Efficient method for collecting power data of ring main unit based on sensor

Abstract

The present invention relates to the field of data processing technology, and in particular to a sensor-based efficient method for collecting electric energy data of a ring main unit. The method obtains electric energy data and a power consumption rate of the ring main unit at each sampling moment; for any sampling moment within a day, the degree of consistency of the electric energy data state at any sampling moment is obtained according to the electric energy data fluctuation characteristics and the power consumption rate within the day; the abnormal value of the electric energy data at any sampling moment is obtained according to the electric energy data state consistency at each historical sampling moment and the electric energy data fluctuation characteristics within the day; for any time period within the day, the monitoring importance of any time period is obtained according to the electric energy data abnormal value at each sampling moment and the electric energy data fluctuation characteristics within the historical day; according to the monitoring importance of each time period, an 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 is 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 in particular to a sensor-based method for efficiently collecting electric energy data of a ring main unit. Background Art

[0002] With the development of smart grids and the continuous advancement of power Internet of Things (IoT) technology, the collection of power data from ring main units (RMUs), a crucial component of power systems, has become increasingly critical. Power data within RMUs, such as voltage, current, and power factor, is crucial for monitoring, analyzing, and optimizing power systems. Traditionally, RMU power data collection is performed 24 hours a day. However, the daily data volume for a single RMU can reach gigabytes, and most data is uploaded in its entirety. This massive data volume can overload central servers and increase data processing latency. Since RMU power data rarely changes during certain time periods, some data is redundant. Therefore, adjusting the adaptive sampling frequency for different time periods can effectively reduce redundant data collection and lower the load on central servers.

[0003] When adjusting the sampling frequency of power data, existing methods generally set the sampling frequency based on 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, differences in behavioral habits among users may be reflected as different situations in the overall data. For example, a user uses it frequently in certain time periods, but other users use it infrequently, which may appear as small changes in power data. At the same time, adjusting only according to user habits may ignore the system's own needs, 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, which can easily lead to data loss or delay, affecting data quality and system monitoring effects.

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

[0005] In view of this, an embodiment of the present invention provides a sensor-based efficient method for collecting power data of a ring main unit to solve the problem of how to adaptively adjust the collection frequency of power data in different time periods.

[0006] An embodiment of the present invention provides a sensor-based method for efficiently collecting power data from a ring main unit, the method comprising the following steps:

[0007] Obtain the power data and power consumption rate of the ring main unit at each sampling moment;

[0008] For any sampling moment on the day, a target window of a preset size is established with the electric energy data at that sampling moment as the center, and the degree of consistency of the electric energy data state at that sampling moment is obtained based on the electric energy data fluctuation characteristics within the target window and the power consumption rate at that sampling moment;

[0009] Obtaining a degree of consistency in the state of electric energy data at each historical sampling moment that is the same as the any sampling moment within a historical day, and obtaining an abnormal value of the electric energy data at the any sampling moment based on the degree of consistency in the state of electric energy data at each historical sampling moment and a fluctuation characteristic of the electric energy data within the target window;

[0010] Divide the day into at least two time periods, and for any time period, obtain the monitoring importance of any time period based on the abnormal value of the power data at each sampling moment in the any time period and the fluctuation characteristics of the power data in the historical day;

[0011] Obtain the monitoring importance of each time period, and use the monitoring importance of each time period to adjust the sampling frequency of each time period within the day to obtain an adaptive sampling frequency for each same time period in the future days, which is used to collect the power data of the ring network cabinet in the future days.

[0012] Preferably, obtaining the consistency of the power data state at any sampling moment according to the power data fluctuation characteristics in the target window and the power usage rate at any sampling moment includes:

[0013] Obtaining a normal data range of the data type to which the power data of the ring main unit belongs, and obtaining a degree of fluctuation of the power data at any sampling moment based on a difference between the power data at any sampling moment and the normal data range and a fluctuation characteristic of the power data within the target window;

[0014] If the electric energy data at any sampling moment is within the normal data range, obtaining the inverse of the absolute value of the difference between the electric energy data fluctuation degree at any sampling moment and the power consumption rate to obtain the consistency degree of the electric energy data state at any sampling moment;

[0015] If the electric energy data at any sampling moment is not within the normal data range, the state consistency degree of the electric energy data at any sampling moment is set to a constant of 0.

[0016] Preferably, obtaining the degree of fluctuation of the electric energy data at any sampling moment according to the difference between the electric energy data at any sampling moment and the normal data range, and the electric energy data fluctuation characteristics within the target window, includes:

[0017] Obtaining an absolute value of a difference between the electric energy data at any sampling moment and a minimum value within the normal data range to obtain an actual difference, obtaining a difference between a maximum value and a minimum value within the normal data range to obtain a maximum difference, obtaining a ratio of the actual difference to the maximum difference to obtain a first fluctuation value at any sampling moment;

[0018] Obtaining the absolute value of the difference between each two adjacent electric energy data in the target window, obtaining the corresponding average absolute value of the difference, obtaining the sum of a constant 1 and the average absolute value of the difference, calculating the difference between the constant 1 and the reciprocal of the sum, and obtaining the second fluctuation value at any sampling moment;

[0019] The fluctuation degree of the electric energy data at any sampling moment is obtained according to the average value between the first fluctuation value and the second fluctuation value.

[0020] Preferably, obtaining the abnormal value of the electric energy data at any sampling moment according to the consistency of the electric energy data state at each historical sampling moment and the electric energy data fluctuation characteristics within the target window includes:

[0021] If the electric energy data at any sampling moment is within the normal data range, obtaining the absolute value of the difference between the average value of the state consistency of the electric energy data at all historical sampling moments and the state consistency of the electric energy data at any sampling moment to obtain the state consistency difference, obtaining the difference between a constant 1 and the reciprocal of the state consistency difference to obtain the first difference value at any sampling moment;

[0022] The state consistency levels of the electric energy data at all historical sampling moments are combined into a state consistency data sequence, the absolute value of the difference between the state consistency levels of each two adjacent electric energy data in the state consistency data sequence is obtained, and the mean of the electric energy data state consistency differences is obtained accordingly. The second difference value at any sampling moment is obtained by obtaining the difference between a constant 1 and the reciprocal of the mean of the electric energy data state consistency differences;

[0023] A weighted summation process is performed on the first difference value and the second difference value to obtain an abnormal value of the electric energy data at any sampling moment.

[0024] Preferably, the step of obtaining the abnormal value of the electric energy data at any sampling moment according to the consistency of the electric energy data status at each historical sampling moment and the electric energy data fluctuation characteristics within the target window further includes:

[0025] If the electric energy data at any sampling moment is not within the normal data range, then in the target window, obtain the electric energy data change rate between any sampling moment and its previous sampling moment, recorded as the left change rate, obtain the electric energy data change rate between any sampling moment and its next sampling moment, recorded 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;

[0026] The inverse of the data change value is obtained as the abnormal value of the electric energy data at any sampling moment.

[0027] Preferably, dividing the day into at least two time periods includes:

[0028] According to the fluctuation characteristics of the electric energy data in the target window, the data change value at any sampling moment is obtained, and the difference between a constant 1 and the reciprocal of the data change value at any sampling moment is obtained to obtain the data turning point value at any sampling moment;

[0029] If the data turning point value at any sampling moment is greater than a preset data turning point threshold, then the sampling moment is determined to be a time period node;

[0030] Get all time period nodes of the day, and divide the day into at least two time periods based on all time period nodes of the day.

[0031] Preferably, obtaining the monitoring importance of any time period according to the abnormal value of the electric energy data at each sampling moment in any time period and the fluctuation characteristics of the electric energy data in historical days includes:

[0032] Obtain a historical time period that is in 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 a constant 1 and the abnormal value of the electric energy data at the any historical sampling moment to obtain the reliability of the electric energy data at the any historical sampling moment; obtain a fluctuation weight at the any historical sampling moment based on the time difference between the any historical day and the current day; obtain the product of the fluctuation weight at the any historical sampling moment, the degree of fluctuation of the electric energy data, and the reliability of the electric energy data to obtain the degree of abnormal usage at the any historical sampling moment;

[0033] Obtaining the usage anomaly level for each historical sampling moment that is the same as any of the historical sampling moments within the historical day, obtaining a corresponding accumulated value of the usage anomaly levels, and recording it as the total usage anomaly level for any of the historical sampling moments; obtaining the total usage anomaly level for each of the historical sampling moments within the historical time period, and obtaining a corresponding average value of the total usage anomaly levels, and recording it as the historical anomaly mean for any of the time periods;

[0034] Obtaining the average value of the abnormal values ​​of the electric energy data at each sampling moment in any time period to obtain the abnormal mean value of the day in any time period;

[0035] The monitoring importance of any time period is obtained according to the average value between the historical abnormal mean value and the abnormal mean value of the day in any time period.

[0036] Preferably, obtaining the fluctuation weight of any historical sampling moment according to the time difference between any historical day and the current day includes:

[0037] The number of days between any historical day and the current day is obtained, and the reciprocal of the number of days between the two days is normalized to obtain the fluctuation weight of any historical sampling moment.

[0038] Preferably, the sampling frequency of each time period within the day is adjusted by utilizing the monitoring importance of each time period to obtain an adaptive sampling frequency for each same time period in the future days, including:

[0039] Obtain the quantity of electric energy data in each time period of the day, and obtain the corresponding quantity mean. For any time period of the day, obtain the product of the quantity mean and the monitoring importance of any time period to obtain the adjustment value;

[0040] If the monitoring importance of any time period is greater than or equal to the monitoring importance of the same time period on the previous historical day, then the sampling frequency of any time period and the adjustment value are added together to obtain the adaptive sampling frequency of the same time period in the future day;

[0041] If the monitoring importance of any time period is less than the monitoring importance of the same time period on the previous historical day, the difference between the sampling frequency of any time period and the adjustment value is obtained to obtain the adaptive sampling frequency of the same time period in the future days.

[0042] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0043] The present invention obtains the electric energy data and power consumption rate of the ring main unit at each sampling moment; for any sampling moment in the day, a target window of a preset size is established with the electric energy data at the any sampling moment as the center, and the degree of consistency of the electric energy data state at the any sampling moment is obtained according to the electric energy data fluctuation characteristics within the target window and the power consumption rate at the any sampling moment; the degree of consistency of the electric energy data state at each historical sampling moment belonging to the same moment as the any sampling moment in the historical day is obtained, and the abnormal value of the electric energy data at the any sampling moment is obtained according to the electric energy data state consistency at each historical sampling moment and the electric energy data fluctuation characteristics within the target window; the day is divided into at least two time periods, and for any time period, the monitoring importance of the any time period is obtained according to the electric energy data abnormal value at each sampling moment in the any time period and the electric energy data fluctuation characteristics in the historical day; the monitoring importance of each time period is obtained, and the sampling frequency of each time period in the day is adjusted using the monitoring importance of each time period to obtain an adaptive sampling frequency for each same time period in the future day, which is used to collect the electric energy data of the ring main unit in the future day. Among them, the consistency of the power data status at any sampling moment is obtained, and it is judged whether the fluctuation of the power data at any sampling moment is caused by user needs, and the possibility of failure or other abnormalities in the ring network cabinet is detected; then the abnormal value of the power data at each sampling moment is obtained, and the monitoring importance of any time period is obtained according to the abnormal value of the power data and the fluctuation characteristics of the power data in historical days. Finally, the adaptive sampling frequency of the same time period in the future days is obtained according to the monitoring importance, and the power data of the ring network cabinet in 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 network cabinet and the system monitoring effect are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a method flow chart of a sensor-based method for efficiently collecting power data of a ring main unit provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0046] The embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0047] It should be noted that the terms "first," "second," and the like in the specification of the present disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, 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. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0048] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0049] See also Figure 1 , is a flow chart of a method for efficiently collecting power data of a ring main unit based on a sensor provided in the first embodiment of the present invention, such as Figure 1 As shown, the method may include:

[0050] Step S101: Obtain the power data and power usage rate of the ring main unit at each sampling moment.

[0051] The scenario targeted by the present invention is the adjustment of the sampling frequency of power data of ring main unit (RMU) with residential users in the community as the main users. When adjusting the sampling frequency of power data, the existing method generally sets the sampling frequency according to the performance of power data and historical power data. Since power data is generally related to user usage, and the RMU generally serves multiple users, the differences in behavioral habits between users may be reflected in different situations in the overall data. For example, a user frequently uses it in certain time periods, but other users do not use it frequently, which may show the characteristics of small changes in power data. At the same time, adjusting only according to user habits may ignore the system's own needs, 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, which may easily lead to data loss or delay, affecting data quality and system monitoring effects.

[0052] Therefore, this embodiment obtains the abnormal value of the electric energy data based on the fluctuation characteristics of the electric energy data of the ring main unit on the current day and in historical days, combined with the usage characteristics of the user, and then obtains the monitoring importance of each time period based on the abnormal value of the electric energy data and the fluctuation characteristics of the electric energy data in historical days. Finally, according to the monitoring importance, the adaptive sampling frequency of the same time period in the future days is obtained, and the electric energy data of the ring main unit in the future days is collected. While not missing key events, the collection of redundant electric energy data is reduced, and the quality of the electric energy data of the ring main unit and the system monitoring effect are improved.

[0053] The electric energy data in the ring main unit includes voltage, current, etc. This embodiment takes a type of electric energy data of the ring main unit as an example, adjusts the collection frequency of the electric energy data of the ring main unit, and uses the sensor corresponding to the electric energy data to collect the electric energy data of the ring main unit at each sampling moment (voltage data is collected using a voltage sensor, and current data is collected using a current sensor). In this embodiment, the initial collection frequency is set to once per second, and the subsequent daily collection frequency needs to be collected according to the actual calculated collection frequency. There is no restriction here, and it can be set according to the specific implementation scenario.

[0054] Because the power data from the RMUs within a residential complex at each sampling moment is comprehensive, representing the dispatch and allocation of all electricity within the community at that moment, focusing solely on the RMUs' power data is incomplete. High current data at a given sampling moment can indicate a high number of users, but in reality, this may not necessarily be the case. This could be due to factors such as some users using high-power appliances, RMU failures, or sensor interference. Therefore, the power consumption rate at each sampling moment can be obtained by analyzing the actual power consumption characteristics of residents using the RMUs' power meters. This can then be combined with the RMUs' power data and power consumption rate to analyze the actual usage characteristics of the power data.

[0055] The electricity consumption rate refers to the proportion of users whose electricity meter data changes among all users in the community, that is, ,in, is the power consumption rate at the t-th sampling moment, is the number of users whose electricity data on the electric meter changes at the t-th sampling time, B is the number of all users in the cell, The larger it is, the more users use electricity at the tth sampling moment, and the higher the electricity consumption rate.

[0056] Step S102: For any sampling moment on the day, a target window of a preset size is established with the electric energy data at any sampling moment as the center, and the degree of consistency of the electric energy data state at any sampling moment is obtained based on the electric energy data fluctuation characteristics within the target window and the power consumption rate at any sampling moment.

[0057] If more users use electricity at the tth sampling time, it means that the power data before and after the tth sampling time may have certain fluctuations. The volatility is mainly reflected in the size of the data and the frequency of data changes. If the power data is volatile and the power consumption rate is low, it means that the power data status may be inconsistent. At this time, the ring main unit may have a fault or other abnormal situation.

[0058] Therefore, for any sampling moment in the day, a target window with a preset size of 5 is established with the electric energy data at any sampling moment as the center. There is no restriction here and it can be set according to the specific implementation scenario to analyze the fluctuation characteristics of the electric energy data. According to the fluctuation characteristics of the electric energy data in the target window and the power consumption rate at any sampling moment, the consistency of the electric energy data state at any sampling moment is obtained, and the abnormal characteristics of the electric energy data of the ring network cabinet at any sampling moment are analyzed, that is, whether the electric energy data at the sampling moment is an abnormal situation worthy of attention, and then according to the abnormal characteristics of the electric energy data, whether the electric energy data at the sampling moment needs to be monitored is analyzed.

[0059] Among them, according to the fluctuation characteristics of the electric energy data in the target window and the power consumption rate at any sampling moment, the method for obtaining the consistency of the electric energy data state at any sampling moment is as follows:

[0060] (1) According to the actual information of the ring network cabinet, the normal data range of the data type of the electric energy data of the ring network cabinet is obtained. For example, the rated voltage of the community ring network cabinet is usually designed to be 10kV. Under normal operating conditions, the voltage of the ring network cabinet is allowed to have a certain fluctuation range, and the fluctuation range usually does not exceed 10% of the rated voltage, that is, 9kV to 11kV is the normal data range of the voltage data of the community ring network cabinet. The specific fluctuation range needs to be determined according to the power grid standards and equipment requirements. There is no restriction here. It can be set according to the specific implementation scenario. According to the difference between the electric energy data at any sampling moment and the normal data range, and the electric energy data fluctuation characteristics in the target window, the electric energy data fluctuation degree at any sampling moment is obtained.

[0061] Specifically, obtaining the absolute value of the difference between the electric energy data at 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, obtaining the ratio of the actual difference to the maximum difference to obtain the first fluctuation value at any sampling moment;

[0062] Obtaining the absolute value of the difference between each two adjacent electric energy data in the target window, obtaining the corresponding average absolute value of the difference, obtaining the sum of a constant 1 and the average absolute value of the difference, calculating the difference between the constant 1 and the reciprocal of the sum, and obtaining the second fluctuation value at any sampling moment;

[0063] The fluctuation degree of the electric energy data at any sampling moment is obtained according to the average value between the first fluctuation value and the second fluctuation value.

[0064] In one embodiment, taking the t-th sampling time of the day as an example, the calculation formula for the fluctuation degree of the electric energy data at the t-th sampling time is:

[0065]

[0066] in, is the fluctuation degree of electric energy data at the tth sampling moment; is the electric 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+1th electric energy data in the target window; is the i-th electric energy data in the target window; N is the number of electric energy data in the target window; 1 is a constant.

[0067] It should be noted that is the first fluctuation value at the t-th sampling moment. The greater the difference between the electric energy data at the t-th sampling moment and the minimum value within the normal data range, The larger it is, the more likely the electric energy data at the t-th sampling moment is to be the electric energy data during the peak period of electricity consumption for residential users in the community, and the greater the fluctuation of the electric energy data at the t-th sampling moment; is the average absolute value of the difference between two adjacent electric energy data in the target window, The larger the value, the greater the difference between adjacent electric energy data in the target window, the greater the volatility of the electric energy data in the target window, and the second fluctuation value at the tth sampling time. The larger it is, the greater the fluctuation of the electric energy data at the tth sampling moment.

[0068] (2) If the electric energy data at any sampling moment is within the normal data range, the inverse of the absolute value of the difference between the electric energy data fluctuation degree at any sampling moment and the power consumption rate is obtained to obtain the consistency degree of the electric energy data state at any sampling moment.

[0069] In one embodiment, taking the t-th sampling time of the day as an example, the calculation formula for the consistency degree of the power data state at the t-th sampling time is:

[0070]

[0071] in, is the consistency of the power data state at the t-th sampling moment; is the fluctuation degree of electric energy data at the tth sampling moment; is the power consumption rate at the tth sampling moment; is the absolute value symbol.

[0072] When explanation is needed, Indicates the difference between the fluctuation degree of electric energy data and the power consumption rate at the t-th sampling moment, The smaller it is, the more likely the change in the electric energy data at the t-th sampling moment is caused by the actual power consumption situation. The more consistent the change characteristics of the electric energy data at the t-th sampling moment are with the actual power consumption situation, the greater the consistency of the electric energy data status at the t-th sampling moment.

[0073] (3) If the electric energy data at any sampling moment is not within the normal data range, it means that the fluctuation of the electric energy data at the tth sampling moment is more likely to be caused by a fault in the ring network cabinet or other abnormal conditions, and has nothing to do with the actual power consumption. The consistency degree of the electric energy data state at any sampling moment is set to a constant of 0.

[0074] At this point, the consistency of the power data state at any sampling moment is obtained.

[0075] Step S103, obtaining the degree of consistency of the electric energy data status at each historical sampling moment belonging to the same moment as any of the sampling moments within a historical day, and obtaining the abnormal value of the electric energy data at any of the sampling moments based on the degree of consistency of the electric energy data status at each historical sampling moment and the electric energy data fluctuation characteristics within the target window.

[0076] The greater the degree of consistency in the power data status at any sampling moment, the more consistent the data's changing characteristics are with actual power usage, and the less likely anomalies are to occur. The less consistent the power data status at any sampling moment, the more likely it is that anomalies are occurring in the user's actual power usage, a ring main unit malfunction, or other abnormalities are occurring, making it more important to monitor the power data at any sampling moment. However, the consistency of the power data status at any sampling moment alone cannot accurately determine whether the power data at any sampling moment is abnormal. Analysis must be conducted in conjunction with the power data fluctuation characteristics within the target window and the consistency of the power data status within the historical day.

[0077] Therefore, according to the above-mentioned method for obtaining the degree of consistency of the electric energy data status at the t-th sampling moment, the degree of consistency of the electric energy data status at each historical sampling moment belonging to the same moment as any of the sampling moments is obtained within the historical day. In this embodiment, the number of historical days is set to 5 days, which is not limited here. It can be set according to the specific implementation scenario. According to the degree of consistency of the electric energy data status at each historical sampling moment and the fluctuation characteristics of the electric energy data in the target window, the electric energy data abnormal value at any sampling moment is obtained, that is, whether the electric energy data at the sampling moment is a noteworthy abnormal situation, and then the electric energy data at the sampling moment is analyzed based on the electric energy data abnormal value whether it needs to be monitored.

[0078] Among them, according to the consistency of the power data status at each historical sampling moment and the power data fluctuation characteristics within the target window, the method for obtaining the power data abnormal value at any sampling moment is as follows:

[0079] (1) If the electric energy data at any sampling moment is within the normal data range, when an abnormality occurs, it may be that the user's actual electricity usage is abnormal. Therefore, the abnormal value of the electric energy data at any sampling moment is obtained according to the consistency of the electric energy data status at each historical sampling moment.

[0080] Specifically, the absolute value of the difference between the average value of the state consistency of the power data at all historical sampling moments and the state consistency of the power data at any sampling moment is obtained to obtain the state consistency difference, and the difference between a constant 1 and the reciprocal of the state consistency difference is obtained to obtain the first difference value at any sampling moment;

[0081] The state consistency levels of the electric energy data at all historical sampling moments are combined into a state consistency data sequence, the absolute value of the difference between the state consistency levels of each two adjacent electric energy data in the state consistency data sequence is obtained, and the mean of the electric energy data state consistency differences is obtained accordingly. The second difference value at any sampling moment is obtained by obtaining the difference between a constant 1 and the reciprocal of the mean of the electric energy data state consistency differences;

[0082] A weighted summation process is performed on the first difference value and the second difference value to obtain an abnormal value of the electric energy data at any sampling moment.

[0083] In one embodiment, taking the t-th sampling time of the day as an example, the calculation formula for the abnormal value of the electric energy data at the t-th sampling time is:

[0084]

[0085] in, is the abnormal value of the electric energy data at the t-th sampling moment; is the consistency of the power data state at the t-th sampling moment; is the state consistency degree of the dth electric energy data in the state consistency degree data sequence; is the state consistency level of the d+1th electric energy data in the state consistency level data sequence; C is the number of state consistency levels of the electric energy data in the state consistency level data sequence (that is, 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 of the power data status of the day is more important than the consistency of the power data status of the history, this embodiment sets , , there is no restriction here and it can be set according to the specific implementation scenario.

[0086] It should be noted that is the first difference value. The greater the difference between the consistency of the power data status at the t-th sampling time of the day and the average consistency of the power data status at the historical sampling time, The larger it is, the more abnormal the electric energy data at the t-th sampling time of the day is, and the larger the abnormal value of the electric energy data at the t-th sampling time is; is the second difference value. The greater the difference between the consistency of the power data status at the historical sampling moment, The larger it is, the more chaotic the electric energy data at each historical sampling moment that is the same as the t sampling moment of the day are, the stronger the volatility is. The more likely the electric energy data at the t sampling moment is to be abnormal, the larger the abnormal value of the electric energy data at the t sampling moment is.

[0087] (2) If the electric energy data at any sampling moment is not within the normal data range, when an abnormality occurs, it may be that the ring network cabinet has a fault or other abnormal conditions, which has nothing to do with the actual power consumption. Therefore, according to the fluctuation characteristics of the electric energy data in the target window, the abnormal value of the electric energy data at any sampling moment is obtained.

[0088] Specifically, in the target window, the rate of change of the electric energy data between any sampling moment and its previous sampling moment is obtained, recorded as the left change rate, the rate of change of the electric energy data between any sampling moment and its next sampling moment is obtained, recorded as the right change rate, the absolute value of the difference between the left change rate and the right change rate is calculated, the data change value at any sampling moment is obtained, and the inverse of the data change value is obtained as the abnormal value of the electric energy data at any sampling moment, wherein the change rate belongs to the existing technology and will not be repeated here.

[0089] In one embodiment, taking the t-th sampling time of the day as an example, the calculation formula for the abnormal value of the electric energy data at the t-th sampling time is:

[0090]

[0091] in, The abnormal value of the electric energy data at the t-th sampling moment is; is the left change rate; is the right change rate; is the absolute value symbol.

[0092] It should be noted that is the data change value at the t-th sampling moment, The larger the value is, the more likely the electric energy data at the t-th sampling moment is to be isolated mutation electric energy data, and the more likely the abnormality of the electric energy data at the t-th sampling moment is to be caused by sensor abnormality caused by electromagnetic interference, etc., and the sensor abnormality caused by electromagnetic interference does not need to be paid too much attention, and the lower the abnormal value of the electric energy data at the t-th sampling moment is; if the electric energy data at the t-th sampling moment is the first electric energy data in the target window, the left change rate is recorded as 0, and the data change value at the t-th sampling moment is If the electric energy data at the t-th sampling moment is the last electric energy data in the target window, the right change rate is recorded as 0, and the data change value at the t-th sampling moment is .

[0093] At this point, the abnormal value of the electric energy data at any sampling moment is obtained.

[0094] Step S104, dividing the day into at least two time periods, and for any time period, obtaining the monitoring importance of any time period based on the abnormal value of the power data at each sampling moment in the any time period and the power data fluctuation characteristics in historical days.

[0095] After obtaining the abnormal value of the electric energy data at any sampling moment, the larger the abnormal value of the electric energy data is, the more chaotic and uncertain the change of the electric energy data is, the more likely an abnormal situation is to occur, and the more it needs to be monitored.

[0096] Since the power data presents similar data characteristics within a certain time period, the day can be divided into at least two time periods according to the fluctuation characteristics of the power data within the day, and it is analyzed whether the power data in each time period needs to be monitored.

[0097] Specifically, according to the above-mentioned method for obtaining the data change value at the t-th sampling moment, based on the fluctuation characteristics of the electric energy data in the target window, the data change value at any sampling moment is obtained, and the difference between the constant 1 and the reciprocal of the data change value at any sampling moment is obtained to obtain the data turning degree value at any sampling moment;

[0098] According to experimental statistical analysis, the data turning point value at most sampling moments is around 0.4. Therefore, in this embodiment, the data turning point threshold is set to 0.4. This is not limited here and can be set according to specific implementation scenarios. If the data turning point value at any sampling moment is greater than 0.4, then the sampling moment is determined to be a time period node.

[0099] Get all time period nodes of the day, and divide the day into at least two time periods based on all time period nodes of the day.

[0100] Furthermore, based on the abnormal values ​​of the power data at each sampling moment in each time period and the fluctuation characteristics of the power data in the past day, the monitoring importance of each time period is obtained. Then, based on the monitoring importance of each time period, the sampling frequency of each time period in the day is adjusted to obtain the adaptive sampling frequency of each same time period in the future day. For any time period, the method for obtaining the monitoring importance of any time period is as follows:

[0101] Obtain a historical time period that belongs to the same time period as the any time period in any historical day, calculate, for any historical sampling moment in the historical time period, the difference between a constant 1 and the abnormal value of the electric energy data at the any historical sampling moment, obtain the reliability of the electric energy data at the any historical sampling moment, obtain the number of days between the any historical day and the current day, normalize the reciprocal of the number of days between the any historical day and the current day, obtain the fluctuation weight of the any historical sampling moment, obtain the product of the fluctuation weight of the any historical sampling moment, the degree of fluctuation of the electric energy data, and the reliability of the electric energy data, and obtain the degree of abnormal usage at the any historical sampling moment;

[0102] Obtaining the usage anomaly level for each historical sampling moment that is the same as any of the historical sampling moments within the historical day, obtaining a corresponding accumulated value of the usage anomaly levels, and recording it as the total usage anomaly level for any of the historical sampling moments; obtaining the total usage anomaly level for each of the historical sampling moments within the historical time period, and obtaining a corresponding average value of the total usage anomaly levels, and recording it as the historical anomaly mean for any of the time periods;

[0103] Obtaining the average value of the abnormal values ​​of the electric energy data at each sampling moment in any time period to obtain the abnormal mean value of the day in any time period;

[0104] The monitoring importance of any time period is obtained according to the average value between the historical abnormal mean value and the abnormal mean value of the day in any time period.

[0105] In one embodiment, taking the mth time period of the day as an example, the calculation formula for the monitoring importance of the mth time period is:

[0106]

[0107] in, is the monitoring importance of the mth time period; is the volatility weight of the sth historical sampling moment in the historical time period of the dth historical day; is the fluctuation degree of electric energy data at the sth historical sampling moment in the historical time period of the dth historical day; is the abnormal value of the electric energy data at the sth historical sampling moment in the historical time period of the dth historical day; C is the number of historical days; M is the number of sampling moments in the mth time period (that is, the number of historical sampling moments in the historical time period); is the abnormal value of the electric energy data at the sth sampling moment in the mth time period.

[0108] It should be noted that is the reliability of the electric energy data at the sth historical sampling moment in the historical time period of the dth historical day, indicating the actual usage level at the sth historical sampling moment in the historical time period of the dth historical day. The smaller it is, the more it conforms to the user's actual usage. The larger it is, the more attention should be paid to the data fluctuations at this time, and the more important the monitoring of the mth time period is; The smaller the value, the greater the correlation between the power data of the dth historical day and the power data of the current day. The greater the possibility of data fluctuation, the more attention should be paid, and the more important the monitoring of the mth time period is. is the abnormal mean value of the mth time period on the day, The larger the value is, the greater the possibility of abnormal power data in the mth time period, the more attention the mth time period needs, and the greater the importance of monitoring the mth time period.

[0109] At this point, the monitoring importance of any time period is obtained.

[0110] Step S105, obtain the monitoring importance of each time period, and use the monitoring importance of each time period to adjust the sampling frequency of each time period in the day to obtain an adaptive sampling frequency for each same time period in the future days, which is used to collect the power data of the ring network cabinet in the future days.

[0111] According to the above method for obtaining the monitoring importance of the mth time period, the monitoring importance of each time period on the current day and in previous days is obtained. Then, based on the monitoring importance of each time period, the sampling frequency of each time period on the current day is adjusted to obtain the adaptive sampling frequency for each same time period in the future days, and the power data of the ring main unit in the future days is collected. For any time period on the current day, the method for obtaining the adaptive sampling frequency for the same time period in the future days is as follows:

[0112] Obtaining the quantity of electric energy data in each time period of the day, obtaining a corresponding quantity mean, obtaining the product of the quantity mean and the monitoring importance level of any time period, and obtaining an adjustment value;

[0113] If the monitoring importance of any time period is greater than or equal to the monitoring importance of the same time period on the previous historical day, then the sampling frequency of any time period and the adjustment value are added together to obtain the adaptive sampling frequency of the same time period in the future day;

[0114] If the monitoring importance of any time period is less than the monitoring importance of the same time period on the previous historical day, the difference between the sampling frequency of any time period and the adjustment value is obtained to obtain the adaptive sampling frequency of the same time period in the future days.

[0115] In one embodiment, taking the mth time period of the day as an example, the calculation formula for the adaptive sampling frequency of the same time period in the future day is:

[0116]

[0117] in, is the adaptive sampling frequency of the same time period in the future day; P is the sampling frequency of the mth time period in the day; G is the quantity mean; is the monitoring importance of the mth time period on the day; The monitoring importance of the same time period of the previous historical day.

[0118] It should be noted that is the adjustment value, if , it means that the fluctuation state of the power data in the mth cycle of the day is more unstable, and the collection frequency of power data needs to be increased to collect more effective power data; if , it means that the fluctuation state of the power data in the mth cycle of the day is more stable, and it is necessary to reduce the frequency of power data collection and reduce redundant power data.

[0119] According to the above-mentioned method for obtaining the adaptive sampling frequency of the same time period in the future days (that is, the adaptive sampling frequency of the same time period in the future days corresponding to the mth time period of the day), the adaptive sampling frequency of each same time period in the future days is obtained, and the power data of the ring main unit in the future days is collected.

[0120] In summary, the embodiment of the present invention obtains the electric energy data and power consumption rate of the ring network cabinet at each sampling moment; for any sampling moment in the day, a target window of a preset size is established with the electric energy data at any sampling moment as the center, and the degree of consistency of the electric energy data state at any sampling moment is obtained according to the electric energy data fluctuation characteristics in the target window and the power consumption rate at any sampling moment; the degree of consistency of the electric energy data state at each historical sampling moment belonging to the same moment as the any sampling moment is obtained in the historical day, and the degree of consistency of the electric energy data state at each historical sampling moment and the electric energy data in the target window are obtained according to the degree of consistency of the electric energy data state at each historical sampling moment and the electric energy data in the target window. According to the fluctuation characteristics, the abnormal value of the electric energy data at any sampling moment is obtained; the day is divided into at least two time periods, and for any time period, the monitoring importance of the any time period is obtained according to the abnormal value of the electric energy data at each sampling moment in the any time period and the fluctuation characteristics of the electric energy data in historical days; the monitoring importance of each of the time periods is obtained, and the sampling frequency of each time period in the day is adjusted using the monitoring importance of each of the time periods to obtain an adaptive sampling frequency for each of the same time periods in the future days, which is used to collect the electric energy data of the ring network cabinet in the future days. Among them, the consistency of the power data status at any sampling moment is obtained, and it is judged whether the fluctuation of the power data at any sampling moment is caused by user needs, and the possibility of failure or other abnormalities in the ring network cabinet is detected; then the abnormal value of the power data at each sampling moment is obtained, and the monitoring importance of any time period is obtained according to the abnormal value of the power data and the fluctuation characteristics of the power data in historical days. Finally, the adaptive sampling frequency of the same time period in the future days is obtained according to the monitoring importance, and the power data of the ring network cabinet in 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 network cabinet and the system monitoring effect are improved.

[0121] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A sensor-based method for efficiently collecting power data from ring main units, characterized in that: The method comprises: Obtain the power data and power 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 electric energy data at any sampling moment as the center, and the normal data range of the data type to which the electric energy data of the ring main unit belongs is obtained. Based on the difference between the electric energy data at any sampling moment and the normal data range, and the electric energy data fluctuation characteristics within the target window, the electric energy data fluctuation degree at any sampling moment is obtained; if the electric energy data at any sampling moment is within the normal data range, the inverse of the absolute value of the difference between the electric energy data fluctuation degree at any sampling moment and the power consumption rate is obtained to obtain the electric energy data state consistency degree at any sampling moment; if the electric energy data at any sampling moment is not within the normal data range, the electric energy data state consistency degree at any sampling moment is set to a constant of 0; Obtaining the degree of consistency of the electric energy data state at each historical sampling moment that is the same as the any sampling moment within the historical day; if the electric energy data at any sampling moment is within the normal data range, obtaining the abnormal value of the electric energy data at any sampling moment based on the degree of consistency of the electric energy data state at each historical sampling moment; if the electric energy data at any sampling moment is not within the normal data range, obtaining the abnormal value of the electric energy data at any sampling moment based on the fluctuation characteristics of the electric energy data within the target window; Divide the day into at least two time periods, and for any time period, obtain the monitoring importance of any time period based on the abnormal value of the power data at each sampling moment in the any time period and the fluctuation characteristics of the power data in the historical day; Obtain the monitoring importance of each time period, and use the monitoring importance of each time period to adjust the sampling frequency of each time period within the day to obtain an adaptive sampling frequency for each same time period in the future days, which is used to collect the power data of the ring network cabinet in the future days.

2. The sensor-based ring main unit power data efficient acquisition method according to claim 1 is characterized in that: The obtaining of the degree of fluctuation of the electric energy data at any sampling moment according to the difference between the electric energy data at any sampling moment and the normal data range and the electric energy data fluctuation characteristics within the target window includes: Obtaining an absolute value of a difference between the electric energy data at any sampling moment and a minimum value within the normal data range to obtain an actual difference, obtaining a difference between a maximum value and a minimum value within the normal data range to obtain a maximum difference, obtaining a ratio of the actual difference to the maximum difference to obtain a first fluctuation value at any sampling moment; Obtaining the absolute value of the difference between each two adjacent electric energy data in the target window, obtaining the corresponding average absolute value of the difference, obtaining the sum of a constant 1 and the average absolute value of the difference, calculating the difference between the constant 1 and the reciprocal of the sum, and obtaining the second fluctuation value at any sampling moment; The fluctuation degree of the electric energy data at any sampling moment is obtained according to the average value between the first fluctuation value and the second fluctuation value.

3. The sensor-based ring main unit power data efficient acquisition method according to claim 1 is characterized in that: The obtaining of the abnormal value of the electric energy data at any sampling moment according to the consistency degree of the electric energy data state at each historical sampling moment includes: Obtaining the absolute value of the difference between the average value of the state consistency of the power data at all historical sampling moments and the state consistency of the power data at any sampling moment to obtain a state consistency difference, obtaining the difference between a constant 1 and the reciprocal of the state consistency difference to obtain a first difference value at any sampling moment; The state consistency levels of the electric energy data at all historical sampling moments are combined into a state consistency data sequence, the absolute value of the difference between the state consistency levels of each two adjacent electric energy data in the state consistency data sequence is obtained, and the mean of the electric energy data state consistency differences is obtained accordingly. The second difference value at any sampling moment is obtained by obtaining the difference between a constant 1 and the reciprocal of the mean of the electric energy data state consistency differences; A weighted summation process is performed on the first difference value and the second difference value to obtain an abnormal value of the electric energy data at any sampling moment.

4. The sensor-based ring main unit power data efficient acquisition method according to claim 1 is characterized in that: The obtaining of an abnormal value of the electric energy data at any sampling moment according to the electric energy data fluctuation characteristics within the target window includes: In the target window, the rate of change of the electric energy data between any sampling moment and the previous sampling moment is obtained, recorded as the left rate of change; the rate of change of the electric energy data between any sampling moment and the next sampling moment is obtained, recorded as the right rate of change; the absolute value of the difference between the left rate of change and the right rate of change is calculated to obtain the data change value at any sampling moment; The inverse of the data change value is obtained as the abnormal value of the electric energy data at any sampling moment.

5. The sensor-based ring main unit power data efficient acquisition method according to claim 1 is characterized in that: The dividing of the day into at least two time periods includes: According to the fluctuation characteristics of the electric energy data in the target window, the data change value at any sampling moment is obtained, and the difference between a constant 1 and the reciprocal of the data change value at any sampling moment is obtained to obtain the data turning point value at any sampling moment; If the data turning point value at any sampling moment is greater than a preset data turning point threshold, then the sampling moment is determined to be a time period node; Get all time period nodes of the day, and divide the day into at least two time periods based on all time period nodes of the day.

6. The sensor-based ring main unit power data efficient acquisition method according to claim 1 is characterized in that: The obtaining of the monitoring importance of any time period according to the abnormal value of the electric energy data at each sampling moment in any time period and the fluctuation characteristics of the electric energy data in historical days includes: Obtain a historical time period that is in 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 a constant 1 and the abnormal value of the electric energy data at the any historical sampling moment to obtain the reliability of the electric energy data at the any historical sampling moment; obtain a fluctuation weight at the any historical sampling moment based on the time difference between the any historical day and the current day; obtain the product of the fluctuation weight at the any historical sampling moment, the degree of fluctuation of the electric energy data, and the reliability of the electric energy data to obtain the degree of abnormal usage at the any historical sampling moment; Obtaining the usage anomaly level for each historical sampling moment that is the same as any of the historical sampling moments within the historical day, obtaining a corresponding accumulated value of the usage anomaly levels, and recording it as the total usage anomaly level for any of the historical sampling moments; obtaining the total usage anomaly level for each of the historical sampling moments within the historical time period, and obtaining a corresponding average value of the total usage anomaly levels, and recording it as the historical anomaly mean for any of the time periods; Obtaining the average value of the abnormal values ​​of the electric energy data at each sampling moment in any time period to obtain the abnormal mean value of the day in any time period; The monitoring importance of any time period is obtained according to the average value between the historical abnormal mean value and the abnormal mean value of the day in any time period.

7. The sensor-based efficient ring main unit power data acquisition method according to claim 6 is characterized in that: The obtaining of the fluctuation weight of any historical sampling moment according to the time difference between any historical day and the current day includes: The number of days between any historical day and the current day is obtained, and the reciprocal of the number of days between the two days is normalized to obtain the fluctuation weight of any historical sampling moment.

8. The sensor-based ring main unit power data efficient acquisition method according to claim 1 is characterized in that: 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 in the future days, including: Obtain the quantity of electric energy data in each time period of the day, and obtain the corresponding quantity mean. For any time period of the day, obtain the product of the quantity mean and the monitoring importance of any time period to obtain the adjustment value; If the monitoring importance of any time period is greater than or equal to the monitoring importance of the same time period on the previous historical day, then the sampling frequency of any time period and the adjustment value are added together to obtain the adaptive sampling frequency of the same time period in the future day; If the monitoring importance of any time period is less than the monitoring importance of the same time period on the previous historical day, the difference between the sampling frequency of any time period and the adjustment value is obtained to obtain the adaptive sampling frequency of the same time period in the future days.

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