3060 results about "Data extraction" patented technology

Disclosed in the present invention is a three-dimensional scene point cloud data extraction and recognition system based on a power distribution network. The system comprises: a data collection module used for collecting data for multiple times and fusing same to acquire a complete power distribution network scene; a data processing module used for using the least square method to calibrate closest point matching and perform point cloud registration; a data extraction and segmentation module used for using principal component analysis to calculate a normal vector of the points in a neighborhood of each point cloud data point, measuring the curvature change near each point of a curved surface of a three-dimensional scene of a power distribution network by means of Gaussian curvature, and segmenting and classifying point cloud data by means of a random sampling fitting method and a clustering method; a data recognition and monitoring module used for using PointNet to perform local and global feature learning on the features of each point cloud data point, and acquiring global features by means of a maximum pooling operation for coding and recognition; and a data visual analysis module used for connecting to an open-source point cloud data processing library PCL and a hardware device for visual interaction. The efficiency and sustainability of the power supply system are improved.

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.

An automated data extraction pipeline for large language model (LLM) training may include extracting a set of code segments from a set of natural language question-answer (Q&A) combinations that each include a provided input, a provided output, and a provided code segment formatted to transform the provided input into the provided output. The data extraction pipeline may then generate a predicted output from a question portion of a first natural language Q&A combination using a first LLM. A first extracted code segment from the extracted set of code segments may then be executed to generate a first actual output of the first extracted code segment. One or more data samples may then be generated for training a second LLM based on a comparison of the first actual output to the predicted output. The second LLM may then be trained using the one or more data samples.

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.

The invention relates to the technical field of model review, in particular to a BIM model review method based on text classification and named entity recognition, and provides the following scheme: building a building specification knowledge graph, obtaining BIM review data through a review data extraction strategy, obtaining a matching rule set by utilizing knowledge graph matching, and adjusting the priority for review. Outputting the report; the knowledge graph construction adopts an ontology model and a knowledge extraction network, component attributes, spatial relationships and time dimensions are considered in the examination data extraction, the matching process is combined with multi-dimensional semantic matching and reasoning, and the examination accuracy and efficiency are ensured.

The invention provides an emergency lane dynamic management method and system, and the method comprises the steps: obtaining historical monitoring videos of all monitoring points, and carrying out the video processing of the historical monitoring videos; extracting vehicle information in the processed video, and redefining and calculating traffic flow parameters according to the extracted vehicle information; according to the traffic flow parameters, constructing a traffic flow interruption probability model, and determining a final threshold value of emergency lane opening; based on the traffic flow parameters and the final threshold, training the multiple classification models; training a traffic jam early warning model according to the traffic flow parameters; a monitoring video of a target monitoring point in a target time period is obtained, video processing and data extraction are carried out, based on the calculated real-time traffic flow parameters, a traffic jam early warning model is used for processing, and traffic flow density and an early warning point are predicted; and according to the prediction result, using multiple classification models to decide whether to open the emergency lane, and fusing all decision results. According to the invention, the use of the emergency lane is optimized.

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.

A computer-implemented method for extracting data from printed documents comprises receiving a printed document and identifying the printed document as one of a structured form (including fully structured and semi-structured) and an unstructured form. Where the printed document is identified as a structured form, the method identifies first text features corresponding to keys for key-value pairs and identifies second text features that satisfy a proximity threshold (and optionally one or more key constraints) relative to the respective first text feature as the respective values of the respective key-value pairs, and records the values of the key-value pairs. Where the printed document is identified as a semi-structured form, the method may further comprise identifying at least one unstructured portion of the printed document and applying a trained machine learning model to the unstructured portion of the printed document to obtain additional values for additional key-value pairs.

The invention provides a report generation and optimization method and system based on an offline data warehouse. The system comprises a data extraction layer, a data loading layer, a data cleaning layer, a data integration layer, a model data layer, a report mart layer, a report generation layer and a report optimization layer. The method comprises the following steps: S1, extracting data; s2, data cleaning; s3, integrating the data; s4, ensuring data quality; s5, storing model data; s6, a report market is constructed; and step S7, report generation. A layered architecture is adopted, independent operation of each module of the system is ensured, and expansion and maintenance are facilitated. Data extraction, cleaning and integration processes are simplified by introducing an automatic tool, and redundant calculation is reduced by optimizing SQL and solidifying logic. Bottom logic is optimized for large-scale data volume and complex query, and report generation performance is improved. And monitoring and management functions of the whole life cycle of the data flow are provided, and data quality and report accuracy are ensured.

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 particularly relates to a data analysis method and system. The method comprises the steps of extracting original data corresponding to a target service from a target data source according to a pre-configured data extraction strategy of the target service, loading the original data to an operation data storage layer of a data warehouse, and storing the original data; operating a data storage layer to output the original data to a detail data layer; the detailed data layer performs data preprocessing on the original data to obtain basic data and outputs the basic data to the summarized data layer; the summarizing data layer performs aggregation processing on the basic data according to a current data processing task corresponding to the target business, obtains structured data corresponding to the target business, and outputs the structured data to the application data service layer; and the application data service layer performs data analysis on the structured data according to the service configuration information corresponding to the current data processing task, and outputs a corresponding data analysis result. According to the scheme, efficient and accurate data management and analysis can be realized.

The invention relates to a fatigue driving detection method based on facial recognition analysis, and the method comprises the following steps: S1, video collection: during the driving of a vehicle, a camera collects the facial video of a driver at regular time, and after the continuous driving of the driver reaches a set time length, the camera automatically starts video collection to increase the monitoring of a fatigue state; s2, data extraction: extracting heart rate monitoring data, blood oxygen monitoring data, pulse wave detection data and respiratory rate detection data of a driver; s3, performing data analysis and fatigue judgment according to the data obtained in the step S1 and the step S2; and S4, outputting a result and warning. The method has the advantages that fatigue driving detection is realized through a non-contact facial recognition analysis method, and fatigue driving is judged through cooperation of multiple physiological indexes, so that the analysis method is more accurate and reliable.

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.

A document processing platform transforms unorganized documents into interactive systems where users can ask questions and get answers. The document processing platform implements a User-Driven Contextualization Process, which allows users to add meaning to data. The process also improves user productivity by automatically tagging information and allowing for the prioritization of relevant details. The user may then ask questions for which narrative answers are provided including the relevant details extracted from the document. The process of transforming an unorganized document into an interactive system where users can ask questions and receive answers is broken down into three phases including document upload and data extraction, user-driven contextualization and definition, and configuring the new documents for questions using a large language model such as OpenAI.

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.

Systems and methods for deep learning model development for data extraction using a feedback loop. A system generates an interactive graphical user interface (GUI) on one or more user devices for displaying a document with data extracted from the document by a data extraction model together with a user interaction tool allowing the user to correct the extracted data. The system receives, via the interactive GUI, correction information for the extracted data and monitoring performance characteristics of the extraction model in real-time based on the user correction information. The system automatically updates and trains the extraction model using the correction information responsive to detecting that the performance characteristics meet a predetermined performance reduction condition.