81 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 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 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 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 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 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.

The invention provides a work order service tracking method and platform based on a project knowledge base and a medium, and relates to the technical field of data processing, and the method comprises the steps: constructing the project knowledge base in a project operation and maintenance process; reading the work order information, performing semantic analysis processing, and mapping an analysis result into a project knowledge base to generate a knowledge mapping result; extracting a historical processing path and a standard implementation path of which the correlation degree with the work order meets a preset condition, and constructing a service processing baseline track corresponding to the work order; in the execution process of the work order, processing behavior information of the work order is collected in real time and continuously mapped to the project knowledge base, and a real-time knowledge evolution track is generated; and executing work order service tracking analysis, and establishing a service tracking result. The technical problem that the project progress and quality are affected due to low work order processing efficiency and high error rate caused by the fact that work order management mostly depends on manual input and processing and lacks an automatic knowledge mining and application mechanism in the prior art is solved.

The present disclosure provides a knowledge graph-based industrial knowledge mining modeling method and system, relating to data mining technology, the method comprising: obtaining basic information of a pre-constructed knowledge graph; determining data collection type and data collection amount; collecting relevant data on multiple same-type equipment products of a target device to construct multiple target knowledge graph sets; based on device usage time nodes, traversing and comparing the multiple target knowledge graph sets to obtain a graph similarity feature set; constructing a target device twin model and embedding graph similarity features in the graph similarity feature set into the target device twin model according to device usage time nodes; and performing fault early warning on the target device usage process according to the target device twin model. The present disclosure can solve the technical problem of low accuracy and timeliness of existing industrial equipment fault early warning methods, and can improve the accuracy and efficiency of industrial equipment fault early warning, thereby timely discovering and handling fault hazards.

The invention discloses a knowledge-driven irrigation farmland sample generation method and system, and the method comprises the steps: excavating the mechanism knowledge information of an irrigation activity based on a constructed optical gradient model, and carrying out the inversion to obtain the surface soil water content in image data; calculating the water content of the surface soil based on a surface water balance process, and determining a distribution space range of a potential irrigation farmland through a threshold value method; the method comprises the following steps: mining prior knowledge of a target irrigation farmland drawing product based on a distribution space range of a potential irrigation farmland, and performing time filtering and spatial filtering on the target irrigation farmland drawing product to obtain an irrigation farmland coarse sample; and screening the irrigated farmland coarse samples based on an uncertainty theory to generate high-quality irrigated farmland samples with representativeness and reliability. According to the method, multi-source remote sensing image data and existing irrigation farmland drawing products are utilized, mechanism knowledge mining, priori knowledge mining and uncertainty analysis are combined, and high-quality irrigation farmland samples are screened and generated.

This invention discloses a system for converting tacit knowledge into explicit knowledge based on knowledge graphs and large models. It includes a knowledge system ontology modeling and management module for constructing and maintaining the knowledge system ontology model in large model applications; a data access and knowledge graph construction module for mapping and loading multi-source heterogeneous data onto the knowledge system ontology; an ontology virtual business environment engine module; a tacit knowledge mining and explicit knowledge generation module that, in a virtual environment, uses graph analysis and large model reasoning to mine tacit knowledge and generate explicit knowledge; and a business strategy planning and workflow orchestration module that transforms explicit knowledge into executable business strategy plans and workflow orchestrations. Through the knowledge system ontology modeling and management module, a systematic knowledge framework is constructed, achieving systematic accumulation of enterprise tacit knowledge, effectively preventing knowledge loss, and improving knowledge reusability and scalability.

The invention discloses a multi-modal analysis traditional Chinese medicine knowledge mining method and system, and the method comprises the steps: obtaining the multi-modal data, such as a tongue image, a face image, a pulse image and an inquiry text, of the same patient, extracting features, and mapping the features to a unified traditional Chinese medicine syndrome semantic space; mining a support, supplement or contradictory relationship among different modal semantic vectors by using an attention mechanism, and constructing a cross-modal evidence collaborative network; generating a comprehensive syndrome representation based on the network fusion information, and completing preliminary differentiation; and finally, generating a final dialectical conclusion and report with credibility evaluation by quantitatively analyzing the consistency degree of the network and the integrity of the evidence chain. The system effectively simulates the thinking process of comprehensive judgment of traditional Chinese medicine doctors, not only improves objectivity and accuracy of syndrome differentiation, but also realizes quantitative evaluation of reliability of diagnosis conclusions and automatic identification of evidence contradictions, and provides an intelligent tool for clinical aid decision making and traditional Chinese medicine experience inheritance.

The invention discloses a textile printing intelligent manufacturing production management system based on big data, which relates to the technical field of textile printing intelligent manufacturing and comprises a data acquisition module, a knowledge mining module, a dynamic simulation module, a quality identification module and a feedback correction module. The system forms an empirical rule base containing a specific process mapping relation by collecting whole-process operation state data and mining historical order data, carries out production process dynamic simulation by utilizing the rule base and combining real-time equipment data, and outputs an equipment parameter optimization scheme. And the system identifies quality defects of printed finished products on line and generates a defect position distribution thermodynamic diagram, so that an equipment parameter scheme is fed back and corrected, and a final execution instruction is formed. According to the invention, autonomous extraction of process knowledge and production closed-loop optimization based on quality space information are realized, and the intelligent level and quality control capability of printing production are improved.

The application discloses a kind of intelligent retrieval method and system of fusing text and image semantic features, the method includes: S1.to the quality detection and pretreatment of the image scan of electronic dossier and case simultaneous recording video;S2.structural electronic dossier catalog is built;S3.text semantic features, dossier image semantic features and video image semantic features are extracted as the multimodal feature of text and image;S4.the multimodal feature of text and image is fused and aligned by multimodal big model, and cross-modal uniform feature vector with semantic consistency is generated;S5.case knowledge graph is automatically constructed, and the structured and semantic integration of legal information is realized;S6.based on natural language query and case knowledge graph, semantic analysis and multi-hop reasoning are carried out, and the retrieval result in the form of structured or question and answer is generated and presented.The application realizes deep knowledge mining and efficient intelligent retrieval of electronic dossier by fusing the semantic features of text and image.

The present disclosure provides a content understanding model training method, a content understanding method and related devices, and relates to the technical field of artificial intelligence, in particular to the technical field of deep learning, knowledge graph, knowledge acquisition and knowledge mining. The specific implementation scheme is: sample reference information is used to generate sample data, which can automatically generate sample data, greatly improving the efficiency of data generation, and sample data is used to drive the iterative optimization of the content understanding model. At the same time, the content understanding result output by the content understanding model is used to guide the data generation process in reverse, improving the quality of the sample data. Further, the sample data with better quality can be used as new input of the content understanding model to optimize the iterative process of the content understanding model, and a model with better content understanding capability can be obtained. In this way, the data generation process and the content understanding process form an optimization closed loop, which can realize the double optimization of data generation and content understanding.

The application discloses a kind of military software defect multimodal knowledge graph construction method, device and system, belong to knowledge graph construction field, comprising: S1, mode layer design;S2, defect knowledge mining: data source is collected, information extraction and data preprocessing;S3, multimodal knowledge graph construction: data entry is carried out in multiple databases, the semantic relationship between nodes is stored in graph database, the multimodal information of graph database node is stored in relational database, and multimodal knowledge graph is constructed.The military software defect knowledge graph of the data driving and knowledge guiding combination constructed by the application changes the traditional knowledge management mode, multidirectional rich semantic information and the defect of other information are shown, the problems of military software defect knowledge resource fragmentation, insufficient utilization, high knowledge acquisition cost and updating difficulty are solved, and foundation is laid for military software defect encyclopedia retrieval, intelligent question and answer, intelligent prediction and intelligent recommendation and the like application.

The invention relates to the technical field of cultural heritage digitization and artificial intelligence, in particular to a cultural heritage digitization method based on multi-modal fusion, which comprises the following steps of ancient document processing, multi-modal information integration, knowledge graph construction, AIGC narrative visualization and multi-terminal interaction presentation. According to the method, efficient integration of multi-modal information of the cultural heritage is realized, a foundation is laid for subsequent knowledge mining and spreading, the constructed knowledge graph can mine deep association of the cultural heritage, a cultural heritage knowledge system is more systematic and more logical, researchers and the public are helped to deeply understand the cultural heritage, and the intellectual property of the cultural heritage is improved. According to the AIGC technology, abstract narration is converted into vivid multimedia content, the visual impact and propagation attraction of the cultural heritage are improved, the public can accept and like the cultural heritage more easily, the audience coverage of the cultural heritage is expanded through the multi-end interactive presentation mode, more people can be touched under different platforms and different scenes, and the interestingness of the cultural heritage is improved. And inheritance and propagation of cultural heritage are effectively promoted.

The invention provides an interpretable knowledge mining system, method and device and a medium, and belongs to the technical field of interpretable artificial intelligence and intelligent data mining. The system comprises a data processing module, an algorithm and intelligent interpretation module, a visual analysis module and an operation management module. An explicit mathematical model which is simple in structure and easy to understand is generated through a multi-branch shallow symbol feature learning tree structure, a dynamic branch expansion strategy, LLM guide initialization and a staged grey wolf optimization selection strategy; and an LLM and retrieval enhancement generation (RAG) mechanism is introduced to perform automatic semantic analysis and intelligent interpretation on the generated model. According to the interpretable knowledge mining system, method and equipment and the medium, the technical threshold of interpretable modeling is effectively reduced, meanwhile, the prediction performance, the structure interpretability and the knowledge discovery efficiency of the model are improved, and the interpretable knowledge mining system, method and equipment and the medium are suitable for engineering optimization, scientific modeling and cross-domain data analysis.

The invention discloses a three-layer two-domain space-time affair graph construction method oriented to the public security field, which comprises the following steps of: pre-constructing public security security field ontology knowledge, mining space-time characteristics of events and entities, constructing event and instance knowledge sub-graphs from top to bottom based on a large model according to three layers of'abstraction layer-instance layer-space-time layer ', and oriented to'event domain' and'entity domain '; and meanwhile, the method has the capabilities of guiding extraction of instances such as events and entities based on public security ontology knowledge and complementing public security ontology knowledge based on instance information clustering, and the accuracy and efficiency of event and relationship mining in the public security field are improved.