CN109670927A - The method of adjustment and its device of credit line, equipment, storage medium - Google Patents

The method of adjustment and its device of credit line, equipment, storage medium Download PDF

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CN109670927A
CN109670927A CN201710952035.9A CN201710952035A CN109670927A CN 109670927 A CN109670927 A CN 109670927A CN 201710952035 A CN201710952035 A CN 201710952035A CN 109670927 A CN109670927 A CN 109670927A
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段培
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Tencent Technology Shenzhen Co Ltd
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Abstract

The embodiment of the present invention provides the method for adjustment and its device, equipment, storage medium of a kind of credit line, wherein the described method includes: the identification information of the target user based on acquisition, obtains the data information of the target user；The state vector that the target user is determined according to the data information of the target user determines the corresponding tactful income vector of the state vector according to the state vector of the target user；State vector described in the tactful income vector sum is input to the first full Connection Neural Network, determines the adjustment direction and adjusted value of credit line；The target credit line of the target user is determined according to the current credit degree of the adjustment direction, adjusted value and the target user.

Description

The method of adjustment and its device of credit line, equipment, storage medium
Technical field
The present invention relates to the method for adjustment and its device of Internet technical field more particularly to a kind of credit line, equipment, Storage medium.
Background technique
Currently, with the development of economy, the basic guarantee that people are no longer content with eating one's fill and wearing warm clothes often has some advanced Consumption, and these excessive consumptions needs are got a bank loan.Loan on personal security is bank or other financial institutions to having sure credibility Borrower provide without the RMB fiduciary loan tendered guarantee, and credit line is foundation credit side's income, business revenue, debt Than condition elements such as, occupation, properties, by the amount of money of debit's unilateral decision.This means that debit will have higher credit line, The various conditions of debit all must be quite excellent, and credit side can just be ready to provide higher credit amount.Traditional automatic tune volume system System, is all based on the rule manually customized, can not automatically adapt to the variation of current financial situation and User Status.
Summary of the invention
In view of this, an embodiment of the present invention is intended to provide a kind of method of adjustment of credit line and its device, equipment, storages Medium, the adjustment for solving credit line in prior art can not automatically adapt to current financial situation and User Status The problem of variation, can deeply study utilize the multi-dimensional data information of user, and it is corresponding according to the state vector of user With the corresponding relationship of tactful income vector, the adjustable strategies of credit line are determined, to be adjusted automatically to credit line.
The technical solution of the embodiment of the present invention is achieved in that
The embodiment of the present invention provides a kind of method of adjustment of credit line, which comprises
The identification information of target user based on acquisition obtains the data information of the target user；
The state vector that the target user is determined according to the data information of the target user, according to the target user State vector determine the corresponding tactful income vector of the state vector；
State vector described in the tactful income vector sum is input to the first full Connection Neural Network, determines credit line Adjustment direction and adjusted value；
Determine the target user's according to the current credit degree of the adjustment direction, adjusted value and the target user Target credit line.
The embodiment of the present invention provides a kind of adjustment device of credit line, and described device includes: the first acquisition module, first Determining module, the second determining module and third determining module, in which:
The first acquisition module obtains the target user's for the identification information of the target user based on acquisition Data information；
First determining module, for determining the state of the target user according to the data information of the target user Vector determines the corresponding tactful income vector of the state vector according to the state vector of the target user；
Second determining module, for state vector described in the tactful income vector sum to be input to the first full connection Neural network determines the adjustment direction and adjusted value of credit line；
The third determining module, for according to the adjustment direction, adjusted value and the current credit of the target user Amount determines the target credit line of the target user.
The embodiment of the present invention provides a kind of adjustment equipment of credit line, and the equipment includes at least: memory, communication are total Line and processor, in which:
The memory, for storing the adjustment programme of credit line；
The communication bus, for realizing the connection communication between processor and memory；
The processor, for executing the adjustment programme of the credit line stored in memory, to perform the steps of
The identification information of target user based on acquisition obtains the data information of the target user；
The state vector that the target user is determined according to the data information of the target user, according to the target user State vector determine the corresponding tactful income vector of the state vector；
State vector described in the tactful income vector sum is input to the first full Connection Neural Network, determines credit line Adjustment direction and adjusted value；
Determine the target user's according to the current credit degree of the adjustment direction, adjusted value and the target user Target credit line.
The embodiment of the present invention provides a kind of computer readable storage medium, is stored on the computer readable storage medium The adjustment programme of the adjustment programme of credit line, the credit line realizes credit line as described above when being executed by processor Method of adjustment the step of.
The embodiment of the present invention provides the method for adjustment and its device, equipment, storage medium of a kind of credit line, wherein first The first identification information of the target user based on acquisition obtains the data information of the target user；Then it is used according to the target The data information at family determines the state vector of the target user, and determines the shape according to the state vector of the target user The corresponding tactful income vector of state vector；State vector described in the tactful income vector sum is input to the first full connection mind again Through network, the adjustment direction and adjusted value of credit line are determined；Finally used according to the adjustment direction, adjusted value and the target The current credit degree at family determines the target credit line of the target user, so, it is possible deeply study and utilizes user Multi-dimensional data information determine line of credit and according to the corresponding corresponding relationship with tactful income vector of the state vector of user The adjustable strategies of degree, to be adjusted automatically to credit line.
Detailed description of the invention
Fig. 1 is the schematic diagram of the implementation environment of the method for adjustment of credit line provided in an embodiment of the present invention；
Fig. 2-1 is the implementation process schematic diagram of the method for adjustment of credit line of the embodiment of the present invention；
Fig. 2-2 is the schematic diagram of the full neural network of the embodiment of the present invention first；
Fig. 3 is the implementation process schematic diagram of the method for adjustment of credit line of the embodiment of the present invention；
Fig. 4 is that the composed structure of automated intelligent tune volume system of the embodiment of the present invention based on user's various dimensions information is illustrated Figure；
Fig. 5 is state aware of embodiment of the present invention module diagram；
Fig. 6 is the course of work schematic diagram of strategy of embodiment of the present invention income iteration module；
Fig. 7 is the course of work schematic diagram of strategy of embodiment of the present invention output module；
Fig. 8 is the composed structure schematic diagram of the adjustment device of credit line of the embodiment of the present invention；
Fig. 9 is the composed structure schematic diagram of the adjustment equipment of credit line of the embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention In attached drawing, the specific technical solution of invention is described in further detail.The following examples are intended to illustrate the invention, but does not have to To limit the scope of the invention.
Fig. 1 is the schematic diagram of the implementation environment of the method for adjustment of credit line provided in an embodiment of the present invention, such as Fig. 1 institute Show, which includes credit line adjustment platform 101, loan platform 102, social application server 103 and target user Terminal 104, wherein credit line adjusts between platform 101 and loan platform 102 and credit line adjusts platform 101 and society It hands between application server 103, loan platform 102 and target terminal user 104, target terminal user 104 and social application take It is all to be interacted by being connected to the network row information of going forward side by side between business device 103.
Credit line adjusts platform 101, for obtaining the data information of target user, and root from social application server The credit line adjustable strategies that target user is determined according to data information, further according to the credit line adjustable strategies and mesh of target user The current credit line of mark user determines target credit line, and target credit line is sent to loan platform 102.Line of credit Degree adjustment platform 101 can be a server, or the server cluster consisted of several servers or a cloud Calculate service centre.
Loan platform 102, for being offered loans according to target credit line for user.Loan platform 102 can be one Server, or the server cluster consisted of several servers or a cloud computing service center.
Social application server 103 is the corresponding server of social application program installed in target terminal user 104.It can To be a server, or the server cluster consisted of several servers or a cloud computing service center.
Social application program is installed, which refers to the Tencent similar to China in target terminal user 104 The QQ and wechat of scientific and technological (Shenzhen) Co., Ltd, the Michat of Chinese Beijing Xiaomi Technology Co., Ltd. and China it is new The application programs such as the Sina weibo of unrestrained network technology limited liability company.The target terminal user can be smart phone, plate Computer, pocket computer on knee and desktop computer etc..
Schematic diagram as shown in connection with fig. 1, below to each of the adjustment device of the method for adjustment of credit line and credit line Embodiment is illustrated.
Embodiment in order to better understand the present invention, it is various to deep learning and the present embodiments relate to what is arrived here Neural network is explained.
Deep learning (Deep Learning) has overturned image recognition, natural language understanding, language since two thousand six The fields such as sound identification and synthesis have driven the third time wave of artificial intelligence so that very much " impossible tasks " becomes " possibility " Tide.Neural network can efficiently indicate high dimensional data, be mapped as low-dimensional data.Deep neural network, i.e., study is more The neural network that data are indicated during layer is abstract has greatly improved speech recognition, Object identifying, object detection, prediction medicine Object molecular activity and other many technologies.Deep learning indicates (supervised learning, unsupervised learning, strong by the way that building is distributed Chemistry is practised) discovery labyrinth is concentrated in large data.
General artificial intelligence requires model to emphasize have the same multiple intelligent behavior of people, including perception, decision, reasoning with Planning and exchange and conmmunication.Most important of which is that learning ability.Model should have an independent learning ability, for example people is from small Suffered supervised learning is less, is more continuous autonomous learning after contacting with environment；By exchange, communication and feedback come Study.Currently, this some learning ability is all that deep learning is short of.Interaction with environment is autonomous learning, self evolves Basis, generally entail specific target；Intensified learning (Reinforcement Learning), namely pass through trial and error, simple The study formula that ground is completed by reward or punishment.Intensified learning wishes to be learned according to the interaction and feedback with external environment Acquistion is to an optimal strategy；Intensified learning is the only way which must be passed for realizing strong artificial intelligence.Using deep learning to extraneous ring The powerful expression ability combination intensified learning in border realizes strong artificial intelligence, possesses huge potentiality.
General artificial intelligence be to create it is a kind of without manually program oneself association solve the problems, such as various intelligent bodies, most Whole target is to realize class people the rank even intelligence of superman's rank.The behavior of intelligent body can be attributed to the interaction with the world. Intelligent body observes this world, then according to the observation and the state output of itself movement, this world can therefore and change, Intelligent body is returned to form feedback.General artificial intelligence has been achieved with huge breakthrough in fields such as game AI, gos, together Sample also has many valuable researchs in other field, and then substitutes the existing work needed by being accomplished manually.
Recognition with Recurrent Neural Network: a type of neural network, the node between hidden layer is no longer connectionless but has connection , and it further includes the output of last moment hidden layer that the input of hidden layer, which not only includes the output of input layer,.
Hidden layer: other each layers in addition to input layer and output layer are called hidden layer, that is to say, that hidden layer is not direct Receive extraneous signal, also sends signal not directly to the external world.
Input layer: only serve input signal is fanned out to effect.So be not credited to when calculating the number of plies of neural network, it should Layer is responsible for receiving the information from network-external, is referred to as the 0th layer.
Output layer: being the last layer of neural network, and the maximum level number with the network is responsible for the meter of output nerve network Calculate result.
Convolutional neural networks: by full-mesh layer (corresponding classical neural network) group of one or more convolutional layers and top At, while also including associated weights and pond layer (pooling layer).This structure enables convolutional neural networks to utilize The two-dimensional structure of input data.
Full Connection Neural Network: for n-1 layers and n-layer, n-1 layers any one node, all with all nodes of n-th layer There is connection.That is for each node of n-th layer when being calculated, the input of activation primitive is the output of n-1 layers of all nodes It is weighted with weight.
The embodiment of the present invention provides a kind of method of adjustment of credit line, and Fig. 2-1 is credit line of the embodiment of the present invention The implementation process schematic diagram of method of adjustment the described method comprises the following steps as shown in Fig. 2-1:
Step S201, the identification information of the target user based on acquisition obtain the data information of the target user.
In the present embodiment, the identification information of target user can be unique distinguishable using user's having in social networks The information for the property known, including text (such as user name), image (such as two dimensional code), naturally it is also possible to specific using other of target user Identify such as ID card No., phone number, email address.
User can generate various types of behavioral datas in social networks, such as pay close attention to the dynamic of good friend, browse good friend master Page issues the Social behaviors data such as personal dynamic, message；Information as user class being obtained by crawler software.
Step S202 determines the state vector of the target user according to the data information of the target user, according to institute The state vector for stating target user determines the corresponding tactful income vector of the state vector.
It in the present embodiment, may include the data information of multidimensional in the data information of the target user, such as can be with Including but not limited to interesting data information, consumption data information and behavioral data information, in which:
Interesting data information can reflect the information such as the hobby of user, can read in application and obtain from social category 's.
Consumption data information can be after lend-borrow action occurs for target user, the data information of the consumption of generation and return The also data information of loaning bill principal and interest.Consumption data information can be from loan platform acquisition.
Behavioral data information can be user in the behavioral data of social platform, can be regarded as being made of several behaviors Stream data, reflect the Behavior preference of user, and behavioral data information can be to be obtained from social application server.
During realization, determine that the state vector of the target user is logical according to the data information of the target user It crosses and the data information of each dimension is input in a Recognition with Recurrent Neural Network respectively, the corresponding output letter for obtaining each dimension The output information of each dimension, is then input in a full Connection Neural Network, last output state vector by breath again.At this time Obtained state vector is that the data information for each dimension that deeply has learnt target user obtains, so that final letter It is more accurate, comprehensive with the adjustable strategies of amount.
The state vector according to the target user determines the corresponding tactful income vector of the state vector in reality During now, the income probability distribution letter for determining obtain under tune volume strategy different under the state vector first can be Then breath carries out maximum pondization operation, at this time completion an iteration process, by iteration preset times, determine the state to Measure corresponding tactful income vector.
In the present embodiment, state vector can reflect the hobby of target user, consumption habit, Behavior preference it is comprehensive Conjunction state, the element number for including in the state vector are pre-set, and the state vector of reality output is by the number that inputs According to decision.What tactful income vector reflected is the corresponding relationship of tune volume strategy and income, wherein volume strategy is adjusted to can be primary tune Volume such as raises 100 yuan, can also be the combination for repeatedly adjusting volume, such as can be 100 yuan of up-regulation, 300 yuan of up-regulation, lowers 50 yuan The combination of volume is adjusted three times.The number for the element for including in tactful income vector is also to pre-set.
State vector described in the tactful income vector sum is input to the first full Connection Neural Network, really by step S203 Determine the adjustment direction and adjusted value of credit line.
Fig. 2-2 is the schematic diagram of the full neural network of the embodiment of the present invention first, as shown in Fig. 2-2, in the first full nerve net In network, 221 be input layer, and 222 be the 1st layer of hidden layer, and 223 be the 2nd layer of hidden layer, and 224 be output layer, wherein the 1st layer is hidden Any one node of layer 222, such as node 2221, all have connection with the 2nd layer of all node of hidden layer 223.
In the present embodiment, adjustment direction includes that upper reconciliation is lowered.Up-regulation is to increase credit line, and lowering is to reduce Credit line.Adjusted value is generally as unit of member, such as adjusted value can be 500 yuan, 1000 yuan etc..
Step S204, according to the determination of the current credit degree of the adjustment direction, adjusted value and the target user The target credit line of target user.
In the present embodiment, it is determined according to the current credit degree of the adjustment direction, adjusted value and the target user The target credit line of the target user can obtain the current credit degree of target user first in the actual implementation process, Then if adjustment direction is up-regulation, current credit degree is obtained into the target line of credit of the target user plus adjusted value Degree；If adjustment direction is to lower, current credit degree is subtracted into adjusted value and obtains the target credit line of the target user.
In the method for adjustment of credit line provided in this embodiment, it is primarily based on the mark letter of the target user of acquisition Breath, obtains the data information of the target user；Then the target user is determined according to the data information of the target user State vector, and the corresponding tactful income vector of the state vector is determined according to the state vector of the target user；Again State vector described in the tactful income vector sum is input to the first full Connection Neural Network, determines the adjustment side of credit line To and adjusted value；The mesh is finally determined according to the current credit degree of the adjustment direction, adjusted value and the target user The target credit line for marking user so, it is possible the multi-dimensional data information that deeply study utilizes user, and according to user The corresponding corresponding relationship with tactful income vector of state vector, the adjustable strategies of credit line are determined, thus automatically to credit Amount is adjusted.
Based on embodiment above-mentioned, the embodiment of the present invention provides a kind of method of adjustment of credit line again, and Fig. 3 is the present invention The implementation process schematic diagram of the method for adjustment of embodiment credit line, as shown in figure 3, the described method comprises the following steps:
Step S301, the identification information of the target user based on acquisition obtain the data information of the target user.
Here, the data information includes at least the first dimension data information, the second dimension data information and third dimension Data information, wherein the first dimension data information is in interesting data information, behavioral data information and consumption data information Any one；The second dimension data information is to remove in interesting data information, behavioral data information and consumption data information Go any one except the first dimension data information；The third dimension data information is interesting data information, behavior It is removed in data information and consumption data information except the first dimension data information and the second dimension data information Data information.
In this example, it is assumed that the first dimension data information is interesting data information；Second dimension data information is to disappear Take data information；Third dimension data information is behavioral data information.
Interesting data information, consumption data information and behavioral data information are all data sequences, and each moment is one group corresponding Data information.
The first dimension data information input to first circulation neural network is obtained the first output knot by step S302 Fruit.
Here, in the actual implementation process, the step S302 can be realized by following steps:
Step S3021 is obtained by the first dimension data information input at the first moment to the first circulation neural network First one-dimensional output result；
The first dimension data information and (k-1) one-dimensional output result at kth moment are input to described by step S3022 First circulation neural network obtains the one-dimensional output result of kth, wherein the k=2,3 ..., N, N be first dimension data It is total at the time of in information；
The one-dimensional output result of N is determined as the first output result by step S3023.
In the embodiment where step S3021 to step S3023, the first output result is entirely according to the first number of dimensions It is believed that breath obtained, the data information of other dimensions is not inputted.
The second dimension data information input to second circulation neural network is obtained the second output knot by step S303 Fruit.
Here, the step S303 can be realized by following steps:
The second dimension data information at the first moment and the first one-dimensional output result are input to described by step S3031 Two Recognition with Recurrent Neural Network obtain the first two dimension output result；
Step S3032, the one-dimensional output result of the second dimension data information, kth at kth moment and (k-1) two dimension is defeated Result is input to the second circulation neural network out, obtain kth two dimension output result, wherein the k=2,3 ..., N；
N two dimension output result is determined as the second output result by step S3033.
In the embodiment where step S3031 to step S3033, input second circulation neural network not only has second Dimension data information further includes one-dimensional output result in the same time.
The third dimension data information is input to third Recognition with Recurrent Neural Network by step S304, obtains third output knot Fruit.
Here, the step S304 can be realized by following steps:
Step S3041 exports the third dimension data information at the first moment, the first one-dimensional output result and the first two dimension As a result it is input to the third Recognition with Recurrent Neural Network, obtains the first three-dimensional output result；
The third dimension data information at kth moment, the one-dimensional output result of kth, kth two dimension are exported result by step S3042 It is input to the third Recognition with Recurrent Neural Network with (k-1) three-dimensional output result, obtains kth three-dimensional output result, wherein described K=2,3 ..., N；
N three-dimensional output result is determined as third output result by step S3043.
In the embodiment where step S3041 to step S3043, input third Recognition with Recurrent Neural Network not only has third Dimension data information further includes one-dimensional output result and two dimension output result in the same time.
In other embodiments of the present invention, it if the data information further includes fourth dimension degree data information, also needs Fourth dimension degree data information is input in the 4th Recognition with Recurrent Neural Network, obtains the 4th output as a result, and inputting the 4th mind Not only there is fourth dimension degree data information through network, further includes one-dimensional output result, two dimension output result and three-dimensional in the same time Export result.If the data information further includes the 5th dimension data information, and so on.
The first output result, the second output result and third output result are input to by step S305 Second full Connection Neural Network, obtains the state vector of the target user.
Here, first output result, second output result and third output result be all vector form output as a result, its In include element number pre-set, and these three output results in include element number can be it is identical , it is also possible to different.
The first output result, the second output result and third output result are input to the second full connection Neural network carries out a comprehensive state analysis, obtains the state vector of the target user, the state vector obtained at this time It is that the data information of each dimension that deeply has learnt target user obtains, so that the adjustment plan of final credit line It is slightly more accurate, comprehensive.
Step S306 obtains workable credit line adjustable strategies.
Here, the workable credit line adjustable strategies are pre-set, for example include: up-regulation or downward G A unit, wherein unit can be monetary unit, be also possible to other virtual credit units, such as bit coin, Q coin, credit Coin etc. can raise 100 yuan (RMB), 200 dollars of up-regulation, 500 Euros of up-regulation, 1000 ports of up-regulation by taking monetary unit as an example Member lowers 100 dollars, lowers 200 Euros, lower 500 Hongkong dollars, lower 1000 yuan etc..
Step S307 determines that first income of the state vector under the workable credit line adjustable strategies is general Rate distributed intelligence.
Here, using the principle of convolutional neural networks, determine the state vector in the workable credit line tune The process of the first income probability distribution information under whole strategy does convolution operation similar to convolution kernel on picture, according to the target The state vector of user enumerates the probability distribution information of acquired income under various tune volume strategies.
Step S308, according to the first income probability distribution information determine the corresponding tactful income of the state vector to Amount.
Here, the step S308 can be realized by following steps:
Step S3081 carries out maximum pondization operation to the first income probability distribution information, obtains the first sampling knot Fruit；
Step S3082 carries out strategy to h sampled result and enumerates to obtain (h+1) income probability distribution information, and to institute The operation of (h+1) income probability distribution information maximum pondization is stated, h sampled result, h=1,2 ..., M-1 are obtained, M is preset The number of iterations；
Step S3083 determines the income of the workable credit line adjustable strategies according to (M-1) sampled result；
Step S3084 is adjusted according to the workable credit line adjustable strategies and the workable credit line The income of strategy determines the corresponding tactful income vector of the state vector.
State vector described in the tactful income vector sum is input to the first full Connection Neural Network, really by step S309 Determine the adjustment direction and adjusted value of credit line.
Step S310, according to the determination of the current credit degree of the adjustment direction, adjusted value and the target user The target credit line of target user.
In other embodiments, the adjustment direction of credit line and adjusted value are determined as adjustable strategies, in the step After S309, the method also includes:
Step 41, the estimated revenue value and actual gain value of the adjustable strategies are determined；
Step 42, feedback information is determined according to the estimated revenue value and the actual gain value；
Step 43, back-propagation algorithm training is carried out to the feedback information, adjusts the first circulation neural network, institute Second circulation neural network, the third Recognition with Recurrent Neural Network, the first full Connection Neural Network and described second is stated to connect entirely Connect the network parameter of neural network.
In the embodiment where step 41 to step 43, the network parameter may include biasing, weight.These networks Parameter is to carry out back-propagation algorithm instruction by the feedback information of estimated revenue value and the determination of actual gain value to adjustable strategies It gets.It is the first circulation neural network, the second circulation neural network, the third Recognition with Recurrent Neural Network, described The initial network parameter of first full Connection Neural Network and the second full Connection Neural Network is random number, is needed by anti- Feedforward information is trained and further adjusts.
Back-propagation algorithm (Back propagation) is to be used to train artificial neural network (Artificial at present Neural Network, ANN) the most frequently used and most effective algorithm, comprising the following steps:
1) training set data is input to the input layer of ANN, by hidden layer, finally reach output layer and export as a result, This is the propagated forward process of ANN；
2) since the output result of ANN and actual result have error, then the error between estimated value and actual value is calculated, and By the error from output layer to hidden layer backpropagation, until traveling to input layer；
3) during backpropagation, according to the value of error transfer factor various parameters；The continuous iteration above process, until receiving It holds back.
In the method for adjustment of credit line provided in an embodiment of the present invention, it is primarily based on the mark of the target user of acquisition Information obtains the data information of the target user；The data information includes at least the first dimension data information, the second dimension Data information and third dimension data information；Then by the first dimension data information input to first circulation neural network, Obtain the first output result；By the second dimension data information input to second circulation neural network, the second output knot is obtained Fruit；The third dimension data information is input to third Recognition with Recurrent Neural Network, obtains third output result；And by described first Output result, the second output result and third output result are input to the second full Connection Neural Network, obtain described The state vector of target user；Workable credit line adjustable strategies are obtained again；Determine that the state vector makes described The first income probability distribution information under credit line adjustable strategies is simultaneously true according to the first income probability distribution information Determine the corresponding tactful income vector of the state vector；It is complete that state vector described in the tactful income vector sum is input to first Connection Neural Network determines the adjustment direction and adjusted value of credit line；According to the adjustment direction, adjusted value and the target The current credit degree of user determines the target credit line of the target user, is capable of the more of deep learning target user in this way Dimension data information obtains the comprehensive state of target user, and then realizes automatic tune volume according to comprehensive state, is able to ascend entirety Efficiency, and the network parameter for carrying out each neural network of deep learning is the estimated revenue and practical receipts according to adjustable strategies What the feedback signal that benefit determines was adjusted, it ensure that the accuracy of deep learning.
The embodiment of the present invention first provides a kind of adjustment system of credit line.Credit line provided in an embodiment of the present invention Adjustment system is the thought based on deep learning and intensified learning, and deep learning is used to provide powerful user's various dimensions information The mechanism of study and expression, and intensified learning provides the target of study for deep learning.
Fig. 4 is that the composed structure of automated intelligent tune volume system of the embodiment of the present invention based on user's various dimensions information is illustrated Figure, as shown in figure 4, the system 400 is made of three modules: state aware module 401, tactful 402 and of income iteration module Tactful output module 403.
The working method of three modules and effect are illustrated below.
State aware module 401, the data information for the various dimensions according to user generates comprehensive state expression, at it In his embodiment, state expression can be state vector.
User can generate the data of multiple dimensions after lend-borrow action occurs；Such as user behavior data can be generated, disappeared Take with refund data and interesting data, and these data are all the forms of data sequence, be referred to as user behavior sequence, Consumption and refund sequence and sequence of interest.Wherein, user behavior sequence is behavioral data of the user in social platform, is considered as It is the stream data being made of several behaviors, reflects the Behavior preference of user；Consumption is that debt-credit row occurs for user with refund sequence For later, the consumer behavior of generation and the behavior for the principal and interest that pay back the borrowed money；Sequence of interest is to read to apply from social category Middle acquisition.Reflect the hobby of user.
State aware module comprehensively utilizes the data information of multiple dimensions using the Recognition with Recurrent Neural Network of multilayer.Three dimensions Sequence distribution be input in different Recognition with Recurrent Neural Network, but three networks are not completely independent, this layer of neural network Hiding layer unit input not only comprising this layer of neural network hidden layer of last moment output but also include upper one layer of mind Output through network current time hidden layer.Based on this, the output of the neural network hidden layer of upper layer network may include multiple The information of dimension.
Fig. 5 be state aware of embodiment of the present invention module diagram, as shown in figure 5, first layer Recognition with Recurrent Neural Network 501 by Input of the interesting data as this layer of neural network, t moment hide the output of layer unitIt can indicate are as follows:
In formula (1-1), σ is sigmoid function, Wi1And Si1It is the parameter of function, xtFor the defeated of t moment interesting data Enter,Carving copy layer hides the output of layer unit, b when for t-1iFor the bias vector of first layer.
Sigmoid function is the function of a common S type in biology, also referred to as S sigmoid growth curve.In Information Center It, will since singly properties, the sigmoid function such as increasing and the increasing of inverse function list are often used as the threshold function table of neural network for it in Variable mappings are between 0,1.The advantages of sigmoid, is that output area is limited, so data are not easy during transmitting Diverging.
Input of the second layer Recognition with Recurrent Neural Network 502 by the consumption and refund data of user as this layer of neural network, t The input of moment neural network not only includes the consumption and refund data at current time, but also includes first layer circulation nerve net The hidden layer of the t moment of network exports, so second layer Recognition with Recurrent Neural Network t moment, which hides layer unit output, to be indicated are as follows:
In formula (1-2), xtFor the input of t moment consumption and refund data, bi2For the bias vector of the second layer, The output of layer unit is hidden for this layer of t-1 moment,The output of layer unit is hidden for first layer Recognition with Recurrent Neural Network t moment, σ is Sigmoid function, Wi2、Ti2And Si2It is the parameter of sigmoid function.
Input of the third layer Recognition with Recurrent Neural Network 503 by the behavior sequence data of user as network, t moment nerve net Network input not only include t moment behavioral data, but also include the t moment of first layer Recognition with Recurrent Neural Network hidden layer it is defeated Out and the hidden layer of second layer Recognition with Recurrent Neural Network t moment exports, so the hiding layer unit of third layer Recognition with Recurrent Neural Network Output can indicate are as follows:
In formula (1-3), xtFor the input of t moment behavior sequence data, bi3For the bias vector of third layer,For T-1 moment third layer Recognition with Recurrent Neural Network hides the output of layer unit,For first layer Recognition with Recurrent Neural Network t moment hidden layer list The output of member,The output σ that layer unit is hidden for second layer Recognition with Recurrent Neural Network t moment is sigmoid function, Wi3、Ti3、Ui3 And Si3It is the parameter of sigmoid function.
First layer Recognition with Recurrent Neural Network, second layer Recognition with Recurrent Neural Network and third layer Recognition with Recurrent Neural Network the last one when The state expression for generating synthesis is done in being input to for quarter in the neural network 504 connected entirely；Sigmoid letter in above-mentioned formula Number parameter can be obtained by backpropagation algorithm training.
Tactful income iteration module 402, for according to current state, iteration calculates the income of the acquirement under the state Value is until convergence.Enumeration strategy can generate the financial value generated under different decisions, a decision pair according to the state of user Answer the probability distribution and acquired income of a dbjective state.
Fig. 6 is the course of work schematic diagram of strategy of embodiment of the present invention income iteration module, as shown in fig. 6, strategy is enumerated Process do convolution operation on picture 602 similar to convolution kernel 601, strategy, which is enumerated, to be enumerated according to the state of active user various Tune volume strategy policyiThe probability distribution reward of lower acquired incomei, wherein i=0,1 ..., n；For benefit distribution rewardiIn include 12 states, M0,M1,...,M11, M0User is there is no overdue under the tune volume strategy for representative, M11It represents User refunds overdue 11 phases of generation under the tune volume strategy, and overdue will stop as user's granting loan of more than phase refunding more than 11 occurs Money；MiIncome under state is usedIt indicates；Sampling operation is done using the method in maximum pond, strategy is enumerated and most Great Chiization operation completes the process of an iteration；Multiple income is realized by each tactful enumeration layer of heap overlay and sample level Iterative calculation generates optimal value；This process can be indicated with formula (1-4):
rewardsum=max { rewardi+rewardnext} (1-4)；
In formula (1-4), rewardsumTo adjust volume strategy policyiOptimal financial value, max () be maximizing letter Number, rewardnextThe financial value generated for last iteration；
The number of plies that strategy is enumerated and sampled is network parameter, can test adjustment in actual application, to obtain most Excellent effect.
Tactful output module 403, for determining optimal tune volume strategy according to tactful income vector.
For tactful income iteration module after the sampling of multilayer convolution sum, it is defeated that the tactful income vector of output is input to strategy Out in module, Fig. 7 is the course of work schematic diagram of strategy of embodiment of the present invention output module, as shown in fig. 7, tactful output module It is made of the full Connection Neural Network 701 of multilayer；The parameter of its network can be obtained by back-propagation algorithm training.In training In the process, Attention mechanism is introduced, the status information 702 that state aware module is exported is used as additional information, so that network The status information of user can be efficiently used.
It should be noted that the income for adjusting volume strategy to generate that tactful income iteration module generates, usually practical with strategy There are errors for the actual gain generated after, the error can be known as feedback signal here.Tactful output module is outside The feedback signal L of portion's environment adjusts the parameter in all neural networks used in three modules by back-propagation algorithm； In practical applications, feedback signal can be determined according to formula (1-5), which indicates the income of the strategy prediction of application With square of actual gain difference.
L=(rewardtraget-rewardprediction)2(1-5)；
In formula (1-5), rewardpredictionFor the prediction income for adjusting volume strategy, rewardtragetTo adjust volume strategy Actual gain.
Feedback signal is passed into tactful income iteration module and state aware module simultaneously, the two modules utilize feedback Signal learns the parameter of this module by back-propagation algorithm.
In embodiments of the present invention, the thought based on deep learning and intensified learning is applied to the adjustment system of credit line In system, wherein the block of state in the adjustment system of credit line generates synthesis according to the data information of the various dimensions of user State vector, tactful income iteration module determine tactful income vector, last strategy output mould further according to the state vector of user Root tuber determines optimal tune volume strategy according to tactful income vector, in this way, the adjustment system energy of credit line provided in this embodiment The multilayer circulation neural network for enough utilizing various dimensions user information realizes User Status perception, and then according to the synthesis shape of user State automatically generates optimal tune volume strategy and can aid in the whole efficiency of promotion and provide differentiation financial service.
The embodiment of the present invention provides a kind of adjustment device of credit line, and Fig. 8 is line of credit provided in an embodiment of the present invention The composed structure schematic diagram of the adjustment device of degree, as shown in figure 8, described device 800 includes: that the first acquisition module 801, first is true Cover half block 802, the second determining module 803 and third determining module 804, in which:
The first acquisition module 801 obtains the target user for the identification information of the target user based on acquisition Data information；
First determining module 802, for determining the target user's according to the data information of the target user State vector determines the corresponding tactful income vector of the state vector according to the state vector of the target user；
Second determining module 803 is complete for state vector described in the tactful income vector sum to be input to first Connection Neural Network determines the adjustment direction and adjusted value of credit line；
The third determining module 804, for according to the adjustment direction, adjusted value and the current letter of the target user The target credit line of the target user is determined with amount.
In other embodiments, the data information includes at least: the first dimension data information, the second dimension data information With third dimension data information, accordingly, first determining module 802 includes:
First input unit, for by the first dimension data information input to first circulation neural network, obtaining the One output result；
Second input unit, for by the second dimension data information input to second circulation neural network, obtaining the Two output results；
Third input unit obtains for the third dimension data information to be input to third Recognition with Recurrent Neural Network Three output results；
4th input unit, for tying the first output result, the second output result and third output Fruit is input to the second full Connection Neural Network, obtains the state vector of the target user.
In other embodiments, first input unit is also used to:
By the first dimension data information input at the first moment to the first circulation neural network, it is one-dimensional defeated to obtain first Result out；
The first dimension data information and (k-1) one-dimensional output result at kth moment are input to the first circulation mind Through network, obtain the one-dimensional output result of kth, wherein the k=2,3 ..., N, N be the first dimension data information in when Carve sum；
The one-dimensional output result of N is determined as the first output result.
In other embodiments, second input unit, be also used to the second dimension data information at the first moment and First one-dimensional output result is input to the second circulation neural network, obtains the first two dimension output result；By the of the kth moment The one-dimensional output result of two-dimensions data information, kth and (k-1) two dimension output result are input to the second circulation nerve net Network obtains kth two dimension output result, wherein the k=2,3 ..., N；N two dimension output result is determined as the second output knot Fruit.
In other embodiments, the third input unit is also used to the third dimension data information at the first moment, One one-dimensional output result and the first two dimension output result are input to the third Recognition with Recurrent Neural Network, obtain the first three-dimensional output knot Fruit；The third dimension data information at kth moment, the one-dimensional output result of kth, kth two dimension output result and (k-1) three-dimensional is defeated Result is input to the third Recognition with Recurrent Neural Network out, obtains kth three-dimensional output result, wherein the k=2,3 ..., N；It will N three-dimensional output result is determined as third output result.
In other embodiments, first determining module 802 further include:
First acquisition unit, for obtaining workable credit line adjustable strategies；
First determination unit, for determining the state vector under the workable credit line adjustable strategies One income probability distribution information；
Second determination unit, for determining the corresponding plan of the state vector according to the first income probability distribution information Slightly income vector.
In other embodiments, second determination unit is also used to: being carried out to the first income probability distribution information Maximum pondization operation, obtains the first sampled result；Strategy is carried out to h sampled result to enumerate to obtain (h+1) income probability point Cloth information, and (h+1) income probability distribution information maximum pondization is operated, obtain h sampled result, h=1,2 ..., M-1, M are preset the number of iterations；The receipts of the workable credit line adjustable strategies are determined according to (M-1) sampled result Benefit；Institute is determined according to the income of the workable credit line adjustable strategies and the workable credit line adjustable strategies State the corresponding tactful income vector of state vector.
The description of apparatus above embodiment, be with the description of above method embodiment it is similar, have same embodiment of the method Similar beneficial effect.For undisclosed technical detail in apparatus of the present invention embodiment, embodiment of the present invention method is please referred to Description and understand.
The embodiment of the present invention provides a kind of adjustment equipment of credit line, and Fig. 9 is the tune of credit line of the embodiment of the present invention The composed structure schematic diagram of finishing equipment, as shown in figure 9, the equipment 900 at least one processor 901, at least one communication are total Line 902, user interface 903, at least one external communication interface 904 and memory 905.Wherein, communication bus 902 is configured to reality Connection communication between these existing components.Wherein, user interface 903 may include display screen, and external communication interface 904 can wrap Include the wireline interface and wireless interface of standard.The wherein processor 901, is configured that
The identification information of target user based on acquisition obtains the data information of the target user；
The state vector that the target user is determined according to the data information of the target user, according to the target user State vector determine the corresponding tactful income vector of the state vector；
State vector described in the tactful income vector sum is input to the first full Connection Neural Network, determines credit line Adjustment direction and adjusted value；
Determine the target user's according to the current credit degree of the adjustment direction, adjusted value and the target user Target credit line.
In other embodiments, the data information includes at least: the first dimension data information, the second dimension data information With third dimension data information, accordingly, the state vector that the target user is determined according to the data information is wrapped It includes:
By the first dimension data information input to first circulation neural network, the first output result is obtained；
By the second dimension data information input to second circulation neural network, the second output result is obtained；
The third dimension data information is input to third Recognition with Recurrent Neural Network, obtains third output result；
The first output result, the second output result and third output result are input to the second full connection Neural network obtains the state vector of the target user.
In other embodiments, described that the first dimension data information input to first circulation neural network is obtained One output result, comprising:
By the first dimension data information input at the first moment to the first circulation neural network, it is one-dimensional defeated to obtain first Result out；
The first dimension data information and (k-1) one-dimensional output result at kth moment are input to the first circulation mind Through network, obtain the one-dimensional output result of kth, wherein the k=2,3 ..., N, N be the first dimension data information in when Carve sum；
The one-dimensional output result of N is determined as the first output result.
In other embodiments, described that the second dimension data information input to second circulation neural network is obtained Two output results, comprising:
The second dimension data information at the first moment and the first one-dimensional output result are input to the second circulation nerve Network obtains the first two dimension output result；
By the one-dimensional output result of the second dimension data information, kth at kth moment and the output result input of (k-1) two dimension To the second circulation neural network, kth two dimension output result is obtained, wherein the k=2,3 ..., N；
N two dimension output result is determined as the second output result.
In other embodiments, described the third dimension data information is input to third Recognition with Recurrent Neural Network to obtain Three output results, comprising:
The third dimension data information at the first moment, the first one-dimensional output result and the first two dimension output result are input to The third Recognition with Recurrent Neural Network obtains the first three-dimensional output result；
The third dimension data information at kth moment, the one-dimensional output result of kth, kth two dimension are exported into result and (k-1) Three-dimensional output result is input to the third Recognition with Recurrent Neural Network, obtains kth three-dimensional output result, wherein the k=2, 3,…,N；
N three-dimensional output result is determined as third output result.
In other embodiments, it is described according to the state vector determine the corresponding tactful income of the state vector to Amount, comprising:
Obtain workable credit line adjustable strategies；
Determine first income probability distribution letter of the state vector under the workable credit line adjustable strategies Breath；
The corresponding tactful income vector of the state vector is determined according to the first income probability distribution information.
In other embodiments, described to determine that the state vector is corresponding according to the first income probability distribution information Tactful income vector, comprising:
Maximum pondization operation is carried out to the first income probability distribution information, obtains the first sampled result；
It carries out strategy to h sampled result to enumerate to obtain (h+1) income probability distribution information, and to (h+1) The operation of income probability distribution information maximum pondization, obtains h sampled result, h=1,2 ..., M-1, M is preset the number of iterations；
The income of the workable credit line adjustable strategies is determined according to (M-1) sampled result；
According to the income of the workable credit line adjustable strategies and the workable credit line adjustable strategies Determine the corresponding tactful income vector of the state vector.
In other embodiments, the processor 901, is configured that
The adjustment direction and adjusted value are determined as adjustable strategies；
Determine the estimated revenue value and actual gain value of the adjustable strategies；
Feedback information is determined according to the estimated revenue value and the actual gain value；
Back-propagation algorithm training is carried out to the feedback information, adjusts the first circulation neural network, described second Recognition with Recurrent Neural Network, the third Recognition with Recurrent Neural Network, the first full Connection Neural Network and the second full connection nerve The network parameter of network.
In the embodiment of the present invention, if realizing the method for adjustment of above-mentioned credit line in the form of software function module, And when sold or used as an independent product, it also can store in a computer readable storage medium.Based in this way Understanding, substantially the part that contributes to existing technology can be produced the technical solution of the embodiment of the present invention in other words with software The form of product embodies, which is stored in a storage medium, including some instructions are used so that one Platform computer equipment (can be personal computer, server or network equipment etc.) executes described in each embodiment of the present invention The all or part of method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read Only Memory), the various media that can store program code such as magnetic or disk.In this way, the embodiment of the present invention is not limited to appoint What specific hardware and software combines.
The embodiment of the present invention provides a kind of computer storage medium, and being stored with computer in the computer storage medium can It executes instruction, which is used to execute the method for adjustment of credit line provided in an embodiment of the present invention.
It should be understood that " one embodiment " or " embodiment " that specification is mentioned in the whole text mean it is related with embodiment A particular feature, structure, or characteristic is included at least one embodiment of the present invention.Therefore, occur everywhere in the whole instruction " in one embodiment " or " in one embodiment " not necessarily refer to identical embodiment.In addition, these specific features, knot Structure or characteristic can combine in any suitable manner in one or more embodiments.It should be understood that in various implementations of the invention In example, magnitude of the sequence numbers of the above procedures are not meant that the order of the execution order, the execution sequence Ying Yiqi function of each process It can determine that the implementation process of the embodiments of the invention shall not be constituted with any limitation with internal logic.The embodiments of the present invention Serial number is for illustration only, does not represent the advantages or disadvantages of the embodiments.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row His property includes, so that the process, method, article or the device that include a series of elements not only include those elements, and And further include other elements that are not explicitly listed, or further include for this process, method, article or device institute it is intrinsic Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do There is also other identical elements in the process, method of element, article or device.
In several embodiments provided herein, it should be understood that disclosed device and method can pass through it Its mode is realized.Apparatus embodiments described above are merely indicative, for example, the division of the unit, only A kind of logical function partition, there may be another division manner in actual implementation, such as: multiple units or components can combine, or It is desirably integrated into another system, or some features can be ignored or not executed.In addition, shown or discussed each composition portion Mutual coupling or direct-coupling or communication connection is divided to can be through some interfaces, the INDIRECT COUPLING of equipment or unit Or communication connection, it can be electrical, mechanical or other forms.
Above-mentioned unit as illustrated by the separation member, which can be or may not be, to be physically separated, aobvious as unit The component shown can be or may not be physical unit；Both it can be located in one place, and may be distributed over multiple network lists In member；Some or all of units can be selected to achieve the purpose of the solution of this embodiment according to the actual needs.
In addition, each functional unit in various embodiments of the present invention can be fully integrated in one processing unit, it can also To be each unit individually as a unit, can also be integrated in one unit with two or more units；It is above-mentioned Integrated unit both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above method embodiment can pass through The relevant hardware of program instruction is completed, and program above-mentioned can store in computer-readable storage medium, which exists When execution, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes: movable storage device, read-only deposits The various media that can store program code such as reservoir (Read Only Memory, ROM), magnetic or disk.
If alternatively, the above-mentioned integrated unit of the present invention is realized in the form of software function module and as independent product When selling or using, it also can store in a computer readable storage medium.Based on this understanding, the present invention is implemented Substantially the part that contributes to existing technology can be embodied in the form of software products the technical solution of example in other words, The computer software product is stored in a storage medium, including some instructions are used so that computer equipment (can be with It is personal computer, server or network equipment etc.) execute all or part of each embodiment the method for the present invention. And storage medium above-mentioned includes: various Jie that can store program code such as movable storage device, ROM, magnetic or disk Matter.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain Lid is within protection scope of the present invention.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.

Claims (15)

1. a kind of method for adjusting credit line, which is characterized in that the described method includes:
The identification information of target user based on acquisition obtains the data information of the target user；
The state vector that the target user is determined according to the data information of the target user, according to the shape of the target user State vector determines the corresponding tactful income vector of the state vector；
State vector described in the tactful income vector sum is input to the first full Connection Neural Network, determines the tune of credit line Perfect square to and adjusted value；
The target of the target user is determined according to the current credit degree of the adjustment direction, adjusted value and the target user Credit line.
2. method according to claim 1, which is characterized in that the data information includes at least: the first dimension data Information, the second dimension data information and third dimension data information, it is accordingly, described that the mesh is determined according to the data information Mark the state vector of user, comprising:
By the first dimension data information input to first circulation neural network, the first output result is obtained；
By the second dimension data information input to second circulation neural network, the second output result is obtained；
The third dimension data information is input to third Recognition with Recurrent Neural Network, obtains third output result；
The first output result, the second output result and third output result are input to the second full connection nerve Network obtains the state vector of the target user.
3. method according to claim 2, which is characterized in that described by the first dimension data information input to One Recognition with Recurrent Neural Network obtains the first output result, comprising:
By the first dimension data information input at the first moment to the first circulation neural network, the first one-dimensional output knot is obtained Fruit；
The first dimension data information and (k-1) one-dimensional output result at kth moment are input to the first circulation nerve net Network obtains the one-dimensional output result of kth, wherein the k=2,3 ..., N, N be the first dimension data information at the time of it is total Number；
The one-dimensional output result of N is determined as the first output result.
4. method according to claim 2, which is characterized in that described by the second dimension data information input to Two Recognition with Recurrent Neural Network obtain the second output result, comprising:
The second dimension data information at the first moment and the first one-dimensional output result are input to the second circulation neural network, Obtain the first two dimension output result；
The one-dimensional output result of second dimension data information, kth at kth moment and (k-1) two dimension output result are input to institute Second circulation neural network is stated, obtains kth two dimension output result, wherein the k=2,3 ..., N；
N two dimension output result is determined as the second output result.
5. method according to claim 2, which is characterized in that described that the third dimension data information is input to Three Recognition with Recurrent Neural Network obtain third output result, comprising:
The third dimension data information at the first moment, the first one-dimensional output result and the first two dimension output result are input to described Third Recognition with Recurrent Neural Network obtains the first three-dimensional output result；
By the third dimension data information at kth moment, the one-dimensional output result of kth, kth two dimension output result and (k-1) three-dimensional Output result be input to the third Recognition with Recurrent Neural Network, obtain kth three-dimensional output result, wherein the k=2,3 ..., N；
N three-dimensional output result is determined as third output result.
6. method according to claim 1, which is characterized in that it is described according to the state vector determine the state to Measure corresponding tactful income vector, comprising:
Obtain workable credit line adjustable strategies；
Determine first income probability distribution information of the state vector under the workable credit line adjustable strategies；
The corresponding tactful income vector of the state vector is determined according to the first income probability distribution information.
7. method according to claim 6, which is characterized in that described true according to the first income probability distribution information Determine the corresponding tactful income vector of the state vector, comprising:
Maximum pondization operation is carried out to the first income probability distribution information, obtains the first sampled result；
It carries out strategy to h sampled result to enumerate to obtain (h+1) income probability distribution information, and to (h+1) income The operation of probability distribution information maximum pondization, obtains h sampled result, wherein h=1,2 ..., (M-1), M is preset iteration time Number；
The income of the workable credit line adjustable strategies is determined according to (M-1) sampled result；
It is determined according to the income of the workable credit line adjustable strategies and the workable credit line adjustable strategies The corresponding tactful income vector of the state vector.
8. method according to claim 1, which is characterized in that the method also includes:
The adjustment direction and adjusted value are determined as adjustable strategies；
Determine the estimated revenue value and actual gain value of the adjustable strategies；
Feedback information is determined according to the estimated revenue value and the actual gain value；
Back-propagation algorithm training is carried out to the feedback information, adjusts the first circulation neural network, the second circulation Neural network, the third Recognition with Recurrent Neural Network, the first full Connection Neural Network and the second full Connection Neural Network Network parameter.
9. a kind of adjustment device of credit line, which is characterized in that described device includes: the first acquisition module, the first determining mould Block, the second determining module and third determining module, in which:
The first acquisition module obtains the data of the target user for the identification information of the target user based on acquisition Information；
First determining module, for determined according to the data information of the target user state of the target user to Amount determines the corresponding tactful income vector of the state vector according to the state vector of the target user；
Second determining module, for state vector described in the tactful income vector sum to be input to the first full connection nerve Network determines the adjustment direction and adjusted value of credit line；
The third determining module, for the current credit degree according to the adjustment direction, adjusted value and the target user Determine the target credit line of the target user.
10. device according to claim 9, which is characterized in that the data information includes at least: the first dimension data Information, the second dimension data information and third dimension data information, accordingly, first determining module includes:
First input unit, for first circulation neural network, it is defeated to be obtained first for the first dimension data information input Result out；
Second input unit, for second circulation neural network, it is defeated to be obtained second for the second dimension data information input Result out；
It is defeated to obtain third for the third dimension data information to be input to third Recognition with Recurrent Neural Network for third input unit Result out；
4th input unit, for the first output result, the second output result and third output result is defeated Enter to the second full Connection Neural Network, obtains the state vector of the target user.
11. device according to claim 10, which is characterized in that first determining module further include:
First acquisition unit, for obtaining workable credit line adjustable strategies；
First determination unit, for determining first receipts of the state vector under the workable credit line adjustable strategies Beneficial probability distribution information；
Second determination unit, for determining that the corresponding strategy of the state vector is received according to the first income probability distribution information Beneficial vector.
12. a kind of adjustment equipment of credit line, which is characterized in that the equipment includes at least: memory, communication bus and place Manage device, in which:
The memory, for storing the adjustment programme of credit line；
The communication bus, for realizing the connection communication between processor and memory；
The processor, for executing the adjustment programme of the credit line stored in memory, to perform the steps of
The identification information of target user based on acquisition obtains the data information of the target user；
The state vector that the target user is determined according to the data information of the target user, according to the shape of the target user State vector determines the corresponding tactful income vector of the state vector；
State vector described in the tactful income vector sum is input to the first full Connection Neural Network, determines the tune of credit line Perfect square to and adjusted value；
The target of the target user is determined according to the current credit degree of the adjustment direction, adjusted value and the target user Credit line.
13. equipment according to claim 12, which is characterized in that the data information includes at least: the first number of dimensions It is believed that breath, the second dimension data information and third dimension data information, accordingly, described according to data information determination The state vector of target user, comprising:
By the first dimension data information input to first circulation neural network, the first output result is obtained；
By the second dimension data information input to second circulation neural network, the second output result is obtained；
The third dimension data information is input to third Recognition with Recurrent Neural Network, obtains third output result；
The first output result, the second output result and third output result are input to the second full connection nerve Network obtains the state vector of the target user.
14. equipment according to claim 12, which is characterized in that described to determine the state according to the state vector The corresponding tactful income vector of vector, comprising:
Obtain workable credit line adjustable strategies；
Determine first income probability distribution information of the state vector under the workable credit line adjustable strategies；
The corresponding tactful income vector of the state vector is determined according to the first income probability distribution information.
15. a kind of computer readable storage medium, which is characterized in that be stored with line of credit on the computer readable storage medium It realizes when the adjustment programme of the adjustment programme of degree, the credit line is executed by processor such as any one of claims 1 to 8 institute The step of method of adjustment for the credit line stated.
CN201710952035.9A 2017-10-13 2017-10-13 The method of adjustment and its device of credit line, equipment, storage medium Pending CN109670927A (en)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110348726A (en) * 2019-07-02 2019-10-18 北京淇瑀信息科技有限公司 A kind of user's amount method of adjustment, device and electronic equipment based on social networks network
CN110807527A (en) * 2019-09-30 2020-02-18 北京淇瑀信息科技有限公司 Line adjusting method and device based on guest group screening and electronic equipment
CN111353872A (en) * 2019-12-20 2020-06-30 上海淇玥信息技术有限公司 Credit granting processing method and device based on financial performance value and electronic equipment
CN112288436A (en) * 2020-10-27 2021-01-29 上海淇馥信息技术有限公司 Triggered resource quota adjusting method, device and system
TWI768512B (en) * 2019-12-26 2022-06-21 日商樂天集團股份有限公司 Credit calculation system, credit calculation method and program product

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110348726A (en) * 2019-07-02 2019-10-18 北京淇瑀信息科技有限公司 A kind of user's amount method of adjustment, device and electronic equipment based on social networks network
CN110807527A (en) * 2019-09-30 2020-02-18 北京淇瑀信息科技有限公司 Line adjusting method and device based on guest group screening and electronic equipment
CN110807527B (en) * 2019-09-30 2023-11-14 北京淇瑀信息科技有限公司 Credit adjustment method and device based on guest group screening and electronic equipment
CN111353872A (en) * 2019-12-20 2020-06-30 上海淇玥信息技术有限公司 Credit granting processing method and device based on financial performance value and electronic equipment
TWI768512B (en) * 2019-12-26 2022-06-21 日商樂天集團股份有限公司 Credit calculation system, credit calculation method and program product
CN112288436A (en) * 2020-10-27 2021-01-29 上海淇馥信息技术有限公司 Triggered resource quota adjusting method, device and system

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