CN113627821A - Method and system for identifying abnormal electricity utilization based on electricity utilization behavior characteristics - Google Patents
Method and system for identifying abnormal electricity utilization based on electricity utilization behavior characteristics Download PDFInfo
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Abstract
The invention provides a method and a system for identifying abnormal electricity consumption based on electricity consumption behavior characteristics, which comprises the steps of obtaining the file information, the electricity consumption information and the payment information of an electricity consumption customer; respectively inputting the file information, the electricity utilization information and the payment information into a preset correlation model to obtain electricity utilization behavior characteristics of an electricity utilization customer; sequencing the obtained electricity utilization behavior characteristics according to the time sequence; inputting the sorted electricity utilization behavior characteristics into a preset electricity utilization behavior analysis model to obtain an electricity utilization abnormity analysis result; and carrying out abnormal electricity utilization identification according to the abnormal electricity utilization analysis result. The invention analyzes and judges the abnormal electricity consumption in advance, and guides measurement and inspection professionals to carry out field processing so as to improve the meter reading accuracy.
Description
Technical Field
The invention relates to the technical field of electric power accounting, in particular to a method and a system for identifying abnormal electricity utilization based on electricity utilization behavior characteristics.
Background
With the deep exploration and reformation of the electric power system in China and the optimized upgrade of the power grid, the differentiation of the power utilization behaviors of electric power customers becomes more remarkable, and by carrying out subdivision management on the power utilization behaviors of large customers, the establishment of a subdivision structure model is beneficial to improving the service quality of power supply enterprises and becomes a development direction. At present, related internal data of an electric power company are more and more, and a common processing method is to analyze data of an electric power customer through a big data analysis means and timely and accurately master electricity consumption behavior characteristics of the customer. However, how to improve the accuracy of analyzing the electricity consumption behavior characteristics of the customers is a difficulty at present.
Disclosure of Invention
The invention aims to provide a method and a system for identifying abnormal electricity utilization based on electricity utilization behavior characteristics, and solves the technical problem that the accuracy of analyzing the electricity utilization behavior characteristics of a client is low in the conventional method.
In one aspect, a method for identifying abnormal electricity utilization based on electricity utilization behavior characteristics is provided, and the method comprises the following steps: acquiring file information, power utilization information and payment information of a power utilization customer;
respectively inputting the file information, the electricity utilization information and the payment information into a preset correlation model to obtain electricity utilization behavior characteristics of an electricity utilization customer; sequencing the obtained electricity utilization behavior characteristics according to the time sequence;
inputting the sorted electricity utilization behavior characteristics into a preset electricity utilization behavior analysis model to obtain an electricity utilization abnormity analysis result; and carrying out abnormal electricity utilization identification according to the abnormal electricity utilization analysis result.
Preferably, the association model comprises:
the electric quantity model is used for identifying the electric quantity data in the electric information, classifying the electric quantity data according to types and storing the electric quantity data in a time sequence from front to back;
the electric charge model is used for identifying electric charge data in the payment information, classifying the electric charge data according to electric charge types and user types, and storing the electric charge data in a time sequence from front to back;
the user fluctuation model is used for judging whether the fluctuation condition of the electric quantity data or the electric charge data exceeding a preset fluctuation range exists in a certain time period or not and outputting the electricity utilization client information of the fluctuation condition of the electric quantity data or the electric charge data exceeding the preset fluctuation range in the certain time period;
and the user classification model is used for identifying the power consumption demand of the power consumption customer and classifying the power consumption customer according to the power consumption demand to obtain a customer classification result.
Preferably, the classifying the electricity customers according to the electricity consumption demands includes:
classifying customers with the installation capacity exceeding a preset threshold value in the electricity consumption demand into large customers;
classifying customers needing to ensure stable electricity consumption in electricity consumption requirements as important customers;
classifying customers needing to follow up the electricity demand of the customers and related services in the electricity consumption demand as key attention customers;
and classifying the clients with the client records but with the power consumption of 0 for a period of time in the power consumption demand as long-term non-power-consumption clients.
Preferably, the obtaining of the electricity consumption behavior characteristics of the electricity consumption customer comprises:
judging the power consumption mode of the power consumption customer according to the input archive information, the power consumption information and the payment information;
and identifying each corresponding electricity utilization client load characteristic index of the electricity utilization client in the electricity utilization mode, and outputting the electricity utilization characteristic as the electricity utilization behavior characteristic of the electricity utilization client.
Preferably, the determining the power consumption mode of the power consumption customer includes:
judging the time sequence of electricity utilization and payment of the electricity utilization customer according to the electricity utilization information and the payment information; if the electricity is firstly used and then the fee is paid, the electricity utilization mode of the electricity utilization customer is judged to be the traditional mode; if the electricity is used after the payment, the electricity utilization mode of the electricity utilization customer is judged to be the charge control mode;
judging whether the difference between the monthly power consumption and the past normal power consumption of the power consumption customer is greater than a preset abnormal threshold value or not according to the power consumption information, and if so, judging that the power consumption mode of the power consumption customer is an abnormal power consumption mode;
and judging whether the power utilization scene of the power utilization client meets a preset special scene condition or not according to the file information and the power utilization information, and if the power utilization scene of the power utilization client meets the preset special scene condition, judging that the power utilization mode of the power utilization client is an abnormal power utilization mode.
Preferably, the step of inputting the sorted electricity consumption behavior characteristics into a preset electricity consumption behavior analysis model to obtain an abnormal electricity consumption analysis result includes:
collecting the client classification results into client features; collecting the customer electricity utilization modes as customer behaviors; collecting the customer payment modes as customer payment behaviors; collecting the customer distribution of each power consumption section according to the sequence of the power consumption from small to large;
determining a power utilization mode corresponding to a power utilization customer according to customer distribution, customer characteristics, customer behaviors and a customer payment line;
predicting load characteristic indexes of each corresponding electricity consumer according to the electricity utilization mode to obtain the predicted electricity consumption of the electricity consumer;
and comparing the estimated power consumption of the power consumption customer with historical power consumption data, and predicting whether the power consumption is abnormal or not to obtain a power consumption abnormal analysis result.
On the other hand, a system for identifying abnormal electricity utilization based on electricity utilization behavior characteristics is also provided, and the method for identifying abnormal electricity utilization based on electricity utilization behavior characteristics is implemented and comprises the following steps:
the information acquisition module is used for acquiring the file information, the electricity utilization information and the payment information of the electricity utilization customer;
the characteristic extraction module is used for inputting the file information, the power utilization information and the payment information into a preset correlation model respectively to obtain power utilization behavior characteristics of a power utilization customer; sequencing the obtained electricity utilization behavior characteristics according to the time sequence;
the abnormality identification module is used for inputting the sorted electricity consumption behavior characteristics into a preset electricity consumption behavior analysis model to obtain an electricity consumption abnormality analysis result; and carrying out abnormal electricity utilization identification according to the abnormal electricity utilization analysis result.
Preferably, the feature extraction module is further configured to classify customers with a loading capacity exceeding a preset threshold in the power consumption demand as large customers; classifying customers needing to ensure stable electricity consumption in electricity consumption requirements as important customers; classifying customers needing to follow up the electricity demand of the customers and related services in the electricity consumption demand as key attention customers; and classifying the clients with the client records but with the power consumption of 0 for a period of time in the power consumption demand as long-term non-power-consumption clients.
Preferably, the feature extraction module is further configured to determine a power consumption mode of the power consumption customer according to the input archive information, the power consumption information, and the payment information;
identifying each corresponding electricity utilization client load characteristic index of the electricity utilization client in the electricity utilization mode, and outputting the electricity utilization characteristic as an electricity utilization behavior characteristic of the electricity utilization client;
the time sequence of power utilization and payment of the power utilization customer is judged according to the power utilization information and the payment information; if the electricity is firstly used and then the fee is paid, the electricity utilization mode of the electricity utilization customer is judged to be the traditional mode; if the electricity is used after the payment, the electricity utilization mode of the electricity utilization customer is judged to be the charge control mode;
judging whether the difference between the monthly power consumption and the past normal power consumption of the power consumption customer is greater than a preset abnormal threshold value or not according to the power consumption information, and if so, judging that the power consumption mode of the power consumption customer is an abnormal power consumption mode;
and judging whether the power utilization scene of the power utilization client meets a preset special scene condition or not according to the file information and the power utilization information, and if the power utilization scene of the power utilization client meets the preset special scene condition, judging that the power utilization mode of the power utilization client is an abnormal power utilization mode.
Preferably, the anomaly identification module is further configured to group the customer classification results into customer features; collecting the customer electricity utilization modes as customer behaviors; collecting the customer payment modes as customer payment behaviors; collecting the customer distribution of each power consumption section according to the sequence of the power consumption from small to large; determining a power utilization mode corresponding to a power utilization customer according to customer distribution, customer characteristics, customer behaviors and a customer payment line; predicting load characteristic indexes of each corresponding electricity consumer according to the electricity utilization mode to obtain the predicted electricity consumption of the electricity consumer; and comparing the estimated power consumption of the power consumption customer with historical power consumption data, and predicting whether the power consumption is abnormal or not to obtain a power consumption abnormal analysis result.
In summary, the embodiment of the invention has the following beneficial effects:
according to the method and the system for identifying abnormal electricity consumption based on the electricity consumption behavior characteristics, the electricity consumption condition of a user is subjected to time series analysis, the electricity consumption behavior characteristics of an electricity consumer are extracted, the abnormal electricity consumption is analyzed and judged in advance by combining with the historical electricity consumption condition, and a measurement inspection professional is guided to carry out field processing so as to improve the meter reading accuracy.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings 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 invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a main flow diagram of a method for identifying abnormal electricity consumption based on electricity consumption behavior characteristics according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a system for recognizing abnormal electricity consumption based on electricity consumption behavior characteristics according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an embodiment of a method for identifying abnormal electricity consumption based on electricity consumption behavior characteristics according to the present invention. In this embodiment, the method comprises:
acquiring file information, power utilization information and payment information of a power utilization customer; it can be understood that the file information, the electricity utilization information and the payment information of the electricity utilization customer can be acquired from the marketing system, wherein the file information comprises a user file, basic information of a business expansion work order, meter reading information, metering point information, a metering point relation, transformer information, a metering point transformer relation and the like; the electricity consumption information comprises electric quantity information, electricity charge information, electricity price information and the like; the payment information comprises payment channel information, payment modes and the like.
Respectively inputting the file information, the electricity utilization information and the payment information into a preset correlation model to obtain electricity utilization behavior characteristics of an electricity utilization customer; sequencing the obtained electricity utilization behavior characteristics according to the time sequence; it can be understood that a data model (correlation model) is established in advance through technologies of data cleaning, summary statistics, code conversion, slow change dimension and the like on historical data of electricity consumers, and various user load characteristic indexes of the electricity consumers in various electricity utilization modes (traditional mode, cost control mode, abnormal electricity utilization mode, special electricity utilization mode) can be determined through the correlation model, namely, electricity data (such as loads in summer or winter of light industry, loads in summer or winter of heavy industry, loads in summer or winter of medical health, loads in summer or winter of administrative office, loads in summer or winter of business financial services and the like).
In a specific embodiment, the association model includes:
the electric quantity model is used for identifying the electric quantity data in the electric information, classifying the electric quantity data according to types and storing the electric quantity data in a time sequence from front to back;
the electric charge model is used for identifying electric charge data in the payment information, classifying the electric charge data according to electric charge types and user types, and storing the electric charge data in a time sequence from front to back;
the user fluctuation model is used for judging whether the fluctuation condition of the electric quantity data or the electric charge data exceeding a preset fluctuation range exists in a certain time period or not and outputting the electricity utilization client information of the fluctuation condition of the electric quantity data or the electric charge data exceeding the preset fluctuation range in the certain time period;
and the user classification model is used for identifying the power consumption demand of the power consumption customer and classifying the power consumption customer according to the power consumption demand to obtain a customer classification result. Classifying customers with the installation capacity exceeding a preset threshold value in the electricity consumption demand into large customers; classifying customers needing to ensure stable electricity consumption in electricity consumption requirements as important customers; classifying customers needing to follow up the electricity demand of the customers and related services in the electricity consumption demand as key attention customers; and classifying the clients with the client records but with the power consumption of 0 for a period of time in the power consumption demand as long-term non-power-consumption clients.
Specifically, the association model is used for conducting data training by constructing deep learning of file exception checking, utilizing a large number of past charging files, electric charge and error conditions, electricity utilization information and the like, combing the influence of each field of the charging files on charging results, utilizing computing power to exhaust various file setting modes, and clearing up file setting specifications of various users. The method comprises the steps of taking file abnormity check as a brain, analyzing a service change process, cleaning and summarizing data, and forming a corresponding electric quantity model (a user electric quantity data storage model taking time and type as dimensions), an electric charge model (an electric charge data storage model taking time, electric charge type and user type as dimensions), a user fluctuation model (fluctuating user information of electric quantity and electric charge exists in a certain time), and a user classification model, wherein the information is classified and summarized mainly aiming at large clients (clients reporting the capacity of more than 10000 kVA), important clients (clients optimizing and ensuring the electricity utilization), focused clients (clients needing to follow up the relevant electricity utilization requirements and services of the clients), and long-term electricity-free clients (clients with client records but with 0 electricity utilization for a long time, namely clients without electricity utilization).
In a specific embodiment, firstly, the power consumption mode of the power consumption customer is judged according to the input archive information, the power consumption information and the payment information; the time sequence of power utilization and payment of the power utilization customer is judged according to the power utilization information and the payment information; if the electricity is firstly used and then the fee is paid, the electricity utilization mode of the electricity utilization customer is judged to be the traditional mode; if the electricity is used after the payment, the electricity utilization mode of the electricity utilization customer is judged to be the charge control mode; judging whether the difference between the monthly power consumption and the past normal power consumption of the power consumption customer is greater than a preset abnormal threshold value or not according to the power consumption information, and if so, judging that the power consumption mode of the power consumption customer is an abnormal power consumption mode; and judging whether the power utilization scene of the power utilization client meets a preset special scene condition or not according to the file information and the power utilization information, and if the power utilization scene of the power utilization client meets the preset special scene condition, judging that the power utilization mode of the power utilization client is an abnormal power utilization mode.
And then, identifying each corresponding electricity consumption client load characteristic index of the electricity consumption client in the electricity consumption mode, and outputting the electricity consumption behavior characteristics as electricity consumption behavior characteristics of the electricity consumption client.
Inputting the sorted electricity utilization behavior characteristics into a preset electricity utilization behavior analysis model to obtain an electricity utilization abnormity analysis result; and carrying out abnormal electricity utilization identification according to the abnormal electricity utilization analysis result. It can be understood that a corresponding power utilization behavior analysis model can be established by performing time series analysis on power utilization behaviors of users, performing power prediction on the users, and performing targeted check on different users by combining the power prediction values of the users; when the abnormity identification is needed, time series analysis is carried out on the electricity utilization condition of the user from customer distribution, customer characteristics, customer behaviors, customer payment behaviors and the like, the electricity utilization behavior characteristics of the electricity utilization customer are extracted, and the electricity utilization abnormity is analyzed and judged in advance by combining historical electricity utilization conditions.
In a specific embodiment, the client classification results are collected into client features; collecting the customer electricity utilization modes as customer behaviors; collecting the customer payment modes as customer payment behaviors; collecting the customer distribution of each power consumption section according to the sequence of the power consumption from small to large; it can be understood that the electricity consumption condition of the customers is analyzed at regular time every month, and the customer distribution condition of each electricity consumption section is collected according to the quantity of electricity consumption; the electricity utilization conditions are collected into customer characteristic electricity utilization conditions according to customer types (large customers, important attention customers and long-term electricity utilization customers); collecting the power utilization conditions of the customer behaviors according to the power utilization modes of the customers; and (4) collecting the payment behaviors of the customers according to the payment modes of the customers (WeChat, network hall, Paibao and real rest business hall). Determining a power utilization mode corresponding to a power utilization customer according to customer distribution, customer characteristics, customer behaviors and a customer payment line; the corresponding relation between the file information, the electricity utilization information and the payment information and the electricity utilization mode and the condition of each corresponding electricity utilization customer load characteristic index in the electricity utilization mode can be obtained through the judgment process of the electricity utilization behavior characteristics of the electricity utilization customers; predicting load characteristic indexes of each corresponding electricity consumer according to the electricity utilization mode to obtain the predicted electricity consumption (electricity consumer load) of the electricity consumer; comparing the estimated power consumption of the power consumption customer with historical power consumption data, and predicting whether power consumption is abnormal or not to obtain a power consumption abnormal analysis result; the abnormal electricity consumption condition is judged to be the abnormal electricity consumption condition in advance, and the abnormal condition is analyzed preliminarily to give out relevant abnormal conclusions (such as the condition that the electricity consumption in a certain month is more than that in other months suddenly, the electricity consumption in continuous months is 0, the condition that the electricity consumption in continuous months is more than that in continuous months is less, abnormal meter counting, electricity stealing by users, large amount of applied electricity and the like).
Fig. 2 is a schematic diagram of an embodiment of a system for identifying abnormal electricity consumption based on electricity consumption behavior characteristics according to the present invention. In this embodiment, the system is configured to implement the method for identifying abnormal electricity consumption based on electricity consumption behavior characteristics, and includes:
the information acquisition module is used for acquiring the file information, the electricity utilization information and the payment information of the electricity utilization customer;
the characteristic extraction module is used for inputting the file information, the power utilization information and the payment information into a preset correlation model respectively to obtain power utilization behavior characteristics of a power utilization customer; sequencing the obtained electricity utilization behavior characteristics according to the time sequence;
the abnormality identification module is used for inputting the sorted electricity consumption behavior characteristics into a preset electricity consumption behavior analysis model to obtain an electricity consumption abnormality analysis result; and carrying out abnormal electricity utilization identification according to the abnormal electricity utilization analysis result.
In a specific embodiment, the feature extraction module is further configured to classify customers with a reported capacity exceeding a preset threshold in the power consumption demand as large customers; classifying customers needing to ensure stable electricity consumption in electricity consumption requirements as important customers; classifying customers needing to follow up the electricity demand of the customers and related services in the electricity consumption demand as key attention customers; and classifying the clients with the client records but with the power consumption of 0 for a period of time in the power consumption demand as long-term non-power-consumption clients.
Specifically, the feature extraction module is further configured to determine a power consumption mode of the power consumption customer according to the input archive information, the power consumption information, and the payment information;
identifying each corresponding electricity utilization client load characteristic index of the electricity utilization client in the electricity utilization mode, and outputting the electricity utilization characteristic as an electricity utilization behavior characteristic of the electricity utilization client;
the time sequence of power utilization and payment of the power utilization customer is judged according to the power utilization information and the payment information; if the electricity is firstly used and then the fee is paid, the electricity utilization mode of the electricity utilization customer is judged to be the traditional mode; if the electricity is used after the payment, the electricity utilization mode of the electricity utilization customer is judged to be the charge control mode;
judging whether the difference between the monthly power consumption and the past normal power consumption of the power consumption customer is greater than a preset abnormal threshold value or not according to the power consumption information, and if so, judging that the power consumption mode of the power consumption customer is an abnormal power consumption mode;
and judging whether the power utilization scene of the power utilization client meets a preset special scene condition or not according to the file information and the power utilization information, and if the power utilization scene of the power utilization client meets the preset special scene condition, judging that the power utilization mode of the power utilization client is an abnormal power utilization mode.
More specifically, the anomaly identification module is further configured to aggregate the customer classification results into customer features; collecting the customer electricity utilization modes as customer behaviors; collecting the customer payment modes as customer payment behaviors; collecting the customer distribution of each power consumption section according to the sequence of the power consumption from small to large; determining a power utilization mode corresponding to a power utilization customer according to customer distribution, customer characteristics, customer behaviors and a customer payment line; predicting load characteristic indexes of each corresponding electricity consumer according to the electricity utilization mode to obtain the predicted electricity consumption of the electricity consumer; and comparing the estimated power consumption of the power consumption customer with historical power consumption data, and predicting whether the power consumption is abnormal or not to obtain a power consumption abnormal analysis result.
As to the implementation process of the system for identifying abnormal electricity consumption based on electricity consumption behavior characteristics, reference may be made to the specific implementation process of the method for identifying abnormal electricity consumption based on electricity consumption behavior characteristics, which is not described herein again.
In summary, the embodiment of the invention has the following beneficial effects:
according to the method and the system for identifying abnormal electricity consumption based on the electricity consumption behavior characteristics, the electricity consumption condition of a user is subjected to time series analysis, the electricity consumption behavior characteristics of an electricity consumer are extracted, the abnormal electricity consumption is analyzed and judged in advance by combining with the historical electricity consumption condition, and a measurement inspection professional is guided to carry out field processing so as to improve the meter reading accuracy.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A method for recognizing abnormal electricity utilization based on electricity utilization behavior characteristics is characterized by comprising the following steps:
acquiring file information, power utilization information and payment information of a power utilization customer;
respectively inputting the file information, the electricity utilization information and the payment information into a preset correlation model to obtain electricity utilization behavior characteristics of an electricity utilization customer; sequencing the obtained electricity utilization behavior characteristics according to the time sequence;
inputting the sorted electricity utilization behavior characteristics into a preset electricity utilization behavior analysis model to obtain an electricity utilization abnormity analysis result; and carrying out abnormal electricity utilization identification according to the abnormal electricity utilization analysis result.
2. The method of claim 1, wherein the association model comprises:
the electric quantity model is used for identifying the electric quantity data in the electric information, classifying the electric quantity data according to types and storing the electric quantity data in a time sequence from front to back;
the electric charge model is used for identifying electric charge data in the payment information, classifying the electric charge data according to electric charge types and user types, and storing the electric charge data in a time sequence from front to back;
the user fluctuation model is used for judging whether the fluctuation condition of the electric quantity data or the electric charge data exceeding a preset fluctuation range exists in a certain time period or not and outputting the electricity utilization client information of the fluctuation condition of the electric quantity data or the electric charge data exceeding the preset fluctuation range in the certain time period;
and the user classification model is used for identifying the power consumption demand of the power consumption customer and classifying the power consumption customer according to the power consumption demand to obtain a customer classification result.
3. The method of claim 2, wherein the classifying electricity consumers according to electricity usage demand comprises:
classifying customers with the installation capacity exceeding a preset threshold value in the electricity consumption demand into large customers;
classifying customers needing to ensure stable electricity consumption in electricity consumption requirements as important customers;
classifying customers needing to follow up the electricity demand of the customers and related services in the electricity consumption demand as key attention customers;
and classifying the clients with the client records but with the power consumption of 0 for a period of time in the power consumption demand as long-term non-power-consumption clients.
4. The method of claim 3, wherein obtaining the electricity usage behavior characteristics of the electricity consumer comprises:
judging the power consumption mode of the power consumption customer according to the input archive information, the power consumption information and the payment information;
and identifying each corresponding electricity utilization client load characteristic index of the electricity utilization client in the electricity utilization mode, and outputting the electricity utilization characteristic as the electricity utilization behavior characteristic of the electricity utilization client.
5. The method of claim 4, wherein said determining the power usage pattern of the power consumer comprises:
judging the time sequence of electricity utilization and payment of the electricity utilization customer according to the electricity utilization information and the payment information; if the electricity is firstly used and then the fee is paid, the electricity utilization mode of the electricity utilization customer is judged to be the traditional mode; if the electricity is used after the payment, the electricity utilization mode of the electricity utilization customer is judged to be the charge control mode;
judging whether the difference between the monthly power consumption and the past normal power consumption of the power consumption customer is greater than a preset abnormal threshold value or not according to the power consumption information, and if so, judging that the power consumption mode of the power consumption customer is an abnormal power consumption mode;
and judging whether the power utilization scene of the power utilization client meets a preset special scene condition or not according to the file information and the power utilization information, and if the power utilization scene of the power utilization client meets the preset special scene condition, judging that the power utilization mode of the power utilization client is an abnormal power utilization mode.
6. The method as claimed in claim 5, wherein the step of inputting the sorted electricity consumption behavior characteristics into a preset electricity consumption behavior analysis model to obtain an abnormal electricity consumption analysis result comprises:
collecting the client classification results as client features; collecting the customer electricity utilization modes as customer behaviors; collecting the customer payment modes as customer payment behaviors; collecting the customer distribution of each power consumption section according to the sequence of the power consumption from small to large;
determining a power utilization mode corresponding to a power utilization customer according to customer distribution, customer characteristics, customer behaviors and a customer payment line;
predicting load characteristic indexes of each corresponding electricity consumer according to the electricity utilization mode to obtain the predicted electricity consumption of the electricity consumer;
and comparing the estimated power consumption of the power consumption customer with historical power consumption data, and predicting whether the power consumption is abnormal or not to obtain a power consumption abnormal analysis result.
7. A system for abnormal electricity consumption identification based on electricity consumption behavior characteristics, which is used for realizing the method of any one of claims 1-6, and is characterized by comprising the following steps:
the information acquisition module is used for acquiring the file information, the electricity utilization information and the payment information of the electricity utilization customer;
the characteristic extraction module is used for inputting the file information, the power utilization information and the payment information into a preset correlation model respectively to obtain power utilization behavior characteristics of a power utilization customer; sequencing the obtained electricity utilization behavior characteristics according to the time sequence;
the abnormality identification module is used for inputting the sorted electricity consumption behavior characteristics into a preset electricity consumption behavior analysis model to obtain an electricity consumption abnormality analysis result; and carrying out abnormal electricity utilization identification according to the abnormal electricity utilization analysis result.
8. The system of claim 7, wherein the feature extraction module is further configured to classify customers with a reportedly capacity exceeding a preset threshold in power usage demand as large customers; classifying customers needing to ensure stable electricity consumption in electricity consumption requirements as important customers; classifying customers needing to follow up the electricity demand of the customers and related services in the electricity consumption demand as key attention customers; and classifying the clients with the client records but with the power consumption of 0 for a period of time in the power consumption demand as long-term non-power-consumption clients.
9. The system of claim 8, wherein the feature extraction module is further configured to determine a power consumption mode of the power consumption customer according to the input profile information, the power consumption information, and the payment information;
identifying each corresponding electricity utilization client load characteristic index of the electricity utilization client in the electricity utilization mode, and outputting the electricity utilization characteristic as an electricity utilization behavior characteristic of the electricity utilization client;
the time sequence of power utilization and payment of the power utilization customer is judged according to the power utilization information and the payment information; if the electricity is firstly used and then the fee is paid, the electricity utilization mode of the electricity utilization customer is judged to be the traditional mode; if the electricity is used after the payment, the electricity utilization mode of the electricity utilization customer is judged to be the charge control mode;
judging whether the difference between the monthly power consumption and the past normal power consumption of the power consumption customer is greater than a preset abnormal threshold value or not according to the power consumption information, and if so, judging that the power consumption mode of the power consumption customer is an abnormal power consumption mode;
and judging whether the power utilization scene of the power utilization client meets a preset special scene condition or not according to the file information and the power utilization information, and if the power utilization scene of the power utilization client meets the preset special scene condition, judging that the power utilization mode of the power utilization client is an abnormal power utilization mode.
10. The system of claim 9, wherein the anomaly identification module is further configured to aggregate customer classification results into customer features; collecting the customer electricity utilization modes as customer behaviors; collecting the customer payment modes as customer payment behaviors; collecting the customer distribution of each power consumption section according to the sequence of the power consumption from small to large; determining a power utilization mode corresponding to a power utilization customer according to customer distribution, customer characteristics, customer behaviors and a customer payment line; predicting load characteristic indexes of each corresponding electricity consumer according to the electricity utilization mode to obtain the predicted electricity consumption of the electricity consumer; and comparing the estimated power consumption of the power consumption customer with historical power consumption data, and predicting whether the power consumption is abnormal or not to obtain a power consumption abnormal analysis result.
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