CN117422276A - Resource limit determining method, device, computer equipment and storage medium - Google Patents
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- G—PHYSICS
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- 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
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- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/27—Regression, e.g. linear or logistic regression
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- 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
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- G06Q30/018—Certifying business or products
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- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
Abstract
The application relates to a resource quota determining method, a resource quota determining device, computer equipment and a storage medium, and relates to the technical field of financial science and technology or other related fields. The method comprises the following steps: acquiring enterprise production factor characteristics corresponding to an enterprise to be granted with resources; determining a resource grant state of an enterprise according to the characteristics of the enterprise production factors; under the condition that the resource grant state characterizes the grant resource, acquiring the yield influence factor characteristics of the products produced by the enterprise; obtaining a target resource quota granting category corresponding to an enterprise from a plurality of preset candidate resource quota granting categories according to the yield influencing factor characteristics; determining the resource limit to be granted corresponding to the enterprise according to the target resource limit grant category; the to-be-granted resource amount is an amount for transferring resources to the enterprise. By adopting the method, the accuracy of the resource grant limit can be determined.
Description
Technical Field
The present disclosure relates to the field of financial science and technology, and in particular, to a method, an apparatus, a computer device, and a storage medium for determining a resource quota.
Background
With the high-speed development of economy, the development trend of enterprise product production is rapid, and with the continuous promotion of product production technology, the product yield is rapidly increased, the yield value is greatly increased, but enterprises face the problem of resource shortage in order to enlarge the scale. For example, the planting enterprises need to enlarge the planting area while improving the planting yield, and if the planting enterprises need to acquire planting resources, resource borrowing needs to be performed.
However, the existing product manufacturing enterprises may have guaranteed resources to guarantee, and resource borrowing is difficult to achieve. And the existing resource grant limit of resource borrowing is calculated by simply classifying through designing a scoring card model.
The applicant finds that the existing method for determining the resource quota has the problem of low accuracy in the implementation process of the prior art.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, and a storage medium for determining a resource credit, which can improve accuracy of determining the resource credit.
In a first aspect, the present application provides a method for determining a resource quota, where the method includes:
acquiring enterprise production factor characteristics corresponding to an enterprise to be granted with resources;
Determining a resource grant state of an enterprise according to the characteristics of the enterprise production factors;
under the condition that the resource grant state characterizes the grant resource, acquiring the yield influence factor characteristics of the products produced by the enterprise;
obtaining a target resource quota granting category corresponding to an enterprise from a plurality of preset candidate resource quota granting categories according to the yield influencing factor characteristics;
determining the resource limit to be granted corresponding to the enterprise according to the target resource limit grant category; the to-be-granted resource amount is an amount for transferring resources to the enterprise.
In one embodiment, according to the yield influencing factor feature, obtaining a target resource quota grant category corresponding to the enterprise from a plurality of preset candidate resource quota grant categories includes:
determining the matching degree of the enterprise and a plurality of candidate resource quota grant categories according to the yield influence factor characteristics;
and taking the candidate resource quota granting category corresponding to the maximum matching degree as the target resource quota granting category corresponding to the enterprise.
In one embodiment, determining the matching degree of the enterprise and the plurality of candidate resource credit grant categories according to the yield influence factor features includes:
Determining a current resource credit grant category from a plurality of candidate resource credit grant categories;
acquiring sample output influence factor characteristics of products produced by a sample enterprise;
obtaining target yield influence factor characteristics corresponding to the current resource quota granting category according to the sample yield influence factor characteristics and the current resource quota granting category;
and determining the matching degree of the enterprise and the current resource quota grant category according to the yield influence factor characteristics and the target yield influence factor characteristics.
In one embodiment, obtaining the target yield impact factor characteristic corresponding to the current resource credit granting category according to the sample yield impact factor characteristic and the current resource credit granting category includes:
determining a weight parameter of a reconstructed sample corresponding to the current resource quota grant category according to the sample yield influence factor characteristic and the minimum similarity degree of the yield influence factor characteristic;
the target yield impact factor characteristic is determined based on the weight parameter, the current resource credit grant category, and the sample yield impact factor characteristic.
In one embodiment, determining the matching degree of the enterprise and the current resource credit grant category according to the yield impact factor characteristic and the target yield impact factor characteristic comprises:
Acquiring the distance between the yield influence factor characteristics and the target yield influence factor characteristics;
and obtaining the matching degree of the enterprise and the current resource quota grant category according to the yield influence factor characteristics and the distance between the target yield influence factor characteristics.
In one embodiment, determining the resource grant status of the enterprise according to the enterprise production factor characteristics includes:
acquiring a first sample production factor of a sample resource grant enterprise corresponding to a grant resource and a second sample production factor of a sample non-resource grant enterprise corresponding to a non-grant resource;
and determining the resource grant state of the enterprise according to the first sample production factor, the first sample production factor and the enterprise production factor characteristics.
In one embodiment, determining the resource limit to be granted corresponding to the enterprise according to the target resource limit grant category includes:
acquiring a resource transfer parameter corresponding to a target resource quota grant category;
and determining the resource limit to be granted corresponding to the enterprise according to the resource transfer parameters.
In a second aspect, the present application further provides a resource quota determining apparatus, where the apparatus includes:
the production factor acquisition module is used for acquiring enterprise production factor characteristics corresponding to the enterprise to be granted with the resources;
The grant state determining module is used for determining the resource grant state of the enterprise according to the characteristics of the production factors of the enterprise;
the yield factor obtaining module is used for obtaining yield influence factor characteristics of products produced by enterprises under the condition that the resource grant state characterizes the grant resources;
the grant category acquisition module is used for acquiring a target resource quota grant category corresponding to the enterprise from a plurality of preset candidate resource quota grant categories according to the yield influence factor characteristics;
the resource limit determining module is used for determining the resource limit to be granted corresponding to the enterprise according to the target resource limit grant category; the to-be-granted resource amount is an amount for transferring resources to the enterprise.
In a third aspect, the present application also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method described above.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described above.
The resource limit determining method, the resource limit determining device, the computer equipment and the storage medium are used for obtaining the enterprise production factor characteristics corresponding to the enterprise to be granted with the resources; determining a resource grant state of an enterprise according to the characteristics of the enterprise production factors; under the condition that the resource grant state characterizes the grant resource, acquiring the yield influence factor characteristics of the products produced by the enterprise; obtaining a target resource quota granting category corresponding to an enterprise from a plurality of preset candidate resource quota granting categories according to the yield influencing factor characteristics; according to the target resource quota granting category, the resource quota to be granted corresponding to the enterprise can be determined. Compared with the prior art, the method and the device for determining the grant limit of the resource limit determine the grant category of the target resource limit corresponding to the enterprise through the characteristics of the production factors of the enterprise and the characteristics of the yield influence factors of the product, and further determine the resource limit to be granted corresponding to the enterprise through the grant category of the target resource limit, so that accuracy of determining the grant limit can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is a flowchart illustrating a method for determining a resource credit in an embodiment;
FIG. 2 is a flowchart illustrating a step of determining matching degree between an enterprise and a plurality of candidate resource credit grant categories according to one embodiment;
FIG. 3 is a flowchart illustrating steps for obtaining target yield impact factor characteristics corresponding to a current resource credit grant category according to one embodiment;
FIG. 4 is a flow diagram of the steps for determining the resource grant status of an enterprise in one embodiment;
FIG. 5 is a flowchart illustrating a method for determining a resource credit according to another embodiment;
FIG. 6 is a flowchart illustrating a step of determining a trust category based on a least squares regression classification method in one embodiment;
FIG. 7 is a block diagram illustrating a resource credit determination device in one embodiment;
Fig. 8 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In an exemplary embodiment, as shown in fig. 1, a method for determining a resource quota is provided, where this embodiment is applied to a terminal for illustration, and it is understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following S102 to S110. Wherein:
s102, obtaining enterprise production factor characteristics corresponding to the enterprise to be granted with the resources.
The enterprise to be granted resources may be an enterprise applying for granting resources, an enterprise applying for trust, an enterprise planting products, for example, a tea enterprise. The business production factor characteristics may be basic characteristics of the business, for example, may be product production location information, business condition information, business credit level, etc. of the business.
For example, an enterprise to be granted resources may be obtained, for example, a user may submit an application for granting resources through a terminal, a background person may verify the authenticity of the enterprise to be granted resources, and in the case that verification passes, the server may obtain the enterprise to be granted resources, and may obtain the enterprise production factor characteristics corresponding to the enterprise to be granted resources. The characteristics of the enterprise production factors can be obtained according to the data submitted by the user on the platform. For example, for tea enterprises, the current characteristics of tea planting and resource management agency trust can be combined, and explanatory variables are divided into text type characteristics and numerical type characteristics, which are respectively taken from production factors of the tea enterprises and influencing factors of tea yield. And the characteristics of the production factors of enterprises can be obtained according to the production factors.
S104, determining the resource grant state of the enterprise according to the characteristics of the enterprise production factors.
Where the resource grant status may characterize whether the enterprise resource may be granted, e.g., the enterprise may be divided into an enterprise that provides the resource grant and an enterprise that does not provide the resource grant.
For example, the server may classify an enterprise to be granted resources according to characteristics of enterprise production factors, and determine a resource grant status of the enterprise. For example, for tea enterprises, the naive bayes classification method can be utilized to divide the tea enterprises into enterprises providing resource grant and enterprises not providing resource grant based on the characteristics of the production elements of the enterprises such as lands, factories, tea gardens and the like.
Optionally, let the given N sample sets be x= [ X ] 1 ,x 2 ,...,x N ]The ith sample feature is denoted as x i ＝(x i (1),x i (2),...,x i (k) X), where x i (1),x i (2),...,x i (k) And each may represent a characteristic of an enterprise's production factors. Resource to be grantedClassifying the enterprise using a naive bayes algorithm, the classes being denoted by c1, c2, and calculating the probability P (c 1 |y)、P(c 2 I y), which probability is large, and which class sample y belongs to.
S106, obtaining the yield influence factor characteristics of the products produced by the enterprise under the condition that the resource grant state characterizes the grant resource.
Wherein, the enterprise that can grant the resource can be a trusted enterprise. The granted resource refers to that a resource grant may be provided. The yield-influencing factor characteristic may be a characteristic of a influencing factor for the yield of the product. For example, for an enterprise, the yield impact factors may be meteorological factor characteristics, soil factor characteristics, and the like; the meteorological factor features may include: the accumulated temperature is more than or equal to 10 ℃, the annual average relative humidity, the annual daily rainfall is more than or equal to 10mm, the annual precipitation and the annual average temperature are all over the year. Soil factors may include: soil organic matter content, nitrogen content, ph value and quick-acting potassium.
In an exemplary case where the server determines that the resource grant status characterizes the grantable resource, the server may further determine a resource grant limit for the enterprise, may obtain a yield-affecting factor characteristic of the product produced by the enterprise, and may further determine the resource grant limit for the enterprise through the yield-affecting factor characteristic.
S108, according to the characteristics of the yield influence factors, obtaining a target resource quota granting category corresponding to the enterprise from a plurality of preset candidate resource quota granting categories.
The candidate resource quota granting category may be a category granting the resource quota, and may be a level granting the resource quota. For example, the resource units that can be granted may be classified according to candidate resource unit grant categories, which may be divided into 4 categories, each of which may correspond to a different resource gain rate, as an example. The target resource quota granting category refers to a resource quota granting category corresponding to an enterprise to be resource granted.
For example, the server may further classify the awardable resource credit of the enterprise from a plurality of preset candidate resource credit awarding categories according to the yield influencing factor characteristics of the enterprise, and determine the target resource credit awarding category of the enterprise. For example, from a plurality of candidate resource credit grant categories, the candidate resource credit grant category that best meets the enterprise may be screened as the target resource credit grant category. As an example, the matching degree of the enterprise and each candidate resource quota grant category may be determined according to the yield influence factor characteristic of the enterprise, and the candidate resource quota grant category corresponding to the largest matching degree is taken as the target resource quota grant category. Further, the resource grant can be performed according to the target resource quota grant category corresponding to the enterprise.
Optionally, based on the classification of the least squares regression, determining a target resource credit granting category corresponding to the enterprise according to the matching degree of the output influencing factor characteristic and the preset plurality of candidate resource credit granting categories.
S110, determining the resource limit to be granted corresponding to the enterprise according to the target resource limit grant category; the to-be-granted resource amount is an amount for transferring resources to the enterprise.
The resource amount to be granted may be an amount of resources that may be granted, an amount of trust granted to the enterprise, or an amount of resources to be transferred to the enterprise.
For example, the server may determine, according to a target resource quota grant category of an enterprise, a resource quota to be granted for transferring resources to the enterprise. For example, the resource limit granted to the enterprise may be determined according to the limit coefficient corresponding to the target resource limit grant category, the credit rating of the enterprise may be determined according to the target resource limit grant category, the final credit rating of the enterprise may be determined according to the credit rating and the credit basic limit of the enterprise, and the resource gain rate of the enterprise that needs to return the resource to the resource management mechanism may be determined according to the credit rating.
In the embodiment, the enterprise production factor characteristics corresponding to the enterprise to be granted with the resources are obtained; determining a resource grant state of an enterprise according to the characteristics of the enterprise production factors; under the condition that the resource grant state characterizes the grant resource, acquiring the yield influence factor characteristics of the products produced by the enterprise; obtaining a target resource quota granting category corresponding to an enterprise from a plurality of preset candidate resource quota granting categories according to the yield influencing factor characteristics; according to the target resource quota granting category, the resource quota to be granted corresponding to the enterprise can be determined. Compared with the prior art, the method and the device for determining the grant limit of the resource have the advantages that the target resource limit grant category corresponding to the enterprise is determined through the production factor characteristics of the enterprise and the yield influence factor characteristics of the product, and the resource limit to be granted corresponding to the enterprise can be further determined through the target resource limit grant category, so that accuracy of determining the grant limit can be improved.
In an exemplary embodiment, according to the characteristics of the yield influencing factor, the obtaining, from a plurality of preset candidate resource quota grant categories, a target resource quota grant category corresponding to the enterprise includes:
Determining the matching degree of the enterprise and a plurality of candidate resource quota grant categories according to the yield influence factor characteristics;
and taking the candidate resource quota granting category corresponding to the maximum matching degree as the target resource quota granting category corresponding to the enterprise.
The matching degree can be characterized by the similarity degree between the enterprise and the candidate resource quota grant category, for example, the greater the similarity degree is, the greater the matching degree is. The matching degree can be expressed by an error, and the smaller the error is, the larger the matching degree is; for example, the error may be characterized by distance.
For example, the server may determine a degree of matching between the enterprise and each candidate resource credit granting category based on the yield-affecting factor characteristic, e.g., may determine a degree of matching between the enterprise and each candidate resource credit granting category based on an error between the yield-affecting factor characteristic of the enterprise and the yield-affecting factor characteristic corresponding to each candidate resource credit granting category. The candidate resource quota granting category corresponding to the maximum matching degree can be used as the target resource quota granting category corresponding to the enterprise; for example, the candidate resource credit grant category corresponding to the smallest error may be used as the target resource credit grant category corresponding to the enterprise.
Alternatively, the degree of matching between the enterprise and each candidate resource credit granting category may be determined based on the yield-affecting factor characteristics of the enterprise and based on the distance between the yield-affecting factor characteristics reconstructed from the candidate resource credit granting categories.
In this embodiment, the matching degree of the enterprise and the multiple candidate resource quota grant categories is determined through the yield influence factor characteristics; the candidate resource quota granting category corresponding to the maximum matching degree can be used as the target resource quota granting category corresponding to the enterprise, and the target resource quota granting category corresponding to the enterprise can be accurately determined, so that accuracy of granted resource quota can be improved.
In an exemplary embodiment, as shown in fig. 2, determining the matching degree of the enterprise to the plurality of candidate resource credit grant categories according to the yield impact factor characteristics includes: s202 to S208, wherein:
s202, determining a current resource quota grant category from a plurality of candidate resource quota grant categories;
the current resource quota grant category may be any one of a plurality of candidate resource quota grant categories; it will be appreciated that for any one of the candidate resource credit grant categories, processing may be performed in the manner provided by the present embodiment.
S204, obtaining sample output influence factor characteristics of products produced by a sample enterprise.
The sample enterprise may be a sample enterprise to which a credit may be granted. The yield-affecting factor characteristic of the sample enterprise may be used as the sample yield-affecting factor characteristic, i.e., the sample yield-affecting factor characteristic may be a yield-affecting factor characteristic of a product produced by the sample enterprise.
S206, obtaining target yield influence factor characteristics corresponding to the current resource quota grant category according to the sample yield influence factor characteristics and the current resource quota grant category.
The target yield influence factor characteristic may be a sample yield influence factor characteristic obtained by performing sample reconstruction according to the current resource credit grant category.
And S208, determining the matching degree of the enterprise and the current resource quota grant category according to the yield influence factor characteristics and the target yield influence factor characteristics.
For example, the server may use any one of a plurality of candidate resource credit grant categories as the current resource credit grant category. The server may obtain the sample yield influencing factor characteristics of the product produced by the sample enterprise, for example, may set N sample sets to x= [ X ] 1 ,x 2 ,...,x N ]The ith sample is numerically characterized by x i ＝(x i (k+1),x i (k+2),...,x i (n))。
It can be appreciated that, for any one candidate resource credit grant category, the server may reconstruct the sample yield influence factor feature according to the current resource credit grant category, to obtain the target yield influence factor feature corresponding to the current resource credit grant category. The server can obtain the matching degree of the enterprise and the current resource quota granting category according to the yield influencing factor characteristic of the enterprise to be granted by the resource and the target yield influencing factor characteristic corresponding to the current resource quota granting category. For example, the degree of matching may be determined by the error between the yield-affecting factor characteristic of the enterprise to be resource granted and the target yield-affecting factor characteristic corresponding to the current resource credit grant category.
Optionally, the value ranges and dimensions of different features are different, so that in order to generalize the statistical distribution of the unified sample, the influence of a certain dimension or a plurality of dimensions on data is prevented from being too large, and meanwhile, the convergence speed of the model is increased, and the data set needs to be normalized. For example, the sample yield-influencing factor characteristic and the yield-influencing factor characteristic may be normalized to (0, 1) at the time of data preprocessing.
In this embodiment, the target yield influence factor characteristic corresponding to the current resource credit granting category can be obtained according to the sample yield influence factor characteristic and the current resource credit granting category. And the matching degree of the enterprise and the current resource quota grant category can be determined according to the yield influence factor characteristics and the target yield influence factor characteristics. Therefore, the matching degree can be effectively and accurately determined, and the accuracy of determining the grant limit of the enterprise can be improved.
In an exemplary embodiment, as shown in fig. 3, according to the sample yield impact factor feature and the current resource credit grant category, obtaining a target yield impact factor feature corresponding to the current resource credit grant category includes: s302 to S304, wherein:
s302, determining a weight parameter of a reconstructed sample corresponding to the current resource quota grant category according to the sample yield influence factor characteristic and the minimum similarity degree of the yield influence factor characteristic;
s304, determining target yield influence factor characteristics based on the weight parameters, the current resource quota grant category and the sample yield influence factor characteristics.
Wherein the degree of similarity can be determined based on the distance. The minimum degree of similarity may be a degree of similarity determined by a least squares regression model. The weight parameter may be a weight coefficient for reconstructing a sample corresponding to the current resource credit grant category.
For example, the server may determine a minimum degree of similarity between the sample yield-affecting factor feature and the yield-affecting factor feature based on a least squares regression model, and may further determine the weight parameters for reconstructing the sample based on the sample yield-affecting factor feature and the minimum degree of similarity of the yield-affecting factor feature. The server can reconstruct the sample yield influence factor characteristics according to the weight parameters and the current resource quota grant category to obtain target yield influence factor characteristics corresponding to the current resource quota grant category.
Alternatively, the least squares regression model may be given as formula (1):
where y may be a yield-affecting factor characteristic, X may be a sample yield-affecting factor characteristic, and w may be a weight parameter. Wherein x= [ X ] 1 ,x 2 ,...,x N ]，x i ＝(x i (k+1),x i (k+2),...,x i (n))。
W may be determined by solving the following expression (2):
w＝(X T X) -1 X T y (2)
δ k (w) may be granting category l for candidate resource credit K Function X delta k (w) may be a target yield-influencing factor characteristic after reconstitution.
In this embodiment, the weight parameter of the reconstructed sample corresponding to the current resource quota grant category can be determined according to the sample yield influence factor feature and the minimum similarity of the yield influence factor feature; and by granting the category and sample yield impact factor characteristics based on the weight parameter, the current resource credit, the target yield impact factor characteristics can be determined, thereby improving the accuracy of determining the target yield impact factor characteristics.
In one exemplary embodiment, determining how well an enterprise matches a current resource credit grant category based on yield impact factor characteristics and target yield impact factor characteristics includes:
acquiring the distance between the yield influence factor characteristics and the target yield influence factor characteristics;
and obtaining the matching degree of the enterprise and the current resource quota grant category according to the yield influence factor characteristics and the distance between the target yield influence factor characteristics.
Wherein the distance may be a distance determined from a two-norm.
Illustratively, the server may determine a distance between the yield-affecting factor feature and the target yield-affecting factor feature according to a second norm between the yield-affecting factor feature and the target yield-affecting factor feature, and the server may obtain a matching degree of the enterprise and the current resource credit grant category according to the distance between the yield-affecting factor feature and the target yield-affecting factor feature. For example, it may be determined that the smaller the distance, the greater the degree of matching.
Optionally, the preset sample set may have K candidate resource quota grant categories, which may be { l } 1 ,l 2 ,...,l K After solving the coefficient vector w for the yield impact factor feature y, the error between the reconstructed sample corresponding to each candidate resource credit grant category and the yield impact factor feature y can be calculated, for example, the error can be calculated by the following formula (3):
Wherein delta k (w) may be granting category l for candidate resource credit K Function X delta k (w) may be the target yield-influencing factor characteristic after reconstitution, r k (y) may be an error between the yield-affecting factor characteristic y and the target yield-affecting factor characteristic, and the degree of matching may be determined based on the error.
Wherein delta k (w):R n →R n Calculating to obtain class I k The j-th element of (2) is defined as formula (4)
Finally, a target resource credit grant category corresponding to the enterprise may be determined from the plurality of candidate resource credit grant categories according to formula (5):
in this embodiment, the distance between the yield-affecting factor features and the target yield-affecting factor features is obtained; and according to the output influence factor characteristics and the distance between the target output influence factor characteristics, the matching degree of the enterprise and the current resource quota grant category can be obtained, so that the accuracy of the matching degree can be improved, and the accuracy of the grant quota can be improved.
In an exemplary embodiment, as shown in fig. 4, determining a resource grant status of an enterprise according to an enterprise production factor characteristic includes S402 to S404, wherein:
s402, acquiring a first sample production factor of a sample resource grant enterprise corresponding to a grant resource and a second sample production factor of a sample non-resource grant enterprise corresponding to a non-grant resource;
S404, determining the resource grant state of the enterprise according to the first sample production factor, the first sample production factor and the enterprise production factor characteristics.
Wherein the first sample production factor may be an enterprise production factor characteristic of the sample resource grant enterprise. The sample resource granting enterprise may be a sample enterprise that may grant resources. The second sample production factor may be an enterprise production factor characteristic of the sample non-resource grant enterprise. A sample non-resource granting enterprise may be a sample enterprise that may not grant resources.
For example, the server may obtain a sample resource grant enterprise corresponding to the granted resource and a sample non-resource grant enterprise corresponding to the non-granted resource. The server may determine a first sample production factor characteristic of the sample resource grant enterprise and a second sample production factor of the sample non-resource grant enterprise. The server may classify the enterprise to be granted with resources according to the first sample production factor, the first sample production factor and the enterprise production factor characteristic, to obtain a resource grant status of the enterprise.
Alternatively, for enterprise y to be granted resources, the enterprise may be classified using a naive bayes algorithm, with category c 1 、c 2 Representing that computing enterprise y is assigned to class c 1 、c 2 Probability P (c) 1 |y)、P(c 2 I y), which probability is large, and the enterprise y to be granted with resources belongs to which class. The probability of the belonging classification of the enterprise to be granted resources may be determined according to the following formula (6):
P(c i |y)＝P(y|c i )P(c i )/P(y) (6)
where P (y) may be constant for all classes, equation (6) may be calculated as P (y|c) i )×P(c i ) Then, a category in which the probability is the largest is selected as its category. P (c) i ) Is the probability of belonging to a certain class of enterprises in a plurality of sample enterprises.
In this embodiment, a first sample production factor of a sample resource grant enterprise corresponding to a grant resource and a second sample production factor of a sample non-resource grant enterprise corresponding to a non-grant resource are obtained; according to the first sample production factor, the first sample production factor and the characteristics of the enterprise production factor, the resource grant state of the enterprise can be determined, so that the resource grant state of the enterprise can be accurately determined, and the accuracy of grant resources is improved.
In an exemplary embodiment, determining, according to the target resource quota grant category, a resource quota to be granted corresponding to the enterprise includes:
acquiring a resource transfer parameter corresponding to a target resource quota grant category;
and determining the resource limit to be granted corresponding to the enterprise according to the resource transfer parameters.
The resource transfer parameter may be an adjustment coefficient set for a resource quota grant category, and may be used to adjust the resource transfer quota.
For example, the server may determine a resource transfer parameter corresponding to the target resource quota granting category, and may determine the resource quota to be granted corresponding to the resource transfer parameter according to a preset correspondence between the resource transfer parameter and the resource quota to be granted.
Alternatively, for a tea enterprise, an upper limit value may be preset based on the resource grant limit of a single user (enterprise), the recommended resource grant limit measurement value of a single user (enterprise) =min [ preset upper limit value; total price of theanine purchased in the last year is 0.7 m]. The total price data of the acquired theaters in the last year are derived from a product database platform, M is an adjustment coefficient, and the total price data is determined according to a classification result. Can take K=4, and class labels are respectively l 1 、l 2 、l 3 、l 4 According to class labelsDetermining resource gain rate and regulating coefficient of resource transfer, specifically table 1 below
Table 1 resource gain ratio and adjustment coefficient corresponding to class 1 tags
| Class labels | Minimum resource gain rate | M takes on value |
| l 1 | LPR+20BP in preset period | 0.9 |
| l 2 | LPR+30BP in preset period | 0.8 |
| l 3 | LPR+40BP in preset period | 0.7 |
| l 4 | LPR+50BP in preset period | 0.6 |
Wherein, LPR is the basic gain rate of the resource, BP is the base point, 20bp=0.20%.
In this embodiment, the resource transfer parameters corresponding to the grant category of the target resource quota are obtained; according to the resource transfer parameters, the to-be-granted resource limit corresponding to the enterprise can be determined, so that accuracy of granting the resource limit can be improved.
In an exemplary embodiment, as shown in fig. 5, a method for determining a resource quota is provided, which is applied to a tea enterprise, and includes:
s501, a client submits data on a platform; s502, manually verifying authenticity by a background; s503, preprocessing submitted data; s504, classifying by a naive Bayes classification method; s505, judging whether to provide credit or not; s506, determining a trust category based on a least squares regression classification method; s507, displaying whether credit is granted or not through a screen; if so, displaying the quota and the interest rate.
Wherein the submitted data may include: text-type feature data and numerical-type feature data.
The text-type feature data includes: (1) Tea enterprises which are engaged in tea production, processing and management for more than 3 years and have SC (service center) card have fixed operation places, normal operation conditions and have the capability of paying the cost; (2) Meets the definition standard of small enterprises or micro enterprises, and has the basic admittance condition of small enterprise resource transfer; (3) The enterprises and legal representatives do not have negative records such as credit investigation, business service, resource payment and the like which are not tied; (4) opening a resource management account at the resource management institution; (5) the credit rating of the small micro-enterprises is above BBB level (inclusive); (6) protecting special product marks by using tea geographical marks; (7) a place of business (land); (8) an operation place (factory building); (9) a tea garden obtaining mode; (10) The financing resource management organization that provides the business resource borrowing to the enterprise and enterprise legal representatives is no more than 3.
The numerical characteristic data includes: weather factors: the accumulated temperature is more than or equal to 10 ℃, the annual average relative humidity, the annual daily rainfall is more than or equal to 10mm days, the annual precipitation and the annual average temperature are all over the year; soil factor: soil organic matter content, nitrogen content, ph value and quick-acting potassium.
As shown in fig. 6, determining the trust category based on the least squares regression classification method includes: s601, obtaining a sample to be tested and a sample set; s602, data preprocessing is carried out; s603, establishing a least square regression model; s604, calculating a representation coefficient w; s605, calculating errors of the sample to be detected and each class, and taking the class with the smallest error as a class label; s606, outputting class labels.
In this embodiment, a naive bayes classification method and a least squares regression classification method are combined to provide a method for determining the resource quota of a tea enterprise based on multiple production elements. The class labels can be learned by a machine learning method, a target client capable of providing credit service is obtained by a naive Bayesian classification method, then the class labels are obtained by a least squares regression classification method, and then the class labels are used for predicting credit and interest rate, so that the accuracy of the resource grant credit of a tea enterprise can be improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a resource quota determining device for implementing the above-mentioned related resource quota determining method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the resource credit determining device or devices provided below may be referred to as the limitation of the resource credit determining method hereinabove, and will not be described herein.
In an exemplary embodiment, as shown in fig. 7, there is provided a resource credit determining apparatus, including: a production factor acquisition module 710, a grant status determination module 720, a yield factor acquisition module 730, a grant category acquisition module 740, and a resource credit determination module 750, wherein:
a production factor obtaining module 710, configured to obtain an enterprise production factor characteristic corresponding to an enterprise to be granted with resources;
a grant status determining module 720, configured to determine a resource grant status of the enterprise according to the characteristics of the enterprise production factors;
a yield factor obtaining module 730, configured to obtain a yield impact factor characteristic of a product produced by an enterprise in a case where the resource grant status characterizes a grant resource;
The grant category obtaining module 740 is configured to obtain, according to the yield impact factor feature, a target resource credit grant category corresponding to the enterprise from a plurality of preset candidate resource credit grant categories;
the resource quota determining module 750 is configured to determine, according to the target resource quota granting category, a resource quota to be granted corresponding to the enterprise; the to-be-granted resource amount is an amount for transferring resources to the enterprise.
In one exemplary embodiment, the grant category acquisition module includes a matching degree determination unit and a grant category determination unit.
And the matching degree determining unit is used for determining the matching degree of the enterprise and the plurality of candidate resource quota grant categories according to the yield influence factor characteristics. The grant category determining unit is used for using the candidate resource quota grant category corresponding to the maximum matching degree as the target resource quota grant category corresponding to the enterprise.
In an exemplary embodiment, the matching degree determining unit includes a current category determining unit, a sample yield factor obtaining unit, a target yield factor determining unit, and a current category matching degree unit.
The current category determining unit is used for determining a current resource quota granting category from a plurality of candidate resource quota granting categories. The sample output factor obtaining unit is used for obtaining sample output influence factor characteristics of products produced by a sample enterprise. The target yield factor determining unit is used for obtaining the target yield factor characteristic corresponding to the current resource quota granting category according to the sample yield factor characteristic and the current resource quota granting category. The current category matching degree unit is used for determining the matching degree of the enterprise and the current resource quota grant category according to the yield influence factor characteristics and the target yield influence factor characteristics.
In an exemplary embodiment, the target yield factor determining unit includes a weight parameter determining unit and a target yield factor obtaining unit.
The weight parameter determining unit is used for determining the weight parameter of the reconstructed sample corresponding to the current resource quota grant category according to the sample yield influence factor characteristic and the minimum similarity degree of the yield influence factor characteristic. The target yield factor obtaining unit is used for determining target yield influence factor characteristics based on the weight parameters, the current resource quota grant category and the sample yield influence factor characteristics.
In an exemplary embodiment, the current category matching degree unit includes a distance acquisition unit and a current matching degree determination unit.
The distance acquisition unit is used for acquiring the distance between the output influencing factor characteristics and the target output influencing factor characteristics.
The current matching degree determining unit is used for obtaining the matching degree of the enterprise and the current resource quota grant category according to the yield influence factor characteristics and the distance between the target yield influence factor characteristics.
In one exemplary embodiment, the grant status determining module includes a sample production factor obtaining unit and a resource grant status determining unit.
The sample production factor obtaining unit is used for obtaining a first sample production factor of a sample resource grant enterprise corresponding to the grant resource and a second sample production factor of a sample non-resource grant enterprise corresponding to the non-grant resource. The resource grant status determining unit is used for determining the resource grant status of the enterprise according to the first sample production factor, the first sample production factor and the enterprise production factor characteristic.
In an exemplary embodiment, the resource quota determining module includes a resource transfer parameter obtaining unit and a resource quota determining unit to be granted.
The resource transfer parameter obtaining unit is used for obtaining the resource transfer parameter corresponding to the target resource quota grant category. The to-be-granted resource limit determining unit is used for determining the to-be-granted resource limit corresponding to the enterprise according to the resource transfer parameters.
The above-mentioned respective modules in the resource quota determining apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 8. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of computer devices is used to store enterprise production factor characteristics. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of resource credit determination.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (11)
1. A method for determining a resource credit, the method comprising:
acquiring enterprise production factor characteristics corresponding to an enterprise to be granted with resources;
determining a resource grant state of the enterprise according to the characteristics of the enterprise production factors;
acquiring yield influence factor characteristics of products produced by the enterprise under the condition that the resource grant state characterizes the grantable resource;
Obtaining a target resource quota granting category corresponding to the enterprise from a plurality of preset candidate resource quota granting categories according to the yield influencing factor characteristics;
determining the resource limit to be granted corresponding to the enterprise according to the target resource limit grant category; and the to-be-granted resource limit is the limit for transferring the resources to the enterprise.
2. The method of claim 1, wherein the obtaining, according to the yield-affecting factor feature, the target resource credit grant category corresponding to the enterprise from a plurality of preset candidate resource credit grant categories includes:
determining the matching degree of the enterprise and the candidate resource quota grant categories according to the yield influence factor characteristics;
and taking the candidate resource quota granting category corresponding to the maximum matching degree as the target resource quota granting category corresponding to the enterprise.
3. The method of claim 2, wherein determining a degree of matching of the enterprise to the plurality of candidate resource credit granting categories based on the yield impact factor characteristics comprises:
determining a current resource credit grant category from the plurality of candidate resource credit grant categories;
Acquiring sample output influence factor characteristics of products produced by a sample enterprise;
obtaining target yield influence factor characteristics corresponding to the current resource quota grant category according to the sample yield influence factor characteristics and the current resource quota grant category;
and determining the matching degree of the enterprise and the current resource quota grant category according to the yield influence factor characteristics and the target yield influence factor characteristics.
4. The method of claim 3, wherein the obtaining the target yield impact factor characteristic corresponding to the current resource credit grant category according to the sample yield impact factor characteristic and the current resource credit grant category comprises:
determining a weight parameter of a reconstructed sample corresponding to the current resource quota grant category according to the sample yield influence factor characteristic and the minimum similarity of the yield influence factor characteristic;
and determining the target yield influence factor characteristic based on the weight parameter, the current resource credit grant category and the sample yield influence factor characteristic.
5. The method of claim 3, wherein said determining a degree of match of the enterprise to the current resource credit granting category based on the yield-affecting factor characteristic and the target yield-affecting factor characteristic comprises:
Acquiring the distance between the yield influence factor characteristics and the target yield influence factor characteristics;
and obtaining the matching degree of the enterprise and the current resource quota grant category according to the yield influence factor characteristics and the distance between the target yield influence factor characteristics.
6. The method of claim 1, wherein said determining the resource grant status of the enterprise based on the enterprise production factor characteristics comprises:
acquiring a first sample production factor of a sample resource grant enterprise corresponding to a grant resource and a second sample production factor of a sample non-resource grant enterprise corresponding to a non-grant resource;
and determining the resource grant state of the enterprise according to the first sample production factor, the first sample production factor and the enterprise production factor characteristic.
7. The method of claim 1, wherein the determining, according to the target resource quota granting category, a resource quota to be granted corresponding to the enterprise includes:
acquiring a resource transfer parameter corresponding to the target resource quota grant category;
and determining the resource limit to be granted corresponding to the enterprise according to the resource transfer parameters.
8. A resource credit determining apparatus, the apparatus comprising:
the production factor acquisition module is used for acquiring enterprise production factor characteristics corresponding to the enterprise to be granted with the resources;
the grant state determining module is used for determining the resource grant state of the enterprise according to the characteristics of the enterprise production factors;
the yield factor obtaining module is used for obtaining yield influence factor characteristics of products produced by the enterprise under the condition that the resource grant state characterizes the grantable resource;
the grant category obtaining module is used for obtaining a target resource quota grant category corresponding to the enterprise from a plurality of preset candidate resource quota grant categories according to the yield influence factor characteristics;
the resource limit determining module is used for determining the resource limit to be granted corresponding to the enterprise according to the target resource limit grant category; and the to-be-granted resource limit is the limit for transferring the resources to the enterprise.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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