CN114528462A

CN114528462A - Data processing method and device, electronic equipment and readable storage medium

Info

Publication number: CN114528462A
Application number: CN202210093726.9A
Authority: CN
Inventors: 杨静; 刘晨飞; 巫丽莎; 刘山山; 李洪光; 张喜
Original assignee: Beijing Sankuai Online Technology Co Ltd
Current assignee: Beijing Sankuai Online Technology Co Ltd
Priority date: 2022-01-26
Filing date: 2022-01-26
Publication date: 2022-05-24

Abstract

The embodiment of the disclosure provides a data processing method, a data processing device, an electronic device and a readable storage medium, wherein the method comprises the following steps: aiming at a target commodity of a target merchant, acquiring characteristic parameters of the target commodity, wherein the characteristic parameters comprise: selling the sales data of the target commodity by each merchant in the target area in a preset time period; determining whether the target commodity of the target merchant meets a replenishment reminding condition or not according to the characteristic parameters of the target commodity; and sending replenishment reminding information aiming at the target commodity to the target merchant under the condition that the target commodity of the target merchant meets the replenishment reminding condition. The embodiment of the disclosure can reduce the operation cost of the merchant and the operation cost of searching the substitute commodity for the user, and can assist the merchant in digital operation and assist the merchant in more reasonably configuring commodity supply.

Description

Data processing method and device, electronic equipment and readable storage medium

Technical Field

Embodiments of the present disclosure relate to the field of network technologies, and in particular, to a data processing method and apparatus, an electronic device, and a readable storage medium.

Background

With the development of mobile terminal devices and network technologies, online shopping brings great convenience to users. Online shopping is the sale of consumers linked to sellers via an internet system. The merchant provides the product and service directory for the user to browse by using the internet, and the user uses the terminal equipment to select and purchase through the internet.

In the process of online shopping by a user through a terminal device of the user, the situation that goods are sold out is often encountered. In this case, if the merchant notifies the user that the goods are not available and guides the user to cancel the order after the user places the order, the user experience is very poor. To improve the user experience, the merchant may configure a "sold out" reminder function for each store in the platform. Thus, when a certain commodity is sold out, the platform automatically displays a sold-out prompt message in a detail page of the sold-out commodity so as to prompt a user that the commodity is sold out. In addition, the merchant can also configure an arrival reminding function in the platform, and after the user subscribes the function, the platform can automatically send a reminding message to the user after no goods arrive.

However, when the user encounters a situation that the goods are sold out, if the user puts an urgency on using the goods, the user needs to search for alternative goods again, which increases the operation cost of the user.

Disclosure of Invention

Embodiments of the present disclosure provide a data processing method and apparatus, an electronic device, and a readable storage medium, which may reduce operation costs of merchants and operations for users to search for alternative commodities, and assist merchants in performing digital operations, thereby assisting merchants in configuring commodity supplies more reasonably.

According to a first aspect of embodiments of the present disclosure, there is provided a data processing method, the method including:

aiming at a target commodity of a target merchant, acquiring characteristic parameters of the target commodity, wherein the characteristic parameters comprise: selling the sales data of the target commodity by each merchant in the target area in a preset time period;

determining whether the target commodity of the target merchant meets a replenishment reminding condition or not according to the characteristic parameters of the target commodity;

and sending replenishment reminding information aiming at the target commodity to the target merchant under the condition that the target commodity of the target merchant meets the replenishment reminding condition. .

According to a second aspect of embodiments of the present disclosure, there is provided a data processing apparatus, the apparatus comprising:

the characteristic acquisition module is used for acquiring characteristic parameters of a target commodity of a target merchant, wherein the characteristic parameters comprise: selling the sales data of the target commodity by each merchant in the target area in a preset time period;

the condition determining module is used for determining whether the target commodity of the target merchant meets the replenishment reminding condition or not according to the characteristic parameters of the target commodity;

and the reminding sending module is used for sending the replenishment reminding information aiming at the target commodity to the target merchant under the condition that the target commodity of the target merchant meets the replenishment reminding condition.

According to a third aspect of embodiments of the present disclosure, there is provided an electronic apparatus including: a processor, a memory and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the aforementioned data processing method when executing the program.

The embodiment of the disclosure provides a data processing method, a data processing device, an electronic device and a readable storage medium, wherein the method comprises the following steps:

the embodiment of the disclosure aims at a target commodity of a target merchant, and obtains a characteristic parameter of the target commodity, wherein the characteristic parameter comprises: and each merchant in the target area sells the sales data of the target commodity in a preset time period. Wherein, the target merchant can be any merchant on the online shopping platform. The target item may be any item sold by the target merchant on the online shopping platform. By the aid of the method and the device, whether the target commodity of the target merchant on the online shopping platform meets the replenishment reminding condition or not can be automatically detected, and if the target commodity of the target merchant meets the replenishment reminding condition, replenishment reminding information aiming at the target commodity is sent to the target merchant. Therefore, the condition that the merchant is likely to be out of stock of the commodities can be predicted in advance, the merchant is reminded of replenishing the commodities in time before the commodities are out of stock, and the condition that the supply of a certain commodity is insufficient due to the fact that the merchant does not replenish the commodities in time can be reduced. That is, the embodiment of the disclosure can solve the problems that no goods are available for the merchant and no goods are available for the user, which may occur in a future period of time, and can reduce the operation cost for the user to search for the substitute goods, so that the user can purchase the required goods quickly, and the shopping experience of the user is improved. In addition, the embodiment of the disclosure can also assist merchants to carry out digital operation, and assist merchants to more reasonably configure commodity supply, thereby reducing the situation that the merchants need to configure the functions of sold-out or arrival reminding for each shop in a platform, further reducing the operation cost of the merchants, and improving the unit quantity to a certain extent.

Drawings

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

FIG. 1 illustrates an application scene architecture diagram in one embodiment of the present disclosure;

FIG. 2 shows a flow diagram of steps of a data processing method in one embodiment of the present disclosure;

FIG. 3 illustrates a schematic diagram of predicting a projected rate of sale of a target commodity via a first predictive model according to the present disclosure;

FIG. 4 shows a schematic block diagram of a data processing apparatus in an embodiment of the present disclosure;

FIG. 5 shows a block diagram of an electronic device in an embodiment of the disclosure.

Detailed Description

Technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present disclosure, belong to the protection scope of the embodiments of the present disclosure.

The terms first, second and the like in the description and in the claims of the present disclosure are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the disclosure may be practiced other than those illustrated or described herein, and that the objects identified as "first," "second," etc. are generally a class of objects and do not limit the number of objects, e.g., a first object may be one or more. Furthermore, the term "and/or" in the specification and claims is used to describe an association relationship of associated objects, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The term "plurality" in the embodiments of the present disclosure means two or more, and other terms are similar thereto.

Referring to fig. 1, an application scenario architecture diagram of a data processing method provided by an embodiment of the present disclosure is shown. As shown in fig. 1, an application scenario of the embodiment of the present disclosure may include a terminal 101 and a server 102. The terminal 101 and the server 102 are connected through a wireless or wired network. The terminal 101 includes, but is not limited to, an electronic device such as a mobile phone, a smart robot, an AI manual service, a mobile computer, and a tablet computer. The server 102 may be a server, a server cluster composed of several servers, or a cloud computing center.

The data processing method of the embodiment of the present disclosure may be executed by the terminal 101 alone, or the data processing method of the embodiment of the present disclosure may be executed by the server 102 alone, or the data processing method of the embodiment of the present disclosure may be executed cooperatively through interaction between the terminal 101 and the server 102.

It should be noted that the architecture diagram shown in fig. 1 is for more clearly illustrating the technical solution in the embodiment of the present disclosure, and does not limit the technical solution provided by the embodiment of the present disclosure, and for other application scenario architectures and business applications, the technical method provided by the embodiment of the present disclosure is also applicable to similar problems.

Referring to fig. 2, a flow diagram illustrating steps of a data processing method in one embodiment of the present disclosure may include:

step 201, aiming at a target commodity of a target merchant, obtaining a characteristic parameter of the target commodity, wherein the characteristic parameter comprises: selling the sales data of the target commodity by each merchant in the target area in a preset time period;

step 202, determining whether the target commodity of the target merchant meets replenishment reminding conditions or not according to the characteristic parameters of the target commodity;

step 203, sending replenishment reminding information for the target commodity to the target merchant under the condition that the target commodity of the target merchant meets the replenishment reminding condition.

In an online shopping scene, a merchant can sell a commodity on an online shopping platform, the platform is provided with a plurality of merchants, a user browses and buys the commodity on the platform, the merchant often sells and receives the commodity while selling the commodity, but in the actual selling process, the situation that the merchant does not timely replenish the commodity to cause that the supply of the commodity is not in demand may occur, so that the operation cost of the user and the merchant is increased, and the shopping experience of the user and the selling situation of the merchant are influenced.

The data processing method provided by the embodiment of the disclosure can be applied to an online shopping scene, and by the embodiment of the disclosure, whether the target commodity of the target merchant meets the replenishment reminding condition can be automatically detected, and if the target commodity of the target merchant meets the replenishment reminding condition is detected, replenishment reminding information for the target commodity is sent to the target merchant. Therefore, the condition that the commodities of the merchant are out of stock possibly occurring can be predicted in advance, the merchant is reminded of timely replenishment before the commodities are out of stock, the condition that the supply of certain commodities is not required due to non-timely replenishment of the merchant can be reduced, the condition that the merchant needs to configure a sold out function or a goods arrival reminding function for each shop in a platform can be reduced, the operation cost of the merchant can be reduced, the operation cost of searching for alternative commodities by a user can be reduced, the user can quickly purchase the required commodities, the shopping experience of the user is improved, and the unit quantity can be improved to a certain extent.

Wherein, the target merchant can be any merchant on the online shopping platform. The target item may be any item sold by the target merchant on the online shopping platform.

For a target commodity of a target merchant, in the embodiment of the present disclosure, a characteristic parameter of the target commodity is obtained, where the characteristic parameter may include: and each merchant in the target area sells the sales data of the target commodity in a preset time period.

The characteristic parameter refers to a parameter that affects the selling speed of the target commodity. The characteristic parameter may be, for example, a region-related parameter and/or a time-related parameter. Wherein the sales speed of region-related parameters, such as drugs for rheumatism, in south China is significantly faster than in north China. Time-related parameters, such as Huoxiang Zhengqi water, sell at a significantly faster rate in summer than in winter.

The method for acquiring the characteristic parameters of the target commodity comprises the following steps: and each merchant in the target area sells the sales data of the target commodity in a preset time period. The characteristic parameters comprehensively consider region-related parameters and time-related parameters so as to improve the accuracy of determining whether the target commodity of the target merchant meets the replenishment reminding condition.

Further, the selling data of the target commodity sold by each merchant in the target area in a preset time period may include: average selling speed of all merchants selling the target commodity in the target area for a preset time period.

It should be noted that, the granularity of the preset time period is not limited in the embodiments of the present disclosure. For example, the preset time period may be one month, one week, etc. For convenience of description, the embodiment of the present disclosure is described by taking a month as an example of a preset time period.

In an optional embodiment of the present disclosure, the target area may comprise an area covered by a shipping range of the target merchant.

In the embodiment of the present disclosure, the online shopping scenario includes, but is not limited to, an instant distribution scenario such as take-out, flash purchase, leg running, and the like, and may also include a non-instant distribution scenario such as an online shopping mall.

According to the method and the device for determining the target commodity of the target merchant, the target area is set to be the area covered by the distribution range of the target merchant, so that the obtained sales data can reflect the sales speed of the target commodity in the target area more truly, and the accuracy of determining whether the target commodity of the target merchant meets the replenishment reminding condition is improved. It is understood that the scope of the target area is not limited by the embodiments of the present disclosure.

In an optional embodiment of the present disclosure, the determining, according to the characteristic parameter of the target commodity, whether the target commodity of the target merchant satisfies a replenishment reminding condition may include:

step S11, determining the estimated selling speed of the target commodity according to the selling data of the target commodity sold by each merchant in the target area in a preset time period;

step S12, determining whether the target commodity of the target merchant meets the replenishment reminding condition according to the estimated selling speed of the target commodity, the inventory of the target commodity and the estimated arrival time of the target commodity.

The inventory of the target commodity refers to the quantity of the target commodity which can be sold by the target merchant currently, and the inventory of the target commodity can be directly obtained. The inventory of the target goods is an important factor for measuring whether the target goods of the target merchant meets the replenishment reminding condition. For example, if the stock of the target product is small, the probability that the target product of the target merchant is out of stock in a short term is high; if the target commodity has more stock, the probability that the target commodity of the target merchant is out of stock in a short term is lower.

The estimated sales speed of the target item is an estimated value representing the estimated sales speed of the target item currently or for a future period of time. The estimated selling speed of the target commodity is an important factor for judging whether the target commodity of the target merchant meets the replenishment reminding condition or not. For example, if the estimated sales speed of the target commodity is high, the probability that the target commodity of the target merchant is out of stock in a short term is high; if the estimated sales speed of the target commodity is low, the probability that the target commodity of the target merchant is out of stock in a short term is low.

The estimated time to arrive for the target item is an estimated value representing the total time required from the time the target merchant places an order for the target item to the time the target merchant receives the target item. The estimated arrival time of the target commodity is an important factor for judging whether the target commodity of the target merchant meets the replenishment reminding condition. For example, if the estimated delivery time of the target commodity is long, the probability that the target commodity of the target merchant is out of stock in a short term is high; if the estimated goods time of the target goods is short, the probability that the target goods of the target merchant are out of stock in a short term is low.

In the specific implementation, the three characteristic parameters, namely the inventory of the target commodity, the estimated sales speed of the target commodity and the estimated arrival time of the target commodity, often influence the probability of the target commodity of the target merchant being out of stock in a short term. The embodiment of the disclosure determines whether the target commodity of the target merchant meets the replenishment reminding condition or not according to the three characteristic parameters, and sends the replenishment reminding information aiming at the target commodity to the target merchant under the condition that the target commodity of the target merchant meets the replenishment reminding condition.

It should be noted that, the specific calculation manner of the estimated selling speed of the target product and the estimated arrival time of the target product in the embodiment of the present disclosure is not limited.

In an optional embodiment of the present disclosure, the determining, according to the estimated sales speed of the target product, the inventory of the target product, and the estimated arrival time of the target product, whether the target product of the target merchant satisfies the replenishment reminding condition may include:

step S21, calculating the ratio of the inventory of the target commodity to the estimated selling speed of the target commodity to obtain the remaining selling time of the target commodity;

and step S22, if the remaining time available for sale of the target commodity is less than the estimated arrival time of the target commodity, determining that the target commodity of the target merchant meets the replenishment reminding condition.

After the inventory of the target product and the estimated selling speed of the target product are obtained, the ratio of the inventory of the target product to the estimated selling speed of the target product is calculated, so that the remaining selling time of the target product can be obtained, namely, the remaining selling time of the target product is equal to the inventory of the target product/the estimated selling speed of the target product.

If the remaining selling time of the target commodity is less than the estimated arrival time of the target commodity, the probability that the target commodity of the target merchant is out of stock in a short term is high, and it can be determined that the target commodity of the target merchant meets the replenishment reminding condition. At this time, replenishment reminding information for the target commodity can be sent to the target merchant to remind the target merchant of timely replenishment.

Further, the embodiment of the disclosure may send the estimated sales speed of the target commodity and/or the remaining saleable time of the target commodity and other related information to the target merchant on the basis of sending the replenishment reminding information for the target commodity to the target merchant, so that the target merchant can also know the expected sales speed of the target commodity for a period of time in the future on the basis of timely replenishment, and the target merchant can conveniently and reasonably determine the replenishment quantity and the replenishment time of the target commodity.

In an optional embodiment of the present disclosure, the determining an estimated sales speed of the target product according to sales data of the target product sold by each merchant in the target area in a preset time period may include:

step S31, carrying out preset calculation on sales data of the target commodity sold by each merchant in the target area in a preset time period to obtain input data;

and step S32, inputting the input data into a first prediction model, and outputting the estimated sales speed of the target commodity through the first prediction model, wherein the first prediction model is a neural network model obtained through training according to historical sales data in the target area.

The disclosed embodiments pre-train a first predictive model based on historical sales data within a target area. The trained first predictive model may be used to predict an estimated sales speed of the target good. In one example, the input data of the first prediction model may include an average sales rate of all merchants selling the target commodity in the target area for a preset time period, and the output data of the first prediction model may be an estimated sales rate of the target commodity. In another example, the input data of the first prediction model may include processed data obtained by performing a preset calculation on average sales speeds of all merchants selling the target commodity in the target area for a preset time period, and the output data of the first prediction model may be an estimated sales speed of the target commodity.

The first prediction model may be obtained by supervised training of an existing neural network based on a large number of training samples and a machine learning method. It should be noted that, the embodiment of the present disclosure does not limit the model structure and the training method of the first prediction model. The first predictive model may fuse a plurality of neural networks. The neural network includes, but is not limited to, at least one or a combination, superposition, nesting of at least two of the following: CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory) Network, RNN (Simple Recurrent Neural Network), attention Neural Network, and the like.

In an optional embodiment of the present disclosure, the selling data of each merchant in the target area selling the target product in a preset time period may include: a first average selling speed, a second average selling speed, and a third average selling speed; wherein, the first average selling speed refers to the average selling speed of each merchant in the target area in the same time period in the last year, the second average selling speed refers to the average selling speed of each merchant in the target area in the past time period in the last year, and the third average selling speed refers to the average selling speed of each merchant in the target area in the past time period in the present year; the obtaining of the input data by performing preset calculation on the sales data of the target commodity sold by each merchant in the target area in a preset time period may include: and calculating the ratio of the first average selling speed to the second average selling speed, and calculating the product of the ratio and the third average selling speed to obtain input data.

It should be noted that, the granularity of the one time period is not limited in the embodiments of the present disclosure. For example, the one time period may be one month, one week, or the like. For convenience of description, the embodiment of the present disclosure is described by taking a month as an example of a time period.

In one example, taking a target merchant as a pharmacy a on an online shopping platform a as an example, assume that a target commodity is a drug a sold by the pharmacy a, and assume that the pharmacy a is located in a target area with 10 total pharmacies selling the drug a. The obtaining of the average sales rate of the 10 pharmacies for a preset time period (e.g., one month) according to the embodiment of the present disclosure may include: average sales rates of the 10 pharmacies over the same month of the last year (e.g., noted as a first average sales rate), average sales rates of the 10 pharmacies over the past month of the last year (e.g., noted as a second average sales rate), and average sales rates of the 10 pharmacies over the past month of the present year (e.g., noted as a third average sales rate).

Alternatively, the preset calculation may be calculated by the following formula: (first average sales speed/second average sales speed) × third average sales speed.

The first average selling speed, the second average selling speed and the third average selling speed are specific to all merchants selling the target product in the target area.

In the above example, the input data ═ (first average selling speed/second average selling speed) × third average selling speed. Inputting the input data into the first prediction model, and outputting the estimated sales speed of the target commodity through the first prediction model.

In an optional embodiment of the present disclosure, the characteristic parameters of the target product may further include: and the goods arrival notification click data of each merchant in the target area about the target commodity, and/or the sales data of each merchant in the target area for selling the target commodity when a preset event occurs.

The goods arrival notification click data of each merchant in the target area about the target goods can reflect the selling speed of the target goods in the target area to a certain extent. For example, the larger the hit rate of the arrival notice of the target commodity of each merchant in the target area is, the larger the demand of the target commodity in the target area is, and the faster the sales speed of the target commodity in the target area is.

And the selling data of the target commodity sold by each merchant in the target area when the preset event occurs can reflect the selling speed of the target commodity in the target area to a certain extent. The preset event refers to an event having a great influence on the selling speed of the target commodity, and may include an event that is difficult to predict, such as a natural disaster event, for example, an event such as flood, epidemic situation, and the like.

Further, the determining whether the target commodity of the target merchant meets the replenishment reminding condition according to the characteristic parameter of the target commodity may include:

and determining whether the target commodity of the target merchant meets the replenishment reminding condition or not according to the sales data of the target commodity sold by each merchant in the target area in a preset time period, and/or the arrival notification click data of each merchant in the target area about the target commodity, and/or the sales data of the target commodity sold by each merchant in the target area when a preset event occurs.

The method and the device for determining the replenishment reminding condition can comprehensively consider a plurality of characteristic parameters of the target commodity to determine whether the target commodity of the target merchant meets the replenishment reminding condition, and can improve the accuracy of determining whether the target commodity of the target merchant meets the replenishment reminding condition.

In the embodiment of the present disclosure, the characteristic parameters of the target product may include the association parameters of the target product and the specific parameters of the target product. The influence of the relevant parameters (such as region-related parameters and/or time-related parameters) of the target commodity on the selling speed of the target commodity can be generally predicted according to experience, and the influence of the special parameters (such as parameters which are only possessed when a preset event occurs) of the target commodity on the selling speed of the target commodity is difficult to predict. For example, when a new crown epidemic situation occurs, the sale speed of epidemic prevention articles such as disinfection products and masks is obviously improved.

In an optional embodiment of the present disclosure, the input data of the first prediction model may further include special attributes of the target product, and the special attributes may include: and each merchant in the target area sells the sales data of the target commodity when a preset event occurs.

In order to avoid the situation that the predicted sale speed of the target commodity predicted by the first prediction model is inaccurate when a preset event occurs, the correlation parameters of the target commodity and the special parameters of the target commodity are considered when the first prediction model is trained in the embodiment of the disclosure. That is, when the first prediction model is trained, the specific parameters of the commodity are also used as training data to participate in the training. Further, the selling data of the target commodity sold by each merchant in the target area when the preset event occurs may include: the average selling speed of all merchants selling the target commodity in the target area when a preset event occurs. After the training is completed, the input data of the first prediction model further includes the specific parameters of the target commodity. Referring to fig. 3, a schematic diagram of predicting a predicted sales speed of a target commodity through a first prediction model according to the present disclosure is shown. As shown in fig. 3, the input data of the first prediction model includes: (first average sales speed/second average sales speed) × third average sales speed, the input data of the first prediction model further includes: specific parameters of the target commodity. The output data of the first prediction model is the predicted sales speed of the target commodity.

In an optional embodiment of the present disclosure, the method may further comprise:

step S41, obtaining a vendor supply parameter of the target commodity, where the vendor supply parameter includes at least one of: the target merchant aims at historical ordering information of the target commodity at a manufacturer, the distance between the target merchant and the manufacturer, the delivery mode of the manufacturer and the capacity information of the manufacturer;

and step S42, determining the estimated arrival time of the target commodity according to the supplier supply parameters of the target commodity.

It should be noted that the above listed vendor supply parameters are only an application example of the present disclosure, and the specific content of the vendor supply parameters is not limited in the embodiments of the present disclosure. For example, the vendor supply parameter may also include an average delivery time of the vendor, and the like.

According to the factory supply parameter of the target commodity, the estimated delivery time of the target commodity can be determined.

If the calculated remaining saleable time of the target commodity is less than the estimated arrival time of the target commodity, it can be determined that the target commodity of the target merchant meets the replenishment reminding condition, and replenishment reminding information for the target commodity can be sent to the target merchant.

In an optional embodiment of the present disclosure, the determining the estimated time to reach the target product according to the supplier supply parameter of the target product may include:

inputting the supplier supply parameters of the target commodity into a second prediction model, and outputting the estimated delivery time of the target commodity through the second prediction model, wherein the second prediction model is a neural network model obtained according to the historical supplier supply parameters of the supplier.

The embodiment of the disclosure trains the second prediction model in advance according to the historical supply parameters of the manufacturer. The historical supplier parameters of the manufacturer may include: the historical merchant aims at historical ordering information of the historical commodities at the manufacturer, the distance between the historical merchant and the manufacturer, the delivery mode of the manufacturer and the capacity information of the manufacturer. The trained second predictive model may be used to predict an estimated time to arrival of the target commodity. The input data of the second prediction model may include the supplier supply parameters of the target commodity, and the output data of the second prediction model may be the estimated delivery time of the target commodity.

The second prediction model may be obtained by supervised training of an existing neural network based on a large number of training samples and machine learning methods. It should be noted that, the embodiment of the present disclosure does not limit the model structure and the training method of the second prediction model. The second predictive model may fuse a plurality of neural networks. The neural network includes, but is not limited to, at least one or a combination, superposition, nesting of at least two of the following: CNN networks, LSTM networks, RNN networks, attention neural networks, and the like.

In summary, in the embodiments of the present disclosure, for a target commodity of a target merchant, a feature parameter of the target commodity is obtained, where the feature parameter includes: and each merchant in the target area sells the sales data of the target commodity in a preset time period. The target merchant can be any merchant on the online shopping platform. The target item may be any item sold by the target merchant on the online shopping platform. By the aid of the method and the device, whether the target commodity of the target merchant on the online shopping platform meets the replenishment reminding condition or not can be automatically detected, and if the target commodity of the target merchant meets the replenishment reminding condition, replenishment reminding information aiming at the target commodity is sent to the target merchant. Therefore, the condition that the merchant is likely to be out of stock of the commodities can be predicted in advance, the merchant is reminded of replenishing the commodities in time before the commodities are out of stock, and the condition that the supply of a certain commodity is insufficient due to the fact that the merchant does not replenish the commodities in time can be reduced. That is, the embodiment of the disclosure can solve the problems that no goods are available for the merchant and no goods are available for the user, which may occur in a future period of time, and can reduce the operation cost for the user to search for the substitute goods, so that the user can purchase the required goods quickly, and the shopping experience of the user is improved. In addition, the embodiment of the disclosure can also assist merchants to carry out digital operation, and assist merchants to more reasonably configure commodity supply, thereby reducing the situation that the merchants need to configure the functions of sold-out or arrival reminding for each shop in a platform, further reducing the operation cost of the merchants, and improving the unit quantity to a certain extent.

It is noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the disclosed embodiments are not limited by the described order of acts, as some steps may occur in other orders or concurrently with other steps in accordance with the disclosed embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the disclosed embodiments.

Referring to fig. 4, there is shown a block diagram of a data processing apparatus in one embodiment of the present disclosure, the apparatus comprising:

a feature obtaining module 401, configured to obtain, for a target product of a target merchant, a feature parameter of the target product, where the feature parameter includes: selling the sales data of the target commodity by each merchant in the target area in a preset time period;

a condition determining module 402, configured to determine, according to the characteristic parameter of the target commodity, whether the target commodity of the target merchant meets a replenishment reminding condition;

a prompt sending module 403, configured to send a replenishment reminding message for a target commodity of the target merchant to the target merchant when it is determined that the target commodity of the target merchant meets the replenishment reminding condition.

Optionally, the target area includes an area covered by a distribution range of the target merchant.

Optionally, the condition determining module includes:

the speed estimation submodule is used for determining the estimated selling speed of the target commodity according to the selling data of the target commodity sold by each merchant in the target area in a preset time period;

and the condition determining submodule is used for determining whether the target commodity of the target merchant meets the replenishment reminding condition or not according to the estimated selling speed of the target commodity, the inventory of the target commodity and the estimated arrival time of the target commodity.

Optionally, the condition determining sub-module includes:

the residual time calculating unit is used for calculating the ratio of the inventory of the target commodity to the estimated selling speed of the target commodity to obtain the residual selling time of the target commodity;

and the condition determining unit is used for determining that the target commodity of the target merchant meets the replenishment reminding condition if the remaining selling time of the target commodity is less than the estimated arrival time of the target commodity.

Optionally, the speed estimation sub-module comprises:

the input calculation unit is used for carrying out preset calculation on sales data of the target commodity sold by each merchant in the target area in a preset time period to obtain input data;

and the model prediction unit is used for inputting the input data into a first prediction model and outputting the estimated sales speed of the target commodity through the first prediction model, and the first prediction model is a neural network model obtained by training according to historical sales data in the target area.

Optionally, the selling data of the target commodity sold by each merchant in the target area in a preset time period includes: a first average selling speed, a second average selling speed, and a third average selling speed; wherein, the first average selling speed refers to the average selling speed of each merchant in the target area in the same time period in the last year, the second average selling speed refers to the average selling speed of each merchant in the target area in the past time period in the last year, and the third average selling speed refers to the average selling speed of each merchant in the target area in the past time period in the present year;

the input calculation unit is specifically configured to calculate a ratio of the first average selling speed to the second average selling speed, and calculate a product of the ratio and the third average selling speed to obtain input data.

Optionally, the apparatus further comprises:

a supply parameter obtaining module, configured to obtain a manufacturer supply parameter of the target product, where the manufacturer supply parameter includes at least one of the following: the target merchant aims at historical ordering information of the target commodity at a manufacturer, the distance between the target merchant and the manufacturer, the delivery mode of the manufacturer and the capacity information of the manufacturer;

and the arrival time estimation module is used for determining the estimated arrival time of the target commodity according to the factory supply parameters of the target commodity.

Optionally, the arrival time estimation module is specifically configured to input a supplier supply parameter of the target commodity into a second prediction model, and output the estimated arrival time of the target commodity through the second prediction model, where the second prediction model is a neural network model trained according to a historical supplier supply parameter of the supplier.

Optionally, the characteristic parameters of the target product further include: and the goods arrival notification click data of each merchant in the target area about the target commodity, and/or the sales data of each merchant in the target area for selling the target commodity when a preset event occurs.

The embodiment of the disclosure aims at a target commodity of a target merchant, and obtains a characteristic parameter of the target commodity, wherein the characteristic parameter comprises: and each merchant in the target area sells the sales data of the target commodity in a preset time period. Wherein, the target merchant can be any merchant on the online shopping platform. The target commodity can be any commodity sold by the target merchant on the online shopping platform. By the aid of the method and the device, whether the target commodity of the target merchant on the online shopping platform meets the replenishment reminding condition or not can be automatically detected, and if the target commodity of the target merchant meets the replenishment reminding condition, replenishment reminding information aiming at the target commodity is sent to the target merchant. Therefore, the condition that the merchant is likely to be out of stock of the commodities can be predicted in advance, the merchant is reminded of replenishing the commodities in time before the commodities are out of stock, and the condition that the supply of a certain commodity is insufficient due to the fact that the merchant does not replenish the commodities in time can be reduced. That is, the embodiment of the disclosure can solve the problems that no goods are available for the merchant and no goods are available for the user, which may occur in a future period of time, and can reduce the operation cost for the user to search for the substitute goods, so that the user can purchase the required goods quickly, and the shopping experience of the user is improved. In addition, the embodiment of the disclosure can also assist merchants to carry out digital operation, and assist merchants to more reasonably configure commodity supply, thereby reducing the situation that the merchants need to configure the functions of sold-out or arrival reminding for each shop in a platform, further reducing the operation cost of the merchants, and improving the unit quantity to a certain extent.

For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

An embodiment of the present disclosure also provides an electronic device, referring to fig. 5, including: a processor 501, a memory 502 and a computer program 5021 stored on the memory 502 and executable on the processor, the processor 501 implementing the data processing method of the foregoing embodiments when executing the program.

Embodiments of the present disclosure also provide a readable storage medium, in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform the data processing method of the foregoing embodiments.

The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system is apparent from the description above. In addition, embodiments of the present disclosure are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the embodiments of the present disclosure as described herein, and any descriptions of specific languages are provided above to disclose the best mode of use of the embodiments of the present disclosure.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the embodiments of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, claimed embodiments of the disclosure require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of an embodiment of this disclosure.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

The various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a document processing apparatus according to embodiments of the present disclosure. Embodiments of the present disclosure may also be implemented as an apparatus or device program for performing a portion or all of the methods described herein. Such programs implementing embodiments of the present disclosure may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit embodiments of the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

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

The above description is only for the purpose of illustrating the preferred embodiments of the present disclosure and is not to be construed as limiting the embodiments of the present disclosure, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the embodiments of the present disclosure are intended to be included within the scope of the embodiments of the present disclosure.

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

Claims

1. A method of data processing, the method comprising:

and sending replenishment reminding information aiming at the target commodity to the target merchant under the condition that the target commodity of the target merchant meets the replenishment reminding condition.

2. The method of claim 1, wherein the target area comprises an area covered by a shipping envelope of the target merchant.

3. The method as claimed in claim 1 or 2, wherein the determining whether the target commodity of the target merchant meets the replenishment reminding condition according to the characteristic parameter of the target commodity comprises:

determining the estimated selling speed of the target commodity according to the selling data of the target commodity sold by each merchant in the target area in a preset time period;

and determining whether the target commodity of the target merchant meets the replenishment reminding condition or not according to the estimated selling speed of the target commodity, the inventory of the target commodity and the estimated arrival time of the target commodity.

4. The method of claim 3, wherein the determining whether the target commodity of the target merchant meets the replenishment reminding condition according to the estimated sales speed of the target commodity, the inventory of the target commodity and the estimated arrival time of the target commodity comprises:

calculating the ratio of the inventory of the target commodity to the estimated selling speed of the target commodity to obtain the remaining selling time of the target commodity;

and if the remaining selling time of the target commodity is less than the estimated arrival time of the target commodity, determining that the target commodity of the target merchant meets the replenishment reminding condition.

5. The method of claim 3, wherein determining the estimated sales speed of the target product based on sales data for each merchant in the target area selling the target product for a preset period of time comprises:

carrying out preset calculation on sales data of the target commodity sold by each merchant in the target area in a preset time period to obtain input data;

inputting the input data into a first prediction model, and outputting the estimated sales speed of the target commodity through the first prediction model, wherein the first prediction model is a neural network model obtained by training according to historical sales data in the target area.

6. The method of claim 5, wherein selling data of the target item by each merchant in the target area for a preset period of time comprises: a first average sales speed, a second average sales speed, and a third average sales speed; wherein, the first average selling speed refers to the average selling speed of each merchant in the target area in the same time period in the last year, the second average selling speed refers to the average selling speed of each merchant in the target area in the past time period in the last year, and the third average selling speed refers to the average selling speed of each merchant in the target area in the past time period in the present year;

the preset calculation of the sales data of the target commodity sold by each merchant in the target area in the preset time period to obtain the input data comprises the following steps:

and calculating the ratio of the first average selling speed to the second average selling speed, and calculating the product of the ratio and the third average selling speed to obtain input data.

7. The method of claim 3, further comprising:

obtaining a manufacturer supply parameter of the target commodity, wherein the manufacturer supply parameter comprises at least one of the following items: the target merchant aims at historical ordering information of the target commodity at a manufacturer, the distance between the target merchant and the manufacturer, the delivery mode of the manufacturer and the capacity information of the manufacturer;

and determining the estimated arrival time of the target commodity according to the manufacturer supply parameter of the target commodity.

8. The method of claim 7, wherein determining the estimated time to delivery of the target item based on the vendor supply parameter of the target item comprises:

9. The method of claim 1, wherein the characteristic parameters of the target good further comprise: and the goods arrival notification click data of each merchant in the target area about the target commodity, and/or the sales data of each merchant in the target area for selling the target commodity when a preset event occurs.

10. A data processing apparatus, characterized in that the apparatus comprises:

11. The apparatus of claim 10, wherein the target area comprises an area covered by a shipping envelope of the target merchant.

12. The apparatus of claim 10 or 11, wherein the condition determining module comprises:

13. The apparatus of claim 12, wherein the condition determining sub-module comprises:

and the condition determining unit is used for determining that the target commodity of the target merchant meets the replenishment reminding condition if the remaining saleable time of the target commodity is less than the estimated arrival time of the target commodity.

14. The apparatus of claim 12, wherein the velocity estimation sub-module comprises:

15. The apparatus of claim 14, wherein the sales data for each merchant in the target area selling the target item for a preset period of time comprises: a first average selling speed, a second average selling speed, and a third average selling speed; wherein, the first average selling speed refers to the average selling speed of each merchant in the target area in the same time period in the last year, the second average selling speed refers to the average selling speed of each merchant in the target area in the past time period in the last year, and the third average selling speed refers to the average selling speed of each merchant in the target area in the past time period in the present year;

16. The apparatus of claim 12, further comprising:

a supply parameter obtaining module, configured to obtain a vendor supply parameter of the target product, where the vendor supply parameter includes at least one of: the target merchant aims at historical ordering information of the target commodity at a manufacturer, the distance between the target merchant and the manufacturer, the delivery mode of the manufacturer and the capacity information of the manufacturer;

17. The apparatus of claim 16, wherein the arrival time estimation module is specifically configured to input a supplier arrival time of the target product into a second prediction model, and output the estimated arrival time of the target product through the second prediction model, and the second prediction model is a neural network model trained according to a historical supplier arrival time of the supplier.

18. The apparatus of claim 10, wherein the characteristic parameters of the target product further comprise: and the goods arrival notification click data of each merchant in the target area about the target commodity, and/or the sales data of each merchant in the target area for selling the target commodity when a preset event occurs.

19. An electronic device, comprising:

processor, memory and computer program stored on the memory and executable on the processor, characterized in that the processor implements the data processing method according to any of claims 1 to 9 when executing the program.

20. A readable storage medium, characterized in that instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the data processing method of any of method claims 1 to 9.