CN118036905B - Abnormal electricity user detection method, device, storage medium and electronic equipment - Google Patents
Abnormal electricity user detection method, device, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses a method and a device for detecting abnormal electricity utilization users, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area; performing matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user; performing correlation calculation processing by adopting a Person correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user; and detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users. The method can improve the detection accuracy and the detection efficiency of the abnormal electricity utilization user.
Description
Technical Field
The present invention relates to the field of electrical load detection technologies, and in particular, to a method and apparatus for detecting an abnormal electricity user, a storage medium, and an electronic device.
Background
The line loss rate is an important comprehensive economic and technical index of the power grid enterprise, and is a comprehensive reflection of the management level of the power grid enterprise. The technical line loss is an inevitable necessary loss in the power transmission process, and is in a relatively constant state under the condition that the equipment state and the power grid operation mode are unchanged. Therefore, the change of the line loss rate mainly depends on Non-technical line loss, and Non-technical loss (Non-TECHNICAL LOSS, abbreviated as NTL) refers to a part of power transmission and distribution loss of a power grid, which cannot be explained by technology, and abnormal electricity utilization behavior of users including electricity theft is a main cause of NTL. The electricity stealing behavior is an important cause of electric energy loss and economic benefit loss of power grid enterprises, and brings great potential safety hazard while causing a large amount of loss, and the screening of the existing abnormal electricity users is mainly checked by a manual meter reading method: and checking whether electricity stealing behavior exists or not through manual field meter reading. However, the manual meter reading method has low efficiency and wastes manpower and material resources.
Disclosure of Invention
In view of the above, the invention provides a method, a device, a storage medium and an electronic device for detecting abnormal electricity users, which mainly aims to solve the problems of low screening efficiency and high cost of the abnormal electricity users at present.
In order to solve the above problems, the present application provides a method for detecting abnormal electricity users, comprising:
Acquiring a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area;
Performing matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user;
Performing correlation calculation processing by adopting a Person correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user;
and detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users.
Optionally, before acquiring the target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area, the method further includes: performing stability verification processing on the line loss rate of each station area to obtain each station area to be detected with abnormal line loss rate, wherein the method specifically comprises the following steps:
Acquiring the line loss rate corresponding to each moment point in each arbitrary time interval based on the line loss rate curve of each area corresponding to each arbitrary time interval;
calculating a line loss rate mean, a line loss rate variance and a line loss rate autocovariance of the line loss rate curve at any time interval based on each line loss rate;
and screening based on the line loss rate mean value, the line loss rate variance and the line loss rate autocovariance of each arbitrary time interval corresponding to each station area to obtain each station area to be detected with abnormal line loss rate curve.
Optionally, the matching degree calculating process is performed by using a trend analysis method based on the line loss rate curve of the target to-be-detected platform area and the load curve of each to-be-detected user, so as to obtain each first abnormal electricity user, which specifically includes:
Acquiring a line loss rate curve corresponding to the target to-be-detected area within a preset time range, and acquiring a load curve of each user within the preset time range under the target to-be-detected area;
determining each time interval based on the predetermined time length range;
Matching the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain the corresponding matching degree of each user to be detected;
And screening the users to be detected based on the matching degree to obtain the first abnormal electricity utilization users.
Optionally, the matching processing is performed on the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain a matching degree corresponding to each user to be detected, which specifically includes:
calculating to obtain the difference value of each power consumption at the later moment and the earlier moment of each time interval based on the load curve of the same user to be detected and calculating to obtain the difference value of each line loss rate at the later moment and the earlier moment of each time interval based on the line loss rate curve of the platform region where the same user to be detected is located;
for the same user to be detected, calculating the number of the line loss rate difference value larger than zero and the electricity consumption difference value larger than zero at the same time interval to obtain a first quantity value;
For the same user to be detected, calculating the number of the line loss rate difference value smaller than zero and the electricity consumption difference value smaller than zero at the same time interval to obtain a second number value;
And aiming at the same user to be detected, carrying out calculation processing based on the first quantity value and the second quantity value to obtain the matching degree corresponding to each user to be detected.
Optionally, the calculating the correlation degree based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user by using a pearson correlation analysis method to obtain each second abnormal electricity user specifically includes:
Based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user, calculating and obtaining covariance of the line loss rate of each moment point of the target to-be-detected area and the electricity consumption of each moment point of each to-be-detected user within the preset duration range;
calculating standard deviations of the line loss rates of all points of the target to-be-detected area based on the line loss rate curve to obtain a first standard deviation corresponding to the target to-be-detected area;
calculating the standard deviation of the electricity consumption of each moment point of each user to be detected based on the load curve of each user to be detected to obtain a second standard deviation corresponding to each user to be detected;
And carrying out correlation calculation processing based on the covariance, the first standard deviation and the second standard deviation to obtain second abnormal electricity utilization users.
Optionally, the performing correlation calculation based on each covariance, each first standard deviation, and each second standard deviation to obtain each second abnormal electricity user specifically includes:
Calculating products of the first standard deviation and the second standard deviation respectively for the target to-be-detected area to obtain first product values corresponding to all to-be-detected users;
Dividing operation is respectively carried out on the basis of the covariance and each first product value, and a correlation coefficient value corresponding to each user to be detected is obtained;
and screening processing is carried out based on the correlation coefficient values, and the user to be detected corresponding to the correlation coefficient value which is larger than or equal to a preset threshold value is determined to be the second abnormal electricity utilization user.
Optionally, the filtering processing is performed based on each first abnormal electricity user and each second abnormal electricity user to obtain each abnormal electricity user, which specifically includes:
and performing intersection operation processing based on each first abnormal electricity user and each second abnormal electricity user to obtain different electricity users.
In order to solve the above problems, the present application provides a device for screening abnormal electricity users, comprising:
The acquisition module is used for: the method comprises the steps of acquiring a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area;
A first calculation module: the matching degree calculation processing is carried out by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user, so as to obtain each first abnormal electricity user;
A second calculation module: the method comprises the steps of performing relevance calculation processing by adopting a Pelson relevance analysis method based on a line loss rate curve of the target to-be-detected area and a load curve of each to-be-detected user to obtain each second abnormal electricity user;
and a detection module: and the method is used for detecting the abnormal user based on each first abnormal electricity user and each second abnormal electricity user to obtain a plurality of target abnormal electricity users.
In order to solve the above-mentioned problems, the present application provides a storage medium storing a computer program which, when executed by a processor, implements the steps of the abnormal electricity user detection method described above.
In order to solve the above problems, the present application provides an electronic device, which at least includes a memory, and a processor, wherein the memory stores a computer program, and the processor implements the steps of the abnormal electricity utilization user detection method when executing the computer program on the memory.
The application has the beneficial effects that: the method comprises the steps of obtaining a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area; performing matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user; performing correlation calculation processing by adopting a Person correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user; and detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users. According to the application, the trend analysis method and the Pelson correlation coefficient method are adopted to comprehensively detect the abnormal users, and the intersection of the clients detected by the two analysis methods is solved to obtain the final abnormal electricity utilization user, so that the abnormal electricity utilization user under the abnormal platform area can be accurately positioned, and the labor cost is saved. The abnormal electricity utilization user can be checked on site in a targeted manner, and the detection efficiency of the abnormal user is high.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
Fig. 1 is a schematic flow chart of a method for detecting abnormal electricity users according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for detecting abnormal electricity consumption according to another embodiment of the present application;
Fig. 3 is a block diagram showing a configuration of an abnormal electricity consumption user detection apparatus according to still another embodiment of the present application.
Detailed Description
Various aspects and features of the present application are described herein with reference to the accompanying drawings.
It should be understood that various modifications may be made to the embodiments of the application herein. Therefore, the above description should not be taken as limiting, but merely as exemplification of the embodiments. Other modifications within the scope and spirit of the application will occur to persons of ordinary skill in the art.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and, together with a general description of the application given above, and the detailed description of the embodiments given below, serve to explain the principles of the application.
These and other characteristics of the application will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It is also to be understood that, although the application has been described with reference to some specific examples, those skilled in the art can certainly realize many other equivalent forms of the application.
The above and other aspects, features and advantages of the present application will become more apparent in light of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present application will be described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the application, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the application in unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriately detailed structure.
The specification may use the word "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the application.
The embodiment of the application provides a method for detecting abnormal electricity utilization users, which is shown in figure 1 and comprises the following steps:
Step S101: acquiring a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area;
in the specific implementation process, acquiring the line loss rate corresponding to each moment point in each arbitrary time interval based on the line loss rate curve of each area corresponding to each arbitrary time interval; specifically, the plot 96 point line loss rate curve should remain stable with only technical line loss, irrespective of non-technical line loss. Calculating a line loss rate mean, a line loss rate variance and a line loss rate autocovariance of the line loss rate curve at any time interval based on each line loss rate; and screening based on the line loss rate mean value, the line loss rate variance and the line loss rate autocovariance of each arbitrary time interval corresponding to each station area to obtain each station area to be detected with abnormal line loss rate curve. When the line loss rate mean value of each arbitrary time interval is constant, the variance is always present and the auto-covariance does not fluctuate with time, the line loss rate curve of each arbitrary time interval is stable, the stable line loss rate curve is not affected by non-technical line loss, the line loss rate is not affected by the change of the electricity consumption of users to which the station area belongs, therefore, the station area corresponding to the stable line loss rate curve is confirmed as a normal station area, the line loss rate value is screened out to fluctuate in a first preset interval through stability test, the first preset interval can be in the line loss rate interval range of 0-0.5, the line loss rate value of the normal station area fluctuates up and down around a 0-0.5 axis, and the fluctuation amplitude is basically consistent. When the line loss rate mean value of each arbitrary time interval is constant, the variance and the autocovariance do not meet the condition that the line loss rate mean value is constant, the variance always exists, and the autocovariance does not fluctuate with time, the line loss rate curve fluctuates greatly, when the line loss rate fluctuates outside a first preset interval, the line loss rate is influenced by the power consumption change of a user to which the station area belongs, and each station area, of which each line loss rate curve fluctuates outside the first preset interval, is determined as the station area to be detected. And determining the users corresponding to the target to-be-detected areas as all to-be-detected users one by one, so as to detect abnormal electricity utilization users.
Step S102: performing matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user;
In the specific implementation process, acquiring a line loss rate curve corresponding to the target to-be-detected area within a preset time range and a load curve of each user within the preset time range under the target to-be-detected area; determining each time interval based on the predetermined time length range; matching the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain the corresponding matching degree of each user to be detected; and screening the users to be detected based on the matching degree to obtain the first abnormal electricity utilization users. Specifically, a user to be detected, of which the matching degree is greater than or equal to a preset matching degree threshold, is determined to be a first abnormal electricity user.
Step S103: performing correlation calculation processing by adopting a Person correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user;
In the specific implementation process, based on a line loss rate curve of the target to-be-detected platform area and a load curve of each to-be-detected user, calculating and obtaining covariance of the line loss rate of each moment point of the target to-be-detected platform area and the electricity consumption of each moment point of each to-be-detected user within the preset duration range; calculating standard deviations of the line loss rates of all points of the target to-be-detected area based on the line loss rate curve to obtain a first standard deviation corresponding to the target to-be-detected area; calculating the standard deviation of the electricity consumption of each moment point of each user to be detected based on the load curve of each user to be detected to obtain a second standard deviation corresponding to each user to be detected; and carrying out correlation calculation processing based on the covariance, the first standard deviation and the second standard deviation to obtain second abnormal electricity utilization users.
Step S104: and detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users.
In the implementation process, intersection operation processing is performed on the basis of each first abnormal electricity user and each second abnormal electricity user, so that each abnormal electricity user is obtained.
The method comprises the steps of obtaining a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area; performing matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user; performing correlation calculation processing by adopting a Person correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user; and detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users. According to the application, the trend analysis method and the Pelson correlation coefficient method are adopted to comprehensively detect the abnormal users, and the intersection of the clients detected by the two analysis methods is solved to obtain the final abnormal electricity utilization user, so that the abnormal electricity utilization user under the abnormal platform area can be accurately positioned, and the labor cost is saved. The abnormal electricity utilization user can be checked on site in a targeted manner, and the detection efficiency of the abnormal user is high.
Yet another embodiment of the present application provides another method for detecting abnormal electricity consumption, as shown in fig. 2, including:
step S201: performing stability verification processing on the line loss rate curve of each station area to obtain each station area to be detected with abnormal line loss rate curve;
In the specific implementation process, acquiring the line loss rate corresponding to each moment point in each arbitrary time interval based on the line loss rate curve of each area corresponding to each arbitrary time interval; specifically, the plot 96 point line loss rate curve should remain stable with only technical line loss, irrespective of non-technical line loss. Calculating a line loss rate mean, a line loss rate variance and a line loss rate autocovariance of the line loss rate curve at any time interval based on each line loss rate; and screening based on the line loss rate mean value, the line loss rate variance and the line loss rate autocovariance of each arbitrary time interval corresponding to each station area to obtain each abnormal station area with abnormal line loss rate curve. When the line loss rate mean value of each arbitrary time interval is constant, the variance is always present and the auto-covariance does not fluctuate with time, the line loss rate curve of each arbitrary time interval is stable, the stable line loss rate curve is not affected by non-technical line loss, the line loss rate is not affected by the change of the electricity consumption of users to which the station area belongs, therefore, the station area corresponding to the stable line loss rate curve is confirmed as a normal station area, the line loss rate value is screened out to fluctuate in a first preset interval through stability test, the first preset interval can be in the line loss rate interval range of 0-0.5, the line loss rate value of the normal station area fluctuates up and down around a 0-0.5 axis, and the fluctuation amplitude is basically consistent. When the line loss rate mean value of each arbitrary time interval is constant, the variance and the autocovariance do not meet the condition that the line loss rate mean value is constant, the variance always exists, and the autocovariance does not fluctuate with time, the line loss rate curve fluctuates greatly, when the line loss rate fluctuates outside a first preset interval, the line loss rate is influenced by the power consumption change of a user to which the station area belongs, and each station area, of which each line loss rate curve fluctuates outside the first preset interval, is determined as the station area to be detected. And determining each to-be-detected area as a target to-be-detected area, and determining the users corresponding to the target to-be-detected area as each to-be-detected user so as to detect abnormal electricity utilization users.
Step S202: acquiring a line loss rate curve corresponding to the target to-be-detected area within a preset time range, and acquiring a load curve of each to-be-detected user within the preset time range under the target to-be-detected area;
In the specific implementation process, searching a data source table required by an electricity consumption information acquisition system and an integrated electric quantity and line loss management system within a preset time range through a data sharing platform of a power grid enterprise, analyzing the source table field, and analyzing and processing through SQL language to generate 96 point data of line loss of each area and 96 point data of electric quantity of each user in each area; screening the 96 point data of the line loss of each platform area and the 96 point data of the electricity consumption of each user in each platform area based on each search field to obtain the line loss rate data of each platform area to be detected corresponding to each search field and the electricity consumption data of each user to be detected corresponding to each platform area to be detected; generating a line loss rate curve corresponding to each to-be-detected station area within a preset duration range based on the line loss rate data of each station area; and generating a load curve of each user to be detected under each platform area to be detected based on the electricity consumption data of each user to be detected corresponding to each platform area to be detected.
Step S203: determining each time interval based on the predetermined time length range;
In the specific implementation process, the time point cutting processing is carried out on the range of the preset time length, and each time interval is obtained. For example: when the preset duration range is 1 day, the time interval can be divided according to the frequency of 96-point data acquisition for 15 minutes, the preset duration range is divided into 96 time intervals, and the size of the time intervals can be set according to actual needs.
Step S204: matching the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain the corresponding matching degree of each user to be detected;
In the specific implementation process, calculating and obtaining the difference value of each power consumption at the later moment and the earlier moment of each time interval based on the load curve of the same user to be detected and calculating and obtaining the difference value of each line loss rate at the later moment and the earlier moment of each time interval based on the line loss rate curve of the platform region where the same user to be detected is located; for the same user to be detected, calculating the number of the line loss rate difference value larger than zero and the electricity consumption difference value larger than zero at the same time interval to obtain a first quantity value; for the same user to be detected, calculating the number of the line loss rate difference value smaller than zero and the electricity consumption difference value smaller than zero at the same time interval to obtain a second number value; and aiming at the same user to be detected, carrying out calculation processing based on the first quantity value and the second quantity value to obtain the matching degree corresponding to each user to be detected. For example: the line loss rate time series data within the preset duration range is that The calculation formula of the statistic is shown as the following formula (1):
（1）
time series corresponding to line loss rate curve The corresponding statistics areTime series corresponding to user i load curveThe corresponding statistics areWhen n is equal, the statistic M is calculated as shown in the following formula (2):
(2) Time sequence when m=1 、The same is the upward or downward trend. Aiming at the same user to be detected, carrying out addition operation processing based on the first quantity value and the second quantity value to obtain a target quantity value with the same line loss rate curve and load curve slope trend in the same time interval; and carrying out division operation based on the target quantity value and the time interval quantity value to obtain the matching degree corresponding to the same user to be detected so as to obtain the matching degree corresponding to each user to be detected.
Step S205: screening the users to be detected based on the matching degree to obtain first abnormal electricity users;
in the implementation process, screening is carried out on each user to be detected based on each matching degree, and each first abnormal electricity utilization user is obtained. Specifically, a user to be detected, of which the matching degree is greater than or equal to a preset matching degree threshold, is determined to be a first abnormal electricity user. The preset matching degree threshold value can be 0.8, and the preset matching degree threshold value can be set according to actual needs.
Step S206: based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user, calculating and obtaining covariance of the line loss rate of each moment point of the target to-be-detected area and the electricity consumption of each moment point of each to-be-detected user within the preset duration range;
In the specific implementation process, the step is based on a line loss rate data set corresponding to the line loss rate curve of the target to-be-detected platform area Calculating to obtain the line loss rate data setCorresponding first mean value; The calculation formula of the first mean value is shown in the following formula (3):
(3) Based on the power consumption data set corresponding to the load curve of each user to be detected Calculating to obtain the electricity consumption data setsCorresponding second mean value; The calculation formula of the second mean value is shown in the following formula (4):
(4) And calculating based on the first average value and the second average value to obtain covariance of the line loss rate of each moment point of the target to-be-detected platform area and the electricity consumption of each moment point of each to-be-detected user within the preset duration range. The calculation formula of the covariance is shown as the following formula (5):
（5）。
Step S207: calculating standard deviations of the line loss rates of all points of the target to-be-detected area based on the line loss rate curve to obtain a first standard deviation corresponding to the target to-be-detected area;
In the specific implementation process, calculating standard deviations of line loss rates of all points of the target to-be-detected area based on the line loss rate curve of the target to-be-detected area to obtain a first standard deviation corresponding to the target to-be-detected area 。
Step S208: calculating the standard deviation of the electricity consumption of each moment point of each user to be detected based on the load curve of each user to be detected to obtain a second standard deviation corresponding to each user to be detected;
In the implementation process, the standard deviation of the electricity consumption of each moment point of each user to be detected is calculated based on the load curve of each user to be detected, so as to obtain a second standard deviation corresponding to each user to be detected 。
Step S209: performing correlation calculation processing based on the covariance, the first standard deviation and the second standard deviation to obtain second abnormal electricity users;
in the specific implementation process, the product of the first standard deviation and the second standard deviation is calculated for the target to-be-detected area respectively to obtain a first product value corresponding to each to-be-detected user; dividing operation is respectively carried out on the basis of the covariance and each first product value, and a correlation coefficient value corresponding to each user to be detected is obtained; and screening processing is carried out based on the correlation coefficient values, and the user to be detected corresponding to the correlation coefficient value which is larger than or equal to a preset threshold value is determined to be the second abnormal electricity utilization user. And performing correlation coefficient calculation processing based on the covariance, the first standard deviation and the second standard deviation to obtain a correlation coefficient r of a power consumption load curve and a line loss rate curve corresponding to each user to be detected, wherein the mathematical expression of the correlation coefficient r is as shown in the following formula (6):
(6) The preset threshold value may be 0.8, and the size of the preset threshold value may be set according to actual needs. And when the correlation coefficient is calculated based on the covariance, the first standard deviation and the second standard deviation, and the correlation coefficient is larger than a preset threshold value, determining the user to be detected with the correlation coefficient larger than the preset threshold value as a second abnormal electricity utilization user.
Step S210: and performing intersection operation processing based on each first abnormal electricity user and each second abnormal electricity user to obtain different electricity users.
In the specific implementation process, a user load curve with the matching degree meeting a matching degree threshold value with a line loss rate curve is detected by a trend analysis method to be used as a first abnormal electricity user set A; detecting a user load curve with the line loss rate curve correlation degree meeting a preset threshold value by a Pearson correlation coefficient method to serve as a second abnormal electricity user set B; and solving an intersection of the two analysis methods, and identifying abnormal electricity utilization users causing the line loss of the station area.
The application obtains each abnormal station area with abnormal line loss rate curve by carrying out stability verification treatment on the line loss rate curve of each station area; the normal area can be filtered through stability verification, and the abnormal area is only required to be screened to obtain various abnormal electricity users. Acquiring a line loss rate curve corresponding to the target to-be-detected area within a preset time range, and acquiring a load curve of each to-be-detected user within the preset time range under the target to-be-detected area; determining each time interval based on the predetermined time length range; matching the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain the corresponding matching degree of each user to be detected; screening the users to be detected based on the matching degree to obtain first abnormal electricity users; based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user, calculating and obtaining covariance of the line loss rate of each moment point of the target to-be-detected area and the electricity consumption of each moment point of each to-be-detected user within the preset duration range; calculating standard deviations of the line loss rates of all points of the target to-be-detected area based on the line loss rate curve to obtain a first standard deviation corresponding to the target to-be-detected area; calculating the standard deviation of the electricity consumption of each moment point of each user to be detected based on the load curve of each user to be detected to obtain a second standard deviation corresponding to each user to be detected; performing correlation calculation processing based on the covariance, the first standard deviation and the second standard deviation to obtain second abnormal electricity users; and performing intersection operation processing based on each first abnormal electricity user and each second abnormal electricity user to obtain different electricity users. According to the application, the Pelson correlation analysis method is adopted to screen out each second abnormal electricity utilization user, and the second abnormal electricity utilization user is combined with the trend analysis method to screen out the final abnormal electricity utilization user, so that the abnormal electricity utilization user causing electricity consumption can be accurately positioned.
Still another embodiment of the present application provides an abnormal electricity user detection apparatus, as shown in fig. 3, including:
acquisition module 1: the method comprises the steps of acquiring a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area;
The first calculation module 2: the matching degree calculation processing is carried out by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user, so as to obtain each first abnormal electricity user;
The second calculation module 3: the method comprises the steps of performing relevance calculation processing by adopting a Pelson relevance analysis method based on a line loss rate curve of the target to-be-detected area and a load curve of each to-be-detected user to obtain each second abnormal electricity user;
detection module 4: and the method is used for detecting the abnormal user based on each first abnormal electricity user and each second abnormal electricity user to obtain a plurality of target abnormal electricity users.
In a specific implementation process, the abnormal electricity utilization user detection device further comprises: the verification module is specifically used for: performing stability verification processing on the line loss rate of each station area to obtain each station area to be detected with abnormal line loss rate, wherein the method specifically comprises the following steps: acquiring the line loss rate corresponding to each moment point in each arbitrary time interval based on the line loss rate curve of each area corresponding to each arbitrary time interval; calculating a line loss rate mean, a line loss rate variance and a line loss rate autocovariance of the line loss rate curve at any time interval based on each line loss rate; and screening based on the line loss rate mean value, the line loss rate variance and the line loss rate autocovariance of each arbitrary time interval corresponding to each station area to obtain each station area to be detected with abnormal line loss rate curve.
In a specific implementation process, the first computing module 2 is specifically configured to: acquiring a line loss rate curve corresponding to the target to-be-detected area within a preset time range, and acquiring a load curve of each user within the preset time range under the target to-be-detected area; determining each time interval based on the predetermined time length range; matching the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain the corresponding matching degree of each user to be detected; and screening the users to be detected based on the matching degree to obtain the first abnormal electricity utilization users.
In a specific implementation process, the first computing module 2 is further configured to: calculating to obtain the difference value of each power consumption at the later moment and the earlier moment of each time interval based on the load curve of the same user to be detected and calculating to obtain the difference value of each line loss rate at the later moment and the earlier moment of each time interval based on the line loss rate curve of the platform region where the same user to be detected is located; for the same user to be detected, calculating the number of the line loss rate difference value larger than zero and the electricity consumption difference value larger than zero at the same time interval to obtain a first quantity value; for the same user to be detected, calculating the number of the line loss rate difference value smaller than zero and the electricity consumption difference value smaller than zero at the same time interval to obtain a second number value; and aiming at the same user to be detected, carrying out calculation processing based on the first quantity value and the second quantity value to obtain the matching degree corresponding to each user to be detected.
In a specific implementation process, the second computing module 3 is specifically configured to: based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user, calculating and obtaining covariance of the line loss rate of each moment point of the target to-be-detected area and the electricity consumption of each moment point of each to-be-detected user within the preset duration range; calculating standard deviations of the line loss rates of all points of the target to-be-detected area based on the line loss rate curve to obtain a first standard deviation corresponding to the target to-be-detected area; calculating the standard deviation of the electricity consumption of each moment point of each user to be detected based on the load curve of each user to be detected to obtain a second standard deviation corresponding to each user to be detected; and carrying out correlation calculation processing based on the covariance, the first standard deviation and the second standard deviation to obtain second abnormal electricity utilization users.
In a specific implementation process, the second computing module 3 is further configured to: calculating products of the first standard deviation and the second standard deviation respectively for the target to-be-detected area to obtain first product values corresponding to all to-be-detected users; dividing operation is respectively carried out on the basis of the covariance and each first product value, and a correlation coefficient value corresponding to each user to be detected is obtained; and screening processing is carried out based on the correlation coefficient values, and the user to be detected corresponding to the correlation coefficient value which is larger than or equal to a preset threshold value is determined to be the second abnormal electricity utilization user.
In a specific implementation process, the detection module 4 is specifically configured to: and performing intersection operation processing based on each first abnormal electricity user and each second abnormal electricity user to obtain different electricity users.
The method comprises the steps of obtaining a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area; performing matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user; performing correlation calculation processing by adopting a Person correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user; and detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users. According to the application, the trend analysis method and the Pelson correlation coefficient method are adopted to comprehensively detect the abnormal users, and the intersection of the clients detected by the two analysis methods is solved to obtain the final abnormal electricity utilization user, so that the abnormal electricity utilization user under the abnormal platform area can be accurately positioned, and the labor cost is saved. The abnormal electricity utilization user can be checked on site in a targeted manner, and the detection efficiency of the abnormal user is high.
Another embodiment of the present application provides a storage medium storing a computer program which, when executed by a processor, performs the method steps of:
step one, acquiring a target to-be-detected platform area and each to-be-detected user corresponding to the target to-be-detected platform area;
step two, carrying out matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user;
thirdly, performing correlation computation processing by adopting a Pelson correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user;
and step four, detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The specific implementation process of the above method steps can be referred to the embodiment of any abnormal electricity user detection method, and this embodiment is not repeated here.
The method comprises the steps of obtaining a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area; performing matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user; performing correlation calculation processing by adopting a Person correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user; and detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users. According to the application, the trend analysis method and the Pelson correlation coefficient method are adopted to comprehensively detect the abnormal users, and the intersection of the clients detected by the two analysis methods is solved to obtain the final abnormal electricity utilization user, so that the abnormal electricity utilization user under the abnormal platform area can be accurately positioned, and the labor cost is saved. The abnormal electricity utilization user can be checked on site in a targeted manner, and the detection efficiency of the abnormal user is high.
Another embodiment of the present application provides an electronic device, which may be a server, that includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes non-volatile and/or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the electronic device is used for communicating with an external client through a network connection. The electronic equipment program is executed by the processor to realize the function or the step of the server side of the abnormal electricity utilization user detection method.
In one embodiment, an electronic device is provided, which may be a client. The electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the electronic device is used for communicating with an external server through a network connection. The electronic device program, when executed by the processor, implements a function or step of the abnormal electricity usage user detection method client side.
Another embodiment of the present application provides an electronic device, at least including a memory, a processor, where the memory stores a computer program, and the processor when executing the computer program on the memory implements the following method steps:
step one, acquiring a target to-be-detected platform area and each to-be-detected user corresponding to the target to-be-detected platform area;
step two, carrying out matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user;
thirdly, performing correlation computation processing by adopting a Pelson correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user;
and step four, detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users.
The specific implementation process of the above method steps can be referred to the embodiment of any abnormal electricity user detection method, and this embodiment is not repeated here.
The method comprises the steps of obtaining a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area; performing matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user; performing correlation calculation processing by adopting a Person correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user; and detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users. According to the application, the trend analysis method and the Pelson correlation coefficient method are adopted to comprehensively detect the abnormal users, and the intersection of the clients detected by the two analysis methods is solved to obtain the final abnormal electricity utilization user, so that the abnormal electricity utilization user under the abnormal platform area can be accurately positioned, and the labor cost is saved. The abnormal electricity utilization user can be checked on site in a targeted manner, and the detection efficiency of the abnormal user is high.
The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, the scope of which is defined by the claims. Various modifications and equivalent arrangements of this application will occur to those skilled in the art, and are intended to be within the spirit and scope of the application.
Claims (7)
1. The abnormal electricity utilization user detection method is characterized by comprising the following steps of:
Acquiring a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area;
Performing matching degree calculation processing by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each first abnormal electricity user;
Performing correlation calculation processing by adopting a Person correlation analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user to obtain each second abnormal electricity user;
Detecting abnormal users based on the first abnormal electricity users and the second abnormal electricity users to obtain a plurality of target abnormal electricity users;
The matching degree calculation processing is performed on the line loss rate curve based on the target to-be-detected platform area and the load curve of each to-be-detected user by adopting a trend analysis method, so as to obtain each first abnormal electricity user, which specifically comprises the following steps:
Acquiring a line loss rate curve corresponding to the target to-be-detected area within a preset time range, and acquiring a load curve of each user within the preset time range under the target to-be-detected area;
determining each time interval based on the predetermined time length range;
Matching the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain the corresponding matching degree of each user to be detected;
The matching processing is performed on the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain a matching degree corresponding to each user to be detected, and the matching processing specifically comprises the following steps:
calculating to obtain the difference value of each power consumption at the later moment and the earlier moment of each time interval based on the load curve of the same user to be detected and calculating to obtain the difference value of each line loss rate at the later moment and the earlier moment of each time interval based on the line loss rate curve of the platform region where the same user to be detected is located;
for the same user to be detected, calculating the number of the line loss rate difference value larger than zero and the electricity consumption difference value larger than zero at the same time interval to obtain a first quantity value;
For the same user to be detected, calculating the number of the line loss rate difference value smaller than zero and the electricity consumption difference value smaller than zero at the same time interval to obtain a second number value;
aiming at the same user to be detected, carrying out addition operation processing based on the first quantity value and the second quantity value to obtain a target quantity value with the same line loss rate curve and load curve slope trend in the same time interval;
Dividing operation is carried out based on the target quantity value and the time interval quantity value to obtain matching degrees corresponding to the same user to be detected so as to obtain matching degrees corresponding to all the users to be detected;
screening the users to be detected based on the matching degree to obtain first abnormal electricity users;
The line loss rate curve based on the target to-be-detected area and the load curve of each to-be-detected user are subjected to correlation calculation processing by adopting a pearson correlation analysis method to obtain each second abnormal electricity user, and the method specifically comprises the following steps:
Based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user, calculating and obtaining covariance of the line loss rate of each moment point of the target to-be-detected area and the electricity consumption of each moment point of each to-be-detected user within the preset duration range;
calculating standard deviations of the line loss rates of all points of the target to-be-detected area based on the line loss rate curve to obtain a first standard deviation corresponding to the target to-be-detected area;
calculating the standard deviation of the electricity consumption of each moment point of each user to be detected based on the load curve of each user to be detected to obtain a second standard deviation corresponding to each user to be detected;
And carrying out correlation calculation processing based on the covariance, the first standard deviation and the second standard deviation to obtain second abnormal electricity utilization users.
2. The method of claim 1, wherein prior to acquiring a target to-be-detected zone and each to-be-detected user corresponding to the target to-be-detected zone, the method further comprises: performing stability verification processing on the line loss rate of each station area to obtain each station area to be detected with abnormal line loss rate, wherein the method specifically comprises the following steps:
Acquiring the line loss rate corresponding to each moment point in each arbitrary time interval based on the line loss rate curve of each area corresponding to each arbitrary time interval;
calculating a line loss rate mean, a line loss rate variance and a line loss rate autocovariance of the line loss rate curve at any time interval based on each line loss rate;
and screening based on the line loss rate mean value, the line loss rate variance and the line loss rate autocovariance of each arbitrary time interval corresponding to each station area to obtain each station area to be detected with abnormal line loss rate curve.
3. The method of claim 1, wherein the performing correlation calculation based on each covariance, each first standard deviation, and each second standard deviation to obtain each second abnormal electricity user specifically comprises:
Calculating products of the first standard deviation and the second standard deviation respectively for the target to-be-detected area to obtain first product values corresponding to all to-be-detected users;
Dividing operation is respectively carried out on the basis of the covariance and each first product value, and a correlation coefficient value corresponding to each user to be detected is obtained;
and screening processing is carried out based on the correlation coefficient values, and the user to be detected corresponding to the correlation coefficient value which is larger than or equal to a preset threshold value is determined to be the second abnormal electricity utilization user.
4. The method of claim 1, wherein the filtering based on each of the first abnormal electricity users and each of the second abnormal electricity users to obtain the different electric users specifically comprises:
and performing intersection operation processing based on each first abnormal electricity user and each second abnormal electricity user to obtain different electricity users.
5. An abnormal electricity use user detection device, characterized by comprising:
The acquisition module is used for: the method comprises the steps of acquiring a target to-be-detected area and each to-be-detected user corresponding to the target to-be-detected area;
A first calculation module: the matching degree calculation processing is carried out by adopting a trend analysis method based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user, so as to obtain each first abnormal electricity user; the matching degree calculation processing is performed on the line loss rate curve based on the target to-be-detected area and the load curve of each to-be-detected user by adopting a trend analysis method, so as to obtain each first abnormal electricity user, which specifically comprises the following steps: acquiring a line loss rate curve corresponding to the target to-be-detected area within a preset time range, and acquiring a load curve of each user within the preset time range under the target to-be-detected area; determining each time interval based on the predetermined time length range; matching the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain the corresponding matching degree of each user to be detected; the matching processing is performed on the load curve and the line loss rate curve of the same user to be detected based on each time interval to obtain a matching degree corresponding to each user to be detected, and the matching processing specifically comprises the following steps: calculating to obtain the difference value of each power consumption at the later moment and the earlier moment of each time interval based on the load curve of the same user to be detected and calculating to obtain the difference value of each line loss rate at the later moment and the earlier moment of each time interval based on the line loss rate curve of the platform region where the same user to be detected is located; for the same user to be detected, calculating the number of the line loss rate difference value larger than zero and the electricity consumption difference value larger than zero at the same time interval to obtain a first quantity value; for the same user to be detected, calculating the number of the line loss rate difference value smaller than zero and the electricity consumption difference value smaller than zero at the same time interval to obtain a second number value; aiming at the same user to be detected, carrying out addition operation processing based on the first quantity value and the second quantity value to obtain a target quantity value with the same line loss rate curve and load curve slope trend in the same time interval; dividing operation is carried out based on the target quantity value and the time interval quantity value to obtain matching degrees corresponding to the same user to be detected so as to obtain matching degrees corresponding to all the users to be detected; screening the users to be detected based on the matching degree to obtain first abnormal electricity users;
A second calculation module: the method comprises the steps of performing relevance calculation processing by adopting a Pelson relevance analysis method based on a line loss rate curve of the target to-be-detected area and a load curve of each to-be-detected user to obtain each second abnormal electricity user; the line loss rate curve based on the target to-be-detected area and the load curve of each to-be-detected user are subjected to correlation calculation processing by adopting a pearson correlation analysis method to obtain each second abnormal electricity user, and the method specifically comprises the following steps: based on the line loss rate curve of the target to-be-detected area and the load curve of each to-be-detected user, calculating and obtaining covariance of the line loss rate of each moment point of the target to-be-detected area and the electricity consumption of each moment point of each to-be-detected user within the preset duration range; calculating standard deviations of the line loss rates of all points of the target to-be-detected area based on the line loss rate curve to obtain a first standard deviation corresponding to the target to-be-detected area; calculating the standard deviation of the electricity consumption of each moment point of each user to be detected based on the load curve of each user to be detected to obtain a second standard deviation corresponding to each user to be detected; performing correlation calculation processing based on the covariance, the first standard deviation and the second standard deviation to obtain second abnormal electricity users;
and a detection module: and the method is used for detecting the abnormal user based on each first abnormal electricity user and each second abnormal electricity user to obtain a plurality of target abnormal electricity users.
6. A storage medium storing a computer program which, when executed by a processor, implements the steps of the abnormal electricity usage user detection method of any one of the preceding claims 1-4.
7. An electronic device comprising at least a memory, a processor, the memory having stored thereon a computer program, the processor, when executing the computer program on the memory, implementing the steps of the abnormal electricity usage user detection method of any of the preceding claims 1-4.
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