WO2017133615A1

WO2017133615A1 - Service parameter acquisition method and apparatus

Info

Publication number: WO2017133615A1
Application number: PCT/CN2017/072593
Authority: WO
Inventors: 黄文�
Original assignee: 腾讯科技（深圳）有限公司
Priority date: 2016-02-03
Filing date: 2017-01-25
Publication date: 2017-08-10
Also published as: CN107040397B; CN107040397A

Abstract

Disclosed are a service parameter acquisition method and apparatus. First, feature data of a sample user whose service parameter is to be predicted is acquired; the feature data is input into a logistic regression analysis model to obtain a feature parameter of the feature data, wherein the feature parameter is used for determining the service parameter. It is determined that the sample user has a first service parameter if the feature parameter is within a predetermined first threshold interval, and it is determined that the sample user has a second service parameter if the feature parameter is within a predetermined second threshold interval, wherein the logistic regression analysis model is obtained by performing logistic regression analysis and repeated iterative training using a large amount of feature data of the sample user. Because a great number of sample users are analyzed in advance by using a logistic regression analysis model to determine values corresponding to feature parameters, the service parameters acquired from a user whose service parameter is to be tested are relatively accurate, and relatively objective prediction of default of the sample user can be implemented.

Description

Method and device for acquiring business parameters

The present application claims priority to Chinese Patent Application No. 201610078384.8, entitled "A Method and Apparatus for Obtaining Business Parameters", filed on February 3, 2016, the entire contents of which is incorporated herein by reference. in.

Technical field

The present application relates to the field of Internet technologies, and in particular, to a method and an apparatus for acquiring a service parameter.

Background technique

Currently, many services are directly related to service parameters. Service parameters directly affect whether a service application can be successful. When a service provider allocates a service to a user, it evaluates whether the service is assigned to the user based on the existing service parameters.

However, at present, the service provider has a large number of user service parameter records, and needs to obtain the required target user service parameters. Currently, the service provider cannot accurately accurately evaluate the required target user's service parameters. There is a certain risk that the service provider's target users provide services.

Summary of the invention

In view of this, the embodiment of the present application provides a method and an apparatus for acquiring a service parameter.

An object of the present application is to provide a method for acquiring a service parameter, the method comprising:

Determining that the sample user that meets the preset rule is the target sample user;

Determining a logistic regression analysis model using a plurality of feature data of the target sample user;

Obtaining feature data of the sample user of the service parameter to be predicted;

Entering the feature data into the logistic regression analysis model to obtain feature parameters of the feature data, and the feature parameters are used to determine the service parameter;

Determining that the sample user has the first service parameter when the feature parameter is located in a preset first threshold interval;

Determining that the sample user has the second service parameter when the feature parameter is located in a preset second threshold interval;

The logistic regression analysis model is obtained by performing logistic regression analysis and repeated iterative training using characteristic data of a large number of sample users.

Another object of the present application is to provide a service parameter obtaining apparatus, the apparatus comprising:

An obtaining unit, configured to acquire feature data of a sample user of a service parameter to be predicted;

a processing unit, configured to determine that the sample user that meets the preset rule is the target sample user;

Determining the logistic regression analysis model using a plurality of feature data of the target sample user;

Entering the feature data into a logistic regression analysis model to obtain feature parameters of the feature data, the feature parameters being used to determine the service parameters;

Determining that the sample user has the second service parameter when the feature parameter is located in a preset second threshold interval, wherein the logistic regression analysis model performs logistic regression analysis using a plurality of sample user feature data and repeats Iterative training is obtained.

A further object of the present application is to provide a service parameter obtaining device, which includes a processor and a memory for storing a program for supporting a data processing device to execute the above method, the processor being configured It is used to execute a program stored in the memory. The database processing device can also include a communication interface for the database processing device to communicate with other devices or communication networks.

The embodiment of the present application provides a computer storage medium for storing computer software instructions used by the service parameter obtaining apparatus, which includes a program designed to execute the foregoing aspect for the service parameter obtaining apparatus.

DRAWINGS

1 is a flowchart of an embodiment of a method for acquiring a service parameter in an embodiment of the present application;

2 is a flowchart of another embodiment of a method for acquiring a service parameter according to an embodiment of the present application;

3 is a structural diagram of an embodiment of a service parameter obtaining apparatus according to an embodiment of the present application;

FIG. 4 is a structural diagram of another embodiment of a service parameter obtaining apparatus according to an embodiment of the present application.

detailed description

The technical solutions in the embodiments of the present application are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present application. It is an embodiment of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope shall fall within the scope of the application.

The terms "first", "second", "third", "fourth" and the like in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order. Or prioritization. It is to be understood that the data so used may be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than what is illustrated or described herein. In addition, the terms "comprises" and "comprises" and "the" and "the" are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to Those steps or units may include other steps or units not explicitly listed or inherent to such processes, methods, products or devices.

Before describing the embodiments of the present application, a preliminary introduction to the nouns involved in the embodiments of the present application is first made:

The logistic regression analysis model is based on a machine learning model with supervised training.

Supervised learning: A training method with training samples and training tags.

In the university, the national student loan is often mentioned to help students with poor economic conditions to complete their studies. Similar to the usual loans, it is necessary to prejudge the default of the students and control the risks in various ways. For example, delays in issuing diplomas or degree certificates, etc. These measures are risk control measures for lenders after the loan, and the prediction of loan defaults for the students before the loan is not comprehensive and accurate. It should be noted that the solution of the embodiment of the present application is not limited to the social application, and all publicly available user feature data can be used as the embodiment of the present application.

With the development of technology, more and more social applications have entered our lives. In the case of user authorization, many social applications can publish the user's location and device information in social circles, for example, showing the current location in a circle of friends. Location, labeling the brand model of the microblog device in the microblog information, the information can reflect the user's feature data, and some pre-preparation can be performed through the feature data. Judge.

The present application determines the corresponding service parameters by using the characteristic data of the user. In fact, these service parameters can reflect the integrity of the user in a certain period of time, that is, whether the default situation will occur. The application can reflect whether the user can default the business parameters. Is the probability of default, that is, between 0 and 1, if the probability of default of the business parameter tends to 0, it indicates that the probability of default is small, for example, the probability of default is 0.1. On the contrary, if the probability of default is more toward 1, it indicates The probability of default is greater, such as a default probability of 0.9. The default prediction and the user default probability in the embodiment of the present application are only different in expression, and the principle is the same.

As shown in FIG. 1 , the method for obtaining the service parameter is provided by the embodiment of the present application, and the method includes:

S101. Acquire feature data of a sample user of a service parameter to be predicted.

S102. Enter the feature data into a logistic regression analysis model to obtain a feature parameter of the feature data, where the feature parameter is used to determine the service parameter, where the logistic regression analysis model is performed by using feature data of a large number of sample users. Logistic regression analysis and repeated iteration training.

In the logistic regression analysis model, a large number of sample users can be analyzed in advance to obtain the commonly used characteristic parameters of the sample users. When the business parameters of a sample user continue to be acquired, logistic regression analysis can be used to analyze each characteristic parameter already existing in the model. The corresponding value is determined, because the corresponding sample user can have multiple characteristic parameters, and each of the characteristic parameters is different for the sample user. For example, the sample user has A characteristic parameter, B characteristic parameter and C characteristic respectively. The parameters, the corresponding values can be 0.2, 0.5 and 0.3 respectively. When determining the sample user, the characteristic parameters can be used to determine the business parameters. The business parameters here can represent the credit degree of the sample users, and the characteristic parameters are 0, 1. If the characteristic parameter tends to 1, it indicates that the probability of default is greater, that is, the credit is lower. Conversely, when the characteristic parameter tends to 0, the probability of default is small, that is, the credit is very high, and the intermediate value can usually be selected. Dividing, for example, between 0 and 0.5 as the first threshold interval, between 0.5 and 1 The second threshold interval is determined. When the feature parameter of the sample user is within the first threshold interval, the sample user may be determined to have the first service parameter, and when the feature parameter of the sample user is located in the second threshold interval, the sample user may be determined to have the second Business parameters, because the logistic regression analysis model is a numerical value corresponding to the characteristic parameters determined by analyzing a large number of sample users in advance, so that when a business parameter is acquired for a user whose business parameters are to be tested If it is more accurate, it can objectively estimate the default of the sample users.

As shown in FIG. 2, the embodiment of the present application provides a method for acquiring a service parameter, where the method includes:

S201. Determine a sample user that meets the preset rule as the target sample user.

The preset rule includes: a user whose location is located at the target location, a degree of association with the target sample user reaches a preset association threshold, and the identity information of the sample user meets a preset condition, for example, in progress When the student's default forecast is made, the position of the student can be matched with the geographical location of the major universities in the country. The location function of the equipment can be used for the position of the student, and the user should be authorized for the position of the student. Under the acquisition, you can further use the age and / or network age data to remove some of the people who do not meet the age, because the students are more exposed to new things, more time online, can also be judged by the level of their social media accounts For students who are determined to be sample users, students can be extended according to their associated circle of friends to expand more sample users who meet the conditions of the students, so that a large number of samples can be used for determining the students' samples, and the logistic regression analysis can be improved. The accuracy of the model.

S202. Determine the logistic regression analysis model by using a plurality of feature data of the target sample user.

The characteristic parameters may include statistical analysis on the sample user location migration frequency, the contact update frequency, the push frequency of the social application information, etc., which may be obtained through statistics, and then the repeated feature calculations are performed to determine accurate feature parameters and corresponding parameters. The value, that is, the weight value, for example, the frequency of a person's location migration, the location of the occurrence is not fixed, it can be considered that the user's work or learning state is unstable, when the business is assigned to it, the later progress may not be smooth. When such a feature parameter reassigns the weight, the weight value of the feature parameter can be increased to reflect the importance. For example, when a loan is made to the user, due to unstable work or learning, there will be a situation in which the payment cannot be repaid on time. Such a user default risk will increase, and then more review will be conducted when the loan is made.

S203. Acquire feature data of a sample user of a service parameter to be predicted.

S204. Enter the feature data into a logistic regression analysis model to obtain a feature parameter of the feature data, where the feature parameter is used to determine the service parameter, where the logistic regression analysis model uses feature data of a large number of sample users. Logistic regression analysis was performed and iterative training was obtained.

S205: When the feature parameter is located in a preset first threshold interval, determine that the sample user has the first service parameter, and when the feature parameter is located in a preset second threshold interval, determine the sample user. Having the second service parameter.

The feature parameter output by the logistic regression analysis model according to the feature data may be a probability value, the range of the feature parameter is between 0 and 1, and the service parameter is divided into two types including the first service parameter and the second service parameter, and the first service The parameter can also be set as a good faith user, and the second service parameter can be set as a default user. When performing credit prediction, the service parameter can correspond to the user's default possibility, so that the corresponding user can be a good user and a default user, such as a characteristic parameter. Between 0 and 0.5, at this time, the sample user has more features of the honest user, and it can be said that the sample user is less likely to default. When the feature parameter is between 0.5 and 1, the sample user has a default. The user has more features. It can be said that the sample user has a higher probability of default. When setting the threshold interval, the user can flexibly choose. When it is necessary to judge the integrity of the user, the value of the intermediate value can be closer to 0. For example, the first The threshold interval can be set between 0 and 0.2, and the second threshold interval is set between 0.2 and 1, correspondingly, for integrity If the condition of the household is loose, the value of the intermediate value may be closer to 1, for example, 0.7, the first threshold interval may be set to 0 to 0.7, and the second threshold interval may be set to 0.7 to 1, in short, by the characteristic parameter The value of the sample user can determine the business parameters of the sample user, and the sample user's default condition can be predicted.

An embodiment of a method for establishing a logistic regression analysis model in an embodiment of the present application includes

Deriving feature data of the target sample user and extracting a first parameter having a trend;

Performing dimensionality reduction on the first parameter to obtain an interpreted second parameter;

Performing cluster analysis, discriminant analysis, and de-duplication on the second parameter to obtain a third parameter, wherein N1 parameters are selected from the second parameter by the cluster analysis, and the second parameter is analyzed by the discriminant analysis Select N2 parameters, combine the selected N1 parameters and N2 parameters to obtain the third parameter;

Performing a logistic regression analysis on the third parameter to obtain a fourth parameter;

Performing an iterative operation on the fourth parameter to obtain a model parameter, and determining that the sample user has the second service parameter.

Specifically: according to the definition of the Logistic function

Logit(p)=α+β·X=α+β ₁ x ₁ +β ₂ x ₂ +...+β _n x _n

The probability of occurrence of the p event, β = (β ₁ , β ₂ , ..., β _n ) is the estimated value of the parametric equation, X = (x ₁ , x ₂ , ..., x _n ) ^T is logistic regression analysis Model variable.

Probability of defaulting users:

A y value of 1 indicates a default customer, and a 0 is a good customer.

θ represents the parameters estimated by the model, namely: α, β ₁ , β ₂ , ..., β _n

Probability of honest users:

Because y is a binary classification, 0 or 1, according to the two probabilities p ₁ , p ₀ , the probability distribution of honest users and default users is obtained.

p(y|x,θ)=(1-h _θ (x)) ^y ·h _θ (x) ^1-y

Principle of maximum likelihood estimation

By deriving log(L(θ)), the extremum is obtained, and the iterative function of θ is obtained, which is the estimated parameter of the logistic regression analysis model. The actual corresponding estimated parameter of the model variable can be used as the weight value of each feature parameter. When predicting a user, classifying the feature data of the user to obtain a plurality of feature parameters, and calculating a weight value of the plurality of feature parameters to obtain a service parameter of the user, that is, an estimated default probability, according to the default The value of the probability can be used to estimate the default of the user in order to decide whether to perform related business, such as issuing a loan.

It should be noted that the premise of the selection of the logistic regression analysis model variable is the derivative variable. Usually, the object of the analysis may be the user or the account. The obtained data may have user basic attribute data, social attribute data, transaction attribute data, stable security. Attribute variables and the like can be derived from the data to obtain new variables for use. The process of creating the derived variables should be understood by those of ordinary skill in the art, and will not be described herein.

The embodiment of the present application discloses a method for acquiring a service parameter, which first acquires a service parameter to be predicted. Feature data of the sample user, the feature data is input to a logistic regression analysis model to obtain feature parameters of the feature data, and the feature parameters are used to determine the service parameter, wherein the logistic regression analysis model adopts a large number of The characteristic data of the sample user is subjected to logistic regression analysis and iteratively iterative training is obtained. Because the logistic regression analysis model pre-analyzes the values corresponding to the characteristic parameters determined by a large number of sample users, the business parameter acquisition is performed on the user of a service parameter to be tested. The results are more accurate and can objectively estimate the default of the sample users.

As shown in FIG. 3, a method for acquiring a service parameter is described in the foregoing. Correspondingly, the embodiment of the present application further provides a service parameter obtaining device, where the device includes:

An obtaining unit 301, configured to acquire feature data of a sample user of a service parameter to be predicted;

The analyzing unit 302 is configured to perform categorization analysis on the feature data by using a logistic regression analysis model to obtain a plurality of feature parameters of the feature data;

The processing unit 302 is configured to input the feature data into a logistic regression analysis model to obtain a feature parameter of the feature data, where the feature parameter is used to determine the service parameter, wherein the logistic regression analysis model uses a large number of sample users The eigendata is subjected to logistic regression analysis and iterative training is obtained.

Optionally, the processing unit 302 is further configured to:

A method for determining the logistic regression analysis model using a plurality of feature data of the target sample user.

Optionally, the service parameter includes a first service parameter and a second service parameter, and the processing unit 302 is further configured to:

When the feature parameter is located in a preset second threshold interval, it is determined that the sample user has the second service parameter.

Optionally, the processing unit 302 is further configured to:

Performing cluster analysis, discriminant analysis, and de-duplication on the second parameter to obtain a third parameter;

Optionally, the preset rule includes: the user where the sample user is located at the target location, the degree of association with the target sample user reaches a preset association threshold, and the identity information of the sample user conforms to the preset. condition.

The embodiment of the present application discloses a service parameter obtaining device, which first obtains feature data of a sample user of a service parameter to be predicted, and uses a logistic regression analysis model to classify the feature data to obtain multiple features of the feature data. a parameter determining a value of each of the plurality of characteristic parameters for determining the business parameter, wherein the logistic regression analysis model performs logistic regression analysis using a plurality of sample user characteristic data and repeats Iterative training is obtained, because the logistic regression analysis model pre-calculates the values corresponding to the characteristic parameters determined by a large number of sample users, so that the results of the business parameters acquisition for a user whose business parameters are to be tested are more accurate, and the sample users can be more objective. The default is estimated.

As shown in FIG. 4, FIG. 4 is a schematic structural diagram of a service parameter obtaining apparatus 40 according to an embodiment of the present application. The service parameter acquisition device 40 includes a processor 410, a memory 450, and an input/output I/O device 430. The memory 450 can include read only memory and random access memory, and provides operational instructions and data to the processor 410. A portion of the memory 450 may also include non-volatile random access memory (NVRAM).

In some implementations, the memory 450 stores the following elements, executable modules or data structures, or a subset thereof, or their extended set:

In the embodiment of the present application, by calling an operation instruction stored in the memory 450 (the operation instruction can be stored in the operating system),

Obtaining feature data of the sample user of the business parameter to be predicted.

Entering the feature data into a logistic regression analysis model to obtain feature parameters of the feature data, the feature parameters are used to determine the business parameter, wherein the logistic regression analysis model uses a feature data of a large number of sample users for logistic Regression analysis and iterative training.

The processor 410 controls the operation of the service parameter obtaining device 40, which may also be referred to as CPU (Central Processing Unit). Memory 450 can include read only memory and random access memory and provides instructions and data to processor 410. A portion of the memory 450 may also include non-volatile random access memory (NVRAM). The various components of the business parameter acquisition device 40 in the application are coupled together by a bus system 420. The bus system 420 may include a power bus, a control bus, a status signal bus, and the like in addition to the data bus. However, for clarity of description, various buses are labeled as bus system 420 in the figure.

The method disclosed in the foregoing embodiment of the present application may be applied to the processor 410 or implemented by the processor 410. Processor 410 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the foregoing method may be completed by an integrated logic circuit of hardware in the processor 410 or an instruction in a form of software. The processor 410 described above may be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or discrete hardware. Component. The methods, steps, and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general purpose processor may be a microprocessor or the processor or any conventional processor or the like. The steps of the method disclosed in the embodiments of the present application may be directly implemented by the hardware decoding processor, or may be performed by a combination of hardware and software modules in the decoding processor. The software module can be located in a conventional storage medium such as random access memory, flash memory, read only memory, programmable read only memory or electrically erasable programmable memory, registers, and the like. The storage medium is located in the memory 450, and the processor 410 reads the information in the memory 450 and completes the steps of the above method in combination with its hardware.

A person skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the system, the device and the unit described above can refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.

In the several embodiments provided by the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of the unit is only a logical function division. In actual implementation, there may be another division manner, for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.

The units described as separate components may or may not be physically separate. The components displayed for the unit may or may not be physical units, ie may be located in one place, or may be distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

A person skilled in the art may understand that all or part of the various steps of the foregoing embodiments may be performed by a program to instruct related hardware. The program may be stored in a computer readable storage medium, and the storage medium may include: Read Only Memory (ROM), Random Access Memory (RAM), disk or optical disk.

It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be performed by a program to instruct related hardware, and the program may be stored in a computer readable storage medium, the above mentioned storage. The medium can be a read only memory, a magnetic disk or an optical disk or the like.

The method and device for obtaining a service parameter provided by the present application are described in detail above. For those skilled in the art, according to the idea of the embodiment of the present application, there are changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as limiting the application.

Claims

A method for obtaining a service parameter, the method comprising:

Determining that the sample user that meets the preset rule is the target sample user;

Determining a logistic regression analysis model using characteristic data of the target sample user;

Obtaining feature data of the sample user of the service parameter to be predicted;

Entering the feature data into the logistic regression analysis model to obtain feature parameters of the feature data, and the feature parameters are used to determine the service parameter;

Determining that the sample user has the first service parameter when the feature parameter is located in a preset first threshold interval;

Determining that the sample user has the second service parameter when the feature parameter is located in a preset second threshold interval;

The logistic regression analysis model is obtained by performing logistic regression analysis using sample data of the sample user and repeatedly iterative training.
The method according to claim 1, wherein the determining the logistic regression analysis model by using the feature data of the target sample user comprises:

Deriving feature data of the target sample user and extracting a first parameter having a trend;

Performing dimensionality reduction on the first parameter to obtain an interpreted second parameter;

Performing cluster analysis, discriminant analysis, and de-duplication on the second parameter to obtain a third parameter;

Performing a logistic regression analysis on the third parameter to obtain a fourth parameter;

And performing a iterative operation on the fourth parameter to obtain a model parameter, where the model parameter is used to determine the feature parameter corresponding to the feature data.
The method according to claim 1, wherein the preset rule comprises at least: a user, a location where the sample user is located at a target location, and a degree of association with the target sample user reaches a preset association threshold. The identity information of the sample user meets the preset conditions.
The method of claim 1 wherein said first threshold interval is between 0 and 0.5 and said second threshold interval is between 0.5 and 1.
A service parameter obtaining device, characterized in that the device comprises:

An obtaining unit, configured to acquire feature data of a sample user of a service parameter to be predicted;

a processing unit, configured to determine that the sample user that meets the preset rule is the target sample user;

Determining the logistic regression analysis model using feature data of the target sample user;

Entering the feature data into a logistic regression analysis model to obtain feature parameters of the feature data, the feature parameters being used to determine the service parameters;

Determining that the sample user has the first service parameter when the feature parameter is located in a preset first threshold interval;

Determining that the sample user has the second service parameter when the feature parameter is located in a preset second threshold interval, wherein the logistic regression analysis model performs logistic regression analysis using the sample user's feature data and iterates repeatedly Trained.
The device according to claim 5, wherein the processing unit is further configured to:

Deriving feature data of the target sample user and extracting a first parameter having a trend;

Performing dimensionality reduction on the first parameter to obtain an interpreted second parameter;

Performing cluster analysis, discriminant analysis, and de-duplication on the second parameter to obtain a third parameter;

Performing a logistic regression analysis on the third parameter to obtain a fourth parameter;

And performing a iterative operation on the fourth parameter to obtain a model parameter, where the model parameter is used to determine the feature parameter corresponding to the feature data.
The device according to claim 5, wherein the preset rule comprises at least: a user, a location where the sample user is located at a target location, and a degree of association with the target sample user reaches a preset association threshold. The identity information of the sample user meets the preset conditions.
A service parameter obtaining device, comprising: a processor and a memory, wherein

a computer readable program is stored in the memory;

The processor is operative to execute the method of the above claims 1 to 4 by running a program in the memory.
A non-volatile storage medium for storing one or more computer programs, wherein the computer program includes processor-executable instructions having one or more memories that, when executed by a computer, cause The computer performs the following operations:

Determining that the sample user that meets the preset rule is the target sample user;

Determining a logistic regression analysis model using characteristic data of the target sample user;

Obtaining feature data of the sample user of the service parameter to be predicted;

Importing the feature data into the logistic regression analysis model to obtain the feature data a feature parameter, the feature parameter being used to determine the service parameter;

Determining that the sample user has the first service parameter when the feature parameter is located in a preset first threshold interval;

Determining that the sample user has the second service parameter when the feature parameter is located in a preset second threshold interval;

The logistic regression analysis model is obtained by performing logistic regression analysis using sample data of the sample user and repeatedly iterative training.