CN113191806A - Method and device for determining flow regulation target - Google Patents

Method and device for determining flow regulation target Download PDF

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Publication number
CN113191806A
CN113191806A CN202110481899.3A CN202110481899A CN113191806A CN 113191806 A CN113191806 A CN 113191806A CN 202110481899 A CN202110481899 A CN 202110481899A CN 113191806 A CN113191806 A CN 113191806A
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time period
level sub
flow
determined
time periods
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CN113191806B (en
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张育博
赵宇
朱耀
邹方曼
王媛琼
徐夙龙
龙波
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G06Q30/0222—During e-commerce, i.e. online transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0641—Shopping interfaces
    • G06Q30/0643—Graphical representation of items or shoppers

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Abstract

The application discloses a method and a device for determining a flow regulation target. One embodiment of the method comprises: determining an article set of a flow control target to be determined and a time period to be determined; inputting the characteristic information of the item set and the time period to be determined into a pre-trained prediction model to obtain the ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in the current preset time period, wherein the prediction model is used for representing the corresponding relation between the characteristic information of the item set and the time period to be determined and the ratio; and determining the flow control target of the article set in the time period to be determined according to the ratio. The application provides a method for determining a flow regulation and control target under an electric market scene, and the reasonability and the accuracy of the determined flow regulation and control target are improved.

Description

Method and device for determining flow regulation target
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for determining a flow regulation target.
Background
In the E-commerce scene, the E-commerce platform is responsible for matching supply and demand and determining the commodity display sequence seen by the consumer. In order to meet the marketing appeal of a specific merchant and realize the operation willingness of the platform, the platform needs to give the merchant a certain flow incentive. The E-commerce platform stimulates the flow of the merchant by means of an intelligent flow control system, and the specific implementation measure is to control the exposure position of the commodity. The regulation and control system is very dependent on the input flow regulation and control target, and if the flow regulation and control target input to the regulation and control system is unreasonable, the regulation and control difficulty can be greatly improved. Therefore, before flow regulation is performed, a reasonable flow regulation target needs to be obtained.
Disclosure of Invention
The embodiment of the application provides a method and a device for determining a flow regulation target.
In a first aspect, an embodiment of the present application provides a method for determining a flow regulation target, including: determining an article set of a flow control target to be determined and a time period to be determined; inputting the characteristic information of the item set and the time period to be determined into a pre-trained prediction model to obtain the ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in the current preset time period, wherein the prediction model is used for representing the corresponding relation between the characteristic information of the item set and the time period to be determined and the ratio; and determining the flow control target of the article set in the time period to be determined according to the ratio.
In some embodiments, the above method further comprises: distributing the flow control targets of the item sets in the time periods to be determined to each sub-time period in the time periods to be determined based on the principle that the flow of the corresponding sub-time periods in the corresponding different time periods accounts for the same proportion in the total flow of the time periods, and determining the flow control targets of the item sets in each sub-time period.
In some embodiments, the distributing the flow rate regulation targets of the item sets in the time period to be determined to each sub-time period in the time period to be determined based on the principle that the flow rates of the corresponding sub-time periods in the corresponding different time periods account for the same proportion in the total flow rate of the time period, and determining the flow rate regulation targets of the item sets in each sub-time period includes: distributing the flow control targets corresponding to the time periods to be determined to the first level sub-time periods in the time periods to be determined and determining the flow control targets of the item sets in the first level sub-time periods based on the principle that the proportion of the flow of the corresponding first level sub-time periods in the total flow of the time periods is the same in the first level sub-time periods in the corresponding different time periods; and distributing the flow control targets corresponding to the first-level sub-time periods to the second-level sub-time periods in the first-level sub-time periods based on the principle that the proportion of the flow of the corresponding second-level sub-time periods in the total flow of the first-level sub-time periods is the same in the second-level sub-time periods as the time unit, and determining the flow control targets of the item sets in the second-level sub-time periods.
In some embodiments, the above distributing the flow rate regulation and control target corresponding to the time period to be determined to each first-level sub-time period in the time period to be determined based on a principle that the proportion of the flow rate of the corresponding first-level sub-time period in the total flow rate of the time period is the same in the first-level sub-time periods in the corresponding different time periods by using the first-level sub-time periods as the time unit includes: determining a flow regulation target corresponding to the time period to be determined based on the principle that the proportion of the flow in the corresponding first-level sub-time periods in the time period total flow is the same in the corresponding different time periods, and distributing the flow regulation target to the first initial weight of each first-level sub-time period in the time period to be determined; determining first adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article by taking the first-level sub-time period as a time unit; and adjusting each first initial weight through the first adjustment information to obtain each first adjusted weight, and distributing the flow control target corresponding to the time period to be determined to each first-level sub-time period in the time period to be determined according to each first adjusted weight.
In some embodiments, the distributing the traffic control target corresponding to each first-level sub-period to each second-level sub-period in each first-level sub-period based on the principle that the proportion of the traffic of the corresponding second-level sub-period in the corresponding first-level sub-period in the total traffic of the first-level sub-period is the same in the second-level sub-period as the time unit includes: for each first-level sub-period within the period to be determined, performing the following operations: determining a flow regulation target corresponding to the first-level sub-time period and distributing the flow regulation target to second initial weights of all second-level sub-time periods in the first-level sub-time period on the basis of the principle that the proportion of the flow of the corresponding second-level sub-time period in the corresponding first-level sub-time period in the total flow of the first-level sub-time period is the same; determining second adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article by taking the second-level sub-time period as a time unit; and adjusting the second initial weight through the second adjustment information to obtain a second adjusted weight, and distributing the flow control target corresponding to the first-level sub-time period to each second-level sub-time period in the first-level sub-time period according to the second adjusted weight.
In some embodiments, the predictive model is trained by: acquiring a training sample set, wherein training samples in the training sample set comprise first characteristic information of a sample article set in a plurality of preset time periods before a sample time period, second characteristic information of the sample article set in a time period corresponding to the sample time period, and a ratio of an average flow of the sample article set in the sample time period to an average flow of the sample article set in a preset time period before the sample time period; and training to obtain a prediction model by using a machine learning algorithm and taking the first characteristic information and the second characteristic information in the training sample as input and taking the ratio corresponding to the input first characteristic information and the input second characteristic information as expected output.
In some embodiments, the above method further comprises: taking articles belonging to the same user as an article set to be screened to obtain a plurality of article sets to be screened; and screening a plurality of sample article sets from the plurality of article sets to be screened according to the sales value of each article set to be screened in the plurality of article sets to be screened.
In a second aspect, an embodiment of the present application provides an apparatus for determining a flow regulation target, including: a first determination unit configured to determine an item set of a flow regulation target to be determined and a time period to be determined; the system comprises a prediction unit, a prediction unit and a control unit, wherein the prediction unit is configured to input characteristic information of an article set and a time period to be determined into a pre-trained prediction model to obtain a ratio of an average flow of the article set in the time period to be determined to an average flow of the article set in a current preset time period, and the prediction model is used for representing a corresponding relation between the characteristic information of the article set and the time period to be determined and the ratio; and the second determining unit is configured to determine the flow regulation target of the article set in the time period to be determined according to the ratio.
In some embodiments, the above apparatus further comprises: and the distribution unit is configured to distribute the flow control targets of the item sets in the time periods to be determined to each sub-time period in the time periods to be determined and determine the flow control targets of the item sets in each sub-time period based on the principle that the flow rates of the corresponding sub-time periods in the corresponding different time periods account for the same proportion in the total flow rate of the time periods.
In some embodiments, the distribution unit is further configured to: distributing the flow control targets corresponding to the time periods to be determined to the first level sub-time periods in the time periods to be determined and determining the flow control targets of the item sets in the first level sub-time periods based on the principle that the proportion of the flow of the corresponding first level sub-time periods in the total flow of the time periods is the same in the first level sub-time periods in the corresponding different time periods; and distributing the flow control targets corresponding to the first-level sub-time periods to the second-level sub-time periods in the first-level sub-time periods based on the principle that the proportion of the flow of the corresponding second-level sub-time periods in the total flow of the first-level sub-time periods is the same in the second-level sub-time periods as the time unit, and determining the flow control targets of the item sets in the second-level sub-time periods.
In some embodiments, the distribution unit is further configured to: determining a flow regulation target corresponding to the time period to be determined based on the principle that the proportion of the flow in the corresponding first-level sub-time periods in the time period total flow is the same in the corresponding different time periods, and distributing the flow regulation target to the first initial weight of each first-level sub-time period in the time period to be determined; determining first adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article by taking the first-level sub-time period as a time unit; and adjusting each first initial weight through the first adjustment information to obtain each first adjusted weight, and distributing the flow control target corresponding to the time period to be determined to each first-level sub-time period in the time period to be determined according to each first adjusted weight.
In some embodiments, the distribution unit is further configured to: for each first-level sub-period within the period to be determined, performing the following operations: determining a flow regulation target corresponding to the first-level sub-time period and distributing the flow regulation target to second initial weights of all second-level sub-time periods in the first-level sub-time period on the basis of the principle that the proportion of the flow of the corresponding second-level sub-time period in the corresponding first-level sub-time period in the total flow of the first-level sub-time period is the same; determining second adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article by taking the second-level sub-time period as a time unit; and adjusting the second initial weight through the second adjustment information to obtain a second adjusted weight, and distributing the flow control target corresponding to the first-level sub-time period to each second-level sub-time period in the first-level sub-time period according to the second adjusted weight.
In some embodiments, the above apparatus further comprises: a training unit configured to train a prediction model by: acquiring a training sample set, wherein training samples in the training sample set comprise first characteristic information of a sample article set in a plurality of preset time periods before a sample time period, second characteristic information of the sample article set in a time period corresponding to the sample time period, and a ratio of an average flow of the sample article set in the sample time period to an average flow of the sample article set in a preset time period before the sample time period; and training to obtain a prediction model by using a machine learning algorithm and taking the first characteristic information and the second characteristic information in the training sample as input and taking the ratio corresponding to the input first characteristic information and the input second characteristic information as expected output.
In some embodiments, the above apparatus further comprises: a deriving unit configured to: taking articles belonging to the same user as an article set to be screened to obtain a plurality of article sets to be screened; and screening a plurality of sample article sets from the plurality of article sets to be screened according to the sales value of each article set to be screened in the plurality of article sets to be screened.
In a third aspect, the present application provides a computer-readable medium, on which a computer program is stored, where the program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement a method as described in any implementation of the first aspect.
The method and the device for determining the flow control target provided by the embodiment of the application determine the article set of the flow control target to be determined and the time period to be determined; inputting the characteristic information of the item set and the time period to be determined into a pre-trained prediction model to obtain the ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in the current preset time period, wherein the prediction model is used for representing the corresponding relation between the characteristic information of the item set and the time period to be determined and the ratio; and determining the flow control target of the article set in the time period to be determined according to the ratio, thereby providing a method for determining the flow control target under the electronic market scene, and improving the reasonability and accuracy of the determined flow control target.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a method of determining a flow regulation target according to the present application;
fig. 3 is a schematic diagram of an application scenario of the method of determining a flow rate regulation target according to the present embodiment;
FIG. 4 is a flow chart of yet another embodiment of a method of determining a flow regulation target according to the present application;
FIG. 5 is a block diagram of one embodiment of an apparatus for determining a flow regulation target according to the present application;
FIG. 6 is a block diagram of a computer system suitable for use in implementing embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 illustrates an exemplary architecture 100 to which the present method and apparatus for determining a flow regulation target may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The communication connections between the terminal devices 101, 102, 103 form a topological network, and the network 104 serves to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The terminal devices 101, 102, 103 may be hardware devices or software that support network connections for data interaction and data processing. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices supporting network connection, information acquisition, interaction, display, processing, and the like, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server providing various services, for example, a background processing server that acquires request information sent by a user through the terminal devices 101, 102, and 103, and determines a flow rate regulation target of an item set in a time period to be determined through a pre-trained prediction model according to an item set and the time period to be determined, which are specified by the user in the request information. Optionally, the server may feed back the traffic control target to the terminal device. As an example, the server 105 may be a cloud server.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., software or software modules used to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be further noted that the method for determining the traffic regulation target provided by the embodiment of the present application may be executed by a server, may also be executed by a terminal device, and may also be executed by the server and the terminal device in cooperation with each other. Accordingly, each part (for example, each unit) included in the apparatus for determining the flow rate regulation target may be entirely provided in the server, may be entirely provided in the terminal device, and may be separately provided in the server and the terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. When the electronic device on which the method of determining a traffic regulation target operates does not need to perform data transmission with other electronic devices, the system architecture may include only the electronic device (e.g., server or terminal device) on which the method of determining a traffic regulation target operates.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method of determining a flow regulation target is shown, comprising the steps of:
step 201, determining an article set of a flow control target to be determined and a time period to be determined.
In this embodiment, an execution subject (e.g., the server in fig. 1) of the method of determining a flow regulation target may determine an item set of the flow regulation target to be determined and a time period to be determined.
In this embodiment, the flow rate regulation target is used as a target flow rate of the item set in the time period to be determined, and when the time period to be determined arrives, the display sequence of the items in the item set is adjusted, so that the item set achieves the flow rate regulation target in the time period to be determined.
Wherein the set of items may be a plurality of items assigned to the user as specified by the user. In an e-market setting, the collection of items may be items sold by merchants in the e-commerce platform. Wherein each merchant corresponds to a collection of items.
The period of time to be determined is typically a period of time in the future. As an example, in an e-commerce setting, the time period to be determined may be, for example, a time period corresponding to an upcoming promotional activity held by the e-commerce platform.
In this embodiment, when determining the article set and the time period to be determined of the flow rate control target, the executing body simultaneously obtains the feature information of the article set and the time period to be determined. The item set characteristic information includes attribute information and flow information of the items in the item set. The flow information may include information characterizing flow trend of the article and information characterizing flow periodicity. It will be appreciated that information regarding clicks, orders, sales, etc. for the item displayed may be considered the flow of the item.
By way of example, the attribute information includes information such as a main camp category, a main camp brand, a quantity of goods, etc. of a user characterized by the item set, and the traffic information of the item set on each day includes information such as a mean value and a standard deviation of clicks, exposures, and sales of the item set on the last 3, 7, 15, and 30 days. In order to obtain the flow trend characteristics, the flow information of the past 3 months can be obtained; to obtain the flow periodicity characteristic, flow information for a time period of the collection of items in the previous cycle corresponding to the time period to be determined may be obtained. For example, the current time is 2021.4.16, the to-be-determined period is 2021.6.1-2021.6.18, and the period corresponding to the to-be-determined period is 2020.6.1-2020.6.18.
The characteristic information of the time period to be determined may include information of a month of the time period to be determined, whether it is a weekday, whether it is a holiday, whether there is a large-scale promotion activity, a number of days from the last promotion activity, a number of days of the next promotion activity, and the like.
Step 202, inputting the characteristic information of the item set and the time period to be determined into a pre-trained prediction model, and obtaining the ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in the current preset time period.
In this embodiment, the executing body may input the feature information of the item set and the time period to be determined into a pre-trained prediction model, so as to obtain a ratio of an average flow rate of the item set in the time period to be determined to an average flow rate of the item set in a current preset time period. The forecasting model is used for representing the corresponding relation between the characteristic information and the ratio of the item set and the time period to be determined.
The time length represented by the preset time period can be specifically set according to the actual situation. For example, the preset time period is one month in the past.
The prediction model may employ a neural network model having a prediction function. For example, the Light Gradient Boosting Machine (Light Gradient elevator) model.
In this embodiment, the ratio of the average flow rate of the item set in the time period to be determined to the average flow rate of the item set in the current preset time period is predicted by the prediction model, instead of directly predicting the flow rate of the item set in the time period to be determined, which is considered as follows:
first, in the shopping mall scene, there are spurts and peaks in the distribution of traffic due to the promotion holidays. Such data is relatively rare and is an outlier. Directly predicting flow information can make it difficult for the predictive model to learn flow distribution information during promotions, and can also confuse predictions during normal times (non-promotion times).
Secondly, the quantity difference of the commodity categories of different merchants is large, and the flow difference is large. For such cases, the prediction effect of the prediction model is not ideal.
The ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in the current preset time period is predicted through the prediction model, so that the problems can be solved, and the accuracy of the prediction model is improved.
In some optional implementations of the embodiment, the execution subject may be trained to obtain the prediction model by:
first, a training sample set is obtained.
Wherein, training samples in the training sample set include: the method comprises the steps of obtaining first characteristic information of a sample item set in a plurality of preset time periods before a sample time period, obtaining second characteristic information of the sample item set in a time period corresponding to the sample time period, and obtaining a ratio of an average flow rate of the sample item set in the sample time period to an average flow rate of the sample item set in one preset time period before the sample time period.
It will be appreciated that the data in the training samples is typically historical data that has been generated. As an example, the current time is 2021.4.16, and the execution subject may obtain historical information of all commodities before the current time from the e-commerce platform. The sample time period in one training sample is 2020.6.1-2020.6.18, the time length of each time period in a plurality of preset time periods before the sample time period is one month, specifically three months of 2020.3.1-2021.6.1, and the time period corresponding to the sample time period is 2019.6.1-2019.6.18.
In this implementation, the training samples and the sample item sets may be in a one-to-one correspondence relationship, or may be in a many-to-one relationship. In the many-to-one relationship, the sample time periods in the plurality of training samples corresponding to the same sample item set are different.
Secondly, training to obtain a prediction model by using a machine learning algorithm and taking the first characteristic information and the second characteristic information in the training sample as input and taking the ratio corresponding to the input first characteristic information and the input second characteristic information as expected output.
In the implementation mode, the prediction model determines the loss values of the ratio output by each training sample and the ratio output as expected, and solves the gradient according to the loss values, so as to update the parameters of the initial prediction model until the training end condition is reached, and obtain the prediction model. The training end condition may be loss convergence, the training frequency exceeds a preset frequency threshold, and the training time exceeds a preset time threshold.
In some optional implementations of this embodiment, the executing entity may obtain the plurality of sample item sets by:
firstly, taking articles belonging to the same user as an article set to be screened to obtain a plurality of article sets to be screened.
In this implementation, the set of items to be screened corresponds to the user one-to-one. As an example, each sample item set may be a set designated by one merchant that is made up of a plurality of items belonging to that merchant.
Secondly, screening a plurality of sample article sets from the plurality of article sets to be screened according to the sales value of each article set to be screened in the plurality of article sets to be screened.
In this implementation manner, the execution main body may sort the sales values of each article set to be screened in the plurality of article sets to be screened according to the size order, select a plurality of article sets to be screened ranked ahead, filter out article sets with a smaller scale, and obtain a plurality of sample article sets. For example, the item set corresponding to the merchant with the sale amount of the top 30% under the category of 3 may be selected.
And step 203, determining the flow control target of the article set in the time period to be determined according to the ratio.
In this embodiment, the executing body may determine the flow rate regulation target of the article set in the time period to be determined according to the ratio.
As an example, first, the execution subject determines the average flow rate of the item set in the time period to be determined according to the ratio and the average flow rate of the item set in the preset time period till the present. And then, determining a flow regulation target according to the average flow of the article set in the time period to be determined and the time length of the time period to be determined.
With continued reference to fig. 3, fig. 3 is a schematic diagram 300 of an application scenario of the method for determining a flow regulation target according to the present embodiment. In the application scenario of fig. 3, first, a user 301 sends request information to a server 303 through a terminal device 302. The request information comprises an article set of the flow control target to be determined and a time period to be determined. After the server acquires the request information, firstly, an article set of the flow control target to be determined and a time period to be determined are determined. Then, the characteristic information of the item set and the time period to be determined is input into the pre-trained prediction model 304, so as to obtain the ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in the current preset time period. The prediction model 304 is used for representing the corresponding relationship between the characteristic information and the ratio of the item set and the time period to be determined. And finally, determining the flow control target of the article set in the time period to be determined according to the ratio.
In the method provided by the above embodiment of the present application, the article set and the time period to be determined of the flow rate regulation target are determined; inputting the characteristic information of the item set and the time period to be determined into a pre-trained prediction model to obtain the ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in the current preset time period, wherein the prediction model is used for representing the corresponding relation between the characteristic information of the item set and the time period to be determined and the ratio; and determining the flow control target of the article set in the time period to be determined according to the ratio, thereby providing a method for determining the flow control target under the electronic market scene, and improving the reasonability and accuracy of the determined flow control target.
In some optional implementation manners of this embodiment, the executing body may further distribute the flow rate regulation target of the item set in the time period to be determined to each sub-time period in the time period to be determined, and determine the flow rate regulation target of the item set in each sub-time period. And the time length of the sub-time period is less than that of the time period to be determined.
Specifically, the executing body may distribute the flow rate regulation targets of the item sets in the time period to be determined to each sub-time period in the time period to be determined, and determine the flow rate regulation targets of the item sets in each sub-time period, based on a principle that the flow rates of the corresponding sub-time periods in the corresponding different time periods account for the same total flow rate in the time period.
As an example, the time period to be determined is 2021.6.13-2021.6.18, which corresponds to 2020.6.13-2020.6.18 of the time period to be determined. Wherein, 6 days 2021.6.13-2021.6.18 correspond to 6 days 2021.6.13-2021.6.18 one by one. The proportion information of the item set in 6 days 2020.6.13-2020.6.18 is 0.05, 0.08, 0.12, 0.2, 0.3 and 0.25 respectively, and the proportion information of the item set in 6 days 2021.6.13-2021.6.18 is 0.05, 0.08, 0.12, 0.2, 0.3 and 0.25 respectively.
In some optional implementations of this embodiment, the execution body may implement traffic partitioning with different time granularities. Specifically, the executing body may execute the distribution process in the following manner:
firstly, taking the first-level sub-time periods as time units, distributing the flow control targets corresponding to the time periods to be determined to the first-level sub-time periods in the time periods to be determined based on the principle that the proportion of the flow of the corresponding first-level sub-time periods in the total flow of the time periods is the same in different corresponding time periods, and determining the flow control targets of the item sets in the first-level sub-time periods.
As an example, the first level sub-period may be a day-level period.
Secondly, with the second-level sub-time periods as time units, based on the principle that the proportion of the flow of the corresponding second-level sub-time periods in the corresponding first-level sub-time periods in the total flow of the first-level sub-time periods is the same, the flow control targets corresponding to the first-level sub-time periods are distributed to the second-level sub-time periods in the first-level sub-time periods, and the flow control targets of the item sets in the second-level sub-time periods are determined.
In this implementation, the time length of the second-level sub-period representation is smaller than the time length of the first-level sub-period representation. For example, the second level sub-period may be an hour-level period.
Continuing to take 2021.6.13-2021.6.18 as the time period to be determined and 2020.6.13-2020.6.18 as the time period corresponding to the time period to be determined as an example, the ratio of the total flow of the two hours 12:00-14:00 of the day of 2020.6.13 in the day is 0.1 and 0.15, and the ratio of the total flow of the two hours 12:00-14:00 of the day of 2021.6.13 in the day is also 0.1 and 0.15.
In some optional implementations of the embodiment, in the distribution process of the flow rate regulation target, the execution main body needs to consider an influence of price change of the item on the flow rate, so as to improve accuracy of distributing the flow rate regulation target in the distribution process.
The execution main body may execute the first step as follows:
firstly, based on the principle that the proportion of the flow in the corresponding first-level sub-time periods in the time period total flow is the same in the corresponding different time periods, determining a flow regulation target corresponding to the time period to be determined, and distributing the flow regulation target to the first initial weight of each first-level sub-time period in the time period to be determined.
The first initial weight of each first-level sub-period may be information of a ratio of the flow rate in each first-level sub-period to the total flow rate in the period.
Then, with the first-level sub-time period as a time unit, first adjustment information is determined according to proportion information of the sales value of each article in the article set in the total sales value of the article set and discount information of each article.
In this implementation, the executing entity may determine the first adjustment information according to the following formula:
Figure BDA0003048799080000131
wherein m represents first adjustment information,
Figure BDA0003048799080000132
characterizing a proportion of sales of each item in the set of items to a total sales of the set of items, and discount (i) characterizing discount information for the items. The sales amount is the sales amount counted in units of time for the first level sub-period.
And finally, adjusting the first initial weight through the first adjustment information to obtain a first adjusted weight, and distributing the flow control target corresponding to the time period to be determined to each first-level sub-time period in the time period to be determined according to the first adjusted weight.
As an example, the execution subject multiplies the first initial weight by the first adjustment information to obtain a first adjusted weight. And multiplying the first adjusted weight corresponding to each first-level sub-time period by the flow regulation target corresponding to the time period to be determined to obtain the flow regulation target corresponding to each first-level sub-time period.
In some optional implementations of this embodiment, the performing may perform the second step by:
for each first-level sub-period within the period to be determined, performing the following operations:
firstly, based on the principle that the proportion of the flow of the corresponding second-level sub-time periods in the total flow of the first-level sub-time periods in the corresponding first-level sub-time periods is the same, determining a flow regulation target corresponding to the first-level sub-time periods, and distributing the flow regulation target to second initial weights of the second-level sub-time periods in the first-level sub-time periods.
And secondly, determining second adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article by taking the second-level sub-time period as a time unit.
In this implementation, the execution body refers to a calculation formula of the first adjustment information to obtain the second adjustment information. Specifically, the sales statistics are set to the sales counted in units of time in the second level sub-period.
And finally, adjusting the second initial weight through the second adjustment information to obtain a second adjusted weight, and distributing the flow control target corresponding to the first-level sub-time period to each second-level sub-time period in the first-level sub-time period according to the second adjusted weight.
With continued reference to FIG. 4, an illustrative flow chart 400 of one embodiment of a method of determining a flow regulation target in accordance with the present application is shown, comprising the steps of:
step 401, determining an article set of a flow control target to be determined and a time period to be determined.
Step 402, inputting the characteristic information of the item set and the time period to be determined into a pre-trained prediction model, and obtaining the ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in the current preset time period.
The forecasting model is used for representing the corresponding relation between the characteristic information and the ratio of the item set and the time period to be determined.
And step 403, determining a flow control target of the article set in the time period to be determined according to the ratio.
Step 404, based on the principle that the proportion of the flow in the corresponding first-level sub-time periods in the corresponding different time periods in the total flow in the time periods is the same, determining a flow regulation target corresponding to the time period to be determined, and distributing the flow regulation target to the first initial weight of each first-level sub-time period in the time period to be determined.
Step 405, with the first-level sub-time period as a time unit, determining first adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article.
And step 406, adjusting each first initial weight through the first adjustment information to obtain each first adjusted weight, and distributing the flow control target corresponding to the time period to be determined to each first-level sub-time period in the time period to be determined according to each first adjusted weight.
Step 407, for each first-level sub-period within the period to be determined, performing the following operations:
step 4071, based on the principle that the proportion of the flow of the corresponding second-level sub-period within the corresponding first-level sub-period in the total flow of the first-level sub-period is the same, determining a flow regulation target corresponding to the first-level sub-period, and distributing the flow regulation target to the second initial weights of the second-level sub-periods within the first-level sub-period.
Step 4072, with the second level sub-period as a time unit, determines second adjustment information according to the proportion information of the sales value of each item in the item set in the total sales value of the item set and the discount information of each item.
Step 4073, adjusting the second initial weight by the second adjustment information to obtain a second adjusted weight, and distributing the traffic control target corresponding to the first-level sub-time period to each second-level sub-time period within the first-level sub-time period according to the second adjusted weight.
As can be seen from this embodiment, compared with the embodiment corresponding to fig. 2, the flow 400 of the method for determining a traffic control target in this embodiment specifically illustrates a distribution process of the traffic control target, so that the accuracy of the traffic control target in the sub-time periods with different time granularities in the time period to be determined is improved.
With continuing reference to fig. 5, as an implementation of the method illustrated in the above figures, the present application provides an embodiment of an apparatus for determining a flow regulation target, which corresponds to the embodiment of the method illustrated in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 5, the apparatus for determining a flow rate regulation target includes: a first determining unit 501 configured to determine an item set of a flow regulation target to be determined and a time period to be determined; the prediction unit 502 is configured to input the feature information of the item set and the time period to be determined into a pre-trained prediction model, so as to obtain a ratio of an average flow of the item set in the time period to be determined to an average flow of the item set in a current preset time period, wherein the prediction model is used for representing a corresponding relationship between the feature information of the item set and the time period to be determined and the ratio; a second determining unit 503 configured to determine the flow regulation target of the item set in the time period to be determined according to the ratio.
In some embodiments, the above apparatus further comprises: and the distribution unit (not shown in the figure) is configured to distribute the flow rate regulation targets of the item sets in the time periods to be determined to each sub-time period in the time periods to be determined and determine the flow rate regulation targets of the item sets in each sub-time period based on the principle that the flow rates of the corresponding sub-time periods in the corresponding different time periods account for the same total flow rate in the time periods.
In some embodiments, the dispensing unit (not shown in the figures) is further configured to: distributing the flow control targets corresponding to the time periods to be determined to the first level sub-time periods in the time periods to be determined and determining the flow control targets of the item sets in the first level sub-time periods based on the principle that the proportion of the flow of the corresponding first level sub-time periods in the total flow of the time periods is the same in the first level sub-time periods in the corresponding different time periods; and distributing the flow control targets corresponding to the first-level sub-time periods to the second-level sub-time periods in the first-level sub-time periods based on the principle that the proportion of the flow of the corresponding second-level sub-time periods in the total flow of the first-level sub-time periods is the same in the second-level sub-time periods as the time unit, and determining the flow control targets of the item sets in the second-level sub-time periods.
In some embodiments, the dispensing unit (not shown in the figures) is further configured to: determining a flow regulation target corresponding to the time period to be determined based on the principle that the proportion of the flow in the corresponding first-level sub-time periods in the time period total flow is the same in the corresponding different time periods, and distributing the flow regulation target to the first initial weight of each first-level sub-time period in the time period to be determined; determining first adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article by taking the first-level sub-time period as a time unit; and adjusting each first initial weight through the first adjustment information to obtain each first adjusted weight, and distributing the flow control target corresponding to the time period to be determined to each first-level sub-time period in the time period to be determined according to each first adjusted weight.
In some embodiments, the dispensing unit (not shown in the figures) is further configured to: for each first-level sub-period within the period to be determined, performing the following operations: determining a flow regulation target corresponding to the first-level sub-time period and distributing the flow regulation target to second initial weights of all second-level sub-time periods in the first-level sub-time period on the basis of the principle that the proportion of the flow of the corresponding second-level sub-time period in the corresponding first-level sub-time period in the total flow of the first-level sub-time period is the same; determining second adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article by taking the second-level sub-time period as a time unit; and adjusting the second initial weight through the second adjustment information to obtain a second adjusted weight, and distributing the flow control target corresponding to the first-level sub-time period to each second-level sub-time period in the first-level sub-time period according to the second adjusted weight.
In some embodiments, the above apparatus further comprises: a training unit (not shown in the figure) configured to train the prediction model by: acquiring a training sample set, wherein training samples in the training sample set comprise first characteristic information of a sample article set in a plurality of preset time periods before a sample time period, second characteristic information of the sample article set in a time period corresponding to the sample time period, and a ratio of an average flow of the sample article set in the sample time period to an average flow of the sample article set in a preset time period before the sample time period; and training to obtain a prediction model by using a machine learning algorithm and taking the first characteristic information and the second characteristic information in the training sample as input and taking the ratio corresponding to the input first characteristic information and the input second characteristic information as expected output.
In some embodiments, the above apparatus further comprises: a deriving unit (not shown in the figures) configured to: taking articles belonging to the same user as an article set to be screened to obtain a plurality of article sets to be screened; and screening a plurality of sample article sets from the plurality of article sets to be screened according to the sales value of each article set to be screened in the plurality of article sets to be screened.
In this embodiment, a first determination unit in the apparatus for determining a flow rate regulation target determines an item set and a time period to be determined of the flow rate regulation target; the method comprises the steps that a prediction unit inputs characteristic information of an article set and a time period to be determined into a pre-trained prediction model to obtain the ratio of the average flow of the article set in the time period to be determined to the average flow of the article set in the current preset time period, wherein the prediction model is used for representing the corresponding relation between the characteristic information of the article set and the time period to be determined and the ratio; the second determining unit determines the flow control target of the item set in the time period to be determined according to the ratio, so that the method for determining the flow control target in the E-market scene is provided, and the reasonability and the accuracy of the determined flow control target are improved.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing devices of embodiments of the present application (e.g., devices 101, 102, 103, 105 shown in FIG. 1). The apparatus shown in fig. 6 is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a processor (e.g., CPU, central processing unit) 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The processor 601, the ROM602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the method of the present application.
It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the client computer, partly on the client computer, as a stand-alone software package, partly on the client computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the client computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first determination unit, a prediction unit, and a second determination unit. For example, the prediction unit may be further described as a unit that inputs the characteristic information of the item set and the time period to be determined into a pre-trained prediction model to obtain a ratio of an average flow rate of the item set in the time period to be determined to an average flow rate of the item set in a current preset time period.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the computer device to: determining an article set of a flow control target to be determined and a time period to be determined; inputting the characteristic information of the item set and the time period to be determined into a pre-trained prediction model to obtain the ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in the current preset time period, wherein the prediction model is used for representing the corresponding relation between the characteristic information of the item set and the time period to be determined and the ratio; and determining the flow control target of the article set in the time period to be determined according to the ratio.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims (16)

1. A method of determining a flow regulation target, comprising:
determining an article set of a flow control target to be determined and a time period to be determined;
inputting the characteristic information of the item set and the time period to be determined into a pre-trained prediction model to obtain a ratio of the average flow of the item set in the time period to be determined to the average flow of the item set in a current preset time period, wherein the prediction model is used for representing a corresponding relation between the characteristic information of the item set and the time period to be determined and the ratio;
and determining a flow regulation target of the article set in the time period to be determined according to the ratio.
2. The method of claim 1, further comprising:
distributing the flow rate regulation targets of the item sets in the time period to be determined to each sub-time period in the time period to be determined based on the principle that the flow rates of the corresponding sub-time periods in the corresponding different time periods account for the same proportion in the total flow rate of the time periods, and determining the flow rate regulation targets of the item sets in each sub-time period.
3. The method according to claim 2, wherein the distributing the flow rate regulation targets of the item sets in the time period to be determined to each sub-time period in the time period to be determined based on the principle that the flow rates of the corresponding sub-time periods in the corresponding different time periods are equal to each other in the total flow rate of the time period, and the determining the flow rate regulation targets of the item sets in each sub-time period comprises:
distributing the flow control targets corresponding to the time periods to be determined to the first-level sub-time periods in the time periods to be determined and determining the flow control targets of the item sets in the first-level sub-time periods based on the principle that the proportion of the flow of the corresponding first-level sub-time periods in different corresponding time periods in the total flow of the time periods is the same;
and distributing the flow control targets corresponding to the first-level sub-time periods to the second-level sub-time periods in the first-level sub-time periods based on the principle that the proportion of the flow of the corresponding second-level sub-time periods in the total flow of the first-level sub-time periods is the same in the second-level sub-time periods as the time unit, and determining the flow control targets of the item sets in the second-level sub-time periods.
4. The method according to claim 3, wherein the distributing the flow rate regulation and control targets corresponding to the time periods to be determined to the first-level sub-time periods in the time periods to be determined based on a principle that the proportion of the flow rates of the corresponding first-level sub-time periods in the corresponding different time periods in the total flow rate of the time periods is the same, with the first-level sub-time periods as a time unit, comprises:
determining a flow regulation target corresponding to the time period to be determined and distributing the flow regulation target to a first initial weight of each first-level sub-time period in the time period to be determined based on the principle that the proportion of the flow in the corresponding first-level sub-time periods in different corresponding time periods in the total flow of the time periods is the same;
determining first adjustment information according to proportion information of the sales value of each article in the article set in the total sales value of the article set and discount information of each article by taking the first-level sub-time period as a time unit;
and adjusting each first initial weight through the first adjustment information to obtain each first adjusted weight, and distributing the flow control target corresponding to the time period to be determined to each first-level sub-time period in the time period to be determined according to each first adjusted weight.
5. The method according to claim 3, wherein the distributing the traffic control target corresponding to each first-level sub-period to each second-level sub-period in each first-level sub-period based on a principle that the proportion of the traffic of the corresponding second-level sub-period in the corresponding first-level sub-period in the total traffic of the first-level sub-period is the same in the time unit using the second-level sub-period as a time unit comprises:
for each first-level sub-period within the period to be determined, performing the following operations:
determining a flow regulation target corresponding to the first-level sub-time period and distributing the flow regulation target to second initial weights of all second-level sub-time periods in the first-level sub-time period on the basis of the principle that the proportion of the flow of the corresponding second-level sub-time period in the corresponding first-level sub-time period in the total flow of the first-level sub-time period is the same;
determining second adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article by taking the second-level sub-time period as a time unit;
and adjusting the second initial weight through the second adjustment information to obtain a second adjusted weight, and distributing the flow control target corresponding to the first-level sub-time period to each second-level sub-time period in the first-level sub-time period according to the second adjusted weight.
6. The method of claim 1, wherein the predictive model is trained by:
acquiring a training sample set, wherein training samples in the training sample set comprise first characteristic information of a sample item set in a plurality of preset time periods before a sample time period, second characteristic information of the sample item set in a time period corresponding to the sample time period, and a ratio of an average flow rate of the sample item set in the sample time period to an average flow rate of the sample item set in a preset time period before the sample time period;
and training to obtain the prediction model by using a machine learning algorithm and taking the first characteristic information and the second characteristic information in the training sample as input and taking the ratio corresponding to the input first characteristic information and the input second characteristic information as expected output.
7. The method of claim 6, further comprising:
taking articles belonging to the same user as an article set to be screened to obtain a plurality of article sets to be screened;
and screening a plurality of sample item sets from the plurality of item sets to be screened according to the sales value of each item set to be screened in the plurality of item sets to be screened.
8. An apparatus for determining a flow regulation target, comprising:
a first determination unit configured to determine an item set of a flow regulation target to be determined and a time period to be determined;
the prediction unit is configured to input the characteristic information of the item set and the time period to be determined into a pre-trained prediction model to obtain a ratio of an average flow of the item set in the time period to be determined to an average flow of the item set in a current preset time period, wherein the prediction model is used for representing a corresponding relation between the characteristic information of the item set and the time period to be determined and the ratio;
a second determining unit configured to determine a flow regulation target of the item set in the time period to be determined according to the ratio.
9. The apparatus of claim 8, further comprising:
and the distribution unit is configured to distribute the flow rate regulation targets of the item sets in the time period to be determined to each sub-time period in the time period to be determined and determine the flow rate regulation targets of the item sets in each sub-time period based on the principle that the flow rates of the corresponding sub-time periods in the corresponding different time periods account for the same total flow rate in the time periods.
10. The apparatus of claim 9, wherein the distribution unit is further configured to:
distributing the flow control targets corresponding to the time periods to be determined to the first-level sub-time periods in the time periods to be determined and determining the flow control targets of the item sets in the first-level sub-time periods based on the principle that the proportion of the flow of the corresponding first-level sub-time periods in different corresponding time periods in the total flow of the time periods is the same; and distributing the flow control targets corresponding to the first-level sub-time periods to the second-level sub-time periods in the first-level sub-time periods based on the principle that the proportion of the flow of the corresponding second-level sub-time periods in the total flow of the first-level sub-time periods is the same in the second-level sub-time periods as the time unit, and determining the flow control targets of the item sets in the second-level sub-time periods.
11. The apparatus of claim 10, wherein the distribution unit is further configured to:
determining a flow regulation target corresponding to the time period to be determined and distributing the flow regulation target to a first initial weight of each first-level sub-time period in the time period to be determined based on the principle that the proportion of the flow in the corresponding first-level sub-time periods in different corresponding time periods in the total flow of the time periods is the same; determining first adjustment information according to proportion information of the sales value of each article in the article set in the total sales value of the article set and discount information of each article by taking the first-level sub-time period as a time unit; and adjusting each first initial weight through the first adjustment information to obtain each first adjusted weight, and distributing the flow control target corresponding to the time period to be determined to each first-level sub-time period in the time period to be determined according to each first adjusted weight.
12. The apparatus of claim 10, wherein the distribution unit is further configured to:
for each first-level sub-period within the period to be determined, performing the following operations: determining a flow regulation target corresponding to the first-level sub-time period and distributing the flow regulation target to second initial weights of all second-level sub-time periods in the first-level sub-time period on the basis of the principle that the proportion of the flow of the corresponding second-level sub-time period in the corresponding first-level sub-time period in the total flow of the first-level sub-time period is the same; determining second adjustment information according to the proportion information of the sales value of each article in the article set in the total sales value of the article set and the discount information of each article by taking the second-level sub-time period as a time unit; and adjusting the second initial weight through the second adjustment information to obtain a second adjusted weight, and distributing the flow control target corresponding to the first-level sub-time period to each second-level sub-time period in the first-level sub-time period according to the second adjusted weight.
13. The apparatus of claim 8, further comprising: a training unit configured to train the prediction model by:
acquiring a training sample set, wherein training samples in the training sample set comprise first characteristic information of a sample item set in a plurality of preset time periods before a sample time period, second characteristic information of the sample item set in a time period corresponding to the sample time period, and a ratio of an average flow rate of the sample item set in the sample time period to an average flow rate of the sample item set in a preset time period before the sample time period; and training to obtain the prediction model by using a machine learning algorithm and taking the first characteristic information and the second characteristic information in the training sample as input and taking the ratio corresponding to the input first characteristic information and the input second characteristic information as expected output.
14. The apparatus of claim 13, further comprising:
a deriving unit configured to: taking articles belonging to the same user as an article set to be screened to obtain a plurality of article sets to be screened; and screening a plurality of sample item sets from the plurality of item sets to be screened according to the sales value of each item set to be screened in the plurality of item sets to be screened.
15. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
16. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
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