CN115238988A - Commemorative coin distribution method, system and related equipment - Google Patents
Commemorative coin distribution method, system and related equipment Download PDFInfo
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Abstract
The invention discloses a commemorative coin distribution method, a commemorative coin distribution system and related equipment, which can be applied to the field of big data. Acquiring exchange period information of commemorative coins to be distributed and acquiring an appointment rate of the commemorative coins to be distributed in a network point to be distributed; determining a target exchange period category of the commemorative coins to be distributed according to the exchange period information; determining a prediction model with the same exchange period type as a target prediction model from a plurality of pre-trained prediction models of different exchange period types; inputting the reservation rate into a target prediction model for data prediction, and predicting to obtain a daily predicted exchange ratio deviation value and a predicted exchange rate of the commemorative coins to be distributed in the network points to be distributed; and determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate. The distribution quantity is determined through the prediction model and the reservation rate of the commemorative coins to be distributed in the distribution network points to be distributed, and the distribution accuracy is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a commemorative coin distribution method, a commemorative coin distribution system and related equipment.
Background
When the bank issues the commemorative coin, the bank distributes the commemorative coin to a bank outlet for exchange. The current mode of allocating commemorative coins is as follows: commemorative coins exchanged on the same day are escorted to bank outlets by a cash truck every day. However, because the number of commemorative coins exchanged every day is different, the number of commemorative coins needing to be escorted to a bank outlet cannot be accurately estimated; if the distributed commemorative coins are too many, the bank note transporting vehicle is required to transport the commemorative coins back to the vault when the bank outlets are closed; if too few commemorative coins are allocated, many users cannot exchange the commemorative coins; the distribution accuracy of the existing mode for distributing the commemorative coins is poor.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, a system and a related device for allocating commemorative coins, so as to solve the problems of poor allocation accuracy and the like in the existing manner for allocating commemorative coins.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
the first aspect of the embodiment of the invention discloses a commemorative coin distribution method, which comprises the following steps:
acquiring exchange period information of commemorative coins to be distributed and acquiring the reservation rate of the commemorative coins to be distributed in a network point to be distributed, wherein the exchange period information at least comprises: total days and holiday distribution within the exchange period;
determining the target exchange period category of the commemorative coin to be distributed according to the exchange period information;
determining a prediction model with the same exchange period category as a target prediction model from a plurality of pre-trained prediction models of different exchange period categories, wherein the prediction model is obtained by training a mathematical model based on historical exchange data of the corresponding exchange period category, the historical exchange data at least comprises historical exchange period information, historical reservation rate, historical exchange rate and historical exchange ratio deviation value of allocated commemorative coins, and the exchange period category corresponding to the prediction model is determined based on the historical exchange period information;
inputting the reservation rate into the target prediction model for data prediction, and predicting to obtain a predicted exchange ratio deviation value and a predicted exchange rate of the commemorative coins to be distributed in the to-be-distributed network points every day;
and determining the predicted distribution quantity of the commemorative coins to be distributed in the distribution network point each day according to the predicted exchange ratio deviation value and the predicted exchange rate.
Preferably, the process of training the mathematical model to obtain the prediction model includes:
classifying the historical exchange data according to historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories;
and aiming at the historical exchange data of each exchange period type, training a mathematical model by using the historical exchange data of the exchange period type until the mathematical model is converged to obtain a prediction model corresponding to the exchange period type.
Preferably, the step of classifying the historical exchange data according to the historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories includes:
preliminarily classifying the historical exchange data according to the total days in the exchange period contained in the historical exchange period information in the historical exchange data;
and classifying the preliminarily classified historical exchange data again based on the vacation distribution in the exchange period contained in the historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories.
Preferably, after determining the predicted allocation amount of the commemorative coin to be allocated in the distribution site per day, the method further comprises:
acquiring an actual exchange proportion deviation value and an actual exchange rate of the commemorative coins to be distributed in the distribution network each day;
and updating the target prediction model according to the actual exchange proportion deviation value and the actual exchange rate.
A second aspect of an embodiment of the present invention discloses a distribution system of commemorative coins, the system including:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring exchange period information of commemorative coins to be distributed and acquiring a reservation rate of the commemorative coins to be distributed in a website to be distributed, and the exchange period information at least comprises: total days and holiday distribution within the exchange period;
the first determining unit is used for determining the target exchange period category of the commemorative coin to be distributed according to the exchange period information;
the second determination unit is used for determining a prediction model with the same exchange period class as the target exchange period class as a target prediction model from a plurality of pre-trained prediction models of different exchange period classes, wherein the prediction model is obtained by training a mathematical model based on historical exchange data of the corresponding exchange period class, the historical exchange data at least comprises historical exchange period information, historical reservation rate, historical exchange rate and historical exchange rate deviation value of commemorative coins which are completely distributed, and the exchange period class corresponding to the prediction model is determined based on the historical exchange period information;
the prediction unit is used for inputting the reservation rate into the target prediction model for data prediction, and predicting to obtain a predicted exchange ratio deviation value and a predicted exchange rate of the commemorative coins to be distributed in the to-be-distributed network points every day;
and the third determining unit is used for determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate.
Preferably, the second determination unit includes:
the classification module is used for classifying the historical exchange data according to historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period types;
and the training module is used for training a mathematical model by using the historical exchange data of each exchange period category until the mathematical model converges aiming at the historical exchange data of each exchange period category to obtain a prediction model corresponding to the exchange period category.
Preferably, the classification module is specifically configured to: preliminarily classifying the historical exchange data according to the total days in the exchange period contained in the historical exchange period information in the historical exchange data; and classifying the preliminarily classified historical exchange data again based on the vacation distribution in the exchange period contained in the historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories.
Preferably, the system further comprises:
the second acquisition unit is used for acquiring the actual exchange proportion deviation value and the actual exchange rate of the commemorative coins to be distributed in the network points to be distributed every day;
and the updating unit is used for updating the target prediction model according to the actual exchange ratio deviation value and the actual exchange rate.
A third aspect of an embodiment of the present invention discloses an electronic device, including: the system comprises a processor and a memory, wherein the processor and the memory are connected through a communication bus; the processor is used for calling and executing the program stored in the memory; the memory is used for storing programs, and the programs are used for realizing the commemorative coin distribution method disclosed in the first aspect of the embodiment of the invention.
A fourth aspect of the embodiments of the present invention discloses a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are used for executing the method for distributing commemorative coins disclosed in the first aspect of the embodiments of the present invention.
Based on the commemorative coin distribution method, the commemorative coin distribution system and the related equipment, provided by the embodiment of the invention, the exchange period information of the commemorative coin to be distributed is obtained, and the reservation rate of the commemorative coin to be distributed in the network point to be distributed is obtained; determining a target exchange period category of the commemorative coins to be distributed according to the exchange period information; determining a prediction model with the same exchange period type as a target prediction model from a plurality of pre-trained prediction models of different exchange period types; inputting the reservation rate into a target prediction model for data prediction, and predicting to obtain a predicted exchange ratio deviation value and a predicted exchange rate of the commemorative coins to be distributed in the network points to be distributed every day; and determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate. In the scheme, the exchange period information is utilized to determine the target exchange period type of the commemorative coin to be distributed, and a prediction model with the exchange period type same as the target exchange period type is determined as a target prediction model. And processing the reservation rate of the commemorative coin to be distributed by using a target prediction model, and predicting to obtain a predicted exchange ratio deviation value and a predicted exchange rate of each day. And determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate. The distribution quantity is determined according to the reservation rate of the commemorative coins to be distributed in the distribution network points to be distributed, and the distribution accuracy is 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 described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a commemorative coin distribution method provided by an embodiment of the present invention;
FIG. 2 is a flow chart of training a predictive model according to an embodiment of the present invention;
FIG. 3 is a block diagram of a distribution system for commemorative coins according to an embodiment of the present invention;
FIG. 4 is another block diagram of a commemorative coin dispensing system according to an embodiment of the present invention;
fig. 5 is a block diagram of another structure of a distribution system of commemorative coins according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
It should be noted that the commemorative coin distribution method, system and related equipment provided by the invention can be used in the field of big data. The above is merely an example, and the application fields of the commemorative coin distribution method, the commemorative coin distribution system and the related device provided by the invention are not limited.
As known from the background technology, when commemorative coins are distributed to bank outlets at present, commemorative coins exchanged in quantity on the same day are escorted to the bank outlets by a money transporting vehicle every day. However, because the number of commemorative coins exchanged every day is different, the number of commemorative coins needing to be escorted to a bank outlet cannot be accurately estimated; if the distributed commemorative coins are too many, the bank note transport vehicle is needed to transport the commemorative coins back to the vault when the bank outlets are closed; if the allocated commemorative coin is too few, many users cannot exchange the commemorative coin; the distribution accuracy of the existing mode for distributing the commemorative coins is poor.
Therefore, the embodiment of the invention provides a commemorative coin distribution method, a commemorative coin distribution system and related equipment, wherein the exchange period information is used for determining the target exchange period type of the commemorative coin to be distributed, and a prediction model with the exchange period type being the same as the target exchange period type is determined as a target prediction model. And processing the reservation rate of the commemorative coins to be distributed by using a target prediction model, and predicting to obtain a daily predicted exchange ratio deviation value and a predicted exchange rate. And determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate. The distribution quantity is determined according to the reservation rate of the commemorative coins to be distributed in the distribution network points to improve the distribution accuracy.
Referring to fig. 1, a flow chart of a commemorative coin distribution method provided by an embodiment of the present invention is shown, the distribution method including:
step S101: the method comprises the steps of obtaining exchange period information of the commemorative coins to be distributed and obtaining the reservation rate of the commemorative coins to be distributed in the distribution network points.
It should be noted that the exchange period information at least includes: total days and holiday distribution within the exchange period; wherein, the total days in the exchange period specifically refers to the duration days of the exchange period, such as: setting the exchange period of the commemorative coins to be distributed as 7 months and 12 days to 7 months and 14 days, wherein the total days in the exchange period is 3 days; the distribution of vacations within the exchange may be used to characterize which day of the exchange is a vacation.
In the process of specifically realizing the step S101, acquiring exchange period information of the commemorative coins to be distributed, and acquiring the reservation rate of the commemorative coins to be distributed in each website to be distributed; the to-be-distributed network point is a bank network point needing to distribute the commemorative coins to be distributed.
Step S102: and determining the target exchange period category of the commemorative coin to be distributed according to the exchange period information.
In the process of implementing the step S102 specifically, the target exchange period category of the commemorative coin to be allocated is determined according to the total days and the distribution of the vacation periods in the exchange period included in the exchange period information of the commemorative coin to be allocated.
For example: setting the total days in the exchange period contained in the exchange period information of the commemorative coins to be distributed as 3 days, and setting the distribution of the vacations as that the first day in the exchange period is a vacation (the other two days are non-vacations); the target exchange period category of the commemorative coin to be allocated is determined to be "100", wherein "1" represents a holiday and "0" represents a non-holiday.
Step S103: and determining a prediction model with the same exchange period category as the target exchange period category as a target prediction model from a plurality of pre-trained prediction models of different exchange period categories.
It should be noted that, historical redemption data is obtained in advance, and the historical redemption data at least includes: historical exchange period information of the commemorative coins which are distributed, historical reservation rates at all bank outlets, historical exchange rates and historical exchange proportion deviation values; and dividing all historical redemption data into historical redemption data of different redemption period categories. Specifically, the historical exchange data is classified according to historical exchange period information in the historical exchange data, so that the historical exchange data of different exchange period categories are obtained.
Aiming at the historical exchange data of each exchange period type, training a mathematical model by using the historical exchange data of the exchange period type to obtain a prediction model corresponding to the exchange period type; that is, historical redemption data of each redemption period category can be trained to obtain a corresponding prediction model; and the exchange period category corresponding to the prediction model is determined based on the historical exchange period information.
In some embodiments, the historical redemption data is categorized by: preliminarily classifying the historical exchange data according to the total days in the exchange period contained in the historical exchange period information in the historical exchange data; that is, the historical exchange data is firstly preliminarily classified according to the total days, that is, the historical exchange data with the same total days is firstly preliminarily classified into one type.
And classifying the preliminarily classified historical exchange data again based on the vacation distribution in the exchange period contained in the historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories. That is, for the historical exchange data with the same total days (which are preliminarily classified), the historical exchange data with the same total days are classified again according to the vacation distribution, and the historical exchange data of different exchange period categories are obtained.
For example: for historical exchange data with total days of 3 days, classifying the historical exchange data with the first day of the exchange period as a vacation and the other two days as non-vacation into the exchange period category of '100'; classifying historical redemption data for which the second and third days of the redemption period are vacations (the first day is non-vacation) into a redemption period category of "011"; by analogy, historical exchange data with the total number of days of 3 days are classified into different exchange period categories.
It should be noted that, the exchange rate and the exchange number of the commemorative coins are related to the total days and the distribution of vacation periods in the exchange period; therefore, according to the historical exchange period information in the historical exchange data, the historical exchange data are classified, corresponding prediction models are obtained by training aiming at the historical exchange data of each exchange period type, and the related data of the commemorative coins with different exchange period information (with different total days and/or different vacation period distribution) are predicted through the prediction models of different exchange period types.
In the process of implementing step S103 specifically, determining, from a plurality of prediction models of different exchange period categories, a prediction model whose exchange period category is the same as a target exchange period category as a target prediction model; that is, the target prediction model is: the total days and vacation distribution corresponding to the exchange period category are the same as the prediction model of the target exchange period category.
For example: setting the category of the target exchange period as '010', representing that the total days of the commemorative coins to be distributed are 3, and the distribution of the holidays is that the second day in the exchange period is a holiday (the other two days are non-holidays); then the prediction model for which the redemption period category is also "010" is determined to be the target prediction model, that is, the target prediction model is: and training a prediction model obtained by a mathematical model by using historical exchange data of which the exchange period category is '010'.
Step S104: and inputting the reservation rate into a target prediction model for data prediction, and predicting to obtain a predicted exchange ratio deviation value and a predicted exchange rate of the commemorative coins to be distributed in the network points to be distributed every day.
In the process of the concrete implementation step S104, the reservation rate of the commemorative coin to be distributed in the network site to be distributed is input into the determined target prediction model for data prediction, so as to predict and obtain the predicted exchange ratio deviation value and the predicted exchange rate of the commemorative coin to be distributed in the network site to be distributed each day.
In some embodiments, the predicted redemption proportion deviation value is: and the predicted exchange proportion is compared with the deviation value of the average value of the predicted exchange quantity.
Step S105: and determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate.
In the process of the concrete implementation step 105, determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate; specifically, for a certain day, the predicted exchange ratio deviation value and the predicted exchange rate corresponding to the day are utilized to determine and obtain the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed on the day.
In order to further improve the prediction accuracy of the target prediction model, preferably, after the daily predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed is determined, the daily actual exchange ratio deviation value and the actual exchange ratio of the commemorative coins to be distributed in the network points to be distributed are obtained; and updating the target prediction model according to the actual exchange ratio deviation value and the actual exchange rate.
In the embodiment of the invention, the exchange period information is utilized to determine the target exchange period category of the commemorative coin to be distributed, and the prediction model with the exchange period category being the same as the target exchange period category is determined as the target prediction model. And processing the reservation rate of the commemorative coins to be distributed by using a target prediction model, and predicting to obtain a daily predicted exchange ratio deviation value and a predicted exchange rate. And determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate. The distribution quantity is determined according to the reservation rate of the commemorative coins to be distributed in the distribution network points to be distributed, and the distribution accuracy is improved.
In the above embodiment of the present invention, the contents of training the mathematical model to obtain the prediction model in step S103 in fig. 1 are shown in fig. 2, which is a flowchart of training the obtained prediction model provided in the embodiment of the present invention, and includes the following steps:
step S201: and classifying the historical exchange data according to the historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories.
In the process of the specific implementation step S201, the historical exchange data of each banking point is extracted from the Hadoop local server by using the data conversion tool, and a HIVE table for storing the historical exchange data is established in the data warehouse tool HIVE.
After the historical exchange data are obtained, classifying the historical exchange data according to the historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories; for specific contents of classifying the historical exchange data, refer to the contents in step S103 in fig. 1 in the embodiment of the present invention, which are not described herein again.
Step S202: and aiming at the historical exchange data of each exchange period type, training a mathematical model by using the historical exchange data of the exchange period type until the mathematical model is converged to obtain a prediction model corresponding to the exchange period type.
In the specific implementation process of step S202, a corresponding mathematical model is constructed for the historical exchange data of each exchange period category; and for the historical exchange data of each exchange period type, training a corresponding mathematical model by using the historical exchange data of the exchange period type until the mathematical model converges to obtain a prediction model corresponding to the exchange period type.
Specifically, for the historical exchange data of each exchange period type, training a corresponding mathematical model until the mathematical model converges by using the historical reservation rate, the historical exchange rate and the historical exchange ratio deviation value in the historical exchange data of the exchange period type to obtain a prediction model corresponding to the exchange period type.
The historical exchange proportion deviation value is as follows: and the historical exchange proportion is compared with the deviation value of the average value of the historical exchange quantity.
In the embodiment of the invention, according to the historical exchange period information in the historical exchange data, the historical exchange data are classified, then the corresponding prediction model is obtained by training the historical exchange data of each exchange period type, the relevant data of commemorative coins with different exchange period information is predicted through the prediction models of different exchange period types, finally, the distribution quantity is determined based on the predicted relevant data, and the distribution accuracy is improved.
In correspondence to the commemorative coin distribution method provided by the above-described embodiment of the present invention, referring to fig. 3, the embodiment of the present invention further provides a structural block diagram of a distribution system of commemorative coins, the distribution system comprising: a first acquisition unit 301, a first determination unit 302, a second determination unit 303, a prediction unit 304, and a third determination unit 305;
the first obtaining unit 301 is configured to obtain exchange period information of the commemorative coin to be distributed, and obtain a reservation rate of the commemorative coin to be distributed in a website to be distributed, where the exchange period information at least includes: total days within the exchange period and the distribution of vacations.
A first determination unit 302, configured to determine a target exchange period category of the commemorative coin to be allocated according to the exchange period information.
The second determining unit 303 is configured to determine, as the target prediction model, a prediction model having an exchange period class identical to a target exchange period class from among a plurality of pre-trained prediction models of different exchange period classes, where the prediction model is obtained by training a mathematical model based on historical exchange data of the corresponding exchange period class, the historical exchange data at least includes historical exchange period information, historical reservation rate, historical exchange rate, and historical exchange rate deviation value of the commemorative coin that has completed distribution, and the exchange period class corresponding to the prediction model is determined based on the historical exchange period information.
And the prediction unit 304 is used for inputting the reservation rate into the target prediction model to perform data prediction, and predicting to obtain a predicted exchange ratio deviation value and a predicted exchange rate of the commemorative coin to be distributed in the network points to be distributed every day.
And the third determining unit 305 is used for determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate.
In the embodiment of the invention, the exchange period information is utilized to determine the target exchange period category of the commemorative coin to be distributed, and the prediction model with the exchange period category being the same as the target exchange period category is determined as the target prediction model. And processing the reservation rate of the commemorative coins to be distributed by using a target prediction model, and predicting to obtain a daily predicted exchange ratio deviation value and a predicted exchange rate. And determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate. The distribution quantity is determined according to the reservation rate of the commemorative coins to be distributed in the distribution network points to be distributed, and the distribution accuracy is improved.
Preferably, referring to fig. 4 in conjunction with fig. 3, there is shown another structural block diagram of a distribution system of commemorative coins according to an embodiment of the present invention, and the second determination unit 303 includes: a classification module 3031 and a training module 3032;
the classification module 3031 is configured to classify the historical exchange data according to the historical exchange period information in the historical exchange data to obtain historical exchange data of different exchange period categories.
In a specific implementation, the classification module 3031 is specifically configured to: preliminarily classifying the historical exchange data according to the total days in the exchange period contained in the historical exchange period information in the historical exchange data; and classifying the preliminarily classified historical exchange data again based on the vacation distribution in the exchange period contained in the historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories.
The training module 3032 is configured to train a mathematical model by using the historical exchange data of the exchange period category until the mathematical model converges for the historical exchange data of each exchange period category, so as to obtain a prediction model corresponding to the exchange period category.
In the embodiment of the invention, according to the historical exchange period information in the historical exchange data, the historical exchange data are classified, then the corresponding prediction model is obtained by training aiming at the historical exchange data of each exchange period type, the relevant data of commemorative coins with different exchange period information is predicted through the prediction models of different exchange period types, finally, the distribution quantity is determined based on the predicted relevant data, and the distribution accuracy is improved.
Preferably, referring to fig. 5 in conjunction with fig. 3, there is shown another structural block diagram of a distribution system for commemorative coins according to an embodiment of the present invention, the distribution system further including:
and the second obtaining unit 306 is used for obtaining the actual exchange ratio deviation value and the actual exchange rate of the commemorative coin to be distributed in the network points to be distributed every day.
And the updating unit 307 is used for updating the target prediction model according to the actual exchange ratio deviation value and the actual exchange rate.
Preferably, an embodiment of the present invention further provides an electronic device, including: the processor and the memory are connected through a communication bus; the processor is used for calling and executing the program stored in the memory; and a memory for storing a program for implementing the allocation method of the commemorative coin as provided in the above-described method embodiments.
Preferably, the embodiment of the invention also provides a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are used for executing the commemorative coin distribution method provided by the above method embodiment.
In summary, embodiments of the present invention provide a commemorative coin allocation method, system and related device, which determine a target exchange period category of a commemorative coin to be allocated by using exchange period information, and determine a prediction model with the exchange period category being the same as the target exchange period category as a target prediction model. And processing the reservation rate of the commemorative coins to be distributed by using a target prediction model, and predicting to obtain a daily predicted exchange ratio deviation value and a predicted exchange rate. And determining the predicted distribution quantity of the commemorative coins to be distributed in the network points to be distributed every day according to the predicted exchange ratio deviation value and the predicted exchange rate. The distribution quantity is determined according to the reservation rate of the commemorative coins to be distributed in the distribution network points to be distributed, and the distribution accuracy is improved.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of distributing commemorative coins, the method comprising:
acquiring exchange period information of commemorative coins to be distributed and acquiring the reservation rate of the commemorative coins to be distributed in a network point to be distributed, wherein the exchange period information at least comprises: total days and holiday distribution within the exchange period;
determining the target exchange period category of the commemorative coin to be distributed according to the exchange period information;
determining a prediction model with the same exchange period type as the target exchange period type as a target prediction model from a plurality of pre-trained prediction models of different exchange period types, wherein the prediction model is obtained by training a mathematical model based on historical exchange data of the corresponding exchange period type, the historical exchange data at least comprises historical exchange period information, historical reservation rate, historical exchange rate and historical exchange ratio deviation value of allocated commemorative coins, and the exchange period type corresponding to the prediction model is determined based on the historical exchange period information;
inputting the reservation rate into the target prediction model for data prediction, and predicting to obtain a predicted exchange ratio deviation value and a predicted exchange rate of the commemorative coins to be distributed in the to-be-distributed network points every day;
and determining the predicted distribution quantity of the commemorative coins to be distributed in the distribution network point each day according to the predicted exchange ratio deviation value and the predicted exchange rate.
2. The method of claim 1, wherein the process of training a mathematical model to obtain a predictive model comprises:
classifying the historical exchange data according to historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories;
and aiming at the historical exchange data of each exchange period type, training a mathematical model by using the historical exchange data of the exchange period type until the mathematical model is converged to obtain a prediction model corresponding to the exchange period type.
3. The method of claim 2, wherein classifying the historical redemption data according to historical redemption period information in the historical redemption data to obtain historical redemption data for different redemption period categories comprises:
preliminarily classifying the historical exchange data according to the total days in the exchange period contained in the historical exchange period information in the historical exchange data;
and classifying the preliminarily classified historical exchange data again based on the vacation distribution in the exchange period contained in the historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories.
4. A method according to any one of claims 1 to 3 wherein, after determining the predicted allocated quantity of said memorial coin to be allocated per day at said point of distribution, said method further comprises:
acquiring an actual exchange proportion deviation value and an actual exchange rate of the commemorative coins to be distributed in the distribution network each day;
and updating the target prediction model according to the actual exchange ratio deviation value and the actual exchange rate.
5. A distribution system for commemorative coins, the system comprising:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring exchange period information of commemorative coins to be distributed and acquiring a reservation rate of the commemorative coins to be distributed in a website to be distributed, and the exchange period information at least comprises: total days and holiday distribution within the exchange period;
the first determining unit is used for determining the target exchange period category of the commemorative coin to be distributed according to the exchange period information;
the second determination unit is used for determining a prediction model with the same exchange period class as the target exchange period class as a target prediction model from a plurality of pre-trained prediction models of different exchange period classes, wherein the prediction model is obtained by training a mathematical model based on historical exchange data of the corresponding exchange period class, the historical exchange data at least comprises historical exchange period information, historical reservation rate, historical exchange rate and historical exchange rate deviation value of commemorative coins which are completely distributed, and the exchange period class corresponding to the prediction model is determined based on the historical exchange period information;
the prediction unit is used for inputting the reservation rate into the target prediction model for data prediction, and predicting to obtain a predicted exchange ratio deviation value and a predicted exchange rate of the commemorative coins to be distributed in the to-be-distributed network points every day;
and the third determining unit is used for determining the predicted distribution quantity of the commemorative coins to be distributed in the distribution network point every day according to the predicted exchange ratio deviation value and the predicted exchange rate.
6. The system according to claim 5, wherein the second determination unit comprises:
the classification module is used for classifying the historical exchange data according to historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period types;
and the training module is used for training a mathematical model by using the historical exchange data of each exchange period category until the mathematical model converges aiming at the historical exchange data of each exchange period category to obtain a prediction model corresponding to the exchange period category.
7. The system of claim 6, wherein the classification module is specifically configured to: preliminarily classifying the historical exchange data according to the total days in the exchange period contained in the historical exchange period information in the historical exchange data; and classifying the preliminarily classified historical exchange data again based on the vacation distribution in the exchange period contained in the historical exchange period information in the historical exchange data to obtain the historical exchange data of different exchange period categories.
8. The system according to any one of claims 5-7, further comprising:
the second acquisition unit is used for acquiring the actual exchange ratio deviation value and the actual exchange rate of the commemorative coins to be distributed in the network points to be distributed every day;
and the updating unit is used for updating the target prediction model according to the actual exchange ratio deviation value and the actual exchange rate.
9. An electronic device, comprising: the system comprises a processor and a memory, wherein the processor and the memory are connected through a communication bus; the processor is used for calling and executing the program stored in the memory; the memory for storing a program for implementing the distribution method of commemorative coin as claimed in any one of claims 1-4.
10. A computer-readable storage medium having stored therein computer-executable instructions for performing the method of distributing commemorative coins according to any one of claims 1-4.
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| CN202210852504.0A CN115238988A (en) | 2022-07-20 | 2022-07-20 | Commemorative coin distribution method, system and related equipment |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115909591A (en) * | 2023-01-06 | 2023-04-04 | 北京国旺盛源智能终端科技有限公司 | Goods selling management method, system and equipment based on point exchange cabinet |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115909591A (en) * | 2023-01-06 | 2023-04-04 | 北京国旺盛源智能终端科技有限公司 | Goods selling management method, system and equipment based on point exchange cabinet |
| CN115909591B (en) * | 2023-01-06 | 2023-05-05 | 北京国旺盛源智能终端科技有限公司 | Goods selling management method, system and equipment based on point exchange cabinet |
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