WO2023221359A1 - User security level identification method and apparatus based on multi-stage time sequence and multiple tasks - Google Patents
User security level identification method and apparatus based on multi-stage time sequence and multiple tasks Download PDFInfo
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Definitions
- the present application relates to the field of computer information processing. Specifically, it relates to a user security level identification method, device, electronic equipment and computer-readable medium based on multi-stage timing and multi-tasking.
- Machine learning which uses useful information in historical data to help analyze future data, usually requires a large amount of labeled data to train a good learner.
- Deep learning models are a typical machine learning model. Because this type of model is a neural network with many hidden layers and many parameters, it usually requires millions of data samples to learn accurate parameters.
- some applications, including medical image analysis, cannot meet this data requirement because labeling the data requires a lot of manual labor.
- multi-task learning (MTL) can help alleviate this data sparsity problem by using useful information from other related learning tasks.
- the multi-task learning task is to predict the labels of unseen data based on a training data set (containing training data instances and their labels).
- the "quality" of the data in the training data set plays a crucial role in the effectiveness of multi-task learning.
- the data in the training data set is difficult to accurately reflect the real situation.
- this application provides a user security level identification method, device, electronic equipment and computer-readable medium based on multi-stage timing and multi-task, which can be based on practical problems and application scenarios, and from the perspective of model samples and model parameters. Improve multi-task machine learning methods to ensure application system user data security and transaction security.
- a user security level identification method based on multi-stage timing and multi-tasking includes: generating multiple stage sets based on all users and their corresponding user stages; Arrange; perform multi-task training on the n+1th group of initial models based on the n+1th stage set and the nth group of model parameter vectors in turn, and generate the n+1th group of model parameter vectors, n is a positive integer; until the multiple After the stage set training is completed, multiple groups of stage scoring models are generated based on multiple groups of model parameter vectors; the security level of the current user is identified through the multiple groups of stage scoring models.
- generating multiple stage sets based on all users and their corresponding user stages includes: determining multiple user stages based on product characteristics; matching the user stage corresponding to each user among all users with the multiple user stages. ;Assign users to the stage set corresponding to their user stage based on the matching results.
- generating multiple stage sets based on the total number of users and their corresponding user stages also includes: determining a label strategy for each user stage; and assigning sample labels to users in each stage set according to the label strategy.
- n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, and generate the n+1 group of model parameter vectors, which also includes: Determine a set of machine learning models; assign sample labels to historical users according to the label strategy corresponding to each user stage; train the n+1th group of machine learning models through historical users with sample labels, and generate the n+1th group of initial Model, n is a positive integer.
- inputting the first-stage set into the first group of initial models to generate the first group of model parameter vectors includes: inputting the user information in the first-stage set into the first group of initial models respectively; Model training is performed based on the user information and its corresponding labels. After the training is completed, the first set of model parameter vectors is generated.
- generating an update vector includes: nonlinearly transforming the nth group of model parameter vectors to generate an update vector; or generating the first to nth groups through nonlinear transformation of the first to nth group of model parameter vectors. update vector.
- input the user information in the n+1th stage set into the n+1th group of initial models after the model parameter vector is updated for multi-task training including: inputting the user information in the n+1th stage set Input the updated model parameter vectors into the n+1 initial model respectively; the n+1 initial model performs multi-task training based on user information and its corresponding labels; when the loss function during the training process does not meet the convergence conditions, Redetermine the initial model parameters of the n+1 initial model to perform multi-task training again; when the loss function meets the convergence condition, complete the multi-task training of the n+1 initial model.
- re-determine the initial model parameters of the (n+1)th group of initial models to perform model training again including: re-training the model on the (n+1)th group of initial models to generate new initial model parameters; or re-determining the convergence conditions Model training is performed again on the n+1 group of initial models to generate new initial model parameters.
- a user security level identification device based on multi-stage timing and multi-tasking.
- the device includes: a stage module for generating multiple stage sets based on all users and their corresponding user stages; a sorting module, It is used to arrange multiple stage sets in sequence; the training module is used to perform multi-task training on the n+1th group of initial models based on the n+1th stage set and the nth group of model parameter vectors, and generate the n+1th group of initial models.
- a set of model parameter vectors, n is a positive integer; a model module, used to generate multiple sets of scoring models based on multiple sets of model parameter vectors until the multiple stages of collective training are completed; a grading module, used to use the multiple sets of scoring models to The current user performs security level identification, and the security level of the current user is determined based on the identification result.
- an electronic device includes: one or more processors; a storage device for storing one or more programs; when one or more programs are processed by one or more processors, Execution causes one or more processors to implement the method as above.
- a computer-readable medium on which a computer program is stored.
- the program is executed by a processor, the above method is implemented.
- multiple stage sets are generated according to the total number of users and their corresponding user stages; the multiple stage sets are generated in time sequence Arrange in order; perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, generate the n+1 group of model parameter vectors, and generate the n group of model parameter vectors, n is a positive integer; until the multi-stage set training is completed, multiple groups of stage scoring models are generated based on multiple groups of model parameter vectors; through the multi-group stage scoring model, the security level identification of the current user can be based on actual problems and Starting from the application scenario, the multi-task machine learning method is improved as a whole from the perspective of model samples and model parameters, thereby ensuring the security of user data and transaction security in the application system.
- Figure 1 is a schematic diagram of a sample space according to an exemplary embodiment.
- Figure 2 is a schematic diagram of a sample space according to another exemplary embodiment.
- Figure 3 is a system block diagram of a user security level identification method and device based on multi-stage sequential multi-tasking according to an exemplary embodiment.
- FIG. 4 is a flow chart of a user security level identification method based on multi-stage sequential multi-tasking according to an exemplary embodiment.
- FIG. 5 is a schematic diagram of a user security level identification method based on multi-stage sequential multi-tasking according to another exemplary embodiment.
- FIG. 6 is a flowchart of a user security level identification method based on multi-stage sequential multi-tasking according to another exemplary embodiment.
- FIG. 7 is a schematic diagram of a user security level identification method based on multi-stage sequential multi-tasking according to another exemplary embodiment.
- FIG. 8 is a block diagram of a user security level identification device based on multi-stage sequential multi-tasking according to an exemplary embodiment.
- FIG. 9 is a block diagram of an electronic device according to an exemplary embodiment.
- Figure 10 is a block diagram of a computer-readable medium according to an exemplary embodiment.
- Example embodiments will now be described more fully with reference to the accompanying drawings.
- Example embodiments may, however, be embodied in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concepts of the example embodiments. To those skilled in the art.
- the same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted.
- first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Accordingly, a first component discussed below may be referred to as a second component without departing from the teachings of the present concepts. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
- the Internet financial service platform will be taken as an example to illustrate the actual application.
- the Internet financial platform after the user registers as a website member, he will apply for financial resources before actual services.
- the Internet service platform will score the user's financial risk based on the user's basic information. The score is higher than the threshold. High-quality users are allowed to borrow financial resources, and users whose scores are below the threshold will no longer provide financial services. Not everyone of high-quality users will actually borrow financial resources, and only some users will occupy financial resources when they are actually needed.
- High-quality users may use financial resources on the first day when their qualifications for borrowing financial resources are approved, or they may use financial resources within 30 days of being approved to use financial resources, or they may use financial resources at a further time. Of course, there are Quality users who do not use financial resources at all. Among high-quality users who use financial resources, some users may default after the use period of the financial resources expires. After the default exceeds a certain period, the user will enter the collection process. Some users may in a shorter period of time The defaulted resources will be returned within a certain period of time, and some users may take longer to return the resources.
- the training data set is established by the users in the current stage to train the machine learning model. That is to say, in each stage, the training set data is established based on the users after multiple screenings. thereby establishing an evaluation model.
- the real sample space is the full sample space
- the actual training set data is a biased sample space. In this case, the trained samples cannot truly reflect the actual situation.
- FIG. 3 is a system block diagram of a user security level identification method and device based on multi-stage interaction sequence (MSIS) according to an exemplary embodiment.
- MSIS multi-stage interaction sequence
- the system architecture 30 may include terminal devices 301, 302, 303, a network 304 and a server 305.
- the network 304 is used as a medium for providing communication links between the terminal devices 301, 302, 303 and the server 305.
- Network 304 may include various connection types, such as wired, wireless communication links, fiber optic cables, etc.
- Terminal devices 301, 302, 303 Users can use terminal devices 301, 302, 303 to interact with the server 305 through the network 304 to receive or send messages, etc.
- Various communication client applications can be installed on the terminal devices 301, 302, and 303, such as Internet service applications, shopping applications, web browser applications, instant messaging tools, email clients, social platform software, etc.
- the terminal devices 301, 302, and 303 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.
- the server 305 may be a server that provides various services, such as a backend management server that provides support for Internet service websites browsed by users using the terminal devices 301, 302, and 303.
- the background management server can analyze and process the received user data, and feed back the processing results (such as security level, resource quota) to the administrator of the Internet service website and/or the terminal device 301, 302, 303.
- the server 305 can, for example, obtain user data from the terminal devices 301, 302, and 303 as full user data; the server 305 can, for example, generate multiple stage sets based on the full number of users and their corresponding user stages; the server 305 can, for example, combine the multiple stage sets in time sequence.
- the server 305 can, for example, perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors in sequence, and generate the n+1 group of model parameter vectors, where n is a positive integer, n is a positive integer; until the multiple stage set training is completed, multiple sets of stage scoring models are generated based on multiple sets of model parameter vectors; the server 305 can, for example, use the multiple sets of stage scoring models to evaluate the performance of the terminal devices 301, 302 and 303. Users perform security level identification.
- the user security level identification method based on multi-stage timing and multi-tasking can be executed by the server 305 and/or the terminal devices 301, 302, 303.
- the method based on multi-stage timing and multi-tasking may be provided in the server 305 and/or the terminal devices 301, 302, and 303.
- the web pages provided for users to browse the Internet service platform are generally located in terminal devices 301, 302, and 303.
- FIG. 4 is a flow chart of a user security level identification method based on multi-stage sequential multi-tasking according to an exemplary embodiment.
- the user security level identification method 40 based on multi-stage sequential multi-tasking at least includes steps S402 to S410.
- multiple stage sets are generated based on all users and their corresponding user stages.
- Multiple user stages can be determined based on product characteristics; the user stage corresponding to each user among all users can be matched with the multiple user stages; and users can be assigned to stage sets corresponding to their user stages based on the matching results.
- the users can be individual users or corporate users, and the resources can be financial resources, power resources, water resources, data resources, etc.
- User information may include basic information authorized by the user, which may be, for example, business account information, user's terminal device identification information, user location information, etc.
- User information may also include behavioral information, which may be, for example, the user's page operation data, user information, etc.
- the specific content of user information can be determined according to the actual application scenario and is not limited here.
- users can be divided into "service application stage", "resource payment stage” and "overdue stage”. The above three stages are related in time sequence according to the business content.
- the user information can include the current stage of the user.
- the user When the user is in the "overdue stage”, it can be seen that the user has passed the "service application stage” and "resource activation stage”. At this time, the user needs to be placed separately. into the set of the corresponding stage.
- the user when a user is in the "resource mobilization stage”, he must have passed the "service application stage”, and the user also needs to be put into the collection of the corresponding stage.
- the method further includes: determining a label strategy for each user stage; and assigning sample labels to users in each stage set according to the label strategy. More specifically, the "pass" and “reject” labels can be determined for the "service application stage”; the "first day of expenditure”, “expenditure within 30 days”, and “expenditure within 60 days” labels can be determined for the “resource expenditure stage” ; “Overdue stage” determines "repayment upon reminder", “repayment upon reminder within 30 days”, “repayment upon reminder within 60 days”; according to the user performance in the user information, users at different stages are allocated corresponding to that stage Tag of.
- stage sets are arranged in sequence in time sequence. Arrange the above-mentioned stage sets according to their corresponding order of business occurrence, that is, the three stage sets are arranged in the order of "service application stage", "resource mobilization stage” and "overdue stage”.
- multi-task training is performed on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, and the n+1 group of model parameter vectors are generated.
- the method further includes: determining a set of machine learning models for each user stage; assigning sample labels to historical users according to label strategies corresponding to each user stage; and classifying the nth group of machines through historical users with sample labels.
- the learning model performs multi-task training and generates the nth group of initial models, where n is a positive integer.
- the initial model can be trained separately for each stage and each label through historical data in advance.
- the initial application model can be generated by training corresponding to the "service application stage", and this group of models can be generated by training corresponding to the "resource mobilization stage", which can include The initial model of energy expenditure on the first day, the initial model of energy expenditure within 30 days, the initial model of energy expenditure within 60 days, etc.
- an initial model is constructed respectively, the user information of each user in the training set is input into the initial model to obtain a predicted label, and the predicted label is compared with the corresponding real label. Yes, determine whether the predicted labels are consistent with the real labels, count the number of predicted labels that are consistent with the real labels, and calculate the proportion of the number of predicted labels that are consistent with the real labels in the number of all predicted labels, if If the proportion is greater than or equal to the preset proportion value, the initial model converges and the initial model after training is obtained. If the proportion is less than the preset proportion value, the parameters in the initial model are adjusted. After adjustment, The initial model re-predicts the predicted label of each object until the proportion is greater than or equal to the preset proportion value.
- the method of adjusting the parameters in the initial model can be carried out by using a stochastic gradient descent algorithm, a gradient descent algorithm or a normal equation.
- the first stage set among the multiple stage sets can be extracted; the first stage set is input into the first group of initial models to generate the first group of model parameter vectors; and then the n+1th stage set and the nth group of model parameters are generated.
- the vector performs multi-task training on the n+1 group of initial models and generates the n+1 group of model parameter vectors, where n is a positive integer.
- FIG. 5 shows the multi-stage sequential multi-task machine learning model framework introduced in this application.
- Multi-stage refers to the multiple product stages mentioned above.
- the above multiple product stages are arranged in time sequence and combined with multi-task learning.
- Model training is performed in this way, that is, the multi-stage sequential multi-task machine learning model framework in this application is generated.
- the users in the first stage set and their corresponding user tags can be input from the input layer.
- the sharing layer often organizes the user data and then inputs it into the corresponding application initial model in the first stage to obtain the corresponding The model parameter vector of this input data.
- the sharing layer frequently organizes the user data, and then inputs it together with the model parameter vector obtained in the first stage into the corresponding first-day dynamic expenditure in the second stage. From the initial model, the initial model of movement and expenditure within 30 days, and the initial model of movement and expenditure within 60 days, the model parameter vectors of the three models corresponding to this input data are obtained.
- multiple sets of stage scoring models are generated based on multiple sets of model parameter vectors. After all stage sets are trained, multiple sets of trained stage scoring models can be generated based on the model parameters in each current set of initial models.
- the security level of the current user is identified through the multiple sets of stage scoring models.
- the user information of the current user can be obtained; multiple groups of stage scoring models can be extracted according to the user stage in the user information; for example, if the current user is in the "service application stage", then the "resource dynamic support stage” can be extracted Multiple initial models corresponding to the "overdue stage”.
- the multiple sets of stage models can also be arranged in sequence according to their corresponding stage timings; the user information can be input into the multiple sets of stage scoring models in turn to generate multiple sets of stage scores; according to the multiple sets of stage Rating to determine the service provided to said user.
- a user may have fewer ratings during the application phase and may be initially rejected.
- a multi-stage sequential multi-task model is used to train multiple models in different stages. In practical applications, these models can be used to calculate the user's score respectively. Choosing the largest score is likely to be the final corresponding situation of the user. , for example, if a user has the highest probability of spending money on the 30th day during the overdue phase, then the user will be assigned preferential information and strategies based on this situation to promote the user to spend money. If the user has the highest risk score of overdue payment for 30 days during the overdue phase, then the user will be determined. Deferred repayment strategies and more.
- multiple stage sets are generated according to the total number of users and their corresponding user stages; the multiple stage sets are arranged in sequence according to the n+1th stage set; , the nth group of model parameter vectors perform multi-task training on the n+1th group of initial models, and generate the n+1th group of model parameter vectors, where n is a positive integer; until the multiple stages of collective training are completed, based on the multiple groups of model parameters
- the vector generates multiple groups of stage scoring models; the method of identifying the security level of the current user through the multi-group stage scoring model can comprehensively improve the multi-task machine learning method from the perspective of model samples and model parameters based on actual problems and application scenarios. , thereby ensuring the security of application system user data and transaction security.
- FIG. 6 is a flowchart of a user security level identification method based on multi-stage sequential multi-tasking according to another exemplary embodiment.
- the process 60 shown in Figure 6 is to perform multi-task training on the n+1th group of initial models based on the n+1th stage set and the nth group of model parameter vectors in sequence in S406 of the process shown in Figure 4, and generate the n+th group of initial models.
- 1 set of model parameter vectors, n is a positive integer" detailed description.
- the user information in the first stage set is input into the first set of initial models respectively.
- the users in the first stage set and their corresponding user tags can be input from the input layer.
- the sharing layer often organizes the user data and then inputs it into the corresponding application initial model in the first stage to obtain the corresponding A vector of model parameters for the input data.
- the first set of initial models performs model training based on the user information and its corresponding labels. After the training is completed, the first set of model parameter vectors are generated.
- the model parameter vector of the n+1 group of initial models is updated through the n group of model parameter vectors.
- the model parameter vector of the n+1 group of initial models can be updated through the nonlinear transformation function sigmoid function and the nth group of model parameter vectors; in some embodiments, other nonlinear transformation methods can also be used to
- the nth set of model parameter vectors is used to update the model parameter vectors of the n+1th set of initial models. This application is not limited to this.
- the parameter transfer formula can be expressed as follows:
- e in represents the data input to the current stage after parameter transformation
- e out represents the model parameters of the previous stage
- g() is a nonlinear function.
- the purpose of performing nonlinear transformation instead of linear transformation is to make the function corresponding to the model parameters more consistent with the actual situation, rather than a simple straight line division.
- Feature points may be divided incorrectly; therefore, when passing parameters, using nonlinear functions to fit the model parameters can make the division of feature points more accurate and consistent with the actual situation.
- the nth set of model parameters can be nonlinearly transformed to generate an update vector; the update vector is weighted and superimposed into the model parameter vector of the n+1th set of initial models.
- the formula for parameter output can be written as:
- ⁇ m is the weight of the model parameters of the m-th model in the D-1 stage, that is, the weight of the model parameters of the m-th model in the previous stage
- D is the current stage
- m is the number of models in the D-1 stage.
- the first to nth groups of update vectors may be generated through nonlinear transformation of the first to nth groups of model parameters; the update vectors of the first to nth groups are weighted and superimposed on the In the model parameter vector of n+1 groups of initial models.
- the formula can be written as:
- Represents the weight of the model parameters of the mnth model in stage 1 Represents the model parameter vector of the mn-th model in the first stage, and mn indicates the number of models in the first stage;
- model parameters of this stage are jointly generated based on the model parameters of multiple models in the D-1 stage (previous stage) to the model parameters of multiple models in the first stage.
- the weight of each stage above and The calculation method can refer to ⁇ m , and ⁇ m can be processed according to the following formula:
- g represents the weight of the model parameters of the m-th model in the D-1 stage before normalization
- m is the number of models in the D-1 stage
- ⁇ > represents the dot product function
- ⁇ m is the m-th model in the D-1 stage the weights of the model parameters of the model
- g() is a nonlinear function.
- model parameters of the previous stage and the model parameters of this stage are weighted and added, and the formula for generating the model parameters of this stage can be as follows:
- the g′() function is a nonlinear transformation function, which is the same as the above g() function. They are all nonlinear transformation functions, such as the sigmoid function. Of course, other nonlinear transformation methods can also be used. This plan does not do this. Specially limited.
- ⁇ 1 and ⁇ 2 are the weights, and the specific values of ⁇ 1 and ⁇ 2 are The calculation method can refer to ⁇ m , or the user can set it according to the importance of the model parameter vector, which will not be described in detail here in this application.
- the user information in the n+1th stage set is respectively input into the n+1th group of initial models after the model parameter vector is updated for multi-task training.
- the parameter vector of the model in the previous stage can be transformed and passed into the initial model of the current stage.
- the parameter vector of the previous stage and the parameter vector of the current stage are weighted and summed to obtain the current stage.
- Parameter vector of the new initial model is
- model parameters of the current initial model are models trained in advance based on existing features and labels, directly added to the parameters of the model from the previous stage uploaded, and the new model parameters are used for the current calculation.
- the convergence of the model does not accurately judge all features.
- the initial model parameters after training are not fixed.
- the output will be result.
- the output result of the model is a decimal number between 0 and 1. Therefore, adding the parameters of the imported model to the original model parameters will not necessarily affect the judgment results of the original features. Therefore, whether the parameters of the new model will misjudge existing features and labels.
- the user information in the n+1th stage set can be input into the n+1th group of initial models after the model parameter vector is updated; the n+1th group of initial models are based on the user information and their corresponding tags.
- Perform multi-task training when the loss function during the training process does not meet the convergence conditions, re-determine the initial model parameters of the n+1 initial model to perform multi-task training again; when the loss function meets the convergence conditions, complete the n+ Multi-task training of an initial set of models.
- the original model can be retrained to obtain a new original model. model parameters, and then pass in the model parameters of the previous stage for judgment, and iterate until the new model obtained by the parameters of the original model and the passed model satisfies the conditions for the judgment of the original features.
- redetermining the initial model parameters of the n+1th group of initial models to perform model training again includes: performing model training again on the n+1th group of initial models to generate new initial model parameters; or redetermining Convergence conditions are used to perform model training again on the n+1th group of initial models to generate new initial model parameters.
- the trained model will output results as long as it meets the convergence conditions when it already has features and labels. Since the model parameters corresponding to each trained model are not necessarily the same, when the training loss function of multi-task training does not meet the convergence conditions, the initial model can be trained again and a set of initial model models can be regenerated according to the training stage. parameters, and then use the model parameters of the regenerated initial model to train the model again until the convergence conditions of multi-task training are met.
- the convergence conditions during initial model training can be adjusted so that the initial model can be trained again to obtain the model parameters of the regenerated initial model, Then the model parameters of the regenerated initial model are used to train the model again until the convergence conditions of multi-task training are met.
- FIG. 8 is a block diagram of a user security level identification device based on multi-stage sequential multi-tasking according to an exemplary embodiment.
- the user security level identification device 80 based on multi-stage sequential multi-tasking includes: stage module 802, sorting module 804, training module 806, model module 808, and grading module 810.
- the stage module 802 is used to generate multiple stage sets based on the total number of users and their corresponding user stages;
- the sorting module 804 is used to arrange multiple stage sets in sequence
- the training module 806 is used to perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, and generate the n+1 group of model parameter vectors, where n is a positive integer;
- the model module 808 is used to generate multiple sets of scoring models based on multiple sets of model parameter vectors until the multiple stages of collective training are completed;
- the classification module 810 is configured to identify the security level of the current user through the multiple sets of scoring models, and determine the security level of the current user based on the identification results.
- multiple stage sets are generated according to the total number of users and their corresponding user stages; the multiple stage sets are arranged in sequence according to the n+1th stage set; , the nth group of model parameter vectors perform multi-task training on the n+1th group of initial models, and generate the n+1th group of model parameter vectors, where n is a positive integer; until the multiple stages of collective training are completed, based on the multiple groups of model parameters
- the vector generates multiple groups of stage scoring models; the method of identifying the security level of the current user through the multi-group stage scoring model can comprehensively improve the multi-task machine learning method from the perspective of model samples and model parameters based on actual problems and application scenarios. , thereby ensuring the security of application system user data and transaction security.
- FIG. 9 is a block diagram of an electronic device according to an exemplary embodiment.
- FIG. 9 An electronic device 900 according to this embodiment of the present application is described below with reference to FIG. 9 .
- the electronic device 900 shown in FIG. 9 is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.
- electronic device 900 is embodied in the form of a general computing device.
- the components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), a display unit 940, and the like.
- the storage unit stores program code, and the program code can be executed by the processing unit 910, so that the processing unit 910 performs the steps in this specification according to various exemplary embodiments of the present application.
- the processing unit 910 may perform the steps shown in FIG. 4 and FIG. 6 .
- the storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 9201 and/or a cache storage unit 9202, and may further include a read-only storage unit (ROM) 9203.
- RAM random access storage unit
- ROM read-only storage unit
- the storage unit 920 may also include a program/utility 9204 having a set of (at least one) program modules 9205 including, but not limited to: an operating system, one or more applications, other program modules, and programs. Data, each of these examples or some combination may include an implementation of a network environment.
- Bus 930 may be a local area representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or using any of a variety of bus structures. bus.
- the electronic device 900 may also communicate with one or more external devices 900' (e.g., a keyboard, a pointing device, a Bluetooth device, etc.) so that the user can communicate with the device that the electronic device 900 interacts with, and/or the electronic device 900 can communicate with a Any device (such as a router, modem, etc.) with which multiple other computing devices communicate. This communication may occur through an input/output (I/O) interface 950.
- the electronic device 900 may also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 960.
- Network adapter 960 may communicate with other modules of electronic device 900 via bus 930.
- electronic device 900 may be used in conjunction with electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.
- the technical solution according to the embodiment of the present application can be embodied in the form of a software product.
- the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, U disk, mobile hard disk etc.) or on the network, including several instructions to cause a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present application.
- the software product may take the form of any combination of one or more readable media.
- the readable medium may be a readable signal medium or a readable storage medium.
- the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
- the computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave carrying readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.
- a readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transport the program for use by or in connection with an instruction execution system, apparatus, or device.
- Program code contained on a readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.
- Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., as well as conventional procedural formulas. Programming language—such as "C” or a similar programming language.
- the program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on.
- the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device, such as provided by an Internet service. (business comes via Internet connection).
- LAN local area network
- WAN wide area network
- the above-mentioned computer-readable medium carries one or more programs.
- the computer-readable medium realizes the following functions: generate multiple stages according to the total number of users and their corresponding user stages. Set; arrange multiple stage sets in sequence; perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, and generate the n+1 group of model parameter vectors, n is a positive integer; until the multiple stage set training is completed, multiple sets of stage scoring models are generated based on multiple sets of model parameter vectors; the current user is identified as a security level through the multiple sets of stage scoring models.
- modules can be distributed in devices according to the description of the embodiments, or can be modified accordingly in one or more devices that are only different from this embodiment.
- the modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.
- the example embodiments described here can be implemented by software, or can be implemented by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product.
- the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, U disk, mobile hard disk, etc.) or on the network. , including several instructions to cause a computing device (which may be a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the embodiment of the present application.
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Abstract
The present application relates to a user security level identification method and apparatus based on a multi-stage time sequence and multiple tasks. The method comprises: generating a plurality of stage sets according to all users and user stages corresponding thereto; sequentially arranging the plurality of stage sets according to a time sequence; performing multi-task training on an (n+1)th group of initial models according to an (n+1)th stage set and an nth group of model parameter vectors, so as to generate an (n+1)th group of model parameter vectors, wherein n is a positive integer; generating a plurality of groups of stage scoring models on the basis of the plurality of groups of model parameter vectors until the training of the plurality of stage sets is completed; and performing security level identification on the current user by means of the plurality of groups of stage scoring models. The present application can start from actual problems and application scenarios and integrally improve a multi-task machine learning method from the perspective of model samples and from model parameters, thereby ensuring the security of user data and transactions in an application system.
Description
本申请涉及计算机信息处理领域,具体而言,涉及一种基于多阶段时序多任务的用户安全等级识别方法、装置、电子设备及计算机可读介质。The present application relates to the field of computer information processing. Specifically, it relates to a user security level identification method, device, electronic equipment and computer-readable medium based on multi-stage timing and multi-tasking.
利用历史数据中的有用信息来帮助分析未来数据的机器学习,通常需要大量有标签数据才能训练出一个优良的学习器。深度学习模型是一种典型的机器学习模型,因为这类模型是带有很多隐藏层和很多参数的神经网络,所以通常需要数以百万计的数据样本才能学习得到准确的参数。但是,包括医学图像分析在内的一些应用无法满足这种数据要求,因为标注数据需要很多人力劳动。在这些情况下,多任务学习(MTL)可以通过使用来自其它相关学习任务的有用信息来帮助缓解这种数据稀疏问题。Machine learning, which uses useful information in historical data to help analyze future data, usually requires a large amount of labeled data to train a good learner. Deep learning models are a typical machine learning model. Because this type of model is a neural network with many hidden layers and many parameters, it usually requires millions of data samples to learn accurate parameters. However, some applications, including medical image analysis, cannot meet this data requirement because labeling the data requires a lot of manual labor. In these cases, multi-task learning (MTL) can help alleviate this data sparsity problem by using useful information from other related learning tasks.
多任务学习任务是根据训练数据集(包含训练数据实例和它们的标签)预测未曾见过的数据的标签。训练数据集中数据的“好坏”对多任务学习的效果有着至关重要的作用。但是,在实际的应用场景中,训练数据集中的数据很难准确的反应真实情况。The multi-task learning task is to predict the labels of unseen data based on a training data set (containing training data instances and their labels). The "quality" of the data in the training data set plays a crucial role in the effectiveness of multi-task learning. However, in actual application scenarios, the data in the training data set is difficult to accurately reflect the real situation.
在所述背景技术部分公开的上述信息仅用于加强对本申请的背景的理解,因此它可以包括不构成对本领域普通技术人员已知的现有技术的信息。The above information disclosed in the Background section is only for enhancement of understanding of the context of the application and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.
发明内容Contents of the invention
有鉴于此,本申请提供一种基于多阶段时序多任务的用户安全等级识别方法、装置、电子设备及计算机可读介质,能够从实际问题和应用场景出发,从模型样本角度、模型参数角度整体改进多任务机器 学习方法,从而保证应用***用户数据安全、交易安全。In view of this, this application provides a user security level identification method, device, electronic equipment and computer-readable medium based on multi-stage timing and multi-task, which can be based on practical problems and application scenarios, and from the perspective of model samples and model parameters. Improve multi-task machine learning methods to ensure application system user data security and transaction security.
本申请的其他特性和优点将通过下面的详细描述变得显然,或部分地通过本申请的实践而习得。Additional features and advantages of the invention will be apparent from the detailed description which follows, or, in part, may be learned by practice of the invention.
根据本申请的一方面,提出一种基于多阶段时序多任务的用户安全等级识别方法,该方法包括:根据全量用户和其对应的用户阶段生成多个阶段集合;将多个阶段集合按照时序依次排列;依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数;直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组阶段评分模型;通过所述多组阶段评分模型对当前用户进行安全等级识别。According to one aspect of the present application, a user security level identification method based on multi-stage timing and multi-tasking is proposed. The method includes: generating multiple stage sets based on all users and their corresponding user stages; Arrange; perform multi-task training on the n+1th group of initial models based on the n+1th stage set and the nth group of model parameter vectors in turn, and generate the n+1th group of model parameter vectors, n is a positive integer; until the multiple After the stage set training is completed, multiple groups of stage scoring models are generated based on multiple groups of model parameter vectors; the security level of the current user is identified through the multiple groups of stage scoring models.
可选地,根据全量用户和其对应的用户阶段生成多个阶段集合,包括:根据产品特征确定多个用户阶段;将全量用户中每一个用户对应的用户阶段和所述多个用户阶段进行匹配;根据匹配结果将用户分配至和其用户阶段对应的阶段集合中。Optionally, generating multiple stage sets based on all users and their corresponding user stages includes: determining multiple user stages based on product characteristics; matching the user stage corresponding to each user among all users with the multiple user stages. ;Assign users to the stage set corresponding to their user stage based on the matching results.
可选地,根据全量用户和其对应的用户阶段生成多个阶段集合,还包括:为每一个用户阶段确定标签策略;根据所述标签策略为每一个阶段集合中用户分配样本标签。Optionally, generating multiple stage sets based on the total number of users and their corresponding user stages also includes: determining a label strategy for each user stage; and assigning sample labels to users in each stage set according to the label strategy.
可选地,依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,包括:提取所述多个阶段集合中的第一阶段集合;将第一阶段集合输入第一组初始模型中,生成第一组模型参数向量;根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数。Optionally, perform multi-task training on the n+1 group of initial models based on the n+1 group of stage sets and the n group of model parameter vectors, and generate the n+1 group of model parameter vectors, including: extracting the multiple stages The first stage set in the set; input the first stage set into the first set of initial models to generate the first set of model parameter vectors; based on the n+1th stage set and the nth set of model parameter vectors, the n+1th set of initial The model performs multi-task training and generates the n+1th set of model parameter vectors, where n is a positive integer.
可选地,依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,还包括:为每一个用户阶段确定一组机器学习模型;根据每一个用户阶段对应的标签策略为历史用户分配样本标签;通过带有样本标签的历史用户对第n+1组机器学习模型进行训练,生成第n+1组初始模型,n为正整数。Optionally, perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, and generate the n+1 group of model parameter vectors, which also includes: Determine a set of machine learning models; assign sample labels to historical users according to the label strategy corresponding to each user stage; train the n+1th group of machine learning models through historical users with sample labels, and generate the n+1th group of initial Model, n is a positive integer.
可选地,将第一阶段集合输入第一组初始模型中,生成第一组模型参数向量,包括:将第一阶段集合中的用户信息分别输入第一组初始模型中;第一组初始模型根据用户信息和其对应的标签进行模型训练,在训练完毕后,生成第一组模型参数向量。Optionally, inputting the first-stage set into the first group of initial models to generate the first group of model parameter vectors includes: inputting the user information in the first-stage set into the first group of initial models respectively; Model training is performed based on the user information and its corresponding labels. After the training is completed, the first set of model parameter vectors is generated.
可选地,根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,包括:生成更新向量;将所述更新向量加权后叠加到第n+1组初始模型的模型参数向量中;将第n+1阶段集合中的用户信息分别输入模型参数向量更新后的第n+1组初始模型中以进行多任务训练;在训练完毕后,生成第n+1组模型参数向量。Optionally, perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, and generate the n+1 group of model parameter vectors, including: generating an update vector; The update vector is weighted and superimposed into the model parameter vector of the n+1th group of initial models; the user information in the n+1th stage set is input into the n+1th group of initial models after the model parameter vector is updated for multi-tasking. Training; after training, generate the n+1th set of model parameter vectors.
可选地,生成更新向量,包括:将第n组模型参数向量进行非线性变换以生成更新向量;或通过第一组至第n组模型参数向量的非线性变换生成第一组至第n组的更新向量。Optionally, generating an update vector includes: nonlinearly transforming the nth group of model parameter vectors to generate an update vector; or generating the first to nth groups through nonlinear transformation of the first to nth group of model parameter vectors. update vector.
可选地,将第n+1阶段集合中的用户信息分别输入模型参数向量更新后的第n+1组初始模型中以进行多任务训练,包括:将第n+1阶段集合中的用户信息分别输入模型参数向量更新后的第n+1组初始模型中;第n+1组初始模型根据用户信息和其对应的标签进行多任务训练;在训练过程中的损失函数不满足收敛条件时,重新确定第n+1组初始模型的初始模型参数以再次进行多任务训练;在损失函数满足收敛条件时,完成第n+1组初始模型的多任务训练。Optionally, input the user information in the n+1th stage set into the n+1th group of initial models after the model parameter vector is updated for multi-task training, including: inputting the user information in the n+1th stage set Input the updated model parameter vectors into the n+1 initial model respectively; the n+1 initial model performs multi-task training based on user information and its corresponding labels; when the loss function during the training process does not meet the convergence conditions, Redetermine the initial model parameters of the n+1 initial model to perform multi-task training again; when the loss function meets the convergence condition, complete the multi-task training of the n+1 initial model.
可选地,重新确定第n+1组初始模型的初始模型参数以再次进行模型训练,包括:对第n+1组初始模型再次进行模型训练以生成新的初始模型参数;或重新确定收敛条件以对第n+1组初始模型再次进行模型训练,生成新的初始模型参数。Optionally, re-determine the initial model parameters of the (n+1)th group of initial models to perform model training again, including: re-training the model on the (n+1)th group of initial models to generate new initial model parameters; or re-determining the convergence conditions Model training is performed again on the n+1 group of initial models to generate new initial model parameters.
根据本申请的一方面,提出一种基于多阶段时序多任务的用户安全等级识别装置,该装置包括:阶段模块,用于根据全量用户和其对应的用户阶段生成多个阶段集合;排序模块,用于将多个阶段集合按照时序依次排列;训练模块,用于依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模 型参数向量,n为正整数;模型模块,用于直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组评分模型;分级模块,用于通过所述多组评分模型对当前用户进行安全等级识别,根据识别结果确定所述当前用户的安全分级。According to one aspect of the present application, a user security level identification device based on multi-stage timing and multi-tasking is proposed. The device includes: a stage module for generating multiple stage sets based on all users and their corresponding user stages; a sorting module, It is used to arrange multiple stage sets in sequence; the training module is used to perform multi-task training on the n+1th group of initial models based on the n+1th stage set and the nth group of model parameter vectors, and generate the n+1th group of initial models. A set of model parameter vectors, n is a positive integer; a model module, used to generate multiple sets of scoring models based on multiple sets of model parameter vectors until the multiple stages of collective training are completed; a grading module, used to use the multiple sets of scoring models to The current user performs security level identification, and the security level of the current user is determined based on the identification result.
根据本申请的一方面,提出一种电子设备,该电子设备包括:一个或多个处理器;存储装置,用于存储一个或多个程序;当一个或多个程序被一个或多个处理器执行,使得一个或多个处理器实现如上文的方法。According to one aspect of the present application, an electronic device is proposed. The electronic device includes: one or more processors; a storage device for storing one or more programs; when one or more programs are processed by one or more processors, Execution causes one or more processors to implement the method as above.
根据本申请的一方面,提出一种计算机可读介质,其上存储有计算机程序,该程序被处理器执行时实现如上文中的方法。According to one aspect of the present application, a computer-readable medium is proposed, on which a computer program is stored. When the program is executed by a processor, the above method is implemented.
根据本申请的基于多阶段时序多任务的用户安全等级识别方法、装置、电子设备及计算机可读介质,通过根据全量用户和其对应的用户阶段生成多个阶段集合;将多个阶段集合按照时序依次排列;依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,生成第n组模型参数向量,n为正整数;直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组阶段评分模型;通过所述多组阶段评分模型对当前用户进行安全等级识别的方式,能够从实际问题和应用场景出发,从模型样本角度、模型参数角度整体改进多任务机器学习方法,从而保证应用***用户数据安全、交易安全。According to the user security level identification method, device, electronic equipment and computer-readable medium based on multi-stage timing and multi-task of the present application, multiple stage sets are generated according to the total number of users and their corresponding user stages; the multiple stage sets are generated in time sequence Arrange in order; perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, generate the n+1 group of model parameter vectors, and generate the n group of model parameter vectors, n is a positive integer; until the multi-stage set training is completed, multiple groups of stage scoring models are generated based on multiple groups of model parameter vectors; through the multi-group stage scoring model, the security level identification of the current user can be based on actual problems and Starting from the application scenario, the multi-task machine learning method is improved as a whole from the perspective of model samples and model parameters, thereby ensuring the security of user data and transaction security in the application system.
应当理解的是,以上的一般描述和后文的细节描述仅是示例性的,并不能限制本申请。It should be understood that the above general description and the following detailed description are only exemplary and do not limit the present application.
通过参照附图详细描述其示例实施例,本申请的上述和其它目标、特征及优点将变得更加显而易见。下面描述的附图仅仅是本申请的一些实施例,对于本领域的普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。The above and other objects, features and advantages of the present application will become more apparent by describing in detail example embodiments thereof with reference to the accompanying drawings. The drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.
图1是根据一示例性实施例示出的样本空间示意图。Figure 1 is a schematic diagram of a sample space according to an exemplary embodiment.
图2是根据另一示例性实施例示出的样本空间示意图。Figure 2 is a schematic diagram of a sample space according to another exemplary embodiment.
图3是根据一示例性实施例示出的一种基于多阶段时序多任务的用户安全等级识别方法及装置的***框图。Figure 3 is a system block diagram of a user security level identification method and device based on multi-stage sequential multi-tasking according to an exemplary embodiment.
图4是根据一示例性实施例示出的一种基于多阶段时序多任务的用户安全等级识别方法的流程图。FIG. 4 is a flow chart of a user security level identification method based on multi-stage sequential multi-tasking according to an exemplary embodiment.
图5是根据另一示例性实施例示出的一种基于多阶段时序多任务的用户安全等级识别方法的示意图。FIG. 5 is a schematic diagram of a user security level identification method based on multi-stage sequential multi-tasking according to another exemplary embodiment.
图6是根据另一示例性实施例示出的一种基于多阶段时序多任务的用户安全等级识别方法的流程图。FIG. 6 is a flowchart of a user security level identification method based on multi-stage sequential multi-tasking according to another exemplary embodiment.
图7是根据另一示例性实施例示出的一种基于多阶段时序多任务的用户安全等级识别方法的示意图。FIG. 7 is a schematic diagram of a user security level identification method based on multi-stage sequential multi-tasking according to another exemplary embodiment.
图8是根据一示例性实施例示出的一种基于多阶段时序多任务的用户安全等级识别装置的框图。FIG. 8 is a block diagram of a user security level identification device based on multi-stage sequential multi-tasking according to an exemplary embodiment.
图9是根据一示例性实施例示出的一种电子设备的框图。FIG. 9 is a block diagram of an electronic device according to an exemplary embodiment.
图10是根据一示例性实施例示出的一种计算机可读介质的框图。Figure 10 is a block diagram of a computer-readable medium according to an exemplary embodiment.
现在将参考附图更全面地描述示例实施例。然而,示例实施例能够以多种形式实施,且不应被理解为限于在此阐述的实施例;相反,提供这些实施例使得本申请将全面和完整,并将示例实施例的构思全面地传达给本领域的技术人员。在图中相同的附图标记表示相同或类似的部分,因而将省略对它们的重复描述。Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concepts of the example embodiments. To those skilled in the art. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted.
此外,所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施例中。在下面的描述中,提供许多具体细节从而给出对本申请的实施例的充分理解。然而,本领域技术人员将意识到,可以实践本申请的技术方案而没有特定细节中的一个或更多,或者可以采用其它的方法、组元、装置、步骤等。在其它情况下,不详细示出或描述公知方法、装置、实现或者操作以避免模糊本申请的各方面。Furthermore, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the present application. However, those skilled in the art will appreciate that the technical solutions of the present application may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other instances, well-known methods, apparatus, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the present application.
附图中所示的方框图仅仅是功能实体,不一定必须与物理上独立 的实体相对应。即,可以采用软件形式来实现这些功能实体,或在一个或多个硬件模块或集成电路中实现这些功能实体,或在不同网络和/或处理器装置和/或微控制器装置中实现这些功能实体。The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor devices and/or microcontroller devices. entity.
附图中所示的流程图仅是示例性说明,不是必须包括所有的内容和操作/步骤,也不是必须按所描述的顺序执行。例如,有的操作/步骤还可以分解,而有的操作/步骤可以合并或部分合并,因此实际执行的顺序有可能根据实际情况改变。The flowcharts shown in the drawings are only illustrative, and do not necessarily include all contents and operations/steps, nor must they be performed in the order described. For example, some operations/steps can be decomposed, and some operations/steps can be merged or partially merged, so the actual order of execution may change according to the actual situation.
应理解,虽然本文中可能使用术语第一、第二、第三等来描述各种组件,但这些组件不应受这些术语限制。这些术语乃用以区分一组件与另一组件。因此,下文论述的第一组件可称为第二组件而不偏离本申请概念的教示。如本文中所使用,术语“及/或”包括相关联的列出项目中的任一个及一或多者的所有组合。It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Accordingly, a first component discussed below may be referred to as a second component without departing from the teachings of the present concepts. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
本领域技术人员可以理解,附图只是示例实施例的示意图,附图中的模块或流程并不一定是实施本申请所必须的,因此不能用于限制本申请的保护范围。Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of exemplary embodiments, and the modules or processes in the accompanying drawings are not necessarily necessary to implement the present application, and therefore cannot be used to limit the protection scope of the present application.
为了便于理解本申请内容,现以互联网金融服务平台为例,实际应用情况进行说明。如图1所示,在互联网金融平台中,用户注册网站会员之后,在实际服务之前会进行金融资源的申请,互联网服务平台会根据用户的基础信息对用户的金融风险进行评分,评分高于阈值的优质用户允许其进行金融资源借用,评分低于阈值的用户,则不会再提供金融服务。优质用户并不是每个人都会进行实际的金融资源借用,只有部分用户会在实际需要时占用金融资源。优质用户可能会在批准其金融资源借用资格的首日即使用金融资源,也可能在批准其使用金融资源的30日内使用金融资源,还可能在更远一些的时间使用金融资源,当然,也存在完全不使用金融资源的优质用户。在使用金融资源的优质用户中,在金融资源使用期限到期后,可能会有一部分用户存在违约现象,在违约超过一定时间后,该用户则会进入催收流程,有些用户可能在较短的时间内归还违约的资源,有些用户则可能更长时间归还资源。In order to facilitate the understanding of the content of this application, the Internet financial service platform will be taken as an example to illustrate the actual application. As shown in Figure 1, in the Internet financial platform, after the user registers as a website member, he will apply for financial resources before actual services. The Internet service platform will score the user's financial risk based on the user's basic information. The score is higher than the threshold. High-quality users are allowed to borrow financial resources, and users whose scores are below the threshold will no longer provide financial services. Not everyone of high-quality users will actually borrow financial resources, and only some users will occupy financial resources when they are actually needed. High-quality users may use financial resources on the first day when their qualifications for borrowing financial resources are approved, or they may use financial resources within 30 days of being approved to use financial resources, or they may use financial resources at a further time. Of course, there are Quality users who do not use financial resources at all. Among high-quality users who use financial resources, some users may default after the use period of the financial resources expires. After the default exceeds a certain period, the user will enter the collection process. Some users may in a shorter period of time The defaulted resources will be returned within a certain period of time, and some users may take longer to return the resources.
本案申请人发现,在上述的每个步骤中,均会有用户损失(用户被拒绝服务或者用户主动选择不进行后续的服务),而在每个阶段环节的建模样本中,会将在本阶段环节有表现的用户作为正负样本,将上一步骤筛选之后的用户作为无标签样本,正负样本和无标签样本统一构成样本集合,利用半监督机器学习手段进行建模。The applicant in this case found that in each of the above steps, there will be user losses (users are denied service or users actively choose not to provide subsequent services), and in the modeling samples of each stage, there will be losses in this process. Users who performed well in each stage were used as positive and negative samples, and users screened in the previous step were used as unlabeled samples. Positive and negative samples and unlabeled samples were unified to form a sample set, and semi-supervised machine learning methods were used for modeling.
在上述的每个阶段,均是通过当前阶段中的用户建立训练数据集,从而进行机器学习模型训练的,也就是说,每个阶段中均是基于多次筛选之后的用户建立训练集数据,从而建立评估模型的。如图2所示,真实的样本空间是全量样本空间,而实际训练集的数据是有偏的样本空间,这种情况下,训练出的样本无法真实的反应实际情况。In each of the above stages, the training data set is established by the users in the current stage to train the machine learning model. That is to say, in each stage, the training set data is established based on the users after multiple screenings. thereby establishing an evaluation model. As shown in Figure 2, the real sample space is the full sample space, while the actual training set data is a biased sample space. In this case, the trained samples cannot truly reflect the actual situation.
由上述说明可知,在实际应用阶段的样本集合中,由于受到实际情况的限制,实际建模的样本空间远远小于真实的全量样本空间,这就造成了构建的模型在准确度和精确度上存在偏差。为了解决这个问题,本案申请人提出了一种基于多阶段时序多任务的用户安全等级识别方法,下面借助于具体的实施例,对本申请的内容进行详细说明。From the above description, it can be seen that in the sample set in the actual application stage, due to limitations of the actual situation, the actual modeling sample space is far smaller than the real full sample space, which results in the accuracy and precision of the built model. There is a bias. In order to solve this problem, the applicant of this case proposed a user security level identification method based on multi-stage timing and multi-tasking. The content of this application will be described in detail below with the help of specific embodiments.
图3是根据一示例性实施例示出的一种基于多阶段时序多任务(multi-stage interaction sequence,MSIS)的用户安全等级识别方法及装置的***框图。Figure 3 is a system block diagram of a user security level identification method and device based on multi-stage interaction sequence (MSIS) according to an exemplary embodiment.
如图3所示,***架构30可以包括终端设备301、302、303,网304和服务器305。网304用以在终端设备301、302、303和服务器305之间提供通信链路的介质。网304可以包括各种连接类型,例如有线、无线通信链路或者光纤电缆等等。As shown in Figure 3, the system architecture 30 may include terminal devices 301, 302, 303, a network 304 and a server 305. The network 304 is used as a medium for providing communication links between the terminal devices 301, 302, 303 and the server 305. Network 304 may include various connection types, such as wired, wireless communication links, fiber optic cables, etc.
用户可以使用终端设备301、302、303通过网304与服务器305交互,以接收或发送消息等。终端设备301、302、303上可以安装有各种通讯客户端应用,例如互联网服务类应用、购物类应用、网页浏览器应用、即时通信工具、邮箱客户端、社交平台软件等。Users can use terminal devices 301, 302, 303 to interact with the server 305 through the network 304 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 301, 302, and 303, such as Internet service applications, shopping applications, web browser applications, instant messaging tools, email clients, social platform software, etc.
终端设备301、302、303可以是具有显示屏并且支持网页浏览的各种电子设备,包括但不限于智能手机、平板电脑、膝上型便携计算 机和台式计算机等等。The terminal devices 301, 302, and 303 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.
服务器305可以是提供各种服务的服务器,例如对用户利用终端设备301、302、303所浏览的互联网服务类网站提供支持的后台管理服务器。后台管理服务器可以对接收到的用户数据进行分析等处理,并将处理结果(例如安全等级、资源配额)反馈给互联网服务网站的管理员和/或终端设备301、302、303。The server 305 may be a server that provides various services, such as a backend management server that provides support for Internet service websites browsed by users using the terminal devices 301, 302, and 303. The background management server can analyze and process the received user data, and feed back the processing results (such as security level, resource quota) to the administrator of the Internet service website and/or the terminal device 301, 302, 303.
服务器305可例如由终端设备301、302、303获取用户数据作为全量用户数据;服务器305可例如根据全量用户和其对应的用户阶段生成多个阶段集合;服务器305可例如将多个阶段集合按照时序依次排列;服务器305可例如依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数,n为正整数;直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组阶段评分模型;服务器305可例如通过所述多组阶段评分模型对终端设备301、302、303中的用户进行安全等级识别。The server 305 can, for example, obtain user data from the terminal devices 301, 302, and 303 as full user data; the server 305 can, for example, generate multiple stage sets based on the full number of users and their corresponding user stages; the server 305 can, for example, combine the multiple stage sets in time sequence. Arranged in sequence; the server 305 can, for example, perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors in sequence, and generate the n+1 group of model parameter vectors, where n is a positive integer, n is a positive integer; until the multiple stage set training is completed, multiple sets of stage scoring models are generated based on multiple sets of model parameter vectors; the server 305 can, for example, use the multiple sets of stage scoring models to evaluate the performance of the terminal devices 301, 302 and 303. Users perform security level identification.
需要说明的是,本申请实施例所提供的基于多阶段时序多任务的用户安全等级识别方法可以由服务器305和/或终端设备301、302、303执行,相应地,基于多阶段时序多任务的用户安全等级识别装置可以设置于服务器305和/或终端设备301、302、303中。而提供给用户进行互联网服务平台浏览的网页端一般位于终端设备301、302、303中。It should be noted that the user security level identification method based on multi-stage timing and multi-tasking provided by the embodiment of the present application can be executed by the server 305 and/or the terminal devices 301, 302, 303. Correspondingly, the method based on multi-stage timing and multi-tasking The user security level identification device may be provided in the server 305 and/or the terminal devices 301, 302, and 303. The web pages provided for users to browse the Internet service platform are generally located in terminal devices 301, 302, and 303.
图4是根据一示例性实施例示出的一种基于多阶段时序多任务的用户安全等级识别方法的流程图。基于多阶段时序多任务的用户安全等级识别方法40至少包括步骤S402至S410。FIG. 4 is a flow chart of a user security level identification method based on multi-stage sequential multi-tasking according to an exemplary embodiment. The user security level identification method 40 based on multi-stage sequential multi-tasking at least includes steps S402 to S410.
如图4所示,在S402中,根据全量用户和其对应的用户阶段生成多个阶段集合。可根据产品特征确定多个用户阶段;将全量用户中每一个用户对应的用户阶段和所述多个用户阶段进行匹配;根据匹配结果将用户分配至和其用户阶段对应的阶段集合中。As shown in Figure 4, in S402, multiple stage sets are generated based on all users and their corresponding user stages. Multiple user stages can be determined based on product characteristics; the user stage corresponding to each user among all users can be matched with the multiple user stages; and users can be assigned to stage sets corresponding to their user stages based on the matching results.
继续以金融服务平台为例进行下文的说明,其中,用户可为个人用户或者企业用户,资源可为金融资源,也可为电力资源、水力资源、数据资源等等。用户信息可包括经过用户授权的基础信息,可例如为业务账号信息、用户的终端设备标识信息、用户所处地域信息等;用户信息还可包括行为信息,可例如为用户的页面操作数据、用户的业务访问时长、用户的业务访问频率等,用户信息的具体内容可根据实际应用场景确定,在此不做限制。在金融服务平台中,可将用户分为“服务申请阶段”、“资源动支阶段”、“逾期阶段”,上述三个阶段根据业务内容存在着时序上的关联。The following explanation will continue to take the financial service platform as an example. The users can be individual users or corporate users, and the resources can be financial resources, power resources, water resources, data resources, etc. User information may include basic information authorized by the user, which may be, for example, business account information, user's terminal device identification information, user location information, etc. User information may also include behavioral information, which may be, for example, the user's page operation data, user information, etc. The length of business access, the frequency of user’s business access, etc. The specific content of user information can be determined according to the actual application scenario and is not limited here. In the financial service platform, users can be divided into "service application stage", "resource payment stage" and "overdue stage". The above three stages are related in time sequence according to the business content.
在用户信息中,可包括用户当前所处于的阶段,在用户处于“逾期阶段”的时候,可知该用户经过了“服务申请阶段”、“资源动支阶段”,此时需要将该用户分别放入对应阶段的集合中。同理,当用户处于“资源动支阶段”阶段时,其一定经过了“服务申请阶段”,也需要将该用户分别放入对应阶段的集合中。The user information can include the current stage of the user. When the user is in the "overdue stage", it can be seen that the user has passed the "service application stage" and "resource activation stage". At this time, the user needs to be placed separately. into the set of the corresponding stage. In the same way, when a user is in the "resource mobilization stage", he must have passed the "service application stage", and the user also needs to be put into the collection of the corresponding stage.
在一个实施例中,还包括:为每一个用户阶段确定标签策略;根据所述标签策略为每一个阶段集合中用户分配样本标签。更具体的,可为“服务申请阶段”确定“通过”和“拒绝”标签;为“资源动支阶段”确定“首日动支”、“30日内动支”、“60日内动支”标签;“逾期阶段”确定“入催还款”、“入催30日还款”、“入催60日还款”;按照用户信息中的用户表现,分别为不同阶段的用户分配对应于该阶段的标签。In one embodiment, the method further includes: determining a label strategy for each user stage; and assigning sample labels to users in each stage set according to the label strategy. More specifically, the "pass" and "reject" labels can be determined for the "service application stage"; the "first day of expenditure", "expenditure within 30 days", and "expenditure within 60 days" labels can be determined for the "resource expenditure stage" ; "Overdue stage" determines "repayment upon reminder", "repayment upon reminder within 30 days", "repayment upon reminder within 60 days"; according to the user performance in the user information, users at different stages are allocated corresponding to that stage Tag of.
在S404中,将多个阶段集合按照时序依次排列。将上述阶段集合按照其对应的按照业务发生顺序排列,即为“服务申请阶段”、“资源动支阶段”、“逾期阶段”三个阶段集合顺序排列。In S404, multiple stage sets are arranged in sequence in time sequence. Arrange the above-mentioned stage sets according to their corresponding order of business occurrence, that is, the three stage sets are arranged in the order of "service application stage", "resource mobilization stage" and "overdue stage".
在S406中,依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量。In S406, multi-task training is performed on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, and the n+1 group of model parameter vectors are generated.
在一个实施例中,还包括:为每一个用户阶段确定一组机器学习模型;根据每一个用户阶段对应的标签策略为历史用户分配样本标签;通过带有样本标签的历史用户对第n组机器学习模型进行多任务训 练,生成第n组初始模型,n为正整数。可事先通过历史数据对每个阶段和每个标签分别训练初始模型,可例如,对应于“服务申请阶段”训练生成申请初始模型,对应于“资源动支阶段”训练生成本组模型,可包括首日动支初始模型,30日内动支初始模型,60日内动支初始模型等等。In one embodiment, the method further includes: determining a set of machine learning models for each user stage; assigning sample labels to historical users according to label strategies corresponding to each user stage; and classifying the nth group of machines through historical users with sample labels. The learning model performs multi-task training and generates the nth group of initial models, where n is a positive integer. The initial model can be trained separately for each stage and each label through historical data in advance. For example, the initial application model can be generated by training corresponding to the "service application stage", and this group of models can be generated by training corresponding to the "resource mobilization stage", which can include The initial model of energy expenditure on the first day, the initial model of energy expenditure within 30 days, the initial model of energy expenditure within 60 days, etc.
具体的,针对每个训练集合,分别构建初始模型,将所述训练集合中的各个用户的用户信息输入所述初始模型,以得到预测标签,将所述预测标签与相应的真实的标签进行比对,判断预测标签与真实的标签是否一致,统计与真实的标签一致的预测标签的数量,并计算与真实的标签一致的预测标签的数量在所有预测标签的数量中的占比,若所述占比大于或等于预设占比值,则所述初始模型收敛,得到训练完成的初始模型,若所述占比小于所述预设占比值,则调整所述初始模型中的参数,通过调整后的初始模型重新预测各个对象的预测标签,直至所述占比大于或等于预设占比值。其中,调整所述初始模型中的参数的方法可以采用随机梯度下降算法、梯度下降算法或正规方程进行。Specifically, for each training set, an initial model is constructed respectively, the user information of each user in the training set is input into the initial model to obtain a predicted label, and the predicted label is compared with the corresponding real label. Yes, determine whether the predicted labels are consistent with the real labels, count the number of predicted labels that are consistent with the real labels, and calculate the proportion of the number of predicted labels that are consistent with the real labels in the number of all predicted labels, if If the proportion is greater than or equal to the preset proportion value, the initial model converges and the initial model after training is obtained. If the proportion is less than the preset proportion value, the parameters in the initial model are adjusted. After adjustment, The initial model re-predicts the predicted label of each object until the proportion is greater than or equal to the preset proportion value. Wherein, the method of adjusting the parameters in the initial model can be carried out by using a stochastic gradient descent algorithm, a gradient descent algorithm or a normal equation.
可提取所述多个阶段集合中的第一阶段集合;将第一阶段集合输入第一组初始模型中,生成第一组模型参数向量;然后根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数。The first stage set among the multiple stage sets can be extracted; the first stage set is input into the first group of initial models to generate the first group of model parameter vectors; and then the n+1th stage set and the nth group of model parameters are generated. The vector performs multi-task training on the n+1 group of initial models and generates the n+1 group of model parameter vectors, where n is a positive integer.
图5所示为本申请引入的多阶段时序多任务机器学习模型框架,其中,多阶段指的是上文中的多个产品阶段,将上述多个产品阶段按照时序排列,并结合多任务学习的方式进行模型训练,即生成本申请中的多阶段时序多任务机器学习模型框架。Figure 5 shows the multi-stage sequential multi-task machine learning model framework introduced in this application. Multi-stage refers to the multiple product stages mentioned above. The above multiple product stages are arranged in time sequence and combined with multi-task learning. Model training is performed in this way, that is, the multi-stage sequential multi-task machine learning model framework in this application is generated.
在具体的应用中,可将第一阶段集合中的用户和其对应的用户标签由输入层输入,共享层对用户数据经常整理,然后输入到第一阶段对应的申请初始模型中,得到对应于本次输入数据的模型参数向量。In a specific application, the users in the first stage set and their corresponding user tags can be input from the input layer. The sharing layer often organizes the user data and then inputs it into the corresponding application initial model in the first stage to obtain the corresponding The model parameter vector of this input data.
然后将第二阶段集合中的用户和其对应的用户标签由输入层输入,共享层对用户数据经常整理,然后和第一阶段得到的模型参数向 量一起输入到第二阶段对应的首日动支初始模型,30日内动支初始模型,60日内动支初始模型中,得到对应于本次输入数据的三个模型的模型参数向量。Then the users in the second stage set and their corresponding user labels are input from the input layer. The sharing layer frequently organizes the user data, and then inputs it together with the model parameter vector obtained in the first stage into the corresponding first-day dynamic expenditure in the second stage. From the initial model, the initial model of movement and expenditure within 30 days, and the initial model of movement and expenditure within 60 days, the model parameter vectors of the three models corresponding to this input data are obtained.
后续阶段数据和模型依次处理。其中,“根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数”的详细内容将在图6,图7对应的实施例中详细说明。In subsequent stages, data and models are processed sequentially. Among them, the details of "Perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors to generate the n+1 group of model parameter vectors, n is a positive integer" will be in Detailed descriptions are provided in the corresponding embodiments of FIG. 6 and FIG. 7 .
在S408中,直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组阶段评分模型。在所有的阶段集合训练完毕后,可根据当前每组初始模型中的模型参数生成多组训练完毕的阶段评分模型。In S408, until the multiple stage set training is completed, multiple sets of stage scoring models are generated based on multiple sets of model parameter vectors. After all stage sets are trained, multiple sets of trained stage scoring models can be generated based on the model parameters in each current set of initial models.
在S410中,通过所述多组阶段评分模型对当前用户进行安全等级识别。可例如,在实际的应用场景中,获取当前用户的用户信息;根据用户信息中的用户阶段提取多组阶段评分模型;比如,当前用户处于“服务申请阶段”,则提取“资源动支阶段”和“逾期阶段”对应的多个初始模型。In S410, the security level of the current user is identified through the multiple sets of stage scoring models. For example, in an actual application scenario, the user information of the current user can be obtained; multiple groups of stage scoring models can be extracted according to the user stage in the user information; for example, if the current user is in the "service application stage", then the "resource dynamic support stage" can be extracted Multiple initial models corresponding to the "overdue stage".
在一个实施例中,还可将所述多组阶段模型按照其对应的阶段时序依次排列;依次将所述用户信息输入多组阶段评分模型中,生成多组阶段评分;根据所述多组阶段评分,确定为所述用户提供的服务。In one embodiment, the multiple sets of stage models can also be arranged in sequence according to their corresponding stage timings; the user information can be input into the multiple sets of stage scoring models in turn to generate multiple sets of stage scores; according to the multiple sets of stage Rating to determine the service provided to said user.
在现有技术中,用户可能在申请阶段评分较少,可能最初就被拒绝了。而在本申请中,利用多阶段时序多任务模型训练出多个不同阶段的模型,在实际应用中,可利用这些模型分别计算该用户的评分,选择最大的评分很可能就是用户最终对应的情况,比如,某个用户在动支阶段30日动支概率最高,则根据这个情况为用户指定优惠信息和策略,促进用户动支,如果用户在逾期阶段超期30天风险评分最高,则为用户确定延期还款策略等等。In existing technologies, a user may have fewer ratings during the application phase and may be initially rejected. In this application, a multi-stage sequential multi-task model is used to train multiple models in different stages. In practical applications, these models can be used to calculate the user's score respectively. Choosing the largest score is likely to be the final corresponding situation of the user. , for example, if a user has the highest probability of spending money on the 30th day during the overdue phase, then the user will be assigned preferential information and strategies based on this situation to promote the user to spend money. If the user has the highest risk score of overdue payment for 30 days during the overdue phase, then the user will be determined. Deferred repayment strategies and more.
根据本申请的基于多阶段时序多任务的用户安全等级识别方法,通过根据全量用户和其对应的用户阶段生成多个阶段集合;将多个阶段集合按照时序依次排列;根据第n+1阶段集合、第n组模型参数向 量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数;直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组阶段评分模型;通过所述多组阶段评分模型对当前用户进行安全等级识别的方式,能够从实际问题和应用场景出发,从模型样本角度、模型参数角度整体改进多任务机器学习方法,从而保证应用***用户数据安全、交易安全。According to the user security level identification method based on multi-stage timing and multi-task of this application, multiple stage sets are generated according to the total number of users and their corresponding user stages; the multiple stage sets are arranged in sequence according to the n+1th stage set; , the nth group of model parameter vectors perform multi-task training on the n+1th group of initial models, and generate the n+1th group of model parameter vectors, where n is a positive integer; until the multiple stages of collective training are completed, based on the multiple groups of model parameters The vector generates multiple groups of stage scoring models; the method of identifying the security level of the current user through the multi-group stage scoring model can comprehensively improve the multi-task machine learning method from the perspective of model samples and model parameters based on actual problems and application scenarios. , thereby ensuring the security of application system user data and transaction security.
应清楚地理解,本申请描述了如何形成和使用特定示例,但本申请的原理不限于这些示例的任何细节。相反,基于本申请公开的内容的教导,这些原理能够应用于许多其它实施例。It should be clearly understood that this application describes how to make and use specific examples, but that the principles of this application are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of this disclosure.
图6是根据另一示例性实施例示出的一种基于多阶段时序多任务的用户安全等级识别方法的流程图。图6所示的流程60是对图4所示的流程中S406“依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数”的详细描述。FIG. 6 is a flowchart of a user security level identification method based on multi-stage sequential multi-tasking according to another exemplary embodiment. The process 60 shown in Figure 6 is to perform multi-task training on the n+1th group of initial models based on the n+1th stage set and the nth group of model parameter vectors in sequence in S406 of the process shown in Figure 4, and generate the n+th group of initial models. 1 set of model parameter vectors, n is a positive integer" detailed description.
如图6所示,在S602中,将第一阶段集合中的用户信息分别输入第一组初始模型中。如上文所述,可将第一阶段集合中的用户和其对应的用户标签由输入层输入,共享层对用户数据经常整理,然后输入到第一阶段对应的申请初始模型中,得到对应于本次输入数据的模型参数向量。As shown in Figure 6, in S602, the user information in the first stage set is input into the first set of initial models respectively. As mentioned above, the users in the first stage set and their corresponding user tags can be input from the input layer. The sharing layer often organizes the user data and then inputs it into the corresponding application initial model in the first stage to obtain the corresponding A vector of model parameters for the input data.
在S604中,第一组初始模型根据用户信息和其对应的标签进行模型训练,在训练完毕后,生成第一组模型参数向量。In S604, the first set of initial models performs model training based on the user information and its corresponding labels. After the training is completed, the first set of model parameter vectors are generated.
在S606中,通过第n组模型参数向量更新第n+1组初始模型的模型参数向量。In S606, the model parameter vector of the n+1 group of initial models is updated through the n group of model parameter vectors.
在一个实施例中,可通过非线性变换函数sigmoid函数和第n组模型参数向量更新第n+1组初始模型的模型参数向量;在一些实施例中,还可以采用其他非线性变换的方法对第n组模型参数向量以更新第n+1组初始模型的模型参数向量,本申请不以此为限。In one embodiment, the model parameter vector of the n+1 group of initial models can be updated through the nonlinear transformation function sigmoid function and the nth group of model parameter vectors; in some embodiments, other nonlinear transformation methods can also be used to The nth set of model parameter vectors is used to update the model parameter vectors of the n+1th set of initial models. This application is not limited to this.
参数传递公式可表示如下:The parameter transfer formula can be expressed as follows:
e
in=g(e
out);
e in =g(e out );
其中,e
in是代表参数变换之后输入到当前阶段的数据,e
out是代表上一阶段的模型参数,g()为非线性函数。其中,进行非线性变换而不是线性变换的目的是为了让模型参数对应的函数可以更符合实际情况,而不是单纯的直线划分。
Among them, e in represents the data input to the current stage after parameter transformation, e out represents the model parameters of the previous stage, and g() is a nonlinear function. Among them, the purpose of performing nonlinear transformation instead of linear transformation is to make the function corresponding to the model parameters more consistent with the actual situation, rather than a simple straight line division.
比如,如果计算出的模型参数是一个一次函数,y=ax+b,则可认为在这个直线之上的用户为好,直线之下的用户为坏,这种情况下,有一些靠近直线的特征点可能被错误的划分;所以,在参数传递的时候,利用非线性函数对模型参数进行拟合,能够使得特征点的划分更加准确,符合实际情况。For example, if the calculated model parameters are a linear function, y=ax+b, then users above the straight line can be considered good, and users below the straight line are considered bad. In this case, there are some users close to the straight line. Feature points may be divided incorrectly; therefore, when passing parameters, using nonlinear functions to fit the model parameters can make the division of feature points more accurate and consistent with the actual situation.
在一个实施例中,可将第n组模型参数进行非线性变换以生成更新向量;将所述更新向量加权后叠加到第n+1组初始模型的模型参数向量中。这种情况下,参数输出的公式可写为:In one embodiment, the nth set of model parameters can be nonlinearly transformed to generate an update vector; the update vector is weighted and superimposed into the model parameter vector of the n+1th set of initial models. In this case, the formula for parameter output can be written as:
其中,α
m为第D-1阶段中第m个模型的模型参数的权重,即上一阶段的第m个模型的模型参数的权重,D为当前阶段,
为上一阶段的第m个模型的模型参数向量,m为第D-1阶段的模型数量。
Among them, α m is the weight of the model parameters of the m-th model in the D-1 stage, that is, the weight of the model parameters of the m-th model in the previous stage, D is the current stage, is the model parameter vector of the m-th model in the previous stage, and m is the number of models in the D-1 stage.
在另一个实施例中,可通过第一组至第n组模型参数的非线性变换生成第一组至第n组的更新向量;将第一组至第n组的更新向量加权后叠加到第n+1组初始模型的模型参数向量中。这种情况下,公式可写为:In another embodiment, the first to nth groups of update vectors may be generated through nonlinear transformation of the first to nth groups of model parameters; the update vectors of the first to nth groups are weighted and superimposed on the In the model parameter vector of n+1 groups of initial models. In this case, the formula can be written as:
其中,
表示第D-1阶段的第m1个模型的模型参数的权重,
表示第D-1阶段的第m1个模型的模型参数向量,m1表示第D-1阶段的模型数量;
表示第D-2阶段的第m2个模型的模型参数的权重,
表示第D-2阶段的第m2个模型的模型参数向量,m2表示第D-2阶段的模型数量;
表示第1阶段的第mn个模型的模型参数的权重,
表示第1阶段的第mn个模型的模型参数向量,mn表示 第1阶段的模型数量;
in, Represents the weight of the model parameters of the m1-th model in stage D-1, Represents the model parameter vector of the m1-th model in stage D-1, and m1 represents the number of models in stage D-1; Represents the weight of the model parameters of the m2-th model in the D-2 stage, Represents the model parameter vector of the m2-th model in stage D-2, and m2 represents the number of models in stage D-2; Represents the weight of the model parameters of the mnth model in stage 1, Represents the model parameter vector of the mn-th model in the first stage, and mn indicates the number of models in the first stage;
即为,根据第D-1阶段(上一阶段)的多个模型的模型参数至第一阶段的多个模型的模型参数共同生成本阶段的模型参数。That is, the model parameters of this stage are jointly generated based on the model parameters of multiple models in the D-1 stage (previous stage) to the model parameters of multiple models in the first stage.
更具体的,上文中每个阶段的权重
和
的计算方式可参考α
m进行,α
m可按照如下公式处理:
More specifically, the weight of each stage above and The calculation method can refer to α m , and α m can be processed according to the following formula:
其中,
表示归一化前第D-1阶段中第m个模型的模型参数的权重,m第D-1阶段的模型数量,<>表示点积函数,α
m为第D-1阶段中第m个模型的模型参数的权重,
为第D-1阶段(上一阶段)的第m个模型的模型参数,g()为非线性函数。
in, represents the weight of the model parameters of the m-th model in the D-1 stage before normalization, m is the number of models in the D-1 stage, <> represents the dot product function, α m is the m-th model in the D-1 stage the weights of the model parameters of the model, are the model parameters of the m-th model in the D-1 stage (previous stage), and g() is a nonlinear function.
将上一阶段的模型参数和本阶段的模型参数进行权重叠加,生成本阶段的模型参数的公式可如下:The model parameters of the previous stage and the model parameters of this stage are weighted and added, and the formula for generating the model parameters of this stage can be as follows:
其中,g′()函数为非线性变换函数,与上述g()函数相同,均为非线性变换函数,比如,sigmoid函数,当然,也可以采用其他非线性变换的方法,本方案对此不作特别限定。Among them, the g′() function is a nonlinear transformation function, which is the same as the above g() function. They are all nonlinear transformation functions, such as the sigmoid function. Of course, other nonlinear transformation methods can also be used. This plan does not do this. Specially limited.
其中,
为更新后的当前阶段的第一个初始模型的模型参数向量,
当前阶段原有的第一个初始模型的模型参数向量,如需确定更新后的第n个初始模型的模型参数向量,可参考本公式的计算方式,将模型参数向量进行替换,即将公式中的第一个初始模型的模型参数向量更换为第n个初始模型的模型参数向量,得到更新后的第n个初始模型的模型参数向量,β
1,β
2为权重,β
1,β
2的具体计算方法可参考α
m,或者,由用户根据模型参数向量的重要程度自行进行设置,本申请在此不再赘述。
in, is the updated model parameter vector of the first initial model of the current stage, The model parameter vector of the original first initial model at the current stage. If you need to determine the model parameter vector of the updated nth initial model, you can refer to the calculation method of this formula and replace the model parameter vector, that is, in the formula The model parameter vector of the first initial model is replaced with the model parameter vector of the n-th initial model, and the updated model parameter vector of the n-th initial model is obtained. β 1 and β 2 are the weights, and the specific values of β 1 and β 2 are The calculation method can refer to α m , or the user can set it according to the importance of the model parameter vector, which will not be described in detail here in this application.
在S608中,将第n+1阶段集合中的用户信息分别输入模型参数向量更新后的第n+1组初始模型中以进行多任务训练。In S608, the user information in the n+1th stage set is respectively input into the n+1th group of initial models after the model parameter vector is updated for multi-task training.
如上文所述,在实际计算中,可将前一阶段的模型的参数向量变换后传入当前阶段的初始模型中,前一阶段的参数向量和当前阶段的参数向量进行加权求和得到当前阶段新的初始模型的参数向量。As mentioned above, in actual calculations, the parameter vector of the model in the previous stage can be transformed and passed into the initial model of the current stage. The parameter vector of the previous stage and the parameter vector of the current stage are weighted and summed to obtain the current stage. Parameter vector of the new initial model.
值得注意的是,当前初始模型的模型参数是事先根据现有特征和标签训练好的模型,直接加上传进来的前一阶段的模型的参数,利用新的模型参数进行当前计算。It is worth noting that the model parameters of the current initial model are models trained in advance based on existing features and labels, directly added to the parameters of the model from the previous stage uploaded, and the new model parameters are used for the current calculation.
一方面,模型的收敛并不是对所有特征都会准确判断,另一方面,训练完毕的初始模型参数,其实并不是固定的,在已有特征和标签的情况下只要满足收敛条件了,就会输出结果。而且模型对于输入的用户特征,输出的结果是一个0~1的小数,所以,在原有的模型参数上加上传入的模型的参数,并不一定会影响到对于原有特征的判断结果。所以,新的模型的参数是否会错误判断现有特征和标签。On the one hand, the convergence of the model does not accurately judge all features. On the other hand, the initial model parameters after training are not fixed. When there are existing features and labels, as long as the convergence conditions are met, the output will be result. Moreover, for the input user features, the output result of the model is a decimal number between 0 and 1. Therefore, adding the parameters of the imported model to the original model parameters will not necessarily affect the judgment results of the original features. Therefore, whether the parameters of the new model will misjudge existing features and labels.
在S610中,在训练完毕后,生成第n+1组模型参数向量。In S610, after training is completed, the n+1th set of model parameter vectors are generated.
在一个实施例中,可将第n+1阶段集合中的用户信息分别输入模型参数向量更新后的第n+1组初始模型中;第n+1组初始模型根据用户信息和其对应的标签进行多任务训练;在训练过程中的损失函数不满足收敛条件时,重新确定第n+1组初始模型的初始模型参数以再次进行多任务训练;在损失函数满足收敛条件时,完成第n+1组初始模型的多任务训练。In one embodiment, the user information in the n+1th stage set can be input into the n+1th group of initial models after the model parameter vector is updated; the n+1th group of initial models are based on the user information and their corresponding tags. Perform multi-task training; when the loss function during the training process does not meet the convergence conditions, re-determine the initial model parameters of the n+1 initial model to perform multi-task training again; when the loss function meets the convergence conditions, complete the n+ Multi-task training of an initial set of models.
在实际的计算中,如果新的模型对于原有特征的判断不满足收敛条件,比如,预测准确率达不到要求,可以采用对于原有的模型重新再进行训练,得到的新的原有模型的模型参数,然后传入前一阶段的模型参数进行判断,一直迭代,直至原有模型与传入的模型的参数得到的新的模型对于原有特征的判断满足条件为止。In actual calculations, if the new model does not meet the convergence conditions for the judgment of the original features, for example, the prediction accuracy does not meet the requirements, the original model can be retrained to obtain a new original model. model parameters, and then pass in the model parameters of the previous stage for judgment, and iterate until the new model obtained by the parameters of the original model and the passed model satisfies the conditions for the judgment of the original features.
在一个实施例中,重新确定第n+1组初始模型的初始模型参数以再次进行模型训练,包括:对第n+1组初始模型再次进行模型训练以生成新的初始模型参数;或重新确定收敛条件以对第n+1组初始模型 再次进行模型训练,生成新的初始模型参数。In one embodiment, redetermining the initial model parameters of the n+1th group of initial models to perform model training again includes: performing model training again on the n+1th group of initial models to generate new initial model parameters; or redetermining Convergence conditions are used to perform model training again on the n+1th group of initial models to generate new initial model parameters.
在一个实施例中,如上文所述,训练完毕的模型在已有特征和标签的情况下只要满足收敛条件了,就会输出结果。由于每次训练的模型对应的模型参数并不一定相同,所以,可在多任务训练的训练损失函数不满足收敛条件时,再次对初始模型进行训练,根据训练阶段重新生成一组初始模型的模型参数,然后利用重新生成的初始模型的模型参数再次进行模型训练,直至满足多任务训练的收敛条件为止。In one embodiment, as mentioned above, the trained model will output results as long as it meets the convergence conditions when it already has features and labels. Since the model parameters corresponding to each trained model are not necessarily the same, when the training loss function of multi-task training does not meet the convergence conditions, the initial model can be trained again and a set of initial model models can be regenerated according to the training stage. parameters, and then use the model parameters of the regenerated initial model to train the model again until the convergence conditions of multi-task training are met.
在另一个实施例中,可在多任务训练的训练损失函数不满足收敛条件时,调整初始模型训练时候的收敛条件,以便再次对初始模型进行模型训练,获得重新生成的初始模型的模型参数,然后利用重新生成的初始模型的模型参数再次进行模型训练,直至满足多任务训练的收敛条件为止。In another embodiment, when the training loss function of multi-task training does not meet the convergence conditions, the convergence conditions during initial model training can be adjusted so that the initial model can be trained again to obtain the model parameters of the regenerated initial model, Then the model parameters of the regenerated initial model are used to train the model again until the convergence conditions of multi-task training are met.
本领域技术人员可以理解实现上述实施例的全部或部分步骤被实现为由CPU执行的计算机程序。在该计算机程序被CPU执行时,执行本申请提供的上述方法所限定的上述功能。所述的程序可以存储于一种计算机可读存储介质中,该存储介质可以是只读存储器,磁盘或光盘等。Those skilled in the art can understand that all or part of the steps for implementing the above-described embodiments are implemented as computer programs executed by a CPU. When the computer program is executed by the CPU, the above-mentioned functions defined by the above-mentioned method provided by this application are executed. The program can be stored in a computer-readable storage medium, which can be a read-only memory, a magnetic disk or an optical disk.
此外,需要注意的是,上述附图仅是根据本申请示例性实施例的方法所包括的处理的示意性说明,而不是限制目的。易于理解,上述附图所示的处理并不表明或限制这些处理的时间顺序。另外,也易于理解,这些处理可以是例如在多个模块中同步或异步执行的。In addition, it should be noted that the above-mentioned drawings are only schematic illustrations of processes included in the methods according to the exemplary embodiments of the present application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal sequence of these processes. In addition, it is also easy to understand that these processes may be executed synchronously or asynchronously in multiple modules, for example.
下述为本申请装置实施例,可以用于执行本申请方法实施例。对于本申请装置实施例中未披露的细节,请参照本申请方法实施例。The following are device embodiments of the present application, which can be used to execute method embodiments of the present application. For details not disclosed in the device embodiments of this application, please refer to the method embodiments of this application.
图8是根据一示例性实施例示出的一种基于多阶段时序多任务的用户安全等级识别装置的框图。如图8所示,基于多阶段时序多任务的用户安全等级识别装置80包括:阶段模块802,排序模块804,训练模块806,模型模块808,分级模块810。FIG. 8 is a block diagram of a user security level identification device based on multi-stage sequential multi-tasking according to an exemplary embodiment. As shown in Figure 8, the user security level identification device 80 based on multi-stage sequential multi-tasking includes: stage module 802, sorting module 804, training module 806, model module 808, and grading module 810.
阶段模块802用于根据全量用户和其对应的用户阶段生成多个 阶段集合;The stage module 802 is used to generate multiple stage sets based on the total number of users and their corresponding user stages;
排序模块804用于将多个阶段集合按照时序依次排列;The sorting module 804 is used to arrange multiple stage sets in sequence;
训练模块806用于根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数;The training module 806 is used to perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, and generate the n+1 group of model parameter vectors, where n is a positive integer;
模型模块808用于直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组评分模型;The model module 808 is used to generate multiple sets of scoring models based on multiple sets of model parameter vectors until the multiple stages of collective training are completed;
分级模块810用于通过所述多组评分模型对当前用户进行安全等级识别,根据识别结果确定所述当前用户的安全分级。The classification module 810 is configured to identify the security level of the current user through the multiple sets of scoring models, and determine the security level of the current user based on the identification results.
根据本申请的基于多阶段时序多任务的用户安全等级识别装置,通过根据全量用户和其对应的用户阶段生成多个阶段集合;将多个阶段集合按照时序依次排列;根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数;直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组阶段评分模型;通过所述多组阶段评分模型对当前用户进行安全等级识别的方式,能够从实际问题和应用场景出发,从模型样本角度、模型参数角度整体改进多任务机器学习方法,从而保证应用***用户数据安全、交易安全。According to the user security level identification device based on multi-stage timing and multi-task of the present application, multiple stage sets are generated according to the total number of users and their corresponding user stages; the multiple stage sets are arranged in sequence according to the n+1th stage set; , the nth group of model parameter vectors perform multi-task training on the n+1th group of initial models, and generate the n+1th group of model parameter vectors, where n is a positive integer; until the multiple stages of collective training are completed, based on the multiple groups of model parameters The vector generates multiple groups of stage scoring models; the method of identifying the security level of the current user through the multi-group stage scoring model can comprehensively improve the multi-task machine learning method from the perspective of model samples and model parameters based on actual problems and application scenarios. , thereby ensuring the security of application system user data and transaction security.
图9是根据一示例性实施例示出的一种电子设备的框图。FIG. 9 is a block diagram of an electronic device according to an exemplary embodiment.
下面参照图9来描述根据本申请的这种实施方式的电子设备900。图9显示的电子设备900仅仅是一个示例,不应对本申请实施例的功能和使用范围带来任何限制。An electronic device 900 according to this embodiment of the present application is described below with reference to FIG. 9 . The electronic device 900 shown in FIG. 9 is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.
如图9所示,电子设备900以通用计算设备的形式表现。电子设备900的组件可以包括但不限于:至少一个处理单元910、至少一个存储单元920、连接不同***组件(包括存储单元920和处理单元910)的总线930、显示单元940等。As shown in Figure 9, electronic device 900 is embodied in the form of a general computing device. The components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), a display unit 940, and the like.
其中,所述存储单元存储有程序代码,所述程序代码可以被所述处理单元910执行,使得所述处理单元910执行本说明书中的根据本 申请各种示例性实施方式的步骤。例如,所述处理单元910可以执行如图4,图6中所示的步骤。Wherein, the storage unit stores program code, and the program code can be executed by the processing unit 910, so that the processing unit 910 performs the steps in this specification according to various exemplary embodiments of the present application. For example, the processing unit 910 may perform the steps shown in FIG. 4 and FIG. 6 .
所述存储单元920可以包括易失性存储单元形式的可读介质,例如随机存取存储单元(RAM)9201和/或高速缓存存储单元9202,还可以进一步包括只读存储单元(ROM)9203。The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 9201 and/or a cache storage unit 9202, and may further include a read-only storage unit (ROM) 9203.
所述存储单元920还可以包括具有一组(至少一个)程序模块9205的程序/实用工具9204,这样的程序模块9205包括但不限于:操作***、一个或者多个应用程序、其它程序模块以及程序数据,这些示例中的每一个或某种组合中可能包括网络环境的实现。The storage unit 920 may also include a program/utility 9204 having a set of (at least one) program modules 9205 including, but not limited to: an operating system, one or more applications, other program modules, and programs. Data, each of these examples or some combination may include an implementation of a network environment.
总线930可以为表示几类总线结构中的一种或多种,包括存储单元总线或者存储单元控制器、***总线、图形加速端口、处理单元或者使用多种总线结构中的任意总线结构的局域总线。 Bus 930 may be a local area representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or using any of a variety of bus structures. bus.
电子设备900也可以与一个或多个外部设备900’(例如键盘、指向设备、蓝牙设备等)通信,使得用户能与该电子设备900交互的设备通信,和/或该电子设备900能与一个或多个其它计算设备进行通信的任何设备(例如路由器、调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口950进行。并且,电子设备900还可以通过网络适配器960与一个或者多个网络(例如局域网(LAN),广域网(WAN)和/或公共网络,例如因特网)通信。网络适配器960可以通过总线930与电子设备900的其它模块通信。应当明白,尽管图中未示出,可以结合电子设备900使用其它硬件和/或软件模块,包括但不限于:微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID***、磁带驱动器以及数据备份存储***等。The electronic device 900 may also communicate with one or more external devices 900' (e.g., a keyboard, a pointing device, a Bluetooth device, etc.) so that the user can communicate with the device that the electronic device 900 interacts with, and/or the electronic device 900 can communicate with a Any device (such as a router, modem, etc.) with which multiple other computing devices communicate. This communication may occur through an input/output (I/O) interface 950. Furthermore, the electronic device 900 may also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 960. Network adapter 960 may communicate with other modules of electronic device 900 via bus 930. It should be understood that, although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.
通过以上的实施方式的描述,本领域的技术人员易于理解,这里描述的示例实施方式可以通过软件实现,也可以通过软件结合必要的硬件的方式来实现。因此,如图10所示,根据本申请实施方式的技术方案可以以软件产品的形式体现出来,该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM,U盘,移动硬盘等)中或网络上, 包括若干指令以使得一台计算设备(可以是个人计算机、服务器、或者网络设备等)执行根据本申请实施方式的上述方法。Through the above description of the embodiments, those skilled in the art can easily understand that the example embodiments described here can be implemented by software, or can be implemented by software combined with necessary hardware. Therefore, as shown in Figure 10, the technical solution according to the embodiment of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, U disk, mobile hard disk etc.) or on the network, including several instructions to cause a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present application.
所述软件产品可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以为但不限于电、磁、光、电磁、红外线、或半导体的***、装置或器件,或者任意以上的组合。可读存储介质的更具体的例子(非穷举的列表)包括:具有一个或多个导线的电连接、便携式盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。The software product may take the form of any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
所述计算机可读存储介质可以包括在基带中或者作为载波一部分传播的数据信号,其中承载了可读程序代码。这种传播的数据信号可以采用多种形式,包括但不限于电磁信号、光信号或上述的任意合适的组合。可读存储介质还可以是可读存储介质以外的任何可读介质,该可读介质可以发送、传播或者传输用于由指令执行***、装置或者器件使用或者与其结合使用的程序。可读存储介质上包含的程序代码可以用任何适当的介质传输,包括但不限于无线、有线、光缆、RF等等,或者上述的任意合适的组合。The computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave carrying readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transport the program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.
可以以一种或多种程序设计语言的任意组合来编写用于执行本申请操作的程序代码,所述程序设计语言包括面向对象的程序设计语言—诸如Java、C++等,还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户计算设备上部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。在涉及远程计算设备的情形中,远程计算设备可以通过任意种类的网络,包括局域网(LAN)或广域网(WAN),连接到用户计算设备,或者,可以连接到外部计算设备(例如利用因特网服务提供商来通过因特网连接)。Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., as well as conventional procedural formulas. Programming language—such as "C" or a similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on. In situations involving remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device, such as provided by an Internet service. (business comes via Internet connection).
上述计算机可读介质承载有一个或者多个程序,当上述一个或者 多个程序被一个该设备执行时,使得该计算机可读介质实现如下功能:根据全量用户和其对应的用户阶段生成多个阶段集合;将多个阶段集合按照时序依次排列;根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数;直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组阶段评分模型;通过所述多组阶段评分模型对当前用户进行安全等级识别。The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by a device, the computer-readable medium realizes the following functions: generate multiple stages according to the total number of users and their corresponding user stages. Set; arrange multiple stage sets in sequence; perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors, and generate the n+1 group of model parameter vectors, n is a positive integer; until the multiple stage set training is completed, multiple sets of stage scoring models are generated based on multiple sets of model parameter vectors; the current user is identified as a security level through the multiple sets of stage scoring models.
本领域技术人员可以理解上述各模块可以按照实施例的描述分布于装置中,也可以进行相应变化唯一不同于本实施例的一个或多个装置中。上述实施例的模块可以合并为一个模块,也可以进一步拆分成多个子模块。Those skilled in the art can understand that the above-mentioned modules can be distributed in devices according to the description of the embodiments, or can be modified accordingly in one or more devices that are only different from this embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.
通过以上的实施例的描述,本领域的技术人员易于理解,这里描述的示例实施例可以通过软件实现,也可以通过软件结合必要的硬件的方式来实现。因此,根据本申请实施例的技术方案可以以软件产品的形式体现出来,该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM,U盘,移动硬盘等)中或网络上,包括若干指令以使得一台计算设备(可以是个人计算机、服务器、移动终端、或者网络设备等)执行根据本申请实施例的方法。Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described here can be implemented by software, or can be implemented by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, U disk, mobile hard disk, etc.) or on the network. , including several instructions to cause a computing device (which may be a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the embodiment of the present application.
以上具体地示出和描述了本申请的示例性实施例。应可理解的是,本申请不限于这里描述的详细结构、设置方式或实现方法;相反,本申请意图涵盖包含在所附权利要求的精神和范围内的各种修改和等效设置。Exemplary embodiments of the present application have been specifically shown and described above. It is to be understood that the present application is not limited to the detailed structures, arrangements, or implementation methods described herein; on the contrary, the present application is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (13)
- 一种基于多阶段时序多任务的用户安全等级识别方法,其特征在于,包括:A user security level identification method based on multi-stage timing and multi-tasking, which is characterized by including:根据全量用户和其对应的用户阶段生成多个阶段集合;Generate multiple stage sets based on the total number of users and their corresponding user stages;将多个阶段集合按照时序依次排列;Arrange multiple stage sets in sequence;依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数;Perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors in sequence, and generate the n+1 group of model parameter vectors, where n is a positive integer;直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组阶段评分模型;Until the multiple stage set training is completed, multiple sets of stage scoring models are generated based on the multiple sets of model parameter vectors;通过所述多组阶段评分模型对当前用户进行安全等级识别。The security level of the current user is identified through the multi-group stage scoring model.
- 如权利要求1所述的方法,其特征在于,根据全量用户和其对应的用户阶段生成多个阶段集合,包括:The method according to claim 1, characterized in that multiple stage sets are generated based on all users and their corresponding user stages, including:根据产品特征确定多个用户阶段;Identify multiple user stages based on product characteristics;将全量用户中每一个用户对应的用户阶段和所述多个用户阶段进行匹配;Match the user stage corresponding to each user among all users with the multiple user stages;根据匹配结果将用户分配至和其用户阶段对应的阶段集合中。According to the matching results, users are assigned to the stage set corresponding to their user stage.
- 如权利要求2所述的方法,其特征在于,根据全量用户和其对应的用户阶段生成多个阶段集合,还包括:The method of claim 2, wherein generating multiple stage sets based on all users and their corresponding user stages further includes:为每一个用户阶段确定标签策略;Determine labeling strategies for each user stage;根据所述标签策略为每一个阶段集合中用户分配样本标签。Sample labels are assigned to users in each stage set according to the label policy.
- 如权利要求1所述的方法,其特征在于,依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,包括:The method according to claim 1, characterized in that multi-task training is performed on the n+1 group of initial models according to the n+1 stage set and the n group of model parameter vectors in order to generate the n+1 group of model parameter vectors. ,include:提取所述多个阶段集合中的第一阶段集合;extracting a first stage set among the plurality of stage sets;将第一阶段集合输入第一组初始模型中,生成第一组模型参数向量;Input the first stage set into the first set of initial models to generate the first set of model parameter vectors;根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数。Perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors to generate the n+1 group of model parameter vectors, where n is a positive integer.
- 如权利要求1所述的方法,其特征在于,依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,还包括:The method according to claim 1, characterized in that multi-task training is performed on the n+1 group of initial models according to the n+1 stage set and the n group of model parameter vectors in order to generate the n+1 group of model parameter vectors. ,Also includes:为每一个用户阶段确定一组机器学习模型;Identify a set of machine learning models for each user stage;根据每一个用户阶段对应的标签策略为历史用户分配样本标签;Assign sample tags to historical users according to the tag strategy corresponding to each user stage;通过带有样本标签的历史用户对第n+1组机器学习模型进行训练,生成第n+1组初始模型,n为正整数。The n+1th group of machine learning models are trained through historical users with sample labels to generate the n+1th group of initial models, where n is a positive integer.
- 如权利要求4所述的方法,其特征在于,将第一阶段集合输入第一组初始模型中,生成第一组模型参数向量,包括:The method of claim 4, characterized in that the first stage set is input into the first group of initial models to generate the first group of model parameter vectors, including:将第一阶段集合中的用户信息分别输入第一组初始模型中;Enter the user information in the first stage set into the first set of initial models respectively;第一组初始模型根据用户信息和其对应的标签进行模型训练,在训练完毕后,生成第一组模型参数向量。The first set of initial models perform model training based on user information and their corresponding labels. After the training is completed, the first set of model parameter vectors are generated.
- 如权利要求4所述的方法,其特征在于,根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,包括:The method according to claim 4, characterized in that multi-task training is performed on the n+1 group of initial models according to the n+1 stage set and the n group of model parameter vectors to generate the n+1 group of model parameter vectors, include:生成更新向量;Generate update vector;将所述更新向量加权后叠加到第n+1组初始模型的模型参数向量中;Weight the update vector and add it to the model parameter vector of the n+1 initial model;将第n+1阶段集合中的用户信息分别输入模型参数向量更新后的第n+1组初始模型中以进行多任务训练;Enter the user information in the n+1th stage set into the n+1th group of initial models after the model parameter vector is updated for multi-task training;在训练完毕后,生成第n+1组模型参数向量。After training is completed, the n+1th set of model parameter vectors are generated.
- 如权利要求7所述的方法,其特征在于,生成更新向量,包括:The method of claim 7, wherein generating an update vector includes:将第n组模型参数向量进行非线性变换以生成更新向量;或Perform nonlinear transformation on the nth set of model parameter vectors to generate an update vector; or通过第一组至第n组模型参数向量的非线性变换生成第一组至第n组的更新向量。The first group to the nth group of update vectors are generated through nonlinear transformation of the first group to the nth group of model parameter vectors.
- 如权利要求7所述的方法,其特征在于,将第n+1阶段集合中的用户信息分别输入模型参数向量更新后的第n+1组初始模型中以进行多任务训练,包括:The method according to claim 7, characterized in that the user information in the n+1th stage set is respectively input into the n+1th group of initial models after the model parameter vector is updated for multi-task training, including:将第n+1阶段集合中的用户信息分别输入模型参数向量更新后的第n+1组初始模型中;Enter the user information in the n+1th stage set into the n+1th group of initial models after the model parameter vector is updated;第n+1组初始模型根据用户信息和其对应的标签进行多任务训练;The n+1 initial model performs multi-task training based on user information and its corresponding labels;在训练过程中的损失函数不满足收敛条件时,重新确定第n+1组初始模型的初始模型参数以再次进行多任务训练;When the loss function during the training process does not meet the convergence conditions, re-determine the initial model parameters of the n+1 initial model to perform multi-task training again;在损失函数满足收敛条件时,完成第n+1组初始模型的多任务训练。When the loss function meets the convergence condition, the multi-task training of the n+1 initial model is completed.
- 如权利要求9所述的方法,其特征在于,重新确定第n+1组初始模型的初始模型参数以再次进行模型训练,包括:The method of claim 9, wherein re-determining the initial model parameters of the (n+1)th group of initial models to perform model training again includes:对第n+1组初始模型再次进行模型训练以生成新的初始模型参数;或Perform model training again on the n+1 initial model to generate new initial model parameters; or重新确定收敛条件以对第n+1组初始模型再次进行模型训练,生成新的初始模型参数。Redetermine the convergence conditions to perform model training again on the n+1th group of initial models to generate new initial model parameters.
- 一种基于多阶段时序多任务的用户安全等级识别装置,其特征在于,包括:A user security level identification device based on multi-stage timing and multi-tasking, which is characterized by including:阶段模块,用于根据全量用户和其对应的用户阶段生成多个阶段集合;The stage module is used to generate multiple stage sets based on all users and their corresponding user stages;排序模块,用于将多个阶段集合按照时序依次排列;The sorting module is used to arrange multiple stage sets in sequence;训练模块,用于依次根据第n+1阶段集合、第n组模型参数向量对第n+1组初始模型进行多任务训练,生成第n+1组模型参数向量,n为正整数;The training module is used to perform multi-task training on the n+1 group of initial models based on the n+1 stage set and the n group of model parameter vectors in sequence, and generate the n+1 group of model parameter vectors, where n is a positive integer;模型模块,用于直至所述多个阶段集合训练完毕,基于多组模型参数向量生成多组评分模型;A model module, configured to generate multiple sets of scoring models based on multiple sets of model parameter vectors until the multiple stages of collective training are completed;分级模块,用于通过所述多组评分模型对当前用户进行安全等级识别,根据识别结果确定所述当前用户的安全分级。A classification module, configured to identify the security level of the current user through the multiple sets of scoring models, and determine the security level of the current user based on the identification results.
- 一种电子设备,其特征在于,包括:An electronic device, characterized by including:一个或多个处理器;one or more processors;存储装置,用于存储一个或多个程序;A storage device for storing one or more programs;当所述一个或多个程序被所述一个或多个处理器执行,使得所述一个或多个处理器实现如权利要求1-10中任一所述的方法。When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any one of claims 1-10.
- 一种计算机可读介质,其上存储有计算机程序,其特征在于,所述程序被处理器执行时实现如权利要求1-10中任一所述的方法。A computer-readable medium with a computer program stored thereon, characterized in that when the program is executed by a processor, the method according to any one of claims 1-10 is implemented.
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