143 results about "Knowledge processing" patented technology

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a multi-source knowledge processing and querying method, which comprises the following steps: data acquisition, cleaning and standardization processing, and standardized database construction; extracting core concepts and association relationships, and generating knowledge elements; constructing a multi-dimensional knowledge graph based on knowledge elements, and establishing a semantic index to realize data semantic annotation and bidirectional mapping; and analyzing the query intention, extracting a query constraint condition, and executing association reasoning based on the multi-dimensional knowledge graph to generate a query result. According to the method, the standardized database of the multi-source heterogeneous data is constructed, so that the data consistency is improved; through multi-dimensional knowledge graph construction and semantic index establishment, the relevance and structural expression of data are enhanced, so that the relationship between knowledge elements is clear and traceable; and through association reasoning based on query constraint conditions, the query accuracy and efficiency are improved.

The invention discloses an intelligent question answering system based on a large language model, and relates to the technical field of natural language processing and information retrieval. According to the system, a retrieval enhancement generation (RAG) and knowledge reordering (Re-rankig) technology is utilized, and the knowledge processing capacity for mechanical manufacturing process design, industrial standards and related specifications is improved. The system is mainly composed of four parts, wherein the first part is used for constructing a knowledge base covering machine manufacturing knowledge; in the second part, a LoRA + fine-tuned Qwen2-7B model is used for guaranteeing high-precision requirements such as process parameter calculation; in the third part, the RAG technology is integrated to effectively retrieve and process domain knowledge; and 4, introducing a reordering model to optimize answers. The system is remarkably superior to a traditional large language model LLM in BLEU-4 and ROUGE text matching evaluation indexes, the reliability and speciality of application of the system in the mechanical manufacturing field are verified, an effective scheme is provided for intelligent question answering in the field, and the prospect is wide.

The invention provides an entity link-based multi-source information association system and method in an agricultural scene, and belongs to the technical field of agricultural artificial intelligence, and the system comprises a data analysis module which constructs a multi-modal data analysis layer for data analysis, converts the analyzed data into a knowledge unit in a preset format through a Converter component, and stores the knowledge unit in the preset format; retaining an original semantic hierarchical structure and generating knowledge association anchor points; the graph construction module is used for constructing a cross-modal knowledge graph by utilizing knowledge association anchor points and an entity linking technology; the graph retrieval module is used for retrieving the cross-modal knowledge graph on the basis of a GraphRAG hierarchical retrieval technology; the result optimization module is used for optimizing the retrieval result to obtain an optimized retrieval result; and the result processing module constructs a low-code workflow engine based on the optimized retrieval result, further constructs an agricultural knowledge processing assembly line, and executes the agricultural knowledge processing assembly line to obtain an executable working scheme. And the processing efficiency of agricultural knowledge is improved.

The invention relates to the technical field of knowledge processing, and particularly discloses a knowledge proposition error correction method and system based on knowledge graph optimization upgrading, and the method comprises the steps: extracting effective information from multi-source knowledge data through employing entity linking, relation extraction and attribute alignment methods, and constructing an initial knowledge graph. Through semantic analysis based on deep learning, grammar, semantic and logic analysis is carried out on knowledge propositions, and a proposition element triple is generated. A graph neural network is utilized to encode triples, proposition vectors and neighborhood feature vectors are obtained, structural similarity is calculated accordingly, and proposition errors are detected in combination with rules and a statistical method. An integrated learning algorithm is adopted to classify error traceability, and an error correction decision is made in combination with multi-source evidence, so that an accurate error correction result is obtained, and the accuracy and reliability of knowledge propositions are improved; the error correction process is more logical and reliable, blind error correction is avoided, and the quality and efficiency of knowledge proposition error correction are improved.

A method and system for designing curing processes is disclosed. The method includes obtaining design requirements using a requirement gathering agent, determining potential ranges of recipes and operating conditions with an operations specialist agent, identifying suitable material options and assessing corresponding properties with a material specialist agent, defining dimensions and shape aspects of the design using a design specialist agent, extracting and reasoning relevant information with a knowledge processing and retrieval agent, formulating a final requirement specification with an experiment enabler agent, generating a first set of experiments based on the final requirement specifications using an experiment designer, performing, by a predictive model design agent implementing a prediction model, real-time predictions for dynamic conditions, updating, by a predictive model optimizer agent implementing a continual learning framework, the prediction model in real-time; and optimizing and updating, by a process optimizer agent, the prediction model iteratively based on feedback from experiments and simulations.

The invention belongs to the technical field of information extraction, and discloses a text structuring method and system based on a large language model. The method comprises the following steps: performing training data processing by analyzing a JSON format of a large language model; performing training and fine tuning on the large language model, performing training by using vLLM, and deploying the fine-tuned large language model; and performing text structuring on the deployed large language model by adopting a CoT prompt mode, and further improving the structured output capability of the large language model by adopting a reflection strategy of a JSON analysis result for wrong JSON format data. According to the method, the problem that JSON character strings generated by a large model in a knowledge extraction task do not meet requirements is solved through combination of fine adjustment and instructions; and the large model generates a JSON (JavaScript Object Notation) of a knowledge graph specification according to a schema. Org requirement, so that end-to-end text structuring is realized, further knowledge processing is not needed, and the workload of subsequent knowledge processing is saved.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention discloses a digital reading system based on a knowledge graph and a text image recognition technology. The system comprises a collection module used for collecting financial data and a first financial document from a plurality of financial data sources; the construction module is used for identifying a target entity in the financial field in the financial data and constructing a graph structure of a financial knowledge graph; the first analysis module is used for receiving a second financial document uploaded by the user, identifying a target keyword in the second financial document and generating a mind map corresponding to the target keyword according to a preset hierarchical structure; the second analysis module is used for identifying document elements in the first financial document; the typesetting module is used for acquiring screen information of the mobile terminal equipment; the watermark adding module is used for generating a digital watermark according to the copyright information and the custom information; and the question processing module is used for receiving the query question. The technical problem that financial knowledge processing efficiency of a related digital reading system is low is solved.

The invention discloses a model training method and device, equipment, a storage medium and a product, and the method comprises the steps: obtaining the type of a training text, the type of the training text comprises a fact reasoning text and an anti-fact reasoning text; inputting the input question of the training text into the student model to obtain a predicted answer of the student model; the student model is obtained based on teacher model distillation; training the student model based on the predicted answer of the student model, the standard answer of the training text and the type of the training text to obtain a trained student model; wherein the training text is obtained based on IT system operation and maintenance knowledge; the teacher model is a large language model, and the student model is an operation and maintenance small model. According to the embodiment of the invention, by sharing a thinking chain method of fact reasoning and anti-fact reasoning, the reasoning answering capability of the student model generated by distillation is improved, the anti-fact reasoning answering probability is reduced, and the IT system operation and maintenance knowledge processing capability of the student model is improved.

The invention discloses an electric power big data automatic reasoning platform and an electric power distribution system, and relates to the technical field of electric power systems, and the platform comprises a knowledge acquisition unit which collects and preprocesses electric power internal and external data, and converts the data into structured data through a knowledge processing unit; organizing the triple data by the dynamic knowledge graph construction unit to form an initial graph; the updating unit monitors a new dynamic state in real time and corrects or adds atlas content; the reasoning unit is based on the dynamic graph, power fault accurate reasoning and future demand trend prediction are achieved through a fault diagnosis and demand prediction algorithm, efficient integration and dynamic evolution of power knowledge are achieved through construction and real-time updating of the dynamic knowledge graph, timeliness and accuracy of the graph are ensured, and reliable data are provided for fault diagnosis; through a closed-loop architecture of data acquisition-inference analysis-scheme making-execution control, an intelligent sensor and an optimization algorithm are combined to generate a distribution scheme adaptive to a scene, and the power distribution adaptability and economy are improved.

The invention relates to the technical field of artificial intelligence, in particular to a culture field dynamic ontology intelligent construction method based on a large model, and the method comprises the following steps: constructing a relation network based on vocabulary semantic embedding, generating a graph to recognize a hyponymy and correct the hyponymy, determining a priority in combination with a context evaluation extension demand, and deducing semantic relation to optimize structural connection. And outputting a dynamic update sequence, and identifying a new entity matching inference rule to obtain an updated ontology structure. According to the method, vocabulary similarity is analyzed through semantic embedding, a cultural entity relation network is constructed, entity hyponymy and hyponymy are recognized in combination with atlas analysis, entity attributes are adjusted in real time, an ontology structure is optimized, it is ensured that an ontology can be flexibly expanded and adapt to a changing cultural environment, the adaptability and updating capacity of cultural knowledge are improved, the ontology structure is continuously improved, and the ontology effect is improved. Intelligentization and real-time performance of the scheme in practical application are enhanced, and accuracy and comprehensiveness of knowledge processing in the cultural field are improved.

The invention provides a water conservancy knowledge structured extraction and verification method and device, and belongs to the technical field of artificial intelligence, and the method comprises the steps: carrying out the differential text processing of different formats of files, and generating an intermediate file; classifying the intermediate file into a regulation class or a non-regulation class based on a preset rule base; performing hierarchical title identification on the regulatory files to form entry knowledge blocks, and converting table contents into HTML (Hypertext Markup Language) knowledge blocks; performing semantic segmentation on the non-regulation file to generate knowledge blocks; performing knowledge block checking and filing, and marking an abnormal alarm block; converting the table knowledge blocks into natural language description by utilizing a large model; and positioning the context of the original text of the alarm knowledge block, and performing intelligent correction through a large model. According to the method, a traditional semantic analysis model and a large language model are creatively fused, a closed-loop process of preprocessing, extraction, verification and correction is formed, the problems of structured analysis and error correction of complex texts in the water conservancy field are solved, and the knowledge processing efficiency and accuracy are remarkably improved.

The invention provides a civil engineering field knowledge large model retrieval enhancement generation method. The method comprises civil engineering domain knowledge processing and vectorization storage, civil engineering domain knowledge capability evaluation and adaptation of a large language model, a civil engineering domain knowledge vector database efficient retrieval mechanism and knowledge enhancement generation guidance based on retrieval results. According to the method, knowledge in the civil engineering field is encoded and established into a vector database through preprocessing processes such as data cleaning and text segmentation; a general large language model suitable for civil engineering field knowledge retrieval enhancement generation is selected through a semantic confusion index, target knowledge is retrieved through similarity calculation to form cue words, and the civil engineering field professional knowledge generation ability of the large language model is enhanced.

The invention provides a power grid data multi-mode soft fusion method and system based on Transform, and belongs to the technical field of power grid data processing. According to the scheme, the method comprises the steps of obtaining text resources, multimedia resources, maintenance data, a power grid model, operation data and power transmission and transformation equipment monitoring information, and constructing a power grid data source; carrying out the preprocessing of a power grid data source through employing an improved K-means clustering method, a reinforcement learning method and a synthetic sampling SMOTE method; performing data modeling, data mining, knowledge processing and knowledge management on the preprocessed data to form a power grid data knowledge base; and fusing the power grid data in the power grid data knowledge base by adopting a trained Transform multi-mode soft fusion model. By constructing a knowledge graph and based on technologies of Transform fusion, GAN and the like, the problems of large quality difference of state data of power transmission and transformation equipment and heterogeneous data fusion are solved, and deep fusion of multi-source data is realized.

The invention discloses a railway engineering unfavorable geology knowledge base construction method and system, and the method comprises the steps: collecting and preprocessing multi-source railway engineering text data, and obtaining a multi-source text data set; performing knowledge extraction, knowledge fusion and knowledge processing based on the multi-source file data set; constructing a railway engineering unfavorable geology knowledge base; and carrying out user interaction design based on a railway unfavorable geology knowledge base. According to the method, unfavorable geological knowledge of railway engineering can be automatically extracted from a large amount of text data, a structured knowledge base is constructed, the knowledge base not only improves the knowledge extraction efficiency and accuracy, but also provides scientific and accurate information support for safety management and decision-making of railway engineering in the form of the knowledge graph, and the method is suitable for popularization and application. Therefore, key fault information can be better mastered for railway operation enterprises, and accurate and effective information data and technical support are provided for decision making.

The invention relates to the technical field of artificial intelligence education, and discloses a personalized online education system and method based on artificial intelligence, and the system comprises a data collection and perception module which is used for collecting the dynamic behavior data and multi-mode perception data of a learner; the knowledge processing and constructing module is used for processing teaching contents to construct a structured knowledge graph; the user modeling module is used for constructing a dynamic learner portrait comprising a knowledge state model and a cognitive-emotional state model for the learner based on the collected data and the constructed knowledge graph; and the intelligent decision and intervention module is used for generating and executing personalized learning intervention based on the dynamic learner portrait. According to the method, by fusing multi-mode cognitive state perception, reinforcement learning intervention decision and a knowledge graph dynamic evolution mechanism, accurate and timely personalized intervention for learners and intelligent guidance of optimal learning strategies are realized.

The invention discloses a bank intelligent operation knowledge base implementation method and system based on a large language model and RAG, and the method comprises the steps: firstly extracting business terms from structured, semi-structured and unstructured multi-source heterogeneous multi-modal data of a bank, and then constructing a business dictionary containing term similarity data and an ontology tree; quantitatively calculating the release mechanism weight, the timeliness coefficient and the punishment association degree of the supervision clauses to obtain authority values, and sorting the authority values; then using a double-constraint loss function to finely adjust the pre-trained large language model as a vector representation model, vectorizing service data, and storing the vectorized service data into a vector database; and finally, aiming at user query, retrieving in a vector database, rearranging in combination with a supervision clause sorting result, and generating a business response after compliance verification. According to the method, the bank unstructured knowledge processing capability and retrieval precision can be improved, and efficient intelligent operation is realized.

The invention discloses a knowledge base construction method, a knowledge processing method and a related product. The construction method comprises the following steps: acquiring a plurality of first documents included in a first knowledge base; integrating the plurality of first documents to obtain a plurality of different document sets, wherein each document set in the plurality of different document sets comprises the first documents with semantic association relationship; carrying out inductive reasoning on the first document included in each document set based on the large language model and the document generation instruction to obtain a second document corresponding to each document set; and constructing a second knowledge base based on the plurality of first documents and the second documents corresponding to each document set. Therefore, the cross-document reasoning ability can be introduced, and the knowledge hierarchy of the second knowledge base is expanded through the induction reasoning ability of the large language model, so that the second knowledge base contains more information to meet the use requirements of the large language model, and the accuracy of the output result of the large language model is improved.

The invention discloses an AI-AD intelligent machine design method and system based on a learning-generation mode. The method comprises the following steps: S1, enabling an AI-AD intelligent mechanical design system to understand design knowledge at first; s2, compiling a workflow with low codes; s3, compiling a Prompt strategy of the design Agent and a called knowledge base; s4, repeating the steps S1 to S3, and compiling various agents needing to complete various specific design functions; s5, according to the designed various agents, a multi-agent self-organizing framework is utilized, and product creation type design is completed; and S6, according to the generated design result, generating a VBA file and storing the VBA file in the cloud. And S7, the SW establishes a conversation with the agent through a back-end API of the agent, timely reads a VBA code of the cloud for modeling, and outputs a model. According to the invention, a system structure of knowledge processing, cloud data storage, SolidWorks-based secondary development modeling and a human-computer interface module is constructed, Agents are enabled to learn at first, then design is created, and a 3D model meeting user requirements is generated, so that the current situation that knowledge engineering has extremely high requirements for practitioners' admission threshold can be reduced.

The invention discloses a field event element extraction method and system based on prompt learning and knowledge completion, and the method comprises the steps: converting an extraction task into a constrained generation problem through structured prompt, achieving the stable extraction under a small number of labeled samples through the existing knowledge prior of a large language model, reducing the dependence on large-scale labeled data, and improving the extraction efficiency. And the cross-domain adaptive capacity is improved. A vectorization knowledge retrieval mechanism is introduced, related evidences are obtained from an external knowledge source, elements which are not clearly expressed in a text are complemented, the integrity of information is enhanced, and the accuracy and credibility of a result are improved through knowledge verification. And through a self-adaptive fusion mechanism of prompt and knowledge, the model can flexibly balance text context and external knowledge, and the robustness of complex context and fuzzy expression is improved. And finally, standardized and structured element data are output, a convenient interface is provided for downstream event atlas construction and analysis tasks, and the automation degree of domain knowledge processing and the system integration efficiency are improved.

The invention discloses an AI processing method and system for long-term memory of mechanical design knowledge, and the method comprises the steps: carrying out the semantic analysis and feature extraction of heterogeneous knowledge, such as parameterization formulas, engineering charts, technological processes and design principles, in the field of mechanical design through a multi-agent collaborative learning system; and establishing a multi-dimensional modular knowledge base and context-based Prompt strategy coupling mechanism. The method solves the problems of low efficiency, insufficient analysis accuracy, limited innovation support capability and the like in the existing mechanical design knowledge processing technology, and particularly solves the technical problems of high calculation complexity, inaccuracy in reasoning, poor adaptability and the like when multiple types of design knowledge are processed. Through the implementation of the method, the processing efficiency of the design knowledge and the design automation level are remarkably improved, the technical threshold of a designer is reduced, the design period is shortened, efficient analysis, accurate matching and innovative application of the mechanical design knowledge are achieved, and powerful support is provided for intelligent development of mechanical design.

The invention discloses a method and a system for automatically generating a video based on multi-agent unstructured knowledge, and relates to the technical field of video automatic generation based on knowledge processing, and through a data source weight evaluation mechanism and a content semantic similarity calculation method, repeated or contradictory knowledge fragments are automatically identified, a disputed knowledge unit list is established, and the video is automatically generated according to the disputed knowledge unit list. And analyzing the matching degree of the dispute content and the preset script by adopting a semantic vector similarity algorithm. And for the recognized dispute knowledge points, a standby knowledge replacement algorithm is applied to retrieve replacement contents with high confidence from a reliable knowledge base, a knowledge unit replacement operation is executed, and high-quality video output with dispute identification is generated, so that the content accuracy and credibility of the multi-source knowledge fusion video are effectively improved. According to the method, a whole-process quality control system from knowledge identification to video generation is established, intelligent matching of knowledge credibility evaluation and a video content presentation mode is realized, and practical deployment of the technology is restricted.

The invention discloses a multi-source feature collaborative knowledge mining and semantic association method, and belongs to the technical field of full-life-cycle knowledge processing of railway track engineering. According to the method, field-related documents are screened, an element range and a processing sequence are limited in combination with a knowledge graph, the documents are customized to generate a term candidate set, term statistical weights are calculated based on a full-life-cycle corpus, dynamic semantic vectors are generated by utilizing a field self-adaptive pre-training model, and composite concept representation is obtained through fusion of an attention mechanism. And linking the knowledge graph to construct a heterogeneous graph, mining association through a graph attention network, analyzing, querying and verifying sequential logic, and then outputting structured knowledge. According to the method, the problems of railway track engineering data islands and cross-stage semantic segmentation can be solved, low-frequency key terms are accurately recognized, the result interpretability is enhanced, applications such as design optimization and construction management and control are directly supported, and the method is adaptive to multiple engineering professions and high in practicability.

The invention discloses a multi-modal large model text image data knowledge processing method and device for RAG, and the method comprises the steps: recognizing correct characters from a text watermark image, automatically generating a character data set corresponding to the text watermark image, and forming an image-text sample pair training sample; an enhancement strategy is introduced into the generated image-text sample pair, and the diversity and adaptability of a training sample are enhanced through preprocessing; integrating all the enhanced training sample data, dividing the data into a training set, a verification set and a test set in proportion, and performing training, parameter adjustment and performance evaluation on the multi-model large model; and inputting a text watermark image and a corresponding language instruction into the trained multi-modal large model, and reasoning and outputting correct text content by the multi-modal large model. According to the multi-modal fusion technology, the internal relation between the watermark and the text can be deeply mined, the accuracy and the effect of watermark elimination are greatly improved, and meanwhile, the integrity of text information is reserved.

The invention discloses a cultural relic knowledge processing method and a related device, and relates to the field of big data, and the method comprises the steps: obtaining cultural relic text data corresponding to to-be-processed cultural relic knowledge, carrying out the text embedding of the cultural relic text data through employing a pre-training word vector, obtaining a cultural relic word vector, inputting the cultural relic word vector into a pre-trained encoder of an improved Transform model, and carrying out the text embedding of the cultural relic text data. And mapping the word vectors into a group of context-aware target word vectors, and inputting the target word vectors into a decoder of the improved Transform model for decoding to obtain a cultural relic knowledge processing result. According to the method, automatic processing of cultural relic knowledge is realized, the weight of the feedforward network is initialized by adopting a quantum spin optimization algorithm to ensure the stability of weight initialization, multi-dimensional features in cultural relic text data are processed by adopting an interlayer nonlinear coupling constraint loss function, and the processing speed is accelerated by adopting a quantum acceleration gradient updating mechanism. Therefore, large-scale and multi-dimensional cultural relic knowledge can be efficiently processed.

The embodiment of the invention provides a gas safety knowledge base updating method, device and equipment and a storage medium, and the method aims at a target model, based on a gas safety knowledge base and a target knowledge result corresponding to any safety scene problem output by real-time safety data, and based on a model processing process corresponding to the target knowledge result. Obtaining processing process data, and obtaining result feedback information corresponding to the target knowledge result; determining a performance evaluation value and an effect evaluation value based on the processing process data and the result feedback information; the performance evaluation value is used for representing the processing performance of the knowledge processing process corresponding to the target model, and the effect evaluation value is used for representing the effect of the target knowledge result output by the target model; and updating the gas safety knowledge base based on the performance evaluation value and the effect evaluation value. Through continuous positive feedback optimization of the gas safety knowledge base, the accuracy and timeliness of contents in the gas knowledge base are ensured, and the updating efficiency and updating accuracy of the knowledge base are improved.

The invention provides an intelligence analysis method and system based on a knowledge graph and a large model, and relates to the technical field of artificial intelligence language and knowledge processing, and the method comprises the following steps: S1, constructing an intelligence knowledge graph; construction of the intelligence knowledge graph relates to a plurality of links including ontology modeling, knowledge extraction, knowledge fusion, knowledge storage and the like. Ontology modeling: on the basis of actual business demands, the knowledge graph adopts a top-down mode for modeling, entities are classified according to'people, objects, cases, events' and the like, and the'people, objects, cases and events' are connected in series through the relationship between the entities. According to the scheme, the knowledge graph is finally integrated with multi-source heterogeneous information, a clear knowledge structure is constructed, a unified intelligence analysis system is formed based on the powerful query capability of a graph database and the expansion capability of a technical and tactical model, and the multi-hop reasoning capability and the aggregation query capability based on the graph are provided; and the powerful semantic comprehension and generation capability of the large language model can be combined.

The application provides a knowledge processing method and device for power inspection defect detection, and the method comprises the following steps: obtaining an optimal path to connect a starting point entity and an ending point entity in the power equipment knowledge graph to form a Markov decision process according to the starting point entity and the ending point entity; training the Markov decision process to obtain a trained Markov decision process; an agent in the trained Markov decision process selects output edges in turn by using a random beam search strategy, and traverses to a new entity; when the agent searches for an optimal path by using the random beam search strategy, the path automatically forms a predicted explainable source. The application not only makes the power equipment knowledge graph knowledge completion explainable, but also improves the ability to understand complex environments and infer high-level causal relationships.

The invention discloses a large model knowledge base operation system based on multi-role cooperation and dynamic optimization. The system comprises a user interaction module used for receiving knowledge requirements triggered by user feedback and identifying knowledge gaps through semantic analysis; the multi-role cooperation module is used for defining roles and permissions thereof, generating knowledge optimization tasks according to a current knowledge gap, and distributing the knowledge optimization tasks to the roles so as to supplement optimized knowledge contents through the roles; the knowledge processing module is used for processing and analyzing the optimized knowledge content; the knowledge storage module is used for carrying out structured and unstructured mixed storage on the knowledge content which is qualified in processing and analysis; and the large model interface module is used for synchronizing the optimized knowledge content to a large model training or reasoning link in real time. According to the method, efficient collaboration of editing and expert opinions can be realized through a large model knowledge base with multi-role collaboration, and the accuracy and timeliness of knowledge contents are ensured.

The application discloses a bank intelligent operation knowledge base implementation method and system based on a large language model and RAG. The method first extracts business terms from structured, semi-structured and unstructured multi-source heterogeneous multi-modal data of a bank, and then constructs a business dictionary and an ontology tree containing term similarity data. Then, the release agency weight, time effectiveness coefficient and penalty correlation degree of the regulatory provisions are quantitatively calculated to obtain an authoritative value and sort. Then, the pre-trained large language model is fine-tuned as a vector representation model using a double-constraint loss function, and the business data is vectorized and stored in a vector database. Finally, for user queries, the vector database is retrieved and combined with the regulatory provision sorting result for rearrangement, and a business response is generated after compliance verification. The method can improve the unstructured knowledge processing capability and retrieval accuracy of the bank, and realize efficient intelligent operation.