Resource allocation methods, devices, electronic equipment, and media based on multi-label classification

Abstract

This disclosure relates to a resource allocation method, apparatus, electronic device, and computer-readable medium based on multi-tag classification. The method includes: acquiring user data; processing the user data through a classifier chain to obtain a tag sequence for the user, wherein the tag sequence contains at least two classification tags, and the classifier chain includes multiple classifiers; determining whether the user meets resource allocation conditions based on the tag sequence; and if the user meets the resource allocation conditions, allocating resource quota data corresponding to the user's resource request to the user terminal. The resource allocation method, apparatus, electronic device, and computer-readable medium based on multi-tag classification disclosed herein can classify users based on multiple tags, improving the accuracy of classification results, effectively preventing user churn from the platform, and avoiding rejection during resource allocation, thus allocating resources to some potential users.

Description

Technical Field

[0001] This disclosure relates to the field of computer information processing, and more specifically, to a resource allocation method, apparatus, electronic device, and computer-readable medium based on multi-label classification. Background Technology

[0002] Currently, in the internet finance sector, a binary classification model is typically built using Logistic Regression. This model categorizes users into two labels, such as "good user" and "bad user," and allocates resources based on these labels. However, this method of user classification can lead to user churn because it doesn't account for special circumstances; the classification results are not always accurate. For example, the label in a binary classification model might be defined by whether a loan has been overdue within a certain repayment period—for instance, defining 3+ overdue periods as "bad" and vice versa. However, this definition could treat all customers with 3+ overdue periods as "bad" customers. Some of these customers will eventually repay their loans on time. The reason for the overdue payments might be temporary cash flow difficulties or external factors causing temporary loss of income. For example, during the recent pandemic, these users had a good willingness and ability to repay, but were affected by certain factors and were unable to repay on time. After overcoming the difficulties, they will repay all their loans. If all such customers are defined as "bad" customers, the platform will lose these users.

[0003] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] In view of this, the present disclosure provides a resource allocation method, apparatus, electronic device and computer-readable medium based on multi-tag classification, which can classify users based on multiple tags, improve the accuracy of classification results, effectively prevent users from leaving the platform, and avoid rejection during resource allocation, allocating resources to some potential users.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to one aspect of this disclosure, a resource allocation method based on multi-label classification is proposed, comprising: acquiring user data; processing the user data through a classifier chain to obtain a label sequence of the user, wherein the label sequence contains at least two classification labels, and the classifier chain includes multiple classifiers; determining whether the user meets the resource allocation conditions based on the label sequence; and if the user meets the resource allocation conditions, allocating resource quota data corresponding to the resource request to the user terminal according to the user's resource request.

[0007] Optionally, the classifier chain includes a first classifier, a second classifier, ..., an nth classifier, where n is a natural number greater than or equal to 2.

[0008] Optionally, processing the user data through the classifier chain to obtain the user's label sequence includes: classifying the user according to the user data using the first classifier to obtain the first label; and classifying the user according to the user data and the first label, ..., the (i-1)th label using the i-th classifier to obtain the i-th label, where i is a natural number greater than or equal to 2 and less than or equal to n.

[0009] Optionally, determining whether a user meets the resource allocation conditions based on the tag sequence includes: comparing the predicted values ​​of at least two category tags in the tag sequence with preset values ​​in a preset tag combination; when the predicted values ​​of the at least two category tags match the preset values ​​in the preset tag combination, determining that the user meets the resource allocation conditions; or when the predicted values ​​of the at least two category tags do not match the preset values ​​in the preset tag combination, determining that the user does not meet the resource allocation conditions.

[0010] Optionally, it further includes: when the predicted values ​​of the at least two classification labels do not match the preset values ​​in the preset label combination, sending resource rejection information to the user terminal according to the user's resource request.

[0011] Optionally, the user data includes any one or more of the following: the number of times a user is overdue during the resource return period, the number of days a user is overdue during the resource return period, the number of times a user is overdue at the end of the resource return period, and the number of days a user is overdue at the end of the resource return period.

[0012] According to one aspect of this disclosure, a resource allocation device based on multi-label classification is proposed. The device includes: an acquisition module for acquiring user data; a processing module for processing the user data through a classifier chain to obtain a label sequence of the user, wherein the label sequence contains at least two classification labels, and the classifier chain includes multiple classifiers; a determination module for determining whether the user meets the resource allocation conditions based on the label sequence; and a resource quota sending module for allocating resource quota data corresponding to the resource request to the user terminal if the user meets the resource allocation conditions.

[0013] Optionally, the classifier chain includes a first classifier, a second classifier, ..., an nth classifier, where n is a natural number greater than or equal to 2.

[0014] Optionally, the processing module is further configured to: classify the user according to the user data using the first classifier to obtain a first label; and classify the user according to the user data and the first label, ..., the (i-1)th label using the i-th classifier to obtain the i-th label, where i is a natural number greater than or equal to 2 and less than or equal to n.

[0015] Optionally, the determining module is further configured to: compare the predicted values ​​of at least two category tags in the tag sequence with preset values ​​in the preset tag combination; when the predicted values ​​of the at least two category tags match the preset values ​​in the preset tag combination, determine that the user meets the resource allocation conditions; or when the predicted values ​​of the at least two category tags do not match the preset values ​​in the preset tag combination, determine that the user does not meet the resource allocation conditions.

[0016] Optionally, the resource rejection information sending module sends resource rejection information to the user terminal according to the user's resource request when the predicted values ​​of the at least two classification labels do not match the preset values ​​in the preset label combination.

[0017] Optionally, the user data includes any one or more of the following: the number of times a user is overdue during the resource return period, the number of days a user is overdue during the resource return period, the number of times a user is overdue at the end of the resource return period, and the number of days a user is overdue at the end of the resource return period.

[0018] According to one aspect of this disclosure, an electronic device is proposed, comprising: one or more processors; a storage device for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the method as described above.

[0019] According to one aspect of this disclosure, a computer-readable medium is provided having a computer program stored thereon that, when executed by a processor, implements the method described above.

[0020] According to the resource allocation method, apparatus, electronic device, and computer-readable medium based on multi-tag classification disclosed herein, user data is acquired; the user data is processed through a classifier chain to obtain a tag sequence of the user, wherein the tag sequence contains at least two classification tags, and the classifier chain includes multiple classifiers; based on the tag sequence, it is determined whether the user meets the resource allocation conditions; if the user meets the resource allocation conditions, resource quota data corresponding to the user's resource request is allocated to the user terminal. In this way, users can be classified based on multiple tags, improving the accuracy of classification results, effectively preventing users from leaving the platform, and avoiding rejection during resource allocation, thus allocating resources to some potential users.

[0021] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0022] The above and other objects, features, and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments with reference to the accompanying drawings. The drawings described below are merely some embodiments of this disclosure, and those skilled in the art will be able to obtain other drawings based on these drawings without any inventive effort.

[0023] Figure 1 This is a system block diagram illustrating a resource allocation method and apparatus based on multi-label classification according to an exemplary embodiment.

[0024] Figure 2 This is a flowchart illustrating a resource allocation method based on multi-label classification according to an exemplary embodiment.

[0025] Figure 3 This is a flowchart illustrating a resource allocation method based on multi-label classification according to another exemplary embodiment.

[0026] Figure 4 This is a flowchart illustrating a resource allocation method based on multi-label classification according to another exemplary embodiment.

[0027] Figure 5 This is a block diagram illustrating a resource allocation device based on multi-label classification according to an exemplary embodiment.

[0028] Figure 6 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0029] Figure 7 This is a block diagram illustrating a computer-readable medium according to an exemplary embodiment. Detailed Implementation

[0030] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0031] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0033] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0034] It should be understood that while the terms first, second, third, etc., may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this disclosure. As used herein, the term "and / or" includes all combinations of any and more of the associated listed items.

[0035] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing this disclosure, and therefore cannot be used to limit the scope of protection of this disclosure.

[0036] Figure 1This is a system block diagram illustrating a resource allocation method and apparatus based on multi-label classification according to an exemplary embodiment.

[0037] like Figure 1 As shown, system architecture 10 may include user terminals 101, 102, and 103, network 104, and server 105. Network 104 is used as a medium to provide communication links between user terminals 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0038] Users can use user terminals 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on user terminals 101, 102, and 103, such as financial service applications, shopping applications, web browser applications, instant messaging tools, email clients, social platform software, etc.

[0039] User terminals 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0040] Server 105 can be a server that provides various services, such as a back-end management server that supports financial service websites browsed by users using user terminals 101, 102, and 103. The back-end management server can analyze and process the received user data and feed back the processing results to the administrator of the financial service website and / or user terminals 101, 102, and 103.

[0041] Server 105 can, for example, acquire user data, process the user data through a classifier chain to obtain a user's tag sequence, wherein the tag sequence contains at least two classification tags, and the classifier chain includes multiple classifiers. Based on the tag sequence, it determines whether the user meets the resource allocation conditions. If the user meets the resource allocation conditions, it allocates resource quota data corresponding to the user's resource request to the user terminal. In this way, users can be classified based on multiple tags, improving the accuracy of the classification results, effectively preventing users from leaving the platform, and also avoiding rejection during resource allocation, allocating resources to some potential users.

[0042] Server 105 can be a single physical server, or it can consist of multiple servers. It should be noted that the resource allocation method based on multi-tag classification provided in this embodiment can be executed by server 105, and correspondingly, the resource allocation device based on multi-tag classification can be installed in server 105. The web page provided to users for browsing the financial services platform is generally located in user terminals 101, 102, and 103.

[0043] Figure 2 This is a flowchart illustrating a resource allocation method based on multi-label classification according to an exemplary embodiment.

[0044] like Figure 2 As shown, the resource allocation method based on multi-label classification includes steps S210 to S240.

[0045] In step S210, user data is obtained.

[0046] In step S220, the user data is processed by a classifier chain to obtain the user's tag sequence, the tag sequence containing at least two classification tags, and the classifier chain including multiple classifiers.

[0047] In step S230, it is determined whether the user meets the resource allocation conditions based on the tag sequence.

[0048] In step S240, if the resource allocation conditions are met, resource quota data corresponding to the user's resource request is allocated to the user terminal according to the user's resource request.

[0049] This method acquires user data, processes it through a classifier chain to obtain a user's tag sequence, which contains at least two classification tags. The classifier chain includes multiple classifiers. Based on the tag sequence, it determines whether the user meets the resource allocation conditions. If the user meets the resource allocation conditions, it allocates resource quota data corresponding to the user's resource request to the user's terminal. This method can classify users based on multiple tags, improve the accuracy of classification results, effectively prevent users from leaving the platform, and avoid rejection during resource allocation, allocating resources to some potential users.

[0050] In one embodiment, the user data includes any one or more of the following: the number of times a user is overdue during the resource return period, the number of days a user is overdue during the resource return period, the number of times a user is overdue at the end of the resource return period, and the number of days a user is overdue at the end of the resource return period.

[0051] In one embodiment, the classifier chain includes a first classifier, a second classifier, ..., an nth classifier, where n is a natural number greater than or equal to 2. Here, n represents the number of classifiers in the classifier chain.

[0052] In one embodiment, users are classified according to user data using a first classifier, a second classifier, ..., an nth classifier, respectively, to obtain a user's label sequence. For example, the first classifier is used to classify users according to user data, resulting in the first label, as shown in Table 1:

[0053] Table 1

[0054]

[0055] Where Classifier1 represents the first classifier, X represents user data, and Y1 represents the first label, for example, 0 or 1.

[0056] In one embodiment, a second classifier is used to classify users based on user data and the first label to obtain the second label, as shown in Table 2:

[0057] Table 2

[0058]

[0059] Where Classifier2 represents the second classifier, X represents user data, the gray area represents the classification result of the first classifier, and Y2 represents the second label, such as 0 and 1.

[0060] In one embodiment, the user is classified using the i-th classifier based on the user data and the 1st label, ..., the (i-1)th label, to obtain the i-th label, as shown in Table 2:

[0061] Table i

[0062]

[0063] Where Classifieri represents the i-th classifier, X represents the user data, the gray area represents the classification results of classifiers 1 to i-1, and Yi represents the i-th label, for example, 0 and 1.

[0064] In one embodiment, based on the above method, users can be classified according to user data using each classifier in the classifier chain, and the classification result of each classifier can be obtained. Then, according to the sorting order of each classifier in the classifier chain, the classification result of each classifier is stored in a label sequence, for example, (Y1, Y2, ..., Yi).

[0065] In one embodiment, the number of classifiers in the classifier chain is set according to actual business needs. It should be noted that the number of classifiers in the classifier chain of this invention is greater than or equal to 2.

[0066] In one embodiment, user tags for each classifier are defined based on the actual business application scenario. For example, the classifier's classification tag is defined based on the number of times a user is overdue within the resource repayment period and / or the number of overdue days within the resource repayment period. Specifically, a user with 3 or more overdue times within the resource repayment period is defined as a bad user, and / or a user with more than 30 overdue days within the resource repayment period is also defined as a bad user, denoted by 1. Conversely, a user with 3 or more overdue times within the resource repayment period is defined as a good user, and / or a user with less than 30 overdue days within the resource repayment period is also defined as a good user, denoted by 2.

[0067] For example, the classifier's classification labels can be defined based on the number of overdue payments and / or the number of overdue days at the end of the resource repayment period. Specifically, a user with more than 0 overdue payments at the end of the resource repayment period is defined as a bad user, and / or a user with more than 0 overdue days within the resource repayment period is also defined as a bad user, denoted by 1. Conversely, a user with 0 overdue days at the end of the resource repayment period is defined as a good user, and / or a user with 0 overdue days within the resource repayment period is also defined as a good user, denoted by 2.

[0068] In one embodiment, a user's eligibility for resource allocation is determined based on a label sequence. For example, if a classifier chain contains two classifiers and the resulting label sequence is (1,1), while the preset label combination is (1,0) or (0,0), then the label sequence (1,1) does not match the preset label combination (1,0) or (0,0), and in this case, the user does not meet the resource allocation criteria. Conversely, if the label sequence is (1,0) or (0,0), it matches the preset label combination (1,0) or (0,0), and the user meets the resource allocation criteria. In this embodiment, the preset label combination can be set according to actual conditions and is not limited here.

[0069] Figure 3 This is a flowchart illustrating a resource allocation method based on multi-label classification according to another exemplary embodiment.

[0070] like Figure 3 As shown, step S220 includes steps S310 and S320.

[0071] In step S310, the user is classified according to the user data by the first classifier to obtain the first label.

[0072] In step S320, the user is classified by the i-th classifier based on the user data and the first label, ..., the (i-1)-th label, to obtain the i-th label, where i is a natural number greater than or equal to 2 and less than or equal to n.

[0073] This method can process user data through a classifier chain to achieve user classification based on multiple labels, making the classification results more accurate and effectively preventing users from leaving the platform.

[0074] Figure 4 This is a flowchart illustrating a resource allocation method based on multi-label classification according to another exemplary embodiment.

[0075] like Figure 4 As shown, step S230 includes steps S410 and S420.

[0076] In step S410, the predicted values ​​of at least two classification labels in the label sequence are compared with the preset values ​​in the preset label combination.

[0077] In step S420, when the predicted values ​​of the at least two category labels match the preset values ​​in the preset label combination, it is determined that the user meets the resource allocation conditions; or when the predicted values ​​of the at least two category labels do not match the preset values ​​in the preset label combination, it is determined that the user does not meet the resource allocation conditions.

[0078] This method compares the predicted values ​​of at least two category labels in a label sequence with preset values ​​in a preset label combination. When the predicted values ​​of at least two category labels match the preset values ​​in the preset label combination, the user is confirmed to meet the resource allocation conditions; or when the predicted values ​​of at least two category labels do not match the preset values ​​in the preset label combination, the user is confirmed to not meet the resource allocation conditions. This allows for quick and accurate identification of which users meet the resource allocation conditions, effectively avoiding the misclassification of potential users into groups that do not meet the resource allocation conditions.

[0079] In one embodiment, the predicted values ​​of at least two classification labels in the label sequence are compared with preset values ​​in a preset label combination. For example, the classifier chain contains two classifiers, resulting in a label sequence of (1,1), where 1 is the predicted value obtained by the classifier. The preset label combination is (1,0) or (0,0), where 1 and 0 are preset values ​​set according to actual conditions. Specifically, if the label sequence (1,1) does not match the preset label combination (1,0) or (0,0), the user does not meet the resource allocation conditions. Conversely, if the label sequence is (1,0) or (0,0), it matches the preset label combination (1,0) or (0,0), and the user meets the resource allocation conditions.

[0080] In one embodiment, the method further includes: when the predicted values ​​of at least two classification labels do not match the preset values ​​in the preset label combination, sending resource rejection information to the user terminal according to the user's resource request, so as to selectively reject some bad users and allow resources to be allocated to more potential users.

[0081] Those skilled in the art will understand that all or part of the steps of the above embodiments are implemented as a computer program executed by a CPU. When the computer program is executed by the CPU, it performs the functions defined by the methods provided in this disclosure. The program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk.

[0082] Furthermore, it should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0083] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0084] Figure 5 This is a block diagram illustrating a resource allocation apparatus based on multi-label classification according to another exemplary embodiment.

[0085] like Figure 5 As shown, the resource allocation device 500 based on multi-label classification includes: an acquisition module 501, a processing module 502, a determination module 503, and a resource quota sending module 504.

[0086] Specifically, module 501 is used to acquire user data.

[0087] The processing module 502 is used to process the user data through a classifier chain to obtain the user's tag sequence.

[0088] The determination module 503 is used to determine whether the user meets the resource allocation conditions based on the tag sequence.

[0089] The resource quota sending module 504, if the resource allocation conditions are met, allocates resource quota data corresponding to the user's resource request to the user terminal according to the user's resource request.

[0090] The resource allocation device 500 based on multi-tag classification can acquire user data, process the user data through a classifier chain to obtain a user's tag sequence, wherein the tag sequence contains at least two classification tags, and the classifier chain includes multiple classifiers. Based on the tag sequence, it determines whether the user meets the resource allocation conditions. If the user meets the resource allocation conditions, it allocates resource quota data corresponding to the user's resource request to the user terminal. In this way, users can be classified based on multiple tags, improving the accuracy of the classification results, effectively preventing users from leaving the platform, and avoiding rejection during resource allocation, thus allocating resources to some potential users.

[0091] Figure 6 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0092] The following reference Figure 6 To describe an electronic device 600 according to such an embodiment of the present disclosure. Figure 6 The electronic device 600 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0093] like Figure 6 As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0094] The storage unit stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in this specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 610 can perform actions such as... Figures 2-4 The steps are shown in the figure.

[0095] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only memory unit (ROM) 6203.

[0096] The storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0097] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0098] Electronic device 600 can also communicate with one or more external devices 600 (e.g., keyboard, pointing device, Bluetooth device, etc.), enabling users to communicate with devices that interact with electronic device 600, and / or any device that allows electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0099] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software, or by combining software with necessary hardware. Therefore, as... Figure 7 As shown, the technical solution according to the embodiments of this disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) or on a network, and includes several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the above-described method according to the embodiments of this disclosure.

[0100] The software product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0101] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0102] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0103] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the following functions: acquire user data; process the user data through a classifier chain to obtain a tag sequence of the user, wherein the tag sequence contains at least two classification tags and the classifier chain includes multiple classifiers; determine whether the user meets the resource allocation conditions based on the tag sequence; and if the user meets the resource allocation conditions, allocate resource quota data corresponding to the user's resource request to the user terminal according to the user's resource request.

[0104] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0105] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0106] Exemplary embodiments of this disclosure have been specifically shown and described above. It should be understood that this disclosure is not limited to the detailed structures, arrangements, or implementations described herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A resource allocation method based on multi-label classification, characterized in that, include: The user data is processed by classifying users through each classifier in the classifier chain to obtain a user's tag sequence. The tag sequence contains at least two classification tags. Specifically, the classifier chain includes a first classifier, a second classifier, ..., an nth classifier, where n is a natural number greater than or equal to 2. The first classifier classifies the user based on the user data to obtain a first tag. The i-th classifier classifies the user based on the user data and the first tag, ..., the (i-1)th tag to obtain an i-th tag, where i is a natural number greater than or equal to 2 and less than or equal to n. The classification results of each classifier are stored in the tag sequence according to their order in the classifier chain. The classification tags of the classifiers are defined based on the number of times a user is overdue within the resource repayment period and / or the number of overdue days within the resource repayment period, or based on the number of times a user is overdue at the end of the resource repayment period and / or the number of overdue days at the end of the resource repayment period. Determining whether a user meets the resource allocation conditions by comparing the predicted values ​​of at least two category tags in the tag sequence with the predicted values ​​in the preset tag combination includes: determining that the user meets the resource allocation conditions when the predicted values ​​of the at least two category tags match the preset values ​​in the preset tag combination. If the resource allocation conditions are met, then resource quota data corresponding to the user's resource request is allocated to the user terminal according to the user's resource request.

2. The method as described in claim 1, characterized in that, Determining whether a user meets the resource allocation criteria by comparing the predicted values ​​of at least two category labels in the label sequence with the predicted values ​​in a preset label combination further includes: When the predicted values ​​of the at least two category labels do not match the preset values ​​in the preset label combination, it is determined that the user does not meet the resource allocation conditions.

3. The method as described in claim 1 or 2, characterized in that, Also includes: When the predicted values ​​of at least two classification labels do not match the preset values ​​in the preset label combination, resource rejection information is sent to the user terminal according to the user's resource request.

4. The method as described in claim 1, characterized in that, The user data includes any one or more of the following: the number of times a user is overdue during the resource return period, the number of days a user is overdue during the resource return period, the number of times a user is overdue at the end of the resource return period, and the number of days a user is overdue at the end of the resource return period.

5. A resource allocation device based on multi-label classification, characterized in that, include: The processing module is used to classify user data acquired by the acquisition module through each classifier in a classifier chain to obtain a tag sequence for the user. The tag sequence contains at least two classification tags. Specifically, the classifier chain includes a first classifier, a second classifier, ..., an nth classifier, where n is a natural number greater than or equal to 2. The first classifier classifies the user based on the user data to obtain a first tag. The i-th classifier classifies the user based on the user data and the first tag, ..., the (i-1)th tag to obtain an i-th tag, where i is a natural number greater than or equal to 2 and less than or equal to n. The classification results of each classifier are stored in the tag sequence according to their order in the classifier chain. The classification tags of the classifiers are defined based on the number of times the user is overdue within the resource repayment period and / or the number of overdue days within the resource repayment period, or based on the number of times the user is overdue at the end of the resource repayment period and / or the number of overdue days at the end of the resource repayment period. The determination module is used to determine whether the user meets the resource allocation conditions by comparing the predicted values ​​of at least two category tags in the tag sequence with the predicted values ​​in the preset tag combination, wherein the user meets the resource allocation conditions when the predicted values ​​of the at least two category tags match the preset values ​​in the preset tag combination. If the resource allocation conditions are met, the resource quota sending module allocates resource quota data corresponding to the user's resource request to the user terminal.

6. The resource allocation device as described in claim 5, characterized in that, The determining module is also used for: When the predicted values ​​of the at least two category labels do not match the preset values ​​in the preset label combination, it is determined that the user does not meet the resource allocation conditions.

7. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-4.

8. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-4.

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