98 results about "Knowledge-based systems" patented technology

The invention discloses a knowledge base system construction method and device for multi-source heterogeneous data and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: integrating a dynamic graph convolutional network and a hierarchical attention mechanism to construct a multi-modal document analysis engine; performing semantic structure analysis on the original heterogeneous document on the basis of a multi-modal document analysis engine to extract document structure features and content semantic features, and constructing an original document relationship model on the basis of the document structure features and the content semantic features; based on the original document relationship model, performing classification fusion on heterogeneous data in the original heterogeneous document to obtain a to-be-stored heterogeneous data corpus, and processing the to-be-stored heterogeneous data corpus by using a graph neural network to establish a cross-modal semantic association index; and based on the cross-modal semantic association index, performing classified storage on the to-be-stored heterogeneous data corpora by utilizing a preset heterogeneous database so as to complete knowledge base system construction of the multi-source heterogeneous data.

The invention relates to the technical field of information retrieval, in particular to an intelligent RAG knowledge base system fused with a dynamic knowledge graph. Comprising a data processing module for preprocessing original data to obtain a standardized data set; the graph construction and updating module is used for carrying out knowledge extraction on the standardized data set by utilizing a large model, constructing a knowledge graph and carrying out dynamic updating; the dialogue management and understanding module is used for recording complete information construction of a current dialogue, updating a dialogue state and calculating a retrieval weight adjustment amount; the mixed retrieval module is used for performing mixed retrieval in combination with DPR and PPR retrieval methods to obtain a first retrieval result and a second retrieval result, setting an initial retrieval weight according to the question type, and obtaining a comprehensive retrieval result in combination with the retrieval weight adjustment amount; and the response optimization module is used for outputting an optimal response according to the comprehensive retrieval result and user feedback by utilizing reinforcement learning based on strategy gradient. According to the method, the retrieval and response generation effect can be optimized, and the user satisfaction is improved.

The invention relates to the technical field of machine tool cutter wear monitoring, and particularly provides an integrated multi-dimensional data monitoring system and a monitoring method.The integrated multi-dimensional data monitoring system comprises a multi-dimensional data acquisition module which can acquire multi-dimensional data of the working state of a cutter in real time; the feature information layer and the intelligent analysis layer comprise a multi-source data fusion module, an abnormal state recognition module, a residual life prediction module and a self-adaptive learning module; the decision execution layer comprises an early warning feedback module, a parameter optimization suggestion module and an intervention control module; the knowledge base system comprises a case base module, a rule base module and a model base module, different physical quantities are collected through the multi-dimensional data collection module, full-dimensional sensing of the state of the cutter is achieved in combination with machining features, material information and environment data, multi-sensor collection, physical model construction, feature information integration and intelligent algorithm analysis are combined, and the machining accuracy of the cutter is improved. And comprehensive, real-time and accurate monitoring of various abnormal states of the machine tool cutter is realized.

The invention provides a management method of a multi-modal enterprise knowledge base system, which comprises the following steps: integrating multi-modal data in an enterprise, constructing a private knowledge base, and combining the private knowledge base with a public knowledge base; performing intention analysis on a multi-modal retrieval demand submitted by the user based on the joint result, performing condition retrieval on the private knowledge base and the public knowledge base based on an intention analysis result, and outputting multi-modal query data; and carrying out quality inspection on the condition retrieval process, carrying out dynamic optimization on a joint result based on a quality inspection result to obtain a final multi-modal enterprise knowledge base, and carrying out deployment management on the multi-modal enterprise knowledge base. The management effect of the multi-modal knowledge in the enterprise is greatly improved, and the retrieval accuracy of the knowledge is also improved.

The invention relates to a database table field description intelligent generation and dynamic maintenance method and device based on RAG and a large model. The method comprises the following steps: constructing a domain-based dynamic knowledge base system through multi-source corpus collection, domain adaptation vectorization model processing and a dynamic knowledge base construction and updating process; receiving a description generation request of a table or a field, recalling related knowledge from the knowledge base through an RAG technology, assembling cue words, and then calling a large model for reasoning to generate a description result; the generated description result is confirmed through manual auditing, the knowledge base is updated according to confirmation feedback, and continuous optimization of the knowledge base is achieved. According to the method provided by the invention, the problems of low accuracy, high maintenance cost, poor interpretability and the like in a traditional method are effectively solved by constructing a field-enhanced RAG architecture and combining a three-layer knowledge system and a self-adaptive prompt framework which can be dynamically adjusted according to a business scene, and a new technical implementation path is provided for table and field description in intelligent data governance.

The invention relates to the technical field of data information processing, in particular to a commemorative venue private knowledge base system based on a large language model and an anti-illusion constraint method thereof.The system comprises a multi-modal data processing engine, the multi-modal data processing engine comprises a data processing module and an RAG-LLM collaboration module, and the RAG-LLM collaboration module has an anti-illusion mechanism. The explanation content meeting the memorial venue history requirements is generated through double retrieval of accurate retrieval and semantic retrieval, credibility fusion and constraint decoding technologies; and the dynamic index engine is internally provided with a knowledge enhancement module and a dynamic updating module. According to the method, the problems of low knowledge base construction efficiency, data privacy leakage risk, uncontrollable LLM generation content and the like are solved by fusing retrieval enhancement generation (RAG), a large language model (LLM) and a localized deployment technology.

The invention provides a cross-domain knowledge graph system based on super-dimensional multi-modal calculation and a data processing method, which are applied to the technical field of knowledge graphs, and the system comprises a multi-modal data acquisition and super-dimensional coding module which is used for acquiring multi-modal data from multiple ways and converting the multi-modal data into super-dimensional vectors; the dynamic knowledge graph construction module is used for identifying a multi-modal entity based on the super-dimensional vector and constructing a multi-modal knowledge graph by using the multi-modal entity; and the calculation query processing module is used for analyzing and optimizing the query of the user and executing the optimized query based on the multi-modal knowledge graph to obtain an execution result, and the query uses one or more preset query modes. According to the scheme, various types of data are supported, the retrieval mode is expanded, the computing power is improved, and then the practicability and user experience of the knowledge base system are improved.

The invention discloses a meteorological operation and maintenance intelligent question-answering method based on multi-mode GraphRAG. The method comprises the following steps: S1, a meteorological operation and maintenance intelligent question-answering system divides input data into unstructured data and structured data and processes the unstructured data and the structured data respectively; s2, constructing a knowledge base system composed of a vector database, a relational database and a graph database; s3, analyzing the user query text and dividing question types; s4, selecting a corresponding retrieval strategy according to the question type and starting a processing flow, and finally processing a retrieval result through a fusion and rearrangement module to obtain a knowledge evidence set; and S5, the meteorological operation and maintenance intelligent question-answering system organizes the knowledge evidence set and the user questions into structured cues and submits the structured cues to the large language model for processing to generate structured answers. According to the method, optimization is carried out according to characteristics of the meteorological operation and maintenance field, professional terms and field specific knowledge can be effectively processed, and high-quality field professional services are provided.

A knowledge base system based on a large language model includes a processor configured to construct and enable a specific domain function module to perform a function calling mechanism according to the large language model to generate a function corresponding to one of an industrial process monitoring system information and a nature language query information, and establish a knowledge base. The processor is configured to construct and enable a retrieval augmented generation module to perform an embedding transfer mechanism to generate a plurality of embedding vectors, and retrieve embedding vectors and establish a vector store. The processor is configured to construct and enable a knowledge and information management module to manage the knowledge base and the vector store.

The invention discloses a planetary gear dynamic characteristic prediction method. Comprising the following steps: carrying out a planetary gear dynamic transmission test on a real planetary gear test bed, carrying out dynamic modeling on a healthy planetary gear, describing the vibration of a planetary gear system by adopting a fixed coordinate system fixed on a gear ring, providing a dynamic characteristic type, responding to signal analysis, and carrying out dynamic transmission. Comparing and judging the result obtained by analyzing the response signal obtained by the test with the result obtained by analyzing the response signal, and establishing a planetary gear dynamic fault knowledge base system, so as to form digital-analog combination in the field of planetary gear transmission, which is beneficial for identifying the dynamic characteristics of planetary gear transmission. The prediction efficiency of the dynamic characteristics of the planet gear and the prediction precision in the field of fault identification are improved, and the accuracy of dynamic characteristic identification is improved through continuous test and model interaction. And a planetary gear transmission dynamic characteristic knowledge base system is established to realize rapid and accurate analysis of vibration signals captured in tests and projects.

The invention discloses an intelligent prompting method and system, computer equipment and a storage medium, and the method comprises the steps: receiving a user-defined occupational tag and a knowledge deviation tag, and configuring a data collection path; collecting and analyzing multi-dimensional behavior data of a user on a personal computer in real time to generate structured knowledge entries; starting a cleaning process in a computer idle period or daily fixed time, judging novelty and correlation of knowledge by using a large model, and storing knowledge entries passing judgment into a knowledge base; in the knowledge base, vertical domain frontier knowledge is automatically obtained from the network based on user tags, an abstract is generated through a large model, and low-frequency outdated knowledge is deleted regularly; user input content is captured in real time, and RAG retrieval is performed in combination with historical behavior data and a knowledge base so as to provide intelligent prompts. The embodiment of the invention aims at solving the problems that an existing knowledge base system is static, the user adaptability is insufficient, and the interaction efficiency is low.

The invention discloses an electricity price policy knowledge base system based on artificial intelligence, and the system comprises an electricity price information collection and arrangement module which is used for automatically collecting and arranging electricity price and electricity charge policy information from the Internet and forming policy text data; the knowledge base architecture establishing module is used for designing and establishing a graph knowledge base to store the incidence relation between the policy texts; the data processing and analyzing module is used for deeply analyzing the policy text by utilizing a natural language processing technology, extracting key information, converting the key information into structured data and storing the structured data in the graph knowledge base; the user interaction module is used for establishing user interaction data employees and providing a natural language query function; and the policy analysis model module is used for collecting and analyzing historical data and real-time data, training a model capable of identifying and classifying data related to an electricity price and electricity charge policy to obtain a policy change trend, and predicting electricity price change. The invention further discloses a corresponding method. According to the invention, the timeliness and accuracy of the obtained policy information can be ensured.

The invention discloses an intelligent knowledge base system based on multi-domain model collaboration and a construction method, and relates to the field of artificial intelligence knowledge management. The system realizes accurate generation and dynamic management of cross-domain knowledge through a three-level driving architecture of an intelligent scheduling layer, a knowledge production layer and an intelligent knowledge base. The intelligent scheduling layer schedules professional field models such as medical treatment (such as Med-PaLM 2) and law (such as a power law intelligent contract review model) at a millisecond level through a field intention analysis engine and a three-dimensional matching matrix; the knowledge production layer completes format standardization, conflict verification and timeliness marking by using a knowledge cleaning assembly line; the intelligent knowledge base adopts a three-level storage system of a core library (high frequency and high reliability), an expansion library (medium confidence) and a temporary library (to be verified), and combines a dynamic weight algorithm of dominant score (60%), recessive behavior score (30%) and domain expert calibration (10%) to realize real-time optimization and upgrading and downgrading of knowledge.

The invention relates to the technical field of artificial intelligence, knowledge representation and knowledge retrieval, in particular to a high-performance knowledge base system based on multi-modal mixed retrieval, which comprises a vector calculation module used for converting an unstructured text into vector data represented by feature vectors; the knowledge graph module is used for converting a structured text into knowledge graph data, the mixed retrieval module is used for performing retrieval according to a query request, the mixed retrieval fusion module is used for performing retrieval on the storage module according to the query request, and mixed retrieval comprises vector retrieval and graph retrieval. And generating a fusion retrieval result. The vector calculation module and the knowledge graph module respectively process unstructured and structured data, and the hybrid retrieval fusion module respectively executes vector retrieval and graph retrieval based on the query request and fuses the retrieval results, so that the semantic matching precision and the relationship reasoning ability are greatly improved.

The invention discloses a multi-modal knowledge base system construction method oriented to a large language model, which comprises the following steps: S1, creating a knowledge base instance, including setting a knowledge base name, description and visibility parameters; s2, adding a multi-modal document to the knowledge base, wherein the multi-modal document comprises a text, a PDF (Portable Document Format), an image and a table format; s3, configuring large language model parameters including a model identifier, an API key and a basic API address; s4, realizing a knowledge-based question and answer function, and enhancing a generation technology by combining a standardized API (Application Program Interface) with a retrieval; s5, realizing a dynamic scoring function of knowledge entries, and performing multi-dimensional scoring on the entries through a large language model; and S6, managing knowledge base authority, distinguishing a personal knowledge base mode and a shared knowledge base mode, and setting document visibility. According to the method, a multi-modal analysis engine is adopted, unified storage of multi-modal data is supported, lightweight document indexing is adopted, automatic scoring is performed through LLM, manual auditing dependency is reduced, and meanwhile high-quality knowledge sharing is ensured in combination with permission control.

The invention relates to the technical field of agricultural travel services, and provides a dialect intelligent customer service and culture knowledge base system, the system comprises a perception layer, an analysis layer, a knowledge layer and an interaction layer four-dimensional architecture, the perception layer collects and preprocesses dialect voice, noise reduction is performed through spectral subtraction, and Mel frequency cepstrum coefficient features are extracted; the analysis layer identifies dialects based on a fine tuning Wav2Vec2.0 model, converts the dialects into mandarin through a Transform architecture, and completes intention identification and slot filling by using a BERT related model; the knowledge layer constructs a local culture knowledge graph containing non-abandoned, folk and other entities, and supports dynamic updating; and the interaction layer is combined with multiple rounds of dialogue management to generate multiform responses, and can be connected with an external service system. The system also optimizes a feedback module iterative model and knowledge. The system can cover more than ten dialects, realizes dialect interaction, culture interpretation and service conversion closed loop, and assists rural culture revitalizing and rural cultural travel service upgrading.

The invention provides a government affair element universe digital person-based system for realizing intelligent guide of affair handling personnel, and belongs to the technical field of government affair service intellectualization. The system deeply integrates element universe, intelligent interaction, big data and artificial intelligence technologies, realizes natural interaction with the affair handling personnel by means of an intelligent virtual digital person, and realizes intelligent guide of the affair handling personnel. And the government affair service process can be smoothly completed. A local intelligent knowledge base system and a knowledge base exclusive to multiple government affair departments are built, and a DeepSeek large model is applied. Voice or text query of business knowledge, handling processes, policies and regulations is supported, and contents cover latest policies of tax payment services, meta universe, offline business handling places and modes and the like. The digital human has abundant functions, provides business consultation, hall navigation guidance and intelligent explanation, can also perform daily question and answer interaction, and creates a relaxed communication atmosphere. And after logging in the meta universe cloud hall, the accompanying real-time efficient interactive guide service can be obtained by clicking the guide digital person, so that the government affair service efficiency and the user satisfaction are remarkably improved.

An apparatus includes memory to store computer-readable program code for a knowledge-based system including an inference engine and a knowledge base, and processing circuitry configured to access the memory, and execute the code. The apparatus is caused to at least receive a time series of measurements of operating conditions of a machine recorded during an operation of the machine. The apparatus is also caused to cluster the time series of measurements into one or more respective clusters and identify a pattern across the clusters. The apparatus is also caused to define a current state of the machine that includes the pattern across the clusters, access and search a knowledge base for a historical case describing a respective solution to a historical problem state similar to the current state, the respective solution including a repair action, and generate an output display indicating the repair action to address the current state.

The invention discloses a task book demand standardization method based on retrieval enhancement generation, and the method comprises the following steps: S1, gathering historical demand documents and standardized records, completing cleaning, term unification and structured storage, and forming a maintainable and searchable domain knowledge base; s2, segmenting the document into entries, carrying out keyword labeling and vectorization representation and indexing, and establishing a complete knowledge base system; s3, generating a high-quality reference set through pre-retrieval enhancement, semantic matching retrieval and post-retrieval enhancement; s4, based on the multi-layer template, driving the large model to output a structured demand confirmation document draft; and S5, providing interactive auxiliary revision containing user feedback and a double termination mechanism, and providing powerful intelligent support for finally forming a demand document. According to the method, the defects of element recognition, knowledge reuse and consistency guarantee in the prior art can be overcome, and a feasible engineering path is provided for large-scale and cross-scene demand standardization.

The invention discloses an intelligent table data escape and knowledge base construction method based on a large language model. The method comprises the steps that multiple document formats are imported; identifying the existence of the table, analyzing the row-column structure of the table, positioning the position of each cell, and extracting data in the table; the big language model understands the data significance of each cell according to the context and the data content in the table; performing escape on the original data; merging and storing the original data, logically partitioning the data, and vectorizing the data after escaping; table data in a knowledge base is obtained through a large language model knowledge base system, and a query request of a user is quickly responded through a vector index of the knowledge base. According to the method, the data in the table is intelligently converted by adopting a large language model semantic escape technology, and the data is embedded into the knowledge base through data merging operation, so that a user can conveniently query and utilize the data, and the problem of insufficient table data processing precision in the prior art is solved.

The invention relates to the technical field of OCR (Optical Character Recognition) and natural language processing, and discloses an OCR document intelligent element extraction system based on a large model. Comprising an OCR (Optical Character Recognition) and large model element extraction system, a local forward and reverse knowledge base-based quadratic optimization system, a content extraction standardization and intelligent error correction post-processing system, an expert confirmation system and a knowledge base system, and the OCR recognition and large model element extraction system performs content extraction on a document subjected to OCR recognition by a large model based on an element extraction rule. According to the OCR document intelligent element extraction system based on the large model, a forward knowledge base can automatically correct OCR identification deviation of similar scenes by precipitating expert optimization experience; the reverse knowledge base can filter error results of historical verification, and repeated false detection is avoided; and in the content standardization link, for the problem of semantic impassability caused by handwritten OCR identification deviation, multi-modal cross validation and intelligent error correction of an extraction result are realized by combining font similarity calculation and large model semantic analysis of a CV model.

The invention discloses a liquid rocket engine expert fault knowledge base system, and belongs to the technical field of engine fault diagnosis. The system comprises a data management module and a fault knowledge base module, the data management module carries out data preprocessing on test run simulation data of the liquid rocket engine, and then all the data are stored in a database in a unified mode. The fault knowledge base module constructs and maintains a model fault tree and fault modes and fault cases on the fault tree, and associates the test run simulation data with the fault modes and the fault cases. According to the method, data support is provided for engine fault diagnosis, and potential faults can be identified and processed more accurately and more quickly.

The invention belongs to the technical field of osteoarthritis management, and particularly relates to an osteoarthritis management system based on artificial intelligence and a computer storage medium. The invention provides an osteoarthritis management system based on artificial intelligence. The system integrates an LLM-DL hybrid architecture, a multi-specialist module cooperation framework and an RAG knowledge base system, and can cover a complete clinical process from patient preliminary diagnosis to treatment scheme formulation. The system can comprehensively and accurately evaluate the disease state of a patient, provides personalized disease progress prediction, not only predicts 2-year and 4-year functional results and iconography progress, but also can identify specific risk factors of the patient, and provides a basis for accurate intervention. Through the design of the whole system, the clinical working efficiency and the prediction accuracy are remarkably improved, the dependence on specialist doctors and high-end equipment is reduced, high-quality KOA management can also be implemented in resource limited areas, and therefore the overall medical resource requirement is effectively reduced.

The application discloses a kind of intelligent diagnosis system of diabetic eye disease based on multi-model cluster and cause-effect enhanced LLM cooperation, and it is related to artificial intelligence medical auxiliary diagnosis technical field.The system includes modular detection cluster layer, microaneurysm, bleeding point, exudate, neovascular detection is carried out to input fundus image simultaneously, and the visual feature corresponding to diabetic eye disease is output;Medical text modal alignment layer, the diabetic eye disease visual feature output by modular detection cluster layer is converted into standardized medical description;Large language model diagnosis layer, according to the standardized medical description output by medical text modal alignment layer, combined with the construction of standardized medical description knowledge base system, the input fundus image is carried out diabetic eye disease diagnosis reasoning, and structured diagnostic report is generated.The system has the advantages of high overall detection accuracy, knowledge evolution, diagnosis interpretability, output standardization and the like.

The invention discloses a student portrait analysis and intervention measure design method and system based on generative artificial intelligence, and belongs to the crossing field of artificial intelligence and education technologies. The method comprises the following steps: obtaining a prediction result and feature contribution analysis of an interpretable machine learning model; constructing a hierarchical knowledge base system; the LoRA fine tuning technology is adopted to carry out domain adaptation on the large language model, and output consistency and professionality are ensured; establishing a student evaluation standard system of dynamic weights, and adaptively adjusting the evaluation weight of each dimension according to the grade of the student; designing a structured prompt template, and generating a special prompt for different analysis tasks; personalized student portraits are generated based on knowledge base retrieval and large model reasoning, and qualitative description and quantitative scoring are included; and generating targeted intervention measures in combination with the private experience knowledge base. According to the method, organic combination of quantitative analysis and qualitative description is realized, the generated student portrait is more comprehensive and accurate, and intervention suggestions are more professional and operable.

According to the turbine fault diagnosis system, a sensing acquisition unit is provided with a multi-type sensor array on a turbine shell, and vibration signals, temperature distribution and pressure fluctuation data are acquired in real time; the data processing unit performs abnormal value elimination and missing value compensation on the original signal, and outputs standardized time sequence data; a feature analysis unit extracts time-frequency domain mixed features from the standardized time sequence data to generate a feature vector set; and the decision output unit receives the feature vector set, and outputs a fault type and confidence through a pre-trained hybrid diagnosis model. The three core problems in traditional steam turbine fault diagnosis are effectively solved: firstly, the limitation of single signal analysis is broken through, and collaborative diagnosis of vibration, temperature and pressure parameters is realized; secondly, a self-adaptive feature extraction mechanism is adopted, so that the diagnosis reliability in a variable working condition environment is remarkably improved; thirdly, a dynamically updated knowledge base system is constructed, and autonomous accumulation and evolution of diagnosis experience are achieved.

The invention provides an artificial intelligence nursing risk assessment robot, a storage medium and computer equipment, and relates to the technical field of nursing risk assessment, and the robot comprises a data collection module which is used for collecting multi-dimensional state data of a user; the knowledge base system is used for performing nursing risk assessment according to the medical records of the user to obtain nursing risk point knowledge; the intelligent analysis engine is used for traversing nursing risk point knowledge based on the multi-dimensional state data of the user to carry out risk level analysis to obtain a risk assessment level, and the risk assessment level has a risk point knowledge tag and an intervention scheme tag; and the interactive interface is used for uploading the risk assessment level, the risk point knowledge tag and the intervention scheme tag to medical staff. The technical problems that in the prior art, nursing risk assessment often depends on data information of a single source, so that the accuracy and timeliness of assessment are insufficient, a nursing scheme is often based on a universal template, the individual difference of each patient is difficult to meet, and the nursing effect is poor are solved.

The application discloses a kind of intelligent diagnosis system of diabetic eye disease based on multi-model cluster and cause-effect enhanced LLM cooperation, and it is related to artificial intelligence medical auxiliary diagnosis technical field.The system includes modular detection cluster layer, microaneurysm, bleeding point, exudate, neovascular detection is carried out to input fundus image simultaneously, and the visual feature corresponding to diabetic eye disease is output;Medical text modal alignment layer, the diabetic eye disease visual feature output by modular detection cluster layer is converted into standardized medical description;Large language model diagnosis layer, according to the standardized medical description output by medical text modal alignment layer, combined with the construction of standardized medical description knowledge base system, the input fundus image is diagnosed and reasoned to diabetic eye disease, and structured diagnostic report is generated.The system has the advantages of high overall detection accuracy, knowledge evolution, diagnosis interpretability, output standardization and the like.

The application discloses an intelligent prompting method and system, a computer device and a storage medium. The method comprises the following steps: receiving a user-defined professional label and a knowledge bias label, and configuring a data collection path; collecting and analyzing multi-dimensional behavior data of the user on a personal computer in real time to generate structured knowledge items; starting a cleaning process during a computer idle period or at a fixed time every day, using a large model to judge the novelty and relevance of the knowledge, and storing the knowledge items determined by the judgment in a knowledge base; in the knowledge base, automatically obtaining vertical field frontier knowledge from the network based on the user label and generating an abstract through a large model, and periodically deleting low-frequency obsolete knowledge; capturing user input content in real time, and combining historical behavior data and the knowledge base to perform RAG retrieval to provide intelligent prompts. The embodiments of the application aim to solve the problems of the existing knowledge base system, such as static, insufficient user adaptability and low interaction efficiency.

The application discloses a kind of knowledge base management method, device, computer equipment and storage medium, the knowledge base management method includes steps: quality detection is carried out to knowledge data in knowledge database, obtains quality detection result;According to quality detection result, determine the target knowledge data needing to update;Get reference knowledge data, and calculate the knowledge difference degree between reference knowledge data and target knowledge data;According to the corresponding relationship of knowledge difference degree and data update strategy, determine target data update strategy, update target knowledge data.This method realizes the precision and automation of knowledge update by the dual mechanism of quality detection and difference degree analysis, effectively improves the reliability and efficiency of knowledge base management, is suitable for dynamic maintenance and intelligent optimization of knowledge base system in financial technology, medical health and other business fields, can provide effective knowledge support for decision support and intelligent service in complex scenarios.