CN108961036A - Electric power arrears risk prediction technique and device - Google Patents
Electric power arrears risk prediction technique and device Download PDFInfo
- Publication number
- CN108961036A CN108961036A CN201810609857.1A CN201810609857A CN108961036A CN 108961036 A CN108961036 A CN 108961036A CN 201810609857 A CN201810609857 A CN 201810609857A CN 108961036 A CN108961036 A CN 108961036A
- Authority
- CN
- China
- Prior art keywords
- electricity consumption
- user
- target electricity
- consumption user
- risk
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
This application discloses a kind of electric power arrears risk prediction technique and devices.This method includes acquiring the power information of target electricity consumption user；Determine the credit information of the target electricity consumption user；Risk forecast model is established according to the user information and credit information；The electric power arrears risk grade of the target electricity consumption user is predicted by the risk forecast model；And the arrearage probability of the target electricity consumption user according to the electric power arrears risk grade forecast.The technical issues of can not carrying out Accurate Prediction present application addresses electric power arrearage.The data foundation to equipment regular inspection and breakdown judge is formed by big data analysis result, moves back and decision support foundation is provided for electric power safety operation and overhaul of the equipments throwing.
Description
Technical field
This application involves power domains, in particular to a kind of electric power arrears risk prediction technique and device.
Background technique
Electric power Electricity customers arrearage makes power supply enterprise require to put into a large amount of man power and materials' progress electric charge pressing payments every year.
Inventors have found that the valuable reference data mistake provided in existing prediction technique power supply enterprise's control arrearage
It is few, and the user tag of target electricity consumption user group can not accurately react user group electric power power demand and
Arrearage content.Further it can not also efficiently solve electric power electric charge pressing payment, the problem of electric power electricity charge are supplemented with money.
Aiming at the problem that electric power arrearage in the related technology can not carry out Accurate Prediction, effective solution side is not yet proposed at present
Case.
Summary of the invention
The main purpose of the application is to provide a kind of electric power arrears risk prediction technique, can not be into solve electric power arrearage
The problem of row Accurate Prediction.
To achieve the goals above, according to the one aspect of the application, a kind of electric power arrears risk prediction technique is provided.
Electric power arrears risk prediction technique according to the application includes: to acquire the power information of target electricity consumption user；It determines
The credit information of the target electricity consumption user；Risk forecast model is established according to the user information and credit information；Pass through institute
State the electric power arrears risk grade that risk forecast model predicts the target electricity consumption user；And according to the electric power arrearage wind
The arrearage probability of target electricity consumption user described in dangerous grade forecast.
Further, establishing risk forecast model according to the user information and credit information includes: to judge the target
Whether the user information of electricity consumption user meets default risk class；If it is determined that the user information of the target electricity consumption user meets
Default risk class, then target electricity consumption user belongs to electricity consumption blacklist；If it is determined that the user information of the target electricity consumption user
It is unsatisfactory for default risk class, then target electricity consumption user belongs to electricity consumption white list.
Further, after the arrearage probability of the target electricity consumption user according to the electric power arrears risk grade forecast also
It include: to judge whether the arrearage probability of the target electricity consumption user is greater than default Risk Results；If it is determined that the target electricity consumption
The arrearage probability of user is greater than default Risk Results, then presses for payment of the target electricity consumption subscriber payment by third party's interface.
Further, the power information for acquiring target electricity consumption user includes: the files on each of customers letter for acquiring target electricity consumption user
Breath；User power utilization data are obtained according to User Profile information；Payment data are extracted in user's user data；Pass through payment
Data obtain electricity consumption behavioral data, wherein the electricity consumption is because data include: that degree, delay payment time, delay are paid in time for payment
Expense number, delay payment amount, the electricity charge prestore amount.
Further, it is determined that whether the credit information of the target electricity consumption user comprises determining that the target electricity consumption user
There are the bank card or credit card of binding；If it is determined that the target electricity consumption user has the bank card or credit card of binding, then judge
The target electricity consumption user and the bank card or the associated credit value of credit card；Determine whether the target electricity consumption user ties up
Fixed payment account；If it is determined that the target electricity consumption user has the payment account of binding, then the target electricity consumption user is judged
Whether there are the electricity charge beyond default amount to prestore expense；The judging result of expense and the judging result of credit value are prestored according to the electricity charge
Determine the credit information of the target electricity consumption user.
Further, it includes following any for establishing risk forecast model according to the user information and credit information
Or a variety of variables: average electricity consumption, the average electricity charge, averagely pay the fees duration, reputation, electricity consumption classification, industry code, contract
Capacity, town and country mark.
To achieve the goals above, according to the another aspect of the application, a kind of electric power arrears risk prediction meanss are provided.
It include: acquisition module according to the electric power arrears risk prediction meanss of the application, for acquiring target electricity consumption user's
Power information；Determining module, for determining the credit information of the target electricity consumption user；Module is established, for according to the use
Family information and credit information establish risk forecast model；Risk class module, for being predicted by the risk forecast model
The electric power arrears risk grade of the target electricity consumption user；And prediction module, for according to the electric power arrears risk grade
Predict the arrearage probability of the target electricity consumption user.
Further, the module of establishing includes: the first judging unit, for judging the user of the target electricity consumption user
Whether information meets default risk class；Blacklist unit, for judging that it is pre- that the user information of the target electricity consumption user meets
If when risk class, then target electricity consumption user belongs to electricity consumption blacklist；White list unit, for judging the target electricity consumption user
User information when being unsatisfactory for default risk class, then target electricity consumption user belongs to electricity consumption white list.
Further, device further includes urging expense module, and described to urge expense module include: second judgment unit, third party's unit,
The second judgment unit, for judging whether the arrearage probability of the target electricity consumption user is greater than default Risk Results；It is described
Third party's unit then passes through third party when for judging that the arrearage probability of the target electricity consumption user is greater than default Risk Results
Interface presses for payment of the target electricity consumption subscriber payment.
Further, the determining module includes: the first determination unit, credit judging unit, the second determination unit, the electricity charge
Prestore judging unit, third determination unit, first determination unit, for determining whether the target electricity consumption user has binding
Bank card or credit card；The credit judging unit, for determining that the target electricity consumption user has the bank card or letter of binding
With card, then the target electricity consumption user and the bank card or the associated credit value of credit card are judged；Second determination unit,
For determining whether the target electricity consumption user has the payment account of binding；The electricity charge prestore judging unit, for determining
The payment account that target electricity consumption user has binding is stated, then judges whether the target electricity consumption user there are the electricity charge beyond default amount
Prestore expense；Third determination unit, judging result and the judging result of credit value for prestoring expense according to the electricity charge determine institute
State the credit information of target electricity consumption user.
In the embodiment of the present application, the credit information of the power information of acquisition mark electricity consumption user and the target electricity consumption user
Mode, risk forecast model is established by the user information and credit information, has been reached through the risk forecast model
The purpose of the electric power arrears risk grade of the target electricity consumption user is predicted, to realize according to the electric power arrears risk
The technical effect of the arrearage probability of target electricity consumption user described in grade forecast, so solve electric power arrearage can not carry out it is accurate pre-
The technical issues of survey.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present application, so that the application's is other
Feature, objects and advantages become more apparent upon.The illustrative examples attached drawing and its explanation of the application is for explaining the application, not
Constitute the improper restriction to the application.In the accompanying drawings:
Fig. 1 is the electric power arrears risk prediction technique schematic diagram according to the application first embodiment；
Fig. 2 is the electric power arrears risk prediction technique schematic diagram according to the application second embodiment；
Fig. 3 is the electric power arrears risk prediction technique schematic diagram according to the application 3rd embodiment；
Fig. 4 is the electric power arrears risk prediction technique schematic diagram according to the application fourth embodiment；
Fig. 5 is the electric power arrears risk prediction technique schematic diagram according to the 5th embodiment of the application；
Fig. 6 is the electric power arrears risk prediction meanss schematic diagram according to the application first embodiment；
Fig. 7 is the electric power arrears risk prediction meanss schematic diagram according to the application second embodiment；
Fig. 8 is the electric power arrears risk prediction meanss schematic diagram according to the application 3rd embodiment；And
Fig. 9 is the electric power arrears risk prediction meanss schematic diagram according to the application fourth embodiment.
Specific embodiment
In order to make those skilled in the art more fully understand application scheme, below in conjunction in the embodiment of the present application
Attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
The embodiment of the application a part, instead of all the embodiments.Based on the embodiment in the application, ordinary skill people
Member's every other embodiment obtained without making creative work, all should belong to the model of the application protection
It encloses.
It should be noted that the description and claims of this application and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to embodiments herein described herein.In addition, term " includes " and " tool
Have " and their any deformation, it is intended that cover it is non-exclusive include, for example, containing a series of steps or units
Process, method, system, product or equipment those of are not necessarily limited to be clearly listed step or unit, but may include without clear
Other step or units listing to Chu or intrinsic for these process, methods, product or equipment.
In addition, term " installation ", " setting ", " being equipped with ", " connection ", " connected ", " socket " shall be understood in a broad sense.For example,
It may be a fixed connection, be detachably connected or monolithic construction；It can be mechanical connection, or electrical connection；It can be direct phase
It even, or indirectly connected through an intermediary, or is two connections internal between device, element or component.
For those of ordinary skills, the concrete meaning of above-mentioned term in this application can be understood as the case may be.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
As shown in Figure 1, this method includes the following steps, namely S102 to step S110:
Step S102 acquires the power information of target electricity consumption user；
By selection target electricity consumption user, and the power information of target electricity consumption user is acquired, can be connect by data
It is data pick-ups such as files on each of customers, payment, electricity consumption behavior in mouthful power business system, cleaning, conversion, integrated, finally it is loaded into industry
In the database of business system, become the basis of multi-dimensional data analysis processing and data mining.
Step S104 determines the credit information of the target electricity consumption user；
Credit information can be power customer credit for embodying in payment and electricity consumption behavior, can be divided into current year credit and
Comprehensive credit, the specific deliberated index of current year credit include that degree, delay payment time, delay payment number, delay are paid in time for payment
Expense amount, the electricity charge prestore amount, violating the regulations and electricity stealing.In addition, in addition to customers' credit, Industrial Cycle index and policy risk
It is an important factor for influencing power customer arrears risk grade.
Step S106 establishes risk forecast model according to the user information and credit information；
Risk forecast model is established according to acquisition and the user information determined, credit information, risk forecast model contains:
User information weight, credit information weight, user information content, credit information content etc..
Step S108 predicts the electric power arrears risk etc. of the target electricity consumption user by the risk forecast model
Grade；
Electric power arrears risk grade can be divided into three ranks according to by normal, needs concern and early warning.
Step S110, according to the arrearage probability of target electricity consumption user described in the electric power arrears risk grade forecast.
The arrearage probability for predicting the target electricity consumption user can be through analysis subscriber payment behavior of credit and electricity consumption row
To analyze, and the credit appraisal of power customer current year and synthesis credit appraisal are carried out by analysis result, finally obtains arrears risk
The arrearage probability of evaluation.
It can be seen from the above description that the application realizes following technical effect:
In the embodiment of the present application, the credit information of the power information of acquisition mark electricity consumption user and the target electricity consumption user
Mode, risk forecast model is established by the user information and credit information, has been reached through the risk forecast model
The purpose of the electric power arrears risk grade of the target electricity consumption user is predicted, to realize according to the electric power arrears risk
The technical effect of the arrearage probability of target electricity consumption user described in grade forecast, so solve electric power arrearage can not carry out it is accurate pre-
The technical issues of survey.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in Fig. 2, according to the user information and letter
Establishing risk forecast model with information includes:
Step S202, judges whether the user information of the target electricity consumption user meets default risk class；
Default risk class (100) can be according to current year electricity consumption grading (30)+user credit grading (40)+power purchase amount
(20)+power purchase policy (10) is executed.
Step S204, if it is determined that the user information of the target electricity consumption user meets default risk class, then target is used
Electric user belongs to electricity consumption blacklist；
If the value of default risk class is set to 80, need to judge whether the user information of target electricity consumption user meets
80 risk class is thought to belong to electricity consumption blacklist, needs to be monitored if meeting.
Step S206, if it is determined that the user information of the target electricity consumption user is unsatisfactory for default risk class, then target
Electricity consumption user belongs to electricity consumption white list.
If the value of default risk class is set to 80, need to judge whether the user information of target electricity consumption user meets
80 risk class thinks to belong to electricity consumption white list if being unsatisfactory for, and does not need to be monitored and Risk Pre-control.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 3, according to the electric power arrears risk
After the arrearage probability of target electricity consumption user described in grade forecast further include:
Step S302, judges whether the arrearage probability of the target electricity consumption user is greater than default Risk Results；
Default Risk Results are pre-configured with according to the arrearage probability of electricity consumption user.
For example, arrearage probability is 60%, then presets Risk Results and be not less than 60%.
Step S304, if it is determined that the arrearage probability of the target electricity consumption user is greater than default Risk Results, then by the
Tripartite's interface presses for payment of the target electricity consumption subscriber payment.
Third party's interface can be directed to the higher user of arrears risk, in conjunction with subscriber arrearage situation, by phone, short message,
It delivers the modes such as arrearage notification sheet and carries out electric charge pressing payment.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 4, the use of acquisition target electricity consumption user
Power information includes:
Step S402 acquires the User Profile information of target electricity consumption user；
Step S404 obtains user power utilization data according to User Profile information；
Step S406 extracts payment data in user's user data；
Step S408, by paying the fees, data obtain electricity consumption behavioral data,
Wherein, the electricity consumption is because data include: that degree, delay payment time, delay payment number, delay are paid in time for payment
Expense amount, the electricity charge prestore amount.
Preferably, it may also include that data cleansing in above-mentioned steps: will be dirty using mathematical statistics or predefined cleaning rule
Data are converted into the data for meeting system application requirement.Data conversion: to the business datum of extraction according to requirement of system design into
Row conversion.Typical data conversion has: entity merges and splits, and such as multiple user's machine account informations are merged into a user message table
In；Field merges and splits；Data aggregate carries out data aggregate according to dimension.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 5, determining the target electricity consumption user
Credit information include:
Step S502, determines whether the target electricity consumption user has the bank card or credit card of binding；
Bank card or credit card have the joint integral or total mark of consumption of target electricity consumption user.
Step S504, if it is determined that the target electricity consumption user has the bank card or credit card of binding, then judges the mesh
Mark electricity consumption user and the bank card or the associated credit value of credit card；
Bank card or the associated credit value of credit card can be target electricity consumption user total mark of consumption or whether it is expired also
Borrow etc..
Step S506, determines whether the target electricity consumption user has the payment account of binding；
Payment account can be electric power payment account.
Step S508, if it is determined that the target electricity consumption user has the payment account of binding, then judges the target electricity consumption
Whether user has the electricity charge beyond default amount to prestore expense；
The electricity charge prestore expense and need to meet beyond certain default amount.
Step S510 prestores the judging result of expense according to the electricity charge and the judging result of credit value determines the target electricity consumption
The credit information of user.
Credit information can prestore the judging result of expense according to the electricity charge and the judging result of credit value obtains.
It should be noted that step shown in the flowchart of the accompanying drawings can be in such as a group of computer-executable instructions
It is executed in computer system, although also, logical order is shown in flow charts, and it in some cases, can be with not
The sequence being same as herein executes shown or described step.
According to the embodiment of the present application, additionally provide a kind of for implementing the device of above-mentioned electric power arrears risk prediction, such as Fig. 6
Shown, which includes: acquisition module 10, for acquiring the power information of target electricity consumption user；Determining module 20, for determining
The credit information of the target electricity consumption user；Module 30 is established, for establishing risk according to the user information and credit information
Prediction model；Risk class module 40, for predicting the electric power of the target electricity consumption user by the risk forecast model
Arrears risk grade；And prediction module 50, it is used for the target electricity consumption user according to the electric power arrears risk grade forecast
Arrearage probability.Preferably, risk class, risk assessment score value, number of users are carried out according to conditions such as objects of statistics, statistics times
Equal data statistics and analysis.Files on each of customers, payment data and electricity consumption behavioral data.
By selection target electricity consumption user in the acquisition module 10 of the embodiment of the present application, and acquire target electricity consumption user
Power information, can be by data pick-ups such as files on each of customers, payment, electricity consumption behavior in data-interface power business system, clear
It washes, convert, integrate, be finally loaded into the database of operation system, become multi-dimensional data analysis processing and data mining
Basis.
Credit information can be power customer body in payment and electricity consumption behavior in the determining module 20 of the embodiment of the present application
Existing credit, can be divided into current year credit and comprehensive credit, and the specific deliberated index of current year credit includes that degree, delay are paid in time for payment
Time-consuming, delay payment number, delay payment amount, the electricity charge prestore amount, violating the regulations and electricity stealing.In addition, in addition to client believes
With Industrial Cycle index and policy risk are also an important factor for influencing power customer arrears risk grade.
The establishing in module 30 according to acquisition and the user information determined of the embodiment of the present application, that credit information establishes risk is pre-
Model is surveyed, risk forecast model contains: user information weight, credit information weight, user information content, credit information content
Deng.
In the risk class module 40 of the embodiment of the present application electric power arrears risk grade can according to by it is normal, need to pay close attention to
Three ranks are divided into early warning.
The arrearage probability that the target electricity consumption user is predicted in the prediction module 50 of the embodiment of the present application can be by dividing
Subscriber payment behavior of credit and electricity consumption behavioural analysis are analysed, and the credit appraisal of power customer current year and synthesis are carried out by analysis result
Credit appraisal finally obtains the arrearage probability of arrears risk evaluation.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in fig. 7, the module 30 of establishing includes:
One judging unit 301, for judging whether the user information of the target electricity consumption user meets default risk class；Blacklist list
Member 302, when for judging that the user information of the target electricity consumption user meets default risk class, then target electricity consumption user belongs to
Electricity consumption blacklist；White list unit 303, for judging that the user information of the target electricity consumption user is unsatisfactory for default risk class
When, then target electricity consumption user belongs to electricity consumption white list.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 8, device further includes urging expense module, institute
Stating and urging expense module includes: second judgment unit 601, third party's unit 602, and the second judgment unit 601 is described for judging
Whether the arrearage probability of target electricity consumption user is greater than default Risk Results；Third party's unit 602, for judging the target
When the arrearage probability of electricity consumption user is greater than default Risk Results, then the target electricity consumption user is pressed for payment of by third party's interface and paid
Take.Arrears risk assessment: it is main to realize subscriber payment behavior and electricity consumption behavioural analysis, and power customer is carried out by analysis result
Current year credit appraisal and comprehensive credit appraisal, it is final to realize arrears risk evaluation.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 8, the determining module 20 includes:
One determination unit 201, credit judging unit 202, the second determination unit 203, the electricity charge prestore judging unit 204, third determines list
Member 205, first determination unit 201, for determining whether the target electricity consumption user has the bank card or credit card of binding；
The credit judging unit 202, for determining that the target electricity consumption user has the bank card or credit card of binding, then described in judgement
Target electricity consumption user and the bank card or the associated credit value of credit card；Second determination unit 203, described in determining
Whether target electricity consumption user has the payment account of binding；The electricity charge prestore judging unit 204, for determining the target electricity consumption
User has the payment account of binding, then judges whether the target electricity consumption user has the electricity charge beyond default amount to prestore expense；
Third determination unit 205, judging result and the judging result of credit value for prestoring expense according to the electricity charge determine the target
The credit information of electricity consumption user.
Obviously, those skilled in the art should be understood that each module of above-mentioned the application or each step can be with general
Computing device realize that they can be concentrated on a single computing device, or be distributed in multiple computing devices and formed
Network on, optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are stored
Be performed by computing device in the storage device, perhaps they are fabricated to each integrated circuit modules or by they
In multiple modules or step be fabricated to single integrated circuit module to realize.In this way, the application be not limited to it is any specific
Hardware and software combines.
The foregoing is merely preferred embodiment of the present application, are not intended to limit this application, for the skill of this field
For art personnel, various changes and changes are possible in this application.Within the spirit and principles of this application, made any to repair
Change, equivalent replacement, improvement etc., should be included within the scope of protection of this application.
Claims (10)
1. a kind of electric power arrears risk prediction technique characterized by comprising
Acquire the power information of target electricity consumption user；
Determine the credit information of the target electricity consumption user；
Risk forecast model is established according to the user information and credit information；
The electric power arrears risk grade of the target electricity consumption user is predicted by the risk forecast model；And
According to the arrearage probability of target electricity consumption user described in the electric power arrears risk grade forecast.
2. electric power arrears risk prediction technique according to claim 1, which is characterized in that according to the user information and letter
Establishing risk forecast model with information includes:
Judge whether the user information of the target electricity consumption user meets default risk class；
If it is determined that the user information of the target electricity consumption user meets default risk class, then target electricity consumption user belongs to electricity consumption
Blacklist；
If it is determined that the user information of the target electricity consumption user is unsatisfactory for default risk class, then target electricity consumption user belongs to use
Dianbai list.
3. electric power arrears risk prediction technique according to claim 1, which is characterized in that according to the electric power arrears risk
After the arrearage probability of target electricity consumption user described in grade forecast further include:
Judge whether the arrearage probability of the target electricity consumption user is greater than default Risk Results；
If it is determined that the arrearage probability of the target electricity consumption user is greater than default Risk Results, then institute is pressed for payment of by third party's interface
State target electricity consumption subscriber payment.
4. electric power arrears risk prediction technique according to claim 1, which is characterized in that the use of acquisition target electricity consumption user
Power information includes:
Acquire the User Profile information of target electricity consumption user；
User power utilization data are obtained according to User Profile information；
Payment data are extracted in user's user data；
By paying the fees, data obtain electricity consumption behavioral data, wherein the electricity consumption is because data include: that degree, delay are paid in time for payment
Time-consuming, delay payment number, delay payment amount, the electricity charge prestore amount.
5. electric power arrears risk prediction technique according to claim 1, which is characterized in that determine the target electricity consumption user
Credit information include:
Determine whether the target electricity consumption user has the bank card or credit card of binding；
If it is determined that the target electricity consumption user has the bank card or credit card of binding, then the target electricity consumption user and institute are judged
State bank card or the associated credit value of credit card；
Determine whether the target electricity consumption user has the payment account of binding；
If it is determined that the target electricity consumption user has the payment account of binding, then judges whether the target electricity consumption user has and exceed
The electricity charge of default amount prestore expense；
According to the electricity charge prestore expense judging result and credit value judging result determine the target electricity consumption user credit letter
Breath.
6. electric power arrears risk prediction technique according to claim 1, which is characterized in that according to the user information and letter
Establishing risk forecast model with information includes following any or a variety of variable:
Average electricity consumption, the average electricity charge, averagely pay the fees duration, reputation, electricity consumption classification, industry code, contract capacity, town and country
Mark.
7. a kind of electric power arrears risk prediction meanss characterized by comprising
Acquisition module, for acquiring the power information of target electricity consumption user；
Determining module, for determining the credit information of the target electricity consumption user；
Module is established, for establishing risk forecast model according to the user information and credit information；
Risk class module, for predicting the electric power arrears risk of the target electricity consumption user by the risk forecast model
Grade；And
Prediction module, the arrearage probability for the target electricity consumption user according to the electric power arrears risk grade forecast.
8. electric power arrears risk prediction meanss according to claim 7, which is characterized in that the module of establishing includes:
First judging unit, for judging whether the user information of the target electricity consumption user meets default risk class；
Blacklist unit, when for judging that the user information of the target electricity consumption user meets default risk class, then target is used
Electric user belongs to electricity consumption blacklist；
White list unit, when for judging that the user information of the target electricity consumption user is unsatisfactory for default risk class, then target
Electricity consumption user belongs to electricity consumption white list.
9. electric power arrears risk prediction meanss according to claim 7, which is characterized in that it further include urging expense module, it is described
Urging expense module includes: second judgment unit, third party's unit,
The second judgment unit, for judging whether the arrearage probability of the target electricity consumption user is greater than default Risk Results；
Third party's unit then leads to when for judging that the arrearage probability of the target electricity consumption user is greater than default Risk Results
It crosses third party's interface and presses for payment of the target electricity consumption subscriber payment.
10. electric power arrears risk prediction meanss according to claim 7, which is characterized in that the determining module includes:
One determination unit, credit judging unit, the second determination unit, the electricity charge prestore judging unit, third determination unit,
First determination unit, for determining whether the target electricity consumption user has the bank card or credit card of binding；
The credit judging unit then judges institute for determining that the target electricity consumption user has the bank card or credit card of binding
State target electricity consumption user and the bank card or the associated credit value of credit card；
Second determination unit, for determining whether the target electricity consumption user has the payment account of binding；
The electricity charge prestore judging unit, for determining that the target electricity consumption user has the payment account of binding, then described in judgement
Whether target electricity consumption user has the electricity charge beyond default amount to prestore expense；
Third determination unit, judging result and the judging result of credit value for prestoring expense according to the electricity charge determine the target
The credit information of electricity consumption user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810609857.1A CN108961036A (en) | 2018-06-13 | 2018-06-13 | Electric power arrears risk prediction technique and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810609857.1A CN108961036A (en) | 2018-06-13 | 2018-06-13 | Electric power arrears risk prediction technique and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108961036A true CN108961036A (en) | 2018-12-07 |
Family
ID=64488515
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810609857.1A Pending CN108961036A (en) | 2018-06-13 | 2018-06-13 | Electric power arrears risk prediction technique and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108961036A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109977143A (en) * | 2019-03-06 | 2019-07-05 | 深圳市买买提信息科技有限公司 | Data interactive method and relevant apparatus |
| CN111597221A (en) * | 2020-03-31 | 2020-08-28 | 国网吉林省电力有限公司信息通信公司 | Customer electricity consumption behavior analysis method based on big data technology |
| CN111612323A (en) * | 2020-05-15 | 2020-09-01 | 国网河北省电力有限公司电力科学研究院 | Electric power credit investigation evaluation method based on big data model |
| CN113408923A (en) * | 2021-06-29 | 2021-09-17 | 中国平安人寿保险股份有限公司 | Premium collection method and device, computer equipment and storage medium |
| CN115099478A (en) * | 2022-06-17 | 2022-09-23 | 国网数字科技控股有限公司 | User electricity consumption behavior prediction method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106355518A (en) * | 2016-11-29 | 2017-01-25 | 国网山东省电力公司电力科学研究院 | Electricity fee payment customer screening method and system |
| CN106600151A (en) * | 2016-12-20 | 2017-04-26 | 北京恒华龙信数据科技有限公司 | Power big data credit reporting analysis method and apparatus |
| CN106920110A (en) * | 2017-02-23 | 2017-07-04 | 国网安徽省电力公司合肥供电公司 | A kind of evaluation method of power customer credit and arrears risk |
| CN206340050U (en) * | 2016-12-10 | 2017-07-18 | 广东电网有限责任公司江门供电局 | A kind of big customer's arrearage early warning system |
| CN107133822A (en) * | 2017-04-28 | 2017-09-05 | 国网山东省电力公司泰安供电公司 | User credit evaluation method and device |
-
2018
- 2018-06-13 CN CN201810609857.1A patent/CN108961036A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106355518A (en) * | 2016-11-29 | 2017-01-25 | 国网山东省电力公司电力科学研究院 | Electricity fee payment customer screening method and system |
| CN206340050U (en) * | 2016-12-10 | 2017-07-18 | 广东电网有限责任公司江门供电局 | A kind of big customer's arrearage early warning system |
| CN106600151A (en) * | 2016-12-20 | 2017-04-26 | 北京恒华龙信数据科技有限公司 | Power big data credit reporting analysis method and apparatus |
| CN106920110A (en) * | 2017-02-23 | 2017-07-04 | 国网安徽省电力公司合肥供电公司 | A kind of evaluation method of power customer credit and arrears risk |
| CN107133822A (en) * | 2017-04-28 | 2017-09-05 | 国网山东省电力公司泰安供电公司 | User credit evaluation method and device |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109977143A (en) * | 2019-03-06 | 2019-07-05 | 深圳市买买提信息科技有限公司 | Data interactive method and relevant apparatus |
| CN111597221A (en) * | 2020-03-31 | 2020-08-28 | 国网吉林省电力有限公司信息通信公司 | Customer electricity consumption behavior analysis method based on big data technology |
| CN111612323A (en) * | 2020-05-15 | 2020-09-01 | 国网河北省电力有限公司电力科学研究院 | Electric power credit investigation evaluation method based on big data model |
| CN111612323B (en) * | 2020-05-15 | 2023-03-28 | 国网河北省电力有限公司电力科学研究院 | Electric power credit investigation evaluation method based on big data model |
| CN113408923A (en) * | 2021-06-29 | 2021-09-17 | 中国平安人寿保险股份有限公司 | Premium collection method and device, computer equipment and storage medium |
| CN113408923B (en) * | 2021-06-29 | 2023-02-03 | 中国平安人寿保险股份有限公司 | Premium collection method and device, computer equipment and storage medium |
| CN115099478A (en) * | 2022-06-17 | 2022-09-23 | 国网数字科技控股有限公司 | User electricity consumption behavior prediction method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108961036A (en) | Electric power arrears risk prediction technique and device | |
| CN110264015A (en) | It opposes electricity-stealing and checks monitoring method and platform | |
| CN102947801B (en) | malicious attack detection and analysis | |
| US20040117240A1 (en) | Environmental performance assessment | |
| CN103366121B (en) | Safety detection method, device and system | |
| JP6629827B2 (en) | Power demand management system, power demand management server, and home appliances | |
| CN109816233A (en) | User tag model generating method and device for power grid | |
| CN109450089B (en) | Transformer area low voltage identification method and device and terminal equipment | |
| CN104778591A (en) | Extracting and identifying methods of feature information of abnormal behavior and devices | |
| CN106357751A (en) | Client browser electric charge payment system | |
| CN105719058A (en) | Electric power demand-side management auxiliary decision support system | |
| CN110083633A (en) | Public affairs based on big data analysis estimate company evaluation method, apparatus, terminal and medium | |
| CN105954578A (en) | Meter reading system capable of data integrity, timely response and information safety | |
| CN101901430A (en) | Power grid risk management and control method and device | |
| CN109636178B (en) | Electric power item screening method and device, computer equipment and storage medium | |
| CN108183814A (en) | The malfunction elimination method and apparatus of the communication channel of power information acquisition system | |
| CN114266575A (en) | Method, device, equipment and storage medium for recovering electric charge of credit abnormal user | |
| CN114360222A (en) | State early warning method, device, equipment and medium for rental equipment | |
| WO2017034171A1 (en) | Method for direct dealing of metal scraps through enlarged collection quantities | |
| CN110807542A (en) | Method, apparatus and computer readable storage medium for intelligently orchestrating system business capabilities | |
| KR101927273B1 (en) | Risk value evaluating system for unclaimed construction and risk value evaluating apparatus for unclaimed construction | |
| CN110909129B (en) | Abnormal complaint event identification method and device | |
| CN111127143A (en) | Information management method, system, terminal and readable storage medium | |
| CN105205703A (en) | Business analysis-based grid electricity operation programme determining method and system | |
| CN111191912B (en) | Light-load line identification method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Yang Jie Inventor after: Duan Mingming Inventor after: Luo Chengchen Inventor after: Cheng Bing Inventor after: Hong Feng Inventor after: Zhang Silu Inventor before: Yang Jie Inventor before: Duan Mingming Inventor before: Luo Chengchen Inventor before: Cheng Bing Inventor before: Hong Feng Inventor before: Zhang Silu Inventor before: Substitute for flying |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20190402 Address after: 650011 No. 63 Tuodong Road, Guandu District, Kunming City, Yunnan Province Applicant after: Yunnan Power Grid Corporation Address before: 650011 No. 63 Tuodong Road, Guandu District, Kunming City, Yunnan Province Applicant before: Yunnan Power Grid Corporation Applicant before: Southwest Forestry University |
|
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181207 |