Vehicle-mounted HMI multi-language script updating system, method, device and medium

Abstract

The embodiment of the present application relates to the technical field of vehicles, and discloses a vehicle-mounted HMI multi-language script updating system, method, device and medium.The vehicle-mounted HMI multi-language script updating system at least comprises a data source layer, a data acquisition layer, a core processing layer, a configuration management layer, a quality assurance layer and an output integration layer; the data source layer is at least used for providing dual-mode data input of translation data and local export data; the data acquisition layer is connected with the data source layer; the core processing layer is connected with the data acquisition layer and the configuration management layer respectively; the configuration management layer is at least used for providing an alarm ID mapping table and UI constraint configuration data; the quality assurance layer is connected with the core processing layer; and the output integration layer is connected with the quality assurance layer.The embodiment of the present application can at least realize automatic, intelligent and high-reliability vehicle-mounted HMI multi-language script updating.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to an in-vehicle HMI multilingual text update system, method, device and medium. Background Technology

[0002] With the development of global automobile sales and the trend of software-defined vehicles, in-vehicle instrument and infotainment systems need to support rapid and flexible updates of multilingual alarm text.

[0003] Existing technologies typically employ a semi-automated process, which suffers from at least the following pain points and drawbacks:

[0004] 1. The operation process is cumbersome and prone to errors: It relies heavily on developers to manually perform operations such as "export-find-copy-paste-import", which is not only inefficient, but also prone to human errors such as mismatch and missed updates when processing a large number of alarm IDs, resulting in incorrect alarm information displayed on the vehicle instrument panel and posing a safety hazard.

[0005] 2. Unreliable display quality and high post-production debugging costs: The translated text differs significantly in length from the original language, while the size of the instrument UI pop-up is fixed, with a limited number of characters per line. Existing technology lacks the ability to predict and adapt to the length of the translated text, easily leading to serious UI problems such as incomplete text display, overlapping, and layout errors after direct import. Once these problems are discovered, developers, translators, and UI designers need to communicate and adjust repeatedly, resulting in a long debugging cycle and high costs.

[0006] 3. Commitment of semantic integrity: Existing simple text processing tools typically break lines mechanically based on the number of characters or spaces, which can easily destroy the integrity of key information. For example, breaking the line after the word "over" in "Brake system pressure is too low, please stop safely" would result in "Brake system pressure is too high" and "low, please stop safely," severely distorting the original meaning and potentially misleading the driver.

[0007] 4. Character compatibility risk: The translated content may contain uncommon characters, special symbols (such as ™, €), or icon symbols that are not supported by the target vehicle's embedded system font library. The existing process cannot detect this issue before importing, resulting in garbled characters or "□" boxes being displayed on the instrument panel, affecting user experience and information accuracy, and the problem is discovered late.

[0008] 5. Lack of advanced management functions: Existing processes are unable to support modern and sophisticated HMI management needs such as A / B testing, parameterized dynamic text, and collaborative updates with non-text resources (such as icons). Summary of the Invention

[0009] The purpose of this invention is to provide a multilingual text update system, method, device and medium for vehicle HMI, which can at least achieve automated, intelligent and highly reliable multilingual text update for vehicle HMI.

[0010] To address the aforementioned technical problems, in a first aspect, the present invention provides an in-vehicle HMI multilingual text update system, comprising at least a data source layer, a data acquisition layer, a core processing layer, a configuration management layer, a quality assurance layer, and an output integration layer;

[0011] The data source layer is used at least to provide dual-mode data input of translated data and locally exported data;

[0012] The data acquisition layer establishes a connection with the data source layer and is at least used to perform programmatic retrieval of the translated data and capture the locally exported data, and then parse and standardize the dual-mode data to at least convert it into structured data.

[0013] The core processing layer establishes connections with the data acquisition layer and the configuration management layer, respectively, and is at least used to acquire the structured data and the alarm ID mapping table, and to perform ID mapping operations and generate matching data based on the structured data and the alarm ID mapping table; and to acquire UI constraint configuration data, and then to perform semantic analysis, line break optimization and overflow processing on the matching data based at least on the UI constraint configuration data to obtain non-overflow formatted text;

[0014] The configuration management layer is used to provide at least the alarm ID mapping table and the UI constraint configuration data;

[0015] The quality assurance layer is connected to the core processing layer and is used at least to acquire the non-overflow formatted text and perform character set verification and length pre-check operations on the non-overflow formatted text; and, after the non-overflow formatted text passes the character set verification and the length pre-check, output verification qualified data; and, after the non-overflow formatted text fails the character set verification and / or the length pre-check, output an exception report.

[0016] The output integration layer establishes a connection with the quality assurance layer, and is used at least to receive the verified data and at least serialize the verified data into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0017] Optionally, the data source layer includes at least a translation platform unit and a local import unit;

[0018] The translation platform unit is at least used to provide the translation data;

[0019] The local import unit is used to provide at least the local exported data.

[0020] Optionally, the data acquisition layer includes at least an API interface unit, a file monitoring service unit, and a data parser unit;

[0021] The API interface unit is at least used to perform the programmatic retrieval of the translation data;

[0022] The file monitoring service unit is used at least to capture the locally exported data;

[0023] The data parser unit is at least used to parse and standardize the dual-mode data to at least transform it into the structured data.

[0024] Optionally, the core processing layer includes at least an ID mapping engine unit, a semantic analyzer unit, a line break optimization algorithm unit, and an overflow processor unit;

[0025] The ID mapping engine unit is at least used to obtain the structured data and the alarm ID mapping table, and to perform the ID mapping operation and generate the matching data based on the structured data and the alarm ID mapping table;

[0026] The semantic analyzer unit is at least used to obtain the UI constraint configuration data, and then at least combine the UI constraint configuration data to perform semantic analysis processing on the matching data;

[0027] The line break optimization algorithm unit is at least used to perform line break optimization processing on the matching data after semantic analysis processing;

[0028] The overflow processor unit is used at least to perform overflow processing on the matching data after semantic analysis and line break optimization to obtain the non-overflow formatted text.

[0029] Optionally, the configuration management layer includes at least a UI constraint configuration library unit and an alarm ID mapping table unit;

[0030] The UI constraint configuration library unit is used to provide at least the UI constraint configuration data;

[0031] The alarm ID mapping table unit is used to provide at least the alarm ID mapping table.

[0032] Optionally, the quality assurance layer includes at least a character set validator unit, a length preflight unit, and an anomaly report generator unit;

[0033] The character set validator unit is at least used to obtain the non-overflow formatted text and perform character set validation on the non-overflow formatted text;

[0034] The length pre-detector unit is at least configured to perform the length pre-detection operation on the non-overflow formatted text that has passed the character set verification after the non-overflow formatted text has passed the character set verification; and to output the verification qualified data after the non-overflow formatted text has passed the character set verification and the length pre-detection.

[0035] The exception report generator unit is at least used to output the exception report after the non-overflow formatted text fails the character set checksum and / or the length pre-check.

[0036] Optionally, the output integration layer includes at least a CSV generator unit and an HMI integration unit;

[0037] The CSV generator unit is at least used to receive the verified data and at least serialize the verified data into the final CSV file;

[0038] The HMI integration unit is used at least to achieve end-to-end automated delivery from the data source to the presentation terminal based on the final CSV file.

[0039] Based on the same concept, in a second aspect, the present invention also provides a method for updating multilingual text in an in-vehicle HMI, which is implemented at least by the in-vehicle HMI multilingual text update system described in any one of the first aspects;

[0040] The method for updating multilingual text in the vehicle HMI includes at least the following:

[0041] The data source layer provides dual-mode data input, including translated data and locally exported data.

[0042] The data acquisition layer performs programmatic retrieval of the translation data and captures the locally exported data, thereby parsing and standardizing the dual-mode data to at least convert it into structured data.

[0043] The configuration management layer provides the alarm ID mapping table and the UI constraint configuration data.

[0044] The core processing layer obtains the structured data and alarm ID mapping table, and performs ID mapping operations and generates matching data based on the structured data and alarm ID mapping table.

[0045] The core processing layer obtains UI constraint configuration data, and then performs semantic analysis, line break optimization and overflow processing on the matching data in combination with at least the UI constraint configuration data to obtain non-overflow formatted text.

[0046] The non-overflow formatted text is obtained through the quality assurance layer, and character set verification and length pre-check are performed on the non-overflow formatted text. Then, after the non-overflow formatted text passes the character set verification and the length pre-check, the verification qualified data is output, or, after the non-overflow formatted text fails the character set verification and / or the length pre-check, an error report is output.

[0047] The output integration layer receives the verified data and serializes it into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0048] Based on the same concept, in a third aspect, the present invention also provides an electronic device, including a memory and a processor, the memory storing a computer program executable on the processor, the processor executing the program to implement the steps in the in-vehicle HMI multilingual text update method of any of the second aspects.

[0049] Based on the same concept, in a fourth aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in the in-vehicle HMI multilingual text update method described in any of the second aspects.

[0050] The in-vehicle HMI multilingual text update system provided in this embodiment of the invention includes at least a data source layer, a data acquisition layer, a core processing layer, a configuration management layer, a quality assurance layer, and an output integration layer. The data source layer is used to provide dual-mode data input, including translated data and locally exported data. The data acquisition layer, connected to the data source layer, is used to perform programmatic retrieval of translated data and capture of locally exported data, and then parse and standardize the dual-mode data to at least convert it into structured data. The core processing layer, connected to both the data acquisition layer and the configuration management layer, is used to acquire structured data and an alarm ID mapping table, and perform ID mapping operations and generate matching data based on the structured data and the alarm ID mapping table; and acquire UI constraint configuration data, and then perform semantic analysis, line break optimization, and overflow processing on the matching data, at least in conjunction with the UI constraint configuration data, to obtain non-overflow formatted text. The configuration management layer is used to provide an alarm ID mapping table and UI constraint configuration data. The Quality Assurance Layer, connected to the core processing layer, is at least used to acquire non-overflow formatted text and perform character set validation and length pre-check operations on the non-overflow formatted text; and, after the non-overflow formatted text passes the character set validation and length pre-check, output validated data; and, if the non-overflow formatted text fails the character set validation and / or length pre-check, output an exception report. The Output Integration Layer, connected to the Quality Assurance Layer, is at least used to receive validated data and at least serialize the validated data into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0051] Therefore, the embodiments of the present invention can at least achieve automated, intelligent, and highly reliable multilingual text updates for in-vehicle HMIs, and at least solve the following problems:

[0052] 1. How to automate the entire process from translation to integration to avoid human error;

[0053] 2. How to ensure that the translation is displayed perfectly and correctly in a fixed UI layout before importing data, so as to avoid debugging later;

[0054] 3. How to maintain the semantic integrity and readability of sentences when adapting line breaks;

[0055] 4. How to prevent garbled characters caused by font incompatibility and nip the risk in the bud before deployment.

[0056] The beneficial effects produced by the embodiments of the present invention include at least the following:

[0057] 1. High efficiency and accuracy: Full-process automation greatly improves update efficiency and eliminates errors caused by manual operation;

[0058] 2. Display effect pre-guarantee: Through intelligent line breaking and pre-validation mechanisms, the translation is ensured to be "imported once and displayed correctly", saving a lot of UI debugging time and costs;

[0059] 3. Accurate and reliable information delivery: The semantic understanding-based line-breaking strategy ensures that key information is not fragmented, improving driving safety; the character pre-detection mechanism eliminates garbled text display;

[0060] 4. Powerful and flexible functionality: This invention provides strong underlying support for the international management of vehicle HMIs and can be easily extended to advanced application scenarios such as canary releases and dynamic text. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the structure of an in-vehicle HMI multilingual text update system provided in an embodiment of the present invention;

[0062] Figure 2 This is a flowchart of a method for updating multilingual text in an in-vehicle HMI according to an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of the structure of an in-vehicle HMI multilingual text update device provided in an embodiment of the present invention;

[0064] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0067] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0068] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0069] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0070] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0071] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0072] Figure 1 This is a schematic diagram of a multilingual text update system for an in-vehicle HMI provided in an embodiment of the present invention. This embodiment is applicable to at least any multilingual text update scenario for an in-vehicle HMI in a vehicle, such as... Figure 1 As shown, the in-vehicle HMI multilingual copywriting update system includes at least a data source layer, a data acquisition layer, a core processing layer, a configuration management layer, a quality assurance layer, and an output integration layer.

[0073] The data source layer is used at least to provide bimodal data input, including translated data and locally exported data.

[0074] The data acquisition layer connects with the data source layer and is used at least to perform programmatic fetching of translation data and capture locally exported data, and then to parse and standardize the dual-mode data to at least transform it into structured data.

[0075] The core processing layer establishes connections with the data acquisition layer and the configuration management layer, respectively. It is used at least to acquire structured data and alarm ID mapping tables, and to perform ID mapping operations and generate matching data based on the structured data and alarm ID mapping tables; and to acquire UI constraint configuration data, and then to perform semantic analysis, line break optimization and overflow processing on the matching data based at least on the UI constraint configuration data to obtain non-overflow formatted text.

[0076] The configuration management layer is used at least to provide alarm ID mapping tables and UI constraint configuration data.

[0077] The quality assurance layer establishes a connection with the core processing layer and is used at least to acquire non-overflow formatted text and perform character set verification and length pre-check operations on the non-overflow formatted text; and, after the non-overflow formatted text passes the character set verification and length pre-check, output the verification qualified data; and, after the non-overflow formatted text fails the character set verification and / or length pre-check, output an exception report.

[0078] The output integration layer establishes a connection with the quality assurance layer, and is used at least to receive verified data and at least serialize the verified data into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0079] The technical solution involved in the embodiments of the present invention is essentially a highly automated data pipeline system. Its core design concept is to compile the original multilingual text data into formatted information with display determinism through a series of intelligent processing stages, thereby ensuring that its rendering effect on a specific human-computer interaction interface is completely consistent with the design expectation.

[0080] In one specific implementation, optionally, the data source layer includes at least a translation platform unit (corresponding to...). Figure 1 Translation platforms in China) and local import units (corresponding to Figure 1 (Local import in the middle)

[0081] The translation platform unit is used at least to provide translation data;

[0082] The local import unit is used to provide at least local exported data.

[0083] In another specific implementation, the data acquisition layer may optionally include at least an API interface unit (corresponding to...). Figure 1 API interfaces), file listening service unit (corresponding to) Figure 1 The file listening service and the data parser unit (corresponding to the file parser service in the middle) ... Figure 1 (data parser in the middle)

[0084] The API interface unit is at least used to perform the programmatic fetching of translation data;

[0085] The file listening service unit is at least used to capture locally exported data;

[0086] The data parser unit is used at least to parse and standardize the bimodal data to at least transform it into structured data.

[0087] In yet another specific implementation, the core processing layer may optionally include at least an ID mapping engine unit (corresponding to...). Figure 1 ID mapping engine in the middle), semantic analyzer unit (corresponding to Figure 1 The semantic analyzer in the middle), the line break optimization algorithm unit (corresponding to the semantic analyzer in the middle), and the line break optimization algorithm unit (corresponding to the line break optimization algorithm unit in the middle). Figure 1 The line break optimization algorithm in the middle) and the overflow processor unit (corresponding to Figure 1 Overflow handler in the middle);

[0088] The ID mapping engine unit is at least used to obtain structured data and alarm ID mapping tables, and to perform ID mapping operations and generate matching data based on the structured data and alarm ID mapping tables;

[0089] The semantic analyzer unit is used at least to obtain UI constraint configuration data, and then to perform semantic analysis processing on the matching data in combination with the UI constraint configuration data.

[0090] The line break optimization algorithm unit is used at least to perform line break optimization processing on the matching data after semantic analysis;

[0091] The overflow processor unit is used at least to perform overflow processing on the matching data after semantic analysis and line break optimization to obtain non-overflow formatted text.

[0092] In yet another specific implementation, the configuration management layer may optionally include at least a UI constraint configuration library unit (corresponding to...). Figure 1 The UI constraint configuration library and alarm ID mapping table unit (corresponding to) Figure 1 (Alarm ID mapping table in the table).

[0093] The UI constraint configuration library unit is used at least to provide UI constraint configuration data;

[0094] The alarm ID mapping table unit is used to provide at least an alarm ID mapping table.

[0095] In yet another specific implementation, the quality assurance layer may optionally include at least a character set validator unit (corresponding to...). Figure 1 The character set validator and length preflight unit (corresponding to the character set validator and length preflight unit) in the text. Figure 1 The length pre-detector and the anomaly report generator unit (corresponding to the length pre-detector) ... in the middle. Figure 1 (The exception report generator in the middle)

[0096] The character set validator unit is at least used to acquire non-overflow formatted text and perform character set validation on the non-overflow formatted text;

[0097] The length preflight unit is at least used to perform a length preflight operation on non-overflow formatted text that has passed character set verification after the non-overflow formatted text has passed character set verification; and to output verification-qualified data after the non-overflow formatted text has passed both character set verification and length preflight.

[0098] The exception report generator unit is used at least to output an exception report after non-overflow formatted text fails the character set checksum and / or length pre-check.

[0099] In yet another specific implementation, the output integration layer optionally includes at least a CSV generator unit (corresponding to...). Figure 1 The CSV generator in the middle) and the HMI integration unit (corresponding to Figure 1 (Kanzi integration in the middle).

[0100] The CSV generator unit is at least used to receive valid data and at least serialize the valid data into a final CSV file;

[0101] The HMI integration unit is used at least to enable end-to-end automated delivery from the data source to the presentation terminal based on the final CSV file.

[0102] For example, the specific implementation process of the in-vehicle HMI multilingual copywriting update system can be as follows:

[0103] The initial stage of the process involves data acquisition and standardized preprocessing. The data source layer provides dual-modal input capabilities, supporting both translation platforms (such as POEditor, Crowdin, or enterprise-developed platforms) and manually exported Excel files. The data acquisition layer implements programmatic real-time data retrieval via API interfaces (RESTful API or GraphQL), while simultaneously utilizing a file monitoring service (Watchdog or FileSystemWatcher) to capture manually exported files. After data loading, the data parser uses a high-performance data framework (Pandas, OpenPyXL, or Apache POI) for parsing and standardization, transforming the data into in-memory structured data objects (such as DataFrames), providing a unified data foundation for subsequent high-precision operations.

[0104] The process then proceeds to the key matching and data fusion stage. The ID mapping engine in the core processing layer reads the native CSV template file (part of the structured data) required by the HMI engine. This template defines the complete data structure of the alarm information. At this stage, the alarm ID, as a globally unique key, is used to perform in-memory table join operations (using Pandas merge or hash table indexing) to batch and atomically map and populate the target language fields from the translated data (also part of the structured data) into the corresponding columns of the CSV template. The alarm ID mapping table in the configuration management layer (stored in SQLite or an in-memory database) maintains the mapping relationships for this process. This process achieves seamless integration between the source data and the target template, laying the foundation for full-process automation and fundamentally eliminating mismatches and omissions that might be introduced by manual intervention.

[0105] The core of the process is semantic adaptive formatting based on rendering constraints. The system first loads precise constraint parameters for the target display area from the UI constraint configuration library (stored in JSON, YAML, or XML format) in the configuration management layer. These parameters include the maximum number of characters per line and the maximum number of lines displayed, and can be extended to font metrics (such as font size and font family) for more accurate visual width calculation. Subsequently, the semantic analyzer in the core processing layer is activated. This analyzer operates based on a predefined NLP rule base, which can use Jieba segmentation, SpaCy, or a custom rule engine. Its core algorithm prioritizes semantic integrity over simple character truncation. The rule base defines the segmentation priority of semantic boundaries (e.g., sentence terminators are higher than clause breaks, and clause breaks are higher than conjunctions) and can identify key entities in the text (such as system proper nouns), ensuring that these entities remain intact when line breaks occur.

[0106] The line-breaking optimization algorithm is the core execution unit in this step. This embodiment of the invention supports two algorithm strategies and can adaptively select one according to the scenario. The implementation logic of the greedy algorithm is to process the text line by line, putting as many characters as possible into the current line, while prioritizing the selection of high-priority semantic segmentation points for line breaking. Its advantages are simple implementation and fast calculation speed, and it is suitable for scenarios with moderate text length and high requirements for processing speed. The dynamic programming algorithm models the problem as a global optimization, and its core lies in the design of the cost function. Define the text character sequence as T[1…n], and let dp[i] represent the minimum cost required to process the first i characters. For each line, define cost(j,i) as the cost of treating the j-th character to the i-th character as a line, then the state transition equation is: dp[i] = min{dp[j-1] + cost(j,i)}, where the value of j ranges from 1 to i, and it must be ensured that the number of characters in the line does not exceed the maximum number of characters per line L.

[0107] The cost function cost(j,i) consists of three layers. The first layer is the basic feasibility constraint: if the total width of the characters from j to i (calculated based on font metrics) exceeds the maximum number of characters per line L, then cost(j,i) is directly set to infinity, indicating that the segmentation scheme is not feasible. The second layer is the semantic integrity cost: based on the segmentation point priority output by the semantic analyzer, if the end position of the line happens to be at a high-priority semantic segmentation point (such as a period or comma), a lower cost is assigned; if it is at a low-priority segmentation point (such as a conjunction or space) or a non-semantic boundary, a higher cost is assigned. Specifically, the cost is inversely proportional to the priority—the cost of a line break at a sentence terminator is 0, at a clause break is 1, at a comma is 2, at a conjunction is 3, at a space is 4, and at a non-semantic boundary is 5. The third layer is the line balance cost: when the total number of lines of text may exceed the maximum number of lines to be displayed R, the system introduces an additional line number penalty term to encourage the algorithm to distribute the number of characters in each line as evenly as possible while satisfying the line number constraint, avoiding situations where one line is too full and another line is too empty. The final cost(j,i) is obtained by weighted summation of the three costs. The weight coefficients can be adjusted according to the actual application scenario (such as the display preferences of different car models).

[0108] By designing the aforementioned cost function, the dynamic programming algorithm can search the feasible solution space for the globally optimal line-breaking scheme that minimizes the total cost. While satisfying character and line count limitations, it prioritizes semantic integrity while also considering the visual balance of each line. This algorithm is suitable for scenarios with long text or strict UI constraints, maximizing information retention even when line count is limited.

[0109] For text that still exceeds the display capacity after optimal line breaks, the overflow processor initiates a graceful degradation strategy, adding an ellipsis identifier to the end of the last line and recording it in the audit log, thereby ensuring a clean interface while providing a basis for subsequent optimization.

[0110] After data formatting, the system performs static character set compatibility verification, a mandatory quality gate in the embedded process. The character set validator in the quality assurance layer embeds or references the character set encoding table supported by the target font definition (stored in Unicode ranges or font files) in the configuration management layer. Before output, it performs a full scan of all text content, comparing the code point of each character with the allowed character sets using Unicode code point comparison (internationalization components such as ICU4J can be used). If any unsupported character is detected, the system immediately triggers an exception handling process. An exception report generator (outputting in JSON, CSV, or Log4j format) generates a diagnostic report containing a problem identifier (alarm ID) and the location of the exception character, thus completely preventing display anomalies caused by missing characters before deployment. Simultaneously, a length preflighter (which can integrate the FreeType font rendering engine) performs pixel-level width calculations to ensure that even if the number of characters does not exceed the limit, display anomalies will not occur due to differences in character width.

[0111] Finally, in the output and system integration phase, the CSV generator in the output integration layer (based on Pandas to_csv or custom streaming write) serializes the processed and validated data structures into a final CSV file that conforms to the specifications. Afterward, depending on the integration depth with the HMI engine, a fully automated mode can be achieved through the Kanzi integration interface (calling the Kanzi command-line tool or Python SDK), or the file can be deployed to a predetermined directory and a notification sent. Extended interfaces with other HMI engines (such as Qt and CGI Studio) are also reserved to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0112] In summary, the above technical solutions, through their sophisticated modular design, can produce significant beneficial effects:

[0113] 1. The automated connection between the data acquisition layer and the core processing layer directly achieves a leap in processing efficiency and data accuracy, transforming the original manual "export-find-copy-paste" process into a programmed operation, completely eliminating human errors such as mismatch and omissions.

[0114] 2. The semantic adaptive formatting module in the core processing layer uses UI constraints as compilation parameters and eliminates potential display problems at the data preparation stage through proactive semantic analysis. Traditional solutions can only discover problems such as incomplete display and text overlap after import and require repeated debugging. In contrast, the embodiments of the present invention complete intelligent line breaks according to constraints such as the number of characters per line and the maximum number of lines before import, ensuring the display effect in advance. At the same time, by identifying key entities and semantic boundaries for line breaks, it avoids information fragmentation problems such as "Brake system pressure over" and "Low, please stop safely" caused by mechanical truncation, ensuring the accuracy and reliability of information transmission and enhancing driving safety.

[0115] 3. The static verification of character set compatibility in the quality assurance layer serves as an embedded defense line. Through a pre-prevention mechanism, it discovers and intercepts incompatible characters such as rare Chinese characters and special symbols before deployment, avoiding the passive situation in traditional solutions where problems can only be discovered after being displayed as "square boxes" or garbled characters on the instrument panel, making it difficult to locate problems, and further strengthening the robustness and output reliability of the system at the quality level.

[0116] 4. The introduction of the configuration management layer completely decouples rules such as UI constraints, font library definitions, and ID mappings from the core logic, enabling flexible adaptation to the differentiated requirements of different vehicle models and different pop-up window sizes without modifying the code, greatly enhancing the maintainability and reusability of the system.

[0117] In view of this, the embodiments of the present invention transform a series of discrete and error-prone manual operations into a coherent, reliable, and intelligent industrial-level pipeline. Through the coordinated cooperation of six major modules, it achieves a technical leap from "text transfer" to "display guarantee", providing a solid technical foundation for the multi-language management of user interfaces in complex environments.

[0118] It should be noted that to clearly explain the technical solution of the present invention, a specific application scenario is described below. This embodiment simulates the complete process of updating the Chinese alarm copy for an English-version vehicle, highlighting how the system achieves the best presentation effect for a complex alarm message within a limited display space through the coordinated operation of the six major modules shown in the architecture diagram.

[0119] Scenario Embodiment: Localization Update of Alarm Copy for On-vehicle Electric Brake System

[0120] Input conditions: The data source layer receives an Excel file from a translation platform, containing an entry with the alarm ID ALM_TPMS_BRAKE_ASSIST_ACTIVE, whose corresponding Chinese translation is: "For safety, the electric brake assist system is operating, but performance may be degraded. Please drive with caution." The configuration management layer's preset UI constraint rules are: the target dashboard alarm pop-up displays a maximum of 12 standard Chinese characters per line, and the entire pop-up can hold a maximum of 3 lines of text. The target file is the original CSV template referenced by the Kanzi HMI project, which already contains an entry with the same ID, and its message_text field contains the original English text.

[0121] System processing procedure:

[0122] The data acquisition layer first obtains the aforementioned Excel file through an API interface or file listening service. The data parser then uses Pandas to parse it into a structured DataFrame. Subsequently, the core processing layer's ID mapping engine uses the alarm ID as a unique key and performs a hash table index join operation in memory to accurately update the Chinese translation into the message_text field of the CSV template, completing the initial data integration.

[0123] Upon entering the core formatting stage, the system loads the JSON-formatted display parameters (maximum 3 lines, 12 characters per line) from the UI constraint configuration library of the configuration management layer and starts the semantic analyzer. This analyzer performs word segmentation and semantic boundary analysis on the text based on an NLP rule base, identifying two commas as high-priority semantic segmentation points. The line-breaking optimization algorithm employs a greedy strategy, first attempting to break the line after the first comma, generating the first line "To ensure safety," (6 characters, meeting the requirements). The remaining text "Electric brake assist system is running, but performance may be degraded, please drive carefully." is still too long, so the algorithm continues to break the line after the second comma, generating the second line "Electric brake assist system is running," (11 characters, meeting the requirements). At this point, the remaining text "But performance may be degraded, please drive carefully." is 14 characters long, still exceeding the 12-character limit per line. Since the maximum limit of 3 lines has been reached, the overflow processor initiates a graceful degradation strategy, intelligently pruning the third line of text, retaining "but performance may be reduced" and adding an ellipsis to form the final third line of text. At the same time, this processing is recorded in the audit log, generating a warning message to prompt the developers to review the original text.

[0124] After formatting, the quality assurance layer intervenes to perform mandatory verification. The character set verifier performs a full character scan of the formatted three lines of text, comparing each character with the target character set definition (such as GB2312 or a custom Unicode subset) defined by the configuration management layer through Unicode code point comparison. The check confirms that all Chinese characters and punctuation marks are within the allowed range, with no rare characters or special symbols, and the verification passes. The length pre-checker further combines font measurements to confirm pixel-level width, ensuring that the actual display effect meets expectations.

[0125] Output: The CSV generator in the output integration layer serializes the processed and validated data structure into a final CSV file. In this file, the message_text field value corresponding to the ID ALM_TPMS_BRAKE_ASSIST_ACTIVE has been updated to three lines of formatted text. The Kanzi integration interface then automatically imports the data via command-line tools or the Python SDK, ultimately presenting clearly structured and semantically complete alarm information on the dashboard. The system can also be seamlessly extended for future adaptation to other HMI engines (such as Qt or CGI Studio).

[0126] Technical Effectiveness Verification: This scenario-based embodiment fully verifies the effectiveness of the collaborative operation of the six modules of this invention. The automated connection between the data acquisition layer and the core processing layer eliminates the risk of mismatches caused by manual operation. The combination of the semantic analyzer and the line break optimization algorithm ensures the complete transmission of information within a limited space. The character set validator and length pre-detector constitute a dual quality gate, intercepting display anomalies before deployment. The entire process, from data input to final display, achieves end-to-end automated delivery, significantly improving the efficiency and reliability of in-vehicle multilingual text updates.

[0127] It should also be noted that the following are several alternative technical solutions:

[0128] For intelligent line segmentation: an alternative could be to use a more complex neural network model for semantic segmentation, but the lightweight NLP method based on rule base adopted in this invention is more practical and advantageous in the context of the demanding requirements for real-time performance and computing resources in the in-vehicle environment, balancing effectiveness and cost.

[0129] For character pre-detection: an alternative could be to perform real-time detection and replace characters with backup characters during HMI engine rendering. However, the "source prevention" solution of this invention is more advantageous because it avoids any possibility of display anomalies in front of the end user, resulting in a better experience.

[0130] In general, other technical combinations may exist to achieve some of the functions, but the organic connection and automated execution of the three steps of UI constraints, semantic line breaks and character pre-detection in the data preparation stage constitute the unique and non-obvious overall technical solution of this invention, and the comprehensive benefits it brings are difficult for alternative solutions to match.

[0131] Figure 2 This is a flowchart of a method for updating multilingual text in an in-vehicle HMI according to an embodiment of the present invention. This embodiment is applicable to at least any in-vehicle HMI multilingual text update scenario. This method can be, but is not limited to, executed by the in-vehicle HMI multilingual text update device described in this embodiment of the present invention. This execution entity can be implemented using software and / or hardware. Figure 2 As shown, this method for updating multilingual text in an in-vehicle HMI includes at least the following steps:

[0132] S1 provides dual-mode data input, including translated data and locally exported data, through the data source layer.

[0133] S2. The translation data is programmatically retrieved and locally exported data is captured through the data acquisition layer. Then, the dual-mode data is parsed and standardized to at least convert it into structured data.

[0134] S3. Provide alarm ID mapping table and UI constraint configuration data through the configuration management layer.

[0135] S4. Obtain structured data and alarm ID mapping table through the core processing layer, and perform ID mapping operation and generate matching data based on the structured data and alarm ID mapping table.

[0136] S5. Obtain UI constraint configuration data through the core processing layer, and then perform semantic analysis, line break optimization and overflow processing on the matching data in combination with at least the UI constraint configuration data to obtain non-overflow formatted text.

[0137] S6. Obtain non-overflow formatted text through the quality assurance layer, and perform character set verification and length pre-check on the non-overflow formatted text. Then, output qualified data after the non-overflow formatted text passes the character set verification and length pre-check, or output an error report after the non-overflow formatted text fails the character set verification and / or length pre-check.

[0138] S7. Receive the verified data through the output integration layer and serialize the verified data into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0139] Optionally, the data source layer includes at least a translation platform unit and a local import unit;

[0140] The translation platform unit is used at least to provide translation data;

[0141] The local import unit is used to provide at least local exported data.

[0142] Optionally, the data acquisition layer includes at least an API interface unit, a file listening service unit, and a data parser unit;

[0143] The API interface unit is at least used to perform the programmatic fetching of translation data;

[0144] The file listening service unit is at least used to capture locally exported data;

[0145] The data parser unit is used at least to parse and standardize the bimodal data to at least transform it into structured data.

[0146] Optionally, the core processing layer includes at least an ID mapping engine unit, a semantic analyzer unit, a line break optimization algorithm unit, and an overflow processor unit;

[0147] The ID mapping engine unit is at least used to obtain structured data and alarm ID mapping tables, and to perform ID mapping operations and generate matching data based on the structured data and alarm ID mapping tables;

[0148] The semantic analyzer unit is used at least to obtain UI constraint configuration data, and then to perform semantic analysis processing on the matching data in combination with the UI constraint configuration data.

[0149] The line break optimization algorithm unit is used at least to perform line break optimization processing on the matching data after semantic analysis;

[0150] The overflow processor unit is used at least to perform overflow processing on the matching data after semantic analysis and line break optimization to obtain non-overflow formatted text.

[0151] Optionally, the configuration management layer includes at least a UI constraint configuration library unit and an alarm ID mapping table unit;

[0152] The UI constraint configuration library unit is used at least to provide UI constraint configuration data;

[0153] The alarm ID mapping table unit is used to provide at least an alarm ID mapping table.

[0154] Optionally, the quality assurance layer includes at least a character set validator unit, a length preflighter unit, and an anomaly report generator unit;

[0155] The character set validator unit is at least used to acquire non-overflow formatted text and perform character set validation on the non-overflow formatted text;

[0156] The length preflight unit is at least used to perform a length preflight operation on non-overflow formatted text that has passed character set verification after the non-overflow formatted text has passed character set verification; and to output verification-qualified data after the non-overflow formatted text has passed both character set verification and length preflight.

[0157] The exception report generator unit is used at least to output an exception report after non-overflow formatted text fails the character set checksum and / or length pre-check.

[0158] Optionally, the output integration layer includes at least a CSV generator unit and an HMI integration unit;

[0159] The CSV generator unit is at least used to receive valid data and at least serialize the valid data into a final CSV file;

[0160] The HMI integration unit is used at least to enable end-to-end automated delivery from the data source to the presentation terminal based on the final CSV file.

[0161] The technical solution provided in this invention firstly provides dual-mode data input—translated data and locally exported data—through a data source layer; further, it performs programmatic retrieval of translated data and captures locally exported data through a data acquisition layer, and then parses and standardizes the dual-mode data to at least convert it into structured data; further, it provides an alarm ID mapping table and UI constraint configuration data through a configuration management layer; further, it obtains the structured data and alarm ID mapping table through a core processing layer, and performs ID mapping operations and generates matching data based on the structured data and alarm ID mapping table; further, it obtains UI constraint configuration data through the core processing layer. Then, at least in conjunction with the UI constraint configuration data, semantic analysis, line break optimization, and overflow handling are performed on the matching data to obtain non-overflow formatted text. Further, the non-overflow formatted text is obtained through the quality assurance layer, and character set verification and length pre-check are performed on the non-overflow formatted text. Then, if the non-overflow formatted text passes the character set verification and length pre-check, the verified qualified data is output, or if the non-overflow formatted text fails the character set verification and / or length pre-check, an exception report is output. Finally, the verified qualified data is received through the output integration layer, and at least the verified qualified data is serialized into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0162] Therefore, this invention uses UI display constraints as input parameters for text processing and builds an automated text formatting preprocessing mechanism based on these constraints. Traditional solutions passively wait for display problems to occur after text updates, while this invention proactively formats the translation by configuring the UI constraint configuration library of the management layer and using the pop-up display parameters (such as maximum number of lines and number of characters per line) as preconditions. This achieves a fundamental shift in thinking from "text updates" to "display assurance."

[0163] During the formatting process, this embodiment of the invention employs an intelligent line-breaking strategy based on semantic understanding, rather than simple mechanical line breaking. The semantic analyzer in the core processing layer identifies key entities (such as system proper nouns) and semantic segmentation points in the text through an NLP rule base, and makes line-breaking decisions based on preset priorities (sentence terminators are higher than clause breaks, and clause breaks are higher than conjunctions). This strategy ensures that line breaks do not destroy the semantic integrity of the sentence and avoids misreading of information due to mechanical truncation, which is crucial for ensuring information accuracy and driving safety.

[0164] This invention integrates a character compatibility pre-check into the deployment process as a mandatory quality gate. The character set verifier in the quality assurance layer performs a full character scan of all translations before output, verifying them against the target character set definition through Unicode code point comparison. This intercepts incompatible characters such as rare characters and special symbols before deployment, preventing garbled character display issues at the source.

[0165] The system architecture of this invention, which separates configuration from logic, enables end-to-end automation through multi-module collaboration. The data acquisition layer, core processing layer, quality assurance layer, output integration layer, and configuration management layer form a tightly integrated data pipeline. Driven by structured configuration files such as alarm ID mapping tables, UI constraint configuration libraries, and target character definitions, it can flexibly adapt to the differentiated needs of different vehicle models and pop-up window sizes without modifying the core code, achieving fully automated delivery from the data source to the HMI engine.

[0166] Figure 3 This is a schematic diagram of a multilingual text update device for an in-vehicle HMI provided in an embodiment of the present invention. This embodiment is applicable to at least any in-vehicle HMI multilingual text update scenario. This in-vehicle HMI multilingual text update device can be implemented using software and / or hardware. Figure 3 As shown, the in-vehicle HMI multilingual text update device includes at least:

[0167] Data input module 110 is used to provide dual-mode data input of translated data and locally exported data through the data source layer;

[0168] The data conversion module 120 is used to perform programmatic retrieval of translation data and capture locally exported data through the data acquisition layer, and then parse and standardize the dual-mode data to at least convert it into structured data.

[0169] The configuration management module 130 is used to provide alarm ID mapping table and UI constraint configuration data through the configuration management layer;

[0170] The data generation module 140 is used to obtain structured data and alarm ID mapping table through the core processing layer, and to perform ID mapping operation and generate matching data based on the structured data and alarm ID mapping table;

[0171] The text output module 150 is used to obtain UI constraint configuration data through the core processing layer, and then perform semantic analysis, line break optimization and overflow processing on the matching data in combination with at least the UI constraint configuration data to obtain non-overflow formatted text.

[0172] The verification report module 160 is used to obtain non-overflow formatted text through the quality assurance layer, and perform character set verification and length pre-check operations on the non-overflow formatted text. Then, after the non-overflow formatted text passes the character set verification and length pre-check, it outputs qualified data, or after the non-overflow formatted text fails the character set verification and / or length pre-check, it outputs an error report.

[0173] The serialization module 170 is used to receive valid data through the output integration layer and serialize the valid data into at least a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0174] Optionally, the data source layer includes at least a translation platform unit and a local import unit;

[0175] The translation platform unit is used at least to provide translation data;

[0176] The local import unit is used to provide at least local exported data.

[0177] Optionally, the data acquisition layer includes at least an API interface unit, a file listening service unit, and a data parser unit;

[0178] The API interface unit is at least used to perform the programmatic fetching of translation data;

[0179] The file listening service unit is at least used to capture locally exported data;

[0180] The data parser unit is used at least to parse and standardize the bimodal data to at least transform it into structured data.

[0181] Optionally, the core processing layer includes at least an ID mapping engine unit, a semantic analyzer unit, a line break optimization algorithm unit, and an overflow processor unit;

[0182] The ID mapping engine unit is at least used to obtain structured data and alarm ID mapping tables, and to perform ID mapping operations and generate matching data based on the structured data and alarm ID mapping tables;

[0183] The semantic analyzer unit is used at least to obtain UI constraint configuration data, and then to perform semantic analysis processing on the matching data in combination with the UI constraint configuration data.

[0184] The line break optimization algorithm unit is used at least to perform line break optimization processing on the matching data after semantic analysis;

[0185] The overflow processor unit is used at least to perform overflow processing on the matching data after semantic analysis and line break optimization to obtain non-overflow formatted text.

[0186] Optionally, the configuration management layer includes at least a UI constraint configuration library unit and an alarm ID mapping table unit;

[0187] The UI constraint configuration library unit is used at least to provide UI constraint configuration data;

[0188] The alarm ID mapping table unit is used to provide at least an alarm ID mapping table.

[0189] Optionally, the quality assurance layer includes at least a character set validator unit, a length preflighter unit, and an anomaly report generator unit;

[0190] The character set validator unit is at least used to acquire non-overflow formatted text and perform character set validation on the non-overflow formatted text;

[0191] The length preflight unit is at least used to perform a length preflight operation on non-overflow formatted text that has passed character set verification after the non-overflow formatted text has passed character set verification; and to output verification-qualified data after the non-overflow formatted text has passed both character set verification and length preflight.

[0192] The exception report generator unit is used at least to output an exception report after non-overflow formatted text fails the character set checksum and / or length pre-check.

[0193] Optionally, the output integration layer includes at least a CSV generator unit and an HMI integration unit;

[0194] The CSV generator unit is at least used to receive valid data and at least serialize the valid data into a final CSV file;

[0195] The HMI integration unit is used at least to enable end-to-end automated delivery from the data source to the presentation terminal based on the final CSV file.

[0196] The technical solution provided in this invention embodiment firstly utilizes a data input module to provide dual-mode data input of translated data and locally exported data through a data source layer; further, a data conversion module utilizes a data acquisition layer to perform programmatic retrieval of translated data and capture locally exported data, thereby parsing and standardizing the dual-mode data to at least convert it into structured data; further, a configuration management module utilizes a configuration management layer to provide an alarm ID mapping table and UI constraint configuration data; further, a data generation module utilizes a core processing layer to obtain structured data and an alarm ID mapping table, and performs ID mapping operations and generates matching data based on the structured data and the alarm ID mapping table; further, a text output module utilizes a core processing layer to obtain structured data and an alarm ID mapping table, and performs ID mapping operations and generates matching data based on the structured data and the alarm ID mapping table; further, a text output module utilizes a core processing layer to obtain structured data and an alarm ID mapping table. The layer obtains UI constraint configuration data, and then performs semantic analysis, line break optimization, and overflow handling on the matching data based on the UI constraint configuration data to obtain non-overflow formatted text. Further, the verification report module obtains the non-overflow formatted text through the quality assurance layer, and performs character set verification and length pre-check on the non-overflow formatted text. Then, if the non-overflow formatted text passes the character set verification and length pre-check, the verification qualified data is output, or if the non-overflow formatted text fails the character set verification and / or length pre-check, an exception report is output. Finally, the serialization module receives the verification qualified data through the output integration layer, and at least serializes the verification qualified data into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0197] Therefore, this invention uses UI display constraints as input parameters for text processing and builds an automated text formatting preprocessing mechanism based on these constraints. Traditional solutions passively wait for display problems to occur after text updates, while this invention proactively formats the translation by configuring the UI constraint configuration library of the management layer and using the pop-up display parameters (such as maximum number of lines and number of characters per line) as preconditions. This achieves a fundamental shift in thinking from "text updates" to "display assurance."

[0198] During the formatting process, this embodiment of the invention employs an intelligent line-breaking strategy based on semantic understanding, rather than simple mechanical line breaking. The semantic analyzer in the core processing layer identifies key entities (such as system proper nouns) and semantic segmentation points in the text through an NLP rule base, and makes line-breaking decisions based on preset priorities (sentence terminators are higher than clause breaks, and clause breaks are higher than conjunctions). This strategy ensures that line breaks do not destroy the semantic integrity of the sentence and avoids misreading of information due to mechanical truncation, which is crucial for ensuring information accuracy and driving safety.

[0199] This invention integrates a character compatibility pre-check into the deployment process as a mandatory quality gate. The character set verifier in the quality assurance layer performs a full character scan of all translations before output, verifying them against the target character set definition through Unicode code point comparison. This intercepts incompatible characters such as rare characters and special symbols before deployment, preventing garbled character display issues at the source.

[0200] The system architecture of this invention, which separates configuration from logic, enables end-to-end automation through multi-module collaboration. The data acquisition layer, core processing layer, quality assurance layer, output integration layer, and configuration management layer form a tightly integrated data pipeline. Driven by structured configuration files such as alarm ID mapping tables, UI constraint configuration libraries, and target character definitions, it can flexibly adapt to the differentiated needs of different vehicle models and pop-up window sizes without modifying the core code, achieving fully automated delivery from the data source to the HMI engine.

[0201] This embodiment provides an electronic device. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. See also: Figure 4The electronic device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 1001, the steps in any of the above-described vehicle HMI multilingual text update methods are performed. Through the above technical solution, the processor 1001 and the memory 1002 are interconnected and communicate with each other via a communication bus and / or other forms of connection mechanisms (not shown). The memory 1002 stores a processor-executable computer program. When the electronic device 1000 is running, the processor 1001 executes the computer program to perform the vehicle HMI multilingual text update method in any optional implementation of the above embodiments, to at least achieve the following functions: providing dual-mode data input of translated data and locally exported data through the data source layer; performing programmatic retrieval of translated data and capturing locally exported data through the data acquisition layer, and then parsing and standardizing the dual-mode data to at least convert it into structured data; providing alarm ID mapping tables and UI constraint configuration data through the configuration management layer; and acquiring structured data and... An alarm ID mapping table is used, and ID mapping operations are performed on the structured data and the alarm ID mapping table to generate matching data. UI constraint configuration data is obtained through the core processing layer, and semantic analysis, line break optimization, and overflow handling are performed on the matching data in combination with the UI constraint configuration data to obtain non-overflow formatted text. The non-overflow formatted text is obtained through the quality assurance layer, and character set verification and length pre-check are performed on the non-overflow formatted text. If the non-overflow formatted text passes the character set verification and length pre-check, the verification qualified data is output. If the non-overflow formatted text fails the character set verification and / or length pre-check, an exception report is output. The verification qualified data is received through the output integration layer, and the verification qualified data is serialized into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0202] This embodiment provides a computer-readable storage medium storing a computer program. When executed by a processor, the program implements the in-vehicle HMI multilingual text update method provided in all embodiments of this application: providing dual-mode data input of translated data and locally exported data through a data source layer; performing programmatic retrieval of translated data and capturing locally exported data through a data acquisition layer, and then parsing and standardizing the dual-mode data to at least convert it into structured data; providing an alarm ID mapping table and UI constraint configuration data through a configuration management layer; and acquiring structured data and the alarm ID mapping table through a core processing layer, and performing ID mapping operations and generating matching numbers based on the structured data and the alarm ID mapping table. According to the process, the UI constraint configuration data is obtained through the core processing layer, and then semantic analysis, line break optimization, and overflow processing are performed on the matching data in combination with the UI constraint configuration data to obtain non-overflow formatted text. The non-overflow formatted text is obtained through the quality assurance layer, and character set verification and length pre-check are performed on the non-overflow formatted text. After the non-overflow formatted text passes the character set verification and length pre-check, the verification qualified data is output, or after the non-overflow formatted text fails the character set verification and / or length pre-check, an exception report is output. The verification qualified data is received through the output integration layer, and the verification qualified data is serialized into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

[0203] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0204] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0205] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0206] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0207] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle-mounted HMI multilingual text update system, characterized in that, It includes at least a data source layer, a data acquisition layer, a core processing layer, a configuration management layer, a quality assurance layer, and an output integration layer; The data source layer is used at least to provide dual-mode data input of translated data and locally exported data; The data acquisition layer establishes a connection with the data source layer and is at least used to perform programmatic retrieval of the translated data and capture the locally exported data, and then parse and standardize the dual-mode data to at least convert it into structured data. The core processing layer establishes connections with the data acquisition layer and the configuration management layer, respectively, and is at least used to acquire the structured data and the alarm ID mapping table, and to perform ID mapping operations and generate matching data based on the structured data and the alarm ID mapping table; and to acquire UI constraint configuration data, and then to perform semantic analysis, line break optimization and overflow processing on the matching data based at least on the UI constraint configuration data to obtain non-overflow formatted text; The configuration management layer is used to provide at least the alarm ID mapping table and the UI constraint configuration data; The quality assurance layer is connected to the core processing layer and is used at least to obtain the non-overflow formatted text and perform character set verification and length pre-check operations on the non-overflow formatted text. And, after the non-overflow formatted text passes the character set check and the length pre-check, output the check-pass data; and, after the non-overflow formatted text fails the character set check and / or the length pre-check, output the error report; The output integration layer establishes a connection with the quality assurance layer, and is used at least to receive the verified data and at least serialize the verified data into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

2. The in-vehicle HMI multilingual text update system according to claim 1, characterized in that, The data source layer includes at least a translation platform unit and a local import unit; The translation platform unit is at least used to provide the translation data; The local import unit is used to provide at least the local exported data.

3. The in-vehicle HMI multilingual text update system according to claim 1, characterized in that, The data acquisition layer includes at least an API interface unit, a file listening service unit, and a data parser unit; The API interface unit is at least used to perform the programmatic retrieval of the translation data; The file monitoring service unit is used at least to capture the locally exported data; The data parser unit is at least used to parse and standardize the dual-mode data to at least transform it into the structured data.

4. The in-vehicle HMI multilingual text update system according to claim 1, characterized in that, The core processing layer includes at least an ID mapping engine unit, a semantic analyzer unit, a line break optimization algorithm unit, and an overflow processor unit. The ID mapping engine unit is at least used to obtain the structured data and the alarm ID mapping table, and to perform the ID mapping operation and generate the matching data based on the structured data and the alarm ID mapping table; The semantic analyzer unit is at least used to obtain the UI constraint configuration data, and then at least combine the UI constraint configuration data to perform semantic analysis processing on the matching data; The line break optimization algorithm unit is at least used to perform line break optimization processing on the matching data after semantic analysis processing; The overflow processor unit is used at least to perform overflow processing on the matching data after semantic analysis and line break optimization to obtain the non-overflow formatted text.

5. The in-vehicle HMI multilingual text update system according to claim 1, characterized in that, The configuration management layer includes at least a UI constraint configuration library unit and an alarm ID mapping table unit; The UI constraint configuration library unit is used to provide at least the UI constraint configuration data; The alarm ID mapping table unit is used to provide at least the alarm ID mapping table.

6. The in-vehicle HMI multilingual text update system according to claim 1, characterized in that, The quality assurance layer includes at least a character set validator unit, a length pre-detector unit, and an anomaly report generator unit; The character set validator unit is at least used to obtain the non-overflow formatted text and perform character set validation on the non-overflow formatted text; The length pre-detector unit is at least configured to perform the length pre-detection operation on the non-overflow formatted text that has passed the character set verification after the non-overflow formatted text has passed the character set verification; and to output the verification qualified data after the non-overflow formatted text has passed the character set verification and the length pre-detection. The exception report generator unit is at least used to output the exception report after the non-overflow formatted text fails the character set checksum and / or the length pre-check.

7. The in-vehicle HMI multilingual text update system according to claim 1, characterized in that, The output integration layer includes at least a CSV generator unit and an HMI integration unit; The CSV generator unit is at least used to receive the verified data and at least serialize the verified data into the final CSV file; The HMI integration unit is used at least to achieve end-to-end automated delivery from the data source to the presentation terminal based on the final CSV file.

8. A method for updating multilingual text in an in-vehicle HMI, which is implemented at least by the in-vehicle HMI multilingual text update system described in any one of claims 1-7; The method for updating multilingual text in the vehicle HMI includes at least the following: The data source layer provides dual-mode data input, including translated data and locally exported data. The data acquisition layer performs programmatic retrieval of the translation data and captures the locally exported data, thereby parsing and standardizing the dual-mode data to at least convert it into structured data. The configuration management layer provides the alarm ID mapping table and the UI constraint configuration data. The core processing layer obtains the structured data and alarm ID mapping table, and performs ID mapping operations and generates matching data based on the structured data and alarm ID mapping table. The core processing layer obtains UI constraint configuration data, and then performs semantic analysis, line break optimization and overflow processing on the matching data in combination with at least the UI constraint configuration data to obtain non-overflow formatted text. The non-overflow formatted text is obtained through the quality assurance layer, and character set verification and length pre-check are performed on the non-overflow formatted text. Then, after the non-overflow formatted text passes the character set verification and the length pre-check, the verification qualified data is output, or, after the non-overflow formatted text fails the character set verification and / or the length pre-check, an error report is output. The output integration layer receives the verified data and serializes it into a final CSV file to achieve end-to-end automated delivery from the data source to the presentation terminal.

9. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the in-vehicle HMI multilingual text update method of claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the in-vehicle HMI multilingual text update method of claim 8.

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