126 results about "Knowledge mining" patented technology

The invention discloses a multi-modal knowledge-based automotive body innovative design system, which comprises a multi-modal data fusion module, a multi-modal data fusion module, a multi-modal data fusion module, a multi-modal data fusion module and a multi-modal data fusion module, wherein the multi-modal data fusion module is used for receiving text data, picture data, a three-dimensional CAD (Computer Aided Design) model file and an engineering symbol expression from an automotive body design process and is used for carrying out feature extraction and semantic alignment on input data of four modals; generating a unified semantic vector representation; the cross-modal knowledge mining and graph construction module is used for extracting multi-level entities and relationships from the unified semantic vector and constructing a dynamically weighted multi-modal knowledge graph; the large-model-driven multi-hop collaborative reasoning module is used for analyzing the multi-modal design requirement of a user, carrying out multi-hop reasoning on a knowledge graph, and outputting design parameter recommendation and an interpretable reasoning chain. The method aims at breaking through the limitation of an existing design system in the aspects of multi-modal processing and shallow semantic understanding, and deep fusion and intelligent application of multi-source heterogeneous design data are achieved.

The invention relates to the technical field of data mining and analysis, in particular to a knowledge mining method and system for the tumor field. The method comprises the following steps: acquiring a tumor multi-source heterogeneous data set, and performing data standardization processing to obtain a standardized tumor multi-source heterogeneous data set; performing multi-level feature extraction on the standardized tumor multi-source heterogeneous data set to obtain a tumor fusion feature set; performing hierarchical knowledge graph construction based on the tumor fusion feature set to obtain a tumor hierarchical knowledge graph; and constructing a graph neural network model by using the tumor hierarchical knowledge graph, and carrying out cancer cell dynamic prediction to obtain tumor dynamic knowledge reasoning result data. According to the method, cancer cell prediction is carried out by extracting the multi-level features, constructing the hierarchical knowledge graph and applying the graph neural network, and personalized medical research optimization is provided, so that the accuracy of cancer cell prediction and the personalization of the medical research direction are remarkably improved.

The invention discloses a construction method of an aerospace manufacturing knowledge graph based on a heterogeneous data joint extraction mode. The construction method comprises the following steps: designing and constructing a knowledge model of the knowledge graph by using an automatic modeling tool based on a top-down theory; performing knowledge graph bottom layer storage in a manner of combining a table structure and a graph structure; knowledge extraction and knowledge mining are carried out from structured, semi-structured and unstructured data; carrying out data mode layer fusion and data layer fusion on data from different sources; knowledge discovery and knowledge graph completion are carried out based on rapid knowledge reasoning of a graph neural network; and carrying out intelligent question answering and visual decision support based on semantic search in combination with a specific application form of the knowledge graph and aerospace manufacturing field data and business scenes. According to the method, challenges of various knowledge sources and data formats on knowledge extraction can be handled, so that the aerospace manufacturing knowledge graph can be applied to the ground, and the method has mobility, scientificity and seriousness.

The invention discloses a physics-based method for discovering a control equation from scarce and noise data. According to the method, a physical information neural network and a sparse regression method are combined, and a partial differential control equation of a dynamic system is found from scarce and noisy data. Firstly, the size of a candidate function library is effectively reduced through a dimension verification method. And then modeling a physical system and calculating a candidate function by using the strong nonlinear fitting capability and automatic differential characteristic of the deep neural network. And finally, the form of a control equation and the coefficient of the equation can be obtained through sparse regression, and the coefficient is finely adjusted through DNN. The method not only can discover the control equation from the data, but also can obtain the network model to realize the response prediction of the dynamic system. The method is easy to implement, efficient, high in precision and high in universality. The method can be widely applied to physical knowledge mining, modeling and reasoning of a complex dynamic system.

The invention provides a data mining method and device, equipment and a storage medium, and the method comprises the steps: carrying out the data preprocessing of multi-source heterogeneous scientific research data, generating a data set in a unified format, and mining a multi-dimensional scientific research portrait feature set from the data set; and based on the feature sets, performing pattern mining on academic output data of the researchers to obtain a research interest pattern set and a research trend index. Through predictive mining, a research interest prediction model and a project matching degree score are generated for historical research data, graph structure mining is performed on a researcher cooperation network by using the model, and a potential cooperation relationship is predicted. And finally, performing knowledge mining on the scientific research project data according to the project matching degree score, the network structure feature and the cooperation relationship prediction result, and generating a personalized project recommendation list and an interpretable recommendation basis thereof. According to the method, interest evolution, project characteristics and cooperation potential of researchers can be comprehensively considered, and recommendation accuracy and interpretability are improved.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

The invention discloses a semantic analysis method and device based on power grid domain knowledge mining and electronic equipment, relates to the technical field of semantic analysis, and improves the analysis accuracy. The method comprises the following steps: constructing a pre-training corpus based on an original corpus related to the power grid field, and continuously updating the corpus of the pre-training corpus by adopting a dynamic knowledge graph and incremental learning fusion system; performing unified representation processing and cross-modal semantic alignment processing of multi-modal corpus data on corpora in the pre-training corpus by utilizing a multi-modal graph attention analysis network; based on the processed pre-training corpus, constructing an analytical model, and performing compression processing on the analytical model through a knowledge distillation technology; and deploying the compressed analytical model to edge equipment in the substation power grid, continuously collecting equipment state information of the edge equipment, performing semantic analysis on the equipment state information by adopting the compressed analytical model, and outputting an obtained analysis result.

The invention provides a multi-level collaborative urban physical examination knowledge reasoning method and related equipment. The method comprises the following steps: acquiring a knowledge base constructed by an urban physical examination knowledge graph and a question request; the question request is input into an entity-level reasoning module for entity and attribute recognition, an initial evidence triple is obtained, knowledge mining is conducted on the initial evidence triple according to a knowledge base, and an evidence triple is obtained; inputting the question request and the evidence triple into a function level reasoning module for identification to obtain an urban physical examination analysis function required in the question request, and calling a space-time analysis tool for space-time analysis according to the urban physical examination analysis function to obtain an evaluation analysis result; inputting the problem request, the evidence triple, the knowledge base and the evaluation analysis result into a decision-making reasoning module to perform knowledge reasoning on the problem request to obtain a reasoning result; the problems that in the urban physical examination knowledge reasoning process, knowledge mining is not deep enough, tool arrangement efficiency is low, and knowledge reasoning is not fine enough are solved.

Embodiments of this specification provide a large language model-based knowledge mining method and apparatus. In the large language model-based knowledge mining method, structural knowledge for a source entity is obtained based on a predetermined entity graph; a candidate relation set is determined based on a target property of the source entity in the predetermined entity graph; a corresponding target relation set and inheritable knowledge are output by using a large language model and based on the structural knowledge, the candidate relation set, and additional knowledge for the source entity; a candidate entity word set corresponding to the provided relation is output by using the large language model and based on the source entity, the relation in the target relation set, and at least one of the structural knowledge, the additional knowledge, and the inheritable knowledge; and then an entity related to the source entity and a corresponding relation are obtained.

The invention discloses an intelligent retrieval method and system fusing text and image semantic features. The method comprises the following steps: S1, carrying out quality detection and preprocessing on an image scanning copy of an electronic file and a case simultaneous recording video; s2, constructing a structured electronic file directory; s3, extracting a text semantic feature, a file image semantic feature and a video image semantic feature as multi-modal features of the text and the image; s4, carrying out feature fusion and alignment on the multi-modal features of the text and the image through a multi-modal large model, and generating a cross-modal unified feature vector with semantic consistency; s5, automatically constructing a case knowledge graph, and realizing structured and semantic integration of legal information; and S6, performing semantic analysis and multi-hop reasoning based on natural language query and the case knowledge graph, and generating and presenting a retrieval result in a structured or question and answer form. According to the method, the semantic features of the text and the image are fused, so that deep knowledge mining and efficient intelligent retrieval of the electronic file are realized.

The invention relates to the technical field of data processing, and discloses a traditional Chinese medicine disease differentiation treatment method and system based on machine learning. The method comprises the following steps: converting four diagnosis information of a patient through hierarchical semantic standardization processing to obtain a traditional Chinese medicine semantic data set; feature extraction and pattern recognition are carried out through fusion type feature mapping and depth syndrome classification; generating a prescription compatibility sequence based on treatment serialization; extracting core dialectical data through collaborative correlation calculation and hierarchical clustering analysis; and finally, constructing a target dialectical mode library through progressive knowledge mining and confidence evaluation. According to the invention, standardization and intelligentization of the whole process from four-diagnosis data acquisition to syndrome differentiation and treatment knowledge accumulation are realized, and the technical problems of multi-source heterogeneous data fusion analysis, syndrome type classification objectification, knowledge dynamic updating and the like are solved.

The invention provides a medical knowledge graph construction method and device, an application method and device, equipment and a medium, and relates to the technical field of medical data processing.The construction method comprises the steps that entity conditions of a medical text are determined, and the entity conditions comprise the condition that entities are marked sufficiently, the condition that entities are marked scarce, the condition that entities are regular and the condition that entities are complex; inputting the medical text into a collaborative integration framework for knowledge mining, and generating a corresponding knowledge mining result; integrating all knowledge mining results by adopting a large language model, generating entity relationship data, and constructing a preliminary medical knowledge graph based on the entity relationship data; and inputting the preliminary medical knowledge graph into a quality control framework for verification, and generating a final medical knowledge graph according to a verification result. The quality of the medical knowledge graph can be further ensured, and the precision is improved.

The invention relates to the field of medical information processing, in particular to a deep learning-based medical multi-source heterogeneous data fusion and knowledge discovery system, which comprises a heterogeneous data management and mapping module, a medical data unified representation module, a medical knowledge mining engine, a knowledge-driven decision support module and a hypothesis verification and visual exploration module, the system utilizes the Riemannian manifold theory to construct a multi-modal medical data unified characterization framework, realizes multi-scale medical knowledge characterization in combination with a curvature perception mechanism, excavates multi-entity association through multi-view learning and a graph neural network, calculates the consistency among diseases, symptoms, signs and genes based on a knowledge graph, provides accurate diagnosis and treatment schemes, and realizes the purpose of accurate diagnosis and treatment. According to the method, the technical problems that medical multi-source heterogeneous data are difficult to fuse, complex association is difficult to mine and knowledge representation is not accurate enough are effectively solved, and the data utilization efficiency, representation quality and diagnosis accuracy are remarkably improved.

The invention provides a data value evaluation method and system based on a knowledge mining large model and an analogue simulation agent. The method comprises the following steps: constructing a corpus containing consumption cases, domain knowledge and evaluation methodology, and completing acquisition and vectorization management; according to the design, a general value dimension is manually preset to form an evaluation framework, and FA-GRPO auto-reflection reinforcement learning is carried out on a large language model by using methodological data, so that the reasoning consistency is improved; constructing a data supplier, a data demander and a third-party evaluator agent, simulating bargaining and an attached condition protocol under information asymmetry based on a Rubinstein game, and generating a consensus usage scheme in combination with external retrieval; and finally, according to a consensus scheme, reversely deducing quantization parameters, and automatically outputting a structured evaluation report containing number paths and value estimation, thereby realizing low-subjectivity, high-consistency and verifiable data value evaluation.

The invention provides a cross-modal multi-granularity humor recognition method and device based on composite distinguishing measure, and relates to the technical field of data mining. The method comprises the following steps: acquiring and preprocessing multi-modal data, and establishing a multi-modal data set; performing multi-granularity sampling on the multi-modal data in each modal to obtain a cross-modal multi-granularity sample space; selecting an optimal granularity combination through coordination; and carrying out identification and classification on humor contents by adopting cross-modal fusion of distinguishing measurement and variable coordination measurement. According to the method, the accuracy and robustness of humor recognition are improved through fusion of multi-modal feature collaborative representation and multi-granularity knowledge mining, a new thought is provided for the field of humor recognition, and the method has important theoretical and application values.

The invention discloses a mathematical formula identification coding method, and particularly relates to the technical field of formula coding. The method comprises the following steps: acquiring mathematical formula image data to be identified, and performing symbol boundary extraction and preprocessing to generate symbol feature expression data; and performing visual spatial layout analysis and symbol type semantic classification based on the symbol feature expression data to generate formula layout structure data and symbol semantic classification data. And through graph structure analysis based on a topological relation, determining an inter-symbol topological relation of the mathematical formula, and generating symbol topological relation data. And deriving a dimension constraint relationship and an operator dependency relationship between symbols through a mathematical meta-knowledge mining technology, and generating mathematical meta-knowledge constraint data. Initial mathematical formula structure expression data is generated through decoding of structure and semantic constraint fusion, and a formula coding sequence is generated through formula consistency verification and semantic constraint reconstruction. According to the invention, the accuracy and efficiency of mathematical formula identification can be effectively improved.

The invention discloses a power operation and maintenance knowledge graph generation method based on a large model and a graph neural network. The method comprises the following steps: collecting and preprocessing related data of power operation and maintenance; constructing and training an NLP large model; performing entity recognition, relation extraction and event extraction on the preprocessed related data of the power operation and maintenance by utilizing the trained NLP large model so as to generate an initial power operation and maintenance knowledge graph; performing deep modeling on the initial power operation and maintenance knowledge graph by using a graph neural network to obtain a new power operation and maintenance knowledge graph; and performing data increment updating on the new power operation and maintenance knowledge graph to obtain a final power operation and maintenance knowledge graph. The method solves the problems that in an existing electric power operation and maintenance knowledge graph construction method, although the powerful natural language understanding and generating capacity of an NLP large model is used, the reasoning capacity is weak in a scene needing multi-level and multi-hop logical reasoning, and the requirements of the electric power operation and maintenance field for deep knowledge mining and intelligent decision making are difficult to meet.

The embodiment of the invention discloses a multichannel annular access method and system for meteorological observation data, and belongs to the technical field of meteorological data processing. According to the embodiment of the invention, the annular buffer structure is adopted to manage meteorological observation data, and compared with a traditional linear stack, the annular buffer has obvious advantages. The linear stack is low in efficiency when the data volume is large, and the annular buffer supports multiple independent data streams to share a storage space, so that continuous writing and cyclic coverage are realized, for example, different sensor data streams can be stored orderly. Read-write pointers circularly move to ensure continuous access of data, the parallel access capability is improved in speed and efficiency, and the multi-task concurrence requirement of meteorological services is met. And an overflow and underflow detection mechanism and a capacity management strategy guarantee data integrity and system stability, and prevent problems of data loss or system crash and the like. Attribute knowledge mining and related mapping are beneficial to accurate management of data, management is carried out according to data characteristics, decision accuracy and efficiency are improved, and data management and operation of a meteorological service system are optimized.

The invention discloses a knowledge base construction method and system based on urban planning implicit knowledge deduction, relates to the technical field of urban planning, and aims to solve the problems that an existing planning knowledge base is insufficient in implicit knowledge mining, poor in scene adaptability and lack of dynamic optimization capability. The method comprises the steps of obtaining multi-source data and performing standardization processing to obtain an enhanced data pool; optimizing the large language model to extract explicit and implicit planning knowledge; constructing a planning knowledge association network with association strength; building an initial knowledge base containing classification, retrieval and implicit knowledge deduction units; generating an adaptability evaluation result through a multi-scene test; constructing a knowledge evolution rule based on an evaluation result and completing knowledge base iterative reconstruction; and establishing a real-time spatial data dynamic triggering mechanism to realize continuous optimization of the knowledge base. According to the method, accurate association and deduction of explicit and implicit knowledge are realized, the scene adaptability and dynamic updating capability of the knowledge base are improved, scientific and efficient knowledge support is provided for urban planning decision making, and the method is suitable for various planning scenes.

The invention belongs to the field of artificial intelligence and knowledge engineering, and discloses a dynamic knowledge distillation method and system based on a knowledge graph, which are used for extracting and reconstructing high-value knowledge from structured or unstructured documents. Aiming at the existing problems of static processing, semantic segmentation, recombination stiffness, cross-document association missing and the like, the method realizes dynamic distillation through the following steps: generating separators in real time based on a domain knowledge graph core entity set, breaking through a fixed partitioning rule, and adapting to multi-type document structures; a concept is utilized to trigger a correlation decision model to filter non-correlation texts, and semantic fragmentization is reduced; loading an adaptive prompt template according to the domain attribute of the knowledge graph, and fitting the characteristics of the fields of medical treatment, education and the like; knowledge is dynamically reconstructed through a logic framework defined by a knowledge graph, and cross-document knowledge point semantic association (such as policy document complementary clause linkage) is achieved. According to the system, through dynamic blocking, semantic filtering, field adaptation and logic recombination, the limitation of a traditional technology is solved, the flexibility and field adaptability of knowledge purification are remarkably improved, and the system is suitable for high-value knowledge mining and structured output of massive documents.

The invention relates to a sheep echinococcosis classification model and classification method based on multi-source Raman spectrum domain migration of target related knowledge mining. The sheep echinococcosis classification model based on multi-source Raman spectrum domain migration of target related knowledge mining comprises a Raman spectrum data input module used for obtaining and inputting serum Raman spectrum data of healthy sheep and echinococcosis sick sheep; the MRKMTAR-MPAT module comprises an MRKM-TA R sub-module and an MPAT sub-module, and is used for deeply extracting target related knowledge in a multi-source domain and guiding adaptive reconstruction of a target domain; and the PGST module is used for realizing dynamic alignment of decision boundaries of the multi-source classifier by integrating pseudo labels and uncertain sensing information predicted by the multi-source classifier. According to the sheep echinococcosis classification model and classification method for multi-source Raman spectrum domain migration based on target related knowledge mining, the problem of knowledge degradation caused by multi-domain distribution offset in Raman spectrum medical data is solved.

The invention discloses a forest grass ecological restoration monitoring system, and the system comprises an intelligent sensing layer which carries out the full-coverage access of multi-source data through constructing a self-adaptive sensor network, an unmanned plane inspection module, and a manual inspection terminal; the multi-modal fusion layer is used for performing data alignment by adopting a space-time fusion algorithm and a deep learning framework through data standardization protocol unification, adjusting a fusion weight according to data quality, generating a data set and transmitting the data set to the knowledge mining layer; the knowledge mining layer is used for constructing a unified data semantic standard of an ecological ontology library, mining a space-time dependency relationship in data by utilizing a space-time diagram neural network, and converting an implicit ecological rule into interpretable decision knowledge through a causal reasoning engine; and the decision support layer is used for displaying core indexes based on the data set, utilizing an LSTM network, generating a personalized restoration scheme through an optimization recommendation engine, dynamically adjusting and optimizing a sensor deployment strategy and a weight distribution algorithm according to an effect, and updating an ecological ontology library.

The invention provides a knowledge mining method and device, an intelligent customer service system, equipment, a medium and a product, and relates to the technical field of computers, in particular to the technical fields of artificial intelligence, data processing, large models, intelligent customer service and the like. The method comprises the following steps: extracting candidate question and answer pairs and a first service label from an artificial customer service session log, and inputting questions in the candidate question and answer pairs into a question and answer model to determine a basic question and answer pair and a second service label; based on the first service label, the second service label and the basic question and answer pair, determining whether the candidate question and answer pair is a new question and answer pair; and in response to the candidate question and answer pair being a new question and answer pair, updating a knowledge base of the question and answer model based on the candidate question and answer pair. According to the invention, the knowledge base of the question and answer model of the existing intelligent customer service system can be automatically supplemented and optimized.

This invention provides a method and system for intelligent acquisition of big data from electronic archives, relating to the field of data processing technology. The method includes deconstructing and separating multimodal information from the original data of electronic archives to generate hybrid feature vectors, mapping and locating candidate entity sets in a knowledge graph, constructing a multi-level relationship network to obtain associated entity clusters, forming an archive knowledge association system, realizing semantic association analysis and knowledge mining of archive information, improving the efficiency of intelligent acquisition of archive information, and enhancing the ability to discover implicit relationships between cross-archive entities.

A system and method of building a well construction knowledge mining system using an artificial intelligence ("AI") model and a plurality of databases, the method comprising: accessing, within the plurality of databases, a set of well construction files; wherein the set of well construction files comprise files in a plurality of formats; and wherein the set of well construction files comprise data relating to well construction operations; preprocessing the set of well construction files to generate a training data set, comprising: reformatting at least a portion of the data to standardized machine-readable data; labeling data in the machine-readable files using the model; and cleaning the data; and training the model, using supervised and unsupervised learning and the training data set, to build the well construction knowledge mining system.

The application discloses a method for discovering control equations from scarce and noisy data based on physics. The application combines a physical information neural network and a sparse regression method to discover the partial differential control equations of a dynamic system from scarce and noisy data. Firstly, the size of the candidate function library is effectively reduced through a dimension verification method. Then, the powerful nonlinear fitting capability and automatic differentiation characteristics of a deep neural network are used to model a physical system and calculate candidate functions. Finally, the form of the control equation and the coefficients of the equation are obtained through sparse regression, and the coefficients are fine-tuned through a DNN. The method can not only discover control equations from data, but also obtain a network model to realize response prediction of a dynamic system. The method is simple, efficient, high-precision and highly universal, and can be widely applied to physical knowledge mining, modeling and reasoning of complex dynamic systems.

The invention provides an audit text classification filing method and system suitable for audit large model training, and relates to the technical field, the method comprises the following steps: carrying out entity identification and relation extraction on an unstructured audit document to generate a knowledge graph, and extracting a sub-graph structure as a feature representation; performing node embedding learning by using a graph neural network, generating a document vector containing structure and semantic information, and determining a document attribution category through an attention mechanism; and a natural language retrieval request can be analyzed, multi-hop path reasoning is executed, and a document corresponding to an implicitly associated extension node is obtained. The audit document classification accuracy is improved, efficient association query is realized, and the audit knowledge mining capability is enhanced.

The application belongs to the technical field of map knowledge, and particularly relates to a map knowledge representation method based on a knowledge graph. The method first acquires map data, extracts map knowledge therefrom, including concept class knowledge and rule class knowledge, the concept class knowledge being common sense knowledge in the field of cartography and including map element knowledge; the rule class knowledge being various models, rules and principles formed in the process of map making; then generates a map knowledge network graph based on the knowledge graph according to the map element knowledge; and finally performs knowledge reasoning according to the rule class knowledge, completes the map knowledge network graph, and generates new entities and / or new entity relationships. The application fully mines geographical knowledge contained in a map, provides a basis for all-round knowledge mining and fusion of the map, and has certain reference value for realizing the transformation of 'data-information-knowledge'.

The application provides a cross-modal multi-granularity humor recognition method and device based on a composite discrimination measure, and relates to the technical field of data mining.The application obtains multi-modal data, pre-processes the multi-modal data, and establishes a multi-modal data set; multi-modal data is sampled in each mode to obtain a cross-modal multi-granularity sample space; the optimal granularity combination is selected through coordination; and the discrimination measure and variable coordination are used to measure the cross-modal fusion to recognize and classify the humor content.The application improves the accuracy and robustness of humor recognition through multi-modal feature collaborative representation and multi-granularity knowledge mining fusion, provides a new idea for the humor recognition field, and has important theoretical and application value.

The application provides a project knowledge base-based work order service tracking method, platform and medium, relates to the technical field of data processing, and the method comprises the following steps: constructing a project knowledge base in a project operation process; reading work order information, performing semantic analysis processing, and mapping the analysis result to the project knowledge base to generate a knowledge mapping result; extracting a historical processing path and a standard implementation path associated with the work order and meeting a preset condition, and constructing a service processing baseline track corresponding to the work order; in the work order execution process, real-time collection of work order processing behavior information is performed, continuous mapping to the project knowledge base is performed, and a real-time knowledge evolution track is generated; and work order service tracking analysis is performed, and a service tracking result is established. The application solves the technical problem that work order management in the prior art mostly depends on manual input and processing, lacks an automatic knowledge mining and application mechanism, and work order processing efficiency is low, error rate is high, and project progress and quality are affected.