CN113947470A

CN113947470A - Big data loan management method and system

Info

Publication number: CN113947470A
Application number: CN202111111807.9A
Authority: CN
Inventors: 秦桂珍
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-09-23
Filing date: 2021-09-23
Publication date: 2022-01-18

Abstract

The invention discloses a big data loan management method and a big data loan management system. The face feature vector comprises a face front face feature vector, a face left side face feature vector and a face right side face feature vector. And comparing the face characteristic vector with face characteristic vectors of other users stored in a database to obtain face information. And obtaining the loan classification tree based on the user information. And obtaining the current user information based on the face information. The current user information represents the attribute state of the user attribute currently to be loaned. And inputting the current user information into the loan classification tree to obtain a loan classification set which can be loaned by the user. And according to the user loan category set, based on the loan form submitted by the current user, issuing a loan to the current user.

Description

Big data loan management method and system

Technical Field

The invention relates to the technical field of computers, in particular to a big data loan management method and system.

Background

In order to control the loan risk, banks often have high requirements on the aspects of qualification, reputation, growth and the like of enterprises. The user information is judged so that the user can loan the partial loan type. Because the current loan mode is still a manual mode, the manpower and the time are wasted in the aspect of communication inquiry. However, when the loan transaction is performed online at present, the user identity is not obtained accurately enough. Meanwhile, the situation that the user wants to make a certain loan and returns to apply for again after submitting information without meeting the requirements is caused, so that time and money are wasted.

Disclosure of Invention

The invention aims to provide a big data loan management method and system, which are used for solving the problems in the prior art

In a first aspect, an embodiment of the present invention provides a big data loan management method, including:

acquiring a face image of a current user, wherein the face image comprises a front face image, a left side face image and a right side face image;

obtaining face information based on the face image of the current user; the face information is a user number;

obtaining user information stored in a database, wherein the user information comprises attribute states of a plurality of user attributes, and the user attributes comprise user credit levels, user identity information, user real estate information and user wage information; the attribute states of the user credit level comprise a negative level, a zero level, a first level and a second level; the attribute state of the user identity information comprises personal loan and enterprise loan; the attribute state of the user real estate information comprises mortgageable and non-mortgageable; the attribute state of the user payroll information comprises low payroll, medium payroll and high payroll;

obtaining a loan classification tree based on the user information;

obtaining current user information based on the face information; the current user information represents the attribute state of the user attribute to be loaned currently;

inputting the current user information into the loan classification tree to obtain a loan classification set which can be loaned by the user; the category attribute of the loan category comprises loan amount, repayment date, purpose and repayment mode;

and according to the user loan category set, based on the loan form submitted by the current user, issuing a loan to the current user.

Optionally, obtaining the loan classification tree based on the user information includes:

obtaining a root node, wherein the root node comprises a plurality of loan categories;

obtaining a first classification attribute based on the user attribute and the root node; the information entropy of the first classification attribute for classifying the loan categories in the root node is smaller than the information entropy of the plurality of other user attributes for classifying the loan categories in the root node; the attribute weight represents the importance of each attribute;

classifying the loan categories in the root node based on the first classification attribute to obtain first-layer child nodes, wherein the number of the first-layer child nodes is the number of attribute states of the first classification attribute; a plurality of the first level child nodes comprise a set of classified loan categories;

sorting other classification attributes according to the weight from large to small to obtain node attributes, wherein the node attributes comprise a second classification attribute, a third classification attribute and a fourth classification attribute; the weight of the second classification attribute is greater than the weight of the third classification attribute; the weight of the third classification attribute is greater than that of the fourth classification attribute;

classifying the loan categories in the sub nodes of the first layer of sub nodes based on the second classification attribute to obtain second layer of sub nodes, wherein the number of the second layer of sub nodes is the number of attribute states of the second classification attribute; a plurality of sub-nodes in the second tier of sub-nodes comprise a set of classified loan categories;

and (4) classifying for multiple times until the last layer of node attribute is reached, and stopping classification to obtain the empty node classification tree.

And pruning the classification tree containing the empty nodes to obtain the loan classification tree.

Optionally, the pruning the classification tree containing the empty node to obtain the classification tree includes:

obtaining the leaf nodes of the last layer which are not empty based on the classification tree containing empty nodes;

traversing from bottom to top based on the leaf nodes of the last layer which are not empty to obtain key nodes; the key nodes are all nodes which can reach the leaf nodes of the last layer which are not empty from the root node;

based on the top-down traversal of the key nodes, deleting subtrees where non-key nodes are located, and replacing the non-key nodes with empty nodes to obtain a classification tree; the non-key nodes are all nodes in the empty node classification tree which are not key nodes.

Optionally, the first classification attribute is obtained based on the attribute state and the weight of the user information; the information entropy of the first classification attribute for classifying the loan categories in the root node is smaller than the information entropy of the plurality of other user attributes for classifying the loan categories in the root node; the attribute weight represents the importance of each attribute, including:

obtaining information entropies of various user attributes based on the user attributes;

the information entropy is obtained by the calculation mode described by the following formula:

h (D) is the information entropy of the user attribute D, D represents the set of a plurality of loan categories in the root node, K represents the kth state of the user attribute, the value of K is an integer between 1 and K, K is the number of all the states of the user attribute, and p_kProbability that the state is k;

obtaining weighted information entropy based on the information entropies of the user attributes; the weighted information entropy represents the sum of the reciprocal of the information entropy of the plurality of user attributes multiplied by the respective weight;

obtaining a first classification attribute based on the weighted information entropy; the weighted information entropy of the first classification attribute is larger than the weighted information entropy of other multiple user attributes.

Optionally, obtaining face information based on the face image of the current user includes:

obtaining a face feature vector based on a face image of a current user through a face recognition model;

and comparing the face characteristic vector with face characteristic vectors of other users stored in a database to obtain face information.

In a second aspect, an embodiment of the present invention provides a big data loan management system, including:

the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring a face image of a current user, and the face image comprises a front face image, a left side face image and a right side face image; obtaining user information stored in a database, wherein the user information comprises attribute states of a plurality of user attributes, and the user attributes comprise user credit levels, user identity information, user real estate information and user wage information; the attribute states of the user credit level comprise a negative level, a zero level, a first level and a second level; the attribute state of the user identity information comprises personal loan and enterprise loan; the attribute state of the user real estate information comprises mortgageable and non-mortgageable; the attribute state of the user payroll information comprises low payroll, medium payroll and high payroll;

the face recognition module is used for obtaining face information based on the face image of the current user;

the loan classification module is used for obtaining a loan classification tree based on the user information; obtaining current user information based on the face information; the current user information represents the attribute state of the user who wants to loan; inputting the current user information into the loan classification tree to obtain a loan type set which can be loaned by the user; the attributes of the loan type comprise loan amount, loan interest rate, repayment date, usage and repayment mode; and according to the user loan category set, based on the loan form submitted by the current user, issuing a loan to the current user.

Compared with the prior art, the embodiment of the invention achieves the following beneficial effects:

the embodiment of the invention provides a big data loan management method and a system, comprising the following steps: acquiring a face image of a current user, wherein the face image comprises a front face image, a left side face image and a right side face image; obtaining face information based on the face image of the current user; the face information is a user number; obtaining user information stored in a database, wherein the user information comprises attribute states of a plurality of user attributes, and the user attributes comprise user credit levels, user identity information, user real estate information and user wage information; the attribute states of the user credit level comprise a negative level, a zero level, a first level and a second level; the attribute state of the user identity information comprises personal loan and enterprise loan; the attribute state of the user real estate information comprises mortgageable and non-mortgageable; the attribute state of the user payroll information comprises low payroll, medium payroll and high payroll; obtaining a loan classification tree based on the user information; obtaining current user information based on the face information; the current user information represents the attribute state of the user attribute to be loaned currently; inputting the current user information into the loan classification tree to obtain a loan classification set which can be loaned by the user; the category attribute of the loan category comprises loan amount, repayment date, purpose and repayment mode; and according to the user loan category set, based on the loan form submitted by the current user, issuing a loan to the current user.

The method of automatic loan is adopted, the manpower is saved, the face recognition model is constructed through the front face, the left side face and the right side face of the face, the accuracy of face recognition is increased, the face can be recognized accurately through the face recognition model, the obtained user information is more convenient and safer, and the inaccuracy and the time waste during information filling are prevented. The loan type that the user can loan is provided for the user to select through the loan classification tree, and the user information is limited to the loan type. So that the user can only choose the loan type within his or her own bearing capacity.

In conclusion, the user can make a loan safely, conveniently and quickly, and the burden of the staff is reduced.

Drawings

Fig. 1 is a flowchart of a big data loan management method according to an embodiment of the invention.

Fig. 2 is a schematic block structure diagram of an electronic device according to an embodiment of the present invention.

The labels in the figure are: a bus 500; a receiver 501; a processor 502; a transmitter 503; a memory 504; a bus interface 505.

Detailed Description

The present invention will be described in detail below with reference to the accompanying drawings.

Examples

The embodiment of the invention provides a big data loan management method, which comprises the following steps:

obtaining a loan classification tree based on the user information;

An embodiment of the invention provides another big data loan management method, as shown in fig. 1, the method includes

S101: the method comprises the steps of collecting a face image of a current user, wherein the face image comprises a front face image, a left side face image and a right side face image.

S102: and obtaining a face feature vector based on the face image of the current user through the face recognition model. The face feature vector comprises a face front face feature vector, a face left side face feature vector and a face right side face feature vector.

S103: comparing the face feature vector with face feature vectors of other users stored in a database to obtain face information; the face information is a user number.

S104: the method comprises the steps of obtaining user information stored in a database, wherein the user information comprises attribute states of a plurality of user attributes, and the user attributes comprise user credit levels, user identity information, user real estate information and user wage information. The attribute states of the user credit rating include negative level, zero level, first level, and second level. The attribute state of the user identity information comprises personal loan and enterprise loan; the attribute state of the user real estate information comprises mortgageable and non-mortgageable. The attribute states of the user payroll information include low payroll, medium payroll and high payroll.

S105: and obtaining a loan classification tree based on the user information.

S106: and inputting the current user information into the loan classification tree to obtain a loan classification set which can be loaned by the user. The category attribute of the loan category comprises loan amount, repayment date, purpose and repayment mode.

S107: and according to the user loan category set, based on the loan form submitted by the current user, issuing a loan to the current user.

Through adopting above scheme, adopt the mode of automatic loan, saved the manpower, through the face to the people's face, the left side face, the construction that face identification model was carried out to the right side face, increased the accuracy of discernment people's face, through the discernment people's face that face identification model can be accurate to make the user information who obtains make things convenient for safety more, inaccuracy and waste time when preventing to fill in information. The loan type that the user can loan is provided for the user to select through the loan classification tree, and the user information is limited to the loan type. So that the user can only choose the loan type within his or her own bearing capacity.

The face recognition model comprises a face convolution neural network, a left face convolution neural network and a right face convolution neural network; the face-positive convolution neural network comprises three face-positive residual blocks and a face-positive full-connection network; the front face facial image is a first front face residual block input, the first front face residual block input is the second front face residual block output, the third front face residual block input is the second front face residual block output, the fully connected network input is the third front face residual block output, and the front face fully connected network output is a front face feature vector; the left face neural convolution network comprises five left face residual blocks and a left face full-connection network; the left face facial image is a first left face residual block input, the first left face residual block input is the second left face residual block output, the third left face residual block input is the second left face residual block output, the fourth left face residual block input is the third left face residual block output, the fifth left face residual block input is the fourth left face residual block output, the left face fully-connected network input is the fifth left face residual block output, the left face fully-connected network output is a left face feature vector; the right face convolutional neural network comprises five right face residual blocks and a right face full-connection network; the right side face facial image is the input of first right side face residual block, the input of first right side face residual block is the output of second right side face residual block, the input of third right side face residual block is the output of second right side face residual block, the input of fourth right side face residual block is the output of third right side face residual block, the input of fifth right side face residual block is the output of fourth right side face residual block, the input of right side face full connection network is the output of fifth right side face residual block, the output of right side face full connection network is right side face eigenvector.

The current user submits a loan form to characterize the user's loan request, which includes the user's loan type, amount, length of time, etc. The user can loan category set comprises a plurality of loan items, wherein the loan items comprise loan categories, money amounts and time lengths; the loan category of the loan item corresponds to the user loan type.

According to the user loan category set, based on the loan form submitted by the current user, a loan is issued to the current user, which specifically comprises the following steps: the current user submits a loan form, and one or more target loan items are selected from the user's loan category set to the user, so as to release a loan for the current user. The loan category of the target loan item corresponds to the user loan type, and specifically, the loan category of the target loan item may be the same as the user loan type. The amount of the target credit corresponds to the amount in the user form, and specifically, the amount of the target credit is greater than or equal to the amount in the user form. The time length of the target loan project is consistent with the time length of the loan submitted by the current user, and specifically comprises the following steps: the loan offering item has a time length greater than or equal to the time length of the current loan submission by the user.

Wherein the residual block comprises: four convolutional networks, four normalization layers, three activation function layers.

The front face image is an input of a first convolution network in a first residual block, the first normalization layer is an output of the first convolution network, the input of the first activation function is an output of the first normalization layer, the input of the second convolution network is an output of the first normalization layer, the input of the second normalization layer is an output of the second convolution network, the input of the second activation function is an output of the second normalization layer, the input of the third convolution network is an output of the second activation function, the input of the third normalization layer is an output of the third convolution network, the input of the fourth convolution network is the front face image, the input of the fourth normalization layer is an output of the fourth convolution network, the input of the third activation function is an output of the third normalization layer and an output of the fourth normalization layer, the output of the third activation function is the output of the first residual block.

By adopting the scheme, the face recognition model using the residual error module is constructed, so that the convolutional neural network with enough depth can still carry out parameter learning.

Optionally, the training process of the face recognition model includes:

a training set is obtained, wherein the training set comprises face images and user numbers, and the face images comprise basic faces and difficult faces. The difficult faces include difficult false faces and difficult correct faces. The difficult error face is a face image that is similar to the base face but is not the same user as the base face. The difficult correct face is an image of a face of the same user that is not similar to the base face but is the same as the base face. The face image comprises a front face, a left side face and a right side face; the facial images are facial images of a plurality of users in a bank database. The user number is the number of a plurality of users in the bank database. The number is unique.

Inputting the front face of the face image into a front face convolution neural network to obtain a front face characteristic vector; the positive face feature vector includes elements representing positive face features.

Inputting the left face of the face image into a left face convolution neural network to obtain a left face characteristic vector; the left face feature vector includes elements representing left face features and elements of left face class features.

Inputting the right side face of the face image into a right side face convolution neural network to obtain a right side face characteristic vector; the right face feature vector includes elements representing features of the right face and elements of the right face class features.

And obtaining a loss value based on the front face feature vector, the left side face feature vector and the right side face feature vector.

And obtaining the maximum iteration times of the face recognition model training, and stopping the training until the loss value is not greater than a first threshold value or the maximum iteration times is reached to obtain the trained face recognition model.

By adopting the scheme, the parameters in the front face convolution neural network, the left side face convolution neural network and the right side face convolution neural network are respectively trained through a plurality of face images, so that the parameters can well meet the requirement that the face is input to obtain the face characteristic vector capable of identifying the user. Through training three convolutional networks of the face convolutional neural network, the left face convolutional neural network and the right face convolutional neural network, the different characteristics of the front face, the left face and the right face are better met, and the face characteristics of a user where the face image is located are more accurately output.

Optionally, obtaining a loss value based on the front face feature vector, the left side face feature vector, and the right side face feature vector includes:

obtaining a face loss value based on the face feature vector of the basic face, the face feature vector of the face with the difficulty errors and the face feature vector of the face with the difficulty errors;

the face front loss value is obtained by a calculation method according to the following formula:

therein, loss₁For the value of the face front loss,

is the ith element in the face feature vector of the basic human face,

the ith element of the face feature vector for the difficult-to-correct face,

the ith element of the face feature vector of the face with the difficulty error is shown, n is the length of the feature vector, margin_xThe face threshold is used for judging whether the basic face is the face of the user.

Obtaining a left face loss value based on the left face feature vector of the basic face, the left face feature vector of the difficult and wrong face and the left face feature vector of the difficult and correct face;

the left face loss value is obtained by a calculation method according to the following formula:

therein, loss₂For the left-side face loss value,

an ith element of a partial vector characterizing facial features in a left side face feature vector of the base face,

the ith element of the partial vector characterizing the facial features in the left face feature vector of the difficult-to-correct face,

the ith element of a partial vector for characterizing the facial features in the left face feature vector of the face with the difficulty error is used, n is the length of the feature vector, margin_yThe left face threshold value is a left face threshold value, and whether the left face threshold value is the face of the user of which the basic face is judged;

a k element of a partial vector for representing the user category in the left face feature vector of the basic face;

the k-th element of the user category vector is marked, the element in the user category vector indicates that the vector is 1 when the basic face is the user, and the vector is 0 when the basic face is not the user; k is the number of user categories, K represents a user category, and K is an integer from 1 to K.

Obtaining a right side face loss value through a front face loss function based on the right side face feature vector of the basic face, the right side face feature vector of the correct face and the right side face feature vector of the correct face;

the right side face loss value is obtained by a calculation method according to the following formula:

therein, loss₃For the right-side face loss value,

the ith element of the partial vector of the characteristic facial features in the right side face feature vector of the basic face,

partial vector of characteristic facial features in right side face feature vector of the difficult correct faceThe (c) th element of (a),

the ith element of the partial vector for characterizing the facial features in the right side face feature vector of the difficult error face, n is the length of the right side face feature vector, margin_zThe right-side face threshold value is used for judging whether the right-side face threshold value is the face of the user of which the basic face is the user;

a k element of a partial vector for representing the user category in the right face feature vector of the basic face;

The loss value is obtained by the calculation mode of the following formula:

Loss＝loss₁+0.5loss₂+0.5loss₃

wherein, Loss is Loss value₁Loss value of face₂Left side face loss value, loss₃Right face loss values.

In this embodiment, the front face threshold is 1, the left face threshold is 0.5, and the right face threshold is 0.5.

The front face has more features for judging the face, so the front face loss value is obtained only by comparing the features. Because the features used for judging the face in the left face feature vector and the right face feature vector are less, the left face loss value and the right face loss value are obtained by adopting a mode of comparing the features and judging which user the face belongs to.

By adopting the scheme, the judgment on the characteristics of the left side face and the right side face is enhanced.

a root node is obtained, wherein the root node comprises a plurality of loan categories.

Obtaining a first classification attribute based on the attribute state and the weight of the user information; the information entropy of the first classification attribute for classifying the loan categories in the root node is smaller than the information entropy of the plurality of other user attributes for classifying the loan categories in the root node; the attribute weights represent the importance of each attribute.

Classifying the loan categories in the root node based on the first classification attribute to obtain first-layer child nodes, wherein the number of the first-layer child nodes is the number of attribute states of the first classification attribute; a plurality of the first level sub-nodes comprise a set of categorized loan categories.

And sequencing other classification attributes from large to small according to the weight to obtain node attributes, wherein the node attributes comprise a second classification attribute, a third classification attribute and a fourth classification attribute. The weight of the second classification attribute is greater than the weight of the third classification attribute; the weight of the third classification attribute is greater than the weight of the fourth classification attribute.

Classifying the loan categories in the sub nodes of the first layer of sub nodes based on the second classification attribute to obtain second layer of sub nodes, wherein the number of the second layer of sub nodes is the number of attribute states of the second classification attribute; a plurality of the second level sub-nodes comprise a set of categorized loan categories.

In this embodiment, the loan attribute status of the loan amount includes a large-amount loan, a medium-amount loan, and a small-amount loan. The repayment date comprises one-year repayment, two-year repayment, five-year repayment and ten-year repayment. The loan uses include personal loans and corporate loans. The repayment means includes one-time repayment and installment. The partial loan categories for this embodiment are described in table 1.

TABLE 1

Loan category	Amount of loan	Date of repayment	Use of	Repayment method
					Loan of the first kind	Loan of great margin	Two-year repayment	Company loan	Disposable repayment
Loan of the second kind	Loan of great margin	One year repayment	Personal loan	Disposable repayment
					Loan of the third kind	Loan of small amount	Five-year repayment	Personal loan	Amortization
Loan of the fourth kind	Loan of money in gold	Ten years repayment	Personal loan	Amortization
					Loan of the fifth kind	Loan of money in gold	One year repayment	Company loan	Disposable repayment

Classifying loan categories by the relationship between the attribute status of the user information and the loan attribute status of the loan type

If the relationship between the attribute state of the user's wages and the loan attribute state of the loan category is shown in table 2, if the attribute state of the user's wages is low wages, the loan category of the loan with the loan amount of small-amount loan is obtained.

TABLE 2

Attribute state of user payroll	Low payroll	Medium payroll	High payroll
				Loan attribute status for loan category	Loan of small amount	Loan of money in gold	Loan of great margin

The loan classification tree is established by using the historical user information stored in the database before and the classification attribute used for judging whether the loan is available or not and which type of loan is available if the loan is available. And calculating the classification attribute for classifying the loan categories in the root node through the information entropy, wherein the information entropy represents the chaos degree of the classified loan categories. The smaller the degree of the disorder, the more easily the attribute status that cannot be loaned, and the more utilizing the later pruning operation. Since the branch where the empty node is located needs to be pruned later, the classification attribute is judged only through the weight. Meanwhile, each layer has the same classification attribute, so that the searching operation is convenient to perform when the device is used later.

By adopting the scheme, the loan classification tree is established by adopting a mode of calculating the information entropy, the loan classification tree with smaller nodes can be established, the computer space and the calculation time are saved, and the search is convenient.

Since the loan classification tree is finally an empty node, the node which can not be loaned is represented, and the branch of the attribute state which can not be loaned is deleted and replaced by the empty node.

By adopting the scheme, the path for searching the loan classification tree is reduced.

wherein H (D) is the information entropy of the user attribute D, K represents the kth state of the user attribute, the value of K is an integer between 1 and K, K is the number of all the states of the user attribute, p_kProbability that the state is k;

obtaining a first classification attribute based on the weighted information entropy; the weighted information entropy of the first classification attribute is larger than the weighted information entropy of other user attributes;

optionally, inputting the current user information into the loan classification tree to obtain a loan classification set which can be loaned by the user; the category attribute of the loan category comprises loan amount, repayment date, purpose and repayment mode;

TABLE 1

Based on the above big data loan management method, an embodiment of the present invention further provides a big data loan management system, where the system includes:

an acquisition module: acquiring a face image of a current user, wherein the face image comprises a front face image, a left side face image and a right side face image; obtaining user information stored in a database, wherein the user information comprises attribute states of a plurality of user attributes, and the user attributes comprise user credit levels, user identity information, user real estate information and user wage information; the attribute states of the user credit level comprise a negative level, a zero level, a first level and a second level; the attribute state of the user identity information comprises personal loan and enterprise loan; the attribute state of the user real estate information comprises mortgageable and non-mortgageable; the attribute state of the user payroll information comprises low payroll, medium payroll and high payroll;

a face recognition module: obtaining a face feature vector based on a face image of a current user through a face recognition model; the face feature vector comprises a face front face feature vector, a face left face feature vector and a face right face feature vector; comparing the face feature vector with face feature vectors of other users stored in a database to obtain face information; the face information is a user number;

a loan classification module: obtaining a loan classification tree based on the user information; obtaining current user information based on the face information; the current user information represents the attribute state of the user who wants to loan; inputting the current user information into the loan classification tree to obtain a loan type set which can be loaned by the user; the attributes of the loan type comprise loan amount, loan interest rate, repayment date, usage and repayment mode;

a loan module: according to the user loan category set, based on the loan form submitted by the current user, a loan is issued to the current user;

optionally, the training process of the face recognition model includes:

obtaining a training set, wherein the training set comprises face images and user numbers, and the face images comprise basic faces and difficult faces; the difficult faces comprise difficult error faces and difficult correct faces; the difficult error face is a face image which is similar to the basic face but is not the same as the basic face; the difficult correct face is a face image which is not similar to the basic face but is the same as the basic face; the face image comprises a front face, a left side face and a right side face; the face images are face images of a plurality of users in a bank database; the user numbers are numbers of a plurality of users in a bank database; the number has uniqueness;

inputting the front face of the face image into a front face convolution neural network to obtain a front face characteristic vector; the face feature vector comprises elements representing face features of a face;

inputting the left face of the face image into a left face convolution neural network to obtain a left face characteristic vector; the left face feature vector comprises elements representing left face features and elements representing left face category features;

inputting the right side face of the face image into a right side face convolution neural network to obtain a right side face characteristic vector; the right face feature vector comprises elements representing right face features and elements representing right face category features;

obtaining a loss value based on the front face feature vector, the left face feature vector and the right face feature vector;

therein, loss₁For the value of the face front loss,

is the ith element in the face feature vector of the basic human face,

the ith element of the face feature vector for the difficult-to-correct face,

therein, loss₂For the left-side face loss value,

therein, loss₃For the right-side face loss value,

the ith element of the partial vector characterizing the facial features in the right side face feature vector of the difficult-to-correct face,

The loss value is obtained by the calculation mode of the following formula:

Loss＝loss₁+0.5loss₂+0.5loss₃

Optionally, an embodiment of the present invention further provides another big data loan management system, including:

The specific manner in which the respective modules perform operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

Embodiments of the present invention further provide an electronic device, as shown in fig. 2, including a memory 504, a processor 502 and a computer program stored in the memory 504 and executable on the processor 502, wherein the processor 502 executes the computer program to implement the steps of any one of the methods of big data loan management as described above.

Where in fig. 2 a bus architecture (represented by bus 500) is shown, bus 500 may include any number of interconnected buses and bridges, and bus 500 links together various circuits including one or more processors, represented by processor 502, and memory, represented by memory 504. The bus 500 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 505 provides an interface between the bus 500 and the receiver 501 and transmitter 503. The receiver 501 and the transmitter 503 may be the same element, i.e. a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 502 is responsible for managing the bus 500 and general processing, and the memory 504 may be used for storing data used by the processor 502 in performing operations.

Embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing the steps of any one of the methods of big data loan management described above and the data referred to above.

The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components in an apparatus according to an embodiment of the invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

Claims

1. A big data loan management method, comprising:

obtaining a loan classification tree based on the user information;

2. The method of claim 1, wherein obtaining a loan classification tree based on the user information comprises:

3. The method of claim 2, wherein pruning the classification tree containing null nodes to obtain the classification tree comprises:

4. The method according to claim 2, wherein the first classification attribute is obtained based on the attribute status and weight of the user information; the information entropy of the first classification attribute for classifying the loan categories in the root node is smaller than the information entropy of the plurality of other user attributes for classifying the loan categories in the root node; the attribute weight represents the importance of each attribute, including:

5. The method of claim 1, wherein obtaining face information based on the current user face image comprises:

6. A big data loan management system, comprising:

7. The system of claim 6, wherein the deriving a loan classification tree based on the user information comprises:

8. The system of claim 7, wherein pruning the classification tree containing null nodes to obtain a classification tree comprises:

9. The system according to claim 7, wherein the first classification attribute is obtained based on the attribute status and weight of the user information; the information entropy of the first classification attribute for classifying the loan categories in the root node is smaller than the information entropy of the plurality of other user attributes for classifying the loan categories in the root node; the attribute weight represents the importance of each attribute, including:

10. The system of claim 6, wherein deriving face information based on the current user face image comprises: