Cross-ethnic language place name translation and writing method based on large model
By combining a large model with a knowledge base and workflow architecture, the problems of low configuration efficiency and insufficient model generalization ability in the translation of place names in ethnic languages are solved, realizing efficient and accurate cross-ethnic language translation of place names, lowering the technical threshold, and providing intelligent support for geographic information systems.
Patent Information
- Application Number
- CN202511679563.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-17
- Publication Date
- 2026-02-27
AI Technical Summary
Existing technologies suffer from low configuration efficiency, high maintenance costs, and insufficient model generalization ability in the translation of place names in ethnic languages. In particular, they are difficult to effectively handle complex scenarios caused by dialect differences and language evolution in multi-ethnic language environments.
By adopting a large model combined with a knowledge base and workflow architecture, and through data collection, preprocessing, structured processing, rule arrangement and large model release, a cross-ethnic language place name translation system is built to achieve adaptive learning and optimize syllable segmentation, grammar matching and cultural taboo filtering, thereby reducing the technical threshold.
It significantly improves the accuracy and efficiency of translation results, reduces operational complexity, and provides an intelligent solution for the standardization of geographic information systems in ethnic minority areas.
Smart Images
Figure CN121581072A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of natural language processing technology, and in particular to a method for transliterating place names across ethnic languages based on a large model. Background Technology
[0002] In the field of transcribing place names in ethnic languages, traditional methods have long relied on manually formulated rule files or statistically based machine learning models, but these techniques have significant limitations:
[0003] Rule-driven solutions (such as plist configuration files) require setting hard constraints such as syllable splitting and grammatical conversion line by line, which is not only inefficient in configuration, but also difficult to cover complex scenarios caused by dialect differences and language evolution; each time a new language or special case is added, experts need to manually adjust the parameters, resulting in high maintenance costs.
[0004] While classic machine learning methods (such as Conditional Random Fields (CRF) and Hidden Markov Models (HMM) can capture some sequence features, their performance is highly dependent on the quality and scale of labeled data. Given the uneven distribution of multi-ethnic language resources in my country (especially low-resource languages), the models have insufficient generalization ability and are prone to transliteration deviations or semantic distortions when migrating across regions.
[0005] To address the aforementioned problems, this invention proposes a cross-ethnic language place name translation method based on a large model. Summary of the Invention
[0006] To overcome the shortcomings of existing technologies, this invention provides a simple and efficient method for transcribing cross-ethnic language place names based on a large model.
[0007] This invention is achieved through the following technical solution:
[0008] A cross-ethnic language place name translation method based on a large model includes the following steps:
[0009] Step S1, Data Acquisition
[0010] By compiling translation rules for place names from books translated into different ethnic languages, a multi-dimensional and high-precision corpus of ethnic language place name translations is constructed, and data is selected from it for training and testing.
[0011] Step S2: Data Preprocessing
[0012] The data in the corpus of transliterated place names in ethnic languages is cleaned and validated, outliers are handled, and duplicates are removed; Step S3: Structured processing
[0013] Unstructured rule information is processed into structured rule information, transforming the rules described in the cleaned free text into structured rule information that supports machine reading;
[0014] In step S3, the free text is first pre-parsed using regular expressions, and structured information, including keywords and entity boundaries, is extracted through pattern matching.
[0015] Then, based on dependency grammar theory, a hierarchy tree of sentence components is constructed from statements in free texts to reveal the logical dependency structure between words.
[0016] Based on the extracted structured information and master-slave relationship tree, a domain knowledge graph is constructed using the RDF triple storage framework to achieve formal definition and reasoning capabilities for concepts.
[0017] Step S4, Rule Arrangement
[0018] Based on actual needs, the processing priority of structured rule information can be customized, and corresponding special names can be processed first, thereby increasing the accuracy of the translation results.
[0019] Step S5: Construction of the Large Model Knowledge Base
[0020] The well-organized structured rule information is imported into the large model knowledge base in text form, and the corresponding rule chains are described to help the large model understand the processing flow of structured rules, thereby enabling effective processing of translated data.
[0021] In step S5, a knowledge injection channel architecture is constructed, and structured rules are converted into prompt word templates to achieve explicit knowledge embedding.
[0022] Step S6: Large Model Workflow Orchestration
[0023] Subtasks are decomposed and constructed based on the processing priority of structured rule information, and then the subtasks are deployed to the corresponding intelligent agent work nodes to form a workflow; the output information of each node is summarized and the analysis results of each rule are integrated to form the final output result.
[0024] In step S6, a large model is built using the open-source large language model application development platform dify, and the deepseek large model is introduced for data processing. A checkpoint mechanism is used to save the intermediate results output by each node. If a node fails, the node can be restarted for subsequent calculations.
[0025] Step S7: Large Model Release
[0026] The large model is published as an application programming interface (API), allowing other programs to access the API after authentication and by carrying a token, thereby obtaining the corresponding translation results.
[0027] This cross-ethnic language place name translation system based on a large model includes:
[0028] The data acquisition module is responsible for compiling translation rules related to place names through translation books of different ethnic languages, building a multi-dimensional and high-precision corpus of ethnic language place name translations, and customizing the selection of data from it as training and testing data.
[0029] The data preprocessing module is responsible for cleaning and validating the data in the corpus of transliterated place names in ethnic languages, handling outliers, and removing duplicates.
[0030] The structured processing module is responsible for structuring unstructured rule information, transforming the rules described in the cleaned free text into structured rule information that supports machine reading.
[0031] The rule orchestration module is responsible for customizing the processing priority of structured rule information according to actual needs, giving priority to the corresponding special names, thereby increasing the accuracy of the translation results;
[0032] The large model knowledge base construction module is responsible for importing the sorted structured rule information into the large model knowledge base in text form, and describing the corresponding rule chains to help the large model understand the processing flow of structured rules, thereby enabling effective processing of translated data.
[0033] The large model workflow orchestration module is responsible for decomposing and constructing subtasks based on the processing priority of structured rule information, and then deploying the subtasks to the corresponding intelligent agent work nodes to form a workflow; it also summarizes the output information of each node, integrates the analysis results of each rule, and forms the final output result.
[0034] The large model publishing module is responsible for publishing large models in the form of application programming interfaces (APIs). It allows other programs to access the APIs after authentication and by carrying a token, thereby obtaining the corresponding translation results.
[0035] A cross-ethnic language place name translation device based on a large model includes a memory and a processor; the memory is used to store a computer program, and the processor is used to execute the computer program to implement the above-described method steps.
[0036] A readable storage medium storing a computer program that, when executed by a processor, implements the above-described method steps.
[0037] The beneficial effects of this invention are: the cross-ethnic language place name translation method based on a large model breaks through the excessive reliance of traditional methods on manual rules, significantly reduces the technical threshold and operational complexity of multilingual place name translation, and provides an intelligent solution for the standardization of geographic information system construction in ethnic minority areas. Attached Figure Description
[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Appendix Figure 1 This is a schematic diagram of the cross-ethnic language place name translation method based on a large model according to the present invention. Detailed Implementation
[0040] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions in the embodiments of this invention will be clearly and completely described below in conjunction with the embodiments of this invention. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.
[0041] With breakthroughs in natural language processing technology, large language models have demonstrated powerful context awareness and multi-task adaptability.
[0042] This cross-ethnic language place name translation method based on a large model includes the following steps:
[0043] Step S1, Data Acquisition
[0044] By compiling translation rules for place names from books translated into different ethnic languages, a multi-dimensional and high-precision corpus of ethnic language place name translations is constructed, and data is selected from it for training and testing.
[0045] Step S2: Data Preprocessing
[0046] The data in the corpus of transliterated place names in ethnic languages is cleaned and validated, outliers are handled, and duplicates are removed; Step S3: Structured processing
[0047] Unstructured rule information is processed into structured rule information, transforming the rules described in the cleaned free text into structured rule information that supports machine reading;
[0048] In step S3, the free text is first pre-parsed using regular expressions, and structured information, including keywords and entity boundaries, is extracted through pattern matching.
[0049] Then, based on dependency grammar theory, a hierarchy tree of sentence components is constructed from statements in free texts to reveal the logical dependency structure between words.
[0050] Based on the extracted structured information and master-slave relationship tree, a domain knowledge graph is constructed using the RDF triple storage framework to achieve formal definition and reasoning capabilities for concepts.
[0051] Step S4, Rule Arrangement
[0052] Based on actual needs, the processing priority of structured rule information can be customized, and corresponding special names can be processed first, thereby increasing the accuracy of the translation results.
[0053] Step S5: Construction of the Large Model Knowledge Base
[0054] The well-organized structured rule information is imported into the large model knowledge base in text form, and the corresponding rule chains are described to help the large model understand the processing flow of structured rules, thereby enabling effective processing of translated data.
[0055] In step S5, a knowledge injection channel architecture is constructed, and structured rules are converted into prompt word templates to achieve explicit knowledge embedding.
[0056] Step S6: Large Model Workflow Orchestration
[0057] Subtasks are decomposed and constructed based on the processing priority of structured rule information, and then the subtasks are deployed to the corresponding intelligent agent work nodes to form a workflow; the output information of each node is summarized and the analysis results of each rule are integrated to form the final output result.
[0058] The workflow capabilities are as follows:
[0059] User request --> A{Load Balancer} --> B([Agent Cluster])
[0060] B-->B1 (Translation Module) & B2 (Interpretive Translation Module) & B3 (Verification Module)
[0061] B1 / B2 / B3 --> C {Result Fusion Engine} --> D (Final Output)
[0062] In step S6, a large model is built using the open-source large language model application development platform dify, and the deepseek large model is introduced for data processing. A checkpoint mechanism is used to save the intermediate results output by each node. If a node fails, the node can be restarted for subsequent calculations.
[0063] Step S7: Large Model Release
[0064] The large model is published as an application programming interface (API), allowing other programs to access the API after authentication and by carrying a token, thereby obtaining the corresponding translation results.
[0065] This cross-ethnic language place name translation system based on a large model includes:
[0066] The data acquisition module is responsible for compiling translation rules related to place names through translation books of different ethnic languages, building a multi-dimensional and high-precision corpus of ethnic language place name translations, and customizing the selection of data from it as training and testing data.
[0067] The data preprocessing module is responsible for cleaning and validating the data in the corpus of transliterated place names in ethnic languages, handling outliers, and removing duplicates.
[0068] The structured processing module is responsible for structuring unstructured rule information, transforming the rules described in the cleaned free text into structured rule information that supports machine reading.
[0069] The rule orchestration module is responsible for customizing the processing priority of structured rule information according to actual needs, giving priority to the corresponding special names, thereby increasing the accuracy of the translation results;
[0070] The large model knowledge base construction module is responsible for importing the sorted structured rule information into the large model knowledge base in text form, and describing the corresponding rule chains to help the large model understand the processing flow of structured rules, thereby enabling effective processing of translated data.
[0071] The large model workflow orchestration module is responsible for decomposing and constructing subtasks based on the processing priority of structured rule information, and then deploying the subtasks to the corresponding intelligent agent work nodes to form a workflow; it also summarizes the output information of each node, integrates the analysis results of each rule, and forms the final output result.
[0072] The large model publishing module is responsible for publishing large models in the form of application programming interfaces (APIs). It allows other programs to access the APIs after authentication and by carrying a token, thereby obtaining the corresponding translation results.
[0073] The cross-ethnic language place name translation device based on a large model includes a memory and a processor; the memory is used to store a computer program, and the processor is used to execute the computer program to implement the above-described method steps.
[0074] The readable storage medium stores a computer program that, when executed by a processor, implements the above-described method steps.
[0075] Compared with existing technologies, this cross-ethnic language place name translation method based on a large model has the following characteristics:
[0076] First, it innovatively constructs a fusion architecture of "large model + knowledge base + workflow": it uses a pre-trained large model as the foundation to realize semantic understanding and generation, embeds domain knowledge base to standardize professional terminology, and uses a visual workflow for dynamic calibration.
[0077] Secondly, it breaks through the excessive reliance on manual rules in traditional methods. By automatically optimizing syllable segmentation strategies, grammatical matching logic, and cultural taboo filtering rules through an adaptive learning mechanism, it significantly reduces the technical threshold and operational complexity of multilingual place name translation, providing an intelligent solution for the standardization of geographic information systems in ethnic minority areas.
[0078] The embodiments described above are merely one specific implementation of the present invention. Ordinary changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included within the protection scope of the present invention.
Claims
1. A large model-based cross-ethnic language place name translation method, characterized by: The method comprises the following steps: Step S1, data collection By writing books in different ethnic languages, sorting out the translation and writing rules related to place names, constructing a multi-dimensional ethnic language place name translation and writing corpus, and customizing data selected therefrom as training and test data; Step S2, data preprocessing The data in the ethnic language place name translation and writing corpus is cleaned and verified, abnormal values are processed, and duplicate values are removed; Step S3, structured processing The unstructured rule information is structured, and the rules described in the cleaned free text are converted into structured rule information that can be read by machines; Step S4, rule arrangement According to the actual needs, the processing priority of the structured rule information is customized; Step S5, large model knowledge base construction The structured rule information is imported into the large model knowledge base in the form of text, and the corresponding rule chain is described to assist the large model in understanding the processing flow of the structured rules; Step S6, large model workflow arrangement According to the processing priority of the structured rule information, subtasks are constructed and deployed to the corresponding agent work nodes to form a workflow; The results of each node are summarized, the analysis results of each rule are fused, and the final output result is formed; Step S7, large model release The large model is released in the form of an application program interface API, and other programs can access the application program interface API after authentication with a token to obtain the corresponding translation and writing results.
2. The large model-based cross-ethnic language geographical name translation method according to claim 1, characterized in that: In step S3, structured information is first extracted, including keywords and entity boundaries; Then, based on dependency grammar theory, the master-slave relationship tree between sentence components is constructed according to the sentences in the free text, and the logical dependency structure between words is revealed; Based on the extracted structured information and the master-slave relationship tree, a domain knowledge graph is constructed to store RDF triples, to realize the formal definition and reasoning ability of concepts.
3. The large model-based cross-ethnic language geographical name translation method according to claim 1, characterized in that: In step S3, the free text is pre-analyzed by regular expressions, and the structured information is extracted by pattern matching.
4. The large model-based cross-ethnic language geographical name translation method according to claim 1, characterized in that: In step S5, a knowledge injection channel architecture is constructed to convert structured rules into prompt word templates, realizing explicit knowledge embedding.
5. The large model-based cross-ethnic language geographical name translation method according to claim 1, characterized in that: In step S6, a large model is built through an open-source large language model application development platform dify, and a deepseek large model is introduced for data processing. The checkpoint mechanism is used to save the intermediate results of each node output, and if a node fails, the subsequent calculation can be restarted after the node is restarted.
6. A large model-based cross-ethnic language place name translation system, characterized in that: The method for implementing any one of claims 1 to 5 comprises: A data collection module is responsible for sorting out the translation and writing rules related to place names by writing books in different ethnic languages, constructing a multi-dimensional ethnic language place name translation and writing corpus, and customizing data selected therefrom as training and test data; A data preprocessing module is responsible for cleaning and verifying the data in the ethnic language place name translation and writing corpus, processing abnormal values, and removing duplicate values; A structured processing module is responsible for structuring unstructured rule information, and converting the rules described in the cleaned free text into structured rule information that can be read by machines; A rule arrangement module is responsible for customizing the processing priority of structured rule information according to actual needs; A large model knowledge base construction module is responsible for importing the structured rule information in the form of text into the large model knowledge base, and describing the corresponding rule chain to assist the large model in understanding the processing flow of the structured rule; A large model workflow arrangement module is responsible for decomposing and constructing subtasks according to the processing priority of the structured rule information, then deploying the subtasks to the corresponding agent working node to form a workflow; and collecting the result information output by each node, fusing the analysis results of each rule, and forming the final output result; A large model publishing module is responsible for publishing the large model in the form of an application program interface (API), supporting other programs to access the application program interface (API) after authentication with a token, thereby obtaining the corresponding translation result.
7. A large model-based cross-ethnic language place name translation device, characterized by: The memory is configured to store a computer program, and the processor is configured to execute the computer program to implement the method according to any one of claims 1 to 5.
8. A readable storage medium characterized by: The computer program is stored on the readable storage medium and is executed by the processor to implement the method according to any one of claims 1 to 5.