CN111310120A - Abnormal electricity utilization user identification method, device, terminal and medium based on big data - Google Patents
Abnormal electricity utilization user identification method, device, terminal and medium based on big data Download PDFInfo
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Abstract
The application discloses abnormal electricity consumption user identification, device, terminal and medium based on big data. By utilizing the power consumption fluctuation interval of the target user to carry out targeted judgment on the actual power consumption fluctuation of the target user, the technical situation that the power consumption habits of different user individuals are neglected in the existing identification mode can be avoided, and the technical problem that the misjudgment rate of the existing abnormal power consumption user identification method is high is solved.
Description
Technical Field
The application relates to the field of electric power data processing, in particular to abnormal electricity utilization user identification, device, terminal and medium based on big data.
Background
Due to the continuous improvement of the power grid informatization level in recent years, various information management systems accumulate a large amount of customer service data and exponentially increase, and in order to further improve the marketing service quality of a power grid company and improve the inspection quality of abnormal power utilization users, the important link of improving the customer service quality of a power grid is provided.
The current common abnormal electricity utilization user identification means is a check rule based on a threshold value, taking the fluctuation rate of electric quantity as an example, abnormal records based on the threshold value rule usually depend on the comparison between the current-stage electric quantity value of a user to be checked and the reference electric quantity values of other users of the same type, so that the abnormal electricity utilization judgment of the user is made, however, the electricity utilization habits of different users are ignored by the general identification means, and the technical problem that the misjudgment rate of the existing abnormal electricity utilization user identification method is high is caused.
Disclosure of Invention
The application provides abnormal electricity user identification, device, terminal and medium based on big data, and is used for solving the technical problem that the existing abnormal electricity user identification method is high in misjudgment rate.
In view of this, a first aspect of the present application provides a big data-based abnormal electricity consumption user identification method, including:
acquiring historical electricity consumption data of each user to be identified;
determining the grading interval of the power consumption of each user to be identified according to the comparison result of the historical average power consumption of each user to be identified and a preset average power consumption grading threshold;
according to the user types of the users to be identified, clustering the users to be identified in the power consumption grading intervals respectively to obtain a user set;
calculating a mean value of power consumption in the same proportion, a variance of the power consumption in the same proportion, a mean value of power consumption in the ring proportion and a variance of the power consumption in the ring proportion of the target user according to historical power consumption data of the target user, wherein the target user is one of the users to be identified;
calculating the average value and the variance of the electricity consumption of the same type of users of the target user according to the target user set where the target user is located and the historical electricity consumption data of each user to be identified in the target user set;
carrying out weighted summation according to the average value of the electricity consumption in the same proportion, the average value of the electricity consumption in the ring proportion and the average value of the electricity consumption of the similar users to obtain a historical electricity consumption average value corresponding to the target user;
carrying out weighted summation according to the electricity consumption variance of the same proportion, the electricity consumption variance of the ring proportion and the electricity consumption variance of the same type of users to obtain the historical electricity consumption variance corresponding to the target user;
obtaining a power consumption fluctuation interval of the target user according to the sum and difference of the historical power consumption mean value and the historical power consumption variance;
and comparing the electricity consumption fluctuation interval with the actual electricity consumption fluctuation of the target user to obtain an abnormal electricity consumption identification result of the target user.
Optionally, the comparing the power consumption fluctuation interval with the actual power consumption fluctuation of the target user to obtain the abnormal power consumption identification result of the target user specifically includes:
and according to the electricity consumption fluctuation interval and the actual electricity consumption fluctuation of the target user, if the actual electricity consumption fluctuation exceeds the range limited by the electricity consumption fluctuation interval, setting the target user as an abnormal electricity consumption user, and if the actual electricity consumption fluctuation is in the range limited by the electricity consumption fluctuation interval, setting the target user as a normal user.
Optionally, the obtaining the power consumption fluctuation interval of the target user according to the sum and the difference of the historical power consumption average value and the historical power consumption variance specifically includes:
and obtaining a power consumption fluctuation interval of the target user according to the interval upper limit and the interval lower limit by taking the sum of the historical power consumption average value and the historical power consumption variance as an interval upper limit and the difference of the historical power consumption average value and the historical power consumption variance as an interval lower limit.
Optionally, the user type of the user to be identified specifically includes: residential electricity types, commercial electricity types, and industrial electricity types.
The second aspect of the present application provides a device for identifying abnormal electricity consumption users based on big data, including:
the historical data acquisition unit is used for acquiring historical electricity consumption data of each user to be identified;
the average power consumption grading unit is used for determining the grading interval of the power consumption of each user to be identified according to the comparison result of the historical average power consumption of each user to be identified and a preset average power consumption grading threshold;
the user classification unit is used for clustering the users to be identified in the power consumption classification intervals respectively according to the user types of the users to be identified to obtain a user set;
the first electricity parameter calculation unit is used for calculating a same-ratio electricity consumption mean value, a same-ratio electricity consumption variance, an annular-ratio electricity consumption mean value and an annular-ratio electricity consumption variance of a target user according to historical electricity consumption data of the target user, wherein the target user is one of the users to be identified;
the second electricity parameter calculation unit is used for calculating the average value and the variance of the electricity consumption of the same type of users of the target user according to the target user set where the target user is located and the historical electricity consumption data of each user to be identified in the target user set;
the historical electricity utilization average value calculating unit is used for carrying out weighted summation according to the electricity utilization average value of the same proportion, the electricity utilization average value of the ring proportion and the electricity utilization average value of the similar users to obtain the historical electricity utilization average value corresponding to the target user;
the historical electricity consumption variance calculating unit is used for carrying out weighted summation according to the electricity consumption variance of the same proportion, the electricity consumption variance of the ring proportion and the electricity consumption variance of the same type of users to obtain the historical electricity consumption variance corresponding to the target user;
the power consumption fluctuation interval establishing unit is used for obtaining a power consumption fluctuation interval of the target user according to the sum and difference of the historical power consumption mean value and the historical power consumption variance;
and the abnormal electricity utilization judging unit is used for comparing the electricity utilization fluctuation interval with the actual electricity utilization fluctuation of the target user so as to obtain an abnormal electricity utilization identification result of the target user.
Optionally, the abnormal electricity utilization determination unit is specifically configured to:
and according to the electricity consumption fluctuation interval and the actual electricity consumption fluctuation of the target user, if the actual electricity consumption fluctuation exceeds the range limited by the electricity consumption fluctuation interval, setting the target user as an abnormal electricity consumption user, and if the actual electricity consumption fluctuation is in the range limited by the electricity consumption fluctuation interval, setting the target user as a normal user.
Optionally, the electricity consumption fluctuation interval establishing unit is specifically configured to:
and obtaining a power consumption fluctuation interval of the target user according to the interval upper limit and the interval lower limit by taking the sum of the historical power consumption average value and the historical power consumption variance as an interval upper limit and the difference of the historical power consumption average value and the historical power consumption variance as an interval lower limit.
Optionally, the user type of the user to be identified specifically includes: residential electricity types, commercial electricity types, and industrial electricity types.
A third aspect of the present application provides a terminal, comprising: a memory and a processor;
the memory is used for storing program codes corresponding to the big data based abnormal electricity utilization user identification in the first aspect of the application;
the processor is configured to execute the program code.
A fourth aspect of the present application provides a storage medium having stored therein program code corresponding to the big-data based abnormal electricity usage user identification according to the first aspect of the present application.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides a big data-based abnormal electricity utilization user identification method, which comprises the following steps: acquiring historical electricity consumption data of each user to be identified; determining the grading interval of the power consumption of each user to be identified according to the comparison result of the historical average power consumption of each user to be identified and a preset average power consumption grading threshold; according to the user type of each user to be identified, clustering the users to be identified in each power consumption grading interval respectively to obtain a user set; calculating a mean value of power consumption in the same proportion, a variance of the power consumption in the same proportion, a mean value of power consumption in the ring proportion and a variance of the power consumption in the ring proportion of a target user according to historical power consumption data of the target user, wherein the target user is one of users to be identified; calculating the average value and variance of the electricity consumption of the same type of users of the target user according to the historical electricity consumption data of the target user set where the target user is located and each user to be identified in the target user set; carrying out weighted summation according to the average value of the electricity consumption in the same proportion, the average value of the electricity consumption in the ring proportion and the average value of the electricity consumption of the similar users to obtain the historical electricity consumption average value corresponding to the target user; carrying out weighted summation according to the variance of the electricity consumption of the same proportion, the variance of the electricity consumption of the ring proportion and the variance of the electricity consumption of the similar users to obtain the historical electricity consumption variance corresponding to the target user; obtaining a power consumption fluctuation interval of a target user according to the sum and difference of the historical power consumption mean value and the historical power consumption variance; and comparing the electricity consumption fluctuation interval with the actual electricity consumption fluctuation of the target user to obtain an abnormal electricity consumption identification result of the target user.
According to the method, firstly, historical power consumption data of each user are divided into a plurality of user sets according to average power consumption and user types, then, according to the historical power consumption data of a target user and the historical power consumption data in the target user set, the average value and the variance of the same-proportion power consumption, the ring-proportion power consumption and the power consumption of the same-class user are respectively calculated, and then, a power consumption fluctuation interval representing the historical power consumption habits of the target user is obtained. By utilizing the power consumption fluctuation interval of the target user to carry out targeted judgment on the actual power consumption fluctuation of the target user, the technical situation that the power consumption habits of different user individuals are neglected in the existing identification mode can be avoided, and the technical problem that the misjudgment rate of the existing abnormal power consumption user identification method is high is solved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a first embodiment of a big data-based abnormal electricity consumption user identification method provided in the present application;
fig. 2 is a schematic structural diagram of a first embodiment of an abnormal electricity consumption user identification device based on big data according to the present application.
Detailed Description
The embodiment of the application provides abnormal electricity user identification, device, terminal and medium based on big data, and is used for solving the technical problem that the existing abnormal electricity user identification method is high in misjudgment rate.
In order to make the objects, features and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the embodiments described below are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a first embodiment of the present application provides a big data-based abnormal electricity consumption user identification method, including:
Firstly, when the method for identifying an abnormal electricity consumption user in this embodiment is implemented, historical electricity consumption data of the user to be identified is obtained, where the historical electricity consumption data includes all electricity consumption meter reading data of the user in a certain period, and a preferred range of "the certain period" in this embodiment is greater than two years.
And 102, determining the grading interval of the power consumption of each user to be identified according to the comparison result of the historical average power consumption of each user to be identified and a preset average power consumption grading threshold value.
Then, according to the obtained historical electricity consumption data of each user to be identified, the historical average electricity consumption of each user to be identified, namely the average value of the historical electricity consumption, is calculated, then, the historical average electricity consumption of each user to be identified is sequentially compared with a preset average electricity consumption grading threshold value, the electricity consumption grading interval of each user to be identified is determined, specifically, the average electricity consumption grading threshold value of the embodiment represents the upper limit or the lower limit of the preset electricity consumption grading interval, the historical average electricity consumption of each user to be identified is respectively compared with the upper limit and the lower limit of each electricity consumption grading interval, the historical average electricity consumption is determined in which electricity consumption grading interval, and therefore the electricity consumption grading interval of each user to be identified is determined.
And 103, clustering the users to be identified in each power consumption classification interval according to the user types of the users to be identified to obtain a user set.
Then, based on the average power consumption grading result in the step 102, clustering the users to be identified in each power consumption grading interval in sequence, wherein the specific steps include: firstly, selecting a power consumption grading interval, determining the user type attribute of each user to be identified in the power consumption grading interval one by one, aggregating the users to be identified with the same user type attribute in the same user set, and adopting the same processing flow for other power consumption grading intervals.
The user type of the user to be identified in this embodiment includes: residential electricity type, commercial electricity type, and industrial electricity type
And 104, calculating the average value, the variance, the average value and the variance of the electricity consumption of the same proportion of the target user according to the historical electricity consumption data of the target user. The target user is one of the users to be identified.
Then, based on each user set obtained in step 103, a target user is selected from the users to be identified, the same-ratio measurement and the ring-ratio measurement are respectively performed according to the historical power consumption data of the target user, and the average value of the same-ratio power consumption, the variance of the same-ratio power consumption, the average value of the ring-ratio power consumption and the variance of the ring-ratio power consumption are calculated.
And 105, calculating the average value and the variance of the electricity consumption of the same type of users of the target user according to the target user set where the target user is located and the historical electricity consumption data of each user to be identified in the target user set.
And then, according to the historical power consumption data (including the historical power consumption data of the target user) of the target user set where the target user is located and each user to be identified in the target user set, calculating the historical power consumption data of the target user and other users to be identified in the target user set in the same time range, and performing mean value calculation and variance calculation to obtain the mean value of the power consumption of the same type of users and the power consumption variance of the same type of users.
And 106, carrying out weighted summation according to the average value of the electricity consumption of the same proportion, the average value of the electricity consumption of the ring proportion and the average value of the electricity consumption of the similar users to obtain the historical electricity consumption average value corresponding to the target user.
And 107, carrying out weighted summation according to the variance of the electricity consumption of the same proportion, the variance of the electricity consumption of the ring proportion and the variance of the electricity consumption of the similar users to obtain the historical electricity consumption variance corresponding to the target user.
And 108, obtaining a power consumption fluctuation interval of the target user according to the sum and difference of the historical power consumption mean value and the historical power consumption variance.
More specifically, the sum of the historical electricity average value and the historical electricity variance is used as an upper section limit, the difference between the historical electricity average value and the historical electricity variance is used as a lower section limit, and the electricity consumption fluctuation section of the target user is obtained according to the upper section limit and the lower section limit.
And step 109, comparing the electricity consumption fluctuation interval with the actual electricity consumption fluctuation of the target user to obtain an abnormal electricity consumption identification result of the target user.
More specifically, according to the comparison between the power consumption fluctuation interval and the actual power consumption fluctuation of the target user, if the actual power consumption fluctuation is beyond the range defined by the power consumption fluctuation interval, the target user is set as an abnormal power consumption user, and if the actual power consumption fluctuation is within the range defined by the power consumption fluctuation interval, the target user is set as a normal user.
According to the abnormal electricity consumption user identification method based on the big data, the historical electricity consumption data of each user are divided into a plurality of user sets according to the average electricity consumption and the user types, then the average value and the variance of the same-proportion electricity consumption, the ring-proportion electricity consumption and the electricity consumption of the same-class users are respectively calculated according to the historical electricity consumption data of the target user and the historical electricity consumption data in the target user set, and then the electricity consumption fluctuation interval representing the historical electricity consumption habits of the target user is obtained. By utilizing the power consumption fluctuation interval of the target user to carry out targeted judgment on the actual power consumption fluctuation of the target user, the technical situation that the power consumption habits of different user individuals are neglected in the existing identification mode can be avoided, and the technical problem that the misjudgment rate of the existing abnormal power consumption user identification method is high is solved.
The above is a detailed description of a first embodiment of a method for identifying an abnormal electricity consumer based on big data provided by the present application, and the following is a detailed description of a first embodiment of an apparatus for identifying an abnormal electricity consumer based on big data provided by the present application.
Referring to fig. 2, a second embodiment of the present application provides a big data based abnormal electricity consumption user identification apparatus, including:
a historical data acquisition unit 201, configured to acquire historical power consumption data of each user to be identified;
the average power consumption grading unit 202 is configured to determine a power consumption grading interval of each user to be identified according to a comparison result between the historical average power consumption of each user to be identified and a preset average power consumption grading threshold;
the user classification unit 203 is used for clustering the users to be identified in each power consumption classification interval according to the user types of the users to be identified to obtain a user set;
the first electricity parameter calculating unit 204 is configured to calculate a mean value of electricity consumption in same proportion, a variance of electricity consumption in same proportion, a mean value of electricity consumption in ring proportion and a variance of electricity consumption in ring proportion of a target user according to historical electricity consumption data of the target user, where the target user is one of users to be identified;
the second electricity parameter calculating unit 205 is configured to calculate an average electricity consumption value of similar users and an electricity consumption variance of similar users of a target user according to historical electricity consumption data of each user to be identified in a target user set where the target user is located and the target user set;
the historical electricity consumption average value calculating unit 206 is configured to perform weighted summation according to the electricity consumption average value of the same proportion, the electricity consumption average value of the ring proportion and the electricity consumption average value of the similar users to obtain a historical electricity consumption average value corresponding to the target user;
the historical electricity consumption variance calculating unit 207 is used for carrying out weighted summation according to the same-ratio electricity consumption variance, the ring-ratio electricity consumption variance and the same-type user electricity consumption variance to obtain the historical electricity consumption variance corresponding to the target user;
the power consumption fluctuation interval establishing unit 208 is configured to obtain a power consumption fluctuation interval of the target user according to a sum and a difference of the historical power consumption average value and the historical power consumption variance;
and an abnormal electricity utilization determination unit 209, configured to compare the electricity consumption fluctuation interval with the actual electricity utilization fluctuation of the target user to obtain an abnormal electricity utilization identification result of the target user.
Further, abnormal electricity usage determination section 209 is specifically configured to:
and comparing the fluctuation interval of the electricity consumption with the actual fluctuation of the electricity consumption of the target user, if the actual fluctuation of the electricity consumption is beyond the range limited by the fluctuation interval of the electricity consumption, setting the target user as an abnormal electricity consumption user, and if the actual fluctuation of the electricity consumption is within the range limited by the fluctuation interval of the electricity consumption, setting the target user as a normal user.
Further, the electricity consumption fluctuation interval establishing unit 208 is specifically configured to:
and obtaining a power consumption fluctuation interval of the target user according to the interval upper limit and the interval lower limit by taking the sum of the historical power consumption average value and the historical power consumption variance as an interval upper limit and the difference of the historical power consumption average value and the historical power consumption variance as an interval lower limit.
Further, the user type of the user to be identified specifically includes: residential electricity types, commercial electricity types, and industrial electricity types.
The above is a detailed description of a first embodiment of the abnormal electricity consumption user identification device based on big data provided by the present application, and the following is a detailed description of embodiments of a terminal and a storage medium provided by the present application.
A third embodiment of the present application provides a terminal, including: a memory and a processor;
the memory is used for storing program codes corresponding to the abnormal electricity utilization user identification based on the big data according to the first embodiment of the application;
the processor is configured to execute the program code.
A fourth embodiment of the present application provides a storage medium, in which a program code corresponding to the big data based abnormal electricity consumption user identification described in the first embodiment of the present application is stored.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A big data-based abnormal electricity utilization user identification method is characterized by comprising the following steps:
acquiring historical electricity consumption data of each user to be identified;
determining the grading interval of the power consumption of each user to be identified according to the comparison result of the historical average power consumption of each user to be identified and a preset average power consumption grading threshold;
according to the user types of the users to be identified, clustering the users to be identified in the power consumption grading intervals respectively to obtain a user set;
calculating a mean value of power consumption in the same proportion, a variance of the power consumption in the same proportion, a mean value of power consumption in the ring proportion and a variance of the power consumption in the ring proportion of the target user according to historical power consumption data of the target user, wherein the target user is one of the users to be identified;
calculating the average value and the variance of the electricity consumption of the same type of users of the target user according to the target user set where the target user is located and the historical electricity consumption data of each user to be identified in the target user set;
carrying out weighted summation according to the average value of the electricity consumption in the same proportion, the average value of the electricity consumption in the ring proportion and the average value of the electricity consumption of the similar users to obtain a historical electricity consumption average value corresponding to the target user;
carrying out weighted summation according to the electricity consumption variance of the same proportion, the electricity consumption variance of the ring proportion and the electricity consumption variance of the same type of users to obtain the historical electricity consumption variance corresponding to the target user;
obtaining a power consumption fluctuation interval of the target user according to the sum and difference of the historical power consumption mean value and the historical power consumption variance;
and comparing the electricity consumption fluctuation interval with the actual electricity consumption fluctuation of the target user to obtain an abnormal electricity consumption identification result of the target user.
2. The method as claimed in claim 1, wherein the step of comparing the power consumption fluctuation interval with the actual power consumption fluctuation of the target user to obtain the abnormal power consumption identification result of the target user specifically comprises:
and according to the electricity consumption fluctuation interval and the actual electricity consumption fluctuation of the target user, if the actual electricity consumption fluctuation exceeds the range limited by the electricity consumption fluctuation interval, setting the target user as an abnormal electricity consumption user, and if the actual electricity consumption fluctuation is in the range limited by the electricity consumption fluctuation interval, setting the target user as a normal user.
3. The method as claimed in claim 1, wherein the obtaining of the power consumption fluctuation interval of the target user according to the sum and the difference of the historical power consumption mean value and the historical power consumption variance specifically comprises:
and obtaining a power consumption fluctuation interval of the target user according to the interval upper limit and the interval lower limit by taking the sum of the historical power consumption average value and the historical power consumption variance as an interval upper limit and the difference of the historical power consumption average value and the historical power consumption variance as an interval lower limit.
4. The abnormal electricity consumption user identification method based on big data according to claim 1, wherein the user type of the user to be identified specifically comprises: residential electricity types, commercial electricity types, and industrial electricity types.
5. An abnormal electricity consumption user identification device based on big data is characterized by comprising:
the historical data acquisition unit is used for acquiring historical electricity consumption data of each user to be identified;
the average power consumption grading unit is used for determining the grading interval of the power consumption of each user to be identified according to the comparison result of the historical average power consumption of each user to be identified and a preset average power consumption grading threshold;
the user classification unit is used for clustering the users to be identified in the power consumption classification intervals respectively according to the user types of the users to be identified to obtain a user set;
the first electricity parameter calculation unit is used for calculating a same-ratio electricity consumption mean value, a same-ratio electricity consumption variance, an annular-ratio electricity consumption mean value and an annular-ratio electricity consumption variance of a target user according to historical electricity consumption data of the target user, wherein the target user is one of the users to be identified;
the second electricity parameter calculation unit is used for calculating the average value and the variance of the electricity consumption of the same type of users of the target user according to the target user set where the target user is located and the historical electricity consumption data of each user to be identified in the target user set;
the historical electricity utilization average value calculating unit is used for carrying out weighted summation according to the electricity utilization average value of the same proportion, the electricity utilization average value of the ring proportion and the electricity utilization average value of the similar users to obtain the historical electricity utilization average value corresponding to the target user;
the historical electricity consumption variance calculating unit is used for carrying out weighted summation according to the electricity consumption variance of the same proportion, the electricity consumption variance of the ring proportion and the electricity consumption variance of the same type of users to obtain the historical electricity consumption variance corresponding to the target user;
the power consumption fluctuation interval establishing unit is used for obtaining a power consumption fluctuation interval of the target user according to the sum and difference of the historical power consumption mean value and the historical power consumption variance;
and the abnormal electricity utilization judging unit is used for comparing the electricity utilization fluctuation interval with the actual electricity utilization fluctuation of the target user so as to obtain an abnormal electricity utilization identification result of the target user.
6. The big data-based abnormal electricity consumption user identification device according to claim 1, wherein the abnormal electricity consumption determination unit is specifically configured to:
and according to the electricity consumption fluctuation interval and the actual electricity consumption fluctuation of the target user, if the actual electricity consumption fluctuation exceeds the range limited by the electricity consumption fluctuation interval, setting the target user as an abnormal electricity consumption user, and if the actual electricity consumption fluctuation is in the range limited by the electricity consumption fluctuation interval, setting the target user as a normal user.
7. The abnormal electricity consumption user identification device based on big data according to claim 1, wherein the electricity consumption fluctuation interval establishing unit is specifically configured to:
and obtaining a power consumption fluctuation interval of the target user according to the interval upper limit and the interval lower limit by taking the sum of the historical power consumption average value and the historical power consumption variance as an interval upper limit and the difference of the historical power consumption average value and the historical power consumption variance as an interval lower limit.
8. The abnormal electricity consumption user identification device based on big data according to claim 1, wherein the user type of the user to be identified specifically comprises: residential electricity types, commercial electricity types, and industrial electricity types.
9. A terminal, comprising: a memory and a processor;
the memory is used for storing program codes corresponding to the big data based abnormal electricity utilization user identification of any one of claims 1 to 4;
the processor is configured to execute the program code.
10. A storage medium storing a program code corresponding to the big data based abnormal electricity usage user recognition according to any one of claims 1 to 4.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112669075A (en) * | 2020-12-30 | 2021-04-16 | 广东电网有限责任公司中山供电局 | Method for checking abnormal fluctuation of electric quantity of electricity customer |
| CN112925827A (en) * | 2021-03-04 | 2021-06-08 | 南京怡晟安全技术研究院有限公司 | User property abnormity analysis method based on power acquisition Internet of things data |
| CN113125851A (en) * | 2021-04-21 | 2021-07-16 | 广东电网有限责任公司梅州供电局 | Power consumption statistical method, device, equipment and storage medium |
| CN114784976A (en) * | 2022-05-19 | 2022-07-22 | 珠海市钛芯动力科技有限公司 | Intelligent extension socket for formulating power utilization plan based on cloud computing |
| CN116029480A (en) * | 2023-03-28 | 2023-04-28 | 广东电网有限责任公司 | Proxy purchase electricity measuring method and system thereof |
| CN116613894A (en) * | 2023-07-20 | 2023-08-18 | 国网汇通金财（北京）信息科技有限公司 | Method and device for processing electricity consumption data |
| CN117273811A (en) * | 2023-11-21 | 2023-12-22 | 国网浙江省电力有限公司 | Enterprise power consumption prediction data processing method based on power consumption data |
| CN112925827B (en) * | 2021-03-04 | 2024-05-10 | 南京怡晟安全技术研究院有限公司 | User property anomaly analysis method based on electric power acquisition internet of things data |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325542A (en) * | 2018-10-09 | 2019-02-12 | 烟台海颐软件股份有限公司 | A kind of electricity exception intelligent identification Method and system based on multistage machine learning |
| CN109815084A (en) * | 2018-12-29 | 2019-05-28 | 北京城市网邻信息技术有限公司 | Abnormality recognition method, device and electronic equipment and storage medium |
-
2020
- 2020-02-14 CN CN202010093088.1A patent/CN111310120B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325542A (en) * | 2018-10-09 | 2019-02-12 | 烟台海颐软件股份有限公司 | A kind of electricity exception intelligent identification Method and system based on multistage machine learning |
| CN109815084A (en) * | 2018-12-29 | 2019-05-28 | 北京城市网邻信息技术有限公司 | Abnormality recognition method, device and electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 王卫公等: "基于灰色分析的集抄数据异常判定", 《电网与清洁能源》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112669075A (en) * | 2020-12-30 | 2021-04-16 | 广东电网有限责任公司中山供电局 | Method for checking abnormal fluctuation of electric quantity of electricity customer |
| CN112925827A (en) * | 2021-03-04 | 2021-06-08 | 南京怡晟安全技术研究院有限公司 | User property abnormity analysis method based on power acquisition Internet of things data |
| CN112925827B (en) * | 2021-03-04 | 2024-05-10 | 南京怡晟安全技术研究院有限公司 | User property anomaly analysis method based on electric power acquisition internet of things data |
| CN113125851A (en) * | 2021-04-21 | 2021-07-16 | 广东电网有限责任公司梅州供电局 | Power consumption statistical method, device, equipment and storage medium |
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| CN116029480A (en) * | 2023-03-28 | 2023-04-28 | 广东电网有限责任公司 | Proxy purchase electricity measuring method and system thereof |
| CN116613894A (en) * | 2023-07-20 | 2023-08-18 | 国网汇通金财（北京）信息科技有限公司 | Method and device for processing electricity consumption data |
| CN116613894B (en) * | 2023-07-20 | 2023-10-10 | 国网汇通金财（北京）信息科技有限公司 | Method and device for processing electricity consumption data |
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| CN117273811B (en) * | 2023-11-21 | 2024-02-09 | 国网浙江省电力有限公司 | Enterprise power consumption prediction data processing method based on power consumption data |
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