Electricity utilization information sensing method of single-phase charge-control intelligent electric energy meter

Abstract

The invention relates to the field of data processing, in particular to an electricity consumption information sensing method for a single-phase charge-control intelligent electric energy meter, which comprises the following steps: segmenting an acquired historical electricity consumption sequence to obtain a plurality of subsequences; all the subsequences are clustered, a clustering result is obtained, and the two-dimensional feature of clustering is the fluctuation coefficient of each subsequence and the standard deviation of the electricity consumption in the subsequence; setting a transmission frequency reference value for each cluster according to a clustering result; based on a to-be-transmitted data sequence, calculating the similarity between the to-be-transmitted data sequence and each cluster, distributing an influence weight for each cluster according to the similarity, and obtaining the transmission frequency of the to-be-transmitted data sequence; and sending the to-be-transmitted data sequence to the master station according to the transmission frequency of the to-be-transmitted data sequence. According to the method, the data transmission accuracy is ensured, the energy consumption is reduced to the maximum extent, and the balance between the performance and the energy consumption is realized.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to a method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter. Background Art

[0002] The single-phase cost-controlled smart energy meter is 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 agencies. The single-phase cost-controlled smart energy meter's power consumption information sensing function uses its built-in high-precision metering chip, sensors, and communication modules to collect, process, and transmit various data about a user's electricity usage in real time, enabling accurate monitoring and intelligent management of electricity usage.

[0003] When electricity meters transmit electricity consumption data to the master station, the master station's data processing efficiency 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 transmission 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] In order to solve the technical problem that the fixed transmission frequency has limitations and leads to reduced transmission efficiency, the present invention provides the following technical solution.

[0005] A method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter, comprising: Obtain the electricity consumption sequence of the historical electricity meter within a preset period, and segment the electricity consumption sequence to obtain multiple subsequences; All subsequences are clustered to obtain clustering results, where 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; 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, and an influence weight is assigned to each cluster according to the similarity. The transmission frequency reference value of each cluster and the corresponding influence weight are weighted 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 master station according to the transmission frequency of the data sequence to be transmitted.

[0006] Preferably, the sending of the data sequence to be transmitted to the master station according to the transmission frequency of the data sequence to be transmitted includes: A set of transmission time points corresponding to the data sequence to be transmitted is constructed according to the transmission frequency, and the data corresponding to the transmission time points are sent to the main station for processing through the electric energy meter communication module.

[0007] Preferably, the power consumption sequence is segmented according to a preset fixed rule, and the fixed rule is: the power consumption sequence is segmented into equal length segments at intervals of 30 minutes.

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

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

[0010] Preferably, the transmission frequency reference value satisfies the relationship: Where, For the The transmission frequency benchmark value of the clusters, The lower limit of the transmission frequency is set. Indicates the upper limit of the transmission frequency. Indicates the The fluctuation coefficient corresponding to the centroid of each cluster, For the The standard deviation of electricity consumption corresponding to the centroid of each cluster, Indicates the The maximum value of the total electricity consumption of the subsequences in the clusters, Indicates the The total mean electricity consumption of all subsequences in the cluster, 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 power consumption, are calculated, and the Euclidean distance between the two-dimensional features of the data sequence to be transmitted and the two-dimensional features corresponding to the centroid points of each cluster is calculated, and then the inverse of the Euclidean distance is used as the similarity between the data sequence to be transmitted and each cluster.

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

[0013] The beneficial effects of the present invention are: By analyzing historical electricity consumption data, dividing electricity consumption patterns into different clusters and setting a transmission frequency baseline value for each cluster, we can fully explore the patterns in historical data, avoid relying on fixed frequencies or empirical values, and make the transmission frequency more consistent with actual electricity consumption characteristics; further, by calculating the similarity between real-time transmission data sequences and historical clusters, the transmission frequency can be dynamically adjusted according to current electricity consumption behavior, while ensuring data transmission accuracy, minimizing energy consumption and achieving a balance between performance and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a method flow chart of steps S1 to S4 in a method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0016] Reference Figure 1 A method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter includes steps S1 to S4, which are specifically as follows: S1: Obtain a historical electricity consumption sequence of an electric energy meter within a preset period, and segment the electricity consumption sequence to obtain multiple subsequences.

[0017] In one embodiment, the electricity consumption data of a single electricity meter in a preset time period (such as the past three days) is extracted (the collection frequency in the embodiment of the present invention is once every half minute). If there is a missing data (such as no record at a certain time point), it is supplemented by linear interpolation (estimating the missing value based on the previous and subsequent data), and then the electricity consumption sequence is constructed based on the timestamp. The electricity consumption sequence is divided into equal length segments every 30 minutes according to a fixed rule, thereby obtaining multiple subsequences.

[0018] At the same time, a real-time sequence of data to be transmitted from the electric energy meter is obtained according to the above acquisition frequency.

[0019] S2: Cluster all subsequences to obtain clustering results, where 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; set a transmission frequency reference value for each cluster based on the clustering results.

[0020] In one embodiment, the fluctuation coefficient of each subsequence of S1 is calculated to quantify the power consumption fluctuation characteristics of each power consumption.

[0021] Exemplarily, the standard deviation and mean of the electricity consumption of any subsequence are calculated, and the ratio of the mean to the maximum electricity consumption in the subsequence is converted using an exponential function to obtain the local fluctuation severity of the subsequence, and finally the product of the local fluctuation severity and the standard deviation of the electricity consumption of the subsequence is used as the fluctuation coefficient of the subsequence.

[0022] Then the fluctuation coefficient of the above subsequence satisfies the relationship: Where, For the The volatility coefficient of the subsequence, For the The standard deviation of the electricity consumption of the subsequences, For the The average power consumption of the subsequences, For the The maximum power consumption of a subsequence, is an exponential function with base e.

[0023] Among them, the exponential function The transformation makes the result more sensitive to the relationship between the mean and the maximum power consumption. When the mean is close to the maximum power consumption, Close to 0, ; When the mean value is much smaller than the maximum power consumption, Larger, This will magnify the difference.

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

[0025] The fluctuation coefficients of all subsequences can be calculated similarly using the above method of calculating the fluctuation coefficient of a subsequence.

[0026] If the fluctuation coefficient of a subsequence is large, it means that the electricity consumption during the corresponding period of the subsequence fluctuates greatly and there is high electricity demand. For example, between 7 and 9 pm, household electrical appliances are used intensively, and the electricity consumption fluctuates greatly, so the fluctuation coefficient is high at this time.

[0027] If the fluctuation coefficient of a subsequence is medium, it means that the electricity consumption during the corresponding period of the subsequence is relatively stable, without significant fluctuations. For example, during daytime working hours, household electrical appliances are relatively fixed, and the electricity consumption fluctuation is small.

[0028] If the fluctuation coefficient of a subsequence is small, it means that the power consumption fluctuations during the corresponding period of the subsequence are small and the power demand is low. For example, between 3 and 5 a.m., most household electrical appliances are turned off, and the power consumption fluctuations are 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 the embodiment of the present invention, all the subsequences are divided into peak electricity consumption period, stable electricity consumption period and valley electricity consumption period by clustering.

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

[0031] After the above clustering, you can also calculate the silhouette coefficient to evaluate the clustering effect. If the silhouette coefficient is low, you can try different K values, calculate the average silhouette coefficient corresponding to each K, and select the K that maximizes the average silhouette coefficient as the optimal number of clusters.

[0032] In one embodiment, the transmission frequency requirements of each cluster under its power usage pattern are quantified based on its two-dimensional characteristics, thereby calculating a transmission frequency baseline value for each cluster. This baseline value serves as a "starting point" or "reference point" for subsequent real-time data transmission frequency calculations.

[0033] The calculation process of the above transmission frequency reference value is as follows: First, set the upper and lower limits of the transmission frequency. In this embodiment of the present 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 mean total power consumption of all subsequences within the cluster, the transmission frequency reference value of the cluster is calculated, which satisfies the following relationship: Where, For the The transmission frequency benchmark value of the clusters, The lower limit of the transmission frequency is set. Indicates the upper limit of the transmission frequency. Indicates the The fluctuation coefficient corresponding to the centroid of each cluster, For the The standard deviation of electricity consumption corresponding to the centroid of each cluster, Indicates the The maximum value of the total electricity consumption of the subsequences in the clusters, Indicates the The total mean electricity consumption of all subsequences in the cluster, Indicates normalization.

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

[0036] The calculation formula of the above reference value takes the upper and lower limits of the set reference value as boundaries and uses the normalization term Will Mapped to the interval [0, 1], and finally rounded up to get the integer frequency, ultimately ensuring the high demand cluster (large fluctuation, large difference in power consumption) near , giving priority to ensuring data accuracy, low-demand clusters (small fluctuations, stable power consumption) near , reducing 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, perform a weighted sum of the transmission frequency reference value of each cluster and the corresponding influence weight, and round it up to obtain the transmission frequency of the data sequence to be transmitted.

[0038] In one embodiment, the formula in S2 is used to calculate the power consumption sequence to be transmitted, obtaining the two-dimensional characteristics of the data sequence to be transmitted, namely the fluctuation coefficient and the standard deviation of the power consumption. The Euclidean distance between the two-dimensional characteristics of the data sequence to be transmitted and the two-dimensional characteristics corresponding to the centroid of each cluster is further calculated, and the reciprocal of the 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 reference 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 based on the similarity, that is, the relationship is satisfied: Where, For the The influence weight corresponding to each cluster is The data sequence to be transmitted and The similarity of the clusters.

[0040] The smaller it is, the higher the similarity is, so it affects the weight The larger the value, the greater the sum of all weights is 1.

[0041] Furthermore, the weighted average method is used to calculate the transmission frequency of the data sequence to be transmitted: Where, is the transmission frequency of the data sequence to be transmitted, For the The influence weight corresponding to each cluster is For the The transmission frequency benchmark value of each cluster.

[0042] By calculating the Euclidean distance between the data sequence to be transmitted and the center of each cluster, we can measure the similarity between the data to be transmitted and different electricity usage patterns. Based on this similarity, the transmission frequency is dynamically adjusted, so that important or representative data can be transmitted more frequently, while less important data is transmitted less frequently. This makes the transmission frequency setting more scientific and reasonable, thereby optimizing transmission resources.

[0043] S4: Sending the data sequence to be transmitted to the master station according to the transmission frequency of the data sequence to be transmitted.

[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. Simply put, the transmission frequency determines the time points at which data should be sent. For example, if the transmission frequency is once an hour, the set of transmission time points may be a fixed time point every hour.

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

[0046] It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the appended claims.

Claims

1. A method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter, characterized in that: include: Obtain the electricity consumption sequence of the historical electricity meter within a preset period, and segment the electricity consumption sequence to obtain multiple subsequences; All subsequences are clustered to obtain clustering results, where 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; 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, and an influence weight is assigned to each cluster according to the similarity. The transmission frequency reference value of each cluster and the corresponding influence weight are weighted 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 master station according to the transmission frequency of the data sequence to be transmitted.

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

3. The method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter according to claim 1, characterized in that: The power consumption sequence is segmented according to a preset fixed rule, wherein the fixed rule is: the power consumption sequence is segmented into equal length segments at intervals of 30 minutes.

4. The method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter according to claim 1, characterized in that: The process of obtaining the fluctuation coefficient includes: Calculate the standard deviation and mean of the electricity consumption of any subsequence. Use an exponential function to convert the ratio of the mean to the maximum electricity consumption in the subsequence to obtain the local fluctuation intensity of the subsequence. The product of the local fluctuation intensity and the standard deviation of the electricity consumption of the subsequence is used as the fluctuation coefficient of the subsequence.

5. The method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter according to claim 1, characterized in that: The clustering adopts K-means algorithm.

6. The method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter according to claim 1, characterized in that: The transmission frequency reference value satisfies the relationship: Where, For the The transmission frequency benchmark value of the clusters, The lower limit of the transmission frequency is set. Indicates the upper limit of the transmission frequency. Indicates the The fluctuation coefficient corresponding to the centroid of each cluster, For the The standard deviation of electricity consumption corresponding to the centroid of each cluster, Indicates the The maximum value of the total electricity consumption of the subsequences in the clusters, Indicates the The total mean electricity consumption of all subsequences in the cluster, Indicates normalization.

7. The method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter according to claim 1, characterized in that: The two-dimensional features of the data sequence to be transmitted, namely the fluctuation coefficient and the standard deviation of the power 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 centroid points of each cluster is calculated, and the inverse of the Euclidean distance is used as the similarity between the data sequence to be transmitted and each cluster.

8. The method for sensing electricity consumption information of a single-phase fee-controlled smart energy meter according to claim 1, characterized in that: The clustering adopts the DBSCAN clustering algorithm.

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