Data acquisition method of electricity utilization information acquisition terminal

Abstract

The invention relates to the technical field of data monitoring and processing, in particular to a data acquisition method of an electricity consumption information acquisition terminal. The method comprises the following steps: acquiring a time sequence data curve and a smooth curve; dividing all the time sequence data curves according to the clustering result of the extreme points of the smooth curve to obtain power consumption trend segmentation regions; calculating the fluctuation degree of the power consumption trend segmentation region according to the data on each time sequence data curve in the power consumption trend segmentation region, the standard deviation and the mean value, and calculating the ircorrelation degree of the power consumption trend segmentation region according to a straight line formed by connecting data points located on the boundaries on the two sides on each time sequence data curve in the same power consumption trend segmentation region; the power utilization complexity of the power utilization trend segmentation area is obtained based on the fluctuation degree and the ircorrelation degree, and the collection frequency of the power load data is adjusted; and acquiring power load data based on the adjusted acquisition frequency. According to the invention, the acquisition frequency of power data in different time periods can be adjusted, and the acquisition frequency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring and processing, and in particular to a data collection method of an electricity consumption information collection terminal. Background Art

[0002] The electricity consumption information collection terminal can achieve comprehensive coverage of all electricity users and checkpoints, realize online monitoring of metering devices and real-time collection of important information such as user load, power consumption, voltage, etc., provide basic data for related systems in a timely, complete and accurate manner, provide support for analysis and decision-making in all aspects of enterprise operation and management, and provide an information basis for realizing intelligent two-way interactive services.

[0003] The electricity consumption information collection terminal is a device that collects electricity consumption information at each information collection point at a fixed collection frequency. It is referred to as the collection terminal and can realize the collection, data management, two-way data transmission, and forwarding or execution of control commands of electric energy meter data.

[0004] Traditionally, when collecting electricity consumption information at each information collection point (this solution takes the power load data of the electricity meter as an example), it is often collected at fixed time intervals. However, this fixed time interval collection method cannot capture the changes in electricity load during complex periods of electricity consumption, and it is difficult to discover potential electricity consumption problems or abnormal phenomena. As a result, the subsequent management platform often has low reference value when performing electricity consumption analysis and trend prediction on the collected data. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a data collection method for an electricity consumption information collection terminal, and the technical solution adopted is as follows: An embodiment of the present invention provides a data collection method of an electricity consumption information collection terminal, the method comprising: Obtain the power load data of the preset number of days in history, and the power data of each day forms a time series data curve; smooth each time series data curve to obtain a smooth curve; Cluster the extreme points of each smooth curve to obtain clusters; obtain the centroid of the convex hull of each cluster as the center point of the cluster; divide all time series data curves in a coordinate system according to the center point to obtain the power consumption trend segmentation area; Calculate the fluctuation degree of the power consumption trend segmentation area according to the data, standard deviation and mean on each time series data curve in the power consumption trend segmentation area; The degree of irrelevance of the power consumption trend segmentation area is calculated according to the slope and intercept of the straight line formed by connecting the data points located at the two side boundaries of each time series data curve in the same power consumption trend segmentation area; The power consumption complexity of the power consumption trend segmentation area is obtained by weighted summing up the fluctuation degree and the irrelevance degree of the power consumption trend segmentation area; the collection frequency of power load data of the time period of the day corresponding to the power consumption trend segmentation area is adjusted according to the power consumption complexity of the power consumption trend segmentation area; and the power load data is collected based on the adjusted collection frequency.

[0006] Preferably, smoothing each time series data curve to obtain a smooth curve includes: The time series data curve is smoothed by using the SG filter to obtain the smooth curve corresponding to each time series data curve.

[0007] Preferably, all time series data curves are divided in a coordinate system according to the center point to obtain the power consumption trend segmentation area, including: A straight line perpendicular to the horizontal axis is drawn through the horizontal coordinate of each center point to obtain a straight line corresponding to each center point, which is recorded as a dividing line; the dividing line is used to divide each time series data curve in the coordinate system to obtain the electricity consumption trend segmentation area.

[0008] Preferably, the calculation formula for the fluctuation degree of the electricity consumption trend segmentation area is specifically: , in, It indicates the fluctuation degree in the i-th electricity consumption trend segmentation area; I indicates the number of time series data curves; Represents the standard deviation of the data on the jth time series data curve in the i-th electricity consumption trend segmentation area; represents the mean value of the data on the jth time series data curve in the i-th power consumption trend segmentation area; M represents the number of data on the jth time series data curve in the i-th power consumption trend segmentation area; It represents the slope between the m-1th point and the mth point on the jth time series data curve in the i-th electricity consumption trend segmentation area. It represents the slope between the m+1th point and the mth point on the jth time series data curve in the i-th electricity consumption trend segmentation area; norm() represents the normalization operation.

[0009] Preferably, the degree of irrelevance of the power consumption trend segmentation area is calculated according to the slope and intercept of a straight line connecting data points located at both side boundaries on each time series data curve in the same power consumption trend segmentation area, including: Obtain the slope of a straight line formed by connecting two data points located at the two side boundaries of each time series data curve in a power consumption trend segmentation area, and calculate the average value of the slope to obtain the standard straight line slope of the power consumption trend segmentation area; Obtain the intercept of a straight line connecting two data points located at the two side boundaries of each time series data curve in a power consumption trend segmentation area, and calculate the average value of the intercepts to obtain the standard straight line intercept of the power consumption trend segmentation area; The standard straight line of a power consumption trend segmentation area is constructed by using the standard straight line slope and standard straight line intercept corresponding to the power consumption trend segmentation area; and the degree of irrelevance of the power consumption trend segmentation area is calculated based on the standard straight line slope, standard straight line intercept and standard straight line.

[0010] Preferably, the calculation formula for the degree of irrelevance of the power consumption trend segmentation area is specifically: , in, It indicates the degree of irrelevance of the i-th electricity consumption trend segmentation area; I indicates the number of time series data curves; It represents the slope of the straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area; Represents the standard straight line slope of the ith electricity consumption trend segmentation area; It represents the intercept of the straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area; represents the standard straight line intercept of the ith electricity consumption trend segmentation area; It represents a straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area; represents the standard straight line constructed by using the standard straight line slope and standard straight line intercept of the i-th electricity consumption trend segmentation area; It represents the Pearson correlation coefficient between the straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area and the standard straight line of the i-th electricity consumption trend segmentation area; norm( ) represents the normalization operation.

[0011] Preferably, the power consumption complexity of the power consumption trend segmented area is obtained by weighted summing the fluctuation degree and the irrelevance degree of the power consumption trend segmented area, including: A first weight corresponding to the fluctuation degree and a second weight corresponding to the irrelevance degree are set; and the first weight and the second weight are used to perform weighted summation of the fluctuation degree and the irrelevance degree of the power consumption trend segmentation area to obtain the power consumption complexity of the power consumption trend segmentation area.

[0012] Preferably, the frequency of collecting power load data of a time period in a day corresponding to a power consumption trend segmentation area is adjusted according to the power consumption complexity of the power consumption trend segmentation area, including: Obtain an initial fixed acquisition frequency, and set a first threshold and a second threshold, wherein the first threshold is less than the second threshold; if the power consumption complexity of the power consumption trend segmentation area is less than or equal to the first threshold, reduce the initial fixed acquisition frequency of the time period of the day corresponding to the power consumption trend segmentation area; if the power consumption complexity of the power consumption trend segmentation area is greater than the first threshold and less than the second threshold, the initial fixed acquisition frequency of the time period of the day corresponding to the power consumption trend segmentation area remains unchanged; if the power consumption complexity of the power consumption trend segmentation area is greater than or equal to the second threshold, increase the initial fixed acquisition frequency of the time period of the day corresponding to the power consumption trend segmentation area; the increase or decrease value of the initial fixed acquisition frequency is the same.

[0013] The embodiments of the present invention have at least the following beneficial effects: the present invention collects power load data of a preset number of days in history, organizes the daily power data into a time series data curve, and then smoothes it to obtain a smooth curve, and then clusters the extreme points in the smooth curve, and divides the time series data curve in the coordinate system by the centroid of the convex hull of each cluster cluster to obtain a power consumption trend segmentation area, which can reasonably divide a day into multiple time periods in combination with the user's power consumption, and then adjust the data collection frequency of each time period; then obtain the fluctuation degree and irrelevance degree of the power consumption trend segmentation area, and combine the fluctuation degree and irrelevance degree of the power consumption trend segmentation area to obtain the power consumption complexity of the power consumption trend segmentation area, which can improve the accuracy of the complexity calculation of each power consumption trend segmentation area; finally, use the power consumption complexity of the power consumption trend segmentation area to adjust the collection frequency of the power load data of the time period of the day corresponding to the power consumption trend segmentation area, and use the adjusted collection frequency to collect charge data, so that the data collection is more reasonable and can meet the needs of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A method flow chart of a data collection method of an electricity consumption information collection terminal provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the data collection method of a power consumption information collection terminal proposed by the present invention, its specific implementation method, structure, features and effects are described in detail as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] The following is a detailed description of a data collection method for an electricity consumption information collection terminal provided by the present invention in conjunction with the accompanying drawings.

[0019] The main application scenario of the present invention is: when using an electricity consumption information collection terminal to collect electricity information data, the existing commonly used method usually uses a fixed collection frequency for collection, which fails to pay attention to the time period that requires more detailed data, making potential electricity consumption problems difficult to detect. Therefore, it is necessary to adjust the collection frequency according to the electricity consumption characteristics of a power consumption area so that the collected data can more clearly reflect the electricity consumption information.

[0020] See also Figure 1 , which shows a method flow chart of a data collection method of a power consumption information collection terminal provided by an embodiment of the present invention, the method comprising the following steps: Step S1, obtaining the power load data of a preset number of days in history, and the power data of each day constitutes a time series data curve; smoothing each time series data curve to obtain a smooth curve.

[0021] First of all, since it is necessary to analyze the historical smooth curve to obtain the residential electricity consumption trend and divide the area, and then adjust the collection frequency of each area according to the complexity of electricity consumption in each area, it is necessary to obtain multiple days of historical data for subsequent analysis. Specifically, the power load data of a preset number of days in history is obtained. The daily power data constitutes a time series data curve, and the number of days for collecting historical data can be adjusted by the implementer according to the actual situation.

[0022] When acquiring the time series data of multiple power loads in history, it should be noted that the time series data of multiple power loads in history here represent the same subject, such as the same community or the same office area, etc. This reflects that the trends of these multiple historical power consumption data are basically similar in theory, which provides the basic conditions for the subsequent acquisition of the segmented area of ​​power consumption trends. The power consumption rules of the same subject generally do not change much. Since it is necessary to obtain the segmented area of ​​residential power consumption trends, that is, to obtain the turning points of large fluctuations of each curve, and the slight fluctuations caused by the small changes in power load in the original curve affect the acquisition of the turning points (extreme points) of large fluctuations, it is necessary to smooth the acquired time series data curve.

[0023] Specifically, the SG filter is used to smooth the time series data curve to obtain the smooth curve corresponding to each time series data curve. Here, it is assumed that I smooth curves are obtained. In addition, when obtaining historical data, the historical data is obtained by pushing forward from the current moment. At the same time, the number of smooth curves needs to be determined by the implementer according to the actual situation. For the convenience of subsequent analysis, each time series data curve is placed in a coordinate system.

[0024] Step S2, clustering the extreme points of each smooth curve to obtain clusters; obtaining the centroid of the convex hull of each cluster as the center point of the cluster; and dividing all time series data curves in a coordinate system according to the center point to obtain power consumption trend segmentation areas.

[0025] Since users' electricity consumption has a certain regularity, if the complexity is directly analyzed without trend segmentation of the time series data curve, the calculation of the complexity of the curve will be affected by the trend, making the calculation of the complexity not accurate enough. Therefore, trend segmentation of the time series data curve is helpful to analyze the complexity of electricity consumption in each trend segmentation area. After trend segmentation of the time series data curve, different collection frequencies can be configured for the complexity of electricity consumption in different time periods. In the time interval with high complexity of electricity consumption, if the collection frequency is too low, it may cause data loss or incomplete data collection, and thus fail to accurately reflect the actual changes in the load. At this time, setting a higher collection frequency can more accurately capture the fluctuation of the load, which is helpful to discover potential electricity problems or abnormal phenomena. Therefore, it is necessary to divide the time series data curve in time and further analyze each divided area.

[0026] The extreme points of the smooth curve of each power load are obtained, and the trend segmentation areas of multiple time series data curves are obtained according to the clustering results of the extreme points. Since different acquisition frequencies can be set for the power load characteristics of different regions after segmentation according to the extreme points, and this processing can also avoid data loss and retain the basic power consumption characteristic trends in the scenario, it is necessary to obtain the trend segmentation area; since the smoothed power load curve reflects the overall trend of the curve, the acquisition of the extreme points of the curve is to obtain the nodes where the overall trend of the curve changes, so the acquisition of the extreme points of the curve can obtain the approximate segmentation results of multiple trend areas of the curve; due to the regularity of user electricity consumption, on the smooth curves of multiple historical power loads, the extreme points are distributed more concentratedly, but not completely uniformly, so it is necessary to cluster the obtained extreme points, and use the cluster center point as the segmentation cutoff to divide multiple time series data curves into multiple power consumption trend segmentation areas.

[0027] First, for the jth point on the smooth curve of the i-th power load , and compare it with the left and right points Product of slopes The point is taken as the extreme point of the smooth curve, where is the slope between the j-1th point and the jth point, is the slope between the jth point and the j+1th point, and so on, we can get all the extreme points on I smooth curves.

[0028] Next, perform DBASAN clustering on the extreme points of the I smooth curves. DBASAN is a density-based spatial clustering algorithm that forms clusters by continuously expanding the set of density-reachable points based on density accessibility. Assuming that K clusters are obtained through DBASAN clustering, the steps for obtaining the center points of these K clusters are as follows: For the kth cluster, obtain the convex hull of the cluster. The convex hull is the smallest convex polygon that contains all the points in the cluster, and then use the centroid of the convex hull as the center point of the kth cluster. The convex hull can be obtained by the convex hull algorithm.

[0029] Set the two-dimensional coordinates of the center point of the kth cluster to , then the coordinates of each center point in the K clusters are , draw a straight line perpendicular to the horizontal axis through the horizontal coordinate of each center point, and obtain the straight line corresponding to each center point, which is recorded as the dividing line; use the dividing line to divide each time series data curve into K+1 regions in the coordinate system, each region is a power consumption trend segmentation region, and each power consumption trend segmentation region includes a part of I time series data curves, and the time periods corresponding to the parts are the same. Here, the time series data curve is divided based on the extreme points obtained by the smooth curve and then analyzed in order to retain the characteristics of the original data.

[0030] Step S3, calculating the fluctuation degree of the power consumption trend segmentation area according to the data, standard deviation and mean on each time series data curve in the power consumption trend segmentation area.

[0031] Since the trends of multiple time series data curves contained in each power consumption trend segmentation area are generally consistent, if these multiple time series data curves have a large degree of fluctuation, it means that the power consumption trend segmentation area where these multiple smooth curves are located is more complex. The fluctuation degree of the time series data curve reflects the instability of the power load change. The greater the fluctuation degree, the more unstable the power load change. Analysis of the fluctuation of the time series data curve in the power consumption trend segmentation area can indicate the complexity of the charge change in the time period corresponding to the power consumption trend segmentation area. The greater the fluctuation, the higher the complexity of the power consumption, the more power consumption data needs to be collected, and the collection frequency needs to be increased.

[0032] Considering that the slight fluctuation of power load is also valuable for the calculation of power consumption complexity, this application obtains the segmentation line through the smooth curve, and then segments the time series data curve for further analysis. The fluctuation degree of the power consumption trend segmentation area is calculated according to the data, standard deviation and mean on each time series data curve in the power consumption trend segmentation area. The specific calculation formula is: , in, It indicates the fluctuation degree in the i-th electricity consumption trend segmentation area; I indicates the number of time series data curves; Represents the standard deviation of the data on the jth time series data curve in the i-th electricity consumption trend segmentation area; represents the mean value of the data on the jth time series data curve in the i-th power consumption trend segmentation area; M represents the number of data on the jth time series data curve in the i-th power consumption trend segmentation area; It represents the slope between the m-1th point and the mth point on the jth time series data curve in the i-th electricity consumption trend segmentation area. It represents the slope between the m+1th point and the mth point on the jth time series data curve in the i-th electricity consumption trend segmentation area; norm() represents the normalization operation.

[0033] In the above formula, It represents the coefficient of variation of the jth time series data curve in the i-th trend segmentation area. The larger the value, the greater the discrete degree of the power load on the curve. The difference between the slope of a data point and the data points on its left and right sides. The larger the absolute value of the difference between the two, the greater the degree of change in the slope of the curve, indicating that the change in the power data on the curve is more complex. From this, we can get the degree of fluctuation of each power consumption trend segmentation area. The greater the degree of fluctuation, the more complex the data change, the more attention needs to be paid, and more data needs to be collected for analysis.

[0034] Step S4, calculating the degree of irrelevance of the power consumption trend segmentation area according to the slope and intercept of the straight line connecting the data points located at the two side boundaries of each time series data curve in the same power consumption trend segmentation area.

[0035] In an electricity consumption trend segmentation area, there may be a situation where multiple time series data curves have large fluctuations, so it is necessary to calculate the degree of independence of multiple curves, which is reflected in the change of charge in the time period corresponding to the electricity consumption trend segmentation area. The higher the degree of independence of multiple time series data curves in an electricity consumption trend segmentation area, the more complex the load changes of these multiple time series change curves in the electricity consumption trend segmentation area. Therefore, it is necessary to comprehensively consider the fluctuation degree and independence degree of the time series data curves in the electricity consumption trend segmentation area to obtain the electricity consumption complexity of each electricity consumption trend segmentation area.

[0036] Furthermore, the degree of uncorrelation of the power consumption trend segmentation area is calculated according to the slope and intercept of the straight line connecting the data points at the left and right ends of each time series data curve in the same power consumption trend segmentation area. Specifically, the slope of the straight line connecting the two data points located at the two side boundaries on each time series data curve in a power consumption trend segmentation area is obtained, and the average value of the slope is calculated to obtain the standard straight line slope of the power consumption trend segmentation area. , the calculation formula is: , in, Represents the slope of the straight line connecting the two data points located on the two side boundaries of the jth time series data curve of the i-th power consumption trend segmentation area.

[0037] Furthermore, the intercept of the straight line connecting the two data points located at the two side boundaries of each time series data curve in a power consumption trend segmentation area is obtained, and the average value of the intercept is calculated to obtain the standard straight line intercept of the power consumption trend segmentation area. , the calculation formula is: , in, Represents the intercept of the straight line connecting the two data points located on both sides of the j-th time series data curve of the i-th power consumption trend segmentation area.

[0038] Furthermore, a standard straight line corresponding to a power consumption trend segmentation area is used to construct a standard straight line for the power consumption trend segmentation area; based on the standard straight line slope, the standard straight line intercept and the standard straight line, the irrelevance degree of the power consumption trend segmentation area is calculated, and the calculation formula of the irrelevance degree is: , in, It indicates the degree of irrelevance of the i-th electricity consumption trend segmentation area; I indicates the number of time series data curves; It represents the slope of the straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area; Represents the standard straight line slope of the ith electricity consumption trend segmentation area; It represents the intercept of the straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area; represents the standard straight line intercept of the ith electricity consumption trend segmentation area; It represents a straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area; represents the standard straight line constructed by using the standard straight line slope and standard straight line intercept of the i-th electricity consumption trend segmentation area; It represents the Pearson correlation coefficient between the straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area and the standard straight line of the i-th electricity consumption trend segmentation area; norm( ) represents the normalization operation.

[0039] In the above formula, It represents the absolute value of the difference between the slope of the straight line corresponding to the time series data curve of the ith trend segmentation area and the slope of the standard straight line. It represents the absolute value of the difference between the intercept of the straight line corresponding to the j-th time series data curve in the i-th trend segmentation area and the intercept of the standard straight line. The larger the two items are, the greater the degree to which the j-th time series curve in the trend segmentation area deviates from the standard curve; The smaller the value, the lower the correlation between the two straight lines, and the higher the degree of irrelevance between the time series data curve in the power consumption trend segmentation area and the standard curve. In this way, the correlation between the time series data curves in the power consumption trend segmentation area can be obtained. The less relevant, the more complex the power consumption changes in the power consumption trend segmentation area, and more data are needed for analysis.

[0040] Step S5, taking the weighted sum of the fluctuation degree and the irrelevance degree of the power consumption trend segmentation area to obtain the power consumption complexity of the power consumption trend segmentation area; adjusting the collection frequency of the power load data of the time period of the day corresponding to the power consumption trend segmentation area according to the power consumption complexity of the power consumption trend segmentation area; and collecting the power load data based on the adjusted collection frequency.

[0041] After obtaining the fluctuation degree and irrelevance degree of the power consumption trend segmentation area in steps S3 and S4, it is necessary to perform a weighted summation on the two, set the first weight and the second weight respectively, and use the first weight and the second weight to perform a weighted summation on the fluctuation degree and irrelevance degree of the power consumption trend segmentation area to obtain the power consumption complexity of the power consumption trend segmentation area.

[0042] The specific calculation formula is: , in, is the complexity of electricity consumption in the i-th electricity consumption trend segmentation area, is the fluctuation degree of the curve in the i-th electricity consumption trend segmentation area, is the degree of irrelevance of the curves in the i-th electricity consumption trend segmentation area, and Denote the first weight and the second weight respectively. Since the fluctuation degree and irrelevance degree of the ith electricity consumption trend segmentation area are equally important for the calculation of the regional electricity consumption complexity, , the implementer can adjust the first weight and the second weight according to actual conditions.

[0043] Since a high level of electricity complexity means that the load changes during this period are more drastic, the corresponding collection frequency should be increased at this time. This is because high-frequency collection can more accurately capture the fluctuations in power load and help discover potential electricity problems or abnormal phenomena. If the collection frequency is too low during this period, it may cause data loss or fail to reflect the actual changes in the load; when the electricity complexity is low, the corresponding collection frequency should be reduced to reduce the corresponding waste of resources; therefore, it is necessary to adjust the collection frequency of each area according to the electricity complexity of each trend segmentation area.

[0044] Obtain an initial fixed acquisition frequency, and set a first threshold and a second threshold, wherein the first threshold is less than the second threshold; if the power consumption complexity of the power consumption trend segmentation area is less than or equal to the first threshold, reduce the initial fixed acquisition frequency of the time period of the day corresponding to the power consumption trend segmentation area; if the power consumption complexity of the power consumption trend segmentation area is greater than the first threshold and less than the second threshold, the initial fixed acquisition frequency of the time period of the day corresponding to the power consumption trend segmentation area remains unchanged; if the power consumption complexity of the power consumption trend segmentation area is greater than or equal to the second threshold, increase the initial fixed acquisition frequency of the time period of the day corresponding to the power consumption trend segmentation area; the increase or decrease value of the initial fixed acquisition frequency is the same.

[0045] Preferably, in an embodiment of the present invention, the values ​​of the first threshold and the second threshold are 0.3 and 0.6 respectively. The implementer can adjust the first threshold and the second threshold according to actual conditions, and the increase or decrease value of the initial fixed collection frequency needs to be determined by the implementer according to actual conditions. In this way, the collection frequency of the time period of the day corresponding to each electricity consumption trend segmentation area is adjusted according to the complexity of electricity consumption, and then the power load data is collected according to the adjusted collection frequency.

[0046] This solution analyzes the power load data collected historically to obtain the complexity of electricity consumption in each electricity consumption trend segmented area of ​​residents, and adjusts the collection frequency of each segmented area according to the complexity of electricity consumption in each electricity consumption trend segmented area. When the regional electricity consumption complexity is high, the collection frequency is appropriately increased, and when the electricity consumption complexity is low, the collection frequency is appropriately reduced. In this way, the adjustment of the collection frequency according to the complexity of electricity consumption can not only more accurately capture the fluctuation of power load, but also help to discover potential electricity consumption problems or abnormal phenomena, and reduce the waste of resources when collecting during stable electricity consumption periods, thereby providing a higher utilization value for subsequent load management, line loss analysis, and electricity consumption monitoring based on electricity consumption information.

[0047] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0048] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0049] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data collection method for an electricity consumption information collection terminal, characterized in that: The method includes: Obtain the power load data of the preset number of days in history, and the power data of each day forms a time series data curve; smooth each time series data curve to obtain a smooth curve; Cluster the extreme points of each smooth curve to obtain clusters; obtain the centroid of the convex hull of each cluster as the center point of the cluster; divide all time series data curves in a coordinate system according to the center point to obtain the power consumption trend segmentation area; Calculate the fluctuation degree of the power consumption trend segmentation area according to the data, standard deviation and mean on each time series data curve in the power consumption trend segmentation area; The degree of irrelevance of the power consumption trend segmentation area is calculated according to the slope and intercept of the straight line formed by connecting the data points located at the two side boundaries of each time series data curve in the same power consumption trend segmentation area; The power consumption complexity of the power consumption trend segmentation area is obtained by weighted summing up the fluctuation degree and the irrelevance degree of the power consumption trend segmentation area; the collection frequency of power load data of the time period of the day corresponding to the power consumption trend segmentation area is adjusted according to the power consumption complexity of the power consumption trend segmentation area; and the power load data is collected based on the adjusted collection frequency.

2. The data collection method of the power consumption information collection terminal according to claim 1, characterized in that: The step of smoothing each time series data curve to obtain a smooth curve includes: The time series data curve is smoothed by using the SG filter to obtain the smooth curve corresponding to each time series data curve.

3. The data collection method of the power consumption information collection terminal according to claim 1, characterized in that: The method of dividing all time series data curves in a coordinate system according to the center point to obtain the power consumption trend segmentation area includes: A straight line perpendicular to the horizontal axis is drawn through the horizontal coordinate of each center point to obtain a straight line corresponding to each center point, which is recorded as a dividing line; the dividing line is used to divide each time series data curve in the coordinate system to obtain the electricity consumption trend segmentation area.

4. The data collection method of the power consumption information collection terminal according to claim 1, characterized in that: The calculation formula for the fluctuation degree of the power consumption trend segmentation area is specifically: ， in, It indicates the fluctuation degree in the i-th electricity consumption trend segmentation area; I indicates the number of time series data curves; Represents the standard deviation of the data on the jth time series data curve in the i-th electricity consumption trend segmentation area; represents the mean value of the data on the jth time series data curve in the i-th power consumption trend segmentation area; M represents the number of data on the jth time series data curve in the i-th power consumption trend segmentation area; It represents the slope between the m-1th point and the mth point on the jth time series data curve in the i-th electricity consumption trend segmentation area. It represents the slope between the m+1th point and the mth point on the jth time series data curve in the i-th electricity consumption trend segmentation area; norm() represents the normalization operation.

5. The data collection method of the power consumption information collection terminal according to claim 1, characterized in that: The method of calculating the degree of irrelevance of the power consumption trend segmentation area according to the slope and intercept of a straight line formed by connecting data points located at both side boundaries of each time series data curve in the same power consumption trend segmentation area includes: Obtain the slope of a straight line formed by connecting two data points located at the two side boundaries of each time series data curve in a power consumption trend segmentation area, and calculate the average value of the slope to obtain the standard straight line slope of the power consumption trend segmentation area; Obtain the intercept of a straight line connecting two data points located at the two side boundaries of each time series data curve in a power consumption trend segmentation area, and calculate the average value of the intercepts to obtain the standard straight line intercept of the power consumption trend segmentation area; The standard straight line of a power consumption trend segmentation area is constructed by using the standard straight line slope and standard straight line intercept corresponding to the power consumption trend segmentation area; and the degree of irrelevance of the power consumption trend segmentation area is calculated based on the standard straight line slope, standard straight line intercept and standard straight line.

6. The data collection method of the power consumption information collection terminal according to claim 5, characterized in that: The specific calculation formula of the irrelevance degree of the power consumption trend segmentation area is: ， in, It indicates the degree of irrelevance of the i-th electricity consumption trend segmentation area; I indicates the number of time series data curves; It represents the slope of the straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area; Represents the standard straight line slope of the ith electricity consumption trend segmentation area; It represents the intercept of the straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area; represents the standard straight line intercept of the ith electricity consumption trend segmentation area; It represents a straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area; represents the standard straight line constructed by using the standard straight line slope and standard straight line intercept of the i-th electricity consumption trend segmentation area; It represents the Pearson correlation coefficient between the straight line connecting the data points on both sides of the j-th time series data curve in the i-th electricity consumption trend segmentation area and the standard straight line of the i-th electricity consumption trend segmentation area; norm( ) represents the normalization operation.

7. The data collection method of the power consumption information collection terminal according to claim 1, characterized in that: The weighted sum of the fluctuation degree and the irrelevance degree of the power consumption trend segmentation area to obtain the power consumption complexity of the power consumption trend segmentation area includes: A first weight corresponding to the fluctuation degree and a second weight corresponding to the irrelevance degree are set; and the first weight and the second weight are used to perform weighted summation of the fluctuation degree and the irrelevance degree of the power consumption trend segmentation area to obtain the power consumption complexity of the power consumption trend segmentation area.

8. The data collection method of the power consumption information collection terminal according to claim 1, characterized in that: The step of adjusting the frequency of collecting power load data of a time period in a day corresponding to a power consumption trend segmentation area according to the power consumption complexity of the power consumption trend segmentation area includes: Obtain an initial fixed acquisition frequency, and set a first threshold and a second threshold, wherein the first threshold is less than the second threshold; if the power consumption complexity of the power consumption trend segmentation area is less than or equal to the first threshold, reduce the initial fixed acquisition frequency of the time period of the day corresponding to the power consumption trend segmentation area; if the power consumption complexity of the power consumption trend segmentation area is greater than the first threshold and less than the second threshold, the initial fixed acquisition frequency of the time period of the day corresponding to the power consumption trend segmentation area remains unchanged; if the power consumption complexity of the power consumption trend segmentation area is greater than or equal to the second threshold, increase the initial fixed acquisition frequency of the time period of the day corresponding to the power consumption trend segmentation area; the increase or decrease value of the initial fixed acquisition frequency is the same.

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