A data acquisition method for an electricity consumption information acquisition terminal

Abstract

The present invention relates to the technical field of data monitoring and processing, and particularly relates to a data acquisition method for an electricity consumption information acquisition terminal. The method includes: obtaining a time series data curve and a smoothed curve; dividing all the time series data curves according to the clustering result of the extreme points of the smoothed curve to obtain an electricity consumption trend segmentation region; calculating the fluctuation degree of the electricity consumption trend segmentation region according to the data, standard deviation and mean value on each time series data curve in the electricity consumption trend segmentation region, calculating the irrelevance degree of the electricity consumption trend segmentation region according to the straight line formed by connecting the data points located on both sides of the boundary on each time series data curve in the same electricity consumption trend segmentation region, and further obtaining the electricity consumption complexity of the electricity consumption trend segmentation region based on the fluctuation degree and the irrelevance degree, and then adjusting the acquisition frequency of the power load data; acquiring the power load data based on the adjusted acquisition frequency. The present invention can adjust the acquisition frequency of power data in different time periods and improve its acquisition frequency.

Description

Technical Field

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

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

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

[0004] When traditionally acquiring the electricity consumption information of each information acquisition point (taking the electric energy meter power load data as an example in this solution), it is often acquired at fixed time intervals, and this fixed time interval acquisition method cannot capture the changes in electricity load during complex electricity consumption periods, and it is difficult to detect potential electricity problems or abnormal phenomena, resulting in relatively low reference value when the subsequent management platform conducts electricity consumption analysis and trend prediction on the acquired 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 acquisition method for an electricity consumption information acquisition terminal, and the specific technical solution adopted is as follows:

[0006] An embodiment of the present invention provides a data acquisition method for an electricity consumption information acquisition terminal, and the method includes:

[0007] Obtain the power load data of a 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 smoothed curve;

[0008] Cluster the extreme points of each smoothed curve to obtain a clustering cluster; obtain the centroid of the convex hull of each clustering cluster as the center point of the clustering cluster; divide all time series data curves in a coordinate system according to the center point to obtain an electricity consumption trend segmentation area;

[0009] Calculate the fluctuation degree of the electricity consumption trend segmentation area according to the data, standard deviation, and mean value on each time series data curve in the electricity consumption trend segmentation area;

[0010] Calculate the irrelevance degree of the power consumption trend segmentation region according to the slopes and intercepts of the straight lines formed by connecting the data points on both sides of each time series data curve in the region with the same power consumption trend;

[0011] Weighted sum the fluctuation degree and the irrelevance degree of the power consumption trend segmentation region to obtain the power consumption complexity of the power consumption trend segmentation region; adjust the acquisition frequency of the power load data for the time period corresponding to the power consumption trend segmentation region according to the power consumption complexity of the power consumption trend segmentation region; collect the power load data based on the adjusted acquisition frequency.

[0012] Preferably, smoothing each time series data curve to obtain a smoothed curve, including:

[0013] Use the S-G filter to smooth the time series data curve to obtain the smoothed curve corresponding to each time series data curve.

[0014] Preferably, divide all time series data curves according to the center points in a coordinate system to obtain the power consumption trend segmentation region, including:

[0015] Draw a straight line perpendicular to the horizontal axis through the abscissa of each center point to obtain the straight line corresponding to each center point, denoted as the segmentation line; use the segmentation line to divide each time series data curve in the coordinate system to obtain the power consumption trend segmentation region.

[0016] Preferably, the specific formula for calculating the fluctuation degree of the power consumption trend segmentation region is:

[0017] ,

[0018] Among them, represents the fluctuation degree within the i-th power consumption trend segmentation region; I represents the number of time series data curves; represents the standard deviation of the data on the j-th time series data curve within the i-th power consumption trend segmentation region; represents the mean value of the data on the j-th time series data curve within the i-th power consumption trend segmentation region; M represents the number of data on the j-th time series data curve within the i-th power consumption trend segmentation region; represents the slope of the (m - 1)-th point and the m-th point on the j-th time series data curve within the i-th power consumption trend segmentation region, represents the slope of the (m + 1)-th point and the m-th point on the j-th time series data curve within the i-th power consumption trend segmentation region; norm( ) represents the normalization operation.

[0019] Preferably, calculate the irrelevance degree of the power consumption trend segmentation region according to the slopes and intercepts of the straight lines formed by connecting the data points on both sides of each time series data curve in the region with the same power consumption trend, including:

[0020] Obtain the slope of the straight line formed by connecting two data points on each time series data curve at the two side boundaries in an electricity consumption trend segmentation region, and calculate the average value of the slopes to obtain the standard straight line slope of this electricity consumption trend segmentation region;

[0021] Obtain the intercept of the straight line formed by connecting two data points on each time series data curve at the two side boundaries in an electricity consumption trend segmentation region, and calculate the average value of the intercepts to obtain the standard straight line intercept of this electricity consumption trend segmentation region;

[0022] Construct the standard straight line of an electricity consumption trend segmentation region by using the standard straight line slope and the standard straight line intercept corresponding to this electricity consumption trend segmentation region; Calculate the irrelevance degree of the electricity consumption trend segmentation region based on the standard straight line slope, the standard straight line intercept and the standard straight line.

[0023] Preferably, the specific calculation formula for the irrelevance degree of the electricity consumption trend segmentation region is:

[0024] ,

[0025] where, represents the irrelevance degree of the i-th electricity consumption trend segmentation region; I represents the number of time series data curves; represents the slope of the straight line formed by connecting the data points on the j-th time series data curve at the two side boundaries in the i-th electricity consumption trend segmentation region; represents the standard straight line slope of the i-th electricity consumption trend segmentation region; represents the intercept of the straight line formed by connecting the data points on the j-th time series data curve at the two side boundaries in the i-th electricity consumption trend segmentation region; represents the standard straight line intercept of the i-th electricity consumption trend segmentation region; represents the straight line formed by connecting the data points on the j-th time series data curve at the two side boundaries in the i-th electricity consumption trend segmentation region; represents the standard straight line constructed by using the standard straight line slope and the standard straight line intercept of the i-th electricity consumption trend segmentation region; represents the Pearson correlation coefficient between the straight line formed by connecting the data points on the j-th time series data curve at the two side boundaries in the i-th electricity consumption trend segmentation region and the standard straight line of the i-th electricity consumption trend segmentation region; norm( ) represents the normalization operation.

[0026] Preferably, the weighted sum of the fluctuation degree and the irrelevance degree of the electricity consumption trend segmentation region is used to obtain the electricity consumption complexity of the electricity consumption trend segmentation region, including:

[0027] Set a first weight corresponding to the degree of fluctuation and a second weight corresponding to the degree of irrelevance; use the first weight and the second weight to perform a weighted sum on the degree of fluctuation and the degree of irrelevance of the power consumption trend segmentation region to obtain the power consumption complexity of the power consumption trend segmentation region.

[0028] Preferably, adjust the acquisition frequency of the power load data of the time period in a day corresponding to the power consumption trend segmentation region according to the power consumption complexity of the power consumption trend segmentation region, including:

[0029] Obtain an initial fixed acquisition frequency, and set a first threshold and a second threshold, where the first threshold is less than the second threshold; if the power consumption complexity of the power consumption trend segmentation region is less than or equal to the first threshold, then reduce the initial fixed acquisition frequency of the time period in a day corresponding to the power consumption trend segmentation region; if the power consumption complexity of the power consumption trend segmentation region is greater than the first threshold and less than the second threshold, then the initial fixed acquisition frequency of the time period in a day corresponding to the power consumption trend segmentation region remains unchanged; if the power consumption complexity of the power consumption trend segmentation region is greater than or equal to the second threshold, then increase the initial fixed acquisition frequency of the time period in a day corresponding to the power consumption trend segmentation region; the increased or decreased value of the initial fixed acquisition frequency is the same.

[0030] The embodiments of the present invention have at least the following beneficial effects: By collecting the power load data of the preset number of days in history, forming a time series data curve for the daily power data, and then smoothing it to obtain a smoothed curve, and then clustering the extreme points in the smoothed curve, and dividing the time series data curve in the coordinate system with the centroid of the convex hull of each clustering cluster to obtain the power consumption trend segmentation region, the present invention can reasonably divide the time of a day into multiple time periods in combination with the user's power consumption, and then adjust the data acquisition frequency of each time period; then obtain the degree of fluctuation and the degree of irrelevance of the power consumption trend segmentation region, and combine the degree of fluctuation and the degree of irrelevance of the power consumption trend segmentation region to obtain the power consumption complexity of the power consumption trend segmentation region, which can improve the accuracy of calculating the complexity of each power consumption trend segmentation region; finally, use the power consumption complexity of the power consumption trend segmentation region to adjust the acquisition frequency of the power load data of the time period in a day corresponding to the power consumption trend segmentation region, and collect charge data using the adjusted acquisition frequency, making the data acquisition more reasonable and meeting the needs of data analysis. Description of the Drawings

[0031] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 This is a flowchart of a data acquisition method for an electricity consumption information acquisition terminal provided by an embodiment of the present invention. Detailed implementation manners

[0033] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of a data acquisition method for an electricity consumption information acquisition terminal proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0035] The following specifically describes the specific solution of a data acquisition method for an electricity consumption information acquisition terminal provided by the present invention with reference to the accompanying drawings.

[0036] The main application scenario of the present invention is as follows: when using an electricity consumption information acquisition terminal to collect electricity information data, the commonly used method usually collects data at a fixed collection frequency, so it is impossible to pay attention to the time periods that require more detailed data, making it difficult to detect potential electricity consumption problems. Therefore, it is necessary to adjust the collection frequency according to the electricity consumption characteristics of an electricity consumption area so that the collected data can more clearly reflect the electricity consumption information.

[0037] Please refer to Figure 1 , which shows a flowchart of a data acquisition method for an electricity consumption information acquisition terminal provided by an embodiment of the present invention. The method includes the following steps:

[0038] Step S1, obtain the power load data of a 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 smoothed curve.

[0039] First, since it is necessary to obtain the electricity consumption trend segmentation areas by analyzing the historical smoothed curves, and then adjust the collection frequency of each area according to the electricity consumption complexity of each area, it is necessary to obtain historical data for multiple days for subsequent analysis. Specifically, obtain the power load data of a preset number of days in history, and the power data of each day forms a time series data curve. The number of days for collecting historical data can be adjusted by the implementer according to the actual situation.

[0040] When obtaining the time-series data of multiple historical power loads, it should be noted that the time-series data of these multiple historical power loads all represent the same entity, such as the same community or the same office area, etc. This reflects that the trends of these multiple historical electricity consumption data are generally similar in theory, providing a basic condition for the subsequent acquisition of the segmented areas of electricity consumption trends. The electricity consumption pattern of the same entity generally does not change much. Since it is necessary to obtain the segmented areas of residential electricity consumption trends, that is, to obtain the significant fluctuation turning points of each curve, and the subtle fluctuations caused by the small changes in electricity load in the original curve affect the acquisition of the significant fluctuation turning points (extreme points), it is necessary to smooth the obtained time-series data curve.

[0041] Specifically, use the S-G filter to smooth the time-series data curve to obtain the smoothed curve corresponding to each time-series data curve. Here, it is set that I smoothed curves are obtained. In addition, when obtaining historical data, the historical data is obtained by pushing forward from the current moment, and the number of smoothed 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.

[0042] Step S2: Cluster the extreme points of each smoothed curve to obtain clustering clusters; obtain the centroid of the convex hull of each clustering cluster as the center point of the clustering cluster; divide all the time-series data curves in a coordinate system according to the center points to obtain the segmented areas of electricity consumption trends.

[0043] Since the electricity consumption of users has a certain regularity, if the complexity of the time-series data curve is directly analyzed without trend segmentation, it will lead to the calculation of the curve complexity being affected by the trend, making the calculation of the complexity inaccurate. Therefore, trend segmentation of the time-series data curve helps to analyze the electricity consumption complexity of each segmented area of electricity consumption trends. Also, because after trend segmentation of the time-series data curve, different acquisition frequencies can be configured for the electricity consumption complexity of different time periods. In the time interval with a high electricity consumption complexity, if the acquisition frequency is too low, it may lead to data loss or incomplete data acquisition, and thus unable to accurately reflect the actual change of the load. At this time, setting a higher acquisition frequency can capture the load fluctuations more precisely, helping to discover potential electricity consumption problems or abnormal phenomena. Therefore, it is necessary to divide the time-series data curve in time and further analyze each divided area.

[0044] Obtain the extreme points of the smoothed curve of each power load, and obtain the trend segmentation regions of multiple time series data curves according to the clustering results of the extreme points. Since different acquisition frequencies can be set for the electricity load characteristics in different regions after segmentation according to the extreme points, and this processing can also avoid data loss and retain the basic electricity consumption characteristic trends in the scenario, it is necessary to obtain the trend segmentation regions. Since the smoothed power load curve reflects the overall trend of the curve, obtaining the extreme points of the curve is also obtaining the nodes where the overall trend of the curve changes. Therefore, obtaining the extreme points of the curve can obtain the approximate segmentation results of multiple trend regions of the curve. Due to the regularity of user electricity consumption, the extreme points are relatively concentrated but not completely unified on the smoothed curves of multiple historical power loads. Therefore, it is necessary to cluster the obtained extreme points, and use the clustering center points as the segmentation cut-off points to divide multiple time series data curves into multiple electricity consumption trend segmentation regions.

[0045] First, for the j-th point on the smoothed curve of the i-th power load , multiply it by the slopes of the two adjacent points , and take the point whose product is as the extreme point of this smoothed curve, where is the slope between the (j - 1)-th point and the j-th point, is the slope between the j-th point and the (j + 1)-th point, and so on to obtain all the extreme points on the I smoothed curves.

[0046] Next, perform DBSCAN clustering on the extreme points of these I smoothed curves. DBSCAN is a density-based spatial clustering algorithm. It is based on density reachability and forms clusters by continuously expanding the set of density-reachable points. Suppose K clusters are obtained through DBSCAN clustering. The steps to obtain the center points of these K clusters are as follows: For the k-th cluster, obtain the convex hull of this cluster. The convex hull is the smallest convex polygon that contains all the points in this cluster, and then take the centroid of this convex hull as the center point of the k-th cluster, where the convex hull can be obtained by a convex hull algorithm.

[0047] Set the two-dimensional coordinates of the center point of the k-th cluster as , then the coordinates of the center points of each of the K clusters are . Draw a straight line perpendicular to the horizontal axis through the abscissa of each center point to obtain the corresponding straight line for each center point, denoted as the segmentation line; use the segmentation lines to divide each time series data curve into K + 1 regions in the coordinate system. Each region is an electricity consumption trend segmentation region, and each electricity consumption trend segmentation region includes a part of the I time series data curves, and the corresponding time periods of this part are the same. Here, dividing and analyzing the time series data curves based on the extreme points obtained from the smoothed curve is to retain the characteristics of the original data.

[0048] Step S3: Calculate the fluctuation degree of the power consumption trend segmentation area based on the data, standard deviation, and mean on each time series data curve in the segmented area according to the power consumption trend.

[0049] Since the trends of multiple time series data curves included in each power consumption trend segmentation area are generally consistent, if there is a large degree of fluctuation in these multiple time series data curves, it indicates that the complexity of the power consumption trend segmentation area where these multiple smoothed curves are located is relatively high. Among them, 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. Analyzing the fluctuation of the time series data curve in the power consumption trend segmentation area can represent the complexity of the charge change during the time period corresponding to the power consumption trend segmentation area. The greater the fluctuation, the higher the complexity of power consumption, and the more power consumption data needs to be collected, and the collection frequency needs to be increased.

[0050] Considering that the slight fluctuation of the power load also has calculation value for the calculation of power consumption complexity, after obtaining the segmentation line through the smoothed curve in this application, the time series data curve is segmented and then analyzed. Therefore, the fluctuation degree of the power consumption trend segmentation area is calculated based on the data, standard deviation, and mean on each time series data curve in the power consumption trend segmentation area. The specific calculation formula is:

[0051] ,

[0052] Among them, represents the fluctuation degree within the i-th power consumption trend segmentation area; I represents the number of time series data curves; represents the standard deviation of the data on the j-th time series data curve within the i-th power consumption trend segmentation area; represents the mean of the data on the j-th time series data curve within the i-th power consumption trend segmentation area; M represents the number of data on the j-th time series data curve within the i-th power consumption trend segmentation area; represents the slope between the (m - 1)-th point and the m-th point on the j-th time series data curve within the i-th power consumption trend segmentation area, represents the slope between the (m + 1)-th point and the m-th point on the j-th time series data curve within the i-th power consumption trend segmentation area; norm( ) represents the normalization operation.

[0053] In the above formula, represents the coefficient of variation of the j-th time series data curve in the i-th trend segmentation area. The larger this value is, the greater the degree of dispersion of the power load on this curve, The difference in slopes between a data point and the data points on its left and right sides. The greater the absolute value of the difference between the two, the greater the degree of slope change on the curve, indicating that the change in power data on the curve is more complex. From this, the degree of fluctuation of each power consumption trend segmentation region can be obtained. The greater the degree of fluctuation, the more complex the data change situation. At this time, a higher level of attention needs to be given, and more data needs to be collected for analysis.

[0054] Step S4: Calculate the irrelevance degree of the power consumption trend segmentation region based on the slopes and intercepts of the straight lines formed by connecting the data points on both sides of each time-series data curve in the same power consumption trend segmentation region.

[0055] In a power consumption trend segmentation region, there may be multiple time-series data curves with large degrees of fluctuation. Therefore, it is necessary to calculate the irrelevance degree of multiple curves to reflect the change of charge in the time period corresponding to the power consumption trend segmentation region. The higher the irrelevance degree of multiple time-series data curves in a power consumption trend segmentation region, the more complex the load change of these multiple time-series change curves in the power consumption trend segmentation region. Therefore, it is necessary to comprehensively consider the degree of fluctuation and irrelevance degree of the time-series data curves in the power consumption trend segmentation region to obtain the power consumption complexity of each power consumption trend segmentation region.

[0056] Furthermore, calculate the irrelevance degree of the power consumption trend segmentation region based on the slopes and intercepts of the straight lines formed by connecting the left and right end data points of each time-series data curve in the same power consumption trend segmentation region. Specifically, obtain the slope of the straight line formed by connecting the two data points on both sides of each time-series data curve in a power consumption trend segmentation region, and calculate the average value of the slopes to obtain the standard straight line slope of the power consumption trend segmentation region The calculation formula is:

[0057] ,

[0058] where, represents the slope of the straight line formed by connecting the two data points on both sides of the j-th time-series data curve in the i-th power consumption trend segmentation region.

[0059] Furthermore, obtain the intercept of the straight line formed by connecting the two data points on both sides of each time-series data curve in a power consumption trend segmentation region, and calculate the average value of the intercepts to obtain the standard straight line intercept of the power consumption trend segmentation region The calculation formula is:

[0060] ,

[0061] where, represents the intercept of the straight line formed by connecting the two data points on both sides of the j-th time-series data curve in the i-th power consumption trend segmentation region.

[0062] Further, a standard straight line of the power consumption trend segmentation region is constructed by using the standard straight line slope and the standard straight line intercept corresponding to the power consumption trend segmentation region; 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 region is calculated, and the calculation formula of the irrelevance degree is:

[0063] ,

[0064] wherein, represents the irrelevance degree of the i-th power consumption trend segmentation region; I represents the number of time series data curves; represents the slope of the straight line formed by connecting the data points located on both sides of the j-th time series data curve in the i-th power consumption trend segmentation region; represents the standard straight line slope of the i-th power consumption trend segmentation region; represents the intercept of the straight line formed by connecting the data points located on both sides of the j-th time series data curve in the i-th power consumption trend segmentation region; represents the standard straight line intercept of the i-th power consumption trend segmentation region; represents the straight line formed by connecting the data points located on both sides of the j-th time series data curve in the i-th power consumption trend segmentation region; represents the standard straight line constructed by using the standard straight line slope and the standard straight line intercept of the i-th power consumption trend segmentation region; represents the Pearson correlation coefficient between the straight line formed by connecting the data points located on both sides of the j-th time series data curve in the i-th power consumption trend segmentation region and the standard straight line of the i-th power consumption trend segmentation region; norm( ) represents the normalization operation.

[0065] In the above formula, represents the absolute value of the difference between the slope of the straight line corresponding to the j-th time series data curve in the i-th trend segmentation region and the standard straight line slope, 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 region and the standard straight line intercept. The larger these two items are, the greater the degree of deviation of the j-th time series curve in the trend segmentation region from the standard curve; The smaller it is, the lower the correlation degree between the two straight lines, and the higher the irrelevance degree between the time series data curve in the power consumption trend segmentation region and the standard curve. Thus, the correlation degree between each time series data curve in the power consumption trend segmentation region can be obtained. The more irrelevant, the more complex the power consumption situation changes in the power consumption trend segmentation region, and at this time, more data is required for analysis.

[0066] In step S5, the fluctuation degree and irrelevance degree of the power consumption trend segmentation area are weighted and summed to obtain the power consumption complexity of the power consumption trend segmentation area; the acquisition frequency of the power load data for the time period corresponding to the power consumption trend segmentation area is adjusted according to the power consumption complexity of the power consumption trend segmentation area; the power load data is acquired based on the adjusted acquisition frequency.

[0067] 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 weighted summation on the two. The first weight and the second weight are respectively set, and the first weight and the second weight are used to perform 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.

[0068] The specific calculation formula is:

[0069] ,

[0070] where, is the power consumption complexity of the i-th power consumption trend segmentation area, is the fluctuation degree of the curve within the i-th power consumption trend segmentation area, is the irrelevance degree of the curve within the i-th power consumption trend segmentation area, and respectively represent the first weight and the second weight. Since the fluctuation degree and irrelevance degree of the i-th power consumption trend segmentation area are equally important for calculating the power consumption complexity of the area, so here we take , and the implementer can adjust the first weight and the second weight according to the actual situation.

[0071] Since a high power consumption complexity indicates that the load changes relatively violently during this period, the corresponding acquisition frequency should be increased at this time. This is because high-frequency acquisition can capture the fluctuations of the power load more accurately, which helps to discover potential power consumption problems or abnormal phenomena. If the acquisition frequency is too low during this time period, it may lead to data loss or inability to reflect the actual changes of the load; when the power consumption complexity is low, the corresponding acquisition frequency should be reduced to reduce the corresponding resource waste; therefore, it is necessary to adjust the acquisition frequency of each area according to the power consumption complexity of each trend segmentation area.

[0072] Obtain the initial fixed acquisition frequency, and set the first threshold and the second threshold, where the first threshold is less than the second threshold; if the electricity consumption complexity of the electricity consumption trend segmentation area is less than or equal to the first threshold, then reduce the initial fixed acquisition frequency of the time period of the day corresponding to the electricity consumption trend segmentation area; if the electricity consumption complexity of the electricity consumption trend segmentation area is greater than the first threshold and less than the second threshold, then the initial fixed acquisition frequency of the time period of the day corresponding to the electricity consumption trend segmentation area remains unchanged; if the electricity consumption complexity of the electricity consumption trend segmentation area is greater than or equal to the second threshold, then increase the initial fixed acquisition frequency of the time period of the day corresponding to the electricity consumption trend segmentation area; the increased or decreased value of the initial fixed acquisition frequency is the same.

[0073] Preferably, in the 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 the actual situation, and the increased or decreased value of the initial fixed acquisition frequency needs to be determined by the implementer according to the actual situation. Thus, the result of adjusting the acquisition frequency of the time period of the day corresponding to each electricity consumption trend segmentation area according to the electricity consumption complexity can be obtained, and then the power load data can be collected according to the adjusted acquisition frequency.

[0074] This solution analyzes the historically collected power load data to obtain the electricity consumption complexity of each electricity consumption trend segmentation area of residents, and adjusts the acquisition frequency of each segmentation area according to the electricity consumption complexity of each electricity consumption trend segmentation area. When the electricity consumption complexity of the area is high, the acquisition frequency is appropriately increased, and when the electricity consumption complexity is low, the acquisition frequency is appropriately reduced. Such adjustment of the acquisition frequency according to the electricity consumption complexity can not only capture the fluctuations of the power load more accurately, help to discover potential electricity consumption problems or abnormal phenomena, but also reduce the waste of resources during the acquisition of stable electricity consumption periods, and thus provides high utilization value for subsequent load management, line loss analysis, etc. based on electricity consumption information and electricity consumption monitoring.

[0075] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the 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.

[0076] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0077] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within 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 of the fluctuation degree and the irrelevance degree of the power consumption trend segmentation area; the collection frequency of the 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; 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; each time series data curve in the coordinate system is divided by the dividing line to obtain a power consumption trend segmentation area; 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. 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; 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; Using the standard straight line slope and the standard straight line intercept corresponding to a power consumption trend segmentation area to construct the standard straight line of the power consumption trend segmentation area; calculating the degree of irrelevance of the power consumption trend segmentation area based on the standard straight line slope, the standard straight line intercept and the standard straight line; 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.

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 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.

4. 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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