Real foreign language AI teaching method and device based on instant messaging technology, and medium

Abstract

The invention discloses a real foreign language AI teaching method and device based on an instant messaging technology, and a medium. The method comprises the following steps: building a real-time session scene based on a preset platform and a preset instant messaging message channel; responding to the determined operation instruction of the teaching terminal, and switching the real-time session scene to a teaching mode; issuing a target language setting instruction of the teaching terminal to the student terminal, wherein the student terminal takes the target language as a real-time output language; when the student end is a sending end, processing the collected original voice of the sending end based on a first preset AI strategy to obtain a plurality of sub-voice segments; and sending the plurality of sub-voice segments to a receiving end based on a preset instant messaging message channel, or further processing the plurality of sub-voice segments based on a second preset AI strategy to obtain a translated voice segment and a translated text corresponding to an expected language, and then playing the translated voice segment and the translated text at the receiving end. The method provides a flexible and efficient teaching environment, meets teaching requirements of different languages, and is low in cost and good in convenience.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent teaching technology, and in particular to a real-person foreign language AI teaching method, device, and medium based on instant communication technology. Background Art

[0002] In the current English education market, many families want their children to learn a foreign language, or bosses and business people need to learn a foreign language for overseas business cooperation. They usually pay a high price to hire foreign teachers or foreign language teachers for offline face-to-face teaching or online video learning. Offline face-to-face teaching is expensive. At the same time, these two learning methods are also limited by time and place. Students and teachers must coordinate their time and space, which is often unrealistic.

[0003] Although online learning is more economical than offline learning, it still poses a certain financial pressure on some price-sensitive users. At the same time, existing online foreign teacher teaching is mostly based on real-time remote teaching with video conferencing software. Although this method allows learners to enjoy one-on-one guidance from foreign teachers at home, the learning and teaching process is often limited by the quality of the network. If the network connection is unstable, it will seriously affect the teaching process.

[0004] In addition, for people who do not know foreign languages, how to effectively teach children or other learners foreign languages ​​is a huge challenge. The traditional approach is to rely on books or application software, but these resources often lack interactivity and cannot meet learners' needs for actual dialogue and communication. In addition, although there are some multilingual teaching tools on the market, such as hardware translators and intelligent oral robots, for hardware translators, although they can meet normal face-to-face conversations, they can only be face-to-face conversations, and a relatively quiet environment is required, and both parties need to talk to the hardware device at the same time; for intelligent oral robots, although they can meet students' oral training, they can only conduct oral conversations according to fixed dialogue scenarios, and cannot innovatively ask questions and answer questions like humans, etc. That is, the interactivity and personalization of the tools currently available on the market are limited, and cannot fully meet the needs of users who do not know foreign languages. Summary of the invention

[0005] In view of this, the embodiments of the present disclosure provide a real-person foreign language AI teaching method, device, and medium based on instant messaging technology, which can solve the problems existing in the prior art of high cost of English learning solutions, many environmental restrictions, insufficient language environment, poor online teaching results, and poor flexibility.

[0006] In a first aspect, the embodiments of the present disclosure provide a real-person foreign language AI teaching method based on instant messaging technology, which specifically includes the following solutions:

[0007] Build real-time conversation scenarios based on preset platforms and preset instant messaging channels;

[0008] Determine the teaching end, and in response to an operation instruction of the teaching end, switch the real-time conversation scene to the teaching mode;

[0009] Sending the target language setting instruction of the teaching terminal to the student terminal, and the student terminal uses the target language in the target language setting instruction as the real-time output language;

[0010] When the student terminal is a sending terminal, the collected original speech of the sending terminal is processed based on a first preset AI strategy to obtain a plurality of sub-speech segments;

[0011] When the desired language of the receiving end is consistent with the real-time output language, sending a plurality of sub-speech segments to the receiving end based on a preset instant messaging message channel, and playing the plurality of sub-speech segments in sequence according to time sequence;

[0012] When the expected language of the receiving end is inconsistent with the real-time output language, several of the sub-speech segments are processed based on the second preset AI strategy to obtain translated speech segments and translated texts corresponding to the expected language, and one or more of the translated speech segments and translated texts are played at the receiving end.

[0013] Optionally, the building of a real-time conversation scenario based on a preset platform and a preset instant messaging channel includes:

[0014] In response to a successful login request on a preset platform, calling a preset information library;

[0015] Retrieving the ID information of all the teaching participants from the preset information database;

[0016] Obtaining a corresponding ID in an online state according to the ID information, and recording it as a target ID;

[0017] Establishing a call connection between all the target IDs based on a preset instant messaging message channel;

[0018] sending a teaching call instruction according to the call connection;

[0019] In response to the teaching call instruction triggering the calling of a preset interface function, an interface containing teaching-related information pops up on the user interface of the student end;

[0020] In response to the consent instruction of the student terminal, the target conversation interface and the target teaching scene parameters are called, and the target conversation interface is rendered based on the target teaching scene parameters to form the real-time conversation scene.

[0021] Optionally, the method for constructing the preset instant messaging message channel includes:

[0022] Authenticating all the target IDs;

[0023] The target IDs that meet the authentication requirements respectively establish a two-way business channel with the business server to form the preset instant messaging channel.

[0024] Optionally, the method for determining the teaching end includes:

[0025] Classify all the target IDs based on a preset information database to obtain the number of first-category participants and the number of second-category participants;

[0026] If the number of the first category of participants is different from the number of the second category of participants, the participant with the smaller number of YY+242237P will be used as the teaching end;

[0027] If the number of the first category of participants and the number of the second category of participants are the same, the first category of participants or the second category of participants is determined as the teaching end based on the preset identity setting, or the device of the user who first logs in to the preset platform is used as the teaching end.

[0028] Optionally, in response to an operation instruction of the teaching end, switching the real-time conversation scene to a teaching mode includes:

[0029] In response to the operation instruction of the teaching terminal, calling and loading a preset teaching static interface to switch to the teaching mode;

[0030] Alternatively, call a preset teaching static interface, load and open a file stored in the business server, and switch to the teaching mode.

[0031] Optionally, the collected original speech of the sending end is processed based on the first preset AI strategy to obtain a plurality of sub-speech segments, including:

[0032] Collect the original voice of the sender and record it as the student's original voice;

[0033] Determining student-side environment information based on the student's original voice;

[0034] Determining the environment type based on the student-side environment information;

[0035] Determining the student's voice status based on the environment type and the student's original voice;

[0036] According to the vocalization state of the student terminal and the voice pause setting information, the original voice of the student is segmented to obtain a plurality of sub-speech segments;

[0037] The voice pause setting information includes one or more of a sentence pause duration and a maximum duration of a sentence without a pause.

[0038] Optionally, the sending of the plurality of sub-speech segments to the receiving end based on a preset instant messaging message channel includes:

[0039] Performing encoding processing on each of the sub-speech segments based on a preset audio encoding format;

[0040] Encapsulating the encoded sub-voice into sub-data packets;

[0041] Embed a timestamp in each of the sub-data packets to obtain a target data packet;

[0042] The target data packets are sent to a receiving end based on a preset instant messaging message channel.

[0043] Optionally, before processing the collected original voice of the sending end, the following steps are further included:

[0044] Get the total duration of the collected original speech;

[0045] If the total duration is greater than 2 seconds, the original speech is processed;

[0046] If the total duration is not greater than 2 seconds, the original speech is divided into a plurality of speech segments in a time sequence at preset time intervals to obtain the decibel level of the speech segments;

[0047] Determine whether the decibel of the speech segment is greater than a preset decibel threshold, and if so, record it as a target segment;

[0048] Obtain the total number of all the target segments, and determine whether the total number is not less than a preset ratio of the number of segments of the speech segment decibels. If so, process the original speech; if not, ignore the original speech.

[0049] Optionally, the processing of the plurality of sub-speech segments based on the second preset AI strategy to obtain translated speech segments and translated texts corresponding to the desired language includes:

[0050] Converting the plurality of sub-speech segments into a plurality of original speech translations according to a preset conversion strategy;

[0051] Preprocessing the plurality of original speech translations;

[0052] Calling the integrated knowledge graph to obtain corresponding keyword information based on the preprocessed original speech translations;

[0053] Analyzing the keyword information based on a preset AI task scheduler to obtain a prediction result of each of the original speech translations, wherein the prediction result includes complexity and processing time;

[0054] Dynamically adjusting the translation allocation strategy of each of the original speech translations based on the prediction results;

[0055] The allocation strategy includes first-level allocation, second-level allocation, and third-level allocation in ascending order;

[0056] Intelligently call the number and type of text translation examples of corresponding levels in the preset AI translation library based on the allocation strategy, and translate the corresponding original speech translation into a translation translation corresponding to the desired language;

[0057] A plurality of translated speech segments are obtained based on the plurality of translated texts; the language of the translated speech segments is the desired language.

[0058] Optionally, playing one or more of the translated voice segment and the translated text at the receiving end includes:

[0059] Based on the user preset or operation instruction of the receiving end, several translated voice segments are played in chronological order;

[0060] Based on the user preset at the receiving end, the translated text is played simultaneously with the translated voice segment in a preset area, or all the translated texts are displayed in a preset area.

[0061] Optionally, converting the plurality of sub-speech segments into a plurality of original speech translations according to a preset conversion strategy includes: calling a third-party translation interface, translating the plurality of sub-speech segments received through the third-party translation interface, and obtaining a plurality of original speech translations.

[0062] Optionally, the preprocessing of the plurality of original speech translations includes:

[0063] Obtaining the message length of each of the original voice translations;

[0064] If the message length is greater than the preset half-width length, pre-processing the original speech translation;

[0065] If the message length is not greater than the preset half-width length, determine whether the plurality of original voice translations contain preset rules, if so, ignore the corresponding original voice translations, if not, pre-process the original voice translations;

[0066] The preprocessing includes one or more of text cleaning, text standardization, part-of-speech tagging, text annotation, and redundancy removal;

[0067] The preset rules include: whether two consecutive original speech translations are both a Chinese character, and / or the original speech translation is a letter, and / or the original speech is a combination of a letter, a space, and a word, and / or the original speech is an interjection.

[0068] In a second aspect, the embodiment of the present disclosure further provides a teaching method, which is applied to a student end and includes:

[0069] Build real-time conversation scenarios based on preset platforms and preset instant messaging channels;

[0070] Determine the student end and receive the target language setting issued by the teaching end;

[0071] In response to the target language setting, the target language in the target language setting instruction is used as the real-time output language to interact with the teaching end.

[0072] In a third aspect, the present disclosure also provides a teaching method, which is applied to a teaching end and includes:

[0073] Build real-time conversation scenarios based on preset platforms and preset instant messaging channels;

[0074] Determine the teaching end;

[0075] Acquire information about actual participants in the real-time conversation scenario;

[0076] Matching the actual participant information with the preset participant information, and if the match is successful, triggering the teaching end to issue an operation instruction;

[0077] In response to the operation instruction, switching the real-time conversation scene to a teaching mode;

[0078] Determine the target language setting instructions;

[0079] Send the target language setting instruction to the student terminal.

[0080] In a fourth aspect, the embodiments of the present disclosure further provide a real-person foreign language AI teaching system based on instant messaging technology, including:

[0081] A construction module is used to construct a real-time conversation scenario based on a preset platform and a preset instant messaging channel;

[0082] A switching module is used to determine the teaching end and, in response to an operation instruction of the teaching end, switch the real-time conversation scene to the teaching mode;

[0083] The instruction sending module is used to send the target language setting instruction of the teaching end to the student end, and the student end uses the target language in the target language setting instruction as the real-time output language;

[0084] A processing module, used for processing the collected original speech of the sending end based on the first preset AI strategy to obtain a plurality of sub-speech segments when the student end is the sending end;

[0085] A sending module, used for sending a plurality of sub-speech segments to the receiving end based on a preset instant messaging message channel when the expected language of the receiving end is consistent with the real-time output language, and playing the plurality of sub-speech segments in sequence according to the time sequence;

[0086] The secondary processing module is used to process a plurality of sub-speech segments based on a second preset AI strategy when the expected language of the receiving end is inconsistent with the real-time output language, obtain translated speech segments and translated texts corresponding to the expected language, and play one or more of the translated speech segments and translated texts at the receiving end.

[0087] In a fifth aspect, the embodiment of the present disclosure further provides a computer device, which adopts the following technical solution: YY+242237P

[0088] The computer device comprises:

[0089] at least one processor; and,

[0090] a memory communicatively connected to the at least one processor; wherein,

[0091] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above-mentioned real-person foreign language AI teaching methods based on instant communication technology or the teaching methods.

[0092] In a sixth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium storing computer instructions for enabling a computer to execute any of the above-mentioned real-person foreign language AI teaching methods based on instant communication technology or the teaching methods.

[0093] In a fifth aspect, the embodiments of the present disclosure further provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements any of the above-mentioned real-person foreign language AI teaching methods based on instant communication technology or the steps of the teaching methods.

[0094] The real-person foreign language AI teaching method based on instant messaging technology disclosed in the present application, by constructing a real-time conversation scenario, students can learn languages ​​in an environment close to the real world, thereby improving the practicality and effectiveness of learning. The instant messaging platform can ensure the real-time nature of the teaching process, so that students can get feedback and corrections in a timely manner. At the same time, it can automatically switch to the teaching mode according to the instructions of the teaching end and the settings of the student end, and provide corresponding language exercises according to the students' language ability and needs. This personalized teaching method can meet the learning needs of different students and improve the teaching effect; by processing the voice based on the preset AI strategy, the original voice can be divided into sub-speech segments, and appropriate translation and playback can be performed. This processing method can reduce errors caused by inaccurate voice recognition and translation, and improve the accuracy of language learning; it can process and convert voice and text between different languages, support learning and communication in multiple languages, and if the expected language of the student and the receiving end is inconsistent, it can provide translation and voice output to ensure the accurate transmission of information and meet teaching scenarios with different needs.

[0095] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0097] Figure 1 A flowchart of a live foreign language AI teaching method based on instant messaging technology provided in an embodiment of the present disclosure.

[0098] Figure 2 A flowchart of a method for constructing a real-time conversation scenario provided in an embodiment of the present disclosure.

[0099] Figure 3 A schematic diagram of a flow chart of a method for constructing a preset instant messaging channel provided in an embodiment of the present disclosure.

[0100] Figure 4 A flowchart of a method for determining a teaching end provided in an embodiment of the present disclosure.

[0101] Figure 5 A flowchart of a method for acquiring several sub-speech segments provided in an embodiment of the present disclosure.

[0102] Figure 6 A flowchart of a method for sending several sub-speech segments provided in an embodiment of the present disclosure.

[0103] Figure 7 A flowchart of an analysis method for collecting original speech from a sending end before processing is provided in an embodiment of the present disclosure.

[0104] Figure 8 A flowchart of a method for processing a plurality of sub-speech segments provided in an embodiment of the present disclosure.

[0105] Fig. 9 A flowchart of a method for preprocessing a plurality of original speech translations provided in an embodiment of the present disclosure.

[0106] Fig.10 A flow chart of a teaching method applied to a student end provided in an embodiment of the present disclosure.

[0107] Fig.11 A flow chart of a teaching method applied to a teaching end provided in an embodiment of the present disclosure.

[0108] Fig.12 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0109] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0110] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0111] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0112] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0113] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0114] Reference Figure 1 The present application discloses a real-person foreign language AI teaching method based on instant messaging technology, which specifically includes the following steps:

[0115] S100, building a real-time conversation scenario based on a preset platform and a preset instant messaging channel.

[0116] Through the construction of real-time conversation scenarios, an intelligent and convenient teaching environment is provided. In this real-time conversation scenario, voice conversation, video conversation or text communication and other interactive methods are supported to meet different teaching needs and learners' preferences.

[0117] The preset platform in this step is a stable online teaching platform that can provide high-quality video and audio functions to ensure efficient teaching activities under any network conditions.

[0118] S200, determining the teaching end, and switching the real-time conversation scene to the teaching mode in response to the operation instruction of the teaching end.

[0119] In this step, it is assumed that all persons entering the real-time conversation scenario do not know in advance who the teacher is, so the teacher's identity is determined first.

[0120] Once the teaching end is determined, it will be granted certain permissions that the student end does not have; then the user at the teaching end (i.e., teacher) can select "teaching mode" on the platform interface (for example, click the "enter teaching mode button"), and then respond to the operating instructions of the teaching end, call and load the preset teaching static interface to switch to the teaching mode; or, call the preset teaching static interface, load and open the file stored in the business server to switch to the teaching mode.

[0121] This step ensures that the teaching environment is optimized according to teaching needs to maximize teaching effectiveness. At the same time, some operations can be automated to reduce the operating burden of teachers and allow them to focus on teaching content.

[0122] S300, sending a target language setting instruction from the teaching terminal to the student terminal, and the student terminal uses the target language in the target language setting instruction as the real-time output language.

[0123] Specifically, on the student side, the target language in the target language setting instruction is used as the real-time output language to interact with the teaching side. For example, in this class, students are required to speak English throughout the class, that is, the target language in the target language setting instruction is English, then during the whole class, students must ask questions and answer questions in English.

[0124] This step ensures that students can focus on the target language during the learning process, thus improving the effectiveness of language learning.

[0125] S400, when the student end is the sending end, the collected original speech of the sending end is processed based on the first preset AI strategy to obtain a plurality of sub-speech segments.

[0126] Specifically, when the student terminal is the sending terminal, the student terminal starts to speak, and the collected original voice is segmented through the first preset AI strategy to improve the accuracy of voice processing, reduce noise and errors, and facilitate more accurate voice translation and real-time feedback. Among them, the language of the original voice is the real-time output language.

[0127] S500, when the desired language of the receiving end is consistent with the real-time output language, a plurality of sub-speech segments are sent to the receiving end based on a preset instant messaging message channel, and the plurality of sub-speech segments are played in sequence according to time sequence.

[0128] Taking English learning as an example, this scenario is applicable to the teaching scenarios between students and real English professional foreign teachers. The student speaks English, the original voice of the student on the student side is collected, the collected original voice of the sending end is processed based on the first preset AI strategy, and several sub-speech segments are obtained. The several sub-speech segments are sent to the receiving end based on the preset instant messaging message channel, and the several sub-speech segments are played in chronological order, that is, the student's words are still played in English at the teaching end.

[0129] S600, when the expected language of the receiving end is inconsistent with the real-time output language, a plurality of sub-speech segments are processed based on a second preset AI strategy to obtain translated speech segments and translated texts corresponding to the expected language, and one or more of the translated speech segments and translated texts are played at the receiving end.

[0130] Taking English learning as an example, this scenario is applicable to the teaching scene between students and real teachers, but the real teachers are parents who do not understand English, or teachers of other languages ​​who do not understand English; in the teaching process, the student speaks English, the original voice of the student is collected, the collected original voice of the sending end is processed based on the first preset AI strategy to obtain several sub-speech segments, and then the several sub-speech segments are processed based on the second preset AI strategy to obtain translated voice segments and translated texts corresponding to the expected language, and finally one or more of the translated voice segments and translated texts are played at the receiving end, that is, only voice playback can be performed, or voice playback and translated text display can be performed at the same time.

[0131] In this solution, the processing of the original speech and several sub-speech segments can be performed at the sending end, at the receiving end, or between the sending end and the receiving end, all of which are within the scope of protection of this application.

[0132] If the processing of several sub-speech segments based on the second preset AI strategy is not at the receiving end, the translated speech segments and translated texts obtained after the processing can be sent to the receiving end based on the preset instant messaging message channel, and the translated speech segments can be played in chronological order. The played language is not English, and the displayed translated text is not English, but a language that users at the teaching end can understand and see.

[0133] Specifically, one or more of the translated voice segments and translated texts are played at the receiving end, including: playing several translated voice segments in chronological order based on the user preset or operation instruction of the receiving end; playing the translated texts simultaneously with the translated voice segments in a preset area based on the user preset of the receiving end, or displaying all the translated texts in a preset area.

[0134] In this application, the introduction of AI strategies can effectively enhance the interactivity and personalization of teaching; through this step, the problem of language inconsistency can be effectively solved. For parents, even if they do not understand English, they can provide English tutoring to their children on the student side to help learners better learn foreign languages. It can also help parents or teachers who do not understand foreign languages ​​to better guide learners. At the same time, it creates a foreign language learning environment that is reasonably priced, convenient, and efficient.

[0135] The real-person foreign language AI teaching method based on instant messaging technology disclosed in the present application, by constructing a real-time conversation scenario, students can learn languages ​​in an environment close to the real world, thereby improving the practicality and effectiveness of learning. The instant messaging platform can ensure the real-time nature of the teaching process, so that students can get feedback and corrections in a timely manner. At the same time, it can automatically switch to the teaching mode according to the instructions of the teaching end and the settings of the student end, and provide corresponding language exercises according to the students' language ability and needs. This personalized teaching method can meet the learning needs of different students and improve the teaching effect; by processing the voice based on the preset AI strategy, the original voice can be divided into sub-speech segments, and appropriate translation and playback can be performed. This processing method can reduce errors caused by inaccurate voice recognition and translation, and improve the accuracy of language learning; it can process and convert voice and text between different languages, support learning and communication in multiple languages, and if the expected language of the student and the receiving end is inconsistent, it can provide translation and voice output to ensure the accurate transmission of information and meet teaching scenarios with different needs.

[0136] The solution disclosed in this application uses instant messaging technology to break the time and space limitations of traditional foreign language teaching. Users can communicate with foreign teachers in real time anytime and anywhere according to their own schedule, exchange problems encountered in the learning process, and receive personalized guidance. This flexible learning method greatly improves users' learning enthusiasm and participation, increases the flexibility and convenience of learning, and students and teachers can carry out efficient language learning without face-to-face communication.

[0137] The function of translating voice segments and text can effectively reduce language barriers, so that students will not feel frustrated due to language barriers when learning a foreign language, and their learning confidence will be enhanced; real-time interaction and personalized teaching can increase students' learning interest and participation, make the learning process more vivid and interesting, and stimulate students' enthusiasm for learning.

[0138] Overall, this teaching method can effectively improve the interactivity, personalization and accuracy of language learning by utilizing instant messaging technology and AI voice processing technology, while reducing language barriers and increasing the flexibility and convenience of learning.

[0139] Reference Figure 2 ,The method of constructing the real-time conversation scenario specifically includes:

[0140] S110, in response to a successful login request on a preset platform, calling a preset information library.

[0141] Specifically, after receiving the user login request and successful verification, the preset information database will be accessed to obtain the user's personal information and authority information. This information database can be a local database or a cloud service, which contains user registration, login, authority management, number of personnel, gender, personnel ID and other information.

[0142] This step can not only ensure the legitimacy of the user's identity and protect data security, but also quickly detect YY+242237P

[0143] Search for user information and improve information search efficiency.

[0144] S120, retrieve the ID information of all the persons to be taught from a preset information database.

[0145] Specifically, a list of participants in currently scheduled or ongoing teaching activities is retrieved from a preset information database, and unique identifiers (IDs) of all participants are extracted; this step facilitates management of identity information of all participants, and facilitates subsequent communication and centralized organization of teaching activities.

[0146] S130, obtaining the corresponding ID in the online state according to the ID information, and recording it as the target ID.

[0147] Specifically, the online status function of the instant messaging system can be used to determine which participants' IDs are currently online, and only these online user IDs can be selected as target IDs to prepare for establishing communication connections, focusing on communication with online users to improve efficiency and avoid trying to establish useless connections with offline users.

[0148] S140: Establish call connections between all target IDs based on the preset instant messaging message channel.

[0149] Specifically, use the API or service provided by the instant messaging platform to establish a communication channel for all selected target IDs, which can include functions such as video calls, audio calls or message push, and utilize the mature technology of the instant messaging platform to quickly establish communication between multiple people, while providing flexible and diverse communication methods.

[0150] S150: Send a teaching call instruction according to the call connection.

[0151] Once the call connection is established, the teaching activity can be started according to the preset teaching call instructions. These instructions may include starting a video conference, screen sharing, document distribution, etc. This step can effectively simplify the process of starting the teaching activity and ensure that the teaching activity can proceed smoothly according to the preset plan of the teaching team.

[0152] S160, in response to the teaching call instruction triggering the calling of the preset interface function, an interface containing teaching-related information pops up on the user interface of the student end.

[0153] By calling predefined interface functions, the student's client can display relevant teaching information, such as the course outline, teaching materials, interactive questions, etc. This step makes it easy to provide a unified teaching interface to all students, enhance the learning experience, and centrally present key teaching information, which helps students understand and participate.

[0154] S170, in response to the consent instruction of the student side, calling the target conversation interface and the target teaching scene parameters, rendering the target conversation interface based on the target teaching scene parameters, and forming a real-time conversation scene.

[0155] When students choose to agree to participate in real-time conversations, the conversation interface is customized and rendered according to the preset teaching scene parameters and the students' specifications, thus forming a personalized real-time conversation scene, which is convenient for providing personalized learning experience, enhancing student participation and interaction, and flexibly adjusting the teaching scene to adapt to different teaching needs.

[0156] The method for constructing a real-time conversation scene disclosed in this embodiment can ensure the optimal use of teaching resources by centrally managing the IDs of participants, while ensuring that teaching activities can be carried out in real time to improve teaching efficiency; by adjusting the parameters of the target teaching scene, the learning needs of different students can be met; the security mechanism of the preset platform is used to protect the security and privacy of student data; this embodiment integrates multiple functions such as real-time communication and teaching management, and provides a seamless teaching environment.

[0157] Reference Figure 3 , a method for constructing a preset instant messaging message channel includes:

[0158] A100, all target IDs are authenticated.

[0159] Specifically, the authentication method includes: authenticating through one or more of a text message verification code, an email verification code, biometric technology, a user name and a password.

[0160] Specifically, the system sends an authentication request to all target IDs, which usually includes requesting the user to provide credentials (such as user name and password, token or other authentication information); then the business server receives the authentication request and verifies the validity of the credentials. The verification process may include querying user records in the database and checking the validity and integrity of the credentials; finally, the system returns the authentication result to the requester, marking the authentication success or failure.

[0161] Use encryption protocols (such as HTTPS) to protect the security of credential data during the transmission process of this step.

[0162] Authentication ensures that only legitimate users can access the communication channel, preventing unauthorized access, ensuring that each target ID is valid and identifiable, improving system reliability, and avoiding interference from forged identities or malicious users.

[0163] A200 establishes a two-way business channel between the target ID that meets the authentication and the business server, forming a preset instant messaging channel.

[0164] Specifically, for each successfully authenticated target ID, the system establishes a two-way communication channel with its corresponding client or device, which can be achieved through WebSocket, MQTT, or other instant messaging protocols; configure channel parameters, such as data transmission format, message processing rules, and connection retention strategy, to achieve two-way messaging function, allowing real-time message sending and receiving between the business server and the target ID, ensuring that messages can be delivered in a timely manner and responses can be quickly returned.

[0165] Furthermore, a suitable instant communication protocol (such as WebSocket, MQTT, HTTP / 2) can be selected according to needs to support efficient two-way communication.

[0166] This embodiment also includes error handling and state management. For error handling, it is used to implement a complete error handling and reconnection mechanism to ensure the stability and reliability of the channel. For state management, it is used to maintain the connection status and promptly discover and handle disconnection or abnormal situations.

[0167] Among them, the two-way communication channel supports real-time message transmission, reduces communication delays, supports two-way messaging, and can facilitate operations such as data synchronization and real-time notifications; through reliable connection protocols and mechanisms, the stability and consistency of communication are guaranteed.

[0168] Furthermore, the method for constructing the preset platform includes:

[0169] 1) Configure the microservice architecture.

[0170] This architecture is an architectural style that divides an application into a set of small, independent services. Each microservice is responsible for a specific functional module and can communicate through a lightweight communication protocol (usually HTTP YY+242237P

[0171] or message queues) to interact with other services.

[0172] 2) Login module, which is used to provide users with a login page, allowing users to enter their username and password to verify their identity, or allowing users to log in through facial recognition, pupil recognition or fingerprint recognition, or integrating a third-party social login service (such as Google, Facebook, WeChat) so that users can quickly log in through their existing accounts.

[0173] 3) Authentication module, used to verify the user's identity information and ensure that only legitimate users can access system resources.

[0174] Specifically, identity authentication includes the following schemes: username / password verification, which verifies the username and password entered by the user, where the password should be stored in encrypted form (such as bcrypt, argon2); two-factor authentication (2FA), which enhances security and requires users to provide a second verification method when logging in (such as SMS verification code, dynamic password generated by TOTP application); OAuth2 / OpenID Connect, which implements standardized authorization and authentication protocols to support third-party authorization and single sign-on (SSO).

[0175] The authentication module also includes session management, which is to generate and manage user session tokens (such as JWT, Session ID) and perform expiration management. It supports user logout operations. The authentication module also includes permission control, which is to control access to system resources based on user roles and permissions. It implements role-based access control (RBAC) or attribute-based access control (ABAC). The authentication module also includes security measures, such as limiting the number of login attempts and introducing a CAPTCHA mechanism to prevent malicious attacks, that is, to prevent brute force cracking.

[0176] The preset platform also includes encrypted communication, ensuring that all data transmission during the login process is encrypted via HTTPS to protect the user's sensitive information. The preset platform also includes log auditing, recording user login activities and authentication failure records for security auditing and anomaly detection.

[0177] Reference Figure 4 , the teaching end determination method includes:

[0178] B100, classifies all target IDs based on a preset information base, and obtains the number of first-category participants and the number of second-category participants.

[0179] Specifically, because the preset information database constructed is an information database containing all possible participants, including basic information and role attributes of roles such as students, teachers, and teaching assistants, all target IDs can be classified based on the preset information database, that is, the target ID can be extracted from the user login information to identify all participants.

[0180] All target IDs are classified using the preset information library. For example, the system classifies target IDs into two categories: "teachers" and "students" based on their roles.

[0181] Through the preset information database, participants can be classified quickly and accurately, ensuring the system's identification of different roles and providing a solid data foundation for decision-making in subsequent steps.

[0182] B200, if the number of the first type of participants and the number of the second type of participants are inconsistent, the party with the smaller number of participants will be used as the teaching end.

[0183] In this embodiment, it is assumed that there is only one teacher in each class. Therefore, when the two numbers are inconsistent, that is, the number of teachers is 1 and the number of students is greater than 1, the smaller number of participants is used as the teaching end, that is, the device corresponding to the number 1 is the teaching end. By judging by the number, the degree of automation is high, and it is fast and efficient.

[0184] B300, if the number of the first category of participants and the number of the second category of participants are the same, the first category of participants or the second category of participants are determined as the teaching end based on the preset identity settings, or the device of the user who first logs in to the preset platform is used as the teaching end.

[0185] In this embodiment, when the numbers of the two categories are the same, it refers to one-to-one teaching, and the teaching end can be determined based on the preset identity setting.

[0186] In addition, in teaching scenarios, teachers generally enter the platform first to prepare courseware, etc., so the device of the user who first logs in to the preset platform can also be used as the teaching end.

[0187] The solution disclosed in this embodiment provides a flexible selection mechanism through preset identity settings to adapt to different teaching scenarios. The device login mechanism ensures that even when the identity is ambiguous, the system can still quickly determine the teaching end to ensure the continuity of teaching activities.

[0188] Reference Figure 5 , the method for obtaining a plurality of sub-speech segments specifically comprises the following steps:

[0189] C100 collects the original voice of the sender and records it as the student's original voice.

[0190] Specifically, by recording the sound of students speaking in a natural environment through a microphone or voice acquisition device, the original voice of the students obtained retains all the characteristics of natural voice, providing the most authentic basic data for subsequent analysis.

[0191] C200 determines the student-side environment information based on the student's original voice.

[0192] Specifically, sound analysis technology can be used to detect background noise, echo, sound reflection, etc. in the voice recording environment, and machine learning models or acoustic analysis algorithms can be used to identify environmental characteristics, such as classrooms, conference rooms, homes, etc. Understanding the recording environment helps to better understand the context of the voice data, so that corresponding adjustments can be made in subsequent processing to improve the accuracy of voice analysis.

[0193] C300, determines the environment type based on the student-side environment information.

[0194] Based on the environmental information, the recording environment is classified using predefined environmental categories (such as quiet, noisy, strong reverberation, etc.). For example, the recording environment can be classified as "classroom environment" or "home environment". By classifying the environment type, specific processing can be performed on the voice signal, such as noise suppression or echo cancellation, thereby improving the effect of voice recognition or analysis.

[0195] C400 determines the student’s voice status based on the environment type and the student’s original voice.

[0196] Analyze the volume, intonation, speed and other characteristics of the student's voice, and combine the environment type information to determine whether the student is in a normal state of speaking, whispering or shouting. By understanding the state of speaking, it helps to more accurately analyze the student's intentions and emotions, and also provides more background information for speech processing, making YY+242237P

[0197] The processing results are more targeted.

[0198] C500, according to the student's voice status and voice pause setting information, segment the student's original voice to obtain a number of sub-speech segments; wherein the voice pause setting information includes one or more of the sentence pause duration and the maximum duration of a sentence without pause.

[0199] The speech processing algorithm is used to segment the student's original speech into several sub-segments according to the set pause duration, maximum sentence duration and other rules. For example, if the pause time exceeds the set threshold, the sentence segmentation is performed here. Segmenting speech data into smaller segments helps improve the processing efficiency and accuracy of the speech recognition system, and also facilitates subsequent speech analysis and applications, such as speech transcription or speech synthesis.

[0200] The method for obtaining several sub-speech segments disclosed in this embodiment can perform targeted speech processing through detailed analysis of the environment and vocalization state, reduce noise interference and misrecognition; through sentence segmentation, long sentences can be decomposed into more easily processed sub-speech segments, which can improve the speech recognition system's ability to understand and analyze speech; understanding different environments and vocalization states allows the system to maintain a high processing effect under a variety of conditions, enhancing the system's versatility and adaptability; through precise speech processing and analysis, the system can provide more accurate and personalized feedback, thereby improving the user's overall experience. This set of strategies aims to improve the quality and processing efficiency of speech data through multi-level analysis and processing of original speech, thereby achieving more efficient speech recognition and analysis.

[0201] Reference Figure 6 The method for sending a plurality of sub-speech segments specifically comprises the following steps:

[0202] S510: Encode each sub-speech segment based on a preset audio encoding format.

[0203] Specifically, encoding each sub-speech segment is to convert it into a compressed data stream.

[0204] Furthermore, assuming that the G.711 audio coding format is used, the audio signal can be divided into several sub-segments, for example, each segment is 100 milliseconds. G.711 coding is applied to each sub-segment, which can compress the audio data to a smaller size while maintaining the sound quality. G.711 coding can compress the audio data to half of its original size, reducing the bandwidth requirement for data transmission.

[0205] S520, encapsulating the encoded sub-voice into a sub-data packet.

[0206] Each encoded sub-segment is packaged into a data packet. For example, assume that each data packet contains 200 bytes of audio data. A custom encapsulation protocol can be used to add the encoded data of each sub-segment to the payload portion of the data packet.

[0207] Packetizing audio data can manage and optimize network transmission, reducing the risk of data loss; at the same time, data packets can be adjusted according to network conditions, such as changing the size of data packets to adapt to different bandwidth requirements.

[0208] S530, embed a timestamp in each sub-data packet to obtain a target data packet.

[0209] Add a timestamp in the header or additional field of each data packet. The timestamp can be an absolute time or a relative time from the start of recording.

[0210] For example, a packet header field may contain the timestamp "2024-08-29T11:16:00" to mark the specific time of the audio segment.

[0211] Timestamps enable the receiving end to correctly reconstruct the audio stream, ensuring the order and playback time of the voices, helping to make adjustments and synchronization when network conditions are unstable, and improving audio quality.

[0212] S540, sending a number of target data packets to a receiving end based on a preset instant messaging message channel.

[0213] Low-latency data transmission can be achieved through the instant messaging channel, which is suitable for voice communication applications while ensuring that data is not lost in the network.

[0214] The method for sending several sub-speech segments disclosed in this embodiment reduces data redundancy during transmission and improves the efficiency of network bandwidth utilization through encoding and packetization; the addition of timestamps enables the receiving end to accurately reassemble the audio stream, ensuring the continuity and quality of the voice; the packetization and encapsulation mechanism enables the system to adapt to different network conditions and reduces the impact of packet loss and delay on voice communication.

[0215] Based on standard audio coding and communication protocols, the solution has good compatibility and can be applied on a variety of devices and platforms. Through this solution, users can enjoy high-quality voice transmission, especially in unstable network conditions, and can also ensure the integrity and real-time nature of audio data.

[0216] Reference Figure 7 , the analysis method before processing the collected original voice of the sending end includes:

[0217] D100, obtains the total duration of the collected original speech.

[0218] Specifically, the total duration of the audio file can be calculated by voice processing software or programming interface (such as Python's pydub or librosa library).

[0219] D200, if the total duration is greater than 2s, the original speech is processed;

[0220] If the total duration is not greater than 2 seconds, the original speech is divided into several segments of speech decibels according to the preset time intervals and in chronological order.

[0221] In a preferred embodiment, the preset time interval is preferably 40 ms.

[0222] In this step, if the total duration is greater than 2 seconds, it directly enters the speech processing stage; if it is not greater than 2 seconds, the speech is divided into multiple segments and then analyzed in detail to accurately determine whether the corresponding original speech is useful to prevent misjudgment.

[0223] D300, determines whether the decibel of the speech segment is greater than the preset decibel threshold, if so, it is recorded as the target segment.

[0224] Among them, the preset decibel threshold is preferably 1.5 decibels.

[0225] D400, obtain the total number of all target segments, and determine whether the total number is not less than the preset ratio of the number of segments of the speech segment decibel. If so, process the original speech; if not, ignore the original speech.

[0226] The preset ratio is preferably 1 / 3. For example, the total number of all target segments is M, and the number of speech segment decibels is N. If M≥1 / 3N, it means that the original speech is short but useful, so it is retained.

[0227] By filtering and segmenting, the amount of audio data to be processed is reduced, and the processing speed is improved; filtering low-decibel speech segments can avoid processing noise or background sounds, and improve the quality of the results; by presetting conditions and proportion judgments, the processing of audio that does not meet the conditions is reduced, thereby saving computing resources and storage space; by analyzing high-decibel segments, it is ensured that the processed voice information has sufficient quality and improves the accuracy of the final result. The solution can adjust parameters (such as decibel thresholds and time intervals) according to actual needs to adapt to different application scenarios and requirements.

[0228] Reference Figure 8 , based on the second preset AI strategy, the plurality of sub-speech segments are processed to obtain the translation speech segments and translation texts corresponding to the expected language, that is, the method for processing the plurality of sub-speech segments specifically includes the following steps:

[0229] E100, converting the plurality of sub-speech segments into a plurality of original speech translations according to a preset conversion strategy.

[0230] Specifically, a third-party translation interface is called, and the received plurality of sub-speech segments are translated through the third-party translation interface to obtain a plurality of original speech translations.

[0231] E200, pre-processes several original speech translations.

[0232] E300 calls the integrated knowledge graph to obtain the corresponding keyword information based on several preprocessed original speech translations.

[0233] Keyword extraction helps quickly identify the main information points in the text, making subsequent processing and analysis more efficient, helping to establish the connection between the text and the knowledge base, and improving the contextual understanding of the translation.

[0234] E400 analyzes keyword information based on a preset AI task scheduler to obtain prediction results for each original speech translation, including complexity and processing time.

[0235] Specifically, the preset AI task scheduler determines the predicted results of each original speech translation based on keyword information and historical translation data information; for example, the text "Hello, how are you?" can be classified as low complexity and short processing time after analysis.

[0236] The prediction results can help optimize resource allocation, determine processing priorities, dynamically adjust processing strategies, and improve overall processing efficiency.

[0237] E500,dynamically adjusts the translation assignment strategy for each original speech translation based on,the prediction results.

[0238] Among them, the allocation strategy includes first-level allocation, second-level allocation, and third-level allocation in ascending order.

[0239] Specifically, the first level allocation refers to standard allocation, the second level allocation refers to high-speed allocation, and the third level allocation refers to ultra-high-speed allocation. Standard allocation instances are suitable for ordinary tasks, high-speed allocation instances are suitable for highly complex and demanding translation tasks, and ultra-high-speed allocation instances are used to process extremely high priority tasks.

[0240] The translations are assigned to different levels of translation tasks based on the predicted complexity and processing time. For example, texts with low complexity are assigned to the first level of translation, and texts with high complexity are assigned to the third level of translation.

[0241] In this step, dynamic adjustment can effectively utilize resources and avoid resource waste. It can flexibly respond to translation tasks of different difficulty levels according to actual conditions and improve processing flexibility.

[0242] E600 intelligently calls the number and type of text translation examples of corresponding levels in the preset AI translation library based on the allocation strategy, and translates the corresponding original speech translation into the translation translation of the expected language.

[0243] Intelligent calling of translation database can ensure the consistency and accuracy of translation, effectively improve translation efficiency and reduce manual intervention.

[0244] E700, obtaining a plurality of translated speech segments based on a plurality of translated texts; the language of the translated speech segments is a desired language.

[0245] Finally, the translation results are converted into speech, which can realize the complete speech translation process, allowing users to hear the translation results in the target language and improve the user experience.

[0246] In this embodiment, the efficiency and accuracy of speech translation are greatly improved through automated processing and intelligent scheduling. Dynamic adjustment of translation strategies and allocation of resources can effectively utilize computing resources and reduce processing time and costs. The application of preprocessing, keyword extraction, and knowledge graphs can improve the quality of translation and context understanding. The final generated translated speech segment meets the needs of users and achieves smooth cross-language communication.

[0247] The overall solution can achieve efficient and accurate multi-language voice translation and enhance the user's translation experience through step-by-step processing, intelligent analysis and resource optimization.

[0248] Reference Fig. 9 , the method for preprocessing several original speech translations specifically includes:

[0249] E210, obtain the message length of each original speech translation.

[0250] Calculating the length of each raw speech translation so that further processing can be determined based on length helps quickly identify translations that need processing, especially those that may be too long or too short, thus avoiding unnecessary processing steps.

[0251] E220: If the message length is greater than the preset half-width length, pre-process the original speech translation.

[0252] The preset half-width length is preferably 10 characters. By setting the threshold, it can be ensured that only longer translations are processed, thereby avoiding excessive processing of short information, which may be accurate enough and do not require complex processing.

[0253] E230, if the message length is not greater than the preset half-width length, determine whether a number of original voice translations contain preset rules, if so, ignore the corresponding original voice translations, if not, pre-process the original voice translations.

[0254] For translations whose message length does not exceed the preset half-width length, check whether they meet the preset rules. If they meet the rules, these translations are ignored; otherwise, pre-processing is performed, which can identify and filter out some specific situations that do not need to be processed (such as common single words, letters, spaces or modal particles) to avoid redundant processing of these useless information.

[0255] Among them, preprocessing includes one or more of text cleaning, text standardization, part-of-speech tagging, text annotation, and redundancy removal.

[0256] For example, the original speech translation is "hello, how are u", and through preprocessing, it is obtained as "Hello, how are you". Preprocessing can improve the quality of the text and reduce errors and noise in subsequent processing.

[0257] Among them, the preset rules include: whether two consecutive original speech translations are both a Chinese character, and / or the original speech translation is a letter, and / or the original speech is a combination of a letter, a space, and a word, and / or the original speech is an interjection.

[0258] By setting preset rules, you can avoid processing invalid information: for example, single words, single letters, spaces, etc. may not have a practical impact on the analysis results, and directly ignoring this information can save processing time; by filtering out unnecessary translations and information, the preprocessing process can more efficiently focus on those translations that really need to be analyzed and improved.

[0259] Specifically, the modal particles include yeah, yah, ah, uh, the, hi, ofiss, she, oh, hell, hmm, ahem, mm, yep, yeap, etc. Upper and lower case words are ignored, that is, no matter whether there are spaces or periods behind them, they will be discarded.

[0260] By identifying and processing modal particles, we can save resources, computing costs and unnecessary noise, and achieve accurate feature extraction.

[0261] The real-person foreign language AI teaching method based on instant communication technology disclosed in the present application constructs a real-time conversation scene through instant communication technology, which is not restricted by time and place in traditional teaching, and teaching can be more flexible and convenient. This method can adapt to different time arrangements and geographical locations, and solves the problem of time and space coordination in traditional teaching methods; this solution realizes instant conversion and translation of voice by real-time acquisition and processing of voice, which can not only provide real-time feedback in the learning process, but also enhance the interactivity and authenticity with learners through the instant translation function. This method is more interactive than traditional books or static applications, and can better simulate actual conversations and communication scenarios; through the preset AI strategy Processing can reduce the risk of teaching interruption or information loss due to network fluctuations; this solution not only supports the language setting of the student side, but also supports the language requirements of the receiving side. If the expected language of the receiving side is inconsistent with the real-time output language, the system will automatically perform translation processing and output the translated voice segment or translated text. This two-way language adaptation function can meet the needs of users with different language requirements and make the teaching content more personalized and in line with actual needs. For people who do not understand foreign languages, this solution provides an intelligent teaching solution through AI technology and instant communication technology. AI strategies can process and optimize voice data and provide real-time language translation and feedback, which is more practical and effective than traditional books and applications.

[0262] In summary, the solution disclosed in this application solves the problems of high cost, time and location limitations, insufficient interactivity, network quality impact, and language adaptation difficulties in the prior art through real-time voice processing, instant translation, and flexible teaching modes, thereby providing language learners with a more economical, efficient, and convenient way to learn a foreign language.

[0263] Reference Fig.10 The second aspect of the present application discloses a teaching method, which is applied to the student end, specifically including YY+242237P

[0264] include:

[0265] S11, building a real-time conversation scenario based on a preset platform and a preset instant messaging channel;

[0266] S12, determining the student terminal and receiving the target language setting issued by the teaching terminal;

[0267] S13, in response to the target language setting, using the target language in the target language setting instruction as the real-time output language to interact with the teaching end.

[0268] Through real-time language communication and instant feedback, students can quickly correct mistakes and improve learning efficiency; the immersive language environment and interactive methods help students learn and use the target language more naturally; customized teaching can be provided according to students' needs and language level to enhance the learning experience; the integrated teaching platform and instant communication tools simplify the teaching process and provide more flexibility and convenience.

[0269] Through this approach, an efficient, interactive and personalized language learning environment can be created, thereby improving the overall teaching quality and student learning outcomes.

[0270] Reference Fig.11 The third aspect of the present application discloses a teaching method, which is applied to the teaching end and specifically includes:

[0271] S21, building a real-time conversation scenario based on a preset platform and a preset instant messaging channel;

[0272] S22, determine the teaching end;

[0273] S23, obtaining information of actual participants in the real-time conversation scenario;

[0274] S24, matching the actual participant information with the preset participant information, and if the match is successful, triggering the teaching end to issue an operation instruction;

[0275] S25, in response to the operation instruction, switching the real-time conversation scene to the teaching mode;

[0276] S26, determining the target language setting instruction;

[0277] S27, sending a target language setting instruction to the student terminal.

[0278] Through automated systems and clear operating procedures, manual errors and unnecessary interference are reduced, making the teaching process smoother; real-time conversation scenarios and flexible teaching mode switching can improve the interaction between teachers and students and enhance the learning experience; according to the actual participant information and target language settings, a more personalized learning experience can be provided to meet the needs of different students; by accurately matching participant information, it ensures that only authorized personnel participate in the class, increasing the security and reliability of the teaching process. This method provides an efficient, flexible and secure online teaching solution by integrating real-time communication technology and automated management.

[0279] It should be noted that the solutions in the real-person foreign language AI teaching method based on instant communication technology disclosed in the first aspect of this application are all applicable to the teaching methods disclosed in the second and third aspects of this application, so they will not be described one by one here.

[0280] In a fourth aspect, the present application discloses a real-person foreign language AI teaching system based on instant messaging technology, including:

[0281] A construction module is used to construct a real-time conversation scenario based on a preset platform and a preset instant messaging channel;

[0282] A switching module is used to determine the teaching end and, in response to an operation instruction of the teaching end, switch the real-time conversation scene to the teaching mode;

[0283] The instruction sending module is used to send the target language setting instruction of the teaching end to the student end, and the student end uses the target language in the target language setting instruction as the real-time output language;

[0284] A processing module, used for processing the collected original speech of the sending end based on the first preset AI strategy to obtain a plurality of sub-speech segments when the student end is the sending end;

[0285] A sending module, used for sending a plurality of sub-speech segments to the receiving end based on a preset instant messaging message channel when the expected language of the receiving end is consistent with the real-time output language, and playing the plurality of sub-speech segments in sequence according to the time sequence;

[0286] The secondary processing module is used to process a plurality of sub-speech segments based on a second preset AI strategy when the expected language of the receiving end is inconsistent with the real-time output language, obtain translated speech segments and translated texts corresponding to the expected language, and play one or more of the translated speech segments and translated texts at the receiving end.

[0287] The computer device according to the embodiment of the present disclosure includes a memory and a processor. The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, etc.

[0288] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the computer device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory, so that the computer device executes any of the aforementioned embodiments of the present disclosure, the real-person foreign language AI teaching method based on instant communication technology, or all or part of the steps of the teaching method.

[0289] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0290] like Fig.12 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure is shown, which is a schematic diagram of the structure of a computer device suitable for implementing the embodiment of the present disclosure. Fig.12 The computer device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0291] like Fig.12 As shown, the computer device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the computer device are also stored. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0292] Typically, the following devices can be connected to the I / O interface: input devices such as sensors or visual information acquisition devices; output devices such as display screens; storage devices such as tapes, hard disks, etc.; and communication devices. The communication device can allow the computer device to communicate with other devices (such as edge computing devices) wirelessly or by wire to exchange data. Fig.12 A computer device having various devices is shown, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0293] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by the processor, any of the real-person foreign language AI teaching methods based on instant communication technology or all or part of the steps of the teaching method described in the embodiment of the present disclosure is executed.

[0294] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0295] According to a computer-readable storage medium of an embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, the real-person foreign language AI teaching method based on instant messaging technology or all or part of the steps of the teaching method described in any of the aforementioned embodiments of the present disclosure is executed.

[0296] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0297] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0298] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0299] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0300] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0301] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0302] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0303] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0304] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A real-person foreign language AI teaching method based on instant messaging technology, characterized in that: include: Build real-time conversation scenarios based on preset platforms and preset instant messaging channels; Determine the teaching end, and in response to an operation instruction of the teaching end, switch the real-time conversation scene to the teaching mode; Sending the target language setting instruction of the teaching terminal to the student terminal, and the student terminal uses the target language in the target language setting instruction as the real-time output language; When the student terminal is a sending terminal, the collected original speech of the sending terminal is processed based on a first preset AI strategy to obtain a plurality of sub-speech segments; When the desired language of the receiving end is consistent with the real-time output language, sending a plurality of sub-speech segments to the receiving end based on a preset instant messaging message channel, and playing the plurality of sub-speech segments in sequence according to time sequence; When the expected language of the receiving end is inconsistent with the real-time output language, several of the sub-speech segments are processed based on the second preset AI strategy to obtain translated speech segments and translated texts corresponding to the expected language, and one or more of the translated speech segments and translated texts are played at the receiving end.

2. The real-person foreign language AI teaching method based on instant messaging technology according to claim 1 is characterized in that: The real-time conversation scenario is constructed based on a preset platform and a preset instant messaging channel, including: In response to a successful login request on a preset platform, calling a preset information library; Retrieving the ID information of all the teaching participants from the preset information database; Obtaining a corresponding ID in an online state according to the ID information, and recording it as a target ID; Establishing a call connection between all the target IDs based on a preset instant messaging message channel; sending a teaching call instruction according to the call connection; In response to the teaching call instruction triggering the calling of a preset interface function, an interface containing teaching-related information pops up on the user interface of the student end; In response to the consent instruction of the student terminal, the target conversation interface and the target teaching scene parameters are called, and the target conversation interface is rendered based on the target teaching scene parameters to form the real-time conversation scene.

3. The real-person foreign language AI teaching method based on instant messaging technology according to claim 2 is characterized in that: The method for constructing the preset instant messaging message channel includes: Authenticating all the target IDs; The target IDs that meet the authentication requirements respectively establish a two-way business channel with the business server to form the preset instant messaging channel.

4. The real-person foreign language AI teaching method based on instant messaging technology according to claim 2 is characterized in that: The method for determining the teaching end includes: Classify all the target IDs based on a preset information database to obtain the number of first-category participants and the number of second-category participants; If the number of the first category of participants is different from the number of the second category of participants, the participant with the smaller number is used as the teaching end; If the number of the first type of participants and the number of the second type of participants are the same, the first type of participants or the second type of participants is determined to be the teaching end based on the preset identity setting, or the device of the user who first logs into the preset platform is used as the YY+242237P Teaching side.

5. The real-person foreign language AI teaching method based on instant messaging technology according to claim 4 is characterized in that: The step of switching the real-time conversation scene to the teaching mode in response to the operation instruction of the teaching end includes: In response to the operation instruction of the teaching terminal, calling and loading a preset teaching static interface to switch to the teaching mode; Alternatively, call a preset teaching static interface, load and open a file stored in the business server, and switch to the teaching mode.

6. The real-person foreign language AI teaching method based on instant messaging technology according to claim 1 is characterized in that: The collected original speech of the sending end is processed based on the first preset AI strategy to obtain a plurality of sub-speech segments, including: Collect the original voice of the sender and record it as the student's original voice; Determining student-side environment information based on the student's original voice; Determining the environment type based on the student-side environment information; Determining the student's voice status based on the environment type and the student's original voice; According to the vocalization state of the student terminal and the voice pause setting information, the original voice of the student is segmented to obtain a plurality of sub-speech segments; The voice pause setting information includes one or more of a sentence pause duration and a maximum duration of a sentence without a pause.

7. The real-person foreign language AI teaching method based on instant messaging technology according to claim 6 is characterized in that: The sending of the plurality of sub-speech segments to the receiving end based on the preset instant messaging message channel includes: Performing encoding processing on each of the sub-speech segments based on a preset audio encoding format; Encapsulating the encoded sub-voice into sub-data packets; Embed a timestamp in each of the sub-data packets to obtain a target data packet; The target data packets are sent to a receiving end based on a preset instant messaging message channel.

8. The real-person foreign language AI teaching method based on instant messaging technology according to claim 6 is characterized in that: Before processing the collected original voice at the sending end, the following steps are also included: Get the total duration of the collected original speech; If the total duration is greater than 2 seconds, the original speech is processed; If the total duration is not greater than 2 seconds, the original speech is divided into a plurality of speech segments in a time sequence at preset time intervals to obtain the decibel level of the speech segments; Determine whether the decibel of the speech segment is greater than a preset decibel threshold, and if so, record it as a target segment; Obtain the total number of all the target segments, and determine whether the total number is not less than a preset ratio of the number of segments of the speech segment decibels. If so, process the original speech; if not, ignore the original speech.

9. The real-person foreign language AI teaching method based on instant messaging technology according to claim 1 is characterized in that: The processing of the plurality of sub-speech segments based on the second preset AI strategy to obtain translated speech segments and translated texts corresponding to the desired language includes: Converting the plurality of sub-speech segments into a plurality of original speech translations according to a preset conversion strategy; Preprocessing the plurality of original speech translations; Calling the integrated knowledge graph to obtain corresponding keyword information based on the preprocessed original speech translations; Analyzing the keyword information based on a preset AI task scheduler to obtain a prediction result of each of the original speech translations, wherein the prediction result includes complexity and processing time; Dynamically adjusting the translation allocation strategy of each of the original speech translations based on the prediction results; The allocation strategy includes first-level allocation, second-level allocation, and third-level allocation in ascending order; Intelligently call the number and type of text translation examples of corresponding levels in the preset AI translation library based on the allocation strategy, and translate the corresponding original speech translation into a translation translation corresponding to the desired language; A plurality of translated speech segments are obtained based on the plurality of translated texts; the language of the translated speech segments is the desired language.

10. The real-person foreign language AI teaching method based on instant communication technology according to claim 9 is characterized in that: Playing one or more of the translated voice segment and the translated text at the receiving end includes: Based on the user preset or operation instruction of the receiving end, several translated voice segments are played in chronological order; Based on the user preset at the receiving end, the translated text is played simultaneously with the translated voice segment in a preset area, or all the translated texts are displayed in a preset area.

11. The real-person foreign language AI teaching method based on instant messaging technology according to claim 9 is characterized in that: The converting of the plurality of sub-speech segments into a plurality of original speech translations according to a preset conversion strategy includes: calling a third-party translation interface, translating the plurality of sub-speech segments received through the third-party translation interface, and obtaining a plurality of original speech translations.

12. The real-person foreign language AI teaching method based on instant messaging technology according to claim 9 is characterized in that: The preprocessing of the plurality of original speech translations comprises: Obtaining the message length of each of the original voice translations; If the message length is greater than the preset half-width length, pre-processing the original speech translation; If the message length is not greater than the preset half-width length, determine whether the plurality of original voice translations contain preset rules, if so, ignore the corresponding original voice translations, if not, pre-process the original voice translations; The preprocessing includes one or more of text cleaning, text standardization, part-of-speech tagging, text annotation, and redundancy removal; The preset rules include: whether two consecutive original speech translations are both a Chinese character, and / or the original speech translation is a letter, and / or the original speech is a combination of a letter, a space, and a word, and / or the original speech is an interjection.

13. A teaching method, applied to a student, characterized in that: include: Build real-time conversation scenarios based on preset platforms and preset instant messaging channels; Determine the student end and receive the target language setting issued by the teaching end; In response to the target language setting, the target language in the target language setting instruction is used as the real-time output language to interact with the teaching end.

14. A teaching method, applied to a teaching end, characterized in that: include: Build real-time conversation scenarios based on preset platforms and preset instant messaging channels; Determine the teaching end; Acquire information about actual participants in the real-time conversation scenario; Matching the actual participant information with the preset participant information, and if the match is successful, triggering the teaching end to issue an operation instruction; In response to the operation instruction, switching the real-time conversation scene to a teaching mode; Determine the target language setting instructions; Send the target language setting instruction to the student terminal.

15. A computer device, characterized in that: The computer device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the real-person foreign language AI teaching method based on instant communication technology as described in any one of claims 1-12, or the teaching method as described in claim 13, or the teaching method as described in claim 14.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the real-person foreign language AI teaching method based on instant communication technology as described in any one of claims 1-12, or the teaching method as described in claim 13, or the teaching method as described in claim 14.

17. A computer program product comprising computer instructions, characterized in that When the computer instruction is executed by the processor, the steps of the real-person foreign language AI teaching method based on instant communication technology described in any one of claims 1 to 12 or the teaching method described in claim 13 or the teaching method described in claim 14 are implemented.

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