CN117611239A - Training method of transaction flow prediction model, and transaction flow prediction method and device - Google Patents
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Abstract
The invention discloses a training method of a transaction flow prediction model and a transaction flow prediction method and device, belonging to the technical field of flow prediction, wherein the training method of the transaction flow prediction model comprises the following steps: extracting principal components of the sample transaction data to obtain sample principal component characteristics of the sample transaction data; performing feature coding on the principal component transaction features to obtain sample coding features; and training the transaction flow prediction model according to the sample coding characteristics. According to the invention, the accuracy of the transaction flow prediction model is improved by training the transaction flow prediction model, so that the accuracy of the transaction flow prediction is improved, and the transaction flow in the transaction peak period is controlled by personnel in advance according to the prediction result, thereby improving the stability and safety of the transaction system of the mechanism; meanwhile, the waiting time of customers in the trade peak period is shortened, and the customer satisfaction is improved.
Description
Technical Field
The present invention relates to the field of traffic prediction technologies, and in particular, to a training method of a transaction traffic prediction model, and a transaction traffic prediction method and apparatus.
Background
With the rapid development of internet finance, the transaction amount of institutions is gradually increased, and particularly during peak hours, the surge of transaction flow rate poses a great challenge to the stability and security of the institution transaction system.
The conventional transaction flow control method often cannot meet the increasingly complex demands, so it is important to develop a method capable of intelligently predicting the transaction flow.
Disclosure of Invention
The invention provides a training method of a transaction flow prediction model and a transaction flow prediction method and device, so as to improve the stability and safety of an institution transaction system in a transaction peak period.
According to an aspect of the present invention, there is provided a training method of a transaction flow prediction model, the method comprising:
extracting principal components of the sample transaction data to obtain sample principal component characteristics of the sample transaction data;
performing feature coding on the principal component transaction features to obtain sample coding features;
and training the transaction flow prediction model according to the sample coding characteristics.
According to an aspect of the present invention, there is provided a transaction flow prediction method, the method comprising:
acquiring transaction data to be predicted;
predicting transaction data to be predicted by adopting a transaction flow prediction model to obtain a target flow prediction result; the transaction flow prediction model is trained according to the training method of the transaction flow prediction model of any one of claims 1-5.
According to another aspect of the present invention, there is provided a training apparatus for a transaction flow prediction model, the apparatus comprising:
the principal component feature determining module is used for extracting principal components of the sample transaction data to obtain sample principal component features of the sample transaction data;
the sample coding feature determining module is used for carrying out feature coding on the main component transaction features to obtain sample coding features;
and the model training module is used for training the transaction flow prediction model according to the sample coding characteristics.
According to another aspect of the present invention, there is provided a transaction flow prediction device, the device comprising:
the transaction data acquisition module is used for acquiring transaction data to be predicted;
the prediction result determining module is used for predicting transaction data to be predicted by adopting a transaction flow prediction model to obtain a target flow prediction result; the transaction flow prediction model is trained according to the training method of the transaction flow prediction model of any one of claims 1-5.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the training method of the transaction flow prediction model, or the transaction flow prediction method, of any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a training method of a transaction flow prediction model, or a transaction flow prediction method, according to any of the embodiments of the present invention.
According to the technical scheme, the sample main component characteristics of the sample transaction data are obtained by extracting the main component of the sample transaction data; performing feature coding on the principal component transaction features to obtain sample coding features; and training the transaction flow prediction model according to the sample coding characteristics. According to the technical scheme, the accuracy of the transaction flow prediction model is improved by training the transaction flow prediction model, so that the accuracy of the transaction flow prediction is improved, and the transaction flow in the transaction peak period is controlled by personnel in advance according to the prediction result, so that the stability and the safety of the transaction system of the mechanism are improved; meanwhile, the waiting time of customers in the trade peak period is shortened, and the customer satisfaction is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a training method of a transaction flow prediction model according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a transaction flow prediction method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a training device for a transaction flow prediction model according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a transaction flow prediction device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing a training method of a transaction flow prediction model or a transaction flow prediction method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "original," "sample," and "object" in the description of the present invention and the claims and the above drawings are used for distinguishing similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described 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.
In addition, it should be noted that, in the technical scheme of the invention, the related processes of collection, storage, use, processing, transmission, provision, disclosure and the like of the original transaction data, the sample transaction data, the transaction data to be predicted and the like all conform to the regulations of the related laws and regulations, and do not violate the popular regulations.
Example 1
Fig. 1 is a flowchart of a training method of a transaction flow prediction model according to an embodiment of the present invention, where the method may be performed by a training device of the transaction flow prediction model, and the device may be implemented in hardware and/or software, and may be configured in an electronic device. As shown in fig. 1, the method includes:
s101, extracting principal components of sample transaction data to obtain sample principal component characteristics of the sample transaction data.
The sample transaction data is transaction data for training a transaction flow prediction model. Alternatively, the sample transaction data may refer to concurrent transaction data in a historic transaction rush hour institution transaction system. Optionally, the sample transaction data includes transaction time, transaction type, and behavioral data of the transaction party. A transaction party is a party that sends a transaction request to an institution transaction system. The transaction data of the transaction party includes, but is not limited to, the transaction frequency and the transaction time period of the transaction party. Sample principal component characteristics refer to characteristics that can characterize sample transaction data.
Specifically, principal component extraction can be performed on sample transaction data based on a principal component analysis algorithm to obtain sample principal component characteristics of the sample transaction data, so as to reduce data dimensions of the sample transaction data.
Optionally, the original transaction data may be preprocessed to obtain sample transaction data.
Wherein, the original transaction data can refer to historical transaction data which is not subjected to data processing; wherein, the historical transaction data refers to concurrent transaction data in a transaction system of a historical transaction peak period institution.
Specifically, the original transaction data may be preprocessed based on a data cleansing rule, to obtain sample transaction data. The data cleaning rule may be preset according to an actual service requirement, for example, the data cleaning rule may include a rule such as non-empty check, primary key repetition, missing value cleaning, illegal value cleaning, and data format check, which is not specifically limited in the embodiment of the present invention.
It can be appreciated that by preprocessing the original transaction data, extraneous data and duplicate data in the original transaction data can be removed, and processing of missing values in the original transaction data can be completed, thereby improving the data quality of the sample transaction data.
S102, performing feature coding on the principal component transaction features to obtain sample coding features.
The sample coding feature is a feature obtained by feature coding of the main component transaction feature; alternatively, the sample-encoding features may take the form of vectors or matrices.
For example, a one-hot encoding (one-hot encoding) mode may be used to perform feature encoding on the principal component transaction feature, so as to obtain a sample encoding feature. It should be noted that, at this time, the sample coding feature exists in the form of a binary vector.
S103, training a transaction flow prediction model according to the sample coding characteristics.
The transaction flow prediction model may be a transaction flow prediction model based on a support vector machine, or may be a transaction flow prediction model based on a neural network, which is not particularly limited in the embodiment of the present invention.
Specifically, at least one super parameter required by the transaction flow prediction model can be determined; determining at least one super-parameter combination according to the value list of the at least one super-parameter based on a grid search algorithm; taking the sample coding characteristic as the input of a transaction flow prediction model; and training the transaction flow prediction model by sequentially using different super-parameter combinations, and determining a target super-parameter combination which enables the performance of the transaction flow prediction model to be optimal in cross validation according to a preset performance evaluation index, so as to obtain the transaction flow prediction model with optimal performance. The performance evaluation index may include, among other things, accuracy and recall.
Optionally, time sequence feature extraction can be performed on the sample coding features to obtain time sequence features; performing attention operation on the sequence characteristics to obtain attention characteristics; predicting sample transaction data according to the attention features to obtain a sample flow prediction result; training the transaction flow prediction model according to the sample flow prediction result and the label data of the sample transaction data.
Where a time series characteristic refers to a characteristic that can reflect the time series between sample transaction data. The attention characteristic is a characteristic obtained by attention operation of the time sequence characteristic. The sample flow prediction result refers to a transaction flow prediction result obtained according to sample transaction data.
Specifically, a Long Short-Term Memory (LSTM) network can be used for extracting time sequence features of sample coding features to obtain time sequence features; performing attention operation on the time sequence characteristics by adopting an attention mechanism to obtain attention characteristics; inputting the attention characteristic into a fully-connected neural network, and predicting sample transaction data through the fully-connected neural network to obtain a sample flow prediction result; determining a prediction loss according to the sample flow prediction result and the label data of the sample transaction data; and training a transaction flow prediction model according to the predicted loss.
Optionally, determining a prediction loss according to the sample flow prediction result and the label data of the sample transaction data; training the transaction flow prediction model according to the predicted loss may be: based on a preset loss function, determining a predicted loss according to a sample flow prediction result and label data of sample transaction data; training the transaction flow prediction model by adopting the prediction loss until the prediction loss reaches a preset range or the training iteration number reaches a preset number, stopping training the transaction flow prediction model, and taking the transaction flow prediction model when training is stopped as a final transaction flow prediction model. The preset loss function, the preset range and the preset times can be preset according to actual service requirements, and the embodiment of the invention is not particularly limited.
It can be understood that the gradient vanishing problem caused by the increase of the network layer number and the time lapse can be eliminated by learning the internal transformation rule of the sample coding characteristics through the long and short memory network; by determining different weights of the time sequence features through the attention mechanism, important information in the time sequence features can be amplified, and irrelevant information in the time sequence features can be reduced or ignored, so that the obtained sample flow prediction result is more accurate.
According to the technical scheme, the sample main component characteristics of the sample transaction data are obtained by extracting the main component of the sample transaction data; performing feature coding on the principal component transaction features to obtain sample coding features; and training the transaction flow prediction model according to the sample coding characteristics. According to the technical scheme, the accuracy of the transaction flow prediction model is improved by training the transaction flow prediction model, so that the accuracy of the transaction flow prediction is improved, and the transaction flow in the transaction peak period is controlled by personnel in advance according to the prediction result, so that the stability and the safety of the transaction system of the mechanism are improved; meanwhile, the waiting time of customers in the trade peak period is shortened, and the customer satisfaction is improved.
Example two
Fig. 2 is a flow chart of a transaction flow prediction method provided in a second embodiment of the present invention, where the present embodiment is applicable to predicting transaction flow of an institution transaction system in a transaction peak period, the method may be performed by a transaction flow prediction device, and the device may be implemented in a hardware and/or software form and may be configured in an electronic device. As shown in fig. 2, the method includes:
s201, obtaining transaction data to be predicted.
The transaction data to be predicted refers to concurrent transaction data in a transaction system of a current transaction peak period institution. Alternatively, the transaction data to be predicted may include transaction time, transaction type, and behavioral data of the transaction party. A transaction party is a party that sends a transaction request to an institution transaction system. The transaction data of the transaction party includes, but is not limited to, the transaction frequency and the transaction time period of the transaction party.
Specifically, the data of the transaction to be predicted can be obtained from the institution transaction system in the current transaction peak period through a crawler technology or a mirror technology.
Optionally, preprocessing can be performed on the transaction data to be predicted, irrelevant data and repeated data in the transaction data to be predicted are removed, and processing of missing values in the transaction data to be predicted is completed, so that the preprocessed transaction data to be predicted is obtained, and the data quality of the transaction data to be predicted is improved.
S202, predicting transaction data to be predicted by adopting a transaction flow prediction model to obtain a target flow prediction result; the transaction flow prediction model is trained according to the training method of the transaction flow prediction model of any one of claims 1-5.
The target flow prediction result refers to a transaction flow prediction result obtained according to transaction data to be predicted.
Specifically, a principal component analysis layer in a transaction flow prediction model is adopted to extract principal components of transaction data to be predicted, so as to obtain target principal component characteristics of the transaction data to be predicted, and reduce the data dimension of the transaction data to be predicted; performing feature coding on the target main component features through a coding layer in the transaction flow prediction model to obtain target coding features; performing time sequence feature extraction on target coding features through a long and short memory network layer in the transaction flow prediction model to obtain target time sequence features; performing attention operation on the target time sequence characteristics through an attention layer in the transaction flow prediction model to obtain target attention characteristics; and processing the target attention characteristic through a prediction layer in the transaction flow prediction model to obtain a target flow prediction result.
Wherein the target principal component features refer to features for characterizing transaction data to be predicted. The target coding feature is a feature obtained by feature coding of the target principal component transaction feature. The target timing characteristics refer to characteristics that can reflect the chronological order between transaction data to be predicted. The target attention characteristic is a characteristic obtained by attention operation of the target time sequence characteristic. It should be noted that the target coding feature may take the form of a vector or a matrix, for example, the target coding feature may take the form of a binary vector.
According to the technical scheme, the transaction data to be predicted is obtained; predicting transaction data to be predicted by adopting a transaction flow prediction model to obtain a target flow prediction result; the transaction flow prediction model is trained according to the training method of the transaction flow prediction model of any one of claims 1-5. According to the technical scheme, the transaction data to be predicted is predicted based on the trained transaction flow prediction model, so that a target flow prediction result is obtained, a more accurate target flow prediction result is provided for the relevant personnel of the mechanism, and the relevant personnel of the mechanism can take measures in advance according to the target flow prediction result to control the transaction flow in the current transaction peak period, so that the stability and the safety of a transaction system of the mechanism are improved; meanwhile, the waiting time of customers in the trade peak period is shortened, and the customer satisfaction is improved.
Example III
Fig. 3 is a schematic structural diagram of a training device for a transaction flow prediction model according to a third embodiment of the present invention, where the present embodiment is applicable to a situation where the transaction flow prediction model is optimized, and the device may be implemented in a form of hardware and/or software, and may be configured in an electronic device. As shown in fig. 3, the apparatus includes:
the principal component feature determining module 301 is configured to perform principal component extraction on sample transaction data, so as to obtain sample principal component features of the sample transaction data;
the sample coding feature determining module 302 is configured to perform feature coding on the principal component transaction feature to obtain a sample coding feature;
the model training module 303 is configured to train the transaction flow prediction model according to the sample coding feature.
According to the technical scheme, the sample main component characteristics of the sample transaction data are obtained by extracting the main component of the sample transaction data; performing feature coding on the principal component transaction features to obtain sample coding features; and training the transaction flow prediction model according to the sample coding characteristics. According to the technical scheme, the accuracy of the transaction flow prediction model is improved by training the transaction flow prediction model, so that the accuracy of the transaction flow prediction is improved, and the transaction flow in the transaction peak period is controlled by personnel in advance according to the prediction result, so that the stability and the safety of the transaction system of the mechanism are improved; meanwhile, the waiting time of customers in the trade peak period is shortened, and the customer satisfaction is improved.
Optionally, the model training module 303 includes:
the time sequence feature determining unit is used for extracting time sequence features of the sample coding features to obtain the time sequence features;
the attention characteristic determining unit is used for carrying out attention operation on the time sequence characteristics to obtain attention characteristics;
the sample flow prediction result determining unit is used for predicting sample transaction data according to the attention characteristics to obtain a sample flow prediction result;
the model training unit is used for training the transaction flow prediction model according to the sample flow prediction result and the label data of the sample transaction data.
Optionally, the model training unit is specifically configured to:
determining a prediction loss according to the sample flow prediction result and the label data of the sample transaction data;
and training a transaction flow prediction model according to the predicted loss.
Optionally, the apparatus further comprises:
and the sample transaction data determining module is used for preprocessing the original transaction data to obtain sample transaction data.
Optionally, the sample transaction data includes transaction time, transaction type, and behavioral data of the transaction party.
The training device for the transaction flow prediction model provided by the embodiment of the invention can execute the training method for the transaction flow prediction model provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the training method for each transaction flow prediction model.
Example IV
Fig. 4 is a schematic structural diagram of a transaction flow prediction device according to a fourth embodiment of the present invention, where the embodiment is applicable to predicting transaction flow of an institution transaction system in a transaction peak period, and the device may be implemented in hardware and/or software, and may be configured in an electronic device. As shown in fig. 4, the apparatus includes:
a transaction data acquisition module 401, configured to acquire transaction data to be predicted;
the prediction result determining module 402 is configured to predict transaction data to be predicted by using a transaction flow prediction model, so as to obtain a target flow prediction result; the transaction flow prediction model is trained according to the training method of the transaction flow prediction model of any one of claims 1-5.
According to the technical scheme, the transaction data to be predicted is obtained; predicting transaction data to be predicted by adopting a transaction flow prediction model to obtain a target flow prediction result; the transaction flow prediction model is trained according to the training method of the transaction flow prediction model of any one of claims 1-5. According to the technical scheme, the transaction data to be predicted is predicted based on the trained transaction flow prediction model, so that a target flow prediction result is obtained, a more accurate target flow prediction result is provided for the relevant personnel of the mechanism, and the relevant personnel of the mechanism can take measures in advance according to the target flow prediction result to control the transaction flow in the current transaction peak period, so that the stability and the safety of a transaction system of the mechanism are improved; meanwhile, the waiting time of customers in the trade peak period is shortened, and the customer satisfaction is improved.
The transaction flow prediction device provided by the embodiment of the invention can execute the transaction flow prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the transaction flow prediction methods.
Example five
Fig. 5 shows a schematic diagram of the structure 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. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, 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, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various 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, etc.; 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, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a training method of a transaction flow prediction model, or a transaction flow prediction method.
In some embodiments, the training method of the transaction flow prediction model, or the transaction flow prediction method, may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the 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 ROM12 and/or the communication unit 19. When the computer program is loaded into RAM13 and executed by processor 11, one or more steps of the training method of the transaction flow prediction model, or the transaction flow prediction method described above, may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the training method of the transaction flow prediction model, or the transaction flow prediction method, in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On 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, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out 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 implemented. The computer program may execute entirely on the 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. The 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 portable 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) through 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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. The client and server are typically 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 hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for training a traffic prediction model, comprising:
extracting principal components of sample transaction data to obtain sample principal component characteristics of the sample transaction data;
performing feature coding on the principal component transaction features to obtain sample coding features;
and training a transaction flow prediction model according to the sample coding characteristics.
2. The method of claim 1, wherein training a transaction flow prediction model based on the sample coding features comprises:
extracting time sequence characteristics from the sample coding characteristics to obtain the time sequence characteristics;
performing attention operation on the time sequence characteristics to obtain attention characteristics;
predicting the sample transaction data according to the attention characteristics to obtain a sample flow prediction result;
training a transaction flow prediction model according to the sample flow prediction result and the label data of the sample transaction data.
3. The method of claim 2, wherein training the transaction flow prediction model based on the sample flow prediction result and the tag data of the sample transaction data comprises:
determining a prediction loss according to the sample flow prediction result and the label data of the sample transaction data;
and training a transaction flow prediction model according to the prediction loss.
4. The method according to claim 1, wherein the method further comprises:
preprocessing the original transaction data to obtain sample transaction data.
5. The method of any of claims 1-4, wherein the sample transaction data includes transaction time, transaction type, and behavioral data of a transaction party.
6. A transaction flow prediction method, comprising:
acquiring transaction data to be predicted;
predicting the transaction data to be predicted by adopting a transaction flow prediction model to obtain a target flow prediction result; the transaction flow prediction model is trained according to the training method of the transaction flow prediction model of any one of claims 1-5.
7. A training device for a traffic prediction model, comprising:
the main component feature determining module is used for extracting main components of sample transaction data to obtain sample main component features of the sample transaction data;
the sample coding feature determining module is used for carrying out feature coding on the main component transaction features to obtain sample coding features;
and the model training module is used for training the transaction flow prediction model according to the sample coding characteristics.
8. A transaction flow prediction device, comprising:
the transaction data acquisition module is used for acquiring transaction data to be predicted;
the prediction result determining module is used for predicting the transaction data to be predicted by adopting a transaction flow prediction model to obtain a target flow prediction result; the transaction flow prediction model is trained according to the training method of the transaction flow prediction model of any one of claims 1-5.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of training the transaction flow prediction model of any one of claims 1-5, or the method of transaction flow prediction of claim 6.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of training the transaction flow prediction model of any one of claims 1-5 or the method of transaction flow prediction of claim 6.
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