CN114819614A

CN114819614A - Data processing method, device, system and equipment

Info

Publication number: CN114819614A
Application number: CN202210429444.1A
Authority: CN
Inventors: 王立
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2022-04-22
Filing date: 2022-04-22
Publication date: 2022-07-29
Also published as: WO2023202496A1

Abstract

The embodiment of the specification provides a data processing method, a device, a system and equipment, wherein the method comprises the following steps: receiving a risk identification result of a server for a first user, wherein the risk identification result of the first user is obtained by carrying out risk identification on first characteristic data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the server based on a preset first number of user characteristic data; performing initialization training on a preset second risk recognition model based on the second characteristic data of the first user and the risk recognition result of the first user to obtain the second risk recognition model after initialization training; and performing parameter updating processing on the second risk identification model after the initial training based on the feature data of the second user and the risk identification result of the second user to obtain a pre-trained second risk identification model.

Description

Data processing method, device, system and equipment

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a data processing method, apparatus, system, and device.

Background

With the rapid development of computer technologies, the number of users of enterprises providing resource transfer services for users, such as internet banking, is rapidly increasing, the data risk faced by the enterprises is also increasing, and in order to reduce the data risk, the enterprises can perform risk identification processing in a manner of constructing a risk identification model. For example, an enterprise may train a risk recognition model based on locally stored user sample data and risk labels, and perform risk recognition processing based on the trained risk recognition model.

However, enterprises such as internet banking may have situations of small data amount of user sample data and risk labels and poor data quality, which may result in poor risk identification effect and low risk identification accuracy of the trained risk identification model, and therefore a solution capable of improving the risk identification effect and the risk identification accuracy of the risk identification model is needed.

Disclosure of Invention

An object of the embodiments of the present specification is to provide a data processing method, apparatus, system, and device, so as to provide a solution capable of improving a risk identification effect and a risk identification accuracy of a risk identification model.

In order to implement the above technical solution, the embodiments of the present specification are implemented as follows:

in a first aspect, a data processing method includes: receiving a risk identification result of a server for a first user, wherein the risk identification result of the first user is obtained by carrying out risk identification on first characteristic data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the server based on a preset first number of user characteristic data; performing initialization training on a preset second risk recognition model based on the second characteristic data of the first user and the risk recognition result of the first user to obtain the second risk recognition model after initialization training; and performing parameter updating processing on the second risk identification model after the initialization training based on the characteristic data of the second user and the risk identification result of the second user to obtain a pre-trained second risk identification model, and performing risk identification processing on the user based on the pre-trained second risk identification model.

In a second aspect, a data processing method provided in an embodiment of the present specification includes: under the condition that a target user is detected to trigger execution of a target service, first characteristic data of the target user is obtained; inputting the first characteristic data of the target user into a pre-trained second risk identification model to obtain a target risk identification result of the target user, wherein the pre-trained second risk identification model is obtained by training a second characteristic data based on a first user, a risk identification result of the first user, characteristic data of a second user and a risk identification result of the second user, the risk identification result of the first user is obtained by carrying out risk identification on the first characteristic data of the first user by a service end based on the pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the service end based on a preset first number of user characteristic data; and determining whether the target business is triggered to be executed or not according to the target risk identification result.

In a third aspect, an embodiment of the present specification provides a data processing system, including a server and a client, where: the client is used for sending the user identification of the first user to the server; the server is used for obtaining a risk identification result of the first user based on the user identifier, the risk identification result of the first user is obtained by carrying out risk identification on first characteristic data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the server based on a preset first number of user characteristic data; the client is configured to perform initialization training on a preset second risk recognition model based on the second feature data of the first user and the risk recognition result of the first user returned by the server, to obtain the second risk recognition model after initialization training, and perform parameter update processing on the second risk recognition model after initialization training based on the feature data of the second user and the risk recognition result of the second user, to obtain a pre-trained second risk recognition model, so as to perform risk recognition processing on the user based on the pre-trained second risk recognition model.

In a fourth aspect, an embodiment of the present specification provides a data processing apparatus, including: the result receiving module is used for receiving a risk identification result of a server for a first user, wherein the risk identification result of the first user is obtained by carrying out risk identification on first characteristic data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the server based on a preset first number of user characteristic data; the first training module is used for carrying out initialization training on a preset second risk recognition model based on the second characteristic data of the first user and the risk recognition result of the first user to obtain the second risk recognition model after initialization training; and the second training module is used for updating parameters of the second risk identification model after the initialization training based on the characteristic data of a second user and the risk identification result of the second user to obtain a pre-trained second risk identification model, and performing risk identification processing on the user based on the pre-trained second risk identification model.

In a fifth aspect, an embodiment of the present specification provides a data processing apparatus, including: the data acquisition module is used for acquiring first characteristic data of a target user under the condition that the target user is detected to trigger execution of a target service; a result obtaining module, configured to input first feature data of the target user into a pre-trained second risk identification model to obtain a target risk identification result of the target user, where the pre-trained second risk identification model is obtained by training a second feature data of a first user, a risk identification result of the first user, feature data of a second user, and a risk identification result of the second user, the risk identification result of the first user is obtained by performing risk identification on the first feature data of the first user by using a pre-trained first risk identification model based on a service end, and the pre-trained first risk identification model is obtained by training the service end by using a preset first number of user feature data; and the risk determining module is used for determining whether the risk exists in triggering and executing the target business or not based on the target risk identification result.

In a sixth aspect, an embodiment of the present specification provides a data processing apparatus, including: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to: receiving a risk identification result of a server for a first user, wherein the risk identification result of the first user is obtained by carrying out risk identification on first characteristic data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the server based on a preset first number of user characteristic data; performing initialization training on a preset second risk recognition model based on the second characteristic data of the first user and the risk recognition result of the first user to obtain the second risk recognition model after initialization training; and performing parameter updating processing on the second risk identification model after the initialization training based on the characteristic data of the second user and the risk identification result of the second user to obtain a pre-trained second risk identification model, and performing risk identification processing on the user based on the pre-trained second risk identification model.

In a seventh aspect, an embodiment of the present specification provides a data processing apparatus, including: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to: under the condition that a target user is detected to trigger execution of a target service, first characteristic data of the target user is obtained; inputting the first feature data of the target user into a pre-trained second risk identification model to obtain a target risk identification result of the target user, wherein the pre-trained second risk identification model is obtained by training second feature data based on a first user, a risk identification result of the first user, feature data of a second user and a risk identification result of the second user, the risk identification result of the first user is obtained by carrying out risk identification on the first feature data of the first user by a service end based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the service end based on a preset first number of user feature data; and determining whether the target business is triggered to be executed or not according to the target risk identification result.

In an eighth aspect, embodiments of the present specification provide a storage medium for storing computer-executable instructions, which when executed implement the following process: receiving a risk identification result of a server for a first user, wherein the risk identification result of the first user is obtained by carrying out risk identification on first characteristic data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the server based on a preset first number of user characteristic data; performing initialization training on a preset second risk recognition model based on the second characteristic data of the first user and the risk recognition result of the first user to obtain the second risk recognition model after initialization training; and performing parameter updating processing on the second risk identification model after the initialization training based on the characteristic data of the second user and the risk identification result of the second user to obtain a pre-trained second risk identification model, and performing risk identification processing on the user based on the pre-trained second risk identification model.

In a ninth aspect, embodiments of the present specification provide a storage medium for storing computer-executable instructions, which when executed implement the following process: under the condition that a target user is detected to trigger execution of a target service, first characteristic data of the target user is obtained; inputting the first characteristic data of the target user into a pre-trained second risk identification model to obtain a target risk identification result of the target user, wherein the pre-trained second risk identification model is obtained by training a second characteristic data based on a first user, a risk identification result of the first user, characteristic data of a second user and a risk identification result of the second user, the risk identification result of the first user is obtained by carrying out risk identification on the first characteristic data of the first user by a service end based on the pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the service end based on a preset first number of user characteristic data; and determining whether the target business is triggered to be executed or not based on the target risk identification result.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1A is a flow chart of one embodiment of a data processing method of the present disclosure;

FIG. 1B is a schematic diagram of a data processing method according to the present disclosure;

FIG. 2 is a schematic processing diagram of another data processing method according to the present disclosure;

FIG. 3 is a schematic diagram illustrating an initialization training and parameter updating process of a second risk identification model according to the present disclosure;

FIG. 4A is a flow chart of yet another embodiment of a data processing method herein;

FIG. 4B is a schematic process diagram of another data processing method of the present disclosure;

FIG. 5 is a schematic process diagram of another data processing method of the present disclosure;

FIG. 6 is a schematic diagram of a data processing system according to the present description;

FIG. 7 is a schematic block diagram of another embodiment of a data processing apparatus according to the present disclosure;

FIG. 8 is a block diagram of another embodiment of a data processing apparatus according to the present disclosure;

fig. 9 is a schematic structural diagram of a data processing apparatus according to the present specification.

Detailed Description

The embodiment of the specification provides a data processing method, a data processing device, a data processing system and data processing equipment.

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort shall fall within the protection scope of the present specification.

Example one

As shown in fig. 1A and 1B, an execution main body of the method may be a client, where the client may be a server or a terminal device, where the server may be an independent server or a server cluster composed of multiple servers, and the terminal device may be a device such as a personal computer or a mobile terminal device such as a mobile phone or a tablet computer. The method may specifically comprise the steps of:

in S102, a risk identification result of the server for the first user is received.

Wherein, the risk identification result of the first user may be obtained by risk identifying, by the server, first feature data of the first user based on a pre-trained first risk identification model, the first user may be a historical user using any service provided by the client, the first feature data of the first user may be stored by the server and include attribute information (such as gender, age, region, and the like) of the first user, behavior information (such as transfer behavior, call behavior, and the like), and the like, for example, the first user may be any one or more users who transfer money in the last month using a resource transfer service provided by the client, the pre-trained first risk identification model may be obtained by training, by the server, user feature data (including attribute information, behavior information, and the like) of a preset first number of users, and the first risk identification model may be a model for risk identification constructed based on a preset deep learning algorithm, the preset first number may be greater than a preset sample number threshold, for example, the pre-trained first risk identification model may be obtained by training a risk identification model constructed by a Deep Neural Network (DNN) algorithm based on 1000 pieces of user feature data by the server.

In implementation, with the rapid development of computer technology, the amount of users of enterprises providing resource transfer services for users, such as internet banking, is rapidly increased, data risks faced by the enterprises are increased, and in order to reduce the data risks, the enterprises can perform risk identification processing in a manner of constructing a risk identification model. For example, an enterprise may train a risk recognition model based on locally stored user sample data and risk labels, and perform risk recognition processing based on the trained risk recognition model. However, enterprises such as internet banking may have situations of small data amount of user sample data and risk labels and poor data quality, which may result in poor risk identification effect and low risk identification accuracy of the trained risk identification model, and therefore a solution capable of improving the risk identification effect and the risk identification accuracy of the risk identification model is needed. Therefore, the embodiments of the present disclosure provide a technical solution that can solve the above problems, and refer to the following specifically.

Taking the client used by the mechanism which can provide the resource transfer service for the user as an example, because the user private data owned by different mechanisms are different, and the user private data owned by each mechanism may have the problems of small data volume and poor data quality, in order to make the risk identification model (i.e. the second risk identification model) used locally achieve a better risk identification effect, the model parameters of the risk identification model used locally can be updated by performing data interaction with the server with a large data volume and a strong data processing capability.

The client can construct a first sample data set used for training the second risk recognition model, the first sample data set can comprise user identifications of a plurality of users and corresponding feature data, the client can send the user identifications in the first sample data set to the server, the server can determine whether the server has the users and the feature data corresponding to the user identifications according to the user identifications, if the users and the feature data corresponding to the user identifications exist, the user may be determined to be the first user, and the characteristic data of the user may be determined to be the first characteristic data of the first user, then, the server may input the first feature data of the first user into a pre-trained first risk identification model to obtain a risk identification result for the first user, and the server may return the risk identification result for the first user to the client.

The client side can perform updating and screening processing on the first sample data set based on the received risk identification result of the first user to obtain a target sample data set formed by the second feature data of the first user and the risk identification result of the first user.

For example, the client may construct a first sample data set based on user data obtained during a preset model training period, for example, the client may construct a first sample data set based on the user data using the resource transfer service in approximately half a month, specifically, the constructed first sample data set may include the feature data of the user 1, the user identifier of the user 1, the feature data of the user 2, and the user identifier of the user 2, the client may send the user identifiers of the user 1 and the user 2 to the server, if the server only stores the feature data of the user 2, the user 2 may be determined as a first user, and the server may determine a risk recognition result for the user 2 based on the stored first feature data of the user 2 and the pre-trained first risk recognition model, and return the risk recognition result of the user 2 to the client. The client may determine the feature data of the user 2 in the first sample data set as second feature data of the user 2, and construct a target sample data set according to the second feature data of the user 2 and the risk identification result of the user 2 determined by the server.

In S104, performing initialization training on a preset second risk recognition model based on the second feature data of the first user and the risk recognition result of the first user, to obtain the second risk recognition model after initialization training.

The second risk identification model may be a model for risk identification that is constructed based on a preset deep learning algorithm, and a model structure of the second risk identification model may be the same as or different from that of the first risk identification model, for example, the first risk identification model may be a model constructed based on a decision tree algorithm, and the second risk identification model may be a model constructed based on a decision tree algorithm, or the second risk identification model may be a model constructed based on a neural network algorithm.

In implementation, data in the target sample data set may be input into a preset second risk recognition model for initialization training, so as to obtain the second risk recognition model after initialization training.

In this way, the risk identification result of the first user is obtained based on the pre-trained first risk identification model of the server, and the pre-trained first risk identification model of the server is obtained by training based on the preset first number of user characteristic data, so that the risk identification effect of the pre-trained first risk identification model is good, the pre-trained second risk identification model is initially trained based on the risk identification result of the first user, and the risk identification effect of the second risk identification model can be improved with the aid of the first risk identification model. In addition, in the training process, the private data of the server can also achieve the purpose that the data cannot be out of the domain, namely the data security of the private data of the server can be protected.

In S106, based on the feature data of the second user and the risk recognition result of the second user, performing parameter updating processing on the second risk recognition model after the initialization training to obtain a second risk recognition model trained in advance, so as to perform risk recognition processing on the user based on the second risk recognition model trained in advance.

The second user may include the first user, and the risk identification result of the second user may be a risk identification result determined according to the feature data of the second user by a manual method or the like.

In implementation, the parameters of the second risk identification model after the initial training may be fine-tuned based on the feature data of the second user and the risk identification result of the second user, so that the risk identification effect of the obtained pre-trained second risk identification model is more in conformity with the risk identification requirement of the client.

As described in S102 above, the client may construct the first sample data set, and determine a target sample data set including the second feature data of the first user and the risk identification result of the first user according to the risk identification result of the first user and the first sample data set returned by the server. In addition, the client may further construct, according to the first sample data set, a second sample data set including feature data of the second user and a risk identification result of the second user, for example, the client may obtain a risk identification result corresponding to the feature data of the user in the first sample data set (the risk identification result may be a risk identification result determined by the client based on a manual method or the like), and construct, based on the feature data of the user in the first sample data set and the determined risk identification result, the second sample data set, where the user in the second sample data set is the second user.

And inputting the feature data of the second user and the risk recognition result of the second user into the second risk recognition model after the initial training for parameter updating processing, so as to obtain the second risk recognition model which is trained in advance.

In addition, when the first risk identification model and the second risk identification model have different model structures, the second risk identification model may learn the risk identification capability of the first risk identification model with the aid of the risk identification result of the first user. Namely, the second risk identification model only depends on publicly interactive information (namely the risk identification result of the first user) in the process of initial training, and in the process of parameter updating processing and the subsequent risk identification process, the second risk identification model can be trained and deployed under the condition of complete data isolation without introducing and depending on other additional data, and the risk identification effect of the second risk identification model is improved on the basis of protecting the security of private data without depending on a complex underlying data interaction framework.

The embodiment of the present specification provides a data processing method, which receives a risk identification result of a server for a first user, where the risk identification result of the first user is obtained by performing risk identification on first feature data of the first user by using a first risk identification model trained in advance by the server, the first risk identification model trained in advance is obtained by performing training on user feature data of the first user by using a preset first quantity by using the server, and based on a second feature data of the first user and the risk identification result of the first user, performing initialization training on a preset second risk identification model to obtain the second risk identification model after initialization training, and based on the characteristic data of the second user and the risk identification result of the second user, and performing parameter updating processing on the second risk identification model after the initial training to obtain a pre-trained second risk identification model, and performing risk identification processing on the user based on the pre-trained second risk identification model. Therefore, the server only returns the risk identification result of the first user to the client, and the first characteristic data of the first user is not required to be sent to the client, so that the private data of the server can be prevented from leaving the domain, the safety of the private data is ensured, meanwhile, the client can learn the risk identification capability of the first risk identification model under the assistance of the risk identification result of the first user, and the second risk identification model after initialization training is subjected to parameter updating based on the locally stored characteristic data and risk identification result of the second user, so that the second risk identification model trained in advance can better meet the local risk identification requirement of the client, and the risk identification effect of the second risk identification model trained in advance is improved.

Example two

As shown in fig. 2, an execution main body of the method may be a client, where the client may be a server or a terminal device, where the server may be an independent server or a server cluster composed of multiple servers, and the terminal device may be a device such as a personal computer or a mobile terminal device such as a mobile phone and a tablet computer. The method may specifically comprise the steps of:

The risk identification result of the first user may be obtained by performing risk identification on the first feature data of the first user based on a pre-trained first risk identification model of the service end, and the pre-trained first risk identification model may be obtained by performing training on the feature data of the user based on a preset first number of the service ends.

In S202, a preset second risk recognition model is initially trained based on the second feature data of the first user, the risk recognition result of the first user, and the first loss function, so as to obtain the second risk recognition model after the initial training.

The risk identification result of the first user may include a risk score, and the first loss function may be a mean square error loss function.

In S204, based on the feature data of the second user, the risk classification label of the second user, and the second loss function, the parameter update processing is performed on the second risk identification model after the initialization training, so as to obtain a second risk identification model trained in advance.

Wherein the risk identification result of the second user may include a risk classification label, and the second loss function may be a cross entropy loss function.

In implementation, since the data volume and the data processing capability of the server are possibly better than those of the client, the first risk identification model constructed by the server may have a different model structure from the second risk identification model, and the obtained risk identification structure may be different, for example, the risk identification result of the first user obtained by the first risk identification model constructed by the server may be a more complex continuous risk score (for example, the risk score may be any value between 0 and 1), and the risk identification result of the second user locally determined by the client may be a simpler risk classification label of the second classification (for example, a label of 0 and 1, that is, a label of 0 may represent a risk-free user and a label of 1 may represent a risk user), so that different loss functions may be set for the initialization training process and the parameter updating process.

For example, as shown in fig. 3, the second risk identification model may include a feature extraction layer and a full connection layer, and taking the second risk identification model as a model constructed based on a Convolutional Neural Network (CNN) as an example, the second risk identification model may include a Convolutional layer, a pooling layer and a full connection layer, where the Convolutional layer and the pooling layer are the feature extraction layer.

The client may input the second feature data of the first user into the second risk identification model to obtain a predicted risk score of the first user, and perform initialization training on the second risk identification model according to the risk score of the first user (i.e., a risk identification result of the first user) determined by the server based on the first feature data of the first user and the first risk identification model and a mean square error loss function, to obtain the second risk model after initialization training.

And inputting the characteristic data of the second user into the second risk model after the initialization training to obtain the predicted risk type of the second user, and performing parameter updating processing on the second risk identification model after the initialization training based on the risk classification label and the cross entropy loss function of the second user to obtain a pre-trained second risk identification model.

In addition, in order to maintain the stability of the second risk model, the adjustment ranges of the parameters of the feature extraction layer and the full connection layer of the second risk model can be determined according to the difference of the sample size, and the specific processing process can be referred to the following steps of one step to two steps:

step one, determining a first parameter updating amplitude aiming at a feature extraction layer and a second parameter updating amplitude aiming at a full connection layer based on the data volume of a second user and a preset number threshold.

Wherein the first parameter update amplitude is smaller than the second parameter update amplitude.

In implementation, for example, when the number of the second users is smaller than the preset number threshold, the parameters of the feature extraction layer of the second risk model after the initial training may be maintained not to be updated, and only the parameters of the fully-connected layer of the second risk recognition model after the initial training are subjected to parameter updating processing; under the condition that the number of the second users is not less than the preset number threshold, the feature extraction layer of the second risk model after the initialization training can be controlled through a regular loss function to update based on the first parameter update amplitude, and the parameters of the full connection layer of the second risk identification model after the initialization training are subjected to normal parameter update processing based on the first parameter update amplitude, wherein the first parameter update amplitude can be smaller than the second parameter update amplitude in order to maintain the stability of the second risk identification model.

And secondly, performing parameter updating processing on the second risk identification model after the initial training based on the first parameter updating amplitude, the second parameter updating amplitude, the characteristic data of the second user, the risk classification label of the second user and a second loss function to obtain a pre-trained second risk identification model.

In implementation, assuming that the preset number threshold is 100, under the condition that the number of the second users is less than 100, the parameters of the feature extraction layer of the second risk recognition model after initialization training can be maintained not to be updated, and the parameters of the full connection layer are updated only based on the second parameter update amplitude to obtain a second risk recognition model trained in advance; under the condition that the number of the second users is not less than 100 (that is, the amount of training sample data of the client is large), the parameters of the feature extraction layer of the second risk identification model after the initial training can be finely adjusted based on the first parameter update amplitude, and the parameters of the full connection layer are updated based on the second parameter update amplitude, so as to obtain the second risk identification model trained in advance.

In addition, there are various methods for determining the update amplitude of the first parameter and the update amplitude of the second parameter, which may be different according to different practical application scenarios, and this is not specifically limited in the embodiments of the present specification,

in S206, based on the preset data processing cycle, whether the pre-trained second risk identification model meets the preset risk identification requirement is detected.

In implementation, for example, the detection process may be performed every half month to detect whether the pre-trained second risk identification model meets the preset risk identification requirement, and the specific detection process method may be various, for example, the accuracy of risk identification based on the pre-trained second risk identification model in nearly half a month can be obtained, if the accuracy is less than the preset accuracy threshold, the pre-trained second risk identification model may be deemed to not meet the preset risk identification requirement, or, in a preset data processing period, according to the change situation of the use scene of the pre-trained second risk identification model, it is determined whether the pre-trained second risk identification model satisfies a pre-set risk identification requirement, for example, when the usage scenario of the pre-trained second risk recognition model is converted from the resource transfer scenario to the identity recognition scenario, the pre-trained second risk identification model may be deemed not to meet the preset risk identification requirement.

The method for detecting whether the pre-trained second risk identification model meets the preset risk identification requirement is an optional and realizable detection method, and in an actual application scenario, a plurality of different detection methods may be available, and may be different according to different actual application scenarios, which is not specifically limited in the embodiment of the present specification.

In S208, when it is detected that the pre-trained second risk recognition model does not satisfy the preset risk recognition requirement, the pre-trained second risk recognition model is updated based on the feature data of the third user.

In practice, the processing manner of S208 may be varied in practical applications, and an alternative implementation manner is provided below, which may specifically refer to the following processing from step one to step four:

step one, inputting the characteristic data of the third user into a pre-trained second risk identification model to obtain a risk identification result of the third user.

And step two, under the condition that the model structure of the pre-trained second risk identification model is changed, determining the second risk identification model with the changed model structure as a third risk identification model.

And thirdly, performing initialization training on the third risk recognition model based on the feature data of the third user and the risk recognition result of the third user to obtain the third risk recognition model after initialization training.

In implementation, because the updating speed of the rogue third party is fast, in order to improve the accuracy of risk identification, the model structure of the second risk identification model may be updated (for example, the pre-trained second risk model may be a risk identification model constructed based on a neural network algorithm, and the second risk identification model after the model structure change may be a risk identification model constructed based on a decision tree), so that the risk identification capability of the second risk identification model before the model structure change can be maintained by the second risk identification model after the model structure change, and the third risk identification model (the second risk identification model after the model structure change) may be initially trained based on the feature data of the third user and the risk identification result of the third user, so as to obtain the third risk identification model after the initial training, so that the third risk identification model after the initial training may learn the pre-trained second risk identification model Risk identification capability of type.

And fourthly, updating parameters of the third risk recognition model after the initial training based on the feature data of the fourth user and the risk recognition result of the fourth user to obtain a pre-trained third risk recognition model, and determining the pre-trained third risk recognition model as the pre-trained second risk recognition model.

In implementation, the process of performing parameter update processing on the third risk identification model after initialization training may refer to the process of performing parameter update processing on the second risk identification model after initialization training, which is not described herein again.

EXAMPLE III

As shown in fig. 4A and 4B, an execution main body of the method may be a client, where the client may be a server or a terminal device, where the server may be an independent server or a server cluster composed of multiple servers, and the terminal device may be a device such as a personal computer or a mobile terminal device such as a mobile phone or a tablet computer. The method may specifically comprise the steps of:

in S402, when it is detected that the target user triggers execution of the target service, first feature data of the target user is acquired.

The target service may be any service that the client can provide for the user, for example, the target service may be a resource transfer service, an identity authentication service, and the like, and the first feature data of the target user may include attribute information (such as gender, age, region, and the like) of the first user, behavior information (such as a transfer behavior, a call behavior, and the like), and the like.

In the implementation, taking a client used by a mechanism capable of providing a resource transfer service for a user as an example, a target service may be a resource transfer service, a target user may trigger and start a resource transfer application installed in the client, and trigger and start a resource transfer service (i.e., a target service), and the client may obtain attribute information and resource transfer behavior information (e.g., resource transfer number, resource transfer time, resource transfer object, etc.) of the target user when detecting that the target user triggers and executes the resource transfer service.

In S404, the first feature data of the target user is input into the pre-trained second risk identification model, so as to obtain a target risk identification result of the target user.

The pre-trained second risk identification model may be obtained by training based on second feature data of the first user, a risk identification result of the first user, feature data of the second user, and a risk identification result of the second user, the risk identification result of the first user may be obtained by performing risk identification on first feature data of the first user for the first risk identification model of which the service end is based on the pre-trained first risk identification model, and the pre-trained first risk identification model may be obtained by training for the service end based on a preset first number of user feature data.

In implementation, the attribute information of the target user and the first feature data such as resource transfer behavior information (e.g., resource transfer quantity, resource transfer time, resource transfer object, etc.) may be input into the pre-trained second risk identification model to obtain the target risk identification result of the target user.

In S406, it is determined whether there is a risk in triggering execution of the target service based on the target risk identification result.

In implementation, if it is determined that there is a risk in triggering execution of the target service based on the target risk identification result, preset warning information may be output to the target user, and execution of the target service may be stopped.

The embodiment of the present specification provides a data processing method, where in a case that a target user is detected to trigger execution of a target service, first feature data of the target user is obtained, the first feature data of the target user is input into a pre-trained second risk identification model, a target risk identification result of the target user is obtained, the pre-trained second risk identification model is obtained by training based on second feature data of a first user, a risk identification result of the first user, feature data of a second user, and a risk identification result of the second user, the risk identification result of the first user is obtained by performing risk identification on the first feature data of the first user by using a pre-trained first risk identification model based on a service end, the pre-trained first risk identification model is obtained by training based on a preset first number of user feature data based on the target risk identification result, it is determined whether there is a risk of triggering execution of the target service. When the client trains the second risk model, the client only uses the risk identification result of the first user provided by the server besides the second characteristic data of the first user, the characteristic data of the second user and the risk identification result of the second user locally at the client, therefore, the private data of the server can not be out of the domain, and the security of the private data is ensured, so that the client can learn the risk identification capability of the first risk identification model with the help of the risk identification result of the first user, and identifying the result according to the locally stored characteristic data of the second user and the risk of the second user, the obtained pre-trained second risk identification model is more in line with the local risk identification requirement of the client, the risk identification effect of the pre-trained second risk identification model is improved, and the accuracy of risk detection for triggering and executing the target service aiming at the target user is improved.

Example four

As shown in fig. 5, an execution main body of the method may be a client, where the client may be a server or a terminal device, where the server may be an independent server or a server cluster composed of multiple servers, and the terminal device may be a device such as a personal computer or a mobile terminal device such as a mobile phone and a tablet computer. The method may specifically comprise the steps of:

In S502, the user identifier of the target user is sent to the server, and the first risk identification result of the target user returned by the server is received.

The first risk identification result of the target user can be obtained by carrying out risk identification on second characteristic data of the target user on the basis of a pre-trained first risk identification model by the service terminal.

In S504, it is determined whether there is a risk in triggering execution of the target service based on the first risk identification result and the target risk identification result.

In implementation, since the data volume and the data processing capacity of the server may be better than those of the client, the client may further send the user identifier of the target user to the server, and receive the first risk identification result of the target user returned by the server.

In addition, the model result output by the first risk identification model constructed by the server may be different from the model result of the second risk identification model, for example, the first risk identification result of the first risk identification model constructed by the server may be a relatively complex continuous risk score (for example, the risk score may be any value between 0 and 1), and the target risk identification result locally determined by the client may be a relatively simple risk classification tag of the second classification (for example, 0 and 1 tags, that is, a tag of 0 may represent a risk-free user, and a tag of 1 may represent a risk user), so that it may be determined whether there is a risk in triggering execution of the target service, in combination with the risk score of the target user and the risk classification tag. The specific risk determination method may be different determination methods according to different actual application scenarios, and this is not specifically limited in the embodiments of the present specification.

EXAMPLE five

An embodiment of the present specification provides a data processing system, including: server side and customer end, wherein:

the client side can be used for sending the user identification of the first user to the server side.

The server may be configured to obtain a risk identification result of the first user based on the user identifier, where the risk identification result of the first user may be obtained by performing risk identification on first feature data of the first user for the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model may be obtained by training user feature data of the server based on a preset first number.

The client may be configured to perform initialization training on a preset second risk recognition model based on second feature data of the first user and a risk recognition result of the first user returned by the server, to obtain the second risk recognition model after initialization training, and perform parameter update processing on the second risk recognition model after initialization training based on the feature data of the second user and the risk recognition result of the second user, to obtain a second risk recognition model after pre-training, so as to perform risk recognition processing on the user based on the second risk recognition model after pre-training.

For example, as shown in fig. 6, a data processing system may include multiple servers and clients, and taking risk user identification in a resource transfer scenario as an example, assuming that the clients and the multiple servers respectively store feature data (such as resource transfer behavior information) of a plurality of users, the client may send locally stored user identifiers of a plurality of first users to the multiple servers, respectively, the server may obtain locally stored first feature data of the first user according to the received user identifier of the first user, determine a risk identification result of the first user according to a first risk identification model pre-trained by the server, and return the risk identification result to the client.

The client performs initialization training and parameter updating processing on a preset second risk recognition model based on the second feature data of the first user, the risk recognition result of the first user, the feature data of the second user and the risk recognition result of the second user, which are returned by the server (for a specific processing process, refer to the processing processes in the first to second embodiments), so as to obtain a pre-trained second risk recognition model.

In addition, there may be multiple servers in the data processing system, and the client may receive risk identification results of multiple servers for the same first user, for example, as shown in fig. 6, the client may receive a risk identification result of the first user 1 determined by the server 1 based on the locally stored first feature data 1 of the first user and the pre-trained first risk identification model 1, the client may also receive a risk identification result of the first user 1 determined by the server 2 based on the locally stored first feature data 2 of the first user and the pre-trained first risk identification model 2, and the client may determine a risk identification result of the first user 1 based on a risk identification result of the first user 1 returned by the server 1 and a risk identification result of the first user 1 returned by the server 2.

For example, the client may determine the risk identification result of the first user according to the preset weight value corresponding to the server and the risk identification result of the first user returned by the server, and specifically, if the preset weight of the server 1 is 0.8, the risk identification result of the first user 1 returned by the server 1 is a risk score of 0.8, the preset weight of the server 2 is 0.7, and the risk identification result of the first user 1 returned by the server 1 is a risk score of 0.75, the risk identification result of the first user may be (0.8 +0.7 × 0.75)/2 ═ 0.58.

The determination method for the risk identification result of the first user is an optional and realizable determination method, and in an actual application scenario, there may be a plurality of different determination methods, for example, the risk identification result of the first user may also be determined according to the wind control priority of the server, and different determination methods may be selected according to different actual application scenarios, which is not specifically limited in the embodiment of the present specification.

In addition, the client side can acquire first feature data of the target user when detecting that the target user triggers and executes the target service, and inputs the first feature data of the target user into a pre-trained second risk identification model to obtain a target risk identification result of the target user.

The client side can send the user identification of the target user to the server side and receive a first risk identification result of the target user returned by the server side. The first risk identification result of the target user can be obtained by carrying out risk identification on second characteristic data of the target user on the basis of a pre-trained first risk identification model by the service terminal.

Finally, the client can determine whether the risk exists in triggering and executing the target service based on the first risk identification result and the target risk identification result.

Therefore, the second risk identification model can transfer the risk identification result of the server side in a distillation learning mode, the risk identification capability learning of the first risk identification model of the server side can be applied to the client side, the problem of privacy data interaction in practical application is avoided, in addition, the client side can also perform real-time risk identification through the pre-trained second risk identification model, and the prevention and control loopholes existing in a mode of performing risk identification through data interaction (such as performing risk identification through interaction of a blacklist) are avoided.

The embodiment of the specification provides a data processing system, because the server only returns the risk identification result of the first user to the client, and does not need to send the first feature data of the first user to the client, the private data of the server can be prevented from leaving the domain, and the security of the private data is ensured.

EXAMPLE six

Based on the same idea, the data processing method provided in the embodiment of the present specification further provides a data processing apparatus, as shown in fig. 7.

The data processing apparatus includes: a result receiving module 701, a first training module 702, and a second training module 703, wherein:

a result receiving module 701, configured to receive a risk identification result of a server for a first user, where the risk identification result of the first user is obtained by performing risk identification on first feature data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training user feature data of the server based on a preset first number;

a first training module 702, configured to perform initialization training on a preset second risk identification model based on the second feature data of the first user and the risk identification result of the first user, to obtain a second risk identification model after the initialization training;

the second training module 703 is configured to perform parameter updating processing on the second risk identification model after the initialization training based on feature data of a second user and a risk identification result of the second user, to obtain a second risk identification model trained in advance, and perform risk identification processing on the user based on the second risk identification model trained in advance.

In an embodiment of this specification, the risk identification result of the first user includes a risk score, and the first training module 702 is configured to:

performing initialization training on the preset second risk recognition model based on the second feature data of the first user, the risk recognition result of the first user and a first loss function to obtain the second risk recognition model after the initialization training;

the risk identification result of the second user includes a risk classification label, and the second training module 1103 is configured to:

performing parameter updating processing on the second risk identification model after the initialization training based on the feature data of the second user, the risk classification label of the second user and a second loss function to obtain the second risk identification model which is trained in advance;

the first loss function is a mean square error loss function, and the second loss function is a cross entropy loss function.

In this embodiment of the present specification, the second risk identification model includes a feature extraction layer and a full connection layer, and the second training module 703 is configured to:

determining a first parameter update amplitude aiming at the feature extraction layer and a second parameter update amplitude aiming at the full connection layer based on the data volume of the second user and a preset number threshold, wherein the first parameter update amplitude is smaller than the second parameter update amplitude;

and performing parameter updating processing on the second risk identification model after the initialization training based on the first parameter updating amplitude, the second parameter updating amplitude, the feature data of the second user, the risk classification label of the second user and a second loss function to obtain the pre-trained second risk identification model.

In an embodiment of this specification, the apparatus further includes:

the model detection module is used for detecting whether the pre-trained second risk identification model meets the preset risk identification requirement or not based on a preset data processing period;

and the model updating module is used for updating the pre-trained second risk recognition model based on the feature data of a third user under the condition that the pre-trained second risk recognition model is detected not to meet the preset risk recognition requirement.

In an embodiment of this specification, the model updating module is configured to:

inputting the feature data of the third user into the pre-trained second risk identification model to obtain a risk identification result of the third user;

under the condition that the model structure of the pre-trained second risk identification model is changed, determining the second risk identification model with the changed model structure as a third risk identification model;

performing initialization training on the third risk recognition model based on the feature data of the third user and the risk recognition result of the third user to obtain a third risk recognition model after initialization training;

and updating parameters of the third risk recognition model after the initial training based on the feature data of a fourth user and the risk recognition result of the fourth user to obtain a pre-trained third risk recognition model, and determining the pre-trained third risk recognition model as the pre-trained second risk recognition model.

The embodiment of the present specification provides a data processing apparatus, which receives a risk identification result of a server for a first user, where the risk identification result of the first user is obtained by performing risk identification on first feature data of the first user by the server based on a pre-trained first risk identification model, the pre-trained first risk identification model is obtained by training user feature data of the first user by the server based on a preset first quantity, and based on a second feature data of the first user and the risk identification result of the first user, performing initialization training on a preset second risk identification model to obtain the second risk identification model after initialization training, and based on the characteristic data of the second user and the risk identification result of the second user, and performing parameter updating processing on the second risk identification model after the initial training to obtain a pre-trained second risk identification model, and performing risk identification processing on the user based on the pre-trained second risk identification model. Therefore, the server only returns the risk identification result of the first user to the client, and the first characteristic data of the first user is not required to be sent to the client, so that the private data of the server can be prevented from leaving the domain, the safety of the private data is ensured, meanwhile, the client can learn the risk identification capability of the first risk identification model under the assistance of the risk identification result of the first user, and the second risk identification model after initialization training is subjected to parameter updating based on the locally stored characteristic data and risk identification result of the second user, so that the second risk identification model trained in advance can better meet the local risk identification requirement of the client, and the risk identification effect of the second risk identification model trained in advance is improved.

EXAMPLE seven

Based on the same idea, embodiments of the present specification further provide a data processing apparatus, as shown in fig. 8.

The data processing apparatus includes: a data acquisition module 801, a result acquisition module 802, and a risk determination module 803, wherein:

a data obtaining module 801, configured to obtain first feature data of a target user when it is detected that the target user triggers execution of a target service;

a result obtaining module 802, configured to input first feature data of the target user into a pre-trained second risk identification model to obtain a target risk identification result of the target user, where the pre-trained second risk identification model is obtained by training second feature data of a first user, a risk identification result of the first user, feature data of a second user, and a risk identification result of the second user, the risk identification result of the first user is obtained by performing risk identification on the first feature data of the first user by using a pre-trained first risk identification model by using a server, and the pre-trained first risk identification model is obtained by training the server by using a preset first number of user feature data;

a risk determining module 803, configured to determine whether there is a risk in triggering execution of the target service based on the target risk identification result.

In this embodiment of the present specification, the risk determining module 803 is configured to:

sending the user identification of the target user to the server, and receiving a first risk identification result of the target user returned by the server, wherein the first risk identification result of the target user is obtained by carrying out risk identification on second characteristic data of the target user by the server based on the pre-trained first risk identification model;

and determining whether the target business is triggered to be executed or not according to the first risk identification result and the target risk identification result.

The embodiment of the present specification provides a data processing apparatus, which, when it is detected that a target user triggers execution of a target service, obtains first feature data of the target user, inputs the first feature data of the target user into a pre-trained second risk identification model, and obtains a target risk identification result of the target user, where the pre-trained second risk identification model is obtained by training based on the second feature data of a first user, a risk identification result of the first user, feature data of a second user, and a risk identification result of the second user, the risk identification result of the first user is obtained by performing risk identification on the first feature data of the first user by using a pre-trained first risk identification model based on a service end, the pre-trained first risk identification model is obtained by training based on a preset first number of user feature data based on the target risk identification result, it is determined whether there is a risk of triggering execution of the target service. When the client trains the second risk model, the client only uses the risk identification result of the first user provided by the server besides the second characteristic data of the first user, the characteristic data of the second user and the risk identification result of the second user locally at the client, therefore, the private data of the server can not be out of the domain, and the security of the private data is ensured, so that the client can learn the risk identification capability of the first risk identification model with the help of the risk identification result of the first user, and identifying the result according to the locally stored characteristic data of the second user and the risk of the second user, the obtained pre-trained second risk identification model is more in line with the local risk identification requirement of the client, the risk identification effect of the pre-trained second risk identification model is improved, and the accuracy of risk detection for triggering and executing the target service aiming at the target user is improved.

Example eight

Based on the same idea, embodiments of the present specification further provide a data processing apparatus, as shown in fig. 9.

Data processing apparatus may vary widely in configuration or performance and may include one or more processors 901 and memory 902, where memory 902 may store one or more stored applications or data. Memory 902 may be, among other things, transient storage or persistent storage. The application program stored in memory 902 may include one or more modules (not shown), each of which may include a series of computer-executable instructions for a data processing device. Still further, the processor 901 may be arranged in communication with the memory 902 for executing a series of computer executable instructions in the memory 902 on the data processing device. The data processing apparatus may also include one or more power supplies 903, one or more wired or wireless network interfaces 904, one or more input-output interfaces 905, one or more keyboards 906.

In particular, in this embodiment, the data processing apparatus includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the data processing apparatus, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:

receiving a risk identification result of a server for a first user, wherein the risk identification result of the first user is obtained by carrying out risk identification on first characteristic data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the server based on a preset first number of user characteristic data;

performing initialization training on a preset second risk recognition model based on the second characteristic data of the first user and the risk recognition result of the first user to obtain the second risk recognition model after initialization training;

and performing parameter updating processing on the second risk identification model after the initialization training based on the characteristic data of the second user and the risk identification result of the second user to obtain a pre-trained second risk identification model, and performing risk identification processing on the user based on the pre-trained second risk identification model.

Optionally, the risk identification result of the first user includes a risk score, and the performing initialization training on a preset second risk identification model based on the feature data of the first user and the risk identification result of the first user to obtain the second risk identification model after initialization training includes:

the risk identification result of the second user includes a risk classification label, and the parameter updating processing is performed on the second risk identification model after the initialization training based on the feature data of the second user and the risk identification result of the second user to obtain a pre-trained second risk identification model, including:

Optionally, the second risk recognition model includes a feature extraction layer and a full connection layer, and the parameter updating processing is performed on the second risk recognition model after the initialization training based on the feature data of the second user, the risk classification label of the second user, and a second loss function, so as to obtain the second risk recognition model trained in advance, where the parameter updating processing includes:

Optionally, the method further comprises:

based on a preset data processing period, detecting whether the pre-trained second risk identification model meets a preset risk identification requirement;

and under the condition that the pre-trained second risk identification model is detected not to meet the preset risk identification requirement, updating the pre-trained second risk identification model based on the feature data of a third user.

Optionally, the updating, based on the feature data of the third user and the risk recognition result of the third user, the pre-trained second risk recognition model includes:

Additionally, the data processing apparatus includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the data processing apparatus, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:

under the condition that a target user is detected to trigger execution of a target service, first characteristic data of the target user is obtained;

inputting the first characteristic data of the target user into a pre-trained second risk identification model to obtain a target risk identification result of the target user, wherein the pre-trained second risk identification model is obtained by training a second characteristic data based on a first user, a risk identification result of the first user, characteristic data of a second user and a risk identification result of the second user, the risk identification result of the first user is obtained by carrying out risk identification on the first characteristic data of the first user by a service end based on the pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the service end based on a preset first number of user characteristic data;

and determining whether the target business is triggered to be executed or not according to the target risk identification result.

Optionally, the determining whether there is a risk in triggering execution of the target service based on the risk identification result of the target user includes:

The embodiment of the specification provides a data processing device, because the server only returns the risk identification result of the first user to the client, and does not need to send the first feature data of the first user to the client, the private data of the server can be prevented from leaving the domain, and the security of the private data is ensured.

Example nine

The embodiments of the present disclosure further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the processes of the data processing method embodiments, and can achieve the same technical effects, and in order to avoid repetition, the details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.

The embodiment of the present specification provides a computer-readable storage medium, where a server only returns a risk identification result of a first user to a client, and does not need to send first feature data of the first user to the client, so that private data of the server cannot leave a domain, and security of the private data is ensured.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: the ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the various elements may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Embodiments of the present description are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present disclosure, and is not intended to limit the present disclosure. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims

1. A method of data processing, comprising:

2. The method according to claim 1, wherein the risk recognition result of the first user includes a risk score, and performing initialization training on a preset second risk recognition model based on the feature data of the first user and the risk recognition result of the first user to obtain the second risk recognition model after initialization training, includes:

3. The method according to claim 2, wherein the second risk recognition model includes a feature extraction layer and a full connection layer, and the obtaining the pre-trained second risk recognition model by performing parameter update processing on the second risk recognition model after the initialization training based on the feature data of the second user, the risk classification label of the second user, and a second loss function includes:

4. The method of claim 1, further comprising:

and under the condition that the pre-trained second risk recognition model is detected not to meet the preset risk recognition requirement, updating the pre-trained second risk recognition model based on feature data of a third user.

5. The method of claim 4, wherein the updating the pre-trained second risk identification model based on the feature data of the third user and the risk identification result of the third user comprises:

6. A method of data processing, comprising:

7. The method of claim 6, wherein determining whether there is a risk in triggering execution of the target service based on the risk identification result of the target user comprises:

8. A data processing system comprising a server and a client, wherein:

the client is used for sending the user identification of the first user to the server;

the server is used for obtaining a risk identification result of the first user based on the user identification, wherein the risk identification result of the first user is obtained by carrying out risk identification on first characteristic data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the server based on a preset first number of user characteristic data;

the client is configured to perform initialization training on a preset second risk recognition model based on the second feature data of the first user and the risk recognition result of the first user returned by the server, to obtain the second risk recognition model after initialization training, and perform parameter update processing on the second risk recognition model after initialization training based on the feature data of the second user and the risk recognition result of the second user, to obtain a pre-trained second risk recognition model, so as to perform risk recognition processing on the user based on the pre-trained second risk recognition model.

9. A data processing apparatus comprising:

the result receiving module is used for receiving a risk identification result of a server for a first user, wherein the risk identification result of the first user is obtained by carrying out risk identification on first characteristic data of the first user by the server based on a pre-trained first risk identification model, and the pre-trained first risk identification model is obtained by training the first characteristic data of the first user by the server based on a preset first quantity of user characteristic data;

the first training module is used for carrying out initialization training on a preset second risk recognition model based on the second characteristic data of the first user and the risk recognition result of the first user to obtain the second risk recognition model after initialization training;

and the second training module is used for updating parameters of the second risk identification model after the initialization training based on the characteristic data of a second user and the risk identification result of the second user to obtain a pre-trained second risk identification model, and performing risk identification processing on the user based on the pre-trained second risk identification model.

10. A data processing apparatus comprising:

the data acquisition module is used for acquiring first characteristic data of a target user under the condition that the target user is detected to trigger execution of a target service;

a result obtaining module, configured to input first feature data of the target user into a pre-trained second risk identification model to obtain a target risk identification result of the target user, where the pre-trained second risk identification model is obtained by training a second feature data of a first user, a risk identification result of the first user, feature data of a second user, and a risk identification result of the second user, the risk identification result of the first user is obtained by performing risk identification on the first feature data of the first user by using a pre-trained first risk identification model based on a service end, and the pre-trained first risk identification model is obtained by training the service end by using a preset first number of user feature data;

and the risk determining module is used for determining whether the risk exists in triggering and executing the target business or not based on the target risk identification result.

11. A data processing apparatus, the data processing apparatus comprising:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

12. A data processing apparatus, the data processing apparatus comprising:

a processor; and

13. A storage medium for storing computer-executable instructions, which when executed implement the following:

14. A storage medium for storing computer-executable instructions, which when executed implement the following: