56 results about "Knowledge mining" patented technology

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 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 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 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 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 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 food detection sample management method and system based on big data analysis, particularly relates to the technical field of food detection data processing, and is used for solving the problem that in an existing sample management big data platform, unstructured text semantics are fuzzy, and the intelligent retrieval and knowledge mining ability is insufficient due to the lack of deep semantic association among data. The method comprises the following steps of: acquiring sample full-process data containing structured and unstructured data, judging a semantic fuzzy condition of the unstructured data by adopting a text similarity algorithm, and performing sequence analysis and semantic completion on a fuzzy text by utilizing a hidden Markov model in combination with space-time correlation characteristics so as to generate a semantic clarification result; and then a semantic association rule is established and optimized based on the semantic clarification result and the structured data, and finally a food detection sample information knowledge graph is constructed according to the optimization rule, so that intelligent semantic retrieval and deep knowledge mining of the sample information are realized.

The present application relates to the technical field of precision grinding processing, and discloses a kind of self-adapting iterative optimization system of grinding wheel laser dressing process parameters, comprising: state acquisition module, the surface state data of grinding wheel is collected and multi-dimensional feature is extracted, data fusion and dimension reduction processing are carried out;Working condition identification module, identify working condition category and determine dressing target state parameter;Parameter optimization module, construct agent model and predict dressing effect, carry out quality evaluation and bayesian optimization search;Dressing execution module, execute laser dressing, identify abnormality and adjust parameters by feature matching;Quality evaluation module, carry out feature extraction, calculate single index compliance degree and gather layer by layer, combined with dressing process data package and correct;Parameter iteration module, identify defect index and generate adjustment scheme, iterative execution dressing and quality evaluation;Knowledge learning module, carry out knowledge mining and transfer learning;The present application realizes the self-adapting optimization and closed-loop quality control of dressing parameter.

The application discloses a VR real scene teaching model big data teaching knowledge mining method and system, belongs to the technical field of big data analysis, and first clock drift correction and motion artifact removal are carried out on collected multi-modal data to obtain synchronous and pure data flow; then, based on the data flow, a multi-dimensional teaching behavior characteristic vector containing a cognitive load index and a behavior trajectory is extracted; then, based on the characteristic vector, learning behavior mode clustering is carried out, and a cognitive load weighted PrefixSpan algorithm is used to mine an optimal operation path sequence; finally, the mining result is visualized. Through fine pretreatment of multi-modal data and cognitive load weighted path mining, the application solves the problems of poor data quality and ignoring learning burden in the prior art, can accurately depict learner behavior, recommend scientific and efficient learning paths, and improves the VR teaching effect.

A software-based method for multimodal industrial knowledge throughout the product lifecycle includes the following steps: Step 1: Obtain the sources and requirements of multimodal industrial data throughout the product lifecycle, and perform cluster analysis on the multimodal industrial data; Step 2: Reuse and map existing ontology and standards to construct a hierarchical multimodal industrial knowledge ontology system; Step 3: Convert multimodal industrial data into text modality; utilize text knowledge mining methods to achieve multimodal industrial data knowledge mining, forming clusters of industrial knowledge triplets for each modality; Step 4: Globally encode the clusters of industrial knowledge triplets for each modality, and perform modal fusion and cross-modal alignment to achieve cross-modal industrial knowledge fusion; Step 5: Use the multimodal industrial knowledge graph throughout the product lifecycle as the knowledge reuse foundation to support the personalized configuration development of ubiquitous deployment industrial software for multi-scenario intelligent businesses, realizing a software-based standard process for industrial knowledge acquisition, mining, and reuse.

The application discloses a domain knowledge graph autonomous modeling method and system, and the method comprises the following steps: acquiring unstructured text of a target domain to construct a text corpus, and acquiring a training data set from the text corpus; constructing an ontology model of the target domain according to the text corpus, labeling the training data set based on the ontology model to obtain a model training data set; fine-tuning and training a pre-constructed BERT model based on the model training data set to obtain a triple knowledge mining model of the target domain; loading a text sentence in the text corpus into the triple knowledge mining model for knowledge mining, and constructing a domain knowledge graph according to a result of the knowledge mining. The embodiment of the application abstracts domain multi-source heterogeneous data and text into a structured knowledge graph for knowledge modeling, provides reliable assistance for intelligent manufacturing of enterprise knowledge empowerment under the premise of improving the accuracy and efficiency of domain knowledge graph modeling, and can be widely applied to the technical field of data processing.

The invention belongs to the technical field of natural language processing, and provides a sentiment analysis method and system based on multi-granularity knowledge mining and sentiment prompting, electronic equipment and a storage medium, and the method comprises the steps of statement collection and sentiment analysis; the sentiment analysis model training process comprises phrase extraction and phrase supplement, field adaptive fine tuning, coding processing, sentiment prompt vector initialization, improved SHGN network propagation, multi-dimensional interaction, vector fusion and mapping, and iterative training. According to the method, basic phrase extraction is combined with dynamic n-gram phrase supplementary processing, so that the coverage degree of key phrases is improved, and the phrase recall rate is increased; through dependency relationship mapping, dependency vector calculation and grammar vector calculation, the emotion-related dependency capture and semantic representation capability is improved; and through multi-dimensional interaction, the multi-granularity association depth is improved.

The invention discloses a power equipment fault case text detection and recognition method based on a multi-scale convolution and attention mechanism, and relates to the technical field of power equipment operation and maintenance and computer vision, and the method comprises the steps: constructing and training a text detection model which integrates a multi-scale convolution attention module in a backbone network; performing text region detection on the target image by using the trained text detection model to generate a to-be-recognized text image block; and inputting the text image block into an optical character recognition engine for recognition, and outputting structured text information. According to the power equipment fault case text detection and recognition method based on the multi-scale convolution and the attention mechanism, complex background interference is more effectively restrained, higher robustness is achieved for texts which are written disorderly and arranged irregularly, an established end-to-end text information extraction system is higher in precision and more reliable, and the method is suitable for popularization and application. And a powerful technical tool is provided for knowledge mining and intelligent analysis in operation and maintenance of power equipment.

The application discloses a food detection sample management method and system based on big data analysis, and particularly relates to the technical field of food detection data processing, and is used for solving the problem of insufficient intelligent search and knowledge mining ability caused by unstructured text semantic ambiguity and lack of deep semantic association between data in the existing sample management big data platform; the problem is solved by the following steps: obtaining sample full-process data containing structured and unstructured data, using a text similarity algorithm to determine the semantic ambiguity of unstructured data, using a hidden Markov model combined with space-time correlation characteristics to perform sequence analysis and semantic completion on the ambiguous text to generate a semantic clarification result, then establishing and optimizing semantic association rules based on the semantic clarification result and the structured data, and finally constructing a food detection sample information knowledge graph according to the optimized rules, so as to realize intelligent semantic search and deep knowledge mining of sample information.

The application discloses a coal mine tunneling operation regulation intelligent decision method and system based on semantic analysis, and relates to the field of data processing, wherein the method comprises: preprocessing, semantic analysis and entity relationship extraction of a coal mine tunneling operation regulation text, and constructing a structured tunneling operation knowledge representation; integrating coal mine operation safety regulations, tunneling equipment operation specifications and historical tunneling operation data for knowledge mining and integration, and building a tunneling operation prior knowledge base; based on the knowledge base, performing logical reasoning and compliance verification on the knowledge representation, and generating a tunneling operation decision scheme set; driving the scheme set to perform three-dimensional modeling and process simulation, constructing a coal mine tunneling operation intelligent agent, and performing digital rehearsal and regulation optimization decision. The application solves the technical problems of insufficient multi-source information fusion and lack of decision accuracy in existing coal mine tunneling operation regulation decision, and achieves the technical effects of realizing deep fusion of multi-source heterogeneous information in the field of coal mine tunneling, and improving decision accuracy.

The invention relates to a computer system utilizing a computational model. Systems, methods, media and products for crop gene questions and answers are disclosed. A system for crop gene question answering comprises a computing resource, a storage unit, a large language model, a planning agent, an execution agent and a knowledge mining module. The storage unit is used for storing the knowledge graph. The large language model is to: receive an input; generating task description; an answer to the crop gene question is generated based on the query output. The planning agent is used for determining a target tool; and generating a tool calling instruction. The execution agent is used for generating a function call instruction. The knowledge mining module is used for providing a tool description document; and calling the target tool to execute structured query on the knowledge graph to obtain query output. According to the system, the limitation that a large language model lacks professional knowledge in the crop gene field is overcome, and the crop gene question answering accuracy of the large language model is improved.

The invention discloses a knowledge content self-adaptive generation method and system based on multi-dimensional cognitive parameter decoupling, and belongs to the technical field of artificial intelligence. According to the method, a three-dimensional cognitive coordinate system is constructed, the cognitive difficulty and the knowledge depth are subjected to orthogonal decoupling, and a role semantic filter is combined. The system carries out dynamic interpolation calculation in a two-dimensional strategy space according to cognitive difficulty parameters and knowledge depth parameters (such as 1-10 levels) input by a user, and independently controls the language custom degree and knowledge mining depth of generated content, so that a low-difficulty high-depth custom general recognition mode or a high-difficulty low-depth expert quick browsing mode can be generated, and the user experience is improved. And any intermediate state combination in the parameter space can be covered. Meanwhile, the system controls a vision generation engine through double axes, controls an image style according to a difficulty parameter, controls image information density according to a depth parameter, and ensures perfect alignment of image-text contents on a cognitive level. According to the method, the problems of single cognitive dimension, strong coupling of difficulty and depth and image-text cognitive experience splitting in the prior art are solved.

The application discloses a kind of complex text open source data intelligent analysis method based on depth semantic analysis, and the multi-source heterogeneous text data is collected by adaptive crawler, the data scattering problem is handled using T matrix approximation technique, and the standardized data set is constructed;Relax direct insertion downscaling method is used to optimize data processing flow;Multi-level semantic analysis is carried out using improved Transform and efficient hyper-reduction model;Heterogeneous graph neural network is constructed by geometric parameterization separation solver to realize knowledge fusion;Finally, technical knowledge graph is constructed and intelligent analysis is carried out.The application solves the problems of high consumption of computing resources, insufficient semantic representation ability and difficult cross-domain knowledge mapping when processing large-scale heterogeneous open source data in the prior art, and realizes efficient and accurate technical knowledge mining and trend analysis.