New method for multi-person translation interaction

Abstract

The invention relates to the technical field of multi-user real-time translation, and discloses a novel multi-person translation interaction method, which comprises the following steps that: an initiator starts a multi-person translation session through a main device, selects a source language and a target language, and generates a session invitation code; other users join the session through the invitation code, and the system automatically allocates the initial authority and identifies the equipment type of the user; the system splits the input content of the user into a plurality of translation tasks, and distributes the tasks to the server node with the minimum load according to the priorities of the tasks. Through adaptive transmission, personalized vocabulary recognition and multi-modal data fusion, high real-time performance and accuracy of multi-person translation interaction are achieved, and it is ensured that translation content is still stably transmitted under network fluctuation. Meanwhile, based on cross-platform synchronization of WebSocket and flexible authority management, data consistency and session security of multiple devices are ensured, and user experience of a complex multi-language scene is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-user real-time translation, and in particular to a new method for multi-user translation interaction. Background Art

[0002] With the advancement of globalization, the demand for cross-language communication is increasing, especially in multi-person conversations, meetings, and collaboration scenarios. The need for real-time translation is particularly urgent. Traditional translation interaction methods typically rely on a single translation device or software to convert the language of one party into the target language of the other party. This is suitable for simple one-on-one conversation scenarios.

[0003] Currently, some existing translation interaction methods achieve multi-person language communication through technologies such as device interconnection, voice recognition, text translation, and data synchronization. These methods typically rely on a single translation server or application, using the network to convert user voice or text input into translated content, and then transmit the translation results to all users. Some systems also support basic cross-platform synchronization and permission control, ensuring that multiple users can simultaneously view translation results on different devices.

[0004] Existing translation interaction methods have some shortcomings in multi-person conversation environments. First, data transmission is easily affected by delays when the network fluctuates, resulting in insufficient real-time translation. Second, the accuracy and professionalism of the translated content are poor, and there is a lack of priority recognition and processing of user-specific vocabulary and industry terminology. In addition, the system's synchronization mechanism between multiple platforms or multiple devices is relatively basic, making it difficult to ensure data consistency across devices, and the dynamic management of user permissions is not flexible enough. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a new method for multi-person translation interaction, which solves the problems of insufficient real-time performance, lack of personalized translation, unstable cross-platform synchronization and inflexible authority management in the existing technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a new method for multi-person translation interaction, comprising the following steps:

[0007] The initiator starts a multi-person translation session through the main device, selects the source and target languages, and generates a session invitation code;

[0008] Other users join the session using the invitation code, and the system automatically assigns initial permissions and identifies the user's device type;

[0009] The system divides the user's input into multiple translation tasks and assigns them to the server node with the least load based on their priority.

[0010] The system identifies each user's input type and aligns data of different modalities on the timeline for semantic fusion;

[0011] The system dynamically adjusts the data transmission rate according to the real-time network bandwidth to achieve low-latency data transmission and multi-device synchronization;

[0012] The system records users' language preferences and usage habits, and performs personalized model adjustments and industry terminology matching optimization;

[0013] The system synchronizes data through a cross-platform real-time synchronization protocol and manages user permissions based on access control lists.

[0014] Preferably, the assignment of translation tasks includes the following steps:

[0015] The system assigns translation tasks to the best server nodes based on the priority of the task, the load of the node, and the size of the task;

[0016] The task priority is determined by the processing delay of the task, the priority of the task and the size of the task.

[0017] Preferably, the priority of task assignment is determined by the following formula:

[0018]

[0019] in, Indicates the server node S i The real-time load, P ij is the task priority, S ij is the task size, D ij is the task processing delay, and τ is the time decay factor.

[0020] Preferably, modality recognition includes recognition processing of voice input and text input, specifically:

[0021] Use a speech recognition model to convert speech input into text and evaluate the conversion accuracy;

[0022] Text input directly enters the translation process to avoid secondary conversion.

[0023] Preferably, the data after modality recognition are aligned on the time axis and data fusion is performed through semantic similarity calculation to ensure the consistency of data of different modalities.

[0024] Preferably, the result of the semantic similarity calculation is determined by the following formula:

[0025]

[0026] Among them, S simis the semantic similarity; F(V) and F(W) are the semantic feature vectors of speech and text input.

[0027] Preferably, the data transmission rate is adjusted by an adaptive transmission protocol, specifically comprising the following steps:

[0028] The system sets up a network bandwidth monitoring module in each user device to monitor the network bandwidth status in real time and obtain the current bandwidth B current And with the preset bandwidth threshold B threshold Make comparisons;

[0029] When B current <B threshold When the system reduces the amount of data by lowering the sampling rate of the data, the sampling rate of the audio or video is reduced to the preset minimum value, and the data encoding is adjusted to a low bit rate mode to ensure the continuity of data transmission when the bandwidth is insufficient;

[0030] The system dynamically calculates the transmission rate R adaptive , so that it meets the current bandwidth conditions, the transmission rate calculation formula is:

[0031]

[0032] Where n is the number of users online at the same time; B max is the maximum bandwidth; θ is the transmission adjustment coefficient, which is used to control the adjustment range when the bandwidth is insufficient;

[0033] In the case of high bandwidth, that is, B current ≥B threshold , the system automatically switches to high-definition transmission mode, increases the data sampling rate and transmission bit rate to provide higher audio or video quality, and ensures the clarity and accuracy of the translation content.

[0034] Preferably, the personalized model adjustment includes the following steps:

[0035] Record users’ language preferences and specific expressions;

[0036] Based on user feedback, personalized model parameters are updated to optimize translation results.

[0037] Preferably, the user-defined vocabulary library includes user-defined proprietary terms, common expressions and specific industry vocabulary, and gives priority to matching user-defined terminology, specifically including the following steps:

[0038] Users enter terminology in a specific field into the custom vocabulary library, and the system prioritizes the terms based on their frequency of use and importance to form a personalized vocabulary library;

[0039] During the translation process, the system prioritizes searching for words that meet the translation requirements from the custom vocabulary database and translates them based on the matching priority, ensuring the accuracy and consistency of industry-specific vocabulary in the translated content.

[0040] The custom vocabulary library supports continuous updating and maintenance by users. Users can add or delete vocabulary to meet changing translation needs and ensure professional and personalized translation results.

[0041] Preferably, the cross-platform synchronization protocol is implemented based on the WebSocket protocol. The system ensures the synchronization and consistency of translation content between different platforms through the real-time synchronization protocol and performs permission management through access control lists. Specifically, the following steps are included:

[0042] The system sets access rights for different users based on the initiator of the session, including viewing rights, editing rights, and voice input rights;

[0043] When users view or edit translation content, the system checks whether their permissions comply with the permission control list to ensure that unauthorized users cannot perform sensitive operations.

[0044] The system automatically recognizes the same user account across multiple devices and merges input information from different devices to ensure the synchronization of translation content and consistency of user data.

[0045] The present invention provides a new method for multi-person translation interaction. It has the following beneficial effects:

[0046] 1. This invention achieves precise synchronization and processing of translation content through multimodal data fusion, semantic similarity calculation, and priority task allocation. The system automatically adapts to different users' input methods and professional needs, ensuring the accuracy and consistency of translation results, and providing an efficient and natural real-time translation experience for multiple users.

[0047] 2. The present invention adopts a cross-platform synchronization mechanism based on the WebSocket protocol, enabling the system to update translation content in real time across different devices and platforms. It combines access control lists (ACLs) to implement permission management, ensuring the security of session data and the standardization of operations, making multi-device interaction more stable and smooth.

[0048] 3. Through adaptive transmission protocols and personalized model optimization, the system of the present invention can dynamically adjust the data transmission rate and learn user language preferences in real time under conditions of unstable network bandwidth, meeting the professional and personalized needs of users in multiple scenarios, and providing high-quality and stable translation services for multi-person cross-language conversations. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of the method of the present invention;

[0050] Figure 2 This is a flow chart of the transmission of real-time audio streams according to the present invention;

[0051] Figure 3 This is a multi-client transmission flow chart of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] Please see the attached Figure 1 The embodiment of the present invention provides a new method for multi-person translation interaction, including the following steps:

[0054] The initiator starts a multi-person translation session on the master device, selects the source and target languages, and generates a session invitation code. This invitation code generation mechanism makes the session unique, helps the system identify and organize participants, and ensures session security. This step controls user access to the session through a clear process, ensuring the standardization and accuracy of the session.

[0055] When other users join the session using the invitation code, the system automatically assigns initial permissions and identifies the user's device type. Automatic device identification allows for the allocation of appropriate processing tasks based on device characteristics, improving the efficiency of system resource utilization. At the same time, by setting initial permissions, the system can conduct preliminary control over user access, preventing unauthorized access and ensuring session security.

[0056] The system divides user input into multiple translation tasks and assigns them to the least loaded server nodes based on their priority. Task splitting and prioritization enable efficient task management and processing. By assigning tasks to less loaded nodes, the system can reduce latency and achieve faster translation, ensuring users can smoothly communicate in real-time translation and improving overall system efficiency.

[0057] The system identifies each user's input type and aligns data from different modalities on the timeline for semantic fusion. Multimodal input recognition and timeline alignment effectively improve the system's adaptability to multiple inputs, enabling users with different input modes, such as voice and text, to interact with each other in real-time translation within the same conversation. This step ensures the temporal synchronization and semantic consistency of the data, making translation communication more fluid and natural.

[0058] The system dynamically adjusts the data transmission rate based on real-time network bandwidth conditions to achieve low-latency data transmission and multi-device synchronization. By dynamically adjusting the data transmission rate, the system can adapt to different network bandwidth conditions, ensuring stable transmission of translation data and avoiding translation interruptions or delays caused by network fluctuations. This mechanism improves the user translation experience in various network environments and ensures the continuity and stability of multi-person translation.

[0059] The system records the user's language preferences and usage habits, and performs personalized model adjustments and industry terminology matching optimization. Language preference recording and personalized model adjustments ensure the personalization and professionalism of the translated content. The translation results obtained by users are more in line with their habits and needs, especially the precise matching of industry terminology, which can greatly improve the accuracy of professional translation and enhance the overall user experience.

[0060] The system synchronizes data through a cross-platform real-time synchronization protocol and manages user permissions based on access control lists. This cross-platform synchronization protocol ensures real-time consistency of translation content between users on different devices, allowing users to seamlessly switch devices and continue their conversations. The permissions management mechanism ensures compliance with user behavior, preventing data leaks or misuse due to improper permissions, thereby enhancing session security and reliability.

[0061] Please see the attached Figure 1 In a preferred embodiment of the present invention, the assignment of translation tasks includes the following steps:

[0062] The system assigns translation tasks to the optimal server node based on task priority, node load, and task size. By prioritizing tasks, the system can effectively identify and process content of higher importance in conversations, ensuring that important tasks are prioritized. This allows the system to prioritize responses to core content during multi-person interactions, thereby improving the timeliness and accuracy of translation results. At the same time, the rational distribution of node load improves the overall resource utilization of the system, making the system more fault-tolerant and stable, and avoiding translation delays caused by load imbalance.

[0063] Task priority is determined by the task processing delay, task priority and task size. Through preliminary assessment of task size, the system can reasonably allocate tasks to different nodes, ensure system response speed, reduce the delay of large tasks, and make the overall translation process more efficient and reasonable. The comprehensive allocation strategy effectively integrates priority, load and size factors, ensures the optimization of multi-task allocation, significantly improves the overall translation efficiency of the system, and makes multi-person translation interaction smoother.

[0064] Please see the attached Figure 1 In a preferred embodiment of the present invention, the priority of task assignment is determined by the following formula:

[0065]

[0066] in, Indicates the server node S i The real-time load, P ij is the task priority, S ij is the task size, D ij is the task processing delay, and τ is the time decay factor. Through the above multi-dimensional task allocation mechanism, the system can achieve accurate and efficient task allocation based on priority, load, task size and processing delay in a complex multi-person translation interaction environment, significantly improving the system's processing efficiency and stability, and ensuring users a smooth translation experience.

[0067] Please see the attached Figure 1 In a preferred embodiment of the present invention, modality recognition includes recognition processing of voice input and text input, specifically:

[0068] The system uses a speech recognition model to convert speech input into text and evaluates the conversion accuracy. The system uses a speech recognition model (Automatic Speech Recognition, ASR) to convert speech into text. After the user's voice input is collected by the microphone, the system will first pre-process it (including noise reduction, audio enhancement, etc.) to optimize the voice quality. On this basis, the ASR model will segment and parse the pre-processed speech signal, and realize the text conversion of the speech content through the combination of acoustic model and language model. This recognition process supports multiple languages ​​and multiple dialects, and can recognize speech input with different speaking speeds and accents, and accurately convert it into text to ensure the quality of the translation input. This step improves the recognition accuracy of the translation input and reduces the information loss caused by voice input;

[0069] Text input directly enters the translation processing flow to avoid secondary conversion. Through real-time evaluation of speech recognition accuracy, the system can ensure the accuracy of translation input, promptly prompt users to improve input methods, and avoid translation deviations caused by recognition errors, thereby improving user experience and translation quality.

[0070] Please see the attached Figure 1 In a preferred embodiment of the present invention, modality-identified data is aligned on the timeline and fused using semantic similarity calculations to ensure consistency across modal data. This approach, combined with semantic similarity calculations, achieves timeline alignment, semantic similarity fusion, content prioritization, real-time data updates, and consistent presentation, enabling the system to deliver smooth and accurate translations in a multimodal input environment. This method effectively ensures the logical, real-time nature of translation and user experience, providing efficient and intelligent support for multi-person cross-language interaction.

[0071] Please see the attached Figure 1 In a preferred embodiment of the present invention, the result of the semantic similarity calculation is determined by the following formula:

[0072]

[0073] Among them, S sim is semantic similarity; F(V) and F(W) are the semantic feature vectors of speech and text input. Through the above formula, the multimodal semantic consistency processing method of the present invention implements the full process from feature vector generation, cosine similarity calculation, dynamic adjustment of similarity threshold, data fusion, to user feedback. This method ensures the semantic consistency of the system under different input modalities, effectively guarantees the accuracy and fluency of multi-person translation conversations, and provides users with an intelligent and precise multimodal translation experience.

[0074] Please see the attached Figure 1 In a preferred embodiment of the present invention, the data transmission rate is adjusted by an adaptive transmission protocol, which specifically includes the following steps:

[0075] The system sets up a network bandwidth monitoring module in each user device to monitor the network bandwidth status in real time and obtain the current bandwidth B current And with the preset bandwidth threshold B threshold Make comparisons;

[0076] When B current <B threshold When the system reduces the amount of data by lowering the sampling rate of the data, the sampling rate of the audio or video is reduced to the preset minimum value, and the data encoding is adjusted to a low bit rate mode to ensure the continuity of data transmission when the bandwidth is insufficient;

[0077] Specifically: The network bandwidth monitoring module can detect changes in the user's network environment in real time, enabling the system to flexibly adapt to changes, avoiding translation delays or freezes caused by network fluctuations and improving the stability of the user experience.

[0078] The system dynamically calculates the transmission rate R adaptive , so that it meets the current bandwidth conditions, the transmission rate calculation formula is:

[0079]

[0080] Where n is the number of users online at the same time; B max is the maximum bandwidth; θ is the transmission adjustment coefficient, which is used to control the adjustment range when the bandwidth is insufficient. The dynamic adjustment of the transmission rate ensures that the system can work stably under insufficient bandwidth conditions, avoids transmission interruptions caused by network limitations, and improves the continuity and real-time performance of the translation service.

[0081] In the case of high bandwidth, that is, B current ≥B threshold The system automatically switches to high-definition transmission mode, increases the data sampling rate and transmission bit rate to provide higher audio or video quality, and ensures the clarity and accuracy of the translated content. The high-definition transmission mode restores the high resolution and high quality of the data when the bandwidth is sufficient, ensuring the clarity of the translated content, optimizing the user experience in high-bandwidth environments, and monitoring the network bandwidth in real time. It intelligently adjusts the data transmission rate and switches the transmission mode according to the current bandwidth conditions to ensure the stable transmission of the translated content.

[0082] Please see the attached Figure 1 In a preferred embodiment of the present invention, the personalized model adjustment includes the following steps:

[0083] Recording users' language preferences and specific expressions. Recording users' language preferences enables the system to gradually understand users' expression habits, providing basic data support for the translation model, thereby achieving translation results that better meet user needs.

[0084] Based on user feedback, the personalized model parameters are updated to optimize the translation effect. The feedback optimization mechanism enables the system to continuously improve the personalized translation effect according to user feedback, realize dynamic optimization of the personalized model, and improve the accuracy of the translation content and user satisfaction.

[0085] Please see the attached Figure 1 In a preferred embodiment of the present invention, the user-defined vocabulary includes user-defined proprietary terms, common expressions, and specific industry vocabulary, and gives priority to matching user-defined terminology, specifically including the following steps:

[0086] Users enter field-specific terms into a custom vocabulary database. The system prioritizes these terms based on their frequency of use and importance to create a personalized vocabulary database. This allows the system to identify specific terms in the user's field of expertise, improving the professionalism and personalization of the translated content and making the system more adaptable to user needs in different application scenarios.

[0087] During the translation process, the system prioritizes searching for words that meet the translation requirements from the custom vocabulary database and translates them based on matching priorities, ensuring the accuracy and consistency of industry-specific vocabulary in the translated content. Prioritization ensures that the system can accurately identify and prioritize the user's core terms, reducing unnecessary translation deviations and further improving the accuracy and consistency of the translated content.

[0088] The custom vocabulary library supports continuous updating and maintenance by users. Users can add or delete vocabulary to meet evolving translation needs and ensure professional and personalized translation results. Before translation begins, the system will first query the custom vocabulary library and match high-priority vocabulary with the content to be translated, prioritizing these user-specified terms. When a custom vocabulary matches the translated content, the system directly replaces it with the target language's specialized terminology. This ensures that the terminology used in the translated content aligns with the user's specific needs, avoiding errors that may be introduced by general translation models. This ensures professionalism and consistency, and brings the translation results closer to the user's domain needs.

[0089] Please see the attached Figure 1 In a preferred embodiment of the present invention, the cross-platform synchronization protocol is implemented based on the WebSocket protocol. The system ensures the synchronization and consistency of translation content between different platforms through the real-time synchronization protocol and performs permission management through access control lists. Specifically, the following steps are included:

[0090] The system sets access rights for different users based on the initiator of the session, including viewing, editing, and voice input permissions. Real-time synchronization achieved through the WebSocket protocol allows the system to maintain consistent translation content across multiple devices and platforms, effectively reducing data latency and improving the interactive experience for users across multiple platforms.

[0091] When users view or edit translation content, the system checks whether their permissions comply with the permissions control list, ensuring that unauthorized users cannot perform sensitive operations. The permissions management mechanism based on the control list effectively controls user access rights, ensuring data security and session standardization, avoiding the risk of unauthorized operations and improving system security. By dynamically verifying user permissions, the system can effectively control user behavior in multi-person sessions and prevent unauthorized operations, thereby ensuring the security and standardization of translation sessions.

[0092] The system automatically recognizes the same user account across multiple devices and merges input information from different devices to ensure the synchronization of translation content and the consistency of user data. The automatic recognition and data merging functions of multiple device accounts ensure the consistency and integrity of user data, avoid duplication or conflict of information input across devices, and enhance the system's multi-device compatibility and ease of use.

[0093] Specifically, clients in different regions collect their audio streams through the Agora SDK and send them to a designated channel under the Agora service node in their local network. For example, a client in region A sends its audio stream to Agora service node 1 in its local area, using channel M. Agora internally implements ultra-low latency and zero-blocking transmission of the audio stream between service nodes 1 and 2. This allows the server in region B to read the audio stream sent by the client in region A through channel M at the nearest Agora service node 2, with ultra-low latency and zero blocking.

[0094] In addition, the chat room function provided by RongCloud can be used to solve similar conference scenarios composed of multiple clients. When using it, the request end 1 in area A or the request end 2 in area C, as well as the server in area B, can send their own text messages to RongCloud's nearest service node 1, service node 3, and service node 2 respectively, and specify to send them to the same chat room roomM. At the same time, RongCloud internally implements data transmission between service node 1, service node 2, and service node 3 with low latency and high stability, so that terminals in each area can read the full amount of messages in the real-time chat room from the nearest service node through roomM with low latency.

[0095] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A new method for multi-person translation interaction, characterized in that: The following steps are involved: The initiator starts a multi-person translation session through the main device, selects the source language and target language, and generates a session invitation code; Other users join the session using the invitation code, and the system automatically assigns initial permissions and identifies the user's device type; The system divides the user's input into multiple translation tasks and assigns the tasks to the server node with the least load according to the priority of the tasks; The system identifies the input type of each user and aligns data of different modalities on the timeline for semantic fusion; The system dynamically adjusts the data transmission rate according to the real-time network bandwidth to achieve low-latency data transmission and multi-device synchronization; The system records the user's language preferences and usage habits, and performs personalized model adjustments and industry terminology matching optimization; The system achieves data synchronization through a cross-platform real-time synchronization protocol and manages user permissions based on access control lists.

2. According to the new method of multi-person translation interaction described in claim 1, it is characterized in that: The assignment of translation tasks includes the following steps: The system assigns translation tasks to the best server nodes based on the priority of the task, the load of the node, and the size of the task; The task priority is determined by the processing delay of the task, the priority of the task and the size of the task.

3. A new method for multi-person translation interaction according to claim 2, characterized in that: The priority of task assignment is determined by the following formula: in, Represents the server node S i The real-time load, P ij is the task priority, S ij is the task size, D ij is the task processing delay, and τ is the time decay factor.

4. According to the new method of multi-person translation interaction described in claim 1, it is characterized in that: Modal recognition includes the recognition and processing of voice input and text input, specifically: Use a speech recognition model to convert speech input into text and evaluate the conversion accuracy; The text input directly enters the translation process to avoid secondary conversion.

5. A new method for multi-person translation interaction according to claim 4, characterized in that: The data after modal identification are aligned on the time axis and fused through semantic similarity calculation to ensure the consistency of data in different modalities.

6. A new method for multi-person translation interaction according to claim 5, characterized in that: The result of the semantic similarity calculation is determined by the following formula: Among them, S sim is the semantic similarity; F(V) and F(W) are the semantic feature vectors of speech and text input.

7. The new method for multi-person translation interaction according to claim 1 is characterized in that: The data transmission rate is adjusted through an adaptive transmission protocol, which includes the following steps: The system sets up a network bandwidth monitoring module in each user device to monitor the network bandwidth status in real time and obtain the current bandwidth B current and the preset bandwidth threshold B threshold Make comparisons; When B current <B threshold When the system reduces the amount of data by lowering the sampling rate of the data, lowering the sampling rate of the audio or video to the preset minimum value, and adjusting the data encoding to a low bit rate mode to ensure the continuity of data transmission when the bandwidth is insufficient; The system dynamically calculates the transmission rate R adaptive , so that it meets the current bandwidth conditions, the transmission rate calculation formula is: Where n is the number of users online at the same time; B max is the maximum bandwidth; θ is the transmission adjustment coefficient, which is used to control the adjustment range when the bandwidth is insufficient; In the case of high bandwidth, that is, B current ≥B threshold , the system automatically switches to high-definition transmission mode, increases the data sampling rate and transmission bit rate to provide higher audio or video quality, and ensures the clarity and accuracy of the translation content.

8. The new method for multi-person translation interaction according to claim 1 is characterized in that: Personalized model tuning includes the following steps: Record the user's language preferences and specific expressions; Based on user feedback, update personalized model parameters to optimize translation results.

9. A new method for multi-person translation interaction according to claim 8, characterized in that: The user-defined vocabulary includes user-defined proprietary terms, common expressions, and specific industry terms, and gives priority to matching user-defined terminology. The specific steps include the following: Users enter terminology in a specific field into a custom vocabulary library, and the system prioritizes the terms based on their frequency of use and importance to form a personalized vocabulary library; During the translation process, the system will first retrieve words that meet the translation requirements from the custom vocabulary database and translate them according to the matching priority to ensure the accuracy and consistency of industry-specific vocabulary in the translated content; The custom vocabulary library supports continuous updating and maintenance by users. Users can add or delete vocabulary to meet changing translation needs and ensure the professionalism and personalization of translation results.

10. The new method for multi-person translation interaction according to claim 1, characterized in that: The cross-platform synchronization protocol is implemented based on the WebSocket protocol. The system ensures the synchronization and consistency of translation content between different platforms through the real-time synchronization protocol, and manages permissions through access control lists. The specific steps include: The system sets access rights for different users based on the initiator of the session, including viewing rights, editing rights, and voice input rights; When users view or edit translation content, the system checks whether their permissions comply with the permission control list to ensure that unauthorized users cannot perform sensitive operations. The system automatically recognizes the same user account across multiple devices and merges input information from different devices to ensure the synchronization of translation content and consistency of user data.

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