A power consumption information sensing method of a single-phase fee control intelligent electric energy meter

Abstract

The present application relates to the field of data processing, in particular to a power consumption information sensing method of a single-phase cost control intelligent electric energy meter, the method comprising: segmenting a historical power consumption sequence obtained to obtain a plurality of subsequences; clustering all the subsequences to obtain a clustering result, wherein the two-dimensional features of the clustering are the fluctuation coefficient of each subsequence and the standard deviation of the power consumption within the subsequence; setting a transmission frequency reference value for each clustering cluster according to the clustering result; based on a to-be-transmitted data sequence, calculating the similarity of the to-be-transmitted data sequence and each clustering cluster, assigning an influence weight to each clustering cluster according to the similarity, and obtaining the transmission frequency of the to-be-transmitted data sequence; and transmitting the to-be-transmitted data sequence to a master station according to the transmission frequency of the to-be-transmitted data sequence. The present application can guarantee the accuracy of data transmission while minimizing energy consumption, achieving a balance between performance and energy consumption.

Description

Technical Field

[0001] This invention relates to the field of data processing. More specifically, this invention relates to a method for sensing electricity consumption information in a single-phase prepaid smart energy meter. Background Technology

[0002] A single-phase prepaid smart meter is a type of smart meter designed for single-phase AC power. It integrates energy metering, cost control, data monitoring, and remote management functions, and is widely used in single-phase power consumption scenarios such as residential communities and government offices. The electricity consumption information sensing of a single-phase prepaid smart meter refers to the meter's real-time collection, processing, and transmission of various data information during the user's electricity consumption process through its built-in high-precision metering chip, sensors, and communication module, thereby achieving accurate monitoring and intelligent management of electricity usage.

[0003] During the process of electricity meters transmitting electricity consumption data to the master station, the data processing efficiency of the master station will decrease due to the large number of meters and the increase in data volume. This is because: all meter data are treated equally, and fixed transmission frequency will cause a large number of meters to send data to the master station at the same time, which can easily cause network congestion. Summary of the Invention

[0004] To address the technical problem of reduced transmission efficiency due to limitations of fixed transmission frequencies, the present invention provides the following technical solution.

[0005] A method for sensing electricity consumption information in a single-phase prepaid smart energy meter includes: Obtain the electricity consumption sequence of historical electricity meters within a preset time period, and divide the electricity consumption sequence into multiple sub-sequences; All subsequences are clustered to obtain clustering results, where the two-dimensional features of the clusters are the fluctuation coefficient and the standard deviation of electricity consumption within each subsequence; a transmission frequency reference value is set for each cluster based on the clustering results. Based on the data sequence to be transmitted, the similarity between the data sequence to be transmitted and each cluster is calculated. Based on the similarity, an influence weight is assigned to each cluster. The transmission frequency baseline value of each cluster and the corresponding influence weight are weighted and summed and rounded up to obtain the transmission frequency of the data sequence to be transmitted. The data sequence to be transmitted is sent to the main station according to the transmission frequency of the data sequence to be transmitted.

[0006] Preferably, sending the data sequence to be transmitted to the main station according to the transmission frequency of the data sequence to be transmitted includes: Based on the transmission frequency, a set of transmission time points corresponding to the data sequence to be transmitted is constructed, and the data corresponding to the transmission time points is sent to the main station for processing through the electricity meter communication module.

[0007] Preferably, the electricity consumption sequence is segmented according to a preset fixed rule, wherein the fixed rule is: the electricity consumption sequence is divided into equal-length segments at 30-minute intervals.

[0008] Preferably, the process of obtaining the fluctuation coefficient includes: Calculate the standard deviation and mean of electricity consumption for any subsequence. Use an exponential function to transform the ratio of the mean to the maximum electricity consumption in the subsequence to obtain the local volatility of the subsequence. Use the product of the local volatility and the standard deviation of electricity consumption in the subsequence as the volatility coefficient of the subsequence.

[0009] Preferably, the clustering uses the K-means algorithm.

[0010] Preferably, the transmission frequency reference value satisfies the following relationship: In the formula, For the first The transmission frequency reference value for each cluster, This is the lower limit of the set transmission frequency. This indicates the upper limit of the set transmission frequency. Indicates the first The fluctuation coefficient corresponding to the centroid of each cluster. For the first The standard deviation of electricity consumption corresponding to the centroid of each cluster Indicates the first The maximum total electricity consumption of subsequences within a cluster. Indicates the first The average total electricity consumption of all subsequences in each cluster. This indicates normalization.

[0011] Preferably, the two-dimensional features of the data sequence to be transmitted, namely the fluctuation coefficient and the standard deviation of the electricity consumption, are calculated. The Euclidean distance between the two-dimensional features of the data sequence to be transmitted and the two-dimensional features corresponding to the centroids of each cluster is calculated. Then, the reciprocal of the Euclidean distance is used as the similarity between the data sequence to be transmitted and each cluster.

[0012] Preferably, the clustering uses the DBSCAN clustering algorithm.

[0013] The beneficial effects of this invention are: By analyzing historical electricity consumption data, electricity consumption patterns are divided into different clusters, and a transmission frequency benchmark value is set for each cluster. This allows for the full extraction of patterns from historical data, avoiding reliance on fixed frequencies or empirical values, and making the transmission frequency more consistent with actual electricity consumption characteristics. Furthermore, by calculating the similarity between real-time transmitted data sequences and historical clusters, the transmission frequency can be dynamically adjusted according to current electricity consumption behavior. This ensures data transmission accuracy while minimizing energy consumption, achieving a balance between performance and energy consumption. Attached Figure Description

[0014] Figure 1 This is a flowchart of steps S1-S4 in a method for sensing electricity consumption information of a single-phase prepaid smart energy meter according to an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0016] Reference Figure 1 A method for sensing electricity consumption information in a single-phase prepaid smart energy meter includes steps S1-S4, as detailed below: S1: Obtain the electricity consumption sequence of historical electricity meters within a preset time period, and divide the electricity consumption sequence into multiple sub-sequences.

[0017] In one embodiment, the electricity consumption data of a single electricity meter during a preset period (such as the past three days) is extracted (in this embodiment of the invention, the collection frequency is once every half minute). If there are missing data (such as no record at a certain time point), the missing value is filled in by linear interpolation (based on the data before and after). Then, an electricity consumption sequence is constructed based on the timestamp. The electricity consumption sequence is divided into equal-length segments according to a fixed rule, that is, every 30 minutes, to obtain multiple subsequences.

[0018] At the same time, the real-time data sequence of the electricity meter to be transmitted is obtained based on the above-mentioned acquisition frequency.

[0019] S2: Cluster all subsequences to obtain clustering results, where the two-dimensional features of the cluster are the fluctuation coefficient of each subsequence and the standard deviation of the electricity consumption within the subsequence; set a transmission frequency reference value for each cluster based on the clustering results.

[0020] In one embodiment, the fluctuation coefficients of each subsequence of S1 are calculated to quantify the fluctuation characteristics of each electricity consumption.

[0021] For example, the standard deviation and mean of the electricity consumption of any subsequence are calculated. The ratio of the mean to the maximum electricity consumption in the subsequence is transformed using an exponential function to obtain the local volatility of the subsequence. Finally, the product of the local volatility and the standard deviation of the electricity consumption of the subsequence is taken as the volatility coefficient of the subsequence.

[0022] The volatility coefficients of the above subsequences satisfy the following relationship: In the formula, For the first The volatility coefficient of each subsequence For the first The standard deviation of electricity consumption for each subsequence. For the first The average electricity consumption of each subsequence. For the first The maximum power consumption of each subsequence. It is an exponential function with base e.

[0023] Among them, through the exponential function The transformation makes the results more sensitive to the relationship between the mean and the maximum power consumption; when the mean is close to the maximum power consumption, Approaching 0 When the average is much smaller than the maximum power consumption, Larger This will amplify the difference.

[0024] By standard deviation With exponential function Multiplication not only takes into account the dispersion of the data, but also adjusts for volatility by the relationship between the mean and the maximum electricity consumption, which makes the volatility coefficient better reflect the actual fluctuation of electricity consumption.

[0025] Similarly, the volatility coefficients of all subsequences can be calculated using the same method as for calculating the volatility coefficients of the subsequences described above.

[0026] If the volatility coefficient of a certain subsequence is large, it indicates that the electricity consumption during the corresponding period fluctuates drastically and there is a high demand for electricity. For example, between 7 pm and 9 pm, household electrical appliances are used intensively, resulting in large fluctuations in electricity consumption and a high volatility coefficient at this time.

[0027] If the volatility coefficient of a subsequence is moderate, it indicates that the electricity consumption during the corresponding period is relatively stable and without significant fluctuations. For example, during the daytime working hours, the number of household electrical appliances is relatively fixed, and the electricity consumption fluctuates little.

[0028] If the volatility coefficient of a certain subsequence is small, it indicates that the electricity consumption fluctuation during the corresponding period is small and the electricity demand is low. For example, between 3 and 5 a.m., most household electrical appliances are turned off, and the electricity consumption fluctuation is minimal.

[0029] In one embodiment, clustering is performed based on the fluctuation coefficients of all the above subsequences to cluster all the subsequences into different electricity consumption behavior states. In this embodiment of the invention, clustering divides all the subsequences into peak electricity consumption periods, stable electricity consumption periods, and low electricity consumption periods.

[0030] Specifically, the fluctuation coefficient of the subsequence and the standard deviation of the corresponding electricity consumption are used as two-dimensional features for clustering. The K-means algorithm is used to cluster all subsequences, and the clustering divides all subsequences into three clusters: peak cluster, stationary cluster, and trough cluster.

[0031] After the above clustering, the clustering effect can be evaluated by the silhouette coefficient. If the silhouette coefficient is low, different K values ​​can be tried, the average silhouette coefficient corresponding to each K can be calculated, and the K that maximizes the average silhouette coefficient can be selected as the optimal number of clusters.

[0032] In one embodiment, the transmission frequency requirement under the power consumption mode of the cluster is further quantified based on the two-dimensional characteristics of the cluster, thereby calculating a transmission frequency reference value for each cluster. This reference value serves as a "starting point" or "reference point" for subsequent real-time data transmission frequency calculations.

[0033] The calculation process for the above transmission frequency reference value is as follows: First, set upper and lower limits for the transmission frequency. In this embodiment of the invention, the upper limit is set to 60 transmissions per half hour and the lower limit is set to 10 transmissions per half hour. In other embodiments, the upper and lower limits can be reset based on multiple experimental operations.

[0034] Then, based on the upper and lower limits, the centroid of the cluster, and the maximum and average total power consumption of all subsequences contained within the cluster, the reference value of the transmission frequency of the cluster is calculated, which satisfies the following relationship: In the formula, For the first The transmission frequency reference value for each cluster, This is the lower limit of the set transmission frequency. This indicates the upper limit of the set transmission frequency. Indicates the first The fluctuation coefficient corresponding to the centroid of each cluster. For the first The standard deviation of electricity consumption corresponding to the centroid of each cluster Indicates the first The maximum total electricity consumption of subsequences within a cluster. Indicates the first The average total electricity consumption of all subsequences in each cluster. This indicates normalization.

[0035] Among them, through Amplifying volatility The influence of this ensures that the reference frequency of high-fluctuation clusters is close to the upper limit, and that of low-fluctuation clusters is close to the lower limit; through calculation If the maximum value of the total electricity consumption of a subsequence in a cluster is much higher than the mean, it indicates that there is a significant difference in electricity consumption in that cluster.

[0036] The above formula for calculating the benchmark value uses the set upper and lower limits of the benchmark value as boundaries, and uses a normalization term. Will Mapped to the [0, 1] interval, and finally rounded up to obtain the integer frequency, this ensures that the high-demand cluster (with large fluctuations and significant differences in power consumption) is... near Prioritize data accuracy, and prioritize low-demand clusters (those with small fluctuations and stable power consumption). near This reduces the waste of transmission resources.

[0037] S3: Based on the data sequence to be transmitted, calculate the similarity between the data sequence to be transmitted and each cluster. Assign an influence weight to each cluster according to the similarity. Sum the transmission frequency baseline value of each cluster with the corresponding influence weight and round up to obtain the transmission frequency of the data sequence to be transmitted.

[0038] In one embodiment, the formula in S2 above is used to calculate the two-dimensional features of the data sequence to be transmitted, namely the fluctuation coefficient and the standard deviation of the electricity consumption, to obtain the data sequence to be transmitted. The Euclidean distance between the two-dimensional features of the data sequence to be transmitted and the two-dimensional features corresponding to the centroids of each cluster is further calculated, and the reciprocal of this Euclidean distance is used as the similarity between the data sequence to be transmitted and each cluster.

[0039] In one embodiment, when setting the transmission frequency of the data sequence to be transmitted, it is necessary to comprehensively consider the transmission frequency baseline value of each cluster and the similarity between the data sequence to be transmitted and each cluster. Therefore, the influence weight of each cluster on the transmission frequency of the data sequence to be transmitted is first determined by the similarity, that is, the following relationship is satisfied: In the formula, For the first The influence weights corresponding to each cluster For the data sequence to be transmitted and the first The similarity of individual clusters.

[0040] The smaller the value, the higher the similarity, thus affecting the weight. The larger it is, and the sum of all its weights is 1.

[0041] Furthermore, the transmission frequency of the data sequence to be transmitted is calculated using a weighted average method: In the formula, The transmission frequency of the data sequence to be transmitted. For the first The influence weights corresponding to each cluster For the first The transmission frequency reference value for each cluster.

[0042] By calculating the Euclidean distance between the data sequence to be transmitted and the centroids of each cluster, the similarity between the data to be transmitted and different electricity consumption behavior patterns can be measured. Based on this similarity, the transmission frequency can be dynamically adjusted so that important or representative data can be transmitted more frequently, while the transmission frequency of unimportant data can be reduced, making the setting of the transmission frequency more scientific and reasonable, thereby optimizing transmission resources.

[0043] S4: Based on the transmission frequency of the data sequence to be transmitted, send the data sequence to be transmitted to the main station.

[0044] In one embodiment, a set of transmission time points corresponding to the data sequence to be transmitted is constructed based on the transmission frequency calculated in S3 above. Simply put, it determines which time points need to be sent based on the transmission frequency. For example, if the transmission frequency is once per hour, then the set of transmission time points might be a fixed time point every hour.

[0045] The data corresponding to the transmission time point is then sent to the master station for processing via the electricity meter's communication module. The electricity meter's communication module is the hardware interface for communication between the electricity meter and the master station. At the specified time point, the electricity meter sends the corresponding data to the master station through this module, and the master station then processes and analyzes this data further.

[0046] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for sensing electricity consumption information in a single-phase prepaid smart energy meter, characterized in that, include: Obtain the electricity consumption sequence of historical electricity meters within a preset time period, and divide the electricity consumption sequence into multiple sub-sequences; Clustering is performed on all subsequences to obtain clustering results. The two-dimensional features of the clustering are the fluctuation coefficient and the standard deviation of electricity consumption within each subsequence. The process of obtaining the fluctuation coefficient includes: calculating the standard deviation and mean of electricity consumption for any subsequence; using an exponential function to transform the ratio of the mean to the maximum electricity consumption in the subsequence to obtain the local fluctuation intensity of the subsequence; and using the product of the local fluctuation intensity and the standard deviation of electricity consumption in the subsequence as the fluctuation coefficient of the subsequence. Based on the clustering results, a transmission frequency reference value is set for each cluster; the transmission frequency reference value satisfies the following relationship: In the formula, For the first The transmission frequency reference value for each cluster, This is the lower limit of the set transmission frequency. This indicates the upper limit of the set transmission frequency. Indicates the first The fluctuation coefficient corresponding to the centroid of each cluster. For the first The standard deviation of electricity consumption corresponding to the centroid of each cluster Indicates the first The maximum total electricity consumption of subsequences within a cluster. Indicates the first The average total electricity consumption of all subsequences in each cluster. Indicates normalization; Based on the data sequence to be transmitted, the similarity between the data sequence to be transmitted and each cluster is calculated. Based on the similarity, an influence weight is assigned to each cluster. The transmission frequency baseline value of each cluster and the corresponding influence weight are weighted and summed and rounded up to obtain the transmission frequency of the data sequence to be transmitted. The data sequence to be transmitted is sent to the main station according to the transmission frequency of the data sequence to be transmitted.

2. The method for sensing electricity consumption information in a single-phase prepaid smart energy meter according to claim 1, characterized in that, The step of sending the data sequence to be transmitted to the main station according to the transmission frequency of the data sequence to be transmitted includes: Based on the transmission frequency, a set of transmission time points corresponding to the data sequence to be transmitted is constructed, and the data corresponding to the transmission time points is sent to the main station for processing through the electricity meter communication module.

3. The method for sensing electricity consumption information in a single-phase prepaid smart energy meter according to claim 1, characterized in that, The electricity consumption sequence is segmented according to a preset fixed rule, which is to divide the electricity consumption sequence into equal-length segments with a 30-minute interval.

4. The method for sensing electricity consumption information in a single-phase prepaid smart energy meter according to claim 1, characterized in that, The clustering was performed using the K-means algorithm.

5. The method for sensing electricity consumption information in a single-phase prepaid smart energy meter according to claim 1, characterized in that, Calculate the two-dimensional features of the data sequence to be transmitted, namely the fluctuation coefficient and the standard deviation of electricity consumption. Calculate the Euclidean distance between the two-dimensional features of the data sequence to be transmitted and the two-dimensional features corresponding to the centroids of each cluster. Then, take the reciprocal of the Euclidean distance as the similarity between the data sequence to be transmitted and each cluster.

6. The method for sensing electricity consumption information in a single-phase prepaid smart energy meter according to claim 1, characterized in that, The clustering was performed using the DBSCAN clustering algorithm.

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