217 results about "Knowledge structure" patented technology

The invention relates to the technical field of data processing, and discloses an AI knowledge base construction method and system for intelligent water conservancy. The method comprises the following steps: forming a water conservancy intelligent perception data set by integrating hydrological monitoring data, engineering operation records and expert literatures; knowledge sprouts are identified, and a water conservancy native knowledge unit is generated; key features are extracted, knowledge element decomposition is carried out, and a knowledge evolution chain network is constructed through correlation analysis; screening in combination with a historical application effect and expert evaluation to obtain a preferred knowledge body; performing intelligent reconstruction on a scheduling rule, risk prevention experience and an operation and maintenance method to form an innovative knowledge base; and finally, intelligently matching with real-time engineering requirements to generate an intelligent water conservancy AI knowledge base. According to the application, by establishing a self-adaptive knowledge acquisition, organization and application mechanism, the knowledge base can autonomously update the knowledge content according to the environment change, the knowledge structure is optimized, and the intelligent operation management level of the water conservancy project is improved.

The invention discloses a traditional Chinese medicine intelligent inquiry method and system based on a knowledge graph and medical case enhancement RAG, and the method comprises the following steps: collecting traditional Chinese medicine related data from a plurality of sources, and carrying out the data cleaning, formatting and standardization processing, and constructing a knowledge graph containing traditional Chinese medicine symptoms, disease causes, prescriptions, and the mutual relation of the symptoms, the disease causes, the prescriptions; expressing a knowledge structure in the graph in a triple form; searching a local sub-graph related to query based on query keyword extraction and generalization by utilizing a Leiden community detection algorithm; a mixed retrieval strategy is adopted, global search and local search are combined, recalled contents are sorted and scored, global and local retrieval results are fused, and a large language model is uniformly output. The system aims at improving the intelligent level and individuation ability of traditional Chinese medicine diagnosis, firstly, a knowledge graph covering entities such as traditional Chinese medicine theories, symptoms, pathogenesis and prescriptions and relationships of the entities is constructed, mass traditional Chinese medicine data are systematically integrated, deep understanding and semantic association of traditional Chinese medicine complex diagnosis and treatment logic are ensured, and the traditional Chinese medicine diagnosis and treatment efficiency is improved. Through deep combination of the structured knowledge base and the generated model, the diagnosis and treatment accuracy is improved, and the model fine tuning and updating cost is reduced.

The embodiment of the invention provides a project research and development data key information processing method and device. A research and development content recognition system and a self-adaptive research and development knowledge graph are constructed. Unified processing and time sequence alignment of text, voice and image contents are realized through multi-modal information decomposition and fusion. Semantic completion and error correction are carried out based on research and development of a semantic analysis model, a knowledge graph structure is dynamically constructed and optimized, and a structured document with a traceability relation is generated. The system adopts a deep neural network model to extract research and development key information, constructs a multi-level document framework, and realizes intelligent conversion from research and development data to a project application document. According to the method, the defects of the traditional technology in the aspects of multi-modal information processing and knowledge structure optimization are effectively overcome, and the research and development data management and project declaration efficiency is remarkably improved.

The embodiment of the invention provides a directional training method and device for an enterprise project text processing model, and the method and device achieve the screening of high-quality training samples through the construction of a project text scoring model and the multi-dimensional evaluation of the text coverage degree, the technical correlation degree and the structural integrity. And analyzing a text structure relationship based on the knowledge graph and the graph convolutional network, and constructing a differentiated text template library. The system performs directional training on a pre-training large model in combination with an enterprise portrait vector, and continuously optimizes model parameters and a knowledge base by adopting an incremental learning strategy. According to the method, the defects of a traditional technology in the aspects of training sample screening, knowledge structure analysis and model personalized customization are effectively overcome, and the practicability and adaptability of a project text processing model are remarkably improved.

The invention discloses a declarative knowledge construction method and device for semantic and situational knowledge collaborative modeling, computer equipment and a readable storage medium, and relates to the field of data processing.The method comprises the steps that firstly, a multi-modal document is analyzed, a chapter abstract is extracted, and structured content is obtained; entities, events and multi-modal knowledge points are extracted from the structured content, and cross-modal fusion is carried out on the entities, the events and the multi-modal knowledge points; carrying out anaphora resolution based on the fused knowledge points, and constructing a double atlas containing a knowledge atlas, a affair atlas and a four-dimensional relation triple; clustering the double maps to obtain a theme community, and performing association mapping on the community, the triple and the entity event, the chapter abstract and the multi-modal knowledge point to form association knowledge; vectorizing the associated knowledge and establishing a vector knowledge index; and carrying out compression ratio and accuracy evaluation on the knowledge through an evaluation system, and feeding back and optimizing the whole knowledge construction process. According to the method, multi-modal knowledge deep fusion and semantic scene collaborative modeling are realized, and the knowledge structuring degree and the application reliability are improved.

The invention relates to the technical field of building engineering cost intelligent estimation, and discloses a building engineering cost intelligent estimation and checking method fusing a domain knowledge graph and a large language model, and the method comprises the following steps: S1, building a building engineering domain knowledge graph (KG), integrating a quota library, a material price library and historical project data, and carrying out the calculation of a building engineering domain knowledge graph (KG); and defining an entity relationship and a dynamic updating rule. According to the intelligent building engineering cost estimation and checking method fusing the domain knowledge graph and the large language model, a closed loop of knowledge structuring (KG) + semantic comprehension (LLM) + multi-modal reasoning is realized for the first time, engineering cost management is promoted to be transformed from experience driving to data-knowledge dual driving, a reusable technical normal form is provided for the field of intelligent construction, and the engineering cost is estimated and checked. The method solves the problems of efficiency, precision, dynamics and interpretability of traditional cost management, has technical innovation and practical value, and provides core technical support for digital transformation of constructional engineering.

The invention relates to the field of intelligent automatic driving, in particular to a knowledge-driven end-to-end automatic driving method based on a sparse expert mechanism and a diffusion model. Comprising the following steps: S1, sensing information processing and state coding; s2, sparse expert module construction and multi-task training; constructing a sparse expert module composed of a plurality of experts, and obtaining a reusable driving skill through multi-task behavior cloning training; s3, generating a diffusion strategy network and an action sequence; and on the basis of a diffusion model, a future multi-step control action sequence is generated from the current state condition, and a continuous and stable driving decision is formed. And S4, a continuous learning and task migration mechanism. According to the method, a combinable and explainable modular driving knowledge structure is constructed, so that the strategy modeling capability is remarkably improved; a diffusion generation mechanism effectively improves the smoothness and stability of the decision process; and a continuous learning and task migration mechanism of structural decoupling improves the long-term adaptability and deployment efficiency of the system.

The invention discloses a digital human interaction method and system based on a knowledge graph, a medium and a product program, and the method comprises the steps: extracting a key entity and an entity relation from question information; determining a core node with the highest association degree with the key entity in a preset knowledge graph, and generating a knowledge sub-graph of which the maximum depth is a preset value based on the core node; calculating the shortest path between the nodes in the knowledge sub-graph, and determining the weight of the hydrophilic-hydrophobic relationship between the nodes according to the length of the shortest path; pruning the knowledge sub-graph based on the weight of the close-to-sparse relationship to obtain an optimized knowledge sub-graph; clustering each node in the optimized knowledge sub-graph according to the type of the corresponding connecting edge to obtain a multi-level knowledge structure; performing depth-first traversal in the multi-level knowledge structure according to the entity relationship to generate a knowledge reasoning chain; and generating reply information, and controlling the digital person to output the reply information. According to the method and the device, the limitation of processing cross-domain knowledge interaction is reduced, and high fusion of multi-dimensional information is realized.

The invention discloses a large-model-driven automatic knowledge graph construction method based on a confidence feedback mechanism, and aims to improve the structural accuracy and semantic consistency in a structured triple generation process, and perform structural constraint guidance by using a few-sample prompt mechanism and a cross validation mechanism of a heterogeneous large model. And the control capability of the large language model on the triple format is enhanced, so that format offset and semantic redundancy in the generation process are reduced. And meanwhile, a multi-dimensional confidence evaluation system is constructed, model consensus judgment, semantic rationality analysis and knowledge consistency verification are fused, and refined quantification and screening of triple quality are realized. According to the method, a confidence backtracking feedback strategy is introduced, a generation-verification-optimization closed-loop process is constructed, the expression and correction capability of the system on a complex knowledge structure is enhanced, the dependence on an external API is effectively reduced, the consumption of computing resources is reduced, and the operation efficiency of the system and the feasibility of engineering deployment are remarkably improved.

The invention relates to a new media AI marketing content creation method and device, equipment and a medium. The method comprises the steps of obtaining user demand configuration, and obtaining a creation intention vector through natural language analysis; based on the platform characteristic knowledge base, extracting a corresponding structure specification and a propagation mechanism according to the target platform, and encoding to obtain a platform characteristic vector; obtaining historical content interaction data corresponding to the audience group, and generating a user-content interaction vector by adopting collaborative filtering and a label similarity algorithm in combination with the content keyword; and calling an artificial intelligence large model, and performing content generation according to the creation intention vector, the platform feature vector and the user-content interaction vector to obtain content creation data. By adopting the method, the goal of automatic, high-quality and personalized new media marketing content creation in a multi-platform environment can be realized by means of natural language analysis, knowledge structure extraction, large model generation constraint and the like.

The invention relates to the field of semiconductors, in particular to an answer generation method and device based on a semiconductor knowledge base and a medium. The method comprises the steps of performing data association on structured knowledge according to a semiconductor domain knowledge structure to construct a class of knowledge bases; performing data recombination on the unstructured knowledge according to the layout so as to construct a second-class knowledge base; a query question is received, retrieval is executed in the first-class knowledge base and the second-class knowledge base based on a preset retrieval model, and a plurality of related knowledge fragments are obtained; determining a first fusion weight between the knowledge fragments of the same knowledge type based on the relevancy between each knowledge fragment and the query question and the update time of each knowledge fragment; determining an optimal knowledge type according to the group feature information, and determining a second fusion weight between different knowledge types according to the optimal knowledge type; and based on the first fusion weight and the second fusion weight, obtaining a reference answer according to the plurality of knowledge fragments. And the requirements of the semiconductor field on information comprehensiveness and answer accuracy are considered.

The invention provides a multi-source heterogeneous data dynamic updating and agent knowledge base construction method, and relates to the technical field of data processing, and the method comprises the steps: organizing heterogeneous data through a three-dimensional feature matrix, constructing feature mapping through singular value decomposition and cross decomposition, and executing recursive tensor completion to generate a fusion feature space; extracting multi-scale features and determining a stable knowledge entity based on comprehensive measurement; constructing a network structure and dividing knowledge clusters; and performing differentiation fusion of the knowledge clusters based on the life cycle parameters. According to the method, efficient integration of heterogeneous data, accurate extraction of knowledge entities and dynamic optimization of knowledge structures are realized, and the intelligent level of knowledge management is improved.

The invention provides a water conservancy knowledge structured extraction and verification method and device, and belongs to the technical field of artificial intelligence, and the method comprises the steps: carrying out the differential text processing of different formats of files, and generating an intermediate file; classifying the intermediate file into a regulation class or a non-regulation class based on a preset rule base; performing hierarchical title identification on the regulatory files to form entry knowledge blocks, and converting table contents into HTML (Hypertext Markup Language) knowledge blocks; performing semantic segmentation on the non-regulation file to generate knowledge blocks; performing knowledge block checking and filing, and marking an abnormal alarm block; converting the table knowledge blocks into natural language description by utilizing a large model; and positioning the context of the original text of the alarm knowledge block, and performing intelligent correction through a large model. According to the method, a traditional semantic analysis model and a large language model are creatively fused, a closed-loop process of preprocessing, extraction, verification and correction is formed, the problems of structured analysis and error correction of complex texts in the water conservancy field are solved, and the knowledge processing efficiency and accuracy are remarkably improved.

The invention discloses a water conservancy project knowledge graph large model construction method and device, and the method comprises the steps: constructing a water conservancy project typical business domain knowledge graph based on a water conservancy project management business knowledge base through employing a multi-business knowledge fusion water conservancy knowledge graph data layer, concept layer and instance layer construction method; aiming at the characteristics of complex water conservancy business knowledge structure and abundant potential association, the completeness and accuracy of a water conservancy typical business domain knowledge graph are improved through a self-iteration water conservancy business knowledge graph complementation technology based on a large language model; in order to efficiently combine the advantages of a knowledge graph and a large model in the aspects of knowledge representation and knowledge analysis, a retrieval enhancement generation technology based on the knowledge graph is adopted, entities, relationships and attributes of the entities and the relationships are automatically extracted from structured and unstructured knowledge bases, and a water conservancy project knowledge graph large model is created; and multi-target collaborative scheme reasoning application of flood control, water supply, power generation and the like of water conservancy projects is supported.

The invention discloses a knowledge graph completion method and device and a medium, and the method comprises the steps: extracting a missing triple with a missing field, and an associated image of the missing triple from a multi-mode knowledge graph database of a designated field; generating an enhanced text without the triad; the enhanced text is a semantic enhanced text of a known field in a missing triple; and based on a pre-constructed reasoning prompt word project about the missing field, through a pre-constructed multi-modal large model, reasoning the missing triple, the associated image and the enhanced text to obtain a complemented triple after complementing the missing field. Therefore, the multi-modal information of the triad is fully utilized, the image information and the text information are fused and complemented, and the missing triad is accurately complemented, so that the knowledge graph information of the specified field is more perfect and accurate, and the knowledge structure and relation of the specified field are better reflected; and a reliable basis is provided for a practical application scene of a subsequent knowledge graph.

The invention relates to the technical field of online education, and discloses an online course learning management method based on a knowledge graph. The method comprises the following steps: acquiring multi-modal learning behavior data of a learner, and extracting a deep learning state vector reflecting knowledge understanding depth, learning input degree and cognitive confusion through semantic fusion; and dynamically calculating and updating the logical relationship strength among the knowledge points in the course knowledge graph by using the vector, so that the knowledge structure can adaptively evolve along with the actual cognitive state of the learning group. And generating a real-time personalized learning path based on the updated knowledge graph and the current state vector of the learner. Meanwhile, according to cognitive confusion features in the state vector, intervention measures such as pushing of remedial resources, adjusting of content sequence or starting of self-adaptive testing are triggered in real time. According to the method, the dynamic optimization of the knowledge graph and the accurate and immediate response of learning intervention are realized, and the adaptability and management efficiency of online learning are improved.

The invention provides a knowledge structured extraction method based on a multi-modal large model, and relates to the technical field of data processing. Comprising the steps of determining to-be-extracted attribute information according to a data extraction requirement, and generating a structured data model according to the to-be-extracted attribute information; according to the structured data model and a preset extraction strategy, performing data extraction on a to-be-processed original multi-modal file to obtain a plurality of extraction results; merging the plurality of extraction results to obtain a merged extraction result; and verifying the combined extraction result to obtain a target extraction result. According to the method, in knowledge extraction through a multi-modal large model, through an extraction strategy of combining multi-round extraction with staged extraction, the data volume of one-time extraction of the model can be reduced, data extraction omission is avoided, meanwhile, staged extraction enables the model to be gradually progressive from coarse to fine and from global understanding to field-level fine extraction, context loss is avoided, and the knowledge extraction efficiency is improved. And the precision of the extraction result is improved.

System and method for using knowledge fabric based on knowledge ontology, designed for deep language understanding and mechanistic causal reasoning, and meta-knowledge repository for auditable question answering. The method includes receiving an input text from a user, building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains. The knowledge graph in combination with causal path knowledge and metadata describing digital sources containing answers constitutes the knowledge fabric. The method includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences using the knowledge graph in conjunction with logical inference to achieve deep natural language understanding. The method includes finding / delivering response to the input request as to why / how unknown factors resulted in known outcome, or what outcomes are likely given known causal factors.

The invention relates to the technical field of talent management and data analysis, in particular to a multi-source data driven scientific and technological talent ability portrait accurate matching method. The method comprises the following steps: collecting and preprocessing multi-source talent data, and mining text information by applying a natural language processing technology; constructing a talent knowledge structure graph by identifying the relationship between the key entities and the extracted entities; determining a plurality of capability dimensions, and establishing a quantitative model or rule for each dimension to measure the capability; calculating scores of all dimensions by comprehensively considering personal differences of talents and working situation factors, and generating a visual ability portrait; a demand standard is determined by analyzing the situation of a demand side, the matching degree between talents and the demand standard is calculated after the talents are screened from a talent pool, the recommended talents are ranked according to the matching degree, and related parameters and rules are optimized according to feedback of the demand side. The talent ability can be comprehensively and accurately described, efficient and accurate matching is achieved, the method adapts to dynamic changes, and talent configuration in the science and technology service field is effectively optimized.

The invention provides an educational resource sequence recommendation method and a related device, and relates to the technical field of resource recommendation. After student learning behavior data, learning resource attribute data and knowledge graph structure information are obtained, a regularization matrix decomposition technology is adopted to carry out potential factorization modeling on a student-resource interaction scoring matrix, and basic preference embedding features of students and resources are extracted. Aiming at diversity and complexity of student learning behaviors, a multi-view feature coding module is designed, learning sequence and short-term interest features are extracted from a behavior sequence view, static attribute features of students and courses are extracted from an attribute information view, and knowledge point pre-correction dependence and association relationship features between courses are extracted from a knowledge structure view. In order to improve the consistency and discrimination of feature representation, on the basis of multi-view fusion features, a comparative learning mechanism is introduced, and the discrimination and migration modeling ability of the model to student interests and preferences is enhanced by constructing a positive and negative sample comparison loss function.

The invention relates to the technical field of education science and technology recommendation systems oriented to personalized learning path optimization, and particularly discloses an education science and technology recommendation system oriented to personalized learning path optimization. The method aims at solving the problems that an existing recommendation system is difficult to dynamically perceive a cognitive state, knowledge structure semantics and dependency relationships are ignored, and the recommendation precision is low due to data sparsity. The system comprises a multi-source data acquisition and fusion module, a dynamic cognitive state evaluation module, a knowledge graph construction and semantic enhancement module, a path generation and optimization decision module and a self-adaptive execution and feedback adjustment module. Through multi-source data fusion, real-time cognitive state quantification, knowledge graph semantic modeling, multi-target optimization path generation and closed-loop feedback adjustment, accurate recommendation of personalized learning paths is realized, and the continuity, rationality and educational effectiveness of the paths are effectively improved.

The invention belongs to the technical field of knowledge maps, and discloses an industry knowledge structured modeling method of a knowledge map based on semantic association. Collecting industry production data; constructing a production knowledge graph according to the industry production data; obtaining personalized demand content; performing feature extraction on the personalized demand content to obtain actual demand feature data; inputting the actual demand feature data into the demand evaluation model to obtain a demand evaluation score, and analyzing and processing the demand evaluation score to obtain a demand rationality level; inputting the actual demand feature data and the production knowledge graph into a parameter construction model to obtain a corresponding production parameter set; inputting the demand rationality level, the production parameter set and the production knowledge graph into a demand construction model to obtain predicted demand feature data; the matching degree of the customer demand and the system prediction is dynamically evaluated, a production parameter set meeting the personalized demand of the customer is generated, and the production accuracy is improved.

The invention discloses a method and system for relieving large model illusion through knowledge graph retrieval, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing a knowledge graph; performing document semantic dicing based on the knowledge graph; constructing a community according to the blocks; determining a corresponding community to be retrieved from the communities according to the entity of the query question; and retrieving from the to-be-retrieved community to obtain an answer. The knowledge graph provided by the invention has a clear structure and certainty, and can clearly represent the relationship between entities, so that key fact details can be clearly presented. Through entity disambiguation, knowledge dispersed in a plurality of documents can be connected through entities and relationships to form a net-shaped knowledge structure instead of keeping the documents isolated. According to the method, the accuracy and correlation of knowledge retrieval can be improved, so that the overall performance of the question-answering system is improved.

The invention relates to the technical field of intelligent education, in particular to an intelligent Agent heuristic question and answer teaching system based on knowledge graph driving, and the system comprises a four-layer progressive architecture including a data layer, a knowledge layer, an Agent layer and an interaction layer: the data layer is responsible for the collection, preprocessing and privacy protection of multi-dimensional teaching data, and provides high-quality input for the construction of a knowledge graph; the knowledge layer is used for converting data into a structured knowledge graph and storing a multidisciplinary knowledge network in an entity-relationship-attribute triple form; in the scheme, a'data-knowledge-Agent-interaction 'four-layer architecture is constructed, knowledge structured association and dynamic updating are realized based on a knowledge graph, weak points of students are accurately positioned by means of sensing, reasoning and decision-making modules of an intelligent Agent, a personalized problem chain is generated, and a multi-modal interaction and privacy protection mechanism is combined, so that the knowledge knowledge association and dynamic updating are realized. Systematization of a knowledge system, personalization of question and answer guidance, precision of data utilization and dynamic knowledge updating are achieved, and the defects of a traditional teaching system are effectively overcome.

The invention provides an intelligent question and answer method and system for an entrepreneurship tutor based on a dialogue system, and relates to the technical field of natural language process.The method comprises the steps that firstly, a target dialogue request which is input by a user and contains entrepreneurship guidance related question content is obtained, and semantic analysis is conducted to generate intention categories and context association features; then, matching associated nodes in a preset entrepreneurial knowledge graph based on intention categories, traversing adjacent paths of the associated nodes according to context association features, extracting a knowledge combination relationship, and finally, generating target reply content based on the knowledge combination relationship and returning the target reply content to a user side to complete question and answer interaction, so that the question intention of a user can be deeply understood; the knowledge structure in the field of entrepreneurship guidance is fully utilized, accurate, related and deep reply content is generated, and the performance of the intelligent question and answer system in the field of entrepreneurship guidance is remarkably improved.

The invention provides a cloud-edge collaborative heterogeneous graph neural network vehicle re-identification method, and relates to the technical field of intelligent traffic. The method comprises the following steps: constructing a knowledge structure through a multi-modal heterogeneous graph; extracting a multi-modal semantic relationship through a heterogeneous graph neural network to construct a cloud teacher model; in the method, a dynamic knowledge distillation mechanism driven by feature clustering is introduced to carry out knowledge fine-grained migration to generate a lightweight student model. Experiments are verified on VeRi-776, CityFlow-ReID and a self-built traffic data set, and results show that under the conditions that model parameters are compressed by 63% and a video memory is reduced by 64%, the total precision loss of a student model does not exceed 5%, and the reasoning speed reaches 213FPS. Compared with an existing traditional baseline method, the HGKDF has significant statistical advantages in RMSE and MAE indexes, is superior in training duration and video memory overhead, and is suitable for constructing the deployment of an integrated traffic large model system oriented to city-level intelligent interactive decision and safety monitoring.

The invention relates to the technical field of artificial intelligence and data fusion, in particular to a knowledge graph-based multi-modal data fusion computing system, which comprises a multi-modal data source interface module, a preprocessing module, a fusion alignment module, a knowledge graph module and a reasoning module, different modal features are mapped to a unified semantic space based on a Riemannian manifold theory, cross-modal information interaction is realized through a geometric perception attention mechanism on manifold, and uncertainty is quantified based on manifold entropy. The knowledge graph module provides semantic constraint to guide a fusion process and receives a fusion result to update a knowledge structure, the reasoning module combines fusion features and uncertainty evaluation to perform reliability sorting and interpretation information generation, the system improves the accuracy, reliability and interpretability of multi-modal data fusion, the fusion accuracy is improved by 15%-20%, and the system is suitable for popularization and application. And the cross-modal semantic alignment error rate is reduced by more than 30%.

The invention relates to the technical field of knowledge tracking, and discloses a graph enhanced double-memory collaborative knowledge tracking model based on an ACT-R cognitive architecture. Comprising a static knowledge structure coding module based on hypergraph projection, a batch-level dynamic learning track construction and coding module, a cross-graph gating fusion mechanism, a sequence modeling module and an expert hybrid prediction module. According to the method, long-term stable structured semantic association between concepts in declarative memory is modeled through a static knowledge structure diagram, a dynamic learning trajectory diagram based on batch reconstruction is designed to accurately capture an evolution rule of a behavior sequence in programmed memory, and on the basis, a cross-diagram gating fusion mechanism and a hybrid expert mechanism are introduced, so that the evolution rule of the behavior sequence in the programmed memory is accurately captured. And self-adaptive fusion and multi-path decision of double-graph features are realized.

The present invention discloses a method for sewing process knowledge fusion and intelligent recommendation based on a knowledge graph. Aiming at the characteristics of long processing flow links and decentralized information in the sewing process, it integrates and manages multi-link information of processing from a global perspective, mainly including the following steps: designing a sewing knowledge ontology model in combination with the characteristics of the sewing industry knowledge structure and service system requirements; extracting process knowledge entities based on a bidirectional long short-term memory network model with conditional random fields; extracting process knowledge entity relationships based on a stacked pointer network model with a pre-trained language model; realizing knowledge storage based on the Neo4j graph database; establishing a sewing parameter recommendation method based on the mechanical properties of fabrics, and realizing intelligent applications in combination with intelligent search and intelligent recommendation functions. Based on knowledge graph technology, the present invention realizes the rapid call and recommendation of sewing process knowledge for the massive process information generated in the sewing process, and improves the independent decision-making ability of sewing production.

The invention provides an engineering carbon emission factor dynamic calculation and traceability analysis method and system, and belongs to the field of engineering construction. The method comprises the following steps: acquiring multi-source heterogeneous data of the whole engineering construction process, and preprocessing the multi-source heterogeneous data to form a data set supporting knowledge modeling and computational analysis; constructing a semantic knowledge structure in the field of carbon emission factors in the whole engineering construction process by utilizing a knowledge graph technology; carrying out dynamic calculation on the carbon emission factor and correcting the weight; and carrying out visual analysis on a dynamic calculation result and a correction result to complete dynamic calculation and traceability analysis on the engineering carbon emission factor. According to the method, the defects in a carbon emission accounting system in the existing engineering construction field are overcome.