Online education intelligent customer service feedback method and system based on generative artificial intelligence

Abstract

The invention relates to the technical field of intelligent customer service, in particular to an online education intelligent customer service feedback method and system based on generative artificial intelligence. The scheme comprises the steps of carrying out structure design of an operation and maintenance and management platform; completing type management and vocabulary management of sensitive vocabularies; the plug-in setting of the management platform is completed; after the current question is obtained, an answer in the current knowledge base is extracted for retrieval feedback; for the questions without the answers, according to background inquiry times, screening out schemes and scenes with least background inquiry, updating the knowledge base online, and feeding back retrieval questions; and screening questions and answers intelligently generated by the knowledge base. According to the scheme, information is automatically generated through a generative artificial intelligence method, online information feedback is completed, and the dependency degree of manual customer service is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent customer service, and more specifically, to an online education intelligent customer service feedback method and system based on generative artificial intelligence. Background Art

[0002] The importance of intelligent customer service systems lies in their ability to significantly improve customer service quality and efficiency through automation and intelligence, while reducing costs and providing data-driven decision support for enterprises. With the continuous advancement of technology, intelligent customer service systems will continue to develop and improve, becoming an important part of the core competitiveness of enterprises. When implementing intelligent customer service systems, enterprises should consider key factors such as technology selection, data integration, and staff training to ensure the effective operation of the system and maximize the return on investment. In the future, intelligent customer service systems will pay more attention to emotion recognition, multi-channel interaction, and self-learning capabilities to provide a more humane and personalized customer service experience.

[0003] Prior to the present invention, traditional customer service systems were mainly based on retrieval technology, with limited ability to understand the semantics of questions raised by users and generate responses. They could only answer questions based on fixed rules, and their interaction methods were single, making it difficult to meet users' complex and diverse questioning needs. In addition, the construction of their knowledge base mainly relied on manually written question-answer pairs, which consumed a lot of time and energy, making the construction of the knowledge base cumbersome. Summary of the invention

[0004] In view of the above problems, the present invention proposes an online education intelligent customer service feedback method and system based on generative artificial intelligence, which automatically generates information through generative artificial intelligence methods, completes online information feedback, and reduces dependence on manual customer service.

[0005] According to a first aspect of an embodiment of the present invention, an online education intelligent customer service feedback method based on generative artificial intelligence is provided.

[0006] In one or more embodiments, preferably, the online education intelligent customer service feedback method based on generative artificial intelligence includes:

[0007] Conduct structural design of operation and maintenance and management platform;

[0008] Complete the type management and vocabulary management of sensitive words;

[0009] Complete the plug-in settings of the management platform;

[0010] After obtaining the current question, extract the answer from the current knowledge base for retrieval feedback;

[0011] For the questions that do not have such answers, based on the number of background inquiries, screen out the solutions and scenarios with the least background inquiries, update the knowledge base online, and provide feedback on the search questions;

[0012] Filter the questions and answers intelligently generated by the knowledge base.

[0013] In one or more embodiments, preferably, the structural design of the operation and maintenance and management platform specifically includes:

[0014] Provide a management platform for operation and maintenance and management personnel;

[0015] The operation and maintenance and management platform includes knowledge base management, question management, feedback management, question and answer session management, common question management, associative question management, sensitive word management and statistical management.

[0016] In one or more embodiments, preferably, completing the type management and vocabulary management of sensitive words specifically includes:

[0017] Get the current question and automatically filter out sensitive words contained in the question;

[0018] Sensitive words are divided into type management and vocabulary management:

[0019] The type management page includes fuzzy query, create, edit and delete functions;

[0020] The vocabulary management page includes functions such as query by sensitive word type, fuzzy query by sensitive word, create, edit, and delete.

[0021] In one or more embodiments, preferably, completing the plug-in setting of the management platform specifically includes:

[0022] Set up a pre-set plugin registration method and structure for the management platform;

[0023] Set the triggering and mapping calling mode of the plug-in for the management platform;

[0024] The questions raised in the settings must be filtered through the scenarios of the management platform plug-in. The scenarios are pre-set ranges and cannot be added later.

[0025] In one or more embodiments, preferably, after obtaining the current question, extracting the answer in the current knowledge base for retrieval feedback specifically includes:

[0026] Get the current question, search in the current knowledge base, and directly provide feedback if there is an answer;

[0027] If there is no such answer, a new question feedback template is created, wherein the new question feedback template includes a standard question, a standard answer, and a user ID;

[0028] For questions that do not have such answers in the feedback, submit multi-scene matching and feedback on the screen to start multi-scene adaptive matching.

[0029] In one or more embodiments, preferably, the problem without such an answer is screened out according to the number of background inquiries, and the solutions and scenarios with the least background inquiries are updated online, and the search question is fed back, specifically including:

[0030] Get the current number of background inquiries, sort them and get the solutions with the least inquiries;

[0031] Obtain all scenario categories of the current knowledge base and extract the scenarios with the least queries under the current scenario category;

[0032] Push the fewest scenarios and solutions to users, and select the most relevant scenarios and solutions;

[0033] The most relevant scenarios and solutions are reorganized into a new module, and a number of new questions and new answers are generated using the AI-based question-answer generation method.

[0034] Add new questions and answers to the original knowledge base;

[0035] The analysis is repeated until a matching answer is found, completing the feedback of the retrieval question.

[0036] In one or more embodiments, preferably, the screening of questions and answers intelligently generated by the knowledge base specifically includes:

[0037] Get all newly generated questions and answers in this Q&A;

[0038] Determine the consistency of the scenario, directly delete the questions and answers that are inconsistent with the scenario and the current question, and update the knowledge base.

[0039] According to a second aspect of an embodiment of the present invention, an online education intelligent customer service feedback system based on generative artificial intelligence is provided.

[0040] In one or more embodiments, preferably, the online education intelligent customer service feedback system based on generative artificial intelligence includes:

[0041] The management platform setting module is used for operation and maintenance and structural design of the management platform;

[0042] Sensitive word screening module, used to complete sensitive word type management and vocabulary management;

[0043] The plug-in setting module is used to complete the plug-in settings of the management platform;

[0044] The standard answer library module is used to extract the answer from the current knowledge base for retrieval feedback after obtaining the current question;

[0045] The least question scenario module is used to screen out the solutions and scenarios with the least backend questions for the questions that have no answers according to the number of backend questions, update the knowledge base online, and provide feedback on the search questions;

[0046] The scenario selection module is used to filter the questions and answers intelligently generated by the knowledge base.

[0047] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0048] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0049] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0050] In the solution of the present invention, a question feedback method based on retrieval and rapid question and answer is implemented through rapid matching of questions and scenarios.

[0051] In the solution of the present invention, the unmatched problems are quickly improved and supplemented by matching low usage scenarios with high correlation, thereby realizing intelligent information feedback.

[0052] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0053] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1It is a flowchart of an online education intelligent customer service feedback method based on generative artificial intelligence according to an embodiment of the present invention.

[0056] Figure 2 It is a flowchart of the structural design of the operation and maintenance and management platform in the online education intelligent customer service feedback method based on generative artificial intelligence in one embodiment of the present invention.

[0057] Figure 3 It is a flowchart of completing type management and vocabulary management of sensitive words in an online education intelligent customer service feedback method based on generative artificial intelligence in an embodiment of the present invention.

[0058] Figure 4 It is a flowchart of completing the plug-in setting of the management platform in the online education intelligent customer service feedback method based on generative artificial intelligence in one embodiment of the present invention.

[0059] Figure 5 It is a flowchart of an online education intelligent customer service feedback method based on generative artificial intelligence in an embodiment of the present invention, which extracts answers from the current knowledge base for retrieval feedback after obtaining the current question.

[0060] Figure 6 It is a flowchart of an online education intelligent customer service feedback method based on generative artificial intelligence in an embodiment of the present invention, which selects solutions and scenarios with the least background inquiries for questions that have no answers based on the number of background inquiries, updates the knowledge base online, and feeds back the retrieval questions.

[0061] Figure 7 It is a flowchart of screening questions and answers intelligently generated by a knowledge base in an online education intelligent customer service feedback method based on generative artificial intelligence in an embodiment of the present invention.

[0062] Figure 8 It is a structural diagram of an online education intelligent customer service feedback system based on generative artificial intelligence according to an embodiment of the present invention.

[0063] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0064] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0066] The importance of intelligent customer service systems lies in their ability to significantly improve customer service quality and efficiency through automation and intelligence, while reducing costs and providing data-driven decision support for enterprises. With the continuous advancement of technology, intelligent customer service systems will continue to develop and improve, becoming an important part of the core competitiveness of enterprises. When implementing intelligent customer service systems, enterprises should consider key factors such as technology selection, data integration, and staff training to ensure the effective operation of the system and maximize the return on investment. In the future, intelligent customer service systems will pay more attention to emotion recognition, multi-channel interaction, and self-learning capabilities to provide a more humane and personalized customer service experience.

[0067] Prior to the present invention, traditional customer service systems were mainly based on retrieval technology, with limited ability to understand the semantics of questions raised by users and generate responses. They could only answer questions based on fixed rules, and their interaction methods were single, making it difficult to meet users' complex and diverse questioning needs. In addition, the construction of their knowledge base mainly relied on manually written question-answer pairs, which consumed a lot of time and energy, making the construction of the knowledge base cumbersome.

[0068] In the embodiment of the present invention, a method and system for online education intelligent customer service feedback based on generative artificial intelligence are provided. The solution automatically generates information through a generative artificial intelligence method, completes online information feedback, and reduces the reliance on manual customer service.

[0069] According to a first aspect of an embodiment of the present invention, an online education intelligent customer service feedback method based on generative artificial intelligence is provided.

[0070] Figure 1It is a flowchart of an online education intelligent customer service feedback method based on generative artificial intelligence according to an embodiment of the present invention.

[0071] In one or more embodiments, preferably, the online education intelligent customer service feedback method based on generative artificial intelligence includes:

[0072] S101. Conduct structural design of operation and management platform;

[0073] S102, completing type management and vocabulary management of sensitive words;

[0074] S103, completing the plug-in settings of the management platform;

[0075] S104, after obtaining the current question, extract the answer in the current knowledge base for retrieval feedback;

[0076] S105, for the questions that do not have such answers, screen out the solutions and scenarios with the least backend inquiries based on the number of backend inquiries, update the knowledge base online, and provide feedback on the retrieval questions;

[0077] S106. Screen the questions and answers intelligently generated by the knowledge base.

[0078] In an embodiment of the present invention, by building an intelligent artificial intelligence question-and-answer system, combining plug-in selection and scenario and question-and-answer analysis, and combining automatically identified question generation and answer generation, it is possible to quickly supplement non-standard questions that cannot be quickly fed back, and efficiently complete artificial intelligence information generation.

[0079] Figure 2 It is a flowchart of the structural design of the operation and maintenance and management platform in the online education intelligent customer service feedback method based on generative artificial intelligence in one embodiment of the present invention.

[0080] like Figure 2 As shown, in one or more embodiments, preferably, the structural design of the operation and management platform specifically includes:

[0081] S201. Provide a management platform for operation and maintenance and management personnel;

[0082] S202. Setting the operation and maintenance and management platform includes knowledge base management, question management, feedback management, question and answer session management, common question management, associative question management, sensitive word management and statistical management.

[0083] In an embodiment of the present invention, a management platform system designed for operation and maintenance and management personnel is involved, which provides a series of functional modules including knowledge base management, question management, feedback management, question and answer session management, common question management, associative question management, sensitive word management and statistical management. These functions are designed to help managers update and maintain the knowledge base of the intelligent question and answer system, manage user interactions (such as likes and dislikes), analyze user feedback, and generate statistical reports to evaluate system performance. In addition, the system also supports a multi-user system, allowing different systems to access different knowledge bases. The operation and maintenance and management platform can complete the following operations to edit, add, delete and other update operations on the knowledge base knowledge, and automatically trigger the update of the intelligent question and answer system; support the management of default reply questions for questions that have no matching answers, further understand the questions that the current user needs to consult, and improve the knowledge base; manage and analyze the feedback content of user likes and dislikes; manage the relevant information of the consultation session and whether the answer to the question is accurate; support the screening and spot check of questions and answers to improve the knowledge base; edit, add, delete and other update operations on the commonly used questions on the right side of the intelligent customer service question and answer system client; manage the associative questions of the text entered for the question, and perform update operations such as editing, adding, deleting, etc.; import an existing sensitive word library, and can edit, add, delete and other update operations on sensitive words; perform statistics according to time, and generate statistical analysis reports based on user feedback information, such as reply efficiency, like rate, and failure rate; it is necessary to support a multi-user system for different systems and support different systems to access different knowledge bases.

[0084] Figure 3 It is a flowchart of completing type management and vocabulary management of sensitive words in an online education intelligent customer service feedback method based on generative artificial intelligence in an embodiment of the present invention.

[0085] like Figure 3 As shown, in one or more embodiments, preferably, completing the type management and vocabulary management of sensitive words specifically includes:

[0086] S301, obtaining the current question and automatically filtering sensitive words contained in the question;

[0087] S302. Sensitive words are divided into type management and vocabulary management:

[0088] S303, the type management page includes fuzzy query, create, edit and delete functions;

[0089] S304. The vocabulary management page includes functions of querying by sensitive word type, fuzzy querying by sensitive word, creating, editing, and deleting.

[0090] In an embodiment of the present invention, the current question is obtained, and the sensitive words contained in the question are automatically filtered; the sensitive words are divided into type management and vocabulary management: the type management page includes fuzzy query, create, edit and delete functions; fuzzy query is performed according to the sensitive word type; a new sensitive word type is created; entering the editing interface, the original sensitive word type information can be modified; clicking the delete button, the data is deleted; the vocabulary management page includes query by sensitive word type, fuzzy query by sensitive word, create, edit, and delete functions; query by sensitive word type; fuzzy query by sensitive word; pull down to select the sensitive word type, enter the sensitive word and status; click the edit button in the operation column to enter the editing interface, the original vocabulary information can be modified; click the delete button to delete the data.

[0091] Figure 4 It is a flowchart of completing the plug-in setting of the management platform in the online education intelligent customer service feedback method based on generative artificial intelligence in one embodiment of the present invention.

[0092] like Figure 4 As shown, in one or more embodiments, preferably, the completion of the plug-in setting of the management platform specifically includes:

[0093] S401, setting a pre-set plug-in registration method and structure for the management platform;

[0094] S402, setting a triggering and mapping calling mode of the plug-in for the management platform;

[0095] S403. The questions raised in the settings must be screened by scenarios through the plug-in of the management platform. The scenarios are pre-set ranges and cannot be added later.

[0096] In an embodiment of the present invention, a plug-in processing flow is set, which specifically includes plug-in registration and matching, triggering and automatic routing strategies; 1) Plug-in registration and matching: the functions of the enterprise software are encapsulated into an API, including an API description file and a plug-in description file. The plug-in description file defines the functions and applicable scenarios of the plug-in in detail. The API description file clearly describes the API and its parameters that the plug-in needs to call. After encapsulating the API, the plug-in needs to be registered in the big model: upload the API description file and the plug-in description file. 2) Plug-in triggering and automatic routing strategy: During the dialogue between the user and the system, the big model automatically identifies the user's intention through the pre-defined scenario description, and intelligently maps the user's input to the appropriate plug-in and API interface. First, it is triggered by the user's question: identify the user's intention and hit the corresponding plug-in scenario. Then, ask additional questions to obtain more parameters when necessary. Once there is enough information, the system will automatically match the specific API interface under the plug-in scenario to complete the mapping from user input to API call.

[0097] Figure 5 It is a flowchart of an online education intelligent customer service feedback method based on generative artificial intelligence in an embodiment of the present invention, which extracts answers from the current knowledge base for retrieval feedback after obtaining the current question.

[0098] like Figure 5 As shown, in one or more embodiments, preferably, after obtaining the current question, extracting the answer in the current knowledge base for retrieval feedback specifically includes:

[0099] S501, obtaining the current question, searching in the current knowledge base, and directly providing feedback if there is an answer;

[0100] S502: If there is no such answer, create a new question feedback template, wherein the new question feedback template includes a standard question, a standard answer, and a user ID;

[0101] S503: For questions that do not have such answers, a multi-scenario matching is proposed, and feedback on the screen is given to start the multi-scenario adaptive matching.

[0102] In an embodiment of the present invention, for the questions and answers missing in the knowledge base, temporary supplements can be made in business management-background management-automatic reply management. When asking questions and answers, the question will first be screened to see if it is a question in the automatic reply management. The specific query logic is as follows: obtain the question raised by the client user; search the corpus in the automatic reply management according to the question; obtain the standard question of the question according to the corpus; search in the standard answer management according to the standard question to obtain the standard answer; both pages support fuzzy search according to the course name; create a new automatic reply management including corpus, standard questions (the data source is the standard questions in the standard answer management) and user id (the data source is user management); create a new standard answer management including standard questions, standard answers, user id (the data source is user management), among which, the process of starting multi-scenario adaptive matching is to screen out the solutions and scenarios with the least background inquiries for the questions that do not have such answers based on the number of background inquiries, update the knowledge base online, and feedback the flowchart of the retrieval question.

[0103] Figure 6 It is a flowchart of an online education intelligent customer service feedback method based on generative artificial intelligence in an embodiment of the present invention, which selects solutions and scenarios with the least background inquiries for questions that have no answers based on the number of background inquiries, updates the knowledge base online, and feeds back the retrieval questions.

[0104] like Figure 6 As shown, in one or more embodiments, preferably, the problem without such an answer is screened out according to the number of background inquiries, and the solutions and scenarios with the least background inquiries are selected, and the knowledge base is updated online, and the retrieval question is fed back, which specifically includes:

[0105] S601, obtaining the current number of background inquiries, and sorting to obtain several solutions with the least inquiries;

[0106] S602, obtaining all scenario categories of the current knowledge base, and extracting a number of scenarios with the least queries under the current scenario category;

[0107] S603: Push the least scenarios and the least solutions to the user, and select the most relevant scenarios and solutions;

[0108] S604: The most relevant scenarios and solutions are reorganized into a new module, and a number of new questions and new answers are generated using an artificial intelligence question-answering generation method;

[0109] S605, adding new questions and new answers to the original knowledge base;

[0110] S606: Re-analyze until there is an answer match, completing the feedback of the search question.

[0111] In an embodiment of the present invention, the current number of background inquiries is obtained, which can be achieved by counting the number of various questions raised by users in the background. Then, we sort these data and find several solutions with the least number of inquiries. There is no such answer, which proves that it is generally difficult to obtain answers in the past. Or, the previous recommendations are biased, so the unpopular and strongly related ones are recommended first, so that the corresponding answers are gradually screened. Next, we need to obtain all the scene classifications of the current knowledge base. This can be done by analyzing the content in the knowledge base and classifying it into different scenes. Then, we extract several scenes with the least number of inquiries under the current scene classification. With the least scenes and the least solutions, we push them to users. Users can choose the most relevant scenes and solutions according to actual conditions. In this way, we can ensure that the information provided to users is the most targeted and practical. The most relevant scenes and solutions will be reorganized into a new module. This module will use the question-answer generation method of artificial intelligence to form several new questions and answers. These questions and answers will be closer to the actual needs of users and improve the practicality of the knowledge base. The newly generated questions and answers will be added to the original knowledge base. In this way, the content of the knowledge base will be continuously updated and expanded to meet the ever-changing needs of users. Finally, we need to re-analyze until there is an answer match. This means that we need to constantly check the content in the knowledge base to ensure that it can accurately answer the user's question. Once a matching answer is found, we can complete the feedback of the retrieval question and provide the user with a satisfactory solution.

[0112] The specific method for reading the number of background inquiries includes: using the first calculation formula to calculate the number of inquiries of a single IP per unit time;

[0113] The second calculation formula is used to calculate the number of background inquiries;

[0114] The first calculation formula is:

[0115] IPC=ZZ÷F

[0116] Among them, ZZ is the total number of inquiries of a single IP per unit time, F is the number of question categories, and IPC is the number of inquiries of a single IP per unit time;

[0117] The second calculation formula is:

[0118]

[0119] Among them, zs is the number of background inquiries, IPCi is the number of inquiries of a single IP in the i-th unit time, i is the IP number, and ss is the total number of IPs.

[0120] Figure 7 It is a flowchart of screening questions and answers intelligently generated by a knowledge base in an online education intelligent customer service feedback method based on generative artificial intelligence in an embodiment of the present invention.

[0121] like Figure 7 As shown, in one or more embodiments, preferably, the screening of questions and answers intelligently generated by the knowledge base specifically includes:

[0122] S701, obtaining all newly generated questions and answers in this question-and-answer session;

[0123] S702: Determine the consistency of the scenario, directly delete the questions and answers that are inconsistent with the scenario and the current question, and update the knowledge base.

[0124] In an embodiment of the present invention, all new questions and answers generated by artificial intelligence in this question-and-answer session are first collected. Then, the scenario consistency of each newly generated question and its corresponding answer is determined. Specifically, it is to check whether the question matches the scenario to which it belongs: if the question is inconsistent with the scenario, then the question and its answer are considered irrelevant and need to be deleted from the knowledge base. Finally, after screening, we will update the retained questions and answers with strong relevance to the knowledge base to ensure that the accuracy and practicality of the knowledge base are continuously improved.

[0125] According to a second aspect of an embodiment of the present invention, an online education intelligent customer service feedback system based on generative artificial intelligence is provided.

[0126] Figure 8 It is a structural diagram of an online education intelligent customer service feedback system based on generative artificial intelligence according to an embodiment of the present invention.

[0127] In one or more embodiments, preferably, the online education intelligent customer service feedback system based on generative artificial intelligence includes:

[0128] The management platform setting module 801 is used for operation and maintenance and structural design of the management platform;

[0129] A sensitive word screening module 802 is used to complete the type management and word management of sensitive words;

[0130] The plug-in setting module 803 is used to complete the plug-in setting of the management platform;

[0131] The standard answer library module 804 is used to extract the answer in the current knowledge base for retrieval feedback after obtaining the current question;

[0132] The least question scenario module 805 is used to screen out the solutions and scenarios with the least background questions according to the number of background questions for the questions without such answers, update the knowledge base online, and feedback the search questions;

[0133] The scenario selection module 806 is used to screen the questions and answers intelligently generated by the knowledge base.

[0134] In the embodiment of the present invention, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0135] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0136] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9 The electronic device shown is a general-purpose online education intelligent customer service feedback device based on generative artificial intelligence. The electronic device can be a smart phone, a tablet computer, or other device. As shown, the electronic device 900 includes a processor 901 and a memory 902. Among them, the processor 901 is electrically connected to the memory 902. The processor 901 is the control center of the terminal 900, which uses various interfaces and lines to connect the various parts of the entire terminal, and executes various functions of the terminal and processes data by running or calling the computer program stored in the memory 902, and calling the data stored in the memory 902, thereby monitoring the terminal as a whole.

[0137] In this embodiment, the processor 901 in the electronic device 900 will load the instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 will run the computer program stored in the memory 902 to achieve various functions: perform structural design of the operation and maintenance and management platform; complete type management and vocabulary management of sensitive words; complete plug-in settings of the management platform; after obtaining the current question, extract the answer in the current knowledge base for retrieval and feedback; for the question that has no such answer, according to the number of background inquiries, screen out the solutions and scenarios with the least background inquiries, update the knowledge base online, and feedback the retrieval questions; screen the questions and answers intelligently generated by the knowledge base.

[0138] The memory 902 may be used to store computer programs and data. The computer programs stored in the memory 902 include instructions that can be executed in the processor. The computer programs may constitute various functional modules. The processor 901 executes various functional applications and data processing by calling the computer programs stored in the memory 902.

[0139] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0140] In the solution of the present invention, a question feedback method based on retrieval and rapid question and answer is implemented through rapid matching of questions and scenarios.

[0141] In the solution of the present invention, the unmatched problems are quickly improved and supplemented by matching low usage scenarios with high correlation, thereby realizing intelligent information feedback.

[0142] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0143] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0146] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An online education intelligent customer service feedback method based on generative artificial intelligence, characterized in that: The method includes: Conduct structural design of operation and maintenance and management platform; Complete the type management and vocabulary management of sensitive words; Complete the plug-in settings of the management platform; After obtaining the current question, extract the answer from the current knowledge base for retrieval feedback; For questions that do not have such answers, we screen out the solutions and scenarios with the least backend inquiries based on the number of backend inquiries, update the knowledge base online, and provide feedback on the search questions; Filter the questions and answers intelligently generated by the knowledge base.

2. The online education intelligent customer service feedback method based on generative artificial intelligence as claimed in claim 1, characterized in that: The structural design of the operation and maintenance and management platform specifically includes: Provide a management platform for operation and maintenance and management personnel; The operation and maintenance and management platform includes knowledge base management, question management, feedback management, question and answer session management, common question management, associative question management, sensitive word management and statistical management.

3. The online education intelligent customer service feedback method based on generative artificial intelligence as claimed in claim 1, characterized in that: The completion of the type management and vocabulary management of sensitive words specifically includes: Get the current question and automatically filter out sensitive words contained in the question; Sensitive words are divided into type management and vocabulary management: The type management page includes fuzzy query, create, edit and delete functions; The vocabulary management page includes functions such as query by sensitive word type, fuzzy query by sensitive word, create, edit, and delete.

4. The online education intelligent customer service feedback method based on generative artificial intelligence as claimed in claim 1, characterized in that: The plug-in setting of the completion management platform specifically includes: Set up a pre-set plugin registration method and structure for the management platform; Set the triggering and mapping calling mode of the plug-in for the management platform; The questions raised in the settings must be filtered through the scenarios of the management platform plug-in. The scenarios are pre-set ranges and cannot be added later.

5. The online education intelligent customer service feedback method based on generative artificial intelligence as claimed in claim 1, characterized in that: After obtaining the current question, extracting the answer in the current knowledge base for retrieval feedback specifically includes: Get the current question, search in the current knowledge base, and directly provide feedback if there is an answer; If there is no such answer, a new question feedback template is created, wherein the new question feedback template includes a standard question, a standard answer, and a user ID; For questions that do not have such answers in the feedback, submit multi-scene matching and feedback on the screen to start multi-scene adaptive matching.

6. The online education intelligent customer service feedback method based on generative artificial intelligence as claimed in claim 5, characterized in that: For the questions that do not have such answers, the solutions and scenarios with the least background inquiries are screened out according to the number of background inquiries, the knowledge base is updated online, and the search questions are fed back, including: Get the current number of background inquiries, sort them and get the solutions with the least inquiries; Obtain all scenario categories of the current knowledge base and extract the scenarios with the least queries under the current scenario category; Push the fewest scenarios and solutions to users, and select the most relevant scenarios and solutions; The most relevant scenarios and solutions are reorganized into a new module, and a number of new questions and new answers are generated using the AI-based question-answer generation method. Add new questions and answers to the original knowledge base; The analysis is repeated until a matching answer is found, completing the feedback of the retrieval question.

7. The online education intelligent customer service feedback method based on generative artificial intelligence as claimed in claim 1, characterized in that: The screening of questions and answers intelligently generated by the knowledge base specifically includes: Get all newly generated questions and answers in this Q&A; Determine the consistency of the scenario, directly delete the questions and answers that are inconsistent with the scenario and the current question, and update the knowledge base.

8. An online education intelligent customer service feedback system based on generative artificial intelligence, characterized by: The system is used to implement the method according to any one of claims 1 to 7, and the system comprises: The management platform setting module is used for operation and maintenance and structural design of the management platform; Sensitive word screening module, used to complete sensitive word type management and vocabulary management; The plug-in setting module is used to complete the plug-in setting of the management platform; The standard answer library module is used to extract the answer from the current knowledge base for retrieval feedback after obtaining the current question; The least question scenario module is used to screen out the solutions and scenarios with the least backend questions for the questions that have no answers according to the number of backend questions, update the knowledge base online, and provide feedback on the search questions; The scenario selection module is used to filter the questions and answers intelligently generated by the knowledge base.

9. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 7 when executed by a processor.

10. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-7.

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