A method for monitoring and managing abnormal operation state of intelligent electric energy meter

Abstract

This invention relates to the field of power equipment monitoring technology, specifically to a method for monitoring and managing abnormal operating status of smart meters. The method involves: obtaining fluctuation characteristic values ​​based on local data at any given time in a load data sequence and corresponding fitted data; filtering the fluctuation characteristic values ​​to obtain a smoothed load sequence; obtaining different load time periods from the smoothed load sequence and clustering them; obtaining electricity fluctuation values ​​based on the fluctuation characteristic values ​​of all times within each time period cluster; determining the collection period based on the time period distribution characteristics of the time period cluster; obtaining relative fluctuation based on the electricity fluctuation values ​​of users and other users during the collection period and the distance characteristics of the meter; and obtaining a frequency adjustment coefficient based on the user's load data, temperature sequence, and relative fluctuation. This invention adaptively collects meter monitoring data based on the frequency adjustment coefficient, reducing the data storage pressure while improving the accuracy of monitoring potential meter faults.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring technology, and specifically to a method for monitoring and managing abnormal operating status of smart meters. Background Technology

[0002] With the increasing digitalization and intelligence of power systems, smart meters have been widely adopted in residential, commercial, and industrial electricity consumption scenarios, playing a crucial role in user electricity management, grid dispatching, and energy billing. However, during long-term operation, factors such as voltage fluctuations, abnormal electricity consumption, and aging metering modules can lead to potential or existing malfunctions in the meter's functions, resulting in equipment damage and economic losses.

[0003] Current smart meter operation status monitoring largely relies on power data collected at fixed frequencies for analysis. However, given the vast number of meter terminals, a long-term high-frequency sampling strategy will generate massive amounts of redundant data, creating a burden on storage and transmission. Conversely, fixed low-frequency sampling may miss instantaneous anomalies, affecting monitoring accuracy. Furthermore, different users have unique electricity consumption characteristics. Using a fixed monitoring data collection frequency may lead to redundant or inaccurate data collection, thereby reducing the accuracy of monitoring potential meter faults and increasing data storage pressure. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a method for monitoring and managing abnormal operating states of smart energy meters. The specific technical solution adopted is as follows:

[0005] Obtain the temperature sequence of the electricity meter and the corresponding historical load data sequence of the user;

[0006] The fluctuation characteristic value is obtained by comparing the local data at any time point in the load data sequence with the corresponding fitted data; the filtering window at any time point is adjusted and filtered according to the fluctuation characteristic value to obtain a smoothed load sequence; different load time periods are obtained according to the data change characteristics of the smoothed load sequence.

[0007] The load time periods are clustered based on the load data difference characteristics and time difference characteristics between the load time periods to obtain different time period clusters; the electricity consumption fluctuation value is obtained based on the distribution characteristics of the fluctuation characteristic values ​​of all times within the time period cluster; different collection time periods are obtained based on the time period distribution characteristics of the time period clusters; the relative fluctuation degree is obtained based on the difference characteristics of the electricity consumption fluctuation values ​​between the user and other users in the collection time period and the distance characteristics of the electricity meter; the frequency adjustment coefficient is obtained based on the load data of the user in the collection time period, the temperature sequence, and the relative fluctuation degree.

[0008] The user's electricity meter monitoring data at different collection periods are adaptively collected based on the frequency adjustment coefficient.

[0009] Furthermore, the step of obtaining the fluctuation characteristic value based on the difference characteristics between local data at any time in the load data sequence and the corresponding fitted data includes:

[0010] The data segments within the preset benchmark window at any given time are fitted using a local weighted regression algorithm to obtain a fitted sequence; the mean square error between the fitted sequence and the data segments within the preset benchmark window is calculated and normalized to obtain the fluctuation characteristic value corresponding to the given time.

[0011] Further, the step of adjusting the filtering window at any given time based on the fluctuation characteristic value and performing filtering to obtain a smoothed load sequence includes:

[0012] Calculate the product of the fluctuation characteristic value and the length of the preset benchmark window and round it up to obtain the length of the adaptive filtering window at any time. Within the adaptive filtering window, filter the load data at any time using the mean filtering algorithm to obtain the filtered value. Construct the smoothed load sequence in chronological order based on the filtered values ​​at all times.

[0013] Furthermore, the step of obtaining different load time periods based on the data variation characteristics of the smoothed load sequence includes:

[0014] The local extreme points of the smoothed load sequence are obtained, and the smoothed load sequence is segmented at the local extreme points to obtain different load time periods.

[0015] Furthermore, the step of clustering the load time periods based on the load data difference characteristics and time difference characteristics between the load time periods to obtain different time period clusters includes:

[0016] Calculate and normalize the dynamic time-normalized distance between the load time period and other load time periods to obtain a first distance; calculate and normalize the time interval between the center moments of the load time period and other load time periods to obtain a second distance; calculate and normalize the absolute value of the difference between the load data corresponding to the center moments of the load time period and other load time periods to obtain a third distance; calculate the average of the first distance, the second distance, and the third distance to obtain the difference distance between the load time period and other load time periods; cluster the load time periods according to the difference distance between different load time periods to obtain different time period clusters.

[0017] Further, the step of obtaining the electricity consumption fluctuation value based on the distribution characteristics of the fluctuation characteristic values ​​at all times within the time period cluster includes:

[0018] Calculate the average value of the fluctuation characteristic values ​​of all load time periods within the time period cluster to obtain the average fluctuation characteristic value; calculate the average value of the coefficient of variation of the fluctuation characteristic values ​​corresponding to all load time periods within the time period cluster to obtain the average coefficient of variation; calculate the product of the average fluctuation characteristic value and the average coefficient of variation to obtain the electricity consumption fluctuation value of the time period cluster.

[0019] Furthermore, the step of obtaining different acquisition time periods based on the time period distribution characteristics of the time period cluster includes:

[0020] Segment all time periods that overlap with other time period clusters, and then treat all independent time periods as different acquisition time periods.

[0021] Furthermore, the step of obtaining the relative fluctuation based on the difference characteristics of the electricity consumption fluctuation values ​​between the user and other users during the collection period and the distance characteristics of the electricity meter includes:

[0022] Calculate the Euclidean distance between the user's and any other user's electricity meter locations and perform a negative correlation mapping to obtain a distance weight; calculate the average of the electricity consumption fluctuation values ​​of all time period clusters corresponding to the user in any collection period to obtain a first value; calculate the average of the electricity consumption fluctuation values ​​of all time period clusters corresponding to any other user in the same collection period to obtain a second value; calculate the difference between the first value and the second value and perform a positive correlation mapping to obtain a fluctuation difference value; use the distance weight as the weight of the fluctuation difference value, calculate the weighted average of the fluctuation difference values ​​of the user and all other users to obtain the user's relative volatility.

[0023] Further, the step of obtaining the frequency adjustment coefficient based on the user's load data during the collection period, the temperature sequence, and the relative volatility includes:

[0024] Calculate and normalize the average value of all load data during the acquisition period to obtain load characteristic values; calculate and normalize the average value of the temperature sequence corresponding to the acquisition period to obtain temperature characteristic values; calculate the product of the load characteristic values ​​and a preset first weight to obtain weighted load characteristic values; calculate the product of the temperature characteristic values ​​and a preset second weight to obtain weighted temperature characteristic values; calculate the sum of the weighted load characteristic values ​​and the weighted temperature characteristic values ​​and perform a positive correlation mapping to obtain electricity consumption characteristic values; calculate and normalize the product of the electricity consumption characteristic values ​​and the relative fluctuation to obtain the frequency adjustment coefficient corresponding to the acquisition period.

[0025] Furthermore, the step of adaptively collecting the user's electricity meter monitoring data at different collection periods based on the frequency adjustment coefficient includes:

[0026] Calculate the difference between the preset maximum sampling frequency and the preset minimum sampling frequency to obtain the adjustment benchmark; calculate the product of the adjustment benchmark and the frequency adjustment coefficient to obtain the adjustment degree; calculate the product of the preset minimum sampling frequency and the adjustment degree to obtain the adaptive sampling frequency for the sampling period; and adaptively collect the electricity meter monitoring data for the sampling period according to the adaptive sampling frequency.

[0027] The present invention has the following beneficial effects:

[0028] In this invention, acquiring fluctuation characteristic values ​​can reflect the electricity consumption fluctuation characteristics of local time periods. Adjusting the filtering window based on the fluctuation characteristic values ​​can improve the accuracy of load data filtering. Smoothing the load sequence can remove the shortage fluctuation characteristics in the load data sequence, more accurately reflecting the user's comprehensive electricity consumption behavior and improving the accuracy of subsequent electricity consumption characteristic analysis. Acquiring time period clusters can divide the user's different electricity consumption characteristic time periods, thereby determining the user's electricity consumption characteristics in different time periods. Acquiring electricity consumption fluctuation values ​​can reflect the user's electricity consumption fluctuation characteristics in different time periods. Acquiring the collection period can determine the time interval of different user electricity consumption behaviors, improving the accuracy of adaptive collection. Acquiring relative volatility can more accurately reflect the user's fluctuation characteristics based on the electricity consumption characteristics of other users, avoiding interference from other factors and improving the accuracy of electricity consumption characteristic analysis. Acquiring the frequency adjustment coefficient can accurately reflect the degree of data collection in different collection periods. Finally, adaptive collection of the user's electricity meter monitoring data in different collection periods can be performed based on the frequency adjustment coefficient, which can improve the accuracy of monitoring potential electricity meter faults while reducing the monitoring data storage pressure. Attached Figure Description

[0029] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 The flowchart illustrates a method for monitoring and managing abnormal operating status of a smart energy meter, as provided in one embodiment of the present invention. Detailed Implementation

[0031] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a smart energy meter operation status anomaly monitoring and management method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0033] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent energy meter operation status abnormal monitoring and management method provided by the present invention.

[0034] Please see Figure 1 The diagram illustrates a flowchart of a smart energy meter operation status anomaly monitoring and management method according to an embodiment of the present invention. The method includes the following steps:

[0035] Step S1: Obtain the temperature sequence of the electricity meter and the corresponding historical load data sequence of the user.

[0036] In this embodiment of the invention, the implementation scenario involves adaptive frequency acquisition of electricity meter monitoring data from different users at different time periods. This reduces data storage pressure while improving the accuracy of monitoring potential electricity meter faults. First, the temperature sequence of the electricity meter and the corresponding user's historical load data sequence are acquired. The power consumption is calculated based on the voltage and current monitored by the electricity meter. In this embodiment, the power consumption is acquired every second, and the user's historical power consumption data for 15 days is used to construct the load data sequence. The implementer can determine the data acquisition duration and sampling frequency according to the implementation scenario. Simultaneously, the temperature sequence of the electricity meter, of the same length as the load data sequence, is acquired.

[0037] Step S2: Obtain fluctuation characteristic values ​​based on the difference between local data and corresponding fitted data at any time in the load data sequence; adjust the filtering window at any time based on the fluctuation characteristic values ​​and perform filtering to obtain a smoothed load sequence; obtain different load time periods based on the data change characteristics of the smoothed load sequence.

[0038] During the monitoring of electricity meter operation status, different users may exhibit differences in electricity consumption time distribution, electricity consumption scale, and load type, resulting in varying impacts of their electricity consumption behavior on the power grid and the electricity meter itself. Therefore, when monitoring the operation status of electricity meters for different users, using a uniform sampling frequency makes it difficult to balance data storage pressure and monitoring accuracy. Thus, different monitoring strategies need to be developed based on the electricity consumption characteristics of different users. Since the load data sequence may contain transient changes caused by equipment start-up / shutdown or short-term fluctuations, a mean filtering algorithm is used to smooth the load data sequence, thereby reducing the impact of random fluctuations. The smoothed load curve can more realistically reflect the overall electricity consumption trend and characteristic distribution of users at different time periods, providing stable data support for subsequent feature extraction and the construction of adaptive sampling strategies. Because different users have different equipment usage states at different time periods, their load fluctuation characteristics may also differ. Therefore, the size of the smoothing window needs to vary when smoothing the data. Thus, fluctuation characteristic values ​​are obtained based on the difference between local data at any given time in the load data sequence and the corresponding fitted data.

[0039] Preferably, in this embodiment of the invention, the step of obtaining the fluctuation characteristic value includes: fitting the data segment within a preset benchmark window at any given time using a local weighted regression algorithm to obtain a fitted sequence; in this embodiment of the invention, the data in the preset benchmark window is the data within one minute before and after the arbitrary time, which can be determined by the implementer according to the implementation scenario. The mean square error between the fitted sequence and the data segment within the preset benchmark window is calculated and normalized to obtain the fluctuation characteristic value corresponding to the arbitrary time; it should be noted that the normalization is calculated using the sigmoid function, and the calculation of the local weighted regression algorithm and the mean square error is prior art, and the specific calculation steps will not be elaborated further. When the mean square error is larger, the fluctuation characteristic value is larger, meaning the residual between the fitted sequence and the real data segment is larger, and the load data within the preset benchmark window at that arbitrary time has stronger volatility, then the window for filtering should be larger.

[0040] Furthermore, the filtering window at any given time can be adjusted and filtered according to the fluctuation characteristic value to obtain a smooth load sequence. Preferably, in this embodiment of the invention, the step of obtaining the smooth load sequence includes: calculating the product of the fluctuation characteristic value and the length of the preset reference window and rounding it up to obtain the length of the adaptive filtering window at any given time. The larger the fluctuation characteristic value, the longer the length of the adaptive filtering window, and the more obvious the filtering effect. Within the adaptive filtering window, the load data at any given time is filtered using a mean filtering algorithm to obtain a filtered value. It should be noted that the mean filtering algorithm is existing technology, and the specific calculation steps will not be elaborated here. A smooth load sequence is constructed according to the filtered values ​​at all times in chronological order. The smooth load sequence removes short-term obvious fluctuations compared to the load data sequence, and can better reflect the overall electricity consumption trend of users. The smooth load sequence can divide the time periods of different electricity consumption characteristics of users. Then, different load time periods are obtained according to the data change characteristics of the smooth load sequence. Specifically, this includes: obtaining the local extreme points of the smooth load sequence, dividing the smooth load sequence at the local extreme points to obtain different load time periods, each load time period can reflect different electricity consumption characteristics of users.

[0041] Step S3: Cluster the load time periods according to the load data difference characteristics and time difference characteristics between load time periods to obtain different time period clusters; obtain the electricity fluctuation value according to the distribution characteristics of the fluctuation characteristics of all times within the time period cluster; obtain different collection time periods according to the time period distribution characteristics of the time period clusters; obtain the relative fluctuation degree according to the difference characteristics of the electricity fluctuation values ​​between users and other users in the collection time period and the distance characteristics of the electricity meter; obtain the frequency adjustment coefficient according to the load data, temperature sequence and relative fluctuation degree of users in the collection time period.

[0042] After obtaining the different load time periods of users, ignoring the date of the load time period and only considering the time period of the load time period, the load time periods can be clustered to comprehensively analyze the different electricity consumption characteristics of users in different time periods; therefore, the load time periods are clustered according to the load data difference characteristics and time difference characteristics between load time periods to obtain different time period clusters.

[0043] Preferably, in this embodiment of the invention, the step of obtaining the time period cluster includes: calculating and normalizing the dynamic time warping distance between the load time period and other load time periods to obtain a first distance; the dynamic time warping distance is obtained through a dynamic time warping algorithm, and the specific steps are not described in detail. The more similar the data of the two sequences, the smaller the dynamic time warping distance; therefore, the smaller the first distance, the more similar the load data of the two load time periods are, and the closer the electricity consumption behavior is. Calculating and normalizing the time interval between the center moments of the load time period and other load time periods to obtain a second distance; the center moment of the load time period only represents any moment within 24 hours of a day, excluding the date of the load time period; the time interval of the center moments only calculates the interval between two moments. For example, if the center moment of the load time period is 11 PM on a certain day, and the center moment of other load time periods is 1 AM on another day, then the time interval between the two center moments is 2 hours; the smaller the second distance, the closer the time periods of the two load time periods are. Calculating and normalizing the absolute value of the difference between the load data corresponding to the center moments of the load time period and other load time periods to obtain a third distance; the more similar the third distance is, the more similar the electricity load is. The average of the first, second, and third distances is calculated to obtain the difference distance between this load time period and other load time periods. The smaller the difference distance, the closer the electricity consumption characteristics and time periods of the two load time periods are. The normalization in the formula is linear normalization. The load time periods are clustered according to the difference distance between different load time periods to obtain different time period clusters. Clustering can group the load time periods of the user that are close in time and have similar electricity consumption characteristics into one category. In this embodiment of the invention, the existing DBSCAN density clustering algorithm is used for clustering. The specific clustering steps are not described in detail.

[0044] Furthermore, different time-period clusters characterize the user's different electricity consumption behaviors at different times. The fluctuation characteristic value of the adaptive filtering window at the corresponding moment in each time-period cluster can reflect the load fluctuation of the user during electricity consumption. Therefore, the electricity fluctuation value is obtained based on the distribution characteristics of the fluctuation characteristic values ​​at all moments within the time-period cluster. Preferably, in this embodiment of the invention, the step of obtaining the electricity fluctuation value includes: calculating the average value of the fluctuation characteristic values ​​at all moments of all load time periods within the time-period cluster to obtain the average fluctuation characteristic value; the larger the average fluctuation characteristic value, the stronger the user's electricity consumption fluctuation in the corresponding time period of the time-period cluster. Calculate the average value of the coefficients of variation of the fluctuation characteristic values ​​corresponding to all load time periods in the time-period cluster to obtain the average coefficient of variation; it should be noted that the calculation of the coefficient of variation is existing technology, and the specific steps will not be repeated. The larger the coefficient of variation, the greater the change in the fluctuation characteristic value, and the more unstable the electricity consumption behavior. Calculate the product of the average fluctuation characteristic value and the average coefficient of variation to obtain the electricity fluctuation value of the time-period cluster; the larger the electricity fluctuation value, the stronger the user's electricity consumption fluctuation in that time period. The formula for obtaining the electricity fluctuation value includes:

[0045]

[0046] In the formula, G represents the electricity consumption fluctuation value, K represents the average fluctuation characteristic value, and N represents the number of load time periods within the time cluster. The coefficient of variation represents the fluctuation characteristic values ​​at all times within the nth load time period. This represents the average coefficient of variation.

[0047] Furthermore, different time-period clusters are distributed across different time periods within a 24-hour cycle. Some time-period clusters may overlap. To more accurately analyze a user's electricity consumption characteristics, different collection periods are obtained based on the time-period distribution characteristics of the time-period clusters. Specifically, this involves segmenting all time-period clusters into those overlapping with other clusters, and then using each segmented period as a distinct collection period. The segmented collection periods can more accurately distinguish different electricity consumption characteristics of users, thus making subsequent adaptive frequency collection of electricity meter monitoring data more accurate. During electricity consumption, user behavior, such as the start-up and shutdown of high-power equipment, can cause local load changes. Furthermore, the instability of the power grid can also cause fluctuations in power consumption at the user end. Therefore, it is necessary to further obtain the electricity consumption fluctuation characteristics of a specific user within the same time period and compare them with those of other users within the same time period. Thus, relative volatility is obtained based on the differences in electricity consumption fluctuation values ​​between the user and other users during the collection period, as well as the distance characteristics of the electricity meter.

[0048] Preferably, in this embodiment of the invention, the step of obtaining relative volatility includes: calculating the Euclidean distance between the user and the locations of any other user's electricity meters and performing a negative correlation mapping to obtain a distance weight; when performing relative volatility analysis of user electricity load, it is also necessary to consider the deployment location of the electricity meter in the power grid. For the user currently being analyzed, the closer the deployment distance is to the electricity meters of other users in the power grid, the higher the similarity between the electrical characteristics of the branch and the impact of power grid fluctuations, and thus its load characteristics have higher reference value. The closer the distance, the greater the distance weight. Calculate the average value of the electricity volatility values ​​of all time clusters corresponding to any collection period of the user to obtain a first value; when the arbitrary collection period is an overlapping period of multiple time clusters, the first value is the average value of the electricity volatility values ​​of multiple time clusters; if the arbitrary period is a period in a single time cluster, the first value is the electricity volatility value of that time cluster; the larger the first value, the more obvious the electricity volatility characteristics of the user in that arbitrary collection period. Calculate the average of the electricity consumption fluctuation values ​​of all time clusters corresponding to any other user within any collection period to obtain a second value; the larger the second value, the more pronounced the electricity consumption fluctuation characteristics of other users during that collection period. Calculate the difference between the first and second values ​​and apply a positive correlation to obtain a fluctuation difference value; a larger fluctuation difference value means that the user's electricity consumption fluctuation characteristics are higher than those of other users. Use distance weight as the weight of the fluctuation difference value; the larger the weight, the more reliable the fluctuation difference value. Calculate the weighted average of the fluctuation difference values ​​of this user and all other users to obtain the user's relative volatility; a larger relative volatility means that the user's electricity consumption volatility is higher than that of other users in the same time period. The steps to obtain the relative volatility include:

[0049]

[0050] In the formula, R represents the relative volatility of a user, and M represents the number of other users. This represents an exponential function with the natural constant as its base, used for data mapping. This represents the Euclidean distance between the user's electricity meter and that of the m-th other user. Indicates the first value. This represents the second value of the m-th other user. Indicates the fluctuation difference value. This represents the distance weight.

[0051] Furthermore, a greater relative fluctuation in a user's data during any given data collection period indicates more frequent start-ups and shutdowns of their electrical equipment, drastic load changes, and overall unstable electricity consumption behavior. Such unstable loads may lead to increased harmonic content in the power grid and distortion of the current waveform, resulting in additional heat loss and increased heat generation in the electricity meter and its connected cables. Simultaneously, if the user's power consumption is high during this period, continuous high-power load operation will further exacerbate equipment aging and overheating risks, increasing the probability of overheating or fire in the smart meter and cables. Furthermore, if the electricity meter temperature is detected to be high during this period, it indicates poor heat dissipation in the meter box, posing a potential risk of component aging or damage. Therefore, a higher data collection frequency is required during this period to achieve more accurate monitoring; hence, a frequency adjustment coefficient is obtained based on the user's load data, temperature sequence, and relative fluctuation during the data collection period.

[0052] Preferably, in this embodiment of the invention, the step of obtaining the frequency adjustment coefficient includes: calculating and normalizing the average value of all load data during the collection period to obtain a load characteristic value; the larger the load characteristic value, the greater the power consumption. Calculating and normalizing the average value of the temperature sequence corresponding to the collection period to obtain a temperature characteristic value; the larger the temperature characteristic value, the higher the temperature of the electricity meter, which is more likely to cause abnormal conditions such as component aging; wherein the normalization method is linear normalization. Calculating the product of the load characteristic value and a preset first weight to obtain a weighted load characteristic value; calculating the product of the temperature characteristic value and a preset second weight to obtain a weighted temperature characteristic value; in this embodiment of the invention, the preset first weight is 0.4, and the preset second weight is 0.6. Different weight values ​​characterize the importance of monitoring different characteristics and can be set according to monitoring needs. Calculating the sum of the weighted load characteristic value and the weighted temperature characteristic value and mapping them positively to obtain an electricity consumption characteristic value; the larger the electricity consumption characteristic value, the greater the load of the electricity meter at that collection time, and the more important it is to monitor. The product of the electricity consumption characteristic value and the relative fluctuation is calculated and normalized. This normalization is calculated using the sigmoid function to obtain the frequency adjustment coefficient corresponding to the data collection period. A larger frequency adjustment coefficient indicates that the user's electricity consumption behavior during that period requires closer monitoring, and the monitoring data collection frequency of the electricity meter should be higher. The steps to obtain the frequency adjustment coefficient include:

[0053]

[0054] In the formula, Q represents the frequency adjustment coefficient. R represents the normalization function, and R represents the relative volatility. This indicates the preset first weight. This indicates a preset second weight, where F represents the load characteristic value and B represents the temperature characteristic value. Represents the weighted load characteristic value. Represents the weighted temperature characteristic value. This represents the characteristic value of electricity consumption.

[0055] Step S4: Adaptively collect the user's electricity meter monitoring data for different collection periods based on the frequency adjustment coefficient.

[0056] After obtaining the frequency adjustment coefficients for different data collection periods, adaptive data collection of electricity meter monitoring data for users in different data collection periods can be performed based on the frequency adjustment coefficients. Preferably, in this embodiment of the invention, the difference between the preset maximum collection frequency and the preset minimum collection frequency is calculated to obtain the adjustment benchmark; the product of the adjustment benchmark and the frequency adjustment coefficient is calculated to obtain the adjustment degree; the larger the frequency adjustment coefficient, the greater the adjustment degree and the higher the data collection frequency. The product of the preset minimum collection frequency and the adjustment degree is calculated to obtain the adaptive collection frequency for that data collection period; the implementer can determine the preset maximum collection frequency and the preset minimum collection frequency according to the implementation scenario. The larger the adaptive collection frequency, the more attention needs to be paid to the user's electricity consumption behavior during that period, and the more likely that the electricity meter and cable will malfunction during that period. Adaptive collection of electricity meter monitoring data for the data collection period is performed based on the adaptive collection frequency. This monitoring data includes load data and temperature data, and the implementer can determine the monitoring data content themselves. Adaptive collection results in different collection frequencies for different users and different time periods, thereby reducing the storage pressure of monitoring data and improving the accuracy of monitoring potential electricity meter faults. Implementers can determine the update cycle of the user adaptive collection frequency according to the implementation scenario, and there is no limitation here.

[0057] In summary, this invention provides a method for monitoring and managing abnormal operating status of smart energy meters. It obtains fluctuation characteristic values ​​based on local data at any given time in a load data sequence and corresponding fitted data; filters these fluctuation characteristic values ​​to obtain a smoothed load sequence; obtains different load time periods based on the smoothed load sequence and performs clustering; obtains electricity consumption fluctuation values ​​based on the fluctuation characteristic values ​​of all times within each time period cluster; determines the collection period based on the time period distribution characteristics of the time period cluster; obtains relative fluctuation based on the electricity consumption fluctuation values ​​of users and other users during the collection period and the distance characteristics of the energy meter; and obtains a frequency adjustment coefficient based on the user's load data, temperature sequence, and relative fluctuation. This invention adaptively collects energy meter monitoring data based on the frequency adjustment coefficient, reducing the data storage pressure while improving the accuracy of monitoring potential energy meter faults.

[0058] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for monitoring and managing abnormal operating status of a smart energy meter, characterized in that, The method includes the following steps: Obtain the temperature sequence of the electricity meter and the corresponding historical load data sequence of the user; The fluctuation characteristic value is obtained by comparing the local data at any time point in the load data sequence with the corresponding fitted data; the filtering window at any time point is adjusted and filtered according to the fluctuation characteristic value to obtain a smoothed load sequence; different load time periods are obtained according to the data change characteristics of the smoothed load sequence. The load time periods are clustered based on the load data difference characteristics and time difference characteristics between the load time periods to obtain different time period clusters; the electricity consumption fluctuation value is obtained based on the distribution characteristics of the fluctuation characteristic values ​​of all times within the time period cluster; different collection time periods are obtained based on the time period distribution characteristics of the time period clusters; the relative fluctuation degree is obtained based on the difference characteristics of the electricity consumption fluctuation values ​​between the user and other users in the collection time period and the distance characteristics of the electricity meter; the frequency adjustment coefficient is obtained based on the load data of the user in the collection time period, the temperature sequence, and the relative fluctuation degree. The user's electricity meter monitoring data is adaptively collected based on the frequency adjustment coefficient during different collection periods; The step of clustering load time periods based on load data difference characteristics and time difference characteristics between load time periods to obtain different time period clusters includes: Calculate and normalize the dynamic time-normalized distance between the load time period and other load time periods to obtain a first distance; calculate and normalize the time interval between the center moments of the load time period and other load time periods to obtain a second distance; calculate and normalize the absolute value of the difference between the load data corresponding to the center moments of the load time period and other load time periods to obtain a third distance; calculate the average of the first distance, the second distance, and the third distance to obtain the difference distance between the load time period and other load time periods; cluster the load time periods according to the difference distance between different load time periods to obtain different time period clusters; The step of obtaining the relative fluctuation based on the difference characteristics of the electricity consumption fluctuation values ​​between the user and other users during the collection period and the distance characteristics of the electricity meter includes: Calculate the Euclidean distance between the user's electricity meter location and the location of any other user's electricity meter, and perform a negative correlation mapping to obtain a distance weight; calculate the average value of the electricity consumption fluctuation value of all time period clusters corresponding to the user in any collection period to obtain a first value; calculate the average value of the electricity consumption fluctuation value of all time period clusters corresponding to any other user in the same collection period to obtain a second value; calculate the difference between the first value and the second value, and perform a positive correlation mapping to obtain a fluctuation difference value; use the distance weight as the weight of the fluctuation difference value, and calculate the weighted average of the fluctuation difference values ​​of the user and all other users to obtain the relative volatility of the user; The step of obtaining the frequency adjustment coefficient based on the user's load data during the acquisition period, the temperature sequence, and the relative volatility includes: Calculate and normalize the average value of all load data during the acquisition period to obtain load characteristic values; calculate and normalize the average value of the temperature sequence corresponding to the acquisition period to obtain temperature characteristic values; calculate the product of the load characteristic values ​​and a preset first weight to obtain weighted load characteristic values; calculate the product of the temperature characteristic values ​​and a preset second weight to obtain weighted temperature characteristic values; calculate the sum of the weighted load characteristic values ​​and the weighted temperature characteristic values ​​and perform a positive correlation mapping to obtain electricity consumption characteristic values; calculate and normalize the product of the electricity consumption characteristic values ​​and the relative fluctuation to obtain the frequency adjustment coefficient corresponding to the acquisition period.

2. The method for monitoring and managing abnormal operating status of a smart energy meter according to claim 1, characterized in that, The step of obtaining the fluctuation characteristic value based on the difference between local data and corresponding fitted data at any time in the load data sequence includes: The data segments within the preset benchmark window at any given time are fitted using a local weighted regression algorithm to obtain a fitted sequence; the mean square error between the fitted sequence and the data segments within the preset benchmark window is calculated and normalized to obtain the fluctuation characteristic value corresponding to the given time.

3. The method for monitoring and managing abnormal operating status of a smart energy meter according to claim 1, characterized in that, The step of adjusting the filtering window at any given time based on the fluctuation characteristic value and performing filtering to obtain a smooth load sequence includes: Calculate the product of the fluctuation characteristic value and the length of the preset benchmark window and round it up to obtain the length of the adaptive filtering window at any time. Within the adaptive filtering window, filter the load data at any time using the mean filtering algorithm to obtain the filtered value. Construct the smoothed load sequence in chronological order based on the filtered values ​​at all times.

4. The method for monitoring and managing abnormal operating status of a smart energy meter according to claim 1, characterized in that, The step of obtaining different load time periods based on the data change characteristics of the smoothed load sequence includes: The local extreme points of the smoothed load sequence are obtained, and the smoothed load sequence is segmented at the local extreme points to obtain different load time periods.

5. The method for monitoring and managing abnormal operating status of a smart energy meter according to claim 1, characterized in that, The step of obtaining the electricity consumption fluctuation value based on the distribution characteristics of the fluctuation characteristic values ​​at all times within the time period cluster includes: Calculate the average value of the fluctuation characteristic values ​​of all load time periods within the time period cluster to obtain the average fluctuation characteristic value; calculate the average value of the coefficient of variation of the fluctuation characteristic values ​​corresponding to all load time periods within the time period cluster to obtain the average coefficient of variation; calculate the product of the average fluctuation characteristic value and the average coefficient of variation to obtain the electricity consumption fluctuation value of the time period cluster.

6. The method for monitoring and managing abnormal operating status of a smart energy meter according to claim 1, characterized in that, The steps for obtaining different acquisition time periods based on the time period distribution characteristics of time period clusters include: Segment all time periods that overlap with other time period clusters, and then treat all independent time periods as different acquisition time periods.

7. The method for monitoring and managing abnormal operating status of a smart energy meter according to claim 1, characterized in that, The step of adaptively collecting the user's electricity meter monitoring data at different collection periods based on the frequency adjustment coefficient includes: Calculate the difference between the preset maximum sampling frequency and the preset minimum sampling frequency to obtain the adjustment benchmark; calculate the product of the adjustment benchmark and the frequency adjustment coefficient to obtain the adjustment degree; calculate the product of the preset minimum sampling frequency and the adjustment degree to obtain the adaptive sampling frequency for the sampling period; and adaptively collect the electricity meter monitoring data for the sampling period according to the adaptive sampling frequency.

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