Menu translation optimization method, system and equipment of user terminal and storage medium

Abstract

The invention discloses a menu translation optimization method, system and device of a user terminal and a storage medium. The method comprises the following steps: collecting a plurality of feedback data of a plurality of users for a target menu of a user terminal; generating a target voting form according to the plurality of feedback data; the target voting form comprises a menu initial text and a menu change text set; the target voting form is pushed to other user terminals using the device language, so that the other user terminals vote according to the target voting form, and a first voting result is obtained; obtaining a target auditing result of the target translator for the first voting result; determining a second voting result according to the target auditing result and the first voting result; obtaining a target menu change text corresponding to the second voting result; and updating the menu initial text into the target menu change text according to the second voting result. Through combination of user voting and expert auditing, low-value suggestions are effectively filtered, and translation optimization efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of translation technology, and in particular to a method, system, device and storage medium for optimizing menu translation on a user terminal. Background Technology

[0002] On the user's terminal, the translation content for different menus varies significantly in different language environments. Therefore, when a terminal user provides feedback on the menu translation to the translation system, the translation system pushes the translation suggestion to the translation expert. The translation expert then determines whether to adopt the translation suggestion based on subjective judgment. If the suggestion is adopted, the updated translation content is synchronized to the terminal device.

[0003] However, in the current technology, all feedback suggestions need to be manually reviewed by translation experts one by one. The review process is cumbersome and time-consuming, which greatly increases the workload of reviewers and reduces the efficiency of translation optimization. Therefore, how to improve the efficiency of translation optimization has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a method, system, device, and storage medium for optimizing menu translation on a user terminal. By combining user voting with expert review, disordered user feedback is transformed into structured voting data, effectively filtering low-value suggestions and focusing on high-consensus translation solutions. Simultaneously, it reduces the workload of manual review, thereby improving translation optimization efficiency.

[0005] In a first aspect, embodiments of this application provide a menu translation optimization method for a user terminal, applied to a server; the method includes:

[0006] Collect multiple feedback data from multiple users regarding the target menu on the user terminal; each feedback data corresponds to a menu change text; all multiple users use the same device language;

[0007] A target voting form is generated based on the multiple feedback data; the target voting form includes: initial menu text and a set of menu change texts;

[0008] The target voting form is pushed to other user terminals using the device language, so that the other user terminals vote according to the target voting form to obtain a first voting result; the first voting result includes the voting percentage of each menu change text in the menu change text set; the other user terminals refer to user terminals other than the user terminals that submitted the multiple feedback data;

[0009] Obtain the target translator's review result regarding the first voting result;

[0010] The second voting result is determined based on the target review result and the first voting result;

[0011] Obtain the target menu change text corresponding to the second voting result;

[0012] The initial menu text is updated to the target menu change text based on the second voting result.

[0013] Secondly, embodiments of this application provide a menu translation optimization system for a user terminal, applied to a server; the menu translation optimization system for the user terminal includes: a data collection unit, a voting form generation unit, a first voting result collection unit, a second voting result collection unit, a data processing unit, and a menu translation optimization unit, wherein,

[0014] The data collection unit is used to collect multiple feedback data from multiple users regarding the target menu of the user terminal; each feedback data corresponds to a menu change text; all multiple users use the same device language;

[0015] The voting form generation unit is used to generate a target voting form based on the multiple feedback data; the target voting form includes: initial menu text and a set of menu change texts;

[0016] The first voting result collection unit is used to push the target voting form to other user terminals using the device language, so that the other user terminals can vote according to the target voting form and obtain a first voting result; the first voting result includes the voting percentage of each menu change text in the menu change text set; the other user terminals refer to user terminals other than the user terminals that submitted the multiple feedback data;

[0017] The data collection unit is also used to obtain the target translator's review results regarding the first voting result;

[0018] The second voting result collection unit is used to determine the second voting result based on the target review result and the first voting result;

[0019] The data processing unit is used to obtain the target menu change text corresponding to the second voting result;

[0020] The menu translation optimization unit is used to update the initial menu text to the target menu change text based on the second voting result.

[0021] Thirdly, embodiments of this application provide an electronic device, including: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the steps in the first aspect of embodiments of this application.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of embodiments of this application.

[0023] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of embodiments of this application. The computer program product may be a software installation package.

[0024] It can be seen that the embodiments of this application have the following beneficial effects:

[0025] By implementing the embodiments of this application, multiple feedback data from multiple users regarding the target menu of the user terminal are collected; a target voting form is generated based on the multiple feedback data; the target voting form includes: initial menu text and a set of changed menu texts; the target voting form is pushed to other user terminals using the device language, so that the other user terminals vote according to the target voting form to obtain a first voting result; the target review result of the target translator regarding the first voting result is obtained; a second voting result is determined based on the target review result and the first voting result; the target menu changed text corresponding to the second voting result is obtained; and the initial menu text is updated to the target menu changed text based on the second voting result. It is evident that by combining user voting with expert review, disordered user feedback is transformed into structured voting data, effectively filtering low-value suggestions and focusing on high-consensus translation solutions. Simultaneously, it can also reduce the workload of manual review, thereby improving translation optimization efficiency. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0027] Figure 1 This is a flowchart illustrating a menu translation optimization method for a user terminal provided in an embodiment of this application;

[0028] Figure 2 This is a schematic diagram of a first scenario of a menu translation optimization method for a user terminal provided in an embodiment of this application;

[0029] Figure 3 This is a schematic diagram of a second scenario of a menu translation optimization method for a user terminal provided in an embodiment of this application;

[0030] Figure 4 This is a schematic diagram of a third scenario of a menu translation optimization method for a user terminal provided in an embodiment of this application;

[0031] Figure 5 This is a schematic diagram of the fourth scenario of a menu translation optimization method for a user terminal provided in an embodiment of this application;

[0032] Figure 6 This is a schematic diagram of a process for generating a target review result provided in an embodiment of this application;

[0033] Figure 7 This is a schematic diagram of the structure of a menu translation optimization system for a user terminal provided in an embodiment of this application;

[0034] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0036] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0037] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0038] The following describes the relevant content, concepts, meanings, technical issues, technical solutions, and beneficial effects involved in the embodiments of this application.

[0039] Please see Figure 1 , Figure 1 This is a flowchart illustrating a menu translation optimization method for a user terminal provided in an embodiment of this application. The method is applied to a server and includes, but is not limited to, the following steps:

[0040] S101. Collect multiple feedback data from multiple users regarding the target menu of the user terminal.

[0041] In this embodiment, the user terminal refers to an intelligent electronic device with multilingual support capabilities, including but not limited to smartphones, personal computers, tablets, vehicle diagnostic terminals, and professional medical equipment. User terminals typically come pre-installed with multilingual operating systems or applications, and can automatically switch language environments based on user settings or device location to adapt to the usage needs of users in different regions worldwide.

[0042] The user terminal includes a menu interface, which can take various forms, such as hierarchical, tiled, or drawer-style menus. Each menu item contains multiple menu items, including menu text and an icon. The menu text corresponds to the user terminal's current voice environment.

[0043] For example, in an automotive diagnostic scenario, the user terminal refers to the diagnostic equipment used for vehicle diagnostics. This diagnostic equipment displays a menu interface, which may include menu items such as fault detection, system settings, data reading, and passenger vehicle. When the user terminal's language is set to different languages ​​such as Chinese, English, or Japanese, the menu text in the menu interface can be simultaneously translated to be semantically accurate and conform to localized expressions, ensuring that the user can clearly understand and operate the menu functions. As the interaction medium between the user and the diagnostic system, the translation quality of the menu interface directly affects the user experience and operational efficiency, thus becoming an optimization target in this application's embodiments.

[0044] In a specific embodiment, for the target menu on the user terminal, i.e., the menu interface that currently needs to be optimized for translation, feedback data from multiple users based on the same device language environment can be collected. Each piece of feedback data corresponds to a menu change text; for example, when the device language is English, users can suggest specific changes to the Settings menu (Preferences), Options, etc. By involving multiple users, the sample size can be increased, improving the reliability of the feedback data.

[0045] Optionally, the user terminal includes: a target feedback button and a target feedback input box; the step of collecting multiple feedback data from multiple users regarding the target menu of the user terminal specifically includes the following steps:

[0046] A101. In response to a first operation by a target user on the target menu of the user terminal, the target feedback button is displayed; the target user is any one of the plurality of users.

[0047] A102. In response to the second operation of the target user on the target feedback button, display target indication information and the target feedback input box; the target indication information is used to instruct the target user to enter menu change text in the target feedback input box;

[0048] A103. Obtain the target feedback data in the target feedback input box; the target feedback data is the feedback data corresponding to the target user.

[0049] The user terminal includes a target feedback button and a target feedback input box. The target feedback button is a trigger control embedded in the menu interaction flow. It is presented in a lightweight form and associated with the target menu, bound to an interactive event, and can switch operation scenarios upon triggering. The target feedback input box is a text control for user feedback content. After the target feedback button is triggered, it can load as a pop-up, overlay, or inline area. A validation mechanism ensures that the feedback content is associated with the target menu and is standardized, providing accurate data for the translation optimization process.

[0050] In a specific embodiment, in response to a first operation by a target user on a target menu of a user terminal, a target feedback button is displayed. The target user can be any one of multiple users. The first operation typically refers to the user's focus on or selection of menu text or icons. For example, in a diagnostic system menu interface, when a user long-presses a passenger vehicle menu item, the server can identify the intention through an event listening mechanism and then display a prominent target feedback button in a fixed location on the interface (such as the right or bottom of the menu).

[0051] In response to the target user's second action on the target feedback button, target indication information and a target feedback input box are displayed. The target indication information instructs the target user to enter the menu change text in the target feedback input box. The second action typically refers to the user's click on the target feedback button.

[0052] Next, the target feedback data in the target feedback input box can be obtained. This target feedback data corresponds to the feedback data of the target user. When the user enters the menu change text in the target feedback input box, the server can listen for the submit or confirmation event of the target feedback input box to obtain the target feedback data entered by the user in real time and automatically associate it with the user's device language, account ID, operation time, and other metadata.

[0053] It is evident that this feedback data collection method, through its layered interaction mechanism and structured input design, enhances user participation and accuracy, providing a standardized structured data foundation for subsequent voting form generation, and ensuring data quality and processing efficiency in the translation optimization process from the source.

[0054] Please see Figure 2 , Figure 2 This is a schematic diagram of a first scenario of a menu translation optimization method for a user terminal provided in this application embodiment. In the user terminal interaction scenario, the user's language environment is English, and the target menu is presented in a grid layout (such as the nine-grid structure of "ICON1-ICON9" in the interface). When the target user performs a first operation on ICON4 (PC), i.e., the menu item corresponding to the passenger vehicle (long press, click on a specific trigger area, etc.), the menu interface displays interactive controls such as feedback and setting on the right side of the passenger vehicle menu item. The feedback interactive control is the target feedback button. When the user performs a second operation on the target feedback button (such as clicking), the target indication information and the target feedback input box are displayed.

[0055] Please see Figure 3 , Figure 3 This is a schematic diagram of a second scenario of a menu translation optimization method for a user terminal provided in an embodiment of this application, combined with... Figure 2 When the target user completes the second action on the target feedback button, the target instruction information and the target feedback input box are displayed. The target instruction information can be "Input the translated text" to guide the user to fill in the menu change text (PV) in the target feedback input box. After the user has finished filling in the text, the server can obtain the target feedback data (menu change text) in the target feedback input box by clicking the submit button.

[0056] S102. Generate a target voting form based on the multiple feedback data; the target voting form includes: initial menu text and a set of menu change texts.

[0057] In a specific embodiment, a target voting form can be generated based on multiple feedback data. Specifically, the collected feedback data can be cleaned. Since feedback from different users regarding the same target menu may be duplicated or have inconsistent formats, multiple feedback data can be cleaned to filter out duplicate feedback text.

[0058] Next, a target voting form can be constructed based on the cleaned feedback data. This target voting form includes: initial menu text and a set of changed menu text. The initial menu text is the menu text corresponding to the menu items in the menu interface, while the set of changed menu text is a collection constructed based on feedback data from multiple users. This collection can be used to generate a target voting form through a visual interface and displayed on the menu interface for users to vote on.

[0059] Generating a target voting form from multiple feedback data sets enables the transformation of disorganized feedback into ordered decision-making input. For example, raw feedback data submitted by multiple users may contain issues such as repetitive expressions, semantic ambiguity, or disordered formatting. Directly using such data would require reviewers to expend considerable effort to filter out valid information. A target voting form, however, eliminates redundant data through deduplication, transforming discrete feedback data into clearly structured units. This reduces the amount of information processed by reviewers and further improves the efficiency of the translation optimization process.

[0060] Optionally, generating the target voting form based on the multiple feedback data specifically includes the following steps:

[0061] A201. Obtain the initial text of the menu corresponding to the target menu;

[0062] A202. Deduplicate the menu change text in the multiple feedback data to obtain the menu change text set;

[0063] A203. Generate the target voting form based on the initial menu text and the set of changed menu texts; the set of changed menu texts consists of interactive voting options in the target voting form.

[0064] In a specific embodiment, the initial menu text corresponding to the target menu can be obtained. This initial menu text is the original menu text of the menu item in the menu interface. Next, the menu change text in multiple feedback data sets is deduplicated to obtain a set of menu change texts.

[0065] Next, by integrating the initial menu text with the deduplicated updated menu text, an interactive target voting form is formed, containing voting options. Specifically, at the interface rendering level, front-end development frameworks (such as Angular and Flutter) can be used to display the initial menu text as a fixed title or description, labeled with specific fields. Meanwhile, the deduplicated updated menu text is transformed into clickable, selectable interactive options. Each option can include metadata hints, such as information from a specific user, to enhance voters' judgment of the credibility of the suggestions.

[0066] Optionally, after generating the target voting form based on the multiple feedback data, the following steps are further included:

[0067] Configure the voting cutoff conditions of the target voting form so that the target voting form stops voting and obtains the voting results when the voting cutoff conditions are met; the voting cutoff conditions include: the number of participants in the vote reaches a preset number of voters threshold, or the voting time reaches a preset time threshold.

[0068] In a specific embodiment, after the target voting form is generated, voting deadline conditions can be configured to ensure the standardization of the voting process and the validity of the results.

[0069] Specifically, the voting deadline conditions for the target voting form are configured so that voting stops and the voting results are obtained when the deadline conditions are met. These deadline conditions include: the number of participants reaching a preset threshold, or the voting time reaching a preset time threshold. The voter and time thresholds are preset and can be adjusted according to actual needs.

[0070] To address the voter threshold, the number of participating users can be counted in real time. A counter continuously accumulates events triggered by voting operations. When the count reaches a preset threshold, termination logic is triggered, freezing the voting entry and preventing new users from participating. For the time threshold, a timer can be set based on the server clock or a scheduled task, starting from the moment the voting form is published. Upon reaching the preset time point, the voting operation will also stop. To ensure data accuracy, voting can be stopped if any condition is met.

[0071] It is evident that by configuring clear voting deadlines, the voting process can be controlled in an orderly manner, ensuring the reliability of voting results and improving the efficiency of menu translation optimization.

[0072] Please see Figure 4 , Figure 4This is a schematic diagram of a third scenario of a menu translation optimization method for a user terminal provided in this application embodiment. As shown in the figure, the user terminal's interface language environment is English. The car icon at the top indicates feedback suggestions for the menu text of the passenger vehicle menu item. The form displays the initial menu text and the set of changed texts in list form. Among them, "PC (Default)" represents the initial menu text (marked Default to specify the baseline reference), and PV, Passenger Vehicle, Passenger Car, and Sedan are the sets of changed menu texts after deduplication. Each text corresponds to a text ID and is configured with a Vote button on the right as an interactive voting option. Users can vote on the changed menu text by clicking the corresponding Vote button, intuitively presenting the interactive form of the target voting form.

[0073] S103. Push the target voting form to other user terminals using the device language, so that the other user terminals can vote according to the target voting form and obtain a first voting result; the first voting result includes the voting percentage of each menu change text in the menu change text set.

[0074] In this embodiment, "other user terminals" refers to user terminals other than those that submitted multiple feedback data, i.e., terminal devices that did not participate in the initial submission of feedback data and share the same device language (e.g., English environment) as the terminals that submitted feedback. The server can accurately filter user terminals using their language identifiers (e.g., terminal language settings field, multi-language resource pack version) to ensure that all pushed terminals are in the target language environment, avoiding voting bias due to language differences.

[0075] In a specific embodiment, the target voting form is pushed to other user terminals using the same device language, allowing these terminals to vote according to the form and obtain the first voting result. Once the other user terminals receive the voting form, they complete the voting by clicking the vote button. The terminal devices then convert the voting behavior into structured data (such as a change text ID, user terminal identifier, and voting time) and upload it to the server.

[0076] Optionally, after pushing the target voting form to other user terminals using the device language, the following steps are further included:

[0077] A301. Obtain the first feedback data from the other user terminals regarding the target menu;

[0078] A302. Update the target voting form based on the first feedback data.

[0079] In a specific embodiment, first feedback data from other user terminals regarding the target menu can be obtained. This first feedback data refers to new translation suggestions or optimization opinions that may be generated by other user terminals during the voting process. In multilingual terminal scenarios, users on other terminals may find that the existing set of changed texts is insufficient or that there are translation solutions that better fit localized expressions when voting. In this case, the server can obtain such new feedback through interactive design (such as supplementary suggestion buttons or floating feedback entry points on the voting interface).

[0080] Next, the target voting form is dynamically updated based on the first feedback data. The server can insert the new changed text from the first feedback data into the original menu changed text set.

[0081] Please see Figure 5 , Figure 5 This is a schematic diagram of the fourth scenario of a menu translation optimization method for a user terminal provided in this application embodiment. As shown in the figure, the figure shows the visualization of the first voting result. It displays the voting percentage of each option in the menu change text set through progress bars and percentages. For example, PC, PV, etc. are the initial and changed texts of the menu, and the corresponding progress bars show voting percentage data of 30%, 50%, etc., respectively, clearly quantifying the degree of acceptance of different translation expressions.

[0082] S104. Obtain the target translator's review result for the first voting result.

[0083] In this embodiment of the application, in order to avoid the bias that may arise from relying solely on group voting, the professional knowledge of the target translators (including the accuracy of language and semantics, the standardization of industry terminology, the adaptability of localized expressions, etc.) can be used to make value judgments and output correction suggestions on the first voting results, so as to avoid situations such as conflicts between user preferences and professional translation standards, or niche but better expressions being masked by a high percentage of votes.

[0084] In a specific embodiment, the first voting result (including the voting percentage of each menu change text) is pushed to the target translator's work terminal (such as the management backend of a professional translation platform or terminal device).

[0085] When translators conduct reviews, a multi-layered assessment from a professional perspective is necessary. First, semantic accuracy must be verified to determine whether the changed menu text accurately conveys the essential meaning of each menu function. For example, in the automotive diagnostics menu, while "PV" receives a high percentage of votes, it's crucial to check for potential confusion with industry terms like "photovoltaic." If ambiguity exists, even with a high vote percentage, it should be marked as requiring optimization. Second, industry standardization must be checked. The changed text should be reviewed against standard terminology libraries for the target application's domain (e.g., SAE terminology and ISO standards for automotive diagnostics) to ensure it conforms to common industry expressions and avoids misunderstandings caused by user-generated creative expressions. Third, localization compatibility analysis is essential. This involves considering the target user's regional culture and language habits to determine if the changed text aligns with local users' everyday expressions. For instance, in an English-speaking environment, US and UK users may differ in their terminology for cars (e.g., US users use "Sedan," while UK users use "Saloon"). Compatibility reviews must be conducted based on the primary usage region of the device.

[0086] After the target translators review the first vote result, they can obtain the target review result. The server can obtain the target review result, which can include the review conclusions on each changed text, such as suggestions to adopt, suggestions to modify, and suggestions not to adopt. It can also include detailed review explanations, such as semantic ambiguity analysis, industry terminology comparison, and localization optimization suggestions.

[0087] Optionally, obtaining the target translator's review result for the first voting result specifically includes the following steps:

[0088] A401. The first voting result is simultaneously pushed to the target translator via email, so that the target translator can determine the feasibility of each menu change text in the menu change text set according to the preset review rules, and obtain multiple feasibility scores; the higher the feasibility score, the more accurate the text translation.

[0089] A402. Determine the target audit result based on the multiple feasibility factors.

[0090] In a specific embodiment, the first voting result can be simultaneously pushed to the target translator via email, allowing the target translator to determine the feasibility of each menu change text in the menu change text set according to preset review rules, resulting in multiple feasibility scores. The higher the feasibility score, the more accurate the text translation. The review rules are a judgment system built on linguistic semantics, industry terminology standards, and localization adaptation principles. It includes core dimensions such as semantic accuracy verification (e.g., whether the changed text accurately maps menu functions), terminology standardization comparison (whether it conforms to the standard vocabulary of the industry to which the terminal application belongs), and cultural adaptability assessment (whether it fits the expression habits and cultural context of the target language environment).

[0091] Translators verify each change against the review rules, assigning a feasible quantifiable value to each text change. The higher the feasibility, the closer the text is to the ideal state of accuracy, standardization, and adaptation under professional translation standards.

[0092] Next, the target review result can be determined based on multiple feasibility levels. Through the mapping relationship between feasibility and review conclusion, the feasibility and conclusion are associated and stored. For example, if the feasibility is greater than or equal to 80%, the menu change text is determined to be recommended for adoption; if the feasibility is greater than or equal to 60% and less than 80%, the menu change text is determined to be recommended for adoption after optimization; and if the feasibility is less than 60%, the menu change text is determined to be not recommended for adoption.

[0093] It is evident that by building a professional review portal through email push notifications and using preset review rules as a framework to achieve feasible quantifiable judgments, human expertise is transformed into structured review results, thereby improving the professionalism, consistency, and interpretability of the review results.

[0094] Please see Figure 6 , Figure 6 This is a flowchart illustrating a process for generating a target review result, as provided in an embodiment of this application. The first voting result in the diagram represents data generated from prior user voting (such as the percentage of votes cast for each menu change text), which is synchronized to the reviewers (i.e., the target translators) via email. The reviewers can professionally evaluate the menu change text in the first voting result according to the review rules (corresponding to preset semantic, terminological, and suitability judgment rules), determining the feasibility of each text (quantitative values ​​of professional dimensions such as translation accuracy and standardization). The server receives this feasibility assessment and ultimately obtains the target review result.

[0095] S105. Determine the second voting result based on the target review result and the first voting result.

[0096] In this embodiment, the first voting result reflects the preference distribution of the same language user group for the menu change text (such as the voting percentage of each menu change text), reflecting the actual needs and usage habits of the users. The target review result, based on the knowledge reserves of the target translators, gives a feasibility judgment for each menu change text from the dimensions of language accuracy, industry standardization, and cultural adaptability (such as suggesting adoption, optimization, or rejection), representing the quality control standard of the professional side.

[0097] In a specific embodiment, the second voting result can be determined based on the target review result and the first voting result. Specifically, a correlation mapping and weight allocation mechanism between the two can be established. First, the voting percentage data in the first voting result and the feasibility data in the target review result are standardized, transforming indicators with different dimensions into a comparable and fusionable numerical system. For example, through a normalization algorithm, the voting percentage and feasibility are mapped to the same numerical range (e.g., 0-1), eliminating fusion barriers caused by data differences. Second, based on the user terminal application scenario and translation optimization goals, fusion weights for group preferences and professional standards are set. If the user terminal is geared towards ordinary consumer users, the weight of the first voting result can be increased to highlight user experience orientation; if the user terminal is used in a professional field (e.g., automotive diagnostic equipment), the weight of the target review result can be increased to ensure the standardization of industry terminology.

[0098] Next, a weighted fusion algorithm can be used to integrate the target review results and the first voting results. For example, the score of the second voting result for a menu change text is calculated as follows: (normalized value of the first voting result × group weight) + (feasibility of the target review result × professional weight). This algorithm transforms group preferences and professional judgments into a unified quantitative score, and then re-prioritizes the menu change texts based on the scores to generate the second voting result.

[0099] It is evident that the second vote not only aligns with users' actual usage habits but also meets the requirements for professional translation quality, providing a precise and balanced basis for determining the final translation solution and significantly improving the market adaptability and professional compliance of the translation results.

[0100] Optionally, determining the second voting result based on the target review result and the first voting result specifically includes the following steps:

[0101] A501. Obtain the voting percentage of each menu change text in the first voting result, and the feasibility of each menu change text in the target review result, to obtain multiple voting percentages and multiple feasibility values; each voting percentage corresponds to one feasibility value.

[0102] A502. Based on the multiple voting percentages, the multiple feasibility scores, the preset first weight, and the preset second weight, determine the comprehensive score for each menu change text in the menu change text set, and obtain multiple comprehensive scores; the sum of the first weight and the second weight is 1; the first weight is the influence coefficient of the voting percentage index in the comprehensive score; the second weight is the influence coefficient of the feasibility index in the comprehensive score;

[0103] A503. Select the menu change text corresponding to the highest comprehensive score from the multiple comprehensive scores as the target menu change text;

[0104] A504. If multiple menu change texts have the same overall score and are all the highest, then the menu change text with the highest voting percentage shall be determined as the target menu change text.

[0105] A505. Determine the second voting result based on the target menu change text and its corresponding comprehensive score.

[0106] In a specific embodiment, the voting percentage of each menu change text in the first voting result and the feasibility of each menu change text in the target review result can be obtained to obtain multiple voting percentages and multiple feasibility, wherein each voting percentage corresponds to a feasibility.

[0107] The overall score for each menu change text in the menu change text set is determined based on multiple voting percentages, multiple feasibility scores, a preset first weight, and a preset second weight. Multiple overall scores are obtained, where the sum of the first and second weights is 1. The first weight is the influence coefficient of the voting percentage indicator in the overall score, and the second weight is the influence coefficient of the feasibility indicator in the overall score. Overall score = (voting percentage × first weight) + (feasibility × second weight). For example, if a menu change text has a voting percentage of 60% (0.6 after normalization), a feasibility score of 70% (0.7 after normalization), a first weight of 0.5, and a second weight of 0.5, then the overall score = 0.6 × 0.5 + 0.7 × 0.5 = 0.65.

[0108] The menu change text corresponding to the highest overall score among multiple overall scores is selected as the target menu change text. For example, if PV has an overall score of 0.8, which is higher than other texts, such as Passenger Car with an overall score of 0.6, then PV is initially determined to be the optimal option for the Passenger Car menu item.

[0109] If multiple menu change texts have the same overall score and are all the highest, the menu change text with the highest voting percentage is determined as the target menu change text. This means a tie-breaking rule is set to handle situations where multiple change texts have the same overall score and are tied for the highest. In this case, the original group voting data is revisited, and the text with the highest voting percentage is selected as the target change text. For example, if PV and Passenger Vehicle both have an overall score of 0.6, but PV's voting percentage (50%) is higher than the latter's (30%), then PV is determined as the target change text. This ensures the integration of professional review while respecting the original group preference data when decision-making is deadlocked, maintaining the rationality of the process.

[0110] After the final output of the second voting results is completed, the target menu change text can be associated with its comprehensive score to form a structured result including the optimal text, score, and fusion basis. For example, the output target change text is PV, and the comprehensive score is 0.6 (voting percentage 50% × 0.5 + feasibility 70% × 0.5).

[0111] It is evident that by employing mechanisms such as quantitative integration, weight adjustment, and tie-breaking, a decision-making system that balances user needs and professional standards has been constructed. This ensures that the second vote reflects the true preferences of the group while also guaranteeing professional translation quality, providing accurate and explainable decision support for menu translation optimization.

[0112] S106. Obtain the target menu change text corresponding to the second voting result.

[0113] In a specific embodiment, after determining the second voting result, the target menu change text corresponding to the second voting result can be obtained.

[0114] Specifically, the second voting result is structured data generated after combining the proportion of group voting and the feasibility of professional review, filtering for the highest score, and handling ties. It includes the comprehensive score of the menu change text of the optimal option and the decision-making basis. Therefore, the target menu change text corresponding to the second voting result can be obtained.

[0115] S107. Update the initial menu text to the target menu change text based on the second voting result.

[0116] In a specific embodiment, after determining the second voting result and obtaining the target menu change text, the initial menu text currently displayed on the user terminal can be replaced with the target menu change text obtained through collaborative decision-making by group voting and professional review.

[0117] Specifically, in multilingual terminal scenarios, it is necessary to ensure the language environment adaptability of text updates. For example, when the device language is English, only the menu text in the English environment is updated; if the terminal supports multilingual switching (such as having both English and German interfaces), then updates need to be performed separately for the target menus in different language environments (such as replacing the corresponding text based on the second vote result in German in the German environment) to ensure that the menu translations in each language environment are optimized synchronously.

[0118] It is evident that by combining user voting with expert review, the accuracy and adaptability of menu translations can be addressed, driving the evolution of menu descriptions on user terminals towards a more demand-oriented and professionally standardized direction.

[0119] In summary, by implementing the embodiments of this application, multiple user feedback data regarding the target menu of the user terminal are collected; a target voting form is generated based on the multiple feedback data; the target voting form includes: initial menu text and a set of changed menu texts; the target voting form is pushed to other user terminals using the device language, so that the other user terminals vote according to the target voting form to obtain a first voting result; the target translator's target review result for the first voting result is obtained; a second voting result is determined based on the target review result and the first voting result; the target menu changed text corresponding to the second voting result is obtained; and the initial menu text is updated to the target menu changed text based on the second voting result. It is evident that by combining user voting with expert review, disordered user feedback is transformed into structured voting data, effectively filtering low-value suggestions and focusing on high-consensus translation solutions. Simultaneously, it can reduce the workload of manual review, thereby improving translation optimization efficiency.

[0120] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a menu translation optimization system for a user terminal provided in an embodiment of this application. The menu translation optimization system 700 for the user terminal includes: a data collection unit 701, a voting form generation unit 702, a first voting result collection unit 703, a second voting result collection unit 704, a data processing unit 705, and a menu translation optimization unit 706.

[0121] The data collection unit 701 is used to collect multiple feedback data from multiple users regarding the target menu of the user terminal; each feedback data corresponds to a menu change text; the multiple users all use the same device language;

[0122] The voting form generation unit 702 is used to generate a target voting form based on the multiple feedback data; the target voting form includes: initial menu text and a set of menu change texts;

[0123] The first voting result collection unit 703 is used to push the target voting form to other user terminals using the device language, so that the other user terminals can vote according to the target voting form and obtain a first voting result; the first voting result includes the voting percentage of each menu change text in the menu change text set; the other user terminals refer to user terminals other than the user terminals that submitted the multiple feedback data.

[0124] The data collection unit 701 is also used to obtain the target translator's target review result for the first voting result;

[0125] The second voting result collection unit 704 is used to determine the second voting result based on the target review result and the first voting result;

[0126] The data processing unit 705 is used to obtain the target menu change text corresponding to the second voting result;

[0127] The menu translation optimization unit 706 is used to update the initial menu text to the target menu change text based on the second voting result.

[0128] Optionally, the user terminal includes: a target feedback button and a target feedback input box; in collecting multiple feedback data from multiple users regarding the target menu of the user terminal, the data collection unit 701 is further specifically used for:

[0129] In response to a first operation by a target user on the target menu of the user terminal, the target feedback button is displayed; the target user is any one of the plurality of users.

[0130] In response to a second action by the target user on the target feedback button, target indication information and the target feedback input box are displayed; the target indication information is used to instruct the target user to enter menu change text in the target feedback input box.

[0131] Obtain the target feedback data from the target feedback input box; the target feedback data is the feedback data corresponding to the target user.

[0132] Optionally, in generating the target voting form based on the plurality of feedback data, the voting form generation unit 702 is further specifically used for:

[0133] Obtain the initial text of the menu corresponding to the target menu;

[0134] The menu change text in the multiple feedback data is deduplicated to obtain the menu change text set.

[0135] The target voting form is generated based on the initial menu text and the set of changed menu texts; the set of changed menu texts consists of interactive voting options in the target voting form.

[0136] Optionally, after generating the target voting form based on the multiple feedback data, the menu translation optimization system 700 of the user terminal is further specifically used for:

[0137] Configure the voting cutoff conditions of the target voting form so that the target voting form stops voting and obtains the voting results when the voting cutoff conditions are met; the voting cutoff conditions include: the number of participants in the vote reaches a preset number of voters threshold, or the voting time reaches a preset time threshold.

[0138] Optionally, after the target voting form is pushed to other user terminals using the device language, the menu translation optimization system 700 of the user terminals is further specifically used for:

[0139] Obtain the first feedback data from the other user terminals regarding the target menu;

[0140] The target voting form is updated based on the first feedback data.

[0141] Optionally, in obtaining the target translator's review result regarding the first voting result, the data collection unit 701 is further specifically used for:

[0142] The first voting result is simultaneously pushed to the target translator via email, so that the target translator can determine the feasibility of each menu change text in the menu change text set according to the preset review rules, and obtain multiple feasibility scores; the higher the feasibility score, the more accurate the text translation.

[0143] The target audit result is determined based on the multiple feasibility factors.

[0144] Optionally, in determining the second voting result based on the target review result and the first voting result, the second voting result collection unit 704 is further specifically used for:

[0145] Obtain the voting percentage of each menu change text in the first voting result, and the feasibility of each menu change text in the target review result, to obtain multiple voting percentages and multiple feasibility values; each voting percentage corresponds to one feasibility value;

[0146] The comprehensive score of each menu change text in the menu change text set is determined based on the multiple voting percentages, the multiple feasibility scores, the preset first weight, and the preset second weight, resulting in multiple comprehensive scores; the sum of the first weight and the second weight is 1; the first weight is the influence coefficient of the voting percentage index in the comprehensive score; the second weight is the influence coefficient of the feasibility index in the comprehensive score;

[0147] Select the menu change text corresponding to the highest comprehensive score from the multiple comprehensive scores as the target menu change text;

[0148] If multiple menu change texts have the same overall score and are all the highest, then the menu change text with the highest voting percentage will be determined as the target menu change text.

[0149] The second voting result is determined based on the target menu change text and its corresponding comprehensive score.

[0150] The user terminal menu translation optimization system 700 described in this application can collect multiple feedback data from multiple users regarding a target menu on the user terminal; generate a target voting form based on the multiple feedback data; the target voting form includes: initial menu text and a set of changed menu texts; push the target voting form to other user terminals using the device language, so that the other user terminals can vote according to the target voting form to obtain a first voting result; obtain the target review result of the target translator regarding the first voting result; determine a second voting result based on the target review result and the first voting result; obtain the target menu changed text corresponding to the second voting result; and update the initial menu text to the target menu changed text based on the second voting result. It is evident that by combining user voting with expert review, disordered user feedback is transformed into structured voting data, effectively filtering low-value suggestions and focusing on high-consensus translation solutions. Simultaneously, it can also reduce the workload of manual review, thereby improving translation optimization efficiency.

[0151] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include a processor, a memory, a communication interface, and one or more programs. The processor, memory, and communication interface can be interconnected via a bus. The one or more programs are stored in the memory and configured to be executed by the processor. In this embodiment, the programs include instructions for performing the following steps:

[0152] Collect multiple feedback data from multiple users regarding the target menu on the user terminal; each feedback data corresponds to a menu change text; all multiple users use the same device language;

[0153] A target voting form is generated based on the multiple feedback data; the target voting form includes: initial menu text and a set of menu change texts;

[0154] The target voting form is pushed to other user terminals using the device language, so that the other user terminals vote according to the target voting form to obtain a first voting result; the first voting result includes the voting percentage of each menu change text in the menu change text set; the other user terminals refer to user terminals other than the user terminals that submitted the multiple feedback data;

[0155] Obtain the target translator's review result regarding the first voting result;

[0156] The second voting result is determined based on the target review result and the first voting result;

[0157] Obtain the target menu change text corresponding to the second voting result;

[0158] The initial menu text is updated to the target menu change text based on the second voting result.

[0159] The electronic device described in this application can collect multiple feedback data from multiple users regarding a target menu on a user terminal; generate a target voting form based on the multiple feedback data; the target voting form includes: initial menu text and a set of changed menu texts; push the target voting form to other user terminals using the device's language, so that the other user terminals can vote according to the target voting form to obtain a first voting result; obtain the target review result of the target translator regarding the first voting result; determine a second voting result based on the target review result and the first voting result; obtain the target menu changed text corresponding to the second voting result; and update the initial menu text to the target menu changed text based on the second voting result. It is evident that by combining user voting with expert review, disordered user feedback is transformed into structured voting data, effectively filtering low-value suggestions and focusing on high-consensus translation solutions. Simultaneously, it can reduce the workload of manual review, thereby improving translation optimization efficiency.

[0160] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0161] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0162] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0163] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0164] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0165] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on a processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented using a software program that runs on a processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0166] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A method for optimizing menu translation in a user terminal, characterized in that, Applied to a server; the method includes: Collect multiple feedback data from multiple users regarding the target menu on the user terminal; each feedback data corresponds to a menu change text; all multiple users use the same device language; A target voting form is generated based on the multiple feedback data; the target voting form includes: initial menu text and a set of menu change texts; The target voting form is pushed to other user terminals using the device language, so that the other user terminals vote according to the target voting form to obtain a first voting result; the first voting result includes the voting percentage of each menu change text in the menu change text set; the other user terminals refer to user terminals other than the user terminals that submitted the multiple feedback data; Obtain the target translator's review result regarding the first voting result; The second voting result is determined based on the target review result and the first voting result; Obtain the target menu change text corresponding to the second voting result; The initial menu text is updated to the target menu change text based on the second voting result.

2. The method as described in claim 1, characterized in that, The user terminal includes: a target feedback button and a target feedback input box; the collection of multiple feedback data from multiple users regarding the target menu of the user terminal includes: In response to a first operation by a target user on the target menu of the user terminal, the target feedback button is displayed; the target user is any one of the plurality of users. In response to a second action by the target user on the target feedback button, target indication information and the target feedback input box are displayed; the target indication information is used to instruct the target user to enter menu change text in the target feedback input box. Obtain the target feedback data from the target feedback input box; the target feedback data is the feedback data corresponding to the target user.

3. The method as described in claim 1, characterized in that, The step of generating the target voting form based on the multiple feedback data includes: Obtain the initial text of the menu corresponding to the target menu; The menu change text in the multiple feedback data is deduplicated to obtain the menu change text set. The target voting form is generated based on the initial menu text and the set of changed menu texts; the set of changed menu texts consists of interactive voting options in the target voting form.

4. The method as described in claim 3, characterized in that, After generating the target voting form based on the multiple feedback data, the method further includes: Configure the voting cutoff conditions of the target voting form so that the target voting form stops voting and obtains the voting results when the voting cutoff conditions are met; the voting cutoff conditions include: the number of participants in the vote reaches a preset number of voters threshold, or the voting time reaches a preset time threshold.

5. The method according to any one of claims 1-4, characterized in that, After pushing the target voting form to other user terminals using the device language, the method further includes: Obtain the first feedback data from the other user terminals regarding the target menu; The target voting form is updated based on the first feedback data.

6. The method as described in claim 1, characterized in that, The process of obtaining the target translator's review result for the first voting result includes: The first voting result is simultaneously pushed to the target translator via email, so that the target translator can determine the feasibility of each menu change text in the menu change text set according to the preset review rules, and obtain multiple feasibility scores; the higher the feasibility score, the more accurate the text translation. The target audit result is determined based on the multiple feasibility factors.

7. The method as described in claim 6, characterized in that, The step of determining the second voting result based on the target review result and the first voting result includes: Obtain the voting percentage of each menu change text in the first voting result, and the feasibility of each menu change text in the target review result, to obtain multiple voting percentages and multiple feasibility values; each voting percentage corresponds to one feasibility value; The comprehensive score of each menu change text in the menu change text set is determined based on the multiple voting percentages, the multiple feasibility scores, the preset first weight, and the preset second weight, resulting in multiple comprehensive scores; the sum of the first weight and the second weight is 1; the first weight is the influence coefficient of the voting percentage index in the comprehensive score; the second weight is the influence coefficient of the feasibility index in the comprehensive score; Select the menu change text corresponding to the highest comprehensive score from the multiple comprehensive scores as the target menu change text; If multiple menu change texts have the same overall score and are all the highest, then the menu change text with the highest voting percentage will be determined as the target menu change text. The second voting result is determined based on the target menu change text and its corresponding comprehensive score.

8. A menu translation optimization system for a user terminal, characterized in that, Applied to servers; The menu translation optimization system for the user terminal includes: a data collection unit, a voting form generation unit, a first voting result collection unit, a second voting result collection unit, a data processing unit, and a menu translation optimization unit, wherein... The data collection unit is used to collect multiple feedback data from multiple users regarding the target menu of the user terminal; each feedback data corresponds to a menu change text; all multiple users use the same device language; The voting form generation unit is used to generate a target voting form based on the multiple feedback data; the target voting form includes: initial menu text and a set of menu change texts; The first voting result collection unit is used to push the target voting form to other user terminals using the device language, so that the other user terminals can vote according to the target voting form and obtain a first voting result; the first voting result includes the voting percentage of each menu change text in the menu change text set; the other user terminals refer to user terminals other than the user terminals that submitted the multiple feedback data; The data collection unit is also used to obtain the target translator's review results regarding the first voting result; The second voting result collection unit is used to determine the second voting result based on the target review result and the first voting result; The data processing unit is used to obtain the target menu change text corresponding to the second voting result; The menu translation optimization unit is used to update the initial menu text to the target menu change text based on the second voting result.

9. An electronic device, characterized in that, include: Processor, memory, communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-7.

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