950 results about "Semantic mapping" patented technology

The invention relates to a cross-domain intelligent reasoning method based on knowledge graph enhancement, and the method achieves the precise reasoning in a complex scene through the construction of a hierarchical knowledge expression framework and a dynamic optimization mechanism. A multi-source heterogeneous data fusion technology is adopted, subject fine-grained knowledge units are generated through multi-modal feature extraction, and a three-dimensional knowledge graph structure comprising a core common concept layer, a subject feature ontology layer and a dynamic semantic mapping layer is established; based on a path exploration algorithm driven by reinforcement learning, cross-domain implicit association is mined while subject independence is reserved, and controllability and interpretability of the reasoning process are achieved in combination with an attention fusion mechanism of a large language model. According to the method, the limitation of traditional unified ontology modeling is broken through, the problems of concept drift and path deviation existing in reasoning in the cross fields of medicine-finance, engineering-law and the like are effectively solved, and the accuracy and knowledge traceability of complex decision tasks are remarkably improved.

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention relates to a multi-modal AI data fusion processing method, device and equipment and a medium, and the method comprises the steps: firstly extracting visual, auditory and text modal features through a pre-training encoder, executing dimension alignment, and generating a standard data feature set with unified dimensions; a cross-modal semantic graph is constructed based on a cosine similarity algorithm, and the problem of semantic mismatch of heterogeneous data is solved; residual enhancement is carried out on the map nodes, and noise interference is eliminated; fusing the optimized features and the semantic topology in combination with a graph convolutional network to generate aggregation graph representation; the fusion features are mapped to a low-dimensional semantic space through a variational auto-encoder, and cross-modal correlation essence is captured; the key dimension contribution degree is quantified, a visual report is generated, and semantic association rules among modals are disclosed, so that the dimension isomerism limitation of a traditional fusion technology is broken through, quantifiable cross-modal semantic mapping is established, the whole process traceability from feature fusion to decision interpretation is realized, and the method is suitable for popularization and application. And the multi-modal decision black box problem in the fields of medical diagnosis, automatic driving and the like is effectively solved.

The invention relates to the technical field of data center intelligent management, in particular to a data center construction and intelligent operation and maintenance management system, which comprises a dynamic environment sensing module, a heterogeneous equipment protocol adaptation module, a multi-dimensional resource dynamic scheduling module, a hidden fault prediction module and an energy efficiency optimization execution module. Physical environment data such as temperature gradient, current harmonic component and optical fiber strain rate are acquired by deploying a multi-mode sensor, and an environment characteristic matrix is constructed; standard semantic mapping of the heterogeneous protocol is realized by using a semantic slot migration algorithm; establishing a resource topological graph based on the hypergraph neural network and dynamically updating the resource topological graph; a dual-channel space-time convolutional network is adopted to realize fault prediction; and combining the fault probability matrix to generate a dynamic tuning strategy of dimensions such as cooling, electric power, network and the like, and forming closed-loop optimization control. According to the invention, integrated collaboration of multi-source information fusion, equipment intelligent control and energy efficiency adaptive optimization is realized, and the intelligence, reliability and energy efficiency level of data center operation and maintenance are improved.

The invention relates to the technical field of data processing, in particular to a method and system for processing data based on a large language model.The method comprises the following steps that based on inter-sentence punctuation positioning and part-of-speech tagging, sentence blocks are divided to generate functional partitions, themes and relational words are extracted to judge semantic chain starting points, a trigger index sequence is constructed, and logic jump points and breakpoint positions are recognized; and mapping the label structure to the language model output analysis deviation, and generating a label mapping combination list. According to the method, semantic turning nodes can be captured by analyzing the semantic direction change trend, the relation between the semantic turning nodes and verb and noun combinations is judged, the position of a trigger point of an actual information transfer effect is extracted, and the break point area of a semantic path is recognized through the positioning of key word starting and stopping blocks and the logical judgment of noun group cross combination; the path integrity has a clear fracture identifier, a traceable semantic mapping structure path is established in a language model, and the accuracy, coherence and hierarchy clearness of semantic reconstruction are enhanced.

The invention discloses a causal atlas-driven enterprise intelligent decision-making method and system, and belongs to the technical field of enterprise management. The method comprises the following steps: constructing an enterprise-level causal atlas by obtaining enterprise business processes, operating indexes and unstructured text data; executing path reasoning based on the to-be-decided data, and determining a multi-hop causal path chain; determining a current association rule and a decision suggestion in combination with an expert rule base; causal maps and rule tags of different industries are introduced, and a migratable causal path and an association rule are identified based on structural similarity and a semantic mapping rule; and finally, current and migration knowledge is fused to generate an enterprise decision execution scheme and a causal reasoning result. According to the scheme, based on comprehensive application of current and migrated knowledge, scientificity, accuracy and interpretability of decision making are effectively enhanced, meanwhile, dynamic updating and inter-industry knowledge sharing are supported, enterprise decision making efficiency and response speed are remarkably improved, and enterprises are assisted to achieve intelligent and fine management.

The invention discloses a fusion method for automatic coprocessing of multi-source heterogeneous data, which comprises the following steps of: analyzing the format and semanteme of the multi-source heterogeneous data to obtain metadata features of each data source, the metadata features comprising data types, coding modes and semantic tags, and generating a standardized metadata description set; according to the metadata description set, a pre-established protocol template library is adopted, an applicable template is matched, an initial unified exchange protocol is generated, and the initial unified exchange protocol comprises a format conversion rule and a semantic mapping relation; and if the format conversion rule of the initial unified exchange protocol cannot adapt to the metadata features of the newly added data source, dynamically updating the protocol template through a machine learning algorithm to obtain an exchange protocol adaptive to the new data source.

The invention provides an intelligent station multi-source heterogeneous data acquisition and remote collaborative analysis method and system, and relates to the technical field of heterogeneous data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out the standardization processing, constructing a station real-time adaptive three-dimensional model through a self-calibration laser scanning technology, and carrying out the semantic mapping of the model and a BIM model; constructing a self-evolution feature library based on a digital twinborn model, and carrying out space-time correlation analysis by adopting a graph convolutional neural network; a remote cooperation platform is established to realize real-time cooperation of multiple experts, and a diagnosis process is recorded through a block chain technology. According to the invention, efficient acquisition, analysis and remote collaborative diagnosis of the data of the smart station are realized, and the accuracy and efficiency of fault diagnosis are improved.

The invention relates to the technical field of emergency command, and discloses an emergency command system and method based on a BIM digital base, and the method comprises the steps: building a unified data model through expanding an IFC standard, carrying out the semantic mapping and entity alignment of BIM building information, GIS geographic space data, monitoring data and an emergency plan document, and carrying out the construction of a unified data model. The method comprises the steps of generating an emergency knowledge graph with BIM building information as nodes, coupling physical simulation and an LSTM prediction algorithm, injecting monitoring data into a BIM model to deduce a disaster process, outputting a risk thermodynamic diagram and an evacuation forbidden zone, and calculating space-time accessibility of emergency resources through a reinforcement learning model according to a deduction result. An optimization scheme including an unmanned aerial vehicle path, a personnel evacuation route and an equipment scheduling instruction is generated, the instruction is synchronized to an augmented reality individual soldier terminal and a mobile command shelter through an edge computing node, a resource deployment state is labeled in real time in a BIM model, multi-terminal operation is synchronized, the timeliness and accuracy of disaster situation sensing are remarkably improved, and the disaster situation sensing efficiency is improved. And disaster loss is effectively reduced.

The invention relates to the field of environmental data processing, in particular to a multi-source heterogeneous data collection and fusion method and system for the environmental governance industry, and the method comprises the following steps: obtaining multi-source heterogeneous environmental data, and carrying out the quality evaluation and restoration processing to obtain a standardized environmental data set; semantic mapping and space-time alignment are carried out on the standardized environment data set to obtain a semantic space-time unified data set, and block chain evidence storage is carried out to obtain a multi-source heterogeneous data collection result; performing multi-modal feature extraction on the multi-source heterogeneous data convergence result to establish an environment dynamic knowledge graph, and performing anomaly detection to obtain a knowledge enhancement environment data set; and constructing an environment data traceability model to perform traceability analysis on the knowledge enhanced environment data set to obtain a traceability inference result, and performing data fusion based on the traceability inference result to obtain a multi-source heterogeneous data fusion result. According to the method, efficient data collection fusion processing is realized, and accurate data support is provided for decision making of the environmental governance industry.

The invention relates to a medical information processing and intelligent data management technology, and discloses a hospital multi-source heterogeneous data integration management system and method based on an EMPI, and the system carries out the real-time subscription and batch extraction of the data of an HIS, an EMR, an LIS, a PACS, a DRG and a pre-hospital first-aid system through an interface adapter, and generates a unified main index identifier through the matching of the EMPI, and carries out the real-time subscription and batch extraction of the data of the HIS, the EMR, the LIS, the PACS, the DRG and the pre-hospital first-aid system. Field-level cleaning, standardization and semantic mapping are carried out on the free text and the structured data, an intermediate standardized record set is formed, an event graph is constructed, event cluster recognition is carried out in combination with time, clinical entities, doctor seeing scenes and department similarity, consistency judgment and EM iterative fusion are carried out on the basis of data source reliability, and event cluster recognition is carried out. And outputting a unified patient file set and a downstream service interface. According to the invention, high-precision integration, standardized management and traceable application of multi-source data are realized, and a reliable data basis is provided for clinical decision support, medical insurance management and scientific research.

The invention discloses a data processing method and system based on a bidding and tendering management platform. The method comprises the following steps: obtaining a bidding and tendering data set and constructing an initial supplier set; dynamically screening the initial supplier set based on a preset qualification verification rule to generate a compliance supplier set; performing multi-dimensional semantic analysis on bidding document contents in the compliance supplier set to generate a risk assessment report; and generating a recommended supplier sequence based on the risk assessment report and the bid invitation party demand information. The method has the following advantages and effects: semantic mapping and dynamic weight distribution among data are realized; based on dynamic rules and real-time data updating, response delay caused by manual intervention is reduced; through confidence scoring, a quotation rationality model and performance matching degree calculation, a quantifiable risk assessment report is generated, and subjective deviation is reduced; and the recommendation logic is dynamically adjusted in combination with the customized demand of the bid invitation party, and the intelligent level and the result credibility of the bid invitation and tendering process are improved.

The invention relates to the technical field of telecommunication, in particular to a switch automatic configuration method and system and a medium. The method comprises the following steps: obtaining switch resource configuration data, and carrying out semantic enhancement topological graph modeling to obtain a network environment digital twinborn model; performing configuration instruction analysis based on the network environment digital twin model and a preset configuration management database to obtain an intention configuration semantic set; performing multi-manufacturer semantic mapping arrangement based on the configuration strategy path of the intention configuration semantic set to obtain a native configuration command set; performing identity path integrity auditing on each configuration command in the native configuration command set to obtain a signature command set passing verification; uploading the verified signature command set to a switch management platform to execute the command; and acquiring real-time behavior state data of the switch, and performing semantic deviation calculation to obtain a configuration result report. According to the invention, the effectiveness and adaptability of switch configuration can be improved.

The invention relates to the technical field of data acquisition, and discloses a multi-protocol extensible data acquisition system architecture and method with a unified interface standard, and the architecture comprises a data source management module which is used for storing data source configuration information and distributing protocol identifiers; the protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification; the data conversion module is used for generating target data through format conversion and semantic mapping; the data verification module and the anomaly detection module are used for comparing and monitoring data integrity based on an integrity algorithm and a threshold value; the data quality monitoring module and the early warning module are used for comparing and evaluating data quality through a consistency model and a threshold value; and the result output module transmits the early warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring, early warning and the like. The system expansibility and the data reliability are improved, and the method is suitable for multi-scene data acquisition.

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

The invention relates to an inter-switch multi-protocol conversion and dynamic routing optimization method and system. The method comprises the following steps: carrying out feature analysis processing on routing protocol messages collected by each switch in an interconnection structure, and extracting to obtain a protocol state parameter set comprising protocol type identifiers, path attribute fields and adjacent state information; performing structured semantic mapping on the path attribute field according to the protocol type identifier, and constructing to obtain a standardized path parameter model fusing multiple protocol structure features; and generating a dynamic routing control instruction set for guiding each switch to execute path switching according to a link connection relationship between the standardized path parameter model and the adjacent state information in combination with a path selection strategy associated with the protocol type identifier. By adopting the method, unified routing behavior management of the switch in a multi-protocol environment can be realized.

The invention discloses a unified management method and device for multi-protocol Internet of Things equipment, and relates to the technical field of Internet of Things communication and protocol management, and the method comprises the steps: constructing a cross-protocol semantic knowledge graph through a graph neural network based on a structured gene-semantic mapping table, and generating a dynamic semantic relation matrix through a node embedding method; according to the dynamic semantic relationship matrix, deploying a genetic algorithm engine at an edge node to dynamically recombine and optimize protocol genes, screening protocol variants adapted to a network environment through an evolution sandbox pressure test, and generating an optimized protocol gene pool; and based on the optimized protocol gene pool, performing real-time conversion on the heterogeneous protocol message in a protocol conversion gateway in combination with a multi-thread parallel analysis architecture to generate a standardized semantic data stream. Unified expression and management of complex heterogeneous protocols are achieved, the protocol conversion process is simplified, cross-protocol semantic understanding and compatibility are improved, and device flexibility and analysis efficiency are improved.

The invention relates to the technical field of landscape evaluation, and discloses an urban green land landscape evaluation method based on a large model, and the method comprises the steps: carrying out the real-time analysis and standardization processing of a streetscape image, IoT environment data, satellite vegetation coverage data and RTK high-precision positioning information of a target city district, extracting core landscape elements, building cross-scene semantic mapping, and carrying out the calculation of the cross-scene semantic mapping. Transmitting the standardized data to a central database and deploying edge nodes; extracting a multi-dimensional landscape index by adopting an improved semantic segmentation model, dynamically adjusting an index weight in combination with regional features and seasonal changes, and classifying and correcting deviation data by edge nodes; a visual evaluation result is generated based on a cooperative computing architecture and a digital twinborn model, a landscape space to be promoted is identified, and optimization suggestions are generated; and obtaining planner feedback information, updating the model, the weight rule and the suggestion generation strategy, and forming a closed loop iteration mechanism. According to the method, the dynamic and practical evaluation requirements of the current urban green land landscape can be met.

The invention discloses a law compliance auditing response method driven by an enterprise knowledge graph, and relates to the technical field of artificial intelligence, and the method comprises the following steps: carrying out semantic analysis on a law and regulation text, constructing a context hierarchical expression model, dividing each law and regulation into a title semantic block, a subject semantic block, a limit semantic block and a constraint semantic block, and generating a hierarchical semantic graph. According to the method, the structured analysis of the regulation provisions is realized by constructing the hierarchical semantic graph, and the regulation provisions are accurately aligned with the business entities in the enterprise knowledge graph, so that the reasonable regulation-enterprise semantic mapping relationship is constructed. And boundary reasoning, confidence scoring, context backtracking and semantic correction are combined to realize intelligent discrimination and correction of the applicability of the regulatory obligations. Models and rules are continuously updated through a feedback optimization learning mechanism, the dynamic adaptive capacity of the system and the accuracy of compliance suggestions are improved, the misjudgment risk is remarkably reduced, and the intelligence and the practical application value of the system are enhanced.

The invention discloses a document link construction and evolution relation tracking method based on fragment-level semantic alignment, which relates to the technical field of document management, and comprises the following steps: carrying out structured analysis on a plurality of input documents, and dividing the document content into a plurality of semantic fragments; performing vectorization processing on each semantic fragment, and constructing a document-fragment-vector semantic mapping relationship; different document fragment-level semantic alignment is realized through cross-document semantic vector similarity calculation, a position offset tolerance algorithm and context association analysis, and a semantic link network is established; fusing the link relationship, the timestamp, the version information and the author information, and constructing a document evolution diagram taking the documents or fragments as nodes and evolution links as edges to record semantic flow paths between the documents; and outputting the semantic link network and the document evolution diagram in a structured form. The method can solve the problems that semantic relations between documents are missing, evolution paths are invisible, content reuse cannot be traced, and the structure induction capability is insufficient.

The invention discloses a humanoid robot real-time cooperation decision-making method based on multi-mode perception fusion. The method comprises the following steps: dynamically deploying a sensor array, acquiring multi-modal original sensing data of the humanoid robot through the deployed sensor array, and performing physiological signal fusion processing and multi-modal feature enhancement processing on the multi-modal original sensing data to obtain a multi-level feature set; performing cross-modal semantic mapping and intention reasoning on the multi-level feature set by using a cross-modal semantic reasoning system supported by a large model to obtain a cooperative execution instruction containing a target action and a force control parameter; and dynamically updating a behavior decision model of the humanoid robot by using a double-buffer collaborative distributed incremental learning mechanism based on the collaborative execution instruction, and obtaining a real-time collaborative decision of the humanoid robot based on the behavior decision model. The technical problem that the man-machine cooperation efficiency of the humanoid robot in a complex scene is low is solved.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention discloses a heterogeneous data conversion method and system based on a multi-modal large model, and aims to solve the problems of single-modal limitation, high migration cost and the like in traditional heterogeneous data processing. The method comprises the following steps: firstly, carrying out format adaptation and classification preprocessing on multi-source heterogeneous data such as images, texts, audios and videos; then, through a ViT, BERT and Transform cross attention mechanism, deep analysis and cross-modal semantic association of visual, text and audio features are realized, and visual-text-audio semantic mapping is established; and based on a dynamic prompt project, guiding the multi-modal large model to generate structured data according to a preset Schema zero sample. And then error correction and format normalization are carried out through a Drools rule engine to ensure that the data meet the business standard. And finally, supporting multi-format output of JSON / XML / database table and the like, and meanwhile, associating original data storage to realize data traceability. According to the method, the limitation of single-modal processing is broken through, and cross-modal data deep association is realized.

The invention relates to the technical field of space governance, and discloses a multi-agent space collaborative governance method which comprises the following steps: processing multi-source heterogeneous data such as satellite images and sensor readings, establishing cross-type semantic association through a geographic space data embedding technology, generating a unified structured text after optimizing the satellite images through vLLM, and synchronizing the unified structured text to a central database; an agent role portrait is dynamically generated by the central server large language model based on a preset Prompt template, and generation does not depend on a fixed rule; then, selecting a target node in the edge-center architecture, disassembling a total task into sub-tasks, establishing semantic mapping, calculating a matching probability, and performing optimal distribution by a reward borrowing function; generating a governance scheme in a perception layer-decision layer-execution layer framework, and outputting a coded operation instruction; based on an execution feedback updating strategy, a multi-level mechanism is set, roles are automatically redistributed, and the governance continuity is guaranteed. According to the invention, the overall efficiency and reliability of space governance can be improved in the face of dynamic scenes or emergency situations.

The invention discloses a conversion method and system based on SysML and Modelica model semantic mapping. The method comprises the following steps that S1, a Modelica standard model library is imported into a SysML modeling tool; s2, completing the definition of behavior elements, and forming an SysML model instance; s3, exporting a Modelica model which can be executed by the Modelica simulator; s4, establishing a mapping relation table between SysML model elements and Modelica model elements; s5, when a change event occurs in the SysML model, updating the content of the corresponding Modelica model; s6, the Modelica model is imported into the SysML model, and a corresponding SysML model instance is generated; s7, simulation result mapping and behavior modeling generation are executed, a state transition diagram structure is formed, and structure updating of internal block diagrams in the system model is completed. According to the method, bidirectional conversion and joint simulation optimization between models are realized, and the method is suitable for cross-platform modeling, collaborative design and functional verification scenes of a complex system.

The invention discloses a 3D intelligent simulation generation method for clothing styles based on a model library, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the entity relation extraction and semantic mapping of multi-source clothing data, constructing a cross-modal knowledge graph, and extracting a physical constraint rule of the cross-modal knowledge graph; performing association reasoning and cross validation on the physical constraint rule and the version library to obtain a basic version template set; inputting the basic model template set into an intelligent clothing model, performing structured feature decoupling by the parameter analysis layer, dynamically adjusting a topological structure by the geometric generation layer, and generating 3D simulated clothing; and carrying out digital modeling on the basic model template set through a digital twinning algorithm, constructing a digital twinning body, carrying out physical simulation and optimization verification on the 3D simulation clothing by using the digital twinning body, and outputting a clothing style 3D simulation scheme. According to the invention, by constructing the pattern library and the intelligent garment model, the efficiency, accuracy and individuation level of garment 3D simulation generation are improved.

The invention provides an intelligent interactive question and answer method and system based on multi-modal large model intention recognition, and relates to the technical field of intelligent question and answer, and the method comprises the steps of feature extraction, cross-modal semantic mapping, intention matching, key information extraction and response generation, achieves the unified processing of multi-modal information, can accurately recognize the intention of a user, and improves the user experience. And the association network is dynamically constructed to extract key information, so that the accuracy and semantic coherence of intelligent questions and answers are improved, and the man-machine interaction experience is enhanced.

The invention relates to an intelligent short message template generation method and system based on a large language AI model, and the method comprises the steps: extracting cross-cultural communication frequency, sentence pattern preference analysis results and screen size adaptation data from social media interaction data and equipment use logs of target audiences, and generating a cultural background distribution matrix; identifying regional culture features in the culture background distribution matrix to obtain a taboo matching result; generating an expression habit preference matrix according to the updated culture background distribution matrix; generating a preliminary short message template according to the expression habit preference matrix and the symbol semantic mapping table, and outputting an adjusted template set conforming to message length control; analyzing the acceptability of the adjusted template set on the language conciseness requirement and the interactive button layout, and generating a new template set; evaluating a text alignment mode of the new template set; and screening candidate short message templates according to the information definition score. According to the invention, the accuracy and acceptability of cross-culture short message communication can be effectively improved, and the participation degree of audiences is enhanced.

The invention relates to a multi-source data fusion management method and system for medical record information, and the method comprises the following steps: carrying out the data collection of an electronic medical record system, a medical image workstation and an inspection information system of a medical institution, and obtaining an original medical record data set; performing semantic mapping and concept association on the original medical record data set based on a preset medical ontology knowledge graph to obtain a semantic association medical record information network; performing dynamic time sequence feature analysis on the semantic association medical record information network through a time sequence feature extractor to obtain a time sequence medical record feature sequence; and carrying out heterogeneous data fusion on the time sequence medical record feature sequence to obtain a unified medical record data view, so that the technical problem that the capability of deeply understanding and mining medical record information is limited due to the fact that an existing medical record management method usually ignores semantic relevance between data is solved.

The invention relates to the technical field of natural language processing, knowledge engineering and artificial intelligence, and particularly discloses a method and system for enhancing understanding of professional domain knowledge by a large model. The method comprises the steps that a professional domain entity classification system composed of a core entity, an auxiliary entity and a relation entity is constructed, attributes are expressed in a layered labeling and multi-granularity modeling mode, and semantic vectors are generated through ontology modeling and an embedding algorithm; based on a mixed extraction framework fusing expert rules and a neural network model, high-quality extraction of professional domain knowledge is realized; the method comprises the following steps: integrating multi-source heterogeneous data, and constructing a dynamically updated domain knowledge graph through semantic mapping, entity normalization and metadata weighting strategies; a knowledge graph is embedded into a Transform architecture, a knowledge perception attention mechanism and a multi-hop inference engine driven by reinforcement learning are introduced, and the knowledge fusion and inference ability of a large model is improved; and meanwhile, a triple check mechanism is designed to ensure entity consistency, relation logicality and numerical reasonability of the generated content. According to the method, the knowledge understanding and reasoning capability of a large model in professional scenes such as water conservancy is effectively improved, and the method has good universality and engineering application prospects.