CN112766665A - Risk level-based electric charge recycling risk prevention and control method - Google Patents
Risk level-based electric charge recycling risk prevention and control method Download PDFInfo
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Abstract
The invention discloses an electric charge recovery risk prevention and control method based on risk levels, which comprises the steps of determining the risk levels of a user according to preset rules, wherein the risk levels comprise a high risk level, a medium risk level and a low risk level; and monitoring the electric quantity of the user with low risk level. The method comprises the steps of establishing an electric charge risk assessment model and an application function based on data such as direct customer electricity utilization behaviors and associated behaviors, developing work such as customer credit analysis, electricity utilization trend analysis, industry prospect information evaluation and emergency assessment by using methods such as service personnel research, basic interview, rule induction and the like, and calculating and outputting actual risk customers and potential risk customers by using a data mining algorithm.
Description
Technical Field
The invention relates to the field of power supply systems, in particular to an electric charge recycling risk prevention and control method based on risk levels.
Background
The electricity charge recycling service is a key service for marketing of power supply enterprises. The business mode of first power utilization and then payment directly influences the economic benefits of enterprises for a plurality of potential risks caused by electric charge recycling. With the development of market economy, the electricity demand of customers is increasing day by day, and the prevention and control work of the risk of recovering the electricity charges faces more challenges.
The electric power enterprise adopts reinforced electric charge recovery measures such as: the propaganda strength is increased, and the power consumption payment consciousness is improved; perfecting and promoting payment modes and payment channels; establishing a special electric charge payment hastening team group; and establishing a scientific internal charging management mechanism, and giving important attention to key enterprises by distributor and the like. The methods improve the enterprise electricity charge recovery situation to a great extent, but the work is mainly started from the management aspect, and the effect depends on the management strength of the enterprise and the working experience of electric power practitioners and other factors.
The identification of the electric charge recycling risk of the client mainly depends on the experience judgment of business personnel, which can not only avoid the influence of subjective factors, but also lack the objectivity and standardized evaluation and analysis tool and means based on data; the electric charge hastens the limitation of the staff of the group, and the working efficiency is influenced because the individual experience is still the leading factor in the face of a large number of customers to be hastens the charge without scientific and uniform customer distinguishing arrangement and working plan. This mode seriously affects further promotion and optimization of the electricity charge recovery work.
Disclosure of Invention
The invention aims to provide a method for avoiding earth-atmosphere interference of an agile small satellite star sensor, which can realize stable attitude measurement of the star sensor under various target pointing tasks and is used for attitude control.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a risk prevention and control method for electric charge recovery based on risk level comprises the following steps:
s1, determining the risk level of the user according to preset rules, wherein the risk level comprises a high risk level, a medium risk level and a low risk level;
and S2, carrying out electric quantity monitoring on the users with low risk levels.
Further, the preset rule is specifically as follows: and determining the risk grade of the user through the electric charge model, wherein the predicted value of the electric charge model is a high risk grade above 0.6, the predicted value of the electric charge model is a medium risk grade within the range of 0.4-0.6, and the predicted value of the electric charge model is a low risk grade within the range of 0.25-0.4.
Further, the electric quantity monitoring specifically includes:
s201, judging whether the electricity utilization type of a user is a continuous sudden increase type, if so, performing S202, wherein the electricity utilization type comprises an electric quantity sudden increase type, an electric quantity continuous sudden increase type, an electric quantity sudden decrease type and an electric quantity continuous sudden decrease type;
s202, recommending the value-added service to the continuously and suddenly increased user.
Further, the electric quantity monitoring specifically further includes:
s203, judging whether the power consumption type of the user is a sudden increase type, and if so, performing a step S204;
and S204, recommending the value-added service to the suddenly increased user.
Further, the electric quantity sudden increase type is that the electric quantity of the current month is increased by 50% compared with the electric quantity of the previous month and the electricity consumption per month is required to be more than 50 degrees, the electric quantity continuous sudden increase type is that the increase of the electric quantity of the current month is more than or equal to 30% compared with the electric quantity of the previous month and the electricity consumption per month is required to be more than 50 degrees, the electric quantity sudden decrease type is that the electric quantity of the current month is reduced by 50% compared with the electric quantity of the previous month and the electricity consumption per month is required to be more than 50 degrees, and the electric quantity continuous sudden decrease type is that the decrease of the electric quantity.
Further, step S1 is preceded by: all clients are divided according to the division rule, and the users divided into the white list preferentially perform the steps S1 and S2.
Further, still include:
and S3, carrying out fee urging processing on the users with high risk levels according to the payment characteristics.
Further, the step S3 specifically includes:
s301, determining payment preferences of users with high risk levels, and determining the use frequency of each payment preference;
s302, the user is charged according to the payment channel corresponding to the payment frequency.
Further, the step S3 specifically includes:
s303, urging the user according to a payment channel corresponding to another payment frequency, wherein the payment frequency is lower than the previous payment frequency;
s304, repeating the step S303 until the charging urging steps of all channels are carried out.
Further, the risk level also comprises the arrearage power failure, wherein the system records the information of the user of the arrearage power failure, and the staff makes corresponding instructions according to the information.
Compared with the prior art, the invention has at least one of the following advantages:
the method comprises the steps of utilizing big data mining analysis modeling, constructing an electric charge risk assessment model and an application function based on data such as direct customer electricity utilization behaviors and correlation behaviors, utilizing methods such as service personnel investigation, basic interview, rule induction and the like, carrying out work such as customer credit analysis, electricity utilization trend analysis, industry prospect information evaluation, emergency assessment and the like, simultaneously adopting a data mining algorithm, calculating and outputting fact risk customers and potential risk customers, and respectively giving three-level electric charge risk identification labels of high risk, medium risk and low risk.
Drawings
Fig. 1 is a block diagram of a risk prevention and control method for recovering electricity charges based on risk level according to an embodiment of the present invention;
FIG. 2 is a block diagram of a method for monitoring power consumption according to an embodiment of the present invention;
FIG. 3 is a block diagram of a method for partitioning and post-partitioning a client according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings 1 to 3 and the detailed description thereof. The advantages and features of the present invention will become more apparent from the following description. It is to be noted that the drawings are in a very simplified form and are all used in a non-precise scale for the purpose of facilitating and distinctly aiding in the description of the embodiments of the present invention. To make the objects, features and advantages of the present invention comprehensible, reference is made to the accompanying drawings. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the implementation conditions of the present invention, so that the present invention has no technical significance, and any structural modification, ratio relationship change or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or field device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or field device. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or field device that comprises the element.
Referring to fig. 1 to 3, the method for risk prevention and control of electric charge recycling based on risk level according to the present embodiment includes:
s1, determining the risk level of the user according to preset rules, wherein the risk level comprises a high risk level, a medium risk level and a low risk level;
and S2, carrying out electric quantity monitoring on the users with low risk levels.
In this embodiment, the preset rule specifically includes: and determining the risk grade of the user through the electric charge model, wherein the predicted value of the electric charge model is a high risk grade above 0.6, the predicted value of the electric charge model is a medium risk grade within the range of 0.4-0.6, and the predicted value of the electric charge model is a low risk grade within the range of 0.25-0.4.
In this embodiment, the electric quantity monitoring specifically includes:
s201, judging whether the electricity utilization type of a user is a continuous sudden increase type, if so, performing S202, wherein the electricity utilization type comprises an electric quantity sudden increase type, an electric quantity continuous sudden increase type, an electric quantity sudden decrease type and an electric quantity continuous sudden decrease type;
s202, recommending the value-added service to the continuously and suddenly increased user.
In this embodiment, the electric quantity monitoring specifically further includes:
s203, judging whether the power consumption type of the user is a sudden increase type, and if so, performing a step S204;
and S204, recommending the value-added service to the suddenly increased user.
In this embodiment, the sudden increase of electric quantity is specifically that the electric quantity in this month is increased by 50% compared with the electric quantity in last month and the electric quantity in single month needs to be greater than 50 degrees, the continuous sudden increase of electric quantity is that the increase of the electric quantity in last month cycle is greater than or equal to 30% and the electric quantity in single month needs to be greater than 50 degrees, the sudden decrease of electric quantity is that the electric quantity in this month is decreased by 50% compared with the electric quantity in last month and the electric quantity in single month needs to be greater than 50 degrees, and the continuous sudden decrease of electric quantity is that the decrease of the electric quantity in last month cycle is greater than or equal.
In this embodiment, before the step S1, the method further includes: all clients are divided according to the division rule, and the users divided into the white list preferentially perform the steps S1 and S2.
In this embodiment, the method further includes:
and S3, carrying out fee urging processing on the users with high risk levels according to the payment characteristics.
In this embodiment, the step S3 specifically includes:
s301, determining payment preferences of users with high risk levels, and determining the use frequency of each payment preference;
s302, the user is charged according to the payment channel corresponding to the payment frequency.
In this embodiment, the step S3 specifically further includes:
s303, urging the user according to a payment channel corresponding to another payment frequency, wherein the payment frequency is lower than the previous payment frequency;
s304, repeating the step S303 until the charging urging steps of all channels are carried out.
In this embodiment, the risk level further includes a defaulting power outage, where the system records information of a user of the defaulting power outage, and the staff makes a corresponding instruction according to the information.
The construction of the electric charge risk prevention and control system needs to be carried out through the processes of data mining analysis, strategy design, service system function development, service flow optimization and the like. 1) Modeling is conducted by utilizing big data mining analysis. The method comprises the steps that an electric charge risk assessment model and an application function are built on the basis of data such as direct customer electricity utilization behaviors and associated behaviors, the work such as customer credit analysis, electricity utilization trend analysis, industry prospect information evaluation, emergency assessment and the like is carried out by using methods such as service personnel investigation, basic interview, rule induction and the like, meanwhile, a data mining algorithm is adopted, a factual risk customer and a potential risk customer are calculated and output, and three-level electric charge risk identification labels such as high risk, middle risk and low risk are given respectively; 2) and developing a practical functional application module. The marketing business application system is used as a carrier, a practical functional application module is developed by combining a theme label and a business strategy, seamless fusion of the label, the strategy and the business is realized through post pushing and other modes, and the electricity charge risk prevention and control closed-loop management and differentiated underpayment collection prompting measures are supported and developed.
The electric charge recycling risk prevention and control model is mainly used for evaluating the tendency of the electric charge recycling risk in the future according to the historical payment behavior characteristics of the user and other information. Through approaches such as service research, basic interview, rule induction and the like, the marketing mechanism and the service condition of the electric charge recovery are deeply understood, and finally, the characteristic dimension for inspecting the electric charge recovery risk of a user is determined to have five main aspects:
user attributes: the attributes comprise the industry, the region, the electricity utilization type, the voltage grade, the capacity and the like of a user;
and (4) fee payment behavior: the method comprises the following steps of including indexes such as payment channels and changes, money return duration and changes, cash proportion and the like of a user;
overdue behavior: the method comprises the indexes of overdue frequency, overdue duration, default fund, adjustment and the like of a user;
the electricity utilization action comprises the following steps: the method comprises the following steps of (1) indexes such as electricity consumption and change, proportion, ring ratio and the like;
and (3) interactive behavior: including electricity-rate related service changes such as changes, passing of house, etc.
After the processes of data preparation, data inspection, data processing and the like, an electric charge recovery risk prevention and control model is built. And index screening is performed through the IV value, so that index redundancy is reduced, and the accuracy of model training is improved.
And selecting logistic regression to perform modeling prediction, and performing cross comparison through algorithms such as random forests, SVM, decision trees, boosting and the like to ensure that the model is optimally selected. The result shows that the logistic regression is simple and efficient compared with other algorithms, the parallel processing speed is high, and the output result is easy to explain. The evaluation of the model prediction effect takes the accuracy and the coverage rate of the prediction result of the risk sample in the sample as main references.
And the prediction model takes recent historical data as input parameters and outputs the risk degree and the risk grade of the overdue risk prediction of the electric charge of the user. Meanwhile, different user groups are formed by combining basic labels and inter-derivative labels generated in the model construction process, such as client types, channel preference, overdue behaviors, electric charge amount levels and the like, and data support is provided for implementation of differential charge promotion strategies.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (10)
1. A risk control method for recovering electric charge based on risk level is characterized by comprising the following steps:
s1, determining the risk level of the user according to preset rules, wherein the risk level comprises a high risk level, a medium risk level and a low risk level;
and S2, carrying out electric quantity monitoring on the users with low risk levels.
2. The method according to claim 1, wherein the preset rule is specifically: and determining the risk grade of the user through the electric charge model, wherein the predicted value of the electric charge model is a high risk grade above 0.6, the predicted value of the electric charge model is a medium risk grade within the range of 0.4-0.6, and the predicted value of the electric charge model is a low risk grade within the range of 0.25-0.4.
3. The method of claim 1, wherein the electrical quantity monitoring specifically comprises:
s201, judging whether the electricity utilization type of a user is a continuous sudden increase type, if so, performing S202, wherein the electricity utilization type comprises an electric quantity sudden increase type, an electric quantity continuous sudden increase type, an electric quantity sudden decrease type and an electric quantity continuous sudden decrease type;
s202, recommending the value-added service to the continuously and suddenly increased user.
4. The method of claim 3, wherein the power monitoring specifically further comprises:
s203, judging whether the power consumption type of the user is a sudden increase type, and if so, performing a step S204;
and S204, recommending the value-added service to the suddenly increased user.
5. The method as claimed in any one of claims 2 to 4, wherein the sudden increase of electric quantity is specifically that the electric quantity in this month increases by 50% compared to the electric quantity in last month and the electric quantity per month needs to be more than 50 degrees, the continuous sudden increase of electric quantity is that the electric quantity in last month increases by 30% or more and the electric quantity per month needs to be more than 50 degrees, the sudden decrease of electric quantity is that the electric quantity in this month decreases by 50% compared to the electric quantity in last month and the electric quantity per month needs to be more than 50 degrees, and the continuous sudden decrease of electric quantity is that the electric quantity in last month decreases by 30% or more.
6. The method of claim 1, wherein the step S1 is preceded by: all clients are divided according to the division rule, and the users divided into the white list preferentially perform the steps S1 and S2.
7. The method of claim 1, further comprising:
and S3, carrying out fee urging processing on the users with high risk levels according to the payment characteristics.
8. The method according to claim 7, wherein the step S3 specifically includes:
s301, determining payment preferences of users with high risk levels, and determining the use frequency of each payment preference;
s302, the user is charged according to the payment channel corresponding to the payment frequency.
9. The method according to claim 8, wherein the step S3 further includes:
s303, urging the user according to a payment channel corresponding to another payment frequency, wherein the payment frequency is lower than the previous payment frequency;
s304, repeating the step S303 until the charging urging steps of all channels are carried out.
10. The method of claim 1, wherein the risk level further comprises an arrearage outage, and wherein the system records information about the user of the arrearage outage, and the staff member makes a corresponding instruction based on the information.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113256008A (en) * | 2021-05-31 | 2021-08-13 | 国家电网有限公司大数据中心 | Arrearage risk level determination method, device, equipment and storage medium |
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