CN113706282A

CN113706282A - Data determination method and device, electronic equipment and storage medium

Info

Publication number: CN113706282A
Application number: CN202111199551.1A
Authority: CN
Inventors: 郭永学; 王路遥; 朱凯雁
Original assignee: Lianlian Hangzhou Information Technology Co ltd
Current assignee: Lianlian Hangzhou Information Technology Co ltd
Priority date: 2021-10-14
Filing date: 2021-10-14
Publication date: 2021-11-26
Anticipated expiration: 2041-10-14
Also published as: CN113706282B

Abstract

The embodiment of the application discloses a data determination method, a data determination device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining category information; determining a user identification of the object; acquiring an index data set based on the category information and the user identification; determining reference data based on the set of metric data and/or the category information; determining a coefficient based on the set of metric data and/or the category information; determining quota data based on the category information; based on the reference data, the coefficients and the quota data, capability assessment data of the object under the category information is determined. The method and the device for determining the capacity evaluation data can adapt to various service fields and scenes, and quickly and conveniently determine the capacity evaluation data of different types.

Description

Data determination method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of big data, and in particular, to a data determining method and apparatus, an electronic device, and a storage medium.

Background

In recent years, internet finance is rapidly developed, and the number of users is explosively increased. The method meets the increasing internet convenient operation requirements of users, and meanwhile, the transaction efficiency, the security degree, the user requirement matching degree and other factors need to be guaranteed. Through internet service, when people perform a large number of buying and selling behaviors, interaction behaviors, payment behaviors and other behaviors, a platform or other users need to efficiently screen the capability performance of specific users in different service fields and scenes.

At present, when capacity evaluation data is determined, secondary combination and calculation are required to be carried out on data of single-dimensional scoring results such as user portrait, feature data and the like output by a big data system, and a final result is obtained. In the secondary combination and calculation links, technicians often implement the combination and calculation logic in a hard coding mode, that is, the combination and calculation logic can only be modified by editing source codes and recompiling executable files, and the combination and calculation logic cannot adapt to different service fields and scenes.

Disclosure of Invention

Aiming at the defects in the prior art, the embodiment of the disclosure provides a data determination method which can be adapted to various service fields and scenes, and can quickly and conveniently determine different types of capability evaluation data.

The embodiment of the application provides a data determination method, which comprises the following steps: determining category information; the category information is category information in a category information set corresponding to the object and is used for representing a service scene corresponding to the data; determining a user identification of the object; acquiring an index data set based on the category information and the user identification; determining reference data based on the set of metric data and/or the category information; determining a coefficient based on the set of metric data and/or the category information; determining quota data based on the category information; based on the reference data, the coefficients and the quota data, capability assessment data of the object under the category information is determined.

In an alternative embodiment, the first computational model is determined based on the category information; based on the first calculation model, the reference data, the coefficients and the quota are calculated to determine capability assessment data of the subject.

In an alternative embodiment, the category information includes one or more of a credit score, a risk limit, a credit line, a credit rating, a recommendation rating.

In an optional embodiment, the index data set includes index data, and the index data is used for representing the statistical result of the object in a single statistical dimension or the scoring result in a single evaluation dimension; determining at least one index database to be queried based on the category information; and acquiring an index data set from at least one index database based on the user identification.

In an alternative embodiment, the second calculation model is determined based on the category information; and calculating the index data in the index data set based on the second calculation model, and determining the reference data.

In an alternative embodiment, at least one index mapping coefficient is determined based on at least one index data in the index data set; determining a category mapping coefficient based on the category information; and multiplying the index mapping coefficient and the category mapping coefficient to determine a coefficient.

In an alternative embodiment, the index mapping coefficient is determined based on market information.

In an alternative embodiment, the quota data is a minimum value, a maximum value or a value interval.

Accordingly, an embodiment of the present application provides a data determining apparatus, including: the category information determining module is used for determining category information; the category information is category information in a category information set corresponding to the object and is used for representing a service scene corresponding to the data; the user identification determining module is used for determining the user identification of the object; the index data set acquisition module is used for acquiring an index data set based on the category information and the user identification; the benchmark data determining module is used for determining benchmark data based on the index data set and/or the category information; a coefficient determination module for determining a coefficient based on the set of metric data and/or the category information; the quota data determining module is used for determining quota data based on the category information; and the capacity evaluation data determining module is used for determining the capacity evaluation data of the object under the category information based on the reference data, the coefficient and the quota data.

In an alternative embodiment, the capability assessment data determination module is configured to determine the first computational model based on the category information; based on the first calculation model, the reference data, the coefficients and the quota are calculated to determine capability assessment data of the subject.

In an optional embodiment, the index data set includes index data, and the index data is used for representing the statistical result of the object in a single statistical dimension or the scoring result in a single evaluation dimension; the index data set acquisition module is used for determining at least one index database to be inquired based on the category information; and acquiring an index data set from at least one index database based on the user identification.

In an alternative embodiment, the reference data determination module is configured to determine the second calculation model based on the category information; and calculating the index data in the index data set based on the second calculation model, and determining the reference data.

In an alternative embodiment, the coefficient determination module is configured to determine at least one metric mapping coefficient based on at least one metric data in the set of metric data; determining a category mapping coefficient based on the category information; and multiplying the index mapping coefficient and the category mapping coefficient to determine a coefficient.

In an alternative embodiment, the coefficient determination module is configured to determine the index mapping coefficient based on market information.

Accordingly, an embodiment of the present disclosure provides an electronic device, which includes a processor and a memory, where at least one instruction, at least one program, a code set, or a set of instructions is stored in the memory, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the data determination method.

Accordingly, an embodiment of the present disclosure provides a computer-readable storage medium, wherein at least one instruction, at least one program, a code set, or a set of instructions is stored in the storage medium, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the data determination method.

The embodiment of the application has the following beneficial effects:

(1) can adapt to various service fields and scenes;

(2) based on three sub-result values: the reference data, the coefficients and the quota data are used for obtaining data results of multiple categories, and the calculation logic is simple and efficient;

(3) the computation logic and coefficients can be flexibly adjusted.

Drawings

In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a schematic view of an application scenario of a data determination method provided in an embodiment of the present application;

fig. 2 is a schematic flowchart of a data determination method provided in an embodiment of the present application;

fig. 3 is a schematic structural diagram of a data determination apparatus according to an embodiment of the present application;

fig. 4 is a block diagram of a hardware structure of a server of a data determination method according to an embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings. It should be apparent that the described embodiment is only one embodiment of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

An "embodiment" as referred to herein relates to a particular feature, structure, or characteristic that may be included in at least one implementation of the present application. In the description of the embodiments of the present application, it should be understood that the terms "upper", "lower", "left", "right", "top", "bottom", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only used for convenience in describing the present application and simplifying the description, and do not indicate or imply that the devices/systems or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be taken as limiting the present application. The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. Moreover, the terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein. Furthermore, the terms "comprises," "comprising," and "having"/"is," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system/apparatus, article, or apparatus that comprises a list of steps or elements/modules is not necessarily limited to those steps or elements/modules expressly listed, but may include other steps or elements/modules not expressly listed or inherent to such process, method, article, or apparatus.

A specific embodiment of a data determination method provided by the present application is described below. Referring to fig. 1, fig. 1 is a schematic view of an application scenario of a data determination method according to an embodiment of the present application. As shown in fig. 1, includes a server 101 and a terminal 102. Alternatively, the server 101 and the terminal 102 may be connected through a wireless link or a wired link, which is not limited in this disclosure.

In an alternative embodiment, the server 101 may be used for data determination, and obtain capability evaluation data of the object under the category information. Specifically, the server 101 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and artificial intelligence platform, and the like. Alternatively, the operating system running on the server 101 may include, but is not limited to, an IOS, Linux, Windows, Unix, Android system, and the like.

In an alternative embodiment, the terminal 102 may assist the server 101 to obtain the capability evaluation data of the object under the category information. The terminal 102 may be used to alter the computational model or coefficients in the data determination. The terminal 102 may be a computational model or coefficients for sending the computational model or coefficients for data determination to the server 101. Alternatively, after the server 101 determines the data, the capability evaluation data of the determined object under the category information may be sent back to the terminal 102 for displaying on the terminal 102. In particular, the terminal 102 may include, but is not limited to, a smart phone, a desktop computer, a tablet computer, a laptop computer, a smart speaker, a digital assistant, an Augmented Reality (AR)/Virtual Reality (VR) device, a smart wearable device, and other types of electronic devices. Optionally, the operating system running on the electronic device may include, but is not limited to, an android system, an IOS system, linux, windows, and the like.

In addition, it should be noted that fig. 1 is only one application environment of the data determination method provided by the present disclosure, and in practical applications, other application environments may also be included, for example, obtaining capability evaluation data of an object under category information may also be implemented on the terminal 102.

Fig. 2 is a flow chart of a data determination method provided in an embodiment of the present application, and the present specification provides the method operation steps as shown in the embodiment or the flow chart, but more or less operation steps can be included based on conventional or non-inventive labor. The order of steps recited in the embodiments is only one of many possible orders of execution and does not represent the only order of execution, and in actual execution, the steps may be performed sequentially or in parallel as in the embodiments or methods shown in the figures (e.g., in the context of parallel processors or multi-threaded processing). Specifically, as shown in fig. 2, the method includes:

s201: category information is determined.

In this embodiment of the present application, the category information may be category information in a category information set corresponding to the object, and is used to characterize a service scenario corresponding to the data. Optionally, the category information may include any one of a credit score, a risk limit, a credit line, a credit rating, and a recommendation rating.

In the embodiment of the application, the data determined based on different category information can be adapted to various service fields and scenes.

S202: a user identification of the object is determined.

Alternatively, the object may be any one of an enterprise user, a natural human user, and an unnatural human user. Alternatively, the object may be any one of an inland individual user, an inland enterprise user, a hong kong individual user, and a hong kong enterprise user.

Alternatively, the user identifier may include any one of an enterprise registration number, a personal identification number, a user account number, a user tax identification number, and a user identification serial number.

S203: and acquiring an index data set based on the category information and the user identification.

According to some embodiments, the set of metric data may include one or more metric data characterizing a statistical result of the object in a single statistical dimension or a scoring result in a single evaluation dimension. Alternatively, the index data may include any one of sales of a subject for a specific period of time, the number of premium stores, user ratings, rating rates, credit status, user academic literacy, user income movement, and user liability situation.

In an alternative embodiment, the big data storage device may include a plurality of index databases, and each index database may correspond to different category information. In this way, when the index data set is to be determined, at least one index database to be queried may be determined from the plurality of index databases based on the category information, the index data may be determined from the at least one index database based on the user identifier, and the index data set may be obtained according to the determined index data.

How to obtain the index data set based on the category information and the user identifier is specifically described below by an optional embodiment. If the category information is credit limit, it may be determined that at least one index database to be queried includes a sales database of a last month, an average monthly sales database of a last twelve months, a user rating database, and a high-quality store number database, based on the user identifier, an index data set of an object corresponding to the user identifier may be obtained from the databases, and the index data in the index data set may include sales of a last month, average monthly sales of a last twelve months, a user rating, and a high-quality store number.

S204: based on the set of metric data and/or the category information, the baseline data is determined.

In an alternative embodiment, the reference data may be constant. Alternatively, the server may directly determine the reference data according to the index data set, in other words, if the index data in the index data sets of the two objects are the same, the reference data corresponding to the two objects may also be the same. Alternatively, the server may directly determine the reference data according to the category information, in other words, if the category information of the two objects is the same, the reference data corresponding to the two objects may also be the same. Alternatively, the server may determine the reference data according to the index data set and the category information, in other words, if the index data in the index data sets of the two objects and the category information of the two objects are the same, the reference data corresponding to the two objects may also be the same.

In another alternative embodiment, the server may determine a second calculation model based on the category information, calculate the index data in the index data set based on the second calculation model, and determine the reference data.

Optionally, the second calculation model may be preset according to different category information, and the second calculation model may be flexibly adjusted.

Optionally, if the category information is a credit limit, the sales amount in the last month and the monthly average sales amount in the last twelve months corresponding to the object may be calculated based on the second calculation model, so as to determine the reference data.

S205: based on the set of metric data and/or the category information, a coefficient is determined.

In an optional embodiment, at least one index mapping coefficient is determined based on at least one index data in the index data set, and a category mapping coefficient is determined based on the category information; and multiplying the index mapping coefficient and the category mapping coefficient to determine a coefficient.

In an alternative embodiment, the coefficients may be constant. Alternatively, the server may directly determine the reference data according to the index data set, in other words, if the index data in the index data sets of the two objects are the same, the coefficients corresponding to the two objects may also be the same. Alternatively, the server may directly determine the coefficients according to the category information, in other words, if the category information of the two objects is the same, the coefficients corresponding to the two objects may also be the same. Alternatively, the server may determine the coefficients according to the index data sets and the category information, in other words, if the index data in the index data sets of the two objects and the category information of the two objects are the same, the coefficients corresponding to the two objects may also be the same.

According to some embodiments, the at least one metric data may include a user score, a number of premium stores, and the at least one metric mapping coefficient may include a first metric mapping coefficient and a second metric mapping coefficient.

Optionally, the scoring interval to which the user score belongs may be determined based on the user score, and the first index mapping coefficient may be determined according to a preset scoring mapping rule. Optionally, the preset score mapping rule may include a one-to-one correspondence rule between score intervals and first index mapping coefficients, where different score intervals correspond to different first index mapping coefficients. Based on the scoring interval to which the user score belongs, the first index mapping coefficient may be determined to be a coefficient corresponding to the scoring interval.

Optionally, the number interval to which the number of high-quality shops belongs may be determined based on the number of high-quality shops, and the second index mapping coefficient may be determined according to a preset number mapping rule. Optionally, the preset number mapping rule may include a one-to-one correspondence rule between number intervals and second index mapping coefficients, where different number intervals correspond to different second index mapping coefficients. Based on the number section to which the number of premium stores belongs, the second index mapping coefficient may be determined as a coefficient corresponding to the number section.

Optionally, based on the category information, the category mapping coefficient may be determined according to a preset category mapping rule. The preset category mapping rule may include a one-to-one correspondence rule of category information and category mapping coefficients, and different category information corresponds to different category mapping coefficients. Based on the category information, the category mapping coefficient may be determined to be a coefficient corresponding to the category information.

According to some embodiments, the index mapping coefficient, the category mapping coefficient, and the manual intervention coefficient are multiplied to determine a coefficient. Optionally, the manual intervention coefficients may be input in real time.

S206: quota data is determined based on the category information.

In the embodiments of the present application, the quota data may be a minimum value, a maximum value, or a numerical range.

Optionally, based on the category information, the quota data may be determined according to a preset quota mapping rule. The preset quota mapping rule may include a one-to-one correspondence rule of category information and quota data. If the category information is a credit line, it can be determined that the limit data is credit limit data corresponding to the credit line, for example, 100 ten thousand.

According to some embodiments, the quota data may be determined based on the category information and the user identification. Optionally, if the category information is a credit line and the user identifier is a natural person user, it may be determined that the quota data is 20 ten thousands; if the category information is a credit line and the subscriber is identified as an enterprise subscriber, it may be determined that the quota data is 100 ten thousand.

S207: based on the reference data, the coefficients and the quota data, capability assessment data of the object under the category information is determined.

According to some embodiments, a first computational model may be determined based on the category information; based on the first calculation model, the reference data, the coefficients and the quota are calculated to determine capability assessment data of the subject. Alternatively, the first computational model may be flexibly adjusted.

Optionally, if the category information is a credit limit, it may be determined that the first calculation model is capability evaluation data = min [ reference data x coefficient, limit ], and the reference data, the coefficient, and the limit are calculated according to the calculation model to determine the capability evaluation data of the object.

In the present embodiment, based on three sub-result values: the reference data, the coefficient and the quota data can be obtained by adjusting the calculation logic, so that the data results of multiple categories can be obtained, and the calculation logic is simple and efficient.

Steps S201-S207 will be further described based on three alternative embodiments.

The first method comprises the following steps:

the category information is determined to be a credit limit in step S201, and the user identification of the object is determined to be a business user in step S202, after which execution of steps S203-S207 is continued.

Step S203 is executed to acquire an index data set based on the category information and the user identifier.

Based on the credit limit of the category information, determining that at least one index database to be inquired comprises a sales database in the last month, an average monthly sales database in the last twelve months, a user rating database and a high-quality shop number database; based on the user identification, the sales of the object in the last month, the average sales of the object in the last twelve months, the user score and the number of high-quality shops can be obtained from the database to serve as an index data set.

Step S204 is performed to determine the reference data based on the index data set and/or the category information.

Based on the category information being a credit line, the second calculation model may be determined to be max [ sales last month, monthly average sales last twelve months ], or a weighted sum of sales last month and monthly average sales last twelve months, where the sum of the weighting coefficients is 1. Based on the second calculation model, the sales of the object in the last month and the average sales of the object in the last twelve months can be calculated to determine the reference data.

Step S205 is performed to determine the coefficients based on the index data set and/or the category information.

Based on the user scores in the index data set, the scoring interval to which the user scores belong can be judged, and the first index mapping coefficient is determined according to a preset scoring mapping rule. The preset score mapping rule may be four score intervals, for example, [0,40 ], [40,60 ], [60,75) and [75,100), corresponding to coefficients of 0.5, 1.0, 1.1 and 1.2, respectively. The first index mapping coefficient may be determined to be a coefficient corresponding to a scoring interval to which the user score belongs, based on the scoring interval. It should be noted that the above-mentioned scoring interval is only one implementation, in other embodiments, the scoring interval may also be other optional value intervals, the coefficient may also be other values, and the mapping rule may also include other corresponding relationships, such as other functions.

And judging the number interval to which the number of the high-quality shops belongs based on the number of the high-quality shops, and determining a second index mapping coefficient according to a preset number mapping rule. The preset number mapping rule may be a number interval [2, + ∞ ] of the number of premium stores, 1, 0, corresponding to coefficients of 1.2, 1.1, 1.0, respectively. The second index mapping coefficient may be determined to be a coefficient corresponding to the number section based on the number section to which the number of premium stores belongs. It should be noted that the above scoring interval is only one implementation manner, in other embodiments, the number interval may also be other optional value intervals, the coefficient may also be other values, and the mapping rule may also include other corresponding relationships, such as other functions.

The optimistic degree of the market can be judged based on market quotation information, such as market bargaining conditions, and a third index mapping coefficient is determined according to a preset market mapping rule. The preset market mapping rule may be that a number of intervals of the total market volume, e.g., [200 ten thousand, + ∞), [100 ten thousand, 200 ten thousand, and [0,100 ten thousand), correspond to optimistic, neutral, and pessimistic, respectively, and also to coefficients of 1.2, 1.0, and 0.8, respectively. The third index mapping coefficient may be determined to be a coefficient corresponding to the numerical value interval based on the numerical value interval to which the market transaction total amount belongs. It should be noted that the above value interval is only one implementation manner, in other embodiments, the value interval may also be other optional value intervals, the coefficient may also be other values, and the mapping rule may also include other corresponding relationships, such as other functions.

Based on the category information being a credit line, the category mapping coefficient may be determined to be a coefficient corresponding to the credit line.

The index mapping coefficient, the category mapping coefficient, and the manual intervention coefficient may be multiplied to determine a coefficient. Optionally, at least one of the index mapping coefficient, the category mapping coefficient, and the manual intervention coefficient may be multiplied to obtain a coefficient. The index mapping coefficient may include at least one sub-coefficient, such as at least one of the first index mapping coefficient, the second index mapping coefficient, and the third index mapping coefficient described above.

Step S206 is performed: quota data is determined based on the category information.

Based on the category information being the credit line, the limit data may be determined to be credit limit data corresponding to the credit line, for example, 100 ten thousand. According to some embodiments, the quota data may be determined based on the category information and the user identification. Optionally, if the category information is a credit line and the user identifier is a natural person user, it may be determined that the quota data is 20 ten thousands; if the category information is a credit line and the subscriber is identified as an enterprise subscriber, it may be determined that the quota data is 100 ten thousand. Optionally, if the limit is not desired to be set, the limit data may be set to positive infinity or negative infinity as desired.

Based on the category information being credit, the first calculation model may be determined to be capability assessment data = min [ reference data x coefficient, limit ], and based on this calculation model, the reference data, coefficient and limit are calculated to determine the capability assessment data of the subject.

And the second method comprises the following steps:

the category information is determined to be the credit rating in step S201, and the user identification of the object is determined to be an individual user in step S202, after which execution of steps S203-S207 is continued.

If the category information is credit level, determining that at least one index database to be inquired comprises a user age database, a credit score database, a debt amount database and a credit duration database; based on the user identification, the user age, credit score, arrearage amount and credit duration corresponding to the object can be obtained from the database to serve as an index data set.

The second calculation model may be determined as a mapping function based on the category information as a credit rating, and the reference data may be determined based on an age group to which the age of the user belongs. Optionally, the reference data are 2, 3, 4, and 5, respectively, based on the age of the user, which belongs to teenagers, middle-aged people, and elderly people.

According to some embodiments, the user 'S historical credit rating may be obtained from the historical credit rating database in step S203, and the user' S recent almanac historical credit rating may be determined as reference data.

Based on the credit scores in the index data set, the scoring interval to which the credit scores belong can be judged, and the first index mapping coefficient is determined according to a preset scoring mapping rule. The preset score mapping rule may be four score intervals, for example, [0,60), [60,80), [80,90) and [90,100), corresponding to coefficients of 0.6, 0.8, 1 and 1.2, respectively. The first index mapping coefficient may be determined to be a coefficient corresponding to a score interval to which the credit score belongs, based on the score interval. It should be noted that the above-mentioned scoring interval is only one implementation, in other embodiments, the scoring interval may also be other optional value intervals, the coefficient may also be other values, and the mapping rule may also include other corresponding relationships, such as other functions.

And judging the amount interval to which the arrears amount belongs based on the arrears amount, and determining a second index mapping coefficient according to a preset amount mapping rule. The preset money mapping rule may be the money interval [10000, + ∞ ], (0,10000), 0 for the arrears, corresponding to coefficients of 0.5,0.8, 1.2, respectively. The second index mapping coefficient may be determined to be a coefficient corresponding to the amount interval based on the amount interval to which the owed amount belongs. It should be noted that the above-mentioned money amount interval is only one implementation, in other embodiments, the money amount interval may also be other optional value intervals, the coefficient may also be other values, and the mapping rule may also include other corresponding relationships, such as other functions.

And judging a time interval to which the credit time belongs based on the credit time, and determining a third index mapping coefficient according to a preset time mapping rule. The preset duration mapping rule may be a duration interval of a plurality of credit durations, for example, [2 years, + ∞), [1 year, 2 years), and [0,1 year), corresponding to coefficients of 1.5, 1.2, and 1, respectively. The third index mapping coefficient may be determined to be a coefficient corresponding to the duration interval based on the duration interval to which the credit duration belongs. It should be noted that the duration interval is only one implementation manner, in other embodiments, the duration interval may also be other optional value intervals, the coefficient may also be other values, and the mapping rule may also include other corresponding relationships, such as other functions.

Based on the category information being the credit rating, the category mapping coefficient may be determined to be the coefficient corresponding to the credit line.

Based on the category information being a credit rating, it may be determined that the limit data is credit limit data corresponding to the credit rating. Alternatively, the quota data may be a data interval, such as [1,9 ].

Step S207 is performed: based on the reference data, the coefficients and the quota data, capability assessment data of the object under the category information is determined.

Based on the category information being a credit rating, the first computational model may be determined to be capability assessment data = max [ min [ baseline data coefficient, quota interval maximum ], quota interval minimum ]. The first computational model may also be other optional computational models, for example, capability assessment data = max [ min [ baseline data + coefficient-1, limit interval maximum ], limit interval minimum ]. The reference data, coefficients and limits may be calculated from the calculation model to determine the capability assessment data for the object.

And the third is that:

the category information is determined to be a recommended level in step S201, and the user identification of the object is determined to be an enterprise user in step S202, after which execution of steps S203-S207 is continued. The recommendation level can be used for determining the recommendation order of the shop when the shop under the enterprise user flag is recommended.

Based on the category information as the recommendation level, determining that at least one index database to be queried comprises a store rating database, a store click rate database, a store sale database in the last month and a credit rating database; based on the user identification, the evaluation rate of the store, the click rate of the store and the sales of the store in the previous month corresponding to the object can be obtained from the database to be used as an index data set.

Based on the category information being the recommendation level, the reference data may be determined to be 5.

According to some embodiments, the store classification corresponding to the object may be acquired from the store classification database in step S203, and the reference data may be determined as the reference level corresponding to the store classification.

Based on the store goodness evaluation in the index data set, a goodness evaluation interval to which the store goodness evaluation belongs can be judged, and a first index mapping coefficient is determined according to a preset goodness evaluation mapping rule. The preset score mapping rule may be three score intervals, for example, [0,75%, [75%,95%, [95%,100], corresponding to coefficients of 0.6, 1, and 1.4, respectively. The first index mapping coefficient may be determined to be a coefficient corresponding to the rating on the basis of a rating interval to which the store rating belongs. It should be noted that the above-mentioned favorable rating interval is only one implementation manner, in other embodiments, the favorable rating interval may also be other optional value intervals, the coefficient may also be other values, and the mapping rule may also include other corresponding relationships, such as other functions.

Based on the shop click rate in the index data set, a goodness interval to which the shop click rate belongs can be judged, and a first index mapping coefficient is determined according to a preset goodness mapping rule. The preset score mapping rule may be three score intervals, for example, [0,5%, [5%,15%, [15%,100], corresponding to coefficients of 0.8, 1, and 1.2, respectively. The first index mapping coefficient may be determined to be a coefficient corresponding to the click rate based on the click rate section to which the store goodness-of-appraisal rate belongs. It should be noted that the above good rating interval is only one implementation manner, in other embodiments, the click rate interval may also be other optional value intervals, the coefficient may also be other values, and the mapping rule may also include other corresponding relationships, such as other functions.

And judging the money interval to which the sales of the shop in the last month belongs based on the sales of the shop in the last month, and determining a third index mapping coefficient according to a preset sales mapping rule. The preset sales mapping rule may be a plurality of sales monetary intervals, for example, [10 ten thousand, + ∞), [5 ten thousand, 10 ten thousand, and [0,5 ten thousand), corresponding to coefficients of 1.4, 1.2, and 1, respectively. The third index mapping coefficient may be determined to be a coefficient corresponding to the money amount interval based on the money amount interval to which the sales amount in the last month belongs. It should be noted that the above-mentioned money amount interval is only one implementation, in other embodiments, the money amount interval may also be other optional value intervals, the coefficient may also be other values, and the mapping rule may also include other corresponding relationships, such as other functions. Alternatively, the amount interval may be determined based on the store classification.

Based on the category information being the recommendation level, a coefficient corresponding to the category mapping coefficient being the recommendation level may be determined.

Based on the category information being the recommendation level, credit limit data corresponding to the recommendation level may be determined as the limit data. Alternatively, the quota data may be a data interval, such as [1,9 ].

Based on the category information being a recommendation rating, the first computational model may be determined to be capability assessment data = max [ min [ baseline data coefficient, quota interval maximum ], quota interval minimum ]. The first computational model may also be other optional computational models, for example, capability assessment data = max [ min [ baseline data + coefficient-2, limit interval maximum ], limit interval minimum ]. The reference data, coefficients and limits may be calculated from the calculation model to determine the capability assessment data for the object.

It should be noted that the above three alternative embodiments are only exemplary, and the application does not limit the optional specific calculation logic, coefficients, etc. in the alternative category information, user identification and data determination method. In the embodiment of the application, the capability evaluation data can be efficiently determined by determining the category information and the user identification based on the same computing frame and automatically adapting the mapping rule aiming at different service scenes. Moreover, according to some optional embodiments, the coefficient or the calculation logic can be modified manually in the data determination method of the present application, so as to achieve the effect of flexible adjustment.

An embodiment of the present application further provides a data determining apparatus, and fig. 3 is a schematic diagram of a data determining apparatus 300 provided in the embodiment of the present application, and as shown in fig. 3, the apparatus includes:

a category information determination module 301, configured to determine category information; the category information is category information in a category information set corresponding to the object and is used for representing a service scene corresponding to the data;

a user identifier determining module 302, configured to determine a user identifier of the object;

an index data set obtaining module 303, configured to obtain an index data set based on the category information and the user identifier;

a benchmark data determination module 304, configured to determine benchmark data based on the index data set and/or the category information;

a coefficient determination module 305 for determining a coefficient based on the set of metric data and/or the category information;

a quota data determining module 306 for determining quota data based on the category information;

and a capability evaluation data determination module 307 for determining capability evaluation data of the object under the category information based on the reference data, the coefficient and the quota data.

In an alternative embodiment, the capability assessment data determination module 307 is configured to determine the first computational model based on the category information; based on the first calculation model, the reference data, the coefficients and the quota are calculated to determine capability assessment data of the subject.

In an optional embodiment, the index data set includes index data, and the index data is used for representing the statistical result of the object in a single statistical dimension or the scoring result in a single evaluation dimension; the index data set obtaining module 303 is configured to determine at least one index database to be queried based on the category information; and acquiring an index data set from at least one index database based on the user identification.

In an alternative embodiment, the reference data determining module 304 is configured to determine the second computational model based on the category information; and calculating the index data in the index data set based on the second calculation model, and determining the reference data.

In an alternative embodiment, the coefficient determination module 305 is configured to determine at least one metric mapping coefficient based on at least one metric data in the set of metric data; determining a category mapping coefficient based on the category information; and multiplying the index mapping coefficient and the category mapping coefficient to determine a coefficient.

In an alternative embodiment, the coefficient determination module 305 is configured to determine the index mapping coefficient based on market information.

The apparatus in the embodiments of the present application is based on the same application concept as the method embodiments described above.

The embodiment of the application also provides electronic equipment which can be arranged in the server to store the data determination method used for realizing the method embodiment. The method provided by the embodiment of the application can be executed in a computer terminal, a server or a similar operation device. Taking the example of the application running on a server, fig. 4 is a hardware structure block diagram of the server of the data determination method provided in the embodiment of the present application. As shown in fig. 4, the server 400 may have a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 410 (the processors 410 may include but are not limited to a Processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 430 for storing data, and one or more storage media 420 (e.g., one or more mass storage devices) for storing applications 423 or data 422. Memory 430 and storage medium 420 may be, among other things, transient or persistent storage. The program stored on the storage medium 420 may include one or more modules, each of which may include a series of instruction operations on a server. Further, the central processor 410 may be configured to communicate with the storage medium 420, and execute a series of instruction operations in the storage medium 420 on the server 400. The server 400 may also include one or more power supplies 460, one or more wired or wireless network interfaces 450, one or more input-output interfaces 440, and/or one or more operating systems 421, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and so forth.

The input/output interface 440 may be used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the server 400. In one example, the input/output Interface 440 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the input/output interface 440 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

It will be understood by those skilled in the art that the structure shown in fig. 4 is only an illustration and is not intended to limit the structure of the electronic device. For example, server 400 may also include more or fewer components than shown in FIG. 4, or have a different configuration than shown in FIG. 4.

An embodiment of the present application further provides an electronic device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the data determination method.

The present application further provides a storage medium, which may be disposed in a server to store at least one instruction, at least one program, a code set, or a set of instructions related to implementing the data determination method in the method embodiment, where the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the data determination method.

Optionally, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, a storage medium including: various media that can store program codes, such as a usb disk, a Read-only Memory (ROM), a removable hard disk, a magnetic disk, or an optical disk.

In the present invention, unless otherwise expressly stated or limited, the terms "connected" and "connected" are to be construed broadly, e.g., as meaning either a fixed connection or a removable connection, or an integral part; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.

It should be noted that: the foregoing sequence of the embodiments of the present application is for description only and does not represent the superiority and inferiority of the embodiments, and the specific embodiments are described in the specification, and other embodiments are also within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in the order of execution in different embodiments and achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown or connected to enable the desired results to be achieved, and in some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment is described with emphasis on differences from other embodiments. In particular, for the embodiments of the apparatus/system, since they are based on embodiments similar to the method embodiments, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiments.

The foregoing is a preferred embodiment of the present invention, and it should be noted that it would be apparent to those skilled in the art that various modifications and enhancements can be made without departing from the principles of the invention, and such modifications and enhancements are also considered to be within the scope of the invention.

Claims

1. A method for determining data, comprising:

determining category information; the category information is category information in a category information set corresponding to the object and is used for representing a service scene corresponding to the data;

determining a user identification of the object;

acquiring an index data set based on the category information and the user identification;

determining reference data based on the set of metric data and/or the category information;

determining a coefficient based on the set of metric data and/or the category information;

determining quota data based on the category information;

determining capability assessment data of the subject under the category information based on the baseline data, the coefficients, and the quota data.

2. The data determination method of claim 1, wherein the determining capability assessment data of the subject under the category information based on the baseline data, the coefficients, and the quota data comprises:

determining a first computational model based on the category information;

calculating the benchmark data, the coefficients and the quota based on the first calculation model, and determining the capability assessment data of the object.

3. The data determination method of claim 1, wherein the category information includes one or more of a credit score, a risk limit, a credit line, a credit rating, a recommendation rating.

4. The data determination method of claim 1, wherein the set of metric data comprises metric data characterizing a statistical result of the object in a single statistical dimension or a scoring result in a single evaluation dimension,

the obtaining an index data set based on the category information and the user identifier includes:

determining at least one index database to query based on the category information;

based on the user identification, obtaining the index data set from the at least one index database.

5. The data determination method of claim 1, wherein determining the reference data based on the set of metric data and/or the category information comprises:

determining a second computational model based on the category information;

and calculating the index data in the index data set based on the second calculation model to determine reference data.

6. The data determination method of claim 1, wherein determining coefficients based on the set of metric data and/or the category information comprises:

determining at least one metric mapping coefficient based on at least one metric data in the set of metric data;

determining a category mapping coefficient based on the category information;

and multiplying the index mapping coefficient and the category mapping coefficient to determine a coefficient.

7. The data determination method of claim 6, wherein determining at least one metric mapping coefficient based on at least one metric data in the set of metric data comprises:

and determining an index mapping coefficient based on the market information.

8. The data determination method of claim 1, wherein the quota data is a minimum value, a maximum value, or a value interval.

9. A data determination apparatus, characterized in that the apparatus comprises:

the category information determining module is used for determining category information; the category information is category information in a category information set corresponding to the object and is used for representing a service scene corresponding to the data;

the user identification determining module is used for determining the user identification of the object;

the index data set acquisition module is used for acquiring an index data set based on the category information and the user identification;

a benchmark data determination module, configured to determine benchmark data based on the index data set and/or the category information;

a coefficient determination module for determining a coefficient based on the set of metric data and/or the category information;

a quota data determination module for determining quota data based on the category information;

a capability assessment data determination module for determining capability assessment data of the object under the category information based on the benchmark data, the coefficient and the quota data.

10. An electronic device, comprising a processor and a memory, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the memory, and wherein the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the data determination method according to any one of claims 1 to 8.

11. A computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the data determination method according to any one of claims 1 to 8.

12. A computer program product, characterized in that the computer program product comprises a computer program, which is stored in a readable storage medium, from which at least one processor of a computer device reads and executes the computer program, causing the computer device to perform the data determination method according to any one of claims 1-8.