44 results about "Traditional knowledge" patented technology

The invention discloses a knowledge construction method and system based on a large model and an RAG technology, and belongs to the technical field of large models.According to the knowledge construction method and system based on the large model and the RAG technology, through cross-text-block mutual information calculation and RAG enhanced reasoning, the system can quantify statistical correlation between entities, and in combination with context information of an external knowledge base, potential incidence relations of cross-paragraphs or documents are mined, so that the knowledge construction efficiency is improved. A dynamic semantic segmentation strategy is adopted to ensure that semantics in text blocks are consistent, context continuity is maintained by retaining overlapped parts, multiple expressions of the same entity are comprehensively judged through a multi-dimensional anaphora resolution mechanism, the error resolution rate is reduced, dynamic segmentation and mixed retrieval are combined, and the problem of large model input length limitation is solved. And through semantic retrieval and keyword matching complementation, the recall rate is improved, and the key problems of semantic fracture, cross-text association missing, low entity alignment precision and the like in traditional knowledge construction are remarkably solved.

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 belongs to the field of combination of a large model and a knowledge graph, and particularly relates to a large model and knowledge graph combined reasoning method for false news detection, which breaks through the limitation of a traditional detection method in the aspects of knowledge instantaneity and reasoning controllability through heterogeneous architecture design. Based on multi-dimensional features such as timeliness, propagation mode and information density of news propagation, a double-engine-driven intelligent reasoning system is constructed, on one hand, important entity extraction and semantic understanding are carried out through a large language model, and on the other hand, structured fact verification is provided through a dynamic knowledge graph. And the inherent factual illusion problem of a large language model is effectively solved. Compared with a traditional knowledge iteration mode depending on single model parameter updating, the method supports an incremental updating strategy based on knowledge graph nodes.

The invention provides a knowledge graph completion method combining an embedding method and a large language model, and belongs to the technical field of knowledge graph processing. According to the knowledge graph complementing method, the model based on embedding and the large language model are combined, the graphic characteristics of the structured data and the semantic depth of the text data are fully utilized, and the accuracy and efficiency of knowledge graph complementing are effectively improved. Compared with the traditional knowledge graph completion technology, the method has the advantages that the large language model is introduced to process the text description, and the text description is fused with the embedding generated based on the embedding model, so that the performance of the completion task is greatly improved. According to the method, a comparative learning framework is introduced to optimize prediction and enhance the generalization ability of a model (completion method) on various knowledge maps.

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 belongs to the technical field of nuclear power DCS knowledge base construction, and aims to solve the problems of knowledge dispersion, update lag, single expression, lack of intelligent application, difficulty in sharing and inheriting and the like in a traditional knowledge management mode of a nuclear power DCS. The invention provides a nuclear power DCS knowledge base construction system and method, the system comprises an application layer, a middle layer, a large model layer, a data layer and a computing power resource layer, the application layer comprises a knowledge base management module, an intelligent interaction module, a data service module, an office assistant module and an operation and maintenance assistant module; according to the method, a system architecture of a knowledge base is designed and comprises function division of a computing power resource layer, a data layer, a large model layer, a middle layer and an application layer. According to the method, an intelligent, systematized and efficient DCS knowledge base can be constructed, a powerful guarantee is provided for safe and stable operation of a nuclear power plant, and the efficiency, accuracy and practicability of nuclear power DCS knowledge management can be improved.

The invention relates to the field of education big data mining, graph neural network and student behavior modeling, and provides a knowledge tracking method of a test question characterization heterogeneous graph based on student emotion, the method adopts a heterogeneous graph neural network technology in the deep learning field to characterize multi-dimensional features of test questions, and emotional features generated by students in the learning process are converted into multi-dimensional features of the test questions into multi-dimensional features of the test questions. The method comprises the following steps of: performing modeling on a specific test question, such as concentration degree, confusion degree, boring feeling and contusion feeling, embedding an emotion feature into a specific test question, and modeling a cognitive structure of a learner by adopting a transform mechanism; finally, the test questions with the emotion features of the students and the cognitive structures of the students are fused into a traditional knowledge tracking model, and knowledge tracking and learner performance prediction for different learner groups are achieved. The method can scientifically and comprehensively predict the learning condition of the student, and achieves the purpose of assisting the teacher in precise teaching.

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 discloses an intelligent tea leaf processing state detection method and system based on knowledge distillation, and belongs to the field of tea leaf processing state detection.The method comprises the steps that a tea leaf fixation state training set and a tea leaf rolling state training set are obtained; respectively constructing a teacher model, a student model for detecting a tea leaf fixation state and a student model for detecting a tea leaf rolling state by using the image classification model; respectively carrying out knowledge distillation training by utilizing the tea leaf fixation state training set and the tea leaf rolling state training set to obtain a tea leaf fixation state detection model and a tea leaf rolling state detection model; and detecting the corresponding to-be-detected pictures by using the tea leaf fixation state detection model and the tea leaf rolling state detection model to obtain a tea leaf fixation state and a tea leaf rolling state. The problem that the learning ability of a student model is limited due to the fact that a fixed temperature coefficient is frequently used in a traditional knowledge distillation method and the method cannot adapt to dynamic changes of task complexity in the training process is solved.

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.