if the number of all historical voltage normal fluctuation value sequences is m, then N = , represents the DTW distance between the two historical voltage normal fluctuation value sequences in the nth group.

[0039] It should be noted that the smaller, the more similar the fluctuation conditions between each historical normal voltage sequence are, and the more credible the voltage normal fluctuation threshold obtained according to the historical normal voltage sequence of the electric energy meter is. Furthermore The larger it is, the higher the credibility of the voltage fluctuation eigenvalue obtained according to the normal voltage fluctuation threshold, and the higher the credibility of the voltage fluctuation difference coefficient.

[0040] Among them, the specific method for obtaining the credibility of the power-off characteristic coefficient of the electric energy meter in the current period according to the abnormal performance of the real-time voltage sequence itself is as follows: Using the least squares method, linearly fit the real-time voltage data in the real-time voltage sequence to obtain the fitting straight line of the real-time voltage sequence. The least squares method is a prior art and will not be elaborated here. Calculate the difference between the constant 0 and the slope of the fitting straight line, and select the maximum value between the constant 0 and the difference, denoted as the downward trend eigenvalue. Calculate the sum between the constant 1 and the downward trend eigenvalue to obtain the sum result, and subtract the reciprocal of the sum result from the constant 1 to obtain the credibility of the power-off characteristic coefficient of the electric energy meter in the current period.

[0041] In an embodiment, the calculation method of the credibility of the power-off characteristic coefficient of the electric energy meter in the current period is:

[0042] Among them, represents the credibility of the power-off characteristic coefficient of the electric energy meter in the current period, k represents the slope of the fitting straight line of the real-time voltage sequence, represents the maximum value function.

[0043] It should be noted that when k is negative, the smaller k is, the faster the voltage of the electric energy meter drops in the current period, and thus the larger it is, the more the electric energy meter conforms to the characteristic of continuous voltage drop during power-off; when k is positive, the voltage of the electric energy meter shows an upward trend in the current period, not a downward trend, , , that is, the electric energy meter does not conform to the characteristic of continuous voltage drop during power-off.

[0044] So far, by analyzing the voltage fluctuation situation of the electric energy meter in the current period, the voltage abnormality coefficient of the electric energy meter in the current period has been obtained, which is used to preliminarily judge whether the electric energy meter has a power-off in the current period, improving the accuracy of monitoring the power-off situation of the electric energy meter.

[0045] Step S104, obtain the real-time data sequence of at least one other power data of the electric energy meter in the current period, and obtain the abnormality performance coefficient of each other power data in the current period according to the data change speed and data fluctuation situation in the real-time data sequence of each other power data.

[0046] If the voltage fluctuation of the electricity meter during the current period is caused by power failure, not only will the voltage show abnormal phenomena such as the fluctuation exceeding the normal range or continuously being lower than the critical voltage (i.e., 60% of the reference voltage of the electricity meter), but other electrical data of the electricity meter will also simultaneously show abnormalities. For example, the current will be no greater than 5% of the rated (basic) current, and there will be abnormal fluctuations at the same time. The power changes with the voltage and current, and generally will quickly drop to 0 or close to 0 under power failure conditions, and there will be abnormal fluctuations at the same time. If the voltage fluctuation of the electricity meter during the current period is not caused by power failure, then other electrical data will not simultaneously show abnormal phenomena. Therefore, only analyzing the abnormal situation of the voltage of the electricity meter during the current period cannot accurately determine whether the electricity meter has experienced a power failure.

[0047] Therefore, in the embodiments of the present invention, according to the recording frequency of the voltage, that is, once every second, the current and power monitored by the electricity meter are recorded once every second respectively, and the real-time data sequences of the current and power of the electricity meter during the current period are obtained. Based on the abnormal phenomena of the current and power when the electricity meter experiences a power failure, the real-time data sequences of the current and power during the current period are analyzed, and the abnormal performance coefficients of the current and power during the current period are obtained respectively. Furthermore, in combination with the abnormal situations of all electrical data (i.e., voltage, current, and power) of the electricity meter during the current period, it is determined whether the electricity meter has experienced a power failure during the current period, so as to more comprehensively monitor the power failure situation of the electricity meter and improve the accuracy of the monitoring results. There is no limitation here, and the implementer can set the types of electrical data according to the specific scenario.

[0048] Among them, taking the current as an example, the specific method for obtaining the abnormal performance coefficient of the current during the current period is as follows: Calculate the absolute value of the difference between any two adjacent data in the real-time data sequence of any one of the other electrical data to obtain the change amount of the two adjacent data, obtain the time interval corresponding to the two adjacent data, calculate the ratio between the change amount and the time interval, and obtain the change speed of the two adjacent data; Obtain the change speed of each adjacent two data in the real-time data sequence of the current, record the average value of all change speeds as the average change speed of the current during the current period, calculate the constant 1 minus the reciprocal of the average change speed, and obtain the first abnormal characteristic value of the current during the current period; Obtain the standard deviation of all data in the real-time data sequence of the current, calculate the constant 1 minus the reciprocal of the standard deviation, and obtain the second abnormal characteristic value of the current during the current period; Calculate the average value between the first abnormal characteristic value and the second abnormal characteristic value, and obtain the abnormal performance coefficient of the current during the current period.

[0049] In an implementation manner, the calculation formula for the abnormal performance coefficient of the current during the current period is:

[0050] Among them, represents the abnormal performance coefficient of the current in the current time period, represents the average change speed of the current in the current time period, represents the standard deviation of all data in the real-time data sequence of the current.

[0051] It should be noted that the larger it is, the more likely it is that the current has an abnormal decrease in the current time period, and then the larger it is, the more abnormal the current performance; the larger it is, the more obvious the fluctuation of the current in the current time period, and the more unstable the change. Then the larger it is, the more abnormal the current performance.

[0052] Similarly, according to the method of obtaining the abnormal performance coefficient of the current in the current time period, obtain the abnormal performance coefficient of the power in the current time period, denoted as .

[0053] So far, by analyzing the situation of other power data of the electric energy meter in the current time period, the abnormal performance coefficients of each other power data in the current time period are obtained. Then, combined with the abnormal conditions of all power data (i.e., voltage, current, and power) of the electric energy meter, it is judged whether the electric energy meter has a power failure in the current time period, so as to improve the accuracy of monitoring the power failure situation of the electric energy meter.

[0054] Step S105, comprehensively monitor the power failure situation of the electric energy meter in the current time period based on the voltage abnormal coefficient of the electric energy meter and the abnormal performance coefficients of each of the other power data.

[0055] Based on the voltage abnormal coefficient of the electric energy meter in the current time period and the abnormal performance coefficients of the other power data (i.e., current and power) of the electric energy meter in the current time period, obtain the power failure possibility index of the electric energy meter in the current time period. Then, according to the power failure possibility index, judge whether the electric energy meter has a power failure in the current time period. The specific method for obtaining the power failure possibility index is as follows: Calculate the average value of the abnormal performance coefficients of all other power data to obtain the comprehensive abnormal coefficient of all other power data of the electric energy meter in the current time period. Perform a weighted sum of the voltage abnormal coefficient of the electric energy meter in the current time period and the comprehensive abnormal coefficient to obtain the power failure possibility index of the electric energy meter in the current time period.

[0056] In an embodiment, assuming that the other power data of the electric energy meter are current and power respectively, the calculation formula for the power failure possibility index of the electric energy meter in the current time period is:

[0057] Among them, represents the power-off possibility index of the electricity meter during the current period, represents the first weight, represents the voltage abnormality coefficient of the electricity meter during the current period, represents the second weight, represents the abnormal performance coefficient of the current during the current period, represents the abnormal performance coefficient of the power during the current period.

[0058] It should be noted that since the voltage of the electricity meter is the main monitoring object and other power data are auxiliary monitoring objects, it can be set as , , which is not restricted here, and the implementer can set it according to the specific scenario; The larger the value, the more the voltage fluctuation of the electricity meter exceeds the normal fluctuation range during the current period and shows a continuous downward trend, and the more it conforms to the power-off characteristics of the electricity meter. Furthermore, the larger the value, the greater the possibility of abnormal power-off of the electricity meter during the current period; The larger the value, the greater the abnormal degree of the current and power of the electricity meter during the current period. Furthermore, the larger the value, the greater the possibility of abnormal power-off of the electricity meter during the current period.

[0059] Furthermore, according to experimental statistics, the preset power-off possibility index threshold is set to 0.7, which is not restricted here, and the implementer can set it according to the specific scenario. If the power-off possibility index of the electricity meter during the current period is greater than or equal to 0.7, it is determined that the electricity meter has experienced a power-off during the current period. At this time, relevant equipment should be enabled to record relevant data and notify the maintenance personnel to check and repair the power grid or power system facilities. Enabling relevant equipment to record relevant data is an existing technology and will not be elaborated here.

[0060] In summary, the present invention obtains the real-time voltage sequence of the electricity meter in the current period and the rated voltage of the electricity meter. If the first real-time voltage data in the real-time voltage sequence is less than the rated voltage, the historical voltage data of the electricity meter before the current period is obtained to obtain a historical voltage sequence; according to the data fluctuation situation of the historical voltage sequence, at least three historical normal voltage sequences when the electricity meter is operating normally are obtained, and according to the voltage fluctuation situation in each historical normal voltage sequence, the normal voltage fluctuation threshold of the electricity meter is obtained; according to the difference between the voltage fluctuation situation in the real-time voltage sequence and the normal voltage fluctuation threshold, and the abnormal performance of the real-time voltage sequence itself, the voltage abnormal coefficient of the electricity meter in the current period is obtained; the real-time data sequence of at least one other electrical data of the electricity meter in the current period is obtained, and according to the data change speed and data fluctuation situation in the real-time data sequence of each other electrical data, the abnormal performance coefficient of each other electrical data in the current period is obtained; the power-off situation of the electricity meter in the current period is monitored by comprehensively considering the voltage abnormal coefficient of the electricity meter and the abnormal performance coefficient of each other electrical data. Among them, the normal voltage fluctuation threshold is obtained according to the voltage data (historical normal voltage sequence) when the electricity meter is operating normally in history, so as to reduce the possibility of misjudgment or missed judgment of power-off events caused by a fixed threshold. According to the difference between the voltage fluctuation situation of the real-time voltage sequence and the normal voltage fluctuation threshold, and the abnormal performance of the real-time voltage sequence itself, the voltage abnormal coefficient is obtained. At the same time, according to the abnormal performance of other electrical data of the electricity meter in the same period, the abnormal performance coefficient is obtained, so as to more comprehensively monitor the power-off situation of the electricity meter, improve the accuracy of the monitoring result, and then comprehensively monitor the power-off situation of the electricity meter by combining the voltage abnormal coefficient and the abnormal performance coefficient of other electrical data, thereby improving the accuracy of monitoring the power-off situation of the electricity meter.

[0061] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An efficient monitoring and processing method for power-off data of an electric energy meter, characterized in that, The efficient monitoring and processing method for the power-off data of an electric energy meter includes: Obtain the real-time voltage sequence of the electric energy meter within the current period and the rated voltage of the electric energy meter. If the first real-time voltage data in the real-time voltage sequence is less than the rated voltage, obtain the historical voltage data of the electric energy meter before the current period to obtain a historical voltage sequence; According to the data fluctuation situation of the historical voltage sequence, obtain at least three historical normal voltage sequences of the electric energy meter during normal operation. According to the voltage fluctuation situation within each historical normal voltage sequence, obtain the voltage normal fluctuation threshold of the electric energy meter; Based on the difference between the voltage fluctuation situation in the real-time voltage sequence and the voltage normal fluctuation threshold, and the abnormal performance of the real-time voltage sequence itself, obtain the voltage abnormal coefficient of the electric energy meter during the current period; Obtain the real-time data sequence of at least one other power data of the electric energy meter within the current period. According to the data change speed and data fluctuation situation in the real-time data sequence of each other power data, obtain the abnormal performance coefficient of each other power data within the current period; Comprehensively consider the voltage abnormal coefficient of the electric energy meter and the abnormal performance coefficients of each other power data to monitor the power-off situation of the electric energy meter during the current period.

2. The efficient monitoring and processing method for power-off data of an electric energy meter according to claim 1, wherein The step of obtaining at least three historical normal voltage sequences of the electric energy meter during normal operation according to the data fluctuation situation of the historical voltage sequence includes: For any historical voltage data other than the rated voltage in the historical voltage sequence, record the moment corresponding to the historical voltage data as the fluctuation start moment. Obtain the moment corresponding to the historical voltage data closest to the rated voltage as the fluctuation end moment. If the historical voltage data corresponding to every two adjacent moments between the fluctuation start moment and the fluctuation end moment is different, record the period composed of the fluctuation start moment and the fluctuation end moment as the voltage fluctuation period, and the time length between the fluctuation start moment and the fluctuation end moment is the voltage fluctuation duration of the voltage fluctuation period; Obtain the average duration of the peak electricity consumption within the preset historical period of the electric energy meter. Among all voltage fluctuation periods, record the voltage fluctuation periods with voltage fluctuation durations less than or equal to the average duration of the peak electricity consumption as voltage normal fluctuation periods, and respectively form historical normal voltage sequences with the historical voltage data within each voltage normal fluctuation period.

3. An efficient monitoring and processing method for power-off data of an electric energy meter according to claim 1, characterized in that, The step of obtaining the voltage normal fluctuation threshold of the electric energy meter according to the voltage fluctuation situation within each historical normal voltage sequence includes: For any historical normal voltage sequence, calculate the absolute value of the difference between each historical voltage data in the historical normal voltage sequence and the rated voltage respectively to obtain the historical voltage normal fluctuation value sequence of the historical normal voltage sequence; Obtain the historical voltage normal fluctuation value sequences of all historical normal voltage sequences, and take the maximum value in the union of all historical voltage normal fluctuation value sequences as the voltage normal fluctuation threshold of the electric energy meter.

4. An efficient monitoring and processing method for power-off data of an electric energy meter according to claim 1, characterized in that, Obtaining the voltage anomaly coefficient of the electric energy meter at the current time period according to the difference between the voltage fluctuation condition in the real-time voltage sequence and the normal voltage fluctuation threshold, and the self-abnormal performance of the real-time voltage sequence, includes: Calculating the absolute value of the difference between each real-time voltage data in the real-time voltage sequence and the rated voltage respectively to obtain the real-time voltage fluctuation value sequence of the real-time voltage sequence, and calculating the difference between the maximum value in the real-time voltage fluctuation value sequence and the normal voltage fluctuation threshold to obtain the voltage fluctuation characteristic value of the electric energy meter at the current time period; If the voltage fluctuation characteristic value is greater than 0, then calculate 1 minus the reciprocal of the voltage fluctuation characteristic value to obtain the voltage fluctuation difference coefficient of the electric energy meter at the current time period. If the voltage fluctuation characteristic value is less than or equal to 0, then use the preset error value as the voltage fluctuation difference coefficient of the electric energy meter at the current time period; Calculating the average value of all real-time voltage data in the real-time voltage sequence to obtain the average voltage, obtaining the critical voltage of the electric energy meter, calculating the difference between the average voltage and the critical voltage, selecting the maximum value between 0 and the difference, calculating the reciprocal of the sum of the maximum value and 1, and subtracting the reciprocal from 1 to obtain the power-off characteristic coefficient of the electric energy meter at the current time period; According to the similarity of the fluctuation conditions between each historical normal voltage sequence and the self-abnormal performance of the real-time voltage sequence, obtaining the weights of the voltage fluctuation difference coefficient and the power-off characteristic coefficient of the electric energy meter at the current time period, and weighted summing the voltage fluctuation difference coefficient and the power-off characteristic coefficient to obtain the first anomaly coefficient of the electric energy meter at the current time period; Calculating the standard deviation of all real-time voltage data in the real-time voltage sequence, and calculating 1 minus the reciprocal of the standard deviation to obtain the second anomaly coefficient of the electric energy meter at the current time period; Calculating the average value between the first anomaly coefficient and the second anomaly coefficient to obtain the voltage anomaly coefficient of the electric energy meter at the current time period.

5. An efficient monitoring and processing method for power-off data of an electric energy meter according to claim 4, characterized in that, The obtaining the weights of the voltage fluctuation difference coefficient and the power-off characteristic coefficient of the electric energy meter at the current time period according to the similarity of the fluctuation conditions between each historical normal voltage sequence and the self-abnormal performance of the real-time voltage sequence, includes: For any historical normal voltage sequence, calculating the absolute value of the difference between each historical voltage data in the any historical normal voltage sequence and the rated voltage respectively to obtain the historical voltage normal fluctuation value sequence of the any historical normal voltage sequence, obtaining the historical voltage normal fluctuation value sequences of all historical normal voltage sequences, and obtaining the credibility of the voltage fluctuation difference coefficient of the electric energy meter at the current time period according to the similarity between each historical voltage normal fluctuation value sequence; Performing linear fitting on the real-time voltage data in the real-time voltage sequence to obtain the fitting straight line of the real-time voltage sequence, and obtaining the credibility of the power-off characteristic coefficient of the electric energy meter at the current time period according to the slope of the fitting straight line; Calculate the sum between the credibility of the voltage fluctuation difference coefficient and the credibility of the power-off feature coefficient to obtain the comprehensive credibility, and calculate the proportion of the credibility of the voltage fluctuation difference coefficient in the comprehensive credibility to obtain the weight of the voltage fluctuation difference coefficient of the electric energy meter in the current period; Calculate the proportion of the credibility of the power-off feature coefficient in the comprehensive credibility to obtain the weight of the power-off feature coefficient of the electric energy meter in the current period.

6. The efficient monitoring and processing method for power-off data of an electric energy meter according to claim 5, characterized in that, The obtaining of the credibility of the voltage fluctuation difference coefficient of the electric energy meter in the current period according to the similarity between each of the historical normal voltage fluctuation value sequences includes: Calculate the DTW distance between every two of the historical normal voltage fluctuation value sequences, and take the reciprocal of the sum between the average value of all the DTW distances and the constant 1 as the credibility of the voltage fluctuation difference coefficient of the electric energy meter in the current period.

7. An efficient monitoring and processing method for power-off data of an electric energy meter according to claim 5, characterized in that, The obtaining of the credibility of the power-off feature coefficient of the electric energy meter in the current period according to the slope of the fitting straight line includes: Calculate the difference between the constant 0 and the slope of the fitting straight line, select the maximum value between the constant 0 and the difference, and denote it as the downward trend feature value. Calculate the sum between the constant 1 and the downward trend feature value to obtain the sum result, and take the reciprocal of the difference between the constant 1 and the sum result to obtain the credibility of the power-off feature coefficient of the electric energy meter in the current period.

8. An efficient monitoring and processing method for power-off data of an electric energy meter according to claim 1, characterized in that The obtaining of the abnormal performance coefficient of each of the other power data in the current period according to the data change speed and data fluctuation condition in the real-time data sequence of each of the other power data includes: For the real-time data sequence of any one of the other power data, calculate the absolute value of the difference between any two adjacent data in the real-time data sequence of the any one of the other power data to obtain the change amount of the any two adjacent data, obtain the time interval corresponding to the any two adjacent data, and calculate the ratio between the change amount and the time interval to obtain the change speed of the any two adjacent data; Obtain the change speed of every two adjacent data in the real-time data sequence of the any one of the other power data, denote the average value of all the change speeds as the average change speed of the any one of the other power data in the current period, and calculate the reciprocal of the difference between the constant 1 and the average change speed to obtain the first abnormal feature value of the any one of the other power data in the current period; Obtain the standard deviation of all the data in the real-time data sequence of the any one of the other power data, and calculate the reciprocal of the difference between the constant 1 and the standard deviation to obtain the second abnormal feature value of the any one of the other power in the current period; Calculate the average value between the first abnormal feature value and the second abnormal feature value to obtain the abnormal performance coefficient of the any one of the other power data in the current period.

9. The high-efficiency monitoring and processing method for power-off data of an electric energy meter according to claim 1, wherein Monitoring the power-off situation of the electric energy meter in the current period by synthesizing the voltage abnormality coefficient of the electric energy meter and the abnormal performance coefficient of each of the other power data includes: Calculate the average value of the abnormal performance coefficients of all other power data to obtain the comprehensive abnormal coefficient of all other power data of the electricity meter during the current period. Perform a weighted sum of the voltage abnormal coefficient and the comprehensive abnormal coefficient of the electricity meter during the current period to obtain the power-off possibility index of the electricity meter during the current period; Monitor the power-off situation of the electricity meter during the current period according to the power-off possibility index of the electricity meter during the current period.

10. The efficient monitoring and processing method for power-off data of an electric energy meter according to claim 9, characterized in that, The monitoring of the power-off situation of the electricity meter during the current period according to the power-off possibility index of the electricity meter during the current period includes: If the power-off possibility index of the electricity meter during the current period is greater than or equal to the preset power-off possibility index threshold, it is determined that the electricity meter has experienced a power-off situation during the current period.

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