36 results about "Traditional knowledge" patented technology

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The invention discloses a key marker gene-based intelligent cell type annotation method, which comprises the following steps of: constructing a static knowledge base by using known marker genes in a reference database, and endowing the marker genes with cell specific weights by using a TF-IDF method, so that the annotation accuracy and interpretability are improved. Meanwhile, under the condition that static matching is insufficient, the literature is understood through a large language model, mark information is extracted, dynamic completion of the knowledge base is achieved, the defect that updating of a traditional knowledge base is lagged is overcome, and good adaptability and expansibility are achieved. Besides, static and dynamic matching scores are fused in the annotation process, so that more robust cell type identification is realized, annotation requirements of multi-tissue, multi-species and novel cell states are adapted, high-precision and extensible cell type annotation can be realized in a scene with insufficient reference knowledge or a fuzzy sample, and the annotation efficiency is improved. And the method has good universality and practicability.

The invention belongs to the field of large-model retrieval, and discloses a large-model knowledge retrieval enhancement method and a related system for the field of power dispatching. A dynamic weighting mechanism of time sensitivity and credibility factors is introduced on the basis of a traditional knowledge retrieval enhancement RAG retrieval framework, so that a retrieval result not only depends on semantic similarity, but also depends on credibility factors; and the timeliness and the source reliability of the information can be comprehensively considered. In traditional knowledge retrieval enhancement (RAG), document sorting often mainly depends on semantic similarity, and differentiation processing of content release time and credibility is lacked, so that the problem that information is outdated although the information is similar or the content source is unreliable easily occurs in a scheduling scene. According to the method, after keyword retrieval and vector retrieval are combined to obtain initial semantic related data, the timeliness weight is calculated through the interval between the timestamp of the query content and the current time, and it is ensured that high-timeliness knowledge such as real-time trend data and an operation mode is preferentially presented in sorting.

The invention relates to the technical field of marine ecological informatics, and discloses a habitat suitability prediction method and system for marine ecological management.The habitat suitability prediction method for marine ecological management.The habitat suitability prediction method for marine ecological management.The habitat suitability prediction method for marine ecological managementincludes the steps that traditional knowledge data of fishermen is obtained, and audio data is transcribed into a text format through a natural language processing technology; generating a traditional knowledge graph containing the corresponding relationship between the observation characteristics and the fishing results; carrying out canonical correlation analysis on surface observation characteristics and vortex physical parameters in the traditional knowledge graph, and converting traditional knowledge into quantitative expression of a vortex parameter space based on a mapping matrix; and adopting a random forest to integrate multi-source prediction results, taking a traditional knowledge prediction rule and a physical model calculation result as input features, and outputting a middle-layer fish habitat probability distribution diagram. The technical problem that traditional knowledge and a vortex physical model are difficult to effectively integrate for middle-layer fish habitat prediction is solved.

The invention relates to the technical field of knowledge management, in particular to a knowledge slice optimization method based on semantic analysis and a semantic relation network. On the one hand, semantic knowledge atoms of time sensitivity and domain labels are generated through a multi-dimensional evaluation and standardized processing mechanism of heterogeneous data sources, the integration precision and knowledge representation consistency of multi-source heterogeneous data are remarkably improved, a reliable data basis is provided for dynamic evolution of a semantic relationship network, and the reliability of the system is improved. The problems of data format conflict, semantic association weakening and updating lag in traditional knowledge management are solved; on the other hand, through an improved TransE graph embedding algorithm and a multi-layer constraint driven knowledge slice division mechanism, self-adaptive structure optimization and multi-granularity knowledge slice dynamic recombination of the semantic relation network are realized, the problem of low user retrieval efficiency is effectively avoided, and the semantic retrieval efficiency and scene adaptability of a large-scale knowledge base are enhanced.

The invention discloses a drug incompatibility detection method based on fusion of a map and a large language model. Comprising the following steps: standardizing drug names in a prescription, mapping the drug names into uniform national standard codes, and generating a drug pair set; and extracting a drug interaction triple in combination with a large language model in the medical field, and constructing a heterogeneous drug knowledge graph. Based on a graph neural network and a comparative learning mechanism, embedded representation learning is performed on the knowledge graph, and the discrimination capability of drug node features is improved. Path retrieval and risk scoring are carried out on the drug pairs with the explicit interaction paths; and for a drug pair without an explicit path, screening alternative drugs by using chemical component similarity, inputting a pre-trained large language model through structured prompt, predicting a potential interaction relationship and credibility, and supplementing implicit risk information. According to the method, the problems of insufficient coverage and limited reasoning ability of the traditional knowledge graph are effectively solved, and the comprehensiveness and reliability of detection are improved.

The invention relates to the technical field of avionics fault diagnosis, and discloses an avionics fault diagnosis method based on multi-modal distillation and related equipment. Through a double-stage architecture design of a double-encoder teacher model and a lightweight student model, low-rank adapter injection and a multi-level knowledge distillation strategy are combined; the problems that an existing efficient parameter fine adjustment method is insufficient in cross-modal semantic alignment in a multi-modal diagnosis task, and cross-modal interaction knowledge is difficult to transmit in traditional knowledge distillation are effectively solved. Due to the introduction of a low-rank adapter, parameter efficiency is kept, and meanwhile, the model is assisted to accurately learn specific features of a multi-modal task; the multi-level knowledge distillation not only aligns model output, but also more fully migrates key knowledge such as cross-modal attention association and a multi-level feature fusion mode in the teacher model, and significantly improves the cross-modal joint reasoning ability of the student model.

The present invention discloses a construction and expansion method and system based on dynamic semantic knowledge graph, which belongs to the field of artificial intelligence technology. It includes: first extracting the head entity, tail entity and their relationship from the text, and adding attribute key-value pairs to enhance the semantic information; then constructing a structured knowledge graph and an indexed semantic knowledge graph to improve the knowledge organization ability; generating a credible knowledge graph through a masked reasoning mechanism, that is, randomly masking the entities or relationships in the triples, and using the pre-trained knowledge reasoning of the Large Language Model (LLM) to complete and verify its rationality. Finally, the three types of knowledge graphs, structured, semantic and credible, are integrated to form a dynamic and extensible target knowledge graph. The present invention effectively solves the problems of insufficient content depth, weak semantic understanding ability and insufficient data volume of traditional knowledge graphs through the triple mechanisms of attribute enhancement, semantic indexing and reasoning verification.

The invention relates to the technical field of computers, and discloses a dialogue processing method and device for a virtual seat and electronic equipment. The method comprises the steps of receiving dialogue content input by a user; reasoning the dialogue content to obtain a virtual seat reasoning result; obtaining a dialogue context feature set corresponding to the dialogue content according to the dialogue content; inputting the virtual seat reasoning result and the dialogue context feature set into a preset large language model, and generating a result corresponding to the dialogue content through the large language model; and feeding back a result corresponding to the dialogue content to the user through the virtual seat. According to the method, the system can better understand the background and context of the dialogue by using the dialogue context feature set and the virtual seat reasoning result, and the anthropomorphic performance of the system is improved. Through combination of virtual seat reasoning and large language model generation, the system can exceed the limitation of a traditional knowledge base algorithm and provide more intelligent replies meeting user requirements, so that the problem that the performance of the knowledge base algorithm is limited is solved.

According to the multi-modal spatio-temporal knowledge graph storage and retrieval method and system provided by the embodiment of the invention, cross-modal semantic alignment is realized in the initial stage of data processing by the system through semantic partitioning and dynamic modeling of ontology perception, and semantic splitting among heterogeneous data such as texts, images and tracks is fundamentally eliminated. And in combination with ontology rule constraints, the system can perform deep causal reasoning instead of simple statistical association, so that the accuracy and interpretability of complex query are remarkably improved. Moreover, through an exploration type ontology modeling and evolution mechanism driven by a large model, the system can automatically sense domain changes and trigger data reconstruction and index updating, so that the knowledge graph can efficiently adapt to the fast-changing real world at low cost and become a live system, and the knowledge graph has the advantages of being high in practicability and easy to popularize. The breakthrough is to solve the pain points of static state and lagging updating of the traditional knowledge graph body.

The invention discloses a mechanism knowledge base intelligent classification and indexing system and device based on multi-modal data fusion, and aims at solving the problems that when a traditional knowledge base processes multi-modal data, the efficiency is low, the classification precision is insufficient, the indexing quality is poor, and non-text resource processing is short. According to the system, through a feature extraction technology, features such as colors and textures are extracted for images, features such as keywords and themes are extracted for texts, and multi-type data such as texts, images and videos are converted into uniform feature vectors. A classification model is constructed by combining an SVM, a CNN and a Transform, and an attention mechanism is introduced to realize adaptive fusion. Based on a classification result, through automatic indexing such as keyword extraction and topic modeling, resources are associated through a knowledge graph. Experiments prove that the processing time of the device is shortened to 1 / 20 of that of an original method, the classification accuracy is improved from 30% to 90% or above, the knowledge base management efficiency and service quality are effectively improved, and support is provided for knowledge utilization and decision making.

The invention relates to the technical field of knowledge tree construction, in particular to an intelligent customer service knowledge tree construction method. By introducing an asymmetric self-consistency verification mechanism and an automatic training data set construction method, the problem of high dependence on manual annotation and a data template in a traditional business knowledge tree construction process is effectively solved, full-process automation from text document processing to business knowledge tree generation is realized, and meanwhile, the efficiency of business knowledge tree construction is improved. According to the method, the strong semantic comprehension and generation capability of the large language model is utilized, domain rules or templates do not need to be preset, and the method can adapt to document structures and language characteristics of different industries, so that the universality and cross-domain adaptability of knowledge tree construction are greatly improved, and the method is suitable for popularization and application. The method can be widely applied to intelligent customer service systems in multiple vertical fields of finance, education, medical treatment, law and the like, and the core pain point of insufficient generalization ability of a traditional method is solved; the technical problem that a traditional knowledge tree construction method is poor in automation and universality is solved.

The invention relates to the technical field of data processing systems and commercial methods, and discloses a customs laboratory knowledge co-construction and sharing system and method based on contribution excitation. The system comprises a knowledge input and vectorization module, a dynamic knowledge graph module, a dynamic value and entropy weight calculation module, a guidance and excitation mapping module and a user interaction and visualization module. The method comprises five key steps of knowledge contribution processing, knowledge network construction, dynamic value calculation, contribution excitation mapping and visual interaction. According to the method, a multi-dimensional knowledge evaluation system is constructed, a contribution entropy concept and a value propagation algorithm are introduced, a knowledge value dynamic quantitative evaluation and accurate incentive distribution mechanism is established, and intelligent management of the full life cycle of knowledge is realized; the problem that a traditional knowledge management system is inaccurate in value evaluation, unreasonable in incentive mechanism and lack of dynamic evolution ability is solved.

The invention provides an automatic maintenance method and system for a special bond knowledge base. The method comprises the steps of obtaining a to-be-processed file; identifying the business field and purpose of the to-be-processed file by using the special large model for the special bond field, and based on the business field and purpose, calling a knowledge base tool to carry out analysis and semantic fragmentation on the to-be-processed file to obtain a preliminary special bond knowledge base; based on a preset label set matched with the service field and the purpose, automatically labeling the service indexes of the fragments in the preliminary special bond knowledge base through a large model, and generating a label labeling result; performing label feature matching on the fragments in the knowledge base according to a label labeling result to obtain an optimized fragment set, and synchronously updating the optimized fragment set to the special bond knowledge base to complete automatic maintenance of the knowledge base; by means of the method, automatic maintenance of the special bond knowledge base can be achieved, the updating efficiency and quality of the knowledge base are improved, and the technical problems that in traditional knowledge base maintenance, manual processing is tedious, and standards are not uniform are solved.

The invention relates to the technical field of artificial intelligence, and discloses a knowledge migration method and system based on causal distillation and a structured loss function, and the method comprises the steps: building a causal graph based on a teacher model, and extracting the causal representation of the teacher model through causal intervention operation based on the causal graph; based on causal representation, constructing causal distillation loss; constructing a structured loss function; and performing weighted combination on the causal distillation loss and the structured loss function to form a total loss function, and training a student model based on the total loss function. According to the method, the problems that traditional knowledge distillation lacks causal constraints, a loss function is single and noise is easily introduced can be effectively solved, the causal reasoning ability, semantic hierarchy mastering and logic consistency of a student model are remarkably improved, meanwhile, robustness and transparency are enhanced, and efficient knowledge migration and performance optimization are achieved.

1. The name of the design product: traditional Chinese medicine knowledge database system graphical user interface for electronic devices. 2. The use of the design product: human-computer interaction and display. 3. The design points of the design product: the interface content of the graphical user interface displaying information. 4. The picture or photo that best indicates the design points: front view. 5. Without design points, omit the top view, bottom view, left view, right view, rear view. 6. The use of the graphical user interface: the graphical user interface is used to manage traditional Chinese medicine knowledge database information. The front view shows the system overview interface, which is the system overview interface.

The invention relates to a large-scale language model knowledge extraction method and system without retrieval assistance, and relates to the technical field of large-scale model knowledge extraction, and the method comprises the steps: obtaining a user retrieval problem and a reconstruction retrieval problem, inputting a large-scale language model, and obtaining a target text; performing preprocessing and text segmentation on the target text to obtain basic text segments, and analyzing semantic integrity to obtain text segments and semantic integrity parameters; ambiguity confidence parameters of the text segments are obtained, segment validity scoring is carried out, and a plurality of candidate text segments are obtained; respectively carrying out semantic matching on the candidate text segments and the user retrieval question to obtain a knowledge extraction result; and in combination with the result output template, reconstructing a knowledge extraction result, and obtaining a structured knowledge extraction result. The problems that traditional knowledge extraction often depends on external auxiliary means, is limited by external resources and is prone to deviation, so that the information accuracy is difficult to guarantee, and the knowledge obtaining efficiency is remarkably reduced are solved.

The application relates to a knowledge graph completion method based on deep semantic and entity category information extraction, and belongs to the field of knowledge graph completion. The method proposes a DSET model of an end-to-end neural network, and outputs entities of missing positions of a predicted knowledge graph based on the DSET model; the DSET model comprises a type similarity graph attention encoder TS-GAT which focuses on modeling of entity type information, and a deep semantic information three-dimensional convolution decoder DS-Conv3 which is responsible for extracting deep semantic features of entities and relations. The method solves the problems of neglecting entity category information and insufficient deep semantic feature extraction in traditional knowledge graph completion methods.

Embodiments of the present disclosure may include a culturally sensitive language translation system for Alaska Native languages, including a user interface configured to receive user input and display output. Embodiments may also include a natural language processing module configured to translate between English and at least one Alaska Native language. Embodiments may also include a speech processing module configured to process voice input and output. Embodiments may also include a cultural context module configured to ensure cultural sensitivity of translations. Embodiments may also include a continuous learning module configured to improve system performance based on user feedback and expert input. Embodiments may also include a data management module configured to securely store and manage user data, language data, and traditional knowledge. Embodiments may also include a processor coupled to a memory, the processor configured to execute the modules.

According to the data transaction method based on multi-constraint unsupervised federated knowledge distillation, by constructing various constraint conditions, logit consistency, batch statistical features and information entropy constraints, on the premise that image data privacy is ensured, efficient and accurate knowledge migration from multiple teacher models to student models is achieved, and the data transaction efficiency is improved. And the batch-level statistical constraint enables the model to be more stable when processing different batches of image data. Besides, the data holder only uploads the trained model parameters, and does not need to share original image data, so that the data privacy is guaranteed. The performance of a student model in an image task is enhanced, the problems of insufficient knowledge transmission, poor model stability and difficulty in integration of multi-teacher knowledge in image data transaction of traditional knowledge distillation are solved, and the method has a good application prospect.

A knowledge distillation method based on a dynamic multi-teacher model and structured relationships is presented. The main content is a novel knowledge distillation method that trains a student model using multiple teacher models and dynamically assigns weights to the teacher models based on their performance to guide the student model's training. Simultaneously, structured relationships are introduced to assist training, allowing the student model to learn the spatial relationships of the teacher models' sample outputs, thereby improving training effectiveness. Compared to traditional multi-teacher models, this invention addresses the limitations of single-teacher learning, blind learning, and average learning. By dynamically adjusting learning weights based on the quality of the teacher models, it achieves better training results. Furthermore, by introducing spatial relationships of samples as knowledge for learning, it overcomes the monotony of traditional knowledge distillation methods that rely solely on response learning, enabling the student model to learn richer knowledge and improving its performance.

The application relates to the technical field of information extraction, and particularly discloses a knowledge graph construction method based on word item chain merging, which constructs a sequential relationship network, adopts a node merging mechanism, and step by step constructs a knowledge graph covering an original semantic structure from bottom to top on the basis of retaining the original sequence of word items, so that the accuracy and completeness of graph construction are improved, a solid foundation is provided for subsequent knowledge reasoning and structure conversion of a traditional knowledge graph, and a simplification method based on word item chain merging is adopted to automatically construct a structured relationship network from bottom to top without relying on external dictionaries or pre-training models, so that the word item sequence information in the text is effectively retained to form a knowledge network graph reflecting the original semantic structure.

The invention belongs to the technical field of artificial intelligence, and provides a full-process automatic interview assisting method, system and equipment based on a large language model.The method comprises the steps that firstly, recruitment requirements of a recruitment end, initial face test questions and multi-modal data of job seekers are obtained, and a multi-modal dynamic knowledge base is constructed; wherein the multi-modal dynamic knowledge base is provided with a content dynamic mechanism, a structure dynamic mechanism and an iteration dynamic mechanism which are respectively used for updating knowledge base content and a sub-problem sequence, updating the structure of a knowledge graph in the multi-modal dynamic knowledge base and optimizing a sub-problem generation and retrieval strategy; and then, according to the multi-modal dynamic knowledge base, determining a structured question set of the job seeker, and further determining an interview auxiliary strategy of the job seeker. Through three updating mechanisms, knowledge application is more suitable for an actual interview scene, and the defects that a traditional knowledge base is static and rigid in structure are overcome.

The invention discloses a recommendation system based on a dual-module enhanced knowledge graph framework, and the appearance of a traditional recommendation system based on a knowledge graph brings significant benefits because it makes it possible to obtain more comprehensive information from adjacent nodes. However, in a recommendation system based on a knowledge graph, unpopular items often lack key information related to a given task, which results in imbalance of recommendation results, that is, more popular items are more likely to be recommended than less popular items. In order to solve the problem, the invention provides a recommendation system based on a double-module enhanced knowledge graph framework (DMKGE), and a knowledge selection module and a knowledge transfer module are innovatively introduced to relieve the problem of knowledge imbalance of different project attributes. Experimental evaluation is carried out on two reference data sets, and results show that the method provided by the invention is superior to a powerful baseline method. In addition, the method has the remarkable advantages of being high in calculation efficiency and low in space-time complexity.

The invention discloses a knowledge reasoning method for integrating entity semantics and a graph structure, and relates to the technical field of data processing, and the method comprises the following steps: S1, obtaining a complete relation neighborhood triple set from a knowledge graph for each target entity, and sampling a plurality of relation neighborhood triples and a plurality of type neighborhood triples; s2, according to the type neighborhood triple, generating final semantic type probability distribution; s3, generating structure prediction according to the relation neighborhood and type neighborhood triple; and S4, according to the final semantic type probability distribution and the structure prediction, performing splicing and fusion to generate final prediction. The innovative dynamic arbitration mechanism breaks through the information fusion bottleneck of a traditional knowledge inference model. The correction strategy based on deep feature tracing can effectively identify and reconcile prediction divergence, the professional advantage of each prediction channel is reserved, optimal integration of cross-dimension knowledge is realized, and the decision reliability of the system is greatly improved.

The application relates to a large language model knowledge extraction method and system without retrieval assistance, and relates to the technical field of large model knowledge extraction, and comprises the following steps: acquiring a user retrieval question and reconstructing the retrieval question, inputting a large language model, and acquiring target text; preprocessing and text segmentation are performed on the target text, basic text segmentation is acquired, and semantic integrity is analyzed to obtain text segmentation and semantic integrity parameters; an ambiguity confidence parameter of the text segmentation is acquired, segmentation effectiveness scoring is performed, and multiple candidate text segmentations are acquired; the candidate text segmentations and the user retrieval question are respectively subjected to semantic matching to acquire a knowledge extraction result; a result output template is combined to reconstruct the knowledge extraction result and acquire a structured knowledge extraction result. The application solves the problem that traditional knowledge extraction often depends on external auxiliary means, is limited by external resources and is prone to deviation, so that information accuracy is difficult to guarantee and knowledge acquisition efficiency is significantly reduced.

The present application relates to the technical field of marine ecological information, and discloses a habitat suitability prediction method and system for marine ecological management, wherein the habitat suitability prediction method for marine ecological management comprises the following steps: obtaining fisherman traditional knowledge data, converting audio data into text format through natural language processing technology, and generating a traditional knowledge graph containing the corresponding relationship between observation characteristics and fishing results; performing canonical correlation analysis on the surface observation characteristics in the traditional knowledge graph and vortex physical parameters, and converting the traditional knowledge into quantitative expression in the vortex parameter space based on a mapping matrix; and integrating multiple source prediction results by using a random forest, taking the traditional knowledge data and the physical model calculation results as input characteristics, and outputting a middle-layer fish habitat probability distribution map. The present application solves the technical problem that traditional knowledge and vortex physical models are difficult to effectively integrate for middle-layer fish habitat prediction.

The invention provides a power system knowledge retrieval method and platform based on an RAG framework, and relates to the technical field of knowledge retrieval, and the method comprises the steps: carrying out knowledge base construction processing on a power system file based on the RAG framework, and generating a vector database; service problem reconstruction is carried out, and a service adaptation expression is generated; inputting a word embedding model, and converting the word embedding model into a 1024-dimensional vector of the problem; sub-block vector similarity matching is carried out, and K candidate block metadata are screened and recalled; associating the K parent block IDs to which the K parent block IDs belong; after the 1024-dimensional vectors of the K parent blocks are called, M candidate block metadata are screened and output; and returning complete chapter content and traceability information. The technical problem that a traditional knowledge base is difficult to meet the efficient retrieval requirement of the commemorative inspection business of a power system due to the fact that the representation capability of vectors on business information is insufficient because the vectorization in the prior art mostly adopts general dimensions and is not combined with the text complexity of commemorative inspection files to optimize the vector dimensions is solved.

This invention belongs to the field of artificial intelligence communication technology and discloses an automated large-scale model implementation method for knowledge sharing. It focuses on three novel technical perspectives: intelligent deconstruction and reorganization of knowledge content, cross-circle knowledge adaptation and transmission, and closed-loop iteration of knowledge sharing effects. It constructs three core modules: intelligent deconstruction and reorganization of knowledge content, cross-circle knowledge adaptation and transmission, and closed-loop iteration and optimization of knowledge sharing effects, covering the entire process of knowledge sharing from content processing and circle adaptation to effect optimization. Through core algorithms such as multi-dimensional knowledge deconstruction and reorganization optimization, cross-circle knowledge semantic adaptation and form conversion, and multi-dimensional quantification and strategy iteration of knowledge sharing effects, it breaks through the industry bottlenecks of traditional knowledge sharing, namely "rigid content structure, inefficient cross-circle transmission, and lack of closed-loop effect optimization."

The present invention discloses a method for dynamic knowledge retrieval enhancement based on a large language model, which belongs to the field of knowledge retrieval and aims to solve the problems of traditional LLM knowledge solidification, lack of timeliness and hallucination. By dynamically constructing a multi-granularity knowledge base and combining rule and semantic segmentation technology (, text is converted into a normalized vector and a hybrid index is established. A dual-channel retrieval trigger mechanism is adopted, which integrates keyword matching scores and BERT semantic probability analysis to intelligently judge retrieval needs; vectorized retrieval is realized through the BGE-M3 model, and the cross encoder is combined to re-rank candidate results to improve accuracy. The system supports multi-language adaptive processing, dynamic switching of word segmentation strategies and cross-language retrieval, and introduces real-time knowledge updates and version control. This method significantly improves the timeliness and accuracy of answers, optimizes the contextual coherence of multiple rounds of dialogue, and can be widely used in intelligent customer service, professional Q&A and other fields, effectively reducing the risk of LLM hallucinations and enhancing knowledge traceability.