CN117436928A

CN117436928A - Data processing method, device, electronic equipment and storage medium

Info

Publication number: CN117436928A
Application number: CN202210821445.0A
Authority: CN
Inventors: 张丹阳; 王晶; 樊歆羽; 王全; 马佳琪; 寇天天; 汪晓燕; 谢茵
Original assignee: SF Technology Co Ltd
Current assignee: SF Technology Co Ltd
Priority date: 2022-07-12
Filing date: 2022-07-12
Publication date: 2024-01-23

Abstract

The application discloses a data processing method, a data processing device, electronic equipment and a storage medium. On the other hand, by acquiring transaction payment data meeting market demand conditions and determining purchasing income, the articles to be purchased can be more accurately determined, and erroneous judgment caused by manual experience is reduced. In addition, the method relies on store sales data and market payment data of the target object, so that even if the target object is marketed, transaction payment data can be determined in real time according to market quotations and sales conditions of the target object on the target object at any time, further whether the currently sold object needs to be purchased continuously or not is judged, and flexibility and rationality of determining the object to be purchased are improved.

Description

Data processing method, device, electronic equipment and storage medium

Technical Field

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

Background

The commodity is a labor result produced for sale, is a product of the human society productivity developed to a certain historical stage, and is a labor product for exchange. For profit, the enterprise needs to select the proper commodity as the purchasing commodity with high qualification.

However, the current method for selecting commodities mainly depends on manual selection and experience judgment, so that the selected commodities to be purchased are not necessarily accurate.

Disclosure of Invention

The application provides a data processing method, a data processing device, electronic equipment and a storage medium, and aims to solve the problem that the selected commodity to be purchased is not necessarily accurate because the existing data processing method mainly depends on manual selection and experience judgment.

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

acquiring store sales data of a target object;

predicting the maximum benefit of the target object and target payment data corresponding to the maximum benefit according to sales payment data in the store sales data and sales quantity corresponding to the sales payment data in the store sales data;

fusing the target payment data and the market payment data of the target object to obtain transaction payment data of the target object;

And determining the object to be purchased in the target object according to the purchase income corresponding to the transaction payment data.

In one possible implementation manner of the present application, the predicting the maximum benefit of the target article and the target payment data corresponding to the maximum benefit according to the sales payment data in the store sales data and the sales quantity corresponding to the sales payment data in the store sales data includes:

acquiring sales payment data in the store sales data and sales quantity corresponding to the sales payment data in the store sales data;

determining the benefits of the target object according to the sales payment data and the sales quantity corresponding to the sales payment data, and training an initial benefit calculation model according to the sales payment data and the benefits corresponding to the sales payment data to obtain a trained benefit calculation model;

obtaining the profit calculation parameters in the trained profit calculation model, and carrying out maximum calculation processing according to the profit calculation parameters to obtain the maximum profit of the target object and target payment data corresponding to the maximum profit.

In one possible implementation manner of the present application, the fusing the target payment data and the market payment data of the target object to obtain transaction payment data of the target object includes:

classifying the target object according to the sales time in the store sales data and the sales quantity in the sales time in the store sales data to obtain the object demand category of the target object;

selecting market reference payment data of the target object from market payment data of the target object according to the object demand category of the target object;

and calculating transaction payment data of the target object according to the target payment data and the market reference payment data.

In one possible implementation manner of the present application, the calculating, according to the target payment data and the market reference payment data, transaction payment data of the target object includes:

acquiring cost payment data of the target object, wherein the cost payment data comprises at least one of purchasing cost, warehousing cost, logistics cost, labor cost, service fee and tax fee;

Acquiring a weighting coefficient corresponding to the target object, wherein the weighting coefficient comprises a first coefficient corresponding to the cost payment data, a second coefficient corresponding to the target payment data and a third coefficient corresponding to the market reference payment data;

and carrying out weighted summation processing on the cost payment data, the target payment data and the market reference payment data according to the weighting coefficient to obtain transaction payment data of the target object.

In one possible implementation manner of the present application, the obtaining the weighting coefficient corresponding to the target object includes:

inquiring to obtain the logistics category corresponding to the target object;

acquiring an initial coefficient corresponding to the object demand category, wherein the initial coefficient comprises an initial cost coefficient corresponding to the cost payment data, a second coefficient corresponding to the target payment data and a third coefficient corresponding to the market reference payment data;

taking the product of the cross-border price adding coefficient corresponding to the logistics category and the initial cost coefficient as a first coefficient corresponding to the cost payment data, and setting the first coefficient, the second coefficient and the third coefficient as weighting coefficients corresponding to the target object.

In one possible implementation manner of the present application, the acquiring store sales data of the target object includes:

acquiring an initial article;

inquiring whether a preset article catalogue list contains the initial article or not;

and if the initial item is contained in the item catalog table, taking the initial item as a target item, and acquiring store sales data of the target item.

In one possible implementation manner of the present application, the determining, according to the purchase benefits corresponding to the transaction payment data, an object to be purchased in the target object includes:

and taking the product of the transaction payment data and the preset purchasing quantity of the target object as purchasing gain corresponding to the transaction payment data, and setting the target object with purchasing gain larger than a preset gain threshold as the object to be purchased.

In a second aspect, the present application provides a data processing apparatus comprising:

the receiving unit is used for receiving a data processing instruction of a target object and acquiring store sales data of a target object corresponding to the data processing instruction;

the predicting unit is used for predicting the maximum benefit of the target object and the target payment data corresponding to the maximum benefit according to the sales payment data in the store sales data and the sales quantity corresponding to the sales payment data in the store sales data;

The fusion unit is used for fusing the target payment data and the market payment data of the target object to obtain transaction payment data of the target object;

and the determining unit is used for determining the object to be purchased in the target object according to the purchase income corresponding to the transaction payment data.

In a possible implementation of the present application, the prediction unit is further configured to:

In a possible implementation of the present application, the fusion unit is further configured to:

the calculating, according to the target payment data and the market reference payment data, transaction payment data of the target article includes:

inquiring to obtain the logistics category corresponding to the target object;

In a possible implementation of the present application, the receiving unit is further configured to:

acquiring an initial article;

In a possible implementation of the present application, the determining unit is further configured to:

In a third aspect, the present application also provides an electronic device comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the processor executing steps of any one of the data processing methods provided herein when the processor invokes the computer program in the memory.

In a fourth aspect, the present application also provides a storage medium having stored thereon a computer program which, when executed by a processor, performs steps in any of the data processing methods provided herein.

In summary, the data processing method provided in the embodiment of the present application includes: acquiring store sales data of a target object; predicting the maximum benefit of the target object and target payment data corresponding to the maximum benefit according to sales payment data in the store sales data and sales quantity corresponding to the sales payment data in the store sales data; fusing the target payment data and the market payment data of the target object to obtain transaction payment data of the target object; and determining the object to be purchased in the target object according to the purchase income corresponding to the transaction payment data.

Therefore, the data processing method provided by the embodiment of the application can automatically determine the transaction payment data according to the store sales data and the market payment data, automatically determine the articles to be purchased according to the transaction payment data and the preset purchasing quantity, and reduce the labor cost during manual determination. On the other hand, through obtaining the transaction payment data which accords with the market demand condition, purchasing income is determined, and further, the articles to be purchased can be determined more accurately, and erroneous judgment caused by manual experience is reduced. In addition, the method relies on store sales data and market payment data of the target object, so that even if the target object is marketed, transaction payment data can be determined in real time according to market quotations and sales conditions of the target object on the target object at any time, further whether the currently sold object needs to be purchased continuously or not is judged, and flexibility and rationality of determining the object to be purchased are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

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

FIG. 2 is a schematic flow chart of a data processing method provided in an embodiment of the present application;

FIG. 3 is a flow chart of acquiring transaction payment data provided in an embodiment of the present application;

FIG. 4 is a schematic flow chart of obtaining a type of demand for an item provided in an embodiment of the present application;

FIG. 5 is a schematic diagram of another process for obtaining transaction payment data provided in an embodiment of the present application;

FIG. 6 is a schematic flow chart of acquiring store sales numbers provided in an embodiment of the present application;

FIG. 7 is a schematic diagram of an embodiment of a data processing apparatus provided in an embodiment of the present application;

fig. 8 is a schematic structural diagram of an embodiment of an electronic device provided in an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

In the description of the embodiments of the present application, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or an implicit indication of the number of features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the embodiments of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

The following description is presented to enable any person skilled in the art to make and use the application. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known processes have not been described in detail in order to avoid unnecessarily obscuring descriptions of the embodiments of the present application. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed in the embodiments of the present application.

The embodiment of the application provides a data processing method, a data processing device, electronic equipment and a storage medium. The data processing device may be integrated in an electronic device, which may be a server or a device such as a terminal.

The execution main body of the data processing method in the embodiment of the present application may be a data processing apparatus provided in the embodiment of the present application, or different types of electronic devices such as a server device, a physical host, or a User Equipment (UE) integrated with the data processing apparatus, where the data processing apparatus may be implemented in a hardware or software manner, and the UE may specifically be a terminal device such as a smart phone, a tablet computer, a notebook computer, a palm computer, a desktop computer, or a personal digital assistant (Personal Digital Assistant, PDA).

The electronic device may be operated in a single operation mode, or may also be operated in a device cluster mode.

With reference to fig. 1, fig. 1 is a schematic view of a scenario of a data processing system provided in an embodiment of the present application. The data processing system may include an electronic device 101, where the electronic device 101 has a data processing apparatus integrated therein.

In addition, as shown in FIG. 1, the data processing system may also include a memory 102 for storing data, such as text data.

It should be noted that, the schematic view of the scenario of the data processing system shown in fig. 1 is only an example, and the data processing system and scenario described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application, and those skilled in the art can know that, with the evolution of the data processing system and the appearance of a new service scenario, the technical solutions provided in the embodiments of the present invention are equally applicable to similar technical problems.

Next, a description will be given initially of a data processing method provided in an embodiment of the present application, in which an electronic device is used as an execution body, and in order to simplify and facilitate description, in a subsequent method embodiment, the execution body is omitted, and the data processing method includes: acquiring store sales data of a target object; predicting the maximum benefit of the target object and target payment data corresponding to the maximum benefit according to sales payment data in the store sales data and sales quantity corresponding to the sales payment data in the store sales data; fusing the target payment data and the market payment data of the target object to obtain transaction payment data of the target object; and determining the object to be purchased in the target object according to the purchase income corresponding to the transaction payment data.

Referring to fig. 2, fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the present application. It should be noted that although a logical order is depicted in the flowchart, in some cases the steps depicted or described may be performed in a different order than presented herein. The data processing method specifically may include the following steps 201 to 204, in which:

201. store sales data of the target item is obtained.

In the embodiment of the application, the store sales data of the target object may refer to sales data of the target object currently performing data processing.

The target object may be referred to as an enterprise or an individual. Illustratively, a target object refers to an enterprise when an employee of the enterprise performs data processing with the enterprise identity through software, web pages, or applets that carry the method. When an individual performs data processing by the above-described software, web page, or applet in the identity of the individual, the target object refers to the individual. For example, when the data processing method provided in the embodiment of the present application is applied to determining an item to be purchased, if an employee in an enterprise represents the enterprise, the target object refers to the enterprise when determining the item to be purchased through the software, the web page, or the applet. For another example, if the data processing method provided in the embodiment of the present application is applied to determining an item to be purchased, and when the user determines the item to be purchased with the identity of the person, the target object refers to the person of the user.

In some embodiments, the electronic device may perform step 201 after receiving the data processing instruction of the target object. The data processing instruction may refer to an instruction issued by the target object when data processing is performed by the software, the web page, or the applet. For example, the target object may send the data processing instruction to the electronic device through the virtual control in the software, the web page, or the applet when determining the item to be purchased, and the electronic device starts to execute step 201 and the following steps after receiving the data processing instruction.

It will be appreciated that the data processing instructions should correspond to the item for which it is desired to determine whether to purchase, i.e., the target item in step 201. For example, the target object may select at least one commodity to be determined in the software loaded with the method, enter the purchase page, and click on a virtual control such as "determine to purchase the commodity" to issue a data processing instruction, where the data processing instruction corresponds to the commodity to be purchased, so that the target commodity is the commodity to be priced. In addition, the data processing instruction may be automatically sent to the electronic device periodically after the target object is set, for example, the target object may send the data processing instruction once every one month.

The store sales data may refer to historical sales data of the target object in the store of the target object, that is, sales data generated when the target object once sells the target object. The store may be a physical store or a point of sale, which is not limited in the embodiment of the present application.

For example, the electronic device may read a historical order of the target object from an order database corresponding to the target object, and use data in the historical order as store sales data. For example, the electronic device may count sales of the target item at different prices according to the price of the order placed in the historical order of the target item and sales corresponding to each order, and use these data as store sales data. For ease of illustration, reference may be made to Table 1, one case of store sales data is shown in Table 1:

price/meta	10	11	12	13	14	15	16
								Sales/piece	550	520	510	500	470	460	450

TABLE 1

In other embodiments, to reduce the amount of computation and to ensure timeliness of the data in the store sales data, a portion of the order may be screened from the historical orders for the target item, and the store sales data determined from the data of the portion of the order. For example, the electronic device may select a part of the orders between n months according to the time of placing the order on the historical order of the target object, and count sales situations of the target object at different prices according to the price of the place of the order on the part of the orders and sales corresponding to each order, so as to obtain store sales data of the target object, where n may be set as required.

202. And predicting the maximum benefit of the target object and the target payment data corresponding to the maximum benefit according to the sales payment data in the store sales data and the sales quantity corresponding to the sales payment data in the store sales data.

Sales payment data may refer to sales prices.

Wherein, sales payment data in the store sales data and sales quantity corresponding to the sales payment data may refer to table 1, and if the data in table 1 is store sales data, the sales payment data in the store sales data may include: 10. 11, 12, 13, 14, 15, 16, and the sales payment data corresponds to sales amounts including: 550 pieces corresponding to 10 elements, 520 pieces corresponding to 11 elements, and 510 pieces … … corresponding to 12 elements.

The maximum income can be the maximum income when the target object sells the target object, and the target payment data corresponding to the maximum income can be the price capable of acquiring the maximum income when the target object sells the target object.

In the embodiment of the present application, the method of combining pricing and purchasing quantity is used to determine the object to be purchased, so the purpose of obtaining the target payment data in step 202 is to combine the historical sales situation of the target object for the target object, and improve the matching degree of the subsequent pricing and the market demand, and the sales situation of the target object.

In some embodiments, the product between sales payment data and the sales amount corresponding to the sales payment data may be taken as a benefit, and then the price in the sales payment data for which the corresponding benefit is greatest may be determined as the target payment data.

In other embodiments, the product between the sales payment data and the sales quantity corresponding to the sales payment data may be used as a benefit, the benefit calculation model is trained through store sales data, and the maximum benefit of the target article and the target payment data corresponding to the maximum benefit are determined according to the parameters in the trained benefit calculation model. By the method in the embodiment, the situation that pricing is not matched with market demand and the sales condition of the target object is not matched when the sales payment data does not contain actual target payment data can be avoided.

For example, the step of predicting the maximum benefit of the target item and the target payment data corresponding to the maximum benefit according to the sales payment data in the store sales data and the sales quantity corresponding to the sales payment data in the store sales data may include:

(1.1) acquiring sales payment data in the store sales data and sales quantity corresponding to the sales payment data in the store sales data.

And (1.2) determining the benefits of the target object according to the sales payment data and the sales quantity corresponding to the sales payment data, and training an initial benefit calculation model according to the sales payment data and the benefits corresponding to the sales payment data to obtain a trained benefit calculation model.

The method of obtaining the profit of the target item may refer to the above, and for example, the product between sales payment data and the sales amount corresponding to the sales payment data may be taken as the profit of the target item.

In the embodiment of the present application, a linear regression (linear regression) model may be used as an initial revenue calculation model, and an objective function of the linear regression model may be:

r=xf (x) formula (1)

Wherein R refers to benefits, x refers to sales payment data corresponding to the benefits, and f is a parameter to be adjusted.

And inputting the sales payment data and the benefits corresponding to the sales payment data into an initial benefit calculation model, and training the initial benefit calculation model to obtain a trained benefit calculation model.

The initial profit calculation model can be stored in a background database of the target object, store sales data are obtained in real time when data processing instructions are received, and training is carried out on the initial profit calculation model according to the store sales data.

And (1.3) obtaining the profit calculation parameters in the trained profit calculation model, and carrying out maximum calculation processing according to the profit calculation parameters to obtain the maximum profit of the target object and target payment data corresponding to the maximum profit.

The benefit calculation parameters in the trained benefit calculation model may refer to the objective functions in the trained benefit calculation model, for example, may refer to equation (1) in step (1.2).

For example, the electronic device may derive the adjusted formula (1) to perform maximum calculation processing, so as to obtain the maximum benefit of the target article and the target payment data corresponding to the maximum benefit.

In addition to the method of training the initial revenue calculation model in real time to obtain the maximum revenue and the target payment data, in other embodiments, the store sales data may be obtained in advance before pricing is performed, training the initial revenue calculation model to obtain the trained revenue calculation model, and the maximum revenue and the target payment data, and storing the maximum revenue and the target payment data in the background database of the target object, and then, directly reading the maximum revenue and the target payment data from the background database when pricing is performed. At this time, the data processing instruction in step 201 should refer to an instruction for obtaining the maximum benefit and the target payment data sent by the target object, or refer to an instruction for obtaining the maximum benefit and the target payment data sent by the target terminal automatically after the target object is set.

203. And fusing the target payment data and the market payment data of the target object to obtain the transaction payment data of the target object.

The market payment data may include all of the searchable prices of the target items in the market. For example, the target object may be searched from a third party platform such as an e-commerce website to obtain market payment data.

In some embodiments, since there may be a difference in naming of the target item among different third parties, the price obtained when searching for the target item may not be the price of the target item, and thus outlier processing of the searched price is required. Illustratively, outlier handling may be based on the 2σ principle: assuming that the prices of the target items follow a normal distribution, outliers are defined as values in the set of prices that deviate by more than 2 standard deviations from the average of the prices. After the searched price is repeatedly processed for m times of abnormal values, the rest price can be used as market payment data of the target object, wherein the specific value of m can be set according to requirements, for example, can be set to be 6. It will be appreciated that after outlier processing, the prices contained in the market payment data typically conform to a normal distribution.

By means of abnormal value processing, price errors caused by products such as combined assemblies, small samples and the like can be effectively prevented.

In order to ensure timeliness of the market payment data, a time range of searching may be set, for example, it may be set to one month so far, and a price which can be searched for in one month so far is set as the market payment data of the target article.

It can be understood that the market payment data and the sales payment data are different from each other in that the pricing object corresponding to the market payment data is different from the pricing object corresponding to the sales payment data, and the pricing object corresponding to the market payment data includes all the objects of the sales target object, so that the overall pricing situation of the target object can be represented, and the pricing object corresponding to the sales payment data is only the target object, and the pricing situation of the target object can be represented more.

It should be noted that, the maximum benefit and the target payment data are already calculated before pricing is performed and stored in the background database of the target object, and then the electronic device may execute step 203 when receiving the instruction sent by the target object to determine the item to be purchased.

The transaction payment data may refer to pricing of the target item by the target object. For example, when the target object sends a data processing instruction by clicking a virtual control in software carrying the method, transaction payment data may refer to a purchase price of the target object in the software. At this time, after "fusing the target payment data and the market payment data to obtain transaction payment data of the target item" in step, the method may further include:

And in a sales page corresponding to the target object, updating the original data of the target object by using the transaction payment data.

The sales page corresponding to the target item may refer to a page corresponding to the target item in the software, the web page, or the applet, and the customer may determine the price of the target item through the sales page, or order the target item.

The original data of the target item may refer to the original price of the target item prior to the present pricing, so after the update, the user may see the updated price in the sales page.

In some embodiments, an average of the prices in the market payment data may be calculated, and the average may be averaged with the target payment data to obtain the transaction payment data.

204. And determining the object to be purchased in the target object according to the purchase income corresponding to the transaction payment data.

The object to be purchased refers to the object to be purchased by the target object.

The purchase benefits corresponding to the transaction payment data refer to benefits that can be obtained when the transaction payment data is used as the pricing of the target object.

In some embodiments, the benefits that the target object can obtain for selling the target item can be predicted based on the transaction payment data and the preset purchase amount, and then the item to be purchased is selected based on the benefits. At this time, the step of determining the to-be-purchased object in the target object according to the purchase benefits corresponding to the transaction payment data includes:

The preset purchase quantity refers to a purchase quantity preset by a target object for the target object, which can be set manually or calculated by a preset algorithm, and the embodiment of the application does not limit the preset purchase quantity. The preset purchase amount may be stored in a background database of the target object, and the electronic device may read the preset purchase amount when executing step 204.

The preset profit threshold is used for evaluating the degree of purchasing profit, and can be preset manually and then stored in a background database of the target object, and the specific value can be set according to an actual scene.

In summary, the data processing method provided in the embodiment of the present application includes: acquiring store sales data of a target object, wherein the store sales data is sales data of the target object; predicting the maximum benefit of the target object and target payment data corresponding to the maximum benefit according to sales payment data in the store sales data and sales quantity corresponding to the sales payment data in the store sales data; fusing the target payment data and the market payment data of the target object to obtain transaction payment data of the target object; and determining the object to be purchased in the target object according to the purchase income corresponding to the transaction payment data.

In some embodiments, in order to further improve accuracy of the transaction payment data, it may further determine a requirement condition of the target article, and then obtain data required during fusion from the market payment data in different manners according to different requirement conditions. Referring to fig. 3, at this time, the step of "fusing the target payment data with the market payment data of the target item to obtain transaction payment data of the target item" includes:

301. And classifying the target object according to the sales time in the store sales data and the sales quantity in the sales time in the store sales data to obtain the object demand category of the target object.

The sales time may refer to the time when the store sales data is acquired, defining the range of data. For example, when the electronic device selects a partial order between n months so far from the historical orders of the target item, and takes the data of the partial order as store sales data, the sales time may refer to the n months. For ease of understanding, refer to table 2, wherein daily sales refer to daily sales of a target item:

TABLE 2

If the electronic device selects a partial order of 1 month, 1 day and 1 month, 7 days from the historical orders of the target object, and takes the data of the partial order as store sales data, the sales time is 1 month, 1 day and 1 month, 7 days. The time may be a date, or may be specific to an hour, a minute, or a second, which is not limited in the embodiments of the present application, and for convenience of description, the time is hereinafter understood to be a date.

The sales amount in the sales time refers to the amount of the target article sold in the sales time, and by way of example in Table 2, the sales amount in the sales time includes 0 pieces sold 1 month and 1 day, 5 pieces sold 1 month and 2 days, and 7 pieces … … sold 1 month and 3 days

The item demand category is used to characterize the demand situation of the target item on the market. Illustratively, the method of fig. 4 may be used to classify the target object, and it is understood that the object requirement class of the target object is one of extremely low frequency, extremely small, extremely unstable, high frequency stable, high frequency unstable, low frequency stable, and low frequency unstable.

The number of demands is understood to be the number that the corresponding sales number is not 0 in the sales time. For example, in table 2, since the sales number in 1 month and 1 day and the sales number in 1 month and 5 days are both 0, the number of times of demand is 5, and for convenience of understanding, the sales time corresponding to the sales number other than 0 is referred to as the item sales time. The greater the number of demands for the target item, the more frequent the market demand for the target item is.

The coefficient of variation cv_all may be calculated by a standard deviation of the sales amount in the sales time (hereinafter referred to as a first standard deviation for convenience of understanding) and a sales amount average in the sales time (hereinafter referred to as a first average for convenience of understanding), specifically, a first standard deviation/first average. The larger the coefficient of variation cv_all of the target object, the higher the demand stability of the target object.

The non-zero average value of the demand refers to an average value of sales numbers corresponding to the sales time of the article. The greater the non-zero mean of demand for the target item, the greater the demand level for the target item when there is demand.

The demand average interval refers to an average value of interval time between the selling time of the articles, and can be understood as an average selling interval of the articles. The following describes a method for calculating the demand average interval, but this should not be taken as limiting the embodiments of the present application: taking table 2 as an example, the selling time of the article is 1 month 2 days, 3 days, 4 days, 6 days, 7 days, the latest time and the earliest time, namely 1 month 7 days and 1 month 2 days, can be selected from the selling times, and then the time difference between the two times is obtained and divided by the number of the selling times of the article to obtain the average demand interval. In this example, the demand average interval is 6 days/5, i.e., 1.2 days, and the demand average interval for the target item is 1.2 days. It will be appreciated that the demand averaging interval may be used to characterize the market demand situation for the target item, the greater the demand averaging interval for the target item, the more frequent the market demand for the target item.

The coefficient of variation cv may be calculated by using a standard deviation of sales numbers corresponding to the sales time of the article (hereinafter referred to as a second standard deviation for convenience of understanding) and a mean of sales numbers corresponding to the sales time of the article (hereinafter referred to as a second mean for convenience of understanding), and specifically, the second standard deviation/the second mean. The larger the coefficient of variation cv of the target object, the higher the demand stability of the target object when the demand exists.

302. And selecting market reference payment data of the target object from market payment data of the target object according to the object demand category of the target object.

The market reference payment data may refer to a reference price selected from market price. After obtaining the object demand category of the target object, the electronic device can select market reference payment data of the target object from the market payment data according to rules corresponding to the object demand category. It can be appreciated that compared with the method of directly performing average calculation, the market reference payment data obtained by selecting according to the corresponding rule better accords with the requirement condition of the target object, so that the accuracy of the transaction payment data can be improved.

For example, when the item demand category of the target item is one of extremely low frequency, extremely small, extremely unstable, high frequency stable, high frequency unstable, low frequency stable, low frequency unstable, the electronic device may select to obtain the market reference payment data according to the following rule:

if the object demand category of the object is high-frequency stable or high-frequency unstable, the market demand degree of the object is higher, and the market quotation is better, so that 75 quantiles in the market payment data can be selected as market reference payment data, namely, the price corresponding to the 75 quantiles is selected from the market quotation price and is used as the market reference price.

If the object demand category of the object is stable at low frequency or unstable at low frequency, the market demand of the object is lower, and the market quotation is general, so that the median in the market payment data can be selected as market reference payment data, namely, the price corresponding to the median is selected from the market quotation prices to be used as the market reference price.

If the object demand category of the object is extremely low frequency, extremely small or extremely unstable, the market demand of the object is extremely low, the information of market quotation is less, and the object of selling the object is less, so that 25 quantiles in the market payment data can be selected as market reference payment data, namely, the price corresponding to the 25 quantiles is selected from the market quotation prices to be used as the market reference price.

303. And calculating transaction payment data of the target object according to the target payment data and the market reference payment data.

The electronic device may fuse the target payment data and the market reference payment data to obtain transaction payment data for the target item. For example, the electronic device may perform an average calculation process on the target payment data and the market reference payment data to obtain transaction payment data of the target item.

In some embodiments, considering that the cost of different items is different and the fluctuation condition of sales prices of different items is different, the cost of the target item can be fused when calculating the transaction payment data, and different weighting coefficients are given to the cost, the market reference price and the maximum benefit price according to the different item types, so as to improve the accuracy of the transaction payment data. Referring to fig. 5, at this time, the step of calculating transaction payment data of the target item according to the target payment data and the market reference payment data includes:

401. and acquiring cost payment data of the target object, wherein the cost payment data comprises at least one of purchasing cost, warehousing cost, logistics cost, labor cost, service fee and tax.

The cost payment data may be understood as a cost price of the target item.

The purchase cost refers to the cost required for purchasing the target article.

The warehouse cost refers to the cost required for storing the target object.

The logistic cost refers to the cost required when transporting the target article.

The labor cost refers to the labor cost required for processing the target object.

The service fee refers to the service fee to be paid when the water transport is carried out, and the service fee can be 10% of the purchasing cost.

The tax may refer to the fee paid for the cross-border transportation.

Illustratively, the cost payment data of the target object may be collected and estimated manually, and then stored in a background database of the target object, from which the electronic device may directly read when performing step 401.

402. And obtaining a weighting coefficient corresponding to the target object, wherein the weighting coefficient comprises a first coefficient corresponding to the cost payment data, a second coefficient corresponding to the target payment data and a third coefficient corresponding to the market reference payment data.

The weighting coefficient refers to a fusion coefficient used when fusing cost payment data, target payment data and market reference payment data.

In some embodiments, a corresponding weighting coefficient may be set for each item. For example, the corresponding weighting coefficients may be preset for each item and then stored in the background database of the target object, and when step 402 is performed, the weighting coefficients corresponding to the items are read from the background database. For example, when the target object is a refrigerator, the weighting coefficient corresponding to the refrigerator can be obtained by inquiring from a background database of the target object.

In other embodiments, the corresponding weighting coefficients may be obtained according to the logistic category corresponding to the target item and the item demand category obtained in step 301, so as to reduce the query event, for example, the weighting coefficients corresponding to the target item may be obtained by the following method:

and (2.1) inquiring to obtain the logistics category corresponding to the target object.

The logistic category of the target object may refer to one of a cross-border logistic and a non-cross-border logistic, that is, when the target object is judged to purchase the target object, the corresponding cross-border transaction or non-cross-border transaction is performed.

The reason for querying the logistic category is that in the cross-border transaction, the cross-border provider company will make a certain price, and the cost payment data obtained in step 401 is the cost price before the price is added. Therefore, if the target object corresponds to the cross-border transaction, the cost payment data needs to be added with the price to the same extent, so as to obtain accurate cost payment data.

And (2.2) obtaining an initial coefficient corresponding to the object demand category, wherein the initial coefficient comprises an initial cost coefficient corresponding to the cost payment data, a second coefficient corresponding to the target payment data and a third coefficient corresponding to the market reference payment data.

The description of the category of the demand for the article may refer to step 301, and detailed description thereof will be omitted.

The initial coefficient is a coefficient corresponding to the target article when the cross-border provider company is not considered for price adding. For example, for extremely low frequency, extremely small, extremely unstable, high frequency unstable and low frequency unstable items, because of its unstable market demand, the fluctuation of sales price is generally large, so smaller second and third coefficients can be set, and larger initial cost coefficients can be set, to avoid that the market demand and sales price reduce the calculation accuracy of the transaction payment data, resulting in mismatch of the transaction payment data and market situation. For high-frequency stability and low-frequency stability, because the market demand is stable and the fluctuation of sales price is usually smaller, larger second coefficient and third coefficient can be set, smaller initial cost coefficient is set, and the matching degree of transaction payment data and market condition is improved.

(2.3) taking the product of the cross-border price adding coefficient corresponding to the logistics category and the initial cost coefficient as a first coefficient corresponding to the cost payment data, and setting the first coefficient, the second coefficient and the third coefficient as weighting coefficients corresponding to the target article.

403. And carrying out weighted summation processing on the cost payment data, the target payment data and the market reference payment data according to the weighting coefficient to obtain transaction payment data of the target object.

In some embodiments, it may be first determined whether the commodity corresponding to the data processing instruction is a new commodity that is not on the market or a commodity that is already on the market, and then different processing is performed according to different situations. Referring to fig. 6, at this time, step "acquire store sales data of a target item" includes:

501. an initial item is acquired.

In the embodiment of the application, the commodity corresponding to the data processing instruction is used as an initial commodity, namely whether the commodity is marketed or not is judged. The corresponding relationship between the data processing instruction and the commodity can refer to step 201, and will not be described in detail.

502. And inquiring whether the preset article catalogue list contains the initial article.

The preset article catalog table records information of the articles on the market, if the article catalog table contains initial articles, the initial articles are indicated to be on the market, and if the article catalog table does not contain the initial articles, the initial articles are indicated to be not on the market.

The inventory table may be stored in a background database of the target object, from which the electronic device reads the inventory table when step 502 is performed.

503. And if the initial item is contained in the item catalog table, taking the initial item as a target item, and acquiring store sales data of the target item.

It should be noted that, if the electronic device determines that the initial item is a new item that is not listed through step 502, market payment data and cost payment data of the initial item may be obtained, if the average value of the market payment data is greater than the cost payment data of the initial item, it indicates that there is a possibility of profit of the initial item, at this time, the market payment data and the cost payment data may be fused to obtain transaction payment data of the initial item, and then, according to the transaction payment data of the initial item and a preset purchase amount of the initial item, the item to be purchased in the initial item is determined. When market payment data are fused, a median in the market payment data can be selected as market reference payment data of an initial object, a weighting coefficient corresponding to the initial object is obtained, the market reference payment data and the cost payment data are weighted and fused, and the method of weighted and fused can be referred to above, and detailed description is omitted.

For ease of understanding, the following provides a process for determining items to be purchased:

(A) Receiving a data processing instruction of a target object, determining an initial article corresponding to the data processing instruction, inquiring whether a preset article catalog table contains the initial article, judging whether the initial article is a new article which is not marketed or an article which is marketed, classifying the initial article and the article which is marketed if the initial article contains the new article which is not marketed, and then respectively processing the classified article, wherein for convenience of explanation, the condition that the initial article contains only the new article which is not marketed or only the article which is marketed is contained is explained below, but the limitation of the embodiment of the application cannot be understood.

(B) If the initial item is a new item which is not on the market, market payment data and cost payment data of the initial item are obtained, if the average value of the market payment data is larger than the cost payment data of the initial item, the possibility that the initial item has profit is indicated, the median in the market payment data is selected as market reference payment data of the initial item, a weighting coefficient corresponding to the initial item is obtained, the market reference payment data and the cost payment data are weighted and fused, and the weighting and fusion method can refer to the above and is not repeated specifically.

(C1) If the initial item is a new item which is already marketed, the initial item is taken as a target item, store sales data is obtained, and maximum profit and target payment data of the target item are predicted according to the store sales data, or if the maximum profit and target payment data of the target item are calculated and stored, the maximum profit and target payment data of the target item can be directly read.

(C2) According to the sales time and the sales quantity in the store sales data, the demand times, the variation coefficient cv_all, the non-zero demand mean value, the demand average interval and the variation coefficient cv of the target object are calculated, the object demand category of the target object is determined according to the parameters, and according to the object demand category, market reference payment data of the target object are selected from market payment data of the target object.

(C3) And inquiring to obtain a weighting coefficient corresponding to the target object, and carrying out weighted summation processing on the cost payment data, the target payment data and the market reference payment data according to the weighting coefficient to obtain transaction payment data, wherein the description and the acquisition mode of the weighting coefficient can refer to the above, and detailed description is omitted.

(C4) Taking the product of the transaction payment data and the preset purchasing quantity of the target article as purchasing gain corresponding to the target article, and setting the target article with purchasing gain larger than the preset gain threshold as the article to be purchased.

Therefore, by the method provided by the embodiment of the application, the target object can also judge whether to purchase before the new product is marketed, so that potential inventory and loss can be reduced.

In order to better implement the data processing method in the embodiment of the present application, based on the data processing method, a data processing apparatus is further provided in the embodiment of the present application, as shown in fig. 7, which is a schematic structural diagram of an embodiment of the data processing apparatus in the embodiment of the present application, where the data processing apparatus 600 includes:

a receiving unit 601, configured to acquire store sales data of a target item;

a prediction unit 602, configured to predict a maximum benefit of the target article and target payment data corresponding to the maximum benefit according to sales payment data in the store sales data and a sales amount corresponding to the sales payment data in the store sales data;

a fusion unit 603, configured to fuse the target payment data and market payment data of the target object to obtain transaction payment data of the target object;

And the determining unit 604 is configured to determine an object to be purchased in the target object according to the purchase benefits corresponding to the transaction payment data.

In a possible implementation of the present application, the prediction unit 602 is further configured to:

In a possible implementation of the present application, the fusion unit 603 is further configured to:

inquiring to obtain the logistics category corresponding to the target object;

In a possible implementation of the present application, the receiving unit 601 is further configured to:

acquiring an initial article;

In a possible implementation manner of the present application, the determining unit 604 is further configured to:

In the implementation, each unit may be implemented as an independent entity, or may be implemented as the same entity or several entities in any combination, and the implementation of each unit may be referred to the foregoing method embodiment, which is not described herein again.

Since the data processing device can execute the steps in the data processing method in any embodiment, the beneficial effects that can be achieved by the data processing method in any embodiment of the present application can be achieved, and detailed descriptions are omitted herein.

In addition, in order to better implement the data processing method in the embodiment of the present application, the data processing method is

On the basis, the embodiment of the present application further provides an electronic device, referring to fig. 8, fig. 8 shows a schematic structural diagram of the electronic device in the embodiment of the present application, and specifically, the electronic device provided in the embodiment of the present application includes a processor 701, where the processor 701 is configured to implement steps of the data processing method in any embodiment when executing a computer program stored in a memory 702; alternatively, the processor 701 is configured to implement the functions of each unit in the corresponding embodiment as shown in fig. 7 when executing the computer program stored in the memory 702.

By way of example, a computer program may be partitioned into one or more modules/units that are stored in the memory 702 and executed by the processor 701 to accomplish the embodiments of the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions to describe the execution of the computer program in a computer device.

Electronic devices may include, but are not limited to, processor 701, memory 702. It will be appreciated by those skilled in the art that the illustrations are merely examples of electronic devices and are not limiting of electronic devices, and may include more or fewer components than illustrated, or may combine certain components, or different components.

The processor 701 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center for an electronic device, with various interfaces and lines connecting various parts of the overall electronic device.

The memory 702 may be used to store computer programs and/or modules, and the processor 701 implements the various functions of the computer device by running or executing the computer programs and/or modules stored in the memory 702, and invoking data stored in the memory 702. The memory 702 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 (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the electronic device, and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the data processing apparatus, the electronic device and the corresponding units described above may refer to the description of the data processing method in any embodiment, and will not be described in detail herein.

Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions or by controlling associated hardware, which may be stored in a storage medium and loaded and executed by a processor.

For this reason, the embodiments of the present application provide a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs steps in the data processing method in any embodiment of the present application, and specific operations may refer to the description of the data processing method in any embodiment, which is not repeated herein.

Wherein the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.

The steps of the data processing method in any embodiment of the present application may be executed by the instructions stored in the storage medium, so that the beneficial effects that can be achieved by the data processing method in any embodiment of the present application may be achieved, which is detailed in the foregoing description and will not be repeated herein.

The foregoing has described in detail a data processing method, apparatus, storage medium and electronic device provided in the embodiments of the present application, and specific examples have been applied to illustrate the principles and embodiments of the present application, where the foregoing description of the embodiments is only for aiding in understanding the method and core idea of the present application; meanwhile, those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present application, and the present description should not be construed as limiting the present application in view of the above.

Claims

1. A method of data processing, comprising:

acquiring store sales data of a target object;

2. The data processing method according to claim 1, wherein predicting the maximum benefit of the target item and the target payment data corresponding to the maximum benefit based on sales payment data in the store sales data and the sales quantity corresponding to the sales payment data in the store sales data comprises:

3. The data processing method according to claim 1, wherein the fusing the target payment data and the market payment data of the target item to obtain transaction payment data of the target item includes:

4. A data processing method according to claim 3, wherein said calculating transaction payment data for said target item based on said target payment data and said market reference payment data comprises:

5. The method according to claim 4, wherein the obtaining the weighting coefficient corresponding to the target object includes:

inquiring to obtain the logistics category corresponding to the target object;

6. The data processing method according to claim 1, wherein the acquiring store sales data of the target item includes:

acquiring an initial article;

7. The data processing method according to any one of claims 1 to 6, wherein the determining the item to be purchased in the target item according to the purchase benefit corresponding to the transaction payment data includes:

8. A data processing apparatus, comprising:

a receiving unit for acquiring store sales data of a target item;

9. An electronic device comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the data processing method according to any one of claims 1 to 7 when the computer program is executed by the processor.

10. A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the data processing method of any of claims 1 to 7.