A method, system, medium and electronic device for realizing conversational digital employee human-computer interaction

Abstract

The present invention relates to the field of large-scale model interaction technology, and more specifically, to a method, system, medium, and electronic device for implementing conversational digital employee human-computer interaction. The solution includes setting a memory button, an API interface, and an online data access interface for the human-computer interaction system; specifying a feedback persona for the robot; setting corresponding opening remarks for different personas; pre-setting an enhanced search mode to provide enhanced responses based on the search information; providing targeted feedback suggestions; and setting key reminder information to be stored in the memory button to remind the robot with each response and information feedback. This solution achieves accurate and efficient matching questions and answers by building an online question-and-answer system for digital employees, combined with dynamically adjusted memory buttons.

Description

Technical Field

[0001] The present invention relates to the field of large model interaction technology, and more specifically, to a method, system, medium and electronic equipment for implementing conversational digital employee human-computer interaction. Background Art

[0002] In the current field of large-scale model interaction, the research on the methods, significance, and importance of conversational human-computer interaction lies in how to achieve efficient and intelligent communication between users and large-scale computational models through natural language processing technology. This interaction method not only enhances the user experience and enables people to communicate more naturally with machines, but also has a profound impact on the development of artificial intelligence technology. With the continuous advancement of technology, conversational human-computer interaction has become an important bridge connecting human wisdom and machine intelligence. It not only promotes the rapid flow of information and knowledge sharing, but also plays a significant role in various fields such as education, healthcare, and customer service, greatly improving service efficiency and quality. Therefore, exploring more intelligent and personalized conversational human-computer interaction methods and their system design is crucial to meeting modern society's demand for efficient and convenient information services.

[0003] Prior to the present invention, existing conversational human-computer interaction methods primarily employed system architectures based on modules such as speech recognition, natural language processing, dialogue management, and speech synthesis. These systems convert user voice input into text, then leverage natural language processing for semantic understanding and intent recognition. Finally, a dialogue management module controls the conversation flow and generates responses. However, these often present challenges: depth of understanding and context. While large language models can generate fluent speech, they often lack the ability to deeply understand the context behind questions and the long-term context. For example, during multi-turn conversations, they may forget previous information or fail to fully understand it, resulting in incoherent responses. Knowledge update lags: Large language models are trained on datasets from a specific point in time, resulting in a time lag in their knowledge base. Over time, the models are unable to automatically acquire new information. Issues with the accuracy and reliability of generated content: Generative AI can sometimes generate inaccurate, erroneous, or harmful content. For example, it may generate false information or ambiguous responses. Summary of the Invention

[0004] In view of the above problems, the present invention proposes a method, system, medium and electronic device for realizing conversational digital employee human-computer interaction. By constructing an online question-and-answer system for digital employees and combining it with dynamically adjusted memory buttons, accurate and efficient matching question-and-answer is achieved.

[0005] According to a first aspect of an embodiment of the present invention, a method for implementing conversational digital employee human-computer interaction is provided.

[0006] In one or more embodiments, preferably, the method for implementing a conversational digital employee human-computer interaction includes:

[0007] Set up memory buttons, API interfaces and online data access interfaces for the human-computer interaction system;

[0008] Specify feedback personas for the robot;

[0009] Set up corresponding opening remarks for different characters;

[0010] Pre-set enhanced search mode to provide enhanced responses based on search information;

[0011] Feedback targeted advice information;

[0012] Set key reminder information to be stored in the memory button, and remind the robot in each reply and information feedback;

[0013] After each digital employee feedback response, extract the proportion of declarative opinions and the proportion of problematic opinions, where the proportion of declarative opinions is the number of declarative sentences divided by the total number of sentences, and the proportion of problematic opinions is the number of problematic sentences divided by the total number of sentences. As needed, during each questioning process, increase the tendency based on the proportion of declarative opinions and the proportion of problematic opinions. The tendency includes increasing the proportion of declarative opinions or the proportion of problematic opinions;

[0014] After each digital employee feedback response, the proportion of response sentences containing new nouns is extracted. When the proportion of new nouns is less than the preset value, it is required to increase the proportion of response sentences containing new nouns, where the proportion of response sentences containing new nouns is the number of sentences that do not include the name in the given information divided by the total number of sentences.

[0015] In one or more embodiments, preferably, the human-computer interaction system is provided with a memory button, an API interface, and an online data access interface, specifically including:

[0016] Set up a memory button to store query information online;

[0017] Set up an API interface to extract question and answer information in corresponding professional fields for matching;

[0018] Set up an online data access interface to extract online data for control.

[0019] In one or more embodiments, preferably, the step of defining a feedback personality for the robot specifically includes:

[0020] The feedback format for pre-set characters includes roles, skills, and considerations;

[0021] A table with corresponding characters is pre-set for selecting characters;

[0022] Pre-set the output method of the character's feedback.

[0023] In one or more embodiments, preferably, the setting of corresponding opening remarks for different characters specifically includes:

[0024] Select a pre-set opening line based on the character settings;

[0025] When a robot query of a corresponding character is conducted online, the opening remarks corresponding to the robot of the character are retrieved.

[0026] In one or more embodiments, preferably, the pre-setting of the enhanced search mode and the providing of an enhanced reply based on the search information specifically include:

[0027] In the pre-set enhanced search mode, set the method and order of filtering information;

[0028] By pre-setting enhanced search parameters and rules, the retrieved information can be filtered and sorted.

[0029] In one or more embodiments, preferably, the feedback-oriented suggestion information specifically includes:

[0030] Based on the error information fed back by the robot in each conversation, the direction of directional suggestions is clearly defined;

[0031] Provides targeted suggestions based on the rules set by the questioner in each conversation.

[0032] In one or more embodiments, preferably, the key reminder information is stored in the memory button and reminded to the robot in each reply and information feedback, specifically including:

[0033] Every time a reply is received, the clear declarative opinions are automatically extracted, numbered, and stored in the memory button as clear opinions;

[0034] Number the stated clear points;

[0035] Obtain the upper limit of input for each feedback response, and calculate the remaining word count according to the first calculation formula;

[0036] Use the second calculation formula to calculate the viewpoint number of this time;

[0037] Calculate the updated current viewpoint set using the third calculation formula;

[0038] Calculate the viewpoint number each time using the fourth calculation formula according to the updated current viewpoint set until the fifth calculation formula is satisfied;

[0039] After the fifth calculation formula is satisfied, all the clear viewpoints are updated, and the viewpoint numbers of this time are calculated again using the second, third, and fourth calculation formulas;

[0040] In each reply, start with "It is necessary to remind you" and then provide feedback on the specific opinions corresponding to the opinion number of this time.

[0041] The first calculation formula is:

[0042] SX-YR=SY

[0043] Among them, SX is the input upper limit, YR is the number of words entered, and SY is the number of remaining words;

[0044] The second calculation formula is:

[0045] BC=Argmin(|SY-ΣGZ|)

[0046] SY-ΣGZ>0

[0047] BC∈JH

[0048] Where Argmin is the function used to extract the current opinion number that minimizes |SY-ΣGZ|, ΣGZ is the total number of words in this opinion, BC is the current opinion number, and JH is the current opinion set;

[0049] The third calculation formula is:

[0050] JHq=JH-BC

[0051] Among them, JHq is the updated current view set;

[0052] The fourth calculation formula is:

[0053] BCn=Argmin(|SY-ΣGZ|)

[0054] SY-ΣGZ>0

[0055] BCn∈JHq

[0056] Among them, BCn is the viewpoint number of each time;

[0057] The fifth calculation formula is:

[0058] JHq=kong

[0059] Among them, kong is the empty set.

[0060] According to a second aspect of an embodiment of the present invention, a conversational digital employee human-computer interaction implementation system is provided.

[0061] In one or more embodiments, preferably, the conversational digital employee human-computer interaction implementation system includes:

[0062] Structural construction module, used to set up memory buttons, API interfaces and online data access interfaces for the human-computer interaction system;

[0063] The character generation module is used to specify the feedback character for the robot;

[0064] Opening remarks module, used to set corresponding opening remarks for different characters;

[0065] Enhanced retrieval module, used to pre-set enhanced retrieval mode and provide enhanced responses based on retrieval information;

[0066] Targeted suggestion module, used to feedback targeted suggestion information;

[0067] The key memory information learning module is used to set key reminder information to be stored in the memory button and remind the robot in each reply and information feedback.

[0068] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method according to any one of the first aspect of the embodiment of the present invention is implemented.

[0069] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided, 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.

[0070] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0071] The solution of the present invention combines a clear digital employee question and answer system to conduct online questions and answers according to the pre-set feedback personality.

[0072] In the solution of the present invention, the question and answer information supplement based on the dynamically adjusted memory button is combined to achieve efficient and accurate question and answer feedback.

[0073] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes 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.

[0074] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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.

[0076] Figure 1 The present invention is a flowchart of a method for implementing a conversational digital employee human-computer interaction.

[0077] Figure 2 This is a flowchart of setting a memory button, an API interface, and an online data access interface for a human-computer interaction system in a method for implementing a conversational digital employee human-computer interaction according to an embodiment of the present invention.

[0078] Figure 3 This is a flowchart of specifying feedback personality for a robot in a method for implementing conversational digital employee human-machine interaction according to an embodiment of the present invention.

[0079] Figure 4 This is a flowchart of setting corresponding opening remarks for different personalities in a method for implementing a conversational digital employee human-computer interaction according to an embodiment of the present invention.

[0080] Figure 5 The present invention is a flowchart of presetting an enhanced search mode in a method for implementing a conversational digital employee human-computer interaction and providing an enhanced reply based on the search information in accordance with an embodiment of the present invention.

[0081] Figure 6 The present invention is a flowchart of feedback-oriented suggestion information in a method for implementing a conversational digital employee human-computer interaction according to an embodiment of the present invention.

[0082] Figure 7 This is a flowchart of setting key reminder information in a method for implementing conversational digital employee human-computer interaction in one embodiment of the present invention, storing it in the memory button, and reminding the robot in each reply and information feedback.

[0083] Figure 8 This is a structural diagram of a conversational digital employee human-computer interaction implementation system according to an embodiment of the present invention.

[0084] Figure 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0085] 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 may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between 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., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0086] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0087] In the current field of large-scale model interaction, the research on the methods, significance, and importance of conversational human-computer interaction lies in how to achieve efficient and intelligent communication between users and large-scale computational models through natural language processing technology. This interaction method not only enhances the user experience and enables people to communicate more naturally with machines, but also has a profound impact on the development of artificial intelligence technology. With the continuous advancement of technology, conversational human-computer interaction has become an important bridge connecting human wisdom and machine intelligence. It not only promotes the rapid flow of information and knowledge sharing, but also plays a significant role in various fields such as education, healthcare, and customer service, greatly improving service efficiency and quality. Therefore, exploring more intelligent and personalized conversational human-computer interaction methods and their system design is crucial to meeting modern society's demand for efficient and convenient information services.

[0088] Prior to the present invention, existing conversational human-computer interaction methods primarily employed system architectures based on modules such as speech recognition, natural language processing, dialogue management, and speech synthesis. These systems convert user voice input into text, then leverage natural language processing for semantic understanding and intent recognition. Finally, a dialogue management module controls the conversation flow and generates responses. However, these often present challenges: depth of understanding and context. While large language models can generate fluent speech, they often lack the ability to deeply understand the context behind questions and the long-term context. For example, during multi-turn conversations, they may forget previous information or fail to fully understand it, resulting in incoherent responses. Knowledge update lags: Large language models are trained on datasets from a specific point in time, resulting in a time lag in their knowledge base. Over time, the models are unable to automatically acquire new information. Issues with the accuracy and reliability of generated content: Generative AI can sometimes generate inaccurate, erroneous, or harmful content. For example, it may generate false information or ambiguous responses.

[0089] In an embodiment of the present invention, a method, system, medium, and electronic device for implementing conversational digital employee human-computer interaction are provided. This solution achieves accurate and efficient matching of questions and answers by building an online question-and-answer system for digital employees and combining it with dynamically adjusted memory buttons.

[0090] According to a first aspect of an embodiment of the present invention, a method for implementing conversational digital employee human-computer interaction is provided.

[0091] Figure 1 The present invention is a flowchart of a method for implementing a conversational digital employee human-computer interaction.

[0092] In one or more embodiments, preferably, the method for implementing a conversational digital employee human-computer interaction includes:

[0093] S101. Setting up a memory button, API interface, and online data access interface for the human-computer interaction system;

[0094] S102. Specifying a feedback personality for the robot;

[0095] S103, setting up corresponding opening remarks for different characters;

[0096] S104: pre-set an enhanced search mode and provide an enhanced reply based on the search information;

[0097] S105, feedback of targeted suggestion information;

[0098] S106. Set key reminder information to be stored in the memory button, and remind the robot in each reply and information feedback.

[0099] In this embodiment of the present invention, the core process involves creating a digital bot. Log in to the Button platform, click a button to generate a bot, enter the bot design page, create a persona for the bot, describe the bot's identity and tasks in the Persona and Response Logic panel on the left, and then write a plan. The bot's persona and response logic define its basic personality, which will consistently influence the effectiveness of its responses across all conversations. To achieve the desired effect, the persona and response logic specify the bot's role, design the language style of its responses, and limit the scope of the bot's responses, ensuring that the conversation more closely meets user expectations.

[0100] In addition, in order to make the robot's feedback more responsive to the needs of asking questions, after each digital employee's feedback, the proportion of declarative opinions and the proportion of problematic opinions are extracted, wherein the proportion of declarative opinions is the number of declarative sentences divided by the total number of sentences, and the proportion of problematic opinions is the number of problematic sentences divided by the total number of sentences. According to the needs, in each questioning process, the tendency is increased according to the needs of the proportion of declarative opinions and the proportion of problematic opinions. The tendency includes increasing the proportion of declarative opinions or the proportion of problematic opinions; after each digital employee's feedback, the proportion of reply sentences containing new nouns is extracted. When the proportion of new nouns is less than the preset value, it is required to increase the proportion of reply sentences containing new nouns, wherein the proportion of reply sentences containing new nouns is the number of sentences that do not include the name in the given information divided by the total number of sentences.

[0101] Figure 2 This is a flowchart of setting a memory button, an API interface, and an online data access interface for a human-computer interaction system in a method for implementing a conversational digital employee human-computer interaction according to an embodiment of the present invention.

[0102] like Figure 2 As shown, in one or more embodiments, preferably, the human-computer interaction system is provided with a memory button, an API interface, and an online data access interface, specifically including:

[0103] S201, set a memory button for storing query information online;

[0104] S202: Setting an API interface for extracting question and answer information in corresponding professional fields for matching;

[0105] S203: Setting an online data access interface for online data extraction and control.

[0106] In an embodiment of the present invention, an efficient human-computer interaction system is constructed. This system optimizes the user experience and enhances system responsiveness by integrating a memory button, an API interface, and an online data access interface. First, the system incorporates a functional module called the "memory button," which functions similarly to human memory and stores the user's previous inquiries online. This storage mechanism allows the system to reference previous information in subsequent conversations, achieving a more personalized and consistent conversational experience. For example, if a user has previously asked a question about the weather, the system can proactively provide a weather forecast in subsequent conversations without the user having to repeat the question. Second, the system is configured with an API interface specifically designed to extract and match questions and answers in specific professional fields. By connecting to external knowledge bases or databases, the API interface can quickly retrieve and provide accurate answers based on user queries. For example, when a user asks a medical question, the API interface can connect to a professional medical database to retrieve and return relevant medical information. Finally, the system also includes an online data access interface, which extracts online data for control purposes. This means the system can monitor dynamic data such as network status and user behavior in real time and adjust its operating strategies accordingly. For example, if increased network latency is detected, the system might automatically reduce the frequency of data transmission to ensure smooth conversations. In summary, through the collaborative work of these three key components, the human-computer interaction system of the present invention can not only provide more intelligent and personalized services, but also adapt to changes in the external environment in real time, thereby significantly improving the user's interactive experience.

[0107] Figure 3 This is a flowchart of specifying feedback personality for a robot in a method for implementing conversational digital employee human-machine interaction according to an embodiment of the present invention.

[0108] like Figure 3 As shown, in one or more embodiments, preferably, the step of providing a feedback personality for the robot specifically includes:

[0109] S301. Pre-set the feedback format of the character including role, skills and precautions;

[0110] S302, pre-setting a table corresponding to a persona for selecting a persona;

[0111] S303: Pre-set the output method of the personality feedback.

[0112] In an embodiment of the present invention, a highly customizable and interactive robot feedback system is designed. This system uses pre-set personas (roles) to guide the robot's behavior and communication style, providing a more personalized and contextualized user experience. First, the system assigns a feedback persona to the robot, which consists of three core elements: role, skills, and precautions. Roles define the robot's identity in specific situations, such as "friendly tour guide" or "professional doctor's assistant," helping to shape the robot's personality and behavior. Skills specify the range of tasks the robot can perform. For example, a robot designated as "IT support expert" would be able to resolve technical issues. Precautions ensure that the robot adheres to specific behavioral guidelines during interactions, such as maintaining politeness and avoiding sensitive topics. Next, the system pre-sets a table corresponding to each persona. This table, as part of the user interface, allows users to select different personas as needed. Users can easily switch between the robot's roles through this table to adapt to different conversational scenarios. For example, if a user needs health advice, they can select the "medical advisor" persona; if they need travel information, they can select the "travel guide" persona. Finally, the system also pre-sets the output method for persona feedback. This means that when interacting with users, the robot's feedback content and style will be consistent with its persona. For example, when the robot is set to be a "funny friend," its responses may include more humor and a lighthearted tone; while as a "serious teacher," its feedback will be more educational and instructive. In this way, embodiments of the present invention not only enhance the robot's interactive capabilities, enabling it to better adapt to the diverse needs of users, but also improve the quality of the user experience, enabling the robot to provide more natural and relevant services in different situations.

[0113] Figure 4 This is a flowchart of setting corresponding opening remarks for different personalities in a method for implementing a conversational digital employee human-computer interaction according to an embodiment of the present invention.

[0114] like Figure 4 As shown, in one or more embodiments, preferably, the corresponding opening remarks for different character settings are displayed, specifically including:

[0115] S401, selecting a pre-set opening statement according to the character setting;

[0116] S402: When a robot corresponding to a character is queried online, the opening remarks corresponding to the robot corresponding to the character are retrieved.

[0117] In an embodiment of the present invention, an intelligent dialogue system is constructed that displays opening lines corresponding to different personas (role settings) to enhance the user experience and promote natural interaction. First, the system pre-programs a series of opening lines corresponding to different personas. These opening lines are carefully crafted sentences or paragraphs designed to reflect the personality, style, and function of a specific persona. For example, for a robot configured as a "friendly travel guide," its opening line might be: "Hello! I'm your travel assistant. Are you ready to explore this beautiful city together?" For a robot configured as a "professional legal advisor," its opening line might be: "Welcome to our legal consulting service. I will provide you with the most professional advice." Then, when a user asks a robot with a corresponding persona online, the system automatically retrieves the opening line corresponding to that persona. This process is achieved through an algorithm that identifies the most appropriate persona based on the user's query and context, and triggers the corresponding opening line. For example, if a user enters a question about travel through the chat interface, the system will recognize that the user is seeking travel-related information and automatically select the "friendly travel guide" persona and display its corresponding opening line. This implementation not only improves the robot's response efficiency but also enhances user immersion and satisfaction through personalized opening lines. Users can immediately sense the robot's role and personality, making it easier to build trust and a willingness to interact. Furthermore, this approach allows the robot to be flexibly applied in a variety of scenarios. Whether providing customer service, educational guidance, or entertainment interaction, it can provide appropriate opening lines based on different personas to meet the diverse needs of users.

[0118] Figure 5 The present invention is a flowchart of presetting an enhanced search mode in a method for implementing a conversational digital employee human-computer interaction and providing an enhanced reply based on the search information in accordance with an embodiment of the present invention.

[0119] like Figure 5 As shown, in one or more embodiments, preferably, the pre-setting enhanced search mode and the enhanced reply based on the search information specifically include:

[0120] S501. In a preset enhanced search mode, set a method and order for filtering information;

[0121] S502: Filter and sort the retrieved information by presetting enhanced search parameters and rules.

[0122] In an embodiment of the present invention, an enhanced search mode is designed to improve the accuracy and efficiency of information retrieval. This mode uses pre-set information filtering methods and sequences, as well as enhanced search parameters and rules, to filter and sort retrieved information, thereby providing users with more precise and relevant responses. First, the system pre-sets the enhanced search mode, which defines how and in what order information is filtered. For example, when a user queries for "best restaurants," the system may first filter restaurants near the user's current location, then further filter based on the user's historical preferences (such as cuisine and price range), and finally sort by rating or recommendation. This multi-level filtering mechanism ensures that only the information that best meets the user's needs is displayed. Next, the system optimizes the search results using pre-set enhanced search parameters and rules. These parameters may include keyword weighting, time range restrictions, and source credibility assessment. For example, if a user is looking for the latest technology news, the system can set a time range parameter to only retrieve articles published within the past week. At the same time, using source credibility assessment, it prioritizes reports from reputable technology media. To illustrate this process, let's use a specific example: suppose a user wants to learn about "the latest developments in artificial intelligence." The system first identifies the keywords "artificial intelligence" and "latest developments" and then applies a pre-set enhanced search mode. In this mode, the system might first filter out relevant articles published within the past month. It then assesses the credibility of the articles based on their sources (such as top academic journals, authoritative news websites, etc.), giving higher rankings to articles from highly credible sources. Finally, the system presents the filtered and ranked results to the user, ensuring that they can quickly access the most relevant and reliable information. In this way, the enhanced search mode of the present invention not only improves the efficiency of information retrieval but also significantly enhances the user's search experience and satisfaction through a multi-dimensional screening and sorting mechanism.

[0123] Figure 6 The present invention is a flowchart of feedback-oriented suggestion information in a method for implementing a conversational digital employee human-computer interaction according to an embodiment of the present invention.

[0124] like Figure 6 As shown, in one or more embodiments, preferably, the feedback-oriented suggestion information specifically includes:

[0125] S601. Based on the error information fed back by the robot in each conversation, clarify the direction of directional suggestions;

[0126] S602: Propose targeted suggestions based on the rules set by the questioner in each conversation.

[0127] In this embodiment of the present invention, an intelligent dialogue system is designed that provides targeted feedback. This function aims to provide more accurate and targeted advice by analyzing the error messages fed back by the robot during each conversation and using rules set by the questioner.

[0128] Figure 7 This is a flowchart of setting key reminder information in a method for implementing conversational digital employee human-computer interaction in one embodiment of the present invention, storing it in the memory button, and reminding the robot in each reply and information feedback.

[0129] like Figure 7 As shown, in one or more embodiments, preferably, the key reminder information is stored in the memory button, and the robot is reminded in each reply and information feedback, specifically including:

[0130] S701. Every time a reply message is received, the clear declarative opinions are automatically extracted, numbered, and stored in a memory button as clear opinions;

[0131] S702, numbering the clear viewpoints;

[0132] S703: Obtain the upper limit of input for each feedback reply, and calculate the remaining number of words according to the first calculation formula;

[0133] S704, calculating the current viewpoint number using the second calculation formula;

[0134] S705, calculating the updated current viewpoint set using a third calculation formula;

[0135] S706, calculating the viewpoint number each time using the fourth calculation formula according to the updated current viewpoint set until the fifth calculation formula is satisfied;

[0136] S707: After the fifth calculation formula is satisfied, all the clear viewpoints are updated, and the second, third, and fourth calculation formulas are used again to calculate the viewpoint number of this time;

[0137] S708. In each reply, start with "It is necessary to remind you" and provide feedback on the clear opinions corresponding to the opinion number of this time.

[0138] The first calculation formula is:

[0139] SX-YR=SY

[0140] Among them, SX is the input upper limit, YR is the number of words entered, and SY is the number of remaining words;

[0141] The second calculation formula is:

[0142] BC=Argmin(|SY-ΣGZ|)

[0143] SY-ΣGZ>0

[0144] BC∈JH

[0145] Where Argmin is the function used to extract the current opinion number that minimizes |SY-ΣGZ|, ΣGZ is the total number of words in this opinion, BC is the current opinion number, and JH is the current opinion set;

[0146] The third calculation formula is:

[0147] JHq=JH-BC

[0148] Among them, JHq is the updated current view set;

[0149] The fourth calculation formula is:

[0150] BCn=Argmin(|SY-ΣGZ|)

[0151] SY-ΣGZ>0

[0152] BCn∈JHq

[0153] Among them, BCn is the viewpoint number of each time;

[0154] The fifth calculation formula is:

[0155] JHq=kong

[0156] Among them, kong is the empty set.

[0157] In an embodiment of the present invention, an intelligent conversational system is designed that provides targeted feedback. This feature aims to provide more accurate and targeted recommendations by analyzing the robot's error messages during each conversation and based on user-defined rules. Specifically, the system first records and analyzes the robot's error messages during each conversation. These error messages may include questions the robot cannot understand, inaccurate or irrelevant responses, and so on. Through in-depth analysis of these error messages, the system can identify common question types and user needs. For example, if the robot repeatedly fails to correctly understand questions about a specific area, the system will mark this area as a priority for improvement. Next, the system provides targeted recommendations based on the user-defined rules during each conversation. These rules can be customized by the user to reflect their specific needs or preferences. For example, a user can set a rule requiring the robot to provide clothing recommendations and outdoor activity suggestions along with weather information. When the robot detects a user asking about the weather, it will not only report the current weather conditions but also provide additional recommendations based on the user's rules, such as "It's cold today, please dress appropriately" or "It's sunny today, perfect for outdoor sports." Let's use a specific example to illustrate this process: suppose a user asks for advice on healthy eating, and the robot provides some basic dietary guidelines. However, the user then indicates that they have a specific health condition and need more specific advice. In this case, the system adjusts its answer strategy based on the user's feedback and the set rules. In future conversations, when similar health conditions are mentioned, the robot will be able to provide more personalized and targeted advice, such as recommending low-sugar recipes or high-fiber food options. In this way, the intelligent dialogue system of the present invention is not only able to learn from its mistakes and improve its performance, but also to provide more considerate services based on the personalized needs of users. This mechanism of feedback of targeted recommendation information significantly improves the user experience and enables the robot to better meet the diverse needs of users.

[0158] According to a second aspect of an embodiment of the present invention, a conversational digital employee human-computer interaction implementation system is provided.

[0159] Figure 8 This is a structural diagram of a conversational digital employee human-computer interaction implementation system according to an embodiment of the present invention.

[0160] In one or more embodiments, preferably, the conversational digital employee human-computer interaction implementation system includes:

[0161] The structure construction module 801 is used to set up memory buttons, API interfaces and online data access interfaces for the human-computer interaction system;

[0162] A character generation module 802 is used to define a feedback character for the robot;

[0163] The opening remarks module 803 is used to set corresponding opening remarks for different characters;

[0164] Enhanced retrieval module 804, used to pre-set an enhanced retrieval mode and provide an enhanced reply based on the retrieval information;

[0165] A directional suggestion module 805 is used to feed back directional suggestion information;

[0166] The key memory information learning module 806 is used to set key reminder information to be stored in the memory button and remind the robot in each reply and information feedback.

[0167] In the embodiment of the present invention, a system applicable to 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.

[0168] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method according to any one of the first aspect of the embodiment of the present invention is implemented.

[0169] According to a fourth aspect of the embodiments of the present invention, an electronic device is provided. Figure 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Figure 9 The electronic device shown is a device for implementing conversational digital employee human-computer interaction. It includes a general computer hardware structure, including at least a processor 901 and a memory 902. The processor 901 and the memory 902 are connected via a bus 903. The memory 902 is suitable for storing instructions or programs executable by the processor 901. The processor 901 can be a standalone microprocessor or a collection of one or more microprocessors. Thus, by executing the instructions stored in the memory 902, the processor 901 performs the method flow described above in the embodiment of the present invention to process data and control other devices. The bus 903 connects the above-mentioned multiple components together and also connects them to the display controller 904, the display device, and the I / O device 905. The I / O device 905 can be a mouse, keyboard, modem, network interface, touch input device, somatosensory input device, printer, or other devices known in the art. Typically, the I / O device 905 is connected to the system via an I / O controller 906.

[0170] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0171] The solution of the present invention combines a clear digital employee question and answer system to conduct online questions and answers according to the pre-set feedback personality.

[0172] In the solution of the present invention, the question and answer information supplement based on the dynamically adjusted memory button is combined to achieve efficient and accurate question and answer feedback.

[0173] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, 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 magnetic disk storage and optical storage) containing computer-usable program code.

[0174] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0175] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0176] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0177] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for implementing a conversational digital employee human-computer interaction, characterized in that: The method includes: Set up memory buttons, API interfaces and online data access interfaces for the human-computer interaction system; Specify feedback personas for the robot; Set up corresponding opening remarks for different characters; Pre-set enhanced search mode to provide enhanced responses based on search information; Feedback targeted advice information; Set key reminder information to be stored in the memory button, and remind the robot in each reply and information feedback; Specifically, the process of setting key reminder information to be stored in the memory button and reminding the robot in each reply and information feedback includes: automatically extracting the clear declarative viewpoints in the reply information each time, numbering them, and storing them in the memory button as clear viewpoints; automatically reading the information in the memory button during each reply and information feedback process, extracting all the declarative viewpoints in the memory button, numbering the declarative viewpoints, supplementing the viewpoint numbers of all the declarative viewpoints that meet the second calculation formula according to the input upper limit of each reply, and updating all the clear viewpoints after all the declarative viewpoints are replied and fed back multiple times in a row; The second calculation formula is: BC=Argmin(|SY-ΣGZ|) SY-ΣGZ>0 BC∈JH Where Argmin is the function used to extract the current viewpoint number that minimizes |SY-ΣGZ|, ΣGZ is the total number of words in this viewpoint, BC is the current viewpoint number, JH is the current viewpoint set, and SY is the number of remaining words; After each digital employee feedback response, extract the proportion of declarative opinions and the proportion of problematic opinions, where the proportion of declarative opinions is the number of declarative sentences divided by the total number of sentences, and the proportion of problematic opinions is the number of problematic sentences divided by the total number of sentences. As needed, during each questioning process, increase the tendency based on the proportion of declarative opinions and the proportion of problematic opinions. The tendency includes increasing the proportion of declarative opinions or the proportion of problematic opinions; After each digital employee feedback response, the proportion of response sentences containing new nouns is extracted. When the proportion of new nouns is less than the preset value, it is required to increase the proportion of response sentences containing new nouns, where the proportion of response sentences containing new nouns is the number of sentences that do not include the name in the given information divided by the total number of sentences.

2. A method for implementing a conversational digital employee human-computer interaction according to claim 1, characterized in that: The human-computer interaction system is provided with a memory button, an API interface, and an online data access interface, specifically including: Set up a memory button to store query information online; Set up an API interface to extract question and answer information in corresponding professional fields for matching; Set up an online data access interface to extract online data for control.

3. The method for implementing a conversational digital employee human-computer interaction according to claim 1, wherein: The feedback personality of the robot is specified, including: The feedback format for pre-set characters includes roles, skills, and considerations; A table with corresponding characters is pre-set for selecting characters; Pre-set the output method of the character's feedback.

4. The method for implementing a conversational digital employee human-computer interaction according to claim 1, wherein: The opening remarks corresponding to different character settings specifically include: Select a pre-set opening line based on the character settings; When a robot query of a corresponding character is conducted online, the opening remarks corresponding to the robot of the character are retrieved.

5. The method for implementing a conversational digital employee human-computer interaction according to claim 1, wherein: The pre-set enhanced search mode provides an enhanced reply based on the search information, specifically including: In the pre-set enhanced search mode, set the method and order of filtering information; By pre-setting enhanced search parameters and rules, the retrieved information can be filtered and sorted.

6. The method for implementing a conversational digital employee human-computer interaction according to claim 1, characterized in that: The feedback-oriented suggestion information specifically includes: Based on the error information fed back by the robot in each conversation, the direction of directional suggestions is clearly defined; Provides targeted suggestions based on the rules set by the questioner in each conversation.

7. The method for implementing a conversational digital employee human-computer interaction according to claim 1, wherein: The key reminder information is stored in the memory button and is reminded to the robot in each reply and information feedback, specifically including: Number the stated clear points; Obtain the upper limit of input for each feedback response, and calculate the remaining word count according to the first calculation formula; Use the second calculation formula to calculate the viewpoint number of this time; Calculate the updated current viewpoint set using the third calculation formula; Calculate the viewpoint number each time using the fourth calculation formula according to the updated current viewpoint set until the fifth calculation formula is satisfied; After the fifth calculation formula is satisfied, all the clear viewpoints are updated, and the viewpoint numbers of this time are calculated again using the second, third, and fourth calculation formulas; In each reply, start with the words "It is necessary to remind you that" and provide feedback on the clear opinions corresponding to the opinion number of this time. The first calculation formula is: SX-YR=SY Among them, SX is the input upper limit, YR is the number of words entered, and SY is the number of remaining words; The third calculation formula is: JHq=JH-BC Among them, JHq is the updated current view set; The fourth calculation formula is: BCn=Argmin(|SY-ΣGZ|) SY-ΣGZ>0 BCn∈JHq Among them, BCn is the viewpoint number of each time; The fifth calculation formula is: JHq=kong Among them, kong is the empty set.

8. A conversational digital employee human-computer interaction implementation system, characterized in that: The system is used to implement the method according to any one of claims 1 to 7, and the system comprises: Structural construction module, used to set up memory buttons, API interfaces and online data access interfaces for the human-computer interaction system; The character generation module is used to specify the feedback character for the robot; Opening remarks module, used to set corresponding opening remarks for different characters; Enhanced retrieval module, used to pre-set enhanced retrieval mode and provide enhanced responses based on retrieval information; Targeted suggestion module, used to feedback targeted suggestion information; The key memory information learning module is used to set key reminder information to be stored in the memory button and remind the robot in each reply and information feedback.

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 configured 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 to 7.

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