CN112733456A - Electricity stealing prevention behavior identification method and system - Google Patents
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Abstract
The application discloses an electric larceny prevention behavior identification method and system, wherein the method comprises the steps of obtaining historical electric utilization evaluation index parameter data of a user; constructing a suspected electricity stealing user identification model; adopting a suspected electricity stealing user identification model to identify suspected electricity stealing users; and verifying suspected electricity stealing users by combining the transverse data of the user electricity utilization evaluation index parameters to determine the electricity stealing users. This application can be according to electric wire netting historical data, under the condition that power line does not appear the hardware damage, according to the user power consumption evaluation index parameter data preliminary judgement user's power consumption state that electric wire netting data platform gathered, the prediction user steals electric action, carries out many-sided branch institute to the power consumption user to reduce search range, improve anti-electricity-stealing work efficiency.
Description
Technical Field
The invention belongs to the technical field of power utilization evaluation index parameter data monitoring, and relates to a method and a system for identifying anti-electricity-stealing behaviors.
Background
Aiming at the characteristics of specialization and concealment of the current electricity stealing behavior, the electricity stealing prevention technology gradually changes to intellectualization.
Conventional means for preventing electricity theft include: firstly, the cabinet is installed and locked at the place where the electric energy meter is installed, so that a user cannot contact the electric energy meter, and the electricity stealing behavior can be effectively prevented. However, in some remote areas this method is difficult to use and requires constant inspection by personnel. And secondly, the problem that the electric energy meter is easy to pry and recover is improved, the sealing position of the electric energy meter is sealed again, unified numbering is adopted, and management is enhanced. And thirdly, by adopting the intelligent electric energy meter, although the theoretical outstanding advantages of the intelligent electric energy meter have good effect in the field of electricity stealing prevention, in practical use, due to the fact that the intelligent electric energy meter is limited by the influences of transmission speed, information quantity and the like at present, abnormal operation events such as voltage loss, current loss, phase failure, power failure, voltage reverse phase sequence and the like stored and recorded by the intelligent electric energy meter cannot be uploaded to relevant departments in time, and automatic alarm cannot be completely realized when electricity stealing behaviors occur.
In addition, the background analysis and processing capability of the existing power utilization information acquisition system needs to be further improved.
Disclosure of Invention
In order to overcome the defects in the prior art, the application provides the electric larceny prevention behavior identification method and the electric larceny prevention behavior identification system, the diagnosis effect is good, and various branches can be performed on the electricity utilization user, so that the search range is narrowed, and the work efficiency of electric larceny prevention is improved.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an anti-electricity-stealing behavior identification method is characterized in that:
the method comprises the following steps:
step 1: acquiring historical power utilization evaluation index parameter data of a user;
step 2: establishing and training a suspected electricity stealing user identification model based on the historical electricity utilization evaluation index parameter data of the user;
and step 3: adopting a suspected electricity stealing user identification model to identify suspected electricity stealing users;
and 4, step 4: and verifying suspected electricity stealing users by combining the transverse data of the user electricity utilization evaluation index parameters to determine the electricity stealing users.
The invention further comprises the following preferred embodiments:
preferably, the user historical electricity utilization evaluation index parameter data obtained in step 1 include electricity consumption, power consumption factors, three-phase voltage unbalance rates, three-phase current unbalance rates, meter types and flag bits of whether electricity is stolen or not of electricity stealing users and normal users.
Preferably, the method further comprises the following steps: and 5: and issuing a checking list of the electricity stealing users for manual checking.
Preferably, the method further comprises the following steps: and storing and displaying the data of each step so as to facilitate the user query and retraining of the suspected electricity stealing user identification model.
Preferably, step 2 specifically comprises:
step 2.1: normalizing the historical power utilization evaluation index parameter data of the user:
the raw data is linearly transformed by dispersion normalization, so that the result is mapped between [0-1], and the conversion function is as follows:
wherein x is historical power utilization evaluation index parameter data of the user, x is a processed value, max is the maximum value of the sample data, and min is the minimum value of the sample data;
step 2.2: and constructing and training a suspected electricity stealing user identification model.
Preferably, in step 2.2, a suspected electricity stealing user identification model is constructed based on the BP neural network:
θ2＝θ2+β·(Ypred-Yreal)·Ypred·(1-Ypred)
wherein α and β are learning rates; a is a constant;
a threshold for the jth neuron of the hidden layer;
the connection weight value from the ith neuron to the jth neuron of the hidden layer; hjAnd IiOutputting a vector for a sample of the hidden layer; theta2Is the bias value of the output layer node;
the bias value of the output layer where the jth neuron is located; y ispred、YrealThe predicted target and the target are output respectively.
Preferably, in step 2.2, in the training process, the target output of the BP neural network is obtained: real electricity consumption Yreal；
If the target output YrealAnd predicted target YpredAnd if the error between the training iterations is smaller than a set error threshold value or the training iteration number reaches a preset threshold value, completing model training.
The invention also discloses an anti-electricity-stealing behavior identification system, which comprises:
the data acquisition module is used for acquiring historical user power utilization evaluation index parameter data;
the model building module is used for building and training a suspected electricity stealing user identification model based on the historical electricity utilization evaluation index parameter data of the user;
the identification module is used for identifying suspected electricity stealing users on the basis of the suspected electricity stealing user identification model;
and the verification module is used for verifying suspected electricity stealing users by combining the transverse data of the user electricity utilization evaluation index parameters and determining the electricity stealing users.
The beneficial effect that this application reached:
this application can be according to electric wire netting historical data, under the condition that power line does not appear the hardware damage, according to the user power consumption evaluation index parameter data preliminary judgement user's power consumption state that electric wire netting data platform gathered, the prediction user steals electric action, carries out many-sided branch institute to the power consumption user to reduce search range, improve anti-electricity-stealing work efficiency.
Drawings
FIG. 1 is a flow chart of an anti-electricity-stealing behavior identification method of the present invention;
fig. 2 is a diagram of a suspected electricity stealing user identification model in the embodiment of the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the method for identifying electricity stealing prevention of the present invention is characterized in that:
the method comprises the following steps:
step 1: and acquiring historical power utilization evaluation index parameter data of the user, wherein the historical power utilization evaluation index parameter data comprise power consumption, power consumption factors, three-phase voltage unbalance rates, three-phase current unbalance rates, meter types and flag bits of whether power is stolen or not of the power stealing user and a normal user.
Step 2: based on the historical power utilization evaluation index parameter data of the user, a suspected power-stealing user identification model is constructed and trained, and the method specifically comprises the following steps:
step 2.1: normalizing the historical power utilization evaluation index parameter data of the user;
the raw data is linearly transformed by dispersion Normalization (Min-Max Normalization) to map the results between [0-1], with the following transfer function:
wherein x is historical power utilization evaluation index parameter data of the user, x is a processed value, max is the maximum value of the sample data, and min is the minimum value of the sample data.
Step 2.2: and (3) constructing and training a suspected electricity stealing user identification model, randomly disordering dimensionality reduction data before training the BP neural network model each time, selecting 80% of the dimensionality reduction data as training samples, using the rest 20% of the dimensionality reduction data as test samples, and enabling the iteration times to be 5000 times.
As shown in fig. 2, in the embodiment of the present invention, a suspected electricity-stealing user identification model is constructed based on a BP neural network:
θ2＝θ2+β·(Ypred-Yreal)·Ypred·(1-Ypred)
wherein α and β are learning rates; a is a constant generally selected from 1-10;
a threshold for the jth neuron of the hidden layer;
the connection weight value from the ith neuron to the jth neuron of the hidden layer; theta2Is the bias value of the output layer node;
the bias value of the output layer where the jth neuron is located; hjAnd IiOutputting a vector for a sample of the hidden layer;
and the results of all layers are output by mapping layer by layer among the three layers of the input layer, the hidden layer and the output layer and adjusting by a weight matrix among the layers.
In the training process, obtaining the target output of the BP neural network: real electricity consumption Yreal；
If the target output YrealAnd predicted target YpredThe error between the training iteration times is less than the set error threshold value or the training iteration times reachAnd finishing model training by a preset threshold value.
And step 3: inputting the data in the step 1 by adopting a suspected electricity stealing user identification model, and outputting the real electricity consumption YrealIf the error is larger, a suspected electricity stealing user is identified;
and 4, step 4: and verifying suspected electricity stealing users by combining the transverse data of the user electricity utilization evaluation index parameters, namely the data of the users with the same electricity utilization type in the same area, and determining the electricity stealing users.
The specific embodiment of the present invention may further include: and 5: and issuing a checking list of the electricity stealing users for manual checking.
And storing the data of each step by adopting an Excel database management system, inputting the read Excel target file and displaying the data on an interface of a software platform so as to facilitate user query and retraining of a suspected electricity stealing user identification model.
The invention relates to an anti-electricity-stealing behavior identification system, which comprises:
the data acquisition module is used for acquiring historical user power utilization evaluation index parameter data;
the model building module is used for building and training a suspected electricity stealing user identification model based on the historical electricity utilization evaluation index parameter data of the user;
the identification module is used for identifying suspected electricity stealing users on the basis of the suspected electricity stealing user identification model;
and the verification module is used for verifying suspected electricity stealing users by combining the transverse data of the user electricity utilization evaluation index parameters and determining the electricity stealing users.
This application can be according to electric wire netting historical data, under the condition that power line does not appear the hardware damage, according to the user power consumption evaluation index parameter data preliminary judgement user's power consumption state that electric wire netting data platform gathered, the prediction user steals electric action, carries out many-sided branch institute to the power consumption user to reduce search range, improve anti-electricity-stealing work efficiency.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (8)
1. An anti-electricity-stealing behavior identification method is characterized in that:
the method comprises the following steps:
step 1: acquiring historical power utilization evaluation index parameter data of a user;
step 2: establishing and training a suspected electricity stealing user identification model based on the historical electricity utilization evaluation index parameter data of the user;
and step 3: adopting a suspected electricity stealing user identification model to identify suspected electricity stealing users;
and 4, step 4: and verifying suspected electricity stealing users by combining the transverse data of the user electricity utilization evaluation index parameters to determine the electricity stealing users.
2. The electricity stealing behavior identification method according to claim 1, wherein:
the user historical electricity utilization evaluation index parameter data obtained in the step 1 comprise electricity consumption, power consumption factors, three-phase voltage unbalance rates, three-phase current unbalance rates, meter types and flag bits of whether electricity is stolen or not of electricity stealing users and normal users.
3. The electricity stealing behavior identification method according to claim 1, wherein:
further comprising: and 5: and issuing a checking list of the electricity stealing users for manual checking.
4. The electricity stealing behavior identification method according to claim 1, wherein:
further comprising: and storing and displaying the data of each step so as to facilitate the user query and retraining of the suspected electricity stealing user identification model.
5. The electricity stealing behavior identification method according to claim 1, wherein:
the step 2 specifically comprises the following steps:
step 2.1: normalizing the historical power utilization evaluation index parameter data of the user:
the raw data is linearly transformed by dispersion normalization, so that the result is mapped between [0-1], and the conversion function is as follows:
wherein x is historical power utilization evaluation index parameter data of the user, x is a processed value, max is the maximum value of the sample data, and min is the minimum value of the sample data;
step 2.2: and constructing and training a suspected electricity stealing user identification model.
6. An electricity stealing behavior recognition method according to claim 5, characterized in that:
in step 2.2, a suspected electricity stealing user identification model is constructed based on the BP neural network:
θ2＝θ2+β·(Ypred-Yreal)·Ypred·(1-Ypred)
wherein α and β are learning rates; a is a constant;
a threshold for the jth neuron of the hidden layer;
the connection weight value from the ith neuron to the jth neuron of the hidden layer; hjAnd IiOutputting a vector for a sample of the hidden layer; theta2Is the bias value of the output layer node;
the bias value of the output layer where the jth neuron is located; y ispred、YrealThe predicted target and the target are output respectively.
7. An electricity stealing behavior recognition method according to claim 6, characterized in that:
in step 2.2, in the training process, the target output of the BP neural network is obtained: real electricity consumption Yreal；
If the target output YrealAnd predicted target YpredAnd if the error between the training iterations is smaller than a set error threshold value or the training iteration number reaches a preset threshold value, completing model training.
8. An electric larceny behavior recognizing system of an electric larceny behavior recognizing method according to any one of claims 1 to 7, characterized in that:
the system comprises:
the data acquisition module is used for acquiring historical user power utilization evaluation index parameter data;
the model building module is used for building and training a suspected electricity stealing user identification model based on the historical electricity utilization evaluation index parameter data of the user;
the identification module is used for identifying suspected electricity stealing users on the basis of the suspected electricity stealing user identification model;
and the verification module is used for verifying suspected electricity stealing users by combining the transverse data of the user electricity utilization evaluation index parameters and determining the electricity stealing users.
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