313 results about "Semantic annotation" patented technology

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

The invention provides a scientific and technological literature intelligent retrieval method and related equipment based on generative artificial intelligence, and the method comprises the steps: carrying out the multi-layer semantic annotation of medical scientific and technological literatures, constructing a symptom-disease dynamic association map, and building a medical scientific and technological literature knowledge base; performing medical context analysis and multi-modal feature extraction on user query, and generating a unified retrieval vector in combination with Boolean operation nested analysis and semantic alignment processing; performing evidence grading retrieval and clinical scene matching based on the unified retrieval vector to obtain a preliminary candidate literature set, and optimizing the preliminary candidate literature set into a target candidate literature set through fine-grained semantic recalculation; and calculating a retrieval prior probability according to the evaluation dimension, performing knowledge weighted fusion on the candidate literature, and generating a medical science and technology literature recommendation report. According to the method, the medical term association relationship is deeply understood through the association map, the result is ensured to be matched with the patient characteristics through evidence grading retrieval and clinical scene matching and screening, and the accuracy of document retrieval is improved.

The invention discloses an AI-based automatic financial information processing method, and relates to the field of financial automation, and the method comprises the steps: achieving the automatic collection and storage of structured and unstructured data through the access of enterprise multi-source financial data; systematic preprocessing is carried out on the collected multi-source heterogeneous financial data, and a unified and high-quality financial data set is constructed; based on natural language processing and a knowledge graph technology, performing text semantic understanding, transaction automatic classification, field standardization and label generation on the cleaned and integrated financial data; comprehensively quantifying enterprise operation and financial performance based on the structured transaction data and the semantic annotation result; based on historical financial indexes, establishing a multi-model architecture to predict key financial variables; and based on the structured data, the prediction result and the historical rule, identifying potential financial abnormity and risk behaviors, and realizing intelligent early warning. According to the method, the intelligence, the real-time performance and the accuracy of financial information processing can be remarkably improved.

The invention discloses a low-altitude airspace management method and system based on data analysis, and relates to the technical field of air traffic management and low-altitude airspace planning, and the method comprises the steps: 1, multi-scale dynamic grid division: adjusting the grid granularity in real time according to the aircraft density and environment complexity, generating a multi-scale grid by adopting an optimized quadtree data structure, and carrying out the multi-scale dynamic grid division; boundary smoothing processing is carried out on boundary areas of the grids with different scales; 2, intelligent airspace layering: dynamically adjusting the thickness of a height layer according to a risk score, and setting a buffer layer to enhance the flight safety; and 3, intelligent semantic annotation: giving dynamic semantic information such as risk levels and traffic priorities to the grid units, embedding airspace control regulations and flight rules, and realizing multi-source data fusion and real-time updating of semantic information. According to the method, the problems of static division limitation, boundary problem, insufficient real-time performance and the like in the prior art are solved, and the airspace resource utilization rate and the flight safety are improved.

The invention discloses a dynamic authority management system and method based on multi-source salary data integration, and relates to the technical field of salary data management, and the method comprises the following steps: collecting role names, authority boundaries and operation granularity information from a plurality of business systems, and generating structured role semantic ontology entries based on a field-level semantic annotation mode; and disassembling the to-be-mapped role according to the authority dimension, calculating a semantic similarity index between the to-be-mapped role and the standard role based on the role semantic ontology library, and outputting a role consistency scoring matrix. According to the method, the authority difference is identified through the role semantic ontology and the similarity score, the minimum authorization judgment is realized in combination with the rule engine and reinforcement learning, the audit traceability and anomaly detection are guaranteed by using the block chain and the dynamic graph, and finally a strategy self-evolution closed loop is constructed. And the problems of permission mismatching and data leakage caused by role semantic inconsistency are effectively solved.

The invention relates to the technical field of knowledge graph retrieval, in particular to a genuine medicinal material intelligent retrieval method and system based on a graph. The method comprises the following steps: performing semantic granularity analysis on a retrieval request input by a user, constructing a multi-level semantic edge and generating a hierarchical semantic graph structure; semantic enhancement is carried out on the map relation through semantic annotation, and a multi-condition intention is extracted in combination with dimensions such as regions, drug properties and channel tropism; further, the system executes multi-hop path combination, a structured semantic path conforming to the composite intention is mined, edge nodes in the path are inferred and complemented, and a genuine medicinal material retrieval result with a closed structure and complete semantics is generated. Compared with a traditional keyword matching and static field retrieval mode, the method has higher semantic perception ability and reasoning intelligence, and the accuracy and adaptability of the system in processing fuzzy, composite and path-incomplete retrieval scenes are remarkably improved.

The invention provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

The invention relates to an industrial equipment maintenance intelligent question-answering system based on multi-agent collaboration. Wherein the input unit is used for receiving text, voice, image or equipment scanning and other multi-mode user input information and analyzing the information into structured problem information; the scheduling unit performs semantic understanding and problem classification on the structured problem information based on the fine-tuned cross-language pre-training language model and an incremental training mechanism; the processing unit calls a corresponding domain agent according to the classification result, and generates an intelligent question and answer processing result including predictive maintenance suggestions, structured reply content and semantic annotation information; and the fusion unit fuses the local knowledge base, the graph database and the networking retrieval information, performs multi-hop semantic reasoning on the intelligent question and answer processing result, and generates multi-modal reply information including text description, image screenshots, prediction curves and recommendation links. The system can support multi-language and multi-mode intelligent question answering and predictive maintenance in a complex industrial maintenance scene.

The invention discloses a highway data fusion management method, and relates to the technical field of intelligent traffic management, and the method comprises the steps: collecting the global operation data of a highway in real time through a multi-source heterogeneous sensing network; performing space-time alignment and semantic annotation processing on the operation data to construct a fusion data set; inputting the feature subset into a lightweight space-time fusion model to form a multi-target decision set; executing the traffic control instruction sequence in a preset space-time window according to the multi-target decision set; and generating a self-optimization report including a model iteration path and road network health assessment based on the deviation degree of the execution feedback data and the expected optimization target. According to the expressway data fusion management method provided by the invention, collaborative optimization of passing efficiency and safety of the expressway is guaranteed.

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention discloses a quadruped robot obstacle avoidance control method and system based on path planning, and relates to the technical field of quadruped robot obstacle avoidance control, and the method comprises the steps: obtaining environment point cloud data, image data and quadruped robot motion speed data, and carrying out the terrain semantic segmentation and semantic pixel back projection of the image data, thereby obtaining a quadruped robot obstacle avoidance result; generating semantic point cloud data, combining the semantic point cloud data with the environment point cloud data subjected to motion compensation, and constructing a semantic annotation grid map; performing global path search on the semantic annotation grid map to generate a global smooth path; when the quadruped robot advances along the global smooth path, path nodes on the global smooth path are extracted at a fixed step pitch, the terrain category and the grid occupation state of each path node are judged according to the semantic annotation grid map, and a local reference path is generated; and executing a multi-stage obstacle avoidance strategy on the local reference path, and generating a foot end track sequence.

The invention relates to the technical field of electric digital data processing, in particular to a traditional Chinese medicine data management system and method for data retrieval. The method comprises the following steps: acquiring morphology data of a traditional Chinese medicinal material sample; performing grade evaluation based on the traditional Chinese medicinal material morphology data to generate traditional Chinese medicinal material grade label data; acquiring a to-be-detected traditional Chinese medicinal material image; preprocessing the to-be-detected traditional Chinese medicinal material image, and performing entity recognition and relation extraction to obtain traditional Chinese medicinal material semantic annotation data; and fusing the traditional Chinese medicinal material grade label data and the traditional Chinese medicinal material semantic annotation data to obtain traditional Chinese medicinal material semantic annotation enhanced data. According to the method, the information of the traditional Chinese medicinal materials is expressed through structured processing and semantic enhancement, the knowledge graph containing dominant and implicit semantic relationships is constructed, and the retrieval depth, flexibility and intelligent application of knowledge of the traditional Chinese medicinal materials are improved.

The invention discloses an AI-based medical and health care integrated data management method and system and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the data preprocessing and data standardization of collected multi-source medical and health care data, and obtaining a standardized medical data set; performing classification and semantic annotation on the data of the standardized medical data set by using a preset medical ontology, and constructing a structured medical knowledge graph; performing virtual mapping based on the structured medical knowledge graph by adopting a digital twinborn model to form a virtual medical twinborn body; based on the virtual medical twins, combining real-time monitoring data and historical patient flow data, through multi-dimensional feature extraction and pension mode recognition, dynamically optimizing and presetting a personalized medical scheme and a resource scheduling strategy, and outputting a medical care service optimization scheme; generating a target service list in combination with the analyzed doctor-patient dialogue text, and distributing the target service list to a preset pension service cooperation terminal; the method has the effect of improving the real-time performance and the accuracy of the old people medical care service strategy.

The invention discloses a multi-modal metadata alignment fusion method and device, equipment and a storage medium, and the method comprises the steps: carrying out the metadata extraction of structured data, unstructured text data and image data, and generating multi-modal metadata with semantic annotations; establishing a shared semantic embedding space, and mapping the multi-modal metadata to the shared semantic embedding space for coding to obtain a unified spatial vector; determining an alignment candidate pair from the unified spatial vector through similarity calculation, and identifying a semantic relationship of the alignment candidate pair; and performing conflict detection on the aligned candidate pairs and the corresponding semantic relationships, and resolving conflicts based on weight weighting fusion to obtain a unified metadata system. According to the method, improvement and optimization are carried out from multiple aspects of multi-modal data processing, semantic understanding, alignment accuracy, conflict resolution and the like, the defects in the prior art are overcome, and a more accurate and comprehensive multi-modal metadata alignment fusion result can be provided.

The invention relates to the field of artificial intelligence, and discloses an intelligent AI semantic annotation method and system based on multi-modal analysis, images, audios, texts and multi-modal data of a sensor are synchronously collected through AI, deep semantic features of all modals are extracted by using a deep learning model after preprocessing, vectors are generated, and the multi-modal data are subjected to semantic annotation; fusing into a semantic scene vector by means of a cross-modal attention mechanism, constructing a cross-modal association graph, and learning a causal logic path by using a graph neural network; constructing a spatiotemporal causal diagram by combining spatiotemporal information, and integrating an event time sequence and spatial dependence by using a time sequence diagram neural network to form a venation path; according to the method, the two knowledge maps are fused into a comprehensive knowledge map, preliminary annotations are generated through rule base reasoning, a high-precision result is output after optimization, the problems of semantic loss and annotation fragmentation caused by modal splitting and context missing in a traditional method can be effectively solved, meanwhile, the manual verification cost is reduced, and the integrity and efficiency of semantic annotation in a complex scene are improved.

The invention discloses a multi-language knowledge graph construction method, system and device and a medium. The method comprises the steps of obtaining text data, voice data and image data; performing standardization processing on the text data, the voice data and the image data to obtain multi-source heterogeneous data after format unification; mapping the multi-source heterogeneous data with the unified format into a unified semantic representation space, and performing semantic annotation to generate a corresponding semantic annotation result; the embedding representations of different languages are aligned based on a comparative learning technology, the embedding of multiple languages is mapped to a unified semantic space, and a semantic annotation result is updated; and constructing a knowledge graph based on the updated semantic annotation result, and updating the knowledge graph in real time based on an incremental updating algorithm. According to the technical scheme, precise analysis of complex semantics and efficient annotation of unseen knowledge can be achieved, dynamic real-time updating of the knowledge graph is achieved, and the response speed and knowledge updating efficiency of a system are remarkably improved.

The invention provides an intelligent electronic medical record generation method based on a portable robot, and the method comprises the steps: firstly constructing a medical knowledge graph from an ICD-11 term library, real-time data and real-time data, generating a standardized entity relationship set, and generating knowledge vectors supporting semantic reasoning through a graph embedding algorithm; then converting the doctor-patient dialogue audio into a structured text through a medical voice model, and generating semantic annotation data in combination with a knowledge vector matching atlas entity; when the matching degree exceeds a dynamic threshold value, the data are automatically converted into HL7 FHIR standard data, and after the compatibility with an existing map is automatically checked, synchronous updating of the electronic medical record is finally completed through template alignment and API adaptation. According to the method, the automatic conversion from the unstructured dialogue to the standardized electronic medical record is realized, and the quality and the utilization efficiency of the medical data are improved.

The present disclosure provides a method for generating annotation data for robotic training using a hierarchical transformer-based model with multiple layers. The transformer-based model includes Alpha models generating low-level control outputs and Beta models generating high-level control outputs. The method receives multimodal input data comprising visual sensor data and natural language instructions, processes this data through the hierarchical transformer-based model to generate annotations at different abstraction levels, wherein Beta models create semantic annotations describing task objectives and Alpha models generate motor command annotations specifying robotic actions, and stores these annotations with the input data to create annotated training data for robotic control systems.

The invention relates to a smart city ESG multi-source data visualization analysis method, device and equipment and a medium, and the method comprises the steps: carrying out the standardized semantic annotation and space-time alignment of city multi-source heterogeneous data, and generating a semantic triple set with a space-time label; the real-time intensity of the causal relationship in the ESG dynamic knowledge graph is constructed and quantified; semantic binding is carried out on the map and the city information model, and a visual descriptor is generated; discovering and rendering a causal relationship chain in the three-dimensional scene based on user interaction; and deducing simulation intervention and optimizing model parameters. According to the method, dynamic and integrated presentation and interactive deduction of complex causal relationships of environment, society and governance in a multi-dimensional space-time scene are realized, and the problems of surface analysis, disjunction of data and space and insufficient decision support in the prior art are effectively solved.

The invention relates to a method, a system and equipment for generating a large model SQL (Structured Query Language) based on business domain semantic enhancement and multi-round voting. The method comprises the following steps: acquiring a natural language input by a user to recognize and judge a business field related to user query; performing semantic annotation and expansion on the input query; organizing related information into Prompt, inputting the Prompt into a large language model, and generating an SQL statement; generating a plurality of candidate SQL statements, and selecting the candidate SQL statements with the highest occurrence frequency as output; and submitting the finally output SQL statement to an actual database system for execution, and returning and displaying a query result. Through the technologies of business domain division, data structure semantic annotation, multi-round candidate generation, voting screening and the like, SQL generation accuracy and system robustness of natural language query are remarkably improved, query intentions and field constraints in business scenes of enterprises such as banks and the like can be accurately recognized, field matching errors are avoided, and the query efficiency is improved. And through multi-round iterative generation and consistent voting screening, the correctness and stability of the output SQL statement are ensured.

The invention provides a semantic aerial view visual relocation method and device in a non-exposed scene, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a multi-view image sequence under a non-exposed scene (such as a tunnel, an underground pipe gallery or an underground parking lot); semantic recognition is carried out based on a pre-trained semantic target detection model, and spatial consistency semantic features are extracted through a semantic-geometric dual-channel fusion mechanism combining a semantic mask and geometric constraints; the method comprises the following steps of: realizing three-dimensional reconstruction by using a voxel micro-renderable modeling method (VGGT), and generating a dense three-dimensional semantic point cloud fusing semantics and a geometric structure; two-dimensional semantics are mapped to a three-dimensional space through a projection and back projection relation, and point cloud semantics are endowed; main structure planes such as the ground, the left wall surface and the right wall surface are extracted, and a two-dimensional semantic aerial view with semantic annotation is generated; and pose estimation is carried out based on a reciprocal matching strategy guided by a semantic mask, so that visual repositioning with high precision, high robustness and semantic interpretability is realized. The method breaks through the problems of low precision, sparse features and poor semantic consistency of traditional visual repositioning in a non-exposed environment, and can be widely applied to the fields of intelligent transportation, underground inspection and unmanned system positioning.

The invention discloses a graphical user interface test case generation method based on a multi-agent framework, and relates to the field of graphical user interface tests.The graphical user interface test case generation method based on the multi-agent framework comprises the following steps that S1, a user demand document is obtained, and file text content and file format information are analyzed and extracted; s2, performing paragraph segmentation on the file text content to form logic semantic units; s3, carrying out correlation analysis on the segmented paragraphs, and obtaining a ternary file group; s4, performing paragraph annotation addition on the ternary file group, and constructing a semantic annotation result set; s5, extracting the agent framework to generate a graphical user interface test case, performing test recording, and storing a test result; s6, the result is verified, and a test case list and a software test report are generated. By conducting multi-dimensional analysis on the text content and the format structure in the document, the reduction efficiency from natural language description to structural semantic construction is improved.

The invention provides a ship design drawing structured vector analysis method and an intelligent question and answer method. The method comprises the following steps: constructing a drawing primitive initial data set; generating a semantic annotation entity set; mapping the semantic annotation entity set to a two-dimensional coordinate system to generate a relation annotation entity pair set; repeated entities with the same semantic content and the coordinate position error lower than a set threshold value are removed, and an effective entity relation set is constructed; converting the effective entity relationship set into a structured semantic relationship set; inputting the drawing primitive initial data set into a neural network target detection model to generate an image recognition entity set; and fusing the structured semantic relationship set and the image recognition entity set to construct a drawing semantic map, finely adjusting the large language model, and outputting a text question and answer result. According to the method, ship drawing automatic analysis and semantic question and answer efficient fusion are realized, and the structure understanding precision and the interaction response capability are improved.

The invention discloses a wearable path guiding system based on visual-tactile cross-modal mapping. The wearable path guiding system adopts a three-module architecture, namely a visual environment sensing module, a simplified path planning module and a tactile guiding module. The environment perception module constructs an incremental scene map with semantic annotations based on a depth camera. And the simplified path planning module is used for generating a steering vector containing gait information and a steering angle according to the advancing direction and simplified path pointing and performing real-time iterative optimization to realize real-time matching of the advancing direction of the user and the target direction. And the tactile guidance module is used for realizing synchronization of gait and tactile stimulation, generating priority gradient intensity field tactile feedback with a direction indication characteristic according to path deviation information, correcting the advancing direction, and starting the voice auxiliary module when multiple times of tactile correction do not reach the expectation. The method is suitable for a visual signal limited scene, the visual signal is mapped into the tactile signal to realize self-adaptive synchronization with the motion rhythm, and path guidance is completed.

The invention discloses a supply chain finance heterogeneous data cleaning and fusion processing system, and relates to the technical field of supply chain finance data management, and the system comprises a heterogeneous data processing platform which is in communication connection with the following modules: a data obtaining module which is used for obtaining heterogeneous data from each participant of supply chain finance, comprising structured data, semi-structured data and unstructured data, and preprocessing the obtained multi-source heterogeneous data; and the knowledge graph construction module is used for performing semantic annotation on entities and relationships in the heterogeneous data and constructing a supply chain finance knowledge graph model. According to the method, modeling is carried out through semantic annotation and relation extraction, the incidence relation between heterogeneous data is defined, then semantic alignment and fusion processing are carried out, data from different sources are mapped into a unified knowledge graph framework, the accuracy and reliability of data fusion are improved, and deeper data insight is provided for supply chain financial services.

The invention relates to the technical field of water network intelligent management and control, and discloses a water network management and control multi-source space digital intelligent fusion base construction and decision optimization method, which comprises the following steps: cooperatively obtaining multi-source space data; format conversion, coordinate system unification and systematic semantic annotation are carried out through a data preprocessing platform, and a normalized spatial data set is formed; constructing a data fusion model, quantitatively evaluating the quality of a data source by adopting a fuzzy comprehensive evaluation method so as to determine a fusion weight, and performing weighted fusion by adopting a specific algorithm aiming at different data types such as vectors, grids and three-dimensional models so as to generate a unified spatial data model; and finally, a decision optimization system performs decision scheme generation and iterative optimization based on the model in combination with a hydrodynamic model and a genetic optimization algorithm. By establishing a standardized data processing and targeted weighted fusion process, the problems that multi-source heterogeneous data is difficult to integrate and the fusion precision is not high are effectively solved, and a reliable data base is constructed.

The invention provides an automatic action trajectory labeling system and method based on a visual language model, and relates to the field of visual technical models. The automatic action trajectory labeling system based on the visual language model comprises a key frame extraction module, a VLM semantic labeling module, a historical labeling database, a diffusion model optimization module and an reflection correction module, and the key frame extraction module extracts a key frame sequence from an input video through optical flow analysis and a TSN network scene change detection algorithm; and the VLM semantic annotation module is used for outputting a candidate segmentation point set containing action starting and ending points, the VLM semantic annotation module adopts a multi-modal VLM, takes a key frame image and a context text instruction as input, and generates a preliminary semantic tag and a corresponding vision-language embedding vector. According to the method, multiple bottlenecks of the existing action track labeling technology are solved, the labeling precision is remarkably improved, and the target of time boundary optimization is achieved.

The invention provides a traffic control guiding method, device and equipment based on vehicle track identification, and aims to accurately obtain a vehicle moving path and identify a travel purpose type by constructing a multi-source wireless signal positioning data fusion and track semantic annotation identification mechanism, and generate a standardized track with a semantic tag. Establishing a similar behavior pattern library by adopting a behavior similarity analysis and clustering division technology, accurately identifying traffic key control points, evaluating node importance, and determining a control key area; a personalized and mastered guide strategy coordination design and a double-layer guide rule set construction technology are introduced, individual demands and group benefits are balanced, and a coordinated path recommendation and guide control scheme is generated; combining an effect tracking monitoring and trajectory mutual influence analysis mechanism to construct a feedback adjustment mechanism to generate a trajectory optimization guide scheme; and finally, a self-adaptive comprehensive traffic control system is formed, and accurate guidance and intelligent management and control of urban traffic are realized.

The invention discloses a three-dimensional point cloud building automatic modeling system based on deep learning. The system comprises a point cloud acquisition and input module, a point cloud preprocessing module, a depth feature extraction module, a semantic segmentation and component identification module, a structure parameter analysis module and a parameterized model generation module. According to the invention, through deep fusion of the point cloud deep learning network and the parametric modeling engine, full-process automation of building three-dimensional modeling is realized; the depth feature extraction module is used, so that the recognition accuracy of structures such as doors, windows and roof edges can be effectively improved, the precision of a building model is ensured, and the depth feature extraction module can continuously improve the recognition accuracy along with continuous use of the system; and by using the semantic segmentation and component identification module, the irregularity of the building point cloud, the complexity of the structure and the shielding noise can be identified and distinguished, and the problem that semantic annotation and topological repair need to be manually performed in the prior art is solved.

The invention provides an electric power industry-oriented A I intelligent question answering and professional report generation method, which comprises the steps of extracting key information from a constructed knowledge graph to form a preliminary electric power knowledge base structure, and the extraction process comprises the steps of identifying high-frequency nodes, analyzing connectivity among the nodes and evaluating importance weights of the nodes; based on the preliminary knowledge base structure, integrating structured and unstructured data in the power field to form a complete power knowledge base, and keeping synchronous updating with the knowledge graph; based on the electric power knowledge base, training a natural language processing model in the electric power field, which comprises named entity recognition and relation extraction by adopting a deep learning model in sequence; and performing semantic annotation on nodes and edges in the knowledge graph by utilizing a trained natural language processing model, and integrating new semantic information into the knowledge graph through entity alignment and relation mapping.