Ring main unit electric energy data efficient acquisition method based on sensor

Abstract

The invention relates to the technical field of data processing, in particular to a ring main unit electric energy data efficient acquisition method based on a sensor, and the method comprises the steps: obtaining the electric energy data and the power utilization rate of a ring main unit at each sampling moment; for any sampling moment in the current day, according to the electric energy data fluctuation characteristics and the power consumption rate in the current day, the electric energy data state consistency degree at any sampling moment is obtained; obtaining an electric energy data abnormal value at any sampling moment according to the electric energy data state consistency degree at each historical sampling moment and the electric energy data fluctuation characteristics in the current day; for any time period in the current day, acquiring the monitoring importance degree of any time period according to the electric energy data abnormal value at each sampling moment and the electric energy data fluctuation characteristics in the historical day; according to the monitoring importance degree of each time period, the self-adaptive sampling frequency of each same time period in the future day is obtained, the electric energy data of the ring main unit in the future day are collected, and the quality of the electric energy data of the ring main unit is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an efficient method for collecting power data of ring main units based on sensors. Background Art

[0002] With the development of smart grids and the continuous progress of power Internet of Things technology, as an important part of the power system, the collection of power data of ring main units has become increasingly crucial. Power data such as voltage, current, and power factor in ring main units are of great significance for the monitoring, analysis, and optimization of the power system. When collecting power data of ring main units traditionally, it is often collected for 24 hours a day. However, the daily data volume of a single ring main unit can reach the GB level, and the full-volume upload mode of original data is mostly adopted. Such a huge amount of data will cause the central server to be overloaded and the data processing delay to be too high. Since the power data of ring main units changes less in some time periods of daily use and some data belongs to redundant data, the adaptive sampling frequency in different time periods can be adjusted to effectively reduce the redundant data collected and reduce the load of the central server.

[0003] When adjusting the sampling frequency of power data by existing methods, the sampling frequency is generally set according to the performance of power data and historical power data. Since power data is generally related to user usage, and ring main units generally serve multiple users, the differences in user behavior habits may be shown as different situations in the overall data. For example, a certain user uses frequently in some time periods, but the other users use infrequently, which may present the characteristic of small changes in power data. At the same time, adjusting only according to user habits may ignore the system's own requirements, resulting in missed detection of key events, such as sudden voltage fluctuations or faults. Moreover, the uncertainty and mutation of user behavior habits may not be obvious in historical power data, easily leading to data loss or delay, affecting data quality and system monitoring effects.

[0004] Therefore, how to adaptively adjust the sampling frequency of power data in different time periods has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides an efficient method for collecting power data of ring main units based on sensors to solve the problem of how to adaptively adjust the sampling frequency of power data in different time periods.

[0006] An embodiment of the present invention provides an efficient method for collecting power data of ring main units based on sensors, and the method includes the following steps: Obtain the power data and electricity consumption rate of the ring main unit at each sampling moment; For any sampling moment within the day, a target window of a preset size is established with the power data at the any sampling moment as the center, and the degree of consistency of the power data state at the any sampling moment is obtained according to the power data fluctuation characteristics within the target window and the power consumption rate at the any sampling moment; The degree of consistency of the power data state at each historical sampling moment belonging to the same moment as the any sampling moment is obtained within the historical days. According to the degree of consistency of the power data state at each historical sampling moment and the power data fluctuation characteristics within the target window, the outlier of the power data at the any sampling moment is obtained; The day is divided into at least two time periods. For any time period, the monitoring importance of the any time period is obtained according to the outlier of the power data at each sampling moment within the any time period and the power data fluctuation characteristics within the historical days; The monitoring importance of each time period is obtained, and the sampling frequency of each time period within the day is adjusted by using the monitoring importance of each time period to obtain the adaptive sampling frequency of each same time period within the future days, which is used to collect the power data of the ring main unit within the future days.

[0007] Preferably, the obtaining the degree of consistency of the power data state at the any sampling moment according to the power data fluctuation characteristics within the target window and the power consumption rate at the any sampling moment includes: Obtain the normal data range of the data type to which the power data of the ring main unit belongs. According to the difference between the power data at the any sampling moment and the normal data range, and the power data fluctuation characteristics within the target window, obtain the degree of power data fluctuation at the any sampling moment; If the power data at the any sampling moment is within the normal data range, obtain the reciprocal of the absolute value of the difference between the degree of power data fluctuation at the any sampling moment and the power consumption rate, and obtain the degree of consistency of the power data state at the any sampling moment; If the power data at the any sampling moment is not within the normal data range, set the degree of consistency of the power data state at the any sampling moment to the constant 0.

[0008] Preferably, the obtaining the degree of power data fluctuation at the any sampling moment according to the difference between the power data at the any sampling moment and the normal data range, and the power data fluctuation characteristics within the target window includes: Obtain the absolute value of the difference between the power data at the any sampling moment and the minimum value within the normal data range to obtain the actual difference. Obtain the difference between the maximum value and the minimum value within the normal data range to obtain the maximum difference. Obtain the ratio of the actual difference to the maximum difference to obtain the first fluctuation value of the any sampling moment; Obtain the absolute value of the difference between every two adjacent power data within the target window, correspondingly obtain the average absolute difference, obtain the addition result of constant 1 and the average absolute difference, calculate the difference between constant 1 and the reciprocal of the addition result, and obtain the second fluctuation value at any sampling moment; Obtain the power data fluctuation degree at any sampling moment according to the average value between the first fluctuation value and the second fluctuation value.

[0009] Preferably, the obtaining of the power data outlier at any sampling moment according to the consistency degree of the power data states at each historical sampling moment and the power data fluctuation characteristics within the target window includes: If the power data at any sampling moment is within the normal data range, obtain the absolute value of the difference between the average value of the consistency degrees of the power data states at all historical sampling moments and the consistency degree of the power data state at any sampling moment, obtain the consistency degree difference, obtain the difference between constant 1 and the reciprocal of the consistency degree difference, and obtain the first difference value at any sampling moment; Form a consistency degree data sequence with the consistency degrees of the power data states at all historical sampling moments, obtain the absolute value of the difference between every two adjacent consistency degrees of the power data states in the consistency degree data sequence, correspondingly obtain the average value of the consistency degree differences of the power data states, obtain the difference between constant 1 and the reciprocal of the average value of the consistency degree differences of the power data states, and obtain the second difference value at any sampling moment; Perform a weighted summation process on the first difference value and the second difference value to obtain the power data outlier at any sampling moment.

[0010] Preferably, the obtaining of the power data outlier at any sampling moment according to the consistency degree of the power data states at each historical sampling moment and the power data fluctuation characteristics within the target window further includes: If the power data at any sampling moment is not within the normal data range, within the target window, obtain the power data change rate between any sampling moment and its previous sampling moment, denoted as the left change rate, obtain the power data change rate between any sampling moment and its next sampling moment, denoted as the right change rate, calculate the absolute value of the difference between the left change rate and the right change rate, and obtain the data change value at any sampling moment; Obtain the reciprocal of the data change value as the power data outlier at any sampling moment.

[0011] Preferably, the dividing the day into at least two time periods includes: According to the power data fluctuation characteristics within the target window, obtain the data change value at any sampling moment, obtain the difference between the constant 1 and the reciprocal of the data change value at any sampling moment, and obtain the data turning degree value at any sampling moment; If the data turning degree value at any sampling moment is greater than the preset data turning degree threshold, confirm that the any sampling moment is a time period node; Obtain all the time period nodes within the day, and divide the day into at least two time periods according to all the time period nodes within the day.

[0012] Preferably, the obtaining of the monitoring importance degree of any time period according to the power data outlier at each sampling moment within any time period and the power data fluctuation characteristics within the historical days includes: Obtain the historical time period belonging to the same time period as any time period within any historical day. For any historical sampling moment within the historical time period, calculate the difference between the constant 1 and the power data outlier at any historical sampling moment to obtain the power data credibility at any historical sampling moment. According to the time difference between any historical day and the current day, obtain the fluctuation weight at any historical sampling moment, and obtain the product of the fluctuation weight, the power data fluctuation degree, and the power data credibility at any historical sampling moment to obtain the usage anomaly degree at any historical sampling moment; Obtain the usage anomaly degree of each historical sampling moment belonging to the same moment as any historical sampling moment within the historical day, and correspondingly obtain the cumulative value of the usage anomaly degree, denoted as the total usage anomaly degree of any historical sampling moment. Obtain the total usage anomaly degree of each historical sampling moment within the historical time period, and correspondingly obtain the average value of the total usage anomaly degree, denoted as the historical anomaly mean of any time period; Obtain the average value of the power data outliers at each sampling moment within any time period to obtain the current day anomaly mean of any time period; Obtain the monitoring importance degree of any time period according to the average value between the historical anomaly mean and the current day anomaly mean of any time period.

[0013] Preferably, the obtaining of the fluctuation weight at any historical sampling moment according to the time difference between any historical day and the current day includes: Obtain the number of days between any historical day and the current day, and perform normalization processing on the reciprocal of the number of days to obtain the fluctuation weight at any historical sampling moment.

[0014] Preferably, adjusting the sampling frequency of each time period within the day according to the monitoring importance of each time period to obtain the adaptive sampling frequency of each same time period within the next day includes: Obtaining the quantity of power data in each time period within the day, correspondingly obtaining the average quantity, and for any time period within the day, obtaining the product of the average quantity and the monitoring importance of the any time period to obtain an adjustment value; If the monitoring importance of the any time period is greater than or equal to the monitoring importance of the same time period of the previous historical day of the day, then obtaining the addition result of the sampling frequency of the any time period and the adjustment value to obtain the adaptive sampling frequency of the same time period within the next day; If the monitoring importance of the any time period is less than the monitoring importance of the same time period of the previous historical day of the day, then obtaining the difference between the sampling frequency of the any time period and the adjustment value to obtain the adaptive sampling frequency of the same time period within the next day.

[0015] The beneficial effects of the embodiments of the present invention compared with the prior art are: The present invention obtains the electrical energy data and power consumption rate of the ring main unit at each sampling moment; for any sampling moment within a day, a target window of a preset size is established with the electrical energy data at the any sampling moment as the center, and according to the fluctuation characteristics of the electrical energy data within the target window and the power consumption rate at the any sampling moment, the degree of consistency of the electrical energy data state at the any sampling moment is obtained; the degree of consistency of the electrical energy data state at each historical sampling moment that belongs to the same moment as the any sampling moment is obtained within historical days, and according to the degree of consistency of the electrical energy data state at each historical sampling moment and the fluctuation characteristics of the electrical energy data within the target window, the outlier of the electrical energy data at the any sampling moment is obtained; the day is divided into at least two time periods, and for any time period, according to the outlier of the electrical energy data at each sampling moment within the any time period and the fluctuation characteristics of the electrical energy data within historical days, the monitoring importance degree of the any time period is obtained; the monitoring importance degree of each of the time periods is obtained, and by using the monitoring importance degree of each of the time periods, the sampling frequency of each time period within the day is adjusted to obtain the adaptive sampling frequency of each same time period within the future days, which is used to collect the electrical energy data of the ring main unit within the future days. Among them, the degree of consistency of the electrical energy data state at any sampling moment is obtained to judge whether the fluctuation of the electrical energy data at any sampling moment is caused by the needs of users, and the possibility of the ring main unit having a fault or other abnormality is detected; then the outlier of the electrical energy data at each sampling moment is obtained, and according to the outlier of the electrical energy data and the fluctuation characteristics of the electrical energy data within historical days, the monitoring importance degree of any time period is obtained, and finally the adaptive sampling frequency of the same time period within the future days is obtained according to the monitoring importance degree, and the electrical energy data of the ring main unit within the future days is collected according to the adaptive sampling frequency, while not missing key events, reducing the collection of redundant electrical energy data, and improving the quality of the electrical energy data of the ring main unit and the system monitoring effect. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of 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.

[0017] Figure 1 It is a flowchart of a method for efficiently collecting electrical energy data of a ring main unit based on sensors provided in Embodiment 1 of the present invention. Detailed Embodiments

[0018] The following will describe in detail the embodiments of the present disclosure, and the examples of the embodiments are shown in the drawings. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present disclosure and should not be construed as a limitation to the present disclosure.

[0019] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0020] In order to illustrate the technical solution of the present invention, the following will be described through specific embodiments.

[0021] See Figure 1 , which is a method flow chart of an efficient acquisition method for ring main unit power data based on sensors provided by Embodiment 1 of the present invention. As Figure 1 shown, the method may include: Step S101, obtaining the power data and power consumption rate of the ring main unit at each sampling moment.

[0022] The scenario targeted by the present invention is the adjustment of the sampling frequency of ring main unit power data mainly for residential users in a community. When adjusting the sampling frequency of power data by existing methods, the sampling frequency is generally set according to the performance of power data and historical power data. Since power data is generally related to user usage, and a ring main unit generally serves multiple users, the differences in user behavior habits may be shown as different situations in the overall data. For example, a certain user uses frequently during some time periods, while the other users do not use frequently, which may present the characteristic of small changes in power data. At the same time, adjusting only according to user habits may ignore the system's own requirements, resulting in missed detection of key events, such as sudden voltage fluctuations or faults. Moreover, the uncertainty and mutability of user behavior habits may not be obvious in historical power data, easily leading to data loss or delay, affecting data quality and system monitoring effects.

[0023] Therefore, in this embodiment, according to the power data fluctuation characteristics of the ring main unit on the current day and historical days, combined with the usage characteristics of users, power data outliers are obtained. Then, according to the power data outliers and the power data fluctuation characteristics within historical days, the monitoring importance of each time period is obtained. Finally, according to the monitoring importance, the adaptive sampling frequency for the same time period within future days is obtained, and the power data of the ring main unit within future days is collected, reducing the acquisition of redundant power data while not missing key events, and improving the quality of ring main unit power data and system monitoring effects.

[0024] The electrical energy data in the ring main unit includes voltage, current, etc. In this embodiment, taking a kind of electrical energy data of the ring main unit as an example, the acquisition frequency of the electrical energy data of the ring main unit is adjusted. The sensors corresponding to the electrical energy data are used to collect the electrical energy data of the ring main unit at each sampling moment (voltage data is collected by a voltage sensor, and current data is collected by a current sensor). In this embodiment, the initial acquisition frequency is set to once per second, and the acquisition frequency for each subsequent day needs to be collected according to the actually calculated acquisition frequency, which is not limited here and can be set according to the specific implementation scenario.

[0025] Since the electrical energy data of the ring main unit in the community at each sampling moment is the overall data, that is, the usage situation of all the current power in the community for scheduling and distribution at the current moment, therefore, only focusing on the electrical energy data of the ring main unit is not comprehensive. If the current data is large at a certain sampling moment, it is considered that there are more users using electricity at that sampling moment, but in fact, the number of users using electricity may not be large. It may be caused by some users using high-power electrical appliances, faults in the ring main unit, interference to the sensors, etc. Therefore, the electricity consumption rate at each sampling moment can be obtained through the power change situation of the residential electricity meters in the community, the actual electricity consumption characteristics of the community residents can be analyzed, and the actual usage characteristics of the electrical energy data can be analyzed by combining the electrical energy data of the ring main unit and the electricity consumption rate.

[0026] The electricity consumption rate refers to the proportion of the number of users with power data changes in the electricity meters among all the users in the community, that is , where is the electricity consumption rate at the t-th sampling moment, is the number of users with power data changes in the electricity meters at the t-th sampling moment, B is the number of all users in the community, The larger it is, the more users are using electricity at the t-th sampling moment, and the higher the electricity consumption rate is.

[0027] Step S102, for any sampling moment within the day, taking the electrical energy data at the any sampling moment as the center, establish a target window with a preset size, and obtain the degree of consistency of the electrical energy data state at the any sampling moment according to the fluctuation characteristics of the electrical energy data within the target window and the electricity consumption rate at the any sampling moment.

[0028] If there are more users using electricity at the t-th sampling moment, it means that the electrical energy data before and after the t-th sampling moment may have certain fluctuations. The fluctuations are mainly reflected in the magnitude of the data and the frequency of data changes. If the electrical energy data has fluctuations while the electricity consumption rate is low, it indicates that the state of the electrical energy data may be inconsistent, and at this time, there may be a fault or other abnormal situations in the ring main unit.

[0029] Therefore, for any sampling moment within a day, with the electrical energy data at any sampling moment as the center, a target window with a preset size of 5 is established (there is no limit here and it can be set according to the specific implementation scenario) to analyze the fluctuation characteristics of the electrical energy data. According to the fluctuation characteristics of the electrical energy data within the target window and the electricity consumption rate at any sampling moment, the degree of consistency of the electrical energy data at any sampling moment is obtained, and the abnormal characteristics of the electrical energy data of the ring main unit at any sampling moment are analyzed, that is, whether the electrical energy data at this sampling moment is an abnormal situation worthy of attention. Furthermore, according to the abnormal characteristics of the electrical energy data, it is analyzed whether the electrical energy data at this sampling moment needs to be monitored.

[0030] Among them, the method for obtaining the degree of consistency of the electrical energy data at any sampling moment according to the fluctuation characteristics of the electrical energy data within the target window and the electricity consumption rate at any sampling moment is as follows: (1) According to the actual information of the ring main unit, obtain the normal data range of the data type to which the electrical energy data of the ring main unit belongs. For example, the rated voltage of the ring main unit in a community is usually designed to be 10 kV. Under normal operating conditions, the voltage of the ring main unit is allowed to have a certain fluctuation range, and the fluctuation range usually does not exceed 10% of the rated voltage, that is, 9 kV to 11 kV is the normal data range of the voltage data of the ring main unit in the community. The specific fluctuation range needs to be determined according to the grid standard and equipment requirements (there is no limit here and it can be set according to the specific implementation scenario). According to the difference between the electrical energy data at any sampling moment and the normal data range, and the fluctuation characteristics of the electrical energy data within the target window, obtain the fluctuation degree of the electrical energy data at any sampling moment.

[0031] Specifically, obtain the absolute value of the difference between the electrical energy data at any sampling moment and the minimum value within the normal data range to obtain the actual difference, obtain the difference between the maximum value and the minimum value within the normal data range to obtain the maximum difference, and obtain the ratio of the actual difference to the maximum difference to obtain the first fluctuation value at any sampling moment; Obtain the absolute value of the difference between every two adjacent electrical energy data within the target window to correspondingly obtain the average absolute value of the difference, obtain the addition result of the constant 1 and the average absolute value of the difference, and calculate the difference between the constant 1 and the reciprocal of the addition result to obtain the second fluctuation value at any sampling moment; According to the average value between the first fluctuation value and the second fluctuation value, obtain the fluctuation degree of the electrical energy data at any sampling moment.

[0032] In an implementation manner, taking the t-th sampling moment of the day as an example, the calculation formula for the fluctuation degree of the electrical energy data at the t-th sampling moment is:

[0033] Among them, is the fluctuation degree of the electrical energy data at the t-th sampling moment; is the electrical energy data at the t-th sampling moment; is the maximum value within the normal data range; is the minimum value within the normal data range; is the absolute value symbol; is the (i + 1)-th electrical energy data within the target window; is the i-th electrical energy data within the target window; N is the number of electrical energy data within the target window; 1 is a constant.

[0034] It should be noted that is the first fluctuation value at the t-th sampling moment. The greater the difference between the electrical energy data at the t-th sampling moment and the minimum value within the normal data range, the greater it is, indicating that the electrical energy data at the t-th sampling moment is more likely to be the electrical energy data during the peak electricity consumption period of residential users in the community, and the greater the fluctuation degree of the electrical energy data at the t-th sampling moment; is the absolute value of the average difference between two adjacent electrical energy data within the target window, the greater it is, indicating that the difference between adjacent electrical energy data within the target window is greater, the greater the volatility of the electrical energy data within the target window, and the second fluctuation value at the t-th sampling moment is greater, and the greater the fluctuation degree of the electrical energy data at the t-th sampling moment.

[0035] (2) If the electrical energy data at any sampling moment is within the normal data range, then obtain the reciprocal of the absolute value of the difference between the fluctuation degree of the electrical energy data at any sampling moment and the electricity consumption rate to obtain the consistency degree of the electrical energy data state at any sampling moment.

[0036] In one embodiment, taking the t-th sampling moment of the day as an example, the calculation formula for the consistency degree of the electrical energy data state at the t-th sampling moment is:

[0037] Wherein, is the consistency degree of the electrical energy data state at the t-th sampling moment; is the fluctuation degree of the electrical energy data at the t-th sampling moment; is the electricity consumption rate at the t-th sampling moment; is the absolute value symbol.

[0038] It should be noted that represents the difference between the fluctuation degree of the electrical energy data at the t-th sampling moment and the electricity consumption rate, the smaller it is, indicating that the change in the electrical energy data at the t-th sampling moment is more likely to be caused by the actual electricity consumption situation, the more consistent the change characteristics of the electrical energy data at the t-th sampling moment are with the actual electricity consumption situation, and the greater the consistency degree of the electrical energy data state at the t-th sampling moment.

[0039] (3) If the power data at any sampling moment is not within the normal data range, it indicates that the fluctuation of the power data at the t-th sampling moment is more likely to be caused by a fault or other abnormal conditions in the ring main unit, which has nothing to do with the actual power consumption situation. Set the degree of consistency of the power data state at any sampling moment to the constant 0.

[0040] Thus, the degree of consistency of the power data state at any sampling moment is obtained.

[0041] Step S103: Obtain the degree of consistency of the power data state at each historical sampling moment that belongs to the same moment as the any sampling moment within the historical days. According to the degree of consistency of the power data state at each historical sampling moment and the power data fluctuation characteristics within the target window, obtain the abnormal value of the power data at the any sampling moment.

[0042] When the degree of consistency of the power data state at any sampling moment is larger, it indicates that the change characteristics of the power data are more consistent with the actual power consumption situation, and the possibility of abnormal conditions is smaller; when the degree of consistency of the power data state at any sampling moment is smaller, it may be that the actual power consumption situation of the user has an abnormality, a fault occurs in the ring main unit, or other abnormal conditions, and it is more necessary to monitor the power data at any sampling moment. However, it is impossible to accurately judge whether the power data at any sampling moment is abnormal only based on the degree of consistency of the power data state at any sampling moment. It is necessary to analyze by combining the power data fluctuation characteristics within the target window and the degree of consistency of the power data state within the historical days.

[0043] Therefore, according to the method for obtaining the degree of consistency of the power data state at the above-mentioned t-th sampling moment, obtain the degree of consistency of the power data state at each historical sampling moment that belongs to the same moment as the any sampling moment within the historical days. In this embodiment, the number of historical days is set to 5 days, which is not limited here and can be set according to the specific implementation scenario. According to the degree of consistency of the power data state at each historical sampling moment and the power data fluctuation characteristics within the target window, obtain the abnormal value of the power data at any sampling moment, that is, whether the power data at this sampling moment is an abnormal situation worthy of attention, and then analyze whether the power data at this sampling moment needs to be monitored according to the abnormal value of the power data.

[0044] Among them, the method for obtaining the abnormal value of the power data at any sampling moment according to the degree of consistency of the power data state at each historical sampling moment and the power data fluctuation characteristics within the target window is as follows: (1) If the power data at any sampling moment is within the normal data range, when an abnormality occurs, it may be that the actual power consumption situation of the user has an abnormality. Therefore, according to the degree of consistency of the power data state at each historical sampling moment, obtain the abnormal value of the power data at any sampling moment.

[0045] Specifically, obtain the absolute value of the difference between the average of the consistency degrees of the power data states at all historical sampling moments and the consistency degree of the power data state at any sampling moment to obtain the consistency degree difference. Then, obtain the difference between the constant 1 and the reciprocal of the consistency degree difference to obtain the first difference value at any sampling moment. Form a consistency degree data sequence from the consistency degrees of the power data states at all historical sampling moments. Obtain the absolute value of the difference between every two adjacent consistency degrees of the power data states in the consistency degree data sequence, and correspondingly obtain the average value of the consistency degree differences of the power data states. Then, obtain the difference between the constant 1 and the reciprocal of the average value of the consistency degree differences of the power data states to obtain the second difference value at any sampling moment. Perform a weighted summation process on the first difference value and the second difference value to obtain the power data outlier at any sampling moment.

[0046] In one embodiment, taking the t-th sampling moment of the current day as an example, the calculation formula for the power data outlier at the t-th sampling moment is:

[0047] Wherein, is the power data outlier at the t-th sampling moment; is the consistency degree of the power data state at the t-th sampling moment; is the d-th consistency degree of the power data state in the consistency degree data sequence; is the (d + 1)-th consistency degree of the power data state in the consistency degree data sequence; C is the number of the consistency degrees of the power data states in the consistency degree data sequence (i.e., the number of historical days); is the absolute value symbol; is the weight coefficient of the first difference value, is the weight coefficient of the second difference value. Since the consistency degree of the power data state on the current day is more important than the consistency degrees of the power data states in history, in this embodiment, , , which is not limited herein and can be set according to specific implementation scenarios.

[0048] It should be noted that is the first difference value. The greater the difference between the consistency degree of the power data state at the t-th sampling moment on the current day and the average of the consistency degrees of the power data states at historical sampling moments, the greater it is, indicating that the power data at the t-th sampling moment on the current day is more abnormal, and the power data outlier at the t-th sampling moment is greater; is the second difference value. The greater the difference between the consistency degrees of the power data states at historical sampling moments, The larger it is, it indicates that the power data of each historical sampling moment belonging to the same moment as the t-th sampling moment on the same day is relatively chaotic, with strong volatility. The power data at the t-th sampling moment is more likely to be abnormal, and the abnormal value of the power data at the t-th sampling moment is larger.

[0049] (2) If the power data at any sampling moment is not within the normal data range, when an abnormality occurs, it may be a fault in the ring main unit or other abnormal conditions, which has nothing to do with the actual power consumption situation. Therefore, according to the power data fluctuation characteristics within the target window, the abnormal value of the power data at any sampling moment is obtained.

[0050] Specifically, in the target window, obtain the change rate of the power data between any sampling moment and its previous sampling moment, denoted as the left change rate, and obtain the change rate of the power data between any sampling moment and its next sampling moment, denoted as the right change rate. Calculate the absolute value of the difference between the left change rate and the right change rate to obtain the data change value at any sampling moment. Obtain the reciprocal of the data change value as the abnormal value of the power data at any sampling moment. Among them, the change rate belongs to the prior art and will not be elaborated here.

[0051] In one embodiment, taking the t-th sampling moment on the same day as an example, the calculation formula for the abnormal value of the power data at the t-th sampling moment is:

[0052] Among them, is the abnormal value of the power data at the t-th sampling moment; is the left change rate; is the right change rate; is the absolute value symbol.

[0053] It should be noted that is the data change value at the t-th sampling moment. The larger it is, it indicates that the power data at the t-th sampling moment is more likely to be an isolated and mutated power data. The abnormality of the power data at the t-th sampling moment is more likely to be caused by sensor abnormalities such as electromagnetic interference. And the sensor abnormalities caused by electromagnetic interference do not require much attention, and the abnormal value of the power data at the t-th sampling moment is lower. If the power data at the t-th sampling moment is the first power data in the target window, then record the left change rate as 0, and the data change value at the t-th sampling moment is If the power data at the t-th sampling moment is the last power data in the target window, then record the right change rate as 0, and the data change value at the t-th sampling moment is .

[0054] Thus, the abnormal value of the power data at any sampling moment is obtained.

[0055] Step S104: Divide the current day into at least two time periods. For any time period, based on the abnormal values of power data at each sampling moment within the time period and the power data fluctuation characteristics within the historical days, obtain the monitoring importance level of the time period.

[0056] After obtaining the abnormal value of power data at any sampling moment, the larger the abnormal value of power data, the more chaotic and uncertain the change of power data is, the more likely an abnormal situation will occur, and the more it needs to be monitored.

[0057] Since the power data presents similar data characteristics within a certain time period, the current day can be divided into at least two time periods according to the power data fluctuation characteristics within the current day, and analyze whether the power data within each time period needs to be monitored.

[0058] Specifically, according to the method for obtaining the data change value at the t-th sampling moment as described above, based on the power data fluctuation characteristics within the target window, obtain the data change value at any sampling moment, obtain the difference between the constant 1 and the reciprocal of the data change value at any sampling moment, and obtain the data turning degree value at any sampling moment. According to experimental statistical analysis, the data turning degree values of most sampling moments are around 0.4. Therefore, in this embodiment, the data turning degree threshold is set to 0.4. This is not limited here and can be set according to specific implementation scenarios. If the data turning degree value at any sampling moment is greater than 0.4, then confirm that the sampling moment is a time period node. Obtain all the time period nodes within the current day, and divide the current day into at least two time periods according to all the time period nodes within the current day.

[0059] Furthermore, based on the abnormal values of power data at each sampling moment within each time period and the power data fluctuation characteristics within the historical days, obtain the monitoring importance level of each time period. Then, based on the monitoring importance level of each time period, adjust the sampling frequency of each time period within the current day to obtain the adaptive sampling frequency of each same time period within the future days. The method for obtaining the monitoring importance level of any time period is as follows: Obtain a historical time period that belongs to the same time period as the any time period on any historical day. For any historical sampling moment within the historical time period, calculate the difference between the constant 1 and the abnormal value of the electrical energy data at the any historical sampling moment to obtain the reliability of the electrical energy data at the any historical sampling moment. Obtain the number of days between the any historical day and the current day, and perform normalization processing on the reciprocal of the number of days to obtain the fluctuation weight at the any historical sampling moment. Obtain the product of the fluctuation weight, the degree of electrical energy data fluctuation, and the reliability of the electrical energy data at the any historical sampling moment to obtain the degree of abnormal use at the any historical sampling moment; Obtain the degree of abnormal use at each historical sampling moment that belongs to the same moment as the any historical sampling moment within the historical day, and correspondingly obtain the cumulative value of the degree of abnormal use, denoted as the total degree of abnormal use at the any historical sampling moment. Obtain the total degree of abnormal use at each historical sampling moment within the historical time period, and correspondingly obtain the average value of the total degree of abnormal use, denoted as the historical abnormal mean of the any time period; Obtain the average value of the abnormal values of the electrical energy data at each sampling moment within the any time period to obtain the daily abnormal mean of the any time period; Obtain the monitoring importance of the any time period based on the average value between the historical abnormal mean and the daily abnormal mean of the any time period.

[0060] In an embodiment, taking the m-th time period of the current day as an example, the calculation formula for the monitoring importance of the m-th time period is:

[0061] Wherein, is the monitoring importance of the m-th time period; is the fluctuation weight at the s-th historical sampling moment within the historical time period of the d-th historical day; is the degree of electrical energy data fluctuation at the s-th historical sampling moment within the historical time period of the d-th historical day; is the abnormal value of the electrical energy data at the s-th historical sampling moment within the historical time period of the d-th historical day; C is the number of historical days; M is the number of sampling moments within the m-th time period (i.e., the number of historical sampling moments within the historical time period); is the abnormal value of the electrical energy data at the s-th sampling moment within the m-th time period.

[0062] It should be noted that, is the reliability of the electrical energy data at the s-th historical sampling moment within the historical time period of the d-th historical day, indicating the true usage degree at the s-th historical sampling moment within the historical time period of the d-th historical day, The smaller it is, the more it conforms to the user's actual usage situation. The larger it is, the more attention should be paid to the data fluctuations that occur at this time, and the greater the monitoring importance of the m-th time period; The smaller it is, the greater the correlation between the electrical energy data of the d-th historical day and the electrical energy data of the current day. At this time, the possibility of data fluctuations is greater, and more attention is needed. The monitoring importance of the m-th time period is greater; is the abnormal mean value of the current day in the m-th time period of the current day, The larger it is, the greater the possibility that the electrical energy data is abnormal within the m-th time period, the more attention should be paid to the m-th time period, and the greater the monitoring importance of the m-th time period.

[0063] Thus, the monitoring importance of any time period is obtained.

[0064] Step S105: Obtain the monitoring importance of each of the time periods, and use the monitoring importance of each of the time periods to adjust the sampling frequency of each time period within the current day, so as to obtain the adaptive sampling frequency of each same time period within the future days, which is used to collect the electrical energy data of the ring main unit within the future days.

[0065] According to the method for obtaining the monitoring importance of the above-mentioned m-th time period, obtain the monitoring importance of each time period within the current day and historical days, and then adjust the sampling frequency of each time period within the current day according to the monitoring importance of each time period, so as to obtain the adaptive sampling frequency of each same time period within the future days, and collect the electrical energy data of the ring main unit within the future days. Then, for any time period within the current day, the method for obtaining the adaptive sampling frequency of the same time period within the future days is as follows: Obtain the quantity of the electrical energy data in each time period within the current day, and correspondingly obtain the quantity mean value. Obtain the product of the quantity mean value and the monitoring importance of the any time period to obtain an adjustment value; If the monitoring importance of the any time period is greater than or equal to the monitoring importance of the same time period of the previous historical day of the current day, then obtain the sum of the sampling frequency of the any time period and the adjustment value to obtain the adaptive sampling frequency of the same time period within the future days; If the monitoring importance of the any time period is less than the monitoring importance of the same time period of the previous historical day of the current day, then obtain the difference between the sampling frequency of the any time period and the adjustment value to obtain the adaptive sampling frequency of the same time period within the future days.

[0066] In an embodiment, taking the m-th time period of the current day as an example, the calculation formula for the adaptive sampling frequency of the same time period within the future days is:

[0067] Among them, is the adaptive sampling frequency for the same time period within the next n days; P is the sampling frequency for the m-th time period within the current day; G is the quantity mean; is the monitoring importance level for the m-th time period within the current day; is the monitoring importance level for the same time period on the previous historical day of the current day.

[0068] It should be noted that is the adjustment value. If , it indicates that the fluctuation state of the power data within the m-th cycle of the current day is more unstable, and it is necessary to increase the acquisition frequency of the power data to collect more effective power data; if , it indicates that the fluctuation state of the power data within the m-th cycle of the current day is more stable, and it is necessary to reduce the acquisition frequency of the power data to reduce redundant power data.

[0069] According to the above method for obtaining the adaptive sampling frequency for the same time period within the next n days (i.e., the adaptive sampling frequency for the same time period within the next n days corresponding to the m-th time period of the current day), obtain the adaptive sampling frequency for each same time period within the next n days, and collect the power data of the ring main unit within the next n days.

[0070] In summary, the embodiments of the present invention obtain the power data and power consumption rate of the ring main unit at each sampling moment; for any sampling moment within a day, a target window of a preset size is established with the power data at the any sampling moment as the center, and according to the power data fluctuation characteristics within the target window and the power consumption rate at the any sampling moment, the consistency degree of the power data state at the any sampling moment is obtained; the consistency degree of the power data state at each historical sampling moment belonging to the same moment as the any sampling moment is obtained within historical days, and according to the consistency degree of the power data state at each historical sampling moment and the power data fluctuation characteristics within the target window, the power data outlier at the any sampling moment is obtained; the day is divided into at least two time periods, for any time period, according to the power data outliers at each sampling moment within the any time period and the power data fluctuation characteristics within historical days, the monitoring importance degree of the any time period is obtained; the monitoring importance degree of each time period is obtained, and the sampling frequency of each time period within the day is adjusted by using the monitoring importance degree of each time period to obtain the adaptive sampling frequency of each same time period within the future days, which is used to collect the power data of the ring main unit within the future days. Among them, the consistency degree of the power data state at any sampling moment is obtained to judge whether the fluctuation of the power data at any sampling moment is caused by the needs of users, and the possibility of the ring main unit having a fault or other anomalies is detected; then the power data outliers at each sampling moment are obtained, and according to the power data outliers and the power data fluctuation characteristics within historical days, the monitoring importance degree of any time period is obtained. Finally, according to the monitoring importance degree, the adaptive sampling frequency of the same time period within the future days is obtained, and the power data of the ring main unit within the future days is collected according to the adaptive sampling frequency. While not missing key events, the collection of redundant power data is reduced, and the quality of the power data of the ring main unit and the system monitoring effect are improved.

[0071] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An efficient acquisition method for power energy data of ring main units based on sensors, characterized in that, The method includes: Obtaining the power data and power consumption rate of the ring main unit at each sampling moment; For any sampling moment within the current day, taking the power data at the any sampling moment as the center, establishing a target window of a preset size, and obtaining the degree of consistency of the power data state at the any sampling moment according to the power data fluctuation characteristics within the target window and the power consumption rate at the any sampling moment; Obtaining the degree of consistency of the power data state at each historical sampling moment that belongs to the same moment as the any sampling moment within the historical days, and obtaining the abnormal value of the power data at the any sampling moment according to the degree of consistency of the power data state at each historical sampling moment and the power data fluctuation characteristics within the target window; Dividing the current day into at least two time periods, and for any time period, obtaining the monitoring importance degree of the any time period according to the abnormal value of the power data at each sampling moment within the any time period and the power data fluctuation characteristics within the historical days; Obtaining the monitoring importance degree of each of the time periods, and using the monitoring importance degree of each of the time periods to adjust the sampling frequency of each time period within the current day, so as to obtain the adaptive sampling frequency of each same time period within the future days, which is used to collect the power data of the ring main unit within the future days.

2. The method for efficiently collecting power energy data of a ring main unit based on sensors according to claim 1, characterized in that The obtaining the degree of consistency of the power data state at the any sampling moment according to the power data fluctuation characteristics within the target window and the power consumption rate at the any sampling moment includes: Obtaining the normal data range of the data type to which the power data of the ring main unit belongs, and obtaining the degree of power data fluctuation at the any sampling moment according to the difference between the power data at the any sampling moment and the normal data range and the power data fluctuation characteristics within the target window; If the power data at the any sampling moment is within the normal data range, obtaining the reciprocal of the absolute value of the difference between the degree of power data fluctuation at the any sampling moment and the power consumption rate, so as to obtain the degree of consistency of the power data state at the any sampling moment; If the power data at the any sampling moment is not within the normal data range, setting the degree of consistency of the power data state at the any sampling moment to the constant 0.

3. The method for efficiently collecting power data of a ring main unit based on sensors according to claim 2, characterized in that, The obtaining the degree of power data fluctuation at the any sampling moment according to the difference between the power data at the any sampling moment and the normal data range and the power data fluctuation characteristics within the target window includes: Obtaining the absolute value of the difference between the power data at the any sampling moment and the minimum value within the normal data range to obtain the actual difference, obtaining the difference between the maximum value and the minimum value within the normal data range to obtain the maximum difference, and obtaining the ratio of the actual difference to the maximum difference to obtain the first fluctuation value at the any sampling moment; Obtaining the absolute value of the difference between every two adjacent power data within the target window to correspondingly obtain the average absolute value of the difference, obtaining the sum result of the constant 1 and the average absolute value of the difference, and calculating the difference between the constant 1 and the reciprocal of the sum result to obtain the second fluctuation value at the any sampling moment; Obtain the degree of fluctuation of the power data at any sampling moment based on the average value between the first fluctuation value and the second fluctuation value.

4. The method for efficiently collecting power data of a ring main unit based on sensors according to claim 2, wherein The obtaining of the abnormal value of the power data at any sampling moment according to the consistency degree of the power data states at each historical sampling moment and the power data fluctuation characteristics within the target window includes: If the power data at any sampling moment is within the normal data range, obtain the absolute value of the difference between the average value of the consistency degrees of the power data states at all historical sampling moments and the consistency degree of the power data state at any sampling moment, to obtain the consistency degree difference. Obtain the difference between the constant 1 and the reciprocal of the consistency degree difference, to obtain the first difference value at any sampling moment. Form a consistency degree data sequence with the consistency degrees of the power data states at all historical sampling moments. Obtain the absolute value of the difference between every two adjacent consistency degrees of the power data states in the consistency degree data sequence, and correspondingly obtain the average value of the consistency degree differences of the power data states. Obtain the difference between the constant 1 and the reciprocal of the average value of the consistency degree differences of the power data states, to obtain the second difference value at any sampling moment. Perform a weighted summation process on the first difference value and the second difference value to obtain the abnormal value of the power data at any sampling moment.

5. The method for efficiently collecting power data of a ring main unit based on sensors according to claim 4, wherein The obtaining of the abnormal value of the power data at any sampling moment according to the consistency degree of the power data states at each historical sampling moment and the power data fluctuation characteristics within the target window further includes: If the power data at any sampling moment is not within the normal data range, within the target window, obtain the power data change rate between any sampling moment and its previous sampling moment, denoted as the left change rate. Obtain the power data change rate between any sampling moment and its next sampling moment, denoted as the right change rate. Calculate the absolute value of the difference between the left change rate and the right change rate to obtain the data change value at any sampling moment. Obtain the reciprocal of the data change value as the abnormal value of the power data at any sampling moment.

6. The method for efficiently collecting power data of a ring main unit based on sensors according to claim 1, characterized in that, The dividing of the current day into at least two time periods includes: According to the power data fluctuation characteristics within the target window, obtain the data change value at any sampling moment. Obtain the difference between the constant 1 and the reciprocal of the data change value at any sampling moment, to obtain the data turning degree value at any sampling moment. If the data turning degree value at any sampling moment is greater than the preset data turning degree threshold, confirm that any sampling moment is a time period node. Obtain all the time period nodes within the current day. According to all the time period nodes within the current day, divide the current day into at least two time periods.

7. The method for efficiently collecting power data of a ring main unit based on sensors according to claim 2, wherein The obtaining of the monitoring importance degree of any time period according to the abnormal values of the power data at each sampling moment within any time period and the power data fluctuation characteristics within the historical days includes: Obtain a historical time period that belongs to the same time period as the any time period within any historical day. For any historical sampling moment within the historical time period, calculate the difference between the constant 1 and the abnormal value of the electrical energy data at the any historical sampling moment to obtain the reliability of the electrical energy data at the any historical sampling moment. According to the time difference between the any historical day and the current day, obtain the fluctuation weight of the any historical sampling moment, and obtain the product of the fluctuation weight, the degree of fluctuation of the electrical energy data, and the reliability of the electrical energy data at the any historical sampling moment to obtain the degree of abnormal use at the any historical sampling moment; Obtain the degree of abnormal use at each historical sampling moment that belongs to the same moment as the any historical sampling moment within the historical day, and correspondingly obtain the accumulated value of the degree of abnormal use, denoted as the total degree of abnormal use at the any historical sampling moment. Obtain the total degree of abnormal use at each historical sampling moment within the historical time period, and correspondingly obtain the average value of the total degree of abnormal use, denoted as the historical abnormal mean of the any time period; Obtain the average value of the abnormal values of the electrical energy data at each sampling moment within the any time period to obtain the current day abnormal mean of the any time period; Obtain the monitoring importance level of the any time period based on the average value between the historical abnormal mean and the current day abnormal mean of the any time period.

8. The method for efficiently collecting power data of a ring main unit based on sensors according to claim 7, wherein The obtaining of the fluctuation weight of the any historical sampling moment according to the time difference between the any historical day and the current day includes: Obtain the number of days between the any historical day and the current day, and perform normalization processing on the reciprocal of the number of days to obtain the fluctuation weight of the any historical sampling moment.

9. The method for efficiently collecting power data of a ring main unit based on sensors according to claim 1, characterized in that, The adjustment of the sampling frequency of each time period within the current day by using the monitoring importance level of each time period to obtain the adaptive sampling frequency of each same time period within the future day includes: Obtain the number of electrical energy data within each time period within the current day, and correspondingly obtain the average value of the number. For any time period within the current day, obtain the product of the average value of the number and the monitoring importance level of the any time period to obtain the adjustment value; If the monitoring importance level of the any time period is greater than or equal to the monitoring importance level of the same time period of the previous historical day of the current day, then obtain the sum of the sampling frequency of the any time period and the adjustment value to obtain the adaptive sampling frequency of the same time period within the future day; If the monitoring importance level of the any time period is less than the monitoring importance level of the same time period of the previous historical day of the current day, then obtain the difference between the sampling frequency of the any time period and the adjustment value to obtain the adaptive sampling frequency of the same time period within the future day.

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