CN116797250A

CN116797250A - Data processing method, device, server and storage medium

Info

Publication number: CN116797250A
Application number: CN202210240163.1A
Authority: CN
Inventors: 秦浩然
Original assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Wodong Tianjun Information Technology Co Ltd
Current assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Wodong Tianjun Information Technology Co Ltd
Priority date: 2022-03-10
Filing date: 2022-03-10
Publication date: 2023-09-22

Abstract

The application provides a data processing method, a data processing device, a server and a storage medium. According to the method, historical data of article sales are obtained from a local database, a first relation is determined according to the business quantization difficulty level, the article sales volume and a preset regression function, a second relation is determined according to the business quantization difficulty level, the article sales volume, the article exchange volume and the preset regression function, and finally a target business quantization difficulty level is determined according to the first relation, the second relation and a preset target business quantization difficulty level range, wherein the target business quantization difficulty level is the business quantization difficulty level with higher article exchange rate and sales volume increase rate. In the technical scheme, from historical data of the sales of the goods, the business quantization difficulty level capable of improving the sales sum and the exchange rate is determined, and a reference is provided for the establishment of a subsequent business strategy, so that the sales amount and the exchange rate of the goods are improved.

Description

Data processing method, device, server and storage medium

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a data processing method, a data processing device, a server, and a storage medium.

Background

With the continuous innovation of the marketing mode, the air ticket is gradually mapped into the public view in a blind box mode, specifically, after the user purchases the blind box, when a satisfactory air line exists, the air line air ticket can be exchanged by an exchange platform of the blind box, and then the order is completed; if none of the recommended periods is redeemed, the order is returned to the user after expiration.

In the prior art, the mainstream blind box airline recommendation strategy mainly comprises user participation recommendation, namely, some interaction strategies are designed, such as a user invites new users, and the probability of the user willing to take an airline increases along with the increase of the number of the invited new users, so that the blind box exchange rate of the user is improved.

In the process of implementing the present application, the inventor finds that at least the following problems exist in the prior art: in the recommendation strategy in the prior art, the number of new users invited is randomly designated by a background manager, and a better business quantification difficulty level is difficult to obtain, so that the sales amount and the exchange rate of the blind boxes are not high.

Disclosure of Invention

The embodiment of the application provides a data processing method, a data processing device, a server and a storage medium, which are used for solving the problem that in the prior art, a better business quantization difficulty level is difficult to obtain, so that the sales amount and exchange rate of an article are not high.

In a first aspect, an embodiment of the present application provides a data processing method, including:

obtaining historical data of sales of the item from a local database, the historical data comprising: the method comprises the steps of business quantization difficulty level, article sales volume corresponding to the business quantization difficulty level and article exchange volume corresponding to the business quantization difficulty level;

determining a first relation according to the business quantization difficulty level, the commodity sales volume and a preset regression function, wherein the first relation is a corresponding relation between the business quantization difficulty level and the sales volume increase rate;

determining a second relation according to the business quantization difficulty level, the article sales amount, the article exchange amount and a preset regression function, wherein the second relation is a corresponding relation between the business quantization difficulty level and the article exchange rate;

determining the target business quantization difficulty level according to the first relation, the second relation and a preset target business quantization difficulty level range, wherein the target business quantization difficulty level is the business quantization difficulty level with higher article exchange rate and sales volume increase rate.

In one possible design of the embodiment of the present application, after the determining the target traffic quantization difficulty level according to the first relationship, the second relationship, and the preset target traffic quantization difficulty level range, the method further includes:

Determining a target strategy of commodity sales according to the target service quantification difficulty level, wherein the target strategy is a service mode adopted under the target service quantification difficulty level;

and sending the target strategy to a shopping platform.

In another possible design of the embodiment of the present application, the determining the first relationship according to the business quantization difficulty level, the article sales amount, and a preset regression function includes:

aiming at each business quantization difficulty level, determining the article sales increase rate corresponding to each business quantization difficulty level according to the article sales volume corresponding to each business quantization difficulty level;

determining a first distance formula according to each business quantization difficulty level, an article sales increase rate corresponding to the business quantization difficulty level and the preset regression function, wherein the first distance formula is a calculation formula corresponding to the minimum value of the distance square sum between each business quantization difficulty level, the article sales increase rate and the preset regression function;

respectively conducting derivation processing on a first independent variable and a first dependent variable in the first distance formula to obtain a first formula and a second formula, wherein the first formula is a formula obtained by conducting derivation on the first independent variable, the second formula is a formula obtained by conducting derivation on the first dependent variable, the first independent variable is an independent variable corresponding to a business quantization difficulty level in the preset regression function, and the first dependent variable is a dependent variable corresponding to an article sales growth rate in the preset regression function;

Determining coefficients and constant terms of a first argument of the first relationship according to the first formula and the second formula;

the first relationship is determined from coefficients and constant terms of a first argument of the first relationship.

In still another possible design of the embodiment of the present application, the determining the second relationship according to the business quantization difficulty level, the item sales amount, the item exchange amount, and a preset regression function includes:

aiming at each business quantization difficulty level, determining an article exchange rate corresponding to each business quantization difficulty level according to the article exchange quantity corresponding to each business quantization difficulty level and the article exchange quantity;

determining a second distance formula according to each business quantization difficulty level, an article exchange rate corresponding to each business quantization difficulty level and the preset regression function, wherein the second distance formula is a calculation formula corresponding to the minimum value of the distance square sum between each business quantization difficulty level and the article exchange rate and the preset regression function;

respectively conducting derivation processing on a second independent variable and a second dependent variable in the second distance formula to obtain a third formula and a fourth formula, wherein the third formula is a formula obtained by conducting derivation on the second independent variable, the fourth formula is a formula obtained by conducting derivation on the second dependent variable, the second independent variable is an independent variable corresponding to a business quantization difficulty level in the preset regression function, and the second dependent variable is a dependent variable corresponding to an article exchange rate in the preset regression function;

Determining coefficients and constant terms of a second argument of the second relationship according to the third formula and the fourth formula;

and determining the second relation according to the coefficient and constant term of the second independent variable of the second relation.

In still another possible design of the embodiment of the present application, the determining the target service quantization difficulty level according to the first relationship, the second relationship, and a preset target service quantization difficulty level range includes:

determining the total sales amount of the article according to the first relationship, the second relationship, the unit price of the article sales and the total sales amount of the article;

determining candidate business quantization difficulty levels according to the commodity sales total;

and determining the target service quantization difficulty level according to the candidate service quantization difficulty level and the preset target service quantization difficulty level range.

In this possible design, prior to the obtaining historical data of item sales from the local database, the method further comprises:

acquiring the commodity sales and commodity exchange quantity corresponding to each business quantification difficulty level in the shopping platform;

and storing the commodity sales volume and the commodity exchange volume corresponding to each business quantification difficulty level into the local database.

Optionally, the determining, according to the sales volume of the article corresponding to each business quantization difficulty level, the sales growth rate of the article corresponding to each business quantization difficulty level includes:

determining the commodity sales increase rate corresponding to the next business quantization difficulty level according to the commodity sales volume corresponding to the previous business quantization difficulty level and the commodity sales volume corresponding to the next business quantization difficulty level;

and determining the article sales growth rate corresponding to each business quantization difficulty level according to the article sales growth rate corresponding to each latter business quantization difficulty level.

In a second aspect, an embodiment of the present application provides a data processing apparatus, including: the device comprises an acquisition module, a determination module and a processing module;

the acquisition module is used for acquiring historical data of article sales from a local database, wherein the historical data comprises: the method comprises the steps of business quantization difficulty level, article sales volume corresponding to the business quantization difficulty level and article exchange volume corresponding to the business quantization difficulty level;

the determining module is configured to determine a first relationship according to the business quantization difficulty level, the article sales volume and a preset regression function, and determine a second relationship according to the business quantization difficulty level, the article sales volume, the article exchange volume and the preset regression function, where the first relationship is a corresponding relationship between the business quantization difficulty level and a sales volume increase rate, and the second relationship is a corresponding relationship between the business quantization difficulty level and an article exchange rate;

The processing module is configured to determine, according to the first relationship, the second relationship, and a preset target service quantization difficulty level range, a target service quantization difficulty level, where the target service quantization difficulty level is a service quantization difficulty level with a higher item exchange rate and sales volume increase rate.

In one possible design of the second aspect, the processing module is further configured to determine a target policy for selling the article according to the target traffic quantization difficulty level, where the target policy is a traffic pattern adopted under the target traffic quantization difficulty level;

the apparatus further comprises: a transmitting module;

the sending module is used for sending the target strategy to the shopping platform.

In another possible design of the second aspect, the determining module determines the first relationship according to the business quantization difficulty level, the article sales amount and a preset regression function, and specifically is configured to:

In still another possible design of the second aspect, the determining module determines the second relationship according to the business quantization difficulty level, the item sales amount, the item exchange amount, and a preset regression function, and specifically is configured to:

In a further possible design of the second aspect, the processing module determines the target traffic quantization difficulty level according to the first relationship, the second relationship, and a preset target traffic quantization difficulty level range, and is specifically configured to:

In this possible design, the obtaining module is further configured to obtain, in the shopping platform, an item sales amount and an item exchange amount corresponding to each business quantization difficulty level;

the processing module is further configured to store the article sales amount and the article exchange amount corresponding to each business quantization difficulty level to the local database.

Optionally, the determining module determines, according to the sales volume of the article corresponding to each service quantification difficulty level, an increase rate of sales of the article corresponding to each service quantification difficulty level, and is specifically configured to:

In a third aspect, an embodiment of the present application provides a server, including: a processor, a memory;

the memory stores computer-executable instructions;

the processor executes the computer-executable instructions to cause the server to perform the data processing method as described in the first aspect and various possible designs described above.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, are adapted to carry out a data processing method as described in the first aspect and in various possible designs described above.

In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program for implementing a data processing method as described in the first aspect and various possible designs above, when the computer program is executed by a processor.

The embodiment of the application provides a data processing method, a device, a server and a storage medium. In the method, historical data of article sales is obtained from a local database, wherein the historical data comprises: the method comprises the steps of determining a first relation according to a business quantization difficulty level, an article sales volume corresponding to the business quantization difficulty level and an article exchange volume corresponding to the business quantization difficulty level, determining a second relation according to the business quantization difficulty level, the article sales volume, the article exchange volume and a preset regression function, determining a target business quantization difficulty level according to the first relation, the second relation and a preset target business quantization difficulty level range, wherein the first relation is the corresponding relation between the business quantization difficulty level and the sales volume increase rate, and the target business quantization difficulty level is the business quantization difficulty level with higher article exchange rate and sales volume increase rate. In the technical scheme, from historical data of the sales of the goods, the business quantization difficulty level capable of improving the sales sum and the exchange rate is determined, and a reference is provided for the establishment of a subsequent business strategy, so that the sales amount and the exchange rate of the goods are improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.

Fig. 1 is a schematic diagram of an application scenario of a data processing method according to an embodiment of the present application;

FIG. 2 is a flowchart of a first embodiment of a data processing method according to an embodiment of the present application;

FIG. 3 is a schematic diagram of a relationship between a business quantization difficulty level and an item sales growth rate according to an embodiment of the present application;

fig. 4 is a schematic diagram of a relationship between a business quantization difficulty level and an article exchange rate according to an embodiment of the present application;

FIG. 5 is a schematic flow chart of a second embodiment of a data processing method according to the present application;

FIG. 6 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application.

Specific embodiments of the present disclosure have been shown by way of the above drawings and will be described in more detail below. These drawings and the written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the disclosed concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

Before describing embodiments of the present application, the background art of the present application will be explained first:

with the continuous development of shopping forms, products such as air ticket blind boxes gradually reflect the lives of people, one or more airlines can be regularly recommended for the order after a user purchases the air ticket blind box products, when the user has a satisfactory airlines, the user can exchange the airlines with a platform for buying the blind boxes, and the order is completed; if none of the recommended periods is redeemed, the order is returned to the user after expiration.

At present, the blind box route recommended schemes are as follows:

1. completely random recommendation: in a completely random recommending mode, when the air ticket blind box recommends routes, all orders of the routes to be pushed are randomly matched in the range of the routes capable of being pushed, and when the routes are matched, the routes are pushed to the current orders, the pushing result is determined by the random result, and no human intervention is performed;

2. Partial random recommendation: when the air ticket blind box pushes the route, certain manual intervention can be performed. Such as: combining the recommended historical data, and removing the route which is pushed by the history from the recommended route before each route pushed, so that the recommended route received by the user each time is not repeated, and a part of user experience is optimized;

3. user participation recommendation: the blind box product can set some interactions participating in the route recommendation for the user, so that the user can also determine the result of a part of route recommendation. Such as: the wish list mode enables the user to wish before the route recommendation, and tells the shopping platform of the route which the user wishes to obtain, and the shopping platform can moderately adjust the recommendation result according to the wish situation of the user, so that part of users wish to meet.

However, the above recommended manner has the following problems:

1. completely random recommendation: the recommendation result is uncontrollable, and in an extreme case, the same route is probably recommended to the user each time, so that the user experience is greatly influenced;

2. partial random recommendation: on the premise of random recommendation, manual intervention is added, so that the relative controllability of a recommendation result is ensured, but information communication with a user is lacking, and the exchange rate of the user is not ensured;

3. User participation recommendation: the platform designs interaction strategies for users, such as users inviting new users, forwarding friend circles or contact groups, and the like, so that the purpose is to pull and popularize products and improve user exchange rate. However, in the mode, the activity effect cannot be measured, if the activity is too difficult, the interaction and the completion of the user can not be realized, and the overall exchange rate is difficult to improve; if the activity is too simple, the product can be pulled up and promoted, the activity result is not attractive, and the product is difficult to promote in a large scale.

Based on the problems in the prior art, fig. 1 is a schematic application scenario diagram of a data processing method according to an embodiment of the present application, so as to solve the technical problems. As shown in fig. 1, the application scenario schematic includes: a user 11, a terminal device 12 and a server 13.

It should be appreciated that embodiments of the present application are illustrated with respect to a user 11 wishing to purchase an air ticket blind box during which the user 11 may wish to select the route of his own cardiology instrument from the platform. The activity form is to invite new users, the more new users are invited, the higher the probability of being satisfied. Wherein the type of blind box is not limited.

Optionally, the terminal device 12 may have a platform such as a website and an application program for purchasing a blind box and exchanging the blind box, the user 11 may purchase the blind box through the platform on the terminal device 12, the blind box is a product randomly recommended by a designated departure city to reach the city, the user may recommend an airline for reaching the city to the user 11 within a designated date period after purchasing, and if the user 11 is satisfied, the ticket may be exchanged, and the blind box order is completed; if none of the users 11 has redeemed the airline tickets within the specified date period, the blind box order is returned to the users 11.

To pull a new or generalized platform may push out activities that user 11 may engage in, i.e., increase the probability of acquiring a cardiometer route. Such as: the activity is in the form of the user 11 increasing the probability of acquisition of the cardiology route by inviting new people, i.e. the strategy is issued by the server 13 to the platform.

Further, the server 13 collects information such as the blind box product purchased by the user 11, the cardiology route wishing to select, the wish activity completion condition, the blind box product exchange, and the like, and stores the collected data in the local database, and it should be understood that the data collection mode may be to collect specific information of different users under different activity difficulty levels in a preset time length.

The server 13 can sort and analyze the sales amount of the blind boxes, the increase rate of the sales amount of the blind boxes, the exchange rate of the blind boxes, the increase rate of the exchange of the blind boxes, the completion of the wishing activities of the users, and the like according to the acquired data.

The server 13 adjusts the difficulty level of the activity according to the analyzed result (for example, initially, the difficulty level of the activity may be 10 new users are invited, the cardiology route may be 100% obtained, but the blind box exchange rate and the blind box sales are both low, at this time, the difficulty level of the activity is adjusted, only 3 new users are invited, the cardiology route may be 100% obtained, and the blind box exchange rate and the blind box sales are both relatively improved).

Finally, according to the difficulty level of the activity, the server 13 determines the probability of the recommended current route of the user according to the completion level of the user activity in the data analysis result, and then selects the route of the cardiometer according to the probability. If the route of the cardiometer is not selected, selecting the route to be recommended for the second time from the rest routes according to a random recommendation mode.

It should be understood that the above-described embodiments are merely application scenarios in which an air ticket type blind box is taken as an example, i.e. the items in the embodiments described below may be air ticket type blind boxes.

The technical conception process of the inventor aiming at the technical problems is as follows: the shortcomings of the complete random recommendation and the partial random recommendation in the prior art are relatively obvious, the user experience is greatly influenced, the method starts from a mode of participating in recommendation by a user, for example, the selling amount and the exchanging amount of a blind box are combined with some collected historical data under different activity difficulties, if a rule can be found out from the historical data, the selling amount and the exchanging amount of a follow-up blind box can be improved under the condition of determining which activity difficulty, the problem existing in the mode of participating in recommendation by the user can be solved, and then win-win of sellers and buyers is realized.

The technical scheme of the present application is described in detail below by a specific embodiment with an application scenario schematic diagram shown in fig. 1. It should be noted that the following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

Fig. 2 is a flowchart of a first embodiment of a data processing method according to an embodiment of the present application.

Step 21, acquiring historical data of article sales from a local database.

Wherein the history data includes: the business quantization difficulty level, the commodity sales volume corresponding to the business quantization difficulty level and the commodity exchange volume corresponding to the business quantization difficulty level.

The article may be a blind box in the above technical background, and may be exemplified by an air ticket.

In this step, the local database stores the related data of the sold articles (such as blind boxes) in advance, and the related data stored in a preset time length from the current time to the current time period is obtained, so that data is provided for obtaining a better service quantification difficulty level later.

Optionally, the business quantization difficulty level may refer to the difficulty level of the activity, for example, invite 1 new user, 2 new users, up to 10 new users; or the number of forwarding circles of friends or groups of contacts, etc. The ease of activity can range from a simple 1 to a complex 10 where the invitation to the new user successfully purchases the item for the new user.

Optionally, the sales amount of the article (such as a blind box) corresponding to the business quantization difficulty level can refer to the sales amount of the article under the difficulty level of different activities, for example, when 1 new user is invited, the sales amount of the blind box is 1023; when 2 new users are invited, the sales volume of the blind box is 1043.

Optionally, the conversion amount of the article (such as a blind box) corresponding to the business quantization difficulty level can refer to the conversion amount of the blind box under the difficulty level of different activities, for example, when 1 new user is invited, the conversion amount of the blind box is 989; when 2 new users are invited, the blind box redemption amount is 995.

It should be appreciated that as the business quantization difficulty level increases, the sales volume of the blind boxes increases due to new users invited by the users also purchasing the blind boxes, but the blind box redemption volume generally tends to decrease due to the increase in the business quantization difficulty level.

For example, table 1 shows the sales amount and the conversion amount of the blind boxes corresponding to the business quantization difficulty level provided by the embodiment of the application.

TABLE 1

Service quantization difficulty level	0	5	10	15	20
						Blind box sales 1	998	1097	1233	1321	1403
Blind box exchange quantity 1	961	1019	977	985	875
						Blind box sales volume 2	1014	1107	1195	1307	1399
Blind box exchange amount 2	995	1001	940	911	783
						Blind box sales volume 3	1011	1124	1213	1297	1411
Blind box exchange amount 3	987	980	1014	948	913

As can be seen from table 2: along with the increase of the business quantization difficulty level (0-20), the blind box sales volume 1, the blind box sales volume 2 and the blind box sales volume 3 are increased along with the increase; the blind box exchange amount 1, the blind box exchange amount 2 and the blind box exchange amount 3 are increased between 0 and 5 of the business quantization difficulty level, but the business quantization difficulty level is larger than 5, and then the descending trend is presented.

Optionally, before this step, a description should be further given of a data source in the local database, and the method of this data source may include the following implementation manner:

step 1, acquiring the commodity sales and commodity exchange quantity corresponding to each business quantification difficulty level in a shopping platform.

The shopping platform collects purchase events of different users on the articles, exchange events of the articles and business quantification difficulty levels corresponding to each article, and achieves article sales and article exchange corresponding to each business quantification difficulty level.

The time for collecting the information may be a period of time from the current time to the time before the current time, may be 1 month, 2 months, etc.

And step 2, storing the commodity sales volume and the commodity exchange volume corresponding to each business quantization difficulty level into a local database.

And storing the commodity sales volume and the commodity exchange volume corresponding to each business quantification difficulty level into a local database according to a certain rule. For example, when the shopping platform acquires a new item sales event, the item sales event is stored in the local database at the item sales volume under the corresponding business quantization difficulty level, and the item sales volume is increased by 1.

And step 22, determining a first relation according to the business quantification difficulty level, the commodity sales and a preset regression function.

The first relation is a corresponding relation between the business quantification difficulty level and the sales volume increase rate.

In the scheme, the corresponding relation between the business quantization difficulty level and the sales volume increase rate and the corresponding relation between the business quantization difficulty level and the article exchange rate are determined according to the acquired historical data.

In this step, the corresponding relationship between the business quantization difficulty level and the sales volume increase rate can be regarded as a unitary linear function, wherein the independent variable is the business quantization difficulty level, the dependent variable is the sales volume increase rate, and the relationship between the business quantization difficulty level and the sales volume increase rate can be obtained by determining the coefficients of the independent variable and the magnitudes of the constant terms.

That is, the regression function is preset to Y _i ＝a+bx _i Wherein b is a coefficient of an independent variable, a is a constant term, x _i To quantify the difficulty level of the service, Y _i The sizes of a and b need to be determined for the sales growth rate, i.e. in this step.

In one possible implementation, this step may be implemented as follows:

step 1, aiming at each business quantization difficulty level, determining the article sales increase rate corresponding to each business quantization difficulty level according to the article sales volume corresponding to each business quantization difficulty level.

Specifically, according to the sales volume of the article corresponding to the former business quantization difficulty level and the sales volume of the article corresponding to the latter business quantization difficulty level, determining the sales growth rate of the article corresponding to the latter business quantization difficulty level, and according to the sales growth rate of the article corresponding to each of the latter business quantization difficulty levels, determining the sales growth rate of the article corresponding to each of the business quantization difficulty levels.

For example, in table 1, when the business quantization difficulty level (the latter business quantization difficulty level) is 5, and when the business quantization difficulty level (the former business quantization difficulty level) is 0, the blind box sales increase rate is (1097-998)/998 is about 10%.

Alternatively, table 2 shows the blind box sales growth rate provided by the embodiment of the present application through the above calculation.

TABLE 2

It should be appreciated that the calculation of the blind box sales growth rate and the blind box redemption rate may be business quantization difficulty level 1, 2, 3..20, growth rate and redemption rate relative to business quantization difficulty level 0.

Further, corresponding to table 2, fig. 3 is a schematic diagram of a relationship between a business quantization difficulty level and an article sales increase rate according to an embodiment of the present application. As shown in fig. 3, this schematic is merely illustrative of the data described above, wherein the article is referred to as a blind box in the example described above.

Aiming at the article sales growth rate 1, the article sales growth rate 2 and the article sales growth rate 3, the article sales growth rate is correspondingly increased along with the increase of the business quantization difficulty level, and the method accords with a unitary primary function, and at the moment, a function capable of replacing the article sales growth rate needs to be continuously determined.

And step 2, determining a first distance formula according to each business quantization difficulty level, the commodity sales growth rate corresponding to the business quantization difficulty level and a preset regression function.

The first distance formula is a calculation formula corresponding to the minimum value of the sum of squares of the distances between each business quantization difficulty level and the article sales growth rate and a preset regression function.

Alternatively, to describe the above relationship, y may be set _i Representing each business quantization difficulty level x _i For the rate of increase of sales of items, in order to make point (x _i ，y _i ) More fitting preset regressionThe function, i.e. the distance of the point to the preset regression function, is smallest, at which time it is ensured (y _i -Y _i ) ² The value of (2) is the smallest.

For each point of the image, the image is displayed,a is a minimum value. n is the number of business quantization difficulty levels.

Further, the method comprises the steps of,in this case, the magnitude of the sum of squares of the deviations depends on the values of a and b, and a pair of values of a and b is provided to minimize the sum of squares of the deviations.

And step 3, respectively carrying out derivative processing on the first independent variable and the first dependent variable in the first distance formula to obtain a first formula and a second formula.

The first formula is a formula obtained by deriving a first independent variable, the second formula is a formula obtained by deriving a first dependent variable, the first independent variable is an independent variable corresponding to a business quantization difficulty level in a preset regression function, and the first dependent variable is a dependent variable corresponding to an article sales growth rate in the preset regression function.

The first independent variable is x _i I.e. deriving the first formula:

the first dependent variable being y _i I.e. deriving the second formula:

and step 4, determining coefficients and constant terms of a first independent variable of the first relation according to the first formula and the second formula.

Further, the values of a and b are jointly solved for the two equations, which can be seen as follows:

and step 5, determining the first relation according to the coefficient and constant term of the first independent variable of the first relation.

Optionally, the above (x _i ，y _i ) And quantifying the difficulty level for each business and selling and increasing rate of the goods corresponding to each business quantification difficulty level.

Substituting the business quantization difficulty levels and the commodity sales growth rates corresponding to the business quantization difficulty levels into a formula to obtain coefficients and constant items of a first independent variable of a first relation, thereby obtaining Y _i ＝a+bx _i Is a calculation formula of (2).

Wherein, in the given data, the calculation can be known: a=0.0029, b= 0.01967. I.e. Y _i ＝0.0029+0.01967x _i 。

And step 23, determining a second relation according to the business quantification difficulty level, the commodity sales amount, the commodity exchange amount and a preset regression function.

Wherein the second relation is the corresponding relation between the business quantization difficulty level and the article exchange rate

In this step, in accordance with step 22 described above, only the item sales amount is replaced with the item exchange amount, and the principle is similar.

In one possible implementation, this step may be implemented as follows:

step 1, aiming at each business quantization difficulty level, determining the article exchange rate corresponding to each business quantization difficulty level according to the article exchange quantity and the article exchange quantity corresponding to each business quantization difficulty level.

For example, in table 1, when the business quantization difficulty level is 0, the item sales amount 1 is 998 and the item exchange amount 1 is 961, and the item exchange rate 1 is 961/998=96.3%.

Optionally, after the above calculation, table 3 is an article exchange rate provided in an embodiment of the present application.

TABLE 3 Table 3

Further, fig. 4 is a schematic diagram of a relationship between a business quantization difficulty level and an article exchange rate according to an embodiment of the present application. As shown in fig. 4, this schematic is merely illustrative of the data described above.

Aiming at the article exchange rate 1, the article exchange rate 2 and the article exchange rate 3, the article exchange rate correspondingly decreases along with the increase of the business quantification difficulty level, and accords with the unitary primary function, and a function capable of replacing the article exchange rate needs to be continuously determined at the moment.

And step 2, determining a second distance formula according to each business quantization difficulty level, the article exchange rate corresponding to each business quantization difficulty level and a preset regression function.

The second distance formula is a calculation formula corresponding to the minimum value of the sum of squares of the distances between each business quantization difficulty level and the article exchange rate and a preset regression function.

Alternatively, the preset regression function may be K _i ＝c+dx _i 。

Further, to describe the above relationship, a k may be set _i Representing each business quantization difficulty level x _i For item exchange rate, in order to make point (x _i ，k _i ) More closely fitting the preset regression function, i.e. the distance from the point to the preset regression function is the smallest, at which time (k _i -K _i ) ² The value of (2) is the smallest.

For each point of the image, the image is displayed,b is a minimum value. n is the number of business quantization difficulty levels.

Further, the method comprises the steps of,in this case, the magnitude of the sum of squares of the deviations depends on the values of c and d, and a pair of values of c and d is provided to minimize the sum of squares of the deviations.

And step 3, respectively carrying out derivative processing on the second independent variable and the second dependent variable in the second distance formula to obtain a third formula and a fourth formula.

The third formula is a formula obtained by deriving a second independent variable, the fourth formula is a formula obtained by deriving the second independent variable, the second independent variable is an independent variable corresponding to the business quantization difficulty level in the preset regression function, and the second independent variable is an independent variable corresponding to the article exchange rate in the preset regression function.

It should be understood that: the first independent variable and the second independent variable are the business quantization difficulty level x _i 。

The second independent variable is x _i That is, a third formula is obtained after derivation:

the second dependent variable is k _i That is, a fourth formula is obtained after derivation:

and 4, determining coefficients and constant terms of a second independent variable of the second relation according to the third formula and the fourth formula.

Further, the values of c and d are jointly solved for the two equations, as follows:

and step 5, determining the second relation according to the coefficient and constant term of the second independent variable of the second relation.

Optionally, the above (x _i ，k _i ) And quantifying the difficulty level for each service and the article exchange rate corresponding to each service quantification difficulty level.

Substituting the business quantization difficulty levels and the article exchange rates corresponding to the business quantization difficulty levels into a formula to obtain coefficients and constant items of a second independent variable of a second relation, thereby obtaining K _i ＝c+dx _i Is a calculation formula of (2).

Wherein, in the given data, the calculation can be known: c=0.9826, d= -0.01801. Namely K _i ＝0.9826-0.01801x _i 。

And step 24, determining the target service quantification difficulty level according to the first relation, the second relation and the preset target service quantification difficulty level range.

The target business quantization difficulty level is a business quantization difficulty level with higher article exchange rate and sales volume increase rate.

In the step, the relation between the business quantization difficulty level and the sales volume increase rate and the article exchange is a main factor influencing the article exchange rate and the sales volume increase rate, and according to the first relation and the second relation, the candidate business quantization difficulty level which can be better can be obtained can be determined, and the target business quantization difficulty level which is suitable for practical application can be obtained through limiting the preset target business quantization difficulty level range.

After the step, determining a target strategy of the commodity sales according to the target business quantification difficulty level, and sending the target strategy to the shopping platform.

The target policy is a service mode adopted under the target service quantification difficulty level.

In one possible implementation, after determining the target traffic quantization difficulty level, a target policy for sales of the item (e.g., blind box), i.e., the traffic pattern, may be formulated, for example, when the target traffic quantization difficulty level is 3: when a user invites 3 new users to purchase the blind box, 100% probability exists that the route of the cardiometer is taken from the blind box; when a user invites 2 new users to purchase the blind box, the probability of taking the route of the cardiometer from the blind box is 70%; when a user invites 1 new user to purchase the blind box, the probability of 40% is that the route of the cardiometer is taken from the blind box; when the user does not invite a new user to purchase a blind box, there is a 10% probability of taking the route from the blind box to the cardiology instrument.

Further, the target strategy is published on the shopping platform so that the user can acquire the target strategy in real time and encourage the user to purchase interests.

According to the data processing method provided by the embodiment of the application, historical data of commodity sales are obtained from a local database, a first relation is determined according to the commodity quantization difficulty level, the commodity sales volume and a preset regression function, a second relation is determined according to the commodity quantization difficulty level, the commodity sales volume, the commodity exchange volume and the preset regression function, and finally a target commodity quantization difficulty level is determined according to the first relation, the second relation and a preset target commodity quantization difficulty level range, wherein the target commodity quantization difficulty level is the commodity quantization difficulty level with higher commodity exchange rate and sales volume increase rate. In the technical scheme, from historical data of the sales of the goods, the business quantization difficulty level capable of improving the sales sum and the exchange rate is determined, and a reference is provided for the establishment of a subsequent business strategy, so that the sales amount and the exchange rate of the goods are improved.

Based on the foregoing embodiments, fig. 5 is a schematic flow chart of a second embodiment of a data processing method according to the embodiment of the present application. As shown in fig. 5, the above step 24 may be implemented as follows:

Step 51, determining the total sales amount of the article according to the first relationship, the second relationship, the unit price of the article sales and the total sales amount of the article.

In this step, after the activity of setting the business quantization difficulty level, the calculation method of the sales total of the article is as follows: item sales unit price total item sales amount item exchange rate (1+ sales volume increase rate).

Wherein the item exchange rate corresponds to the second relationship and the sales volume increase rate corresponds to the first relationship.

And 52, determining candidate business quantification difficulty levels according to the commodity sales total.

In this step, the item sales total w=item sales unit price p×item sales total m×k _i *Y _i Specific:

w＝pm*[1+(0.0029+0.01967x _i )]*(0.9826-0.01801x _i )

w＝(-0.00035x _i ² +0.00127x _i +0.9854)mp。

to maximize the value of w, the derivative of the above formula is taken to give x _i About 1.8, i.e. x _i May be 1 or 2.

Alternatively, the candidate traffic quantization difficulty level may be 1 or 2.

And step 53, determining the target service quantization difficulty level according to the candidate service quantization difficulty level and the preset target service quantization difficulty level range.

In this step, the candidate service quantification difficulty level is at least 0, and at this time, it should be considered that the item exchange rate is greater than a certain threshold value, and a large number of items (such as blind boxes) cannot be sold, but the items are returned due to being not exchanged, for example, the item exchange rate is greater than 50%.

I.e. 0.9826-0.01801x _i > 50%, then x _i Less than or equal to 26.8.

Further, presetting a target service quantification difficulty level range x _i May take a value between 0 and 26.8.

In addition, candidate service quantization difficulty levels 1 and 2 are both within the preset target service quantization difficulty level range, and w= (-0.00035 x) can be calculated _i ² +0.00127x _i + 0.9854) mp, gives:

x _i when=1, w= 0.98632mp; x is x _i When=2, w=0.98654mp。

Thus, the target traffic quantization difficulty level may be a traffic quantization difficulty level of 2.

According to the data processing method provided by the embodiment of the application, the article sales total is determined according to the first relation, the second relation, the article sales unit price and the article sales total, the candidate business quantization difficulty level is determined according to the article sales total, and then the target business quantization difficulty level is determined according to the candidate business quantization difficulty level and the preset target business quantization difficulty level range, so that the better business quantization difficulty level is realized, and a foundation is provided for improving the article sales total and the article exchange rate.

On the basis of the above method embodiment, fig. 6 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application. As shown in fig. 6, the apparatus includes: comprising the following steps: an acquisition module 61, a determination module 62 and a processing module 63;

An obtaining module 61, configured to obtain, from a local database, historical data of sales of the article, where the historical data includes: the business quantization difficulty level, the commodity sales volume corresponding to the business quantization difficulty level and the commodity exchange volume corresponding to the business quantization difficulty level;

the determining module 62 is configured to determine a first relationship according to the business quantization difficulty level, the article sales amount, and the preset regression function, and determine a second relationship according to the business quantization difficulty level, the article sales amount, the article exchange amount, and the preset regression function, where the first relationship is a correspondence between the business quantization difficulty level and the sales amount increase rate, and the second relationship is a correspondence between the business quantization difficulty level and the article exchange rate;

the processing module 63 is configured to determine a target business quantization difficulty level according to the first relationship, the second relationship, and a preset target business quantization difficulty level range, where the target business quantization difficulty level is a business quantization difficulty level with a higher item exchange rate and a higher sales volume increase rate.

In one possible design of the embodiment of the present application, the processing module 63 is further configured to determine a target policy of sales of the article according to the target business quantization difficulty level, where the target policy is a business mode adopted under the target business quantization difficulty level;

The apparatus further comprises: a transmission module 64;

and a sending module 64, configured to send the target policy to the shopping platform.

In another possible design of the embodiment of the present application, the determining module 62 determines the first relationship according to the business quantization difficulty level, the sales of the article, and the preset regression function, specifically for:

determining a first distance formula according to each business quantization difficulty level, an article sales growth rate corresponding to the business quantization difficulty level and a preset regression function, wherein the first distance formula is a calculation formula corresponding to the minimum value of the distance square sum between each business quantization difficulty level and the article sales growth rate and the preset regression function;

respectively carrying out derivative processing on a first independent variable and a first dependent variable in a first distance formula to obtain a first formula and a second formula, wherein the first formula is a formula obtained by carrying out derivative on the first independent variable, the second formula is a formula obtained by carrying out derivative on the first dependent variable, the first independent variable is an independent variable corresponding to the business quantization difficulty level in a preset regression function, and the first dependent variable is a dependent variable corresponding to the commodity sales increase rate in the preset regression function;

Determining coefficients and constant terms of a first argument of a first relation according to a first formula and a second formula;

the first relationship is determined based on coefficients and constant terms of a first argument of the first relationship.

In yet another possible design of the embodiment of the present application, the determining module 62 determines the second relationship according to the business quantization difficulty level, the sales amount of the article, the exchange amount of the article, and the preset regression function, specifically for:

aiming at each business quantization difficulty level, determining an article exchange rate corresponding to each business quantization difficulty level according to the article exchange quantity and the article exchange quantity corresponding to each business quantization difficulty level;

determining a second distance formula according to each business quantization difficulty level, the article exchange rate corresponding to each business quantization difficulty level and a preset regression function, wherein the second distance formula is a calculation formula corresponding to the minimum value of the distance square sum between each business quantization difficulty level and the article exchange rate and the preset regression function;

respectively carrying out derivative processing on a second independent variable and a second dependent variable in a second distance formula to obtain a third formula and a fourth formula, wherein the third formula is a formula obtained by carrying out derivative on the second independent variable, the fourth formula is a formula obtained by carrying out derivative on the second dependent variable, the second independent variable is an independent variable corresponding to the business quantization difficulty level in a preset regression function, and the second dependent variable is a dependent variable corresponding to the article exchange rate in the preset regression function;

Determining coefficients and constant terms of a second independent variable of the second relation according to the third formula and the fourth formula;

the second relationship is determined based on coefficients and constant terms of a second argument of the second relationship.

In still another possible design of the embodiment of the present application, the processing module 63 determines the target service quantization difficulty level according to the first relationship, the second relationship and the preset target service quantization difficulty level range, and is specifically configured to:

determining candidate business quantification difficulty levels according to the commodity sales total;

In this possible design, the obtaining module 61 is further configured to obtain, in the shopping platform, an item sales amount and an item exchange amount corresponding to each business quantization difficulty level;

the processing module 63 is further configured to store the sales amount and the exchange amount of the article corresponding to each business quantization difficulty level to a local database.

Optionally, the determining module 62 determines, according to the sales volume of the article corresponding to each business quantization difficulty level, an article sales growth rate corresponding to each business quantization difficulty level, which is specifically configured to:

Determining the sales increase rate of the articles corresponding to the business quantization difficulty level according to the sales of the articles corresponding to the business quantization difficulty level and the sales of the articles corresponding to the business quantization difficulty level;

The data processing device provided by the embodiment of the application can be used for executing the technical scheme corresponding to the data processing method in the embodiment, and the implementation principle and the technical effect are similar, and are not repeated here.

It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.

Fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application. As shown in fig. 7, the server may include: a processor 70, a memory 71, and computer program instructions stored on the memory 71 and executable on the processor 70.

Processor 70 executes server-executed instructions stored in memory 71, causing processor 70 to perform the aspects of the embodiments described above. The processor 70 may be a general purpose processor including a central processing unit CPU, a network processor (network processor, NP), or the like; but may also be a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

Optionally, the server may further include: a transceiver 72.

The memory 71 and the transceiver 72 are connected to the processor 70 via a system bus and communicate with each other, and the memory 71 is used for storing server program instructions.

Alternatively, in terms of hardware implementation, the acquisition module 61 and the transmission module 64 in the embodiment shown in fig. 7 described above correspond to the transceiver 72 in this embodiment.

The system bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The system bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.

The server provided by the embodiment of the application can be used for executing the technical scheme corresponding to the data processing method in the embodiment, and the implementation principle and the technical effect are similar, and are not repeated here.

The embodiment of the application also provides a chip for running the instruction, which is used for executing the technical scheme of the data processing method in the embodiment.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and when the computer instructions run on a computer, the computer is caused to execute the technical scheme of the data processing method in the embodiment.

The embodiment of the application also provides a computer program product, which comprises a computer program, wherein the computer program is used for executing the technical scheme of the data processing method in the embodiment when being executed by a processor.

The computer-readable storage medium described above can be implemented by any type of volatile or nonvolatile memory computer or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method of data processing, comprising:

2. The method of claim 1, wherein after said determining said target traffic quantization difficulty level based on said first relationship, said second relationship, and a preset target traffic quantization difficulty level range, said method further comprises:

and sending the target strategy to a shopping platform.

3. The method according to claim 1 or 2, wherein said determining said first relationship according to said business quantization difficulty level, said item sales amount, and a preset regression function comprises:

4. The method according to claim 1 or 2, wherein said determining said second relationship according to said business quantization difficulty level, said item sales amount, said item redemption amount and a preset regression function comprises:

5. The method according to claim 1 or 2, wherein the determining the target traffic quantization difficulty level according to the first relationship, the second relationship, and a preset target traffic quantization difficulty level range comprises:

6. The method of claim 2, wherein prior to said retrieving historical data of item sales from the local database, the method further comprises:

7. The method of claim 3, wherein determining the rate of increase in sales of the item for each business level of difficulty based on the amount of sales of the item for each business level of difficulty comprises:

8. A data processing apparatus, comprising: the device comprises an acquisition module, a determination module and a processing module;

9. The apparatus of claim 8, wherein the processing module is further configured to:

the apparatus further comprises: a transmitting module;

10. The apparatus according to claim 8 or 9, wherein the determining module is configured to determine the first relationship according to the business quantization difficulty level, the item sales amount, and a preset regression function, specifically configured to:

11. The apparatus according to claim 8 or 9, wherein the determining module is configured to determine the second relationship according to the business quantization difficulty level, the item sales amount, the item redemption amount and a preset regression function, specifically configured to:

12. The apparatus according to claim 8 or 9, wherein the processing module is configured to determine the target traffic quantization difficulty level according to the first relationship, the second relationship, and a preset target traffic quantization difficulty level range, and is specifically configured to:

13. The apparatus of claim 9, wherein the obtaining module is further configured to obtain, in the shopping platform, an item sales amount and an item redemption amount corresponding to each business quantization difficulty level;

14. The apparatus of claim 10, wherein the determining module is configured to determine, according to the sales volume of the item corresponding to each business quantization difficulty level, a sales growth rate of the item corresponding to each business quantization difficulty level, specifically configured to:

15. A server, comprising: a processor, a memory and computer program instructions stored on the memory and executable on the processor, which processor, when executing the computer program instructions, implements the data processing method according to any one of the preceding claims 1 to 7.

16. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein computer-executable instructions, which when executed by a processor are for implementing the data processing method according to any of the preceding claims 1 to 7.

17. A computer program product comprising a computer program for implementing the data processing method according to any of the preceding claims 1 to 7 when the computer program is executed by a processor.