288 results about "Structured text" patented technology

The invention discloses an electric power operation risk early warning method and system based on knowledge enhancement and multi-modal fusion. The method comprises the steps that video monitoring data, sensor monitoring data and service system data are collected in real time through multi-source sensing equipment deployed on an electric power operation site; the method comprises the following steps of: extracting entities and relationships from unstructured texts such as regulation documents and job logs by utilizing a natural language processing technology based on deep learning, extracting behavior characteristics from video streams by adopting a computer vision algorithm, and constructing an electric power security knowledge graph with dynamic updating capability; designing a multi-modal feature fusion algorithm based on an attention mechanism, and effectively integrating visual features, text features and sensor data; a graph neural network is adopted to train a dynamic risk prediction model to carry out risk prediction, intelligent research and judgment of electric power operation risks are realized, accurate management and control of the risks are realized through a grading early warning mechanism, and closed-loop management from risk perception to early warning treatment is formed.

The invention discloses an intelligent medical record quality control method based on a rule and large language model hybrid engine, and relates to the technical field of natural languages, and the method comprises the steps: obtaining original electronic medical record data from a medical information system; performing post-structured processing on the original electronic medical record data, and converting an unstructured or semi-structured text in the original electronic medical record data into a unified structured data object readable by a machine; traversing a plurality of quality control points in a preset quality control rule base, and for each quality control point, adaptively selecting and executing a corresponding quality control path based on the converted structured data object; and based on the quality control results of all the quality control points, performing score calculation and generating a quality control report. According to the method, the automatic and deep quality control of the full-dose medical record can be realized, form inspection is covered, connotation logic analysis can be executed, the system is endowed with excellent flexibility and regional expansion capability, and reliable technical support is provided for improving the overall level of medical quality management.

The invention discloses an alloy field-oriented text and table double-track collaborative data extraction method and system. The method comprises the following steps of: obtaining an alloy related document DOI; taking the DOI as an index to obtain a full-text literature and storing the full-text literature in a JSON format; performing document screening based on an LDA topic distribution model and establishing a target topic document library; preprocessing the unstructured table, and obtaining structured table data by using a large language model; performing automatic text segment division on the JSON format text through rule matching analysis; and sorting the text segments by using a large language model to generate semi-structured text data, fusing and normalizing the text and table data, and outputting a component-process-organization-performance-strengthening mechanism alloy data chain. According to the method, through a double-track collaborative data extraction mechanism, the problems of multi-source heterogeneity, format fragments, information islands, semantic layering and the like of material subject data are effectively solved, structured treatment of literature data in the alloy field is achieved, and a unified technical path is provided for constructing a high-quality alloy database.

The invention relates to the field of drawing recognition, in particular to a PDF drawing data extraction method and system based on intelligent recognition. Comprising the following steps: reading an internal structure of a PDF engineering drawing to obtain a native text stream, a vector path and a grating image; identifying the native text flow through a shunt preprocessing framework to form structured text data; rendering the vector path and the grating image to obtain a background image; analyzing the structured text data by utilizing the intelligent recognition model through the character recognition and extraction sub-model, obtaining drawing metadata and recording the position, and obtaining a character recognition result; analyzing the background image through a graphic element recognition and classification sub-model, recognizing and classifying component elements, and obtaining a graphic recognition result; and performing fusion according to the visual space corresponding relation to form a drawing analysis result. According to the method, the adaptive capacity of engineering drawings with various sources and different qualities is improved through the shunting preprocessing framework and the intelligent identification model.

The invention discloses an industrial measurement data intelligent analysis report generation method and system based on a large language model, and relates to the technical field of industrial measurement, and the method comprises a multi-source data and abstraction module which carries out the structural processing of original data DAT output by measurement analysis software; and the large language model service and interface module is used for submitting the PCT to a selected large language model LLM through a multi-model adaptation interface to execute semantic reasoning and output a structured text TXT. Structured analysis and semantic association modeling are carried out on original measurement data through multi-source data and an abstract module, when overall alignment deviation or local feature anomaly exists in the original data, a hierarchical dependency relationship between the data can be established through a semantic graph structure, a deviation transmission path is revealed, and the accuracy of the measurement data is improved. The large language model is analyzed in a unified data context, and the utilization depth of measurement data can be effectively improved, so that the accuracy and integrity of report analysis are improved.

The invention discloses a self-adaptive text extraction method and system based on artificial intelligence, and the method comprises the steps: carrying out the analysis of the document structure entropy of an example document set, quantifying the noise density, geometric distortion degree and background complexity of the example document set, and carrying out the self-adaptive selection of a preprocessing assembly line intensity grade according to the above; dynamically configuring image preprocessing parameters and AI recognition model parameters, and generating a recognition engine instance to output a preliminary recognition text; after regularized coarse screening extraction is carried out based on key field description, a multi-candidate generation strategy is started for low-confidence-coefficient candidate text fragments, a multi-person cooperative verification process is triggered for lower-confidence-coefficient fragments, finally all the fragments are processed through a text standardization module, and structured text extraction information is output. According to the method, accurate adaptation of processing intensity is achieved through document quality quantitative evaluation, the extraction accuracy and system robustness of complex heterogeneous documents are effectively improved through a multi-level confidence coefficient verification mechanism, and the identification error risk caused by image quality fluctuation or rule solidification is reduced.

The invention discloses a chip data batch structured processing method and device, equipment and a storage medium, and relates to the technical field of chip data management, and the method comprises the steps: analyzing a chip data manual into structured text content through a document processor, and generating a target abstract document corresponding to the chip data manual; constructing an initial chip data table based on chip basic information, extracting and classifying the target abstract document to obtain a target parameter, and constructing a target chip data table based on the target parameter and the initial chip data table; abstract document blocks are extracted based on the target abstract document through a document partitioning device, vectorization processing is conducted on the abstract document blocks, and a target semantic vector is generated and stored in a vector knowledge base. By constructing the chip data table and the vector database, the processing automation degree, the query efficiency and the retrieval flexibility and accuracy of the chip data are improved.

The invention discloses a document structure extraction and model training method and device, equipment and a medium, and relates to the technical field of artificial intelligence and computer vision. The method comprises the following steps: constructing a special training data set containing data of at least two document understanding tasks (including optical character recognition, layout analysis, text positioning, regional text extraction, image description and chart title generation), and a fine tuning data set for converting a document image into a machine-readable structured text format; constructing a multi-modal large model comprising a shape adaptive cutting module, a visual encoder, a visual token compression module, a modal connector and a language decoder; pre-training the model by using the special training data set to jointly learn various document understanding tasks; and performing fine tuning on the pre-trained model by using the fine tuning data set, and adapting to a document structure extraction task to obtain a document structure extraction model. By means of the technical scheme, efficient and accurate document structure extraction can be achieved.

The invention provides a consumer right protection intelligent review method and system based on a large language model, and relates to the technical field of artificial intelligence and natural language process.The method comprises the steps that a to-be-reviewed text is preprocessed, and structured text feature data is obtained; performing semantic understanding on the structured text feature data by using a pre-trained large language model to obtain a text semantic vector containing field semantic information; performing joint retrieval and matching on the text semantic vector in combination with a knowledge graph and a rule base, and identifying and outputting a risk element set; and performing compliance determination based on the risk factor set to obtain a preliminary auditing conclusion including a risk level, evidence sufficiency, a rule matching degree and a reference confidence coefficient. Through full-process intelligent processing, the original text is automatically converted into a high-credibility auditing conclusion with interpretability.

The invention discloses a classroom content evaluation system based on student questions and answers, and the system comprises a classroom information collection module, a data processing module, a classroom question generation module, a student answer interaction module, a semantic analysis module, a classroom evaluation generation module, a classified storage module, and a teaching strategy optimization module. The classroom information acquisition module monitors and acquires videos, audios, blackboard-writing texts and courseware images in real time, the data processing module converts multi-modal data into structured texts through voice recognition and OCR technologies, and the classroom question generation module generates a question set based on teaching content. The student answering interaction module receives answering data in a text, voice or image form, and the semantic analysis module analyzes teaching texts and answering contents and marks knowledge blind areas; the system has the advantages that multi-modal teaching content is integrated in real time, adaptability problems are dynamically generated, knowledge blind areas are accurately recognized, and the classroom effect is quantitatively evaluated in a multi-dimensional mode.

The invention relates to the technical field of bill text recognition, and discloses a bill text recognition system and method based on deep learning. The method comprises the following steps: acquiring target bill original image data containing a multi-channel pixel matrix and spatial resolution information; based on a matching result of the bill edge features and a preset template, geometric distortion correction is carried out on the original image, and a corrected bill image is generated; inputting the corrected image into a pre-training text region detection network to obtain positioning information containing text line boundary coordinates and region confidence; text line image blocks are extracted according to the positioning information, character segmentation preprocessing is executed, and a character-level image sequence is generated; calling a deep character recognition model to classify the sequence character by character, and generating an initial text recognition result; semantic verification and error correction are performed on the initial result based on the bill type knowledge base, final structured text data are generated, and the processing requirements of bills of different types and qualities can be met.

This invention provides a transformer fault diagnosis method and system based on the integration of knowledge graphs and large language models. [Solution] The knowledge graph question-and-answer system includes acquiring unstructured and semi-structured text; constructing a question-and-answer module including an encoding layer, a head entity recognition layer, a relationship extraction model including a tail entity and relationship recognition layer, a LangChain model, and a large language model that takes the output of the LangChain as input; acquiring fault problem text; inputting the fault problem text into the LangChain model; outputting expert knowledge; inputting the expert knowledge and fault problem text into the large language model; outputting an expert fault diagnosis answer; alternately combining the expert fault diagnosis answer with high-quality knowledge triple text; and outputting a final fault diagnosis answer. The present invention can significantly improve the health management level of power transformers.

The application provides a consumer rights protection intelligent review method and system based on a large language model, and relates to the technical field of artificial intelligence and natural language processing. The method comprises: preprocessing a text to be reviewed to obtain structured text feature data; using a pre-trained large language model to perform semantic understanding on the structured text feature data to obtain a text semantic vector containing domain semantic information; performing joint retrieval and matching on the text semantic vector in combination with a knowledge graph and a rule base to identify and output a risk element set; and performing compliance determination based on the risk element set to obtain a preliminary review conclusion containing a risk level, evidence sufficiency, rule matching degree and benchmark confidence. The application automatically converts an original text into a high-confidence review conclusion with explainability through intelligent processing of the whole process.

The embodiment of the invention provides a ship industry standard document intelligent processing method, which comprises the following steps: S1, document analysis: carrying out structured analysis and restoration on a ship industry standard document to generate a structured text supporting vectorization storage and semantic retrieval; s2, database construction: constructing a document vector index database based on the structured text to form a semantic retrieval task-oriented efficient data structure; and S3, content retrieval: performing user-oriented query, constructing a semantic retrieval mechanism, and completing high-precision matching from a natural language problem to a structured document content and result return. According to the embodiment of the invention, the document structure can be accurately restored, the content segmentation is more reasonable, the retrieval matching is more accurate, the retrieval result has more content depth, the technical path is clear, and the project landing is easy.

The invention belongs to the technical field of image restoration and computer vision, and particularly relates to a multi-mode face restoration and expression recognition system and method based on semantic guidance of a facial action unit. According to the system, multi-scale features are extracted through visual coding, and an AU activation probability is detected by using a graph neural network; the semantic conversion module converts the numerical probability into an interpretable biomechanical structured text; the multi-modal reasoning module fuses vision and text information, introduces the common sense reasoning ability of a multi-modal large language model, and improves the student network performance through knowledge distillation; and finally, the conditional generation module realizes image restoration by taking the semantic features as guidance. The facial action unit is used as a biomechanical medium, the multi-modal reasoning ability is converted into restoration constraint, the defects that in the prior art, restoration of physiology is distorted, recognition depends on image quality, and two tasks are isolated are overcome, collaborative enhancement of face restoration and expression recognition is achieved, and it is ensured that the restoration result is clear in vision and conforms to the physiological law of facial muscles.

The invention provides a multi-source bill processing method and device, electronic equipment and a medium, and relates to the technical field of computer software, and the method comprises the steps: carrying out the preprocessing of obtained bill data, and obtaining target bill image data; inputting the target bill image data into a multi-modal model for feature extraction to obtain structured text information, visual layout information and semantic association information of the bill data; based on the structured text information, the visual layout information and the semantic association information, whether repeated bills exist or not is determined; when it is determined that no repeated bills exist, compliance verification is carried out on the bill data, and a compliance verification result is obtained; processing the bill data of which the compliance verification result is passed and the configured business bill to obtain a bill knowledge graph; and carrying out cost collection processing on the bill data corresponding to the bill knowledge graph to generate accounting document data. The method can greatly improve the bill processing efficiency, and guarantees the credibility and traceability of the whole process of the bill.

The invention provides a bidding document review method and system. The method comprises the steps of obtaining a to-be-reviewed bidding document text and a corresponding bidding document text; at least one type of business compliance auditing prompt words, technical compliance auditing prompt words, qualification matching auditing prompt words and format normalization auditing prompt words is obtained, and each type of auditing prompt words is designed based on bid invitation document terms in advance; the bidding information at least comprises an auditing type, a bidding clause / format, bidding content, problem description, a risk level, a clause basis, a modification suggestion and influence evaluation; based on the obtained auditing prompt word and the structured text of the bidding document, analyzing the structured text of the bidding document to be audited, and integrating multi-category analysis results to generate an analysis report; according to the bidding document review method and system provided by the invention, the review accuracy is improved through multi-dimensional deep review, and more powerful support is provided for intelligent development of the bidding field.

Systems, methods, and computer-readable media are provided for accessing a user request received in a user session with an application. Item(s) of data may be selected in the user session in association with the user request. A data management system determines that the request is for or otherwise relevant to item(s) of application functionality such as financial exceptions, account balances, and / or operations detail. The data management system generates a prompt by adding structured data return template(s) for triggering the item(s) of application functionality, the user request, and, if applicable, structured text representing the item(s) of data to a prompt template. The data management system prompts a large language model with the prompt and receives a result. The result includes a data structure conforming to the structured data return template(s) and that is based at least in part on the item(s) of data. The data management system triggers the relevant item(s) of application functionality based at least in part on the result and causes display of information indicating the item(s) of application functionality have been triggered.

The invention relates to a contract generation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a natural language contract demand input by a user; performing structured analysis on the natural language contract demand to generate a structured text of the natural language contract demand; based on the structured text, performing multi-dimensional entity recognition, constructing semantic association and demand structured modeling, and generating a contract business demand map; based on the contract business demand graph, performing bidirectional graph vector matching with a preset model graph vector library to obtain at least one candidate contract model; based on the contract business demand graph and the candidate contract model, performing term filling and complementing to obtain an initial contract text; and performing compliance bidirectional calibration on the initial contract text, and performing optimization according to a calibration result to obtain a final contract text. By adopting the method, the accuracy of contract text generation can be improved.

The invention relates to the technical field of artificial intelligence and the field of financial services, in particular to a contract content verification method and device based on artificial intelligence, equipment and a medium. According to the method, a to-be-checked contract text is obtained, structured extraction is carried out on the contract text, a structured text representing paragraph marks, clause types and title levels is determined, compliance judgment results and result accuracy corresponding to each input collaborative agent are aggregated, and a check result with a comparison result being the corresponding contract text is obtained. The method can be applied to a financial service scene, the to-be-checked contract text is firstly obtained and subjected to structured extraction to obtain the structured text containing the paragraph mark, the clause type and the title level, and the contract text check result is obtained by comparing the aggregation result with the preset check condition. Therefore, the to-be-checked contract text is comprehensively subjected to structured analysis, and the contract text is automatically checked to obtain a check result.

The invention discloses a multi-modal conference summary generation method and system based on a large language model, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving conference basic information uploaded by a user and a conference file containing a picture and an audio; the audio file is transferred into a structured text with a timestamp through an ASR voice transfer technology, and the picture file is recognized as an editable text through an OCR technology; conference basic information, a structured text and an editable text are integrated and converted into a structured conference summary first draft, then text cleaning and content error correction are performed on the first draft in combination with a large language model and predefined logic, participant information is extracted, and finally a customized instruction is input into the large language model through a prompt project to obtain a complete conference summary. And performing accurate filling according to a structured field of the predefined Word template to generate an editable conference summary Word document. The conference summary can be automatically output, and the writing efficiency of the conference summary is improved.

The invention relates to the technical field of artificial intelligence, in particular to an official document semantic verification and intelligent error correction method and system based on a large language model, and the method comprises the steps: executing document analysis and preprocessing to form structured text data; key entities and association relationships are extracted from a large language model subjected to official document special corpus fine adjustment and stored as an official document knowledge graph; based on the official document knowledge graph, verifying entity expression uniformity, fact data matching degree and context logic consistency; marking and checking inconsistency, and generating a modification suggestion with a modification basis by using a large language model subjected to official document special prediction fine adjustment. The document semantic verification and intelligent error correction method realizes document semantic verification and intelligent error correction, and is suitable for processing scenes of documents (such as reports, notices, requests, replies and the like) of administrative organizations and enterprises and public institutions.

The invention discloses a multilevel intelligent mind map generation device based on a large model, and the device comprises a multi-modal data conversion module which processes audio and video data through a multi-modal large model, and converts the input text data and the audio and video processing data into a unified structured text format; the data preprocessing module is used for performing deep semantic analysis on the structured text, extracting key information elements, and performing document slicing and entity structure processing to obtain structured data; the graph fusion module is used for constructing a knowledge graph based on the structured data and ensuring the data quality through an entropy increase resistant mechanism; the graph generation module is used for extracting a community structure from the knowledge graph and generating hierarchical node data of the mind map; the mapping module is used for rendering the node data into a multi-level mind map; and the output module provides interactive display and file export functions of the mind map. According to the device, automatic and multi-level generation of the mind map is realized, and the generation efficiency and accuracy are remarkably improved.

The invention relates to the technical field of power system safety measures, and particularly provides a method for constructing a safety measure generation model based on a graph theory and a large language model, and the method comprises the following steps: constructing a power grid knowledge graph according to a power grid topological graph, and a sequential rule and a restrictive rule of equipment operation; inputting the structured text instruction of the power grid knowledge graph into the large language model, and generating an initial work ticket through the large language model; generating a training sample according to topological features extracted from the power grid topological graph and the initial work ticket, and training the large language model to obtain a topological perception enhancement model; and according to the multi-modal auxiliary data and the topological features, generating a multi-modal feature vector, and re-training the topological perception enhancement model to obtain a security measure generation model, so as to solve the technical problems that the security measure generation model cannot process image data and the generated security measure is easy to violate equipment operation security requirements.

This electronic device comprises a memory for storing instructions, a display, and at least one processor including processing circuitry. The instructions, when executed individually or collectively by the at least one processor, cause the electronic device to: when a first user command for capturing a first screen of the display is identified, acquire a captured image of the first screen and additional information related to the first screen; when a text included in the captured image is identified, convert the identified text into structured text data on the basis of the additional information; acquire paste content on the basis of the structured text data and context information about a second screen of the display; and input the paste content to an input field included in the second screen.

The application relates to an intelligent alarm method for industrial safety monitoring and belongs to the technical field of industrial safety production monitoring, which solves the problems of insufficient scene understanding ability and high false alarm rate of a traditional AI monitoring system. The method comprises the following steps: acquiring an industrial production scene video stream in real time and extracting a key frame image; determining a scene description structured text based on the key frame image and a scene classification prompt word by using a multi-modal visual language model; acquiring a determined high-risk scene type based on the scene description structured text and a scene reasoning prompt word by using a large language model; determining a violation feature of the scene based on the determined scene type and a violation feature prompt word by using a model calling platform; determining a judgment conclusion about the violation feature based on the violation feature of the scene and the key frame image by using a multi-modal visual language model; and generating an alarm event according to the judgment conclusion. The application realizes an intelligent alarm method for industrial safety monitoring.

A large language model (LLM) system includes a personal AI agent, an enterprise AI agent, and a public AI agent. A method for operating the LLM includes: executing a first action plan to obtain a first integrated answer and a first integrated score based on a first query using the personal AI agent; and executing a second action plan to obtain a second integrated answer and a second integrated score based on a second query using the enterprise AI agent. A method for applying the LLM system includes: transforming a bitmap of a block diagram into a scalable vector graphics and integrating a semantic tag into the scalable vector graphics; training the large language model by using the scalable vector graphics integrated with the semantic tag and using a functional specification document; and generating a structured text description of the block diagram using the large language model which is trained.

The invention relates to an e-commerce scene-oriented commodity label generation method and device, and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining native text information and an image set of a target commodity; performing information identification and extraction on the image set, converting related visual data into structured text description data, and fusing the structured text description data into a to-be-processed text corpus with multi-dimensional information; dynamically generating a theme label structure for the target commodity; performing text segmentation on the to-be-processed text corpus according to the topic tag structure to obtain grouped texts corresponding to different topic tags, and feeding back each grouped text to a corresponding topic expert sub-model to perform candidate tag extraction; and semantic fusion is carried out, and a fused label set is used as a final label of the target commodity. According to the method, the integrity, accuracy and robustness of commodity label generation in an e-commerce scene are remarkably improved, and the limitation of dependence on manpower or a fixed label library is reduced.

The application relates to the technical field of space governance, and discloses a multi-agent space collaborative governance method, which comprises the following steps: processing multi-source heterogeneous data such as satellite images and sensor readings, establishing cross-type semantic association through geographic space data embedding technology, generating unified structured text after optimizing the satellite image through a vLLM, and synchronizing to a central database; an agent role image is dynamically generated by a central server large language model based on a preset Prompt template, and the generation is independent of fixed rules; then, a target node is selected in an edge-center architecture, a total task is decomposed into subtasks, semantic mapping is established and a matching probability is calculated, and distribution is optimized by means of a reward function; a governance scheme is generated in a perception layer-decision layer-execution layer architecture, and an encoded operation instruction is output; a strategy is updated based on execution feedback, a multistage mechanism is established, roles are automatically redistributed, and governance continuity is ensured. The application can improve the overall efficiency and reliability of space governance when facing dynamic scenes or emergency situations.

The invention provides a two-ticket face generation method based on multiple modes, and belongs to the technical field of intelligent electronic ticket faces, and the method comprises five steps: firstly, collecting multi-source data such as a structured text and a monitoring video, and carrying out the targeted preprocessing; entity recognition, event extraction and multi-source data fusion are realized through a deep learning model and a cross-modal association algorithm, and a dynamic knowledge graph is constructed; then, a ticket face is automatically generated based on a multi-modal generation model of a Transform architecture, and iterative optimization is carried out in combination with user feedback; then supporting user interaction and personalized customization, and adapting to differentiated requirements; and finally, the face specification and safety are ensured through safety auditing and compliance checking. According to the method, deep fusion and automatic generation of multi-modal data are realized, richness, generation efficiency and suitability of ticket information are improved, compliance and safety of operation are guaranteed, and intelligent upgrading of operation and maintenance of a power system is assisted.