2282 results about "Data extraction" patented technology

The method is suitable for the technical field of automatic driving, an automatic driving large model framework based on 3D space-time perception and human-like decision reasoning is provided, through deep integration of cross-modal dynamic semantic patch embedding and multi-scale chain reasoning, the three-dimensional space-time semantic reasoning ability of an automatic driving system in a complex scene is effectively improved, and the automatic driving efficiency is improved. The efficient cooperative processing of the full-view scene video stream and the global aerial view is realized; meanwhile, through a multi-level space-time semantic self-adaptive data extraction normal form and semantic action representation generation based on rule driving and semantic action interaction, and in combination with a cross-modal task-oriented multi-level training matrix architecture, the model shows excellent performance in the aspects of scene understanding, decision transparency and end-to-end driving task optimization. The accuracy of the system in dynamic behavior deduction is effectively improved, the decision interpretability is improved, and powerful technical support is provided for global path planning and human-like decision making of automatic driving.

Systems and methods are disclosed for optimization, management, and merging of processes. This system integrates a multifaceted technological framework, including generative artificial intelligence, quantum computing simulations, and blockchain technology. It features a user interface for inputting diverse workflow requirements, a generative AI module for processing these inputs, and a quantum computing module for simulating and optimizing workflows. The system utilizes blockchain for secure workflow deployment and a suite of specialized engines for prompt management, data extraction, analysis, optimization, deployment orchestration, and continuous monitoring. These components ensure the system's adaptability to user-specific needs, scalability across various industries, and capability for integration with existing enterprise systems. This invention revolutionizes BPM by streamlining processes, enhancing efficiency, and maintaining high security and customization standards.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device, equipment and medium based on a context agreement, comprising: receiving a business demand event, extracting a demand text, and disassembling the demand text to generate a plurality of subtasks; obtaining a data source context according to the sub-tasks and task subject information, generating a corresponding data extraction path and an analysis model strategy, connecting a target data source through an access strategy, collecting original data, preprocessing the original data, and dividing the original data into training data and verification data; and training the model based on the analysis model strategy and generating a reasoning result, and completing verification output through a result verification model. According to the method, task disassembly, data access and model generation processes are driven through a model context protocol, and a knowledge graph reasoning and verification mechanism is combined, so that a closed-loop automatic analysis path is formed, and the processing efficiency and intelligent adaptation capability of cross-domain data tasks are effectively improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an enhanced decision method, device, equipment and medium based on thinking chain annotation, which comprises the following steps: extracting core information features to generate a structured data set, loading a basic language model and executing supervision fine tuning to generate a fine-tuned model, fusing multi-modal input to generate fusion features, constructing a state observation space to receive the fusion features as input, generating reward signals based on a double reward mechanism and optimizing model parameters to generate an optimized model, deploying a monitoring module to dynamically adjust parameter configuration to generate an adaptive decision model, and outputting a decision response result. According to the method, multi-source data extraction, structured expression, multi-modal fusion, reinforcement learning optimization and dynamic adaptive mechanism fusion are carried out, so that the understanding ability of the model to complex data, reasoning transparency and the adaptive ability of the model to coping with environmental changes are remarkably improved.

A robotic surgical system. a surgeon console operatively coupled to a patient console and one or more surgical instruments. A surgeon computer is coupled to or integrated with the surgeon console, the surgeon computer further operatively connected to the one or more surgical instruments; A surgical robot is coupled to a robotic surgery control system and a feedback loop. The robotic surgery control system includes or is coupled to an artificial intelligence (AI) system. A feedback loop is further configured to receive performance-related data from the one or more sensors, the data analyzed by the robotic surgery control system or the AI system to dynamically adjust the robotic system's operation as needed. A data extraction module retrieves, from the robotic surgery control system or the AI system. one or more programmed steps executed by the surgeon for positioning at least one of the surgical instruments during the surgical procedure.

A system and method for automatically extracting data from tables and graphs in scientific literature, particularly in life sciences and healthcare, is presented. The invention employs a hybrid approach combining computer vision, natural language processing (NLP), and a large language model (LLM)-based data extraction module. A neural network enhances accuracy by providing contextual information. The system performs table structure detection, optical character recognition (OCR), Vision Transformers (ViTs) for text recognition, and graph-to- table conversion. An LLM refines the extracted data using advanced prompt engineering. A user interface enables data review, validation, and iterative refinement. The system employs a novel two-stage approach: de-rendering tables and graphs into a machine-readable format, followed by interpretation and user-defined mapping. A knowledge graph enhances extraction by resolving ambiguities and inferring relationships. Designed for scalability and continuous improvement, the invention significantly enhances data extraction efficiency and accuracy, accelerating research and knowledge discovery.

The invention relates to the technical field of equipment testing, and discloses a motor operation state vibration noise combined diagnosis generation method, which comprises the following steps: when a motor operates, performing bimodal data extraction on operation state data to obtain a vibration signal and a noise signal; performing order tracking analysis on the vibration signal to obtain an electric vibration characteristic order component; calculating a time domain energy envelope line, and performing synchronous time domain segmentation on the noise signal to obtain noise signal slices; filtering non-related components of the vibration characteristic order component in the noise signal slice, and extracting a vibration synchronous noise component of the vibration characteristic order component synchronous with the current rotation period of the motor according to a filtered result; constructing a sound-vibration fusion spectrum; analyzing the distribution rule of the sound and vibration intensity in the sound and vibration fusion spectrum, identifying a correlation mutation point, and diagnosing a specific operation state fault based on the distribution mode of the correlation mutation point; according to the invention, the efficiency of vibration noise combined diagnosis generation of the motor operation state can be improved.

The embodiment of the invention discloses a community governance decision generation method and device based on a knowledge graph. The method comprises the following steps: collecting community governance data; performing information extraction and text processing on the community governance data by adopting a data extraction model to generate structured data; constructing a causal analysis atlas, a decision treatment atlas and a space-time correlation atlas by using the structured data; fusing the causal analysis graph, the decision governance graph and the space-time association graph to obtain a community governance knowledge graph, wherein the community governance knowledge graph supports semantic-based query and reasoning; acquiring resident appeal information, and performing risk reasoning based on a preset reasoning rule to obtain a risk reasoning result; and based on a risk reasoning result, in combination with spatio-temporal information defined by a spatio-temporal association graph, querying in the community governance knowledge graph, and generating a community governance scheme according to a query result. According to the invention, the dynamism, the accuracy and the intelligent level of community management can be improved.

The invention belongs to the technical field of geological information processing, and discloses a geological mineral exploration data extraction system which comprises the following steps: constructing an air-ground-hole three-in-one acquisition network, and respectively acquiring earth surface mineral spectrum data, seismic longitudinal wave and transverse wave reflection data and rock core main quantity and trace element composition data; processing the collected data to form a five-dimensional data cube containing space coordinates, spectrum parameters, physical property parameters, element parameters and time indexes, performing physical layer attribute extraction on the five-dimensional data cube, performing analysis to obtain a wave impedance value and a lithology category probability array, performing semantic layer attribute extraction on the five-dimensional data cube, and performing semantic layer attribute extraction on the five-dimensional data cube; an alteration type probability array is obtained through analysis, an alteration zoning graph layer is generated, the mineralization potential index and a five-dimensional data cube are fused, a four-dimensional dynamic database is constructed, a mineralization potential prediction graph layer and a drilling priority graph are generated based on the four-dimensional dynamic database, and therefore the decision-making level of ore body drilling deployment is improved.

The invention discloses a geological mineral exploration data extraction method and system, and relates to the technical field of geological mineral exploration data extraction, and the method comprises the steps: carrying out the data collection of a target region through a multi-source data collection method, obtaining satellite remote sensing data, unmanned aerial vehicle aerial survey data and ground geophysical prospecting data, and obtaining an original data set; preprocessing the collected data by adopting a preliminary data processing method, removing noise and correcting errors to obtain a high-quality data set; integrating data from different sources in the high-quality data set by adopting a weighted fusion algorithm to generate a comprehensive database and obtain a comprehensive data set; performing high-resolution geophysical exploration on the fused comprehensive data set by adopting a geophysical exploration technology to obtain underground structure information; analyzing the exploration data by adopting a geophysical inversion technology, and identifying the position of a potential ore body to obtain an ore body distribution map; the method is based on the comprehensive data set, underground structure information and the ore body distribution diagram.

The invention relates to the technical field of artificial intelligence and large models, in particular to a multi-chain collaborative retrieval enhancement system, method and device based on a large model and a storage medium. The data extraction module is used for analysis; the mixed retrieval knowledge base construction module is used for cutting the document and constructing a mixed retrieval knowledge base by using the obtained document fragments; the query processing module is used for acquiring a query request of a user, performing mixed retrieval in the mixed retrieval knowledge base according to the query request, and outputting a document fragment corresponding to a retrieval result; the fusion sorting module is used for weighted fusion sorting; the answer generation module is used for inputting the document fragments and the query request into a large language model to generate answers; and the result output module is used for outputting an answer, wherein the answer comprises a traceability mark. According to the method, unified analysis and deep fusion can be carried out on the multi-modal heterogeneous data, multi-dimensional retrieval reasoning can be carried out, and the retrieval efficiency and the retrieval accuracy are improved.

The invention relates to the field of document processing and data extraction, and particularly discloses a PDF text extraction method and system based on a large language model.The method includes the steps that content of all pages of a target PDF document is positioned and marked to obtain a first to-be-recognized area and a second to-be-recognized area, and noise interference features of the to-be-recognized areas are removed; formulating a multi-level text logic reconstruction strategy to complete reconstruction of a logic sequence of the target PDF document, preliminarily outputting a first-level PDF document, and performing primary image-text association degree analysis to output first association strength; performing intelligent anomaly recognition and correction on the content of the first-level PDF document on a semantic structure through a large language model to obtain a second-level PDF document, and outputting second association strength; judging whether the secondary PDF document is qualified or not based on the first association strength and the second association strength; according to the method, the logic sequence and the semantic integrity of the document can be recovered, and the text purity and the structural integrity are improved.

The invention belongs to the technical field of intelligent power grids, and particularly relates to a circuit breaker control method, system and device of the intelligent Internet of Things, and the method comprises the steps: obtaining the operation data of a sensor; data extraction is carried out on the operation parameters of the sensor, the operation parameters are transmitted to a cloud server through the wide area Internet of Things for analysis, and whether the circuit breaker has an abnormal condition or not is judged according to a preset rule and a historical data training model; if an abnormal condition exists, based on a risk assessment matrix and real-time data monitoring, generating a safety control strategy and establishing a strategy library, performing different-level response according to an abnormal type, and generating a control instruction; and after an instruction is received, dynamically adjusting parameters according to the state of the circuit breaker, interactively verifying the instruction through the block chain node, operating the circuit breaker according to a safety control strategy after the instruction passes, and meanwhile, feeding back and adjusting the strategy. Therefore, the problems that in the prior art, the data processing dimension is single, strategy response lags behind, and the abnormal type recognition capacity is insufficient are solved.

The invention provides a multi-agent system for water quality purification material development, and belongs to the technical field of computer systems based on specific calculation models. The system comprises a data extraction agent, a material design agent, a material evaluation and screening agent, a synthesis method generation agent, a material characterization agent, a working condition matching agent, an effect verification agent, a mechanism mining agent and a material field knowledge base. According to the system, through cooperative work of a plurality of intelligent agents, a development report including structure information, an evaluation report, a synthesis scheme, a characterization scheme, a working condition matching scheme, an effect verification report and a deep mechanism analysis report of a water quality purification material for realizing a target task of the water quality purification material is directly generated according to the target task of the water quality purification material; the method does not depend on manpower and computing power resources, and saves time and labor.

The invention discloses an intelligent digital human training method and system based on multi-modal interaction, and belongs to the technical field of semantic indexing.The method specifically comprises the steps that voice, vision and text data are analyzed and converted into high-dimensional feature vectors through a modal exclusive encoder, the high-dimensional feature vectors are projected to a unified semantic space through a cross-modal semantic mapping model, and the high-dimensional feature vectors are obtained; generating a semantic primitive containing a modal identifier, a core semantic tag and a feature weight; semantic primitives are used as nodes, directed edges and edge weight table association strength are established based on semantic similarity, typical scene node connection weights are strengthened, and a mesh map containing intra-modal hierarchy and inter-modal cross association is formed; constructing a double-layer index on the basis of the mesh map; semantic primitives are extracted from newly added data, the position of a new node in an association graph is determined through a graph matching algorithm, an association edge with an existing node is automatically established, and a lower-layer modal exclusive index is synchronously updated.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent collaborative data question-answering system and method based on a large model, and the system comprises an agent cluster module which is composed of five special agents, namely a data question-answering agent, a data extraction agent, a data analysis agent, a visualization agent and a quality examination agent, and achieves the task decomposition and collaborative execution through dynamic scheduling; the knowledge management module comprises a business knowledge base, a data element knowledge base and a user feedback base, and adopts a hierarchical knowledge fusion technology to provide domain knowledge support for the intelligent agent; and the supporting function module covers a front-end dialogue component and a verification and execution engine and is responsible for interactive interface rendering and result reliability verification. According to the method, the fine tuning requirement on the large model is remarkably reduced, the illusion of the large model is effectively intercepted through a dual verification mechanism, and the accuracy and reliability of question and answer results are improved.

The invention relates to the technical field of agricultural datamation, in particular to an agricultural meteorology intelligent decision support method and system combined with multi-source heterogeneous data, and the method comprises the following steps: S1, obtaining and processing the multi-source heterogeneous data: obtaining agricultural multi-source heterogeneous data through official and monitoring equipment, and preprocessing the multi-source heterogeneous data; s2, data extraction and fusion: extracting multi-source heterogeneous data according to relevance; the method integrates meteorological, soil and crop multi-dimensional data through the space-time diagram neural network, combines the dynamic adjacency matrix and the attention mechanism, effectively captures the space-time correlation characteristics of the farmland area, solves the problem of data island of a traditional method, improves the disaster recognition precision, and improves the disaster recognition accuracy through introducing a transfer learning mechanism and a federated learning framework. Rapid migration of a main production area model to a new planting area is achieved, meanwhile, farm node data are encrypted and aggregated through a parameter server, collaborative modeling is completed on the premise of guaranteeing privacy of agricultural enterprises, and the risk of sensitive data leakage is avoided.

The invention provides a visualization system capable of synchronously displaying multiple types of data according to time. Comprising a distributed service cluster composed of a micro-service, a user management micro-service, a data transmission micro-service, a data storage management micro-service, a data extraction micro-service, a data visualization display micro-service, a data visualization template management micro-service and a resource monitoring micro-service; the micro-services are packaged in a containerized manner and interact by adopting a lightweight communication protocol, and automatic deployment and dynamic expansion are realized through a container arrangement tool; the system is provided with a basic parameter set with customizable micro-service configuration and a graphical configuration interface which are used for cooperatively generating a system operation environment configuration strategy. The user management micro-service establishes a multi-dimensional authority system based on a role access control model, and records an operation log to realize audit tracking; the data transmission micro-service integrates a plurality of heterogeneous data access modes, carries out transmission process verification and carries out secure channel transmission through the service gateway.

The invention discloses a big language model illusion detection method and system based on a knowledge graph structure, and belongs to the field of big model illusion detection. The method comprises the following steps: acquiring text data generated by a large language model, and extracting a triple of the text data to obtain a knowledge graph triple representation set; on the basis of a large language model and a natural language inference model, whether triads in the knowledge graph triad representation set have illusion or not is judged, and results of the two models are output as final illusion probabilities; and outputting the illusion existence probability of each triple, and performing visual display. The double-model verification mechanism provided by the invention improves the reliability of the hallucination detection result, the weighted fusion algorithm can balance the advantages of different models, the position of the hallucination is accurately positioned, and visual display is carried out to facilitate the understanding of a user.

The invention provides a face forgery detection method based on multi-domain clue reconstruction, and the method comprises the following steps: collecting face video data, and extracting face images of an Xi-th frame and an (Xi + n)-th frame; constructing a convolutional neural network model, and initializing parameters of the convolutional neural network model; generating a feature enhanced image by using the face image of the Xi frame and the face image of the Xi + n frame; potential spatial features are generated from the feature enhanced image, a reconstructed image is generated, and multi-scale features of the potential spatial features are extracted; generating fusion features by using the potential spatial features and the multi-scale features; generating a residual image by using the reconstructed image and the (Xi + n) th frame of face image, and generating a global attention feature by using the fusion feature and the residual image; and calculating a loss function, and carrying out face counterfeiting detection to judge whether a counterfeiting trace exists or not. The method provided by the invention can realize accurate detection of the forged image.

Computer-based techniques for performing systematic literature reviews are provided. Some embodiments include an SLR system including various components that interoperate to generate regulation-compliant SLR reports regarding an issue based on user-defined parameters. In several embodiments, the SLR system may integrate various artificial intelligence techniques to facilitate generation of SLR reports. For example, various SLR systems disclosed hereby may utilize generative pre-trained transformers to assist in aspects of systematic literature reviews, such as literature database queries, literature screening, data question generation, level of evidence classification, qualitative assessments, data extraction, and report generation.

The invention relates to the technical field of financial management, and discloses a causal interpretability and illusion suppression method, system and device for text generation, and the key point of the technical scheme is that the method comprises the following steps: S1, obtaining related data according to a generation target, extracting the causal relationship between entities, and constructing a causal map; s2, extracting a causal chain related to the generated target from the causal atlas, and inputting the causal chain and the input variables into the large language model to obtain a target text; s3, performing anti-fact intervention processing on the input variable, inputting the input variable into the large language model, and recording a logic test result; s4, identifying an entity from the target text, comparing the entity with the fact data related to the generated target, and calculating an entity alignment score; and S5, according to the causal chain, the target text, the logic test result and the entity alignment score, generating an interpretation report and performing structured output, so that the output text has higher logicality and higher credibility.

The invention relates to a geological mineral exploration data extraction method which comprises the following steps: S1, data acquisition: acquiring multi-source geological data through scientific drilling, remote sensing images and a geological radar; according to the method, through scientific drilling, remote sensing image and geological radar multi-source data collection, in combination with data preprocessing, feature extraction and three-dimensional model construction, massive geological data can be rapidly obtained and processed, and accurate positioning of mineral resources is achieved; the remote sensing image is combined to identify surface mineralization abnormity, the problem of surface and deep data separation in a traditional exploration method is effectively solved, meanwhile, the data preprocessing module ensures consistency and high-precision fusion of data from different sources through cleaning, format unification and geographical registration, and the data preprocessing module analyzes abundance and distribution rules of main metallogenic elements to obtain the data of different sources. And identifying combinations of elements closely related to mineralization.

The invention belongs to the technical field of artificial intelligence and information retrieval, and provides a multi-modal retrieval method and system based on a lightweight knowledge graph and an index table, and the method comprises the steps: obtaining multi-modal source data, and extracting a structured semantic tag set; constructing a lightweight knowledge graph and a metadata index table; analyzing a natural language query input by a user to obtain a semantic query vector and a keyword set, executing semantic retrieval in the knowledge graph to obtain a text candidate result, and executing keyword matching in the metadata index table to obtain a non-text candidate result; for each non-text meta record, fusing the cross-modal similarity between the non-text meta record and the text candidate result, performing index matching on an original score and a graph semantic evidence score, calculating a comprehensive correlation score, and performing reordering; and generating a natural language answer containing a non-text record link according to a reordering result. The method is suitable for efficient cross-modal knowledge retrieval in a high-security and low-resource scene.

The invention provides a material data curve identification and fitting method based on deep learning, and relates to the technical field of material data extraction, and the method comprises the steps: obtaining an image file containing a plurality of material data curves; separating the material data curve from the image background by adopting an image segmentation technology, and extracting feature points of the material data curve through a convolutional neural network; and taking the feature points as control points, performing parametric fitting on the curve through polynomial or spline interpolation, and generating a smooth continuous curve. The method further comprises the following steps: detecting the position and boundary of a coordinate axis in the image file through an image processing technology; the OCR technology is used to identify the scale label on the coordinate axis, and the deep learning model is combined to analyze the numerical value and unit of the scale. According to the method, through innovative technologies such as multi-modal feature fusion, self-adaptive preprocessing and intelligent data labeling, precise recognition of complex curves, multi-curve separation and association, intelligent recognition of non-standard coordinate axes and efficient real-time processing are achieved.

Disclosed is a new document processing solution that combines the powers of machine learning and deep learning and leverages the knowledge of a knowledge base. Textual information in an input image of a document can be converted to semantic information utilizing the knowledge base. A semantic image can then be generated utilizing the semantic information and geometries of the textual information. The semantic information can be coded by semantic type determined utilizing the knowledge base and positioned in the semantic image utilizing the geometries of the textual information. A region-based convolutional neural network (R-CNN) can be trained to extract regions from the semantic image utilizing the coded semantic information and the geometries. The regions can be mapped to the textual information for classification / data extraction. With semantic images, the number of samples and time needed to train the R-CNN for document processing can be significantly reduced.

The invention provides a nuclear power safety report data extraction method and system based on multi-modal feature fusion, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a nuclear power safety report document; carrying out OCR (Optical Character Recognition) and paragraph segmentation on the report document to obtain first text data; performing text semantic understanding on the first text data by adopting a dynamic template matching and semantic driving extraction mechanism to obtain a text feature vector; segmenting pages of the report document and identifying fonts and fonts to generate first format data; adopting a cross-page table reconstruction algorithm to detect table cells of the first format data and splicing a cross-page table to obtain format feature vectors; performing feature alignment on the text feature vector and the format feature vector, and inputting the text feature vector and the format feature vector into a large pre-training language model to obtain a semantic vector; and fusing the text feature vector, the format feature vector and the semantic vector to form a composite expression unit, and intelligently extracting structured data based on the composite expression unit. The semantic entity and structural relationship in the nuclear power report can be effectively identified.

System and methods are disclosed for matching extracted text data based on one or more similarity scores. The method may include receiving one or more documents from a plurality of data sources, utilizing an optical character recognition algorithm for extracting text data from the one or more documents, comparing, utilizing a fuzzy matching algorithm, the extracted text data to reference dataset(s) to determine one or more matches between the extracted text data and at least one of the reference dataset(s), wherein the one or more matches are based on at least one similarity score, inputting the determined one or more matches and the at least one similarity score into a trained machine-learning model to refine the one or more matches, and outputting a representation of the refined one or more matches and the at least one similarity score to a graphical user interface of a device.

The invention relates to the technical field of point cloud data extraction, in particular to a river water surface extraction method based on SWOT point cloud and center line data. The method comprises the following steps: acquiring PIXC point cloud data; performing data preprocessing on the PIXC point cloud data, and performing spatial index construction to obtain point cloud KD tree index data and center line KD tree index data; based on the point cloud KD tree index data and the center line KD tree index data, carrying out topology shape-preserving cutting to obtain river cutting center line data; performing candidate water surface point set construction on the river cutting center line data; therefore, through multi-level spatial index construction, topological conformal cutting, combined space-elevation modeling and structural processing of geometric and hydrological feature fusion, the problems of difficulty in data association, much space isolated noise and insufficient water surface continuity recognition in traditional river water surface extraction are solved; and the extraction precision and the processing efficiency of the river water surface point cloud data are improved.

The invention relates to the technical field of intelligent optimization, and discloses a 3D printing powder proportion optimization method and system based on artificial intelligence. The method comprises the following steps: collecting historical data corresponding to a powder material based on a printing process of a 3D printed finished product, and performing multivariate analysis on the historical data to generate a plurality of powder compositions; the material simulation model is used for carrying out property simulation on any powder composition, outputting material property data and extracting a powder ratio meeting the printing requirement; the finished product prediction model performs finished product prediction on any powder ratio, outputs a finished product prediction result, performs matching optimization on the finished product prediction result and ideal finished product data, and screens out an optimal powder ratio and a corresponding optimal printing process; and performance testing is conducted on the actual printing finished product, if the actual printing finished product does not meet the requirement, the step is executed repeatedly, and a new optimal powder proportion and a new optimal printing technology are screened out again. According to the method, the precision of 3D printing powder proportion optimization is improved.