International remote translation system based on AI and implementation method thereof

Abstract

The invention discloses an international remote translation system based on AI and an implementation method thereof, and belongs to the technical field of artificial intelligence. Comprising a voice input module, an emotion perception module, a predictive simultaneous interpretation module, a language adaptation module based on few-sample learning, a dynamic translation quality evaluation and feedback module and a multi-language cooperative processing module, and can automatically recognize an input language and dynamically adjust a translation model in a multi-language environment. The emotion information in the voice and the text is captured in real time, the translation path is optimized in combination with the context weight, the emotion transmission accuracy and the translation context consistency are remarkably improved, the term requirements of different fields can be rapidly met, the translation quality is continuously optimized through dynamic feedback, the translation accuracy and smoothness are ensured, and the translation efficiency is improved. And the method has wide applicability in multi-language, cross-cultural communication and professional field application, and has relatively high innovativeness and practical value.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an AI-based international remote translation system and an implementation method thereof, which are applied to multilingual international remote communication, and are particularly suitable for high-precision real-time translation scenarios that require the combination of emotion recognition and context optimization. Background Art

[0002] With the development of globalization, cross-cultural and multilingual international communication is becoming more and more frequent, and real-time translation systems play a key role in this. Traditional translation systems mainly rely on large language models (LLMs) and pre-training data. Although they have made progress in the translation of common languages, they still face some serious deficiencies in practical applications. These systems perform poorly in emotion recognition and cannot effectively capture the speaker's tone, speech speed and emotional changes, resulting in stiff translation results and lack of accuracy in emotional transmission. Existing systems have limited capabilities in context processing and often translate only based on current input, ignoring the overall coherence of the conversation, resulting in inconsistent context in translation results, especially in continuous conversations and cross-cultural communication. Existing systems lack sufficient adaptability when dealing with professional terminology, especially in high-precision fields such as medicine and law. Existing technologies are difficult to quickly adapt to professional terminology, affecting the accuracy of translation.

[0003] These technical deficiencies have led to limitations in the practical application of existing translation systems. First, in multilingual international communication, the loss of emotional information may lead to misunderstandings or improper emotional transmission, especially in scenarios involving customer service, negotiations, etc. This emotional mismatch has a significant impact on the results of communication. Secondly, translation systems that ignore context are prone to logical incoherence or semantic errors when dealing with long paragraphs, continuous conversations, or conversations from different cultural backgrounds, which affects the effectiveness of communication. Finally, for terminology in different fields, especially in time-sensitive real-time translation environments, it is difficult for existing systems to quickly adjust translation models to adapt to the requirements of specific fields, and the quality of professional translation is therefore limited. These problems show that existing translation systems cannot fully meet the needs in multilingual, cross-cultural, and professional applications. Summary of the invention

[0004] In response to the problems of insufficient emotion recognition ability, weak dialogue context processing ability, poor domain adaptability, and lack of dynamic optimization and feedback mechanism, the present invention provides an AI-based international remote translation system and its implementation method. By integrating multiple modules, it can dynamically adjust the translation path in a multilingual environment, evaluate the translation quality in real time, and optimize the translation process by combining emotion perception and context weight, thereby overcoming the shortcomings of the prior art.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0006] An AI-based international remote translation system, the system comprising:

[0007] A voice input module, used to receive the user's real-time voice and convert it into text data;

[0008] The emotion perception module is used to process the received voice data and text data in parallel and recognize the emotional information in the voice or text in real time;

[0009] A predictive simultaneous interpretation module is used to generate multiple translation paths based on the sentiment information and the large language model, and introduce a context history weight H t Dynamically adjusting the priority of the translation path;

[0010] A language adaptation module based on few-shot learning, which is used to quickly adapt to domain terminology or special language environments through few-shot learning;

[0011] Dynamic translation quality assessment and feedback module, which is used to conduct real-time assessment based on contextual consistency and semantic accuracy after the translation is completed, and continuously optimize the translation path based on the assessment feedback;

[0012] The multi-language collaborative processing module is used to automatically identify the input language and dynamically adjust the translation model in a multi-language environment.

[0013] As a preferred solution of the present invention, the voice input module includes:

[0014] A voice capture unit, used to receive a real-time voice signal from a user;

[0015] The speech noise reduction unit uses an adaptive filtering algorithm to reduce the noise of the speech signal;

[0016] The context extraction unit is used to extract context information from the speech signal and generate a context tag.

[0017] As a preferred solution of the present invention, the emotion perception module includes:

[0018] Speech emotion recognition unit, used to extract speech features, including intonation, speaking speed, volume, and generate speech emotion information;

[0019] A text sentiment analysis unit, used to perform sentiment analysis on the text data and identify sentiment words and semantic tendencies in the text;

[0020] The emotion fusion unit is used to combine the speech emotion information and the text emotion information to generate the emotion embedding E combined , the generation formula of the emotion embedding is:

[0021] E combined =concat(Eaudio ,E text ,C t )

[0022] Among them, E audio is the emotional information extracted from speech features, E text is the sentiment information extracted from the text, C t The current context information.

[0023] As a preferred solution of the present invention, the emotion fusion unit includes:

[0024] The adaptive learning unit is used to dynamically adjust the generation method of emotion embedding through continuous learning based on the user's personalized voice features and text usage habits.

[0025] As a preferred embodiment of the present invention, the predictive simultaneous interpretation module includes:

[0026] A translation path generating unit, used for generating a plurality of translation paths based on the emotion information;

[0027] The context weight calculation unit is used to calculate the context history weight H based on the context information of the conversation. t ;

[0028] The path priority adjustment unit is used to adjust the path priority based on the context history weight H t and the probability of the translation path P(T i |C t ) Dynamically adjust the priority of the translation path. The priority adjustment formula is:

[0029] P(T i |C t ,H t )=α*P(T i |C t )+β*H t

[0030] Among them, P(T i |C t ) is the probability of the current translation path; H t is the historical context weight; α and β are adaptive weight parameters used to balance the selection of context history and current translation path.

[0031] As a preferred embodiment of the present invention, the predictive simultaneous interpretation module further includes:

[0032] The semantic conflict detection unit is used to detect semantic conflicts in multiple translation paths and select the optimal translation path to eliminate the semantic conflicts by comparing the sentiment information and context information.

[0033] As a preferred solution of the present invention, the language adaptation module based on few-sample learning includes:

[0034] Domain context extraction unit, used to extract domain terms and context markers;

[0035] Few-shot learning unit, used to train models based on a small amount of domain data and generate domain adaptation models;

[0036] Reinforcement learning unit, used to define the state S t 、Action A t and the reward mechanism R t Perform translation path optimization, the optimization formula is:

[0037]

[0038] Among them, Q(S t ,A t ) represents the action value in the current state; η is the learning rate; γ is the discount factor; The best expected reward for the next state.

[0039] As a preferred solution of the present invention, the few-sample learning module further includes:

[0040] The knowledge graph extension unit is used to associate domain terms with concepts in the existing knowledge base and dynamically adapt the translation model to domain needs.

[0041] As a preferred solution of the present invention, the dynamic translation quality evaluation and feedback module includes:

[0042] The quality scoring unit is used to score the translation results based on contextual consistency and semantic accuracy to generate a translation quality score Q s , the calculation formula is:

[0043] Q s =λ1*sim(T ref ,T out )+λ2*C consistency

[0044] Among them, T ref For reference translation; T out The translation result generated by the system; C consistency is the context consistency score; λ1 and λ2 are the score weight parameters;

[0045] A feedback optimization unit is used to adjust the translation path selection according to the quality score.

[0046] As a preferred solution of the present invention, the multi-language collaborative processing module includes:

[0047] Language detection unit, used to automatically detect the source language and target language;

[0048] The language embedding calculation unit selects the best translation path based on the language embedding similarity. The calculation formula of embedding similarity is:

[0049] M(L1,L2)=argmaxsim(EL1,EL2)

[0050] Among them, M(L1,L2) is the embedding mapping between the source language L1 and the target language L2; sim(EL1,EL2) is the embedding similarity between the source language L1 and the target language L2.

[0051] The implementation method of the AI-based international remote translation system includes the following steps:

[0052] Step 1: Receive a user's real-time voice signal through a voice input module, convert the voice signal into text data, and extract context information from the voice signal to generate a context tag;

[0053] Step 2: The emotion perception module processes the speech data and the text data in parallel, extracts and identifies the emotion information therein, and generates speech emotion information and text emotion information;

[0054] Step 3: Combine speech emotion information and text emotion information to generate emotion embedding through emotion fusion unit;

[0055] Step 4: using a predictive simultaneous interpretation module, generating multiple translation paths based on the emotion embedding, calculating context history weights, and dynamically adjusting the priority of each translation path based on the context weights;

[0056] Step 5: Use the language adaptation module based on few-shot learning to quickly adapt to domain terms or special language environments. Use the domain context extraction unit to extract domain terms and perform few-shot learning based on a small amount of domain data to generate a domain adaptation model.

[0057] Step 6: The dynamic translation quality assessment and feedback module evaluates the translation results in real time, generates a translation quality score based on contextual consistency and semantic accuracy, optimizes the translation path based on the evaluation result feedback, and finally outputs the translation result.

[0058] Preferably, the emotion perception module and the translation path priority adjustment mechanism are updated in real time according to the user feedback data;

[0059] Dynamically adjust the model parameters of the few-shot learning module based on the user's historical translation records;

[0060] In the multi-language collaborative processing module, translation paths with higher semantic similarity are given priority.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows: the speech input module of the present invention can receive user speech in real time and convert it into text data, and with the help of adaptive filtering and context extraction technology, effectively reduce background noise and retain important context information, thereby improving the accuracy of speech recognition. The emotion perception module processes speech and text data in parallel, recognizes the user's emotional information in real time, makes the translation result more natural and emotional, and significantly improves the accuracy of emotional transmission compared with the prior art. The predictive simultaneous interpretation module generates multiple translation paths based on emotional information and a large language model, and dynamically adjusts the priority of the translation path by introducing context history weights to ensure the coherence and accuracy of the translation results in continuous dialogues. The language adaptation module based on few-sample learning can quickly adapt to the terminology and special language environment in different fields, and greatly improves the accurate translation ability of the present invention in multi-field and multi-language environments compared with the traditional method of relying on a large amount of data training. The dynamic translation quality evaluation and feedback module evaluates the contextual consistency and semantic accuracy of the translation in real time, and continuously optimizes the translation path according to the feedback results to ensure the continuous improvement of the translation quality. The multi-language collaborative processing module automatically identifies the input language and dynamically adjusts the translation model, so that the present invention can work efficiently in multi-language communication scenarios, significantly improving the translation efficiency and accuracy. Overall, the present invention enhances emotion recognition, context processing and multilingual adaptability through the synergy of various modules, which not only solves the limitations of the existing technology, but also significantly improves the fluency and accuracy of translation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0063] in:

[0064] Figure 1 It is a schematic diagram of the overall structure of the embodiment of the present invention;

[0065] Figure 2 The following is a schematic diagram of the working process of the voice input module in the present embodiment;

[0066] Figure 3 This is a schematic diagram of the structure of the emotion perception module in the embodiment of the present invention;

[0067] Figure 4 A schematic diagram of path selection of a predictive simultaneous interpretation module in an embodiment of the present invention;

[0068] Figure 5 This is a structural diagram of a language adaptation module based on few-sample learning in an embodiment of the present invention;

[0069] Figure 6 This is a working diagram of the dynamic translation quality evaluation and feedback module in the embodiment of the present invention;

[0070] Figure 7 The figure is a schematic diagram of the method flow of the embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0072] Example 1

[0073] like Figure 1 As shown in FIG. 1 , an embodiment of the present invention is provided, which provides an international remote translation system based on AI, including:

[0074] like Figure 2 As shown, the voice input module is used to receive the user's real-time voice and convert it into text data;

[0075] In this specific example, the voice input module includes:

[0076] A voice capture unit, used to receive a real-time voice signal from a user;

[0077] The speech noise reduction unit uses an adaptive filtering algorithm to reduce the noise of the speech signal;

[0078] The context extraction unit is used to extract context information from the speech signal and generate a context tag.

[0079] like Figure 3 As shown, the emotion perception module is used to process the received voice data and text data in parallel and recognize the emotional information in the voice or text in real time;

[0080] In this example, the emotion perception module preferably includes:

[0081] The text sentiment analysis unit is used to perform sentiment analysis on the text data and identify the sentiment words and semantic tendencies in the text; the sentiment fusion unit is used to combine the speech sentiment information and the text sentiment information to generate the sentiment embedding E combined, the generation formula of the emotion embedding is:

[0082] E combined =concat(E audio ,E text ,C t )

[0083] Among them, E audio is the emotional information extracted from speech features, E text is the sentiment information extracted from the text, C t The current context information.

[0084] Furthermore, the emotion integration unit includes:

[0085] The adaptive learning unit is used to dynamically adjust the generation method of emotion embedding through continuous learning based on the user's personalized voice features and text usage habits.

[0086] like Figure 4 As shown, the predictive simultaneous interpretation module is used to generate multiple translation paths based on the sentiment information and the large language model, and introduce the context history weight H t Dynamically adjusting the priority of the translation path;

[0087] In this example, the predictive simultaneous interpretation module preferably includes:

[0088] A translation path generating unit, used for generating a plurality of translation paths based on the emotion information;

[0089] The context weight calculation unit is used to calculate the context history weight H based on the context information of the conversation. t ;

[0090] The path priority adjustment unit is used to adjust the path priority based on the context history weight H t and the probability of the translation path P(T i |C t ) Dynamically adjust the priority of the translation path. The priority adjustment formula is:

[0091] P(T i |C t ,H t )=α*P(T i |C t )+β*H t

[0092] Among them, P(T i |C t ) is the probability of the current translation path; H t is the historical context weight; α and β are adaptive weight parameters used to balance the selection of context history and current translation path.

[0093] Furthermore, the predictive simultaneous interpretation module also includes:

[0094] The semantic conflict detection unit is used to detect semantic conflicts in multiple translation paths and select the optimal translation path to eliminate the semantic conflicts by comparing the sentiment information and context information.

[0095] like Figure 5 As shown, a language adaptation module based on few-shot learning is used to quickly adapt to domain terminology or special language environments through few-shot learning;

[0096] In this example, the language adaptation module based on few-sample learning preferably includes:

[0097] Domain context extraction unit, used to extract domain terms and context markers;

[0098] Few-shot learning unit, used to train models based on a small amount of domain data and generate domain adaptation models;

[0099] Reinforcement learning unit, used to define the state S t 、Action A t and the reward mechanism R t Perform translation path optimization, the optimization formula is:

[0100]

[0101] Among them, Q(S t ,A t ) represents the action value in the current state; η is the learning rate; γ is the discount factor; The best expected reward for the next state.

[0102] Furthermore, the few-sample learning module also includes:

[0103] The knowledge graph extension unit is used to associate domain terms with concepts in the existing knowledge base and dynamically adapt the translation model to domain needs.

[0104] like Figure 6 As shown, the dynamic translation quality evaluation and feedback module is used to perform real-time evaluation based on contextual consistency and semantic accuracy after the translation is completed, and continuously optimize the translation path based on the evaluation feedback;

[0105] In this example, the dynamic translation quality assessment and feedback module preferably includes:

[0106] The quality scoring unit is used to score the translation results based on contextual consistency and semantic accuracy to generate a translation quality score Q s , the calculation formula is:

[0107] Q s =λ1*sim(T ref ,T out )+λ2*C consistency

[0108] Among them, T re f is the reference translation; T out The translation result generated by the system; C consistency is the context consistency score; λ1 and λ2 are the score weight parameters;

[0109] A feedback optimization unit is used to adjust the translation path selection according to the quality score.

[0110] The multi-language collaborative processing module is used to automatically identify the input language and dynamically adjust the translation model in a multi-language environment.

[0111] Specifically, in this example, the multi-language collaborative processing module includes:

[0112] Language detection unit, used to automatically detect the source language and target language;

[0113] The language embedding calculation unit selects the best translation path based on the language embedding similarity. The calculation formula of embedding similarity is:

[0114] M(L1,L2)=argmaxsim(EL1,EL2)

[0115] Among them, M(L1,L2) is the embedding mapping between the source language L1 and the target language L2; sim(EL1,EL2) is the embedding similarity between the source language L1 and the target language L2.

[0116] like Figure 7 As shown, this embodiment is based on the above system and also provides a method for implementing an international remote communication system based on AI real-time translation, including the following steps:

[0117] Step 1: Receive a user's real-time voice signal through a voice input module, convert the voice signal into text data, and extract context information from the voice signal to generate a context tag;

[0118] Step 2: The emotion perception module processes the speech data and the text data in parallel, extracts and identifies the emotion information therein, and generates speech emotion information and text emotion information;

[0119] Step 3: Combine speech emotion information and text emotion information to generate emotion embedding through emotion fusion unit;

[0120] Step 4: using a predictive simultaneous interpretation module, generating multiple translation paths based on the emotion embedding, calculating context history weights, and dynamically adjusting the priority of each translation path based on the context weights;

[0121] Step 5: Use the language adaptation module based on few-shot learning to quickly adapt to domain terms or special language environments. Use the domain context extraction unit to extract domain terms and perform few-shot learning based on a small amount of domain data to generate a domain adaptation model.

[0122] Step 6: The dynamic translation quality assessment and feedback module evaluates the translation results in real time, generates a translation quality score based on contextual consistency and semantic accuracy, optimizes the translation path based on the evaluation result feedback, and finally outputs the translation result.

[0123] This embodiment further updates the emotion perception module and the translation path priority adjustment mechanism in real time according to the user feedback data;

[0124] Dynamically adjust the model parameters of the few-shot learning module based on the user's historical translation records;

[0125] In the multi-language collaborative processing module, translation paths with higher semantic similarity are given priority.

[0126] Example 2

[0127] In this embodiment, the user can personalize the translation options through the system's custom settings interface, including the following functions:

[0128] Translation style selection: Users can choose different translation styles according to different communication occasions. For example, in formal occasions, users can choose more formal and professional translation output; in daily communication, users can choose a more colloquial translation style.

[0129] Sensitivity adjustment of emotion perception: Users can customize the system's sensitivity to the emotion perception module. For example, users can set the system's sensitivity to the speaker's tone, speaking speed, and volume, so that when the other party's emotions change greatly, the system can provide more accurate translation results.

[0130] Multi-language switching shortcut keys: Users can quickly switch input and output languages ​​through system settings, and can save the language switching history.

[0131] Through the personalized settings of the users, the present invention can provide the most suitable translation style and emotion perception accuracy for different types of users and different communication occasions, thereby greatly improving the accuracy of translation and the satisfaction of the users.

[0132] Example 3

[0133] In this embodiment, an information integration function based on multimodal perception is introduced, which captures the speaker's facial expressions through a camera and combines them with voice data to improve the translation quality. The specific steps are as follows:

[0134] Multimodal information collection: When translating, the system not only captures the user's voice through the voice input module, but also captures the speaker's facial expressions, eyes, posture and other non-verbal information in real time through the camera.

[0135] Multimodal emotion fusion: The system integrates facial expression information with speech emotion information and text emotion information. For example, when a speaker smiles, the system will further determine the speaker's emotion based on the tone of voice and expression, thereby adjusting the translation content.

[0136] Optimized translation generation: Based on the information of emotional integration, the system generates more accurate and natural translation results. For example, if the speaker smiles and uses a relaxed tone in a formal setting, the system will adjust the translation to a slightly humorous form to better match the speaker's actual emotional expression.

[0137] The present invention can accurately understand the speaker's intention through comprehensive analysis of speech, text and visual information, thereby generating translation content that is more in line with actual emotions. The integration of multimodal information effectively enhances the system's translation capabilities in complex emotional communication scenarios.

[0138] Example 4

[0139] This experiment evaluated the performance of the AI ​​real-time translation system in three key areas: translation style, emotion recognition, and terminology translation, in order to verify the actual application effect and technical advantages of the system.

[0140] Experimental design:

[0141] Translation style testing: In formal and informal scenarios, users customize their translation style through the system and evaluate its accuracy, contextual consistency, and user satisfaction.

[0142] Multimodal emotion recognition test: By comparing the emotion recognition accuracy of single modality (voice) and multimodality (voice + facial expression), the system's emotion perception ability is verified.

[0143] Domain terminology translation test: Test the system’s terminology translation accuracy based on few-shot learning in the medical and legal fields, and compare the performance of this system with traditional large-scale language models in specific fields.

[0144] Experimental data:

[0145]

[0146] Data description:

[0147] Translation style test: The formal translation style provided by the system in formal occasions is highly accurate, and user satisfaction has reached 90%. In daily chat scenarios, users prefer informal translation styles, with a satisfaction rate of 92%, slightly higher than the formal translation style. The choice of translation style in different scenarios reflects the flexibility and personalization of the system.

[0148] Multimodal emotion recognition test: The accuracy of multimodal emotion recognition combined with facial expressions was 96%, significantly higher than the 83% that relied solely on voice input, indicating that multimodal perception can significantly improve the accuracy of emotion recognition.

[0149] Terminology translation test: In the medical field, the accuracy of terminology translation based on few-shot learning is 95%; in the legal field, the accuracy of terminology translation is 93%. This is better than the performance of traditional large-scale language models in specific fields, especially when dealing with highly professional terms.

[0150] Experimental data show that the system of the present invention performs well in multiple key areas, especially in emotion recognition, personalized translation and domain terminology translation, where the system demonstrates obvious technical advantages. Through different scenario tests, the system can provide personalized services according to user needs and effectively improve translation accuracy and user satisfaction.

[0151] In summary, the present invention realizes real-time translation in multilingual international remote communication by integrating a voice input module, an emotion perception module, a predictive simultaneous interpretation module, a language adaptation module based on few-sample learning, a dynamic translation quality assessment and feedback module, and a multilingual collaborative processing module. Each module works together to effectively overcome the shortcomings of the prior art in emotion recognition, context processing, and domain adaptability. Compared with traditional translation systems, the present invention has the following significant advantages: it can accurately capture and transmit emotional information, ensure the consistency of context, quickly adapt to the terminology requirements of different fields, and continuously optimize the translation path through dynamic feedback, thereby improving the fluency and accuracy of translation. The present invention has wide applicability and high practical value in applications in multilingual, cross-cultural, and professional fields.

[0152] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0153] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0154] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. AI-based international remote translation system, characterized by: include: A voice input module, used to receive the user's real-time voice and convert it into text data; The emotion perception module is used to process the received voice data and text data simultaneously and recognize the emotional information in the voice or text in real time; A predictive simultaneous interpretation module generates multiple translation paths based on the sentiment information and a large language model, and dynamically adjusts the priority of the translation paths by introducing context history weights; Language adaptation module based on few-shot learning, which can quickly adapt to domain terminology or special language environment through few-shot learning; Dynamic translation quality assessment and feedback module, which is used to conduct real-time assessment based on contextual consistency and semantic accuracy after the translation is completed, and continuously optimize the translation path based on the assessment feedback; The multi-language collaborative processing module is used to automatically identify the input language and dynamically adjust the translation model in a multi-language environment.

2. The AI-based international remote translation system according to claim 1, characterized in that: The voice input module comprises: A voice capture unit, used for receiving a user's voice signal; The speech noise reduction unit uses an adaptive filtering algorithm to reduce the noise of the speech signal; The context extraction unit is used to extract context information from the speech signal and generate a context tag.

3. The AI-based international remote translation system according to claim 1, characterized in that: The emotion perception module comprises: Speech emotion recognition unit, used to extract speech features, including intonation, speaking speed, volume, and generate speech emotion information; The text sentiment analysis unit is used to perform sentiment analysis on the text data and identify the sentiment words and semantic tendencies in the text; the sentiment fusion unit is used to combine the speech sentiment information and the text sentiment information to generate the sentiment embedding E combined , the generation formula of the emotion embedding is: AND combined =concat(E audio ,AND text ,C t ) Among them, E audio is the emotional information extracted from speech features, E text is the sentiment information extracted from the text, C t The current context information.

4. The AI-based international remote translation system according to claim 3, characterized in that: The emotion fusion unit comprises: The adaptive learning unit is used to dynamically adjust the generation method of emotion embedding through continuous learning based on the user's personalized voice features and text usage habits.

5. The AI-based international remote translation system according to claim 1, characterized in that: The predictive simultaneous interpretation module includes: A translation path generating unit, used for generating a plurality of translation paths based on the emotion information; The context weight calculation unit is used to calculate the context history weight H based on the context information of the conversation. t ; The path priority adjustment unit is used to adjust the path priority based on the context history weight H t and the probability of the translation path P(T i |C t ) Dynamically adjust the priority of the translation path. The priority adjustment formula is: P(T i |C t ,H t )=α*P(T i |C t )+β*H t Among them, P(T i |C t ) is the probability of the current translation path; H t is the historical context weight; α and β are adaptive weight parameters used to balance the selection of context history and current translation path.

6. The AI-based international remote translation system according to claim 1, characterized in that: The predictive simultaneous interpretation module also includes: The semantic conflict detection unit is used to detect semantic conflicts in multiple translation paths and select the optimal translation path to eliminate the semantic conflicts by comparing the sentiment information and context information.

7. The AI-based international remote translation system according to claim 1, characterized in that: The language adaptation module based on few-sample learning includes: Domain context extraction unit, used to extract domain terms and context markers; Few-shot learning unit, used to train models based on a small amount of domain data and generate domain adaptation models; Reinforcement learning unit, used to define the state S t 、Action A t and the reward mechanism R t To optimize the translation path, the optimization formula is: Among them, Q(S t ,A t ) represents the action value in the current state; η is the learning rate; γ is the discount factor; The best expected reward for the next state.

8. The AI-based international remote translation system according to claim 1, characterized in that: The few-sample learning module also includes: The knowledge graph extension unit is used to associate domain terms with concepts in the existing knowledge base and dynamically adapt the translation model to domain needs.

9. The AI-based international remote translation system according to claim 1, characterized in that: The dynamic translation quality assessment and feedback module includes: The quality scoring unit is used to score the translation results based on contextual consistency and semantic accuracy to generate a translation quality score Q s , the calculation formula is: Q s =λ1*sim(T ref ,T out )+λ2*C consistency Among them, T ref For reference translation; T out The translation result generated by the system; C consistency is the context consistency score; λ1 and λ2 are the score weight parameters; A feedback optimization unit is used to adjust the translation path selection according to the quality score.

10. The AI-based international remote translation system according to claim 1, characterized in that: The multi-language collaborative processing module includes: Language detection unit, used to automatically detect the source language and target language; The language embedding calculation unit selects the best translation path based on the language embedding similarity. The calculation formula of embedding similarity is: M(L1,L2)=argmaxsim(EL1,EL2) Among them, M(L1,L2) is the embedding mapping between the source language L1 and the target language L2; sim(EL1,EL2) is the embedding similarity between the source language L1 and the target language L2.

11. A method for implementing an AI-based international remote translation system according to any one of claims 1 to 10, characterized in that: The method comprises the following steps: Step 1: Receive a user's real-time voice signal through a voice input module, convert the voice signal into text data, and extract context information from the voice signal to generate a context tag; Step 2: The emotion perception module processes the speech data and the text data in parallel, extracts and identifies the emotion information therein, and generates speech emotion information and text emotion information; Step 3: Combine speech emotion information and text emotion information to generate emotion embedding through emotion fusion unit; Step 4: using a predictive simultaneous interpretation module, generating multiple translation paths based on the emotion embedding, calculating context history weights, and dynamically adjusting the priority of each translation path based on the context weights; Step 5: Use the language adaptation module based on few-shot learning to quickly adapt to domain terms or special language environments. Use the domain context extraction unit to extract domain terms and perform few-shot learning based on a small amount of domain data to generate a domain adaptation model. Step 6: The dynamic translation quality assessment and feedback module evaluates the translation results in real time, generates a translation quality score based on contextual consistency and semantic accuracy, optimizes the translation path based on the evaluation result feedback, and finally outputs the translation result.

12. The method for implementing the AI-based international remote translation system according to claim 11, characterized in that: The method further comprises the following steps: Update the emotion perception module and translation path priority adjustment mechanism in real time based on user feedback data; Dynamically adjust the model parameters of the few-shot learning module based on the user's historical translation records; In the multi-language collaborative processing module, translation paths with higher semantic similarity are given priority.

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