Electricity meter electricity consumption metering method combined with power consumer grouping

Abstract

The invention provides an electricity meter electricity metering method combined with power user grouping, and relates to the technical field of electricity metering, and the method comprises the steps: querying historical electricity records of enterprise users in a region, carrying out the user grouping, and determining a plurality of user clusters; daily electricity utilization interval division is carried out on the multiple user clusters, electricity utilization metering strategy optimization is carried out according to division results, and multiple optimized metering schemes are determined; establishing an electricity metering anomaly detection plug-in; and executing electricity metering of the enterprise user according to the plurality of optimized metering schemes, synchronously monitoring electricity metering abnormity through an electricity metering abnormity detection plug-in, and dynamically adjusting the plurality of optimized metering schemes according to an abnormity detection result. According to the method and the device, the technical problem of insufficient metering efficiency and accuracy caused by the fact that power utilization metering cannot effectively adapt to dynamic changes of power utilization behaviors in the prior art can be solved, more accurate and efficient electricity utilization metering of the electricity meter is realized by combining user grouping, and the pertinence and efficiency of metering are improved.

Description

Technical Field

[0001] The present application relates to the technical field of electricity metering, and in particular to an electricity metering method combined with power user grouping. Background Art

[0002] Electricity metering refers to the process of using an electric meter to accurately record and calculate the amount of electric energy consumed by a user within a certain period of time. Electricity user grouping refers to dividing users into different groups based on their electricity consumption characteristics, behavior patterns, living habits, and other factors, helping power companies to better understand users' electricity needs, thereby achieving personalized electricity management and services. In electricity metering, grouping can be used to implement differentiated electricity price policies, such as peak and valley electricity prices, or to develop energy-saving incentives for different user groups to encourage users to use electricity when the grid load is low, thereby optimizing the overall operation efficiency and energy distribution of the grid. At present, most electricity metering is based on fixed peak and valley time settings, while electricity consumption behavior is dynamic and may change with time, season, working mode, and other factors. Although the current electricity metering method has taken into account peak and valley time periods and some electricity consumption characteristics, its adaptability to complex or dynamic electricity consumption behavior patterns is still limited, resulting in insufficient metering efficiency and accuracy. Especially when faced with diversified electricity consumption patterns and dynamically changing user needs, static metering strategies cannot adapt effectively. Differences in behavior among different users can easily cause metering deviations, affecting the accuracy and efficiency of the overall metering.

[0003] In summary, the existing technology has technical problems such as the lack of dynamic adaptability of electricity metering and the mismatch between metering strategies and dynamic electricity consumption behaviors, which will cause deviations between metering results and actual electricity consumption, resulting in insufficient metering efficiency and accuracy. Summary of the invention

[0004] The purpose of this application is to provide an electricity metering method combined with electricity user grouping, in order to solve the technical problems in the prior art that the electricity metering lacks dynamic adaptability, the metering strategy does not match the dynamic electricity consumption behavior, and the metering results deviate from the actual electricity consumption, resulting in insufficient metering efficiency and accuracy.

[0005] In view of the above problems, the present application provides an electric meter electricity consumption metering method combined with power user grouping, wherein the electric meter electricity consumption metering method combined with power user grouping includes: querying the historical electricity consumption records of enterprise users in the area, grouping users according to the historical electricity consumption records, and determining multiple user clusters; dividing the multiple user clusters into daily electricity consumption intervals, performing electricity metering strategy optimization according to the division results, and determining multiple optimized metering schemes; establishing an electricity metering anomaly detection plug-in based on the multiple user clusters; performing electricity metering for enterprise users according to the multiple optimized metering schemes, synchronously monitoring electricity metering anomalies through the electricity metering anomaly detection plug-in, and performing dynamic adjustment of the multiple optimized metering schemes according to the anomaly detection results.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: By querying the historical electricity consumption records of enterprise users in the area, users are grouped according to the historical electricity consumption records to determine multiple user clusters; the multiple user clusters are divided into daily electricity consumption intervals respectively, and the electricity metering strategy optimization is performed according to the division results to determine multiple optimized metering schemes; an electricity metering anomaly detection plug-in based on the multiple user clusters is established; the electricity metering of enterprise users is performed according to the multiple optimized metering schemes, and the electricity metering anomaly is synchronously monitored through the electricity metering anomaly detection plug-in, and the dynamic adjustment of the multiple optimized metering schemes is performed according to the anomaly detection results. In other words, users are classified according to similar electricity consumption behavior patterns through grouping, and the electricity consumption data of each user cluster is further analyzed, and their daily electricity consumption is divided into different intervals, and the metering strategy is customized to achieve more refined management. According to the historical behavior pattern of the user cluster, abnormal electricity consumption is identified, and the metering strategy is dynamically adjusted to achieve dynamic adaptability of electricity metering, so that the metering scheme can be flexibly adjusted according to the real-time behavior of the user and the abnormal changes detected, avoiding the accumulation of long-term deviations, and achieving more accurate and efficient metering of electricity consumption, and improving the pertinence and efficiency of metering.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0009] Figure 1 This is a flow chart of a method for measuring electricity consumption by an electric meter combined with grouping of electric power users in this application.

[0010] Figure 2 A flow chart of determining an optimized metering scheme in an electricity metering method for grouping electricity users in combination with an electric meter in this application. DETAILED DESCRIPTION

[0011] This application solves the technical problems in the prior art that the lack of dynamic adaptability in electricity metering and the mismatch between metering strategies and dynamic electricity consumption behaviors will cause deviations between metering results and actual electricity consumption, resulting in insufficient metering efficiency and accuracy, by providing an electricity metering method combined with electricity user grouping. Through grouping, users are classified according to similar electricity consumption behavior patterns, and the electricity consumption data of each user cluster is further analyzed, and their daily electricity consumption is divided into different intervals. The metering strategy is customized to achieve more refined management, and abnormal electricity consumption is identified according to the historical behavior patterns of the user cluster. The metering strategy is dynamically adjusted to achieve dynamic adaptability of electricity metering, so that the metering scheme can be flexibly adjusted according to the real-time behavior of the user and the abnormal changes detected, avoiding the accumulation of long-term deviations, and achieving more accurate and efficient metering of electricity consumption, and improving the pertinence and efficiency of metering.

[0012] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0013] For examples, please see the attached Figure 1 The present application provides an electric meter electricity consumption measurement method combined with electric power user grouping, wherein the electric meter electricity consumption measurement method combined with electric power user grouping specifically comprises the following steps: Step 1: query the historical electricity consumption records of enterprise users in the area, group the users according to the historical electricity consumption records, and determine multiple user clusters.

[0014] Specifically, the power company's database or data management system queries the historical electricity consumption records of enterprise users in the area, including the electricity consumption, electricity consumption time, peak electricity consumption and other information of enterprise users in this area in the past period of time. From the historical electricity consumption records, the monthly electricity consumption feature set, weekly electricity consumption feature set and daily electricity consumption feature set of multiple enterprise users are obtained. Each electricity consumption feature includes the peak and valley curve of electricity consumption, the power factor curve, the electricity load curve and the load fluctuation curve, but the collection frequency is different. For example, the monthly electricity consumption feature is collected once every 6 hours, the weekly electricity consumption feature is collected once every half an hour, and the daily electricity consumption feature is collected once every minute. The high-frequency features are extracted from the monthly electricity consumption feature set, weekly electricity consumption feature set and daily electricity consumption feature set respectively, and the monthly electricity consumption information, weekly electricity consumption information and daily electricity consumption information that best represent the whole are selected. Through the clustering algorithm, the monthly electricity consumption information, weekly electricity consumption information and daily electricity consumption information are used to group multiple enterprise users three times in turn, and the groups are grouped according to the electricity consumption behavior and needs of the enterprise users to ensure that the users in each group have similar electricity consumption behavior. Finally, multiple user clusters are obtained. Through feature extraction and clustering at the monthly, weekly, and daily levels, we can have a more comprehensive understanding of users’ electricity usage behavior and ensure that users are accurately classified.

[0015] Step 2: Divide the multiple user clusters into daily electricity consumption intervals, optimize the electricity metering strategy according to the division results, and determine multiple optimized metering schemes.

[0016] Specifically, for each user cluster, its daily electricity consumption characteristic data is obtained, including electricity peak and valley curves, power factor curves, load curves, and fluctuation curves. The daily electricity consumption intervals are divided for each user cluster, and a day is divided according to a preset time step to form multiple time intervals, and the preset time step is usually short. The electricity consumption data of each time interval is fitted, and the centroid of the interval is calculated, that is, the middle value of each segmented curve, reflecting the electricity consumption characteristics (such as peak and valley values, load fluctuations, etc.) in this period. The centroid data is used to cluster each time interval, and the intervals with similar electricity consumption characteristics are divided into the same category. Multiple time thresholds and multiple electricity consumption feature centroid sets are obtained through clustering, indicating the demarcation points of different electricity consumption modes, and different electricity consumption feature intervals are clearly distinguished to form a time-sharing electricity consumption mode diagram within a day. The electricity consumption feature centroid set represents the electricity consumption characteristics of each cluster center. The electricity metering strategy is configured and optimized according to multiple electricity consumption feature centroid sets, including adjusting the sampling frequency and data sampling accuracy, to obtain multiple optimized metering strategies. The first optimized metering scheme is constructed based on multiple time thresholds and multiple optimized metering strategies, so that the metering strategy can adapt to the electricity consumption characteristics of each time period. The first week electricity consumption feature set of each user cluster is analyzed for discreteness. If the discreteness is less than or equal to the predetermined discrete scalar, the first optimized metering scheme is added to multiple optimized metering schemes. Conversely, the overall difference between each user and other users is analyzed, and the first optimized metering scheme is compensated and added to multiple optimized metering schemes. Through the division of daily electricity consumption intervals and the optimization of metering strategies, the electricity metering scheme of each user cluster can be personalized to adapt to its unique electricity consumption characteristics, effectively improve the pertinence and efficiency of metering, and ensure accurate electricity consumption data in different time periods.

[0017] Step 3: Establish an electricity metering anomaly detection plug-in based on the multiple user clusters.

[0018] Specifically, the monthly, weekly, and daily electricity consumption information of each user cluster is obtained, including peak and valley electricity consumption, power factor, load fluctuation, voltage and current fluctuation, etc. The electricity consumption pattern and change trend of the user cluster are analyzed based on the daily electricity consumption characteristics and historical data. According to the electricity consumption characteristics of the user cluster, some key abnormal factors of electricity metering are defined, including but not limited to current exceeding the standard, voltage deviation, electricity deviation, and power factor abnormality. Through statistical analysis and setting thresholds at different time scales (such as month, week, and day), the abnormal factor threshold is defined for each user cluster to adapt to different electricity consumption needs and patterns. An independent detection branch is established for the abnormal electricity metering factor of each user cluster, and all abnormal factors of the user cluster are comprehensively considered. Each user cluster and the corresponding abnormal electricity metering detection branch are mapped and integrated to obtain the abnormal electricity metering detection plug-in. The abnormal electricity metering detection plug-in manages the monitoring logic and alarm mechanism of each user cluster, dynamically adapts the electricity consumption behavior of different user clusters, and performs abnormal monitoring based on actual data. At the same time, the abnormal electricity metering detection plug-in should be able to access the data of smart meters and other devices to achieve real-time monitoring. When an anomaly is detected, the power meter anomaly detection plug-in can quickly issue an alarm and notify relevant management personnel. Based on long-term monitoring data, the threshold and detection rules of the anomaly factor are regularly updated to adapt to the changes in the power consumption pattern of the user cluster. By building a power meter anomaly detection plug-in based on user clusters, the power consumption anomalies of each user cluster can be centrally monitored, and rapid responses and alarms can be issued.

[0019] Step 4: Execute electricity consumption metering of enterprise users according to the multiple optimized metering schemes, synchronously monitor electricity metering anomalies through the electricity metering anomaly detection plug-in, and perform dynamic adjustment of the multiple optimized metering schemes according to the anomaly detection results.

[0020] Specifically, the electricity consumption of enterprise users is measured in real time according to multiple optimized metering schemes, including real-time data collection, data storage and regular reporting. According to multiple optimized metering schemes, it means collecting parameters such as current, voltage, and power for metering according to the set sampling frequency and accuracy. At the same time, the power meter anomaly detection plug-in is started to monitor the power consumption data in real time and analyze it according to the preset power meter anomaly factors (such as current exceeding the standard, voltage deviation, etc.). The power meter anomaly detection plug-in continuously receives the real-time data stream and compares it with the set threshold to determine whether an anomaly occurs. When an anomaly is detected, the power meter anomaly detection plug-in records the occurrence of the abnormal event, including information such as the type of anomaly, the time of occurrence and duration, and immediately feedbacks these abnormal information to notify relevant management personnel for follow-up processing.

[0021] According to the anomaly detection results of the electricity metering anomaly detection plug-in, analyze the abnormal electricity metering frequency and type of each user cluster, and evaluate its impact on the electricity metering plan. Dynamically adjust multiple optimized metering plans based on multiple abnormal electricity metering frequencies. If the abnormal electricity metering frequency is low, fine-tune the metering plan for individual users, adjust the compensation coefficient or optimize the metering parameters; if the abnormal electricity metering frequency is high, it is necessary to analyze and adjust the overall metering plan, re-evaluate the electricity consumption characteristics and update the metering plan. After the adjustment, continue to monitor the electricity consumption, and continuously optimize the metering plan based on the new data to form a closed-loop management. Through real-time anomaly detection and dynamic adjustment mechanism, it is possible to respond to electricity anomalies in a timely manner, and the electricity metering of enterprise users can be more accurate and adapt to the actual electricity needs of different users.

[0022] Furthermore, step one of this application includes: According to the historical electricity consumption records, monthly electricity consumption feature sets, weekly electricity consumption feature sets and daily electricity consumption feature sets are collected for multiple corporate users, wherein the electricity consumption features include electricity peak and valley curves, power factor curves, electricity load curves and load fluctuation curves; high-frequency features are extracted from the monthly electricity consumption feature sets, weekly electricity consumption feature sets and daily electricity consumption feature sets respectively to determine monthly electricity consumption information, weekly electricity consumption information and daily electricity consumption information; using the K-means algorithm, a primary grouping is performed on the multiple corporate users according to the monthly electricity consumption information to obtain a primary grouping result; a secondary grouping is performed on the primary grouping result according to the weekly electricity consumption information to obtain a secondary grouping result; a tertiary grouping is performed on the secondary grouping result according to the daily electricity consumption information to obtain the multiple user clusters.

[0023] Specifically, historical electricity consumption records are collected through smart meters, power company databases or other power detection equipment, including data such as electricity consumption, electricity consumption time, and power. Based on the historical electricity consumption records, monthly electricity consumption feature sets, weekly electricity consumption feature sets and daily electricity consumption feature sets of multiple corporate users are collected to construct electricity consumption feature sets of different time scales, including electricity peak and valley curves, power factor curves, electricity load curves and load fluctuation curves. Each curve reflects the user's electricity consumption behavior and pattern at different time scales. The electricity peak and valley curve describes the changes in electricity consumption peaks and valleys, reflecting the user's peak electricity consumption period and its fluctuations; the power factor curve is used to evaluate the effective utilization of electricity, which can reflect the quality and efficiency of electricity consumption; the electricity load curve shows the changes in electricity load over time and analyzes the overall trend of electricity consumption; the load fluctuation curve describes the fluctuation of load within a certain period of time and identifies the stability of electricity consumption. The collection frequency of each power consumption characteristic curve is different. For example, the monthly power consumption characteristic set is collected every 6 hours to analyze the long-term power consumption trend and pattern of users within a month; the weekly power consumption characteristic set is collected every half hour to analyze the medium-term power consumption pattern and changes of users within a week; the daily power consumption characteristic set is collected every minute to analyze the short-term power consumption behavior and fluctuations of users within a day. Enterprise users usually refer to different enterprises in a specific area (such as industrial parks, commercial areas, etc.). As an independent power consumption entity, the power consumption behavior and pattern of each enterprise may vary due to factors such as industry type, production scale, and operation time.

[0024] The high-frequency features of the monthly electricity consumption feature set, weekly electricity consumption feature set and daily electricity consumption feature set are extracted respectively. Through multiple similarity comparisons and center calculations, it is ensured that the final selected features have high representativeness and effectiveness. Multiple similar high-frequency features are summarized into a comprehensive curve to make the results smoother and easier to understand. Finally, the monthly electricity consumption information, weekly electricity consumption information and daily electricity consumption information are determined. The K-means algorithm is used to preliminarily group multiple enterprise users according to the monthly electricity consumption information to form initial user clusters. The K-means algorithm is an iterative clustering analysis algorithm that selects K initial center points, which can be randomly selected or selected based on some heuristic methods. For each data point in the data set, its distance from each center point is calculated, and it is assigned to the cluster represented by the nearest center point. The center point of each cluster is updated, that is, the mean of all data points in each cluster is calculated to obtain a new center point. Repeat the steps until the stop condition is met, such as the change of the center point is less than a certain threshold, the maximum number of iterations is reached, etc. By clustering based on monthly electricity consumption information, the differences between users can be preliminarily divided, and users are grouped according to the similarities in their monthly electricity consumption information, so that users with similar electricity consumption behaviors are gathered into the same cluster.

[0025] On the basis of the first clustering, similarly, the user groups are further refined according to the weekly electricity consumption information to capture the characteristics and changes of weekly electricity consumption, making each user cluster more representative. Finally, the secondary clustering results are clustered for the third time according to the daily electricity consumption information to obtain the final user cluster. The purpose of the third clustering is to capture the subtle differences in daily electricity consumption changes, to ensure that the electricity consumption behaviors within each user cluster are as similar as possible, and the electricity consumption behaviors outside are as different as possible. Through step-by-step clustering, users can be accurately divided into different groups, so that personalized metering solutions can be implemented for different groups in metering and management strategies. Through feature extraction and clustering at the monthly, weekly, and daily levels, users' electricity consumption behaviors can be understood more comprehensively, ensuring that users are accurately classified. Different users can formulate different metering strategies based on their characteristics, making metering more accurate and targeted.

[0026] Furthermore, the present application also includes the following steps: The first month's electricity consumption features are randomly selected, wherein the first month's electricity consumption features include the first month's electricity consumption peak-valley curve, the first month's power factor curve, the first month's electricity load curve, and the first month's load fluctuation curve; the first month's electricity consumption peak-valley curve is similarly compared with other monthly electricity consumption peak-valley curves in the monthly electricity consumption feature set, and the amount of data with a similarity greater than a similarity threshold is counted, which is set as the first centrality; if the first centrality is greater than a predetermined scalar, the first month's power factor curve is similarly compared with other monthly power factor curves in the monthly electricity consumption feature set, and the amount of data with a similarity greater than a similarity threshold is counted, which is set as the second centrality; if the second centrality is greater than the predetermined scalar, the third centrality of the first month's electricity load curve is calculated and obtained, and if the third centrality is greater than the predetermined scalar, the fourth centrality of the first month's load fluctuation curve is calculated and obtained; if the fourth centrality is greater than the predetermined scalar, the first month's electricity consumption features are added to the high-frequency monthly electricity consumption feature set, and the high-frequency monthly electricity consumption feature set is curve fitted to obtain the monthly electricity consumption information.

[0027] Specifically, the electricity consumption characteristics of a month are randomly selected from the monthly electricity consumption characteristic set as the first month electricity consumption characteristics, including the first month electricity consumption peak and valley curve, the first month power factor curve, the first month electricity load curve and the first month load fluctuation curve, which respectively show the peak and valley of electricity consumption, power efficiency, overall electricity load and monthly load fluctuation of the month. The first month electricity consumption peak and valley curve is compared with the monthly electricity consumption peak and valley curves of other monthly electricity consumption characteristics in the monthly electricity consumption characteristic set to calculate the similarity. Similarity comparison is a data analysis method used to evaluate the similarity between two or more curves. A similarity threshold is set in advance to determine whether two curves are similar enough. The amount of data with a similarity greater than the similarity threshold is counted, that is, how many other monthly electricity consumption peak and valley curves are similar to the first month electricity consumption peak and valley curve is calculated as the first centrality. The first centrality reflects the similarity between the electricity consumption peak and valley curve of the month and other months. If there are many high similarities, it means that the electricity consumption peak and valley curve of the month is representative.

[0028] The predetermined scalar is a pre-set threshold used to determine whether the representativeness of the peak-valley curve of electricity consumption in the first month is high enough. If the first centrality exceeds the predetermined scalar, it means that the peak-valley curve of electricity consumption in the first month is highly representative in the data set, so it is necessary to further analyze the similarity of the power factor curve of the first month. Similar to the similarity comparison of the general electricity peak-valley curve, the power factor curve of the first month is compared with the power factor curves of other months to calculate the similarity. The amount of data with a similarity greater than the similarity threshold is counted, that is, how many other power factor curves are similar to the power factor curve of the first month. This number is the second centrality. When the second centrality is greater than the predetermined scalar, it means that the power factor curve of the first month is also highly representative.

[0029] Similarly, the first month's electricity load curve is compared with other monthly electricity load curves for similarity, and the third centrality is calculated. When the third centrality is greater than the predetermined scalar, it means that the first month's electricity load curve is also highly representative. Then the first month's load fluctuation curve is calculated to obtain the fourth centrality. This series of conditional judgments and calculations ensures that each feature has undergone strict similarity analysis, thereby ensuring that the high-frequency features finally selected have higher credibility. When the fourth centrality is greater than the preset scalar, it means that the electricity consumption characteristics of the first month are highly representative, and it is added to the high-frequency monthly electricity consumption feature set. The high-frequency monthly electricity consumption feature set is curve fitted, and the same high-frequency feature curves are fitted into a comprehensive curve to obtain comprehensive monthly electricity consumption information. Comprehensive monthly electricity consumption information is obtained. Curve fitting is a mathematical method used to find a mathematical function that can best describe a set of data points. Through multiple similarity comparisons and centrality calculations, it is ensured that the final selected features have high representativeness and validity. Curve fitting aggregates multiple similar high-frequency features into a comprehensive curve, making the results smoother and easier to understand, which helps to identify the electricity consumption patterns of enterprise users.

[0030] Further, as attached Figure 2 As shown, step 2 of this application includes: A first user cluster is randomly selected from the multiple user clusters, and the first day's electricity consumption information of the first user cluster is obtained, wherein the first day's electricity consumption information includes a first day's electricity peak-valley fitting curve, a first day's power factor fitting curve, a first day's electricity load fitting curve, and a first day's load fluctuation fitting curve; a plurality of time intervals are determined by dividing the day according to a predetermined time step, and a plurality of peak-valley fitting segment centroids, a plurality of factor fitting segment centroids, a plurality of load fitting segment centroids, and a plurality of fluctuation fitting segment centroids of the plurality of time intervals are obtained; the plurality of time intervals are clustered according to the plurality of peak-valley fitting segment centroids, the plurality of factor fitting segment centroids, the plurality of load fitting segment centroids, and the plurality of fluctuation fitting segment centroids, and a plurality of time thresholds and a plurality of electricity consumption feature centroid sets are determined; an electricity consumption metering strategy optimization configuration is performed according to the plurality of electricity consumption feature centroid sets, and a plurality of optimized metering strategies are determined, wherein the electricity consumption metering strategy includes a sampling frequency and a data sampling accuracy; a first optimized metering scheme is constructed according to the plurality of time thresholds and the plurality of optimized metering strategies, and is added to the plurality of optimized metering schemes.

[0031] Specifically, a user cluster is randomly selected from multiple user clusters as the first user cluster, and the electricity consumption information of the first user cluster on a certain day, that is, the electricity consumption information of the first day, is obtained, including the peak and valley fitting curve of electricity consumption on the first day, the power factor fitting curve on the first day, the electricity load fitting curve on the first day, and the load fluctuation fitting curve on the first day. According to the preset time step, a day is divided into multiple time intervals. Usually this predetermined time step is short, such as once every 5 minutes. For each time interval, the segmented centroid of the peak and valley fitting curve, the power factor fitting curve, the electricity load fitting curve, and the load fluctuation fitting curve is calculated. The centroid is usually the statistical center point of the segmented curve, such as the mean or median, representing the electricity consumption characteristics of the interval.

[0032] Using multiple peak-valley fitting segment centroids, multiple factor fitting segment centroids, multiple load fitting segment centroids, and multiple fluctuation fitting segment centroids, a clustering algorithm (such as K-means) is used to cluster multiple time intervals, find the similarity between time intervals, group similar time intervals into a group, and determine multiple time thresholds and multiple power consumption feature centroid sets. Based on the similarity of the segment centroids, find time intervals with the same or similar power consumption features and cluster them into a class, representing the time period with similar power consumption features within a day, which is used to further determine the turning point (time threshold) and typical features of the power consumption pattern. When the centroid values ​​of different time intervals are significantly different, they will be identified as different classes, and the boundary between each class forms a time threshold. The central centroid of each class represents the typical power consumption feature value of the class, and these centroids form a power consumption feature centroid set.

[0033] The electricity metering strategy is optimized and configured according to multiple sets of electricity consumption feature centroids, including adjusting the sampling frequency and data sampling accuracy. For example, during peak hours and periods of large load fluctuations, it is recommended to set a higher sampling frequency to accurately capture electricity consumption fluctuations and ensure real-time data; during low periods or periods of relatively stable load, the sampling frequency can be appropriately reduced to reduce data storage and transmission costs. During periods of peak electricity consumption or large changes in power factor, a higher sampling accuracy is set to ensure that the acquired data information is accurate and facilitate subsequent analysis and anomaly detection; during periods of stable load, the sampling accuracy can be reduced to balance the requirements for data volume and accuracy. By having different sampling frequency and accuracy requirements for each time period, multiple different optimized metering strategies are formed, so that the metering solution can flexibly respond to changes in users' electricity consumption patterns.

[0034] According to the determined multiple time thresholds and multiple optimized metering strategies, the time thresholds and metering strategies are combined to form a highly targeted and detailed first optimized metering scheme, including sampling frequency, accuracy and other configurations in different time intervals. The first week electricity consumption feature set of the first user cluster is obtained, and the dispersion analysis is performed on the first week electricity consumption feature set to evaluate the stability of the electricity consumption characteristics within the user cluster. If the dispersion is small (that is, the electricity consumption behaviors of the user clusters are similar), it is considered that the user clusters have a consistent electricity consumption pattern, and the first optimized metering scheme can be directly added to multiple optimized metering schemes. If the dispersion is large, it indicates that there is a large difference in electricity consumption within the user cluster. The overall difference between each user in the first user cluster and other users is analyzed separately. If the difference exceeds the tolerance range, the weekly electricity consumption compensation coefficient is calculated based on the user electricity consumption difference deviation to balance the users with large differences. After compensating the first optimized metering scheme according to the weekly electricity consumption compensation coefficient, it is added to multiple optimized metering schemes, including the sampling frequency and accuracy settings for different time periods to meet the personalized electricity consumption characteristics requirements of the user cluster. By refining the time period division of daily electricity consumption characteristics within user clusters, based on centroid clustering and optimized metering strategy configuration, the metering solution can be more accurately adapted to the user's electricity consumption behavior, thereby improving the efficiency and accuracy of the metering system.

[0035] Furthermore, the present application also includes the following steps: Arrange the multiple peak-valley fitting segment centroids in the order of time intervals to determine a peak-valley segment centroid sequence, cluster the peak-valley segment centroid sequence to determine a first time threshold set; obtain a second time threshold set, a third time threshold set and a fourth time threshold set by clustering the multiple factor fitting segment centroids, the multiple load fitting segment centroids and the multiple fluctuation fitting segment centroids; perform a union operation on the first time threshold set, the second time threshold set, the third time threshold set and the fourth time threshold set in the order of thresholds to obtain the multiple time thresholds; wherein, clustering the peak-valley segment centroid sequence to determine the first time threshold set includes: if the deviation of two adjacent peak-valley fitting segment centroids is less than or equal to a predetermined deviation threshold, then merge the time intervals corresponding to the two adjacent peak-valley fitting segment centroids, and the merged time interval corresponds to the mean of the two adjacent peak-valley fitting segment centroids; if the deviation of two adjacent peak-valley fitting segment centroids is greater than the predetermined deviation threshold, then set a time dividing line and continue to iterate and merge.

[0036] Specifically, multiple peak-valley fitting segment centroids are arranged in the order of time intervals to form an ordered centroid sequence, which reflects the changing trend of peak-valley electricity consumption behavior over time. The peak-valley segment centroid sequence is clustered to identify time periods with similar electricity consumption characteristics. During the clustering process, the peak-valley segment centroid sequence is traversed to compare the deviations of two adjacent peak-valley fitting segment centroids. The predetermined deviation threshold is a pre-set critical value for judging whether two centroids are close enough so that the corresponding time intervals can be merged. If the deviation between two adjacent peak-valley fitting segment centroids is less than or equal to the predetermined deviation threshold, it is considered that the peak-valley electricity consumption behaviors of the two time intervals are similar, so the time intervals corresponding to the two peak-valley fitting segment centroids are merged into a larger time interval, and the merged time interval corresponds to the mean of the two adjacent peak-valley fitting segment centroids. If the deviation between the two adjacent peak-valley fitting segment centroids is greater than the predetermined deviation threshold, the two time intervals should be regarded as different, then a time dividing line is set between the centroids, and the iterative merging process continues.

[0037] Exemplarily, assume that there is the following peak-valley segmented centroid sequence (in kilowatt-hours): 100, 120, 140, 150, 152, 200; the preset deviation threshold is 10; compare 100 and 120, the deviation 20 is greater than the threshold, set the time dividing line; compare 120 and 140, the deviation 20 is greater than the threshold, set the time dividing line; compare 140 and 150, the deviation 10 is equal to the threshold, merge into 145; compare 150 and 152, the deviation 2 is less than the threshold, merge into 151; compare 152 and 200, the deviation 18 is greater than the threshold, set the time dividing line. According to the division result of the centroid deviation, the boundaries of the user's peak, valley and transition time periods in a day are obtained, thereby forming a first time threshold set.

[0038] Similarly, multiple factor fitting segment centroids, multiple load fitting segment centroids, and multiple fluctuation fitting segment centroids are clustered independently to obtain the second time threshold set, the third time threshold set, and the fourth time threshold set. The first time threshold set, the second time threshold set, the third time threshold set, and the fourth time threshold set are subjected to a union operation in the order of thresholds. The union operation refers to merging different time threshold sets (such as the first time threshold set, the second time threshold set, the third time threshold set, and the fourth time threshold set) to form a comprehensive threshold set containing all time segmentation points. The core of the union operation is to integrate the time thresholds in each set without duplication to ensure that the final generated time threshold can fully reflect the dynamic trend of user power consumption. In chronological order, the time nodes in each time threshold set are merged into a comprehensive time series. For thresholds of the same order, a merge operation is performed, that is, if two or more thresholds are adjacent or overlapped in time, they will be merged into a wider time interval. According to the changes in actual electricity consumption data, the time dividing line is flexibly set to ensure the true reflection of electricity consumption characteristics. Through the deviation analysis of adjacent centroids, the clustering results are ensured to have high accuracy and avoid the influence of noise on the clustering effect. The time thresholds from multiple sources are merged to form a comprehensive time threshold set.

[0039] Furthermore, the present application also includes the following steps: A first weekly electricity consumption feature set of the first user cluster is obtained, wherein the first weekly electricity consumption feature set includes a first weekly electricity consumption peak and valley curve set, a first weekly power factor curve set, a first weekly electricity consumption load curve set, and a first weekly load fluctuation curve set; a comprehensive discreteness calculation is performed according to the first weekly electricity consumption peak and valley curve set, the first weekly power factor curve set, the first weekly electricity consumption load curve set, and the first weekly load fluctuation curve set to determine a first discreteness; if the first discreteness is less than or equal to a predetermined discrete scalar, the first optimized metering scheme is added to the multiple optimized metering schemes; if the first discreteness is greater than the predetermined discrete scalar, weekly electricity consumption difference analysis is performed on multiple first users of the first user cluster respectively, and if the difference exceeds a predetermined tolerance interval, a weekly electricity consumption compensation coefficient is set according to the difference deviation to determine multiple weekly electricity consumption compensation coefficients; after mapping and compensating the first optimized metering scheme according to the multiple weekly electricity consumption compensation coefficients, the first optimized metering scheme is added to the multiple optimized metering schemes.

[0040] Specifically, the first week electricity consumption feature set of the first user cluster is obtained, including the first week electricity consumption peak and valley curve set, the first week power factor curve set, the first week electricity load curve set and the first week load fluctuation curve set. The first week electricity consumption peak and valley curve set, the first week power factor curve set, the first week electricity load curve set and the first week load fluctuation curve set are subjected to dispersion analysis to evaluate the stability of the electricity consumption characteristics within the user cluster. For each electricity consumption feature set (peak and valley curve set, power factor curve set, electricity load curve set and load fluctuation curve set), its average value is calculated; for each data point, the square of the difference between it and the average value of the corresponding feature set is calculated; the square differences of all data points are summed and divided by the total number of data points; the square root is taken to obtain the comprehensive dispersion, i.e., the first dispersion. If the dispersion is small (i.e., the electricity consumption behaviors of the user cluster are similar), it is considered that the user cluster has a consistent electricity consumption pattern, and the first optimized metering scheme can be directly applied to the user cluster.

[0041] The predetermined discrete scalar is a threshold value pre-set for evaluating the stability of electricity consumption behavior. If the first discreteness is less than or equal to the predetermined discrete scalar, the first optimized metering scheme is added to the multiple optimized metering schemes. If the first discreteness is greater than the predetermined discrete scalar, it indicates that there is a large difference in electricity consumption within the user cluster, and further detailed analysis is required. Weekly electricity consumption difference analysis is performed on multiple users in the first user cluster, that is, the difference between the electricity consumption characteristics of each first user in a week and the corresponding characteristics of other users is calculated. The difference can be determined by calculating the variance, and the variance reflects the average difference between the electricity consumption characteristics of each user and the electricity consumption characteristics of other users. Compare whether the difference of each user exceeds the predetermined tolerance interval. The predetermined tolerance interval is a threshold used to determine whether the difference is within an acceptable range. If the difference exceeds the tolerance interval, the weekly electricity consumption compensation coefficient is set according to the difference deviation. After judging all differences, multiple weekly electricity consumption compensation coefficients are determined. The weekly electricity consumption compensation coefficient is an adjustment factor used to correct the electricity consumption data of a specific user during electricity metering to more accurately reflect its electricity consumption behavior.

[0042] According to multiple weekly electricity compensation coefficients, the first optimized metering scheme is adjusted. The compensation coefficient is mapped to the configuration parameters of the first optimized metering scheme (such as sampling frequency, accuracy, etc.). For example, users with large load fluctuations can increase the sampling frequency through the compensation coefficient to improve the metering accuracy, thereby more accurately capturing their electricity fluctuations; while users with small load fluctuations may reduce the sampling frequency to save resources. Multiple optimization schemes are generated based on the compensation coefficient to ensure that the actual electricity consumption characteristics of each user are more matched with the metering scheme settings, and the compensated optimized metering scheme is added to the overall multiple optimized metering schemes. Mapping compensation is to add a compensation strategy on the basis of the original optimization scheme to ensure that the metering is accurate and applicable. Through multi-level analysis and dynamic compensation adjustment, the scheme ensures that the optimized metering scheme for the overall electricity consumption behavior of users is more adaptable, which helps to take into account individual differences in the metering process, achieve a more intelligent and accurate electricity metering effect, effectively reduce the impact of electricity consumption differences on metering results, and improve the adaptability and efficiency of the metering scheme.

[0043] Further, step three of this application includes: Obtain the first month's electricity usage information, the first week's electricity usage information and the first day's electricity usage information of the first user cluster; configure a first electricity metering anomaly factor according to the first month's electricity usage information, the first week's electricity usage information and the first day's electricity usage information, and construct a first electricity metering anomaly detection branch according to the first electricity metering anomaly factor, wherein the electricity metering anomaly factor includes at least current exceeding the standard, voltage deviation, electricity quantity deviation and power factor anomaly; obtain multiple electricity metering anomaly detection branches of the multiple user clusters, and construct the electricity metering anomaly detection plug-in based on the mapping between the multiple user clusters and the multiple electricity metering anomaly detection branches.

[0044] Specifically, the power consumption information of the first user cluster at different time scales (month, week, day) is obtained, and each power consumption information includes a power consumption peak and valley curve, a power factor curve, a power consumption load curve, and a load fluctuation curve. According to the extracted power consumption information, configure the power meter abnormal factors, including but not limited to current exceeding the standard, voltage deviation, power deviation, and power factor abnormality. Current exceeding the standard refers to the situation where the current exceeds the normal working range; voltage deviation refers to the situation where the voltage deviates from the standard value; power deviation refers to the difference between the actual power consumption and the expected power consumption; power factor abnormality refers to the power factor being lower than the normal level, indicating that the power utilization efficiency is low. The abnormal factors can be flexibly configured according to the data at the three levels of month, week, and day to identify subtle abnormal changes. According to each abnormal factor, establish the corresponding abnormal detection logic and branch, and set up a first power meter abnormal detection branch in the first user cluster to detect the power consumption abnormality of the user cluster, such as power fluctuation exceeding the standard, abnormal power usage, etc. Repeat the above steps for each user cluster, establish a corresponding power meter abnormal detection branch for each user cluster, and obtain multiple power meter abnormal detection branches. Each user and the corresponding power meter anomaly detection branch are mapped and integrated to form a unified power meter anomaly detection plug-in, which can flexibly adapt to the power consumption characteristics of each user cluster, and can simultaneously receive and process power consumption data from multiple user clusters to perform real-time anomaly detection. The power meter anomaly detection plug-in can continuously monitor the power consumption of each user cluster during operation, identify anomalies through the configuration of each branch, and once an anomaly is detected, the plug-in can trigger corresponding alarms or automatic corrective measures. By extracting abnormal factors using power consumption information at different time scales and monitoring the user's power consumption behavior through detection branches, refined identification of abnormal power consumption is achieved.

[0045] Further, step 4 of this application includes: According to the abnormal detection results, the abnormal electricity metering frequencies of the multiple user clusters are counted to determine multiple abnormal electricity metering frequencies; according to the multiple abnormal electricity metering frequencies, the multiple optimized metering schemes are dynamically adjusted, wherein, if the abnormal electricity metering frequency is less than or equal to the predetermined frequency scalar, the optimized metering scheme is targetedly fine-tuned according to the abnormal detection results, and if the abnormal electricity metering frequency is greater than the predetermined frequency scalar, the optimized metering scheme is overall adjusted according to the abnormal detection results.

[0046] Specifically, in the process of running the power metering anomaly detection plug-in, the abnormal power metering events of each user cluster are collected and recorded in real time. The abnormal detection results of each user cluster are classified and counted, and the frequency of abnormal power metering events of each user cluster is regularly counted to form multiple abnormal power metering frequencies. The multiple optimized metering schemes previously determined are dynamically adjusted according to the multiple abnormal power metering frequencies counted. Based on historical data and industry standards, a predetermined frequency scalar is pre-determined to judge the level of abnormal power metering frequency. If the abnormal power metering frequency is less than or equal to the predetermined frequency scalar, it means that the power consumption stability of the user cluster is good, but there may be some small-scale abnormal situations, and targeted fine-tuning is performed. On the basis of maintaining the stability of the overall solution, the metering strategy of specific users is fine-tuned, such as adjusting the sampling frequency, improving the data sampling accuracy, or adopting different metering methods. For example, if a user frequently has voltage deviations in a specific time period, a voltage compensation coefficient is set for the user to optimize its metering scheme. If the abnormal power metering frequency is greater than the predetermined frequency scalar, it is considered that the abnormal situation is relatively common, and the optimized metering scheme of the entire user cluster needs to be re-analyzed and established. Re-evaluate the electricity consumption behavior patterns and needs of these users and identify potential systemic problems. Based on the evaluation results, establish a new optimized metering scheme, including resetting the metering threshold; adjusting the electricity metering strategy, such as changing the sampling method or introducing more electricity monitoring parameters; introducing more advanced data analysis algorithms to more accurately identify and predict abnormal behavior. After fine-tuning or overall regulation, continue to monitor the metering behavior of the user cluster and observe the effect of the adjustment. Regularly evaluate the effectiveness of the optimized metering scheme based on the implementation effect, and make necessary adjustments to ensure that the metering scheme always adapts to the user's electricity demand and behavior patterns. By dynamically adjusting the optimized metering scheme based on the statistical results of abnormal metering frequency, more efficient and accurate electricity metering management can be achieved. Targeted fine-tuning ensures that the special needs of individual users are met, while overall regulation helps to deal with systemic problems and maintain the effectiveness and stability of the metering scheme, which not only improves the flexibility of power management, but also effectively reduces the risks brought by abnormal metering, thereby improving the overall performance of the power system and user satisfaction.

[0047] In summary, the electric meter electricity consumption measurement method combined with electric power user grouping provided in this application has the following technical effects: By querying the historical electricity consumption records of enterprise users in the area, users are grouped according to the historical electricity consumption records to determine multiple user clusters; the multiple user clusters are divided into daily electricity consumption intervals respectively, and the electricity metering strategy optimization is performed according to the division results to determine multiple optimized metering schemes; an electricity metering anomaly detection plug-in based on the multiple user clusters is established; the electricity metering of enterprise users is performed according to the multiple optimized metering schemes, and the electricity metering anomaly is synchronously monitored through the electricity metering anomaly detection plug-in, and the dynamic adjustment of the multiple optimized metering schemes is performed according to the anomaly detection results. In other words, users are classified according to similar electricity consumption behavior patterns through grouping, and the electricity consumption data of each user cluster is further analyzed, and their daily electricity consumption is divided into different intervals, and the metering strategy is customized to achieve more refined management. According to the historical behavior pattern of the user cluster, abnormal electricity consumption is identified, and the metering strategy is dynamically adjusted to achieve dynamic adaptability of electricity metering, so that the metering scheme can be flexibly adjusted according to the real-time behavior of the user and the abnormal changes detected, avoiding the accumulation of long-term deviations, and achieving more accurate and efficient metering of electricity consumption, and improving the pertinence and efficiency of metering.

[0048] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0049] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for measuring electricity consumption by electric meter combined with grouping of electric power users, characterized in that: include: Query the historical electricity consumption records of enterprise users in the area, group the users according to the historical electricity consumption records, and determine multiple user clusters; Dividing the multiple user clusters into daily electricity consumption intervals respectively, optimizing the electricity metering strategy according to the division results, and determining multiple optimized metering schemes; Establishing an electricity metering anomaly detection plug-in based on the multiple user clusters; The electricity consumption of enterprise users is measured according to the multiple optimized metering schemes, the electricity metering anomalies are synchronously monitored through the electricity metering anomaly detection plug-in, and the multiple optimized metering schemes are dynamically adjusted according to the anomaly detection results.

2. The method for measuring electricity consumption by electric meter combined with grouping of electric power users according to claim 1, characterized in that: The user groups are grouped according to the historical electricity consumption records to determine a plurality of user clusters, including: Collecting monthly electricity consumption feature sets, weekly electricity consumption feature sets and daily electricity consumption feature sets of multiple enterprise users according to the historical electricity consumption records, wherein the electricity consumption features include electricity peak and valley curves, power factor curves, electricity load curves and load fluctuation curves; Extracting high-frequency features from the monthly electricity usage feature set, weekly electricity usage feature set, and daily electricity usage feature set respectively to determine monthly electricity usage information, weekly electricity usage information, and daily electricity usage information; Using a K-means algorithm, a plurality of enterprise users are grouped according to the monthly electricity consumption information to obtain a grouping result; Performing secondary grouping on the primary grouping result according to the weekly electricity consumption information to obtain a secondary grouping result; The secondary grouping result is grouped a third time according to the daily electricity consumption information to obtain the multiple user clusters.

3. The method for measuring electricity consumption by electric meter combined with grouping of electric power users according to claim 2, characterized in that: Extracting high-frequency features from the monthly electricity consumption feature set to determine monthly electricity consumption information includes: Randomly select the electricity consumption characteristics of the first month, wherein the electricity consumption characteristics of the first month include the electricity consumption peak and valley curve of the first month, the power factor curve of the first month, the electricity load curve of the first month, and the load fluctuation curve of the first month; Perform similarity comparison on the first monthly electricity consumption peak-valley curve and other monthly electricity consumption peak-valley curves in the monthly electricity consumption feature set, and count the amount of data whose similarity is greater than a similarity threshold, and set it as a first centrality; If the first centrality is greater than a predetermined scalar, the first monthly power factor curve is compared with other monthly power factor curves in the monthly power consumption feature set for similarity, and the amount of data with a similarity greater than a similarity threshold is counted and set as a second centrality; If the second centrality is greater than the predetermined scalar, the third centrality of the first monthly power load curve is calculated and obtained; if the third centrality is greater than the predetermined scalar, the fourth centrality of the first monthly load fluctuation curve is calculated and obtained; If the fourth centrality is greater than the predetermined scalar, the first monthly electricity consumption feature is added to a high-frequency monthly electricity consumption feature set, and curve fitting is performed on the high-frequency monthly electricity consumption feature set to obtain the monthly electricity consumption information.

4. The method for measuring electricity consumption by electric meter combined with grouping of electric power users according to claim 2, characterized in that: The multiple user clusters are respectively divided into daily electricity consumption intervals, and electricity metering strategy optimization is performed according to the division results to determine multiple optimized metering schemes, including: Randomly selecting a first user cluster from the multiple user clusters, and obtaining the first-day electricity consumption information of the first user cluster, wherein the first-day electricity consumption information includes a first-day electricity consumption peak-valley fitting curve, a first-day power factor fitting curve, a first-day electricity consumption load fitting curve, and a first-day load fluctuation fitting curve; Determine multiple time intervals by dividing the day according to a predetermined time step, and obtain multiple peak-valley fitting segment centroids, multiple factor fitting segment centroids, multiple load fitting segment centroids, and multiple fluctuation fitting segment centroids of the multiple time intervals; Clustering the multiple time intervals according to the multiple peak-valley fitting segment centroids, the multiple factor fitting segment centroids, the multiple load fitting segment centroids, and the multiple fluctuation fitting segment centroids, to determine multiple time thresholds and multiple power consumption feature centroid sets; Execute an optimization configuration of the electricity metering strategy according to the plurality of electricity feature centroid sets to determine a plurality of optimized metering strategies, wherein the electricity metering strategy includes a sampling frequency and a data sampling accuracy; A first optimized metering scheme is constructed according to the multiple time thresholds and the multiple optimized metering strategies, and is added to the multiple optimized metering schemes.

5. The method for measuring electricity consumption by electric meter combined with grouping of electric power users according to claim 4, characterized in that: Determine multiple time thresholds, including: Arranging the multiple peak-valley fitting segment centroids in chronological order of time intervals to determine a peak-valley segment centroid sequence, and clustering the peak-valley segment centroid sequence to determine a first time threshold set; Acquire a second time threshold set, a third time threshold set and a fourth time threshold set by clustering the plurality of factor fitting segment centroids, the plurality of load fitting segment centroids and the plurality of fluctuation fitting segment centroids; performing a union operation on the first time threshold set, the second time threshold set, the third time threshold set, and the fourth time threshold set in a threshold order to obtain the multiple time thresholds; The step of clustering the peak-valley segment centroid sequence to determine a first time threshold set includes: If the deviation of the centroids of two adjacent peak-valley fitting segments is less than or equal to a predetermined deviation threshold, the time intervals corresponding to the centroids of the two adjacent peak-valley fitting segments are merged, and the merged time interval corresponds to the average of the centroids of the two adjacent peak-valley fitting segments; If the deviation between the centroids of two adjacent peak-valley fitting segments is greater than a predetermined deviation threshold, a time segmentation line is set and the iterative merging continues.

6. The method for measuring electricity consumption by electric meter combined with grouping of electric power users according to claim 4, characterized in that: Build the first optimized metering solution, which will also include: Acquire a first-week electricity consumption feature set of the first user cluster, wherein the first-week electricity consumption feature set includes a first-week electricity consumption peak and valley curve set, a first-week power factor curve set, a first-week electricity consumption load curve set, and a first-week load fluctuation curve set; Perform comprehensive dispersion calculation based on the first week's electricity peak and valley curve set, the first week's power factor curve set, the first week's electricity load curve set, and the first week's load fluctuation curve set to determine a first dispersion; If the first discreteness is less than or equal to a predetermined discrete scalar, adding the first optimized metering scheme to the plurality of optimized metering schemes; If the first discreteness is greater than a predetermined discrete scalar, performing weekly electricity consumption difference analysis on a plurality of first users of the first user cluster respectively, and if the difference exceeds a predetermined tolerance interval, setting a weekly electricity consumption compensation coefficient according to a difference deviation, and determining a plurality of weekly electricity consumption compensation coefficients; After mapping and compensating the first optimized metering scheme according to the multiple weekly electricity consumption compensation coefficients, the first optimized metering scheme is added to the multiple optimized metering schemes.

7. The method for measuring electricity consumption by electric meter combined with grouping of electric power users according to claim 2, characterized in that: Establishing an electricity meter anomaly detection plug-in based on the multiple user clusters, including: Obtaining electricity usage information of the first month, the first week, and the first day of the first user cluster; According to the first monthly electricity consumption information, the first weekly electricity consumption information and the first daily electricity consumption information, a first electricity meter abnormality factor is configured, and according to the first electricity meter abnormality factor, a first electricity meter abnormality detection branch is constructed, wherein the electricity meter abnormality factor includes at least current exceeding the standard, voltage deviation, power deviation and power factor abnormality; A plurality of power meter anomaly detection branches of the plurality of user clusters are obtained, and the power meter anomaly detection plug-in is constructed based on the mapping of the plurality of user clusters and the plurality of power meter anomaly detection branches.

8. The method for measuring electricity consumption by electric meter combined with grouping of electric power users according to claim 1, characterized in that: The method further comprises: performing dynamic adjustment of the plurality of optimized metering schemes according to the abnormality detection result, comprising: According to the abnormal detection result, the abnormal electricity metering frequencies of the plurality of user clusters are counted to determine a plurality of abnormal electricity metering frequencies; The multiple optimized metering schemes are dynamically adjusted according to the multiple abnormal electricity metering frequencies, wherein if the abnormal electricity metering frequency is less than or equal to the predetermined frequency scalar, the optimized metering scheme is targetedly fine-tuned according to the abnormal detection result; if the abnormal electricity metering frequency is greater than the predetermined frequency scalar, the optimized metering scheme is overall controlled according to the abnormal detection result.

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