Multi-language front-end interface automatic localization method and system based on AI

Abstract

The invention discloses an AI-based multi-language front-end interface automatic localization method and system. The automatic localization method comprises the following steps: automatically loading and displaying a corresponding translation text according to language preference of a user; the language preference of the user is detected and recognized, and a translation version is automatically selected; automatically translating the application content by using machine learning and natural language processing technologies; adjusting the translation content according to the cultural background of the target language; the dynamic content is translated in real time; training the translation data based on a deep learning model; and multi-language resources are managed in a unified manner. And through an artificial intelligence technology, translation and localization of a multi-language front-end interface are automatically realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of front-end interface processing, and in particular to a multi-language front-end interface automatic localization method and system based on AI. BACKGROUND

[0002] With the popularization of the global Internet and the development of multinational enterprises, multi-language front-end interfaces have become a basic requirement for global applications. Front-end applications not only need to adapt to users in different regions, but also need to provide interface experiences that conform to local languages and cultures. Therefore, multi-language localization of front-end interfaces has become one of the important challenges faced by developers.

[0003] Currently, the localization of front-end interfaces usually relies on manual translation and manual management of language resource files. This process usually includes translating text content, adapting the layout and layout of different languages, and adjusting the performance of text and interactive elements according to different cultural backgrounds. This traditional manual translation and configuration method has some obvious shortcomings:

[0004] High labor costs: Multi-language support requires a large amount of manual translation and proofreading, especially for large-scale applications, the workload of translators is huge and the translation content needs to be updated frequently, increasing the development and operation costs.

[0005] Translation quality and consistency problems: In the process of manual translation, there are problems such as inaccurate translation, unclear context understanding, or inconsistent style. Especially between multiple language versions, there may be inconsistencies in translation, affecting user experience.

[0006] Translation update lag: As the application updates and iterates, the text content of multiple language versions often needs to be updated simultaneously. The traditional translation update method often has a delay, resulting in delayed multi-language version updates.

[0007] Cultural adaptation problems: Users in different languages and cultural backgrounds have different understandings and expectations of content, and manual translation is difficult to accurately capture cultural differences, which may result in large differences in user experience.

[0008] Drawbacks of existing technologies:

[0009] Poor translation accuracy

[0010] Existing machine translation technology, although it can quickly generate translation results, cannot accurately translate according to context and cultural differences. Especially in the translation of professional terms, special situations, and polysemous words, the translation result may not be accurate, affecting user experience. For example, some text translation directly uses literal translation, which cannot consider the cultural background and habits of the target language, resulting in unnatural translation or ambiguity.

[0011] Inconsistent translation quality

[0012] In a solution based on the combination of machine translation and manual translation, although machine translation provides preliminary translation, a large amount of time and effort is still required for manual translation personnel to proofread and correct. Differences in language style, expression, etc. during manual translation may result in inconsistent translation styles between different language versions of the same interface, affecting the overall user experience.

[0013] Real-time updating and dynamic translation difficulties

[0014] Most existing translation solutions rely on static resource files. For frequently updated or dynamically generated content, the front-end page needs to be manually updated with the corresponding language translation content, and the updating process is time-consuming and laborious. Even machine translation systems usually cannot achieve instant translation and dynamic updating, and cannot adapt to rapidly changing application content.

[0015] High complexity of multi-language management

[0016] In multi-language localization, especially the translation management of large-scale applications is very complex. With the increase of supported languages, the management and updating of resource files become more cumbersome, and the differences between multiple language versions need to be coordinated. In addition, version control of translation may also be problematic, resulting in lag or inconsistency of text content in some language versions.

[0017] Insufficient cultural adaptation

[0018] Existing technical solutions usually lack cultural adaptation functions, i.e. cannot adjust text content according to cultural differences in different languages and regions. The influence of cultural background on language is often ignored during translation, which may cause users to misunderstand or feel uncomfortable. For example, the use of words or expression in some languages may have different emotional or cultural colors in different cultures. SUMMARY

[0019] In view of the above problems, the present application is proposed in order to provide an AI-based multi-language front-end interface automatic localization method and system to overcome the above problems or at least partially solve the above problems.

[0020] According to one aspect of the present application, an AI-based multi-language front-end interface automatic localization method is provided, the automatic localization method comprising:

[0021] automatically loading and displaying corresponding translated text according to user language preferences;

[0022] detecting and identifying user language preferences and automatically selecting translation versions;

[0023] using machine learning and natural language processing techniques to automatically translate application content;

[0024] Adjusting translation content according to the cultural background of the target language;

[0025] Real-time translation of dynamic content;

[0026] Training translation data based on deep learning models;

[0027] Unified management of multilingual resources.

[0028] Optionally, the automatic loading and display of corresponding translated text according to the user's language preference specifically includes:

[0029] According to the user's language preference, including browser language settings, language selection of user account;

[0030] Automatic loading and display of corresponding translated text;

[0031] The front-end interface reloads the translation content according to dynamic data updates.

[0032] Optionally, the detection and identification of the user's language preference and automatic selection of the translation version specifically includes:

[0033] Detect and identify the user's language preference, including the language settings of the user's device, browser language, IP address information, and real-time selection of the appropriate language environment for the user.

[0034] Optionally, the automatic translation of application content using machine learning and natural language processing techniques specifically includes:

[0035] Automatic translation of application content using machine learning and natural language processing techniques;

[0036] Intelligent adjustment of translation content according to context and context;

[0037] Responsible for managing all language versions of resources, including dynamic content updates.

[0038] Optionally, the adjustment of translation content according to the cultural background of the target language specifically includes:

[0039] Adjusting translation content according to the cultural background of the target language, considering cultural differences, expression habits, and cultural characteristics of the language, to ensure that the translation content meets the expectations of local users.

[0040] Optionally, the real-time translation of dynamic content specifically includes:

[0041] Real-time capture of changes in the front-end interface content, including user input, interactive interface elements, and instant translation and update to the interface.

[0042] Optionally, the training of the translation data based on the deep learning model specifically includes:

[0043] Based on the deep learning model, the AI optimization module is trained on a large amount of translation data;

[0044] Through continuous learning and feedback, the translation quality is optimized, and adaptive adjustment is made according to the characteristics of the language.

[0045] Optionally, the unified management of the multi-language resources specifically includes:

[0046] The unified management of the multi-language resources includes loading, updating and maintaining of the language files;

[0047] The change of the translation content is automatically detected, and the language resources of the front-end page are updated in time to ensure the consistency and synchronization between the language versions.

[0048] The application also provides an AI-based multi-language front-end interface automatic localization system, which applies the AI-based multi-language front-end interface automatic localization method described above, and the automatic localization system includes:

[0049] The front-end interface module is used for automatically loading and displaying the corresponding translated text according to the language preference of the user;

[0050] The language recognition module is used for detecting and recognizing the language preference of the user, and automatically selecting the translation version;

[0051] The translation management module is used for automatically translating the application content using machine learning and natural language processing technology;

[0052] The cultural adaptation module is used for adjusting the translation content according to the cultural background of the target language;

[0053] The real-time translation module is used for real-time translation of dynamic content;

[0054] The AI optimization module is used for training of the translation data based on the deep learning model;

[0055] The multi-language resource management module is used for unified management of the multi-language resources.

[0056] The application provides an AI-based multilingual front-end interface automatic localization method and system, the automatic localization method comprising: automatically loading and displaying corresponding translated text according to the language preference of a user; detecting and identifying the language preference of the user, and automatically selecting a translated version; automatically translating application content using machine learning and natural language processing technology; adjusting the translated content according to the cultural background of the target language; real-time translation of dynamic content; training translated data based on a deep learning model; and uniformly managing multilingual resources. Through artificial intelligence technology, the translation and localization of the multilingual front-end interface are automatically realized.

[0057] The above description is only a summary of the technical solutions of the application, in order to enable the technical means of the application to be more clearly understood and implemented according to the content of the specification, and in order to enable the above and other purposes, features and advantages of the application to be more apparent and easy to understand, the following specific embodiments of the application are described. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, on the premise of not creating labor, can also obtain other drawings according to these drawings.

[0059] Figure 1 The flow chart of the AI-based multilingual front-end interface automatic localization method provided by the embodiments of the application. DETAILED DESCRIPTION

[0060] The exemplary embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the present disclosure are shown. It should be understood that the present disclosure can be embodied in many forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0061] The terms "include" and "have" and any variations thereof in the specification and claims of the present application and the drawings are intended to cover the non-exclusive inclusion, for example, the inclusion of a series of steps or units.

[0062] The technical solutions of the present application will be further described in detail below in combination with the drawings and embodiments.

[0063] As shown in Figure 1 An AI-based multilingual front-end interface automatic localization method, the automatic localization method comprising:

[0064] According to the user's language preference, automatically load and display the corresponding translated text;

[0065] Detect and identify the user's language preference, automatically select the translation version;

[0066] Use machine learning and natural language processing technology to automatically translate application content;

[0067] Adjust the translation content according to the cultural background of the target language;

[0068] Real-time translation of dynamic content;

[0069] Based on deep learning model, the translation data is trained; the multilingual resources are uniformly managed.

[0070] An AI-based multilingual front-end interface automatic localization system, comprising:

[0071] Front-end interface module, presenting application interface and content, according to the user's language preference (browser language settings, user account language selection) automatically load and display the corresponding translated text. The front-end interface updates according to the dynamic data and reloads the translation content.

[0072] Language recognition module, detects and identifies the user's language preference, including the user's device language settings, browser language, IP address and other information, and selects the appropriate language environment for the user in real time.

[0073] Translation management module, using machine learning and natural language processing technology to automatically translate application content. The translation content is intelligently adjusted according to the context and context to ensure the accuracy and naturalness of the translation. The translation management module is responsible for managing all language versions of resources, including the update of dynamic content

[0074] Cultural adaptation module, according to the cultural background of the target language, adjust the translation content, consider cultural differences, expression habits and cultural characteristics of language, ensure that the translation content meets the expectations of local users.

[0075] Real-time translation module, real-time translation function of dynamic content is realized. It can capture the changes of front-end interface content (such as user input, interactive interface elements) in real time, and translate and update to the interface immediately.

[0076] AI optimization module, based on deep learning model, AI optimization module improves the accuracy and adaptability of translation model through training a large amount of translation data. Through continuous learning and feedback, the translation quality is optimized, and adaptive adjustment is made according to the characteristics of different languages.

[0077] The multi-language resource management module manages the multi-language resources uniformly, including loading, updating and maintaining language files. It can automatically detect changes in translation content and update the language resources of the front-end page in time to ensure consistency and synchronization between different language versions.

[0078] The front-end interface module obtains user language preferences through the language recognition module, then cooperates with the translation management module to obtain translated texts from the translation management module, loads and displays them to the interface.

[0079] The real-time translation module receives new text information during user interaction and timely delivers it to the translation management module for translation, then returns the translation result to the interface for updating.

[0080] The cultural adaptation module and the translation management module work together to adjust the translation result based on context understanding and cultural differences.

[0081] The AI optimization module continuously optimizes the translation management module to ensure the accuracy and fluency of translation.

[0082] The multi-language resource management module continuously synchronizes and updates the translation content of all language versions to ensure that the front-end page can load the latest translation text in real time.

[0083] The workflow of the present application includes the following steps:

[0084] Language detection and user preference recognition;

[0085] The system first detects the user's language preference through the language recognition module, which includes device language settings, browser language, location (through IP address), and user selection information.

[0086] According to the recognition result, the system selects the appropriate language environment for the user and loads the corresponding language resources.

[0087] Content loading and intelligent translation

[0088] The front-end interface loads application content, and all text content (such as button text, prompt information) will be transmitted to the translation management module.

[0089] The translation management module uses deep learning models to translate text content. At this time, translation is not only based on literal translation, but also considers context and context for optimization.

[0090] With the help of the cultural adaptation module, the translation content will be further adjusted to ensure compliance with the culture and habits of the target language.

[0091] Dynamic content real-time translation

[0092] When users interact with the page (such as clicking, inputting), the real-time translation module will translate the new content on the interface and automatically update it to the page.

[0093] The real-time translation module can handle dynamic content such as form input, real-time data, etc., ensuring the immediacy and accuracy of translation.

[0094] Multi-language resource synchronization

[0095] The multi-language resource management module is responsible for synchronously updating the content of all language versions, ensuring the consistency of translated content among different language versions.

[0096] The updated translation content will take effect immediately, and the front-end interface will reload the text resources according to the latest translation file.

[0097] User experience optimization

[0098] The system continuously adjusts translation content and localization solutions based on user usage habits and cultural preferences (through the AI optimization module) to improve user experience.

[0099] Through the continuous learning and feedback of the AI optimization module, the accuracy and naturalness of the system's translation gradually improve over time.

[0100] Benefits:

[0101] AI-based automatic translation and optimization mechanism, combined with cultural adaptation and context optimization translation system.

[0102] Real-time translation and dynamic update interface capabilities support real-time text translation based on user input or language environment.

[0103] Cultural adaptation through AI ensures that translation is not just a language conversion, but also conforms to the expression methods and habits of the target culture.

[0104] Multi-language switching function of user interface, and can automatically update and adjust translation content according to user preferences.

[0105] Automated translation process and AI interaction, reducing manual intervention, improving system intelligence and efficiency.

[0106] API and modular design make the translation service flexible and adaptable to different technical requirements and environmental changes.

[0107] The above detailed description of the specific implementation is further detailed for the purpose, technical solution and beneficial effect of the present application, and it should be understood that the above is only the specific implementation of the present application and is not used to limit the protection scope of the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. An AI-based method for automatic localization of multilingual front-end interfaces, characterized in that, The automatic localization method includes: Automatically load and display the corresponding translated text based on the user's language preferences; Detect and identify the user's language preferences and automatically select the translation version; Use machine learning and natural language processing technologies to automatically translate application content; Adjust the translation content according to the cultural background of the target language; Real-time translation of dynamic content; The translation data is trained using a deep learning model; Implement unified management of multilingual resources.

2. The AI-based automatic localization method for multilingual front-end interfaces according to claim 1, characterized in that, The automatic loading and display of corresponding translated text based on the user's language preferences specifically includes: Based on the user's language preferences, including browser language settings and the user account's language selection; Automatically load and display the corresponding translated text; The front-end interface reloads the translated content based on dynamic data updates.

3. The AI-based automatic localization method for multilingual front-end interfaces according to claim 1, characterized in that, The process of detecting and identifying the user's language preferences and automatically selecting a translation version specifically includes: It detects and identifies users' language preferences, including the language settings of the user's device, browser language, and IP address information, and selects the appropriate language environment for the user in real time.

4. The AI-based automatic localization method for multilingual front-end interfaces according to claim 1, characterized in that, The automatic translation of application content using machine learning and natural language processing technologies specifically includes: Use machine learning and natural language processing technologies to automatically translate application content; The translated content is intelligently adjusted based on the context and language. Responsible for managing resources for all language versions, including updates to dynamic content.

5. The AI-based automatic localization method for multilingual front-end interfaces according to claim 1, characterized in that, The adjustment of translation content based on the cultural background of the target language specifically includes: The translation should be tailored to the cultural context of the target language, taking into account cultural differences, expression habits, and the cultural characteristics of the language, to ensure that the translation meets the expectations of local users.

6. The AI-based automatic localization method for multilingual front-end interfaces according to claim 1, characterized in that, The real-time translated dynamic content specifically includes: Capture changes in the front-end interface content in real time, including user input and interactive interface elements, and translate and update them on the interface instantly.

7. The AI-based automatic localization method for multilingual front-end interfaces according to claim 1, characterized in that, The training of translation data based on a deep learning model specifically includes: Based on a deep learning model, the AI ​​optimization module is trained on a large amount of translation data; We optimize translation quality through continuous learning and feedback, and make adaptive adjustments based on the characteristics of the language.

8. The AI-based automatic localization method for multilingual front-end interfaces according to claim 1, characterized in that, The unified management of multilingual resources specifically includes: Unified management of multilingual resources, including loading, updating and maintaining language files; It automatically detects changes in the translated content and updates the language resources on the front-end page in a timely manner to ensure consistency and synchronization between different language versions.

9. An AI-based multilingual front-end interface automatic localization system, employing the AI-based multilingual front-end interface automatic localization method described in any one of claims 1-8, characterized in that, The automated localization system includes: The front-end interface module is used to automatically load and display the corresponding translated text based on the user's language preferences; The language recognition module is used to detect and identify the user's language preferences and automatically select the translation version. The translation management module is used to automatically translate application content using machine learning and natural language processing technologies; The culture adaptation module is used to adjust the translated content according to the cultural background of the target language. The real-time translation module is used for translating dynamic content in real time. The AI ​​optimization module is used to train translation data based on a deep learning model; The multilingual resource management module is used for unified management of multilingual resources.

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