CN113902543A

CN113902543A - Resource quota adjusting method and device and electronic equipment

Info

Publication number: CN113902543A
Application number: CN202111124691.2A
Authority: CN
Inventors: 张丹丹; 张瑞军; 陆达飞; 苏绥绥
Original assignee: Beijing Qiyu Information Technology Co Ltd
Current assignee: Beijing Qiyu Information Technology Co Ltd
Priority date: 2021-09-25
Filing date: 2021-09-25
Publication date: 2022-01-07

Abstract

The disclosure relates to a resource quota adjusting method, a resource quota adjusting device, an electronic device and a computer readable medium. The method comprises the following steps: the method comprises the steps of obtaining a plurality of user data corresponding to a plurality of users in a target guest group, wherein the user data comprises basic data and behavior data; inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve; generating an efficiency curve according to the income curve and the loss curve; when the efficiency curve meets a preset strategy, determining a resource quota allocation value according to the efficiency curve; adjusting a resource quota for a plurality of users in the target guest group based on the resource quota allocation value. The resource quota adjusting method, the resource quota adjusting device, the electronic equipment and the computer readable medium can quickly adjust the resource quota for the user, improve the resource utilization efficiency, reduce the calculation pressure of the server and improve the satisfaction degree of the user.

Description

Resource quota adjusting method and device and electronic equipment

Technical Field

The present disclosure relates to the field of computer information processing, and in particular, to a resource quota adjusting method, an apparatus, an electronic device, and a computer-readable medium.

Background

With the development of economy, in order to meet the development requirements of an individual user or an enterprise user, the resource borrowing activity of the individual user or the enterprise user is often carried out through a resource service organization, and for the resource service organization, the resource borrowing activity of the user is likely to bring risks to a resource service company. Before the payment deadline expires, a great adverse change of the business condition of the borrowed object (company or individual) is likely to affect the performance capability of the borrowed object, so that the resource arrears and the borrowed object are at risk, and therefore, in order to reduce the occurrence probability of such risk, the resource service organization needs to perform risk assessment on the borrowed object and further formulate the corresponding resource allocation limit.

Currently, financial risk discrimination is often obtained by analyzing the credit risk of a user. Credit risk is also called counterparty risk or performance risk and refers to the risk that counterparty does not fulfill due debt. Credit risk, which is a major risk faced by the internet financial services industry, has been the core content of credit risk management. However, with the increasing sophistication of resource service companies and products, there is a new demand for allocation of resource quota to users from different perspectives. For example, from a company perspective, proper profit is a necessary revenue support for a company's benign operations, and from a developer operator perspective, a large amount of computation over time can also reduce server performance and increase server maintenance costs. It is preferable for the operator to make full use of the resources.

Therefore, a new resource quota adjusting method, apparatus, electronic device, and computer readable medium are needed.

The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

In view of this, the present disclosure provides a resource quota adjusting method, a resource quota adjusting device, an electronic device, and a computer readable medium, which can quickly adjust a resource quota for a user, improve resource utilization efficiency, reduce server computation pressure, and improve user satisfaction.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to an aspect of the present disclosure, a resource quota adjusting method is provided, where the method includes: the method comprises the steps of obtaining a plurality of user data corresponding to a plurality of users in a target guest group, wherein the user data comprises basic data and behavior data; inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve; generating an efficiency curve according to the income curve and the loss curve; when the efficiency curve meets a preset strategy, determining a resource quota allocation value according to the efficiency curve; adjusting a resource quota for a plurality of users in the target guest group based on the resource quota allocation value.

Optionally, the method further comprises: when the efficiency curve does not meet a preset strategy, determining a temporary quota allocation value according to the efficiency curve; adjusting a resource quota for a plurality of users in the target guest group based on the temporary quota allocation value; tracking efficiency curves of the plurality of users in the target guest group to update the temporary quota allocation value.

Optionally, the method further comprises: acquiring user data of a current user; comparing the user data to a plurality of guest groups to determine a target guest group for the current user; and adjusting the resource quota of the current user based on the resource quota allocation value or the temporary quota allocation value of the target guest group.

Optionally, inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve, comprising: inputting the plurality of user data into a response rate model and a resource balance model in a revenue loss model to generate a plurality of response rates and a plurality of resource balance values corresponding to the plurality of users; generating the benefit curve based on the plurality of response rates and the plurality of resource balance values.

Optionally, inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve, further comprising: inputting the plurality of user data into an overdue model and a loss model in an income loss model to generate a plurality of overdue rates and a plurality of loss rates corresponding to the plurality of users; generating the loss curve based on the plurality of overdue rates and the plurality of loss rates.

Optionally, generating an efficiency curve from the yield curve and the loss curve comprises: and generating the efficiency curve according to the derivative relation of the gain curve and the loss curve.

Optionally, when the efficiency curve meets a preset policy, determining a resource quota allocation value according to the efficiency curve includes: and when an extreme value exists in the efficiency curve in a calculation interval, determining the resource quota allocation value according to the extreme value.

Optionally, adjusting the resource quota for the plurality of users in the target guest group based on the resource quota allocation value includes: acquiring users in the target guest group one by one; acquiring the response rate, the resource allowance value, the overdue rate and the loss rate of the user; constructing a distribution coefficient matrix based on the response rate, the resource allowance value, the overdue rate and the loss rate; and adjusting the resource quota for the plurality of users in the target guest group based on the allocation coefficient matrix and the resource quota allocation value.

Optionally, when the efficiency curve does not satisfy a preset policy, determining a temporary quota allocation value according to the efficiency curve includes: and when the efficiency curve has no extreme value in the calculation interval, determining the temporary quota allocation value according to the maximum value in the calculation interval.

Optionally, tracking efficiency curves of the plurality of users in the target guest group to update the temporary quota allocation value, comprising: after the behavior data of the users in the target guest group are updated, calculating an efficiency curve of the target guest group in real time; when the efficiency curve meets a preset strategy, generating a resource quota allocation value of the target guest group; and when the efficiency curve does not meet a preset strategy, updating the temporary quota allocation value of the target guest group according to the efficiency curve.

According to an aspect of the present disclosure, a resource quota adjusting apparatus is provided, the apparatus including: the data module is used for acquiring a plurality of user data corresponding to a plurality of users in a target guest group, and the user data comprises basic data and behavior data; a model module for inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve; a curve module for generating an efficiency curve according to the profit curve and the loss curve; the quota module is used for determining a resource quota allocation value according to the efficiency curve when the efficiency curve meets a preset strategy; an adjusting module, configured to adjust the resource quota for the plurality of users in the target guest group based on the resource quota allocation value.

According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.

According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.

According to the resource quota adjusting method, the resource quota adjusting device, the electronic equipment and the computer readable medium, a plurality of user data corresponding to a plurality of users in a target guest group are obtained, wherein the user data comprise basic data and behavior data; inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve; generating an efficiency curve according to the income curve and the loss curve; when the efficiency curve meets a preset strategy, determining a resource quota allocation value according to the efficiency curve; the resource quota can be quickly adjusted for the users based on the mode that the resource quota allocation value is adjusted for the users in the target customer group, so that the resource utilization efficiency is improved, the calculation pressure of the server is reduced, and the satisfaction degree of the users is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.

Fig. 1 is a system block diagram illustrating a resource quota adjusting method and apparatus according to an example embodiment.

Fig. 2 is a flowchart illustrating a resource quota adjusting method according to an example embodiment.

Fig. 3 is a flowchart illustrating a resource quota adjusting method according to another exemplary embodiment.

Fig. 4 is a diagram illustrating a resource quota adjusting method according to another exemplary embodiment.

Fig. 5 is a diagram illustrating a resource quota adjusting method according to another exemplary embodiment.

Fig. 6 is a flowchart illustrating a resource quota adjusting method according to another exemplary embodiment.

Fig. 7 is a block diagram illustrating a resource quota adjusting apparatus according to an example embodiment.

FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.

FIG. 9 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

It will be understood that, although 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 element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.

In this disclosure, resources refer to any substance, information, time that may be utilized, information resources including computing resources and various types of data resources. The data resources include various private data in various domains. The innovation of the present disclosure is how to make the resource quota adjustment process more automated, efficient and reduce human cost using information interaction technology between the server and the client. Thus, in essence, the present disclosure can be applied to the distribution of various types of resources, including physical goods, water, electricity, and meaningful data. However, for convenience, the resource allocation is illustrated as being implemented by taking financial data resources as an example in the disclosure, but those skilled in the art will understand that the disclosure can also be used for allocation of other resources.

Although some customers of the financial service company cannot pay timely, due to financial profits such as default money and interest, when the customers pay the default money, the profits brought by the customers are higher than those of the customers who pay timely. Moreover, among the clients who are refused after the credit risk judgment, the profit brought by some clients can cover the risk, and profit income is brought to the financial service company. How to increase the profit of a company while trying to reduce the risk of the company is what is to be discussed in the embodiments of the present disclosure. As mentioned above, with the increasing sophistication of resource service companies and products, there is a new demand for allocation of user resource quota from different perspectives. The specific technical details are only described in the present disclosure in terms of matching with benign operation requirements of the financial service company, however, the method in the present disclosure may also match with allocation of other resources and other requirements, such as maximum utilization of server resources, maximum working efficiency of servers, and the like, and the present disclosure is not limited thereto.

In the embodiment of the disclosure, in order to improve the determination accuracy of the resource quota adjusting method, the method provided in the embodiment of the disclosure may construct the revenue loss model corresponding to each service type based on sample data of each service type acquired from multiple data acquisition paths. The user data includes, but is not limited to, service account information of the user, page operation data of the user, service access duration of the user, service access frequency of the user, terminal device identification information of the user, and region information where the user is located, and may be specifically determined according to an actual application scenario, and is not limited herein.

As shown in fig. 1, the system architecture 10 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a financial services application, a shopping application, a web browser application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server that provides various services, such as a background management server that supports financial services websites browsed by the user using the

terminal apparatuses

101, 102, and 103. The backend management server may analyze and/or otherwise process the received user data and feed back the processing results (e.g., resource quotas) to the administrator of the financial services website and/or the

terminal devices

101, 102, 103.

The server 105 and/or the

terminal devices

101, 102, 103 may, for example, obtain a plurality of user data corresponding to a plurality of users in the target guest group, where the user data includes basic data and behavior data; the server 105 and/or the

terminal devices

101, 102, 103 may, for example, input the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve; the server 105 and/or the

terminal devices

101, 102, 103 may generate an efficiency curve, for example, from the gain curve and the loss curve; the server 105 and/or the

terminal devices

101, 102, 103 may determine a resource quota allocation value according to the efficiency curve, for example, when the efficiency curve satisfies a preset policy; the server 105 and/or the

end devices

101, 102, 103 can adjust the resource quota for the plurality of users in the target guest group, e.g., based on the resource quota allocation value.

The server 105 and/or the

terminal devices

101, 102, 103 may determine a temporary quota allocation value according to the efficiency curve, for example, when the efficiency curve does not satisfy a preset policy; the server 105 and/or the

end devices

101, 102, 103 may adjust the resource quota for the plurality of users in the target guest group, e.g., based on the temporary quota allocation value; the server 105 and/or the

end devices

101, 102, 103 can, for example, track efficiency curves of the plurality of users in the target guest group to update the temporary quota allocation value.

The server 105 and/or the

terminal devices

101, 102, 103 may for example obtain user data of the current user; the server 105 and/or the

terminal devices

101, 102, 103 may, for example, compare the user data to a plurality of guest groups to determine a target guest group for the current user; the server 105 and/or the

end devices

101, 102, 103 can adjust the resource quota for the current user, e.g., based on the resource quota allocation value or a temporary quota allocation value for the target guest group.

The server 105 may be an entity server, and may also be composed of a plurality of servers, for example, it should be noted that the resource quota adjusting method provided by the embodiment of the present disclosure may be executed by the server 105 and/or the

terminal devices

101, 102, and 103, and accordingly, the resource quota adjusting apparatus may be disposed in the server 105 and/or the

terminal devices

101, 102, and 103. And the web page end provided for the user to browse the financial service platform is generally positioned in the

terminal equipment

101, 102 and 103.

Fig. 2 is a flowchart illustrating a resource quota adjusting method according to an example embodiment. The resource quota adjusting method 20 includes at least steps S202 to S210.

As shown in fig. 2, in S202, a plurality of user data corresponding to a plurality of users in the target guest group is obtained, where the user data includes basic data and behavior data.

In the embodiment of the present disclosure, the user may be an individual user or an enterprise user, and the allocation of the resource amount may be adjustment of a financial resource amount, or allocation of an electric power resource and a hydraulic resource. The user data may include basic data, such as service account information, terminal device identification information of the user, region information where the user is located, and the like; the user data may also include behavior data, which may be, for example, page operation data of the user, service access duration of the user, service access frequency of the user, and the like, and specific content of the user data may be determined according to an actual application scenario, which is not limited herein. More specifically, the user data of the current user can be obtained in a webpage embedding mode based on user authorization. The remote information can be user data of the user on other transaction platforms or other business departments.

More specifically, behavior data of a user on a website can be acquired through a Fiddler tool, the Fiddler tool works in a web proxy server mode, a client side firstly sends out request data, the Fiddler proxy server intercepts a data packet, and the proxy server impersonates the client side to send the data to a server; similarly, the server returns the response data, and the proxy server intercepts the data and returns the intercepted data to the client. And the Fiddler can acquire the related browsing data of residence time, residence page, click operation and the like of the user network browsing.

In the embodiment of the disclosure, different users can be divided into different guest groups through user images, each guest group has a similar user image, the whole users formed by the similar users can be analyzed based on the similar user images to obtain statistical type data, the analysis of a single user is more focused on considering individual differences of the users, but other hidden features of the user can be ignored, the statistical analysis is performed on the guest group where the user is located, common features can be extracted, the hidden features of the user can be mined, and further, the service can be better provided for the user.

In S204, the plurality of user data is input into a revenue loss model to generate a revenue curve and a loss curve. The plurality of user data may be input into a response rate model and a resource balance model in a revenue loss model to generate a plurality of response rates and a plurality of resource balance values for the plurality of users, for example; generating the benefit curve based on the plurality of response rates and the plurality of resource balance values. For example, the user data is input into an overdue model and a loss model in the income loss model to generate a plurality of overdue rates and a plurality of loss rates corresponding to the users; generating the loss curve based on the plurality of overdue rates and the plurality of loss rates.

A plurality of feature information may be generated based on the user data and a feature policy. The data cleaning and data fusion can be carried out on the user data so as to convert the user data into a plurality of characteristic data, and more particularly, the variable loss rate analysis and processing and abnormal value processing can be carried out on the user data; and the user data discretized by continuous variables can be subjected to WOE conversion, discrete variable WOE conversion, text variable processing, text variable word2vec processing and the like.

In one embodiment, a criticality index of at least one base datum and at least one behavior datum in the user data may be calculated, for example; extracting partial information from the user data based on the criticality index to generate a plurality of feature information. More specifically, the variable parameters, the discrimination parameters, the information values and the model characteristic parameters of the plurality of characteristic information can be calculated; and extracting a plurality of multi-dimensional characteristic information from the plurality of characteristic information based on the variable parameter, the discrimination parameter, the information value and the model characteristic parameter.

The machine learning model is trained by a plurality of users of the target customer base to obtain a revenue loss model. The income loss model can be composed of a response rate model, a resource balance model, an overdue rate model and a loss rate model, wherein the response rate model identifies the probability that the possibility of the user dynamic support is increased after the user is subjected to the quota lifting operation; the resource balance model represents that after the limit of the user is promoted and the user moves, the unused balance in the account remains; the overdue rate model represents the probability of overdue payment after the user moves and pays; the loss rate model represents the specific outstanding resource quotas after the user is overdue.

Specifically, an adjustment model may be respectively constructed for each model, a plurality of user data may be input into the adjustment model to obtain a predicted tag, the predicted tag is compared with a corresponding real tag, whether the predicted tag is consistent with the real tag is judged, the number of the predicted tags consistent with the real tag is counted, the ratio of the number of the predicted tags consistent with the real tag to the number of all the predicted tags is calculated, if the ratio is greater than or equal to a preset ratio, the adjustment model converges to obtain a trained model, if the ratio is smaller than the preset ratio, parameters in the adjustment model are adjusted, and the predicted tag of each object is predicted again through the adjusted adjustment model until the ratio is greater than or equal to the preset ratio. The method for adjusting the parameters in the adjustment model may be performed by using a random gradient descent algorithm, a gradient descent algorithm, or a normal equation. If the times of adjusting the parameters of the adjusting model exceed the preset times, the model used for building the adjusting model can be replaced, so that the model training efficiency is improved.

In the results obtained by calculating the response rate model, the resource balance model, the overdue rate model and the loss rate model, the response rate and the resource balance can be regarded as the income Q (x) possibly increased by the financial service company; the overdue rate and the loss rate can be regarded as the loss L (x) possibly suffered by the financial service company, wherein x represents different resource limit values. The gain Q (x) and the loss L (x) are functions related to the value of the resource quota.

In S206, an efficiency curve is generated from the yield curve and the loss curve. And generating the efficiency curve according to the derivative relation of the gain curve and the loss curve.

More specifically, the functional relationship of the efficiency curve may be

In S208, when the efficiency curve meets a preset policy, a resource quota allocation value is determined according to the efficiency curve. The resource quota allocation value may be determined, for example, when an extremum exists in the efficiency curve within a calculation interval, according to the extremum.

In one embodiment, an quota interval may be allocated to x, and may be, for example, a common quota interval of the guest group user, and when the efficiency curve is within the interval of x and an extreme value can be obtained, it is considered that there is an optimal value in the resource quota of the guest group user. As shown in fig. 4, the term "extremum" means that the relationship between the efficiency curve of the guest group and x is in a poisson distribution or an inverted U-shaped distribution within the interval.

In S210, adjusting a resource quota for a plurality of users in the target guest group based on the resource quota allocation value. The resource quota can be directly allocated to a plurality of users in the target guest group.

In practical application scenarios, there is still a difference between users of the same customer group, and if the same quota is simply allocated, the same quota may be lost. In one embodiment, multidimensional qualifying relationships can be introduced, for example. Acquiring users in the target guest group one by one; acquiring the response rate, the resource allowance value, the overdue rate and the loss rate of the user; constructing a distribution coefficient matrix based on the response rate, the resource allowance value, the overdue rate and the loss rate; and adjusting the resource quota for the plurality of users in the target guest group based on the allocation coefficient matrix and the resource quota allocation value.

The coefficient matrix can be a 4-dimensional matrix corresponding to different values among the response rate, the resource allowance value, the overdue rate and the loss rate. For example, for the a user, the response rate, the resource quota value, the overdue rate, and the loss rate are (0.8,0.7,0.5, and 0.9), the adjustment coefficient corresponding to the combination may be searched according to the coefficient matrix, and then, the adjustment may be performed again on the basis of the resource quota calculated above.

According to the resource quota adjusting method, a plurality of user data corresponding to a plurality of users in a target guest group are obtained, wherein the user data comprise basic data and behavior data; inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve; generating an efficiency curve according to the income curve and the loss curve; when the efficiency curve meets a preset strategy, determining a resource quota allocation value according to the efficiency curve; the resource quota can be quickly adjusted for the users based on the mode that the resource quota allocation value is adjusted for the users in the target customer group, so that the resource utilization efficiency is improved, the calculation pressure of the server is reduced, and the satisfaction degree of the users is improved.

It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.

Fig. 3 is a flowchart illustrating a resource quota adjusting method according to another exemplary embodiment. The process 30 shown in fig. 3 is a supplementary description of the process shown in fig. 2.

As shown in fig. 3, in S302, an efficiency curve is generated from the profit curve and the loss curve.

In S304, when the efficiency curve does not satisfy a preset policy, a temporary quota allocation value is determined according to the efficiency curve. The temporary quota allocation value may be determined, for example, according to a maximum value within a calculation interval when the efficiency curve does not have an extreme value within the calculation interval.

In one embodiment, an quota interval may be allocated to x, which may be, for example, a common quota interval of the guest group user, and when the efficiency curve is in the interval of x and cannot take an extreme value, it is considered that an optimal value does not currently exist in the resource quota of the guest. As shown in fig. 5, the non-extremum means that the relationship between the efficiency curve of the guest group and x does not conform to the poisson distribution in the interval, the positive correlation between the efficiency curve and x, and the efficiency increases as x increases. At this time, the resource quota corresponding to the maximum value x is taken as the temporary quota.

In S306, adjusting a resource quota for a plurality of users in the target guest group based on the temporary quota allocation value. Acquiring users in the target guest group one by one; acquiring the response rate, the resource allowance value, the overdue rate and the loss rate of the user; constructing a distribution coefficient matrix based on the response rate, the resource allowance value, the overdue rate and the loss rate; adjusting resource quotas for a plurality of users in the target guest group based on the allocation coefficient matrix and the temporary quota allocation value.

In S308, efficiency curves of the plurality of users in the target guest group are tracked to update the temporary quota allocation value. The efficiency curve of the target customer group can be calculated in real time after the behavior data of the users in the target customer group is updated, for example; when the efficiency curve meets a preset strategy, generating a resource quota allocation value of the target guest group; and when the efficiency curve does not meet a preset strategy, updating the temporary quota allocation value of the target guest group according to the efficiency curve.

Fig. 6 is a flowchart illustrating a resource quota adjusting method according to another exemplary embodiment. The process 60 shown in fig. 6 is a supplementary description of the process shown in fig. 2.

As shown in fig. 6, in S602, user data of the current user is acquired. The current user may be a new user of the financial services platform, the new user not belonging to any guest group.

In S604, the user data is compared to a plurality of guest groups to determine a target guest group for the current user. A user representation of the user may be first computed, the user representation may include a plurality of feature representations, and the user representation of the user may be compared to user representations of a plurality of existing customer groups to determine a target customer group.

And determining key users in each passenger group, wherein the key users can be users which are most consistent with the user portrait of the passenger group, for example, the newly added users and the key users in the plurality of passenger groups are compared in similarity, and the target passenger group is determined according to the comparison result.

In S606, the resource quota of the current user is adjusted based on the resource quota allocation value or the temporary quota allocation value of the target guest group. Users in the target guest group can be acquired one by one, for example; acquiring the response rate, the resource allowance value, the overdue rate and the loss rate of the user; constructing a distribution coefficient matrix based on the response rate, the resource allowance value, the overdue rate and the loss rate; and adjusting the resource quota for the plurality of users in the target guest group based on the allocation coefficient matrix and the resource quota allocation value.

Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.

Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.

Fig. 7 is a block diagram illustrating a resource quota adjusting apparatus according to an example embodiment. As shown in fig. 7, the resource quota adjusting apparatus 70 includes: a data module 702, a model module 704, a curve module 706, a quota module 708, and an adjustment module 710.

The data module 702 is configured to obtain a plurality of user data corresponding to a plurality of users in a target guest group, where the user data includes basic data and behavior data;

a model module 704 for inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve;

the curve module 706 is configured to generate an efficiency curve according to the profit curve and the loss curve;

the quota module 708 is configured to determine a resource quota allocation value according to the efficiency curve when the efficiency curve meets a preset policy; the quota module 708 is further configured to determine a temporary quota allocation value according to the efficiency curve when the efficiency curve does not satisfy a preset policy;

the adjustment module 710 is configured to adjust the resource quota for the plurality of users in the target guest group based on the resource quota allocation value. The adjustment module 710 is further configured to adjust the resource quota for the plurality of users in the target guest group based on the temporary quota allocation value.

According to the resource quota adjusting device, a plurality of user data corresponding to a plurality of users in a target guest group are obtained, wherein the user data comprise basic data and behavior data; inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve; generating an efficiency curve according to the income curve and the loss curve; when the efficiency curve meets a preset strategy, determining a resource quota allocation value according to the efficiency curve; the resource quota can be quickly adjusted for the users based on the mode that the resource quota allocation value is adjusted for the users in the target customer group, so that the resource utilization efficiency is improved, the calculation pressure of the server is reduced, and the satisfaction degree of the users is improved.

An electronic device 800 according to this embodiment of the disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.

As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one memory unit 820, a bus 830 connecting the various system components (including the memory unit 820 and the processing unit 810), a display unit 840, and the like.

Wherein the storage unit stores program code that can be executed by the processing unit 810, such that the processing unit 810 performs the steps according to various exemplary embodiments of the present disclosure in this specification. For example, the processing unit 810 may perform the steps as shown in fig. 2, 3, 6.

The memory unit 820 may include readable media in the form of volatile memory units such as a random access memory unit (RAM)8201 and/or a cache memory unit 8202, and may further include a read only memory unit (ROM) 8203.

The memory unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 830 may be any of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 800 may also communicate with one or more external devices 800' (e.g., keyboard, pointing device, bluetooth device, etc.) such that a user can communicate with devices with which the electronic device 800 interacts, and/or any devices (e.g., router, modem, etc.) with which the electronic device 800 can communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 860. The network adapter 880 may communicate with other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 9, the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present disclosure.

The software product may employ any combination of one or more readable media. The 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 electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: the method comprises the steps of obtaining a plurality of user data corresponding to a plurality of users in a target guest group, wherein the user data comprises basic data and behavior data; inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve; generating an efficiency curve according to the income curve and the loss curve; when the efficiency curve meets a preset strategy, determining a resource quota allocation value according to the efficiency curve; adjusting a resource quota for a plurality of users in the target guest group based on the resource quota allocation value.

Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A resource quota adjusting method is characterized by comprising the following steps:

the method comprises the steps of obtaining a plurality of user data corresponding to a plurality of users in a target guest group, wherein the user data comprises basic data and behavior data;

inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve;

generating an efficiency curve according to the income curve and the loss curve;

when the efficiency curve meets a preset strategy, determining a resource quota allocation value according to the efficiency curve;

adjusting a resource quota for a plurality of users in the target guest group based on the resource quota allocation value.

2. The method of claim 1, further comprising:

when the efficiency curve does not meet a preset strategy, determining a temporary quota allocation value according to the efficiency curve;

adjusting a resource quota for a plurality of users in the target guest group based on the temporary quota allocation value;

tracking efficiency curves of the plurality of users in the target guest group to update the temporary quota allocation value.

3. The method of claim 1, further comprising:

acquiring user data of a current user;

comparing the user data to a plurality of guest groups to determine a target guest group for the current user;

and adjusting the resource quota of the current user based on the resource quota allocation value or the temporary quota allocation value of the target guest group.

4. The method of claim 1, wherein inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve comprises:

inputting the plurality of user data into a response rate model and a resource balance model in a revenue loss model to generate a plurality of response rates and a plurality of resource balance values corresponding to the plurality of users;

generating the benefit curve based on the plurality of response rates and the plurality of resource balance values.

5. The method of claim 1, wherein the plurality of user data is input into a revenue loss model to generate a revenue curve and a loss curve, further comprising:

inputting the plurality of user data into an overdue model and a loss model in an income loss model to generate a plurality of overdue rates and a plurality of loss rates corresponding to the plurality of users;

generating the loss curve based on the plurality of overdue rates and the plurality of loss rates.

6. The method of claim 1, wherein generating an efficiency curve from the yield curve and the loss curve comprises:

and generating the efficiency curve according to the derivative relation of the gain curve and the loss curve.

7. The method of claim 1, wherein determining a resource quota allocation value from the efficiency curve when the efficiency curve satisfies a preset policy comprises:

and when an extreme value exists in the efficiency curve in a calculation interval, determining the resource quota allocation value according to the extreme value.

8. The method of claim 5, wherein adjusting the resource quota for the plurality of users in the target guest group based on the resource quota allocation value comprises:

acquiring users in the target guest group one by one;

acquiring the response rate, the resource allowance value, the overdue rate and the loss rate of the user;

constructing a distribution coefficient matrix based on the response rate, the resource allowance value, the overdue rate and the loss rate;

and adjusting the resource quota for the plurality of users in the target guest group based on the allocation coefficient matrix and the resource quota allocation value.

9. The method of claim 2, wherein determining a temporary quota allocation value from the efficiency curve when the efficiency curve does not satisfy a preset policy comprises:

and when the efficiency curve has no extreme value in the calculation interval, determining the temporary quota allocation value according to the maximum value in the calculation interval.

10. The method of claim 2, wherein tracking efficiency curves of the plurality of users in the target guest group to update the temporary quota allocation value comprises:

after the behavior data of the users in the target guest group are updated, calculating an efficiency curve of the target guest group in real time;

when the efficiency curve meets a preset strategy, generating a resource quota allocation value of the target guest group;

and when the efficiency curve does not meet a preset strategy, updating the temporary quota allocation value of the target guest group according to the efficiency curve.

11. A resource quota adjusting apparatus, comprising:

the data module is used for acquiring a plurality of user data corresponding to a plurality of users in a target guest group, and the user data comprises basic data and behavior data;

a model module for inputting the plurality of user data into a revenue loss model to generate a revenue curve and a loss curve;

a curve module for generating an efficiency curve according to the profit curve and the loss curve;

the quota module is used for determining a resource quota allocation value according to the efficiency curve when the efficiency curve meets a preset strategy;

an adjusting module, configured to adjust the resource quota for the plurality of users in the target guest group based on the resource quota allocation value.

12. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-10.

13. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-10.