Method and system for monitoring abnormal power consumption during time period based on dynamic adjustment of meter code curve

Abstract

The invention discloses a time period power anomaly monitoring method and system based on dynamic distance adjustment of meter code curve, relates to the field of power meter data anomaly monitoring, solves the problem that existing technical means cannot troubleshoot time period parameter power division errors, and the key points of the technical solution are: constructing a daily time-sharing parameter power curve Q based on the daily frozen data of the power meter, and calculating a daily time-sharing actual power curve R based on the hourly frozen data of the power meter; calculating the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R through dynamic time warping DTW; when the path cumulative distance exceeds a threshold, judging that there is a power meter time-sharing parameter power anomaly in the daily time-sharing parameter power curve Q and locating the abnormal power, and when the path cumulative distance does not exceed the threshold, judging that there is no power meter time-sharing parameter power anomaly in the daily time-sharing parameter power curve Q; and analyzing whether the time period parameter power is divided incorrectly by comparing the daily time-sharing parameter power with the daily time-sharing actual power data.

Description

Technical Field

[0001] The invention relates to the field of abnormal monitoring of electric energy meter data, and more specifically, to a method and system for monitoring abnormal power quantity during a period of time based on dynamic adjustment of meter code curve. Background Art

[0002] The electric energy meter is a device used to measure the user's electricity consumption. For most industrial and commercial users, different electricity prices will correspond to different periods of electricity consumption during the day. Therefore, when the electric energy meter measures the user's electricity consumption, it will count the electricity consumption in each period according to the specific time of electricity consumption in the preset period parameters in the meter. The period parameters include "peak period electricity consumption", "peak period electricity consumption", "flat period electricity consumption" and "valley period electricity consumption". When actually calculating the user's electricity bill, the system will calculate the fee according to the corresponding electricity price based on the electricity consumption data of each period transmitted by the electric energy meter.

[0003] However, in actual operation, the energy meter may make mistakes in the time period division when recording the time period parameter electricity, resulting in inconsistency between the recorded time period parameter electricity and the actual electricity consumption. Since the total electricity consumption of the user has not changed and there is no error in the wiring of the energy meter, it is difficult to intuitively check such abnormal situations with existing technical means, including line loss analysis and on-site inspection. At present, such problems can only be handled through post-event electricity fee correction, and monitoring abnormalities cannot be achieved, which to a certain extent limits the timely response and handling of abnormal situations of the energy meter.

[0004] Therefore, the present application provides a method and system for monitoring abnormal power consumption during a period of time based on dynamic adjustment of the meter code curve to solve the above problems. Summary of the invention

[0005] The purpose of this application is to provide a method and system for monitoring power anomalies in time periods based on dynamic adjustment of meter code curves, so as to solve the problem that existing technical means cannot troubleshoot errors in the division of time period parameter power. This application can calculate the characteristics of the power situation of the user in the correct time period based on the hourly frozen value of the meter code curve recorded by the meter, and analyze whether the time period parameter power is divided incorrectly by comparing the daily time-sharing parameter power with the daily time-sharing actual power data.

[0006] The present application first provides a method for monitoring power anomalies during a time period based on dynamic distance adjustment of a meter code curve, including: S1, constructing a daily time-sharing parameter power curve Q based on the daily frozen data of the power meter, and calculating the daily time-sharing actual power curve R based on the hourly frozen data of the power meter; S2, calculating the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R through dynamic time warping DTW; S3, when the path cumulative distance exceeds a threshold, judging that there is an abnormality in the time-sharing parameter power of the power meter in the daily time-sharing parameter power curve Q and locating the abnormal power, and when the path cumulative distance does not exceed the threshold, judging that there is no abnormality in the time-sharing parameter power of the power meter in the daily time-sharing parameter power curve Q.

[0007] In a possible implementation, S1 includes: obtaining daily frozen data of electric energy meters for multiple consecutive days, extracting parameter electricity data sequences of peak segments, peak segments, flat segments and valley segments based on the daily frozen data of electric energy meters for multiple consecutive days, and drawing a daily time-sharing parameter electricity curve Q based on the parameter electricity data sequences of different time periods; obtaining hourly frozen data of electric energy meters for multiple consecutive days, calculating the actual electricity of peak segments, peak segments, flat segments and valley segments for multiple consecutive days based on the hourly frozen data of electric energy meters for multiple consecutive days, obtaining the actual electricity data sequences of peak segments, peak segments, flat segments and valley segments, and drawing a daily time-sharing actual electricity curve R based on the actual electricity data sequences of different time periods.

[0008] In a possible implementation, S2 includes: calculating the distance between corresponding data points in the daily time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R based on the Euclidean distance formula to obtain a distance matrix; selecting the path with the smallest cumulative distance from the upper left corner to the lower right corner of the distance matrix as the path cumulative distance of the daily time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R.

[0009] In a possible implementation, S3 includes: setting a threshold value according to the maximum allowable metering error of the electric energy meter; when the cumulative path distance is greater than the threshold value, it is considered that there is an abnormality in the time-sharing electric energy meter in the daily time-sharing parameter electric energy curve Q, and the specific time period of the abnormal electric energy is located; when the cumulative path distance is less than or equal to the threshold value, it is considered that the electric energy data in the daily time-sharing parameter electric energy curve Q does not contain an abnormality in the time-sharing parameter electric energy of the electric energy meter.

[0010] In a possible implementation, the specific time period of locating abnormal electricity in S3 includes: S31, querying the data point in the daily time-sharing parameter electricity curve Q with the largest path distance difference from the daily time-sharing actual electricity curve R, and replacing the data point with the corresponding data point in the daily time-sharing actual electricity curve R to obtain an updated daily time-sharing parameter electricity curve Q'; S32, calculating the cumulative path distance between the updated daily time-sharing parameter electricity curve Q' and the daily time-sharing actual electricity curve R, if it does not exceed the threshold, proceed to the next step, if it exceeds the threshold, replace the daily time-sharing parameter electricity curve Q with the updated daily time-sharing parameter electricity curve Q', return and re-execute S31; S33, all replaced data points in the updated daily time-sharing parameter electricity curve Q' are abnormal points, and the time periods corresponding to the abnormal points all have abnormal time-sharing parameter electricity of the energy meter.

[0011] The present application also provides a time period power anomaly monitoring system based on dynamic distance adjustment of meter code curves, including: a curve construction unit, used to construct a daily time-sharing parameter power curve Q based on the daily frozen data of the power meter, and calculate the daily time-sharing actual power curve R based on the hourly frozen data of the power meter; a distance calculation unit, used to calculate the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R through dynamic time warping DTW; an anomaly analysis unit, used to determine whether there is an electric energy meter time-sharing parameter power anomaly in the daily time-sharing parameter power curve Q and locate the abnormal power when the path cumulative distance exceeds a threshold, and determine whether there is no electric energy meter time-sharing parameter power anomaly in the daily time-sharing parameter power curve Q when the path cumulative distance does not exceed the threshold.

[0012] In a possible implementation, the curve construction unit specifically includes: a Q curve construction unit, which is used to obtain the daily frozen data of the electric energy meter for multiple consecutive days, extract the parameter electricity data sequences of the peak segment, peak segment, flat segment and valley segment according to the daily frozen data of the electric energy meter for multiple consecutive days, and draw the daily time-sharing parameter electricity curve Q according to the parameter electricity data sequences of different time periods; an R curve construction unit, which is used to obtain the hourly frozen data of the electric energy meter for multiple consecutive days, calculate the actual electricity of the peak segment, peak segment, flat segment and valley segment for multiple consecutive days according to the hourly frozen data of the electric energy meter for multiple consecutive days, obtain the actual electricity data sequences of the peak segment, peak segment, flat segment and valley segment, and draw the daily time-sharing actual electricity curve R according to the actual electricity data sequences of different time periods.

[0013] In one possible implementation, the distance calculation unit specifically includes: a distance matrix construction unit, which is used to calculate the distance between corresponding data points in the daily time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R based on the Euclidean distance formula to obtain a distance matrix; a cumulative distance calculation unit, which is used to select the path with the smallest cumulative distance from the upper left corner to the lower right corner of the distance matrix as the path cumulative distance of the daily time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R.

[0014] In a possible implementation, the abnormality analysis unit specifically includes: a threshold setting unit, which is used to set a threshold according to the maximum allowable metering error of the electric energy meter; an abnormal data point positioning unit, which is used to consider that there is an abnormality in the time-sharing electric energy meter in the daily time-sharing parameter electric energy curve Q when the cumulative path distance is greater than the threshold, and locate the specific time period of the abnormal electric energy; when the cumulative path distance is less than or equal to the threshold, it is considered that the electric energy data in the daily time-sharing parameter electric energy curve Q does not have an abnormality in the time-sharing parameter electric energy of the electric energy meter.

[0015] In a possible implementation, the abnormal data point locating unit specifically includes: a data point updating unit, which is used to query the data point in the daily time-sharing parameter electricity curve Q with the largest path distance difference from the daily time-sharing actual electricity curve R, and replace the data point with the corresponding data point in the daily time-sharing actual electricity curve R to obtain the updated daily time-sharing parameter electricity curve Q'; an updating data point analysis unit, which calculates the cumulative path distance between the updated daily time-sharing parameter electricity curve Q' and the daily time-sharing actual electricity curve R, and if it does not exceed the threshold, it is transferred to the abnormal data point time period extraction unit, and if it exceeds the threshold, the daily time-sharing parameter electricity curve Q is replaced with the updated daily time-sharing parameter electricity curve Q' and returned to the data point updating unit; an abnormal data point time period extraction unit, all replaced data points in the updated daily time-sharing parameter electricity curve Q' are abnormal points, and the time periods corresponding to the abnormal points all have abnormal time-sharing parameter electricity of the electric energy meter.

[0016] Compared with the prior art, the present application has the following beneficial effects: the time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R are used to intuitively describe the situation of the segmented electricity used for user billing and the user's actual segmented electricity; by updating the two types of electricity of the user on a daily basis and comparing the difference between the two sequences, an early warning is issued for situations where the difference is large, ensuring that the segmented parameters of the meter are set correctly; at the same time, by using the DTW algorithm to compare the similarity of the two curve data sequences for abnormality judgment, it has a good recognition effect and provides a theoretical basis for the detection results; in addition, the meter electricity data can also be technically analyzed through the electricity collection system to avoid interference from human factors, and the user's electricity consumption can be continuously monitored, and an early warning is generated at the early stage of the abnormality, and the abnormal electricity point is quickly located, and the difference in electricity charges caused by electricity errors is statistically analyzed. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0018] Figure 1 A flow chart of a method for monitoring abnormal power consumption during a time period based on dynamic adjustment of a meter code curve provided in an embodiment of the present application;

[0019] Figure 2It is a schematic diagram of a user's daily time-sharing parameter power curve Q and a daily time-sharing actual power curve R;

[0020] Figure 3 A schematic diagram of the cumulative distance distribution of a user's daily path;

[0021] Figure 4 This is a structural diagram of a system for monitoring power anomalies during a time period based on dynamic adjustment of a meter code curve provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] Hereinafter, the term "include" or "may include" used in various embodiments of the present application indicates the presence of the function, operation or element applied for, and does not limit the increase of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or the possibility of increasing one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items.

[0023] The terms used in the various embodiments of the application are only used to describe the purpose of specific embodiments and are not intended to limit the various embodiments of the application. As used herein, the singular form is intended to also include the plural form, unless the context clearly indicates otherwise. Unless otherwise limited, all terms used here (including technical terms and scientific terms) have the same meaning as the meanings commonly understood by ordinary technicians in the field of the various embodiments of the application. The terms (such as the terms defined in the dictionary generally used) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning, unless clearly defined in the various embodiments of the application.

[0024] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0025] First, some of the terms and related technologies involved in this application are explained to facilitate understanding by those skilled in the art.

[0026] 1. A meter is a measuring device used to measure and record certain quantities, such as electricity, water, gas, etc.

[0027] 2. The meter code is the reading displayed on the meter. This reading represents the total amount of the measured quantity flowing through the meter from a certain starting point (usually the zero point when the meter is installed or the last time the meter was read) to the current moment.

[0028] 3. Daily freezing function means that the electric energy meter can record and store electricity data on a daily basis. These data can be used to generate a daily time-sharing parameter electricity curve Q, which can reflect the daily electricity usage.

[0029] 4. The hourly freeze function means that the electric energy meter can record and store power data in an hourly cycle. These data can be used to generate the daily time-sharing actual power curve R, which can reflect the power usage of each hour in a day.

[0030] 5. Dynamic Time Warping (DTW) is an algorithm for measuring the similarity between two time series. Its core idea is to find the best time alignment between the two sequences by nonlinearly adjusting the time axis of the time series, thereby calculating the similarity between them.

[0031] See also Figure 1 As shown, Figure 1 A flow chart of a method for monitoring power anomaly during a time period based on dynamic distance adjustment of a meter code curve provided in an embodiment of the present application. The method includes: S1, constructing a daily time-sharing parameter power curve Q based on the daily frozen data of the power meter, and calculating a daily time-sharing actual power curve R based on the hourly frozen data of the power meter; S2, calculating the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R through dynamic time warping DTW; S3, when the path cumulative distance exceeds a threshold, judging that there is a power anomaly of the power meter time-sharing parameter power in the daily time-sharing parameter power curve Q and locating the abnormal power, and when the path cumulative distance does not exceed the threshold, judging that there is no power anomaly of the power meter time-sharing parameter power in the daily time-sharing parameter power curve Q.

[0032] Specifically, the present application uses the daily frozen data of the electric energy meter to obtain the daily time-sharing electricity data recorded by the electric energy meter (i.e., the electricity consumption during the peak period, peak period, normal period, and valley period of each day), and constructs the daily time-sharing parameter electricity curve Q; at the same time, the hourly frozen data of the electric energy meter is used to calculate the actual daily time-sharing electricity data, and obtain the daily time-sharing actual electricity curve R. Next, the dynamic time warping DTW algorithm is used to calculate the path cumulative distance D between the two curves. The DTW algorithm can adapt to the time expansion and contraction changes in the time series and find the best time alignment method to evaluate the similarity of the two curves; under normal circumstances, the two curves are similar, and the path cumulative distance D is small; however, when the electric energy meter records the time period parameter electricity and there is an error in the time division, the difference between the two curves becomes larger, and the path cumulative distance D is also larger. Therefore, when the path cumulative distance D exceeds the set threshold, it can be considered that there is an error in the electric energy meter time-sharing parameter electricity in the daily time-sharing parameter electricity curve Q, and the specific period of abnormal electricity can be further located at this time; if the path cumulative distance D does not exceed the threshold, it can be considered that the electricity data in the daily time-sharing parameter electricity curve Q is normal, and there is no abnormality in the electric energy meter time-sharing parameter electricity.

[0033] It should be noted that the daily time-sharing power data is composed of the user's time period power data for a single day. The daily time-sharing parameter power can be directly obtained from the meter record data. The daily time-sharing actual power data needs to be calculated based on the difference between the positive active value at the beginning and end of the time period corresponding to the meter code curve. The time period power is calculated in hours as the time node. By extracting the frozen positive active value of the meter for 24 hours a day, the actual power value of the user in each time period on any day can be obtained, which can be expressed as:

[0034]

[0035] Among them, p 尖峰 ,p 峰 ,p 平 ,p 谷 Indicates the actual power value of the "peak period", "peak period", "flat period" and "valley period" of a certain day, k 尖峰 ,k 峰 ,k 平 ,k 谷 Indicates the number of time periods included in the "peak period", "peak period", "flat period" and "valley period" of the day, w i尖峰末 ,w i峰末 ,w i平末 ,w i谷末 Indicates the positive active power indication value of the user's electric energy at the end of the i-th time period recorded by the table code, w i尖峰初 ,w i峰初 ,w i平初 ,w i谷初 It indicates the positive active power indication of the user's electric energy at the beginning of the i-th time period as recorded by the table code.

[0036] The improvement of this application is that a monitoring method specifically for the abnormal power consumption in the time period of the electric energy meter is proposed, which effectively makes up for the shortcomings of the existing technology in the monitoring of abnormal power consumption in the time-sharing electric energy meter. Specifically, this method fully considers the source of the segmented power consumption data on which the user's electricity bill calculation depends, and the technical principle of the electric energy meter recording the user's electricity consumption. Compared with the existing technology, this method has the following advantages: 1. Real-time monitoring: through daily monitoring, the cause of the abnormality can be located faster and more accurately, and the timeliness of abnormality detection is improved; 2. Convenient operation: using the original data of the electric energy meter, the actual segmented power consumption of the user is directly calculated through a simple mathematical formula, and the difference value is quickly compared, which greatly reduces the complexity of manual calculation; 3. Result authenticity: through the analysis and processing of the original data of the electric energy meter, the actual segmented power consumption of the user is calculated, which effectively avoids the interference of human factors and ensures the authenticity and accuracy of the algorithm results.

[0037] In a possible implementation, step S1 includes: obtaining daily frozen data of electric energy meters for multiple consecutive days, extracting parameter electricity data sequences of peak segments, peak segments, flat segments and valley segments based on the daily frozen data of electric energy meters for multiple consecutive days, and drawing a daily time-sharing parameter electricity curve Q based on the parameter electricity data sequences of different time periods; obtaining hourly frozen data of electric energy meters for multiple consecutive days, calculating the actual electricity of peak segments, peak segments, flat segments and valley segments for multiple consecutive days based on the hourly frozen data of electric energy meters for multiple consecutive days, obtaining the actual electricity data sequences of peak segments, peak segments, flat segments and valley segments, and drawing a daily time-sharing actual electricity curve R based on the actual electricity data sequences of different time periods.

[0038] Specifically, step S1 constructs a daily time-sharing parameter power curve Q based on the daily frozen data of the power meter, and calculates the daily time-sharing actual power curve R based on the hourly frozen data of the power meter. Define the daily time-sharing parameter power curve Q = {q1, q2, q3, q4}, q1~q4 represent the peak section, peak section, flat section and valley section parameter power data sequence, and the parameter power data sequence q in different time periods i ={x1,x2,...,x n},i∈{1,2,3,4}, where n represents the total number of days recorded, x i , i∈{1,2,...,n} is the corresponding time period parameter power data of a single day, and a 1×4n daily time-sharing parameter power curve Q={q1,q2,q3,q4}={x1,x2,...,x 4n Define the daily time-sharing actual electricity curve R = {r1, r2, r3, r4}, where r1~r4 represent the actual electricity data sequences of the peak section, peak section, flat section and valley section respectively. The actual electricity data sequences of different time periods q i ={y1,y2,...,y n},i∈{1,2,3,4},y i ,i∈{1,2,...,n} is the corresponding time period electricity data of a single day, according to p 尖峰 ,p 峰 ,p 平 ,p 谷 Calculate the actual power consumption curve of the day by time by constructing a 1×4n curve R={r1,r2,r3,r4}={y1,y2,...,y 4n}.

[0039] In a possible implementation, step S2 includes: calculating the distance between corresponding data points in the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R based on the Euclidean distance formula to obtain a distance matrix; selecting the path with the smallest cumulative distance from the upper left corner to the lower right corner of the distance matrix as the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R.

[0040] Specifically, step S2 is to calculate the cumulative path distance D of Q and R by dynamic time warping (DTW). The distance is calculated using the Euclidean distance formula, and the Euclidean distance of Q and R can form a 4N×4N distance matrix:

[0041]

[0042] Among them, d(x i ,y j )=|x i -y j |,i,j∈{1,2,...,4n};

[0043] Find the shortest path in the distance matrix. The shortest path refers to the path from the distance matrix d(x1,y1) to d(x1,y1). 4n ,y 4n ) is the path with the smallest cumulative distance value. Considering the normal situation, any term d(x i ,y j ) has only one path through which the previous item to reach this item is d(x i ,y j-1 )、d(x i-1 ,y j-1 ) or d(x i-1 ,y j ), so the cumulative path distance is: D(x i ,y j )=d(x i ,y j )+min[D(x i ,yj-1 ),D(x i-1 ,y j-1 ),D(x i-1 ,y j )].

[0044] In a possible implementation, step S3 includes: setting a threshold value according to the maximum allowable metering error of the electric energy meter; when the cumulative path distance is greater than the threshold value, it is considered that there is an abnormality in the time-sharing electric energy meter in the daily time-sharing parameter electric energy curve Q, and the specific time period of the abnormal electric energy is located; when the cumulative path distance is less than or equal to the threshold value, it is considered that the electric energy data in the daily time-sharing parameter electric energy curve Q does not have an abnormality in the time-sharing parameter electric energy of the electric energy meter.

[0045] Specifically, step S3 is to determine whether there is an abnormality in the time-sharing power consumption of the electric energy meter based on the path cumulative distance D. First, define t to represent a reasonable threshold, which is usually set to verify the total power consumption E s , E s is the maximum allowable measurement error of the electric energy meter; when the cumulative path distance D>t, there is an abnormal time-sharing power of the electric energy meter in Q, and the abnormal power location begins; when the cumulative path distance D≤t, there is no abnormal time-sharing power of the electric energy meter in Q, and the time-sharing power of the electric energy meter can be used to calculate the electricity fee normally.

[0046] Furthermore, the specific time period of locating abnormal electricity in step S3 includes: S31, querying the data point with the largest path distance difference from the daily time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R, and replacing the data point with the corresponding data point in the daily time-sharing actual electricity curve R to obtain the updated daily time-sharing parameter electricity curve Q'; S32, calculating the cumulative path distance between the updated daily time-sharing parameter electricity curve Q' and the daily time-sharing actual electricity curve R, if it does not exceed the threshold, proceed to the next step, if it exceeds the threshold, replace the daily time-sharing parameter electricity curve Q with the updated daily time-sharing parameter electricity curve Q', return and re-execute S31;

[0047] S33. All replaced data points in the updated daily time-sharing parameter electricity curve Q' are abnormal points, and the time periods corresponding to the abnormal points all have abnormal time-sharing parameter electricity of the energy meter.

[0048] Specifically, when locating the specific time period of abnormal electricity, first identify the data point in the daily time-sharing parameter electricity curve Q with the largest path distance to the daily time-sharing actual electricity curve R, which may be a point where the electricity is recorded incorrectly; then replace the point with the actual electricity data point corresponding to the point in R, thereby obtaining the updated daily time-sharing parameter electricity curve Q'; next calculate the cumulative path distance D between the updated curve Q' and the actual electricity curve R, if D does not exceed the preset threshold, it means that the consistency between the replaced curve and the actual electricity curve is within an acceptable range, that is, all abnormal data points are queried; if D exceeds the threshold, it means that there are still abnormal data points, and the original curve Q needs to be replaced by the updated curve Q', and start again from step S31, continue to find and replace other possible abnormal data points, until all abnormal data points are queried, and there are abnormal time-sharing parameter electricity of the energy meter in the time period corresponding to these abnormal data points.

[0049] It can be understood that the time period power anomaly monitoring method based on dynamic adjustment of meter code curve provided in the present application can be used to monitor the time period setting of the user's meter in real time, and intuitively describe the segmented power used for user billing and the user's actual segmented power through the time-sharing parameter power curve Q and the daily time-sharing actual power curve R. By updating the two types of power of the user on a daily basis and comparing the difference between the two sequences, an early warning is issued for situations where the difference is large, ensuring that the segmented parameter settings of the meter are correct; at the same time, by using the DTW algorithm to compare the similarity of the two curve data sequences to make an abnormal judgment, it has a good recognition effect and provides a theoretical basis for the detection results; in addition, the meter power data can be technically analyzed through the power collection system to avoid interference from human factors, and the user's power consumption can be continuously monitored. An early warning is generated at the early stage of the abnormality and the power anomaly point is quickly located, and the difference in electricity charges caused by power errors is statistically analyzed.

[0050] The following is a verification example using a user's electricity consumption data for the first quarter. Figure 2 and Figure 3 Verify the rationality of this application method.

[0051] Step 1: Through step S1, we can obtain the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R of a certain user, such as Figure 2 As shown;

[0052] Step 2: Calculate the cumulative path distance D of Q and R through DTW. The results are shown in Table 1. The distribution of daily path cumulative distance is as follows: Figure 3 As shown;

[0053] Table 1

[0054]

[0055] Step 3: According to the total power of 33486 kWh, the electric energy meter to be determined is 0.5S level, and the threshold E is calculated. S =167.43, since D = 374 > E S , it is determined that there is an abnormality in the time-sharing power of the electric energy meter in the daily time-sharing parameter power curve Q;

[0056] Furthermore, through calculation, we can find that the points with abnormalities are:

[0057] [9,10,11,12,13,22,23,24,55,56,57,58,59,60,61,62,63,64,65,80,81,82,83];

[0058] After replacing the abnormal point in step S33, the final path distance between Q' and R is D=0≤t, which indicates that the time-sharing power of the electric energy meter is correct and all abnormal points are correctly located.

[0059] It can be seen that the time period power anomaly monitoring method based on dynamic adjustment of the meter code curve provided in the present application can accurately respond to the power anomaly of the time-sharing parameters of the electric energy meter and accurately locate the data anomaly points.

[0060] See also Figure 4 , Figure 4 The structural diagram of the time period power anomaly monitoring system based on the dynamic distance adjustment of the meter code curve provided in the embodiment of the present application. The system includes: a curve construction unit, which is used to construct a daily time-sharing parameter power curve Q based on the daily frozen data of the power meter, and calculate the daily time-sharing actual power curve R based on the hourly frozen data of the power meter; a distance calculation unit, which is used to calculate the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R through dynamic time warping DTW; an abnormality analysis unit, which is used to determine that there is an abnormality in the power meter time-sharing parameter power curve Q and locate the abnormal power when the cumulative path distance exceeds a threshold, and determine that there is no abnormality in the power meter time-sharing parameter power curve Q when the cumulative path distance does not exceed the threshold.

[0061] In a possible implementation, the curve construction unit specifically includes: a Q curve construction unit, which is used to obtain the daily frozen data of the electric energy meter for multiple consecutive days, extract the parameter electricity data sequences of the peak segment, peak segment, flat segment and valley segment according to the daily frozen data of the electric energy meter for multiple consecutive days, and draw the daily time-sharing parameter electricity curve Q according to the parameter electricity data sequences of different time periods; an R curve construction unit, which is used to obtain the hourly frozen data of the electric energy meter for multiple consecutive days, calculate the actual electricity of the peak segment, peak segment, flat segment and valley segment for multiple consecutive days according to the hourly frozen data of the electric energy meter for multiple consecutive days, obtain the actual electricity data sequences of the peak segment, peak segment, flat segment and valley segment, and draw the daily time-sharing actual electricity curve R according to the actual electricity data sequences of different time periods.

[0062] In one possible implementation, the distance calculation unit specifically includes: a distance matrix construction unit, which is used to calculate the distance between corresponding data points in the daily time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R based on the Euclidean distance formula to obtain a distance matrix; a cumulative distance calculation unit, which is used to select the path with the smallest cumulative distance from the upper left corner to the lower right corner of the distance matrix as the path cumulative distance of the daily time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R.

[0063] In a possible implementation, the abnormality analysis unit specifically includes: a threshold setting unit, which is used to set a threshold according to the maximum allowable metering error of the electric energy meter; an abnormal data point positioning unit, which is used to consider that there is an abnormality in the time-sharing electric energy meter in the daily time-sharing parameter electric energy curve Q when the cumulative path distance is greater than the threshold, and locate the specific time period of the abnormal electric energy; when the cumulative path distance is less than or equal to the threshold, it is considered that the electric energy data in the daily time-sharing parameter electric energy curve Q does not have an abnormality in the time-sharing parameter electric energy of the electric energy meter.

[0064] In a possible implementation, the abnormal data point locating unit specifically includes: a data point updating unit, which is used to query the data point in the daily time-sharing parameter electricity curve Q with the largest path distance difference from the daily time-sharing actual electricity curve R, and replace the data point with the corresponding data point in the daily time-sharing actual electricity curve R to obtain the updated daily time-sharing parameter electricity curve Q'; an updating data point analysis unit, which calculates the cumulative path distance between the updated daily time-sharing parameter electricity curve Q' and the daily time-sharing actual electricity curve R, and if it does not exceed the threshold, it is transferred to the abnormal data point time period extraction unit, and if it exceeds the threshold, the daily time-sharing parameter electricity curve Q is replaced with the updated daily time-sharing parameter electricity curve Q' and returned to the data point updating unit; an abnormal data point time period extraction unit, all replaced data points in the updated daily time-sharing parameter electricity curve Q' are abnormal points, and the time periods corresponding to the abnormal points all have abnormal time-sharing parameter electricity of the electric energy meter.

[0065] It can be understood that the time period electricity anomaly monitoring system based on dynamic adjustment of the meter code curve provided in the present application is used to implement the above-mentioned time period electricity anomaly monitoring method based on dynamic adjustment of the meter code curve, corresponds one to one with the above-mentioned method, and has corresponding technical effects, so it will not be elaborated on.

[0066] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring abnormal power consumption during a period based on dynamic adjustment of the meter code curve, characterized in that: include: S1. Construct a daily time-sharing parameter power curve Q based on the daily frozen data of the power meter, and calculate a daily time-sharing actual power curve R based on the hourly frozen data of the power meter; S2. Calculate the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R through dynamic time warping (DTW). S3. When the cumulative distance of the path exceeds the threshold, it is determined that there is an abnormality in the time-sharing parameter power of the electric energy meter in the daily time-sharing parameter power curve Q and the abnormal power is located. When the cumulative distance of the path does not exceed the threshold, it is determined that there is no abnormality in the time-sharing parameter power of the electric energy meter in the daily time-sharing parameter power curve Q; Among them, S3 includes: setting a threshold value according to the maximum allowable measurement error of the electric energy meter; when the cumulative path distance is greater than the threshold value, it is considered that there is an abnormality in the time-sharing electric energy meter in the daily time-sharing parameter electric energy curve Q, and the specific time period of the abnormal electric energy is located; when the cumulative path distance is less than or equal to the threshold value, it is considered that there is no abnormality in the time-sharing parameter electric energy meter in the electric energy data in the daily time-sharing parameter electric energy curve Q; The specific time period for locating abnormal electricity in S3 includes: S31, querying the data point with the largest path distance difference from the daily time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R, and replacing the data point with the corresponding data point in the daily time-sharing actual electricity curve R to obtain the updated daily time-sharing parameter electricity curve Q'; S32, calculating the cumulative path distance between the updated daily time-sharing parameter electricity curve Q' and the daily time-sharing actual electricity curve R, if it does not exceed the threshold, proceed to the next step, if it exceeds the threshold, replace the daily time-sharing parameter electricity curve Q with the updated daily time-sharing parameter electricity curve Q', return and re-execute S31; S33, all replaced data points in the updated daily time-sharing parameter electricity curve Q' are abnormal points, and the time periods corresponding to the abnormal points all have abnormal time-sharing parameter electricity of the energy meter.

2. The method for monitoring abnormal power consumption during a period based on dynamic adjustment of the table code curve according to claim 1 is characterized in that: S1 includes: Obtain the daily frozen data of the electric energy meter for multiple consecutive days, extract the parameter power data sequences of the peak section, peak section, flat section and valley section according to the daily frozen data of the electric energy meter for multiple consecutive days, and draw the daily time-sharing parameter power curve Q according to the parameter power data sequences of different time periods; Obtain the hourly frozen data of the energy meter for multiple consecutive days, calculate the actual electricity consumption in the peak section, peak section, flat section and valley section for multiple consecutive days based on the hourly frozen data of the energy meter for multiple consecutive days, obtain the actual electricity consumption data sequence of the peak section, peak section, flat section and valley section, and draw the daily time-sharing actual electricity consumption curve R according to the actual electricity consumption data sequence of different time periods.

3. The method for monitoring abnormal power consumption during a period based on dynamic adjustment of the table code curve according to claim 1 is characterized in that S2 include: Based on the Euclidean distance formula, the distance between the corresponding data points in the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R is calculated to obtain a distance matrix; The path with the smallest cumulative distance from the upper left corner to the lower right corner of the distance matrix is ​​selected as the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R.

4. The time period power anomaly monitoring system based on the dynamic adjustment of the meter code curve is characterized by: include: A curve construction unit, used to construct a daily time-sharing parameter power curve Q based on the daily frozen data of the power meter, and calculate a daily time-sharing actual power curve R based on the hourly frozen data of the power meter; A distance calculation unit, used to calculate the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R through dynamic time warping DTW; The abnormality analysis unit is used to determine that there is an abnormality in the time-sharing parameter power of the electric energy meter in the daily time-sharing parameter power curve Q and locate the abnormal power when the cumulative path distance exceeds the threshold, and to determine that there is no abnormality in the time-sharing parameter power of the electric energy meter in the daily time-sharing parameter power curve Q when the cumulative path distance does not exceed the threshold; The abnormality analysis unit specifically includes: a threshold setting unit, which is used to set a threshold according to the maximum allowable metering error of the electric energy meter; an abnormal data point positioning unit, which is used to consider that there is an abnormality in the daily time-sharing parameter electric energy curve Q when the cumulative path distance is greater than the threshold, and locate the specific time period of the abnormal electric energy; when the cumulative path distance is less than or equal to the threshold, it is considered that there is no abnormality in the electric energy meter time-sharing parameter electric energy data in the daily time-sharing parameter electric energy curve Q; The abnormal data point locating unit specifically includes: a data point updating unit, which is used to query the data point with the largest path distance difference from the daily time-sharing parameter electricity curve Q and the daily time-sharing actual electricity curve R, and replace the data point with the corresponding data point in the daily time-sharing actual electricity curve R to obtain the updated daily time-sharing parameter electricity curve Q'; an updating data point analyzing unit, which calculates the cumulative path distance between the updated daily time-sharing parameter electricity curve Q' and the daily time-sharing actual electricity curve R, and if it does not exceed the threshold, it is transferred to the abnormal data point time period extraction unit, and if it exceeds the threshold, the daily time-sharing parameter electricity curve Q is replaced with the updated daily time-sharing parameter electricity curve Q' and returned to the data point updating unit; an abnormal data point time period extraction unit, all replaced data points in the updated daily time-sharing parameter electricity curve Q' are abnormal points, and the time periods corresponding to the abnormal points all have abnormal time-sharing parameter electricity of the electric energy meter.

5. The time period power abnormality monitoring system based on dynamic adjustment of table code curve according to claim 4 is characterized in that: The curve construction unit specifically includes: A Q curve construction unit is used to obtain the daily frozen data of the electric energy meter for multiple consecutive days, extract the parameter power data sequences of the peak section, peak section, flat section and valley section according to the daily frozen data of the electric energy meter for multiple consecutive days, and draw the daily time-sharing parameter power curve Q according to the parameter power data sequences of different time periods; The R curve construction unit is used to obtain the hourly frozen data of the electric energy meter for multiple consecutive days, calculate the actual electricity consumption of the peak section, peak section, flat section and valley section for multiple consecutive days according to the hourly frozen data of the electric energy meter for multiple consecutive days, obtain the actual electricity consumption data sequence of the peak section, peak section, flat section and valley section, and draw the daily time-sharing actual electricity consumption curve R according to the actual electricity consumption data sequence of different time periods.

6. The time period power abnormality monitoring system based on dynamic adjustment of table code curve according to claim 4 is characterized in that: The distance calculation unit specifically includes: A distance matrix construction unit is used to calculate the distance between corresponding data points in the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R based on the Euclidean distance formula to obtain a distance matrix; The cumulative distance calculation unit is used to select the path with the smallest cumulative distance from the upper left corner to the lower right corner of the distance matrix as the path cumulative distance of the daily time-sharing parameter power curve Q and the daily time-sharing actual power curve R.

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