Vehicle multi-language adaptive translation method, device and equipment and storage medium

Abstract

The invention discloses an in-vehicle multi-language adaptive translation method, device and equipment and a storage medium, and relates to the technical field of intelligent translations. The method comprises the following steps: acquiring imported to-be-translated data and a target translation type; translating the to-be-translated data according to a preset translation corpus and the target translation type to obtain an initial translation file; and calibrating the initial translation file based on a preset calibration strategy to obtain a target translation file. By using the preset training rule and the natural language processing capability of the large model, the problems that the icon interface is not matched and the multi-language localization translation is inaccurate due to different lengths of different languages in the current multi-language translation of the vehicle are solved, and the accuracy and adaptability of the multi-language interface translation of the vehicle are improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent translation, and in particular to a vehicle-mounted multi-language adaptation translation method, device, equipment and storage medium. Background Art

[0002] In the car system, with the acceleration of the globalization process, the existing car multi-language translation solutions have many shortcomings. First, each icon component of the car interface has a fixed size, and in the multi-language translation scenario, due to the differences in character length and typesetting of different languages, the length of the English, Russian, Arabic and other languages ​​corresponding to the content of Chinese characters will exceed the fixed size of the original icon. This leads to frequent problems such as text out of the box and word wrapping during the translation process, which seriously affects the user experience. Secondly, different cultural languages ​​have their own fixed language habits and language culture, which requires the translation to not only accurately convey the original meaning, but also conform to the cultural background and expression habits of the target language. However, the existing car translation system often ignores this point, resulting in the translation results not being able to express the meaning, or even misunderstandings. And although the existing methods can achieve multi-language translation, they are not optimized for the special needs of the car system, and cannot solve the problems of car software language habit adaptation and car UI adaptation.

[0003] Therefore, how to improve the accuracy and adaptability of the translation of the multi-language interface of the car computer is a problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of this application is to provide a vehicle-mounted multi-language adaptation translation method, device, equipment and storage medium, aiming to solve the technical problem of how to improve the accuracy and adaptability of vehicle-mounted multi-language interface translation.

[0005] To achieve the above objectives, the present application proposes a vehicle-mounted multi-language adaptation translation method, the method comprising:

[0006] Get the imported data to be translated and the target translation type;

[0007] Translating the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file;

[0008] The initial translation file is calibrated based on a preset calibration strategy to obtain a target translation file.

[0009] In one embodiment, the step of calibrating the initial translation file based on a preset calibration strategy to obtain a target translation file includes:

[0010] According to the preset calibration strategy, the semantic calibration rule, the adaptation calibration rule and the character length threshold of the current vehicle computer are obtained;

[0011] The initial translation file is calibrated according to the semantic calibration rule, the adaptation calibration rule and the character length threshold of the current vehicle computer to obtain a target translation file.

[0012] In one embodiment, the step of calibrating the initial translation file according to the semantic calibration rule, the adaptation calibration rule and the character length threshold of the current vehicle computer to obtain the target translation file includes:

[0013] Performing semantic calibration on the initial translation file according to the semantic calibration rule to obtain a revised translation file;

[0014] The modified translation file is adapted and calibrated according to the adaptation and calibration rule and the character length threshold to obtain a target translation file.

[0015] In one embodiment, the step of performing semantic calibration on the initial translation file according to the semantic calibration rule to obtain a revised translation file comprises:

[0016] Performing semantic alignment detection on the initial translation file according to the semantic alignment rule to obtain a semantic matching degree;

[0017] When the semantic matching degree is lower than a preset semantic matching degree, updating the preset translation corpus to obtain a revised corpus;

[0018] The initial translation file is semantically calibrated according to the revised corpus to obtain a revised translation file.

[0019] In one embodiment, the step of performing adaptation and calibration on the revised translation file according to the adaptation and calibration rule and the character length threshold to obtain a target translation file comprises:

[0020] Performing adaptation and calibration detection on the revised translation file according to the adaptation and calibration rule to obtain the character length;

[0021] When the character length is greater than the character length threshold, obtaining a preset adaptation strategy;

[0022] The characters in the revised translation file are adjusted according to the preset adaptation strategy to obtain a target translation file.

[0023] In one embodiment, the step of adjusting the characters in the revised translation file according to the preset adaptation strategy to obtain the target translation file includes:

[0024] Reduce or replace characters in the revised translation file according to the preset adaptation strategy, and integrate the adjusted content into the revised corpus to obtain the target corpus;

[0025] The revised translation file is adjusted according to the target corpus to obtain a target translation file.

[0026] In one embodiment, the step of reducing or replacing characters in the revised translation file according to the preset adaptation strategy includes:

[0027] Obtaining a first adaptation ratio and a second adaptation ratio according to the preset adaptation strategy;

[0028] When the ratio of the character length to the character length threshold is greater than a first adaptation ratio and less than or equal to a second adaptation ratio, replacing the characters in the revised translation file;

[0029] When the ratio of the character length to the character length threshold is greater than a second adaptation ratio, the characters in the revised translation file are reduced in format.

[0030] In addition, to achieve the above purpose, the present application also proposes a vehicle-mounted multi-language adaptation translation device, the device comprising:

[0031] The data import module is used to obtain the imported data to be translated and the target translation type;

[0032] A file translation module, used for translating the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file;

[0033] The format calibration module is used to calibrate the initial translation file based on a preset calibration strategy to obtain a target translation file.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle-mounted multi-language adaptation translation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the vehicle-mounted multi-language adaptation translation method as described above.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the vehicle-computer multi-language adaptation translation method as described above are implemented.

[0036] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the vehicle-computer multi-language adaptation translation method as described above are implemented.

[0037] The present application provides a method for multi-language adaptation translation of a vehicle computer, and the method of the present application includes: obtaining imported data to be translated and a target translation type; translating the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file; calibrating the initial translation file based on a preset calibration strategy to obtain a target translation file. In summary, the present application solves the current problem of incompatible icon interface and inaccurate localized translation of multiple languages ​​in vehicle computer multi-language translation due to the different lengths of different languages ​​by utilizing preset training rules and the natural language processing capabilities of a large model, thereby improving the accuracy and adaptability of vehicle computer multi-language interface translation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A flowchart diagram of the first embodiment of the vehicle-mounted multi-language adaptation translation method of the present application;

[0041] Figure 2 This is a detailed flowchart of multi-language translation in an embodiment of the multi-language adaptation translation method for a vehicle computer of the present application;

[0042] Figure 3 A flowchart diagram of the second embodiment of the vehicle-mounted multi-language adaptation translation method of the present application;

[0043] Figure 4 A flowchart diagram of the third embodiment of the vehicle-mounted multi-language adaptation translation method of the present application;

[0044] Figure 5 This is a schematic diagram of the module structure of the vehicle multi-language adaptation translation device according to an embodiment of the present application;

[0045] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the vehicle-mounted multi-language adaptation translation method in the embodiment of the present application.

[0046] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The main solution of the embodiment of the present application is: obtaining imported data to be translated and a target translation type; translating the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file; and calibrating the initial translation file based on a preset calibration strategy to obtain a target translation file.

[0050] In the car system, with the acceleration of the globalization process, the existing car multi-language translation scheme has many shortcomings. First, each icon component of the car interface has a fixed size, and in the multi-language translation scenario, due to the differences in character length and typesetting of different languages, the length of English, Russian, Arabic and other languages ​​corresponding to the content of Chinese characters will exceed the fixed size of the original icon. This leads to frequent problems such as text out of the box and word wrapping during the translation process, which seriously affects the user experience. Secondly, different cultural languages ​​have their fixed language habits and language culture, which requires the translation to not only accurately convey the original meaning, but also conform to the cultural background and expression habits of the target language. However, the existing car translation system often ignores this point, resulting in the translation results not being able to express the meaning, or even misunderstandings. And although the existing methods can achieve multi-language translation, they are not optimized for the special needs of the car system, and cannot solve the problems of car software language habit adaptation and car UI adaptation. Therefore, how to improve the accuracy and adaptability of car multi-language interface translation is a problem that needs to be solved urgently.

[0051] This application solves the current problem of incompatible icon interface and inaccurate multi-language localization translation in vehicle computers due to the different lengths of different languages ​​by utilizing preset training rules and the natural language processing capabilities of large models, thereby improving the accuracy and adaptability of multi-language interface translation in vehicle computers.

[0052] It should be noted that the execution subject of this embodiment can be a vehicle-mounted multi-language adaptation translation system, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device that can realize the above-mentioned vehicle-mounted multi-language adaptation translation function, etc., and this embodiment does not specifically limit this. The following takes the vehicle-mounted multi-language adaptation translation system as an example to illustrate this embodiment and the following embodiments.

[0053] Based on this, the embodiment of the present application provides a vehicle multi-language adaptation translation method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle-mounted multi-language adaptation translation method of the present application.

[0054] In this embodiment, the vehicle computer multi-language adaptation translation method includes steps S10 to S30:

[0055] Step S10: Obtain the imported data to be translated and the target translation type.

[0056] It should be noted that in this step, the system will obtain the target translation type that the user selects through the vehicle interface. That is, into which language the interface data to be translated is desired to be translated, such as English, French, German, etc. After the system receives the user's translation instruction, it will extract the data to be translated of the vehicle interface from the database of the vehicle system and record the target translation type specified by the user. In addition, it should be noted that the data to be translated can be any text data in the vehicle system (such as menu items, button texts, prompt information, etc.), as long as it needs to be translated into the corresponding language. The target translation type refers to the specific language type into which the user wishes the data to be translated to be translated.

[0057] It can be understood that the role of this step is to provide the original file to be translated and the translation direction for the subsequent translation process.

[0058] Step S20: translating the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file.

[0059] It should be noted that in this step, the system will call the preset large language model and parameters from the preset translation corpus according to the target translation type specified by the user. Subsequently, the system will use the large language model and parameters to translate the data to be translated and generate a preliminary translation file. It can be understood that the role of this step is to use the deep learning and natural language processing capabilities of the large language model to achieve automated multilingual translation. For example, when the user chooses to translate the Chinese car interface into English, the translation model will first identify the various elements (such as words, phrases, etc.) in the car interface text and search for the corresponding English translation in the translation corpus. At the same time, the large model will also make appropriate adjustments and optimizations to the translation results based on the contextual information of the text to ensure the accuracy of the translation results. Finally, the large model will output the translated text in the form of a file as the initial translation file.

[0060] In addition, it should be noted that the preset translation corpus is a continuously updated database that contains a large amount of translation examples and corpus information. This information is obtained through deep learning and analysis of a large amount of translation texts, and can provide rich translation references and basis for the translation system. At the same time, the corpus will be regularly updated and optimized based on user feedback and usage to improve the performance and accuracy of the translation system.

[0061] Step S30: calibrating the initial translation file based on a preset calibration strategy to obtain a target translation file.

[0062] It should be noted that if Figure 2 As shown, in this step, the system will calibrate the initially generated translation file according to the preset calibration strategy. It can be understood that the role of this step is to further correct and optimize the translation results to ensure the adaptability and accuracy of the translation results to different vehicle interface elements. The calibration strategy includes but is not limited to semantic judgment, UI adaptability monitoring and other aspects. Specifically, the system will perform semantic judgment on the translation results. This step mainly uses the automobile general expression monitoring module to perform semantic analysis on the translated text to check whether there is unreasonable translation or unclear semantics. If these problems exist, the system will correct or adjust the translation results to ensure the accuracy and professionalism of the translation results. Then the system will monitor the UI adaptability of the translation results. If it is found that the translated text exceeds the display range of the UI interface or there are typesetting problems, the system will make appropriate adjustments and optimizations to the text. Such as abbreviations, replacement of short character words, and reduction of text font size, etc., to ensure that the translation results can be perfectly adapted to the UI interface. Finally, the calibrated translation file will be output as the target translation file. This file has been processed through a strict translation and calibration process, has high accuracy and adaptability, and can be directly used for multi-language adaptation and updating of the vehicle system.

[0063] In addition, it should be noted that the preset calibration strategy is a continuous optimization process. As the various elements in the vehicle interface are updated, the system will continuously update and improve the calibration strategy of the corresponding vehicle interface to improve the accuracy and adaptability of the translation results. In addition, since the layout, text, element size and other contents of the vehicle interface of most different cars are different, the preset calibration strategy will also be adaptively adjusted according to the vehicle interface type of different vehicles.

[0064] In a feasible implementation manner, the step S30 specifically includes:

[0065] Step S301: acquiring semantic calibration rules, adaptation calibration rules and a character length threshold of the current vehicle computer according to a preset calibration strategy.

[0066] It should be noted that the preset calibration strategy is a complete and systematic calibration process that is pre-set. It includes two core contents: semantic calibration rules and adaptation calibration rules, as well as specific threshold settings for the length of text characters of different elements in the current car computer interface. The semantic calibration rules are mainly used to determine whether the translated content in the initial translation file accurately conveys the meaning of the original text, and whether there are semantic deviations or misunderstandings. These rules are based on the statistics of a large corpus and deep learning of large language models, and can automatically identify and correct semantic errors in translation. The adaptation calibration rules focus on ensuring that the translated content can be adapted to the UI interface of the car computer, avoiding problems such as disordered interface layout or incomplete information display due to differences in the length of the translated language.

[0067] In addition, it should be noted that the character length threshold is determined according to the specific design of the vehicle interface, which takes into account factors such as the size and layout of the interface elements and the user's reading logic. Therefore, in actual applications, the character length threshold may vary depending on factors such as vehicle model, interface version or user preference.

[0068] Step S302: calibrating the initial translation file according to the semantic calibration rule, the adaptation calibration rule and the character length threshold of the current vehicle computer to obtain a target translation file.

[0069] It should be noted that after obtaining the semantic calibration rules, adaptation calibration rules and character length threshold, the system will start formal calibration of the initial translation file. This step includes operations such as semantic accuracy check of the translated content, character length adaptability adjustment and necessary format optimization. Specifically, in this step, the large language model will first check each sentence in the initial translation file one by one according to the semantic calibration rules to determine whether it accurately conveys the meaning of the original text. If there is a semantic deviation or misunderstanding, the large language model will automatically make corrections or make modification suggestions for manual review. Then, the large language model will adjust the character length of the translated content according to the adaptation calibration rules and character length threshold. If the character length of the corresponding sentence or vocabulary in a certain element exceeds the character length threshold of the corresponding element in the current car interface, the large language model will try to use abbreviations, replace short character vocabulary, reduce the font size and other solutions for adjustment. At the same time, the large language model will also use visualization tools to pre-process the UI adaptability to ensure that the display effect of the translated content on the interface meets the design requirements.

[0070] It can be understood that after the fine calibration of the above steps, the final target translation file will not only convey the meaning of the original text, but also adapt to the car UI interface, providing car users with a smoother and more comfortable reading experience.

[0071] This embodiment provides a method for multi-language adaptation translation of a vehicle computer, and the method of this embodiment includes: obtaining imported data to be translated and a target translation type; translating the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file; calibrating the initial translation file based on a preset calibration strategy to obtain a target translation file. In summary, this embodiment solves the problem of incompatibility of icon interfaces and inaccurate localized translation of multiple languages ​​in the current vehicle computer multi-language translation due to the different lengths of different languages, and improves the accuracy and adaptability of the vehicle computer multi-language interface translation.

[0072] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 3 , Figure 3 This is a flow chart of the second embodiment of the vehicle multi-language adaptation translation method of the present application, wherein step S302 specifically includes:

[0073] Step A10: semantically calibrate the initial translation file according to the semantic calibration rule to obtain a revised translation file.

[0074] It should be noted that in this step, the system will input the initial translation file into the automotive general expression monitoring module, which has built-in semantic calibration rules. These rules are based on a large number of automotive industry terms and are constructed using expressions and language logic. The module will analyze each sentence, phrase and word in the initial translation file one by one to determine whether it conforms to the general expression habits of the automotive industry and whether there are semantic ambiguities or errors. Once content that does not conform to the semantic calibration rules is found, the module will immediately mark it and give suggestions and corrections based on the large language model. Subsequently, the system will make corresponding corrections to the initial translation file based on these corrections to obtain a corrected translation file. It can be understood that the semantic calibration rules are a dynamically updated process. With the continuous emergence of new terms in the automotive industry and the update of the car-machine interface, the system will regularly update the semantic calibration rule base to ensure the accuracy and timeliness of the translation results.

[0075] In a feasible implementation manner, the step A10 specifically includes:

[0076] Step A101: performing semantic alignment detection on the initial translation file according to the semantic alignment rule to obtain a semantic matching degree.

[0077] It should be noted that in this step, the system will analyze the sentences, phrases or words corresponding to each element in the initial translation file one by one according to the semantic calibration rules and the large language model to determine whether the translated content conforms to the common expression habits of the automotive industry and whether it accurately conveys the meaning of the original text. Through this detection process, the system will give a semantic match, which reflects the degree of conformity between the initial translation file and the semantic calibration rules.

[0078] Step A102: When the semantic matching degree is lower than a preset semantic matching degree, updating the preset translation corpus to obtain a revised corpus.

[0079] It should be noted that in this step, when the semantic matching degree given by the automobile general expression monitoring module is lower than the preset threshold (for example, the preset threshold is 85%, and if it is lower than this value, the translation quality is considered to be substandard), the system will trigger the update mechanism. This mechanism includes two parts: one is automatic update, the system will automatically search for relevant translations on the Internet through the wrong translation content, and add it to the translation corpus; the other is manual intervention update, the system will feedback the wrong translation content and related context information to the R&D personnel or language experts for manual review and correction, and then add the correct translation content to the translation corpus. Through the update of these two parts, the system can obtain a corrected corpus, which has improved translation accuracy and richness. In addition, it should be noted that the preset semantic matching degree threshold is a parameter that can be adjusted according to actual needs, and it varies according to the vehicle interface elements, language and user settings.

[0080] Step A103: semantically calibrating the initial translation file according to the revised corpus to obtain a revised translation file.

[0081] It should be noted that in this step, after obtaining the revised corpus, the system will once again perform semantic calibration on the initial translation file based on the large speech model. It is understandable that this semantic calibration is based on the revised corpus, so it can more accurately identify and correct errors in the translation. After this step, the system will output a revised translation file, which has been improved in terms of semantic accuracy, standardization of industry terminology, and adaptability to cultural differences. In addition, it should be noted that the revised translation file is not the final translation result. In actual applications, R&D personnel will further adjust and optimize the process of generating revised translation files based on factors such as the specific layout of the vehicle interface, icon size, and user feedback.

[0082] Step A20: performing adaptation and calibration on the revised translation file according to the adaptation and calibration rule and the character length threshold to obtain a target translation file.

[0083] It should be noted that after obtaining the corrected translation file, the system will process it according to the adaptability monitoring module of the vehicle computer. This module will further analyze and process the corrected translation file according to the character length threshold of the current vehicle computer (that is, the maximum number of characters that each vehicle computer interface, icon or pop-up window can accommodate) and the adaptation calibration rules. Such as abbreviation, replacement of short character words or reducing the font size. Subsequently, the system will make corresponding adjustments to the corrected translation file according to the adaptation calibration rules to obtain the target translation file adapted to the vehicle computer interface.

[0084] In addition, it should be noted that the adaptation calibration rules will dynamically adjust the character length threshold according to the specific UI type, pop-up window size, and icon size in the current vehicle interface to ensure that the character length of the translated content is within a reasonable display range.

[0085] In a feasible implementation manner, the step A20 specifically includes:

[0086] Step A201: performing adaptation and calibration detection on the revised translation file according to the adaptation and calibration rule to obtain character length.

[0087] It should be noted that the adaptation calibration rules are pre-set and are used to detect whether the character length in the revised translation file meets the display requirements of the vehicle UI interface. It is understandable that this step is implemented through the UI adaptability monitoring module in the large voice model, which can automatically read the revised translation file and detect and calculate the character length to obtain the character length of the corresponding element in each UI interface. At the same time, by comparing the design specifications of each UI interface (such as pop-up window size, icon size, etc.), the module can determine the maximum character length that can be accommodated by the corresponding element in each interface, that is, the character length threshold.

[0088] Step A202: When the character length is greater than the character length threshold, obtain a preset adaptation strategy.

[0089] It should be noted that when the character length of an element in the translated car interface is greater than the character length threshold, the large language model will automatically trigger the acquisition process of the preset adaptation strategy. The preset adaptation strategy is pre-set and is used to solve the problem of character length exceeding the threshold. The strategy includes abbreviations, replacement of short character words, and reducing the font size. For example, when the system detects that the character length of the translated English string in an icon in the car interface exceeds the character length threshold corresponding to the icon in the corresponding car interface, the AI ​​large model will automatically determine one or more feasible adaptation strategies from the preset adaptation strategies. The strategy includes abbreviating long words into abbreviated forms, replacing long words with shorter synonyms, or reducing the font size to adapt to the interface display requirements.

[0090] In addition, it should be noted that the selection and application of the preset adaptation strategy will be flexibly adjusted according to the actual situation. Different interfaces, different languages ​​and character length thresholds of elements in different interfaces will affect the selection of the adaptation strategy.

[0091] Step A203: adjusting the characters in the revised translation file according to the preset adaptation strategy to obtain a target translation file.

[0092] It should be noted that after obtaining the preset adaptation strategy, the large language model will adjust the characters in the revised translation file according to the adaptation strategy. Including abbreviations of long words, replacement of short character vocabulary, reducing the font size and many other operations. By adjusting the length and layout of the characters, the large language model can ensure that each string in the revised translation file can adapt to the display requirements of the corresponding interface. It can be understood that after the above steps are processed, the system will obtain a target translation file that meets the display requirements of the vehicle UI interface. Each string in this file has been adapted and calibrated to ensure that it can be correctly and clearly displayed on the vehicle interface in different language environments.

[0093] In this embodiment, by performing dual calibration of semantic calibration and adaptation calibration on the initial translation file, a target translation file that is adapted to each element in the current vehicle UI interface and has accurate semantics is obtained, thereby improving the quality of the multilingual translation file and ensuring the adaptability and accuracy of the multilingual translated vehicle interface.

[0094] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the first and second embodiments can be referred to the above description, and will not be described in detail later. Figure 4 , Figure 4 This is a flow chart of the third embodiment of the vehicle-mounted multi-language adaptation translation method of the present application, wherein step A203 specifically includes:

[0095] Step B10: reducing or replacing characters in the revised translation file according to the preset adaptation strategy, and integrating the adjusted content into the revised corpus to obtain the target corpus.

[0096] It should be noted that in this step, when the corresponding characters in the revised translation file exceed the character length threshold of the vehicle interface UI elements (such as icons, pop-up windows, etc.) due to the character length, the system will automatically process the characters according to the preset adaptation strategy. The specific process is as follows: the system will detect the length of the characters in each corresponding element in the revised translation file and compare it with the character length threshold of the corresponding UI element. If the character length exceeds a certain proportion of the threshold (the proportion can be set according to actual needs, such as 10%), an adjustment mechanism is triggered, such as reduction or replacement. The reduction operation refers to shortening the character length by reducing the font size or adjusting the font style while ensuring the readability of the characters. The replacement operation replaces longer characters with synonyms but shorter words or abbreviations. These adjusted contents will be immediately integrated into the revised corpus for subsequent translation reference and use, thereby obtaining the target corpus.

[0097] In a feasible implementation manner, the step B10 specifically includes:

[0098] Step B101: Obtain a first adaptation ratio and a second adaptation ratio according to the preset adaptation strategy.

[0099] It should be noted that in this step, the system will set the corresponding character length threshold according to factors such as the pop-up window size and icon size of the UI interface. The threshold represents the upper limit of the number of characters that can be fully displayed in different displayed elements in the UI interface. At the same time, the system will determine the first adaptation ratio and the second adaptation ratio according to the preset adaptation strategy. The first adaptation ratio is a relatively small value, which is used to determine whether to replace characters based on the length of the characters; the second adaptation ratio is a relatively large value, which is used to determine that the character length has obviously exceeded the threshold, and the character format needs to be reduced.

[0100] In addition, it should be noted that the preset adaptation strategy is a judgment strategy pre-defined according to the size of corresponding elements in different vehicle-mounted interfaces, including a first adaptation ratio and a second adaptation ratio. The adaptation ratio refers to the ratio of the character length to the character length threshold. The first adaptation ratio and the second adaptation ratio are not fixed, and they can be adjusted according to the specific vehicle-mounted interface and user needs.

[0101] Step B102: When the ratio of the character length to the character length threshold is greater than a first adaptation ratio and less than or equal to a second adaptation ratio, replace the characters in the revised translation file.

[0102] It should be noted that when the ratio of the character length to the character length threshold is greater than the first adaptation ratio and less than or equal to the second adaptation ratio, the system will trigger the character replacement mechanism. Specifically, the system will find words or abbreviations that are close to the current character length but can be expressed more concisely for replacement according to the replacement rules in the translation corpus. The length of the replaced character should meet the requirement of being less than the length of the character it replaces. For example, assuming that the character length threshold in a button in a certain car interface is 10 characters, the character length in the corresponding button in the current revised translation file is 11 characters (ie 11 / 10 = 110%), which exceeds the first adaptation ratio (105%) but does not exceed the second adaptation ratio (115%). At this time, the system will search the translation corpus for words or phrases that are close in meaning to the current character and can be expressed more concisely for replacement.

[0103] Step B103: When the ratio of the character length to the character length threshold is greater than a second adaptation ratio, the characters in the revised translation file are reduced in format.

[0104] It should be noted that when the ratio of the character length to the character length threshold is greater than the second adaptation ratio, the system will trigger the character format reduction mechanism. Specifically, when the ratio of the character length in different elements in the vehicle interface to the character length threshold is greater than the corresponding second adaptation ratio, the system will adjust the font size, line spacing, etc. of the corresponding characters in the correction translation file to reduce the display area of ​​the characters so that they can be fully displayed in the vehicle interface. For example, assuming that the character length threshold of a pop-up window in a vehicle interface is 20 characters, the corresponding character length in the current correction translation file is 26 characters (ie 26 / 20 = 130%), which exceeds the second adaptation ratio (115%). At this time, the system will reduce the font size of the character, or adjust parameters such as line spacing to reduce the display area of ​​the character. It can be understood that the adjusted character length should meet the requirement of not exceeding the character length threshold.

[0105] Step B20: adjusting the revised translation file according to the target corpus to obtain a target translation file.

[0106] It should be noted that after the target corpus is formed, the large language model will use the target corpus to adaptively adjust the revised translation file. This adjustment process mainly replaces or modifies the corresponding parts in the revised translation file according to the corresponding character adjustment strategy in the target corpus. After this adjustment, the revised translation file becomes the target translation file. It can be understood that the target translation file not only maintains the semantics and information content of the original text, but also improves the adaptability of the vehicle interface.

[0107] In this embodiment, the characters in the revised translation file are reduced or replaced by a preset adaptation strategy, and the adjusted content is integrated into the revised corpus to obtain the target corpus. This not only solves the UI incompatibility problem caused by different character lengths in multi-language translation, but also improves translation efficiency and accuracy.

[0108] This application also provides a multi-language adaptation translation device for vehicle computers, please refer to Figure 5 The vehicle multi-language adaptation translation device includes:

[0109] The data import module 10 is used to obtain the imported data to be translated and the target translation type;

[0110] A file translation module 20, configured to translate the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file;

[0111] The format calibration module 30 is used to calibrate the initial translation file based on a preset calibration strategy to obtain a target translation file.

[0112] The vehicle-mounted multi-language adaptation translation device provided by the present application adopts the vehicle-mounted multi-language adaptation translation method in the above-mentioned embodiment, and can solve the technical problem of how to improve the accuracy and adaptability of the vehicle-mounted multi-language interface translation. Compared with the prior art, the beneficial effects of the vehicle-mounted multi-language adaptation translation device provided by the present application are the same as the beneficial effects of the vehicle-mounted multi-language adaptation translation method provided by the above-mentioned embodiment, and the other technical features in the vehicle-mounted multi-language adaptation translation device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0113] In one embodiment, the format calibration module 30 is further used to obtain semantic calibration rules, adaptation calibration rules and a character length threshold of the current vehicle computer according to a preset calibration strategy; and calibrate the initial translation file according to the semantic calibration rules, the adaptation calibration rules and the character length threshold of the current vehicle computer to obtain a target translation file.

[0114] In one embodiment, the format calibration module 30 is further used to perform semantic calibration on the initial translation file according to the semantic calibration rule to obtain a revised translation file; and perform adaptation calibration on the revised translation file according to the adaptation calibration rule and the character length threshold to obtain a target translation file.

[0115] In one embodiment, the format calibration module 30 is further used to perform semantic calibration detection on the initial translation file according to the semantic calibration rule to obtain a semantic matching degree; when the semantic matching degree is lower than a preset semantic matching degree, update the preset translation corpus to obtain a revised corpus; and perform semantic calibration on the initial translation file according to the revised corpus to obtain a revised translation file.

[0116] In one embodiment, the format calibration module 30 is further used to perform adaptation calibration detection on the corrected translation file according to the adaptation calibration rule to obtain the character length; when the character length is greater than the character length threshold, obtain a preset adaptation strategy; and adjust the characters in the corrected translation file according to the preset adaptation strategy to obtain a target translation file.

[0117] In one embodiment, the format calibration module 30 is further used to reduce or replace characters in the revised translation file according to the preset adaptation strategy, and integrate the adjusted content into the revised corpus to obtain a target corpus; and adjust the revised translation file according to the target corpus to obtain a target translation file.

[0118] In one embodiment, the format calibration module 30 is further used to obtain a first adaptation ratio and a second adaptation ratio according to the preset adaptation strategy; when the ratio of the character length to the character length threshold is greater than the first adaptation ratio and less than or equal to the second adaptation ratio, the characters in the corrected translation file are replaced; when the ratio of the character length to the character length threshold is greater than the second adaptation ratio, the characters in the corrected translation file are formatted in a reduced size.

[0119] The present application provides a vehicle-mounted multi-language adaptation and translation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle-mounted multi-language adaptation and translation method in the above-mentioned embodiment 1.

[0120] Reference below Figure 6 , which shows a schematic diagram of the structure of a vehicle-mounted multi-language adaptation translation device suitable for implementing the embodiment of the present application. The vehicle-mounted multi-language adaptation translation device in the embodiment of the present application includes but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The multi-language adaptation translation device for the vehicle shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0121] like Figure 6As shown, the vehicle-mounted multi-language adaptation translation device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the vehicle-mounted multi-language adaptation translation device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the vehicle-mounted multi-language adaptation translation device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a vehicle-mounted multi-language adaptation translation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0122] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0123] The vehicle-mounted multi-language adaptation translation device provided by the present application adopts the vehicle-mounted multi-language adaptation translation method in the above-mentioned embodiment, which can solve the technical problem of how to improve the accuracy and adaptability of the vehicle-mounted multi-language interface translation. Compared with the prior art, the beneficial effects of the vehicle-mounted multi-language adaptation translation device provided by the present application are the same as the beneficial effects of the vehicle-mounted multi-language adaptation translation method provided by the above-mentioned embodiment, and the other technical features in the vehicle-mounted multi-language adaptation translation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0124] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0125] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of 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.

[0126] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the vehicle-computer multi-language adaptation translation method in the above-mentioned embodiment.

[0127] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0128] The computer-readable storage medium may be included in the vehicle-mounted multi-language adaptation and translation device; or may exist independently without being assembled into the vehicle-mounted multi-language adaptation and translation device.

[0129] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the vehicle-mounted multi-language adaptive translation device, the vehicle-mounted multi-language adaptive translation device: obtains the imported data to be translated and the target translation type; translates the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file; and calibrates the initial translation file based on a preset calibration strategy to obtain a target translation file.

[0130] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0131] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0132] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0133] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned vehicle-machine multi-language adaptation translation method, and can solve the technical problem of how to improve the accuracy and adaptability of vehicle-machine multi-language interface translation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the vehicle-machine multi-language adaptation translation method provided in the above-mentioned embodiment, and will not be repeated here.

[0134] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned vehicle-computer multi-language adaptation translation method.

[0135] The computer program product provided by this application can solve the technical problem of how to improve the accuracy and adaptability of the multi-language interface translation of the vehicle computer. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the multi-language adaptation translation method of the vehicle computer provided by the above embodiment, which will not be repeated here.

[0136] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A multi-language adaptation translation method for a vehicle computer, characterized in that: The method comprises: Get the imported data to be translated and the target translation type; Translating the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file; The initial translation file is calibrated based on a preset calibration strategy to obtain a target translation file.

2. The method according to claim 1, characterized in that The step of calibrating the initial translation file based on a preset calibration strategy to obtain a target translation file comprises: According to the preset calibration strategy, the semantic calibration rule, the adaptation calibration rule and the character length threshold of the current vehicle computer are obtained; The initial translation file is calibrated according to the semantic calibration rule, the adaptation calibration rule and the character length threshold of the current vehicle computer to obtain a target translation file.

3. The method according to claim 2, characterized in that The step of calibrating the initial translation file according to the semantic calibration rule, the adaptation calibration rule and the character length threshold of the current vehicle computer to obtain a target translation file comprises: Performing semantic calibration on the initial translation file according to the semantic calibration rule to obtain a revised translation file; The modified translation file is adapted and calibrated according to the adaptation and calibration rule and the character length threshold to obtain a target translation file.

4. The method according to claim 3, characterized in that The step of performing semantic calibration on the initial translation file according to the semantic calibration rule to obtain a revised translation file comprises: Performing semantic alignment detection on the initial translation file according to the semantic alignment rule to obtain a semantic matching degree; When the semantic matching degree is lower than a preset semantic matching degree, updating the preset translation corpus to obtain a revised corpus; The initial translation file is semantically calibrated according to the revised corpus to obtain a revised translation file.

5. The method according to claim 3, characterized in that The step of performing adaptation and calibration on the revised translation file according to the adaptation and calibration rule and the character length threshold to obtain a target translation file comprises: Performing adaptation and calibration detection on the revised translation file according to the adaptation and calibration rule to obtain the character length; When the character length is greater than the character length threshold, obtaining a preset adaptation strategy; The characters in the revised translation file are adjusted according to the preset adaptation strategy to obtain a target translation file.

6. The method according to claim 5, characterized in that The step of adjusting the characters in the revised translation file according to the preset adaptation strategy to obtain the target translation file comprises: Reduce or replace characters in the revised translation file according to the preset adaptation strategy, and integrate the adjusted content into the revised corpus to obtain the target corpus; The revised translation file is adjusted according to the target corpus to obtain a target translation file.

7. The method according to claim 6, characterized in that The step of reducing or replacing characters in the revised translation file according to the preset adaptation strategy comprises: Obtaining a first adaptation ratio and a second adaptation ratio according to the preset adaptation strategy; When the ratio of the character length to the character length threshold is greater than a first adaptation ratio and less than or equal to a second adaptation ratio, replacing the characters in the revised translation file; When the ratio of the character length to the character length threshold is greater than a second adaptation ratio, the characters in the revised translation file are reduced in format.

8. A multi-language adaptation translation device for a vehicle, characterized in that: The device comprises: The data import module is used to obtain the imported data to be translated and the target translation type; A file translation module, used for translating the data to be translated according to a preset translation corpus and the target translation type to obtain an initial translation file; The format calibration module is used to calibrate the initial translation file based on a preset calibration strategy to obtain a target translation file.

9. A multi-language adaptation translation device for a vehicle, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the vehicle-mounted multi-language adaptation translation method as claimed in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the vehicle-mounted multi-language adaptation translation method according to any one of claims 1 to 7 are implemented.

Images