A digital employee question and answer method, system, device and storage medium

Abstract

The application discloses a digital employee question and answer method, system, device and storage medium. The digital employee question and answer method comprises the following steps: in the case that a user consultation message and historical context information are received, a first product category and a first core question corresponding to the user consultation message and the historical context information are extracted; based on the first product category, a product knowledge query result is determined through a preset product knowledge base; based on the first core question, a process query result is determined through a preset experience process base; based on the first core question, an answer query result is determined through a preset question and answer knowledge base; and based on the product knowledge query result, the process query result and the answer query result, a reply message corresponding to the user consultation message is determined, so that the efficiency and accuracy of the digital employee question and answer are improved.

Description

Technical Field

[0001] This application relates to the technical field of digital employee question-and-answer, and in particular to a digital employee question-and-answer method, system, device and storage medium. Background Technology

[0002] Currently, existing technologies for responding to user inquiries mainly fall into two categories: manual responses and intelligent responses. Manual responses rely on professional customer service personnel who, based on their product knowledge, experience in handling inquiries, and by analyzing user inquiries and possible historical records, determine the user's needs and provide a response. However, this requires a large number of customer service personnel to meet response demands, and it is difficult to accurately link historical context information, easily leading to duplicate inquiries and disjointed responses. Existing intelligent response methods are mostly based on a single knowledge base for retrieval and reply. At the same time, existing intelligent response methods often ignore the correlation between user inquiry messages and historical context information, and only perform isolated analysis on the current inquiry message, resulting in a lack of targeted replies and often "answering the wrong question", leading to a low accuracy rate. Summary of the Invention

[0003] This application aims to at least address the technical problems existing in the prior art. To this end, this application proposes a digital employee question-and-answer method, system, device, and storage medium that can improve the efficiency and accuracy of digital employee question-and-answer.

[0004] A first aspect of this application provides a digital employee question-and-answer method, comprising the following steps: Upon receiving user inquiry messages and historical context information, extract the first product category and the first core question corresponding to the user inquiry messages and historical context information; Based on the first product category, the product knowledge query results are determined through a preset product knowledge base; Based on the first core issue, the process query results are determined through a pre-set experience process library; Based on the first core question, the answer query result is determined through a pre-set question-and-answer knowledge base; Based on the product knowledge query results, process query results, and answer query results, the corresponding reply message for the user's inquiry message is determined.

[0005] The digital employee question-and-answer method according to the embodiments of this application has at least the following beneficial effects: This application combines user inquiry messages with historical context information to determine the primary product category and the primary core issue, thereby accurately linking the user's previous inquiry records, interaction processes, and demand tendencies. This avoids repeatedly asking the user for information already provided, ensuring the continuity of inquiry responses. Furthermore, by linking preset product knowledge bases, preset experience process bases, and preset question-and-answer knowledge bases, it obtains product knowledge query results, process query results, and answer query results respectively. Based on these results, it determines the corresponding response message for the user's inquiry message, resulting in a logically clear and complete response message, thus improving the efficiency and accuracy of digital employee Q&A.

[0006] A second aspect of this application provides a digital employee question-and-answer system, the digital employee question-and-answer system comprising: The category extraction module is used to extract the first product category and the first core question corresponding to the user inquiry message and the historical context information when receiving user inquiry messages and historical context information. The product knowledge query result determination module is used to determine the product knowledge query result based on the first product category and through a preset product knowledge base; The process query result determination module is used to determine the process query result based on the first core question and through a preset experience process library. The answer query result determination module is used to determine the answer query result based on the first core question and through a preset question-and-answer knowledge base. The response message determination module is used to determine the response message corresponding to the user inquiry message based on the product knowledge query results, process query results, and answer query results.

[0007] This system combines user inquiry messages with historical context information to determine the primary product category and the primary core issue. This allows for precise correlation with the user's previous inquiry records, interaction processes, and demand tendencies, avoiding the repetitive questioning of information already provided by the user and ensuring the continuity of inquiry responses. Furthermore, by linking preset product knowledge bases, preset experience process bases, and preset question-and-answer knowledge bases, it obtains product knowledge query results, process query results, and answer query results respectively. Based on these results, the system determines the corresponding response message for the user's inquiry message, resulting in a logically clear and complete response message, thus improving the efficiency and accuracy of digital employee Q&A.

[0008] A third aspect of this application provides an electronic device including at least one controller and a memory for communicatively connecting to the controller; the memory stores instructions executable by the at least one controller to cause the at least one controller to perform a digital employee question-and-answer method as described in the first aspect of this application.

[0009] A fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform a digital employee question-and-answer method as described in the first aspect of this application.

[0010] It should be noted that the beneficial effects of the third and fourth aspects of this application with respect to the prior art are the same as the beneficial effects of the aforementioned digital employee question-and-answer method with respect to the prior art, and will not be elaborated here.

[0011] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0012] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart illustrating an embodiment of the digital employee question-and-answer method provided in this application; Figure 2 This is a schematic diagram of the structure of an embodiment of the digital employee question-and-answer system provided in this application; Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device provided in this application. Detailed Implementation

[0013] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0014] In the description of this application, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0015] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0016] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0017] Currently, existing technologies for responding to user inquiries mainly fall into two categories: manual responses and intelligent responses. Manual responses rely on professional customer service personnel who, based on their product knowledge, experience in handling inquiries, and by analyzing user inquiries and possible historical records, determine the user's needs and provide a response. However, this requires a large number of customer service personnel to meet response demands, and it is difficult to accurately link historical context information, easily leading to duplicate inquiries and disjointed responses. Existing intelligent response methods are mostly based on a single knowledge base for retrieval and reply. At the same time, existing intelligent response methods often ignore the correlation between user inquiry messages and historical context information, and only perform isolated analysis on the current inquiry message, resulting in a lack of targeted replies and often "answering the wrong question", leading to a low accuracy rate.

[0018] To address the aforementioned technical deficiencies, embodiments of this application provide a digital employee question-and-answer method, system, device, and storage medium.

[0019] Please see Figure 1 This is a flowchart illustrating a digital employee question-and-answer method provided in an embodiment of this application. The method is applied to an electronic device, which may be a server, etc. Figure 1 As shown, the digital employee question-and-answer method includes: Step S101: Upon receiving user inquiry messages and historical context information, extract the first product category and the first core question corresponding to the user inquiry messages and historical context information; In step S101, the aforementioned receiving of user consultation messages and historical context information can be receiving user consultation messages input by the user at the current moment, extracting the first n historical messages of the user consultation messages, and using the first n historical messages as the aforementioned historical context information, where n is a value preset according to actual needs.

[0020] Step S102: Based on the first product category, determine the product knowledge query results through a preset product knowledge base; In step S102, the product knowledge query result determined by the preset product knowledge base based on the first product category can be obtained by inputting the first product category into the preset product knowledge base for matching, obtaining the first matching result output by the preset product knowledge base, and using the first matching result as the product knowledge query result.

[0021] Step S103: Based on the first core question, determine the process query results through a preset experience process library; In step S103, the above-mentioned process query result determined by the preset experience process library based on the first core problem can be obtained by inputting the first core problem into the preset experience process library for matching, and the second matching result output by the preset experience process library is used as the process query result.

[0022] Step S104: Based on the first core question, determine the answer query results through a preset question-and-answer knowledge base; In step S104, the above-mentioned method of determining the answer query result based on the first core question through a preset question-and-answer knowledge base can be to input the first core question into the preset question-and-answer knowledge base for matching, obtain the third matching result output by the preset question-and-answer knowledge base, and use the third matching result as the answer query result.

[0023] Step S105: Based on the product knowledge query results, process query results, and answer query results, determine the reply message corresponding to the user's inquiry message.

[0024] This application combines user inquiry messages with historical context information to determine the primary product category and the primary core issue, thereby accurately linking the user's previous inquiry records, interaction processes, and demand tendencies. This avoids repeatedly asking the user for information already provided, ensuring the continuity of inquiry responses. Furthermore, by linking preset product knowledge bases, preset experience process bases, and preset question-and-answer knowledge bases, it obtains product knowledge query results, process query results, and answer query results respectively. Based on these results, it determines the corresponding response message for the user's inquiry message, resulting in a logically clear and complete response message, thus improving the efficiency and accuracy of digital employee Q&A.

[0025] In some embodiments, before determining the product knowledge query results based on a preset product knowledge base according to a first product category, the method further includes: Obtain text and image information for each product; The text and image information obtained for each product can be derived from product documents, product detail page images, and product knowledge summarized by the merchant, which can then be used as the corresponding text and image information for each product.

[0026] Text and image information are input into the multimodal large model to obtain the second product category and product knowledge corresponding to each product output by the multimodal large model. The product knowledge includes product positioning, product process characteristics and product size. The aforementioned multimodal large model can be either the GLM4.1V model or the Qwen2.5 model.

[0027] A pre-defined product knowledge base is constructed based on the second product category and product knowledge of all products.

[0028] The above-mentioned construction of a preset product knowledge base based on the second product category and product knowledge of all products can be achieved by comprehensively considering the second product category and product knowledge of all products to obtain a preset product knowledge base.

[0029] This application improves the accuracy of digital employee Q&A by building a pre-set product knowledge base, ensuring the professionalism of product knowledge.

[0030] In some embodiments, before determining the process query result based on the first core question and using a preset experience process library, the method further includes: Obtain several sets of historical dialogues for each product; Input several sets of historical dialogues for each product into the pre-built first language model to obtain the second core problem solved by each set of historical dialogues output by the first language model. The first major language model mentioned above can be the Qwen3-8B model.

[0031] Extract all the secondary core questions corresponding to each product; and perform cluster analysis on all the secondary core questions corresponding to each product to obtain all question categories corresponding to each product; Construct structured chat templates corresponding to question categories. The structured chat templates include question categories and experience flow units. The experience flow units include preset standard prompts, preset questions, and preset solution presentation methods. The structured chat templates corresponding to the aforementioned question categories can be manually created.

[0032] A library of pre-defined experience processes is built based on each question category and corresponding structured chat template for each product.

[0033] The above-mentioned pre-set experience process library, which is based on each question category and corresponding structured chat template for each product, can be obtained by combining each question category and corresponding structured chat template for each product.

[0034] This application improves the accuracy and efficiency of digital employee Q&A by constructing a pre-set experience process library, which provides a standardized service path for experience processes.

[0035] In some embodiments, before determining the answer query result based on the first core question and using a preset question-and-answer knowledge base, the method further includes: From several sets of historical dialogues for each product, potential knowledge points are identified, which are explanations, suggestions or tips given for specific questions about the product. The specific questions mentioned above can be product knowledge not recorded in the aforementioned preset product knowledge base.

[0036] A pre-defined question-and-answer knowledge base is constructed based on specific questions and potential knowledge points.

[0037] The above-mentioned construction of a pre-defined question-and-answer knowledge base based on specific questions and potential knowledge points can be used to synthesize specific questions and potential knowledge points to obtain a pre-defined question-and-answer knowledge base.

[0038] This application improves the accuracy of digital employee Q&A by constructing a pre-defined question-and-answer knowledge base, thereby implicitly supplementing the pre-defined product knowledge base with experience.

[0039] In some embodiments, the response message corresponding to the user's inquiry message is determined based on the product knowledge query results, process query results, and answer query results, including: Input the product knowledge query results, process query results, and answer query results into the pre-built second language model to obtain the response message corresponding to the user inquiry message output by the second language model.

[0040] The second major language model mentioned above can be the Qwen3-8B model.

[0041] This application improves the accuracy of digital employee Q&A by integrating product knowledge search results, process search results, and answer search results to obtain logically clear and complete response messages.

[0042] In some embodiments, extracting the first product category and the first core question corresponding to the user inquiry message and historical context information includes: Input user inquiry messages and historical context information into the multimodal big model to obtain the first product category output by the multimodal big model; Input user inquiry messages and historical context information into the first language model to obtain the first core question output by the first language model.

[0043] This application defines the product range by extracting the first product category and identifies the user's specific needs by identifying the first core question, thereby providing more accurate data for answering subsequent questions and improving the accuracy of digital employee Q&A.

[0044] In some embodiments, the method further includes: Receive user feedback messages; End the conversation once the feedback message indicates that the problem has been resolved.

[0045] Additionally, refer to Figure 2 One embodiment of this application provides a digital employee question-and-answer system, including a category extraction module 1100, a product knowledge query result determination module 1200, a process query result determination module 1300, an answer query result determination module 1400, and a reply message determination module 1500, wherein: The category extraction module 1100 is used to extract the first product category and the first core question corresponding to the user inquiry message and historical context information when receiving user inquiry messages and historical context information; The product knowledge query result determination module 1200 is used to determine the product knowledge query result based on the first product category and through a preset product knowledge base. The process query result determination module 1300 is used to determine the process query result based on the first core question and through a preset experience process library. The answer query result determination module 1400 is used to determine the answer query result based on the first core question and through a preset question and answer knowledge base. The reply message determination module 1500 is used to determine the reply message corresponding to the user's inquiry message based on the product knowledge query results, process query results, and answer query results.

[0046] This system combines user inquiry messages with historical context information to determine the primary product category and the primary core issue. This allows for precise correlation with the user's previous inquiry records, interaction processes, and demand tendencies, avoiding the repetitive questioning of information already provided by the user and ensuring the continuity of inquiry responses. Furthermore, by linking preset product knowledge bases, preset experience process bases, and preset question-and-answer knowledge bases, it obtains product knowledge query results, process query results, and answer query results respectively. Based on these results, the system determines the corresponding response message for the user's inquiry message, resulting in a logically clear and complete response message, thus improving the efficiency and accuracy of digital employee Q&A.

[0047] It should be noted that the system embodiments described above are based on the same inventive concept as the method embodiments described above. Therefore, the relevant content of the method embodiments described above is also applicable to the system embodiments described above, and will not be repeated here.

[0048] 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, data stored, data displayed, 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 related data must comply with relevant regulations. The acquisition, storage, use and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

[0049] like Figure 3 One embodiment of this application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned digital employee question-and-answer method. The electronic device includes: At least one battery; At least one memory; At least one processor; At least one program; The program is stored in memory, and the processor executes at least one program to implement a digital employee question-and-answer method according to the above embodiments of this disclosure.

[0050] Electronic devices can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0051] The electronic devices according to embodiments of this application will now be described in detail.

[0052] The processor 1600 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure. The memory 1700 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1700 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1700 and is called and executed by the processor 1600 to implement a digital employee question-and-answer method according to an embodiment of this disclosure.

[0053] The input / output interface 1800 is used to implement information input and output. The communication interface 1900 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 2000 transmits information between various components of the device (e.g., processor 1600, memory 1700, input / output interface 1800, and communication interface 1900); The processor 1600, memory 1700, input / output interface 1800 and communication interface 1900 are connected to each other within the device via bus 2000.

[0054] This disclosure also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described digital employee question-and-answer method.

[0055] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0056] The above is a detailed description of the preferred embodiments of this application. However, the embodiments of this application are not limited to the above-described implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the embodiments of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of the embodiments of this application.

Claims

1. A digital employee question-and-answer method, characterized in that, The digital employee Q&A method includes: Upon receiving user inquiry messages and historical context information, extract the first product category and the first core question corresponding to the user inquiry messages and historical context information; Based on the first product category, the product knowledge query results are determined through a preset product knowledge base; Based on the first core issue, the process query results are determined through a pre-set experience process library; Based on the first core question, the answer query result is determined through a pre-set question-and-answer knowledge base; Based on the product knowledge query results, process query results, and answer query results, the corresponding reply message for the user's inquiry message is determined.

2. The digital employee question-and-answer method according to claim 1, characterized in that, Before determining the product knowledge query results based on the first product category and a preset product knowledge base, the method further includes: Obtain text and image information for each product; The text information and the image information are input into the multimodal large model to obtain the second product category and product knowledge corresponding to each product output by the multimodal large model, wherein the product knowledge includes product positioning, product process characteristics and product size; Based on the second product category of all products and the product knowledge, the preset product knowledge base is constructed.

3. The digital employee question-and-answer method according to claim 2, characterized in that, Before determining the process query result based on the first core problem using the preset experience process library, the method further includes: Obtain several sets of historical dialogues for each product; Each product's set of historical dialogues is input into a pre-built first large language model to obtain the second core problem solved by each set of historical dialogues output by the first large language model. Extract all the secondary core questions corresponding to each product; and perform cluster analysis on all the secondary core questions corresponding to each product to obtain all question categories corresponding to each product; Construct structured chat templates corresponding to question categories, wherein the structured chat templates include question categories and experience flow units, and the experience flow units include preset standard introductory text, preset question items, and preset solution presentation methods; The preset experience process library is constructed based on each question category and corresponding structured chat template for each product.

4. The digital employee question-and-answer method according to claim 3, characterized in that, Before determining the answer query result based on the first core question using the preset question-and-answer knowledge base, the method further includes: From the aforementioned sets of historical dialogues for each product, potential knowledge points are identified, wherein the potential knowledge points are explanations, suggestions, or tips given for specific questions about the product. Based on the specific questions and the potential knowledge points, the preset question-and-answer knowledge base is constructed.

5. The digital employee question-and-answer method according to claim 4, characterized in that, The step of determining the response message corresponding to the user inquiry message based on the product knowledge query results, process query results, and answer query results includes: The product knowledge query results, the process query results, and the answer query results are input into a pre-built second language model to obtain the reply message corresponding to the user inquiry message output by the second language model.

6. The digital employee question-and-answer method according to claim 5, characterized in that, The step of extracting the first product category and the first core question corresponding to the user inquiry message and the historical context information includes: The user inquiry message and the historical context information are input into the multimodal large model to obtain the first product category output by the multimodal large model; The user inquiry message and the historical context information are input into the first large language model to obtain the first core question output by the first large language model.

7. The digital employee question-and-answer method according to claim 1, characterized in that, The method further includes: Receive user feedback messages; If the feedback message indicates that the problem has been resolved, the conversation ends.

8. A digital employee question-and-answer system, characterized in that, The digital employee Q&A system includes: The category extraction module is used to extract the first product category and the first core question corresponding to the user inquiry message and the historical context information when receiving user inquiry messages and historical context information. The product knowledge query result determination module is used to determine the product knowledge query result based on the first product category and through a preset product knowledge base; The process query result determination module is used to determine the process query result based on the first core question and through a preset experience process library. The answer query result determination module is used to determine the answer query result based on the first core question and through a preset question-and-answer knowledge base. The response message determination module is used to determine the response message corresponding to the user inquiry message based on the product knowledge query results, process query results, and answer query results.

9. An electronic device, characterized in that, It includes at least one controller and a memory for communicatively connecting with the controller; the memory stores instructions executable by the at least one controller to cause the at least one controller to perform a digital employee question-and-answer method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions for causing a computer to perform a digital employee question-and-answer method as described in any one of claims 1 to 7.

Images