166 results about "Web of knowledge" patented technology

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

According to the agent construction method and the multi-agent cooperation method provided by the invention, the agent parameter configuration file is acquired, and the agent basic information, the ECA rule, the behavior tree path index and the performance function parameter configuration are loaded from the agent parameter configuration file; the knowledge hypergraph is adopted to aggregate agent basic information, ECA rules, behavior tree path indexes and performance function parameter configuration, and a dynamic knowledge network with semantic association is obtained; and after a prompt word used for calling the large language model is dynamically generated based on the dynamic knowledge network, the large language model is driven to generate an executable code, and when the executable code passes verification, an agent corresponding to the agent parameter configuration file is obtained. And then, aiming at a received user instruction, performing task processing by adopting the constructed multi-agent. Therefore, the autonomous decision-making capability and the dynamic adaptability of the intelligent agent are enhanced, and higher flexibility and expansibility are achieved.

The invention provides a knowledge question and answer library agent construction method and system. Efficient knowledge management and question and answer are achieved through cooperation of multiple agents. According to the system, firstly, a multi-modal information extraction agent is constructed, and heterogeneous data such as texts, images and tables are converted into structured vectors and stored; meanwhile, the knowledge graph is dynamically constructed and continuously optimized by the self-adaptive knowledge graph construction agent, and a new relationship is derived through combination of symbolic logic and a graph neural network, so that an evolvable knowledge network is formed. In the question and answer stage, a query analysis agent deeply analyzes the intention of a user and generates sub-queries; retrieving the vector library and the knowledge graph in parallel by the retrieval enhancement generation agent; and the reasoning and synthesizing agent integrates multi-source information and generates an accurate answer with a complete source label through a large language model. Dynamic knowledge management, precise semantic analysis and system self-evolution are achieved, and the method is particularly suitable for professional field scenes needing high-reliability questions and answers.

The invention relates to the technical field of vocational education, in particular to a vocational ability training system based on an artificial intelligence technology, which comprises a user portrait modeling module for generating a dynamic personal ability map based on historical data and an ability evaluation model of a user; the knowledge graph engine is used for integrating industry capability standards, post demand data and a real-time updated vocational skill knowledge base to form a structured knowledge network; the AI training recommendation module is used for dynamically generating a personalized training path by adopting a reinforcement learning algorithm in combination with a user portrait and a knowledge graph; the digital human application module constructs a simulation working scene through virtual reality and NLP technologies, and supports a user to complete training through natural dialogue and operation; and the real-time feedback and correction module is used for analyzing user performance by using multi-modal data and generating instant guidance suggestions. The system has the following beneficial effects of training personalized depth improvement, learning resource and scene expansion, learning effect evaluation and feedback optimization, and technology fusion and interactive innovation.

A knowledge graph construction method based on active learning and incremental learning comprises the following steps: S1, preprocessing data from a plurality of heterogeneous data sources, and extracting entities, relationships and attributes to form an initial knowledge network; s2, vectorizing elements in the initial knowledge network by using a knowledge graph embedding model, and performing entity alignment based on vector similarity to obtain an initial knowledge graph; s3, screening out candidate knowledge triples with high uncertainty and / or high representativeness from the initial knowledge graph by adopting an active learning strategy, and obtaining user labeling information corresponding to the candidate knowledge triples; s4, performing iterative optimization on a knowledge extraction model and / or a knowledge graph embedding model according to the user labeling information; s5, new data are fused into the optimized knowledge graph in an incremental learning mode, and knowledge conflict detection and resolution are carried out in the fusion process; and S6, circularly executing the steps S3 to S5 until the knowledge graph meets a preset quality condition.

The invention discloses a large language model auxiliary optimization strategic decision-making system and a large language model auxiliary optimization strategic decision-making method. The system comprises six core modules. The dynamic knowledge fusion module constructs a three-layer distributed knowledge network, constructs an entity association weight matrix through a bidirectional Transform model based on an attention mechanism, and realizes knowledge dynamic association in combination with a time attenuation factor and a hybrid coding technology. The large language model module performs field fine tuning by adopting incremental pre-training and low-rank adaptation technologies, and introduces an exclusive word segmentation list to improve professional analysis precision. The full-process intelligent writing module covers submodules for report generation, revision and the like, and supports full-life-cycle management of reports. The strategic decision intelligent deduction module integrates scene impact factors, and realizes multi-scene deduction through reinforcement learning and Monte Carlo tree search. The interaction display module provides a visual interface, and the multi-mode interaction module realizes full task chain management. According to the invention, real-time knowledge support and intelligent deduction capability are provided for strategic decision making, and decision making efficiency and accuracy are improved.

The invention belongs to the technical field of artificial intelligence and educational informatization, and relates to a multi-mode knowledge graph enhanced intelligent teaching-assistant question-answering system. According to the method, the knowledge graph construction technology, the multi-modal content analysis technology and the large language model reasoning enhancement technology are comprehensively applied, and the semantic understanding, knowledge integration and reasoning generation capabilities of the intelligent teaching assisting system in an education and teaching scene are improved. The related technology comprises layout analysis of textbook documents, semantic description generation of image content, entity and relation extraction of text content, multi-modal knowledge graph construction and question and answer reasoning and natural language generation combined with the knowledge graph. Through cooperative application of the technologies, semantic interconnection can be carried out on various modal information such as texts, images and tables in the textbook, and a searchable and traceable textbook-level knowledge network is formed.

The invention discloses an industrial equipment fault inference system based on a knowledge graph, and the system comprises a knowledge graph construction module, a fault data collection module, an inference analysis module and a response processing module. The entity extraction unit extracts equipment components, fault types and maintenance record entities from an industrial equipment operation document, equipment manual unstructured data supplementation attributes are integrated, the attributes and association weights are marked, and the relationship construction unit establishes a fault causal relationship between the entities and a component association relationship to form a multi-level knowledge network; a knowledge verification unit verifies entity attribute consistency and relation rationality, a dynamic updating unit receives data updating nodes and relation strength of each unit and receives feedback data optimization weights, and in a fault data acquisition module, a real-time monitoring unit acquires operation parameters and state signals and associates equipment identifiers.

The invention discloses a shield risk tracing method based on a knowledge graph, and belongs to the technical field of shield risk analysis. Determining an entity set and a relationship set, and constructing a mode layer of the knowledge graph; performing data preprocessing on the document data to obtain a processed text, extracting a knowledge triple meeting requirements, and outputting a structured knowledge triple according to a predetermined format; performing entity alignment to obtain a normalized knowledge triad to construct a knowledge graph; calculating the membership degree of the node on the basis of the knowledge graph, and obtaining the posterior probability of the node in combination with the posterior probability of the node; and inferring a potential risk propagation path and relation strength based on the posterior probability of the node in the knowledge graph, and realizing dynamic analysis and prediction of the knowledge network. According to the method, structural association of risk factors is realized through the knowledge graph, and the uncertainty is quantified by combining fuzzy Bayesian reasoning, so that the problem that a complex causal relationship and information fuzziness are difficult to process by a traditional method is solved.

The invention relates to the technical field of artificial intelligence, and particularly provides an intelligent proposition method, system and device for simulating expert propositions and a storage medium, and the method comprises the steps: constructing a structured knowledge network fusing knowledge points, cognitive levels and proposition specifications; establishing a material vector database associated with the knowledge point system; according to the target knowledge point, proposition constraint information is extracted from the knowledge network, related materials are retrieved from a vector library, and the related materials are combined into a generative prompt to be input into a large language model to generate a test question first draft; inputting the test question first draft into at least one large language model serving as a simulation answering agent, evaluating the quality of the test questions by analyzing the answering process and result, and outputting the test questions reaching the standard. According to the method, the proposition efficiency and consistency can be remarkably improved, absolute dependence on expert experience is reduced, and automatic output of high-quality test questions is achieved.

The invention discloses an IETM intelligent retrieval method based on multi-modal fusion and a knowledge graph, and the method comprises the following steps: data layer construction: integrating multi-dimensional data, and establishing a multi-modal database; knowledge layer modeling: defining an IETM domain ontology through an ontology modeling tool, constructing a knowledge network, cleaning data and unifying entity annotations by using a rule engine, and forming an RDF triple knowledge graph which can be understood by a machine; and application layer retrieval: based on hierarchical semantic fusion of multi-modal data and a three-level progressive retrieval mechanism, carrying out deep analysis and accurate retrieval on the query intention of the user. According to the method, semantic enhancement is carried out on the text, three-level feature extraction is carried out on the image, and spatio-temporal joint coding is carried out on the video, so that deep semantic alignment and cross-modal semantic consistency improvement of the multi-modal data are realized, and the quality and effect of multi-modal data fusion are effectively improved.

The invention provides an intelligent document understanding method and system combining natural language processing and deep learning, and relates to the technical field of natural language processing and information management. Fusing the semantic pre-annotation result and the original text feature of the document, inputting the fused semantic pre-annotation result and the original text feature of the document into a cross-modal semantic enhancement model to obtain a document enhanced semantic representation, performing bidirectional semantic interaction with a dynamic business knowledge network based on the document enhanced semantic representation, generating an associated interaction result, and constructing a structured semantic asset; finally, the structured semantic assets are input into an intelligent document application engine, semantic feedback data are collected to optimize model parameters, document understanding accuracy and practicability can be improved, and diversified business requirements are met.

The invention belongs to the technical field of intelligent electric charge questions and answers, and particularly relates to a hierarchical knowledge network construction and retrieval method for intelligent electric charge questions and answers. The invention provides an iterative adaptive enhanced RAG method, based on a hierarchical knowledge network, information retrieved by a first round of problems is used as a knowledge carrier, newly retrieved information is continuously updated into initialized information in subsequent multi-round iterations, information is adaptively collected from the perspective of knowledge increase, and the information retrieval efficiency is improved. New knowledge and collected knowledge are flexibly integrated, and the problem that information interaction among different retrieval steps is insufficient is solved; in addition, a self-adaptive exploration stopping strategy is formulated, uncertain active retrieval is replaced, time deviation of active retrieval prediction is effectively avoided, and continuous knowledge increase is ensured.

The invention discloses a dialogue type AI intelligent outbound service automatic control method combined with a knowledge graph, and the method comprises the following steps: S1, constructing a multi-dimensional knowledge graph: integrating a domain ontology, a user portrait and a dialogue context, and forming a structured knowledge network; s2, dynamically generating a dialogue strategy: fusing rule reasoning and a machine learning model, dynamically selecting a dialogue strategy path based on a user response probability, and screening an optimal strategy through conflict detection; s3, real-time interaction control: realizing multi-round management through voice recognition, intention classification and dialogue state tracking, and triggering an emergency strategy in combination with sentiment analysis and silence detection; and S4, dynamically optimizing the system: updating knowledge graph entities / relationships by utilizing incremental learning, and optimizing strategy parameters and business rules in real time through reinforcement learning and association analysis. According to the method, the knowledge integration efficiency is improved, and the CRM data association response time is from gt; the result is reduced to 1t in 2 seconds; 200 milliseconds; the high-net-value customer conversion rate is improved by 42%, and the manual intervention demand is reduced by 70%.

The invention relates to the technical field of textbook construction and optimization, in particular to an automatic textbook construction and optimization method and device.The method comprises the following steps that original resources of multiple types of textbooks are collected, and core knowledge elements in the original resources are recognized; constructing a structured mapping relationship between the knowledge elements and the teaching resources based on the core knowledge elements; on the basis of the structured mapping relationship, constructing a knowledge graph covering multidisciplinary knowledge points and a preposition dependency relationship, and automatically identifying knowledge faults and complementing missing association so as to form a knowledge network with self-evolution ability; based on the knowledge network, utilizing a hybrid generation model to cooperatively work, and quickly generating a structured textbook framework containing knowledge veins, teaching cases and exercise resources; learning condition data including classroom behaviors, learning feedback and knowledge mastering conditions are obtained, and a quantity and chemical condition report is obtained after data processing; and updating data based on the quantitative chemical situation report and the knowledge graph.

The invention discloses an equipment full-life-cycle intelligent operation and maintenance management method and system based on AI deep learning. The method comprises the following steps: collecting multi-source heterogeneous data generated in each stage in a full life cycle of equipment, deeply exploring a knowledge network for the heterogeneous data through semantics, converging and fusing associated equipment entities, constructing a triple knowledge network, and obtaining an equipment knowledge graph network; constructing an equipment digital twinborn body corresponding to the equipment entity in a high-fidelity manner through the digital twinborn virtual mapping and the equipment experiment sand table; carrying out equipment operation simulation deduction according to the real-time monitoring data to obtain digital twin simulation deduction data; machine learning deep intelligent analysis is carried out according to the digital twinborn simulation deduction data, the equipment health state is evaluated and predicted, and equipment degradation track life prediction information is obtained; and querying equipment knowledge graph information according to an equipment knowledge graph network, calibrating digital twin simulation deduction data, and reasoning and generating an equipment dynamic maintenance decision by referring to equipment degradation track life prediction information.

The invention relates to a multi-source information knowledge fusion and intelligent retrieval method and system, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the unified knowledge modeling of the multi-source heterogeneous data, and obtaining a unified knowledge framework; and based on the unified knowledge framework, calling a large language model to perform structured preprocessing on the multi-source heterogeneous data to generate a structured knowledge triple. And performing structured processing on the structured knowledge triple through the attribute graph, and performing vectorization storage on each entity associated data through the embedded model to obtain a fused knowledge body. And receiving a user query request, and in response to the user query request, performing deep analysis on the user input data to obtain a user query intention and query elements. According to the method, the condition graph is constructed according to the query intention and the query elements of the user, and the condition graph is mapped into the knowledge network for entity positioning and condition graph matching to generate the structured retrieval result, so that the network overhead and scheduling delay are reduced, and the retrieval response speed and accuracy are improved.

Implementations include a computer-implemented method comprising: obtaining data representing observed conditions in an enterprise network, each observed condition being associated with at least one cybersecurity issue, a cybersecurity issue comprising one of (i) a vulnerability comprising an instance of a vulnerable condition or (ii) a weakness that is likely to cause a vulnerability to occur; using a plurality of exploitation prediction models to determine probabilities of exploitation of the cybersecurity issues associated with the observed conditions in the enterprise network, wherein the plurality of exploitation prediction models are trained using a knowledge mesh generated using data from cybersecurity repositories; assigning a priority ranking to each of the observed conditions in the enterprise network based on the respective probabilities of exploitation for the cybersecurity issues associated with the observed conditions; and performing one or more actions to mitigate the observed conditions in the enterprise network based on the priority rankings.

The invention relates to the technical field of knowledge maps, in particular to a power grid production operation plan intelligent generation and correction method and system based on artificial intelligence, and the method comprises the following steps: constructing an equipment entity, a personnel entity and a power grid production operation plan task entity, and building a power grid multi-dimensional constraint entity knowledge map. According to the method, equipment, personnel and tasks are explicitly defined as entities, meanwhile, abstract constraint conditions such as safety regulations, equipment intervals and personnel qualification are modeled as entity nodes in a knowledge network, a power grid operation knowledge system containing a multi-dimensional constraint relation is constructed, and after a preliminary plan is generated, by means of the activation and satisfaction relation between the entities in a graph, the power grid operation knowledge system with the multi-dimensional constraint relation is established. Triggered but unsatisfied constraint items can be retrieved and positioned, and rapid and accurate identification and traceability of plan conflicts are realized.

The invention provides an educational policy retrieval method and system fusing a policy corpus and a large model, and relates to the technical field of artificial intelligence. Firstly, through a dynamic knowledge network construction module, an educational policy knowledge graph capable of being dynamically updated is constructed from four dimensions of time, space, effectiveness and a subject, version evolution, regional application and effectiveness relationships among policies are accurately modeled, and a structured basis is provided. And secondly, based on the map, a collaborative inference engine analyzes the complex intention of natural language query of the user by using a generative artificial intelligence model subjected to field fine tuning, and ensures that interpretation answers are accurate and compliant through a controllable generation technology under knowledge constraints. And finally, the feedback optimization system enables the system to be continuously adaptive and evolved by collecting multi-dimensional user behaviors and dominant feedback, mining high-frequency contradictory points in policy execution and driving bidirectional optimization of a knowledge graph and a generative model, and dynamic reasoning and closed-loop optimization of educational policy interpretation are realized.

The invention belongs to the technical field of computers, particularly relates to an enterprise consultation service system based on big data, and aims to solve the problems of data islands, decision lagging and insufficient predictive ability in traditional enterprise consultation service. The system comprises a global data access module, a knowledge graph construction module, a dynamic simulation engine, an intelligent consultation generation module and an interactive feedback optimization module, a time sequence evolution knowledge network is constructed by fusing internal and external multi-source heterogeneous data, multi-scene parallel simulation and intelligent suggestion generation are realized, and closed-loop feedback learning is supported. According to the scheme, the accuracy, foresight and response efficiency of enterprise strategic decision can be improved, and large-scale concurrent service and real-time dynamic optimization are supported.

The invention relates to the technical field of knowledge maps, in particular to a personalized question and answer recommendation method based on a knowledge map and two-layer attention, and the method comprises the steps: constructing and storing a structured knowledge network containing a plurality of entities and association relationships thereof, analyzing an input natural language query, and recognizing query elements; based on semantic comprehension, calculating a semantic matching degree between the query elements and candidate answers extracted from the structured knowledge network, and further identifying a deep semantic intention of the natural language query; and based on the identified query elements and semantic intentions, in combination with the semantic matching degree, performing retrieval in the structured knowledge network to obtain an initial result, performing structured recombination and natural language generation on the initial result, and outputting a readable answer. The method has higher question and answer matching precision and query generation quality in a data sparse and relation complex scene, and is widely applied to the fields of education question and answer, intelligent recommendation, knowledge retrieval and the like.

The invention discloses a transformer substation work ticket knowledge graph construction method based on a large language model, and belongs to the field of energy power and artificial intelligence. The method comprises the following steps: based on standardized electrical drawing annotation data, generating an independent knowledge graph through data recombination preprocessing, rough knowledge graph construction and attribute supplementary reasoning; all the independent knowledge maps are divided into a framework layer map and a detail layer map according to voltage grades, and full map fusion is completed by adopting a hierarchical fusion strategy; carrying out breakpoint detection by using an isolated point detection algorithm based on degree centrality, carrying out connectivity analysis through depth-first search, and re-fusing multiple non-connected sub-graphs; and screening abnormal nodes based on an electrical connection rule, inputting the abnormal nodes into a large language model to automatically supplement Agents to generate correction suggestions, and completing map optimization. Through cooperation of the large model Agent and multiple algorithms, the electrical data structuring efficiency and accuracy are improved, and knowledge network support is provided for automatic operation and fault diagnosis of the transformer substation.

The invention relates to the technical field of code analysis, in particular to a code business logic vulnerability static analysis method based on a large language model, which comprises the following steps: performing semantic feature extraction based on a source code file set and a business requirement document to obtain a code business semantic feature map; constructing a business logic constraint knowledge network based on the code business semantic feature map, and generating a business scene simulation test case set based on the business logic constraint knowledge network; and executing the service scene simulation test case set, obtaining corresponding function runtime state data, and carrying out state transition trajectory analysis based on the function runtime state data to obtain an actual service state circulation sequence. By executing the test case and capturing the state data when the function runs, the state change of the program can be monitored in real time, an objective basis is provided for subsequent state transition trajectory analysis, and the reliability of an analysis result is ensured.

The invention relates to the cross technical field of computer science and technology and hydraulic engineering, and particularly discloses a hydraulic engineering large language model response accuracy and context understanding ability assessment method, which comprises the following steps: constructing a dynamically updated hydraulic engineering field knowledge base, accessing hydrological monitoring data in real time, analyzing engineering drawings and tracking industry standard update, and evaluating the response accuracy and context understanding ability of a hydraulic engineering large language model. Forming a space-time correlation knowledge network; executing a multi-dimensional test, including response verification based on a terminology library, multi-modal consistency check across texts and drawings, and continuous decision chain pressure test for simulating a dam break scene; errors are positioned to standard specific terms through a semantic matching algorithm, and historical cases are associated to generate a fine-grained report; generating a confrontation training data set; according to the method, the problems of knowledge lag, insufficient multi-modal fusion, extensive error positioning and the like in large language model evaluation in the water conservancy field are solved, and the reliability and decision support capability of the model in a complex engineering scene are improved.

The invention relates to the technical field of information engineering knowledge graph construction, in particular to a supervision knowledge graph dynamic modeling method based on multi-source heterogeneous data fusion, and the method comprises the steps: designing a four-dimensional knowledge graph modeling framework, and combining the entity alignment of a graph neural network, the relation extraction of remote supervision and the attribute processing of weight fusion. A dynamic evolution and cross-source consistent knowledge network is constructed, virtual features are generated through a virtual feature compensation mechanism (historical event retrieval and multi-dimensional feature weighted fusion) for a multi-modal data missing scene and mapped to a graph, data are effectively filled, the problem of incomplete knowledge coverage in a complex engineering scene is solved, a compliance entropy evaluation model is introduced, and the knowledge coverage is evaluated. The compliance state of the knowledge graph is quantitatively supervised from the three dimensions of rule coverage rate, data consistency and tracing integrity, and the hysteresis and subjectivity limitation of traditional manual compliance check is broken through.

The invention provides a method for constructing a knowledge graph based on complex structural parts. According to the method, feature parameters are fused in a hidden layer through a neural network model, and a high-precision classification result is output to define a knowledge graph category; defining and standardizing a process knowledge concept from a data source to form a mode layer of a tree structure; extracting entities from technical documents and historical regulations in combination with a bottom-up method to construct a data layer; multi-dimensional mapping of a mode layer and a data layer is realized through a Neo4j graph database, and a visual knowledge graph containing a process knowledge relationship is generated; and finally, generating a process route of the complex structural part by using the semantic association network in the map. According to the method, the knowledge graph is constructed on the basis of the complex structural parts, scattered technical documents, expert experience and historical data are integrated, a unified knowledge network is formed, data islands are effectively broken, the structured level, relevance and reuse efficiency of complex structural part process knowledge are enhanced, and the machining efficiency of the complex structural parts is effectively improved.

The invention relates to the technical field of data intelligent matching, and discloses an industrial data matching method and system based on artificial intelligence. The method comprises the following steps: receiving multi-source industrial data, and dynamically constructing an associated knowledge network; analyzing a natural language query intention of a user, and injecting the natural language query intention into the knowledge network as a constraint condition to generate an intentional knowledge network; interactive exploration is carried out on the network, paths and weights are dynamically adjusted according to feedback, and an optimized path set is formed; decision optimization is carried out in combination with a service rule, and a multi-level matching scheme is generated; and directionally retrieving and fusing data according to the scheme, and outputting a matching result. According to the method, through intention-driven network focusing and interactive adaptive exploration, the accuracy and the intelligent degree of complex industry data matching are improved.