CN115481833A - Method, device, equipment and medium for determining quantity of available resources - Google Patents

Method, device, equipment and medium for determining quantity of available resources Download PDF

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CN115481833A
CN115481833A CN202110599115.7A CN202110599115A CN115481833A CN 115481833 A CN115481833 A CN 115481833A CN 202110599115 A CN202110599115 A CN 202110599115A CN 115481833 A CN115481833 A CN 115481833A
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sample
group
candidate
available resources
objects
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王晶
黄文�
董井然
陈守志
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Tencent Technology Shenzhen Co Ltd
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis

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Abstract

The application discloses a method, a device, equipment and a medium for determining the number of available resources, and belongs to the technical field of data processing. The method comprises the following steps: acquiring quantity influence factors of the target objects, wherein the quantity influence factors of the target objects are information influencing the quantity of the available resources acquired by the target objects; determining a target group from at least two candidate groups according to the quantity influence factor of the target object, wherein the at least two candidate groups are determined by grouping the quantity influence factors of the plurality of sample objects; determining a quantity adjusting coefficient corresponding to a target group according to the quantity of available resources which can be acquired by a plurality of sample objects; and determining the number of available resources which can be acquired by the target object according to the number adjustment coefficient corresponding to the target group. The quantity adjusting coefficient is determined according to the quantity of the available resources of the plurality of sample objects, so that the accuracy of the quantity adjusting coefficient is improved, and the quantity of the available resources of the target object determined according to the quantity adjusting coefficient is more accurate.

Description

Method, device, equipment and medium for determining quantity of available resources
Technical Field
The embodiment of the application relates to the technical field of data processing, in particular to a method, a device, equipment and a medium for determining the number of available resources.
Background
With the development of social economy, the available resource allocation service becomes a service related to individuals or enterprises, and the available resource allocation service refers to a service for evaluating the quantity of available resources for individuals or enterprises after a series of risk evaluations are performed on the individuals or the enterprises by an available resource allocation mechanism, wherein the quantity of the available resources is related to a quantity adjustment coefficient of the individuals or the enterprises.
In the related art, when the available resource allocation mechanism evaluates the number of the available resources of an individual or an enterprise, a number adjustment coefficient is determined according to manual experience, then, a product of a basic number and the number adjustment coefficient is calculated, and a product result is used as the number of the available resources of the individual or the enterprise, wherein the basic number is a fixed value. Since the quantity adjustment coefficient is determined from manual experience, the accuracy of the quantity adjustment coefficient is not high, thereby reducing the accuracy of the quantity of available resources.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a medium for determining the number of available resources, which can be used for solving the problem of low accuracy of the number of available resources.
In one aspect, an embodiment of the present application provides a method for determining the number of available resources, where the method includes:
acquiring a quantity influence factor of a target object, wherein the quantity influence factor of the target object is information influencing the quantity of available resources acquired by the target object;
determining a target group from at least two candidate groups according to the number influence factors of the target object, wherein the at least two candidate groups are determined by grouping the number influence factors of a plurality of sample objects;
determining a quantity adjusting coefficient corresponding to the target group according to the quantity of available resources which can be obtained by the plurality of sample objects;
and determining the number of available resources which can be acquired by the target object according to the number adjustment coefficient corresponding to the target group.
In another aspect, an embodiment of the present application provides an apparatus for determining an amount of available resources, where the apparatus includes:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a quantity influence factor of a target object, and the quantity influence factor of the target object is information influencing the quantity of available resources acquired by the target object;
a determining module, configured to determine a target group from at least two candidate groups according to the number influence factor of the target object, where the at least two candidate groups are determined by grouping the number influence factors of the plurality of sample objects;
the determining module is further configured to determine a quantity adjustment coefficient corresponding to the target group according to the quantity of available resources that can be acquired by the plurality of sample objects;
the determining module is further configured to determine, according to the quantity adjustment coefficient corresponding to the target group, the quantity of the available resources that can be acquired by the target object.
In a possible implementation manner, the obtaining module is further configured to obtain, for any sample object, an object label of the any sample object and at least two candidate impact factors, where the object label is a negative sample label or a positive sample label;
the determining module is further configured to determine, for any one of the candidate impact factors, a sample discrimination indicator of the any one of the candidate impact factors based on the any one of the candidate impact factors of the plurality of sample objects and object labels of the plurality of sample objects, the sample discrimination indicator indicating a degree of discrimination between sample objects with negative sample labels and sample objects with positive sample labels; determining the number influence factor from the at least two candidate influence factors according to the sample distinguishing indices of the various candidate influence factors.
In a possible implementation manner, the determining module is configured to group the candidate impact factors of the sample objects to obtain at least two first groups; determining the number of sample objects of the negative sample labels and the number of sample objects of the positive sample labels corresponding to each first group according to the object labels of the plurality of sample objects; and determining the sample distinguishing index of any candidate influence factor according to the number of the sample objects of the negative sample label and the number of the sample objects of the positive sample label corresponding to each first group.
In one possible implementation, the determining module is configured to rank the candidate impact factors of the sample objects; and grouping any one candidate influence factor of the plurality of sample objects based on any one candidate influence factor at the head after sorting and any one candidate influence factor at the tail after sorting to obtain at least two first groups.
In a possible implementation manner, the determining module is configured to rank the respective first groups; for any first group, calculating the sum of the number of sample objects of the negative sample labels according to the number of sample objects of the negative sample labels corresponding to the any first group and the number of sample objects of the negative sample labels corresponding to each first group which is arranged before the any first group, and calculating a first ratio between the number of sample objects of the negative sample labels and the number of sample objects of the negative sample labels in the plurality of sample objects; calculating the sum of the number of sample objects of the positive sample label according to the number of sample objects of the positive sample label corresponding to any one first group and the number of sample objects of the positive sample label corresponding to each first group before any one first group in sequence, and calculating a second ratio between the number of sample objects of the positive sample label and the number of sample objects of the positive sample label in the plurality of sample objects; calculating the difference between a first ratio and a second ratio corresponding to any one first group to obtain the ratio difference corresponding to any one first group; and determining the maximum ratio difference from the ratio differences corresponding to the first groups, and determining the maximum ratio difference as the sample distinguishing index of any one candidate influence factor.
In one possible implementation, the apparatus further includes:
the grouping module is used for grouping the quantity influence factors of the plurality of sample objects to obtain a plurality of second groups;
the calculating module is used for calculating the negative sample probability of each second group;
a merging module, configured to merge the plurality of second groups into the at least two candidate groups according to the negative sample probabilities of the second groups.
In a possible implementation manner, the calculation module is configured to determine, for any one second group, a number of sample objects of a negative sample label corresponding to the any one second group; calculating the negative sample probability of any second group according to the sample object number of the negative sample label corresponding to any second group
In a possible implementation manner, the merging module is configured to sort the plurality of second groups; for any two second groups which are adjacent in sequence, calculating a negative sample probability difference value between negative sample probabilities of the any two second groups which are adjacent in sequence, and calculating a ratio of the negative sample probability difference value to a negative sample probability of any one of the any two second groups which are adjacent in sequence; and if the ratio corresponding to any two second groups which are adjacent in sequence is smaller than the target ratio, combining the any two second groups which are adjacent in sequence into a candidate group.
In a possible implementation manner, the determining module is configured to determine, for any candidate group, the number of available resources of the sample object with the negative sample label and the number of available resources of the sample object with the positive sample label, which correspond to any candidate group, from the number of available resources that can be acquired by the plurality of sample objects; determining an average value of the number of available resources of the sample objects of the negative sample label corresponding to any one candidate group based on the number of available resources of the sample objects of the negative sample label corresponding to any one candidate group, and determining an average value of the number of available resources of the sample objects of the positive sample label corresponding to any one candidate group based on the number of available resources of the sample objects of the positive sample label corresponding to any one candidate group; and determining the quantity adjusting coefficient corresponding to the target group based on the average value of the quantity of the available resources of the sample object with the negative sample label and the average value of the quantity of the available resources of the sample object with the positive sample label corresponding to each candidate group.
In a possible implementation manner, the determining module is configured to determine an efficiency cost ratio based on an average value of the number of available resources of the negative exemplar label exemplar object and an average value of the number of available resources of the positive exemplar label exemplar object corresponding to each candidate group, wherein the efficiency cost ratio is used for characterizing a relationship between cost and efficiency; and determining a quantity adjusting coefficient corresponding to the target group based on the efficiency cost ratio.
In a possible implementation manner, the determining module is configured to determine a first number adjustment coefficient threshold, a second number adjustment coefficient threshold, and a number adjustment coefficient difference between any two candidate groups; determining the average value of the number of the available resources of the plurality of sample objects according to the number of the available resources of the plurality of sample objects; determining a number adjustment coefficient corresponding to the target group based on the first number adjustment coefficient threshold, the second number adjustment coefficient threshold, the number adjustment coefficient difference, the average number of available resources for the plurality of sample objects, and the efficiency cost ratio.
In a possible implementation manner, the average value of the number of available resources of the plurality of sample objects is equal to the sum of the number portions of the available resources corresponding to the respective candidate groups, and the number portion of the available resources corresponding to any candidate group is obtained based on the average value of the number of available resources of the sample object of the positive sample tag corresponding to any candidate group and the number adjustment coefficient corresponding to any candidate group.
In another aspect, an embodiment of the present application provides a computer device, where the computer device includes a processor and a memory, where the memory stores at least one instruction, and the at least one instruction, when executed by the processor, causes the computer device to implement any one of the above methods for determining the amount of available resources.
In another aspect, a computer-readable storage medium is provided, where at least one instruction is stored, and when executed, the at least one instruction implements any one of the above methods for determining an amount of available resources.
In another aspect, a computer program or a computer program product is provided, in which at least one computer instruction is stored, and the at least one computer instruction is loaded and executed by a processor to implement any of the above methods for determining the amount of available resources.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
according to the technical scheme provided by the embodiment of the application, the target group is determined from the at least two candidate groups according to the quantity influence factor of the target object, and the quantity adjustment coefficient of the target object is determined according to the quantity adjustment coefficient corresponding to the target group.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an implementation environment of a method for determining the amount of available resources according to an embodiment of the present application;
fig. 2 is a flowchart of a method for determining the amount of available resources according to an embodiment of the present application;
FIG. 3 is a flow chart of adjusting available resources of various objects according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a sample object grouping section provided in an embodiment of the present application;
fig. 5 is a schematic diagram of a quantity adjustment coefficient determining part provided in an embodiment of the present application;
FIG. 6 is a schematic diagram of a policy application section provided in an embodiment of the present application;
fig. 7 is a block diagram of an apparatus for determining the amount of available resources according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a terminal device according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an implementation environment of a method for determining an amount of an available resource according to an embodiment of the present application, where the implementation environment includes a computer device 11 as shown in fig. 1, and the method for determining an amount of an available resource according to an embodiment of the present application may be executed by the computer device 11. Illustratively, the computer device 11 may comprise at least one of a terminal device or a server.
The terminal device may be at least one of a desktop computer 101, a tablet computer 102, and a laptop portable computer 103, a smart phone 104, a game console. The server may be one server, or a server cluster formed by multiple servers, or any one of a cloud computing platform and a virtualization center, which is not limited in this embodiment of the present application. The server can be in communication connection with the terminal device through a wired network or a wireless network. The server may have functions of data processing, data storage, data transceiving, and the like, and is not limited in the embodiment of the present application.
Data related to the method for determining the number of available resources in the embodiment of the application can be realized based on a cloud technology, data processing/data calculation related to implementation of a scheme can be realized based on cloud calculation, and data related to implementation of the scheme can be stored in a block chain.
Cloud technology refers to a hosting technology for unifying serial resources such as hardware, software, network and the like in a wide area network or a local area network to realize calculation, storage, processing and sharing of data. Cloud technology (Cloud technology) is based on a general term of network technology, information technology, integration technology, management platform technology, application technology and the like applied in a Cloud computing business model, can form a resource pool, is used as required, and is flexible and convenient. Cloud computing technology will become an important support. Background services of the technical network system require a large amount of computing and storage resources, such as video websites, picture-like websites and more web portals. With the high development and application of the internet industry, each article may have its own identification mark and needs to be transmitted to a background system for logic processing, data in different levels are processed separately, and various industrial data need strong system background support and can only be realized through cloud computing.
Cloud computing (cloud computing) is a computing model that distributes computing tasks over a pool of resources formed by a large number of computers, enabling various application systems to obtain computing power, storage space, and information services as needed. The network that provides the resources is referred to as the "cloud". Resources in the "cloud" appear to the user as being infinitely expandable and available at any time, available on demand, expandable at any time, and paid for on-demand.
As a basic capability provider of cloud computing, a cloud computing resource pool (called as an ifas (Infrastructure as a Service) platform for short is established, and multiple types of virtual resources are deployed in the resource pool and are selectively used by external clients.
According to the logic function division, a PaaS (Platform as a Service) layer can be deployed on an IaaS (Infrastructure as a Service) layer, a SaaS (Software as a Service) layer is deployed on the PaaS layer, and the SaaS can be directly deployed on the IaaS. PaaS is a platform on which software runs, such as a database, a web container, etc. SaaS is a variety of business software, such as web portal, sms, and mass texting. Generally speaking, saaS and PaaS are upper layers relative to IaaS.
Cloud computing (cloud computing) refers to a mode of delivery and use of Information Technology (IT) infrastructure, and refers to obtaining required resources in an on-demand, easily-extensible manner over a network; the generalized cloud computing refers to a delivery and use mode of a service, and refers to obtaining a required service in an on-demand and easily-extensible manner through a network. Such services may be IT and software, internet related, or other services. Cloud Computing is a product of development and fusion of traditional computers and Network Technologies, such as Grid Computing (Grid Computing), distributed Computing (Distributed Computing), parallel Computing (Parallel Computing), utility Computing (Utility Computing), network Storage (Network Storage Technologies), virtualization (Virtualization), load balancing (Load Balance), and the like.
With the development of diversification of internet, real-time data stream and connecting equipment and the promotion of demands of search service, social network, mobile commerce, open collaboration and the like, cloud computing is rapidly developed. Different from the prior parallel distributed computing, the generation of cloud computing can promote the revolutionary change of the whole internet mode and the enterprise management mode in concept.
Based on the foregoing implementation environment, an embodiment of the present application provides a method for determining the amount of available resources, as shown in fig. 2, fig. 2 is a flowchart of the method for determining the amount of available resources, which can be executed by the computer device 11 in fig. 1, and the method includes steps S21 to S24.
Step S21, acquiring the quantity influence factor of the target object, wherein the quantity influence factor of the target object is information influencing the quantity of the available resources acquired by the target object.
The target object includes a business or an individual, etc. The amount of available resources is the amount of available resources that can be rated for a business or an individual after the available resource allocation mechanism has performed a series of risk assessments on the business or the individual. The quantity influence factor is information that influences the quantity of available resources, including but not limited to basic information of the target object, for example, the age of the individual, the city of the individual or business, the credit score of the individual, the occupation of the individual, the size of the business, the research and development field of the business, etc. The number of the number influence factors is not limited in the embodiment of the application, and the number influence factors are one or more.
In the embodiment of the application, the target object inputs object information in a program related to an available resource distribution mechanism, so that the quantity influence factor input by the target object is obtained.
Step S22, according to the quantity influence factor of the target object, determining the target group from at least two candidate groups, wherein the at least two candidate groups are determined by grouping the quantity influence factors of the plurality of sample objects.
In the embodiment of the application, the number influence factors of a plurality of sample objects are obtained, and the number influence factors of the plurality of sample objects are grouped to obtain at least two candidate groups. Sample objects include, but are not limited to, businesses or individuals. The embodiment of the present application does not limit the manner of acquiring the quantity influence factor of any sample object, and for example, historically, an object enters object information in a program related to the available resource allocation mechanism, and the quantity influence factor is extracted from the object information of the object and used as the quantity influence factor of any sample object.
The method comprises the steps of grouping quantity influence factors of a plurality of sample objects to obtain at least two candidate groups, achieving the purpose of establishing a mapping relation between the quantity influence factors and the candidate groups, and determining a target group corresponding to a target object from the at least two candidate groups according to the quantity influence factors of the target object and the mapping relation after the quantity influence factors of the target object are obtained.
In a possible implementation manner, any sample object corresponds to at least two candidate impact factors, in this embodiment of the present application, a quantitative impact factor needs to be determined from the at least two candidate impact factors, and a manner of determining the quantitative impact factor from the at least two candidate impact factors is as follows.
The method for determining the number of available resources in the embodiment of the application further comprises the following steps: for any sample object, acquiring an object label and at least two candidate influence factors of any sample object, wherein the object label is a negative sample label or a positive sample label; for any one candidate influence factor, determining a sample discrimination index of any one candidate influence factor based on any one candidate influence factor of the plurality of sample objects and object labels of the plurality of sample objects, wherein the sample discrimination index is used for indicating the discrimination degree between the sample objects with the negative sample labels and the sample objects with the positive sample labels; a quantitative impact factor is determined from the at least two candidate impact factors based on the sample discrimination indices for the various candidate impact factors. Wherein, the above steps may be performed before step S22.
In the embodiment of the present application, the obtaining manner of the at least two candidate impact factors of any sample object is not limited. Illustratively, historically, an object enters object information in a program related to the available resource allocation mechanism, and at least two candidate influence factors are extracted from the object information of the object and serve as the at least two candidate influence factors of any sample object.
For example, historically, the user enters name, gender, age, credit score, occupation, etc. in the programs associated with the available resource allocation mechanism, and extracts age, credit score, and occupation from the information entered by the user as three candidate impact factors for a sample object.
And for any sample object, labeling the object label of the sample object through a manual or trained binary classification model, wherein the object label is a negative sample label or a positive sample label. The model structure and the training mode of the two-class model are not limited in the embodiment of the present application. Illustratively, negative exemplar labels are default labels, loss of confidence labels, low-prime labels, etc., and positive exemplar labels are non-default labels, loss of confidence labels, high-prime labels, etc.
For any candidate influence factor, calculating a sample distinguishing index of any candidate influence factor based on the any candidate influence factor in the multiple sample objects and the object labels of the multiple sample objects, so as to obtain the sample distinguishing indexes of various candidate influence factors, determining the sample distinguishing index meeting the condition from the sample distinguishing indexes of various candidate influence factors, and taking the candidate influence factor corresponding to the sample distinguishing index meeting the condition as the number influence factor. The sample discrimination indexes meeting the conditions include, but are not limited to, the maximum sample discrimination index or a sample discrimination index greater than a preset sample discrimination index threshold.
Wherein, for any one candidate influence factor, determining a sample discrimination indicator for any one candidate influence factor based on any one candidate influence factor of the plurality of sample objects and object labels of the plurality of sample objects comprises: grouping any one candidate influence factor of a plurality of sample objects to obtain at least two first groups; determining the number of sample objects of the negative sample label and the number of sample objects of the positive sample label corresponding to each first group according to the object labels of the plurality of sample objects; and determining the sample distinguishing index of any candidate influence factor according to the number of the sample objects of the negative sample label and the number of the sample objects of the positive sample label corresponding to each first group.
In the embodiment of the present application, when any one of the candidate impact factors of the plurality of sample objects is grouped, the grouping manner is not limited. In a possible implementation manner, grouping is performed in a clustering manner, and any one candidate influence factor of a plurality of sample objects is grouped into at least two classes, wherein each class corresponds to one first group.
For example, any one of the candidate influence factors is a city, and the cities of the plurality of sample objects are grouped into five classes according to five criteria of a first-line city, a second-line city, a third-line city, a fourth-line city, and other cities, thereby obtaining five first groups, which are the first-line city, the second-line city, the third-line city, the fourth-line city, and the other cities, respectively. Wherein other cities include cities other than first-line cities, second-line cities, third-line cities, and fourth-line cities.
In another possible implementation manner, grouping any one of the candidate impact factors of the plurality of sample objects to obtain at least two first groups includes: ranking any one of the candidate impact factors for the plurality of sample objects; any candidate influence factor of the plurality of sample objects is grouped based on any candidate influence factor at the head after sorting and any candidate influence factor at the tail after sorting, and at least two first groups are obtained.
In the embodiment of the application, based on any candidate influence factor at the head after sorting and any candidate influence factor at the tail after sorting, any candidate influence factor of a plurality of sample objects can be randomly grouped or equidistantly grouped.
Illustratively, any one of the candidate influencing factors is age, the ages of the plurality of sample objects are ranked, the credit score at the top of the ranking is 20, the credit score at the bottom of the ranking is 50, and the credit scores of all the sample objects are equally grouped based on 20 and 50 to obtain three first groups, wherein one first group corresponds to the age being greater than or equal to 20 and less than 30, another first group corresponds to the age being greater than or equal to 30 and less than 40, and the rest first group corresponds to the age being greater than or equal to 40 and less than 50.
After the plurality of first groups are obtained, according to the object labels of the plurality of sample objects, the number of sample objects with negative sample labels in the sample objects corresponding to the first groups is determined, and the number of sample objects with positive sample labels in the sample objects corresponding to the first groups is determined. And then determining the sample distinguishing index of any candidate influence factor according to the number of the sample objects of the negative sample label corresponding to each first group and the number of the sample objects of the positive sample label corresponding to the first group.
In a possible implementation manner, determining a sample distinguishing index of any candidate impact factor according to the number of sample objects of the negative sample label and the number of sample objects of the positive sample label corresponding to each first group includes: sorting the first groups; for any one first group, calculating the sum of the number of sample objects of the negative sample labels according to the number of sample objects of the negative sample labels corresponding to any one first group and the number of sample objects of the negative sample labels corresponding to each first group which is arranged before any one first group, and calculating a first ratio between the sum of the number of sample objects of the negative sample labels and the number of sample objects of the negative sample labels in the plurality of sample objects; calculating the sum of the number of sample objects of the positive sample label according to the number of sample objects of the positive sample label corresponding to any one first group and the number of sample objects of the positive sample label corresponding to each first group before any one first group, and calculating a second ratio between the number of sample objects of the positive sample label and the number of sample objects of the positive sample label in the plurality of sample objects; calculating the difference between the first ratio and the second ratio corresponding to any one first group to obtain the ratio difference corresponding to any one first group; and determining the maximum ratio difference from the ratio differences corresponding to the first groups, and determining the maximum ratio difference as the sample distinguishing index of any candidate influence factor.
In the embodiment of the present application, each first group is sorted. For any first group, on one hand, the sum of the number of sample objects of the negative sample label corresponding to any first group and the number of sample objects of the negative sample label corresponding to each first group in the sequence before any first group is calculated, the calculated result is used as the sum of the number of sample objects of the negative sample label, then the ratio between the number of sample objects of the negative sample label and the number of sample objects of the negative sample label in the plurality of sample objects is calculated, and the calculated ratio is used as a first ratio, so that the first ratio corresponding to any first group is obtained; on the other hand, the sum of the number of sample objects of the positive sample label corresponding to any one first group and the number of sample objects of the positive sample label corresponding to each first group before any one first group is sequenced is calculated, the calculated result is used as the sum of the number of sample objects of the positive sample label, then the ratio between the number of sample objects of the positive sample label and the number of sample objects of the positive sample label in the plurality of sample objects is calculated, and the calculated ratio is used as a second ratio, so that the second ratio corresponding to any one first group is obtained.
And then calculating the difference between the first ratio corresponding to any one of the first groups and the second ratio corresponding to any one of the first groups to obtain the ratio difference corresponding to any one of the first groups, then determining the maximum ratio difference from the ratio differences corresponding to the first groups, and determining the maximum ratio difference as the sample distinguishing index of any one of the candidate influence factors.
Illustratively, any one of the candidate impact factors is the age of the individual, the age of the individual of 10 ten thousand sample subjects is divided into five first groups, and the five first groups are ranked, resulting in first groups 1-5. Among the 10 ten thousand sample objects, the number of sample objects with negative sample labels is 5 ten thousand, and the number of sample object data with positive sample labels is 5 ten thousand.
For the first group 1, there is no first group that is ranked before the first group 1, and the number of sample objects of the negative sample label corresponding to the first group 1 is 50, and the number of sample objects of the positive sample label corresponding to the first group 1 is 10, then the sum of the number of sample objects of the negative sample label corresponding to the first group 1 is 50 (i.e., the sum of 50 and 0), the ratio of 50 to 5 ten thousand (i.e., the number of sample objects of the negative sample label in 10 ten thousand sample objects) is calculated, the calculated ratio 0.001 is used as the first ratio corresponding to the first group 1, the sum of the number of sample objects of the positive sample label corresponding to the first group 1 is 10 (i.e., the sum of 10 and 0), the ratio of 10 to 5 ten thousand (i.e., the number of sample objects of the positive sample label in 10 ten thousand sample objects) is calculated, the calculated ratio 0.0002 is used as the second ratio corresponding to the first group 1, and then the difference between 0.001 and 0.0008 is calculated, and the difference corresponding to the first group 1 is obtained.
For the first group 2, the first group before the first group 2 is ranked as a first group 1, the number of sample objects of the negative sample label corresponding to the first group 2 is 100, the number of sample objects of the positive sample label corresponding to the first group 1 is 190, the sum of the number of sample objects of the negative sample label corresponding to the first group 2 is 150 (i.e., the sum of 50 and 100), a ratio of 150 to 5 ten thousand is calculated, the calculated ratio 0.003 is used as a first ratio corresponding to the first group 3, the sum of the number of sample objects of the positive sample label corresponding to the first group 2 is 200 (i.e., the sum of 10 and 190), a ratio of 200 to 5 ten thousand is calculated, the calculated ratio 0.004 is used as a second ratio corresponding to the first group 2, and then the difference between 0.003 and 0.004 is calculated, so as to obtain the difference of the ratio 0.001 corresponding to the first group 2.
According to the above manner, the ratio differences corresponding to the first groups 1 to 5 can be respectively calculated, the maximum ratio difference is determined from the ratio differences corresponding to the first groups 1 to 5, and if the ratio difference 0.001 corresponding to the first group 2 is the maximum ratio difference, 0.001 is used as the sample discrimination index of the personal age.
Further, the sample distinguishing index satisfying the condition is determined from the sample distinguishing indexes of the various candidate influence factors, and the candidate influence factor corresponding to the sample distinguishing index satisfying the condition is used as the number influence factor.
Wherein, before determining the target group from the at least two candidate groups according to the number influence factor of the target object, the method further comprises: grouping the quantity influence factors of the plurality of sample objects to obtain a plurality of second groups; calculating the negative sample probability of each second group; and combining the plurality of second groups into at least two candidate groups according to the negative sample probability of each second group.
When the quantity influence factors of the plurality of sample objects are grouped, the grouping can be performed in a clustering mode, the quantity influence factors of the plurality of sample objects are grouped into at least two classes, and each class corresponds to one second group. Or the quantity influence factors of the plurality of sample objects may be sorted first, and the quantity influence factors of the plurality of sample objects may be grouped based on the quantity influence factor at the head after sorting and the quantity influence factor at the tail after sorting to obtain a plurality of second groups.
And then, calculating the negative sample probability of each second group, then calculating the negative sample probability difference according to the negative sample probability of every two adjacent second groups, if the negative sample probability difference is smaller than a negative sample probability difference threshold value, combining the two adjacent second groups into a candidate group, and if the negative sample probability difference is not smaller than the negative sample probability difference threshold value, taking the two adjacent second groups as the two candidate groups. The negative sample probability difference threshold is set according to manual experience or actual scenes, and the magnitude of the value is not limited.
For example, the number influence factor of any sample object is the credit score of any sample object, the credit scores of 5 ten thousand sample objects are sorted, the credit score at the first position after sorting is 518, the credit score at the last position after sorting is 603, the number influence factors of 5 ten thousand sample objects are grouped based on 518 and 603, 20 second groups are obtained, the negative sample probability of each second group is calculated, and the relationship between the 20 second groups and the corresponding credit score and negative sample probability is shown in table 1.
TABLE 1
Figure BDA0003092274480000121
Figure BDA0003092274480000131
Then, according to the negative sample probability of the second group 1 and the negative sample probability of the second group 2, calculating a negative sample probability difference, if the negative sample probability difference is smaller than a negative sample probability difference threshold, merging the second group 1 and the second group 2 into one candidate group, and if the negative sample probability difference is not smaller than the negative sample probability difference threshold, taking the second group 1 and the second group 2 as two candidate groups. In this way, it is determined that the second group 2 and the second group 3 are combined into one candidate group or the second group 2 and the second group 3 are regarded as two candidate groups, based on the negative sample probability of the second group 2 and the negative sample probability of the second group 3. And so on, until the second group 19 and the second group 20 are determined to be merged into one candidate group according to the negative sample probability of the second group 19 and the negative sample probability of the second group 20, or the second group 19 and the second group 20 are taken as two candidate groups, so that whether every two second groups in the 20 second groups are merged is determined.
In the embodiment of the present application, second groups 1 and 2 are merged into candidate group 1, second groups 3-10 are merged into candidate group 2, second groups 11 and 12 are merged into candidate group 3, second groups 13-18 are merged into candidate group 4, and second groups 19 and 20 are merged into candidate group 5, and at this time, the relationship between the candidate group and the corresponding second group, the credit score, and the negative sample probability is shown in table 2.
TABLE 2
Figure BDA0003092274480000141
In one possible implementation manner, combining multiple second groups into at least two candidate groups according to the negative sample probability of each second group includes: sorting the plurality of second groups; for any two second groups with adjacent sequencing, calculating a negative sample probability difference value between the negative sample probabilities of the any two second groups with adjacent sequencing, and calculating a ratio of the negative sample probability difference value to the negative sample probability of any one of the any two second groups with adjacent sequencing; and if the ratio corresponding to any two second groups which are adjacent in sequence is smaller than the target ratio, combining any two second groups which are adjacent in sequence into a candidate group.
In the embodiment of the application, the plurality of second groups are sorted, when the probability difference of the negative samples is calculated according to the probability of the negative samples of every two second groups which are adjacent to each other in the sorting, the probability difference of the negative samples between the probabilities of the negative samples of the two second groups which are adjacent to each other in the sorting is firstly calculated, then the ratio between the probability difference of the negative samples and the probability of the negative samples of any one of the two second groups which are adjacent to each other in the sorting is calculated, and the calculated ratio is used as the probability difference of the negative samples. In a possible implementation manner, any one of the two second groups that are adjacent to each other in the order is the second group that is located at the back of the two second groups that are adjacent to each other in the order, and in this case, the above calculation process is shown in formula (0).
g＝(p i -p j )/p j PublicFormula (0)
Where g is the negative sample probability difference, p i Probability of negative sample for the second group, p, preceding the two second groups that are adjacent in the sequence j And (4) the probability of the negative sample of the second group which is ranked at the back in two second groups which are ranked adjacently is given.
If the ratio of any two second groups in adjacent ranks is smaller than the target ratio (the negative sample probability difference threshold shown above), that is, the negative sample probability difference of any two second groups in adjacent ranks is smaller than the negative sample probability difference threshold, combining any two second groups in adjacent ranks into a candidate group; and if the ratio corresponding to any two second groups which are adjacent in sequence is not smaller than the target ratio, namely the probability difference of the negative samples corresponding to any two second groups which are adjacent in sequence is not smaller than the probability difference threshold of the negative samples, taking any two second groups which are adjacent in sequence as two candidate groups.
In the embodiment of the present application, calculating the negative sample probability of each second group includes: for any second group, determining the number of sample objects of the negative sample label corresponding to any second group; and calculating the negative sample probability of any second group according to the sample object number of the negative sample label corresponding to any second group.
For any second group, the any second group is obtained by grouping the number influence factors of the multiple sample objects, that is, any second group corresponds to at least one sample object, the number of negative sample labels in the object labels of the sample objects corresponding to the any second group is counted, the counted number of negative sample labels is used as the number of sample objects of the negative sample labels corresponding to the any second group, the ratio of the number of sample objects of the negative sample labels corresponding to the any second group to the total number of sample objects is calculated, and the calculated ratio is used as the negative sample probability of any second group.
For example, 20 second groups are obtained by grouping credit scores of 5 ten thousand sample objects, and are respectively recorded as second groups 1 to 20, where the second group 1 corresponds to 100 sample objects, the number of negative sample labels in the object labels of the 100 sample objects is 55, the number of sample objects of the negative sample labels corresponding to the second group 1 is 55, a ratio of 5 ten thousand of the number of the sample objects to the number of the total sample objects is calculated, and the calculated ratio 0.0011 is used as the negative sample probability of the second group 1.
Step S23, determining a quantity adjustment coefficient corresponding to the target group according to the quantity of available resources that can be acquired by the plurality of sample objects.
In the embodiment of the present application, the number of available resources of a plurality of sample objects is obtained, and the obtaining manner of the number of available resources of any sample object is not limited. For example, when determining the amount of available resources of a certain object historically, a number adjustment coefficient of the certain object is determined according to human experience, then the product of the number adjustment coefficient and a base number is used as the amount of available resources of the certain object, and the amount of available resources is obtained as the amount of available resources of any sample object.
In a possible implementation manner, determining a quantity adjustment coefficient corresponding to a target group according to the quantity of available resources that can be acquired by a plurality of sample objects includes: for any candidate group, determining the number of available resources of the sample object with the negative sample label and the number of available resources of the sample object with the positive sample label corresponding to any candidate group from the number of available resources which can be acquired by a plurality of sample objects; determining the average value of the number of available resources of the sample object of the negative sample label corresponding to any candidate group based on the number of available resources of the sample object of the negative sample label corresponding to any candidate group, and determining the average value of the number of available resources of the sample object of the positive sample label corresponding to any candidate group based on the number of available resources of the sample object of the positive sample label corresponding to any candidate group; and determining the quantity adjusting coefficient corresponding to the target group based on the average value of the quantity of the available resources of the sample object with the negative sample label and the average value of the quantity of the available resources of the sample object with the positive sample label corresponding to each candidate group.
In the embodiment of the application, for any candidate group, the number of available resources of the sample object corresponding to any candidate group is determined from the number of available resources of a plurality of sample objects, and according to the object tag of the sample object corresponding to any candidate group, the number of available resources of the sample object of the negative sample tag corresponding to any candidate group and the number of available resources of the sample object of the positive sample tag corresponding to any candidate group are determined respectively from the number of available resources of the sample object corresponding to any candidate group.
In one aspect, the average of the number of available resources for negative exemplar labeled exemplar objects corresponding to any candidate group is determined based on the number of available resources for negative exemplar labeled exemplar objects corresponding to any candidate group. The sample object with the negative sample label may be referred to as a negative sample, and the average value of the number of available resources of the sample object with the negative sample label corresponding to any candidate group may be referred to as the average number of available resources of the negative sample corresponding to any candidate group.
The formula for calculating the amount of the negative sample average available resources is shown in formula (1).
Figure BDA0003092274480000171
Wherein the content of the first and second substances,
Figure BDA0003092274480000172
is the number of negative sample average available resources,
Figure BDA0003092274480000173
is the number of resources available for the ith negative example, and n is the total number of negative examples.
In general, the calculation formula for averaging the amount of available resources is shown in formula (2).
Figure BDA0003092274480000174
Wherein, avg (q) i ) Is the average number of available resources, q i Is the firstThe number of available resources of i objects, n is the total number of objects, t is the number of groups obtained by grouping all the objects, i.e., the number of candidate groups in the embodiment of the present application, if credit scores of 5 ten thousand sample objects are grouped to obtain 5 candidate groups, t =5,n k Is the number of objects corresponding to the kth candidate group.
Equation (2) above includes the object ratio corresponding to the kth candidate group and the average number of available resources corresponding to the kth candidate group. The object ratio corresponding to the kth candidate group is shown in formula (3), and the average available resource quantity corresponding to the kth candidate group is shown in formula (4).
Figure BDA0003092274480000175
Figure BDA0003092274480000176
Wherein r is k Is the object ratio corresponding to the kth candidate group,
Figure BDA0003092274480000177
is the average number of available resources, q, corresponding to the kth candidate group i Is the number of resources available for the ith object, n is the total number of objects, n k Is the number of objects corresponding to the kth candidate group.
The formula (1) is modified according to the formulas (2), (3) and (4), resulting in the formula (5) shown below.
Figure BDA0003092274480000178
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003092274480000179
is the number of negative sample average available resources,
Figure BDA00030922744800001710
is the negative sample ratio corresponding to the kth candidate group,
Figure BDA00030922744800001711
is the number of negative sample average available resources corresponding to the kth candidate group, and t is the number of candidate groups.
The above equation (5) includes the number of available resources in the negative sample weighted average corresponding to the kth candidate group. The number of available resources for the negative sample weighted average corresponding to the kth candidate group is shown in equation (6).
Figure BDA0003092274480000181
Wherein the content of the first and second substances,
Figure BDA0003092274480000182
is the number of available resources for the negative sample weighted average corresponding to the kth candidate group,
Figure BDA0003092274480000183
is the negative sample ratio corresponding to the kth candidate group,
Figure BDA0003092274480000184
is the number of negative sample average available resources corresponding to the kth candidate group.
At this time, the formula (5) is modified according to the formula (6) and the number adjustment coefficient corresponding to each candidate group, and the following formula (7) is obtained.
Figure BDA0003092274480000185
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003092274480000186
is the number of negative sample average available resources,
Figure BDA0003092274480000187
is the number of available resources, c, of the negative sample weighted average corresponding to the kth candidate group k The number adjustment coefficient corresponding to the kth candidate group is t, and the number of candidate groups is t.
For example, the correspondence between the candidate groups and the credit score and the quantity adjustment coefficient is shown in table 3 below.
TABLE 3
Group of candidates Credit scoring Number adjustment factor
Candidate set 1 531 below c 1
Candidate group 2 531-573 c 2
Candidate group 3 573-579 c 3
Candidate group 4 579-599 c 4
Candidate group 5 599 or more c 5
The formula (7) can be modified to the formula (8) shown below.
Figure BDA0003092274480000188
Wherein the content of the first and second substances,
Figure BDA0003092274480000189
is the number of negative sample average available resources,
Figure BDA00030922744800001810
the number of negative sample weighted average available resources, c, corresponding to candidate groups 1,2,3,4, 5, respectively 1 、c 2 、c 3 、c 4 、c 5 The number adjustment coefficients corresponding to candidate groups 1,2,3,4, 5, respectively.
On the other hand, the average value of the number of available resources of the sample object of the positive sample label corresponding to any candidate group is determined based on the number of available resources of the sample object of the positive sample label corresponding to any candidate group. The sample object with the positive sample label may be referred to as a positive sample, and the average value of the number of available resources of the sample object with the positive sample label corresponding to any candidate group may be referred to as the average number of available resources of the positive sample corresponding to any candidate group.
The calculation formula of the amount of the positive sample average available resources is shown in formula (9).
Figure BDA0003092274480000191
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003092274480000192
is the amount of resources available on average for the positive sample,
Figure BDA0003092274480000193
is the number of available resources for the jth positive sample, and m is the total number of positive samples.
The formula (9) is modified according to the formulas (2), (3) and (4), resulting in the formula (10) shown below.
Figure BDA0003092274480000194
Wherein the content of the first and second substances,
Figure BDA0003092274480000195
is the amount of resources available on average for the positive sample,
Figure BDA0003092274480000196
is the positive sample ratio corresponding to the kth candidate group,
Figure BDA0003092274480000197
is the number of positive sample average available resources corresponding to the kth candidate group, and t is the number of candidate groups.
The formula (10) is modified according to the formula (6) and the number adjustment coefficient corresponding to each candidate group, to obtain the formula (11) shown below.
Figure BDA0003092274480000198
Wherein the content of the first and second substances,
Figure BDA0003092274480000199
is the amount of resources available on average for the positive sample,
Figure BDA00030922744800001910
is the number of positive sample weighted average available resources, c, corresponding to the kth candidate group k Is the number adjustment coefficient corresponding to the kth candidate group, and t is the number of candidate groups.
For the correspondence between the candidate group and the credit score, the number adjustment coefficient shown in the foregoing table 3, equation (11) may be modified to equation (12) shown below.
Figure BDA00030922744800001911
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00030922744800001912
is the amount of resources available on average for the positive sample,
Figure BDA00030922744800001913
the number of available resources, c, of the positive sample weighted average corresponding to candidate groups 1,2,3,4, 5, respectively 1 、c 2 、c 3 、c 4 、c 5 The number adjustment coefficients corresponding to candidate groups 1,2,3,4, 5, respectively.
Then, a quantity adjustment coefficient corresponding to the target group is determined based on the average value of the quantity of available resources of the sample object of the negative sample label corresponding to each candidate group and the average value of the quantity of available resources of the sample object of the positive sample label corresponding to each candidate group.
The average value of the number of available resources of the sample object with the negative sample label corresponding to any candidate group is equivalent to the number of the negative sample weighted average available resources corresponding to the kth candidate group, that is, the number of the available resources
Figure BDA00030922744800001914
The average value of the number of available resources of the sample object of the positive sample label corresponding to any candidate group is equivalent to the aforementioned weighted average number of available resources of the positive sample corresponding to the kth candidate group, that is, the number of available resources is
Figure BDA00030922744800001915
In this embodiment of the present application, determining a quantity adjustment coefficient corresponding to a target group based on an average value of the quantity of available resources of a negative sample label sample object and an average value of the quantity of available resources of a positive sample label sample object corresponding to each candidate group includes: determining an efficiency cost ratio based on the average value of the number of available resources of the sample object with the negative sample label and the average value of the number of available resources of the sample object with the positive sample label corresponding to each candidate group, wherein the efficiency cost ratio is used for representing the relation between the cost and the efficiency; and determining a quantity adjusting coefficient corresponding to the target group based on the efficiency cost ratio.
Using the above formulas (7), (11) and the calculation formula of the efficiency Cost Ratio (BCR), the number adjustment coefficient corresponding to the target group is determined based on the average value of the number of available resources of the sample object of the negative sample label corresponding to each candidate group and the average value of the number of available resources of the sample object of the positive sample label corresponding to each candidate group. The calculation formula of BCR is shown in formula (13).
Figure BDA0003092274480000201
Wherein, BCR is the efficiency cost ratio,
Figure BDA0003092274480000202
is the number of negative sample average available resources,
Figure BDA0003092274480000203
is the number of resources available on average for the positive sample.
In practical applications, the value of the efficiency cost ratio should be as small as possible, and therefore, the following formula (14) can be obtained from the formulas (7), (11), and (13).
Figure BDA0003092274480000204
Wherein BCR is the efficiency cost ratio,
Figure BDA0003092274480000205
is the negative example corresponding to the kth candidate groupWeighted average of the number of available resources, c k Is the number adjustment coefficient corresponding to the kth candidate group, t is the number of candidate groups,
Figure BDA0003092274480000206
is the number of positive sample weighted average available resources, c, corresponding to the kth candidate group k Is the number adjustment coefficient, w, corresponding to the kth candidate group k Is the difference between the number of available resources in the positive sample weighted average and the number of available resources in the negative sample weighted average corresponding to the kth candidate group.
In the embodiment of the present application, c is obtained by calculation according to formula (14) k And calculating to obtain the quantity adjusting coefficient corresponding to each candidate group. Since the target group is determined from at least two candidate groups, after the number adjustment coefficient corresponding to each candidate group is obtained by calculation, it is equivalent to obtaining the number adjustment coefficient corresponding to the target group by calculation.
In one possible implementation, determining a number adjustment coefficient corresponding to the target group based on the efficiency cost ratio includes: determining a first number adjustment coefficient threshold, a second number adjustment coefficient threshold, and a number adjustment coefficient difference between any two candidate groups; determining the average value of the number of the available resources of the plurality of sample objects according to the number of the available resources of the plurality of sample objects; determining a number adjustment coefficient corresponding to the target group based on the first number adjustment coefficient threshold, the second number adjustment coefficient threshold, the number adjustment coefficient difference, the average number of available resources for the plurality of sample objects, and the efficiency cost ratio.
In the embodiment of the application, specific data of the first quantity adjustment coefficient threshold value is determined according to artificial experience, specific data of the second quantity adjustment coefficient threshold value is determined according to artificial experience, and a specific numerical value of a quantity adjustment coefficient difference value between any two candidate groups is determined according to artificial experience.
When the average value of the number of the available resources of the plurality of sample objects is calculated, the sum of the number of the available resources of the plurality of sample objects is calculated, then the ratio between the sum of the number of the available resources and the number of the sample objects is calculated, and the calculated ratio is used as the average value of the number of the available resources of the plurality of sample objects.
The average value of the number of the available resources of the plurality of sample objects is equal to the sum of the number parts of the available resources corresponding to each candidate group, and the number part of the available resources corresponding to any candidate group is obtained based on the average value of the number of the available resources of the sample objects of the positive sample tags corresponding to any candidate group and the number adjusting coefficient corresponding to any candidate group.
In the embodiment of the present application, the average value of the number of available resources of a plurality of sample objects satisfies the following formula (15).
∑ k c k q gk = Q equation (15)
Wherein, c k Is the number adjustment factor corresponding to the kth candidate group,
Figure BDA0003092274480000211
is the number of available resources of the positive sample weighted average corresponding to the kth candidate group (i.e. the average of the number of available resources of the sample object of the positive sample label corresponding to the kth candidate group), and Q is the average of the number of available resources of the plurality of sample objects.
And determining the quantity adjusting coefficient corresponding to the target group based on the first quantity adjusting coefficient threshold, the second quantity adjusting coefficient threshold, the quantity adjusting coefficient difference, the average value of the quantity of available resources of the plurality of sample objects, the average value of the quantity of available resources of the sample objects with the negative sample labels corresponding to the candidate groups and the average value of the quantity of available resources of the sample objects with the positive sample labels. That is, the conditional constraint shown in equation (16) is applied to equation (14) to obtain the number adjustment coefficient corresponding to the target group.
Figure BDA0003092274480000221
Wherein, c k Is the number adjustment coefficient, w, corresponding to the kth candidate group k Is that the kth candidate group corresponds toThe difference between the number of available resources of the positive sample weighted average and the number of available resources of the negative sample weighted average,
Figure BDA0003092274480000222
is the number of available resources of the positive sample weighted average corresponding to the kth candidate group, Q is the average of the number of available resources of the plurality of sample objects, c min Is a first quantity adjustment coefficient threshold, c max Is a second quantity adjustment coefficient threshold, a is a quantity adjustment coefficient difference, c i Is the number adjustment coefficient corresponding to the ith candidate group, c i+1 The number adjustment coefficient corresponding to the (i + 1) th candidate group, and t is the number of candidate groups. Wherein k is less than or equal to t.
For example, for the correspondence between the candidate group and the credit score and the number adjustment coefficient shown in table 3, equation (16) may be modified to equation (17) shown below.
max∑ k w k *c k
Figure BDA0003092274480000223
c min ≤c 1 ≤c 2 ≤c 3 ＝1≤c 4 ≤c 5 ≤c max
c i+1 -c i ≧ a, i =1,2,3,4 equation (17)
Wherein, c k Is the number adjustment coefficient, w, corresponding to the kth candidate group k Is the difference between the number of positive sample weighted average available resources and the number of negative sample weighted average available resources corresponding to the kth candidate group,
Figure BDA0003092274480000224
is the number of available resources of the positive sample weighted average corresponding to the kth candidate group, Q is the average of the number of available resources of the plurality of sample objects, c min Is a first quantity adjustment coefficient threshold, c max Is a second quantity adjustment coefficient threshold, c 1 、c 2 、c 3 、c 4 、c 5 Number adjustment coefficients corresponding to candidate groups 1,2,3,4, 5, respectively, a being the number adjustment coefficient difference, exemplarily, a =0.1, c i Is the number adjustment coefficient corresponding to the ith candidate group, c i+1 The number adjustment coefficient corresponding to the (i + 1) th candidate group, and t is the number of candidate groups. Wherein k is less than or equal to t.
By solving equation (16), the quantity adjustment coefficient corresponding to the kth candidate group, i.e. c, can be obtained k Thus, after the quantity adjustment coefficient corresponding to each candidate group is obtained, the quantity adjustment coefficient corresponding to the target group is obtained through calculation.
And step S24, determining the number of available resources which can be acquired by the target object according to the number adjustment coefficient corresponding to the target group.
In the embodiment of the application, the product of the quantity adjusting coefficient corresponding to the target group and the basic quantity is calculated, and the product result is used as the quantity of the available resources of the target object. The basic number is determined according to manual experience or actual conditions, and the size of specific data is not limited in the embodiment of the application.
According to the technical scheme provided by the embodiment of the application, the target group is determined from the at least two candidate groups according to the quantity influence factor of the target object, and the quantity adjustment coefficient of the target object is determined according to the quantity adjustment coefficient corresponding to the target group.
The above method for determining the amount of available resources is set forth from the perspective of method steps, and will be described in detail with reference to a specific scenario. The scene of the embodiment of the application is a scene of adjusting the amount of the available resources, that is, the amount of the available resources of each object whose amount of the available resources is to be adjusted is adjusted by the resource allocation mechanism every fixed time. That is, the aforementioned sample object and target object are each objects whose amount of available resources is to be adjusted. When the available resource allocation mechanism adjusts the amount of the available resources of each object, as shown in fig. 3, fig. 3 is a flowchart for adjusting the available resources of each object according to the embodiment of the present application, and the whole flow sequentially includes a sample object grouping part, a quantity adjustment coefficient determining part, and a policy application part.
First, a sample object grouping part is executed, as shown in fig. 4, fig. 4 is a schematic diagram of a sample object grouping part provided in an embodiment of the present application. When the sample object grouping part is executed, sample object data is acquired, and current object data of each object with the number of available resources to be adjusted is collected, wherein the object data comprises at least two candidate influence factors, the number of available resources and an object tag. The number influencing factors are then selected, and as indicated above, the sample discrimination indicators for the various candidate influencing factors are first calculated, and then the number influencing factors are determined from the at least two candidate influencing factors based on the sample discrimination indicators for the various candidate influencing factors. Then, the number influence factors are grouped to obtain at least two candidate groups, and the process can be seen in the related description above, which is not described herein again.
Next, a quantity adjustment coefficient determining part is executed, as shown in fig. 5, and fig. 5 is a schematic diagram of a quantity adjustment coefficient determining part provided in an embodiment of the present application. In the embodiment of the present application, the problem of the number adjustment coefficient determination section may be defined as: under the condition that the total amount of the available resources is fixed, the number of the available resources of each sample object is dynamically adjusted, the number of the available resources corresponding to the sample objects with negative sample labels is reduced, and the number of the available resources corresponding to the sample objects with positive sample labels is improved, which is essentially the problem of dynamic allocation of the number of the available resources. Based on the above problem definition, formula derivation is performed according to formulas (1) to (17) shown above, thereby determining the number adjustment coefficients corresponding to the respective candidate groups, i.e., determining the number adjustment coefficients.
Then, a policy application part is executed, as shown in fig. 6, and fig. 6 is a schematic diagram of a policy application part provided in an embodiment of the present application. After determining the number adjustment coefficients corresponding to the candidate groups, the target object is the object for which the number of available resources is to be adjusted. The number of available resources of the target object is determined first, as shown in the foregoing, according to the number influence factor of the target object, a target group is determined from at least two candidate groups, a number adjustment coefficient corresponding to the target group is determined, a product between the base number and the number adjustment coefficient corresponding to the target group is calculated, and a product result is taken as the number of available resources of the target object, so that the number of available resources of each object whose number of available resources is to be adjusted is obtained. And then, performing effect evaluation on the target strategy, wherein in the embodiment of the application, the target strategy is a comparison strategy, the quantity of available resources in the object data of each object collected in the sample object grouping part is used as the quantity of available resources of a comparison group, the quantity of available resources of each object determined in the strategy application part is used as the quantity of available resources of an experimental group, and the effect evaluation is performed by calculating and comparing the BCR value of the experimental group and the BCR value of the comparison group. The calculation method of the BCR value is shown in the formula (13) shown above, and is not described herein again.
In practical applications, the determination of the number of available resources in the embodiment of the present application may be applied in a scenario in which the number of available resources is adjusted, in addition to a scenario in which the number of available resources is adjusted, and the like.
As shown in fig. 7, fig. 7 is a structural diagram of an available resource amount determining apparatus 70 according to an embodiment of the present application, and the available resource amount determining apparatus 70 includes an obtaining module 71 and a determining module 72.
An obtaining module 71, configured to obtain a quantity influence factor of the target object, where the quantity influence factor of the target object is information that influences the quantity of the target object to obtain the available resources.
A determining module 72, configured to determine a target group from at least two candidate groups according to the number influence factor of the target object, where the at least two candidate groups are determined by grouping the number influence factors of the multiple sample objects.
The determining module 72 is further configured to determine a quantity adjustment coefficient corresponding to the target group according to the quantity of available resources that can be acquired by the plurality of sample objects.
The determining module 72 is further configured to determine, according to the quantity adjustment coefficient corresponding to the target group, the quantity of the available resources that can be acquired by the target object.
In a possible implementation manner, the obtaining module 71 is further configured to obtain, for any sample object, an object label of any sample object and at least two candidate influence factors, where the object label is a negative sample label or a positive sample label.
A determining module 72, further configured to determine, for any candidate impact factor, a sample discrimination indicator of any candidate impact factor based on any candidate impact factor of the plurality of sample objects and object labels of the plurality of sample objects, the sample discrimination indicator being used for indicating a degree of discrimination between sample objects with negative sample labels and sample objects with positive sample labels; a quantitative impact factor is determined from the at least two candidate impact factors based on the sample discrimination indices for the various candidate impact factors.
In a possible implementation manner, the determining module 72 is configured to group any one of the candidate impact factors of the plurality of sample objects to obtain at least two first groups; determining the number of sample objects of the negative sample label and the number of sample objects of the positive sample label corresponding to each first group according to the object labels of the plurality of sample objects; and determining the sample distinguishing index of any candidate influence factor according to the number of the sample objects of the negative sample label and the number of the sample objects of the positive sample label corresponding to each first group.
In one possible implementation, the determining module 72 is configured to rank any one of the candidate impact factors of the plurality of sample objects; and grouping any candidate influence factor of the plurality of sample objects based on any candidate influence factor at the head after sorting and any candidate influence factor at the tail after sorting to obtain at least two first groups.
In a possible implementation, the determining module 72 is configured to rank the first groups; for any one first group, calculating the sum of the number of sample objects of the negative sample labels according to the number of sample objects of the negative sample labels corresponding to any one first group and the number of sample objects of the negative sample labels corresponding to each first group which is arranged before any one first group, and calculating a first ratio between the sum of the number of sample objects of the negative sample labels and the number of sample objects of the negative sample labels in the plurality of sample objects; calculating the sum of the number of sample objects of the positive sample label according to the number of sample objects of the positive sample label corresponding to any one first group and the number of sample objects of the positive sample label corresponding to each first group before any one first group, and calculating a second ratio between the number of sample objects of the positive sample label and the number of sample objects of the positive sample label in the plurality of sample objects; calculating the difference between the first ratio and the second ratio corresponding to any one first group to obtain the ratio difference corresponding to any one first group; and determining the maximum ratio difference from the ratio differences corresponding to the first groups, and determining the maximum ratio difference as the sample distinguishing index of any candidate influence factor.
In one possible implementation, the available resource amount determining device 70 further includes a grouping module, a calculating module, and a merging module.
And the grouping module is used for grouping the quantity influence factors of the plurality of sample objects to obtain a plurality of second groups.
And the calculating module is used for calculating the negative sample probability of each second group.
And the merging module is used for merging the plurality of second groups into at least two candidate groups according to the negative sample probability of each second group.
In a possible implementation manner, the calculation module is configured to determine, for any one of the second groups, a number of exemplar objects of the negative exemplar labels corresponding to any one of the second groups; and calculating the negative sample probability of any second group according to the sample object number of the negative sample label corresponding to any second group.
In a possible implementation manner, the merging module is configured to sort the plurality of second groups; for any two second groups which are adjacent in sequence, calculating a negative sample probability difference value between the negative sample probabilities of the any two second groups which are adjacent in sequence, and calculating a ratio of the negative sample probability difference value to the negative sample probability of any one of the any two second groups which are adjacent in sequence; and if the ratio corresponding to any two second groups which are adjacent in sequence is smaller than the target ratio, combining the any two second groups which are adjacent in sequence into a candidate group.
In a possible implementation manner, the determining module 72 is configured to determine, for any candidate group, the number of available resources of the sample object with the negative sample label and the number of available resources of the sample object with the positive sample label corresponding to any candidate group from the number of available resources that can be acquired by the multiple sample objects; determining the average value of the number of available resources of the sample object of the negative sample label corresponding to any candidate group based on the number of available resources of the sample object of the negative sample label corresponding to any candidate group, and determining the average value of the number of available resources of the sample object of the positive sample label corresponding to any candidate group based on the number of available resources of the sample object of the positive sample label corresponding to any candidate group; and determining the quantity adjusting coefficient corresponding to the target group based on the average value of the quantity of the available resources of the sample object with the negative sample label and the average value of the quantity of the available resources of the sample object with the positive sample label corresponding to each candidate group.
In one possible implementation, the determining module 72 is configured to determine an efficiency cost ratio based on the average of the number of available resources of the negative exemplar label exemplar object and the average of the number of available resources of the positive exemplar label exemplar object corresponding to each candidate group, wherein the efficiency cost ratio is used for characterizing the relationship between the cost and the efficiency; and determining a quantity adjusting coefficient corresponding to the target group based on the efficiency cost ratio.
In a possible implementation, the determining module 72 is configured to determine a first number adjustment coefficient threshold, a second number adjustment coefficient threshold, and a number adjustment coefficient difference between any two candidate groups; determining the average value of the number of the available resources of the plurality of sample objects according to the number of the available resources of the plurality of sample objects; and determining the quantity adjusting coefficient corresponding to the target group based on the first quantity adjusting coefficient threshold, the second quantity adjusting coefficient threshold, the quantity adjusting coefficient difference, the average quantity of the available resources of the plurality of sample objects and the efficiency cost ratio.
In a possible implementation manner, the average value of the number of available resources of the plurality of sample objects is equal to the sum of the number portions of the available resources corresponding to the respective candidate groups, and the number portion of the available resources corresponding to any candidate group is obtained based on the average value of the number of available resources of the sample object of the positive sample tag corresponding to any candidate group and the number adjustment coefficient corresponding to any candidate group.
It should be understood that, when the apparatus provided in fig. 7 implements its functions, it is only illustrated by the division of the functional modules, and in practical applications, the above functions may be distributed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above. In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.
Fig. 8 shows a block diagram of a terminal device 800 according to an exemplary embodiment of the present application. The terminal device 800 may be a portable mobile terminal such as: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion Picture Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. The terminal device 800 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, etc.
In general, the terminal device 800 includes: a processor 801 and a memory 802.
The processor 801 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so forth. The processor 801 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 801 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 801 may be integrated with a GPU (Graphics Processing Unit) that is responsible for rendering and drawing content that the display screen needs to display. In some embodiments, the processor 801 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
Memory 802 may include one or more computer-readable storage media, which may be non-transitory. Memory 802 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 802 is used to store at least one instruction for execution by processor 801 to implement the method of determining the amount of available resources provided by the method embodiments herein.
In some embodiments, the terminal device 800 may further include: a peripheral interface 803 and at least one peripheral. The processor 801, memory 802 and peripheral interface 803 may be connected by bus or signal lines. Various peripheral devices may be connected to peripheral interface 803 by a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 804, a display screen 805, a camera assembly 806, an audio circuit 807, a positioning assembly 808, and a power supply 809.
The peripheral interface 803 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 801 and the memory 802. In some embodiments, the processor 801, memory 802, and peripheral interface 803 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 801, the memory 802, and the peripheral interface 803 may be implemented on separate chips or circuit boards, which are not limited by this embodiment.
The Radio Frequency circuit 804 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 804 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 804 converts an electrical signal into an electromagnetic signal to be transmitted, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 804 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuit 804 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: the world wide web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the radio frequency circuit 804 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.
The display screen 805 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display 805 is a touch display, the display 805 also has the ability to capture touch signals on or above the surface of the display 805. The touch signal may be input to the processor 801 as a control signal for processing. At this point, the display 805 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display 805 may be one, and is disposed on the front panel of the terminal device 800; in other embodiments, the number of the display screens 805 may be at least two, and the at least two display screens are respectively disposed on different surfaces of the terminal device 800 or are in a folding design; in other embodiments, the display 805 may be a flexible display, disposed on a curved surface or a folded surface of the terminal device 800. Even further, the display 805 may be arranged in a non-rectangular irregular pattern, i.e., a shaped screen. The Display 805 can be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), and other materials.
The camera assembly 806 is used to capture images or video. Optionally, camera assembly 806 includes a front camera and a rear camera. Generally, a front camera is disposed at a front panel of the terminal, and a rear camera is disposed at a rear surface of the terminal. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, the main camera and the wide-angle camera are fused to realize panoramic shooting and a VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, camera assembly 806 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.
The audio circuit 807 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 801 for processing or inputting the electric signals to the radio frequency circuit 804 to realize voice communication. For the purpose of stereo sound collection or noise reduction, a plurality of microphones may be provided at different positions of the terminal device 800. The microphone may also be an array microphone or an omni-directional pick-up microphone. The speaker is used to convert electrical signals from the processor 801 or the radio frequency circuit 804 into sound waves. The loudspeaker can be a traditional film loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, or converting an electric signal into a sound wave inaudible to a human being to measure a distance. In some embodiments, the audio circuitry 807 may also include a headphone jack.
The positioning component 808 is used to locate the current geographic Location of the terminal device 800 to implement navigation or LBS (Location Based Service). The Positioning component 808 may be a Positioning component based on the Global Positioning System (GPS) in the united states, the beidou System in china, or the galileo System in russia.
The power supply 809 is used to supply power to various components in the terminal apparatus 800. The power supply 809 can be ac, dc, disposable or rechargeable. When the power supply 809 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge technology.
In some embodiments, terminal device 800 also includes one or more sensors 810. The one or more sensors 810 include, but are not limited to: acceleration sensor 811, gyro sensor 812, pressure sensor 813, fingerprint sensor 814, optical sensor 815 and proximity sensor 816.
The acceleration sensor 811 can detect the magnitude of acceleration in three coordinate axes of the coordinate system established with the terminal apparatus 800. For example, the acceleration sensor 811 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 801 may control the display 805 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 811. The acceleration sensor 811 may also be used for acquisition of motion data of a game or a user.
The gyro sensor 812 may detect a body direction and a rotation angle of the terminal device 800, and the gyro sensor 812 may cooperate with the acceleration sensor 811 to acquire a 3D motion of the user on the terminal device 800. The processor 801 may implement the following functions according to the data collected by the gyro sensor 812: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.
Pressure sensors 813 may be disposed on the side bezel of terminal device 800 and/or underneath display screen 805. When the pressure sensor 813 is arranged on the side frame of the terminal device 800, the holding signal of the user to the terminal device 800 can be detected, and the processor 801 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 813. When the pressure sensor 813 is disposed at a lower layer of the display screen 805, the processor 801 controls the operability control on the UI interface according to the pressure operation of the user on the display screen 805. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.
The fingerprint sensor 814 is used for collecting a fingerprint of the user, and the processor 801 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 814, or the fingerprint sensor 814 identifies the identity of the user according to the collected fingerprint. Upon identifying that the user's identity is a trusted identity, the processor 801 authorizes the user to perform relevant sensitive operations including unlocking a screen, viewing encrypted information, downloading software, paying for and changing settings, etc. Fingerprint sensor 814 may be disposed on the front, back, or side of terminal device 800. When a physical button or a vendor Logo is provided on the terminal device 800, the fingerprint sensor 814 may be integrated with the physical button or the vendor Logo.
The optical sensor 815 is used to collect ambient light intensity. In one embodiment, processor 801 may control the display brightness of display 805 based on the ambient light intensity collected by optical sensor 815. Specifically, when the ambient light intensity is high, the display brightness of the display screen 805 is increased; when the ambient light intensity is low, the display brightness of the display 805 is adjusted down. In another embodiment, the processor 801 may also dynamically adjust the shooting parameters of the camera assembly 806 according to the ambient light intensity collected by the optical sensor 815.
A proximity sensor 816, also called a distance sensor, is typically provided on the front panel of the terminal device 800. The proximity sensor 816 is used to collect the distance between the user and the front surface of the terminal device 800. In one embodiment, when the proximity sensor 816 detects that the distance between the user and the front surface of the terminal device 800 is gradually reduced, the processor 801 controls the display 805 to switch from the bright screen state to the dark screen state; when the proximity sensor 816 detects that the distance between the user and the front surface of the terminal device 800 becomes gradually larger, the display screen 805 is controlled by the processor 801 to switch from the screen-on state to the screen-on state.
Those skilled in the art will appreciate that the configuration shown in fig. 8 is not limiting of terminal device 800 and may include more or fewer components than shown, or may combine certain components, or may employ a different arrangement of components.
Fig. 9 is a schematic structural diagram of a server provided in this embodiment, where the server 900 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 901 and one or more memories 902, where the one or more memories 902 store at least one program code, and the at least one program code is loaded and executed by the one or more processors 901 to implement the method for determining the amount of available resources provided by the above-mentioned method embodiments. Certainly, the server 900 may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input and output, and the server 900 may also include other components for implementing device functions, which are not described herein again.
In an exemplary embodiment, a computer-readable storage medium is also provided, having stored therein at least one instruction, which when executed, implements any of the above-described methods for determining the amount of available resources.
Alternatively, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, a computer program or a computer program product is also provided, in which at least one computer instruction is stored, which is loaded and executed by a processor to implement any of the above methods for determining the amount of available resources.
It should be understood that reference to "a plurality" herein means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the advantages and disadvantages of the embodiments.
The above description is only exemplary of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (15)

1. A method for determining the amount of available resources, the method comprising:
acquiring a quantity influence factor of a target object, wherein the quantity influence factor of the target object is information influencing the quantity of available resources acquired by the target object;
determining a target group from at least two candidate groups according to the number influence factors of the target object, wherein the at least two candidate groups are determined by grouping the number influence factors of a plurality of sample objects;
determining a quantity adjusting coefficient corresponding to the target group according to the quantity of available resources which can be acquired by the plurality of sample objects;
and determining the number of available resources which can be acquired by the target object according to the number adjustment coefficient corresponding to the target group.
2. The method of claim 1, further comprising:
for any sample object, acquiring an object label and at least two candidate influence factors of the sample object, wherein the object label is a negative sample label or a positive sample label;
for any one candidate impact factor, determining a sample discrimination indicator of the any one candidate impact factor based on the any one candidate impact factor of the plurality of sample objects and object labels of the plurality of sample objects, the sample discrimination indicator indicating a degree of discrimination between sample objects of negative sample labels and sample objects of positive sample labels;
determining the number influence factor from the at least two candidate influence factors according to the sample distinguishing indices of the various candidate influence factors.
3. The method of claim 2, wherein said determining a sample discrimination metric for said any one candidate impact factor based on said any one candidate impact factor of said plurality of sample objects and object labels of said plurality of sample objects comprises:
grouping the any one candidate influence factor of the plurality of sample objects to obtain at least two first groups;
determining the number of sample objects of the negative sample label and the number of sample objects of the positive sample label corresponding to each first group according to the object labels of the plurality of sample objects;
and determining the sample distinguishing index of any candidate influence factor according to the number of the sample objects of the negative sample label and the number of the sample objects of the positive sample label corresponding to each first group.
4. The method according to claim 3, wherein said grouping of said any one of said candidate impact factors for said plurality of sample objects into at least two first groups comprises:
ranking the any one of the candidate impact factors for the plurality of sample objects;
and grouping any one candidate influence factor of the plurality of sample objects based on any one candidate influence factor at the head after sorting and any one candidate influence factor at the tail after sorting to obtain at least two first groups.
5. The method according to claim 3, wherein said determining a sample differentiation index for said any one candidate impact factor according to the number of sample objects with negative sample labels and the number of sample objects with positive sample labels for said each first group comprises:
sorting the respective first groups;
for any first group, calculating the sum of the number of the sample objects with negative sample labels according to the number of the sample objects with negative sample labels corresponding to the any first group and the number of the sample objects with negative sample labels corresponding to each first group before the any first group, and calculating a first ratio between the number of the sample objects with negative sample labels and the number of the sample objects with negative sample labels in the plurality of sample objects;
calculating the sum of the number of sample objects of the positive sample label according to the number of sample objects of the positive sample label corresponding to any one first group and the number of sample objects of the positive sample label corresponding to each first group before any one first group in sequence, and calculating a second ratio between the number of sample objects of the positive sample label and the number of sample objects of the positive sample label in the plurality of sample objects;
calculating the difference between a first ratio and a second ratio corresponding to any one first group to obtain the ratio difference corresponding to any one first group;
and determining the maximum ratio difference from the ratio differences corresponding to the first groups, and determining the maximum ratio difference as the sample distinguishing index of any one candidate influence factor.
6. The method according to any one of claims 2-5, wherein before determining the target group from the at least two candidate groups according to the number of target objects influencing factor, further comprising:
grouping the number influence factors of the plurality of sample objects to obtain a plurality of second groups;
calculating the negative sample probability of each second group;
and combining the plurality of second groups into the at least two candidate groups according to the negative sample probabilities of the second groups.
7. The method of claim 6, wherein calculating negative sample probabilities for each second group comprises:
for any second group, determining the number of sample objects of the negative sample label corresponding to the any second group;
and calculating the negative sample probability of any second group according to the sample object number of the negative sample label corresponding to any second group.
8. The method according to claim 6, wherein said combining the plurality of second groups into the at least two candidate groups according to the negative sample probabilities of the respective second groups comprises:
sorting the plurality of second groups;
for any two second groups which are adjacent in sequence, calculating a negative sample probability difference value between negative sample probabilities of the any two second groups which are adjacent in sequence, and calculating a ratio of the negative sample probability difference value to a negative sample probability of any one of the any two second groups which are adjacent in sequence;
and if the ratio corresponding to any two second groups which are adjacent in sequence is smaller than the target ratio, combining the any two second groups which are adjacent in sequence into a candidate group.
9. The method according to any one of claims 2 to 5, wherein the determining a quantity adjustment factor corresponding to the target group according to the quantity of available resources that can be obtained by the plurality of sample objects comprises:
for any candidate group, determining the number of available resources of the sample object with the negative sample label and the number of available resources of the sample object with the positive sample label corresponding to any candidate group from the number of available resources which can be acquired by the plurality of sample objects;
determining an average value of the number of available resources of the sample object of the negative sample label corresponding to any candidate group based on the number of available resources of the sample object of the negative sample label corresponding to any candidate group, and determining an average value of the number of available resources of the sample object of the positive sample label corresponding to any candidate group based on the number of available resources of the sample object of the positive sample label corresponding to any candidate group;
and determining the quantity adjusting coefficient corresponding to the target group based on the average value of the quantity of the available resources of the sample object with the negative sample label and the average value of the quantity of the available resources of the sample object with the positive sample label corresponding to each candidate group.
10. The method of claim 9, wherein determining the quantity adjustment factor corresponding to the target group based on the average value of the quantity of available resources of the negative exemplar label exemplar objects and the average value of the quantity of available resources of the positive exemplar label exemplar objects corresponding to each candidate group comprises:
determining an efficiency cost ratio based on the average value of the number of available resources of the sample object with the negative sample label and the average value of the number of available resources of the sample object with the positive sample label corresponding to each candidate group, wherein the efficiency cost ratio is used for representing the relation between the cost and the efficiency;
and determining a quantity adjusting coefficient corresponding to the target group based on the efficiency cost ratio.
11. The method of claim 10, wherein determining a quantity adjustment factor corresponding to the target group based on the efficiency cost ratio comprises:
determining a first number adjustment coefficient threshold, a second number adjustment coefficient threshold, and a number adjustment coefficient difference between any two candidate groups;
determining the average value of the number of the available resources of the plurality of sample objects according to the number of the available resources of the plurality of sample objects;
determining a number adjustment coefficient corresponding to the target group based on the first number adjustment coefficient threshold, the second number adjustment coefficient threshold, the number adjustment coefficient difference, the average number of available resources for the plurality of sample objects, and the efficiency cost ratio.
12. The method of claim 11, wherein the average of the number of available resources of the plurality of sample objects is equal to the sum of the number portions of available resources corresponding to each candidate group, and the number portion of available resources corresponding to any candidate group is obtained based on the average of the number of available resources of the sample object corresponding to the positive sample label corresponding to any candidate group and the number adjustment coefficient corresponding to any candidate group.
13. An apparatus for determining the amount of available resources, the apparatus comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring the quantity influence factor of a target object, and the quantity influence factor of the target object is information influencing the quantity of the target object to acquire available resources;
a determining module, configured to determine a target group from at least two candidate groups according to the number influence factor of the target object, where the at least two candidate groups are determined by grouping the number influence factors of the plurality of sample objects;
the determining module is further configured to determine a quantity adjustment coefficient corresponding to the target group according to the quantity of available resources that can be acquired by the plurality of sample objects;
the determining module is further configured to determine, according to the quantity adjustment coefficient corresponding to the target group, the quantity of the available resources that can be acquired by the target object.
14. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction which, when executed by the processor, causes the computer device to carry out a method of determining an amount of available resources according to any one of claims 1 to 12.
15. A computer-readable storage medium having stored therein at least one instruction which, when executed, performs a method for determining an amount of available resources as recited in any one of claims 1 to 12.
CN202110599115.7A 2021-05-31 2021-05-31 Method, device, equipment and medium for determining quantity of available resources Pending CN115481833A (en)

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