Data processing method, terminal, server, electronic device and storage medium

Abstract

The embodiments of the present application provide a data processing method, a terminal, a server, an electronic device, and a computer-readable storage medium, and relate to the computer field. The method includes: in response to updating the selection operation of the goods to be billed in the target application, determining the label of the object, the object includes at least one of a store or a shopper, and the label is used to indicate whether the object has the authority to preload the bill of lading page; if it is determined according to the label that the object has the authority to preload the bill of lading page, then determining the probability of entering the bill of lading page, and if the probability is greater than a preset value, requesting the page data of the bill of lading page corresponding to the selection operation from the server. Compared with the prior art, the embodiments of the present application further improve the accuracy of judging whether to preload the bill of lading page, save resource investment, and increase commercial support, laying the foundation for realizing the seamless loading of the bill of lading page and ensuring a good experience for shoppers.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data processing method, a terminal, a server, an electronic device and a storage medium. Background Art

[0002] When a shopper updates a product to be billed in the target application, if the target application can respond in time and preload the updated bill page data, the bill page can be generated based on the preloaded bill page data when the shopper enters the bill page. This can significantly increase the speed of generating the bill page, compared to loading the bill page data when the shopper is sure to enter the bill page, and provide a good user experience for the shopper.

[0003] However, in the related art, the billing page data is preloaded every time the shopper updates the goods to be billed, which will significantly increase the traffic of the preloaded data and cause pressure on the server. In addition, since the shopper does not enter the billing page every time he updates the data to be billed, preloading the billing page every time the goods to be billed are updated will cause a waste of traffic. Summary of the invention

[0004] The embodiments of the present application provide a data processing method, a terminal, a server, an electronic device and a storage medium, which can solve the above-mentioned problems of the prior art. The technical solution is as follows:

[0005] According to one aspect of an embodiment of the present application, a data processing method is provided, which is applied to a terminal and includes:

[0006] In response to updating a selection operation of a to-be-billed commodity in a target application, determining a tag of an object, the object comprising at least one of a store or a shopper, the tag being used to indicate whether the object has permission to preload a bill of lading page;

[0007] If it is determined according to the tag that the user has the authority to preload the bill of lading page, the probability of entering the bill of lading page is determined. If the probability is greater than a preset value, the page data of the bill of lading page corresponding to the cargo selection operation is requested from the server.

[0008] As an optional implementation, determining the probability of entering the bill of lading page includes:

[0009] Acquire operation information of the shopper during at least one operation of the target application;

[0010] Input the operation information into a bill of lading prediction model pre-stored locally, and obtain the probability of entering the bill of lading page output by the bill of lading prediction model;

[0011] Among them, the bill of lading prediction model is determined based on the first prediction model, and the first prediction model is trained with the operation information of the sample shoppers during at least one operation of the target application as the training sample, and whether the sample shoppers enter the bill of lading page after triggering the add-to-cart operation of adding goods in the target application as the sample label.

[0012] As an optional implementation manner, the requesting the server for page data of the bill of lading page corresponding to the cargo selection operation further includes:

[0013] Receiving and caching the page data returned by the server;

[0014] In response to a settlement operation that triggers entry into a bill of lading page, the bill of lading page is loaded according to the cached page data.

[0015] As an optional implementation manner, the requesting the server for page data of a bill of lading page corresponding to the cargo selection operation includes:

[0016] Determine the timestamp of the product selection operation and the updated product information of the product to be shipped;

[0017] Sending a page data acquisition request to the server, wherein the page data acquisition request includes a timestamp of the product selection operation and updated product information of the product to be shipped;

[0018] The receiving and caching the page data returned by the server further includes: receiving and caching a timestamp of a product selection operation corresponding to the page data returned by the server;

[0019] The step of loading the bill of lading page according to the cached page data comprises:

[0020] The timestamp of the cargo selection operation is determined, and the bill of lading page is loaded according to the page data corresponding to the timestamp.

[0021] As an optional implementation manner, determining the label of the object includes:

[0022] If it is determined that the tag of the object does not exist locally, sending a tag acquisition request to the server, the tag acquisition request including at least one of the unique identifier of the store or the unique identifier of the shopper;

[0023] According to the label of the object returned by the server, the label is saved locally.

[0024] As an optional implementation, the step of inputting the operation information into a bill of lading prediction model pre-stored locally also includes the step of obtaining the bill of lading prediction model:

[0025] Periodically receiving a second prediction model sent by a server, where the second prediction model is obtained by the server after compressing the first server of the current period;

[0026] The parameters of the second prediction model are adjusted based on the locally stored operation information of the shopper during at least one operation of the target application as a training sample, and whether the shopper enters the bill of lading page after triggering the add-to-cart operation of adding items in the target application as a sample label, and the adjusted second prediction model is used as the bill of lading prediction model for the current period.

[0027] As an optional implementation manner, the data processing method further includes:

[0028] The parameters of the bill of lading prediction model of the current period are sent to the server, so that the server adjusts the parameters of the first prediction model of the current period according to the parameters of the bill of lading prediction model, and uses the adjusted first prediction model as the first prediction model of the next period.

[0029] As an optional implementation manner, the requesting the server for page data of a bill of lading page corresponding to the cargo selection operation includes:

[0030] If it is determined that the goods to be shipped after the goods selection operation meet the preset conditions, the page data of the shipping order page corresponding to the goods selection operation is requested from the server.

[0031] According to one aspect of an embodiment of the present application, a data processing method is provided, which is applied to a server and includes:

[0032] The receiving terminal sends a label acquisition request in response to a selection operation of updating a to-be-billed commodity in a target application, the label acquisition request including a unique identifier of an object, the object including at least one of a store or a shopper;

[0033] If a tag corresponding to the unique identifier of the object is retrieved, the tag is returned to the terminal. The tag is used to indicate whether the object has the authority to preload the bill of lading page, so that if the terminal determines that it has the authority to preload the bill of lading page based on the tag, it determines the probability of entering the bill of lading page. If the probability is greater than the preset value, the page data of the bill of lading page corresponding to the cargo selection operation is requested from the server.

[0034] As an optional implementation manner, before returning the label to the terminal, the method further includes:

[0035] Determine the peak flow rate, flow buffer value and flow instantaneous value of loading bill of lading page within a preset time period;

[0036] If it is determined that the instantaneous flow value is less than the peak flow value, the difference between the flow buffer value and the instantaneous flow value is used as the flow for preloading the bill of lading page.

[0037] As an optional implementation manner, the data processing method further includes:

[0038] The operation information of the sample shopper during at least one operation of the target application is used as a training sample, and whether the sample shopper enters the bill of lading page after triggering the additional purchase operation of the additional product in the target application is used as a sample label to train an initial model, so as to obtain a first prediction model;

[0039] The first prediction model is compressed, and the compressed first prediction model is sent to the terminal as the second prediction model, so that the terminal obtains the bill of lading prediction model according to the second prediction model.

[0040] As an optional implementation manner, the compressed first prediction model is sent to the terminal as the second prediction model, and then further includes:

[0041] Receive the parameters of the bill of lading prediction model sent by the terminal, adjust the parameters of the first prediction model of the current cycle according to the parameters, and use the adjusted first prediction model as the first prediction model of the next cycle.

[0042] According to another aspect of an embodiment of the present application, a terminal is provided, the terminal including:

[0043] A tag determination module, configured to determine a tag of an object in response to a selection operation of updating a to-be-billed commodity in a target application, the object comprising at least one of a store or a shopper, the tag being used to indicate whether the object has permission to preload a bill of lading page;

[0044] The page request module is used to determine the probability of entering the bill of lading page if it is determined based on the tag that the user has the authority to preload the bill of lading page. If the probability is greater than a preset value, the page data of the bill of lading page corresponding to the cargo selection operation is requested from the server.

[0045] According to another aspect of an embodiment of the present application, a server is provided, the server comprising:

[0046] A label request receiving module, used to receive a label acquisition request sent by a terminal in response to a selection operation of updating a to-be-billed commodity in a target application, wherein the label acquisition request includes a unique identifier of an object, and the object includes at least one of a store or a shopper;

[0047] The tag return module is used to return the tag to the terminal if a tag corresponding to the unique identifier of the object is retrieved. The tag is used to indicate whether the object has the authority to preload the bill of lading page, so that if the terminal determines that it has the authority to preload the bill of lading page based on the tag, it determines the probability of entering the bill of lading page. If the probability is greater than a preset value, it requests the server for the page data of the bill of lading page corresponding to the cargo selection operation.

[0048] According to another aspect of an embodiment of the present application, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the data processing method of the above aspect.

[0049] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the data processing method in the above aspect are implemented.

[0050] The beneficial effects of the technical solution provided by the embodiment of the present application are:

[0051] In response to updating the selection operation of the goods to be billed in the target application, the label of the object is determined. The label of the present application is used to indicate whether the object has the authority to preload the bill of lading page. The object of the present application includes at least one of a store or a shopper, and it is possible to predict whether to preload the bill of lading page from multiple dimensions. Compared with the prior art, the accuracy of judging whether to preload the bill of lading page is further improved, resource investment is saved, and commercial support is increased. After determining that the authority to preload the bill of lading page is possessed by the label, the probability of entering the bill of lading page is further calculated. If the probability is greater than a preset value, the page data of the bill of lading page corresponding to the selection operation is requested from the server. The embodiment of the present application determines whether to request page data from the server based on the label and the probability of entering the bill of lading page, which can not only reduce the number and flow of preloading the bill of lading page, but also more accurately predict whether it is suitable to preload the bill of lading page, laying the foundation for realizing the senseless loading of the bill of lading page and ensuring a good experience for shoppers. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in describing the embodiments of the present application are briefly introduced below.

[0053] Figure 1 A schematic diagram of a system architecture for implementing a data processing method provided in an embodiment of the present application;

[0054] Figure 2 A flowchart of a terminal-side data processing method provided in an embodiment of the present application;

[0055] Figure 3 A schematic diagram of updating a pending bill of lading commodity in a target application provided in an embodiment of the present application;

[0056] Figure 4 A flowchart of another terminal-side data processing method provided in an embodiment of the present application;

[0057] Figure 5 A flowchart of a data processing method on a terminal side according to another embodiment of the present application;

[0058] Figure 6 A flow chart of a data processing method on a server side provided in an embodiment of the present application

[0059] Figure 7 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0060] Figure 8 A schematic diagram of the structure of a server provided in an embodiment of the present application;

[0061] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The embodiments of the present application are described below in conjunction with the drawings in the present application. It should be understood that the implementation methods described below in conjunction with the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions of the embodiments of the present application.

[0063] It will be understood by those skilled in the art that, unless specifically stated, the singular forms "one", "an" and "the" used herein may also include plural forms. It should be further understood that the terms "including" and "comprising" used in the embodiments of the present application refer to that the corresponding features can be implemented as the presented features, information, data, steps, operations, elements and / or components, but do not exclude the implementation as other features, information, data, steps, operations, elements, components and / or combinations thereof supported by the technical field. It should be understood that when we call an element "connected" or "coupled" to another element, the one element can be directly connected or coupled to the other element, or it can refer to that the one element and the other element establish a connection relationship through an intermediate element. In addition, the "connection" or "coupling" used here may include wireless connection or wireless coupling. The term "and / or" used here indicates at least one of the items defined by the term, for example, "A and / or B" can be implemented as "A", or as "B", or as "A and B".

[0064] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0065] The data processing method, terminal, electronic device, electronic device and computer-readable storage medium provided in this application are intended to solve the above technical problems in the prior art.

[0066] The following describes several exemplary embodiments to illustrate the technical solutions of the embodiments of the present application and the technical effects produced by the technical solutions of the present application. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0067] Figure 1 The system architecture diagram for implementing the data processing method provided in the embodiment of the present application is as shown in the figure. The system includes a terminal 101 and a server 102. The terminal 101 is communicatively connected with the server 102 via a network 103.

[0068] In this embodiment, the terminal 101 may be any type of mobile computing device, including a mobile computer (e.g., a personal digital assistant (PDA), a laptop computer, a notebook computer, such as a tablet computer, a netbook, etc.), a mobile phone (e.g., a cellular phone), a wearable computing device (e.g., a smart watch, a head-mounted device, including smart glasses), or other types of mobile devices. In some embodiments, the terminal 101 may also be a fixed computing device, such as a desktop computer, a game console, a smart TV, etc.

[0069] The server 102 can store and run instructions that can execute the various methods described herein, and can be a single server or a server cluster or a cloud server, respectively, or any two or three of them can be the same server or the same server cluster or cloud server. It should be understood that the server mentioned herein is typically a server computer with a large amount of memory and processor resources, but other embodiments are also possible.

[0070] Examples of the network 103 include a local area network (LAN), a wide area network (WAN), a personal area network (PAN), and / or a combination of communication networks such as the Internet. Each of the server 102 and the terminal 101 may include at least one communication interface (not shown) capable of communicating through the network 103. Such a communication interface may be one or more of the following: any type of network interface (e.g., a network interface card (NIC)), a wired or wireless (such as an IEEE 802.11 wireless LAN (WLAN)) wireless interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth™ interface, a near field communication (NFC) interface, etc.

[0071] like Figure 1 As shown, the terminal 101 may include a display screen and a terminal application that can interact with the terminal user via the display screen. The terminal 101 may, for example, interact with the server 102 via the network 103, for example, send data to it or receive data from it. The terminal application may be a local application, a web page (Web) application, or a small program (LiteApp, such as a mobile phone small program) as a lightweight application. In the case where the terminal application is a local application that needs to be installed, the terminal application can be installed in the user terminal 101. In the case where the terminal application is a Web application, the terminal application can be accessed through a browser. In the case where the terminal application is a small program, the terminal application can be directly opened on the user terminal 101 by searching for relevant information of the terminal application (such as the name of the terminal application, etc.), scanning a graphic code (such as a barcode, a QR code, etc.) of the terminal application, etc., without installing the terminal application.

[0072] The present application provides a terminal side data processing method, such as Figure 2 As shown, the method includes:

[0073] S101. In response to a selection operation of updating a to-be-billed commodity in a target application, a label of an object is determined.

[0074] It should be understood that the target application may be an application with an online shopping function, and the target application may display a shopping interface including information about the goods available for purchase, and shoppers may add goods to a shopping cart (i.e., a bill of lading) by triggering the information about the goods on the shopping page. The operation of updating the selection of goods to be billed in the embodiment of the present application may include adding goods to a shopping cart or deleting goods from a shopping cart.

[0075] See also Figure 3 , which exemplarily shows a schematic diagram of updating the goods to be billed in the target application in an embodiment of the present application, as shown in the figure, Figure 3The left and right figures in the figure are the product display pages before and after the products to be ordered are updated, respectively. The side of the product display page can display products of different categories, including four categories of recommendations, must-buys, meal sets and drinks in this embodiment. The quantity of product 1 in the left figure is 1, the quantity of product 2 is 2, and the quantity of product 3 is not displayed, indicating that product 3 does not belong to the products to be ordered at this time. The quantity of product 1 in the right figure remains unchanged, while the quantity of product 2 is 1, indicating that the shopper has reduced the purchase of product 2. Finally, there are only 1 product 1 and 1 product 2 left in the products to be ordered.

[0076] In response to a product selection operation, an embodiment of the present application first determines a label of an object, wherein the object includes at least one of a store or a shopper. That is, the present application supports setting labels for stores and shoppers, and the labels are used to indicate whether the object has permission to preload a bill of lading page.

[0077] In some embodiments, the application can set labels according to the business type of the store or the shopping habits of the shoppers. For example, if the business type of the store is barbecue, spicy hot pot, etc., since the finished products of this type of business require users to select more ingredients, there is a common situation of frequently updating the bill of lading goods. The label for this type of store can be set to not have the permission to preload the bill of lading page. For business types such as drinks and cooking, what users choose is the final product, so the label for this type of store can be set to have the permission to preload the bill of lading page.

[0078] For a shopper, if it is determined that the number of times the shopper places an order within a preset time period exceeds a threshold, the shopper is considered to be a frequent user of the target application, and the tag of the shopper can be set with permission to preload the billing page. On the contrary, if the frequency of use of a shopper is not high, the tag of the shopper can be set not to have permission to preload the billing page.

[0079] In the embodiment of the present application, the generation of object tags can be performed by a terminal or by a server. When performed by a terminal, in response to a shopper entering a store's shopping interface, the business type of the store can be determined by identifying text information in the shopping interface. Whenever a shopper launches a target application or places an order, the corresponding behavior and timestamp can be recorded locally, thereby counting the shopper's usage habits. According to the shopper's usage habits, the shopper's tag can be regularly updated locally on the terminal.

[0080] When the server is used, the server can set the store's label when the store reports / updates the business type, and can provide the store with services that support the pre-loading of the bill of lading page, increase commercial support, and make more reasonable use of traffic. When the terminal needs the label of the object, it can initiate a request to the server.

[0081] S102. If it is determined based on the tag that the user has the authority to preload the bill of lading page, the probability of entering the bill of lading page is determined. If the probability is greater than a preset value, the page data of the bill of lading page corresponding to the cargo selection operation is requested from the server.

[0082] The objects of the embodiment of the present application include at least one of a store and a shopper. When at least one object has the permission to preload the bill of lading page, it is determined that the current selection operation has the permission to preload the bill of lading page. After that, the probability of the shopper entering the bill of lading page is further determined. The embodiment of the present application can predict the probability through a pre-trained neural network model. The prediction using machine learning technology can improve the accuracy of the prediction. If the probability is greater than a preset value, the page data of the bill of lading page corresponding to the selection operation is requested from the server to preload the bill of lading page.

[0083] In some embodiments, if the probability is not greater than a preset value, the page data of the bill of lading page corresponding to the cargo selection operation is not requested from the server. Instead, when it is determined that the bill of lading page has been entered, the page data of the bill of lading page is requested from the server.

[0084] It should be understood that each time the terminal requests page data from the server based on the latest items to be ordered, for example, the items to be ordered after the shopper updates at time 1 include 1 item A and 2 items B. At this time, the probability of entering the order page is low, and no page data is requested from the server. At the subsequent time 2, the items to be ordered are updated, and the updated items to be ordered are 1 item A and 1 item B. At this time, the probability of entering the order page is high, and the page data requested from the server refers to the order page corresponding to 1 item A and 1 item B.

[0085] The data processing method of the embodiment of the present application, in response to the selection operation of updating the goods to be billed in the target application, determines the label of the object. The label of the present application is used to indicate whether the object has the authority to preload the bill of lading page. The object of the present application includes at least one of a store or a shopper, and it is possible to prejudge whether to preload the bill of lading page from multiple dimensions. Compared with the prior art, the accuracy of judging whether to preload the bill of lading page is further improved, resource investment is saved, and commercial support is increased. After determining that the authority to preload the bill of lading page is available through the label, the probability of entering the bill of lading page is further calculated. If the probability is greater than the preset value, the page data of the bill of lading page corresponding to the selection operation is requested from the server. The embodiment of the present application determines whether to request page data from the server based on the label and the probability of entering the bill of lading page, which can not only reduce the number and flow of preloading the bill of lading page, but also more accurately predict whether it is suitable to preload the bill of lading page, laying the foundation for realizing the senseless loading of the bill of lading page and ensuring the good experience of shoppers.

[0086] Based on the above embodiments, as an optional embodiment, determining the probability of entering the bill of lading page includes:

[0087] S201. Obtain operation information of a shopper during at least one operation of a target application.

[0088] Specifically, the embodiment of the present application can obtain at least one of the following operation information:

[0089] The number of purchases and deductions, the number of visits to the store pages (such as product display pages, bill of lading pages, etc.), the length of stay, etc.

[0090] S202: Input the operation information into a bill of lading prediction model pre-stored locally, and obtain the probability of entering the bill of lading page output by the bill of lading prediction model.

[0091] This application uses the bill of lading prediction model of the terminal to perform probabilistic prediction. Compared with the method of deploying the bill of lading prediction model on the server, it can achieve real-time prediction without bandwidth waiting time.

[0092] It should be understood that the bill of lading prediction model of the embodiment of the present application is determined based on the first prediction model. The embodiment of the present application also includes a step of training the first prediction model before step S202. Specifically, the first prediction model can be trained in the following manner: first, collect the operation information of a certain number of sample shoppers during at least one operation of the target application, obtain the bill of lading behavior information of whether the sample shoppers enter the bill of lading page after triggering the purchase operation of the additional purchase goods in the target application, and then, based on the operation information of the sample shoppers during at least one operation of the target application and the bill of lading behavior information of whether the sample shoppers enter the bill of lading page after triggering the purchase operation of the additional purchase goods in the target application, train the initial model, wherein the operation information of the sample shoppers during at least one operation of the target application is used as the training sample, and whether the sample shoppers enter the bill of lading page after triggering the purchase operation of the additional purchase goods in the target application is used as the sample label, so as to obtain the first prediction model. The initial model can be a single neural network model or a combination of multiple neural network models.

[0093] In the embodiment of the present application, the step of training the first prediction model can be executed by the terminal or by the server.

[0094] In some embodiments, the embodiments of the present application can first obtain a first prediction model based on a large number of training samples on the server side, and then compress the first prediction model, and send the compressed model to the terminal for storage, so that the terminal can obtain a bill of lading prediction model based on the compressed model. The compressed model occupies less storage space and will not cause pressure on the terminal operation. Although the accuracy rate is reduced, in some embodiments, the terminal can re-train the compressed first prediction model based on the user's operation information stored locally, and use the re-trained model as the bill of lading prediction model, thereby taking into account both accuracy and space occupation.

[0095] Based on the above embodiments, as an optional embodiment, the operation information is input into a bill of lading prediction model pre-stored locally, and the step of obtaining the bill of lading prediction model is also included:

[0096] Periodically receiving a second prediction model sent by a server, where the second prediction model is obtained by the server after compressing the first server of the current period;

[0097] The parameters of the second prediction model are adjusted based on the locally stored operation information of the shopper during at least one operation of the target application as a training sample, and whether the shopper enters the bill of lading page after triggering the add-to-cart operation of adding items in the target application as a sample label, and the adjusted second prediction model is used as the bill of lading prediction model for the current period.

[0098] Based on the above embodiments, as an optional embodiment, it also includes:

[0099] The parameters of the bill of lading prediction model of the current period are sent to the server, so that the server adjusts the parameters of the first prediction model of the current period according to the parameters of the bill of lading prediction model, and uses the adjusted first prediction model as the first prediction model of the next period.

[0100] In the embodiment of the present application, the server periodically compresses the first prediction model to obtain the second prediction model, sends the second prediction model to the terminal, and then the terminal trains the second prediction model based on local operation information to obtain the bill of lading prediction model of the current period. The terminal then sends the parameters of the bill of lading prediction model to the server, and the server optimizes the prediction model according to the parameters to obtain the first prediction model of the next period, thereby realizing continuous iteration of the bill of lading prediction model and always maintaining the accuracy of the predicted probability of entering the bill of lading page at a high level.

[0101] Based on the above embodiments, as an optional embodiment, the page data of the bill of lading page corresponding to the cargo selection operation is requested from the server, and then the following is further included:

[0102] S301, receiving and caching page data returned by the server;

[0103] S302: In response to a settlement operation that triggers entry into a bill of lading page, the bill of lading page is loaded according to cached page data.

[0104] The embodiment of the present application preloads the bill of lading page by accepting and caching the page data returned by the server. Therefore, after the settlement operation of entering the bill of lading page is triggered, the bill of lading page is loaded according to the cached page data, so that the bill of lading page can be quickly displayed to the shopper, realizing seamless loading of the bill of lading page and improving user experience.

[0105] See also Figure 4 , which exemplarily shows a flow chart of the data processing method on the terminal side of an embodiment of the present application. As shown in the figure, the terminal displays the product information of the store through the target application. In response to the selection operation of the product to be billed that is updated in the target application (illustrated by adding to purchase in the figure), the trigger strategy is: the label of the object is determined by the server. The server can pre-classify the user and the store and determine the corresponding label. If it is determined that the authority to preload the bill page is available based on the label of the current store or the current shopper, the probability of entering the bill page is determined based on terminal intelligence (referring to the technology of running artificial intelligence applications on mobile devices). If the probability is greater than a preset value, the page data of the bill page corresponding to the selection operation is requested from the server. The acquired page data has a certain validity period, which is 10 minutes (min) in the embodiment of the present application. If the shopper triggers the settlement operation of entering the bill page within 10 minutes, the bill page is loaded based on the cached page data to achieve seamless loading of the bill page.

[0106] Based on the above embodiments, as an optional embodiment, requesting the server for page data of a bill of lading page corresponding to the cargo selection operation includes:

[0107] Determine the timestamp of the picking operation and the updated product information of the pending order items.

[0108] In the embodiment of the present application, each time the user performs a product selection operation, it is necessary to determine the timestamp of the product selection operation and the updated product information of the product to be shipped. The optional product information may include price, purchase quantity, remarks, etc.

[0109] Furthermore, the terminal sends a page data acquisition request to the server, the page data acquisition request includes the timestamp of the product selection operation and the updated product information of the product to be picked up; the terminal receives and caches the page data returned by the server, and also includes: receiving and caching the timestamp of the product selection operation corresponding to the page data returned by the server.

[0110] The terminal sends the timestamp of the goods selection operation to the server, and the server carries the corresponding timestamp when returning the page data. In this way, when the terminal receives the page data, it knows which goods selection operation the page data corresponds to. In this way, when loading the bill of lading page according to the cached page data, the timestamp of the goods selection operation can be determined, and the bill of lading page can be loaded according to the page data corresponding to the timestamp, which solves the problem that when the user frequently performs goods selection operations, the loaded bill of lading page is not the page data corresponding to the most recent goods selection operation.

[0111] Based on the above embodiments, as an optional embodiment, determining a label of an object includes:

[0112] If it is determined that the tag of the object does not exist locally, a tag acquisition request is sent to the server, where the tag acquisition request includes at least one of a unique identifier of the store or a unique identifier of the shopper;

[0113] According to the label of the object returned by the server, the label is saved locally.

[0114] It should be noted that the terminal of the embodiment of the present application first determines whether there is a tag of the object stored locally. If so, the authority is directly determined based on the local tag. If not, a tag acquisition request is sent to the server. The tag acquisition request may include the unique identifier of the store or the unique identifier of the shopper. The server can store the corresponding tag according to the unique identifier of the object, so that after receiving the tag acquisition request, the corresponding tag is returned according to the unique identifier. After receiving the tag, the terminal will save the tag locally and start the timer. When the timer expires, the tag will be deleted, so that the terminal must request the latest tag from the server at regular intervals, which lays the foundation for supporting the commercialization of senseless loading.

[0115] Based on the above embodiments, as an optional embodiment, requesting the server for page data of the bill of lading page corresponding to the cargo selection operation includes:

[0116] If it is determined that the goods to be shipped after the goods selection operation meet the preset conditions, the page data of the shipping order page corresponding to the goods selection operation is requested from the server.

[0117] In some cases, a store may set some mandatory items for placing an order or require the quantity or price of the items to reach a certain level. Therefore, when the embodiment of the present application requests the page data of the bill of lading page corresponding to the product selection operation from the server, it first determines whether the products to be ordered after the product selection operation meet the preset conditions, such as whether the mandatory products are included. If the conditions are met, the server is requested to receive the page data of the bill of lading page corresponding to the product selection operation, which can more accurately avoid invalid loading of the bill of lading page and save traffic.

[0118] See also Figure 5 , which exemplarily shows a flow chart of a data processing method on a terminal side in another embodiment of the present application, as shown in the figure, including:

[0119] S401, in response to the operation of updating the selection of the goods to be shipped in the target application, determining whether there is a label of the object locally, if not, executing S402, if so, executing S403;

[0120] S402, sending a tag acquisition request to the server, and saving the tag locally according to the tag of the object returned by the server;

[0121] S403, judging whether the user has the authority to preload the bill of lading page according to the local tag object, if so, executing S404, if not, executing S409;

[0122] S404, obtaining operation information of the shopper during at least one operation of the target application, inputting the operation information into a bill of lading prediction model pre-stored locally, and obtaining the probability of entering the bill of lading page output by the bill of lading prediction model;

[0123] S405, determine whether the probability is greater than a preset value, if so, execute S406, if not, execute S409;

[0124] S406: Determine the timestamp of the product selection operation and the updated product information of the product to be shipped, and send a page data acquisition request to the server, where the page data acquisition request includes the timestamp of the product selection operation and the updated product information of the product to be shipped;

[0125] S407, receiving and caching the page data returned by the server and the timestamp of the product selection operation corresponding to the page data;

[0126] S408. In response to the settlement operation that triggers the entry into the bill of lading page, determine the timestamp of the cargo selection operation, load the bill of lading page according to the page data corresponding to the timestamp, and end the process.

[0127] S409, in response to the settlement operation that triggers the entry into the bill of lading page, determining the updated commodity information of the commodity to be billed, and sending a page data acquisition request to the server, the page data acquisition request including the updated commodity information of the commodity to be billed;

[0128] S410, receiving the page data returned by the server, loading the bill of lading page according to the page data, and ending the process.

[0129] See also Figure 6 , which exemplarily shows a flow chart of a data processing method on the server side of an embodiment of the present application, as shown in the figure, including:

[0130] S601: a label acquisition request sent by a receiving terminal in response to a selection operation of updating a product to be billed in a target application, wherein the label acquisition request includes a unique identifier of an object, and the object includes at least one of a store or a shopper;

[0131] S602. If a tag corresponding to the unique identifier of the object is retrieved, the tag is returned to the terminal. The tag is used to indicate whether the object has the authority to preload the bill of lading page, so that if the terminal determines that it has the authority to preload the bill of lading page based on the tag, it determines the probability of entering the bill of lading page. If the probability is greater than a preset value, the page data of the bill of lading page corresponding to the cargo selection operation is requested from the server.

[0132] The server of the embodiment of the present application receives a tag acquisition request sent by the terminal, and returns the tag to the server. The tag is used to indicate whether the object has the permission to preload the bill of lading page, so that the terminal can preliminarily determine whether seamless loading is supported based on the tag. If it is determined that the object has the permission to preload the bill of lading page, the terminal further determines the probability of entering the bill of lading page. If the probability is greater than a preset value, the server is requested to obtain page data of the bill of lading page corresponding to the product selection operation. The present application determines whether to request page data from the server based on the tag and the probability of entering the bill of lading page, which can reduce the number and traffic of preloading the bill of lading page and more accurately predict whether it is suitable to preload the bill of lading page, laying the foundation for realizing seamless loading of the bill of lading page and ensuring a good experience for shoppers.

[0133] It should be understood that when the terminal adds items to a purchase, reduces items from a purchase, or enters the bill of lading page, it interacts with the server, and these interactions require the server to deploy corresponding traffic. Since traffic usage fluctuates, that is, there are peaks and troughs, when the relevant technology deploys traffic on the server side, if the bill of lading page is preloaded for all cargo selection operations, the traffic difference between the peak period and the trough period can be as high as 4 times, the traffic burden on the server is extremely large, and it will increase costs considerably.

[0134] In order to solve the above problems in the prior art, based on the above embodiments, as an optional embodiment, the label is returned to the terminal, and the method also includes:

[0135] Determine the peak flow rate, flow buffer value and flow instantaneous value of loading bill of lading page within a preset time period;

[0136] If it is determined that the instantaneous value of the flow is less than the peak value of the flow, the difference between the flow buffer value and the instantaneous value of the flow is used as the flow of preloading the bill of lading page.

[0137] The embodiment of the present application sets a traffic cache value and further determines the traffic peak value and traffic instantaneous value of a preset time period (for example, one day). When the traffic instantaneous value is less than the traffic peak value, the difference between the traffic cache value and the traffic instantaneous value is calculated, and the difference is used as the traffic for preloading the bill of lading page.

[0138] In the embodiment of the present application, when the traffic is at a trough, a larger amount of traffic required for preloading the bill of lading page can be allocated from the total traffic, and even all the bill of lading pages can be preloaded. However, the amount of pre-requests given during peak times is smaller. By releasing more idle traffic during non-peak hours, the optimized coverage can be enhanced, the deployment efficiency of traffic can be improved, and more users can be supported to enjoy the preloading service in disguised form.

[0139] Based on the above embodiments, as an optional embodiment, the method further includes:

[0140] S701, using the operation information of the sample shopper during at least one operation of the target application as training samples, and using whether the sample shopper enters the bill of lading page after triggering the add-to-purchase operation of the add-to-purchase item in the target application as sample labels to train an initial model, and obtain a first prediction model;

[0141] S702: compress the first prediction model, and send the compressed first prediction model to the terminal as the second prediction model, so that the terminal obtains the bill of lading prediction model according to the second prediction model.

[0142] In the embodiment of the present application, the server side obtains a first prediction model based on training samples and training labels. Considering the large scale of the prediction model, the server further compresses the first prediction model and sends the compressed second prediction model to the terminal.

[0143] In some embodiments, the terminal can directly use the compressed second prediction model as the bill of lading prediction model to predict probability, or it can iteratively train the second prediction model based on the shopper's operation information stored locally, and use the iteratively trained second prediction model as the bill of lading prediction model to predict probability.

[0144] Based on the above embodiments, as an optional embodiment, the server sends the compressed first prediction model as the second prediction model to the terminal, and then further includes:

[0145] Receive the parameters of the bill of lading prediction model sent by the terminal, adjust the parameters of the first prediction model of the current cycle according to the parameters, and use the adjusted first prediction model as the first prediction model of the next cycle.

[0146] The present application embodiment provides a terminal, such as Figure 7As shown, the terminal may include: a tag determination module 701 and a page request module 702, wherein:

[0147] The tag determination module 701 is used to determine the tag of an object in response to the selection operation of updating the to-be-billed commodity in the target application, where the object includes at least one of a store or a shopper, and the tag is used to indicate whether the object has the authority to preload the bill of lading page;

[0148] The page request module 702 is used to determine the probability of entering the bill of lading page if it is determined that the pre-loaded bill of lading page is authorized according to the tag. If the probability is greater than a preset value, the page data of the bill of lading page corresponding to the cargo selection operation is requested from the server.

[0149] The terminal of the embodiment of the present application can execute the data processing method on the terminal side provided by the embodiment of the present application, and the implementation principle is similar. The actions performed by each module in the terminal of each embodiment of the present application correspond to the steps in the data processing method on the terminal side of each embodiment of the present application. For the detailed functional description of each module of the terminal, please refer to the description in the corresponding method shown in the previous text, which will not be repeated here.

[0150] The present application embodiment provides a server, such as Figure 8 As shown, the server may include: a label request receiving module 801 and a label returning module 802, wherein:

[0151] The label request receiving module 801 is used to receive a label acquisition request sent by the terminal in response to the selection operation of updating the pending bill of lading goods in the target application, the label acquisition request including the unique identifier of the object, and the object includes at least one of a store or a shopper;

[0152] The tag return module 802 is used to return the tag corresponding to the unique identifier of the object to the terminal if it is retrieved. The tag is used to indicate whether the object has the authority to preload the bill of lading page, so that if the terminal determines that it has the authority to preload the bill of lading page based on the tag, it determines the probability of entering the bill of lading page. If the probability is greater than a preset value, it requests the server for the page data of the bill of lading page corresponding to the cargo selection operation.

[0153] The server of the embodiment of the present application can execute the server-side data processing method provided by the embodiment of the present application, and the implementation principle is similar. The actions performed by each module in the server of each embodiment of the present application correspond to the steps in the server-side data processing method of each embodiment of the present application. For the detailed functional description of each module of the server, please refer to the description in the corresponding method shown in the previous text, which will not be repeated here.

[0154] In an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the data processing method. Compared with the related art, the following can be achieved:

[0155] In response to updating the selection operation of the goods to be billed in the target application, the label of the object is determined. The label of the present application is used to indicate whether the object has the authority to preload the bill of lading page. The object of the present application includes at least one of a store or a shopper, and it is possible to predict whether to preload the bill of lading page from multiple dimensions. Compared with the prior art, the accuracy of judging whether to preload the bill of lading page is further improved, resource investment is saved, and commercial support is increased. After determining that the authority to preload the bill of lading page is possessed by the label, the probability of entering the bill of lading page is further calculated. If the probability is greater than a preset value, the page data of the bill of lading page corresponding to the selection operation is requested from the server. The embodiment of the present application determines whether to request page data from the server based on the label and the probability of entering the bill of lading page, which can not only reduce the number and flow of preloading the bill of lading page, but also more accurately predict whether it is suitable to preload the bill of lading page, laying the foundation for realizing the senseless loading of the bill of lading page and ensuring a good experience for shoppers.

[0156] In an alternative embodiment, an electronic device is provided, such as Fig. 9 As shown, Fig. 9 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present application.

[0157] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0158] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0159] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0160] The memory 4003 is used to store the computer program for executing the embodiment of the present application, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the computer program stored in the memory 4003 to implement the steps shown in the above method embodiment.

[0161] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps and corresponding contents of the aforementioned method embodiment can be implemented.

[0162] The embodiment of the present application also provides a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiment when executed by a processor.

[0163] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that shown or described in the drawings.

[0164] It should be understood that, although each operation step is indicated by arrows in the flowchart of the embodiment of the present application, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present application, the implementation steps in each flowchart can be performed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on actual implementation scenarios. Some or all of these sub-steps or stages may be executed at the same time, and each sub-step or stage in these sub-steps or stages may also be executed at different times respectively. In different scenarios of execution time, the execution order of these sub-steps or stages may be flexibly configured according to demand, and the embodiment of the present application does not limit this.

[0165] The above is only an optional implementation method for some implementation scenarios of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the solution of the present application, other similar implementation methods based on the technical ideas of the present application are also within the protection scope of the embodiments of the present application.

Claims

1. A data processing method, applied to a terminal, characterized in that: The method comprises: In response to updating a selection operation of a to-be-billed commodity in a target application, determining a tag of an object, the object comprising at least one of a store or a shopper, the tag being used to indicate whether the object has permission to preload a bill of lading page; If it is determined according to the tag that the user has the authority to preload the bill of lading page, the probability of entering the bill of lading page is determined, and if the probability is greater than a preset value, the page data of the bill of lading page corresponding to the cargo selection operation is requested from the server; Wherein, the determination of the probability of entering the bill of lading page includes: Acquire operation information of the shopper during at least one operation of the target application; Input the operation information into a bill of lading prediction model pre-stored locally, and obtain the probability of entering the bill of lading page output by the bill of lading prediction model; Among them, the bill of lading prediction model is determined based on the first prediction model, and the first prediction model is trained with the operation information of the sample shoppers during at least one operation of the target application as the training sample, and whether the sample shoppers enter the bill of lading page after triggering the add-to-cart operation of adding goods in the target application as the sample label.

2. The method according to claim 1, characterized in that The step of requesting the server for page data of the bill of lading page corresponding to the cargo selection operation further includes: Receiving and caching the page data returned by the server; In response to a settlement operation that triggers entry into a bill of lading page, the bill of lading page is loaded according to the cached page data.

3. The method according to claim 2, characterized in that The requesting the server for page data of the bill of lading page corresponding to the cargo selection operation includes: Determine the timestamp of the product selection operation and the updated product information of the product to be shipped; Sending a page data acquisition request to the server, wherein the page data acquisition request includes a timestamp of the product selection operation and updated product information of the product to be shipped; The receiving and caching the page data returned by the server further includes: receiving and caching a timestamp of a product selection operation corresponding to the page data returned by the server; The step of loading the bill of lading page according to the cached page data comprises: The timestamp of the cargo selection operation is determined, and the bill of lading page is loaded according to the page data corresponding to the timestamp.

4. The method according to claim 1, characterized in that: The determining of the label of the object includes: If it is determined that the tag of the object does not exist locally, sending a tag acquisition request to the server, the tag acquisition request including at least one of the unique identifier of the store or the unique identifier of the shopper; According to the label of the object returned by the server, the label is saved locally.

5. The method according to claim 1, characterized in that The step of inputting the operation information into a bill of lading prediction model pre-stored locally also includes the step of obtaining the bill of lading prediction model: Periodically receiving a second prediction model sent by a server, where the second prediction model is obtained by the server after compressing the first server of the current period; The parameters of the second prediction model are adjusted based on the locally stored operation information of the shopper during at least one operation of the target application as a training sample, and whether the shopper enters the bill of lading page after triggering the add-to-cart operation of adding items in the target application as a sample label, and the adjusted second prediction model is used as the bill of lading prediction model for the current period.

6. The method according to claim 5, characterized in that Also includes: The parameters of the bill of lading prediction model of the current period are sent to the server, so that the server adjusts the parameters of the first prediction model of the current period according to the parameters of the bill of lading prediction model, and uses the adjusted first prediction model as the first prediction model of the next period.

7. The method according to claim 1, characterized in that The requesting the server for page data of the bill of lading page corresponding to the cargo selection operation includes: If it is determined that the goods to be shipped after the goods selection operation meet the preset conditions, the page data of the shipping order page corresponding to the goods selection operation is requested from the server.

8. A data processing method, applied to a server, characterized in that: The method comprises: The receiving terminal sends a label acquisition request in response to a selection operation of updating a product to be billed in a target application, wherein the label acquisition request includes a unique identifier of an object, wherein the object includes at least one of a store or a shopper; If a tag corresponding to the unique identifier of the object is retrieved, the tag is returned to the terminal, and the tag is used to indicate whether the object has the authority to preload the bill of lading page, so that if the terminal determines that it has the authority to preload the bill of lading page according to the tag, it determines the probability of entering the bill of lading page, and if the probability is greater than a preset value, it requests the server for page data of the bill of lading page corresponding to the cargo selection operation; Wherein, the determination of the probability of entering the bill of lading page includes: Acquire operation information of the shopper during at least one operation of the target application; Input the operation information into a bill of lading prediction model pre-stored locally, and obtain the probability of entering the bill of lading page output by the bill of lading prediction model; Among them, the bill of lading prediction model is determined based on the first prediction model, and the first prediction model is trained with the operation information of the sample shoppers during at least one operation of the target application as the training sample, and whether the sample shoppers enter the bill of lading page after triggering the add-to-cart operation of adding goods in the target application as the sample label.

9. The method according to claim 8, characterized in that Before returning the label to the terminal, the method further comprises: Determine the peak flow rate, flow buffer value and flow instantaneous value of loading bill of lading page within a preset time period; If it is determined that the instantaneous flow value is less than the peak flow value, the difference between the flow buffer value and the instantaneous flow value is used as the flow for preloading the bill of lading page.

10. The method according to claim 8, characterized in that Also includes: The operation information of the sample shopper during at least one operation of the target application is used as a training sample, and whether the sample shopper enters the bill of lading page after triggering the additional purchase operation of the additional product in the target application is used as a sample label to train an initial model, so as to obtain a first prediction model; The first prediction model is compressed, and the compressed first prediction model is sent to the terminal as the second prediction model, so that the terminal obtains the bill of lading prediction model according to the second prediction model.

11. The method according to claim 10, characterized in that The compressed first prediction model is sent to the terminal as the second prediction model, and then the method further includes: Receive the parameters of the bill of lading prediction model sent by the terminal, adjust the parameters of the first prediction model of the current cycle according to the parameters, and use the adjusted first prediction model as the first prediction model of the next cycle.

12. A terminal, characterized in that: include: A label determination module, configured to determine a label of an object in response to a selection operation of updating a to-be-billed commodity in a target application, wherein the object includes at least one of a store or a shopper, and the label is used to indicate whether the object has permission to preload a bill of lading page; A page request module, for determining the probability of entering the bill of lading page if it is determined according to the tag that the user has the authority to preload the bill of lading page, and for requesting page data of the bill of lading page corresponding to the cargo selection operation from the server if the probability is greater than a preset value; Wherein, the determination of the probability of entering the bill of lading page includes: Acquire operation information of the shopper during at least one operation of the target application; Input the operation information into a bill of lading prediction model pre-stored locally, and obtain the probability of entering the bill of lading page output by the bill of lading prediction model; Among them, the bill of lading prediction model is determined based on the first prediction model, and the first prediction model is trained with the operation information of the sample shoppers during at least one operation of the target application as the training sample, and whether the sample shoppers enter the bill of lading page after triggering the add-to-cart operation of adding goods in the target application as the sample label.

13. A server, characterized in that: include: a label request receiving module, configured to receive a label acquisition request sent by a terminal in response to a selection operation of updating a product to be billed in a target application, wherein the label acquisition request includes a unique identifier of an object, wherein the object includes at least one of a store or a shopper; a tag returning module, for returning the tag corresponding to the unique identifier of the object to the terminal if the tag corresponding to the unique identifier of the object is retrieved, the tag being used to indicate whether the object has the authority to preload the bill of lading page, so that if the terminal determines that the object has the authority to preload the bill of lading page according to the tag, the probability of entering the bill of lading page is determined, and if the probability is greater than a preset value, the page data of the bill of lading page corresponding to the cargo selection operation is requested from the server; Wherein, the determination of the probability of entering the bill of lading page includes: Acquire operation information of the shopper during at least one operation of the target application; Input the operation information into a bill of lading prediction model pre-stored locally, and obtain the probability of entering the bill of lading page output by the bill of lading prediction model; Among them, the bill of lading prediction model is determined based on the first prediction model, and the first prediction model is trained with the operation information of the sample shoppers during at least one operation of the target application as the training sample, and whether the sample shoppers enter the bill of lading page after triggering the add-to-cart operation of adding goods in the target application as the sample label.

14. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the data processing method according to any one of claims 1 to 11.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data processing method according to any one of claims 1 to 11 are implemented.

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