CN112085499A - Processing method and device of quota account data - Google Patents
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Abstract
The invention discloses a method and a device for processing quota account data, wherein the method comprises the following steps: acquiring quota account data needing prediction, wherein the account data comprises: the system information and the predetermined date are included, and the system comprises one of the following: a small amount system and an online banking system; inputting account data into the trained neural network model to output account prediction data of the quota account on a predetermined date, wherein the account prediction data comprises: account initiation amount data and account receiving amount data in a preset period granularity, and training a neural network model based on historical account data of a date label to which a preset date belongs; and managing the quota account according to the account initiating amount data and the account receiving amount data. The invention can accurately control the liquidity of the net debit quota account of the bank, thereby effectively improving the efficiency of business processing.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method and a device for processing quota account data.
Background
Second generation payment systems provide a wide variety of flexible liquidity management mechanisms, such as a fund pool, for system participants (e.g., various commercial banks), but these liquidity management mechanisms are not fully open to participants due to a variety of factors. The net debit limit account opened by the participant in the payment system is mainly used for the service clearing use of the participant in a micropayment system and a cross bank clearing system on the network. The business processing logic of the petty payment system and the online cross-bank clearing system is to carry out rolling difference on batch business at regular time, and clear the business after obtaining rolling difference net amount. In practice, insufficient net debit limits may result in a batch of traffic not being properly cleared, affecting traffic processing efficiency. There is a need for more accurate control of participant net debit account liquidity.
However, there is currently no solution that has more accurate control over participant net debit limit account liquidity.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for processing data of a quota account to solve at least one of the above-mentioned problems.
According to a first aspect of the invention, there is provided a method of processing quota account data, the method comprising:
acquiring quota account data needing prediction, wherein the account data comprises: the system information and the predetermined date, the system comprises one of the following: a small amount system and an online banking system;
inputting the account data into a trained neural network model to output account prediction data of the quota account on the predetermined date, wherein the account prediction data comprises: account initiation amount data and account receiving amount data in a preset period granularity, wherein the neural network model is trained on historical account data of a date label to which the preset date belongs;
and managing the quota account according to the account initiating amount data and the account receiving amount data.
According to a second aspect of the present invention, there is provided an apparatus for processing quota account data, the apparatus comprising:
the prediction data acquisition unit is used for acquiring quota account data needing prediction, and the account data comprises: the system information and the predetermined date, the system comprises one of the following: a small amount system and an online banking system;
a prediction unit, configured to input the account data into a trained neural network model to output account prediction data of the quota account on the predetermined date, where the account prediction data includes: account initiation amount data and account receiving amount data in a preset period granularity, wherein the neural network model is trained on historical account data of a date label to which the preset date belongs;
and the management unit is used for managing the quota account according to the account initiating amount data and the account receiving amount data.
According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method when executing the program.
According to a fourth aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
According to the technical scheme, the acquired quota account data needing to be predicted is input into the neural network model, and the neural network model can predict the account data of the quota account on the preset date, so that the flow condition of the amount data of the quota account can be further predicted, the flow of the bank net debit quota account can be conveniently and accurately controlled, and the efficiency of business processing can be effectively improved.
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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow diagram of a method of processing quota account data according to an embodiment of the invention;
FIG. 2 is a detailed flow diagram of quota account data prediction according to an embodiment of the present invention;
FIG. 3 is a flow diagram of data exploration and preprocessing, according to an embodiment of the invention;
fig. 4 is a block diagram of the structure of a limited-account data processing apparatus according to an embodiment of the present invention;
fig. 5 is a detailed configuration block diagram of a limit account data processing apparatus according to an embodiment of the present invention;
fig. 6 is a schematic block diagram of a system configuration of an electronic apparatus 600 according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
The lack of net debit limit funds may result in a batch of traffic not being properly cleared, thereby affecting the efficiency of traffic processing. Therefore, there is a need for more accurate control of bank net debit account liquidity. However, there is currently no solution for more accurate control of the liquidity of the net debit limit account for the bank. Based on the above, the embodiment of the invention provides a processing scheme of quota account data, which can predict the data of the bank net debit quota account, so that the liquidity of the bank net debit quota account can be accurately mastered, and the service processing efficiency is improved. Embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a processing method of limit account data according to an embodiment of the present invention, as shown in fig. 1, the method including:
In actual practice, dates having similar data characteristics within a certain period (e.g., one week, one month, etc.) may be set as the same date tag according to a predetermined rule. The predetermined rule may be a rule for setting a date label, and may be determined according to actual situations.
For example, taking banking as an example, if the specific period is one week, then each saturday has similar business characteristics to the last saturday, each monday has similar characteristics to the previous monday, and the same day of the week; while the traffic characteristics on tuesday through friday are all similar. Therefore, the data of saturday, sunday and monday can be spliced into a group belonging to the same date tag, for example, the date tag is set as data _ weekend, the data of tuesday to friday is spliced into a group, and the date tag is set as data _ weekday.
And 103, managing the quota account according to the account initiating amount data and the account receiving amount data.
In actual operation, determining account net amount data according to the account initiating amount data and the account receiving amount data; and managing the quota account according to the account initiating amount data, the account receiving amount data and the account net amount data.
The acquired quota account data needing to be predicted is input into the neural network model, and the neural network model can predict the account data of the quota account on the preset date, so that the flow condition of the amount data of the quota account can be further predicted, the mobility of the bank net debit quota account can be conveniently and accurately mastered, and the efficiency of business processing can be effectively improved.
Preferably, the neural network model may be an LSTM (Long Short-Term Memory network) model.
In practical operation, the training process of the neural network model comprises the following steps (1) to (3):
(1) acquiring historical account data of the same date label as the preset date in the step 102, wherein the historical account data comprises: date, date label, historical account origination amount data and historical account receipt amount data at the predetermined period granularity (e.g., 15 minutes).
(2) And preprocessing the historical account data, and classifying the preprocessed historical account data into training set data and prediction set data.
The pretreatment here includes: performing missing data filling processing on the historical account data, for example, filling missing data with a value of 0; and then, normalizing the historical account data after the missing data filling processing.
(3) And training the neural network model according to the training set data based on a grid search method, and then verifying the trained neural network model according to the prediction set data to obtain the trained neural network model.
In order to better understand the embodiment of the present invention, the embodiment of the present invention is described in detail below by taking banking as an example. In this example, the account data may be payment transaction data in a payment system, and the quota account flow situation for the target participant (i.e., bank) is predicted using the LSTM model, and the prediction data includes: the forecasts are made at 15 minute granularity according to the participants' origination to their limited account (i.e., account origination amount data), receipt (i.e., account receipt amount data), and net (i.e., account net data). This example is described below in conjunction with the quota account data prediction flow shown in FIG. 2, and the data exploration and preprocessing flow shown in FIG. 3.
Referring to fig. 2 and 3, the quota account data prediction process mainly includes: data preprocessing, parameter searching and modeling prediction.
In the data preprocessing stage, the original data (corresponding to the CSV data in fig. 2) are obtained first, that is, the initiation amount data and the reception amount data summarized by the network banking system service and the small amount system service in the production environment according to the 15-minute granularity are obtained.
In one example, the raw data may include: date (date), bank number (bank _ id), line name (name), summary time interval (time, e.g., 00:00-00:15, representing a 15 minute time interval), total number of sent-out strokes in summary time interval (send _ count), total amount of sent-out strokes in summary time interval (send _ amount), total number of received strokes in summary time interval (rcv _ count), total amount of received strokes in summary time interval (rcv _ amount), etc.
And for the obtained original data, aligning the initiating amount and the receiving amount data summarized by the two systems according to the granularity of 15 minutes according to a time axis. And exploring the original data, and checking the overall distribution condition of the data, whether the data is missing, whether abnormal points exist and the overall trend rule of the data as shown in fig. 3.
Then, the data is preprocessed, as shown in fig. 3, the preprocessing step mainly includes: missing data is filled with 0 values, partial group point data is replaced by mean values at the same time, and then normalization processing is carried out on the data.
In the actual data exploration process, data with different dates are found to have a certain periodic rule. Therefore, the original data can be added with a date tag according to the operation time sequence of the payment system based on the service periodicity characteristic. The date label has: holidays, special holidays, second holidays and ordinary days. Data with the same date label are stitched together for later modeling.
Taking certain bank data as an example, after the preprocessed data is obtained, a peak value appears in the initiated amount data at about 9 am every Monday by looking up data trends. The data change rule from tuesday to friday is basically the same. And carrying out data exploration on the received amount data, and finding that the change rule of the received amount data is similar to that of the initiated amount data.
Given the periodic nature of the data, the participants (i.e., banks) are similar to the last Saturday business feature every Saturday, similar to the previous Monday, and on the same day of the week. While the traffic characteristics on tuesday through friday are all similar. Therefore, data of saturday, sunday and monday can be spliced into a group, data of tuesday to friday can be spliced into a group, and the two groups of data are modeled independently. For example, data is split into data _ weekend groups (including saturday, sunday, monday data) and data _ weekday (including tuesday to friday data).
Based on the data packets described above, in one example, the prediction method may be: the prediction of Monday, Saturday and Sunday is based on the data of last Monday, last Saturday and last Sunday, the prediction of Tuesday is based on last Friday, the prediction of Wednesday, Thursday and Friday is based on the data of the previous day respectively, and so on. According to the method, the prediction efficiency and accuracy of the model can be effectively improved according to the comparison experiment result of the data transmitted and received by the participant.
In actual operation, the pre-processed data is proportionally divided into a training set and a prediction set. And then, using a grid searching method to level the model parameters. And then, combining grid search results, performing data verification for multiple times, and selecting the optimal parameters of the model. And finally, verifying the accuracy of the model parameters on the long-term data set by adopting a cross verification method.
For a certain date needing prediction, data within the length of the previous month can be taken as a training set for training. And finally, obtaining the initiating and receiving amount prediction results of a certain time granularity of the prediction date, and calculating to obtain the net amount prediction result.
The prediction error of the neural network model of the embodiment of the invention is basically within 10%, the prediction accuracy is higher, and the neural network model has stronger guiding significance for the prediction of the net debit quota account of the participant.
Fig. 4 is a block diagram showing a configuration of a limited-account data processing apparatus according to an embodiment of the present invention, as shown in fig. 4, the apparatus including: a prediction data acquisition unit 41, a prediction unit 42, and a management unit 43, wherein:
a prediction data obtaining unit 41, configured to obtain quota account data that needs to be predicted, where the account data includes: the system information and the predetermined date, the system comprises one of the following: a small amount system and an online banking system;
a prediction unit 42, configured to input the account data into the trained neural network model to output account prediction data of the quota account on the predetermined date, where the account prediction data includes: account initiation amount data and account receiving amount data in a preset period granularity, wherein the neural network model is trained on historical account data of a date label to which the preset date belongs;
and the management unit 43 is used for managing the quota account according to the account initiating amount data and the account receiving amount data.
In one embodiment, the management unit specifically includes: net amount data determination module and management module, wherein: the net amount data determining module is used for determining account net amount data according to the account initiating amount data and the account receiving amount data; and the management module is used for managing the quota account according to the account initiating amount data, the account receiving amount data and the account net amount data.
The quota account data which needs to be predicted and is acquired by the prediction data acquisition unit 41 is input to the neural network model through the prediction unit 42, and the neural network model can predict the account data of the quota account on a preset date, so that the flow condition of the amount data of the quota account can be further predicted, the mobility of the bank net debit quota account can be conveniently and accurately controlled, and the efficiency of business processing can be effectively improved.
In actual practice, the neural network model may be an LSTM model.
Specifically, as shown in fig. 5, the quota account data processing apparatus further includes: a model training unit 44, configured to train the neural network model.
The model training unit 44 specifically includes: historical data acquisition module, preprocessing module and training module, wherein:
a historical data obtaining module, configured to obtain the historical account data, where the historical account data includes: date, date label, historical account origination amount data and historical account receipt amount data within the predetermined period granularity.
And the preprocessing module is used for preprocessing the historical account data and classifying the preprocessed historical account data into training set data and prediction set data.
Specifically, the preprocessing module specifically includes: missing data filling processing submodule and normalization processing submodule, wherein: the missing data filling processing submodule is used for filling missing data into the historical account data; and the normalization processing submodule is used for performing normalization processing on the historical account data after missing data filling processing.
And the training module is used for training the neural network model according to the training set data based on a grid searching method, and then verifying the trained neural network model according to the prediction set data to obtain the trained neural network model.
With continued reference to fig. 5, the quota account data processing apparatus further includes: a date label setting unit 45 for setting the above date label. Specifically, the date label setting unit sets dates having similar data characteristics in a specific period as the same date label according to a predetermined rule.
For specific execution processes of the units, the modules, and the sub-modules, reference may be made to the description in the foregoing method embodiments, and details are not described here again.
In practical operation, the units, the modules and the sub-modules may be combined or may be arranged singly, and the present invention is not limited thereto.
The present embodiment also provides an electronic device, which may be a desktop computer, a tablet computer, a mobile terminal, and the like, but is not limited thereto. In this embodiment, the electronic device may be implemented by referring to the above method embodiment and the limited account data processing apparatus embodiment, and the contents thereof are incorporated herein, and repeated details are not repeated.
Fig. 6 is a schematic block diagram of a system configuration of an electronic apparatus 600 according to an embodiment of the present invention. As shown in fig. 6, the electronic device 600 may include a central processor 100 and a memory 140; the memory 140 is coupled to the central processor 100. Notably, this diagram is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the limit account data processing functionality may be integrated into the central processor 100. The central processor 100 may be configured to control as follows:
acquiring quota account data needing prediction, wherein the account data comprises: the system information and the predetermined date, the system comprises one of the following: a small amount system and an online banking system;
inputting the account data into a trained neural network model to output account prediction data of the quota account on the predetermined date, wherein the account prediction data comprises: account initiation amount data and account receiving amount data in a preset period granularity, wherein the neural network model is trained on historical account data of a date label to which the preset date belongs;
and managing the quota account according to the account initiating amount data and the account receiving amount data.
As can be seen from the above description, in the electronic device provided in the embodiment of the present application, the acquired data of the quota account to be predicted is input to the neural network model, and the neural network model can predict the account data of the quota account on the predetermined date, so that the flow condition of the amount data of the quota account can be further predicted, the flow of the bank net debit quota account can be conveniently and accurately controlled, and the efficiency of business processing can be effectively improved.
In another embodiment, the limit account data processing apparatus may be configured separately from the central processor 100, for example, the limit account data processing apparatus may be configured as a chip connected to the central processor 100, and the limit account data processing function is realized by the control of the central processor.
As shown in fig. 6, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in FIG. 6; furthermore, the electronic device 600 may also comprise components not shown in fig. 6, which may be referred to in the prior art.
As shown in fig. 6, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the limited account data processing method.
In conclusion, in order to enhance the liquidity risk management capability, more accurately predict the liquidity condition of the participants, the embodiment of the invention provides a scheme for predicting liquidity of a net debit quota account of a participant by utilizing transaction detail services of a participant micropayment system and an online cross-bank clearing system by utilizing real payment transaction service data of a payment system, after a series of preprocessing steps are carried out on original transaction detail data, the initiating amount, the receiving amount and the net amount of a net debit quota account of every 15 minutes of the next day of a participant can be automatically predicted by utilizing a parameter-adjusted LSTM model, the prediction error is less than 10 percent of the fluctuation range of real data, the average training time is less than 1 minute, and the change mode of the data can be accurately predicted, and the requirements of the production environment on the precision and the performance of the intelligent prediction of the fluidity of the participants can be met.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings. The many features and advantages of the embodiments are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the embodiments which fall within the true spirit and scope thereof. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the embodiments of the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope thereof.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (12)
1. A method for processing quota account data, the method comprising:
acquiring quota account data needing prediction, wherein the account data comprises: the system information and the predetermined date, the system comprises one of the following: a small amount system and an online banking system;
inputting the account data into a trained neural network model to output account prediction data of the quota account on the predetermined date, wherein the account prediction data comprises: account initiation amount data and account receiving amount data in a preset period granularity, wherein the neural network model is trained on historical account data of a date label to which the preset date belongs;
and managing the quota account according to the account initiating amount data and the account receiving amount data.
2. The method of claim 1, wherein the neural network model is an LSTM model, and wherein the neural network model is trained by:
obtaining the historical account data, wherein the historical account data comprises: date, date label, historical account origination amount data and historical account receipt amount data in the predetermined cycle granularity;
preprocessing the historical account data, and classifying the preprocessed historical account data into training set data and prediction set data;
and training the neural network model according to the training set data based on a grid search method, and then verifying the trained neural network model according to the prediction set data to obtain the trained neural network model.
3. The method of claim 2, wherein the date tag is set by:
dates having similar data characteristics within a specific period are set as the same date tag according to a predetermined rule.
4. The method of claim 2, wherein preprocessing the historical account data comprises:
performing missing data filling processing on the historical account data;
and normalizing the historical account data after the missing data filling processing.
5. The method of claim 1, wherein managing the quota account based on the account origination amount data and the account receipt amount data comprises:
determining account net amount data according to the account initiating amount data and the account receiving amount data;
and managing the quota account according to the account initiating amount data, the account receiving amount data and the account net amount data.
6. An apparatus for processing quota account data, the apparatus comprising:
the prediction data acquisition unit is used for acquiring quota account data needing prediction, and the account data comprises: the system information and the predetermined date, the system comprises one of the following: a small amount system and an online banking system;
a prediction unit, configured to input the account data into a trained neural network model to output account prediction data of the quota account on the predetermined date, where the account prediction data includes: account initiation amount data and account receiving amount data in a preset period granularity, wherein the neural network model is trained on historical account data of a date label to which the preset date belongs;
and the management unit is used for managing the quota account according to the account initiating amount data and the account receiving amount data.
7. The apparatus of claim 6, wherein the neural network model is an LSTM model, the apparatus further comprising:
a model training unit for training the neural network model,
the model training unit specifically comprises:
a historical data obtaining module, configured to obtain the historical account data, where the historical account data includes: date, date label, historical account origination amount data and historical account receipt amount data in the predetermined cycle granularity;
the preprocessing module is used for preprocessing the historical account data and classifying the preprocessed historical account data into training set data and prediction set data;
and the training module is used for training the neural network model according to the training set data based on a grid searching method, and then verifying the trained neural network model according to the prediction set data to obtain the trained neural network model.
8. The apparatus according to claim 7, wherein the preprocessing module specifically comprises:
the missing data filling processing submodule is used for filling missing data into the historical account data;
and the normalization processing submodule is used for performing normalization processing on the historical account data after missing data filling processing.
9. The apparatus of claim 7, further comprising:
a date label setting unit for setting the date label,
the date label setting unit is specifically configured to: dates having similar data characteristics within a specific period are set as the same date tag according to a predetermined rule.
10. The apparatus of claim 6, wherein the management unit comprises:
the net amount data determining module is used for determining account net amount data according to the account initiating amount data and the account receiving amount data;
and the management module is used for managing the quota account according to the account initiating amount data, the account receiving amount data and the account net amount data.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 5 are implemented when the processor executes the program.
12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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Application publication date: 20201215 |