103 results about "Knowledge element" patented technology

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention discloses a method for constructing a cross-modal knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the collection, structural analysis, modal recognition and classification, cleaning and standardization processing, so as to form structured multi-modal data; extracting entities and relationships of the identified modals from the structured multi-modal data, and summarizing the entities and relationships to form a multi-modal knowledge element set; mapping different modal entities in the multi-modal knowledge element set to a unified semantic space, and generating a unified entity relationship set through semantic matching, alignment and fusion; and normalizing the data into knowledge triples, and storing and organizing the knowledge triples through a graph database to form a cross-modal knowledge graph. According to the method, the problems of difficulty in multi-modal heterogeneous information alignment and difficulty in entity relationship extraction can be relieved, semantic association is enhanced, and knowledge graph integrity and multi-scene adaptability are improved.

The invention discloses a document analysis and query method based on a knowledge graph, and the method comprises the steps: carrying out the part-of-speech tagging of a received to-be-analyzed document, and obtaining a part-of-speech tagging result; extracting knowledge element information from the part-of-speech tagging result based on a preset power grid domain ontology knowledge base, and constructing an initial knowledge graph based on the knowledge element information; combining nodes in the initial knowledge graph to obtain a fused knowledge graph; constructing a mapping table according to the fused knowledge graph, and generating a candidate query template set based on the mapping table; receiving a natural language query statement input by a user, selecting a target query template from the candidate query template set based on the natural language query statement, and generating a target query statement; querying from the fused knowledge graph by using the target query statement, and outputting a query result; according to the method, the accuracy and comprehensiveness of information analysis can be effectively improved, the query intention of the user can be accurately understood, and the accurate query and analysis requirements of professionals on project documents are met.

The invention relates to the technical field of process scheme generation, and discloses an intelligent scheme reasoning generation method based on a process knowledge graph, and the method comprises the steps: firstly collecting multi-source process data, constructing a knowledge element extraction system, and outputting standardized knowledge entries through semantic analysis; establishing a process knowledge graph, and when a scheme reasoning demand is detected, positioning a target knowledge node through a logic association algorithm and generating an association relationship label; meanwhile, constructing a historical scheme case information base, and dynamically recording reasoning to generate a record; establishing a multi-dimensional weight configuration model, calculating a scheme matching degree score, and generating a recommendation priority sequence; solving an optimal scheme reasoning result by adopting an improved ant colony algorithm; and finally, outputting a result through the interactive verification platform, tracking and feeding back. The method improves the efficiency and quality of process scheme generation, and is suitable for process scheme formulation in industrial production.

The invention relates to the technical field of artificial intelligence, in particular to a self-organizing knowledge base construction method, system and device based on a multi-agent system and a storage medium, and the method comprises the steps: S1, monitoring a dialogue information flow, calculating the self-reply confidence coefficient of an agent, and if the self-reply confidence coefficient exceeds a preset dynamic threshold value, judging that a potential knowledge value exists and triggering an extraction request; s2, in response to the request, performing semantic coding on a dialogue fragment, extracting knowledge elements, and generating a structured knowledge unit by utilizing graph neural network modeling and relation calibration; s3, through a pre-training semantic coding model, mapping the multi-dimensional semantic coding model into a multi-dimensional semantic vector; s4, carrying out hierarchical clustering comparison and combination, and if a threshold value is exceeded, establishing a new classification and adjusting a knowledge base index; and S5, knowledge fingerprints are generated and compared, and fusion updating is carried out based on the reputation scoring model when semantics conflict or redundancy occurs. According to the method, unstructured dialogue high-precision knowledge extraction is realized, so that the knowledge base structure is dynamically self-organized according to semantics, multi-source knowledge is coordinated, and the knowledge management automation level and quality are improved.

The invention provides a knowledge-enhanced medical illusion static detection and correction method and system, and relates to the technical field of medical illusion detection.The method comprises the steps that first feature information is obtained; determining a causal association strength coefficient; constructing a dynamic knowledge element network; obtaining first evidence information according to the first feature information and the dynamic knowledge element network; obtaining a first consistency coefficient; determining a timeliness coefficient and a confidence coefficient of the first evidence information; processing the causal association strength coefficient, the first consistency coefficient, the timeliness coefficient and the confidence coefficient through a trained medical illusion detection model to generate a medical illusion detection probability; determining whether the first feature information needs to be corrected; and when it is determined that the first feature information needs to be corrected, correcting the first feature information through a trained medical illusion correction model. According to the invention, the accuracy of medical illusion static detection and correction can be improved.

The invention provides an intelligent scheduling method and system applied to cable operation and maintenance, and the method comprises the steps: firstly deconstructing migration knowledge elements in a cable operation and maintenance migration knowledge base, dividing resource adaptive, process adaptive and risk adaptive knowledge elements, and generating a knowledge element classification set; constructing an operation and maintenance scene dynamic portrait of the to-be-scheduled cable, and integrating the operation characteristics, the operation and maintenance task requirements and the historical operation and maintenance performance data to generate a three-dimensional scene portrait; calculating an adaptation coefficient of each migration knowledge element and the portrait, and screening out a target knowledge element combination; constructing an initial operation and maintenance scheduling scheme framework based on the target knowledge element combination, and integrating various adaptation rules; and updating and adjusting a scheme framework through multi-turn portrait iteration, optimizing resource configuration, a step sequence and an abnormity coping plan, obtaining a final scheme, generating an operation and maintenance scheduling instruction, and sending the operation and maintenance scheduling instruction to an operation and maintenance execution terminal. According to the invention, intelligentization, precision and high efficiency of cable operation and maintenance scheduling are realized.

The invention provides a knowledge graph-driven medical data agent collaborative governance method, system and device and a medium, and belongs to the technical field of medical data governance, and the method comprises the following steps: constructing a knowledge graph containing a quintuple, and defining mapping relationships among medical data entities, attributes and standards; extracting multi-modal medical data knowledge elements through a structured / unstructured engine, and converting the multi-modal medical data knowledge elements into a quintuple for storage; executing table / field / value domain mapping based on the knowledge graph to generate a data mapping result; binding the quality control rules and the medical specifications to form a quality control quintuple; arranging the task into a directed acyclic execution sequence through an agent group cooperation task; and dynamically updating the knowledge graph and performing version management. Medical data full-link governance, knowledge graph unification heterogeneous data semantics, intelligent agent cooperation automatic processing complex tasks, update guarantee knowledge timeliness and quality control rule binding guarantee data quality are achieved, and medical data governance efficiency and accuracy are improved.

The invention belongs to the technical field of electric power intelligent operation and maintenance, and relates to an electric power equipment fault prediction knowledge graph updating method which comprises the following steps: continuously receiving multivariate heterogeneous information from an electric power system operation environment, and applying at least one technology of natural language processing, time sequence analysis and statistical pattern recognition. Extracting new knowledge elements to form a to-be-verified new knowledge set, performing semantic consistency verification on the to-be-verified new knowledge set and an existing knowledge graph, detecting logic conflicts between the to-be-verified new knowledge set and existing knowledge triples and rules through logical reasoning, and obtaining a to-be-verified new knowledge graph based on a context environment where the logic conflicts occur. A conflict resolution strategy is automatically selected and executed by evaluating evidence reliability of new and old knowledge, and an optimized knowledge rule is generated, so that an existing knowledge graph is dynamically updated, the problems that an updating mechanism of the existing knowledge graph is lagged and evolution of context-related knowledge is difficult to process are effectively solved, and the reliability of the knowledge graph is improved. And the knowledge graph is pushed to be converted from static storage to dynamic evolution intelligent agents.

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 relates to the technical field of virtual reality simulation, in particular to a virtual reality immersive practical training content generation method, which comprises the following steps of: constructing a domain knowledge graph: extracting atomic knowledge elements representing operation actions, equipment entities, state parameters and safety rules from a standard document and a case library of a target training domain, establishing a multi-dimensional logic relationship including a causal relationship, a time sequence relationship, a hierarchical relationship and a probability relationship among the atomic knowledge elements, and forming and storing a domain knowledge graph; acquiring trainee data in real time; constructing and updating a student state model; a dynamic content decision step; and generating and injecting nonlinear practical training content. By collecting student data in real time and matching the student data with the knowledge graph, the system can provide accurate feedback for the student after each practical training, and help the student find and overcome own knowledge blind spots.

The invention relates to the technical field of computers, and discloses a learning difficulty intelligent adjusting device and method, and the method comprises the steps: extracting key knowledge elements from pre-obtained user learning data, and constructing a learning graph; according to the learning map, analyzing the knowledge mastering degree and the learning ability of the user to obtain a user ability evaluation result; generating corresponding learning content in combination with the user capability evaluation result and the node association relationship in the learning map; in the learning process of the user, the updating condition of the knowledge elements and the change condition of the learning ability of the user are analyzed, and the learning graph is dynamically updated to obtain an updated learning graph; according to the updated learning map, the difficulty of the learning content is adjusted in real time, updated learning content is obtained, and intelligent adjustment of the learning difficulty is achieved; the learning content difficulty is intelligently adjusted, the requirements of different users in different learning stages are met, and the learning effect and the knowledge mastering degree are effectively improved.

The invention provides a clinical decision knowledge graph construction method and system, and the method comprises the steps: extracting standardized entities corresponding to diseases, symptoms and diagnosis and treatment elements from medical knowledge data; constructing a clinical concept knowledge graph for representing a medical concept logic relationship and a causal relationship according to the semantic association relationship and the causal dependency relationship among the standardized entities; a diagnosis event, an examination event and a treatment event related to the patient are extracted, link evidences among the events are determined based on the event chain relation among the events, and a clinical event knowledge graph used for representing the disease course evolution process of the patient is constructed according to all the link evidences; and performing knowledge element fusion based on an entity association relationship between the clinical concept knowledge graph and the clinical event knowledge graph, and generating a target knowledge graph for clinical decision analysis. By adopting the scheme of the invention, the cross-map fusion of the static medical concept knowledge and the dynamic disease course event chain relationship can be realized, and the clinical decision knowledge structure with the reasoning ability can be constructed.

The invention provides an automatic construction and semantic understanding enhancement method and device for a large model knowledge base. Internet multi-type data sources are integrated through multi-source data collection, and text data related to the large model application field is obtained; a knowledge extraction and fusion technology is utilized to extract key knowledge elements from the collected data and eliminate redundant contradictory information to form a unified knowledge representation system; based on the unified knowledge representation system, automatically constructing a knowledge base with rich semantic association according to a predefined structure mode, and introducing a data quality evaluation mechanism to guarantee the reliability of the knowledge base; and based on the constructed knowledge base, the deep semantic analysis technology is used to enhance the semantic understanding ability of the large model, including semantic coding, knowledge graph embedding, semantic reasoning and knowledge completion. According to the scheme, efficient, accurate and automatic construction of the knowledge base can be achieved, the semantic understanding ability of a large model is remarkably improved, the large model can understand and apply knowledge more accurately, and powerful support is provided for artificial intelligence application.

The invention relates to the technical field of data generation, and discloses a training content generation method and system based on a deep learning model and a storage medium, and the method comprises the steps: obtaining basic content data, and extracting text, table and format content to generate structured data; identifying a content structure and extracting training knowledge elements through deep learning model semantic analysis, and constructing a knowledge graph in combination with an association relationship between the elements; calculating the occurrence frequency, the content chapter and the core degree of the training knowledge elements to obtain a difficulty coefficient; obtaining training parameters, calculating a knowledge coverage coefficient and a knowledge depth coefficient, and generating initial content in combination with the first text framework and the large language model; and performing full-content verification and correction through the second text framework to obtain training content. According to the method, the training content is automatically generated, the problems of low efficiency, high cost, unstable quality and the like of manual question setting are solved, and the scientificity and pertinence of the training content are improved.

The invention discloses a natural resource data management method and device, equipment and a storage medium, and relates to the technical field of data management, and the method comprises the steps: collecting original natural resource data, and initializing a natural resource data set; performing structured processing operation on the natural resource data set, constructing a storage structure for storing and managing the data set, and forming a structured natural resource data set; performing element extraction on the structured natural resource data set, performing fusion processing on key knowledge elements obtained by extraction, and generating a target retrieval database according to a processing result; according to demand information input by a user, knowledge information matched with the demand information is retrieved and determined from the target retrieval database, and a corresponding visual scene is generated and displayed to the user based on the knowledge information. According to the method provided by the invention, the efficiency and convenience of acquiring and using the natural resource data by the user can be improved, and the requirements on visual display and analysis of the data in a natural resource management service are met.

The invention discloses an autonomous evolution type 5G protocol fuzzy test system based on a large language model, and the system comprises a protocol extraction unit which employs the large language model to analyze a 5G protocol specification document, and extracts structured protocol knowledge elements containing a message format, state machine logic and parameter constraints; the vulnerability analysis unit generates a potential vulnerability description list through big language model reasoning based on the knowledge elements; the test strategy generation unit drives a large language model to generate a targeted test case based on protocol knowledge, vulnerability description and historical test knowledge in the dynamic test knowledge base; the test execution unit executes the use case and captures a result; and feeding back an analysis result of the internalization unit, extracting a causal rule, generating new test knowledge and feeding back the new test knowledge to the knowledge base. According to the system disclosed by the invention, test targets and resources can be autonomously scheduled according to continuous updating of the knowledge base, so that the test process has an iterative optimization capability, and full-process automation and intelligentization of 5G protocol fuzzy testing are realized.

The invention relates to the technical field of knowledge graph construction, and provides a knowledge graph construction method and system based on a traditional Chinese medicine classical prescription, and the method comprises the steps: obtaining multi-source heterogeneous data composed of a digital text of a classical ancient book, a teaching material, a traditional Chinese medicine authority dictionary, an academic journal literature and structured clinical data; the standard text data is subjected to mixed strategy knowledge extraction, discrete knowledge elements such as entities, relations and attributes are separated out, and the discrete knowledge elements form an original fact unit of a knowledge graph; the extracted discrete knowledge elements are subjected to ontology modeling and formalized representation, and a multi-dimensional semantic network with the prescription-syndrome-disease-machine as the core is constructed; and performing semantic similarity comprehensive judgment by calculating the character string similarity of the entity names and combining context features of the entity names. The system comprises a heterogeneous data processing module, an entity recognition module, a semantic network construction module and a similarity judgment module. Structured organization, deep association and intelligent utilization of classical prescription knowledge are realized.

The invention discloses an academic publication knowledge service intelligent management system and method based on digital twinning, and relates to the field of knowledge service and technology, and the system comprises a data storage module, a dimension weight quantification module, a weight distribution difference analysis module, an effective trigger distribution model construction module and a response priority analysis module. According to the method, the initial weight of each dimension is quantified through six dimensions including multi-dimensional intelligent retrieval, covering knowledge elements, semantic understanding and user portraits, the weight can be dynamically allocated according to user retrieval content features such as whether keywords contain limits or not, new and old attributes of the users and keyword expansion capacity, the weight is updated based on subjective operation contents of the users, and the user experience is improved. The retrieval strategy is deeply matched with the individual requirements of the user, the problem of low retrieval efficiency is greatly reduced, and the operation cost of repeatedly modifying retrieval conditions by the user is reduced.

The invention discloses an intelligent decision-making method based on a road material design knowledge graph system, relates to the field of intelligent design of traffic infrastructure materials, and solves the technical problems that design and optimization of the traffic infrastructure materials depend on artificial experience and a large number of tests for a long time, and the design effect is poor. The method comprises the following steps: extracting knowledge elements from material related data and carrying out standardization processing; constructing a knowledge graph by taking the knowledge elements as nodes; embedding the trained prediction model in the knowledge graph as an executable model node; constructing a combined reasoning mechanism in the knowledge graph; constructing a multi-target optimization model according to a material design target set by a user, and solving according to a performance prediction result generated by a plurality of executable model nodes in the knowledge graph; outputting an interpretable material design decision result based on the solving result; according to the method provided by the invention, the number of experiments and the design cost are greatly reduced, and the efficiency, accuracy and reliability of material design are improved.

The invention discloses a cultural transmission knowledge graph construction method based on game video content analysis, and the method combines deep learning, natural language processing and multi-modal data fusion technologies, and is dedicated to deep mining and structured representation of cultural knowledge elements and transmission association thereof from mass and multi-modal game video data. And constructing a knowledge graph serving cultural transmission research and practice. The method not only provides an important theoretical tool for understanding a new mechanism of cultural transmission in the digital era, but also opens up an innovative technical path for actively, accurately and efficiently bearing and transmitting the cultural value by using game media, and has remarkable theoretical value and application potential.

The invention relates to the technical field of data processing, and particularly provides a multi-source heterogeneous operation and maintenance data analysis method and system based on an industry subject portrait, and the method comprises the steps: collecting industry subject data from a multi-heterogeneous data source, and carrying out the cleaning and standardization processing to form standard data; based on this, constructing an industry knowledge graph comprising industry subjects, knowledge elements and association relationships thereof; meanwhile, in combination with the standard data and the knowledge graph, the quantitative score of the industry subject on the multi-dimensional portrait is calculated and mapped into a portrait label; and realizing anomaly detection, root cause positioning, prediction and early warning or multi-dimensional dynamic analysis by utilizing the knowledge graph and the main body portrait. And finally, generating an industry health state and risk situation conclusion based on an analysis result. According to the invention, the efficiency and reliability of operation, maintenance and management are obviously improved.

The invention discloses a fair competition review case library construction and expansion method based on knowledge modeling and large language model generation, and the method comprises a case library construction stage and an intelligent expansion stage. Collecting data from the heterogeneous data source, analyzing the data into structured case data through an information extraction technology, and finally constructing case knowledge elements for describing case types, subjects and law article application relationships; in the expansion stage, based on existing case knowledge elements, a'case-legal provision 'relation model with variable scene parameters is constructed, diversified candidate case texts are generated by utilizing a large language model, and the candidate case texts are stored after being subjected to validity and logic consistency rechecking, so that efficient and high-quality automatic expansion of a case library is realized.

The invention provides a multi-source fusion knowledge point labeling method and device based on hypergraph knowledge enhancement, and relates to the technical field of knowledge point labeling, and the method comprises the steps: inputting a knowledge point text set into a large language model to obtain a knowledge element system matrix and a knowledge point incidence matrix, constructing a hypergraph matrix according to the knowledge point text set, the knowledge element system matrix and the knowledge point incidence matrix, and inputting the hypergraph matrix into a hypergraph convolutional neural network to obtain a hyperedge matrix; inputting the test question text into a large language model to obtain a vocabulary sequence, inputting the vocabulary sequence into a bidirectional semantic encoder to obtain a vocabulary vector matrix, and inputting the vocabulary vector matrix into a bidirectional long-short-term memory network to obtain a vocabulary feature matrix; splicing the hyperedge matrix and the vocabulary feature matrix to obtain a spliced feature matrix, and inputting the spliced feature matrix into a multilayer perception neural network to obtain a knowledge point prediction matrix; and performing knowledge point labeling on the test question text according to the knowledge point prediction matrix to obtain all predicted knowledge point texts of the test question text.

The invention relates to the technical field of computers, and discloses a table metadata processing method and system, electronic equipment and a storage medium, and the method comprises the following steps: carrying out regional semantic segmentation through weighted fusion based on frame geometric features, character coordinates and language features of a table; constructing a structural feature vector based on the row number, the column number and the row list header cell style of the table, performing geometric semantic integrity reconstruction, executing element standardization and semantic extraction, and performing geometric semantic integrity reconstruction on the basis of a region coding library, a unit conversion chain and a knowledge element rule library by utilizing normalized index names, time and region information. And calculating a regional code, a standard measurement value, a knowledge element ID and a retrieval field, matching the normalized index with an existing index system, and outputting a structured record. According to the method, the requirements of high accuracy and traceable structuralization during data processing in a big data environment can be met.

The application discloses a carbon footprint tracing system fusing a knowledge graph and space-time attention, which comprises a multi-source data fusion module, a power grid, product and auxiliary data are used to obtain space-time characteristics and a knowledge graph original data set; an electric carbon factor dynamic prediction module, preliminary prediction is carried out through a fusion model, and a dynamic error correction model is used to calibrate to obtain a final electric carbon factor; a knowledge graph construction module, based on preset entities and relations and attributes, extracts and fuses knowledge elements from original data, and constructs a special knowledge graph; a carbon footprint accurate accounting module, based on the correlation of the special knowledge graph, automatically matches the final electric carbon factor with the power consumption of each production process of the product, combines non-power carbon emission data, and obtains the total amount of product carbon footprint through an accounting model; and a carbon footprint tracing module, based on the special knowledge graph, traces the production link of the total amount of product carbon footprint. The application improves the product carbon footprint accounting accuracy and full-link tracing capability.

This invention relates to the field of virtual reality simulation technology, and more particularly to a method for generating immersive virtual reality training content. The method includes: a step of constructing a domain knowledge graph: extracting atomic knowledge elements representing operational actions, equipment entities, state parameters, and safety rules from standard documents and case libraries of the target training domain, and establishing multi-dimensional logical relationships between these atomic knowledge elements, including causal, temporal, hierarchical, and probabilistic relationships, to form and store the domain knowledge graph; a step of real-time collection of trainee data; a step of constructing and updating a trainee state model; a step of dynamic content decision-making; and a step of generating and injecting non-linear training content. By collecting trainee data in real time and matching it with the knowledge graph, the system can provide trainees with precise feedback after each training session, helping them identify and overcome their knowledge gaps.

The invention discloses a learning plan analysis system based on beauty teaching, which comprises an education server, a learning analysis module and a learning strategy generation module, and is characterized in that the education server receives a learning progress result of a learner, and the learning analysis module identifies vulnerable knowledge elements by analyzing the result and generates a learning strategy; the learning strategy generation module generates supplementary learning content in a targeted manner; and meanwhile, functional modules such as a learning psychological analysis unit, a learning career analysis unit and an art learning evaluation unit are integrated, and algorithms such as deep learning, natural language processing and reinforcement learning are combined, so that functions such as learning mode recognition, beauty ability multi-dimensional evaluation, personalized learning strategy generation and career path recommendation are realized. According to the system, knowledge weak points and learning requirements of the learner can be accurately positioned, customized learning planning and feedback are provided in combination with beauty teaching characteristics, the learning efficiency and beauty literacy are effectively improved, and support is provided for comprehensive development and occupational planning of the learner.

The application provides a relationship triple construction method, a knowledge enhancement method and related devices, which can fine-tune a pre-training model using knowledge corpus of an application scenario of a knowledge graph to obtain a language model. The language model learns the knowledge of the application scenario and can distinguish whether a target sentence meets the part-of-speech requirement of a relationship triple, whether it is a knowledge element meeting the knowledge of the application scenario of the knowledge graph, and then construct a relationship triple according to the target sentence meeting the above requirements. Manual annotation is not required, which avoids the problem that manual annotation may not cover all possible entities and relationships, resulting in low quality of the knowledge graph.

This application discloses a method, apparatus, device, and storage medium for constructing UAV technical data, relating to the field of computer technology. The method includes: extracting target cognitive behavior data of UAV aviation support personnel during mission execution to construct target triples and establishing a cognitive process model using a target BERT model; clustering knowledge elements related to UAV use and maintenance to obtain a knowledge hierarchy framework; extracting unstructured data from the UAV domain and constructing a target knowledge model using knowledge graph technology; determining the cognitive vectors at each stage of the cognitive process model; determining the matching relationship between the cognitive process and knowledge based on the similarity between the knowledge vectors and cognitive vectors corresponding to the target knowledge model; and constructing UAV technical data by combining the cognitive process model and the target knowledge model. While ensuring the completeness of the UAV technical data construction, the method also considers the cognitive characteristics and knowledge needs of users at different stages.