3695 results about "Information extraction" patented technology

The invention provides a multi-agent collaborative question and answer enhancement method and system based on heterogeneous data knowledge, and belongs to the technical field of information management. The planning optimization intelligent agent carries out structured processing on the input complex natural language problem; a multi-modal retrieval mechanism on the heterogeneous knowledge source is constructed based on the problem disassembly and entity recognition result, and a multi-path recall agent passes through a semantic matching model of a double-tower structure; the abstract extraction agent performs semantic fusion and information extraction on the text segments and the knowledge graph sub-graphs output by the multi-path recall module; logic verification and quality evaluation are carried out on the answers generated by the abstract extraction module by the reflection iteration agent; and by setting a threshold mechanism and combining importance weights of the sub-questions, performing scoring and reflection optimization on the generated answers by utilizing a large language model LLM (Language Language Model). The method can effectively cope with cross-domain and multi-level complex question and answer tasks, and has good expandability and intelligent level.

The invention relates to a cross-modal knowledge reasoning method based on a multi-modal large model. In a cross-modal knowledge reasoning process, an existing model is usually limited by single-modal information extraction and shallow feature fusion, so that deep semantic association among data such as texts, images and videos is difficult to fully capture. In order to solve the problem, the invention provides a model for fusing multi-modal information such as texts, images, videos, documents and the like, and processing of multi-modal data is converted into unified feature extraction, interaction and deep reasoning tasks by fully utilizing a supervision fine tuning strategy, a self-adaptive attention mechanism and a cross-language processing technology. The model adopts a modular design, integrates multi-source data complementary analysis, spatial-temporal feature modeling and emotional semantic analysis, and realizes multi-modal collaborative interaction, dynamic scene understanding, long video key event analysis and man-machine co-emotional response. Through sufficient training, the multi-modal large model shows excellent logical reasoning ability and emotion understanding ability in a complex cognitive task, and a brand new solution is provided for efficient extraction, deep semantic analysis and intelligent response of cross-modal information.

The invention discloses a point cloud semantic segmentation method and system based on local neighborhood attention, and belongs to the technical field of three-dimensional point cloud processing and deep learning, and the method comprises the following steps: S101, dividing three-dimensional point cloud data into training, verification and test sets, and carrying out the preprocessing; s102, constructing a deep learning network based on local neighborhood attention, training by using the training set, and obtaining an optimal model by using the verification set; and S103, inputting the test set into the optimal model to obtain a semantic segmentation result. According to the method, the semantic segmentation model is constructed by fusing local neighborhood attention of global position coding and local position coding, so that the problem of poor space geometric structure information extraction due to the fact that a conventional model only pays attention to the relative position of points in a neighborhood is effectively solved, and the feature extraction capability of an attention mechanism is enhanced; and the robustness, generalization and segmentation accuracy of the three-dimensional point cloud semantic segmentation model are improved.

The invention relates to the technical field of automatic report generation, and provides a test analysis and report generation method and system based on large model retrieval enhancement, and the method comprises the steps: firstly carrying out the semantic task decomposition of user query, and meanwhile, achieving the multi-dimensional information extraction through the butt joint of a knowledge vector library and a structured image-text knowledge system established by a private knowledge base module. And three strategies of RAG, Self-RAG and Graph-RAG are combined to enhance the generation capability of the large model so as to accurately obtain background knowledge. And constructing a standard SQL statement according to a structured query requirement, and performing data filling according to template prompt by an output result fusion module in combination with a query background and an SQL execution result to form a test report. By optimizing a retrieval enhancement generation method, professional knowledge supplement related to query is realized, and the reasoning ability of a large model in a professional scene is effectively enhanced, so that the accuracy and the intelligent level of data analysis are improved.

The invention belongs to the technical field of information, and relates to a multi-level dynamic intelligent automatic data processing and information evaluation method and system. The method comprises the steps that a database environment operation data set is constructed, targeted training and fine adjustment are conducted on a large language model in combination with reinforcement learning, and full-automatic data analysis, query and information extraction of multiple tables in a database are achieved; a multi-level agent interaction and cooperation framework is established, a scheduling agent is responsible for global decision making and strategy planning, an operation agent is responsible for execution of specific tasks, and the scheduling agent and the operation agent share information and coordinate the tasks to form a highly cooperative whole; and establishing a cyclic supervision and error correction mechanism, scheduling the intelligent agent to learn a normative workflow example of the target working environment and track the state of the operation intelligent agent in real time, identifying the behavior of the operation intelligent agent deviating from a predetermined path, and taking corrective measures. According to the method, high efficiency, intelligence and automation of data processing are realized, and the method has remarkable practical value and innovative significance.

The present invention provides a screenshot content extraction method based on a multimodal large model, which is used to extract OTA hotel structured information, including the following steps: obtaining a screenshot image with hotel information; performing text recognition on the image through an optical character recognition model to obtain a text information box set; performing text area positioning on the image through an image positioning model, and extracting the text content of the text area to obtain a text content set; inputting the text content set into a text large model, extracting structured information through preset prompt words, and obtaining first structured information; inputting the image into a visual language model, extracting structured information through prompt words, and obtaining second structured information; comparing and fusing the first structured information and the second structured information to obtain hotel structured information. The present invention can understand the image content more comprehensively and accurately, significantly improve the accuracy of hotel structured information extraction, and reduce the error rate of information extraction.

The invention relates to the technical field of intelligent vending, solves the problem that in the prior art, commodity identification cannot be accurately carried out in a multi-target scene, and provides a multi-target commodity identification method, device and system based on multi-modal data processing. The method comprises the following steps: acquiring multiple frames of real-time images in a commodity transaction scene; performing preprocessing and label information extraction on the real-time image, and determining character information corresponding to the target image and the commodity label; performing instance segmentation on the target image, and determining commodity position information; performing feature extraction on the target image, and determining commodity image feature information; according to pre-collected multi-source privatized data in an intelligent vending scene, performing fine adjustment and optimization processing on the open-source multi-modal visual language model to obtain a multi-modal large model; and inputting the commodity image feature information and the text information into the multi-modal large model for information fusion, and determining a commodity target identification result. According to the invention, commodity identification can be accurately carried out in a multi-target scene.

The invention discloses a thermal imaging temperature rise trend early warning system based on time-space sequence prediction, and particularly relates to the technical field of thermal imaging data prediction and early warning. The thermal imaging temperature rise trend early warning system comprises an image conversion module, a fluctuation feature extraction module, an edge prediction module, an anomaly characterization module and a prediction decision module; a temperature dynamic change rate and gradient intensity are calculated, edge model prediction is carried out based on a fluctuation index combination, when the fluctuation index combination does not exceed a stable interval, a lightweight deep network model deployed at a thermal imaging acquisition end is called, and when the fluctuation index combination exceeds the stable interval, a prediction decision module determines whether to switch to a high-order multi-modal model; the space-time information extraction capability is improved by constructing the temperature evolution data body, the prediction path is dynamically controlled based on the fluctuation index combination, and the prediction stability and efficiency are improved; and the abnormal activation index and the prediction offset index are combined to realize adaptive switching of model calling, so that the accuracy and adaptability of the early warning system are enhanced.

The invention discloses an enterprise audit information extraction method based on artificial intelligence, and relates to the technical field of enterprise audit information processing, and the method comprises the steps: obtaining an audit original document from an enterprise, carrying out the multi-mode conversion processing, and generating a standard data set; performing deep semantic analysis on the standard data set, and constructing an audit knowledge graph; performing cross-modal alignment on the auditing knowledge graph, the image features, the table features and the time sequence features, and constructing a joint auditing analysis model; performing dynamic verification on an audit analysis result and a historical case library to form a dynamic evolution knowledge base; and generating a final audit document based on the updated knowledge base. Through cross-modal feature vector alignment and attention-driven clause applicability scoring, dynamic association analysis of financial anomaly and business pipeline fluctuation is realized, and the cross-modal clue discovery efficiency is improved.

The invention relates to the technical field of archive information management, and provides an archive information extraction management method and system based on multi-modal learning, and the method comprises the steps: collecting multi-modal archive data, carrying out the preprocessing of the multi-modal archive data, and obtaining the processed multi-modal archive data; performing feature extraction on the multi-modal archive data, and obtaining an archive comprehensive feature vector through weight distribution and feature alignment; based on a preset archive knowledge graph, performing multi-modal entity recognition on the archive comprehensive feature vector, constructing an archive relation graph, and performing iterative calculation through a graph neural network to obtain an archive association rule; updating a preset archive knowledge graph according to the archive association rule to obtain an archive enhanced knowledge graph, and performing iterative optimization on the archive association rule to form and store an archive management strategy network; and constructing an archive information index system based on the archive enhanced knowledge graph and the archive management strategy network. According to the invention, efficient management and accurate retrieval of archive information are realized.

The invention relates to the field of computer vision, and discloses a photovoltaic panel defect category detection algorithm based on feature pyramid and cascade group attention, comprising the following steps: S1, data acquisition, labeling and preprocessing; s2, carrying out model training by using an improved YOLOv11 algorithm; and S3, predicting the input infrared picture, accurately positioning the defect and marking the category. According to the photovoltaic panel defect category detection algorithm based on the feature pyramid and the cascading group attention, the feature pyramid and the cascading group attention can be detected according to the internal circuit structure of the photovoltaic panel and an infrared imaging picture; accurately positioning, marking and classifying the defects on the photovoltaic panel; a newest YOLOv11 target detection algorithm is adopted and improved, and part of C3k2 modules are replaced by SCC3k2 modules, so that information capture on space and channels is more accurate, and adverse effects caused by redundant information extraction are reduced; and meanwhile, different input segmentation is provided for each attention head in the attention module, and the diversity of the characteristics input to the attention heads is enhanced by cross-head cascade output characteristics.

The invention provides a semi-structured file processing method based on an LLMs (Language Language Model), which is used for efficiently analyzing a semi-structured file and converting the semi-structured file, and belongs to the technical field of intelligent document processing and multi-modal learning. The method is characterized by comprising the following steps: multi-modal fusion analysis: adopting a hierarchical cross-modal attention mechanism HCMA, and combining a multi-head attention mechanism with a graph neural network GNN to realize multi-level feature fusion of a text and an image; adaptive structured conversion: adopting a dynamic structure analysis network DPN, and combining reinforcement learning and a generation model to realize adaptive analysis of a document structure; and model generation and multi-modal reasoning: adopting a multi-modal generation reasoning network MGRN, and combining a diffusion model and neural symbol reasoning to realize joint generation and reasoning of the text and the image. According to the method, a remarkable technical breakthrough is brought to the aspects of document analysis, information extraction and intelligent reasoning, and powerful support is provided for data-driven innovation of various industries.

The invention discloses an engineering drawing label identification method and system based on multi-modal information extraction, and the method comprises the steps: obtaining an engineering drawing image, carrying out the preprocessing of the image, and setting a data structure Schema for controlling an extraction target and a relation mode; detecting a drawing label area in the drawing through the trained drawing label detection model to obtain a bounding box coordinate of each drawing label, mapping the detected bounding box coordinates back to a coordinate system of the original image, and cutting out a corresponding drawing label image from the original image; performing text recognition on the cut-out signature image, and extracting text content and corresponding textbox coordinate information; and inputting the clipped signature image and the text recognition result into a trained multi-modal information extraction model, performing information extraction according to a set Schema, and outputting a structured extraction result. According to the method, the extraction target can be flexibly defined, unified extraction of the two-tuples and the three-tuples is supported, and the extraction accuracy is high.

The invention relates to a government affair file information extraction and question answering method and device and a medium, and the method comprises the steps: carrying out the entity extraction of a government affair file through employing a BERT-CRF joint model, and obtaining a structured entity set; performing relation extraction on the structured entity set to generate a semantic relation set between the entities; constructing a knowledge graph according to the structured entity set and the semantic relationship set, storing entity nodes into a graph database, and storing an embedded vector of an entity text into a vector database; when a query request of a user is received, relation query of the graph database and semantic retrieval of the vector database are carried out, sub-graph structures and semantic matching vectors related to query are extracted, and a mixed retrieval result is obtained; and inputting the mixed retrieval result into a large language model, and generating a question and answer response text conforming to a preset format by applying a dynamic prompt template. According to the method, the document processing efficiency and accuracy are effectively improved, and a solid technical support is provided for intelligent management of government affair documents.

The invention relates to a bill voucher information extraction method based on multi-mode and OCR model fusion. The method comprises the following steps: S1, obtaining an image of a bill voucher; s2, preprocessing the image; s3, identifying the preprocessed image by using an OCR engine to obtain the text content and the corresponding two-dimensional coordinates of each text block; s4, taking the recognized text segments and the original image as input, performing joint coding by using a pre-trained multi-modal model, evaluating and outputting the matching degree of each text segment and a predefined field category by the model, and determining candidate texts of each field and confidence of the candidate texts; s5, accurately positioning and extracting the key field, and verifying the consistency of the OCR output and the semantic result; s6, if the verification result conflicts or the identification reliability of a certain field is lower than a threshold value, error correction operation is carried out; and S7, outputting the structured bill voucher information. Through multi-modal fusion and iterative correction, the error rate of non-standard voucher information extraction is effectively reduced, and the method is suitable for various voucher formats and complex scenes.

The invention discloses a power grid field table recognition and information extraction method based on a large model, and relates to the technical field of power grids, comprising the following steps: obtaining a table image in the power grid field, improving image definition through an image enhancement algorithm, eliminating noise and background interference, and performing tilt correction and resolution optimization on the table to obtain a table image; generating a standardized image; and analyzing the structure of the table by using a large model based on deep learning, and constructing a spatial layout model of table units. According to the method, a large model is processed by combining vision and natural language through a multi-modal fusion system, a complex table is accurately analyzed, and the analysis efficiency and accuracy are improved. Based on deep learning and logical reasoning technologies, the system can automatically detect and repair semantic and logical errors, and data integrity and reliability are ensured. And through structured conversion and standardized output, seamless joint with a scheduling platform is realized, automatic scheduling and monitoring are supported, and the power grid management efficiency and the emergency processing capability are remarkably improved.

The invention discloses a government affair processing method based on a big language model of a knowledge graph and electronic equipment. The method comprises the steps of performing named entity and relation extraction on government affair related texts by utilizing the big language model; performing named entity classification and entity disambiguation on the named entities and the relationships, and constructing a knowledge graph; performing community detection on the constructed knowledge graph by using a Leiden graph calculation method to generate a hierarchical community structure, and generating a community abstract from bottom to top by using a large language model; and extracting entities from government affair questions input by a user by using the large language model, performing mixed retrieval on the entities by using the constructed knowledge graph and the retrieval model to obtain retrieval results related to the entities, and enhancing the retrieval results by using the large language model to generate result answers. According to the method, information extraction is carried out by utilizing the large language model, so that the information of the constructed knowledge graph is more complete, and the knowledge graph is combined with a retrieval enhancement generation method, so that the interpretability and comprehensiveness of content generated by the large language model in the field of government affairs are improved.

The invention relates to the field of standard comparison analysis, in particular to a method for automatically identifying differences of domestic and overseas standard files. A method for automatically identifying differences of standard files at home and abroad comprises the following four steps of S1, standard file analysis and information extraction, S2, semantic understanding and information integration, S3, standard difference comparison, S4, compliance analysis and suggestion, and in combination with a rule engine and a natural language generation technology, evaluating difference influences and outputting adjustment suggestions. According to the method, comprehensive analysis of the standard file is realized through multi-modal integration and structured processing, and a knowledge base with cross-language and format is constructed; clause association reasoning is realized through deep semantic analysis and knowledge graph construction; according to the method, accurate difference recognition is achieved through hybrid embedding and a dynamic threshold strategy, the accuracy and efficiency of standard comparison are remarkably improved, the manual intervention cost is reduced, and an efficient solution is provided for standard system analysis in international trade and technical cooperation.

The invention relates to the technical field of text data analysis, in particular to an electronic medical record free text analysis method, system and device, which improve the efficiency of information extraction and reduce the demand of manual intervention. The method comprises the steps of receiving free text data of the electronic medical record, performing cleaning, word segmentation and medical term standardization processing, and converting an unstructured text into structured data; based on deep learning and a medical knowledge base, text features are extracted through a pre-training language model, entity boundaries are captured on an output layer in combination with a conditional random field, medical entities in a text are recognized, and the recognized entities are classified and labeled; extracting a causal relationship, a treatment relationship and an examination relationship among entities through a dependency syntactic analysis and semantic role labeling technology, and constructing an entity association network; performing dynamic correction and supplementation on entity classification and relationships in combination with medical record context information; and outputting an analysis result in a structured JSON format to generate a computable semantic map.

The invention provides a literature structured extraction method and system based on artificial intelligence, and aims to solve the problem that information extraction is incomplete and inaccurate in an existing literature processing technology. Firstly, an original literature data set comprising a plurality of literature units is obtained and preprocessed to obtain a standardized literature data set; calling a pre-trained literature feature extraction model to process the standardized literature data, extracting content features and structural features of the literature units, based on the content features and the structural features of the literature units, executing structured analysis processing to generate a structured element set containing theme elements, logic relation elements and key information elements, and finally, storing the structured element set. And integrating and optimizing the structured element set to generate a final structured literature result containing the element association relationship, thereby realizing automatic and structured extraction of the literature information, improving the literature processing efficiency and accuracy, and providing a new way for intelligent management and application of the literature.

The invention belongs to the technical field of remote sensing image segmentation, and particularly relates to a method for generating an adversarial frequency perception image segmentation network, which comprises the following steps of: 1, acquiring a public remote sensing image data set; 2, inputting the remote sensing image data set into a self-adaptive frequency driving feature enhancement module, and decoupling high-frequency features and low-frequency features; the frequency perception adversarial feature enhancement generates more real high and low frequency features through a generative adversarial network, and the resolution capability of the model is improved; and the CLIP text encoder converts the text description into semantic feature vectors to provide support for subsequent cross-modal matching. According to the remote sensing image semantic segmentation method, innovative design is carried out in the aspects of multi-modal fusion, high and low frequency information extraction, adversarial training and the like for remote sensing image semantic segmentation tasks with complex modals, and the segmentation precision, generalization ability and robustness are effectively improved.

The invention relates to the technical field of image processing, and provides an image target tracking method and device based on deep learning, and the method comprises the steps: obtaining an original frame sequence of a target video, carrying out the adaptive frame screening of the original frame sequence, and obtaining a plurality of feature frame sequences, key frames and common frames, performing feature extraction and feature mapping on all the feature frame sequences to obtain target trajectory nodes, performing trajectory optimization calculation on the key frames and the common frames by using all the target trajectory nodes to obtain key frame trajectories and common frame trajectories, and inputting the key frame trajectories and the common frame trajectories into a preset deep learning model to perform trajectory reconstruction optimization so as to obtain a target trajectory; and obtaining a tracking target trajectory. By performing adaptive frame screening on the original frame sequence and generating the feature frame sequence, the key frame and the common frame, the data processing efficiency and the information extraction accuracy are improved, the accurate generation of the target motion track is realized, and the problems of reduced tracking accuracy and delayed system response in a complex scene are solved.

The invention discloses a government affair intelligent interaction and information extraction method and device, equipment and a medium, and relates to the technical field of digital government affair, digital cities, intelligent interaction and the like. The traditional manual item-by-item filling is converted into end-to-end automatic processing, so that the operation steps of a user are remarkably reduced, the filling burden of a hundred-item-level form is solved, and the filling efficiency is improved; and an ID voice corrector is adopted to realize three-layer progressive verification, so that the output ID offset of the multi-modal large model is effectively inhibited, the accurate mapping of field identifiers is ensured, and meanwhile, a lightweight knowledge graph is adopted to automatically capture logic rules among fields, interspecific compliance conflicts of government affair scenes are intercepted in real time, the logic error risk is reduced, and the filling accuracy is improved.

The invention discloses a database information extraction and data analysis method based on a large language agent, and belongs to the crossing field of database management and artificial intelligence. In order to solve the problems that traditional database query depends on manual SQL writing, and complex analysis efficiency is low, the method constructs an intelligent agent through a large language model (LLM) and a prompt engineering technology, and end-to-end conversion from a natural language instruction to structured data analysis is achieved. The method specifically comprises the following steps: 1) disassembling an instruction and associating database metadata; 2) generating a precise SQL query; 3) automatically adapting a visual chart according to a query result, and generating an analysis report; and 4) integrating data conclusions in different scenes. According to the method, in a judicial operation situation judgment scene test, the complex query generation accuracy is improved to 80%, meanwhile, the method can be applied to data analysis platforms in the fields of science, finance, medical treatment and the like, and non-technical users are supported to rapidly complete the whole process of'instruction-query-visualization-decision '.

The invention discloses a four-dimensional-based resume and post matching analysis method and system, and relates to the technical field of data information processing. The method comprises the steps of obtaining an original JD text and performing text preprocessing; four-dimensional information is set, and information extraction is carried out based on four dimensions; establishing a mapping relation between the JD extraction field and the resume four-dimensional degree; performing resume matching based on four dimensions; carrying out dynamic weight distribution; performing comprehensive scoring based on the dynamic weight distribution information; and result verification and output are carried out. The system comprises a preprocessing module, a four-dimensional information extraction module, a matching calculation module, an interpretability module and a verification and output module. According to the method, a four-dimensional extraction framework directly corresponding to a resume system is proposed for the first time, a four-dimensional mapping model is formed, semantic alignment of recruitment requirements and talent resumes is realized, and the analysis accuracy of complex semantics is improved in combination with a rule engine and a machine learning model.

The present invention relates to the field of content marketing. Disclosed is a content label establishment method based on electronic commerce data and social media marketing content data. Aiming at the problems of inaccuracy and low efficiency in existing labeled entity extraction results, the present invention provides the content label establishment method based on electronic commerce data and social media marketing content data, comprising: constructing a commodity knowledge map; constructing a content category database; and carrying out information extraction on the content category database to construct a category content label tree, and on the basis of the category content label tree, labeling data in the content category database. According to the present invention, when information extraction is carried out on the content category database, a RaNER model is used to identify a concrete entity, then a large language model is used in combination with an information extraction type prompt and a chain of though summary type prompt to identify an abstract entity, and results extracted by a plurality of model entities are fused, such that the problems of inaccurate entity extraction and identification and incomplete entity recall of a single model are effectively solved, and high entity extraction accuracy and efficiency are achieved.

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 embodiment of the invention provides a test case generation method and device.The method comprises the steps that a test case generation task is determined, and the test case generation task carries a target document, a task type and a document type of a to-be-generated test case; analyzing the target document according to the document type by using an information extraction agent to generate an analysis result of the target document; generating an intelligent agent by using a test point, and generating an initial test point according to the task type and the analysis result; and generating a first test case corresponding to the test case generation task by using a test case generation agent according to the initial test point and the analysis result. The analysis logic of a qualification test expert is simulated through a plurality of agents, and the efficiency and integrity of test case output are greatly improved.

The invention discloses a visible light and infrared image fusion target tracking method based on visual cue learning, and relates to the field of extended target tracking. Comprising the steps of inputting video image data, converting an image through a block embedding module, extracting multi-scale information through scale perception attention, extracting each modal feature through a double-branch backbone network, performing specific modal visual prompt learning and inter-modal visual prompt interaction, and performing cross fusion of an adapter and a prediction head. According to the method, a more reliable and efficient target tracking system is realized by introducing a multi-scale information extraction mechanism, enhancing visual prompt learning and interaction capability and developing a more advanced multi-modal fusion strategy, and through the improvements, the performance of target tracking in a complex environment can be remarkably improved, and the requirements of practical application are met.

The invention provides an intelligent interactive question and answer method and system based on multi-modal large model intention recognition, and relates to the technical field of intelligent question and answer, and the method comprises the steps of feature extraction, cross-modal semantic mapping, intention matching, key information extraction and response generation, achieves the unified processing of multi-modal information, can accurately recognize the intention of a user, and improves the user experience. And the association network is dynamically constructed to extract key information, so that the accuracy and semantic coherence of intelligent questions and answers are improved, and the man-machine interaction experience is enhanced.