CN115311071A

CN115311071A - Multi-product composite limit management method, device, equipment and storage medium

Info

Publication number: CN115311071A
Application number: CN202210951577.5A
Authority: CN
Inventors: 冯立阳
Original assignee: Ping An Consumer Finance Co Ltd
Current assignee: Ping An Consumer Finance Co Ltd
Priority date: 2022-08-09
Filing date: 2022-08-09
Publication date: 2022-11-08

Abstract

The invention relates to an artificial intelligence technology, and discloses a multi-product composite limit management method, which comprises the following steps: extracting user information characteristics from user historical information; identifying the quota type and quota mode of the service request; calculating credit scores of various limit types according to the user information characteristics; selecting a preset limit calculation model to construct a limit tree according to a debit and credit APP related by a user, products related by the user, limit types, limit modes and credit scores; traversing the limit tree according to the total limit, the used limit and the used limit type to obtain the balance of each node of the limit tree; and comparing the balance of each node with the service request, and executing the service request according to the comparison result. In addition, the invention also relates to a block chain technology, and the credit tree can be stored in the node of the block chain. The invention also provides a multi-product composite limit management device, electronic equipment and a storage medium. The invention can improve the accuracy of managing the multi-product quota.

Description

Multi-product composite limit management method, device, equipment and storage medium

Technical Field

The invention relates to the technical field of artificial intelligence, in particular to a multi-product composite limit management method and device, electronic equipment and a computer readable storage medium.

Background

With the development of the consumption financial market, each loan APP and the APP corresponding to the bank provide consumption loan services, and a user can pay money after consuming the money and perform line management according to the consumption loan services.

In the existing quota management method in the industry, due to the product form, the APPs are relatively independent, the provided service generally only acts on quota management of a user under the product of the user, and quota calculation of the user only needs to control concurrence of quota use and recovery, and generally realizes optimistic lock (CAS) and newly added logic relationship.

However, for the same user corresponding to multiple APPs and multiple products, because the total limit of the user has upper limit control and each APP allocates the total limit of the same user, when the total limit of the user changes, the current limit management method is not linked strongly, so that the limit of each product cannot be adjusted accurately according to the total limit of the user, and when special limits (such as temporary limits and active lottery tickets) occur, the special limits can only be realized through additional business logic, so that the business function is unnecessarily and transversely expanded, and the subsequent maintenance is difficult.

Disclosure of Invention

The invention provides a multi-product composite credit management method, a device and a computer readable storage medium, and mainly aims to solve the problem of low multi-product credit management accuracy.

In order to achieve the above object, the present invention provides a multi-product composite quota management method, which comprises:

receiving a service request of a user, acquiring user history information from a preset platform according to the service request, and extracting user information characteristics from the user history information;

identifying the quota type and quota mode of the service request;

calculating credit scores of all limit types of the user according to the user information characteristics;

analyzing a user related loan APP and a user related product from the user historical information, and selecting a preset quota calculation model to construct a quota tree according to the user related loan APP, the user related product, the quota type, the quota mode and the credit score;

acquiring the current total limit of the user, the used limit and the used limit type of the related product, and traversing the limit tree according to the total limit, the used limit and the used limit type to obtain the balance of each node of the limit tree;

and comparing the balance of each node with the service request, and executing the service request according to the comparison result.

Optionally, the calculating credit scores of various quota types of the user according to the user information characteristics includes:

acquiring all historical users, screening associated users of the user information characteristics from the historical users, and acquiring historical information of the associated users;

constructing a user knowledge graph according to the user information characteristics and the associated user historical information characteristics;

and analyzing the user knowledge graph by using a pre-constructed graph learning model to obtain credit scores of all limit types of the user.

Optionally, the selecting a preset credit calculation model to construct a credit tree according to the user related to loan APP, the user related to the product, the credit type, the credit model, and the credit score includes:

selecting a preset quota calculation model according to the quota mode, selecting different tree structures according to different preset quota calculation models, and taking the user preset total quota as a root node of a quota tree according to the tree structures;

taking the user related loan APP and the user related product as primary child nodes of a quota tree according to the tree structure;

according to the tree structure, the quota type in the loan-related APP of the user is used as a second-level child node, and the credit score is used as the weight of the corresponding second-level child node;

and taking the transaction type of the secondary child node as a tertiary child node according to the tree structure to obtain the credit trees corresponding to different credit modes.

Optionally, the selecting a preset quota calculation model according to the quota mode includes:

judging whether the quota type has a special quota type;

when the type of the quota has a special quota type, the quota mode directly selects a preset quota calculation model;

and when the type of the quota does not have a special quota type, selecting a preset quota calculation model according to the quota mode.

Optionally, before the selecting the preset credit calculation model according to the credit mode, the method further includes:

determining the quota coefficient of the preset quota calculation model according to the quota mode;

determining a first business logic relationship between each quota type and the total quota of the user;

determining a second business logic relation between the available quota of each quota type and the used quota;

and constructing a preset quota calculation model according to the quota coefficient, the first service logic relationship and the second service logic relationship.

Optionally, the comparing the balance of each node with the service request, and executing the service request according to a comparison result includes:

comparing the balance of each node with the transaction amount in the service request;

when the balance of each node is not less than the transaction amount, the balance of each node meets a service request, and a product list and a limit type list meeting the service request are displayed;

and when the balance of each node is less than the transaction amount, rejecting the service request.

Optionally, the extracting the user information feature from the user history information includes:

performing word segmentation and part-of-speech tagging on the historical user information to obtain word segmentation and part-of-speech tagging results;

extracting nouns and noun phrases in the participles according to the results of the participles and part-of-speech tagging, counting to obtain a user historical information characteristic frequency set according to the nouns and the noun phrases, and generating a frequent pattern tree according to the user historical information characteristic frequency set;

identifying features in the frequent pattern tree to obtain a candidate user historical information feature set;

and calculating the mutual point information value of each characteristic in the candidate user historical information characteristic set, and filtering out the user historical information characteristics with the mutual point information value smaller than a preset standard threshold value from the candidate user historical information characteristic set to obtain the user historical information characteristics.

In order to solve the above problems, the present invention further provides a device for managing a composite amount of multiple products, the device comprising:

the system comprises a characteristic extraction module, a service request acquisition module and a service processing module, wherein the characteristic extraction module is used for receiving a service request of a user, acquiring user history information from a preset platform according to the service request and extracting user information characteristics from the user history information;

the type and mode identification module is used for identifying the quota type and quota mode of the service request;

the credit score calculating module is used for calculating the credit scores of all the quota types of the user according to the user information characteristics;

the credit line tree construction module is used for analyzing a user loan-related APP and a user-related product from the user historical information, and selecting a preset credit line calculation model to construct a credit line tree according to the user loan-related APP, the user-related product, the credit line type, the credit line mode and the credit score;

the execution module is used for acquiring the current total limit of the user, the used limit and the used limit type of the related product, and traversing the limit tree according to the total limit, the used limit and the used limit type to obtain the balance of each node of the limit tree; and comparing the balance of each node with the service request, and executing the service request according to the comparison result.

In order to solve the above problem, the present invention also provides an electronic device, including:

at least one processor; and (c) a second step of,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program which can be executed by the at least one processor, and the computer program is executed by the at least one processor so as to enable the at least one processor to execute the multi-product composite credit management method.

In order to solve the above problem, the present invention further provides a computer-readable storage medium, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is executed by a processor in an electronic device to implement the multi-product composite quota managing method.

The embodiment of the invention receives a service request of a user, acquires user history information from a preset platform according to the service request, and extracts user information characteristics from the user history information; identifying the quota type and quota mode of the service request; calculating credit scores of various quota types of the user according to the user information characteristics, so that quota risk control is facilitated when quota allocation is performed, and quota accuracy is improved; analyzing a user related loan APP and a user related product from the user historical information, selecting a preset quota calculation model to construct a quota tree according to the user related loan APP, the user related product, the quota type, the quota mode and the credit score, combining the credit score of each quota type with the quota tree, better distributing the quota of each quota type, accurately controlling the quota of each quota type, controlling loan risk, further selecting different quota calculation models to construct the quota tree according to different quota modes, meeting the requirements of different users, and accurately realizing quota control of corresponding products according to corresponding scenes; traversing the quota tree according to the total quota, the used quota and the used quota type to obtain the balance of each node of the quota tree, clearly displaying the balance condition of each node, and facilitating the subsequent iterative optimization of the quota calculation model; and comparing the balance of each node with the service request, and executing the service request according to the comparison result, so that the separate management of the credit lines of the same user under different products can be realized, and the accurate management and control of the total credit line of the user can be realized under the same account system. Therefore, the multi-product composite credit management method, the multi-product composite credit management device, the electronic equipment and the computer readable storage medium provided by the invention can solve the problem of low accuracy of multi-product credit management.

Drawings

FIG. 1 is a schematic flow chart illustrating a multi-product composite quota management method according to an embodiment of the present invention;

FIG. 2 is a schematic diagram illustrating a detailed implementation process of one step in the method for managing multiple product composite credits shown in FIG. 1;

FIG. 3 is a flowchart illustrating another step of the method for managing multiple product quota of FIG. 1;

FIG. 4 is a functional block diagram of a multi-product composite credit management device according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device for implementing the multi-product compound credit management method according to an embodiment of the present invention.

The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.

The embodiment of the application provides a multi-product composite limit management method. The executing subject of the multi-product composite line management method includes but is not limited to at least one of the electronic devices that can be configured to execute the method provided by the embodiment of the application, such as a server, a terminal and the like. In other words, the multi-product composite credit management method can be executed by software or hardware installed in the terminal device or the server device, and the software can be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides 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 Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.

Referring to fig. 1, a flow chart of a multi-product composite quota management method according to an embodiment of the present invention is shown. In this embodiment, the method for managing multiple product compound lines includes:

s1, receiving a service request of a user, acquiring user history information from a preset platform according to the service request, and extracting user information characteristics from the user history information.

In the embodiment of the invention, the service request is a loan service request of consumption loan service in each loan APP and the APP corresponding to the bank, and is consumed and withdrawn by a main user.

The historical user information in the embodiment of the invention comprises basic information, behavior data, operator data, network public information and the like of the user, and the main sources are credit investigation platforms, ordinary Hui Xiaoe credit platforms, banks, social media and the like.

In detail, the extracting of the user information features from the user history information in S1 includes:

identifying the characteristics in the frequent pattern tree to obtain a candidate user historical information characteristic set;

In the embodiment of the invention, the frequent pattern tree is decomposed into a plurality of conditional frequent pattern trees, each conditional frequent pattern tree is subjected to frequent pattern mining, and the user historical information characteristics which are lower than the preset frequency in each conditional frequent pattern tree are filtered out, so that the candidate user historical information characteristic set is obtained.

Further, the higher the point mutual information value in the candidate user historical information feature set is, the higher the historical user and feature association degree is, and when the point mutual information value is lower than a preset standard threshold, the corresponding user historical information feature is filtered; and when the mutual point information value is higher than a preset standard threshold value, retaining corresponding user historical information characteristics, and combining to obtain the user historical information characteristics.

S2, identifying the credit type and the credit mode of the service request.

In the embodiment of the invention, the quota modes are divided into modes of including, mutual exclusion, public use, preferential use and the like; the amount types include use, cash-out, consumption, red envelope, etc.

And S3, calculating credit scores of all limit types of the user according to the user information characteristics.

In detail, referring to fig. 2, the S3 includes:

s31, acquiring all historical users, screening the associated users of the user information characteristics from the historical users, and acquiring the historical information of the associated users;

s32, constructing a user knowledge graph according to the user information characteristics and the associated user historical information characteristics;

and S33, analyzing the user knowledge graph by using a pre-constructed graph learning model to obtain credit scores of all limit types of the user.

In the embodiment of the invention, the user information characteristics are descriptions of the basic attributes, information requirements, information behaviors, psychological states, environmental influences and the like of the user, and are mainly divided into dimensional attributes such as identity traits, performance capability, credit history, interpersonal relationships, behavior preference and the like, and the final model predicts the credit score of the corresponding user according to each piece of dimensional information.

In the embodiment of the present invention, the associated user is a user having a certain similarity with the user store, and the historical user corresponding to the historical user information feature meeting the requirement of the similarity can be screened as the associated user by extracting the historical user information feature of the historical user information and by the similarity between the historical user information feature and the user information feature.

In an embodiment of the present invention, the pre-constructed graph learning model may be an R-GCN (relational graph convolution network) model, and a graph convolution network is formed by a plurality of convolution layers. For example: the network comprises a plurality of convolutional layers to form a Squeezenet network, wherein the Squeezenet network is a lightweight network and has more efficient computing capability than a common network.

And S4, analyzing a user loan related APP and a user related product from the user historical information, and selecting a preset limit calculation model to construct a limit tree according to the user loan related APP, the user related product, the limit type, the limit mode and the credit score.

In detail, referring to fig. 3, in S4, selecting a preset credit calculation model to construct a credit tree according to the user related loan APP, the user related product, the credit type, the credit model, and the credit score includes:

s41, selecting a preset credit calculation model according to the credit mode, selecting different tree structures according to different preset credit calculation models, and taking the user preset total credit as a root node of a credit tree according to the tree structures;

s42, taking the loan borrowing APP related to the user and the product related to the user as primary child nodes of a quota tree according to the tree structure;

s43, according to the tree structure, taking the quota type in the loan-related APP of the user as a second-level child node, and taking the credit score as the weight of the corresponding second-level child node;

and S44, taking the transaction type of the second-level child node as a third-level child node according to the tree structure to obtain the credit trees corresponding to different credit modes.

In the embodiment of the invention, the transaction types comprise non-stage consumption and stage consumption.

In the embodiment of the invention, the limit tree is preset in a bank system and can be identified by marking each root node with a user identity.

In detail, before the selecting the preset credit calculation model according to the credit mode in S41, the method further includes:

In one embodiment of the present invention, when the credit type is public and the credit type is withdrawal and consumption, the credit coefficient is 100%, and the withdrawal credit of the user = the consumption credit of the user = the total credit of the user 100%; the total user limit = the user withdrawal use limit + the user consumption limit; the user cash withdrawal available amount = (user cash withdrawal amount-user cash withdrawal using amount) · min (user total amount-user total using amount); the user consumption available line = (the user consumption line-the user consumption use line) · min (the user total line-the user total use line).

In one embodiment of the present invention, when the credit types are mutually exclusive and the credit types are withdrawal and consumption types, the credit coefficient is 50%, and the withdrawal credit of the user = the user consumption credit = the total user credit 50%; the user cash withdrawal available amount = the user cash withdrawal amount-the user cash withdrawal use amount; the user consumption available line = the user consumption line-the user consumption usage line.

In one embodiment of the present invention, when the credit type is inclusion and the credit type is withdrawal and consumption, the credit coefficient is 100%, and the user consumption credit = the user total credit; the user's withdrawal limit = user consumption limit 50%, the user's total usage limit = user withdrawal usage limit + user consumption usage limit; the user cash withdrawal available amount = (the user cash withdrawal amount-the user cash withdrawal using amount) · min (the total user consumption amount-the user total using amount); the user consumption available line = user consumption line-user total usage line.

Further, the selecting a preset quota calculation model according to the quota mode in S41 includes:

judging whether the quota type has a special quota type;

and when the quota type does not have a special quota type, selecting a preset quota calculation model according to the quota mode.

In the embodiment of the invention, the special quota type comprises a red packet, a lottery ticket, a point and the like.

In the embodiment of the invention, the preset quota calculation model is a quota calculation model corresponding to a preferred use or public mode.

In one embodiment of the invention, when the special quota type such as red envelope, lottery ticket and integral is included, a quota calculation model corresponding to a public mode is selected, and the rated coefficient is 100%; the user's withdrawal limit = user consumption limit = user total limit 100%; the total user limit = user current use limit + user consumption use limit; the user cash-withdrawal available amount = (user cash-withdrawal amount + red envelope amount-user cash-withdrawal use amount) · min (user total amount + red envelope amount-user total use amount); the user consumption available line = (the user consumption line + the red packet line-the user consumption use line) · min (the user total line + the red packet line-the user total use line).

In the embodiment of the invention, when special limit types such as red packets, lottery tickets, points and the like are involved, the lottery ticket limit is preferentially used for deducting money, a user shows the logic of preferential deduction of the red packet limit in the red packet limit and the consumption limit, and the user does not participate in the calculation of the red packet limit because the user does not involve the red packets; the red envelope amount is the most accumulated amount, and the usage scene is not considered, so that the red envelope amount is used as a one-time additional amount.

In the embodiment of the invention, different credit calculation models are selected according to different credit modes to construct the credit tree, so that the requirements of different users can be met, the credit management and control of corresponding products can be accurately realized according to corresponding scenes, furthermore, the credit scores of all credit types are combined with the credit tree, the credit of all credit types is better distributed, the credit of all credit types is accurately managed and controlled, and the loan risk is controlled.

S5, obtaining the current total limit of the user, the used limit and the used limit type of the related product, and traversing the limit tree according to the total limit, the used limit and the used limit type to obtain the balance of each node of the limit tree.

In the embodiment of the invention, the current total limit of the user is the integration of the balance of a plurality of accounts under the name of the user, no transaction money is carried out on the user, and the total limit is the credit amount granted to the user by a bank.

In the embodiment of the invention, for example, the current user relates to 2 loan APPs (A, B), a relates to two products (A1 and A2), B relates to one product (B1), and due to the control limitation, the current total limit of the user is 10w yuan, when the user trusts and uses 3w yuan under the product A1 and 5w yuan under the product A2, the user finally trusts 2w under the product B1 at most.

In another embodiment of the present invention, for example, the current user relates to 2 loan APPs (A, B), a relates to two products (A1, A2), B relates to one product (B1), and due to the control limitation, the current total limit of the user is 10w yuan, when the user trusts and uses 3w yuan under the A1 product and 5w yuan under the A2 product, the user initiates a balance of 4w on the A1 product, and finally the user trusts 1w at most under the B1 product.

In the embodiment of the invention, the quota of each node can be obtained by traversing the quota tree, so that a manager can know and use the quota calculation model more clearly, and the subsequent iterative optimization of the quota calculation model is facilitated.

S6, comparing the balance of each node with the service request, and executing the service request according to the comparison result.

In detail, the S6 includes:

In the embodiment of the invention, the user can select the corresponding product to carry out transaction operation according to the displayed product list and the quota type list which meet the service request.

The embodiment of the invention receives a service request of a user, acquires user history information from a preset platform according to the service request, and extracts user information characteristics from the user history information; identifying the quota type and quota mode of the service request; calculating credit scores of various quota types of the user according to the user information characteristics, so that quota risk control is facilitated when quota allocation is performed, and quota accuracy is improved; analyzing a user related loan APP and a user related product from the user historical information, selecting a preset quota calculation model to construct a quota tree according to the user related loan APP, the user related product, the quota type, the quota mode and the credit score, combining the credit score of each quota type with the quota tree, better distributing the quota of each quota type, accurately controlling the quota of each quota type, controlling loan risk, further selecting different quota calculation models to construct the quota tree according to different quota modes, meeting the requirements of different users, and accurately realizing quota control of corresponding products according to corresponding scenes; traversing the quota tree according to the total quota, the used quota and the used quota type to obtain the balance of each node of the quota tree, clearly displaying the balance condition of each node, and facilitating the subsequent iterative optimization of the quota calculation model; and comparing the balance of each node with the service request, and executing the service request according to the comparison result, so that the separate management of the credit lines of the same user under different products can be realized, and the accurate management and control of the total credit line of the user can be realized under the same account system. Therefore, the multi-product composite quota management method provided by the invention can solve the problem of low accuracy of multi-product quota management.

FIG. 4 is a functional block diagram of a multi-product composite credit management device according to an embodiment of the present invention.

The multi-product composite credit line management device 100 of the invention can be installed in an electronic device. According to the realized function, the multi-product composite credit line management device 100 can include a feature extraction module 101, a type and pattern recognition module 102, a credit score calculation module 103, a credit line tree construction module 104 and an execution module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and are stored in a memory of the electronic device.

In the present embodiment, the functions regarding the respective modules/units are as follows:

the feature extraction module 101 is configured to receive a service request of a user, obtain user history information from a preset platform according to the service request, and extract user information features from the user history information;

the type and mode identification module 102 is configured to identify a credit type and a credit mode of the service request;

the credit score calculating module 103 is configured to calculate credit scores of various quota types of the user according to the user information characteristics;

the quota tree building module 104 is configured to analyze a user loan APP and a user product from the user history information, and select a preset quota calculation model to build a quota tree according to the user loan APP, the user product, the quota type, the quota mode, and the credit score;

the execution module 105 is configured to obtain a current total limit of the user, an used limit related to a product, and a used limit type, and traverse the limit tree according to the total limit, the used limit, and the used limit type to obtain a balance of each node of the limit tree; and comparing the balance of each node with the service request, and executing the service request according to the comparison result.

In detail, in the embodiment of the present invention, each module in the multi-product composite limit management device 100 adopts the same technical means as the multi-product composite limit management method described in fig. 1 to 3, and can produce the same technical effect, which is not described herein again.

Fig. 5 is a schematic structural diagram of an electronic device for implementing a method for managing multiple product composite credits according to an embodiment of the present invention.

The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further include a computer program, such as a multi-product quota management program, stored in the memory 11 and executable on the processor 10.

In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., executing a multi-product composite quota management program, etc.) stored in the memory 11 and calling data stored in the memory 11.

The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various data, such as codes of the multi-product limit management program, but also temporarily store data that has been output or will be output.

The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.

The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.

Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.

For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

It is to be understood that the embodiments described are illustrative only and are not to be construed as limiting the scope of the claims.

The multi-product composite quota management program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can realize that:

identifying the quota type and quota mode of the service request;

Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiment corresponding to the drawing, and is not repeated here.

Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).

The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:

identifying the quota type and quota mode of the service request;

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.

The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a string of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the same, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-product composite limit management method is characterized by comprising the following steps:

identifying the quota type and quota mode of the service request;

2. The multi-product composite credit line management method of claim 1, wherein the calculating credit scores of each credit line type of the user according to the user information characteristics comprises:

3. The method as claimed in claim 2, wherein the selecting a predetermined credit calculation model to construct a credit tree according to the user related loan APP, the user related product, the credit type, the credit model and the credit score comprises:

taking the loan related APP of the user and the related products of the user as primary sub-nodes of a quota tree according to the tree structure;

4. The method as claimed in claim 3, wherein the selecting a predetermined credit calculation model according to the credit model comprises:

judging whether the quota type has a special quota type;

when the credit type has a special credit type, the credit mode directly selects a preset credit calculation model;

5. The multi-product composite credit management method of claim 3, wherein before selecting the preset credit calculation model according to the credit mode, the method further comprises:

determining a second service logic relationship between the user available quota of each quota type and the used quota;

6. The multi-product compound credit management method of claim 1, wherein the comparing the balance of each node with the service request, and executing the service request according to the comparison result comprises:

7. The multi-product compound credit management method of claim 1, wherein the extracting of the user information features from the user history information comprises:

performing word segmentation and part-of-speech tagging on the historical information of the user to obtain the result of word segmentation and part-of-speech tagging;

8. A multi-product composite limit management device is characterized in that the device comprises:

the system comprises a feature extraction module, a service request processing module and a feature extraction module, wherein the feature extraction module is used for receiving a service request of a user, acquiring user historical information from a preset platform according to the service request and extracting user information features from the user historical information;

the credit score calculating module is used for calculating the credit score of each limit type of the user according to the user information characteristics;

the line tree building module is used for analyzing a user loan-related APP and a user-related product from the user historical information, and selecting a preset line calculation model to build a line tree according to the user loan-related APP, the user-related product, the line type, the line model and the credit score;

the execution module is used for acquiring the current total limit of the user, the used limit related to the product and the used limit type, and traversing the limit tree according to the total limit, the used limit and the used limit type to obtain the balance of each node of the limit tree; and comparing the balance of each node with the service request, and executing the service request according to the comparison result.

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and the number of the first and second groups,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the multi-product compound credit management method of any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for managing a multi-product composite credit as recited in any one of claims 1 to 7.