CN114662988A - Discount roll wind control method and device, electronic equipment and computer storage medium - Google Patents
Discount roll wind control method and device, electronic equipment and computer storage medium Download PDFInfo
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Abstract
The application discloses a method and a device for wind control of a coupon, electronic equipment and a computer storage medium, which can be applied to the field of network security or the field of finance. The method comprises the steps of obtaining device data corresponding to a device terminal when a user carries out coupon obtaining operation on the device terminal; the method comprises the steps that device data are used as input of a wind control recognition model, the device data are processed based on the wind control recognition model, target risk factors corresponding to device terminals are output, and the wind control recognition model is obtained according to device data training of the device terminals in a historical time period; and when the target risk factor is determined to be greater than or equal to the preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation. The right of the user can be timely guaranteed through the mode.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for controlling a coupon by using wind, an electronic device, and a computer storage medium.
Background
In order to improve the viscosity of customers, a bank promotes a large amount of coupon offers so that users can take coupons based on own equipment terminals. Because the user can take the coupon from the same equipment terminal by creating a plurality of forged accounts, the right of the user cannot be guaranteed.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for controlling a coupon by wind, an electronic device, and a computer storage medium, so as to solve a problem that a user right in the prior art cannot be guaranteed.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
the first aspect of the embodiment of the present invention shows a method for controlling a coupon by wind, where the method includes:
acquiring device data corresponding to a device terminal when a user carries out coupon getting operation on the device terminal;
the equipment data is used as the input of a wind control identification model, the equipment data is processed based on the wind control identification model, and a target risk factor corresponding to the equipment terminal is output, wherein the wind control identification model is obtained by training the equipment data of the equipment terminal in a historical time period;
and when the target risk factor is determined to be greater than or equal to a preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation.
Optionally, the method further includes:
judging whether the target risk factor is larger than a preset risk factor threshold value or not;
when the target risk factor is determined to be greater than or equal to a preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation;
and when the target risk factor is determined to be smaller than a preset risk factor threshold value, determining that the coupon fetching operation is a normal operation.
Optionally, the obtaining of the wind control recognition model according to the device data training of the device terminal in the historical time period includes:
acquiring corresponding equipment data of each equipment terminal when a user carries out coupon fetching operation in a historical time period;
calculating the repeated occurrence times of the equipment data of each equipment terminal in a preset time range;
and training a general neural network model based on the repeated occurrence times of the equipment data of each equipment terminal in a preset time range and a preset risk factor to obtain a trained wind control recognition model.
Optionally, the process of processing the device data based on the wind control identification model and outputting the target risk factor corresponding to the device terminal includes:
the wind control identification model calculates the target times of repeated occurrence of the equipment data within a preset time range;
and searching a target risk factor corresponding to the target times by the wind control identification model, and outputting the target risk factor corresponding to the equipment terminal.
A second aspect of the embodiment of the present invention shows a wind control device for a coupon, where the device includes:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring device data corresponding to a device terminal when a user carries out coupon receiving operation on the device terminal;
the wind control identification model is used for taking the equipment data as the input of the wind control identification model, processing the equipment data based on the wind control identification model and outputting a target risk factor corresponding to the equipment terminal, and the wind control identification model is constructed according to the construction unit;
and the determining unit is used for determining that the coupon fetching operation is abnormal operation when the target risk factor is determined to be greater than or equal to a preset risk factor threshold value.
Optionally, the determining unit is further configured to: judging whether the target risk factor is larger than a preset risk factor threshold value or not; and when the target risk factor is determined to be smaller than a preset risk factor threshold value, determining that the coupon fetching operation is a normal operation.
Optionally, the constructing unit is configured to: acquiring corresponding equipment data of each equipment terminal when a user carries out preferential roll obtaining operation in a historical time period; calculating the repeated occurrence times of the equipment data of each equipment terminal in a preset time range; and training a general neural network model based on the repeated occurrence times of the equipment data of each equipment terminal in a preset time range and a preset risk factor to obtain a trained wind control recognition model.
Optionally, the wind control identification model is specifically configured to:
the wind control identification model calculates the target times of repeated occurrence of the equipment data within a preset time range; and searching a target risk factor corresponding to the target times by the wind control identification model, and outputting the target risk factor corresponding to the equipment terminal.
A third aspect of the embodiment of the present invention shows an electronic device, where the electronic device is configured to run a program, where the program executes the method for controlling the coupon according to the first aspect of the embodiment of the present invention when running.
A fourth aspect of the embodiments of the present invention shows a computer storage medium, where the storage medium includes a storage program, and when the program runs, the apparatus where the storage medium is located is controlled to execute the method for controlling the coupon according to the first aspect of the embodiments of the present invention.
Based on the above-mentioned wind control method, device, electronic device and computer storage medium for the coupon, provided by the embodiments of the present invention, the method includes: acquiring device data corresponding to a device terminal when a user carries out coupon getting operation on the device terminal; the equipment data is used as the input of a wind control identification model, the equipment data is processed based on the wind control identification model, and a target risk factor corresponding to the equipment terminal is output, wherein the wind control identification model is obtained by training the equipment data of the equipment terminal in a historical time period; and when the target risk factor is determined to be greater than or equal to a preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation. In the embodiment of the invention, the acquired equipment data corresponding to the equipment terminal is processed through a wind control identification model obtained by training the equipment data of the equipment terminal in a historical time period, a corresponding risk factor is output, and whether the preferential roll getting operation performed on the equipment terminal by a user is normal operation or not is determined through the risk factor; and if the target risk factor is larger than or equal to a preset risk factor threshold value, determining that the coupon obtaining operation is abnormal operation. The right of the user can be timely guaranteed through the mode.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart illustrating a method for controlling a coupon according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a process of constructing a wind control identification model according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a wind control device for a coupon according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As is known in the art, blackouts can often bypass the restrictions of traffic by forging new devices or forging certain system-underlying parameters (e.g., geographic location, imei number, etc.). The device parameters of the terminal device are forged, and the device ID calculated based on the forged data has no meaning.
In the embodiment of the invention, the acquired equipment data corresponding to the equipment terminal is processed through a wind control identification model obtained by training the equipment data of the equipment terminal in a historical time period, a corresponding risk factor is output, and whether the preferential roll getting operation performed on the equipment terminal by a user is normal operation or not is determined through the risk factor; and if the target risk factor is determined to be greater than or equal to the preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation. By the method, the right of the user can be timely guaranteed.
It should be noted that the method and the device for controlling the coupon by wind provided by the invention can be used in the network security field or the financial field. The above is merely an example, and the application fields of the method, the apparatus, the electronic device, and the computer storage medium for controlling the wind of the coupon provided by the present invention are not limited.
Referring to fig. 1, a schematic flow chart of a method for controlling a coupon according to an embodiment of the present invention is shown, where the method includes:
step S101: and acquiring device data corresponding to the device terminal when a user carries out coupon getting operation on the device terminal.
In the process of implementing step S101 specifically, when the user performs a coupon pickup operation on the device terminal, the device data of the device terminal itself is acquired.
The device data refers to data that can uniquely identify the device terminal.
The equipment terminal can be mobile phone, ipad, or computer and other equipment.
The coupon refers to a volume provided by a bank for a customer and the like.
Step S102: and taking the equipment data as the input of a wind control identification model, processing the equipment data based on the wind control identification model, and outputting a target risk factor corresponding to the equipment terminal.
In step S102, the wind control identification model is trained according to the device data of the device terminal in the historical time period.
Specific contents of S102: the process of specifically realizing the step S102 based on the constructed wind control identification model comprises the following steps:
step S11: and the wind control identification model calculates the target times of repeated occurrence of the equipment data in a preset time range.
In the process of implementing the step S11, the login application time in the device data of a certain device terminal within the preset time range is counted; and calculating the target times of repeated login application of the equipment terminal based on the login application time.
It should be noted that the preset time range is also set according to a plurality of experiments or experiences, for example, the preset time range may be set to the time between the starting day a and the ending day B, and may be set to a range of 31 days from 3 months and 1 days to 4 months and 1 days.
Step S12: and searching a target risk factor corresponding to the target times by the wind control identification model, and outputting the target risk factor corresponding to the equipment terminal.
In the process of specifically implementing step S12, the wind control identification model traverses the mapping relationship between the target times and the risk factors to find the risk factors corresponding to the target times, and sets the risk factors as target risk factors.
Step S103: and judging whether the target risk factor is greater than or equal to a preset risk factor threshold value, executing the step S104 when the target risk factor is determined to be greater than or equal to the preset risk factor threshold value, and executing the step S105 when the target risk factor is determined to be less than the preset risk factor threshold value.
In the process of specifically implementing step S103, comparing the target risk factor with a preset risk factor threshold, when it is determined that the target risk factor is greater than or equal to the preset risk factor threshold, indicating that there is an extraction anomaly, then executing step S104, when it is determined that the target risk factor is less than the preset risk factor threshold, indicating that there is no extraction anomaly, and executing step S105.
Such as: setting a target risk factor a to be 0.6 and a preset risk factor threshold K to be 0.5, comparing the target risk factor a with the preset risk factor threshold K, determining that the target risk factor a is 0.6 and is greater than the preset risk factor threshold K, and determining that the operation has a fraud behavior, namely determining that the current coupon picking operation has the fraud behavior.
Step S104: determining that the coupon pickup operation is an abnormal operation.
In the process of implementing step S104 specifically, it is determined that there is a fraudulent behavior in the current coupon pickup operation.
Step S105: determining that the coupon pickup operation is a normal operation.
In the process of specifically implementing step S105, it is determined that there is no fraudulent activity in the current coupon pickup operation.
In the embodiment of the invention, the acquired equipment data corresponding to the equipment terminal is processed through a wind control identification model obtained by training the equipment data of the equipment terminal in a historical time period, a corresponding risk factor is output, and whether the preferential roll getting operation performed on the equipment terminal by a user is normal operation or not is determined through the risk factor; and if the target risk factor is determined to be greater than or equal to the preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation. The right of the user can be timely guaranteed through the mode.
Based on the above-mentioned wind control method for a coupon, which is shown in the embodiment of the present invention, a process of obtaining a wind control recognition model according to device data training of a device terminal in a historical time period, as shown in fig. 2, includes the following steps:
step S201: and acquiring the corresponding equipment data of each equipment terminal when the user carries out the coupon fetching operation in the historical time period.
In the process of implementing step S201 specifically, in a historical time period, and when the user performs the coupon pickup operation, the device data of each device terminal itself is acquired.
The device data refers to data that can uniquely identify the device terminal.
The equipment terminal can be mobile phone, ipad, or computer and other equipment.
The historical time period is set according to a plurality of experiments or experiences, and can be set to the past half year.
Step S202: and calculating the repeated occurrence times of the equipment data of each equipment terminal in a preset time range.
In the process of implementing the step S202 specifically, the login application time in the device data of each device terminal in the preset time range is counted; and calculating the times of repeatedly logging in the application by each equipment terminal based on the login application time.
It should be noted that the preset time range is also set according to a plurality of experiments or experiences, for example, the preset time range may be set as the time between the starting day a and the ending day B, and the preset time range is smaller than the historical time period, for example, the preset time range may be set as the range of 31 days from 3 months and 1 days to 4 months and 1 days.
Step S203: and training a general neural network model based on the repeated occurrence times of the equipment data of each equipment terminal in a preset time range and a preset risk factor to obtain a trained wind control recognition model.
In the process of specifically implementing step S203, a mapping relationship between the number of times that the device data of each device terminal repeatedly appears within a preset time range and each preset risk factor is first established; and training a universal neural network model based on the mapping relation between the repeated occurrence times of the equipment data of each equipment terminal in the preset time range and each preset risk factor, so that the trained neural network model, namely the wind control identification model, can process the input equipment data of each equipment terminal to output the corresponding risk factor.
It should be noted that the preset risk factors are set according to multiple experiments, the value range is a number between (0, 1), and the number of the preset risk factors is multiple.
In the embodiment of the invention, the corresponding equipment data of each equipment terminal when the user carries out the coupon obtaining operation is obtained in the historical time period; calculating the repeated occurrence times of the equipment data of each equipment terminal in a preset time range; and training a general neural network model based on the repeated occurrence times of the equipment data of each equipment terminal in a preset time range and a preset risk factor to obtain a trained wind control recognition model. Processing the acquired equipment data corresponding to the equipment terminal by using a trained wind control identification model, outputting a corresponding risk factor, and determining whether the coupon getting operation performed by a user on the equipment terminal is normal operation or not by using the risk factor; if the target risk factor is determined to be larger than or equal to a preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation; and when the target risk factor is determined to be smaller than a preset risk factor threshold value, determining that the coupon fetching operation is a normal operation. The right of the user can be timely guaranteed through the mode.
Based on the above-mentioned correspondence between the wind control methods for the coupons, the embodiments of the present invention also disclose a schematic structural diagram of a wind control device for the coupons, as shown in fig. 3, the device includes:
an obtaining unit 301, configured to obtain device data corresponding to a device terminal when a user performs a coupon pickup operation on the device terminal.
And the wind control identification model 302 is configured to use the device data as an input of the wind control identification model, process the device data based on the wind control identification model, and output a target risk factor corresponding to the device terminal, where the wind control identification model is constructed according to the construction unit 304.
A determining unit 303, configured to determine that the coupon fetching operation is an abnormal operation when it is determined that the target risk factor is greater than or equal to a preset risk factor threshold.
It should be noted that, the specific principle and the execution process of each unit in the above-mentioned wind control device for a coupon disclosed in the embodiment of the present application are the same as the above-mentioned wind control method for a coupon implemented and shown in the present application, and reference may be made to the corresponding parts in the above-mentioned wind control method for a coupon disclosed in the embodiment of the present application, and details are not described here again.
In the embodiment of the invention, the acquired equipment data corresponding to the equipment terminal is processed through the wind control identification model obtained through equipment data training of the equipment terminal in a historical time period, and the corresponding risk factor is output, so that whether the coupon picking-up operation performed on the equipment terminal by a user is normal operation or not is determined through the risk factor; and if the target risk factor is larger than or equal to a preset risk factor threshold value, determining that the coupon obtaining operation is abnormal operation. The right of the user can be timely guaranteed through the mode.
Optionally, based on the above-described wind control apparatus for a coupon, the determining unit 303 is further configured to: judging whether the target risk factor is larger than a preset risk factor threshold value or not; and when the target risk factor is determined to be smaller than a preset risk factor threshold value, determining that the coupon fetching operation is a normal operation.
In the embodiment of the invention, the acquired equipment data corresponding to the equipment terminal is processed through a wind control identification model obtained by training the equipment data of the equipment terminal in a historical time period, a corresponding risk factor is output, and whether the preferential roll getting operation performed on the equipment terminal by a user is normal operation or not is determined through the risk factor; if the target risk factor is larger than or equal to a preset risk factor threshold value, determining that the coupon obtaining operation is abnormal operation; and when the target risk factor is determined to be smaller than a preset risk factor threshold value, determining that the coupon fetching operation is a normal operation. The right of the user can be timely guaranteed through the mode.
Optionally, based on the above-mentioned wind control device for a coupon, the constructing unit 304 is configured to: acquiring corresponding equipment data of each equipment terminal when a user carries out coupon fetching operation in a historical time period; calculating the repeated occurrence times of the equipment data of each equipment terminal in a preset time range; and training a general neural network model based on the repeated occurrence times of the equipment data of each equipment terminal in a preset time range and a preset risk factor to obtain a trained wind control recognition model.
In the embodiment of the invention, the corresponding equipment data of each equipment terminal when the user carries out the coupon obtaining operation is obtained in the historical time period; calculating the repeated occurrence times of the equipment data of each equipment terminal in a preset time range; and training a universal neural network model based on the repeated occurrence times of the equipment data of each equipment terminal in a preset time range and a preset risk factor to obtain a trained wind control identification model. Processing the acquired equipment data corresponding to the equipment terminal by using a trained wind control identification model, outputting a corresponding risk factor, and determining whether the coupon getting operation performed by a user on the equipment terminal is normal operation or not by using the risk factor; if the target risk factor is determined to be larger than or equal to a preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation; and when the target risk factor is determined to be smaller than a preset risk factor threshold value, determining that the coupon fetching operation is a normal operation. The right of the user can be timely guaranteed through the mode.
Optionally, based on the above-described wind control device for a coupon, the wind control identification model 302 is specifically configured to:
the wind control identification model calculates the target times of repeated occurrence of the equipment data within a preset time range; and searching a target risk factor corresponding to the target times by the wind control identification model, and outputting the target risk factor corresponding to the equipment terminal.
In the embodiment of the invention, the acquired equipment data corresponding to the equipment terminal is processed based on the trained wind control identification model, and the corresponding risk factor is output, so that whether the coupon picking operation performed by a user on the equipment terminal is normal operation or not is determined through the risk factor; if the target risk factor is larger than or equal to a preset risk factor threshold value, determining that the coupon obtaining operation is abnormal operation; and when the target risk factor is determined to be smaller than a preset risk factor threshold value, determining that the coupon fetching operation is a normal operation. The right of the user can be timely guaranteed through the mode.
Based on the data processing apparatus disclosed in the embodiment of the present disclosure, the modules may be implemented by a hardware device including a processor and a memory. Specifically, the modules are stored in a memory as program units, and a processor executes the program units stored in the memory to implement text processing.
The processor comprises a kernel, and the kernel calls a corresponding program unit from the memory. The kernel can be set to be one or more, and mirror image security reinforcement is realized by adjusting kernel parameters.
The embodiment of the present disclosure provides a computer storage medium, where the storage medium includes a stored text processing program, where the program, when executed by a processor, implements the method for windmilling a coupon described in fig. 1.
The embodiment of the disclosure provides a processor, where the processor is configured to execute a program, where the program executes the method for controlling the coupon in fig. 1 when running.
The disclosed embodiment provides an electronic device, and as shown in fig. 4, the electronic device 40 in the disclosed embodiment may be a server, a PC, a PAD, a mobile phone, or the like.
The electronic device 40 comprises at least one processor 401 and at least one memory 402 connected to the processor 401, and a bus 403.
The processor 401 and the memory 402 communicate with each other via the bus 33. A processor 401 for executing programs stored in the memory 402.
A memory 402 for storing a program for at least: acquiring device data corresponding to a device terminal when a user carries out coupon getting operation on the device terminal; the equipment data is used as the input of a wind control identification model, the equipment data is processed based on the wind control identification model, and a target risk factor corresponding to the equipment terminal is output, wherein the wind control identification model is obtained by training the equipment data of the equipment terminal in a historical time period; and when the target risk factor is determined to be greater than or equal to a preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on an electronic device:
acquiring device data corresponding to a device terminal when a user carries out coupon obtaining operation on the device terminal; the equipment data is used as the input of a wind control identification model, the equipment data is processed based on the wind control identification model, and a target risk factor corresponding to the equipment terminal is output, wherein the wind control identification model is obtained by training the equipment data of the equipment terminal in a historical time period; and when the target risk factor is determined to be greater than or equal to a preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transmyedia) such as modulated data signals and carrier waves.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for windmilling coupons, the method comprising:
acquiring device data corresponding to a device terminal when a user carries out coupon getting operation on the device terminal;
the equipment data is used as the input of a wind control identification model, the equipment data is processed based on the wind control identification model, and a target risk factor corresponding to the equipment terminal is output, wherein the wind control identification model is obtained by training the equipment data of the equipment terminal in a historical time period;
and when the target risk factor is determined to be greater than or equal to a preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation.
2. The method of claim 1, further comprising:
judging whether the target risk factor is larger than a preset risk factor threshold value or not;
when the target risk factor is determined to be greater than or equal to a preset risk factor threshold value, determining that the coupon fetching operation is abnormal operation;
and when the target risk factor is smaller than a preset risk factor threshold value, determining that the coupon obtaining operation is a normal operation.
3. The method of claim 1, wherein training the wind control recognition model according to the device data of the device terminal in the historical time period comprises:
acquiring corresponding equipment data of each equipment terminal when a user carries out coupon fetching operation in a historical time period;
calculating the repeated occurrence times of the equipment data of each equipment terminal in a preset time range;
and training a general neural network model based on the repeated occurrence times of the equipment data of each equipment terminal in a preset time range and a preset risk factor to obtain a trained wind control recognition model.
4. The method of claim 1, wherein the processing the device data based on the wind control identification model and outputting a target risk factor corresponding to the device terminal comprises:
the wind control identification model calculates the target times of repeated occurrence of the equipment data within a preset time range;
and the wind control identification model searches for a target risk factor corresponding to the target times, and outputs the target risk factor corresponding to the equipment terminal.
5. A wind-controlled apparatus for coupons, said apparatus comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring device data corresponding to a device terminal when a user carries out coupon receiving operation on the device terminal;
the wind control identification model is used for taking the equipment data as the input of the wind control identification model, processing the equipment data based on the wind control identification model and outputting a target risk factor corresponding to the equipment terminal, and the wind control identification model is constructed according to the construction unit;
and the determining unit is used for determining that the coupon fetching operation is abnormal operation when the target risk factor is determined to be greater than or equal to a preset risk factor threshold value.
6. The apparatus of claim 5, wherein the determining unit is further configured to: judging whether the target risk factor is larger than a preset risk factor threshold value or not; and when the target risk factor is determined to be smaller than a preset risk factor threshold value, determining that the coupon fetching operation is a normal operation.
7. The apparatus of claim 5, wherein the construction unit is configured to: acquiring corresponding equipment data of each equipment terminal when a user carries out preferential roll obtaining operation in a historical time period; calculating the repeated occurrence times of the equipment data of each equipment terminal in a preset time range; and training a general neural network model based on the repeated occurrence times of the equipment data of each equipment terminal in a preset time range and a preset risk factor to obtain a trained wind control recognition model.
8. The apparatus of claim 5, wherein the wind control identification model is specifically configured to:
the wind control identification model calculates the target times of repeated occurrence of the equipment data within a preset time range; and searching a target risk factor corresponding to the target times by the wind control identification model, and outputting the target risk factor corresponding to the equipment terminal.
9. An electronic device, characterized in that the electronic device is configured to run a program, wherein the program when running performs the method of windmilling a coupon according to any of claims 1-4.
10. A computer storage medium, characterized in that the storage medium comprises a stored program, wherein the program, when running, controls a device on which the storage medium is located to execute a method of windmilling a coupon according to any one of claims 1-4.
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| CN202210442122.0A CN114662988A (en) | 2022-04-25 | 2022-04-25 | Discount roll wind control method and device, electronic equipment and computer storage medium |
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| CN202210442122.0A CN114662988A (en) | 2022-04-25 | 2022-04-25 | Discount roll wind control method and device, electronic equipment and computer storage medium |
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