CN111709831B - Method and device for analyzing blacklist - Google Patents
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Abstract
The application provides a blacklist analysis method and device, wherein the method comprises the following steps: firstly, when a blacklist analysis instruction triggered by a user is received, personal information of the user is obtained; wherein, the personal information of the user comprises: basic information of a user and current ETC card credit attribute information of the user; then, inputting the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient to obtain the credit coefficient of the user; the calculation model of the user credit coefficient is obtained by training the decision tree model by the basic information and the ETC card credit attribute information corresponding to each of a plurality of training sample users; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users; and finally, if the credit coefficient of the user is larger than the threshold value, adding the user into the high-speed passing blacklist. The judgment standard of the high-speed passing black list is matched with the actual asset condition of the user.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for analyzing a blacklist.
Background
An electronic toll collection system (Electronic Toll Collection, ETC) is a system for automatic highway tolling. As the use of ETC has become more widespread, so has the number of users who use ETC cards to pay highway tolls. Currently, the ETC card used by most users adopts a billing charging mode. That is, the ETC accounting card is bound with a bank card of the user, and after the user passes through the toll station using the ETC accounting card, the ETC deducts the toll from the bound bank card according to the pass record. Because some users pay tolls without sufficient funds in the bank cards bound by the users, arrearages occur, and financial institutions such as banks set up high-speed transit black lists to limit the transit of the users.
In the prior art, whether the user enters a high-speed passing blacklist is judged mainly according to the times of arrearages or the amounts of arrearages of ETC accounting cards of the user. For example, when the number of times the ETC billing card owes the user reaches n, the user may be set as a high-speed transit blacklist user. However, since only the arrearage number or arrearage amount of the ETC accounting card is considered in the existing setting manner of the high-speed passing blacklist, the arrearage number or arrearage amount of the ETC accounting card cannot actually reflect the actual asset condition of the user, and the setting standard of the high-speed passing blacklist is not matched with the actual asset condition of the user.
Disclosure of Invention
In view of this, the present application provides a method and apparatus for analyzing a blacklist, which are used for setting a standard of a high-speed passing blacklist to be more fit with actual asset conditions of users.
The first aspect of the present application provides a method for analyzing a blacklist, including:
when a blacklist analysis instruction triggered by a user is received, personal information of the user is obtained; wherein the personal information of the user includes: basic information of a user and current ETC card credit attribute information of the user;
inputting the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient to obtain the user credit coefficient; the calculation model of the user credit coefficient is obtained by training a decision tree model by using basic information and ETC card credit attribute information corresponding to a plurality of training sample users; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users;
and if the credit coefficient of the user is greater than a threshold value, adding the user into a high-speed passing blacklist.
Optionally, the method for constructing the calculation model of the user credit coefficient includes:
determining a correlation coefficient corresponding to each attribute in the training sample set; wherein the training sample set comprises: training the decision tree model by the basic information and the ETC card credit attribute information corresponding to each training sample user; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users;
sequentially selecting attributes corresponding to the correlation coefficients as nodes according to the size sequence of the correlation coefficients from the root node of the decision tree model;
and selecting different values of the nodes to establish sub-nodes corresponding to the nodes until all the attributes of the training sample set are established, obtaining a final decision tree model, and taking the final decision tree model as a calculation model of the user credit coefficient.
Optionally, the determining the correlation coefficient corresponding to each attribute in the training sample set includes:
inputting each attribute in the training sample set into a correlation coefficient formula respectively, and calculating to obtain a correlation coefficient corresponding to each attribute in the training sample set;
wherein, the correlation coefficient formula is:
wherein D represents a training sample set, D i Represents the i-th sample set, |D| is the lumped number of training samples, |D i I represents the number of sample sets of the i-th class, and a represents the attribute input to the correlation coefficient formula.
Optionally, before inputting the basic information of the user and the current ETC card credit attribute information of the user into the calculation model of the user credit coefficient, the method further includes:
judging whether the user has added a high-speed traffic blacklist or not;
and if the user is judged not to be added into the high-speed passing black list, executing a calculation model for inputting the basic information of the user and the current ETC card credit attribute information of the user into the credit coefficient of the user.
Optionally, the high-speed passing blacklist sample user is a user who has the condition that ETC card debt is not returned in a preset repayment time period; and the high-speed passing white list sample user is a user without the condition that ETC card debt is not returned in the preset repayment time period.
A second aspect of the present application provides an analysis device for a blacklist, including:
the acquisition unit is used for acquiring the personal information of the user when receiving a blacklist analysis instruction triggered by the user; wherein the personal information of the user includes: basic information of a user and current ETC card credit attribute information of the user;
the input unit is used for inputting the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient to obtain the user credit coefficient; the calculation model of the user credit coefficient is obtained by training a decision tree model by using basic information and ETC card credit attribute information corresponding to a plurality of training sample users; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users;
and the first execution unit is used for adding the user into a high-speed passing blacklist if the credit coefficient of the user is larger than a threshold value.
Optionally, the construction unit of the calculation model of the user credit coefficient includes:
the determining unit is used for determining a correlation coefficient corresponding to each attribute in the training sample set; wherein the training sample set comprises: training the decision tree model by the basic information and the ETC card credit attribute information corresponding to each training sample user; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users;
the selecting unit is used for sequentially selecting attributes corresponding to the correlation coefficients as nodes according to the magnitude sequence of the correlation coefficients from the root node of the decision tree model;
and the model determining unit is used for selecting different values of the nodes to establish sub-nodes corresponding to the nodes until all the attributes of the training sample set are established, obtaining a final decision tree model, and taking the final decision tree model as a calculation model of the user credit coefficient.
Optionally, the determining unit includes:
the computing unit is used for respectively inputting each attribute in the training sample set into the correlation coefficient formula and computing to obtain the correlation coefficient corresponding to each attribute in the training sample set;
wherein, the correlation coefficient formula is:
wherein D represents a training sample set, D i Represents the i-th sample set, |D| is the lumped number of training samples, |D i I represents the number of sample sets of the i-th class, and a represents the attribute input to the correlation coefficient formula.
Optionally, the black list analysis device further includes:
the judging unit is used for judging whether the user has added into a high-speed passing blacklist;
and the second execution unit is used for executing a calculation model for inputting the basic information of the user and the current ETC card credit attribute information of the user into the user credit coefficient if the judgment unit judges that the user does not join the high-speed passing blacklist.
Optionally, the high-speed passing blacklist sample user is a user who has the condition that ETC card debt is not returned in a preset repayment time period; and the high-speed passing white list sample user is a user without the condition that ETC card debt is not returned in the preset repayment time period.
According to the technical scheme, in the blacklist analysis method and the blacklist analysis device provided by the embodiment of the application, the personal information of the user is obtained when a blacklist analysis instruction triggered by the user is received; wherein the personal information of the user includes: basic information of a user and current ETC card credit attribute information of the user; then, inputting the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient to obtain the credit coefficient of the user; the calculation model of the user credit coefficient is obtained by training a decision tree model by using basic information and ETC card credit attribute information corresponding to a plurality of training sample users; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users; and if the credit coefficient of the user is greater than a threshold value, adding the user into a high-speed passing blacklist. According to the method and the device for verifying the ETC card credit attribute information, whether the user to be verified should be added into the high-speed passing blacklist is judged according to the basic information of the user and the current ETC card credit attribute information of the user, and the basic information of the user and the current ETC card credit attribute information of the user can reflect the actual asset condition of the user, so that the judging standard of the high-speed passing blacklist is matched with the actual asset condition of the user.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a specific flowchart of a method for analyzing a blacklist according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for constructing a model for calculating a user credit factor according to another embodiment of the present application;
fig. 3 is a specific flowchart of a method for analyzing a blacklist according to another embodiment of the present application;
fig. 4 is a schematic diagram of an analysis device for a blacklist according to another embodiment of the present application;
FIG. 5 is a schematic diagram of a construction unit of a calculation model of a user credit coefficient according to another embodiment of the present application;
fig. 6 is a schematic diagram of an analysis device for a blacklist according to another embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the terms "first," "second," and the like in this application are used merely to distinguish between different devices, modules, or units and are not intended to limit the order or interdependence of functions performed by such devices, modules, or units, but the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiment of the application provides a method for analyzing a blacklist, as shown in fig. 1, specifically including the following steps:
s101, acquiring personal information of a user when receiving a blacklist analysis instruction triggered by the user.
Wherein, the personal information of the user comprises: basic information of the user and credit attribute information of the ETC card of the user. The user's basic information may include, but is not limited to, gender, annual income, etc.; the current ETC card credit attribute information of the user can include, but is not limited to, a historical total arrearage amount, a current arrearage amount, a line number of the ETC card, a daily average asset under the name of an ETC-associated bank account, a historical passing total number of the ETC card, a historical passing total amount of the ETC card, a current earliest arrearage of the ETC card, and the like. The content in the personal information of the user to be acquired may be selected according to the actual application situation, and thus is not limited herein.
Specifically, when the user fails to pay by using the ETC card, a blacklist analysis instruction is triggered, and personal information of the user is obtained.
S102, inputting the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient to obtain the user credit coefficient.
The calculation model of the user credit coefficient is obtained by training the decision tree model by the basic information and the ETC card credit attribute information corresponding to each of a plurality of training sample users; the plurality of training sample users includes a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users.
Optionally, in another embodiment of the present application, a method for constructing a calculation model of a user credit coefficient, as shown in fig. 2, includes:
s201, determining a correlation coefficient corresponding to each attribute in the training sample set.
Wherein the training sample set comprises: training the decision tree model by the basic information and the ETC card credit attribute information corresponding to each training sample user; the plurality of training sample users includes a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users.
Optionally, in another embodiment of the present application, the high-speed passing blacklist sample user is a user who has a situation that the ETC card debt is not returned within the preset repayment period; the high-speed passing white list sample user is a user without the condition that ETC card debt is not returned in a preset repayment time period.
Optionally, in another embodiment of the present application, an implementation of step S201 includes:
and respectively inputting each attribute in the training sample set into a correlation coefficient formula, and calculating to obtain a correlation coefficient corresponding to each attribute in the training sample set.
Wherein, the correlation coefficient formula is:
wherein D represents a training sample set, D i Represents the i-th sample set, |D| is the lumped number of training samples, |D i I represents the number of sample sets of the i-th class, and a represents the attribute input to the correlation coefficient formula.
Referring to Table 1, the process of calculating the correlation coefficient for both attributes will now be illustrated by way of example for gender and annual revenue.
| Sex (sex) | Annual income | Whether or not to blacklist |
| M | 120K | 0 |
| L | 100K | 0 |
| L | 70K | 0 |
| M | 120K | 0 |
| L | 95K | 1 |
| L | 60K | 0 |
| M | 220K | 0 |
| L | 85K | 1 |
| L | 75K | 0 |
| L | 90K | 1 |
Statistics on table 1 yield the data as shown in table 2:
| sex (sex) | Number of blacklists 1 | Number of blacklists of 0 |
| M | 0 | 3 |
| L | 3 | 4 |
According to the formula, the correlation coefficient of whether the data is blacklisted is obtained as follows:
and the correlation coefficient of the influence of which the attribute is sex is:
s202, starting from a root node of the decision tree model, sequentially selecting attributes corresponding to the correlation coefficients as nodes according to the magnitude order of the correlation coefficients.
S203, selecting different values of the nodes to establish sub-nodes corresponding to the nodes until all the attributes of the training sample set are established, and obtaining a final decision tree model.
S204, taking the final decision tree model as a calculation model of the user credit coefficient.
It should be noted that, the calculation model of the user credit coefficient needs to be updated and optimized according to the latest acquired data every preset time.
Optionally, in another embodiment of the present application, an implementation before step S102, as shown in fig. 3, includes:
s301, judging whether the user has joined a high-speed traffic blacklist.
Specifically, if it is determined that the user does not join the high-speed traffic blacklist, step S302 is executed; if the user is judged to be in the high-speed traffic blacklist, then
Specifically, whether the user has added a high-speed passing blacklist or not can be judged through a black-and-white list mark in personal information of the user; and matching the users in the high-speed passing blacklist to obtain a matching result of whether the users are added into the high-speed passing blacklist. Whether the user has added to the high-speed traffic blacklist is quite diversified is judged, and the limitation is not limited. If it is determined that the user does not join the high-speed traffic blacklist, step S302 is performed. If it is determined that the user is already in the high-speed traffic blacklist, the reminding information can be sent to the user by means of short messages and the like.
S302, inputting the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the credit coefficient of the user.
And S103, if the credit coefficient of the user is larger than the threshold value, adding the user into the high-speed passing blacklist.
After the user is added into the high-speed passing blacklist, the user can be informed that the user is added into the high-speed passing blacklist by sending a short message to the user, the user needs to pay the delinquent amount in a preset time, if the user is successful in paying the delinquent amount in the preset time, the user is judged whether the user should be added into the high-speed passing blacklist by using the evaluation model of the user credit again, and if the user is not paying in the preset time, the user needs to pay corresponding interest according to the delinquent time until the user pays the delinquent amount and the corresponding interest. And are not limited herein.
According to the scheme, in the blacklist analysis method provided by the application, firstly, personal information of a user is obtained when a blacklist analysis instruction triggered by the user is received; wherein, the personal information of the user comprises: basic information of a user and current ETC card credit attribute information of the user; then, inputting the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient to obtain the credit coefficient of the user; the calculation model of the user credit coefficient is obtained by training the decision tree model by the basic information and the ETC card credit attribute information corresponding to each of a plurality of training sample users; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users; and if the credit coefficient of the user is greater than the threshold value, adding the user into the high-speed passing blacklist. According to the method and the device for verifying the ETC card credit attribute information, whether the user to be verified should be added into the high-speed passing blacklist is judged according to the basic information of the user and the current ETC card credit attribute information of the user, and the basic information of the user and the current ETC card credit attribute information of the user can reflect the actual asset condition of the user, so that the judging standard of the high-speed passing blacklist is matched with the actual asset condition of the user.
Another embodiment of the present application provides an analysis device for a blacklist, as shown in fig. 4, specifically including:
the acquiring unit 401 is configured to acquire personal information of a user when receiving a blacklist analysis instruction triggered by the user.
Wherein, the personal information of the user comprises: basic information of the user and credit attribute information of the ETC card of the user.
And the input unit 402 is configured to input the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient, so as to obtain the credit coefficient of the user.
The calculation model of the user credit coefficient is obtained by training the decision tree model by the basic information and the ETC card credit attribute information corresponding to each of a plurality of training sample users; the plurality of training sample users includes a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users.
Optionally, in another embodiment of the present application, the high-speed passing blacklist sample user is a user who has a situation that the ETC card debt is not returned within the preset repayment period; the high-speed passing white list sample user is a user without the condition that ETC card debt is not returned in a preset repayment time period.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer to the content of the corresponding method embodiment, which is not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the building unit of the calculation model of the user credit coefficient, as shown in fig. 5, includes:
a determining unit 501, configured to determine a correlation coefficient corresponding to each attribute in the training sample set.
Wherein the training sample set comprises: training the decision tree model by the basic information and the ETC card credit attribute information corresponding to each training sample user; the plurality of training sample users includes a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users.
Optionally, in another embodiment of the present application, an implementation of the determining unit 501 includes:
the computing unit is used for respectively inputting each attribute in the training sample set into the correlation coefficient formula, and computing to obtain the correlation coefficient corresponding to each attribute in the training sample set.
Wherein, the correlation coefficient formula is:
wherein D represents a training sample set, D i Represents the i-th sample set, |D| is the lumped number of training samples, |D i I represents the number of sample sets of the i-th class, and a represents the attribute input to the correlation coefficient formula.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer to the content of the corresponding method embodiment, which is not described herein again.
The selecting unit 502 is configured to sequentially select, from the root node of the decision tree model, the attribute corresponding to the correlation coefficient as a node according to the order of magnitude of the correlation coefficient.
The model determining unit 503 is configured to select different values of the nodes to establish sub-nodes corresponding to the nodes until all the attributes of the training sample set are established, obtain a final decision tree model, and use the final decision tree model as a calculation model of the user credit coefficient.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer to the content of the corresponding method embodiment, as shown in fig. 2, which is not described herein again.
The first execution unit 403 is configured to add the user to the high-speed traffic blacklist if the credit coefficient of the user is greater than the threshold value.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer to the content of the corresponding method embodiment, as shown in fig. 1, which is not repeated herein.
Optionally, in another embodiment of the present application, the apparatus for analyzing a blacklist, as shown in fig. 6, further includes:
a judging unit 601, configured to judge whether the user has joined the high-speed traffic blacklist.
And the second execution unit 602 is configured to execute a calculation model for inputting the basic information of the user and the credit attribute information of the current ETC card of the user into the credit coefficient of the user if the determination unit 601 determines that the user does not join the high-speed traffic blacklist.
The specific working process of the unit disclosed in the foregoing embodiments of the present application may refer to the content of the corresponding method embodiment, as shown in fig. 3, which is not described herein again.
As can be seen from the above solution, in the blacklist analysis device provided in the present application, first, when receiving a blacklist analysis instruction triggered by a user, the obtaining unit 401 obtains personal information of the user; wherein, the personal information of the user comprises: basic information of a user and current ETC card credit attribute information of the user; then, the input unit 402 inputs the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient to obtain the user credit coefficient; the calculation model of the user credit coefficient is obtained by training the decision tree model by the basic information and the ETC card credit attribute information corresponding to each of a plurality of training sample users; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users; if the credit factor of the user is greater than the threshold, the first execution unit 403 adds the user to the high-speed traffic blacklist. According to the method and the device for verifying the ETC card credit attribute information, whether the user to be verified should be added into the high-speed passing blacklist is judged according to the basic information of the user and the current ETC card credit attribute information of the user, and the basic information of the user and the current ETC card credit attribute information of the user can reflect the actual asset condition of the user, so that the judging standard of the high-speed passing blacklist is matched with the actual asset condition of the user.
In the above embodiments of the disclosure, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus and method embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in various embodiments of the present disclosure may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present disclosure may be embodied in essence or a part contributing to the prior art or a part of the technical solution, or in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a live device, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those skilled in the art can make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A method for analyzing a blacklist, comprising:
when a blacklist analysis instruction triggered by a user is received, personal information of the user is obtained; wherein the personal information of the user includes: basic information of a user and current ETC card credit attribute information of the user;
inputting the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient to obtain the user credit coefficient;
if the credit coefficient of the user is larger than a threshold value, adding the user into a high-speed passing blacklist;
the construction method of the calculation model of the user credit coefficient comprises the following steps:
determining a correlation coefficient corresponding to each attribute in the training sample set; wherein the training sample set comprises: basic information and ETC card credit attribute information corresponding to each training sample user; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users;
sequentially selecting attributes corresponding to the correlation coefficients as nodes according to the size sequence of the correlation coefficients from the root node of the decision tree model;
selecting different values of the nodes to establish sub-nodes corresponding to the nodes until all the attributes of the training sample set are established, obtaining a final decision tree model, and taking the final decision tree model as a calculation model of a user credit coefficient;
wherein, determining the correlation coefficient corresponding to each attribute in the training sample set includes:
inputting each attribute in the training sample set into a correlation coefficient formula respectively, and calculating to obtain a correlation coefficient corresponding to each attribute in the training sample set;
wherein, the correlation coefficient formula is:
wherein D represents a training sample set, D i Represents the i-th sample set, |D| is the lumped number of training samples, |D i I represents the number of sample sets of the i-th class, A represents the input to the correlation coefficient formulaAttributes.
2. The analysis method according to claim 1, wherein before inputting the basic information of the user and the current ETC card credit attribute information of the user into the calculation model of the user credit coefficient, further comprising:
judging whether the user has added a high-speed traffic blacklist or not;
and if the user is judged not to be added into the high-speed passing black list, executing a calculation model for inputting the basic information of the user and the current ETC card credit attribute information of the user into the credit coefficient of the user.
3. The analysis method according to claim 1, wherein the high-speed through blacklist sample user is a user who has a situation in which ETC card debt is not returned within a preset repayment period; and the high-speed passing white list sample user is a user without the condition that ETC card debt is not returned in the preset repayment time period.
4. An apparatus for analyzing a blacklist, comprising:
the acquisition unit is used for acquiring the personal information of the user when receiving a blacklist analysis instruction triggered by the user; wherein the personal information of the user includes: basic information of a user and current ETC card credit attribute information of the user;
the input unit is used for inputting the basic information of the user and the current ETC card credit attribute information of the user into a calculation model of the user credit coefficient to obtain the user credit coefficient; the first execution unit is used for adding the user into a high-speed passing blacklist if the credit coefficient of the user is larger than a threshold value;
the construction method of the calculation model of the user credit coefficient comprises the following steps:
determining a correlation coefficient corresponding to each attribute in the training sample set; wherein the training sample set comprises: basic information and ETC card credit attribute information corresponding to each training sample user; the plurality of training sample users comprise a plurality of high-speed traffic blacklist sample users and a plurality of high-speed traffic whitelist sample users;
sequentially selecting attributes corresponding to the correlation coefficients as nodes according to the size sequence of the correlation coefficients from the root node of the decision tree model;
selecting different values of the nodes to establish sub-nodes corresponding to the nodes until all the attributes of the training sample set are established, obtaining a final decision tree model, and taking the final decision tree model as a calculation model of a user credit coefficient;
wherein, determining the correlation coefficient corresponding to each attribute in the training sample set includes:
inputting each attribute in the training sample set into a correlation coefficient formula respectively, and calculating to obtain a correlation coefficient corresponding to each attribute in the training sample set;
wherein, the correlation coefficient formula is:
wherein D represents a training sample set, D i Represents the i-th sample set, |D| is the lumped number of training samples, |D i I represents the number of sample sets of the i-th class, and a represents the attribute input to the correlation coefficient formula.
5. The analysis device according to claim 4, further comprising:
the judging unit is used for judging whether the user has added into a high-speed passing blacklist;
and the second execution unit is used for executing a calculation model for inputting the basic information of the user and the current ETC card credit attribute information of the user into the user credit coefficient if the judgment unit judges that the user does not join the high-speed passing blacklist.
6. The analysis device according to claim 4, wherein the high-speed through blacklist sample user is a user who has a situation in which ETC card arrears have not been returned within a preset repayment period; and the high-speed passing white list sample user is a user without the condition that ETC card debt is not returned in the preset repayment time period.
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| CN110738565A (en) * | 2019-10-11 | 2020-01-31 | 中山市银鹿金科信息科技有限公司 | Real estate finance artificial intelligence composite wind control model based on data set |
| CN111080442A (en) * | 2019-12-21 | 2020-04-28 | 湖南大学 | Credit scoring model construction method, device, equipment and storage medium |
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