CN114153894A - Real-time online identification system for electricity stealing users - Google Patents
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Abstract
The invention discloses a real-time online identification system of an electricity stealing user, belongs to the technical field of electric power systems, and aims to solve the problem that the existing real-time online identification method of the electricity stealing user is easy to have a state misjudgment phenomenon. It includes: the user electricity real-time monitoring module monitors the user electricity load in real time; the user type distinguishing module is used for distinguishing different user types respectively; the user classification module classifies different types of users; the electricity stealing suspected user judging module is used for carrying out similarity retrieval judgment on the real-time electricity loads of the users of the same type by adopting a similarity retrieval judging method to obtain an electricity stealing suspected user; and the electricity stealing user judging module calls the electricity load historical data of the suspected electricity stealing user at the time interval, trains the machine learning model, inputs the real-time electricity data into the machine learning model, judges whether the real-time electricity data is matched with the machine learning model or not, and judges the user as the electricity stealing user if the real-time electricity data is not matched with the machine learning model. The method is used for the power system to judge the electricity stealing users on line.
Description
Technical Field
The invention relates to a real-time online identification system for electricity stealing users, belonging to the technical field of electric power systems.
Background
There is a serious conflict between the ever-increasing demand for electricity and the increasingly scarce resources of electricity, and therefore, a population of electricity stealing users is generated. For a long time, phenomena such as electricity stealing and fraud are frequently prohibited in the society, and the behaviors seriously damage the life and property safety of other users, the management and power supply order of power enterprises and even bring serious threat to the development of national economy. Therefore, how to judge the electricity stealing users is a problem to be solved urgently at present. Aiming at electricity stealing users, a real-time online identification system of the electricity stealing users is particularly important.
At present, a real-time online identification method of a power stealing user mainly aims at address source information of the power stealing user, searches the position of the user according to an information flow channel, utilizes a position system to crack a password channel, searches data center information and adjusts the state of a structural system. However, in practical applications, the intensity of collecting user data is too large, so that much irrelevant information is also collected, and the phenomenon of state misjudgment is caused because real user data information cannot be completely reproduced.
Disclosure of Invention
The invention aims to solve the problem that the existing real-time online identification method of an electricity stealing user is easy to have a state misjudgment phenomenon, and provides a real-time online identification system of the electricity stealing user.
The invention relates to a real-time online identification system of an electricity stealing user, which is characterized by comprising the following components:
the user electricity real-time monitoring module monitors the electricity load of the user in real time;
the user type distinguishing module is used for distinguishing different user types respectively;
the user classification module classifies different types of users;
the electricity stealing suspected user judging module adopts a similarity retrieval judging method to carry out similarity retrieval judgment on the real-time electricity loads of the users of the same type so as to obtain the electricity stealing suspected user;
and the electricity stealing user judging module calls the electricity load historical data of the electricity stealing suspected user at the time interval, trains the machine learning model, inputs the real-time electricity data into the machine learning model, judges whether the real-time electricity data is matched with the machine learning model, and judges the user as the electricity stealing user if the real-time electricity data is not matched with the machine learning model.
Preferably, the user types include residential electricity, commercial electricity, industrial electricity, and agricultural production electricity.
Preferably, the similarity retrieval judgment method is adopted to perform similarity retrieval judgment on the real-time power loads of users of the same type, and the specific method for acquiring the electricity stealing suspicion user is as follows:
s3-1, performing curve fitting on the real-time power load of the same type of user;
s3-2, carrying out cluster analysis on the obtained curves;
s3-3, calculating the Euclidean distance between each user and the clustered curve;
and S3-4, when the Euclidean distance is larger than the Euclidean distance between the clustered curve and the center acquired at S3-2, judging that the user corresponding to the curve is a suspected electricity stealing user.
The invention has the advantages that: the real-time online identification system of the electricity stealing user provided by the invention eliminates the influence of interference electricity data, retains complete user data information, reduces unnecessary operation waste, shortens real-time judgment time, can improve system judgment efficiency to a higher degree, enhances identification performance, processes identification data in a centralized manner, and completely reproduces data information conditions.
Drawings
Fig. 1 is a schematic block diagram of a real-time online identification system for electricity stealing users according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
The first embodiment is as follows: the present embodiment is described below with reference to fig. 1, and the present embodiment provides a real-time online identification system for electricity stealing users, which includes:
the user electricity real-time monitoring module monitors the electricity load of the user in real time;
the user type distinguishing module is used for distinguishing different user types respectively;
the user classification module classifies different types of users;
the electricity stealing suspected user judging module adopts a similarity retrieval judging method to carry out similarity retrieval judgment on the real-time electricity loads of the users of the same type so as to obtain the electricity stealing suspected user;
and the electricity stealing user judging module calls the electricity load historical data of the electricity stealing suspected user at the time interval, trains the machine learning model, inputs the real-time electricity data into the machine learning model, judges whether the real-time electricity data is matched with the machine learning model, and judges the user as the electricity stealing user if the real-time electricity data is not matched with the machine learning model.
The second embodiment is as follows: the embodiment further describes the first embodiment, and the user types include residential electricity, commercial electricity, industrial electricity and agricultural production electricity.
The third concrete implementation mode: the first embodiment is further described, a similarity retrieval and judgment method is adopted to perform similarity retrieval and judgment on real-time power loads of users of the same type, and the specific method for acquiring the electricity stealing suspected user is as follows:
s3-1, performing curve fitting on the real-time power load of the same type of user;
s3-2, carrying out cluster analysis on the obtained curves;
s3-3, calculating the Euclidean distance between each user and the clustered curve;
and S3-4, when the Euclidean distance is larger than the Euclidean distance between the clustered curve and the center acquired at S3-2, judging that the user corresponding to the curve is a suspected electricity stealing user.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (3)
1. A real-time on-line identification system for electricity stealing users, comprising:
the user electricity real-time monitoring module monitors the electricity load of the user in real time;
the user type distinguishing module is used for distinguishing different user types respectively;
the user classification module classifies different types of users;
the electricity stealing suspected user judging module adopts a similarity retrieval judging method to carry out similarity retrieval judgment on the real-time electricity loads of the users of the same type so as to obtain the electricity stealing suspected user;
and the electricity stealing user judging module calls the electricity load historical data of the electricity stealing suspected user at the time interval, trains the machine learning model, inputs the real-time electricity data into the machine learning model, judges whether the real-time electricity data is matched with the machine learning model, and judges the user as the electricity stealing user if the real-time electricity data is not matched with the machine learning model.
2. The system for real-time on-line identification of electricity-stealing users according to claim 1, wherein the user types include residential electricity, commercial electricity, industrial electricity, and agricultural production electricity.
3. The system of claim 1, wherein the similarity retrieval determination method is used for performing similarity retrieval determination on real-time power loads of users of the same type, and the specific method for obtaining the suspected electricity stealing user comprises the following steps:
s3-1, performing curve fitting on the real-time power load of the same type of user;
s3-2, carrying out cluster analysis on the obtained curves;
s3-3, calculating the Euclidean distance between each user and the clustered curve;
and S3-4, when the Euclidean distance is larger than the Euclidean distance between the clustered curve and the center acquired at S3-2, judging that the user corresponding to the curve is a suspected electricity stealing user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111350594.5A CN114153894A (en) | 2021-11-15 | 2021-11-15 | Real-time online identification system for electricity stealing users |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202111350594.5A CN114153894A (en) | 2021-11-15 | 2021-11-15 | Real-time online identification system for electricity stealing users |
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| CN114153894A true CN114153894A (en) | 2022-03-08 |
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| CN202111350594.5A Pending CN114153894A (en) | 2021-11-15 | 2021-11-15 | Real-time online identification system for electricity stealing users |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116660621A (en) * | 2023-07-27 | 2023-08-29 | 江西琰圭技术服务有限公司 | Electricity larceny prevention intelligent management system for local sampling analysis |
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2021
- 2021-11-15 CN CN202111350594.5A patent/CN114153894A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116660621A (en) * | 2023-07-27 | 2023-08-29 | 江西琰圭技术服务有限公司 | Electricity larceny prevention intelligent management system for local sampling analysis |
| CN116660621B (en) * | 2023-07-27 | 2023-09-26 | 江西琰圭技术服务有限公司 | Electricity larceny prevention intelligent management system for local sampling analysis |
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