Task information processing method and device and storage medium

Abstract

The invention provides a task information processing method and device and a storage medium wherein the method comprises: in response to a task processing instruction, determining target task information corresponding to the task processing instruction, the target task information comprising at least one task node associated with the task processing instruction, the target task information is preset according to historical task processing data of a target task type and a preset rule; executing a corresponding task node according to the target task information, and generating a task execution result; and returning the task execution result to the user. According to the method, automation of the task processing process is realized, the labor cost is saved, and the task processing efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to a task information processing method, device, and storage medium. Background Art

[0002] A digital mall is a place where the merchandise in a physical store is digitized through computers and the data is shared online. The goal is to allow people to find out where they can buy the goods they need without leaving their homes, whether in a physical store or online, thus creating a truly digital mall.

[0003] Traditional digital department store operations involve complex supply chains involving suppliers, department store staff, and shopping guides, with varying degrees of bottlenecks and deficiencies in information and logistics flows. In actual store operations, this entirely manual approach often leads to a lack of focus on counter management issues and low efficiency. For example, when faced with specific counter issues, brands, shopping guides, and store brand managers lack the knowledge to resolve them and lack standardized guidance. Furthermore, counter operations involve extensive basic tasks, and information transfer relies heavily on manual labor, resulting in low efficiency. Decisions regarding counter product removal and marketing promotions are often based on empirical analysis, resulting in inefficient decision-making. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to provide a task information processing method, device and storage medium, which realize the automation of the task processing process, not only saving labor costs but also improving task processing efficiency.

[0005] In a first aspect, an embodiment of the present application provides a task information processing method, comprising: in response to a task processing instruction, determining target task information corresponding to the task processing instruction, the target task information including at least one task node associated with the task processing instruction, the target task information being pre-set based on historical task processing data and preset rules of the target task type; executing the corresponding task node according to the target task information to generate a task execution result; and returning the task execution result to the user.

[0006] In one embodiment, the determining, in response to the task processing instruction, target task information corresponding to the task processing instruction includes: determining, in response to the task processing instruction triggered by a user, target task information corresponding to the task processing instruction;

[0007] In one embodiment, determining target task information corresponding to the task processing instruction in response to the task processing instruction includes: determining target task information corresponding to the task processing instruction in response to the task processing instruction triggered by a preset condition.

[0008] In one embodiment, the task processing instruction triggered in response to a preset condition includes: obtaining historical sales status information of a specified item; determining whether the specified item meets the preset condition based on the historical sales status information; if the specified item meets the preset condition, triggering the task processing instruction corresponding to the preset condition.

[0009] In one embodiment, in response to a task processing instruction, determining the target task information corresponding to the task processing instruction includes: in response to the task processing instruction, searching for the target task information that matches the task processing instruction from a preset task library according to the task processing instruction, wherein the preset task library includes multiple preset task information.

[0010] In one embodiment, before searching for the target task information that matches the task processing instruction from the preset task library according to the task processing instruction, it also includes: obtaining historical task processing data of a specific type of task; determining at least one task node corresponding to the specific type of task based on the historical task processing data; connecting the at least one task node in series according to the preset rules corresponding to the specific type of task to generate preset task information corresponding to the specific type of task, and storing the preset task information in the preset task library.

[0011] In one embodiment, the specific type of tasks includes one or more of: a product replenishment task, a product placement task, a product price adjustment task, and a product information promotion task.

[0012] In one embodiment, the method further includes: in response to an adjustment instruction for the preset task person information, adjusting the task nodes included in the preset task information to generate the adjusted preset task information.

[0013] In one embodiment, executing the corresponding task node according to the target task information includes: determining at least one execution system corresponding to each task node in the target task information; and calling the at least one execution system to execute the corresponding task node in the target task information.

[0014] In the second aspect, an embodiment of the present application provides a task information processing method, including: in response to a task processing instruction for product information, determining the product information processing task corresponding to the processing instruction, the product information processing task including at least one task node associated with the processing instruction, and the product information processing task is pre-set based on historical task processing data and preset rules of the target task type; executing the corresponding task node according to the product information processing task, generating a task execution result regarding the product information; and returning the task execution result to the user.

[0015] In a third aspect, an embodiment of the present application provides a task information processing device, comprising:

[0016] a determination module, configured to determine, in response to a task instruction, target task information corresponding to the task instruction, the target task information including at least one task node associated with the task instruction, the target task information being pre-set based on historical task processing data of the target task type and preset rules;

[0017] An execution module is used to execute the corresponding task node according to the target task information and generate a task execution result;

[0018] The feedback module is used to return the task execution result to the user.

[0019] In one embodiment, the determining module is configured to determine target task information corresponding to the task processing instruction in response to the task processing instruction triggered by the user;

[0020] In one embodiment, the determining module is configured to determine target task information corresponding to the task processing instruction in response to the task processing instruction triggered by a preset condition.

[0021] In one embodiment, the determination module is used to obtain historical sales status information of a specified item; determine whether the specified item meets the preset conditions based on the historical sales status information; if the specified item meets the preset conditions, trigger a task processing instruction corresponding to the preset conditions.

[0022] In one embodiment, the determining module is configured to respond to a task processing instruction and search a preset task library for the target task information matching the task processing instruction according to the task processing instruction, wherein the preset task library includes a plurality of preset task information.

[0023] In one embodiment, the device also includes: a preset module, which is used to obtain historical task processing data of a specific type of task before searching for the target task information matching the task processing instruction from the preset task library according to the task processing instruction; determine at least one task node corresponding to the specific type of task based on the historical task processing data; connect the at least one task node in series according to the preset rule corresponding to the specific type of task to generate preset task information corresponding to the specific type of task, and store the preset task information in the preset task library.

[0024] In one embodiment, the specific type of tasks includes one or more of: a product replenishment task, a product placement task, a product price adjustment task, and a product information promotion task.

[0025] In one embodiment, the device further includes: an adjustment module, configured to adjust the task nodes included in the preset task information in response to an adjustment instruction for the preset task person information, and generate the adjusted preset task information.

[0026] In one embodiment, the execution module is configured to determine at least one execution system corresponding to each task node in the target task information; and call the at least one execution system to execute the corresponding task node in the target task information.

[0027] In a fourth aspect, an embodiment of the present application provides an electronic device, including:

[0028] at least one processor; and

[0029] a memory communicatively coupled to the at least one processor;

[0030] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the electronic device to execute the method described in any one of the above aspects.

[0031] In a fifth aspect, an embodiment of the present application provides a cloud device, including:

[0032] at least one processor; and

[0033] a memory communicatively coupled to the at least one processor;

[0034] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the cloud device to execute the method described in any one of the above aspects.

[0035] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the method described in any one of the above aspects is implemented.

[0036] In a seventh aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements the method described in any of the above aspects when executed by a processor.

[0037] The task information processing method, device and storage medium provided in the embodiments of the present application automatically detect task processing instructions. When a task processing instruction is detected, the target task information to be processed is determined according to the task processing instruction. The target task information is pre-configured with executable task nodes contained in the target task. By automatically executing the task nodes contained in the target task information, the target task can be automatically executed, and the task execution results can be fed back to the user. The entire task processing process is automated, saving labor costs and improving task processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are some embodiments of the present invention, and it is clear that those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0039] Figure 1 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of an application scenario of a task information processing system provided in an embodiment of the present application;

[0041] Figure 3 A schematic diagram of a framework flow of a task information processing system provided in an embodiment of the present application;

[0042] Figure 4 A schematic diagram of the framework structure of a task information processing system provided in an embodiment of the present application;

[0043] Figure 5 A flowchart of a task information processing method provided in an embodiment of the present application;

[0044] Figure 6A This is an information diagram of an automatic delivery task of potentially explosive materials provided in an embodiment of the present application;

[0045] Figure 6B A schematic diagram of the source of suggested prices in a price reduction item determination task provided in an embodiment of the present application;

[0046] Figure 7 A collaborative interaction diagram of a task information processing method provided in an embodiment of the present application;

[0047] Figure 8 A flowchart of a task information processing method provided in an embodiment of the present application;

[0048] Figure 9A flowchart of a task information processing method provided in an embodiment of the present application;

[0049] Figure 10 A schematic diagram of the structure of a task information processing device provided in an embodiment of the present application;

[0050] Figure 11 A schematic diagram of the structure of a cloud device provided in an embodiment of the present application.

[0051] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0052] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0053] The term "and / or" in this article is used to describe the association relationship of associated objects, specifically indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0054] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0055] In order to clearly describe the technical solutions of the embodiments of the present application, the following definitions are given first:

[0056] AI: Artificial Intelligence.

[0057] RPA: Robotic process automation.

[0058] IM: Instant Messaging, instant messaging.

[0059] SOP: Standard operating procedure.

[0060] ELT: Extraction-Loading-Transformation, data extraction, loading and transformation, a data warehouse technology.

[0061] LLM: Large Language Model, large language model.

[0062] HTTP: Hypertext Transfer Protocol.

[0063] SPU: Standard Product Unit: Standard product unit.

[0064] HSF: High-speed Service Framework.

[0065] REST: Representational State Transfer, a software architectural style.

[0066] CSPU: child standard product unit, child standard product unit.

[0067] LLM (Large Language Model) is a natural language processing model based on deep learning. It learns the syntax and semantics of natural languages ​​to generate human-readable text. Large language models can handle a variety of natural language tasks, such as text classification, question-answering, and conversation, and are an important path to artificial intelligence.

[0068] The task information processing method of the embodiment of the present application can be applied to any field that requires human-computer interaction processing.

[0069] Taking the digital shopping mall scenario as an example, the traditional digital department store operation mechanism features a complex supply chain consisting of suppliers, department store staff, and shopping guides, with varying degrees of bottlenecks and deficiencies in information and logistics flows. In actual store operations, this entirely manual operation often leads to a lack of focus on counter management issues and low efficiency. For example, when faced with specific counter issues, brand owners, shopping guides, and store brand managers lack knowledge of how to resolve them and lack standardized guidance. Furthermore, counter operations involve extensive basic tasks, and information transfer is largely manual, resulting in low efficiency. Decisions regarding tasks such as stocking and unstocking counter products and marketing promotions are often based on empirical analysis, resulting in inefficient task processing.

[0070] In order to solve the above problems, an embodiment of the present application provides a task information processing solution, which automatically detects task processing instructions. When a task processing instruction is detected, the target task information that needs to be processed is determined according to the task processing instruction. The target task information is pre-configured with the executable task nodes contained in the target task. By automatically executing the task nodes contained in the target task information, the target task can be automatically executed, and the task execution results can be fed back to the user. The entire task processing process is automated, saving labor costs and improving task processing efficiency.

[0071] The following detailed description of some embodiments of the present application is provided in conjunction with the accompanying drawings. The following embodiments and features thereof may be combined with one another unless they conflict with each other. Furthermore, the sequence of steps in the following method embodiments is provided for illustrative purposes only and is not intended to be a strict limitation.

[0072] like Figure 1 As shown, this embodiment provides an electronic device 1, including: at least one processor 11 and a memory 12, Figure 1 In the example, a processor 11 and a memory 12 are connected via a bus 10. Memory 12 stores instructions executable by processor 11. The instructions are executed by processor 11 so that electronic device 1 can perform all or part of the method described in the following embodiments, thereby automating task processing, saving labor costs, and improving task processing efficiency.

[0073] In one embodiment, the electronic device 1 may be a mobile phone, a tablet computer, a laptop computer, a desktop computer, or a large computing system composed of multiple computers.

[0074] Figure 2 This is a schematic diagram of an application scenario 200 of a task information processing system provided in an embodiment of the present application. Figure 2 As shown, the system includes: a server 210 and a terminal 220, wherein:

[0075] The server 210 may be a data platform that provides task information processing services, such as a digital shopping mall platform. In actual scenarios, a digital shopping mall platform may have multiple servers 210. Figure 2 Here, one server 210 is taken as an example.

[0076] The terminal 220 can be a computer, mobile phone, tablet or other device used by the user to log in to the digital shopping mall platform. There can also be multiple terminals 220. Figure 2 Two terminals 220 are used as an example for illustration.

[0077] The terminal 220 and the server 210 can transmit information via the Internet, so that the terminal 220 can access the data on the server 210. The terminal 220 and / or the server 210 can be implemented by the electronic device 1.

[0078] The task information processing solution of the embodiment of the present application can be deployed on the server 210, can also be deployed on the terminal 220, or can be deployed partially on the server 210 and partially on the terminal 220. In actual scenarios, the choice can be based on actual needs, and this embodiment does not limit it.

[0079] When the task information processing solution is fully or partially deployed on the server 210 , a calling interface may be opened to the terminal 220 to provide algorithm support to the terminal 220 .

[0080] The method provided in the embodiments of the present application can be implemented by executing corresponding software code on electronic device 1 and by interacting with a server. The electronic device 1 can be a local terminal device. When the method is run on a server, the method can be implemented and executed based on a cloud interaction system, which includes a server and a client device.

[0081] In a possible implementation, the method provided in the embodiment of the present application provides a graphical user interface through a terminal device, wherein the terminal device can be the local terminal device mentioned above, or it can be a client device in the cloud interaction system mentioned above.

[0082] like Figure 3 The figure shows a schematic diagram of the architecture flow of the task information processing system according to one embodiment of the present application. Taking the task information processing scenario in a digital shopping mall as an example, it is assumed that the preset tasks are pre-programmed into standard operating procedures using RPA technology. The user is a supplier, for example:

[0083] After a supplier user logs into the digital mall through terminal 221 and activates the automated task processing function, the system automatically detects the task processing instruction. After detecting the task processing instruction, server 210 automatically searches the preset task library for the target SOP (target task information) that matches the task processing instruction. The target SOP includes pre-configured executable task nodes. Server 210 automatically executes the task nodes in the target SOP, completes the task processing process, and provides feedback on the processing results to the user. The entire task processing process is automated, saving labor costs and improving task processing efficiency.

[0084] like Figure 4 The figure shows a framework diagram of a task information processing system according to an embodiment of the present application. Taking a digital shopping mall scenario as an example, the system mainly includes: a rule calculation platform, an RPA driving engine, and a business management gateway, among which:

[0085] The rule calculation platform includes a rule execution calculation engine, rule management and rule analysis, which are used to determine the preset rules for various tasks in the digital shopping mall. Big data can be used to precipitate task data indicators involved in the digital shopping mall, such as the sales, business methods, and 30-day redemption coupon redemption amounts of each counter in the past week. These task indicators are combined and calculated on the rule calculation platform to determine a group of counters that can participate in the rule configuration from the digital shopping mall. For example, counters with sales exceeding 300,000 in the past week are selected, and the corresponding task processes are subsequently executed for these selected counters. Based on the execution result data of each task, the preset rules corresponding to each task are summarized. The preset rules may include the execution process information of the corresponding task. For example, the preset rules corresponding to the counter replenishment task may include the execution sequence information of each task node in the replenishment process.

[0086] The RPA driver engine includes the RPA orchestration center, configuration center, task instance center, RPA scheduling engine, business action factory, and data transaction factory. The RPA orchestration center is used to orchestrate RPA process nodes for various tasks. The aforementioned metrics are applied to these nodes to simulate real-world business process interactions. In real-world scenarios, each business action involved in a digital shopping mall corresponds to a business unit in the actual business (such as product selection and placement unit or replenishment unit). By combining business actions, the corresponding business process can be implemented. To achieve this, a SOP (Business Process Specification) can be configured in the configuration center. Each individual business action in the SOP is configured and combined into a complete business process. The business action nodes in the business process are executed by the SOP execution system. During the actual business process execution, if the functions of external systems need to be called, the system can implement this capability through digital transactions. The external system will register its functions with the digital transaction center.

[0087] The IM-based robot access platform includes a collaborative space factory (for example, enabling group or individual chat), a robot factory, and a platform access SDK. Through the platform access SDK, robots are connected to platforms (such as DingTalk, WeChat, and Lark) and domain robots within the system. The robot window allows complex business processes to be integrated with the system's domain robots. Business actions and SOPs (standard operating procedures) are then used to orchestrate real-world business operations and execute these actions through the system.

[0088] The business management gateway includes service routing and a business registration center. Service routing specifically includes HSF, HTTP, service registration / discovery, REST applications, and service management. The business registration center includes login / authentication / interception, a business service subscription center, and IM plug-in management.

[0089] The system may also include a user system for managing user information, where the user may be a natural person or a digital person.

[0090] The role system is used to manage business role information.

[0091] Business entity, used to manage counter information in digital shopping malls.

[0092] Users can subscribe to corresponding service items through the backend subscription service and the frontend subscription service. For example, they can subscribe to: water and electricity bill warnings, communication letter generation, product selection and delivery, group automation management, equity delivery, community operation, questionnaires and new product incubation or many other service items through the backend subscription service.

[0093] Through the front-end subscription service, you can subscribe to: engineering repair reports, product turnover, same style and same price, replenishment or many other services.

[0094] Please see Figure 5 , which is a task information processing method of an embodiment of the present application, the method can be Figure 1 The electronic device 1 shown is used to perform and can be applied to Figure 2-Figure 4 In the application scenario of the digital shopping mall shown in , the task processing process is automated, labor costs are saved, and task processing efficiency is improved. In this embodiment, the terminal 220 is used as the execution end as an example. The method includes the following steps:

[0095] Step 501: In response to a task processing instruction, determine target task information corresponding to the task processing instruction, where the target task information includes at least one task node associated with the task processing instruction, and the target task information is pre-set based on historical task processing data and preset rules of the target task type.

[0096] In this step, a tool is provided to department store counter staff or suppliers, allowing them to activate the task processing function. Once activated, the system automatically detects the task processing instructions. The task processing instructions can carry a target task identifier, allowing the system to determine the target task information to be processed based on the target task identifier. The target task can be a specific type of preset task. For example, in a digital mall, the target task can be a task related to counter operations or product information configured by a supplier in the digital mall, such as counter replenishment tasks, product listing and unlisting tasks, product selection and delivery tasks, and automatic delivery tasks for potential hot-selling products.

[0097] The target task information pre-configures the executable task nodes that the target task should have, and a task node can correspond to a specific operation in the target task. Figure 6AAs shown, it is an information diagram of the automatic delivery task of potential explosive products provided in an embodiment of the present application. The task includes 4 task nodes, which are respectively as follows in order of habit: Node 1 Automatic delivery of potential explosive products - online notification, Node 2 Questionnaire result query, Node 3 Automatic delivery of potential explosive products - product selection recommendation card, and Node 4 Automatic delivery of potential explosive products - check.

[0098] In actual scenarios, target task information is pre-set based on the historical task processing data and preset rules of the target task type. The historical task processing data can be obtained based on big data, and the preset rules are used to characterize the execution rules of the target type tasks. Each task can have corresponding preset rules so that the digital system can simulate the execution process of the task in the real scenario.

[0099] In one embodiment, step 501 may specifically include: responding to a task processing instruction triggered by a user, determining target task information corresponding to the task processing instruction.

[0100] In this embodiment, the task processing instruction can be actively triggered by the user. For example, if a supplier user in a digital shopping mall wants to replenish the stock of his own counter, he can directly trigger the replenishment task processing instruction. After receiving the task processing instruction, the system will automatically process it. This gives users the flexibility to make decisions.

[0101] In one embodiment, step 501 may specifically include: responding to a task processing instruction triggered by a preset condition, determining target task information corresponding to the task processing instruction.

[0102] In this embodiment, task processing instructions can be triggered by preset conditions. These conditions can be set based on the actual needs of each task, such as triggering at a fixed time each day or triggering when product sales reach a certain value. Upon receiving a task processing instruction, the system automatically processes it, eliminating the need for user interaction and improving processing efficiency.

[0103] In one embodiment, responding to a task processing instruction triggered by a preset condition includes: obtaining historical sales status information of a specified item; determining whether the specified item meets the preset condition based on the historical sales status information; and triggering the task processing instruction corresponding to the preset condition if the specified item meets the preset condition.

[0104] In this embodiment, designated items refer to items requiring task processing, such as merchandise in a digital mall. For example, if a supplier user in the digital mall has subscribed to a task for automatically placing potentially explosive products, the designated items are the supplier's products. The system periodically retrieves historical sales status information for each product in the supplier's counter over a period of time. This historical sales status information includes, but is not limited to, sales per unit (SPU), number of units in stock, merchant time, product type, and price. Each piece of information can be configured with trigger conditions. The system uses historical sales status information to determine whether each product meets the corresponding pre-set conditions. If the pre-set conditions are met, the corresponding task processing instruction is automatically triggered.

[0105] Preset conditions refer to the triggering conditions for task processing instructions for specific tasks. Taking the digital shopping mall scenario as an example, based on big data capabilities, the triggering conditions for various dimensions of business operations of store counters and brands in the digital shopping mall can be accumulated. For example, ELT data warehouse technology can be used to extract, convert, and load data from the data source to the destination, integrating the scattered, messy, and non-uniform data in store counters together to provide an analytical basis for the processing of business decision-making tasks for store counters. Based on the accumulation of large amounts of data, the triggering conditions of various tasks are defined, forming the ability to derive trigger conditions and open services for trigger conditions. Ultimately, the trigger conditions are used to trigger the execution of the corresponding task processing process.

[0106] For example, the triggering conditions for the processing instruction of the potential explosive material delivery task can be as follows:

[0107] Group seasonal products: SPU color storage age <= 90 days.

[0108] Seasonal products in store counters: store CSPU inventory age <= 90 days.

[0109] Secondary industries: women's clothing, luxury goods, leisure, children's clothing, and sports.

[0110] Optionally, the triggering conditions for replenishment task processing can be as follows:

[0111] Same SPU sales volume >= 10, and inventory quantity >= 10. Also, offline sales in the past 30 days are required (offline best-selling product selection). TAG = 'BEST_SELL_OFFLINE'.

[0112] Products newly added to the shelves in the past 15 days, with the same SPU for sale (new product selection) TAG = 'BEST_SELL_NEW_ONLINE'.

[0113] The click-through rate of product recommendations is adjusted to 70% of the original rate.

[0114] Optionally, the triggering conditions for price adjustment task processing may be as follows:

[0115] Average sales volume of the same style_no (model number) in the past 14 days in stores of the same level and with the same counter name;

[0116] Average discount of style_no in the same store and counter in the past 14 days;

[0117] The product pricing model architecture provides a recommended price reduction range.

[0118] like Figure 6B The figure below shows the source of recommended prices for the price reduction task. A price range can be suggested based on a preset algorithm. For example, a preset algorithm can recommend a price reduction range based on the goals of maximizing store profit, maximizing store turnover, or maximizing store sales. The recommended price range can also be determined based on the lowest price of the same item, such as the lowest price or discounted price of the same item.

[0119] Taking the triggering conditions of the processing instructions for the potential explosive product delivery task as an example, when certain products are determined to meet the triggering conditions of the potential explosive product delivery task based on the historical sales status information of each product in the supplier's counter over a period of time, the processing instructions for the automatic delivery of potential explosive products are triggered for these products.

[0120] In one embodiment, step 501 may specifically include: in response to the task processing instruction, searching for target task information matching the task processing instruction from a preset task library according to the task processing instruction, where the preset task library includes a plurality of preset task information.

[0121] In this embodiment, the preset task library contains information about multiple preset tasks, along with the identifiers and corresponding trigger conditions for each preset task. Task processing instructions can be parsed to obtain the target task identifier, and then the preset task library can be searched for the target task information associated with the target task identifier based on the target task identifier. Searching through the preset task library can save online computing resources and improve computing efficiency.

[0122] In one embodiment, before searching a preset task library for target task information that matches the task processing instruction in step 501, the method further includes: obtaining historical task processing data for tasks of a specific type. Determining at least one task node corresponding to the specific type of task based on the historical task processing data. Connecting the at least one task node in series according to a preset rule corresponding to the specific type of task generates preset task information corresponding to the specific type of task, and storing the preset task information in the preset task library.

[0123] In this embodiment, a preset task library is first established. The preset task library may include multiple different types of tasks. In the process of establishing the task library, historical task processing data of various types of tasks may be collected, and the task nodes that should be included in the task of this type may be determined based on the historical task processing data. Taking the digital shopping mall scenario as an example, for the automatic delivery of potentially explosive products to the counter, the historical processing records of the potentially explosive product delivery tasks in the mall may be obtained, and one or more task nodes that the potentially explosive product delivery tasks should have may be analyzed based on the historical processing records. The task nodes here may be the operation nodes in the task processing process. For details, please refer to the aforementioned Figure 6A Then, one or more task nodes of the potential explosive delivery task are connected in series according to the preset rules corresponding to the potential explosive delivery task to generate the preset task information corresponding to the potential explosive delivery task. The preset rules here can refer to the execution order rules of each task node in the potential explosive delivery task. The preset rules can adopt the aforementioned Figure 4 The preset task information is then stored in the preset task library. The above operations can be performed for various types of tasks in the digital shopping mall, and finally a preset task library containing various task information is established for subsequent scheduling.

[0124] Optionally, RPA can be used to establish a standardized operating process for each preset task. Figure 4 The RPA driver engine shown in the figure uses standardized workflows as task information for pre-set tasks. For example, it orchestrates RPA process nodes for each pre-set task based on historical task processing data. The trigger conditions corresponding to each task are applied to the process nodes, using digital information to automatically execute real-world business process interactions. This ensures that each task is processed according to pre-defined execution rules and procedures, reducing the manpower required for task processing and preventing human errors.

[0125] Optionally, assuming that a preset task includes multiple task nodes, each task node may be configured with a corresponding trigger condition, and the corresponding task node is triggered to be executed according to the corresponding trigger condition.

[0126] For example, in the business scenario of automatic delivery of potentially explosive products, the precipitated trigger conditions are matched to each action node, and then through visual SOP process configuration, each node is connected in series according to the execution order corresponding to the task, thereby automating the interaction of the business process of delivering potentially explosive products.

[0127] Optionally, you can configure responsible individuals for RPA process nodes based on actual needs to maintain the RPA process and achieve collaboration across all links. Different business RPA processes can be assigned to individuals with different roles, and the responsible individuals can initiate the execution of the RPA process.

[0128] In one embodiment, the specific type of tasks includes one or more of: a product replenishment task, a product placement task, a product price adjustment task, and a product information promotion task.

[0129] In this embodiment, taking the digital shopping mall scenario as an example, fixed-type tasks include but are not limited to product replenishment tasks, product placement tasks, product price adjustment tasks, and product information promotion tasks. In actual scenarios, task types can be increased or decreased according to actual business needs to meet the diverse needs of users.

[0130] Optionally, a specific business type can be divided into multiple sub-task types based on actual business needs. For example, for automated product selection and placement in digital shopping malls, robots can leverage accumulated sales data to regularly push product selection and placement notifications, accurately targeting specific products for placement. Products can also be categorized into, for example, potential hot sellers, discounted items, and replenishment items, with task processing guidance tailored to each type of product. This allows for the integration of online data and traffic analysis to guide stores or brand suppliers in implementing product placement, marketing, and product listing and delisting.

[0131] Step 502: Execute the corresponding task node according to the target task information and generate a task execution result.

[0132] In this step, each task node in the target task information can be called to automatically execute the task nodes in sequence to generate corresponding task execution results. The task execution results include but are not limited to notification information on whether the task has been completed.

[0133] In one embodiment, step 502 may specifically include: determining at least one execution system corresponding to each task node in the target task information, and calling the at least one execution system to execute the task node corresponding to the target task information.

[0134] In this embodiment, different task nodes in the target task information may correspond to different execution systems. The execution systems of these task nodes may be the same or different. For example, it may be the free system of a digital shopping mall or the execution system of a product supplier. Different execution systems can correspond to different calling methods to coordinate the data processing methods of different systems. During the execution process, the target task information first determines the execution system required for each task node. Then, the corresponding execution system is called to execute the corresponding task node, ensuring that each task node can be executed smoothly, coordinating the data processing methods of each execution system, and improving the system data processing efficiency.

[0135] For example, when actually executing a task process, it may be necessary to call the functions of an external system. The system's ability to call the external system can be achieved through digital transactions. The external system will register its own functions into the digital transaction center for easy calling and use.

[0136] Step 503: Return the task execution result to the user.

[0137] In this step, task processing results can be fed back to users via a chatbot, allowing them to review task progress in real time and enhance the interactive experience. The introduction of chatbots, leveraging natural language processing and artificial intelligence technologies, automates conversational processes. Based on user needs and instructions, chatbots can automatically handle business processes such as price comparison, replenishment, stocking, and commission distribution, assisting in achieving big data business targets and improving department store operational efficiency.

[0138] For example, in proactive product selection scenarios, when stores or brand suppliers autonomously add new products or replace existing ones, they can perform one-stop operations by contacting the chatbot and entering the corresponding instructions. Based on the digital information of the products, the system determines candidate products for new or replacement products. Supplier users can view, add, delete, and adjust the prices of the candidate products. After the supplier confirms, the robot automatically and intelligently evaluates whether the selected products meet the launch requirements. If they pass the evaluation, the products will be launched.

[0139] In one embodiment, the method further includes: in response to an instruction to adjust the preset task person information, adjusting the task nodes included in the preset task information, and generating adjusted preset task information.

[0140] In this embodiment, the task information in the preset task library supports updating. For example, task types can be increased or decreased according to actual business needs, and task nodes and / or trigger conditions of specific task types can be adjusted to meet the diverse needs of users.

[0141] like Figure 7As shown, it is a collaborative interaction diagram of a task information processing method provided by an embodiment of the present application. Taking the digital shopping mall scenario as an example, big data processing is performed in advance to form indicators for various businesses and generate a business library. The scheduling system reconfigures multiple tasks, and by detecting the trigger conditions of each task, when the trigger conditions are met, the corresponding task processing instructions are triggered. Assume that the supplier user subscribes to a certain business automatic realization function, such as subscribing to the automatic realization function of task A "check whether the container is tidy every day". First, the two parties select an enterprise-level IM communication platform, which can bring together internal personnel of upstream and downstream companies and provide qualification certification. Form an upstream and downstream collaborative group of XX department store to realize real-time data transmission and information sharing between suppliers and digital shopping mall operations. After the scheduling system detects that Task A meets the trigger conditions, it sends the daily operations required for the subscription Task A in the group through the robot, triggering the reporting node. The relevant operation users, shopping guide users and supplier users involved can fill in the information required for Task A online. After filling in the information, the robot automatically initiates the RPA corresponding to Task A. Task A is pre-orchestrated through the RPA process and the corresponding task nodes are set, such as Step 1, Step 2..., where Step 1 needs to call the supplier system to complete the execution, and Step 2 needs to call the department store system to execute. The system automatically executes the RPA process corresponding to Task A and can use the robot to feed back the execution results to the upstream and downstream collaboration group of XX Department Store so that relevant users in the group can view the task processing results.

[0142] In the traditional department store operation system, there is a misalignment of goals between upstream and downstream, opaque communication, the same thing needs to be discussed across platforms, the data between them is discrete, and there is a lack of application of operational data. Shopping guides and suppliers do not have an industry sense that is sensitive to the business. Most business operations are performed based on experience, and the operating conditions of the counters are unclear. In the solution of the embodiment of the present application, the system will analyze business data in real time and compile it into an industry analysis report to help shopping guides and suppliers provide auxiliary assistance when making business decisions. After the business decision is issued, the system will automatically execute the RPA process through the system's business RPA process, simulating the real operations of the shopping guide under business execution, quickly and conveniently automating the common operations of the shopping guide, and the communication of the entire process is transparent. The business decision is issued in the group by the robot, and the supplier determines the direction of the business decision through the analysis of the business operation report given by the robot. After the direction is determined, the shopping guide is responsible for executing the marketing action.

[0143] For example, through the system SOP configuration, the operating data of a certain counter can be periodically counted to generate monthly and annual operating data, and then sent to suppliers to assist them in making business analysis decisions.

[0144] The above-mentioned task information processing method aligns the upstream and downstream partners of the digital mall through the IM platform, introduces AI-based chatbots, and relies on the business data accumulation and rule system establishment of the data indicator center to form corresponding business process SOPs. Then, corresponding RPA is initiated according to different business processes, and finally task scheduling is carried out to realize automated task execution and improve the task processing efficiency of the digital mall.

[0145] Compared with the traditional shopping mall operation mechanism, the task processing solution provided by the embodiment of the present application has at least the following advantages:

[0146] 1. Through a fully online process-driven approach, we strengthen collaboration across all roles in store operations. We digitize counters, providing foundational capabilities for counter operational collaboration. We provide benchmarks for resource allocation across counter inventory, marketing, traffic flow, and fulfillment. We also provide an online system for identifying and optimizing business actions for counter diagnosis, execution, inspection, and optimization.

[0147] 2. By using AI robots, the counters operate based on integrated online and offline operations. The AI ​​robots communicate directly with stores or brand suppliers to select products, launch products, and review the results. With the assistance of AI robots, stores or suppliers can carry out a series of product sales operations such as replenishing best-selling products, clearing out slow-selling products, and incubating new products at low cost.

[0148] 3. AI robots rely on data indicators integrated from big data (referring to the triggering conditions for the various tasks mentioned above) to provide analytical basis for store counters' operational decisions. They can provide intelligent product recommendations, inventory and pricing guidance, precise traffic supply, and synchronized results, guiding store counters and brand suppliers in replenishing and setting discounts. Furthermore, based on algorithmic technology and big data, AI robots can automatically capture high-quality products from suppliers and recommend sales strategies. Once the supplier confirms the recommended action, the product is automatically delivered to accurately matched potential customers, reaching the final sales point and promoting transactions.

[0149] Please see Figure 8 , which is a task information processing method of an embodiment of the present application, the method can be Figure 1 The electronic device 1 shown is used to perform and can be applied to Figure 2-Figure 4 In the application scenario of the digital shopping mall shown in , the task processing process is automated, labor costs are saved, and task processing efficiency is improved. In this embodiment, the terminal 220 is used as the execution end as an example. The method includes the following steps:

[0150] Step 801: Obtain historical sales status information of a specified commodity.

[0151] Step 802: Determine whether the specified product meets the preset conditions based on the historical sales status information. If yes, proceed to step 803; otherwise, return to step 801 and proceed to the next round of determination.

[0152] Step 803: If the designated product meets the preset conditions, a task processing instruction corresponding to the preset conditions for the designated product is triggered.

[0153] Step 804: In response to the task processing instruction for the specified product, a preset task library is searched for target task information that matches the task processing instruction. The preset task library includes multiple preset task information. The target task information includes at least one task node associated with the task processing instruction. The target task information is pre-set based on historical task processing data and preset rules for the target task type.

[0154] Step 805: Determine at least one execution system corresponding to each task node of the target task information.

[0155] Step 806: Call at least one execution system to execute the task node corresponding to the target task information and generate a task execution result.

[0156] Step 807: Return the task execution result to the user.

[0157] For details of each step of the above task information processing method, please refer to the relevant description of the above embodiment, which will not be repeated here.

[0158] Please see Figure 9 , which is a task information processing method of an embodiment of the present application, the method can be Figure 1 The electronic device 1 shown is used to perform and can be applied to Figure 2-Figure 4 In the application scenario of the digital shopping mall shown in , the task processing process is automated, labor costs are saved, and task processing efficiency is improved. This embodiment takes the terminal 220 as an example of the execution end. Compared with the previous embodiment, this embodiment takes the task processing in the digital shopping mall scenario as an example. The method includes the following steps:

[0159] Step 901: In response to a task processing instruction for product information, determine the product information processing task corresponding to the processing instruction. The product information processing task includes at least one task node associated with the processing instruction. The product information processing task is pre-set based on historical task processing data and preset rules of the target task type.

[0160] Step 903: Execute the corresponding task node according to the product information processing task to generate a task execution result related to the product information.

[0161] Step 904: Return the task execution result to the user.

[0162] For details of each step of the above task information processing method, please refer to the relevant description of the above embodiment, which will not be repeated here.

[0163] Please see Figure 10 , which is a task information processing device 1000 according to an embodiment of the present application, which can be applied to Figure 1 The electronic device 1 shown can be applied to Figure 2-Figure 4 In the application scenario of the digital shopping mall shown in , the task processing process is automated, labor costs are saved, and task processing efficiency is improved. The device includes: a determination module 1001, an execution module 1002, and a feedback module 1003. The functional principles of each module are as follows:

[0164] Determination module 1001 is used to determine the target task information corresponding to the task instruction in response to the task instruction. The target task information includes at least one task node associated with the task instruction. The target task information is pre-set based on historical task processing data and preset rules of the target task type.

[0165] The execution module 1002 is used to execute the corresponding task node according to the target task information and generate a task execution result.

[0166] Feedback module 1003 is used to return the task execution result to the user.

[0167] In one embodiment, the determination module 1001 is configured to determine target task information corresponding to a task processing instruction in response to a task processing instruction triggered by a user.

[0168] In one embodiment, the determining module 1001 is configured to determine target task information corresponding to the task processing instruction in response to a task processing instruction triggered by a preset condition.

[0169] In one embodiment, the determination module 1001 is configured to obtain historical sales status information of a specified item, determine whether the specified item meets a preset condition based on the historical sales status information, and trigger a task processing instruction corresponding to the preset condition if the specified item meets the preset condition.

[0170] In one embodiment, the determining module 1001 is configured to respond to a task processing instruction and search a preset task library for target task information matching the task processing instruction according to the task processing instruction, where the preset task library includes a plurality of preset task information.

[0171] In one embodiment, the apparatus further includes a preset module configured to obtain historical task processing data for a specific type of task before searching a preset task library for target task information matching the task processing instruction based on the task processing instruction. The apparatus further includes determining at least one task node corresponding to the specific type of task based on the historical task processing data. The apparatus further includes connecting the at least one task node in series according to a preset rule corresponding to the specific type of task to generate preset task information corresponding to the specific type of task, and storing the preset task information in the preset task library.

[0172] In one embodiment, the specific type of tasks includes one or more of: a product replenishment task, a product placement task, a product price adjustment task, and a product information promotion task.

[0173] In one embodiment, the device further includes: an adjustment module for adjusting the task nodes included in the preset task information in response to an adjustment instruction for the preset task person information, and generating adjusted preset task information.

[0174] In one embodiment, the execution module 1002 is configured to determine at least one execution system corresponding to each task node in the target task information, and call the at least one execution system to execute the corresponding task node in the target task information.

[0175] For a detailed description of the task information processing device 1000 , please refer to the description of the relevant method steps in the above embodiment. The implementation principles and technical effects are similar and will not be repeated here in this embodiment.

[0176] Figure 11 This is a schematic diagram of the structure of a cloud device 110 provided in an exemplary embodiment of the present application. The cloud device 110 can be used to run the method provided in any of the above embodiments. Figure 11 As shown, the cloud device 110 may include: a memory 1104 and at least one processor 1105, Figure 11 A processor is used as an example.

[0177] The memory 1104 is used to store computer programs and can be configured to store various other data to support operations on the cloud device 110. The memory 1104 can be an object storage service (OSS).

[0178] Memory 1104 can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0179] The processor 1105 is coupled to the memory 1104 and is used to execute the computer program in the memory 1104 to implement the solution provided by any of the above method embodiments. The specific functions and technical effects that can be achieved are not repeated here.

[0180] Furthermore, if Figure 11 The cloud device also includes: a firewall 1101, a load balancer 1102, a communication component 1106, a power supply component 1103 and other components. Figure 11 Only some components are shown schematically, which does not mean that the cloud device only includes Figure 11 Components shown.

[0181] In one embodiment, the above Figure 11 The communication component 1106 is configured to facilitate wired or wireless communication between the device where the communication component 1106 is located and other devices. The device where the communication component 1106 is located can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, LTE (Long Term Evolution, Long Term Evolution, referred to as LTE), 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component 1106 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1106 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0182] In one embodiment, the above Figure 11 The power supply component 1103 provides power to various components of the device where the power supply component 1103 is located. The power supply component 1103 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply component is located.

[0183] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method of any of the aforementioned embodiments is implemented.

[0184] An embodiment of the present application also provides a computer program product, including a computer program, which implements the method of any of the aforementioned embodiments when executed by a processor.

[0185] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is only a logical function division. In actual implementation, other division methods may be used. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not implemented.

[0186] The above-mentioned integrated module implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some steps of the methods of various embodiments of the present application.

[0187] It should be understood that the above-mentioned processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The memory may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile storage NVM (Nonvolatile memory, NVM for short), such as at least one disk memory, and can also be a USB flash drive, a mobile hard disk, a read-only memory, a disk or an optical disk, etc.

[0188] The storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random-access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0189] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0190] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, apparel, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, apparel, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, apparel, or apparatus comprising the element.

[0191] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0192] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of each embodiment of the present application.

[0193] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user data and other information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0194] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A task information processing method, characterized in that: include: In response to a task processing instruction, determining target task information corresponding to the task processing instruction, the target task information including at least one task node associated with the task processing instruction, the target task information being pre-set based on historical task processing data of the target task type and preset rules; Execute the corresponding task node according to the target task information and generate a task execution result; Return the task execution result to the user.

2. The method according to claim 1, characterized in that The step of determining target task information corresponding to the task processing instruction in response to the task processing instruction includes: In response to the task processing instruction triggered by the user, determining target task information corresponding to the task processing instruction; Alternatively, in response to the task processing instruction triggered by a preset condition, target task information corresponding to the task processing instruction is determined.

3. The method according to claim 2, characterized in that The task processing instruction triggered in response to a preset condition includes: Get the historical sales status information of the specified item; Determining whether the designated item meets the preset conditions based on the historical sales status information; If the designated item meets the preset condition, a task processing instruction corresponding to the preset condition is triggered.

4. The method according to claim 1, wherein The step of determining target task information corresponding to the task processing instruction in response to the task processing instruction includes: In response to the task processing instruction, the target task information matching the task processing instruction is searched from a preset task library according to the task processing instruction, where the preset task library includes a plurality of preset task information.

5. The method according to claim 4, characterized in that Before searching the preset task library for the target task information matching the task processing instruction according to the task processing instruction, the method further includes: Get historical task processing data for a specific type of task; Determining at least one task node corresponding to the specific type of task according to the historical task processing data; The at least one task node is connected in series according to a preset rule corresponding to the specific type of task, preset task information corresponding to the specific type of task is generated, and the preset task information is stored in the preset task library.

6. The method according to claim 5, characterized in that The specific type of tasks includes: one or more of: commodity replenishment tasks, commodity delivery tasks, commodity price adjustment tasks and commodity information promotion tasks.

7. The method according to claim 5, characterized in that Also includes: In response to an adjustment instruction for the preset task person information, the task nodes included in the preset task information are adjusted to generate the adjusted preset task information.

8. The method according to claim 1, characterized in that The executing the corresponding task node according to the target task information includes: Determine at least one execution system corresponding to each task node of the target task information; The at least one execution system is called to execute the task node corresponding to the target task information.

9. A task information processing method, characterized in that: include: In response to a task processing instruction for product information, determining a product information processing task corresponding to the processing instruction, the product information processing task including at least one task node associated with the processing instruction, the product information processing task being pre-set based on historical task processing data of a target task type and preset rules; Execute the corresponding task node according to the commodity information processing task, and generate a task execution result related to the commodity information; Return the task execution result to the user.

10. A task information processing device, characterized in that: include: a determination module, configured to determine, in response to a task instruction, target task information corresponding to the task instruction, the target task information including at least one task node associated with the task instruction, the target task information being pre-set based on historical task processing data of the target task type and preset rules; An execution module is used to execute the corresponding task node according to the target task information and generate a task execution result; The feedback module is used to return the task execution result to the user.

11. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the electronic device to perform the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the method according to any one of claims 1 to 9 is implemented.

13. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 9 when the computer program is executed by a processor.

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