87 results about "Knowledge engineering" patented technology

The invention discloses a structured decision-making method based on multi-agent collaborative decision-making and reinforcement learning, and relates to the technical field of natural language processing, knowledge engineering and agent collaboration, and the method comprises the steps: receiving an original rule document, analyzing the document type, complexity and constraint conditions, and defining a task target and a success standard; and according to the task target, matching and scheduling the intelligent agent from the registered intelligent agent library, and further analyzing the capacity configuration of the intelligent agent for standby. Through the multi-agent cooperation and reinforcement learning technology, full-process automation of rule documents from input to structured analysis is realized, document types, complexity evaluation and constraint condition analysis can be automatically identified, and a clear task target and a success standard are generated; and the large language model generates a structured workflow according to task requirements and agent capabilities, so that the performability is ensured through logic verification, manual intervention is greatly reduced, and the processing efficiency and the system intelligence degree are improved.

The invention belongs to the technical field of equipment knowledge engineering and natural language processing, and discloses an LLM-based constraint and self-repairing uniformization equipment knowledge graph automatic construction method. The method comprises the following steps: firstly, acquiring equipment related document data through network collection, document arrangement and database query; then, utilizing an equipment domain ontology and constraints as preposed soft and hard constraints, driving LLM to generate semantic intermediate representation, and obtaining candidate triples through structured compiling; then, a self-repairing closed loop is formed through semantic unit testing, logic consistency detection and evidence binding verification, and triples which violate constraints and have conflicts or illusions are automatically recognized and repaired; and finally, entity standard identification, cross-document duplicate removal combination and conflict resolution are realized through cross-segment unification and incremental alignment. According to the method, the fragile path that the LLM directly generates the triple and blindly stores the triple is avoided, the illusion and inconsistency problems are effectively inhibited, and the correctness, interpretability and maintainability of the equipment knowledge graph are remarkably improved.

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.

The invention provides a risk conduction prediction method and system based on combined deduction of a time sequence diagram and a large model, and relates to the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: extracting a prospective time sequence fact based on a hierarchical cue word and a structured constraint mechanism; extracting conflicts and generating a sequential relationship; the historical time sequence knowledge graph is updated based on the time sequence relation and a confidence coefficient weighted updating strategy; determining a central entity, and initiating a structured query to the updated historical time sequence knowledge graph by taking the central entity as a starting point to generate a context knowledge sub-graph; converting the context knowledge sub-graph into a natural language description with a logic relationship, injecting a risk hypothesis event, constructing a cue word as a new input of a large model, and outputting to obtain a structured JSON object containing a complete reasoning chain; and converting the structured JSON object based on a risk path extraction and visualization algorithm influencing weight attenuation to obtain risk early warning information and a visual conduction path diagram.

The invention discloses an industry knowledge graph automatic construction method oriented to multi-source heterogeneous data, and relates to the technical field of knowledge engineering. Structured, semi-structured, unstructured and distributed data are supported, and high-concurrency data are cached through Kafka; preprocessing and standardizing, and executing cleaning, field unification, text conversion and format conversion; a pre-training model and a rule engine are adopted to cover entities and relations of multiple industries; designing a dynamic Schema, storing the dynamic Schema in a graph database, and constructing an index; evaluating and correcting from multiple dimensions; and dynamic updating and maintenance, incremental updating based on data change, and support of version management and knowledge service. According to the method, the knowledge graph is automatically constructed through multi-source heterogeneous data, the construction efficiency and quality are improved, cross-domain fusion is supported, dynamic updating is adapted, and multi-industry intelligent application is assisted.

The invention provides a traditional Chinese medicine electronic medical record natural language processing and knowledge graph construction method and system. The method belongs to the technical field of the crossing field of traditional Chinese medicine informatization, artificial intelligence natural language processing and knowledge engineering, and comprises the following steps: performing multi-granularity semantic unit division on the traditional Chinese medicine electronic medical record to generate traditional Chinese medicine text semantic unit data; constructing a multi-granularity semantic decoupling engine according to the traditional Chinese medicine text semantic unit data so as to construct a traditional Chinese medicine text semantic analysis framework; performing traditional Chinese medicine unstructured text deep analysis according to the traditional Chinese medicine text semantic analysis framework, and performing multi-dimensional semantic feature extraction to obtain traditional Chinese medicine text semantic feature data and medical record time sequence data; through multi-granularity semantic unit division and deep analysis, the unstructured text in the traditional Chinese medicine electronic medical record can be effectively converted into structured semantic data, and the ability to understand and process traditional Chinese medicine clinical information is further improved.

The invention belongs to the technical field of natural language processing and knowledge engineering, and discloses a method, a system, equipment and a medium for automatically constructing a multi-field scientific knowledge base, and the method comprises the following steps: based on an input identifier list or a field search word, retrieving a full text of a literature, analyzing and converting the full text into a structured text; combining a pre-configured large language model with a field cue word, extracting key information in the structured text, and outputting structured data; the automatic script filters irrelevant, repeated or incomplete structured data according to a pre-configured filtering rule; performing standardization processing on the filtered structured data so as to realize the consistency and comparability of the data; and inserting the standardized structured data into an interactive knowledge base or database, establishing data association, and verifying association logic. The method supports cross-field rapid adaptation, solves the problems of low efficiency and high error rate of a traditional method, and can be widely applied to the fields of biomedicine, material science, synthetic biology and the like.

The invention relates to the technical field of artificial intelligence and knowledge engineering, and provides a digital human knowledge graph dynamic iteration system and method based on user feedback. According to the method, the timeliness and automation level of knowledge updating are improved; the configuration efficiency of technical resources is optimized; a data-driven iterative verification closed loop is constructed; according to the knowledge service system, the maintainability and robustness of the system are enhanced, the system has the capabilities of tracking, querying and rollback any change, the risk of system service degradation caused by misoperation or invalid updating is greatly reduced, and the stability of the whole knowledge service system is improved.

The invention discloses a knowledge graph construction method, device and equipment and a readable storage medium, and is applied to the technical field of natural language processing and knowledge engineering.The method comprises the steps that document content is divided to obtain initial document fragments, and all the initial document fragments are merged and divided based on semantic similarity to obtain target division blocks; based on the target division block, subject-predicate-object formatting processing is carried out to obtain a subject-predicate-object formatting result; entity and relation extraction is carried out according to the subject-predicate-object formatting result to obtain a display triple, implicit relation reasoning is carried out to obtain an implicit relation triple, entity and relation type normalization is carried out to obtain a normalized triple, and the knowledge graph is constructed based on the normalized triple. The block segmentation driven by semantic similarity is adopted to avoid sentence breakage, cascade errors are reduced based on subject-object formatting processing, and the problems of fragmentation, link missing and the like are solved based on implicit relations, so that the integrity and accuracy of knowledge graph construction are improved.

The invention provides a knowledge graph generation method and system combined with dynamic semantic association mining in the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: S1, collecting basic data which is used for generating a knowledge graph and comprises relational data and non-relational data, and carrying out the preprocessing of each basic data through a kettle tool; s2, reasoning each piece of basic data through a LangChain framework in combination with a large model so as to dynamically and semantically associate and mine ontology information from each piece of basic data; s3, initializing a knowledge graph, converting the ontology information into a graph structure of the knowledge graph, and dynamically updating the knowledge graph based on the newly mined ontology information; and step S4, storing the knowledge graph in a graph database Neo4j in real time, and performing visual display on the knowledge graph stored in the graph database Neo4j through a Neo4jBLOOM tool. The knowledge graph generation method has the advantages that the flexibility, the automation degree, the efficiency, the semantic consistency, the dynamic adaptability and the expansibility of knowledge graph generation are greatly improved.

The invention provides a dynamic updating method and system of a knowledge graph based on reasoning enhancement. The method belongs to the cross technical field of artificial intelligence, knowledge engineering and dynamic system modeling. The method comprises the following steps: performing multi-dimensional feature extraction on original knowledge data to generate a knowledge feature vector set; constructing an initial knowledge graph structure based on the knowledge feature vector set, and defining an initial association rule between knowledge nodes to form a basic knowledge graph model; building a multi-order causal inference engine according to the basic knowledge graph model, performing deep mining on potential causal relationships among knowledge nodes, and generating a knowledge causal relationship network; and performing confidence evaluation on each causal chain in the knowledge causal relationship network to obtain the knowledge causal relationship network subjected to quality verification. Based on a multi-order causal reasoning engine, the system can mine and verify potential causal relationships among knowledge nodes in real time, and continuous updating and self-optimization of the knowledge graph are ensured.

The invention provides a traffic real-time road condition prediction method and system based on knowledge engineering, and the method comprises the steps: collecting multi-source traffic data in real time, carrying out the missing data filling and data space-time alignment processing of the multi-source traffic data, and obtaining the processed multi-source traffic data; fusing the processed multi-source traffic data by adopting a method based on dynamic space-time tensor modeling to obtain fused traffic data; based on the fused traffic data, constructing a self-evolution traffic knowledge graph with time-space attributes; on the basis of the self-evolution traffic knowledge graph, constructing a road condition hybrid prediction model by adopting a knowledge-guided meta-learning framework; and performing traffic real-time road condition prediction based on the road condition hybrid prediction model. According to the invention, the robustness of the system is obviously enhanced, and more accurate road condition prediction is realized.

Multi-stage hybrid network integrates relationship regularization links and explainable elements to improve alignment with human values, explainability, robustness, and efficiency. The network comprises neural components, event prediction elements, and probability models across multiple stages, with relationship constraints enforcing structured knowledge representation. Explainable elements provide interpretable rationales for decisions, enhancing transparency. Training incorporates supervised learning, human-guided refinement, semi-automated knowledge engineering, and adversarial robustness techniques. A Socratic reasoning module detects contradictions and refines outputs for logical consistency. Indexed model elements enable dynamic memory optimization for improved efficiency. Candidate outputs may be scored, verified, or selected using neural and symbolic criteria. The invention supports retry loops and configurable subsystem pipelines to improve output quality. Applications include text generation, speech recognition, translation, and decision support. By combining structured constraints, human oversight, and modular architectures, the system improves the trustworthiness, safety, and adaptability of AI systems across diverse modalities and tasks.

The invention belongs to the technical field of artificial intelligence and knowledge engineering, and relates to a multi-modal knowledge graph construction and interactive exploration method and system oriented to game cognition enhancement. The method comprises the following steps: acquiring multi-source heterogeneous multi-modal data, converting non-text modal data into a description text, and extracting a knowledge triple; constructing a multi-modal knowledge graph according to the extracted knowledge triad; and based on the constructed multi-modal knowledge graph, game cognition enhanced interactive exploration and multi-modal visual presentation are carried out. According to the method, end-to-end multi-source heterogeneous multi-modal unified integration can be realized, cross-modal concrete support of a cognitive process is realized, multi-modal knowledge traceability verification of a decision basis is realized, real-time perception and synchronization of a dynamic game environment are supported, and the method is suitable for popularization and application. According to the method, newly generated key information can be quickly digested, absorbed and updated to the knowledge graph in a quickly changing game environment, and is immediately retrieved and explored, so that approximate real-time dynamic cognition and decision support is provided for a user.

The invention discloses a big language model illusion detection method and device based on a knowledge graph and a medium, and relates to the technical field of natural language processing and knowledge engineering. The method comprises the steps of generating a to-be-detected text in response to a large language model, and obtaining a domain knowledge graph; performing structured analysis on the to-be-detected text to obtain an entity set and a relation triple corresponding to the to-be-detected text; based on the domain knowledge graph, performing factual illusion detection on the entity set and the relation triple to obtain a detection result and confidence corresponding to the factual illusion detection; and generating an illusion detection result corresponding to the to-be-detected text based on the detection result and the confidence coefficient. Therefore, the detection precision is high, the coverage range is wide, and the interpretability is high.

The invention discloses a dynamic adaptive management system for road traffic safety planning driven by artificial intelligence. According to the system, multi-source data are acquired through a data acquisition and preprocessing layer and are cleaned and standardized; the knowledge engineering layer constructs a policy knowledge graph and a rule engine; the large model service layer realizes natural language generation, semantic understanding and multi-modal reasoning; the intelligent generation layer uses a GAN model to generate multi-modal planning content and dynamically adapts the multi-modal planning content; the implementation process management layer monitors planning implementation, middle-stage evaluation and last-stage summarization in real time; and the interaction and output layer provides visual editing and multi-format output. The system integrates the technologies of deep learning, generative adversarial network and the like, realizes intelligent planning generation and dynamic supervision, has the capabilities of multi-modal data processing, dynamic self-adaption and full-process management, can effectively improve the scientificity, high efficiency and adaptability of road traffic safety planning, and provides powerful support for road traffic safety management.

The invention relates to the cross technical field of artificial intelligence and knowledge engineering, in particular to a local knowledge base question-answering system based on a new energy automobile and a construction method thereof, and the method comprises the following steps: integrating a knowledge graph, a ChatGLM2-6B language model and a LangChain framework to construct a local knowledge base question-answering system; obtaining a ternary collaborative architecture for structured management, retrieval and semantic question and answer of heterogeneous data in the new energy field; and based on the ternary collaborative architecture, correspondingly designing a plurality of functional modules for providing an intelligent question-answering solution for the field of new energy vehicles, and forming the local knowledge base question-answering system for the new energy vehicles. According to the'knowledge graph-pre-trained large model-LangChain 'ternary collaborative architecture provided by the invention, structured management, efficient retrieval and high-precision semantic question and answer of heterogeneous data in the field can be realized, and structural management, efficient retrieval and high-precision semantic question and answer of multi-source heterogeneous data in the new energy automobile field can be realized; and local deployment, large model generation and interpretable retrieval can be combined.

The invention belongs to the technical field of network security, and discloses a construction method of a network security knowledge graph based on SecureBERTPlus, and the method comprises the steps: achieving the knowledge conversion of threat intelligence through the cooperative processing of a data preprocessing module, a semantic information extraction module and a knowledge graph construction and optimization module; and a complete knowledge engineering link from original data to decision support is formed. According to the construction method adopted by the invention, synchronous optimization of entity recognition and relation extraction is realized through a joint extraction framework; semantic features of terms in the network security field are accurately captured through a field-adaptive SecureBERTPlus pre-training model, time sequence dependence in an attack chain is modeled by using a BiLSTM layer, a cross-sentence key relationship is focused by means of an attention mechanism, and ATTamp is injected based on a CRF layer; a CK rule constrains a decoding process, and a hierarchical clustering HAC based on context embedding is particularly introduced to realize entity dynamic disambiguation and semantic fusion.

The invention discloses a knowledge graph multi-modal document analysis and image table semantization knowledge recall method, and belongs to the technical field of knowledge engineering and information retrieval. The invention provides an innovative scheme for fusing a visual language model, semantic abstract generation and knowledge graph modeling. The method comprises the following steps: constructing a vertical domain knowledge graph by adopting a BERT-BiLSTM-CRF model; according to the method, multi-modal document analysis is realized through models such as DocLayout-YOLO, TableMaster, UniMERNet and the like; the method comprises the following steps of: segmenting an image into 16 * 16 block sequences by adopting a vit-gpt2-image-adaptation model, and realizing image semantization through 768-dimensional vector space mapping and Transform coding; constructing a document summary tree based on DBSCAN clustering and LLM recursive summary; and designing a hybrid retrieval space fusing semantic vectors and structured vectors, and reordering by adopting a double-attention mechanism. According to the method, the knowledge base document retrieval recall rate is increased to 99%, the question and answer accuracy rate reaches 90% or above, the index construction time is shortened by 60%, and the problem that semantic understanding and recall of non-text elements in complex documents are difficult is effectively solved.

The invention discloses a logic knowledge construction method and device based on a large language model and logic programming, electronic equipment and a storage medium, and belongs to the field of artificial intelligence knowledge engineering. According to the method, automatic conversion from multi-source heterogeneous knowledge to machine reasonable logic knowledge is realized through combination of semantic understanding capability of a large language model and standardized processing of logic programming. The method specifically comprises the steps of obtaining unstructured / semi-structured original knowledge data, performing entity relationship recognition and predicate logic detection by utilizing a large language model to generate a preliminary logic expression, performing grammar normalization processing to form standard logic facts and rules, and constructing a consistent logic knowledge base through a conflict detection and resolution mechanism. According to the method, a mixed conversion framework of natural language and formalized logic is innovatively provided, and the maintainability and expandability of the knowledge base are remarkably improved by introducing timestamp metadata, a rule dependence graph and an incremental updating mechanism. The technology provides structured logic support for subsequent large model reasoning, and can be widely applied to intelligent questions and answers, decision support and other scenes requiring precise logic reasoning.

The invention relates to a CATIA knowledge engineering-based rule plate automatic generation method and system and a medium. The method comprises the following steps of: creating text parameters in CATIA and inputting a graph-free code; analyzing the codes to obtain modeling parameters; generating coordinate points according to the parameters and forming a contour; inserting a geometry, drawing a curved surface based on the contour, and then performing thickening and chamfering treatment; boolean operation is carried out on the geometry; activating or cancelling the corresponding geometry according to the structural form code; defining attributes for the geometry and associating modeling parameters; packaging the model into user features and issuing parameters; and calling characteristics by inputting codes and version numbers, and automatically generating a graph-free model with attributes. According to the method, codes can be automatically identified, accurate models and attributes are quickly generated, and the design efficiency and quality are improved.

The invention provides a document knowledge retrieval method and system based on a two-way quantity library hybrid architecture, relates to the technical field of crossing of artificial intelligence and knowledge engineering, and solves the technical problems that a traditional RAG knowledge retrieval system is delayed in response to high-frequency repeated problems, high in cost and poor in answer consistency due to indifference processing. The method comprises the steps of obtaining a user query request and historical document data; constructing an FAQ vector library and a document vector library based on the historical document data; calculating the matching similarity between the user query request and the FAQ vector library through a similarity algorithm to obtain an FAQ similarity score; comparing the FAQ similarity score with a preset similarity threshold value; when the FAQ similarity score is greater than or equal to a similarity threshold value, marking a standard answer corresponding to the FAQ similarity score as a query result; when the FAQ similarity score is smaller than the similarity threshold value, the document vector library is retrieved, and a big language model is called for TOP-M related fragments to generate answers to serve as query results. The method and device are used in the knowledge retrieval process.

The invention belongs to the technical field of knowledge engineering, and provides a multi-dimensional verification knowledge base construction method based on user requirements, which comprises the following steps: importing materials provided by a user, identifying a knowledge base construction target for the materials and analyzing the user requirements, and constructing the target and the user requirements based on the knowledge base obtained by analysis. Further designing a knowledge representation structure and an association relationship model according to knowledge types and business scenes, preliminarily generating a knowledge base content file, performing multi-dimensional verification on the knowledge base content by utilizing a calibration tool, calculating a comprehensive score according to a preset rule weight, and performing multi-dimensional verification on the output knowledge base content file. And the user decides verification and outputs the knowledge base after passing the verification. And through double verification of multi-dimensional automatic verification and manual recheck verification, the accuracy, normalization and logicality of the stored knowledge are ensured, and the overall quality of the knowledge base is ensured from the source.

The application discloses a dynamic enhanced multi-modal hypergraph retrieval enhancement generation method and system, belonging to the technical field of information retrieval and knowledge engineering, comprising: in the offline stage, cross-modal encoding is performed on multi-source heterogeneous data to generate cross-modal vector index and candidate theme / entity set; knowledge drift detection is performed on the cross-modal vector index and the candidate theme / entity set, if it is determined that there is drift, then local subgraph incremental hypergraph update is triggered, and after multi-expert voting alignment verification, it is written into a multi-modal double hypergraph index library; in the online stage, the user request is subjected to semantic analysis, theme words and entity words are extracted; according to the theme words and the entity words, coarse retrieval and fine retrieval are performed based on the multi-modal double hypergraph index library, evidence subgraphs are extracted, multi-modal context is obtained; the user request and the multi-modal context are input into a multi-modal large language model to generate an answer and feedback the user; the timeliness, stability and reliability of retrieval are improved, and the integrity of complex knowledge reasoning is improved.

The invention discloses a big data platform monitoring method and device based on knowledge engineering, electronic equipment and a storage medium. The method comprises the steps of collecting real-time state data of a to-be-monitored platform; reasoning the real-time state data according to a pre-training prediction model and a pre-constructed reasoning mechanism to obtain a reasoning conclusion; the pre-training prediction model is obtained through at least one round of training according to each piece of fault knowledge in a pre-constructed knowledge base; each piece of fault knowledge comprises a fault conclusion and a fault symptom; the pre-constructed reasoning mechanism is realized through a target reasoning tool according to a fault judgment rule in the pre-constructed knowledge base; and generating a fault diagnosis report according to the reasoning conclusion, and displaying the fault diagnosis report to an operation and maintenance party of the to-be-monitored platform. According to the technical scheme, intelligent operation and maintenance of the big data platform are realized, and the operation and maintenance efficiency, adaptability and fault diagnosis accuracy are improved.

The application discloses a domain knowledge triple extraction method, system, medium and equipment, and relates to the technical field of knowledge engineering.The domain knowledge triple extraction method and system provided by the application can provide high-quality and efficient data support for triple extraction by analyzing and converting the obtained domain professional text into a structured vector knowledge base which can be efficiently searched, then the domain professional text is subjected to quantitative calculation and fusion screening, the initial entities of domain core high-frequency words which have global high frequency and cross-text universality are mined, the problem of "local optimum and global deviation" caused by traditional single word frequency is avoided, non-core term interference is effectively avoided, and the stability, relevance and efficiency of iterative extraction are improved, and further, the high-frequency word initial entities are subjected to closed-loop iteration through iterative RAG algorithm combined with the structured vector knowledge base, accurate extraction of the large model and iterative correlation entities, the explicit and implicit semantic correlation triples in the text can be mined layer by layer, the strong relevance and structural integrity of the extracted triples are ensured, and the professionalism and accuracy of the model in extracting triples are improved.

The application relates to the technical field of knowledge engineering, and discloses a production and manufacturing digital twin modeling method based on a knowledge graph, which comprises the following steps: performing data fusion and model construction on original multi-source data, obtaining multi-source fusion data, and generating a digital twin architecture; performing ontological design and semantic modeling on the multi-source fusion data, and generating a manufacturing knowledge graph; wherein the manufacturing knowledge graph is represented by triplets, and the triplets comprise entities-relation-entities / attributes; performing entity modeling on the manufacturing knowledge graph, and obtaining a knowledge-driven geometric model; and performing reasoning based on the manufacturing knowledge graph and historical data, and obtaining a knowledge-driven decision model. The method can construct a digital twin model with knowledge-driven prediction and decision-making capabilities, and provides a high-robustness and sustainable-evolution digital twin modeling method for intelligent manufacturing, smart factories and other fields. The application also discloses a production and manufacturing digital twin modeling device based on the knowledge graph and an electronic device.

The invention relates to the technical field of artificial intelligence and knowledge engineering, and discloses a knowledge graph completion method, system and device based on entity relation attention and a medium, and the method comprises the steps: distributing an embedded vector with a fixed length for each entity and relation in a knowledge graph, and employing a first initialization method to initialize the embedded vector; respectively mapping and remodeling the embedded vectors of the head entity and the relationship into a matrix form; capturing a deep relationship between the head entity and the relationship through an attention mechanism, and generating a joint feature vector; calculating the joint feature vector and the embedded vector of the tail entity to obtain a scoring result of the triple; and sorting and selecting all the candidate triads according to the scoring result, and adding the first selection result as a completion result into the knowledge graph. According to the method, the deep semantic association between the entity and the relationship can be effectively captured, and key information is dynamically focused through an attention mechanism, so that knowledge graph completion with better performance is realized.

The invention discloses a general scientific research entity corpus construction method and device based on scientific and technical literature and a medium, and relates to the technical field of natural language processing and knowledge engineering. Performing automatic pre-labeling of general scientific research entities on the scientific and technical literature abstract set to obtain an extracted general scientific research entity set and a confidence coefficient set; filtering and screening the confidence coefficient set to obtain a difficult sample set; extracting the difficult sample set to obtain a large language model pre-labeling result set; and obtaining a large language model correction labeling result set and a large language model correction degree set. According to the method and the device, the technical problems of low scientific research entity extraction efficiency and insufficient accuracy caused by incomplete general scientific research corpus construction process and lack of an effective feedback optimization mechanism of a model in the prior art are solved, and the technical effects of realizing a high-quality general scientific research entity corpus construction process and improving the entity extraction efficiency and accuracy are achieved.

The application discloses a modular design method for automobile eye ellipse and head envelope, and is characterized in that, based on the secondary development function of CATIA software, a knowledge engineering template file (part file) capable of directly displaying a design target structure is established, a plurality of fixed input parameters, a plurality of variable parameters and intermediate parameters are selected, the corresponding relationship between each parameter and an output parameter is automatically converted and associated, and an intelligent design template for automobile eye ellipse and head envelope is obtained; then, the template is called during design, different variable parameter information is input according to design requirements, the knowledge engineering template is automatically run, and the calculated output parameter is directly displayed in the form of a graph. The application can simplify the calculation process, reduce repetitive labor, avoid human calculation errors, improve design accuracy, and better improve design efficiency and accuracy.