CN112508698B - User policy triggering method and device and electronic equipment - Google Patents
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Abstract
The disclosure relates to a user policy triggering method, a device, an electronic device and a computer readable medium. The method comprises the following steps: acquiring user data of a target user in a preset state; inputting the user data into an unsteady state risk analysis model to generate a plurality of risk values and corresponding time nodes, wherein the unsteady state risk analysis model is generated through training of a plurality of historical user data sets with the time nodes; determining a plurality of risk policies based on the plurality of risk values; and generating a plurality of user strategy triggering tasks according to the plurality of risk strategies and the corresponding time nodes. The user policy triggering method, the device, the electronic equipment and the computer readable medium can analyze risks of users facing different external environments or different user policies in a future period of time, and determine the user risk policies according to analysis results, so that the allocation efficiency of the user policies is greatly improved, and the enterprise resource loss is reduced.
Description
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to a user policy triggering method, a device, an electronic apparatus, and a computer readable medium.
Background
With the development of economies, in order to meet the needs of its own development, individual users or enterprise users often conduct borrowing activities by financial service institutions, for which the borrowing activities of the users are likely to pose a risk to the financial service companies. Before the repayment period expires, the financial business condition of the borrower (credit user) is likely to be affected by significant adverse changes of the financial business condition of the borrower, so that risks such as bad accounts and bad accounts occur, and therefore, in order to reduce the occurrence probability of such risks, the financial service institution needs to perform risk assessment on the borrower, further formulate a corresponding user policy, and the user policy is used for determining the amount of resources occupied by the user and the period of resource return for the user, and the user policy can also be used for allocating special resources for the user, and the like.
In user policy formulation, the prior art is to formulate a user policy by experiential knowledge and then analyzing based on user basic information and personal behavior data. Moreover, most of the user policies in the prior art are established in the initial stage of user trust, and many changes of external environments, such as economic slip of certain industry, increased rate of loss of business, change of credit status of users, or adjustment of the overall user policies by the financial service company, are faced in the process of using financial products by users, and all the changes affect personal risk data of users. If the risk possibly generated by the user cannot be timely early-warned, the risk is extremely high for the financial service company.
Accordingly, there is a need for a new user policy triggering method, apparatus, electronic device, and computer readable medium.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the disclosure provides a method, an apparatus, an electronic device, and a computer readable medium for triggering a user policy, which can analyze risks of a user facing different external environments or different user policies in a future period of time, and determine a user risk policy according to an analysis result, thereby greatly improving allocation efficiency of the user policy and reducing resource loss of enterprises.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to an aspect of the present disclosure, a user policy triggering method is provided, which includes: acquiring user data of a target user in a preset state; inputting the user data into an unsteady state risk analysis model to generate a plurality of risk values and corresponding time nodes, wherein the unsteady state risk analysis model is generated through training of a plurality of historical user data sets with the time nodes; determining a plurality of risk policies based on the plurality of risk values; and generating a plurality of user strategy triggering tasks according to the plurality of risk strategies and the corresponding time nodes.
Optionally, the method further comprises: acquiring user data of a plurality of historical users; determining a plurality of time nodes; generating the plurality of historical user data sets based on the time node; training a machine learning model through the plurality of historical user data sets to generate the unsteady state risk analysis model.
Optionally, determining the plurality of time nodes includes: determining a time node according to the user state characteristic change time; determining a time node according to the online time of the historical user strategy; and determining a time node according to the external environment change time.
Optionally, generating the plurality of historical user data sets based on the time node includes: and dividing the plurality of user data according to the time node to generate a plurality of historical user data sets.
Optionally, dividing the plurality of user data according to the time node to generate the plurality of historical user data sets includes: generating a plurality of time periods according to the time node; corresponding the time in the plurality of historical user data and the plurality of time periods; a plurality of historical user data sets are generated from partial historical user data containing a period of time.
Optionally, generating a plurality of historical user data sets from partial historical user data comprising a period of time includes: generating a subset t 0, a subset t 1, a subset t 2, … …, a subset t k from partial historical user data comprising a certain period of time; generating a historical user data set through subsets t j and t j+1; wherein,。
Optionally, training a machine learning model through the plurality of historical user data sets to generate the unsteady state risk analysis model includes: randomly extracting historical user data sets in sequence from the plurality of historical user data sets; training the machine learning model sequentially through the randomly extracted historical user data set; and when the data extraction in all the historical user data sets is finished, generating the unsteady state risk analysis model according to the current machine learning model parameters.
Optionally, training the machine learning model sequentially through the randomly extracted historical user data set includes: reading one historical user data in a subset t j in the historical user data set; training the machine learning model through historical user data to generate an intermediate state risk analysis model; reading one historical user data in a subset t j+1 in the historical user data set; the machine learning model is trained with historical user data to update the intermediate risk analysis model.
Optionally, generating the unsteady state risk analysis model according to current machine learning model parameters includes: and generating the unsteady state risk analysis model according to the parameters of the current intermediate state risk analysis model.
Optionally, the method further comprises: and updating the user policy of the target user when the user policy triggering time node in the task is reached.
According to an aspect of the present disclosure, a user policy triggering apparatus is provided, the apparatus including: the data module is used for acquiring user data of a target user in a preset state; the risk analysis module is used for inputting the user data into an unsteady state risk analysis model to generate a plurality of risk values and corresponding time nodes, and the unsteady state risk analysis model is generated through training of a plurality of historical user data sets with the time nodes; a policy module to determine a plurality of risk policies based on the plurality of risk values; and the strategy triggering module is used for generating a plurality of user strategy triggering tasks according to the plurality of risk strategies and the corresponding time nodes.
Optionally, the method further comprises: the historical data module is used for acquiring user data of a plurality of historical users; a time node module for determining a plurality of time nodes; a data collection module for generating the plurality of historical user data collections based on the time node; and the model training module is used for training a machine learning model through the plurality of historical user data sets to generate the unsteady state risk analysis model.
Optionally, the time node module is further configured to determine a time node according to the user state feature change time; determining a time node according to the online time of the historical user strategy; and determining a time node according to the external environment change time.
Optionally, the data set module is further configured to divide the plurality of user data according to the time node to generate the plurality of historical user data sets.
Optionally, the data collection module includes: a time period unit, configured to generate a plurality of time periods according to the time node; a correspondence unit, configured to correspond time in the plurality of historical user data and the plurality of time periods; and the screening unit is used for generating a plurality of historical user data sets through partial historical user data containing a certain time period.
Optionally, the screening unit is further configured to generate a subset t 0, a subset t 1, a subset t 2, … …, and a subset t k by including part of the historical user data of a certain period of time; generating a historical user data set through subsets t j and t j+1; wherein,。
Optionally, the model training module includes: an extracting unit, configured to randomly extract a historical user data set from the plurality of historical user data sets in sequence; the training unit is used for training the machine learning model sequentially through the randomly extracted historical user data set; and the generation unit is used for generating the unsteady state risk analysis model according to the current machine learning model parameters when the data in all the historical user data sets are extracted.
Optionally, the extracting unit is further configured to read one historical user data in a subset t j in the set of historical user data; training the machine learning model through historical user data to generate an intermediate state risk analysis model; reading one historical user data in a subset t j+1 in the historical user data set; the machine learning model is trained with historical user data to update the intermediate risk analysis model.
Optionally, the extracting unit is further configured to generate the unsteady state risk analysis model according to parameters of the current intermediate state risk analysis model.
Optionally, the method further comprises: and the policy allocation module is used for allocating the user policy to the target user when the user policy reaches the time node in the user policy triggering task.
According to an aspect of the present disclosure, there is provided an electronic device including: one or more processors; a storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the methods as described above.
According to an aspect of the present disclosure, a computer-readable medium is presented, on which a computer program is stored, which program, when being executed by a processor, implements a method as described above.
According to the user policy triggering method, device, electronic equipment and computer readable medium, user data of a target user in a preset state are obtained; inputting the user data into an unsteady state risk analysis model to generate a plurality of risk values and corresponding time nodes, wherein the unsteady state risk analysis model is generated through training of a plurality of historical user data sets with the time nodes; determining a plurality of risk policies based on the plurality of risk values; the method for generating the plurality of user strategy trigger tasks according to the plurality of risk strategies and the corresponding time nodes can analyze risks of users facing different external environments or different user strategies in a future period of time, and determine the user risk strategies according to analysis results, so that the allocation efficiency of the user strategies is greatly improved, and the resource loss of enterprises is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely examples of the present disclosure and other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a system block diagram illustrating a user policy triggering method and apparatus according to an example embodiment.
FIG. 2 is a flowchart illustrating a user policy triggering method according to an example embodiment.
Fig. 3 is a flow chart illustrating a user policy triggering method according to another example embodiment.
Fig. 4 is a flow chart illustrating a user policy triggering method according to another example embodiment.
Fig. 5 is a flow chart illustrating a user policy triggering method according to another example embodiment.
Fig. 6 is a block diagram illustrating a user policy triggering apparatus according to an example embodiment.
Fig. 7 is a block diagram illustrating a user policy triggering apparatus according to another example embodiment.
Fig. 8 is a block diagram of an electronic device, according to an example embodiment.
Fig. 9 is a block diagram of a computer-readable medium shown according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many 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 concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
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 give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
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 element from another element. Accordingly, a first component discussed below could be termed a second component without departing from the teachings of the concepts of the present disclosure. As used herein, the term "and/or" includes any one of the associated listed items and all combinations of one or more.
Those skilled in the art will appreciate that the drawings are schematic representations of example embodiments and that the modules or flows in the drawings are not necessarily required to practice the present disclosure, and therefore, should not be taken to limit the scope of the present disclosure.
In the present invention, resources refer to any substance, information, time that can be utilized, information resources including computing resources and various types of data resources. The data resources include various dedicated data in various fields. The innovation of the invention is how to use the information interaction technology between the server and the client to make the resource allocation process more automatic, efficient and reduce the labor cost. Thus, the invention can be applied to the distribution of various resources, including physical goods, water, electricity, meaningful data and the like. However, for convenience, the present invention is described in terms of resource allocation by taking financial data resources as an example, but those skilled in the art will appreciate that the present invention may be used for allocation of other resources.
FIG. 1 is a system block diagram illustrating a user policy triggering method and apparatus according to an example embodiment.
As shown in fig. 1, the system architecture 10 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as financial service class applications, shopping class applications, web browser applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server providing support for financial service-like websites browsed by the user using the terminal devices 101, 102, 103. The background management server may analyze the received user data and the like, and feed back the processing result (e.g., the user policy trigger task) to the administrator and/or the terminal device 101, 102, 103 of the financial service website.
The server 105 may, for example, acquire user data of a target user in a preset state; server 105 may, for example, input the user data into an unsteady state risk analysis model that is trained from a plurality of historical user data sets with time nodes, generating a plurality of risk values and their corresponding time nodes; server 105 may determine a plurality of risk policies, for example, based on the plurality of risk values; server 105 may generate a plurality of user policy trigger tasks, e.g., from the plurality of risk policies and their corresponding time nodes.
The server 105 may be an entity server, or may be formed by a plurality of servers, for example, it should be noted that the user policy triggering method provided in the embodiment of the disclosure may be executed by the server 105, and accordingly, the user policy triggering device may be disposed in the server 105. And the web page end provided for the user to browse the financial service platform is generally located in the terminal devices 101, 102, 103.
FIG. 2 is a flowchart illustrating a user policy triggering method according to an example embodiment. The user policy triggering method 20 includes at least steps S202 to S208.
As shown in fig. 2, in S202, user data of a target user in a preset state is acquired. The preset state may be, for example, a default state, and the user may be determined to enter the default state when the user has not repaid the resource within a prescribed time period. User risk for the offending user over a period of time in the future may then be analyzed in accordance with the methods of the present disclosure.
The user data may include information about the name, age, occupation, income, address, etc. of the user, and may also include information about the behavior of the user on the financial service platform, such as browsing, logging in, etc.
In S204, the user data is input into an unsteady state risk analysis model, and a plurality of risk values and corresponding time nodes thereof are generated, where the unsteady state risk analysis model is generated through training of a plurality of historical user data sets with the time nodes. The process of establishing the unsteady state risk analysis model will be described in detail later.
Inputting user data into an unsteady state risk analysis model, and inputting a plurality of risk values, wherein each risk value corresponds to effective time, for example, the risk value is 0.4 from the current time to within 1 month; within 1 month to 2 months, a risk value of 0.6, etc. Furthermore, the risk value may also correspond to a change time, for example, when the user is online from the current time to a preferential activity of a certain user, the risk value is 0.4; within one month from the online of a user's preferential activity, the risk value is 0.6, etc.
In S206, a plurality of risk policies is determined based on the plurality of risk values. And determining corresponding risk strategies based on different risk values, and determining the risk strategy contents together according to the user strategies to be online of the financial service platform or other preferential activities.
In S208, a plurality of user policy trigger tasks are generated according to the plurality of risk policies and the corresponding time nodes thereof.
In one embodiment, further comprising: and updating the user policy of the target user when the user policy triggering time node in the task is reached. The specifics of the user policy may include updating the resource quota for the user, or re-doing a risk assessment for the user, or requiring the user to provide more credit material, etc.
According to the user policy triggering method, user data of a target user in a preset state is obtained; inputting the user data into an unsteady state risk analysis model to generate a plurality of risk values and corresponding time nodes, wherein the unsteady state risk analysis model is generated through training of a plurality of historical user data sets with the time nodes; determining a plurality of risk policies based on the plurality of risk values; the method for generating the plurality of user strategy trigger tasks according to the plurality of risk strategies and the corresponding time nodes can analyze risks of users facing different external environments or different user strategies in a future period of time, and determine the user risk strategies according to analysis results, so that the allocation efficiency of the user strategies is greatly improved, and the resource loss of enterprises is reduced.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 3 is a flow chart illustrating a user policy triggering method according to another example embodiment. The flow 30 shown in FIG. 3 is a detailed description of "generating an unsteady state risk analysis model through training of multiple sets of historical user data with time nodes".
As shown in fig. 3, in S302, user data of a plurality of history users is acquired. The historical user may be a user who entered the default state.
In S304, a plurality of time nodes are determined. Comprising the following steps: determining a time node according to the user state characteristic change time; determining a time node according to the online time of the historical user strategy; and determining a time node according to the external environment change time.
In S306, the plurality of historical user data sets are generated based on the time node. Comprising the following steps: and dividing the plurality of user data according to the time node to generate a plurality of historical user data sets. The details will be described in detail in the corresponding embodiment of fig. 4.
In S308, training a machine learning model through the plurality of historical user data sets to generate the unsteady state risk analysis model. The details will be described in detail in the corresponding embodiment of fig. 5.
Fig. 4 is a flow chart illustrating a user policy triggering method according to another example embodiment. The flow 40 shown in fig. 4 is a detailed description of S306 "generate the plurality of historical user data sets based on the time node" in the flow shown in fig. 3.
As shown in fig. 4, in S402, a plurality of time periods are generated from the time node. The time period may be t 0,t1,t2,……,tk.
In S404, the time in the plurality of historical user data and the plurality of time periods are associated.
In S406, a plurality of historical user data sets are generated from the partial historical user data containing a certain period of time. Comprising the following steps: generating a subset t 0, a subset t 1, a subset t 2, … …, a subset t k from partial historical user data comprising a certain period of time; generating a historical user data set through subsets t j and t j+1; wherein,。
More specifically, the user data included in the subset t 0 is the user data between (t 0,t1), and the user data included in the subset t 1 is the user data between (t 1,t2); the user data included in the subset t j is the user data between (t j,tj+1), … …, and the user data included in the subset t k is the user data between (t k,tk+1).
Fig. 5 is a flow chart illustrating a user policy triggering method according to another example embodiment. The process 50 shown in fig. 5 is a detailed description of the process of S308 "training a machine learning model through the plurality of historical user data sets to generate the unsteady state risk analysis model" shown in fig. 3.
As shown in fig. 5, in S502, a historical user data set is randomly extracted sequentially from the plurality of historical user data sets.
In S504, the machine learning model is trained sequentially through the randomly extracted historical user data set. Comprising the following steps: reading one historical user data in a subset t j in the historical user data set; training the machine learning model through historical user data to generate an intermediate state risk analysis model; reading one historical user data in a subset t j+1 in the historical user data set; the machine learning model is trained with historical user data to update the intermediate risk analysis model.
In one embodiment, a historical user data set is selected randomly during model training, and may be (t 1, t 2);
Then selecting one user data training from t1 of the historical user data set to generate a model m0;
selecting one user data from t2 of the historical user data set to train the model m0, and generating a model m1;
then randomly selecting a historical user data set, which can be (t 0, t 1);
Selecting one user data from t0 of the historical user data set to train the model m1, and generating a model m2;
selecting one user data from t1 of the historical user data set to train the model m2, and generating a model m3;
And performing training in an iterative manner until all the user data are trained, and obtaining a final user risk analysis model.
In S506, when the data extraction in all the historical user data sets is completed, the unsteady state risk analysis model is generated according to the current machine learning model parameters. Comprising the following steps: and generating the unsteady state risk analysis model according to the parameters of the current intermediate state risk analysis model.
Those skilled in the art will appreciate that all or part of the steps implementing the above described embodiments are implemented as a computer program executed by a CPU. The above-described functions defined by the above-described methods provided by the present disclosure are performed when the computer program is executed by a CPU. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic disk or an optical disk, etc.
Furthermore, it should be noted that the above-described figures are merely illustrative of the processes involved in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
The following are device embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure. For details not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the method of the present disclosure.
Fig. 6 is a block diagram illustrating a user policy triggering apparatus according to another example embodiment. As shown in fig. 6, the user policy triggering apparatus 60 includes: the data module 602, the risk analysis module 604, the policy module 606, the policy triggering module 608, the user policy triggering device 60 may further include: policy assignment module 610.
The data module 602 is configured to obtain user data of a target user in a preset state;
The risk analysis module 604 is configured to input the user data into an unsteady state risk analysis model, and generate a plurality of risk values and corresponding time nodes thereof, where the unsteady state risk analysis model is generated by training a plurality of historical user data sets with the time nodes;
the policy module 606 is configured to determine a plurality of risk policies based on the plurality of risk values;
The policy triggering module 608 is configured to generate a plurality of user policy triggering tasks according to the plurality of risk policies and the corresponding time nodes.
The policy allocation module 610 is configured to allocate the user policy to the target user when reaching a time node in the user policy triggering task.
Fig. 7 is a block diagram illustrating a user policy triggering apparatus according to an example embodiment. As shown in fig. 7, the user policy triggering apparatus 70 includes: a historical data module 702, a time node module 704, a data collection module 706, and a model training module 708.
The historical data module 702 is configured to obtain user data of a plurality of historical users;
The time node module 704 is configured to determine a plurality of time nodes; the time node module 704 is further configured to determine a time node according to the user state characteristic change time; determining a time node according to the online time of the historical user strategy; and determining a time node according to the external environment change time.
The data collection module 706 is configured to generate the plurality of historical user data collections based on the time node; the data set module 706 is further configured to divide the plurality of user data according to the time node to generate the plurality of historical user data sets.
The data aggregation module 706 includes: a time period unit, configured to generate a plurality of time periods according to the time node; a correspondence unit, configured to correspond time in the plurality of historical user data and the plurality of time periods; and the screening unit is used for generating a plurality of historical user data sets through partial historical user data containing a certain time period. The screening unit is further configured to generate a subset t 0, a subset t 1, a subset t 2, … …, and a subset t k by including part of the historical user data of a certain period of time; generating a historical user data set through subsets t j and t j+1; wherein,。
Model training module 708 is configured to train a machine learning model through the plurality of historical user data sets to generate the unsteady state risk analysis model. The model training module 708 includes: an extracting unit, configured to randomly extract a historical user data set from the plurality of historical user data sets in sequence; the extracting unit is further configured to read one historical user data in a subset t j in the historical user data set; training the machine learning model through historical user data to generate an intermediate state risk analysis model; reading one historical user data in a subset t j+1 in the historical user data set; the machine learning model is trained with historical user data to update the intermediate risk analysis model. The extraction unit is further configured to generate the unsteady state risk analysis model according to parameters of the current intermediate state risk analysis model. The training unit is used for training the machine learning model sequentially through the randomly extracted historical user data set; and the generation unit is used for generating the unsteady state risk analysis model according to the current machine learning model parameters when the data in all the historical user data sets are extracted.
According to the user policy triggering device, user data of a target user in a preset state are obtained; inputting the user data into an unsteady state risk analysis model to generate a plurality of risk values and corresponding time nodes, wherein the unsteady state risk analysis model is generated through training of a plurality of historical user data sets with the time nodes; determining a plurality of risk policies based on the plurality of risk values; the method for generating the plurality of user strategy trigger tasks according to the plurality of risk strategies and the corresponding time nodes can analyze risks of users facing different external environments or different user strategies in a future period of time, and determine the user risk strategies according to analysis results, so that the allocation efficiency of the user strategies is greatly improved, and the resource loss of enterprises is reduced.
Fig. 8 is a block diagram of an electronic device, according to an example embodiment.
An electronic device 800 according to such an embodiment of the present disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 8, the electronic device 800 is embodied in the form of a general purpose computing device. Components of electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one memory unit 820, a bus 830 that connects the different system components (including memory unit 820 and processing unit 810), a display unit 840, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 810 such that the processing unit 810 performs steps in the present specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 810 may perform the steps as shown in fig. 2, 3, 4, 5.
The storage unit 820 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM) 8201 and/or a cache memory unit 8202, and may further include a read only memory unit (ROM) 8203.
The storage unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 830 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 800 may also communicate with one or more external devices 800' (e.g., keyboard, pointing device, bluetooth device, etc.), devices that enable a user to interact with the electronic device 800, and/or any devices (e.g., routers, modems, etc.) that the electronic device 800 can communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 850. Also, electronic device 800 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 860. Network adapter 860 may communicate with other modules of electronic device 800 via bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 800, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, as shown in fig. 9, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-described method according to the embodiments of the present disclosure.
The software product may employ 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 can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a 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), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. 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, partly on a remote computing device, or entirely on the remote computing device or server. In the case of 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 (e.g., connected via the Internet using an Internet service provider).
The computer-readable medium carries one or more programs, which when executed by one of the devices, cause the computer-readable medium to perform the functions of: acquiring user data of a target user in a preset state; inputting the user data into an unsteady state risk analysis model to generate a plurality of risk values and corresponding time nodes, wherein the unsteady state risk analysis model is generated through training of a plurality of historical user data sets with the time nodes; determining a plurality of risk policies based on the plurality of risk values; and generating a plurality of user strategy triggering tasks according to the plurality of risk strategies and the corresponding time nodes.
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and include several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that this disclosure is not limited to the particular arrangements, instrumentalities and methods of implementation described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (7)
1. A method for triggering a user policy, comprising:
Acquiring user data of a plurality of historical users, wherein the user data comprises names, ages, professions, income, address information of the users and behavior information of the users on a financial service platform;
Splitting user data of a plurality of historical users based on a plurality of preset time nodes; the method comprises the steps that a plurality of preset time nodes are determined according to user state characteristic change time and/or historical user strategy online time and/or external environment change time;
Generating a plurality of historical user data sets with time nodes based on the time nodes, comprising: generating a subset t 0, a subset t 1, a subset t 2, … …, a subset t k from partial historical user data comprising a certain period of time; splicing the subsets t 0, t 1, t 2, … … and t k to generate a plurality of historical user data sets through the subsets t j and t j+1, wherein j is [0, k-1];
training the machine learning model through a plurality of historical user data sets to generate an unsteady state risk analysis model, comprising:
Randomly extracting historical user data sets in sequence from the plurality of historical user data sets; reading one historical user data in a subset t j in the historical user data set; training a machine learning model through the historical user data to generate an intermediate risk analysis model; reading one historical user data in a subset t j+1 in the historical user data set again, and training a machine learning model through the historical user data to update the intermediate state risk analysis model; sequentially performing iterative training until the training is finished when the historical user data in all the historical user data sets are extracted, and generating the unsteady state risk analysis model based on iterative updating of each historical user data set;
acquiring user data of a target user in a preset state;
inputting the user data into an unsteady state risk analysis model to generate a plurality of risk values and corresponding effective time thereof;
determining a plurality of risk policies based on the plurality of risk values;
and generating a plurality of user strategy triggering tasks according to the plurality of risk strategies and the corresponding time nodes.
2. The method of claim 1, wherein generating the plurality of historical user data sets with time nodes based on the time nodes further comprises:
Dividing the plurality of historical user data according to the time node to generate a plurality of historical user data sets with the time node.
3. The method of claim 2, wherein dividing the plurality of historical user data according to the time node generates the plurality of historical user data sets with time nodes, further comprising:
generating a plurality of time periods according to the time node;
corresponding the time in the plurality of historical user data and the plurality of time periods;
a plurality of historical user data sets are generated from partial historical user data containing a period of time.
4. The method of claim 3, wherein training a machine learning model through the plurality of historical user data sets to generate the unsteady state risk analysis model further comprises:
and when the data extraction in all the historical user data sets is finished, generating the unsteady state risk analysis model according to the current machine learning model parameters.
5. A user policy triggering apparatus, comprising:
the training module is used for acquiring user data of a plurality of historical users, wherein the user data comprise names, ages, professions, income, address information of the users and behavior information of the users on the financial service platform; splitting a plurality of historical user data based on a plurality of preset time nodes; the method comprises the steps that a plurality of preset time nodes are determined according to user state characteristic change time and/or historical user strategy online time and/or external environment change time; a method for generating a plurality of historical user data sets with time nodes based on the time nodes, comprising: generating a subset t 0, a subset t 1, a subset t 2, … …, a subset t k from partial historical user data comprising a certain period of time; splicing the subsets t 0, t 1, t 2, … … and t k to generate a plurality of historical user data sets through the subsets t j and t j+1, wherein j is [0, k-1]; and training the machine learning model with a plurality of historical user data sets to generate an unsteady state risk analysis model, comprising: randomly extracting historical user data sets in sequence from the plurality of historical user data sets; reading one historical user data in a subset t j in the historical user data set; training a machine learning model through the historical user data to generate an intermediate risk analysis model; reading one historical user data in a subset t j+1 in the historical user data set again and training a machine learning model through the historical user data to update the intermediate risk analysis model; sequentially performing iterative training until the training is finished when the historical user data in all the historical user data sets are extracted, and generating the unsteady state risk analysis model based on iterative updating of each historical user data set;
The data module is used for acquiring user data of a target user in a preset state;
The risk analysis module is used for inputting the user data into an unsteady state risk analysis model to generate a plurality of risk values and corresponding effective time;
a policy module to determine a plurality of risk policies based on the plurality of risk values;
And the strategy triggering module is used for generating a plurality of user strategy triggering tasks according to the plurality of risk strategies and the corresponding time nodes.
6. An electronic device, comprising:
One or more processors;
A storage means for storing one or more programs;
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-4.
7. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-4.
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| CN202110169624.6A CN112508698B (en) | 2021-02-07 | User policy triggering method and device and electronic equipment |
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