Order management service method and electronic equipment

Abstract

The embodiment of the invention discloses an order management service method and electronic equipment. The method comprises the following steps: receiving order analysis demand information submitted by a user; generating first prompt information according to the order analysis demand information, and inputting the first prompt information into a first artificial intelligence (AI) large model, so that the first AI large model converts the order analysis demand information expressed by a natural language into a structured query statement; determining an order identifier of a related order from the order database by executing the query statement, and extracting the related data; and generating second prompt information according to the extracted related data, and inputting the second prompt information into a second AI large model, so that the second AI large model analyzes the related data to generate an analysis result, and the analysis result is used for being displayed to the user. Through the embodiment of the invention, accurate order retrieval can be realized according to complex order analysis requirements of the user, and the user is helped to realize efficient order management.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to an order management service method and electronic equipment. Background Art

[0002] The booming development of commodity information service systems (also known as "e-commerce platforms") has brought enormous business opportunities, but also new challenges to sellers. This is especially true in cross-border e-commerce systems, where transaction processes are lengthy and order statuses are complex and varied (for example, they can include pending confirmation, pending payment, pending shipment, pending receipt, completed, canceled, etc.), involving multiple links and platforms. Sellers may need to understand the status of an order at any time to perform appropriate management actions. For example, the shipping time of an order is a common concern for sellers, especially in cross-border scenarios. After the buyer pays, the seller must complete the shipment within a certain timeframe, otherwise it may affect their overall reputation.

[0003] However, a seller usually needs to manage a large number of orders. Relying on traditional screening rules for order retrieval and management is inefficient and prone to problems such as information lag and human errors. It may even lead to serious consequences such as overdue orders and customer complaints, which in turn affects store reputation and sales indicators. Summary of the Invention

[0004] This application provides an order management service method and electronic equipment that can achieve accurate order retrieval based on users' complex order analysis needs, helping users achieve efficient order management.

[0005] This application provides the following solutions:

[0006] An order management service method, comprising:

[0007] Receiving order analysis requirement information submitted by a user, wherein the order analysis requirement information is expressed in natural language;

[0008] Generate first prompt information based on the order analysis requirement information, and input the prompt information into a first artificial intelligence (AI) model, so that the first AI model converts the order analysis requirement information expressed in natural language into a structured query statement;

[0009] By executing the query statement, determining the order identifier of the relevant order from the order database, and extracting the relevant data;

[0010] A second prompt message is generated based on the extracted relevant data and input into the second AI big model, so that the second AI big model analyzes the relevant data to generate an analysis result, and the analysis result is used to be displayed to the user.

[0011] The receiving of the order analysis requirement information submitted by the user includes:

[0012] Receive order analysis requirement information submitted by users through natural language input.

[0013] The receiving of the order analysis requirement information submitted by the user includes:

[0014] providing at least one order analysis requirement option, wherein the order analysis requirement option is determined based on commonly used order analysis requirements;

[0015] The order analysis requirement information is received according to the user's selection of the order analysis requirement option.

[0016] The order analysis requirements include: requirements related to screening of qualified orders;

[0017] The analysis results generated by the second AI model include: order screening results, and text content summarizing the order screening results.

[0018] The order analysis requirement information is submitted through the operation portal provided in the intelligent interactive interface after the intelligent interactive interface is called up on the order list page;

[0019] When presenting the analysis results to the user, the summarized text content and an operation option for viewing the order screening results corresponding to the text content are displayed in the intelligent interactive interface;

[0020] After receiving a viewing request through this operation option, the corresponding order screening results are displayed on the order list page, so that the corresponding order detail information can be viewed based on the order list page.

[0021] The user includes a seller user, and the analysis result includes: personalized order management suggestion information generated based on the order data and operation-related data of the seller user.

[0022] An order management service method, comprising:

[0023] Receiving order analysis requirement information submitted by a user, wherein the order analysis requirement information is expressed in natural language;

[0024] Submitting the order analysis requirement information to a server, so that the server generates first prompt information based on the order analysis requirement information and inputs the prompt information into a first AI model, so that the first AI model converts the order analysis requirement information expressed in natural language into a structured query statement; by executing the query statement, determining the order identifier of the relevant order from the order database and extracting the relevant data; generating second prompt information based on the extracted relevant data and inputting the prompt information into a second AI model, so that the second AI model analyzes the relevant data and generates an analysis result;

[0025] The analysis results returned by the server are displayed.

[0026] The receiving of the order analysis requirement information submitted by the user includes:

[0027] In response to a call-up operation performed based on the order list page, an intelligent interactive interface is displayed, and an operation entry for submitting an order analysis request is provided in the intelligent interactive interface;

[0028] The order analysis requirement information is received through the operation portal.

[0029] The display of the analysis results returned by the server includes:

[0030] The generated analysis results are displayed through the linkage between the intelligent interactive interface and the order list page.

[0031] The order analysis requirements include: requirements related to screening of qualified orders;

[0032] The analysis results generated by the second AI model include: order screening results, and text content summarizing the order screening results;

[0033] The display of the generated analysis results through the linkage between the intelligent interactive interface and the order list page includes:

[0034] Displaying the summarized text content and an operation option for viewing the order screening results corresponding to the text content in the intelligent interactive interface;

[0035] After receiving a viewing request through this operation option, the corresponding order screening results are displayed on the order list page, so that the corresponding order detail information can be viewed based on the order list page.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the aforementioned methods.

[0037] An electronic device, comprising:

[0038] one or more processors; and

[0039] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of any of the aforementioned methods.

[0040] A computer program product comprises a computer program / computer executable instructions, wherein the computer program / computer executable instructions are capable of implementing the steps of any of the aforementioned methods when executed by a processor in an electronic device.

[0041] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0042] Through the embodiments of the present application, users can express their order analysis needs in natural language. Then, a first prompt message can be generated based on the order analysis requirement information and input into the first AI model, so that the first AI model converts the order analysis requirement information expressed in natural language into a structured query statement. Then, by executing the query statement, the order identifier of the relevant order is determined from the order database and the relevant data is extracted. Thereafter, a second prompt message can be generated based on the extracted relevant data and input into the second AI model, so that the second AI model analyzes the relevant data and generates analysis results, which are then displayed to the user. Through this solution, the powerful natural language processing capabilities of the AI model can be utilized to understand the user's complex order analysis needs. Based on intelligent summaries, it can quickly retrieve orders that meet the conditions from the order list, achieve accurate order retrieval, and help users achieve efficient order management. From a platform perspective, this intelligent and automated function can reduce manual operations, improve order processing speed, reduce human errors and order risks, and lower operating costs.

[0043] In an optional implementation method, the AI big model can also be used to generate personalized order management suggestions based on the seller's order data, operational data, etc., to help users complete order management more efficiently.

[0044] Of course, any product implementing the present application does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 It is a schematic diagram of the system architecture provided by the embodiment of the present application;

[0047] Figure 2 This is a flow chart of the server-side method provided in an embodiment of the present application;

[0048] Figure 3 is a schematic diagram of an interface provided in an embodiment of the present application;

[0049] Figure 4 This is a schematic diagram of the interaction timing provided by an embodiment of the present application;

[0050] Figure 5 This is a flowchart of the client-side method provided by an embodiment of the present application;

[0051] Figure 6 Schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0053] In an embodiment of the present application, a corresponding solution is provided to help seller users manage orders more efficiently. In this solution, an AI (Artificial Intelligence) large model can be used to help seller users manage orders. The so-called AI large model refers to a deep learning model containing massive parameters. This AI large model is large in size and can store and process a large amount of information, thereby achieving higher performance in various tasks (including content generation, etc.). Therefore, it is possible to rely on the powerful natural language text understanding and logical thinking ability of the pre-trained AI large model to help seller users manage orders.

[0054] Specifically, in embodiments of the present application, an intelligent interactive interface can be provided for seller users. For example, the intelligent interactive interface can be invoked based on the order list page, etc. Through the interface, seller users can submit their order analysis request information. In embodiments of the present application, such order analysis request information can be expressed in natural language that is easy for humans to understand, for example, "Help me check which orders in my store need to be shipped recently." Since order analysis requests can be submitted based on natural language, seller users do not need to understand complex order screening rules or convert specific requests into formatted query conditions, which makes request submission more convenient and efficient.

[0055] After receiving the order analysis demand information, the AI big model can be used to understand the natural language semantics of the order analysis demand information, thereby analyzing the user's demands, and locating the specific order from the order database, obtaining the relevant order data, and performing analysis based on the obtained order data to generate corresponding analysis results. This analysis result can then be presented to the seller user. For example, the specific analysis results may include: calculating the number of unshipped orders, counting sales, analyzing the geographical distribution of orders, and so on. In an optional manner, the specific analysis results may also include order management suggestions generated by the AI big model. For example, after analyzing which orders need to be shipped recently, corresponding stocking suggestions can be given based on the corresponding shipping dates, quantity of goods, etc. of these orders, and so on.

[0056] Specifically from the perspective of system architecture, see Figure 1, the embodiment of the present application can also provide services such as an intelligent order management assistant in the order service of the commodity information service system. In this service, the order analysis requirement information expressed in natural language submitted by the user can be received through the client on the user side (for example, a workbench provided to the seller user, etc.), and then the server side of the intelligent order management assistant can call the AI big model to perform natural language semantic understanding on the order analysis requirement information, so as to query relevant orders from the order database associated with the user, extract relevant order data, and generate analysis results by analyzing such relevant order data. Specifically, when querying order data, the AI big model can be called first so that the AI big model converts the order analysis requirement information expressed in natural language into a structured query statement, and then the intelligent order management assistant can use the query statement to query the order database, query the relevant orders and obtain the relevant order data, and then re-call the AI big model to reason on the order data to generate analysis results. Accordingly, the analysis results can be returned to the client, and the client renders the analysis results into the user interface for display. Of course, if the client's hardware and software environment supports it, it is also possible to deploy the AI model directly on the client side and call the AI model on the client side. When displaying the analysis results, they can be presented in an easy-to-understand manner, such as charts, lists, and summary text.

[0057] The specific implementation scheme provided in the embodiments of this application is introduced in detail below.

[0058] Example 1

[0059] First, the first embodiment of the present application provides an order management service method from the perspective of the user side server of the intelligent order management assistant, see Figure 2 , the method may specifically include:

[0060] S201: Receive order analysis requirement information submitted by a user, wherein the order analysis requirement information is described in natural language.

[0061] In an embodiment of the present application, an intelligent order management service can be provided to users. When using this service, order analysis request information expressed in natural language can be submitted. In a specific implementation, an intelligent interactive interface can be provided to users on the client side, which can provide an operation entry for submitting order analysis request information.

[0062] Among them, the above-mentioned intelligent interaction interface can be entered in a variety of ways. For example, a special intelligent interaction module can be provided in the user client, and the user can enter the intelligent interaction interface by clicking on the module. Or, in a more preferred way, since the commodity information service system also provides the user with an order list page, the user can view the information of the relevant order through the order list page, and the user may usually generate order management needs in the process of viewing the order information on the order list page. Therefore, the above-mentioned intelligent interaction interface can also be called up in the order list page. For example, an intelligent interaction option can be provided in the order list page, and the user can call up the intelligent interaction interface by clicking on the option, or the above-mentioned intelligent interaction interface can be called up by double-clicking, long pressing, etc. on the blank space of the order list page, etc. Among them, the specific intelligent interaction interface can be displayed in various forms such as an overlay layer of the order list page (for example, a floating layer, a pop-up window, a drawer layer, a drop-down box, etc.) or an auxiliary interface (for example, a sidebar, etc.). After the intelligent interaction interface is called up, specific order analysis requirement information can be submitted through the intelligent interaction interface.

[0063] It should be noted that the order list page usually also has the function of categorizing and displaying orders. For example, various options such as pending payment, pending shipment, and pending receipt can be provided according to the order status, and users can view orders in the corresponding status under the specific option. Although this can solve the user's order management problem to a certain extent, in actual application, the user's order analysis needs may not only be classified by status, but also include more detailed needs. For example, in order to formulate a relatively accurate, phased stocking strategy, it may be necessary to query the orders that need to be shipped in the last 1 day, the last 3 days, the last 5 days, etc. In other words, orders in the same status can be further subdivided. For example, for orders in the same pending shipment status, different shipping deadlines may be corresponding to orders, and orders with different shipping deadlines may require different stocking strategies, etc. At this point, the existing solution of classifying by order status cannot meet the user's needs. Therefore, it can be implemented in combination with the intelligent interaction solution provided in the embodiment of the present application.

[0064] Specifically, the order analysis request information in the embodiment of the present application can be expressed in natural language. That is, the user can directly describe their needs in natural language and enter them into the dialog box of the intelligent interactive interface to complete the submission of the order analysis request. In other words, in the embodiment of the present application, the user is allowed to describe their order analysis needs in natural language, such as "find orders with an amount greater than US$100 and not shipped for 3 days", etc.

[0065] Alternatively, the system can also provide some pre-generated order analysis requirement options. These options can be generated by collecting statistics on users' frequently used order analysis requirements. In other words, the user's potential requirements are expressed in advance and packaged into the form of requirement options. In this way, users can submit their order analysis requirements by clicking on these requirement options, thereby improving efficiency. In other words, the system can provide some common analysis scenarios for users to choose from, such as "orders not shipped in the past 7 days" and "high-risk orders." Of course, if the requirements you need are not included in the requirement options provided by the system, you can manually enter and submit the order analysis requirement information.

[0066] For details on interface display, please refer to Figure 3 The example shown in , which shows the display status after the intelligent interactive interface is called up based on the order list page, the order analysis requirements are submitted, and the AI large model gives the analysis results. Among them, 31 is shown as the intelligent interactive interface, and the user can input the order analysis requirement information through the input box shown in 32, or can also complete the quick input of the requirement options shown in 32, and so on.

[0067] S202: Generate first prompt information based on the order analysis requirement information and input it into the first AI big model, so that the first AI big model converts the order analysis requirement information expressed in natural language into a structured query statement.

[0068] After receiving the user's order analysis demand information, the first AI model can be used to perform natural language semantic understanding of the user's order analysis demand information to analyze the user's demands, and then determine the relevant orders and order data from the order library, so as to further generate analysis results based on the specific order data.

[0069] In the specific implementation, since it is necessary to first determine the relevant orders, and the order analysis requirements submitted by the user are expressed in natural language and cannot be directly used to query the order database, the order analysis requirements expressed in natural language can first be converted into structured query statements. Specifically, the conversion operation of this step can be completed by the AI big model. To this end, the first prompt information (Prompt, used to guide the generation of the big model) can be generated based on the order analysis requirement information and the preset first prompt information template, and input into the first AI big model, so that the first AI big model converts the order analysis requirement information expressed in natural language into a structured query statement. Among them, the first prompt information template can also include specific task description, background description, output format and other aspects of information to guide or control the generation result of the first AI big model.

[0070] S203: By executing the query statement, determine the order ID of the relevant order from the order database and extract the relevant data

[0071] After the first AI model converts the order analysis demand information expressed in natural language into a structured query statement, the server can execute the query statement to determine the order ID of the relevant order from the order database and extract the relevant order data.

[0072] S204: Generate second prompt information based on the extracted relevant data and input it into the second AI big model, so that the second AI big model analyzes the relevant data to generate analysis results, and the analysis results are used to display to the user.

[0073] After obtaining the specific order data, a second prompt information can be generated based on the order data and the preset second prompt information template, and input into the second AI large model, so that the second AI large model can analyze the order data, including summarizing the order data, extracting key information, etc. The extracted key information may include order quantity, total amount, delivery time, etc., and then the analysis results can be generated based on these key information. Among them, the second prompt information template can also include specific task descriptions, background descriptions, output formats and other aspects of information to guide or control the generation results of the second AI large model. In addition, the specific analysis results generated can also be different for different needs. For example, if the user needs to query "which orders need to be shipped in the store in the near future", the analysis results generated by the large model can summarize the number of orders that need to be shipped within different time limits, etc.

[0074] It should be noted here that the first AI large model used in the above step S202 and the second AI large model used in S204 can be the same large model or different large models, which can be determined according to actual needs.

[0075] In addition, under an optional implementation method, especially for seller users, the analysis results generated by the second AI large model can not only include content for answering questions raised by users, but also generate personalized order management suggestions based on the seller user's order data, operation data, etc. For example, in the aforementioned example of querying orders that need to be shipped in the near future, the second AI large model can not only summarize the number of orders that need to be shipped within different time limits, but also further provide relevant suggestions on how to prepare goods based on the seller user's order data, relevant data on actual operations, etc. In this way, through this intelligent interactive service, users can not only more efficiently implement order queries under complex query conditions, but also obtain relevant order management suggestions, thereby further improving the user experience and helping users complete order management more efficiently.

[0076] After the second AI model generates the analysis results, the analysis results can be displayed on the client side. It should be noted that in the specific implementation, the specific analysis results can be displayed in a clear and easy-to-understand manner, for example, in the form of tables, charts, summary texts, etc. Figure 3 In the example shown in , the analysis results of the large model are shown at 34, where summary text such as "2 orders need to be shipped within 1 day" and "5 orders need to be shipped within 3 days" can be displayed intuitively, making it easy for users to understand the overall status of specific orders. In addition, a "click to view" option can be provided, allowing users to view the status of the orders corresponding to the specific summary text content. For example, clicking the view option corresponding to "2 orders need to be shipped within 1 day" will display a list of orders that meet this condition, and users can further click to view the details of a specific order, and so on.

[0077] As previously mentioned, in one specific implementation, users can invoke the intelligent interactive interface through the order list page, then submit their order analysis request within the intelligent interactive interface. The analysis results can then be displayed based on the intelligent interactive interface. In this manner, since order information analysis is typically required during intelligent interaction, and the order list page is also used to display order-related information, the generated analysis results can be displayed through the linkage between the intelligent interactive interface and the order list page.

[0078] For example, in a specific implementation, if the specific order analysis requirement is to filter orders that meet certain conditions, the analysis results generated by the second large AI model may include: the results of filtering orders that meet the conditions, as well as text content summarizing the order filtering results, such as "There are 3 orders that need to be shipped in the last 2 days." In this case, the summary text content can be displayed in the intelligent interactive interface, and an action option for viewing the corresponding order information can be provided. After the user clicks the action option, the corresponding order filtering results can be displayed on the order list page. In other words, instead of displaying the order filtering results directly in the intelligent interactive interface, the order filtering results can be displayed on the original order list page. The advantages of this approach are: first, the display area of the order list page is generally larger, which is more conducive to displaying order information. Second, the order list page already has an order filtering function, and seller users may be more accustomed to viewing the order filtering results on the order list page. Therefore, displaying the order filtering results on the original order list page is more in line with the browsing habits of seller users.

[0079] In order to better understand the specific implementation scheme provided by the embodiment of the present application, Figure 4 The interactive sequence diagram shown in the embodiment of the present application is used to introduce the solution provided by the embodiment of the present application. Figure 4 In the example shown, the intelligent interactive interface of the smart order management assistant is invoked on the order list page. Specifically, the following steps may be included:

[0080] Step 1. The user selects or enters an order analysis requirement. If the specific order analysis requirement is related to order query, it can be called a "query request". Of course, the query request here is usually not a simple query of orders in a certain status, but usually a more complex request. For example, the query conditions include not only status information but also time information, etc.

[0081] Step 2. After receiving the order analysis request, the intelligent order management assistant can obtain the order base table structure.

[0082] Step 3. The intelligent order management assistant generates a prompt for generating a query statement based on the obtained order table structure and the preset prompt template.

[0083] Step 4. The intelligent order management assistant calls the first AI model based on the prompt for generating the query statement.

[0084] Step 5. The first AI model converts the order analysis requirement information expressed in natural language into a structured query statement and returns it to the intelligent order management assistant.

[0085] Step 6. The smart order management assistant executes the query statement and queries the order data service for order data.

[0086] Step 7. The order data service returns the order data to the smart order management assistant.

[0087] Step 8. The intelligent order management assistant generates a prompt for generating analysis results based on the received order data and the preset prompt template.

[0088] Step 9. The intelligent order management assistant calls the second AI model according to the prompt for generating analysis results.

[0089] Step 10. The second AI big model generates analysis results by analyzing and summarizing the order data. Since the demand in this example is mainly to query orders, the analysis results generated by the AI big model may include order summary results and related order lists.

[0090] Step 11. The intelligent order management assistant can display the above order summary results to the user through the intelligent interactive interface.

[0091] Step 12. The intelligent order management assistant receives the order screening and other operations submitted by the user through the intelligent interactive interface.

[0092] Step 13. The order screening results are displayed on the order list page. Users can continue to view the order information that meets the conditions on the order list page, and can further click to view the order details.

[0093] In summary, through the embodiments of the present application, users can express their order analysis needs in natural language. Then, a first prompt message can be generated based on the order analysis requirement information and input into the first AI model, so that the first AI model converts the order analysis requirement information expressed in natural language into a structured query statement. Then, by executing the query statement, the order identifier of the relevant order is determined from the order database, and the relevant data is extracted. After that, a second prompt message can be generated based on the extracted relevant data and input into the second AI model, so that the second AI model analyzes the relevant data and generates analysis results, which are used to display to the user. Through this solution, the powerful natural language processing capabilities of the AI model can be utilized to understand the user's complex order analysis needs. Based on intelligent summaries, orders that meet the conditions can be quickly retrieved from the order list, achieving accurate order retrieval and helping users achieve efficient order management. From a platform perspective, this intelligent and automated function can reduce manual operations, improve order processing speed, reduce manual errors and order risks, and reduce operating costs.

[0094] In an optional implementation method, the AI big model can also be used to generate personalized order management suggestions based on the seller's order data, operational data, etc., to help users complete order management more efficiently.

[0095] Example 2

[0096] The second embodiment corresponds to the first embodiment and provides an order management service method from the perspective of the client. Figure 5 , the method may include:

[0097] S501: Receive order analysis requirement information submitted by a user, wherein the order analysis requirement information is expressed in natural language.

[0098] Among them, in a specific implementation method, the order list page can respond to the user's call-up operation, display an intelligent interactive interface, and provide an operation entrance for submitting order analysis requirements in the intelligent interactive interface; then, the order analysis requirement information can be received through the operation entrance.

[0099] S502: Submitting the order analysis requirement information to the server, so that the server generates first prompt information based on the order analysis requirement information and inputs the prompt information into the first AI model, so that the first AI model converts the order analysis requirement information expressed in natural language into a structured query statement; by executing the query statement, determining the order identifier of the relevant order from the order database and extracting the relevant data; generating second prompt information based on the extracted relevant data and inputting the prompt information into the second AI model, so that the second AI model analyzes the relevant data and generates an analysis result;

[0100] S503: Display the analysis results returned by the server.

[0101] Specifically, when displaying the analysis results returned by the server, the generated analysis results can also be displayed through the linkage between the intelligent interactive interface and the order list page. For example, specific order analysis requirements may include: requirements related to screening of qualified orders; at this time, the analysis results generated by the second AI large model may include: order screening results, and text content that summarizes the order screening results. Specifically, when displaying the generated analysis results through the linkage between the intelligent interactive interface and the order list page, the summarized text content and the operation options for viewing the order screening results corresponding to the text content can be first displayed in the intelligent interactive interface. Then, after receiving the viewing request through the operation option, the corresponding order screening results are displayed in the order list page, so that the corresponding order details information can be viewed based on the order list page.

[0102] For the parts not described in detail in the second embodiment, please refer to the description of the first embodiment and other parts of this specification, which will not be repeated here.

[0103] It should be noted that the embodiments of the present application may involve the use of user data. In actual applications, user-specific personal data can be used in the scheme described herein within the scope permitted by applicable laws and regulations, subject to the requirements of applicable laws and regulations of the country where the user is located (for example, with the user's explicit consent, effective notification to the user, etc.).

[0104] Corresponding to the first embodiment, the embodiment of the present application further provides an order management service device, which may include:

[0105] A demand information receiving unit, configured to receive order analysis demand information submitted by a user, wherein the order analysis demand information is expressed in natural language;

[0106] a query statement conversion unit, configured to generate first prompt information based on the order analysis requirement information and input the first prompt information into a first artificial intelligence (AI) model, so that the first AI model converts the order analysis requirement information expressed in natural language into a structured query statement;

[0107] a data acquisition unit, configured to determine the order identifier of the relevant order from the order database by executing the query statement, and extract the relevant data;

[0108] An analysis result generating unit is used to generate a second prompt information based on the extracted relevant data, and input it into the second AI big model, so that the second AI big model analyzes the relevant data to generate an analysis result, and the analysis result is used to display it to the user.

[0109] The demand information receiving unit may be specifically used for:

[0110] Receive order analysis requirement information submitted by users through natural language input.

[0111] Alternatively, the demand information receiving unit may also be specifically used to:

[0112] providing at least one order analysis requirement option, wherein the order analysis requirement option is determined based on commonly used order analysis requirements;

[0113] The order analysis requirement information is received according to the user's selection of the order analysis requirement option.

[0114] Specifically, the order analysis requirements include: requirements related to screening of qualified orders;

[0115] The analysis results generated by the second AI model include: order screening results, and text content summarizing the order screening results.

[0116] The order analysis requirement information is submitted through the operation portal provided in the intelligent interactive interface after the intelligent interactive interface is called up on the order list page;

[0117] When presenting the analysis results to the user, the summarized text content and an operation option for viewing the order screening results corresponding to the text content are displayed in the intelligent interactive interface;

[0118] After receiving a viewing request through this operation option, the corresponding order screening results are displayed on the order list page, so that the corresponding order detail information can be viewed based on the order list page.

[0119] The user includes a seller user, and the analysis result includes: personalized order management suggestion information generated based on the order data and operation-related data of the seller user.

[0120] Corresponding to the second embodiment, the embodiment of the present application further provides an order management service device, which may include:

[0121] A demand information receiving unit, configured to receive order analysis demand information submitted by a user, wherein the order analysis demand information is expressed in natural language;

[0122] a demand information submission unit, configured to submit the order analysis demand information to a server, so that the server generates first prompt information based on the order analysis demand information and inputs the prompt information into a first AI model, so that the first AI model converts the order analysis demand information expressed in natural language into a structured query statement; determines the order identifier of the relevant order from the order database by executing the query statement, and extracts the relevant data; generates second prompt information based on the extracted relevant data, and inputs the prompt information into a second AI model, so that the second AI model analyzes the relevant data and generates an analysis result;

[0123] The analysis result display unit is used to display the analysis result returned by the server.

[0124] The demand information receiving unit may be specifically used for:

[0125] In response to a call-up operation performed based on the order list page, an intelligent interactive interface is displayed, and an operation entry for submitting an order analysis request is provided in the intelligent interactive interface;

[0126] The order analysis requirement information is received through the operation portal.

[0127] The analysis result display unit can be specifically used for:

[0128] The generated analysis results are displayed through the linkage between the intelligent interactive interface and the order list page.

[0129] The order analysis requirements include: requirements related to screening of qualified orders;

[0130] The analysis results generated by the second AI model include: order screening results, and text content summarizing the order screening results;

[0131] The analysis result display unit may include:

[0132] A first display sub-unit is used to display the summarized text content in the intelligent interactive interface, as well as an operation option for viewing the order screening results corresponding to the text content;

[0133] The second display sub-unit is used to display the corresponding order screening results in the order list page after receiving the viewing request through the operation option, so as to view the corresponding order details information based on the order list page.

[0134] In addition, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of any one of the methods in the aforementioned method embodiments are implemented.

[0135] And an electronic device comprising:

[0136] one or more processors; and

[0137] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of the method described in any one of the aforementioned method embodiments.

[0138] A computer program product includes a computer program / computer executable instructions, which implement the steps of the method described in the above method embodiment when executed by a processor in an electronic device.

[0139] in, Figure 6 The electronic device architecture is shown as an example, and may include a processor 610, a video display adapter 611, a disk drive 612, an input / output interface 613, a network interface 614, and a memory 620. The processor 610, the video display adapter 611, the disk drive 612, the input / output interface 613, the network interface 614, and the memory 620 may be communicatively connected via a communication bus 630.

[0140] Among them, the processor 610 can be implemented by a general-purpose CPU (Central Processing Unit, processor), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., to execute relevant programs to implement the technical solutions provided in this application.

[0141] The memory 620 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 620 can store an operating system 621 for controlling the operation of the electronic device 600, and a basic input and output system (BIOS) for controlling the low-level operations of the electronic device 600. In addition, a web browser 623, a data storage management system 624, and an order management service processing system 625, etc. can also be stored. The above-mentioned order management service processing system 625 can be an application program that specifically implements the operations of the aforementioned steps in the embodiment of the present application. In short, when the technical solution provided by the present application is implemented by software or firmware, the relevant program code is stored in the memory 620 and is called and executed by the processor 610.

[0142] The input / output interface 613 is used to connect to input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0143] The network interface 614 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0144] The bus 630 comprises a pathway for transmitting information between the various components of the device (eg, the processor 610 , the video display adapter 611 , the disk drive 612 , the input / output interface 613 , the network interface 614 , and the memory 620 ).

[0145] It should be noted that although the above device only shows a processor 610, a video display adapter 611, a disk drive 612, an input / output interface 613, a network interface 614, a memory 620, a bus 630, etc., in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may also include only the components necessary to implement the solution of the present application, and does not necessarily include all the components shown in the figure.

[0146] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. 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 can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present application or certain parts of the embodiments.

[0147] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0148] The above describes in detail the order management service method and electronic device provided by this application. Specific examples are used herein to illustrate the principles and implementation methods of this application. The description of the above embodiments is intended only to help understand the method and core concept of this application. At the same time, those skilled in the art will appreciate that variations in the specific implementation methods and scope of application may occur based on the concepts of this application. In summary, the contents of this specification should not be construed as limiting this application.

Claims

1. An order management service method, characterized in that: include: Receiving order analysis requirement information submitted by a user, wherein the order analysis requirement information is expressed in natural language; Generate first prompt information based on the order analysis requirement information, and input the prompt information into a first artificial intelligence (AI) model, so that the first AI model converts the order analysis requirement information expressed in natural language into a structured query statement; By executing the query statement, determining the order identifier of the relevant order from the order database, and extracting the relevant data; A second prompt message is generated based on the extracted relevant data and input into the second AI big model, so that the second AI big model analyzes the relevant data to generate an analysis result, and the analysis result is used to be displayed to the user.

2. The method according to claim 1, characterized in that The receiving of the order analysis requirement information submitted by the user includes: Receive order analysis requirement information submitted by users through natural language input.

3. The method according to claim 1, characterized in that The receiving of the order analysis requirement information submitted by the user includes: providing at least one order analysis requirement option, wherein the order analysis requirement option is determined based on commonly used order analysis requirements; The order analysis requirement information is received according to the user's selection of the order analysis requirement option.

4. The method according to claim 1, wherein The order analysis requirements include: requirements related to screening of qualified orders; The analysis results generated by the second AI model include: order screening results, and text content summarizing the order screening results.

5. The method according to claim 4, characterized in that The order analysis requirement information is submitted through the operation portal provided in the intelligent interactive interface after the intelligent interactive interface is called up on the order list page; When presenting the analysis results to the user, the summarized text content and an operation option for viewing the order screening results corresponding to the text content are displayed in the intelligent interactive interface; After receiving a viewing request through this operation option, the corresponding order screening results are displayed on the order list page, so that the corresponding order detail information can be viewed based on the order list page.

6. The method according to claim 1, characterized in that The user includes a seller user, and the analysis result includes: personalized order management suggestion information generated based on the order data and operation-related data of the seller user.

7. An order management service method, characterized in that: include: Receiving order analysis requirement information submitted by a user, wherein the order analysis requirement information is expressed in natural language; Submitting the order analysis requirement information to a server, so that the server generates first prompt information based on the order analysis requirement information and inputs the prompt information into a first AI model, so that the first AI model converts the order analysis requirement information expressed in natural language into a structured query statement; by executing the query statement, determining the order identifier of the relevant order from the order database and extracting the relevant data; generating second prompt information based on the extracted relevant data and inputting the prompt information into a second AI model, so that the second AI model analyzes the relevant data and generates an analysis result; The analysis results returned by the server are displayed.

8. The method according to claim 7, characterized in that The receiving of the order analysis requirement information submitted by the user includes: In response to a call-up operation performed based on the order list page, an intelligent interactive interface is displayed, and an operation entry for submitting an order analysis request is provided in the intelligent interactive interface; The order analysis requirement information is received through the operation portal.

9. The method according to claim 8, characterized in that The display of the analysis results returned by the server includes: The generated analysis results are displayed through the linkage between the intelligent interactive interface and the order list page.

10. The method according to claim 9, characterized in that The order analysis requirements include: requirements related to screening of qualified orders; The analysis results generated by the AI large model include: order screening results, and text content summarizing the order screening results; The display of the generated analysis results through the linkage between the intelligent interactive interface and the order list page includes: Displaying the summarized text content and an operation option for viewing the order screening results corresponding to the text content in the intelligent interactive interface; After receiving a viewing request through this operation option, the corresponding order screening results are displayed on the order list page, so that the corresponding order detail information can be viewed based on the order list page.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

12. An electronic device, characterized in that: include: one or more processors; as well as A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1 to 10.

13. A computer program product comprising a computer program / computer executable instructions, characterized in that When the computer program / computer executable instructions are executed by a processor in an electronic device, the steps of the method according to any one of claims 1 to 10 are implemented.

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