CN113822703A

CN113822703A - Exposure distribution method and device, electronic equipment and storage medium

Info

Publication number: CN113822703A
Application number: CN202111017418.XA
Authority: CN
Inventors: 仲思惠; 郑云丹; 李学鹏; 李茗萱; 涂一娜; 张野
Original assignee: Beijing Sankuai Online Technology Co Ltd
Current assignee: Beijing Sankuai Online Technology Co Ltd
Priority date: 2021-08-31
Filing date: 2021-08-31
Publication date: 2021-12-21

Abstract

The present invention provides an exposure amount distribution method, device, electronic device and storage medium. The method includes: for any target merchant settled in a recent preset time period, acquiring the basic business data of the target merchant itself, and the exposure-weighted competition data within the traffic competition range where the target merchant is located, and the exposure-weighted The competition data includes at least one of the total exposure data in the traffic competition range and the exposure weighted data of each merchant in the traffic competition range; based on the basic business data and exposure weighted competition data, obtain the target merchant in the next N time units Exposure-weighted estimated revenue score within each time unit in equal to N. Thereby, the accuracy of the exposure weighting timing of the merchants and the exposure balance of the new merchants are improved.

Description

Exposure distribution method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of internet technologies, and in particular, to a method and an apparatus for allocating an exposure amount, an electronic device, and a storage medium.

Background

With the development of internet technology and the popularization of intelligent terminal equipment, more and more off-line merchants are connected to the e-commerce platform so as to get through information interaction channels between the off-line merchants and consumers through the e-commerce platform. After a consumer enters a homepage of the e-commerce platform through application software running on terminal equipment of the consumer, the e-commerce platform pushes shop promotion information of a merchant, namely shop advertisement information, to the application software, so that the shop of the merchant is shown to the consumer, and the consumer is promoted to enter the shop homepage for consumption.

When the platform displays the merchants to the users, the merchants are generally ranked based on comprehensive operation conditions of all aspects of the merchants, and for newly developed merchants, the operation data is less, the operation capacity is relatively low, and the possibility of arranging the merchants at the front position is relatively low. To support a new merchant, the new merchant may be weighted to increase its exposure. The related art allows the merchant to autonomously select when to turn on the weighting and automatically turn on the remaining amount of weighting for the merchant after the expiration date to ensure that the merchant can use all of the weighting. However, since the merchants do not know the competition situation of the weighting opportunity and the traffic, the opportunity for most merchants to start the weighting is not good, and the exposure opportunity brought by the weighting is still less.

Disclosure of Invention

The embodiment of the invention provides an exposure allocation method, an exposure allocation device, electronic equipment and a storage medium, and aims to solve the problems that exposure weighting opportunities are not good and the exposure of new merchants is influenced in the related art.

In order to solve the technical problem, the invention is realized as follows:

in a first aspect, an embodiment of the present invention provides an exposure amount distribution method, including:

aiming at any target merchant staying in a latest preset time period, acquiring the own business basic data of the target merchant and exposure weighted competition data in a flow competition range where the target merchant is located, wherein the exposure weighted competition data comprises at least one of total exposure data in the flow competition range and exposure weighted data of each merchant in the flow competition range;

acquiring estimated income values of the target merchants for exposure weighting in each time unit of N time units in the future based on the operation basic data and the exposure weighting competitive data, wherein N is a positive integer;

and obtaining M time units with the highest estimated income value as exposure weighting time of the target merchant, and carrying out exposure weighting on the target merchant in the exposure weighting time, wherein M is a positive integer and is less than or equal to N.

Optionally, the step of obtaining the predicted revenue score of the target merchant for exposure weighting in each of the N time units in the future based on the business basic data and the exposure weighted competition data includes:

acquiring target operation basic data and target exposure weighting competitive data corresponding to any time unit in the N time units, wherein the target operation basic data comprises operation basic data used for determining estimated income values of the target merchants for exposure weighting in the time units, and the target exposure weighting competitive data comprises exposure weighting competitive data used for determining estimated income values of the target merchants for exposure weighting in the time units;

acquiring a first score element of the target merchant in the time unit according to the target operation basic data;

acquiring a second score element of the target merchant in the time unit according to the target exposure weighted competition data;

and acquiring an estimated income value of the target merchant for exposure weighting in the time unit according to the first and second score elements and the weight of each score element.

Optionally, the step of acquiring, for any time unit of the N time units, target business basic data and target exposure weighted competition data corresponding to the time unit includes:

acquiring a time attribute of any time unit in the N time units, wherein the time attribute comprises whether the time unit is at least one of a holiday and a weather category;

acquiring operation basic data of a target merchant and exposure weighted competition data in a traffic competition range of the target merchant in J target time units closest to the time units according to the time attributes of the time units, wherein the operation basic data and the exposure weighted competition data are used as target operation basic data and target exposure weighted competition data corresponding to the time units, and J is a positive integer;

wherein the target time unit includes the time unit and a time unit that precedes the time unit and has the same time attribute as the time unit.

Optionally, the step of obtaining a first score element of the target merchant in the time unit according to the target business basic data includes:

acquiring the operation basic data of the benchmarking merchants as reference in the flow competition range;

for each business data dimension contained in the target business basic data, taking the business basic data of the benchmark merchant under the business data dimension as a reference, and carrying out standardization processing on the target business basic data of the target merchant under the business data dimension to obtain a score of the target merchant under each business data dimension;

and acquiring a first score element of the target merchant in the time unit according to the score of the target merchant in each business data dimension.

Optionally, the operation data dimension includes at least one of a takeout platform operation data dimension, an to-store platform operation data dimension, an off-line store passenger flow data dimension, a conversion data dimension between the passenger flow and the order, and a store meal-out duration data dimension.

Optionally, the step of obtaining a second score element of the target merchant in the time unit according to the target exposure weighted competition data includes:

acquiring exposure weighted competition data before the time unit from the target exposure weighted competition data as reference exposure weighted competition data of the time unit;

aiming at each competitive data dimension contained in the target exposure weighted competitive data, determining benchmarking data under the competitive data dimension by using reference exposure weighted competitive data under the competitive data dimension;

taking the benchmark data under the competitive data dimension as a reference, and carrying out standardization processing on the target exposure weighted competitive data under the competitive data dimension in the time unit to obtain the score of the target merchant under the competitive data dimension;

and acquiring a second score element of the target merchant in the time unit according to the score of the target merchant in each competitive data dimension.

Optionally, the exposure weighting data includes at least one of real exposure weighting data and predicted exposure weighting data, and the business basis data includes at least one of real business basis data and predicted business basis data. .

In a second aspect, an embodiment of the present invention provides an exposure amount distribution apparatus, including:

the data acquisition module is used for acquiring the self-operation basic data of a target merchant and exposure weighted competition data in a flow competition range of the target merchant aiming at any target merchant staying in the nearest preset time period, wherein the exposure weighted competition data comprises at least one of total exposure data in the flow competition range and exposure weighted data of each merchant in the flow competition range;

the profit prediction module is used for acquiring prediction profit scores of exposure weighting of the target merchant in each time unit of N future time units based on the operation basic data and the exposure weighting competitive data, wherein N is a positive integer;

and the weighting distribution module is used for acquiring M time units with the highest estimated income value, taking the M time units as the exposure weighting time of the target merchant and carrying out exposure weighting on the target merchant in the exposure weighting time, wherein M is a positive integer and is less than or equal to N.

Optionally, the revenue forecasting module includes:

a target data obtaining sub-module, configured to obtain, for any time unit of the N time units, target operation basic data and target exposure weighting competitive data corresponding to the time unit, where the target operation basic data includes operation basic data used to determine an estimated revenue score for exposure weighting performed by the target merchant in the time unit, and the target exposure weighting competitive data includes exposure weighting competitive data used to determine an estimated revenue score for exposure weighting performed by the target merchant in the time unit;

the first scoring element obtaining sub-module is used for obtaining a first scoring element of the target merchant in the time unit according to the target operation basic data;

the second score element acquisition submodule is used for acquiring a second score element of the target merchant in the time unit according to the target exposure weighted competition data;

and the income score acquisition submodule is used for acquiring an estimated income score of the target merchant for exposure weighting in the time unit according to the first and second score elements and the weight of each score element.

Optionally, the target data obtaining sub-module is specifically configured to:

Optionally, the first score element obtaining sub-module is specifically configured to:

Optionally, the second score element obtaining sub-module is specifically configured to:

In a third aspect, an embodiment of the present invention additionally provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the exposure dose distribution method according to the first aspect.

In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores thereon a computer program, and the computer program, when executed by a processor, implements the steps of the exposure amount distribution method according to the first aspect.

In the embodiment of the invention, aiming at any target merchant staying in a latest preset time period, acquiring the self-operation basic data of the target merchant and exposure weighted competition data in a flow competition range of the target merchant, wherein the exposure weighted competition data comprises at least one of total exposure data in the flow competition range and exposure weighted data of each merchant in the flow competition range; acquiring estimated income values of exposure weighting of the target merchant in each time unit of the N time units in the future based on the operation basic data and the exposure weighting competitive data; and obtaining M time units with the highest estimated income value, taking the M time units as exposure weighting time of the target merchant, and carrying out exposure weighting on the target merchant in the exposure weighting time, wherein M is less than or equal to N. Therefore, the accuracy of the exposure weighting time of the merchant and the exposure balance of the new merchant are improved.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without inventive labor.

FIG. 1 is a flowchart illustrating the steps of a method for dispensing exposure in accordance with an embodiment of the present invention;

FIG. 2 is a flowchart illustrating the steps of another exposure dose distribution method according to an embodiment of the present invention;

FIG. 3 is a schematic view of an exposure dose dispensing apparatus according to an embodiment of the present invention;

FIG. 4 is a schematic view showing another example of the structure of the exposure dose dispensing apparatus according to the embodiment of the present invention;

fig. 5 is a schematic diagram of a hardware structure of an electronic device in the 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 some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to FIG. 1, a flow chart of the steps of a method for dispensing an exposure is shown in an embodiment of the present invention.

Step 110, aiming at any target merchant staying in a latest preset time period, acquiring own business basic data of the target merchant and exposure weighted competition data in a flow competition range where the target merchant is located, wherein the exposure weighted competition data comprises at least one of total exposure data in the flow competition range and exposure weighted data of each merchant in the flow competition range;

step 120, obtaining estimated revenue scores of the target merchants for exposure weighting in each of the future N time units based on the business basic data and the exposure weighting competitive data, wherein N is a positive integer;

step 130, obtaining M time units with the highest estimated revenue value as exposure weighting time of the target merchant, and performing exposure weighting on the target merchant at the exposure weighting time, where M is a positive integer and is less than or equal to N.

In order to help the merchants fully utilize weighting tools and optimize weighting use effects, in the embodiment of the invention, a method for performing weighting setting on newly joined merchants based on the business basic data of the merchants and exposure weighting competitive data in a traffic competitive range of the merchants is provided.

Specifically, for any target merchant who is resident within a latest preset time period, that is, any new current merchant, the operation basic data of the target merchant and the exposure weighted competition data in the traffic competition range where the target merchant is located may be obtained, where the exposure weighted competition data includes at least one of total exposure data in the traffic competition range and exposure weighted data of each merchant in the traffic competition range. And then, based on the business basic data and the exposure weighting competition data, acquiring the predicted profit score of the target merchant for exposure weighting in each time unit of the future N time units each time.

And for the target merchant, the main purpose of weighting is to improve the income, and then M time units with the highest income score can be estimated as the exposure weighting time of the target merchant, and the exposure weighting is carried out for the target merchant at the corresponding exposure weighting time. Wherein N and M are positive integers, and M is less than or equal to N.

The business basic data of the target merchant can include any data related to the business capacity of the target merchant, such as menu content, marketing activities, sales volume, people flow, evaluation and the like of the merchant. The exposure weighted competition data can be understood as other data related to the exposure competition capability of the target merchant, for example, when the number of the merchants with weighted weights simultaneously turned on is smaller, the base number of the competition stores is smaller, the average weighted exposure number of the target merchant is larger, and conversely, the average weighted exposure number of the target merchant is smaller; moreover, when the total exposure data provided by the user terminal in the corresponding traffic competition range is more, the average weighted exposure obtained by the merchant is more, and vice versa, the average weighted exposure can be less. Thus, in embodiments of the present invention, exposure weighted competition data may be set to include, but is not limited to, total exposure data that can be provided within the traffic competition range in which the target merchant is located, exposure weighted data for each merchant within the corresponding traffic competition range, and the like. The exposure weighting data of the merchants can be understood as data such as whether the corresponding merchants carry out weighting, weighting time, weight value in each weighting and the like.

In addition, in practical applications, the range of exposure traffic competition for merchants is generally limited. For example, it may generally be set that each merchant in the same merchant type (e.g., a catering merchant type, a convenience merchant type, a fresh merchant type) performs competition for exposure traffic, or it may be set that each merchant in the same area range or the same business circle performs competition for exposure traffic, and then a traffic competition range in which the target merchant is located may be understood as a range corresponding to the merchant type to which the target merchant belongs, or may be an area range to which the target merchant belongs, or a business circle range; and so on. In the embodiment of the present invention, the determination manner of the traffic contention range may be set according to a requirement, and the embodiment of the present invention is not limited. Of course, the traffic competition range may not be divided according to the requirement, that is, the traffic competition ranges corresponding to the same and currently desirable maximum ranges of different target merchants may be set, and the embodiment of the present invention is not limited thereto.

In addition, in the embodiment of the present invention, the above-mentioned business basic data and exposure weighted competition data may be obtained in any available manner, and the correspondence between the predicted revenue score of the target merchant performing exposure weighting in each of the future N time units and the business basic data and the exposure weighted competition data thereof may also be set by user according to requirements, which is not limited in the embodiment of the present invention.

For example, the business basic data and the exposure weighting competitive data can be obtained by analyzing and mining data in the take-out platform, the store-to-store platform, the intelligent equipment of the merchant and the intelligent equipment of the rider, a training weighting scoring model is set, the data is substituted into the weighting scoring model to calculate the estimated income score of the intelligent equipment for exposure weighting in each time unit of the future N time units, so that the optimal scheme of the current weighting setting is decided, the merchant is helped to select relatively optimal weighting time, and the exposure times of the weighting flow are improved. The training sample of the weighted scoring model may include business basis data for a plurality of merchants with known revenue scores and exposure weighted competition data.

Specifically, the M time units with the highest estimated revenue score may be obtained as the exposure weighting time of the target merchant, and the exposure weighting is performed on the target merchant at the exposure weighting time. The weighting value of each exposure weighting can be set by the corresponding merchant and/or platform in a user-defined manner according to the requirement, and the embodiment of the invention is not limited. The time length of the time unit can also be set by self-definition according to requirements, and the embodiment of the invention is not limited.

For example, in the embodiment of the present invention, a day may be set as a time unit, and a mapping relationship between different weighting values and different estimated profit score value ranges may be set, so that for each time unit in the exposure weighting time of the target merchant, the weighting value having the mapping relationship with the weighting value may be determined as the weighting value of the target merchant in the corresponding time unit according to the estimated profit score value range in the corresponding time unit.

Assuming that one day is taken as a time unit, the value of N is 3, the value of M is 1, and the predicted estimated profit score of the target merchant for exposure weighting on the 1 st day in the future of the current time is a1, the estimated profit score of exposure weighting on the 2 nd day in the future is a2, and the estimated profit score of exposure weighting on the 3 rd day in the future is a3, a2> a3> a1, then the 2 nd day in the future with the current time as the reference can be used as the exposure weighting time of the target merchant.

In addition, it should be noted that the aforementioned future N time units may be continuous N time units, or discontinuous N time units, and may be future time for starting to time based on the current time, or future time for starting to time based on a specified time after the current time, and specifically may be set by a user according to a requirement, which is not limited in this embodiment of the present invention.

In the embodiment of the present invention, the above-mentioned

step

110 and 130 may be executed each time the exposure weighting request for the target merchant is received, so as to obtain the exposure weighting time corresponding to the currently triggered exposure weighting request and perform weighting. The total length of time that the same merchant can be weighted may be limited, and the period of time that the merchant can be weighted as a new store after entering the parking platform is generally limited, e.g., each new merchant typically has a 7-day weighting privilege. The merchant may turn on the weighting in units of days and need to run out of all 7 days before some expiration date, otherwise the platform will automatically turn on for the merchant after the expiration date. And the weighted merchants are started at the same time, and compete for the weighted traffic provided by the platform together.

Then, for any merchant, the exposure weighting request may be triggered before the expiration date, and in the case that the activated weighting duration has not reached the total weighting duration, the exposure weighting time of this weighting may be determined by performing the step 110-.

Referring to fig. 2, in another embodiment, the step 120 may further include:

step 121, acquiring, for any time unit of the N time units, target operation basic data and target exposure weighting competitive data corresponding to the time unit, where the target operation basic data includes operation basic data used for determining an estimated revenue score for performing exposure weighting by the target merchant in the time unit, and the target exposure weighting competitive data includes exposure weighting competitive data used for determining an estimated revenue score for performing exposure weighting by the target merchant in the time unit;

step 122, acquiring a first score element of the target merchant in the time unit according to the target operation basic data;

step 123, acquiring a second score element of the target merchant in the time unit according to the target exposure weighted competition data;

and 124, acquiring an estimated income score of the target merchant for exposure weighting in the time unit according to the first score element, the second score element and the weight of each score element.

In practical applications, in order to predict the predicted profit score weighted in any time unit in the future, the more the relevance of the data as a reference to the profit score of the corresponding time unit is, the more accurate the predicted profit score is.

Moreover, when acquiring the business basic data of the target merchant and the exposure weighted competition data within the traffic competition range of the target merchant, and predicting the estimated profit score of the corresponding merchant for exposure weighting in each of the future N time units, the referenced business basic data and the exposure weighted competition data may be the same, but the accuracy of the estimated profit scores in different time units may be correspondingly affected, and the estimated profit score in the later time unit is more easily affected.

Therefore, in the embodiment of the present invention, target business basic data and target exposure weighting competitive data for determining the predicted revenue score of the target merchant for performing exposure weighting in each time unit may be obtained respectively. Specifically, the conditions that the target business basic data and the target exposure weighted competition data corresponding to each time unit need to satisfy may be set by user according to requirements, and the embodiment of the present invention is not limited thereto.

For example, the target business basic data corresponding to each time unit can be set as the real-time business basic data of the corresponding target merchant at the current time, and/or the business basic data at least one time unit before the corresponding time unit, and so on. Of course, the business basic data of the target merchant in the future time unit can also be based on the actual business basic data in the historical time period, and the predicted value of the business basic data in the future time unit can be obtained through prediction to a certain extent. Therefore, in the embodiment of the present invention, when acquiring the target business basic data corresponding to each time unit, the business basic data of the target merchant in at least one time unit (that is, the target time unit corresponding to the time unit) before and closest to the time unit by the corresponding target merchant may also be acquired as the target business basic data corresponding to the time unit, and when acquiring the business basic data of the target merchant in the target time unit, the actual business basic data of the target merchant in the corresponding time unit may be preferentially acquired, and if there is no actual business basic data (for example, the target time unit has not yet come), the business basic data of the target merchant predicted in the corresponding time unit, that is, the predicted value of the business basic data, may be acquired at this time. The prediction model of the business basic data may be obtained by training any available machine learning model, and the embodiment of the present invention is not limited thereto.

For the exposure weighting competitive data, the total exposure data in the future time period can be obtained through prediction, and the exposure weighting data (for example, whether the certain time unit in the future is weighted or not, weighting weight value and other data) of each merchant in the flow competitive range can also be set in advance by each corresponding merchant according to the requirements, that is, the exposure weighting data of each merchant in the future time period can be known in advance to a certain extent. Therefore, in the embodiment of the present invention, the target exposure weighted competition data corresponding to each time unit may be set as the real-time exposure weighted competition data of each merchant in the corresponding traffic competition range at the current time, and/or the exposure weighted competition data of each merchant in at least one time unit before the current time. The exposure weighted competition data of each merchant in the corresponding traffic competition range in at least one time unit (i.e., the target time unit corresponding to the time unit) before and closest to the time unit by the corresponding target merchant may also be acquired as the target exposure weighted competition data corresponding to the time unit, and when the exposure weighted competition data of each merchant in the target time unit is acquired, real exposure weighted competition data may be preferentially acquired, and if there is no real exposure weighted competition data (e.g., the target time unit has not arrived), the predicted exposure weighted competition data may be acquired at this time. The prediction model of the exposure weighted competition data may be obtained by training any available machine learning model, and the embodiment of the present invention is not limited thereto.

For each time unit in the future N time units, a first score element for the target merchant to perform weighting in the time unit may be further obtained according to the target business basic data corresponding to the time unit, a second score element for the target merchant to perform weighting in the time unit may be obtained according to the target exposure weighting competition data corresponding to the time unit, and an estimated revenue score for the target merchant to perform exposure weighting in the time unit may be further obtained according to the first score element and the second score element of the target merchant in the time unit and the weight of each score element. The corresponding relationship between the first score element and the target operation basic data, the corresponding relationship between the second score element and the target exposure weighting competition data, and the weights of the first score element and the second score element in the estimated profit score can be set by user according to requirements, and the embodiment of the invention is not limited.

For example, the specific scoring rules for the predicted revenue scores may be set as:

Score＝∑F_i*W_i,(i＝1,2,...,n),∑W_i＝1，

where Fi represents the i-th scoring element and Wi represents the weight of the i-th scoring element. Score is obtained by accumulating the product of Fi and Wi, Fi is a Score element of a certain influence weighting factor, the range of the Score element is (0-100), Wi is the weight of the element, the range of the weight is (0-1), and the sum of Wi is 1. The scores of each element are accumulated based on the weights, and a predicted profit Score can be calculated.

Optionally, in another embodiment, the step 121 may further include:

a step 1211 of acquiring, for any time unit in the N time units, a time attribute of the time unit, where the time attribute includes whether the time unit is at least one of a holiday and a weather category;

step 1212, according to the time attribute of the time unit, acquiring operation basic data of the target merchant and exposure weighted competition data within a traffic competition range of the target merchant in J target time units closest to the time unit, as target operation basic data and target exposure weighted competition data corresponding to the time unit, where J is a positive integer; wherein the target time unit includes the time unit and a time unit that precedes the time unit and has the same time attribute as the time unit.

In practical applications, for a merchant, the business basic data such as the traffic and sales amount of a typical holiday are more than those of a working day, and then the merchant is correspondingly inclined to perform the weighting of the exposure amount in the holiday. It is of course also possible that there is a tendency for merchants (e.g. merchants near the office location) to weight on weekdays. That is, for the business, there will be a large difference between the business basic data and the exposure weighted competition data in working days and non-working days (i.e. holidays). Accordingly, the business basic data of the merchants is also easily influenced by weather conditions, such as more traffic on sunny days, less traffic on rainy days, and the opposite may be true for other portions of merchants, and so on.

Therefore, in the embodiment of the present invention, when determining the target business basic data and the target exposure weighted competition data corresponding to each time unit, the time attribute of the time unit may be referred to, that is, whether the time unit belongs to a holiday or a working day, and a weather category, so as to obtain the business basic data and the exposure weighted competition data of the target merchant in the target time unit having the same time attribute.

Specifically, for any time unit in the N time units, a time attribute of the time unit may be obtained, where the time attribute includes whether the time unit is at least one of a holiday and a weather category; and further acquiring operation basic data of the target merchant and exposure weighted competition data in a traffic competition range of the target merchant in J historical time units which are closest to the time unit and have the same time attribute with the time unit according to the time attribute of the time unit, wherein the operation basic data and the exposure weighted competition data are used as the target operation basic data and the target exposure weighted competition data corresponding to the time unit, and J is a positive integer. The target time unit corresponding to a certain time unit may include the time unit itself, or may include a time unit before the time unit.

For the same target merchant and the same time unit, the value of J when the target business basic data is obtained and the value of J when the target exposure weighted competition data is obtained may be different or completely the same, the target time unit when the target business basic data corresponding to the time unit is obtained may or may not include the corresponding time unit itself, and in addition, the target time unit when the target exposure weighted competition data corresponding to the time unit is obtained may or may not include the corresponding time unit itself, and the specific value of J when the target business basic data and the target exposure weighted competition data are obtained may be set by user according to requirements, which is not limited in the embodiment of the present invention.

Optionally, in another embodiment, the step 122 may further include:

1221, acquiring the operation basic data of the benchmarking merchants as reference in the flow competition range;

step 1222, for each business data dimension included in the target business basic data, with the business basic data of the target merchant in the business data dimension as a reference, standardizing the target business basic data of the target merchant in the business data dimension to obtain a score of the target merchant in each business data dimension;

and 1223, acquiring a first score element of the target merchant in the time unit according to the score of the target merchant in each business data dimension.

As described above, in the embodiment of the present invention, the factors affecting the weighted scoring model at least include the following two factors: the business self-operation capacity (F1), the business weighted flow competition condition (F2);

for each factor, there may be multiple data dimensions contained therein. For example, the business basic data related to the business capability of the merchant itself may include the business data of the target merchant in the takeout platform (e.g. menu, sales volume, price, marketing advertisement, evaluation, etc. in the takeout platform), the business data in the store-to platform (e.g. menu, sales volume, price, average meal-taking duration, etc. in the store-to platform), the customer flow data of the offline store, the actual meal-taking duration collected by the rider intelligent device, etc. Moreover, the influence of the different data dimensions on the first score element may be different, and therefore, in the embodiment of the present invention, each data dimension included in the business basic data, that is, each business data dimension described above, may be considered separately, so as to improve the accuracy of the first score element.

In addition, in order to convert the business basic data of the target merchant into the first score element in the form of a numerical value, and in order to obtain the strength of the business capacity of the target merchant based on the business basic data of the target merchant, generally, a reference is needed, so in the embodiment of the present invention, in order to achieve the above object, the business basic data of the benchmarking merchant as a reference in the traffic competition range can be obtained, further, for each business data dimension included in the target business basic data corresponding to the time unit, the business basic data of the benchmarking merchant in the business data dimension is taken as a reference, the target business basic data of the target merchant in the business data dimension is standardized to obtain the score of the target merchant in each business data dimension, and finally, according to the score of the target merchant in each business data dimension, and acquiring a first score element weighted by the target merchant in the time unit.

The obtained business basic data of the benchmark merchant may include business basic data of the benchmark merchant corresponding to the target business basic data within a time range, or may be business basic data of the benchmark merchant at the current time, or business basic data of the benchmark merchant within a period of time before the current time, which is not limited in the embodiment of the present invention. And the benchmark merchants used as references can be set by self-defining according to requirements, for example, the merchants with the highest ranking at the top in the same traffic competition range can be used as the current benchmark merchants, or the merchants with the highest ranking at the top in a period of time can be counted as the benchmark merchants, and so on.

Moreover, the value range of the value under the operation data dimension can be set by self according to requirements, and the embodiment of the invention is not limited.

For example, with the above-mentioned specific scoring rule for the predicted profit score, in the embodiment of the present invention, a calculation rule of the score Fi of each factor may also be set to be similar to the overall scoring rule, that is: f_i＝∑f_i*w_i，(i＝1,2,...,n),∑w _i1. That is, each small index Fi affecting the factor, for example, the business basic data in each data dimension, is normalized, the original value is converted into a (0-100) score, and the score is accumulated in combination with each small index, that is, the weight wi of each business data dimension, so as to calculate the Fi value. The small index standardization standards of different factors are different.

Specifically, for the business operation capacity (F1), the business basic data under each business data dimension of the business can be standardized based on the benchmarked business with the same traffic competition range.

For example, for any business data dimension, the business basic data of the benchmarking merchant in the business data dimension can be used as a reference, the target business basic data of the target merchant in the business data dimension can be obtained, and the proportion of the business basic data in the business data dimension relative to the reference data can be realized so as to realize the standardization processing of the business basic data in the business data dimension.

Optionally, in another embodiment, the operation data dimension includes at least one of a take-out platform operation data dimension, a store-to-store platform operation data dimension, an offline store passenger flow data dimension, a conversion data dimension between passenger flow and orders, and a store meal length data dimension.

Accordingly, the business basis data (fi) under each business data dimension specifically participating in determining the first score element may include one or more of:

1. the target merchant and the platform operation data of the benchmarking merchant, such as sales volume, evaluation, activity setting and the like, are easier to arrange at the front position in the weighting competition when the operation level of the target merchant relative to the benchmarking merchant is relatively high;

2. the business-to-store platform operation data of the target merchant and the benchmark merchant can evaluate the business capacity of the merchant and the customer data from the transaction dimension because the business-to-store is transaction dimension data, and can calculate the rank of the merchant in the flow competition range by combining the transverse data (such as the business-to-store platform operation data of each merchant and the business-to-store platform operation data of the benchmark merchant) in the flow competition range, thereby assisting in perfecting the business capacity analysis of the merchant;

3. the online and offline store passenger flow data are counted through in-store visualization equipment and the like, so that the real online and offline store passenger flow data can be evaluated, and meanwhile, conversion data such as conversion rate between the passenger flow and an order can be calculated by combining with the order data of the store, so that the operation basic data of the store can be enriched;

4. the real meal-out time of stores can be collected through rider intelligent equipment and the like, and transverse data (for example, the real meal-out time of each merchant store, the real meal-out time of a benchmarking merchant and the like) in a flow competition range are counted, so that the operation basic data of the stores are enriched.

Optionally, in another embodiment, the step 123 may further include:

step 1231, obtaining exposure weighted competition data before the time unit from the target exposure weighted competition data, as reference exposure weighted competition data of the time unit;

step 1232, determining benchmarking data in the competitive data dimension by using the reference exposure weighted competitive data in the competitive data dimension for each competitive data dimension included in the target exposure weighted competitive data;

step 1233, with the benchmarking data under the competitive data dimension as a reference, standardizing the target exposure weighted competitive data under the competitive data dimension in the time unit to obtain the score of the target merchant under the competitive data dimension;

step 1234, obtaining a second score element of the target merchant in the time unit according to the score of the target merchant in each competitive data dimension.

For the weighted traffic competition score of the target merchant (F2), that is, the second score element generated by the exposure weighted competition data, the current exposure weighted competition data (that is, the exposure weighted competition data of any time unit in the future X time units of the current score to be predicted) may be normalized with the benchmarking data calculated by the exposure weighted competition data in a certain time unit in the past as the reference, so as to determine the change condition of the current exposure weighted competition data relative to the past exposure weighted competition data.

Moreover, the exposure weighted competition data includes at least one of total exposure data within the traffic competition range, and exposure weighted data for each merchant of the traffic competition range. Therefore, when predicting the second score element in any time unit in the future, the exposure weighted competition data in each time unit before the time unit in the target exposure weighted competition data corresponding to the time unit can be acquired as the reference exposure weighted competition data of the time unit. And aiming at each competitive data dimension contained in the target exposure weighted competitive data, determining the benchmark data under the competitive data dimension by using the reference exposure weighted competitive data under the competitive data dimension, and further carrying out standardization processing on the target exposure weighted competitive data under the corresponding competitive data dimension in the time unit by using the benchmark data under the competitive data dimension as a reference to obtain the score of the target merchant under the corresponding competitive data dimension, so that a second score element of the target merchant in the time unit can be obtained according to the score of the target merchant under each competitive data dimension.

That is, the small metric basis data (fi) that specifically participates in the scoring of the second score element may include one or more of:

1. if the number of the merchants which are currently opened and weighted simultaneously is less than that of the past, the base number of the merchants which are currently involved in the exposure weighting competition in the traffic competition range is smaller, and the average weighted exposure number of the corresponding target merchants is more. Therefore, the exposure weighted data of each merchant in the time unit of the second score element to be predicted can be subjected to benchmarking processing based on the exposure weighted data of each merchant before the time unit as reference data, for example, the ratio of the exposure weighted data of each merchant in the time unit to the average of the exposure weighted data in each time unit before the time unit is determined, and the like, so as to determine the score of the target merchant in the competitive data dimension of the exposure weighted data;

2. and when the total exposure data of the user side is more, the average weighted exposure obtained by the target merchant is more. Accordingly, in the embodiment of the present invention, the total exposure data in the time unit of the second score element to be predicted may be normalized based on the total exposure data before the time unit as the reference data, for example, the ratio of the total exposure data in the time unit to the average of the total exposure data in each time unit before the time unit may be determined, so as to determine the score of the target merchant in the competitive data dimension of the total exposure data.

Optionally, in another embodiment, the exposure weighting data includes at least one of real exposure weighting data and predicted exposure weighting data, and the business basis data includes at least one of real business basis data and predicted business basis data.

In practical applications, a user may preset whether to weight the exposure amount in a certain time unit in the future according to a requirement, and at this time, may acquire real exposure amount weighted data in the corresponding time unit, and certainly, a merchant may not preset exposure amount weighted data in a certain time unit in the future, and at this time, the exposure amount weighted data in the unit time may be predicted based on exposure amount weighted data set by the merchant before the time unit and a remaining available exposure amount weighted amount of the merchant, and at this time, the predicted exposure amount weighted data in the time unit may be acquired, which is not limited by the embodiment of the present invention. Accordingly, for any time unit, if the target merchant generates real business data in the time unit, that is, the time unit is a time unit that has already passed, the real business basic data of the target merchant in the time unit can be obtained and used as the business basic data of the target merchant in the time unit, and if the time unit has not reached and belongs to a future time unit, the business basic data can be predicted through the model and used as the business basic data of the target merchant in the time unit. Of course, in the embodiment of the present invention, for each time unit, the business basic data may be predicted by the model regardless of whether the time unit belongs to the future or the past, and the predicted business basic data is used as the business basic data of the target merchant in the time unit, which is not limited in the embodiment of the present invention.

In the embodiment of the present invention, the prediction model of the exposure weighting data and the prediction model of the business basic data may be obtained by training through any available machine learning model, which is not limited in the embodiment of the present invention.

In the embodiment of the invention, data analysis and mining can be carried out from multiple dimensions such as merchants, business circles and the like based on data in a take-out platform, a home-going platform, intelligent equipment and the like, a weighting setting strategy with higher income is calculated by combining a platform weighting model, and a new merchant is helped to select weighting time with better estimated exposure effect, so that extra exposure of the new merchant is reasonably improved, and the exposure of different merchants is balanced.

Referring to FIG. 3, a schematic diagram of an exposure dose dispensing apparatus according to an embodiment of the present invention is shown.

The exposure amount distribution device of the embodiment of the invention comprises: a data acquisition module 210, a revenue prediction module 220, and a weighted distribution module 230.

The functions of the modules and the interaction relationship between the modules are described in detail below.

A data obtaining module 210, configured to obtain, for any target merchant who is located within a latest preset time period, operation basic data of the target merchant and exposure weighted competition data in a traffic competition range where the target merchant is located, where the exposure weighted competition data includes at least one of total exposure data in the traffic competition range and exposure weighted data of each merchant in the traffic competition range;

a profit prediction module 220, configured to obtain, based on the operation basic data and the exposure weighting competitive data, a prediction profit score for performing exposure weighting on each time unit of N future time units by the target merchant, where N is a positive integer;

the weighting distribution module 230 is configured to obtain M time units with the highest estimated revenue score, serve the M time units as exposure weighting time of the target merchant, and perform exposure weighting on the target merchant at the exposure weighting time, where M is a positive integer and is less than or equal to N.

Referring to fig. 4, in an embodiment of the present invention, the revenue estimating module 220 may further include:

a target data obtaining sub-module 221, configured to obtain, for any time unit of the N time units, target operation basic data and target exposure weighting competitive data corresponding to the time unit, where the target operation basic data includes operation basic data used to determine an estimated revenue score for exposure weighting performed by the target merchant in the time unit, and the target exposure weighting competitive data includes exposure weighting competitive data used to determine an estimated revenue score for exposure weighting performed by the target merchant in the time unit;

a first score element obtaining sub-module 222, configured to obtain, according to the target operation basic data, a first score element of the target merchant in the time unit;

a second score element obtaining sub-module 223, configured to obtain, according to the target exposure weighted competition data, a second score element of the target merchant in the time unit;

and the profit score obtaining sub-module 224 is configured to obtain an estimated profit score of the target merchant for exposure weighting in the time unit according to the first score element, the second score element, and the weight of each score element.

Optionally, in an embodiment of the present invention, the target data obtaining sub-module is specifically configured to:

acquiring operation basic data of a target merchant and exposure weighted competition data in a traffic competition range of the target merchant in J target time units closest to the time units according to the time attributes of the time units, wherein the operation basic data and the exposure weighted competition data are used as target operation basic data and target exposure weighted competition data corresponding to the time units, and J is a positive integer; wherein the target time unit includes the time unit and a time unit that precedes the time unit and has the same time attribute as the time unit.

Optionally, in an embodiment of the present invention, the method is specifically configured to:

Optionally, in an embodiment of the present invention, the operation data dimension includes at least one of a takeout platform operation data dimension, a store-to-store platform operation data dimension, an offline store passenger flow data dimension, a conversion data dimension between a passenger flow and an order, and a store meal-out duration data dimension.

Optionally, in this embodiment of the present invention, the second fractional element obtaining sub-module is specifically configured to:

Optionally, in an embodiment of the present invention, the exposure weighting data includes at least one of real exposure weighting data and predicted exposure weighting data, and the business basic data includes at least one of real business basic data and predicted business basic data. .

The exposure amount distribution device provided by the embodiment of the invention can realize the processes realized in the method embodiments of fig. 1 to fig. 2, and is not repeated here to avoid repetition.

Preferably, an embodiment of the present invention further provides an electronic device, including: the processor, the memory, and the computer program stored in the memory and capable of running on the processor, when being executed by the processor, implement the processes of the above embodiment of the exposure allocation method, and can achieve the same technical effects, and in order to avoid repetition, the details are not repeated here.

The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program realizes the processes of the exposure allocation method embodiment, and can achieve the same technical effect, and in order to avoid repetition, the description is omitted here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.

Fig. 5 is a schematic diagram of a hardware structure of an electronic device implementing various embodiments of the present invention.

The electronic device 500 includes, but is not limited to: a radio frequency unit 501, a network module 502, an audio output unit 503, an input unit 504, a sensor 505, a display unit 506, a user input unit 507, an interface unit 508, a memory 509, a processor 510, and a power supply 511. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 5 does not constitute a limitation of the electronic device, and that the electronic device may include more or fewer components than shown, or some components may be combined, or a different arrangement of components. In the embodiment of the present invention, the electronic device includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted terminal, a wearable device, a pedometer, and the like.

It should be understood that, in the embodiment of the present invention, the radio frequency unit 501 may be used for receiving and sending signals during a message sending and receiving process or a call process, and specifically, receives downlink data from a base station and then processes the received downlink data to the processor 510; in addition, the uplink data is transmitted to the base station. In general, radio frequency unit 501 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 501 can also communicate with a network and other devices through a wireless communication system.

The electronic device provides wireless broadband internet access to the user via the network module 502, such as assisting the user in sending and receiving e-mails, browsing web pages, and accessing streaming media.

The audio output unit 503 may convert audio data received by the radio frequency unit 501 or the network module 502 or stored in the memory 509 into an audio signal and output as sound. Also, the audio output unit 503 may also provide audio output related to a specific function performed by the electronic apparatus 500 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 503 includes a speaker, a buzzer, a receiver, and the like.

The input unit 504 is used to receive an audio or video signal. The input Unit 504 may include a Graphics Processing Unit (GPU) 5041 and a microphone 5042, and the Graphics processor 5041 processes image data of a still picture or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 506. The image frames processed by the graphic processor 5041 may be stored in the memory 509 (or other storage medium) or transmitted via the radio frequency unit 501 or the network module 502. The microphone 5042 may receive sounds and may be capable of processing such sounds into audio data. The processed audio data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 501 in case of the phone call mode.

The electronic device 500 also includes at least one sensor 505, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 5061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 5061 and/or a backlight when the electronic device 500 is moved to the ear. As one type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), detect the magnitude and direction of gravity when stationary, and can be used to identify the posture of an electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), and vibration identification related functions (such as pedometer, tapping); the sensors 505 may also include fingerprint sensors, pressure sensors, iris sensors, molecular sensors, gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which are not described in detail herein.

The display unit 506 is used to display information input by the user or information provided to the user. The Display unit 506 may include a Display panel 5061, and the Display panel 5061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.

The user input unit 507 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device. Specifically, the user input unit 507 includes a touch panel 5071 and other input devices 5072. Touch panel 5071, also referred to as a touch screen, may collect touch operations by a user on or near it (e.g., operations by a user on or near touch panel 5071 using a finger, stylus, or any suitable object or attachment). The touch panel 5071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 510, and receives and executes commands sent by the processor 510. In addition, the touch panel 5071 may be implemented in various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. In addition to the touch panel 5071, the user input unit 507 may include other input devices 5072. In particular, other input devices 5072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail herein.

Further, the touch panel 5071 may be overlaid on the display panel 5061, and when the touch panel 5071 detects a touch operation thereon or nearby, the touch operation is transmitted to the processor 510 to determine the type of the touch event, and then the processor 510 provides a corresponding visual output on the display panel 5061 according to the type of the touch event. Although in fig. 5, the touch panel 5071 and the display panel 5061 are two independent components to implement the input and output functions of the electronic device, in some embodiments, the touch panel 5071 and the display panel 5061 may be integrated to implement the input and output functions of the electronic device, and is not limited herein.

The interface unit 508 is an interface for connecting an external device to the electronic apparatus 500. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 508 may be used to receive input (e.g., data information, power, etc.) from external devices and transmit the received input to one or more elements within the electronic apparatus 500 or may be used to transmit data between the electronic apparatus 500 and external devices.

The memory 509 may be used to store software programs as well as various data. The memory 509 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 509 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The processor 510 is a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 509 and calling data stored in the memory 509, thereby performing overall monitoring of the electronic device. Processor 510 may include one or more processing units; preferably, the processor 510 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 510.

The electronic device 500 may further include a power supply 511 (e.g., a battery) for supplying power to various components, and preferably, the power supply 511 may be logically connected to the processor 510 via a power management system, so as to implement functions of managing charging, discharging, and power consumption via the power management system.

In addition, the electronic device 500 includes some functional modules that are not shown, and are not described in detail herein.

It should be noted that, in this document, 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 phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.

While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. 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.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An exposure amount distribution method, comprising:

2. The method of claim 1, wherein said step of obtaining a predicted revenue score for said target merchant for exposure weighting in each of the N time units in the future based on said business basis data and said exposure weighted competition data comprises:

3. The method according to claim 2, wherein the step of acquiring the target business basic data and the target exposure weighted competition data corresponding to the time unit for any one of the N time units comprises:

4. The method of claim 2 or 3, wherein the step of obtaining the first rating element of the target merchant in the time unit according to the target business basic data comprises:

5. The method of claim 4, wherein the business data dimension comprises at least one of a take-out platform business data dimension, a to store platform business data dimension, an off-line store traffic data dimension, a conversion data dimension between traffic and orders, and an store meal length data dimension.

6. The method of claim 2 or 3, wherein the step of obtaining a second score element of the target merchant in the time unit according to the target exposure weighted competition data comprises:

7. The method of claim 1, wherein the exposure weighting data comprises at least one of real exposure weighting data, predicted exposure weighting data, and the business basis data comprises at least one of real business basis data, predicted business basis data. The business basic data includes at least one of real business basic data and predicted business basic data.

8. An exposure dose distribution apparatus, comprising:

9. The apparatus of claim 8, wherein the revenue prediction module comprises:

10. The apparatus of claim 9, wherein the target data acquisition sub-module is specifically configured to:

11. The apparatus according to claim 9 or 10, wherein the first score element obtaining sub-module is specifically configured to:

12. The apparatus of claim 11, wherein the business data dimension comprises at least one of a take-out platform business data dimension, a to store platform business data dimension, an off-line store traffic data dimension, a conversion between traffic and orders data dimension, and an store meal length data dimension.

13. The apparatus according to claim 9 or 10, wherein the second fractional element obtaining submodule is specifically configured to:

14. The apparatus of claim 8, wherein the exposure weighting data comprises at least one of real exposure weighting data, predicted exposure weighting data, and the business basis data comprises at least one of real business basis data, predicted business basis data. The business basic data includes at least one of real business basic data and predicted business basic data.

15. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of the exposure dose distribution method according to any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that a computer program is stored thereon, which when executed by a processor implements the steps of the exposure dose distribution method according to any one of claims 1 to 7.