CN115545341A - Event prediction method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses an event prediction method, an event prediction device, electronic equipment and a storage medium. The method comprises the following steps: aiming at a target object which does not handle a credit card, acquiring target information which is authorized to be used by the target object and is related to credit card handling and a trained event prediction model, wherein the event prediction model is used for predicting the occurrence probability of a credit card handling event; inputting the target information into an event prediction model, and predicting whether a target object transacts a credit card or not according to the occurrence probability output by the event prediction model; the event prediction model comprises a depth convolution layer and a point-by-point convolution layer. According to the technical scheme of the embodiment of the invention, whether the target object is inclined to handle the credit card or not can be quickly and accurately predicted, so that the potential credit card object is excavated, and the credit card business is promoted aiming at the credit card object, which is beneficial to realizing the effective promotion of the credit card business.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to an event prediction method, an event prediction device, electronic equipment and a storage medium.
Background
For most credit card issuers in the market at present, the popularization of the credit card service is mainly realized by schemes such as advertisement putting of television or media, specific market publicity, activity promotion and the like.
However, the popularization effect of the schemes is not good, and needs to be improved.
Disclosure of Invention
The embodiment of the invention provides an event prediction method, an event prediction device, electronic equipment and a storage medium, which are used for realizing accurate prediction of a credit card transacting object so as to realize effective popularization of credit card business.
According to an aspect of the present invention, there is provided an event prediction method, which may include:
aiming at a target object which does not handle a credit card, acquiring target information which is authorized to be used by the target object and is related to the handling of the credit card and a trained event prediction model, wherein the event prediction model is used for predicting the occurrence probability of a credit card handling event;
inputting the target information into an event prediction model, and predicting whether the target object transacts the credit card or not according to the occurrence probability output by the event prediction model;
the event prediction model comprises a depth convolution layer and a point-by-point convolution layer.
According to another aspect of the present invention, there is provided an event prediction apparatus, which may include:
the event prediction model acquisition module is used for acquiring target information which is authorized to be used by a target object and is related to credit card transaction and a trained event prediction model aiming at the target object which does not transact the credit card, wherein the event prediction model is used for predicting the occurrence probability of credit card transaction events;
the event prediction module is used for inputting the target information into the event prediction model and predicting whether the target object transacts the credit card or not according to the occurrence probability output by the event prediction model;
the event prediction model comprises a depth convolution layer and a point-by-point convolution layer.
According to another aspect of the present invention, there is provided an electronic device, which may include:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor, when executed, to implement a method of event prediction as provided by any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium having stored thereon computer instructions for causing a processor to execute a method for event prediction provided by any of the embodiments of the present invention.
According to the technical scheme of the embodiment of the invention, aiming at a target object which does not handle a credit card, target information which is authorized to be used by the target object and related to credit card handling and a trained event prediction model for predicting the occurrence probability of a credit card handling event are obtained, wherein the event prediction model comprises a deep convolutional layer and a point-by-point convolutional layer, and is a light-weight network model which is convenient to deploy to external equipment and has high calculation speed; and inputting the target information into the event prediction model, and predicting whether the target object transacts the credit card or not according to the occurrence probability output by the event prediction model. By the technical scheme, whether the target object is inclined to handle the credit card or not can be quickly and accurately predicted, so that potential credit card objects are excavated, and credit card businesses are promoted aiming at the credit card objects.
It should be understood that the statements in this section do not necessarily identify key or critical features of any embodiment of the present invention, nor do they necessarily limit the scope of the present invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for event prediction according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a convolution module in an event prediction method according to an embodiment of the present invention;
FIG. 3 is a flow diagram of another event prediction method provided in accordance with an embodiment of the present invention;
fig. 4 is a block diagram of an event prediction apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the event prediction method according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. The cases of "target", "original", etc. are similar and will not be described in detail herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be emphasized that the technical solutions of the present application, such as obtaining, storing, using, and processing of data, all conform to relevant regulations of national laws and regulations.
Fig. 1 is a flowchart of an event prediction method provided in an embodiment of the present invention. The embodiment can be applied to the case of predicting the credit card transaction object (namely, whether the prediction target object has the credit card transaction requirement), and is particularly applied to the case of predicting the credit card transaction object based on the lightweight network model. The method can be executed by the event prediction device provided by the embodiment of the invention, the device can be implemented by software and/or hardware, the device can be integrated on electronic equipment, and the electronic equipment can be various user terminals or servers.
Referring to fig. 1, the method of the embodiment of the present invention specifically includes the following steps:
s110, aiming at target objects which are not transacted with credit cards, obtaining target information which is authorized to be used by the target objects and is related to credit card transaction, and a trained event prediction model comprising a deep convolutional layer and a point-by-point convolutional layer, wherein the event prediction model is used for predicting the occurrence probability of credit card transaction events.
The target object may be understood as an object which does not transact a credit card, and the target information may be understood as information which the target object authorizes to use and is related to transacting the credit card, and specifically may be information which the target object authorizes to use and recommends credit card services to the target object. The event prediction model can be understood as a trained model which can be used for predicting the occurrence probability of credit card transaction events, and is combined with the embodiment of the invention, in particular the model which can be used for predicting the probability of credit card transaction of the target object.
In general, in order to improve the classification accuracy of a network model, the number of network layers is generally increased. However, an increase in the number of network layers will certainly lead to a multiplication of the number of parameters and thus to a multiplication of the amount of computations. Therefore, in order to facilitate subsequent deployment and other applications of the network model, a lightweight network model is provided, so that the purpose of lightweight is achieved while a certain classification accuracy of the network model is maintained. In the embodiment of the invention, the convolution module in the event prediction model comprises a deep convolution (Depthwise convolution) layer and a point-by-point convolution (Pointwise convolution) layer, and the parameter quantity of the convolution module is greatly reduced compared with the parameter quantity of a common convolution module, so that the corresponding calculated quantity is reduced, and the event prediction model is a light network model which is convenient to deploy to external equipment, and is beneficial to popularization of service personnel for transacting services of credit cards.
In practical application, optionally, the event prediction model can be built based on a MobileNet network. Still alternatively, the convolution module in the event prediction model includes a depth convolution layer, a Batch Normalization (BN) layer (here, denoted by BN 1), an active layer (here, denoted by ReLU 1), and a point convolution layer, which are connected in sequence, and further includes a Batch Normalization layer (here, denoted by BN 2) connected after the point convolution layer and an active layer (here, denoted by ReLU 2) connected after BN2, as shown in fig. 2 for example.
And S120, inputting the target information into the event prediction model, and predicting whether the target object transacts the credit card according to the occurrence probability output by the event prediction model.
The target information is input into the event prediction model, and since the event prediction model can be used for predicting the occurrence probability of the credit card transaction event, whether the target object is inclined to handle the credit card can be predicted according to the occurrence probability output by the event prediction model, and for example, the target object is inclined to handle the credit card can be determined under the condition that the occurrence probability is greater than a preset probability threshold.
On the basis, optionally, for the predicted target object which is prone to transacting the credit card, professional knowledge explanation about the credit card can be provided for the target object, so that the target object is prompted to transact the credit card, and therefore the effect of effective popularization of the credit card business is achieved.
According to the technical scheme of the embodiment of the invention, aiming at a target object which does not handle a credit card, target information which is authorized to be used by the target object and related to credit card handling and a trained event prediction model for predicting the occurrence probability of a credit card handling event are obtained, wherein the event prediction model comprises a deep convolutional layer and a point-by-point convolutional layer, and is a light-weight network model which is convenient to deploy to external equipment and has high calculation speed; and inputting the target information into the event prediction model, and predicting whether the target object handles the credit card or not according to the occurrence probability output by the event prediction model. According to the technical scheme, whether the target object tends to handle the credit card or not can be quickly and accurately predicted, so that potential credit card objects are excavated, and then credit card businesses are popularized aiming at the credit card objects.
An optional technical solution, after obtaining target information related to authorized use and credit card transaction of the target object, the event prediction method may further include: performing code conversion on the target information according to the information type of the target information; inputting target information into an event prediction model, comprising: the transcoded target information is input to an event prediction model. For example, in the case where the information type of the target information is an enumeration type, the target information may be transcoded based on one-hot (one-hot) encoding; in the case where the information type of the target information is a numerical range, transcoding the target information based on a preset numerical range; etc., and are not specifically limited herein. According to the technical scheme, before the target information is input into the event prediction model, the target information is subjected to code conversion, so that the event prediction model can better understand the target information, and the prediction accuracy of the event prediction model is improved.
Fig. 3 is a flowchart of another event prediction method provided in an embodiment of the present invention. The present embodiment is optimized based on the above technical solutions. In this embodiment, optionally, the event prediction model is obtained by pre-training through the following steps: acquiring sample information which is used by the sample object and is related to credit card transaction, and marking information, wherein the marking information is used for indicating whether the sample object transacts the credit card or not; taking the sample objects and the labeling information as a group of training samples; and training the original prediction model based on a plurality of groups of training samples to obtain an event prediction model, wherein the model structure of the original prediction model is the same as that of the event prediction model. The same or corresponding terms as those in the above embodiments are not explained in detail herein.
Referring to fig. 3, the method of this embodiment may specifically include the following steps:
s210, obtaining sample information which is used by the sample object and is related to credit card transaction, and marking information, wherein the marking information is used for indicating whether the sample object transacts the credit card or not.
The sample object may be an object that has transacted a credit card, or an object that has not transacted a credit card, and is not specifically limited herein. Sample information may be understood as information that the sample object authorizes use and is relevant to credit card transactions, in particular information that the sample object authorizes use and recommends credit card services to itself. The annotation information may be used to indicate whether the sample object has processed a credit card.
And S220, taking the sample object and the labeling information as a group of training samples.
And S230, training the original prediction model based on a plurality of groups of training samples to obtain an event prediction model, wherein the original prediction model comprises a depth convolution layer and a point-by-point convolution layer, and the original prediction model is used for predicting the occurrence probability of credit card transaction events.
The original prediction model may be understood as a model to be trained for predicting the probability of occurrence of a credit card transaction event. The model structure of the original prediction model is the same as that of the event prediction model, that is, the convolution module in the original prediction model includes a depth convolution layer and a point-by-point convolution layer, and is a lightweight network model.
And training the original prediction model based on a plurality of groups of training samples to obtain an event prediction model. In practical application, optionally, a Cross Entropy (Cross Entropy) loss function can be used as a loss function, an Adam optimizer can be used as an optimizer and applied to a model training process, the Cross Entropy loss function and the Adam optimizer can be combined, an original prediction model is trained based on multiple groups of training samples, an event prediction model is obtained, and therefore the prediction accuracy of the trained event prediction model is guaranteed.
S240, aiming at the target object which does not transact the credit card, acquiring target information which is authorized to be used by the target object and is related to credit card transaction.
And S250, inputting the target information into the event prediction model, and predicting whether the target object transacts the credit card or not according to the occurrence probability output by the event prediction model.
According to the technical scheme of the embodiment of the invention, the sample information which is used by the sample object and related to credit card transaction and the marking information which is used for indicating whether the sample object transacts the credit card are used as a group of training samples, and then the original prediction model comprising the deep convolutional layer and the point-by-point convolutional layer is trained on the basis of a plurality of groups of training samples, so that the event prediction model can be obtained through effective training.
An optional technical solution, after obtaining sample information related to authorized use of a sample object and credit card transaction, the event prediction method may further include: performing code conversion on the sample information according to the information type of the sample information; taking the sample objects and the labeling information as a set of training samples, including: and taking the coded and converted sample information and the marking information as a group of training samples. For example, in the case where the information type of the sample information is an enumeration type, the sample information may be code-converted based on one-hot (one-hot) coding; under the condition that the information type of the sample information is a numerical range, carrying out code conversion on the sample information based on a preset numerical gear; etc., and are not specifically limited herein. According to the technical scheme, before the sample information is input into the original prediction model, the sample information is subjected to code conversion, so that the original prediction model can better understand the sample information, and the model training effect is improved.
Fig. 4 is a block diagram of an event prediction apparatus provided in an embodiment of the present invention, which is configured to execute an event prediction method provided in any of the above embodiments. The device and the event prediction method of the above embodiments belong to the same inventive concept, and details that are not described in detail in the embodiments of the event prediction device may refer to the embodiments of the event prediction method. Referring to fig. 4, the apparatus may specifically include: an event prediction model acquisition module 310 and an event prediction module 320.
The event prediction model obtaining module 310 is used for obtaining target information which is authorized to be used by a target object and is related to credit card transaction and a trained event prediction model aiming at the target object which does not transact a credit card, wherein the event prediction model is used for predicting the occurrence probability of a credit card transaction event;
the event prediction module 320 is used for inputting the target information into the event prediction model and predicting whether the target object transacts the credit card according to the occurrence probability output by the event prediction model;
the event prediction model comprises a depth convolution layer and a point-by-point convolution layer.
Optionally, on the basis of the above apparatus, the apparatus may further include:
the target information code conversion module is used for carrying out code conversion on the target information according to the information type of the target information after the target information which is authorized to be used by the target object and is related to credit card transaction is obtained;
an event prediction module 320 comprising:
and a target information input unit for inputting the code-converted target information to the event prediction model.
On the basis of any one of the above technical solutions, optionally, the event prediction model includes a convolution module, and the convolution module includes a depth convolution layer, a batch normalization layer, an activation layer, and a point-by-point convolution layer, which are sequentially connected.
Optionally, the event prediction model is built based on a MobileNet network.
Optionally, the event prediction model is obtained by pre-training through the following modules:
the system comprises a labeling information acquisition module and a labeling information acquisition module, wherein the labeling information acquisition module is used for acquiring sample information which is used for authorizing the sample object to use and is related to the transaction of a credit card, and the labeling information is used for indicating whether the sample object transacts the credit card or not;
a training sample obtaining module for using the sample object and the labeling information as a group of training samples;
and the model training module is used for training the original prediction model based on a plurality of groups of training samples to obtain an event prediction model, wherein the model structure of the original prediction model is the same as that of the event prediction model.
On this basis, optionally, the event prediction apparatus further includes:
the sample information transcoding module is used for transcoding the sample information according to the information type of the sample information after obtaining the sample information which is used by the sample object and related to credit card transaction;
a training sample obtaining module specifically configured to:
and taking the sample information after code conversion and the marking information as a group of training samples.
Optionally, the model training module is specifically configured to:
and training the original prediction model based on a plurality of groups of training samples by combining a cross entropy loss function and an Adam optimizer to obtain an event prediction model.
According to the event prediction device provided by the embodiment of the invention, the event prediction model acquisition module is used for acquiring the target information which is authorized to be used by the target object and related to credit card transaction and the trained event prediction model for predicting the occurrence probability of the credit card transaction event, wherein the event prediction model comprises a deep convolutional layer and a point-by-point convolutional layer, and is a light-weight network model which is easy to deploy to external equipment and has high calculation speed; and then, inputting the target information into the event prediction model through the event prediction module, and predicting whether the target object transacts the credit card according to the occurrence probability output by the event prediction model. The device can quickly and accurately predict whether the target object tends to handle the credit card, so that a potential credit card object is excavated, and then the credit card business is promoted aiming at the credit card object.
The event prediction device provided by the embodiment of the invention can execute the event prediction method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, in the embodiment of the event prediction apparatus, the included units and modules are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the event prediction method.
In some embodiments, the event prediction method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the event prediction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the event prediction method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the Internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An event prediction method, comprising:
for a target object which does not transact a credit card, acquiring target information which is authorized to be used by the target object and is related to credit card transaction, and a trained event prediction model, wherein the event prediction model is used for predicting the occurrence probability of a credit card transaction event;
inputting the target information into the event prediction model, and predicting whether the target object transacts a credit card or not according to the occurrence probability output by the event prediction model;
wherein the event prediction model comprises a depth convolution layer and a point-by-point convolution layer.
2. The method of claim 1, wherein after obtaining the target information that the target object authorizes use and is relevant to credit card transactions, further comprising:
performing code conversion on the target information according to the information type of the target information;
the inputting the target information to the event prediction model comprises:
inputting the target information after transcoding to the event prediction model.
3. The method of claim 1 or 2, wherein the event prediction model comprises a convolution module comprising the depth convolution layer, batch normalization layer, activation layer, and the point-by-point convolution layer connected in sequence.
4. The method according to claim 1 or 2, wherein the event prediction model is built based on a MobileNet network.
5. The method of claim 1, wherein the event prediction model is pre-trained by:
acquiring sample information which is used by a sample object for authorization and is related to credit card transaction, and marking information, wherein the marking information is used for indicating whether the sample object transacts a credit card or not;
taking the sample object and the labeling information as a set of training samples;
and training an original prediction model based on a plurality of groups of training samples to obtain the event prediction model, wherein the model structure of the original prediction model is the same as that of the event prediction model.
6. The method of claim 5, wherein after obtaining sample information regarding authorized use of the sample object and credit card transactions, further comprising:
performing code conversion on the sample information according to the information type of the sample information;
the taking the sample object and the labeling information as a set of training samples comprises:
and using the sample information and the labeling information after code conversion as a group of training samples.
7. The method of claim 5, wherein training an original prediction model based on the plurality of sets of training samples to obtain the event prediction model comprises:
and training an original prediction model based on a plurality of groups of training samples by combining a cross entropy loss function and an Adam optimizer to obtain the event prediction model.
8. An event prediction apparatus, comprising:
the event prediction model acquisition module is used for acquiring target information which is authorized to be used by a target object and is related to credit card transaction and a trained event prediction model aiming at the target object which does not transact a credit card, wherein the event prediction model is used for predicting the occurrence probability of a credit card transaction event;
the event prediction module is used for inputting the target information into the event prediction model and predicting whether the target object transacts a credit card or not according to the occurrence probability output by the event prediction model;
wherein the event prediction model comprises a depth convolution layer and a point-by-point convolution layer.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the event prediction method of any one of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the method of event prediction according to any one of claims 1-7 when executed.
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