Text translation processing method and apparatus, electronic device, and storage medium

Abstract

The text translation processing method and device, the electronic equipment and the storage medium provided by the embodiments of the present application comprise the following steps: obtaining a source text to be translated, loading a corresponding standard knowledge base based on the vertical field context of the source text to be translated, and then determining the initial style features and industry term benchmarks of the target translation language based on the standard knowledge base; filtering and constraining the character set of the translation text according to the script type corresponding to the target translation language, to determine the character set restriction condition during text translation; performing semantic decoupling processing on the source text to be translated to obtain deep semantic intent information, and performing detailed logical reconstruction processing on the deep semantic intent based on the initial style features and the character set restriction condition to obtain preliminary translation text information; then performing shunt correction processing on the preliminary translation text information to obtain remodeled translation text information; and finally performing correction filtering on the remodeled translation text information based on the industry term benchmarks to obtain target translation text information, thereby improving the accuracy and professional rigor of the output target translation text.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence processing technology, and in particular to a text translation processing method, apparatus, electronic device and storage medium. Background Technology

[0002] In the fields of artificial intelligence and natural language processing, machine translation technology has evolved from a rule-based and statistical stage to a deep semantic understanding stage based on Large Language Models (LLMs). The industry has begun to leverage the powerful contextual learning capabilities of LLMs, combined with Retrieval-Augmented Generation (RAG) technology to retrieve terminology from professional knowledge bases. This improves the professionalism and stylistic consistency of terminology in specific domain contexts, thereby providing more accurate text translation services for cross-language communication.

[0003] However, existing text translation processing solutions typically rely on end-to-end direct generation by the model. This approach often leads to significant logical residues in the translation. Furthermore, when large models develop translation habits or training data biases, non-target language script characters can easily be mixed into the translation, resulting in character pollution. At the same time, this direct generation approach lacks confidence assessment and correction mechanisms for complex semantic units such as idioms and metaphors, making it difficult to accurately reconstruct the logical flow in cross-cultural contexts. This results in situations where the final output translation has low accuracy and professional rigor. Summary of the Invention

[0004] The main objective of this application is to propose a text translation processing method, apparatus, electronic device, and storage medium that can improve the accuracy and professional rigor of the output target translation.

[0005] To achieve the above objectives, a first aspect of this application proposes a text translation processing method, the method comprising: The source text to be translated is obtained, and the corresponding standard knowledge base is loaded based on the vertical domain context of the source text to be translated. Then, the initial stylistic features and industry terminology benchmarks of the target translation language are determined based on the standard knowledge base. Based on the script type corresponding to the target translation language, filter and constrain the character set of the translation text to determine the character set restrictions during text translation. The source text to be translated is semantically decoupled to obtain deep semantic intent information. Based on the initial stylistic features and the character set constraints, the deep semantic intent is logically reconstructed in detail to obtain preliminary translation information. Identify the semantic units to be corrected in the preliminary translation information, determine the translation confidence of the semantic units to be corrected, and then perform split correction processing on the preliminary translation information based on the translation confidence to obtain the reconstructed translation information; Based on the industry terminology benchmark, the reconstructed translation information is corrected and filtered to obtain the target translation information.

[0006] In some embodiments, loading the corresponding standard knowledge base based on the vertical domain context of the source text to be translated includes: The semantic feature vector of the source text to be translated is input into a preset domain classification model for classification processing to obtain a vertical domain label used to characterize the vertical domain context. Based on the vertical domain tags, corresponding expert mimicry instructions and industry knowledge weights are matched from a preset expert state database; The large language model is invoked to load the standard knowledge base associated with the expert mimicry instructions and the industry knowledge weights.

[0007] In some embodiments, the step of filtering and constraining the target translation character set according to the script type corresponding to the target translation language to determine the character set restrictions during text translation includes: Based on the script type corresponding to the target translation language, a first logical firewall is enabled in the translation generation stream corresponding to the text translation, and the character set restriction conditions are generated based on the first logical firewall. The first logical firewall is used to monitor the real-time character data to be output in real time. The character set restriction condition is used to instruct the first logical firewall to filter out real-time character data when it identifies that the real-time character data is a character, source language root or affix residue corresponding to a script type that is not the target translation language.

[0008] In some embodiments, the semantic decoupling process performed on the source text to be translated to obtain deep semantic intent information includes: The grammatical and rhetorical shell of the source text to be translated is removed to obtain the core source text, and the core source text is input into a preset large language model for skeleton data extraction to obtain the central semantic skeleton. The central semantic skeleton is subjected to key feature identification to obtain at least one of the following: logical center position, missing action subject, and context connection identifier. Based on the logical center position, the missing action subject, and the context connection identifier, the deep semantic intent information is obtained.

[0009] In some embodiments, the detailed logical reconstruction of the deep semantic intent based on the initial stylistic features and the character set constraints to obtain preliminary translation information includes: Based on the initial stylistic features, the target logical pattern of the target translation language is determined, and the source logical pattern of the source text to be translated is determined. When the source logic pattern is inductive logic and the target logic pattern is deductive logic, the conclusion information in the deep semantic intent is placed at the beginning of the translated text information to obtain logically processed text information. Identify the missing action subject in the central semantic skeleton, and perform subject completion on the logically processed text information based on the missing action subject to obtain the completed text information; The completed text information is filtered based on the character set restrictions to obtain the preliminary translation information.

[0010] In some embodiments, determining the translation confidence level of the semantic unit to be corrected, and then performing a splitting and correction process on the preliminary translated information based on the translation confidence level to obtain reconstructed translated information, includes: The semantic unit to be corrected is input into a preset confidence gating model for data processing to obtain the translation confidence level, which is used to characterize the degree of translation comprehension of the semantic unit to be corrected. When the translation confidence is higher than the first confidence threshold, a native language equivalent search is performed in the target language idiom database to obtain target local idioms that are functionally similar to the semantic unit to be corrected. The semantic unit to be corrected in the preliminary translation information is replaced based on the target local idioms to obtain the reconstructed translation information. When the translation confidence is not higher than the first confidence threshold but higher than the second preset threshold, the source text to be corrected corresponding to the semantic unit to be corrected in the source text to be translated is determined, and the source text to be corrected is directly translated to obtain the translated text unit. Then, the translated text unit replaces the semantic unit to be corrected in the preliminary translation information to obtain the reconstructed translation information. When the translation confidence level is not higher than the second preset threshold, the semantic unit to be corrected is heterogeneously labeled.

[0011] In some embodiments, the step of correcting and filtering the reshaped translation information based on the industry terminology benchmark to obtain the target translation information includes: Based on the semantic constraint logic in the industry terminology benchmark, the incremental semantics in the reconstructed translation information are filtered for compliance, so as to intercept semantic drift content that exceeds the definition scope of the industry terminology benchmark and obtain filtered translation information. Each technical term feature in the filtered translation information is matched and compared with the industry terminology benchmark to identify terminology conflicts in the filtered translation information that deviate from the industry terminology benchmark. Based on the industry terminology benchmark, the term conflict items are replaced atomically to obtain replacement term items. Then, based on the replacement term items, the term conflict items in the filtered translation information are replaced to obtain the target translation information.

[0012] To achieve the above objectives, a second aspect of this application provides a text translation processing apparatus, the apparatus comprising: A translation processing system, the translation processing system being used to perform the text translation processing method as described in the first aspect; The smart glasses are used to send the source text to be translated to the translation processing system and to receive and display the target translation information sent by the translation processing system.

[0013] To achieve the above objectives, a third aspect of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in any one of the embodiments of the first aspect.

[0014] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of the embodiments of the first aspect.

[0015] The text translation processing method, apparatus, electronic device, and storage medium proposed in this application provide accurate industry terminology benchmarks and stylistic navigation for translation tasks through deep coupling of vertical domain context and standard knowledge base, ensuring professional alignment from the source. Furthermore, by introducing script-based character set filtering constraints, a first logical firewall is established, effectively intercepting and eliminating non-target language character residues and character pollution that are easily generated during end-to-end generation of large models. In addition, the core semantic decoupling and logical reconstruction mechanism, supplemented by translation confidence diversion correction for complex semantic units, can deeply reverse the syntactic logic residues of the source language and achieve accurate reshaping of metaphors and idioms in cross-cultural contexts. Finally, the correction filtering mechanism based on industry terminology benchmarks forms a closed-loop verification, ensuring that the output target translation not only fully conforms to the thinking habits and logical expressions of the target language but also achieves pixel-level professional rigor in terminology usage, thereby significantly improving the accuracy and professional rigor of the output target translation. Attached Figure Description

[0016] Figure 1This is a flowchart of one embodiment of the text translation processing method provided in this application.

[0017] Figure 2 yes Figure 1 Flowchart of step S101.

[0018] Figure 3 yes Figure 1 The flowchart of step S102.

[0019] Figure 4 yes Figure 1 The flowchart of step S103.

[0020] Figure 5 yes Figure 1 Another flowchart for step S103.

[0021] Figure 6 yes Figure 1 The flowchart for step S104.

[0022] Figure 7 yes Figure 1 The flowchart for step S105.

[0023] Figure 8 This is a schematic diagram of the text translation processing apparatus provided in an embodiment of this application.

[0024] Figure 9 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0026] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., used in the specification, claims, and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0027] In the fields of artificial intelligence and natural language processing, machine translation technology has evolved from a rule-based and statistical stage to a deep semantic understanding stage based on Large Language Models (LLMs). The industry has begun to leverage the powerful contextual learning capabilities of LLMs, combined with Retrieval-Augmented Generation (RAG) technology to retrieve terminology from professional knowledge bases. This improves the professionalism and stylistic consistency of terminology in specific domain contexts, thereby providing more accurate text translation services for cross-language communication.

[0028] However, existing text translation processing solutions typically rely on end-to-end direct generation by the model. This approach often leads to significant logical residues in the translation. Furthermore, when large models develop translation habits or training data biases, non-target language script characters can easily be mixed into the translation, resulting in character pollution. At the same time, this direct generation approach lacks confidence assessment and correction mechanisms for complex semantic units such as idioms and metaphors, making it difficult to accurately reconstruct the logical flow in cross-cultural contexts. This results in situations where the final output translation has low accuracy and professional rigor.

[0029] Based on this, embodiments of this application provide a text translation processing method, apparatus, electronic device, and storage medium, which can improve the accuracy and professional rigor of the output target translation.

[0030] The text translation processing method provided in this application relates to the field of logistics technology. The text translation processing method provided in this application can be applied to a processing system in a terminal, a processing system in a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application implementing the text translation processing method, but is not limited to the above forms.

[0031] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network personal computers (PCs), minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0032] Please see Figure 1 , Figure 1 This is an optional flowchart of the text translation processing method provided in the embodiments of this application. In some embodiments of this application, Figure 1 The specific methods may include, but are not limited to, steps 101 to 104.

[0033] Step 101: Obtain the source text to be translated, load the corresponding standard knowledge base based on the vertical domain context of the source text to be translated, and then determine the initial stylistic features and industry terminology benchmarks of the target translation language based on the standard knowledge base.

[0034] Step 102: Filter and constrain the character set of the target translation language according to the script type, and determine the character set restrictions during text translation.

[0035] Step 103: Perform semantic decoupling on the source text to be translated to obtain deep semantic intent information, and perform detailed logical reconstruction on the deep semantic intent based on the initial stylistic features and character set constraints to obtain preliminary translation information.

[0036] Step 104: Identify the semantic units to be corrected in the preliminary translation information and determine the translation confidence of the semantic units to be corrected. Then, based on the translation confidence, perform diversion and correction processing on the preliminary translation information to obtain the reconstructed translation information.

[0037] Step 105: Based on industry terminology benchmarks, the reconstructed translation information is corrected and filtered to obtain the target translation information.

[0038] Steps 101 to 105 are described in detail below.

[0039] In step 101 of some embodiments, the processing system first acquires the source text to be translated, loads the corresponding standard knowledge base based on the vertical domain context of the source text, and then determines the initial stylistic features and industry terminology benchmarks of the target translation language based on the standard knowledge base. This step is the resource initialization stage of the entire translation process. It can be understood that the source text to be translated refers to the original data carrier that is in its original language state, input by the user or collected by external sensors, and is to be semantically reconstructed by the system. In practical applications, the source text to be translated is not limited to a simple character sequence, but also includes the layout format, stylistic features, and industry-specific logical order contained in the text. For example, in the application scenario of smart glasses, the source text to be translated can be a fragment of a foreign language equipment repair manual with complex abbreviations and specific grammatical structures captured by the user through a camera, or it can be a cross-border conference dialogue text with colloquial features and severe subject omission generated by real-time speech transcription. The source text to be translated is the starting material of the entire processing process.

[0040] Furthermore, the vertical domain context refers to the set of specific industry attributes, knowledge backgrounds, and logical paradigms deeply embedded in the source text to be translated. It not only covers the proprietary vocabulary systems of different industries such as law, medicine, integrated circuits, or communication protocols, but also includes the unique expression habits, stylistic norms, and semantic logic that has a unique orientation within that specific category.

[0041] By accurately identifying the vertical domain context of the source text to be translated, the processing system can achieve parameter shifting from a general context to a professional context, thereby ensuring that the translation decision-making process can automatically attach the corresponding industry terminology benchmarks and expert-level logical constraints. It can be understood that the vertical domain context enables the translation system to perform in-depth analysis and semantic reconstruction of the source text within a logical framework that conforms to industry consensus, just like a senior expert in the field.

[0042] Determining the initial stylistic features and industry terminology benchmarks of the target translation language based on a standard knowledge base refers to the process by which the processing system, after identifying vertical domain labels, extracts configuration parameters and truth references deeply adapted to that domain from the mounted standard knowledge base. Specifically, the processing system uses industry-standard data from the standard knowledge base to automatically generate a set of control variables for a large language model. These variables constitute the "initial stylistic features" of the target language, constraining the sentence structure, tone weight, and terminology norms of the translation. Simultaneously, the processing system retrieves and activates the corresponding "industry terminology benchmarks" from the knowledge base. These benchmarks, serving as legal-level truth values ​​throughout the translation chain, not only provide semantic boundaries for subsequent logical reconstruction but also act as criteria in the final correction and filtering stage, forcibly calibrating any expressions deviating from the professional scope to ensure that the output translation accurately matches the industry data.

[0043] Initial stylistic features refer to technical parameters pre-set based on the attributes of the source text to be translated, used to constrain the expression style of the target language. For example, legal documents require a "rigorous, neutral, and de-rhetorical" style, while literary works require a "high level of artistic conception, strong rhetoric, and non-linear" style. Industry terminology benchmarks, on the other hand, refer to authoritative and exclusive lexical mapping tables and semantic definitions provided by standard knowledge bases within a specific vertical field. They define the unique and legitimate meaning of key technical terms in translation. For example, in a semiconductor translation task, the initial stylistic feature of the word "Gate" is set to "academic hardcore," while its industry terminology benchmark forces it to be anchored as "gate" rather than the more general "door."

[0044] In step 102 of some embodiments, the processing system filters and constrains the character set of the target translation language according to the script type, determining the character set restrictions during text translation. This step intervenes in the generation logic of the large language model through preset script type rules. Here, "script type" refers to the writing system used by the target language, such as the Latin alphabet script for English or the Chinese character script for Chinese. The "character set restrictions" generated by the processing system forcibly intercept and remove heterogeneous characters that do not conform to the target language by monitoring the token generation stream in real time during the translation process. This mechanism can specifically solve the character pollution problem caused by training data bias or lazy mechanisms in large models, ensuring the purity of the output results in visual presentation and reading experience, and providing a regular character environment for subsequent semantic reconstruction.

[0045] In step 103 of some embodiments, the processing system performs semantic decoupling on the source text to be translated, obtaining deep semantic intent information. Based on initial stylistic features and character set constraints, the system then performs detailed logical reconstruction of the deep semantic intent to obtain preliminary translation information. This step involves reshaping the deep logic of the language. "Semantic decoupling" refers to stripping away the literal syntactic shell of the source language and utilizing the semantic extraction capabilities of a large model to obtain "deep semantic intent information" that is independent of specific language forms. Based on this, the processing system combines the stylistic settings established in step 101 and the character constraints in step 102 to perform "logical reconstruction," aiming to adjust the syntactic focus. For example, considering the differences between Chinese and English, the inductive logic of the source text is reversed to the deductive logic commonly used in the target language, and syntactic components such as subjects that are frequently omitted in the source language are forcibly completed, thereby eliminating translation habits and logical remnants at the source.

[0046] In step 104 of some embodiments, the processing system identifies semantic units to be corrected in the preliminary translated information and determines the translation confidence of these units. Based on the translation confidence, the preliminary translated information is then subjected to a split-processing correction to obtain reconstructed translated information. This step introduces a quality gating mechanism to handle complex semantic translations. "Semantic units to be corrected" typically refer to complex parts of the text involving cultural idioms, metaphors, or industry jargon. The processing system uses the "translation confidence" to assess the reliability probability of the model's understanding of these units and performs "split-processing correction" based on the probability value: for high-confidence units, it performs native language equivalence mapping; for medium- and low-confidence units, it adopts semantic dimensionality reduction or heterogeneous annotation. This hierarchical processing strategy mimics the decision-making process of human proofreading, ensuring the robustness and semantic safety of the translation in cross-cultural context transformation.

[0047] In step 105 of some embodiments, the processing system corrects and filters the reshaped translation information based on industry terminology benchmarks to obtain the target translation information. As the final verification step of the entire process, the processing system uses the industry terminology benchmarks loaded in step 101 as criteria to perform pixel-level scanning and filtering on the reshaped translation. This step not only identifies and corrects terminology conflicts that deviate from the standard definition, but also intercepts semantic drift that may be caused by excessive explicit processing based on the semantic constraint logic in the benchmark. Through this closed-loop correction based on truth benchmarks, the processing system can perform a final professional calibration of the translation, ensuring that the output target translation information not only conforms to the cultural expression habits of the target language, but also meets strict industry compliance standards in the use of technical terminology.

[0048] Through steps 101 to 105 above, a collaborative architecture of multi-layered logical firewalls and cross-cultural context reshaping is constructed, achieving precise end-to-end control from underlying character constraints to deep logical reconstruction. The solution in this application ensures the professional depth of the translation through vertical domain context anchoring and initial style setting, and completely solves the character pollution problem commonly found in machine translation by using script filtering constraints. At the same time, through semantic decoupling and a confidence-based diversion correction mechanism, the syntactic logic residues of the source language are effectively reversed, and idioms and metaphors are accurately reshaped in a cross-cultural context. Ultimately, this ensures that the output target translation conforms to the inertia of native language thinking in terms of logical expression and achieves higher rigor and adaptability in terms of professional terminology.

[0049] In some embodiments, to establish rigorous professional contextual constraints from the initial stage of the translation task, the solution of this application aims to accurately construct an expert-level translation environment adapted to specific industries by performing deep domain attribute identification on the source text. By transforming a general large language model into an expert mimic with vertical domain knowledge reserves, the professionalism and terminology consistency of subsequent translation outputs are ensured from the ground up.

[0050] Based on this, such as Figure 2 As shown, the "loading the corresponding standard knowledge base based on the vertical domain context of the source text to be translated" in step 101 may include, but is not limited to, steps 201 to 203.

[0051] Step 201: Input the semantic feature vector of the source text to be translated into a preset domain classification model for classification processing to obtain vertical domain labels used to represent the vertical domain context.

[0052] Step 202: Based on the vertical domain tags, match the corresponding expert mimicry instructions and industry knowledge weights from the preset expert state database.

[0053] Step 203: Invoke the large language model to load the standard knowledge base associated with expert mimicry instructions and industry knowledge weights.

[0054] In step 201 of some embodiments, the processing system inputs the semantic feature vector of the source text to be translated into a preset domain classification model for classification processing to obtain a vertical domain label used to represent the vertical domain context. This step falls under the category of feature engineering and pattern recognition. In the implementation process, the processing system first uses a pre-trained semantic encoder to perform deep mapping on the source text to be translated, transforming it into a semantic feature vector carrying global semantic information, that is, a set of multi-dimensional numerical sequences that can quantitatively describe the text topic, lexical distribution, and logical density. Subsequently, the semantic feature vector is input into a pre-trained domain classification model optimized for multi-industry corpora. The model calculates the cosine similarity between the text feature and the benchmark cluster centers of each industry in the vector space or performs probability attribution determination through neural network layers, outputting the industry attribute with the highest probability value as the vertical domain label (such as "semiconductor manufacturing", "intellectual property law", or "biomedicine").

[0055] This vertical domain label not only digitally defines the industry to which the source text belongs, but also serves as a core index for activating specific standard knowledge bases and invoking expert mimicry instructions in subsequent processes, achieving a precise leap from unstructured text to structured domain perception. Furthermore, the "semantic feature vector" refers to the numerical representation in a high-dimensional space converted from unstructured text through a pre-trained encoder, carrying the deep semantic information of the source text. The "domain classification model," on the other hand, is a discriminator trained on a large corpus of industry data, capable of identifying the domain of the text, whether in medicine, law, finance, or engineering.

[0056] In step 202 of some embodiments, the processing system matches corresponding expert mimicry instructions and industry knowledge weights from a pre-defined expert state database based on vertical domain labels. This step realizes the transformation from domain recognition to execution logic. The "expert state database" stores customized parameter sets for different industries. The "expert mimicry instructions" are a set of meticulously designed prompt word constraints designed to guide the large language model to simulate the thinking logic and expression habits of specific industry experts (such as patent agents, clinicians, etc.). The "industry knowledge weights" are a set of configuration parameters used to adjust the activation intensity of model neurons. By increasing the weight of specific professional lexical spaces, the model can more sensitively capture and apply industry-specific logical structures when generating translations, rather than common-sense expressions in general contexts.

[0057] In step 203 of some embodiments, the processing system calls the large language model to load a standard knowledge base associated with expert mimicry instructions and industry knowledge weights. This step is the implementation stage of resource loading, and its core lies in achieving deep integration between the large language model and external professional knowledge. Here, the "standard knowledge base" refers to industry benchmark resources that have been manually verified or published by authoritative institutions, containing standard terminology tables, industry stylistic paradigms, and specific taboo language norms. By loading the associated knowledge base, the processing system enables the large language model to acquire relevant professional knowledge while possessing general language capabilities. Thus, by loading a standard knowledge base highly correlated with instructions and weights, it ensures that the model can retrieve and adhere to the highest standards in the vertical field in real time when handling complex translation tasks, thereby anchoring the "translation quality benchmark" at the entry point of the generation process.

[0058] Through steps 201 to 203 above, and using a progressive architecture of "feature recognition - logical matching - resource loading," the technical problem of general translation models "understanding language translation but not industry knowledge" in vertical industries has been successfully solved. The solution in this application achieves instantaneous switching of a large language model from a general state to a specialized state through precise indexing of vertical domain tags, logical guidance from expert mimicry instructions, and dynamic adjustment of industry knowledge weights. This resource loading mechanism based on a standard knowledge base not only provides authoritative reference coordinates for subsequent logical reconstruction and terminology filtering but also significantly enhances the compliance and rigor of the translation system when processing texts with high professional thresholds.

[0059] In some embodiments, to avoid script contamination during the translation process at the character physical level, this solution introduces a real-time monitoring mechanism to ensure character purity and language consistency in the output translation. This mechanism establishes an automated filtering barrier to accurately identify and block heterogeneous characters accidentally carried during the large language model generation process, thereby ensuring the readability and professionalism of the final translation.

[0060] Based on this, such as Figure 3 As shown, "perform coverage detection on the initial candidate data and obtain coverage detection results" in step 102 may include, but is not limited to, step 301.

[0061] Step 301: Based on the script type corresponding to the target translation language, enable the first logical firewall in the corresponding translation generation stream during text translation, and generate character set restriction conditions based on the first logical firewall. The first logical firewall is used to monitor the real-time character data to be output in real time. The character set restriction conditions are used to indicate that when the first logical firewall identifies that the real-time character data is a character corresponding to a script type that is not the target translation language, or a source language root or affix residue, the real-time character data will be filtered out.

[0062] In step 301 of some embodiments, a dynamic, low-level character-level security access mechanism is introduced to directly intervene in the Large Language Model (LLM) generation process. The processing system first parses the physical writing conventions followed by the target translation language, i.e., the script type (e.g., Latin alphabet character set for English, Chinese character set for Chinese). Based on this type, the processing system mounts a first logical firewall in real-time to the translation engine's "Token Stream." This firewall is no longer a passive check after generation, but rather an active monitoring probe that monitors the underlying encoding properties of each token (text unit) generated during the translation process in real-time within milliseconds before its formal output.

[0063] Understandably, the character set constraint is the logical truth table for the firewall to perform filtering tasks. It clearly defines the "legal character boundaries" in the target context and focuses on intercepting two defects that LLM is prone to: First, non-target script characters. For example, in Chinese-to-English translation, when a large model accidentally outputs Chinese characters, full-width punctuation, or Japanese / Korean interference characters due to overfitting the source text, the firewall will identify the encoding range of these characters and determine them as illegal. Second, residual roots or affixes of the source language. That is, although the characters themselves may be legal, their combination clearly belongs to the morphological features of the source language (such as the "-ing" suffix in English words appearing in Chinese translations). Once the firewall identifies this heterogeneous data, it will immediately perform a filtering action and forcibly trigger model resampling or path correction, thereby ensuring that the final generated translation reaches the standard of purity and no contamination at the physical character level. This allows the formation chain of "character contamination" to be broken from the root, preventing subsequent semantic drift caused by the generation of a single erroneous character.

[0064] Through step 301 above, by placing "character-level filtering logic" at the forefront of the generation chain, the script pollution problem caused by overfitting the source corpus when large language models process complex technical terms is effectively solved. This dynamic monitoring mechanism based on the first logical firewall can eliminate translation errors at the physical stage of character generation, ensuring a high degree of visual consistency in the translated text. By strictly constraining the legal boundaries of the character set, the solution in this application not only significantly improves the purity of the translation but also provides a standardized character foundation for subsequent higher-level semantic reconstruction and logical reshaping, thereby greatly reducing the cost of manual error correction.

[0065] In some embodiments, to remove the syntactic constraints of the source language from a deep semantic dimension, this solution deconstructs and extracts core features from the source text to be translated, aiming to obtain a pure semantic intent independent of any specific linguistic form. This process provides a standardized semantic foundation for subsequent cross-cultural logical reconstruction, ensuring that the translated content can transcend literal limitations and accurately align with the core technology.

[0066] Based on this, such as Figure 4 As shown, the "perform coverage detection on the initial candidate data and obtain the coverage detection result" in step 103 may include, but is not limited to, steps 401 to 402.

[0067] Step 401: Remove the grammatical and rhetorical shell of the source text to be translated to obtain the core source text, and input the core source text into the preset large language model for skeleton data extraction to obtain the central semantic skeleton.

[0068] Step 402: Key feature identification is performed on the central semantic skeleton to obtain at least one of the logical center position, missing action subject, and context connection marker. Based on at least one of the logical center position, missing action subject, and context connection marker, deep semantic intent information is obtained.

[0069] In step 401 of some embodiments, the processing system removes the grammatical and rhetorical shell of the source text to be translated, obtaining the core source text. This core source text is then input into a pre-defined large language model for skeleton data extraction, resulting in a central semantic skeleton. This step belongs to the semantic preprocessing stage. The "grammatical and rhetorical shell" refers to non-core semantic components in the source language that exist to satisfy specific syntactic norms or literary rhetoric, such as flowing sentences in Chinese expressions or modal particles added to balance syllables. The processing system obtains the "core source text" by stripping away these shells. Then, the processing system inputs the core source text, stripped of rhetorical interference, into the pre-defined large language model, utilizing its deep semantic representation capabilities to perform a highly abstract structured extraction task. This process is not a simple keyword retrieval, but rather scans the logical dependencies in the text through the model's internal attention mechanism, thereby identifying the "subject-predicate-object" chain and core predicate logic that support the meaning of the entire sentence. The resulting central semantic skeleton is a pure logical architecture that has removed modifying adverbs, limiting adjectives, and redundant conjunctions. The semantic skeleton of the center precisely solidifies the initiator of the action, the action itself, and the audience in the original text, providing the most concise and stable instruction base for subsequent cross-cultural logical reversal and syntactic reshaping without losing the core meaning.

[0070] In step 402 of some embodiments, the processing system identifies key features of the central semantic skeleton to obtain at least one of the following: logical focus position, missing action subject, and contextual cohesion marker. Based on at least one of these, deep semantic intent information is obtained. This step is an analysis stage that performs a deep scan of the semantic skeleton. The "logical focus position" refers to the location of the emphasis or conclusion of the sentence; the "missing action subject" refers to the explicit subject or actor that is often omitted in the source language (such as Chinese); and the "contextual cohesion marker" refers to the logical guiding features used to define the logical relationship of the current semantic unit within the overall paragraph. By identifying these key features, the processing system integrates fragmented skeleton data into "deep semantic intent information" with a complete logical chain. This information is no longer a simple set of words, but a high-dimensional semantic expression containing action direction, audience orientation, and logical priority, directly determining whether the subsequent translation can achieve "dispersed form but cohesive spirit."

[0071] Through steps 401 to 402 above, the problem of logical residue caused by literal structure interference in machine translation is successfully solved by semantically decoupling the source text to be translated. The solution in this application, by extracting the central semantic skeleton and accurately identifying the logical focus and the subject of the action, can capture the hidden deep intentions in the source text, thus providing a precise basis for reshaping the logical flow in a cross-cultural context. This feature-based semantic processing method not only improves the system's ability to parse complex and long sentences, but also ensures that the final target translation maintains professional rigor while completely eliminating the syntactic traces of the source language, achieving professional, native-speaker-like expression.

[0072] In some embodiments, to achieve deep adaptation to cross-cultural contexts based on semantic decoupling, this solution aims to fundamentally eliminate translation habit problems caused by differences in syntactic structure by aligning and transforming the logical patterns of the source and target languages. This process, through dynamic adjustment of the logical focus and automatic completion of missing morphemes, ensures that the translation, while meeting stylistic norms, fully conforms to the thinking habits and reading needs of the target language readers.

[0073] Based on this, such as Figure 5 As shown, the step 103, "based on the initial stylistic features and character set constraints, performs detailed logical reconstruction of the deep semantic intent to obtain preliminary translation information", may include, but is not limited to, steps 501 to 504.

[0074] Step 501: Based on the initial stylistic features, determine the target logical pattern of the target translation language and the source logical pattern of the source text to be translated.

[0075] Step 502: When the source logic mode is inductive logic and the target logic mode is deductive logic, the conclusion information in the deep semantic intent is placed at the beginning of the translated text information to obtain the logically processed text information.

[0076] Step 503: Determine the missing action subject in the central semantic skeleton, and perform subject completion on the logical processing text information based on the missing action subject to obtain the completed text information.

[0077] Step 504: Filter the completed text information based on character set constraints to obtain preliminary translation information.

[0078] In step 501 of some embodiments, the processing system determines the target logical pattern of the target translation language and the source logical pattern of the source text to be translated based on initial stylistic features. This step is a prerequisite for the decision-making of logical reshaping. "Initial stylistic features" refer to the language style tendencies set in the initialization stage, such as the rigor of legal texts or the intuitiveness of market documents. During implementation, based on the cultural dimension parameters in the initial stylistic features, the processing system first performs a macro-structural scan of the source text to be translated, identifying its inherent source logical pattern, i.e., the thought process followed by the source language in organizing information, such as the inductive tendency of "cause and effect" or "setting the background before giving the conclusion" common in Chinese technical texts. Subsequently, the processing system determines the corresponding target logical pattern according to the cultural attributes and industry norms of the target translation language, i.e., the logical presentation method most easily accepted by the target audience during reading, such as the deductive tendency of "conclusion first" or "main point information at the top" typical in English patents or legal documents. This determination process, by calculating the "cultural shift" between the source and target in the logic matrix, provides clear reorganization instructions for subsequent logical reversal surgery, ensuring that the translation system not only performs conversion at the lexical level, but also completes the logical alignment from the "gradual" expression of the source language to the "impactful" expression of the target language at the underlying level of thinking.

[0079] In step 502 of some embodiments, when the source logic pattern is inductive logic and the target logic pattern is deductive logic, the conclusion information in the deep semantic intent is placed at the beginning of the translated text information to obtain logically processed text information. This step makes a structural adjustment to the differences in focus in cross-cultural contexts. The processing system identifies the core conclusion in the deep semantic intent and breaks the inherent framework of "cause and effect" or "conclusion before context" in the source language, forcibly moving the concluding information to the beginning of the sentence or paragraph. The "logically processed text information" generated by this rearrangement mechanism can significantly improve the information acquisition efficiency of the target language reader, enabling them to grasp the core technology at the beginning of reading, thereby achieving a true reshaping of the logical architecture.

[0080] In step 503 of some embodiments, the processing system determines the missing action subject in the central semantic skeleton and completes the logical processing text information based on the missing action subject to obtain the completed text information. This step focuses on restoring syntactic integrity, where the "central semantic skeleton" refers to the core logical chain after removing rhetorical interference. Since languages ​​such as Chinese often have the phenomenon of subject omission, the processing system identifies the "missing action subject," that is, determines that the current action lacks an explicit executor, and automatically fills in the missing subject according to the contextual logic, thereby generating the "complete text information." This mandatory subject completion mechanism solves the common semantic fragmentation problem in machine translation, ensuring the logical rigor of the translated text in rigorous scenarios such as legal or technical documents.

[0081] In step 504 of some embodiments, the processing system filters the completed text information based on character set constraints to obtain preliminary translation information. As a quality gate in the reconstruction stage, the processing system applies the "character set constraints" determined in step 102 to the text that has undergone logical adjustments. This filtering process is not a simple search and replace, but a secondary purification targeting heterogeneous characters or residual morphemes that may be introduced during the reconstruction process. By performing rigorous script compliance checks on the completed text, it is ensured that the final generated "preliminary translation information" not only achieves professional conversion in the logical chain but also fully conforms to the purity standards of the target translation language at the underlying character level, providing high-quality benchmark material for subsequent terminology calibration.

[0082] Through steps 501 to 504 above, a progressive process of "logical alignment - reversal of focus - morpheme completion - character filtering" successfully achieves a deep leap from source language thinking to target language thinking. This application's solution not only eliminates syntactic inconsistencies through logical pattern transformation but also enhances the certainty of the translation through automated repair of the action subject. Combined with real-time character set constraints, this solution fundamentally reverses the logical lag phenomenon in cross-context translation of general large-scale models, resulting in preliminary translations that maintain a high degree of professional rigor while possessing excellent readability and cultural adaptability.

[0083] In some embodiments, to provide a more robust translation correction mechanism for complex semantic units, this scheme introduces a quantitative quality evaluation system, aiming to implement differentiated reshaping strategies for vocabulary at different levels of understanding. This mechanism, by establishing multi-level diversion paths, ensures that cultural idioms and complex expressions in the translation retain their core intent while achieving deep adaptation to the target language environment.

[0084] Based on this, such as Figure 6 As shown, the step 104, "determine the translation confidence of the semantic unit to be corrected, and then perform diversion and correction processing on the preliminary translation information based on the translation confidence to obtain the reconstructed translation information", may include, but is not limited to, steps 601 to 604.

[0085] Step 601: Input the semantic unit to be corrected into a preset confidence gating model for data processing to obtain the translation confidence level used to characterize the translation comprehension level of the semantic unit to be corrected.

[0086] Step 602: When the translation confidence is higher than the first confidence threshold, a native language equivalent search is performed in the target language idiom database to obtain target local idioms that are functionally similar to the semantic units to be corrected. Based on the target local idioms, the semantic units to be corrected in the preliminary translation information are replaced to obtain the reconstructed translation information.

[0087] Step 603: When the translation confidence is not higher than the first confidence threshold but higher than the second preset threshold, determine the source text to be corrected corresponding to the semantic unit to be corrected in the source text to be translated, and directly translate the source text to be corrected to obtain the translated text unit. Then replace the semantic unit to be corrected in the preliminary translation information with the translated text unit to obtain the reconstructed translation information.

[0088] Step 604: When the translation confidence is not higher than the second preset threshold, heterogeneous annotation is performed on the semantic unit to be corrected.

[0089] In step 601 of some embodiments, the processing system introduces a quantitative quality assessment mechanism to input the identified semantic units to be corrected (i.e., text fragments containing complex idioms, technical metaphors, or highly ambiguous meanings) into a preset confidence gating model. This model utilizes multi-dimensional probability estimation logic to perform deep computation on the prediction entropy value, lexical probability distribution, and semantic consistency of the large language model in this specific context, ultimately producing a scalarized translation confidence score. This translation confidence score accurately characterizes the credibility of the system's understanding of the current translation, that is, it determines whether the translation result is an "accurate output" based on precise semantic alignment or a "fuzzy generation" based on probabilistic guessing. This provides a scientific and objective hard trigger criterion for subsequent triage decisions such as native language equivalence replacement, dimensionality reduction paraphrasing, or heterogeneous annotation.

[0090] In step 602 of some embodiments, when the translation confidence level is higher than a first confidence threshold, the processing system performs a native language equivalence search in the target language idiom database to obtain target local idioms that are functionally similar to the semantic unit to be corrected. Based on these target local idioms, the system replaces the semantic unit to be corrected in the preliminary translation information to obtain the reconstructed translation information. In this high-confidence scenario, the processing system determines that the model has accurately captured the semantics of the source text, thus pursuing the highest level of professional translation. This "native language equivalence search" refers to retrieving professional expressions with the same rhetorical function or emotional polarity from the target language corpus. By replacing the identified "target local idioms" into the preliminary translation, the reconstructed translation can be fully integrated into the cultural background of the target language, eliminating cultural semantic gaps.

[0091] In step 603 of some embodiments, when the translation confidence level is not higher than a first confidence threshold but higher than a second preset threshold, the processing system determines the source text to be corrected corresponding to the semantic unit to be corrected in the source text to be translated, and directly interprets the source text to be corrected to obtain the interpretive text unit. The interpretive text unit then replaces the semantic unit to be corrected in the preliminary translation information to obtain the reconstructed translation information. This step is for cases with medium confidence levels, where the system determines it is difficult to find a perfect cultural equivalent. To ensure "semantic accuracy," a safety strategy is adopted. The processing system backtracks to the "source text to be corrected" segment in the source text, discards the original metaphorical imagery or rhetorical shell, and converts it into a straightforward and professional "interpretive text unit." This dimensionality reduction ensures the accurate transmission of core technical information and avoids semantic misunderstandings caused by forced translation.

[0092] In step 604 of some embodiments, when the translation confidence level is not higher than a second preset threshold, the system performs heterogeneous annotation on the semantic unit to be corrected. In risk scenarios with extremely low confidence, the processing system adopts "heterogeneous annotation" as a fallback protection mechanism. Here, heterogeneous annotation refers to retaining the parts of the translation to be confirmed through quotation marks, italics, or specific identifiers, and marking them as externally imported cultural or specific contextual content. This processing method can explicitly alert readers to the extremely high risk of cross-cultural translation at this point, preventing readers from being misled. It also provides accurate risk positioning labels for subsequent manual proofreading or interactive correction, ensuring the safety baseline of the translation system.

[0093] Through steps 601 to 604 above, by introducing a three-tiered diversion mechanism driven by "confidence gating," the technical challenges of "rigid application" or "semantic loss" that easily occur in machine translation when handling idioms and metaphors are successfully solved. This application's solution matches semantic units with different levels of understanding (high, medium, and low) to equivalent mapping, dimensionality reduction translation, and heterogeneous annotation paths, respectively. This solution, while ensuring translation professionalism, greatly enhances the system's resilience and semantic transparency. This multi-level correction strategy not only reshapes the logical flow in cross-cultural contexts but also ensures that the resulting reconstructed translations meet the high standards of industrial applications in terms of accuracy and cultural adaptability.

[0094] In some embodiments, to ensure that the translation output remains strictly within the scope of industry expertise after cross-cultural reshaping, this solution introduces a truth-based end-of-line verification mechanism to eliminate ambiguities and deviations that may arise during semantic reorganization. This process utilizes authoritative industry benchmarks as logical safeguards to conduct pixel-level compliance reviews and precise corrections of the translation, thereby guaranteeing the professional rigor and legal validity of the final deliverables.

[0095] Based on this, such as Figure 7As shown, the step 105, "correcting and filtering the reconstructed translation information based on industry terminology benchmarks to obtain the target translation information", may include, but is not limited to, steps 701 to 703.

[0096] Step 701: Based on the semantic constraint logic in the industry terminology benchmark, perform compliance filtering on the incremental semantics in the reconstructed translation information to intercept semantic drift content that exceeds the definition scope of the industry terminology benchmark, and obtain the filtered translation information.

[0097] Step 702: Match and compare each technical term feature in the filtered translation information with the industry terminology benchmark to identify terminology conflicts in the filtered translation information that deviate from the industry terminology benchmark.

[0098] Step 703: Perform atomic-level replacement of term conflict items based on industry terminology benchmarks to obtain replacement term items, and replace term conflict items in the filtered translation information based on the replacement term items to obtain target translation information.

[0099] In step 701 of some embodiments, the processing system performs compliance filtering on the incremental semantics in the reconstructed translation information based on the semantic constraint logic in the industry terminology benchmark. This is to intercept semantically drifting content that exceeds the definition scope of the industry terminology benchmark, thus obtaining filtered translation information. This step is a quality control gate. The "industry terminology benchmark" refers to the set of authoritative terms and their definitions loaded during the initialization phase. "Incremental semantics" refers to the semantic content that is not explicitly expressed in the original text, which is automatically completed or generated by the system based on the context during the implicit explicit processing or diversion correction in step 104. The system uses the "semantic constraint logic" preset in the benchmark, that is, the semantic boundaries of specific terms within their respective industries, to perform "compliance filtering" on these new contents. Once it finds that the explicit logical description exceeds the industry-recognized definition scope (i.e., "semantic drift"), it immediately performs interception to ensure that the generated "filtered translation information" maintains professional restraint in terms of logical depth.

[0100] In step 702 of some embodiments, the processing system matches each technical term feature in the filtered translation information with an industry terminology benchmark to identify terminology conflicts in the filtered translation information that deviate from the industry terminology benchmark. This step is a refined comparison at the lexical level. The processing system automatically segments the filtered text to extract "technical terminology features" with industry attributes. By aligning each term with the "industry terminology benchmark," the processing system can accurately locate "terminology conflicts" in the translation that are grammatically correct but use non-standard terms. These conflicts originate from the misuse of non-professional vocabulary or polysemous words caused by interference from a general corpus during the generation process of the large language model. This identification process provides precise targets for subsequent physical correction.

[0101] In step 703 of some embodiments, the processing system performs atomic-level replacement of terminological conflicts based on industry terminology benchmarks to obtain replacement terminology items. Based on these replacement terminology items, it then replaces the terminological conflicts in the filtered translation information to obtain the target translation information. This step performs a final calibration action. The "atomic-level replacement" refers to the point-to-point mandatory coverage of identified non-compliant words without disrupting the overall grammatical structure of the sentence. The processing system directly retrieves standard "replacement terminology items" from the "industry terminology benchmarks" and fills them back into the corresponding positions in the translation. Through this truth-based correction, the system ultimately eliminates all potential professional biases, thereby generating "target translation information" that possesses both professional logical flow and meets industry standards in technical terminology.

[0102] Through steps 701 to 703 above, a closed-loop mechanism of "semantic filtering-feature comparison-forced replacement" solves the technical problem that machine translation easily sacrifices professional accuracy in the pursuit of linguistic fluency. This application's solution transforms industry terminology benchmarks into an executable quality firewall, effectively suppressing semantic expansion and terminology pollution that may occur during translation reorganization. By enforcing compliance constraints on incremental semantics and atomic-level repair of conflicting terms, this solution ensures that the final output target translation achieves pixel-level rigor in the use of professional terminology, not only conforming to the reading habits of the target language but also meeting the stringent requirements for translation certainty in high-value documents such as legal and technical documents.

[0103] In the practical application of this solution, based on the relevant steps of the text translation processing method described above, a comprehensive reshaping from the underlying characters to the deep logic is achieved through a "multi-dimensional nested translation enhancement architecture." The following section, combining the technical content of each module of the processing system, details its implementation example in a specific application scenario.

[0104] In the initial application phase, the processing system uses Module A (the expert mimicry recognition and industry benchmark initialization module) to perform domain probing on the source text to be translated. For example, when a user scans a "technical maintenance agreement" for German industrial equipment using smart glasses, the processing system identifies its vertical domain context as "German engineering standards." At this point, the processing system doesn't just call up a dictionary; instead, it executes an "expert mimicry" instruction, switching the large language model to "senior German engineer" mode. The processing system instantly loads the domain-specific standard knowledge base, establishing an initial stylistic feature of "extremely rigorous (Formal)," and anchoring specific words such as "Anlage" as the industry benchmark term "device / equipment," rather than the generic "accessory," thus locking in the professional precision of the translation from the source.

[0105] During the translation generation process, Module B (i.e., the script firewall interception and character purity guarantee module) operates in real time based on the "monolingual reader agreement". Assuming the target translation language is Chinese, the processing system will automatically activate the first logical firewall and generate strict character set restrictions. At this time, if the large model attempts to retain the abbreviation "API" or residual English suffix when translating "Application Programming Interface" due to training data bias, the firewall will execute elimination filtering according to the "non-Latin language forced isolation" rule. Except for necessary mathematical formulas, the processing system strictly requires the output of a pure Chinese character script, effectively intercepting any remnants of source language roots or affixes, ensuring that the translated text fully conforms to the reading habits of target language readers in terms of visual presentation.

[0106] In the solution of this application, the core of the actual application lies in the four-step deep processing protocol of Module C (i.e., the deep semantic de-shelling and cross-cultural logic reversal module). The processing system first performs "semantic de-shelling" on the source text. For example, it decouples the Chinese context-colored word "granularity" and identifies its deep intention as "level of detail", so as to match "Level of detail" in English instead of a rigid literal translation. Subsequently, it performs the key "logical reconstruction": for Chinese-to-English translation, the processing system automatically recognizes the inductive logic of Chinese (first presenting the background and then the conclusion), and performs "syntactic surgery" to place the core conclusion information in the front. At the same time, based on the central semantic skeleton, it forcibly complements the action subject that is often missing in Chinese, enabling the preliminary translated text to be fully aligned with the native language in terms of logical flow.

[0107] For highly difficult cultural idioms, Module D (i.e., the idiom confidence shunting and safety processing mechanism module) performs precise shunting through a decision tree. When the idiom "casting pearls before swine" appears in the source text, the confidence gating model first evaluates the degree of understanding. If the confidence is extremely high, the processing system will initiate "native language equivalent search" and replace it with the native English professional expression "Casting pearls before swine"; if the confidence is in the medium range and there is no equivalent idiom, the processing system will adopt the "abstract dimensionality reduction" strategy and directly translate it as "explaining complex things to those who don't understand"; if the confidence is extremely low, it will be marked using the "heterogeneous fallback" mechanism. This hierarchical processing ensures that cultural images neither generate ambiguity nor lose the rigorous transmission of semantics during the conversion process.

[0108] In the final output stage, Module E (i.e., the cultural awareness explicitation and specialized orthography verification module) acts as the last line of defense for quality control, performing fine-tuning. The processing system performs subtext explicitation processing to address differences in "high / low context culture," ensuring that implicit logical connections are explicitly expressed in the translation. Simultaneously, the system performs rigorous "language-specific red-line checks" for specific languages, such as automatically scanning and ensuring all nouns are capitalized in German translations, or proofreading non-breaking spaces before punctuation marks in French translations. This pixel-level scanning, combined with final filtering based on industry terminology benchmarks, completely eliminates semantic drift and formatting errors, ultimately outputting accurate and highly specialized target translation information.

[0109] Reference Figure 8 This application also provides a text translation processing apparatus, which includes: The translation processing system, as a processing system, is used to execute the text translation processing methods described above; Smart glasses are used to send the source text to be translated to the translation processing system and to receive and display the target translation information sent by the translation processing system.

[0110] Understandably, this text translation processing device utilizes edge-cloud collaboration between smart glasses and the translation processing system to construct a real-time cross-language interactive hardware solution. The smart glasses, acting as the front-end sensing and display terminal, integrate a miniature camera, microphone array, and waveguide display module, responsible for acquiring the source text to be translated from the external environment (such as road signs, documents, or audio-to-text transcription of conversations). The translation processing system, deployed in the cloud or at an edge computing gateway, handles computationally intensive tasks such as complex Large Language Model (LLM) inference, logical firewall filtering, and semantic reconstruction. This architecture ensures that the terminal device remains lightweight and low-power while receiving high-performance computing support.

[0111] During operation, the smart glasses use wireless communication links (such as Wi-Fi 7 or 5G) to send the collected source text to the translation processing system in real time. Upon receiving the data, the translation processing system rigorously executes a series of processing steps, including loading the vertical domain context, filtering scripts via a firewall, reversing and reshaping logic, and performing confidence-based stream routing correction. After processing, the translation processing system sends the generated target translation information, which possesses industry rigor and native-speaker idiomaticity, to the smart glasses. The smart glasses' display control unit projects the target translation onto the augmented reality (AR) layer in the user's field of vision in real time, achieving a seamless integration of the translation with the real-world scene.

[0112] Taking industrial technical inspection as an example, when technicians wear smart glasses to observe complex German-made machinery, the glasses capture the German technical specifications (the source text to be translated) on the equipment and transmit it to the system. The system automatically identifies that it belongs to the "German engineering standards" field, loads the corresponding industry terminology benchmarks, performs logical reversal to adapt to Chinese reading habits, and enforces orthographic checks such as capitalization of German nouns. Ultimately, what technicians see on the glasses screen is not a rigid word-for-word translation, but "native-speaker-level" operating instructions that conform to Chinese engineering expression habits and are accurate in terminology. In international conference scenarios, smart glasses can also process real-time transcribed speeches from other parties and display them in logically clear Chinese, eliminating "translationese," at the bottom of the field of vision, greatly improving the naturalness and efficiency of cross-language communication.

[0113] Furthermore, in practical applications, besides smart glasses, this translation processing device can also achieve equivalent technical functions through smart mobile terminals (such as smartphones or tablets with AR capabilities), embedded professional handheld translation devices, or transparent display translation terminals. Taking a smartphone as an example, the device uses its high-resolution camera to capture or scan the source text to be translated, engages in two-way data interaction with the backend translation processing system via a mobile network, and uses the real-time preview interface on the display screen to directly overlay the reconstructed target translation information onto the original text location using AR spatial positioning technology. This alternative lowers the barrier to hardware wearing, and by leveraging the powerful computing power and sensor integration of mobile phones, it can also achieve accurate loading of vertical domain context and intuitive presentation of logically reconstructed translations, making it a highly universal implementation path for office, business travel, and academic research scenarios.

[0114] The text translation processing device provided in this application combines a high-performance translation processing system with lightweight smart glasses, transforming complex logic reshaping translation technology into a portable visual enhancement experience. Utilizing smart glasses as an interaction point, and in conjunction with the multi-layered logic firewall and self-checking loop within the translation processing system, it effectively solves the technical pain points of traditional translation devices during real-time translation, such as character contamination, inaccurate terminology, and logical residue. This device not only significantly improves the immediacy of information acquisition but also ensures that users in professional technical fields can obtain accurate, idiomatic, and industry-standard translation results, greatly expanding the application value of large language model translation technology in professional and mobile scenarios.

[0115] This application also provides an electronic device (i.e., a computer device), which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the text translation processing methods described in the above embodiments. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0116] Reference Figure 9, Figure 9 This illustration shows the hardware structure of an electronic device according to another embodiment, the electronic device comprising: The processor 910 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 920 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 920 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 920 and called by the processor 910 to execute the text translation processing method of the embodiments of this application. The input / output interface 930 is used to implement information input and output; The communication interface 940 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 950 transmits information between various components of the device (e.g., processor 910, memory 920, input / output interface 930, and communication interface 940); The processor 910, memory 920, input / output interface 930 and communication interface 940 are connected to each other within the device via bus 950.

[0117] This application also provides a computer-readable storage medium storing a computer program for causing a computer to perform the text translation processing method described in the above embodiments.

[0118] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0119] This invention also provides a computer program product that stores program instructions, which, when executed by a computer, cause the computer to implement the text translation processing method described in any of the above embodiments.

[0120] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0121] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0122] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0124] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. 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 comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0125] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0127] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0130] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A text translation processing method, characterized in that, The method includes: The source text to be translated is obtained, and the corresponding standard knowledge base is loaded based on the vertical domain context of the source text to be translated. Then, the initial stylistic features and industry terminology benchmarks of the target translation language are determined based on the standard knowledge base. Based on the script type corresponding to the target translation language, filter and constrain the character set of the translation text to determine the character set restrictions during text translation. The source text to be translated is semantically decoupled to obtain deep semantic intent information. Based on the initial stylistic features and the character set constraints, the deep semantic intent is logically reconstructed in detail to obtain preliminary translation information. Identify the semantic units to be corrected in the preliminary translation information, determine the translation confidence of the semantic units to be corrected, and then perform split correction processing on the preliminary translation information based on the translation confidence to obtain the reconstructed translation information; Based on the industry terminology benchmark, the reconstructed translation information is corrected and filtered to obtain the target translation information.

2. The text translation processing method according to claim 1, characterized in that, The loading of the corresponding standard knowledge base based on the vertical domain context of the source text to be translated includes: The semantic feature vector of the source text to be translated is input into a preset domain classification model for classification processing to obtain a vertical domain label used to characterize the vertical domain context. Based on the vertical domain tags, corresponding expert mimicry instructions and industry knowledge weights are matched from a preset expert state database; The large language model is invoked to load the standard knowledge base associated with the expert mimicry instructions and the industry knowledge weights.

3. The text translation processing method according to claim 1, characterized in that, The step of filtering and constraining the character set of the target translation language based on the script type to determine the character set restrictions during text translation includes: Based on the script type corresponding to the target translation language, a first logical firewall is enabled in the translation generation stream corresponding to the text translation, and the character set restriction conditions are generated based on the first logical firewall. The first logical firewall is used to monitor the real-time character data to be output in real time. The character set restriction condition is used to instruct the first logical firewall to filter out real-time character data when it identifies that the real-time character data is a character, source language root or affix residue corresponding to a script type that is not the target translation language.

4. The text translation processing method according to claim 1, characterized in that, The semantic decoupling process performed on the source text to be translated to obtain deep semantic intent information includes: The grammatical and rhetorical shell of the source text to be translated is removed to obtain the core source text, and the core source text is input into a preset large language model for skeleton data extraction to obtain the central semantic skeleton. The central semantic skeleton is subjected to key feature identification to obtain at least one of the following: logical center position, missing action subject, and context connection identifier. Based on the logical center position, the missing action subject, and the context connection identifier, the deep semantic intent information is obtained.

5. The text translation processing method according to claim 1, characterized in that, The process of logically reconstructing the deep semantic intent based on the initial stylistic features and the character set constraints yields preliminary translation information, including: Based on the initial stylistic features, the target logical pattern of the target translation language is determined, and the source logical pattern of the source text to be translated is determined. When the source logic pattern is inductive logic and the target logic pattern is deductive logic, the conclusion information in the deep semantic intent is placed at the beginning of the translated text information to obtain logically processed text information. Identify the missing action subject in the central semantic skeleton, and perform subject completion on the logically processed text information based on the missing action subject to obtain the completed text information; The completed text information is filtered based on the character set restrictions to obtain the preliminary translation information.

6. The text translation processing method according to claim 1, characterized in that, The process of determining the translation confidence level of the semantic unit to be corrected, and then performing a splitting and correction process on the preliminary translated information based on the translation confidence level to obtain reconstructed translated information includes: The semantic unit to be corrected is input into a preset confidence gating model for data processing to obtain the translation confidence level, which is used to characterize the degree of translation comprehension of the semantic unit to be corrected. When the translation confidence is higher than the first confidence threshold, a native language equivalent search is performed in the target language idiom database to obtain target local idioms that are functionally similar to the semantic unit to be corrected. The semantic unit to be corrected in the preliminary translation information is replaced based on the target local idioms to obtain the reconstructed translation information. When the translation confidence is not higher than the first confidence threshold but higher than the second preset threshold, the source text to be corrected corresponding to the semantic unit to be corrected in the source text to be translated is determined, and the source text to be corrected is directly translated to obtain the translated text unit. Then, the translated text unit replaces the semantic unit to be corrected in the preliminary translation information to obtain the reconstructed translation information. When the translation confidence level is not higher than the second preset threshold, the semantic unit to be corrected is heterogeneously labeled.

7. The text translation processing method according to claim 1, characterized in that, The process of correcting and filtering the reshaped translation information based on the industry terminology benchmark to obtain the target translation information includes: Based on the semantic constraint logic in the industry terminology benchmark, the incremental semantics in the reconstructed translation information are filtered for compliance, so as to intercept semantic drift content that exceeds the definition scope of the industry terminology benchmark and obtain filtered translation information. Each technical term feature in the filtered translation information is matched and compared with the industry terminology benchmark to identify terminology conflicts in the filtered translation information that deviate from the industry terminology benchmark. Based on the industry terminology benchmark, the term conflict items are replaced atomically to obtain replacement term items. Then, based on the replacement term items, the term conflict items in the filtered translation information are replaced to obtain the target translation information.

8. A text translation processing apparatus, characterized in that, The device includes: A translation processing system, the translation processing system being used to perform the text translation processing method as described in any one of claims 1 to 7; The smart glasses are used to send the source text to be translated to the translation processing system and to receive and display the target translation information sent by the translation processing system.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the text translation processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the text translation processing method according to any one of claims 1 to 7.

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