285 results about "Document structuring" patented technology

The invention discloses a knowledge base system construction method and device for multi-source heterogeneous data and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: integrating a dynamic graph convolutional network and a hierarchical attention mechanism to construct a multi-modal document analysis engine; performing semantic structure analysis on the original heterogeneous document on the basis of a multi-modal document analysis engine to extract document structure features and content semantic features, and constructing an original document relationship model on the basis of the document structure features and the content semantic features; based on the original document relationship model, performing classification fusion on heterogeneous data in the original heterogeneous document to obtain a to-be-stored heterogeneous data corpus, and processing the to-be-stored heterogeneous data corpus by using a graph neural network to establish a cross-modal semantic association index; and based on the cross-modal semantic association index, performing classified storage on the to-be-stored heterogeneous data corpora by utilizing a preset heterogeneous database so as to complete knowledge base system construction of the multi-source heterogeneous data.

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.

According to the knowledge text retrieval method and device, the storage medium and the computer equipment, when knowledge text retrieval is carried out, the retrieval statement and the target knowledge document corresponding to the retrieval statement are obtained firstly, the document structure tree of the target knowledge document is determined, the document structure tree is of a tree structure constructed by utilizing the directory structure of the target knowledge document, and the retrieval statement is stored in the document structure tree; according to the method, a target knowledge document is taken as a component of the target knowledge document, an index structure between each index node in the component of the target knowledge document and the target knowledge document is recorded, and leaf nodes in the index nodes are connected through pointers to form a linked list structure. The corresponding index node can be dynamically retrieved based on the association relationship between the index structure and the linked list structure of the tree structure, and the context content corresponding to the retrieval statement in the target knowledge document is intercepted by adopting different modes based on the type of the index node. And omission of unrelated contents of other chapters and key contents of the same chapter is avoided, so that the retrieval precision and quality are improved.

The invention relates to a knowledge retrieval method, system and device based on document structure context enhancement and a medium, and the method comprises the following steps: obtaining a multi-source heterogeneous structured document, carrying out unified structure analysis and formatting, and extracting metadata and hierarchical structure information of the document; constructing a document directory tree and chapter association according to the extracted document metadata and hierarchical structure information, and segmenting chapters and paragraphs into knowledge blocks; performing hierarchical context enhancement on the knowledge blocks according to the document directory tree to form enhanced knowledge blocks; vectorizing the enhanced knowledge block by using the embedded model, and storing the enhanced knowledge block and a vectorization result thereof into a knowledge base; performing multi-path and multi-layer retrieval in the knowledge base according to a user query text, and recalling related knowledge blocks; and sorting the recalled knowledge blocks, and extracting the most relevant knowledge block to obtain a retrieval result. Compared with the prior art, the method has the advantages that the document retrieval efficiency is improved, and the like.

The invention relates to the technical field of document processing, in particular to a knowledge graph-based document intelligent writing and analysis system, which comprises a graph dynamic updating module, an entity recognition and mapping module, a context connection analysis module, a semantic structure rearrangement module and a semantic coherence verification module. According to the method, the real-time data source is adopted, the knowledge graph is dynamically updated and expanded, synchronization of document content and the current information trend is ensured, noun phrases and entity distribution in a text are accurately analyzed through intelligent entity recognition, the accuracy of information extraction is improved, the coincidence degree of keywords between paragraphs is calculated, and synonymous entities are intelligently inserted, so that the information extraction efficiency is improved. According to the method, the context connection quality of the document is improved, the document structure is automatically rearranged according to the correlation of the content, the logic presentation of the information is optimized, the reading experience is enhanced, and the overall semantic consistency of the document is ensured and the interpretation ambiguity is reduced through complex semantic coherence verification.

The invention discloses a contract filing method, system and device based on automatic recognition and a medium, and the method specifically comprises the steps: taking a contract document uploaded by a user as input, and extracting contract key data; inputting the signature area coordinate, the signature date metadata and the clause keyword into a space-time correlation model to generate a context enhanced candidate contract number set; inputting the candidate contract number set into a preset knowledge graph, and outputting a target contract number; inputting the distribution characteristics of the clause keywords into a bidirectional long-short-term memory network classifier for processing, and constructing a multi-dimensional metadata matrix in combination with the target contract number; and inputting the desensitized contract text content and the multi-dimensional metadata matrix into a file DNA atlas generator, and generating a unique feature code by fusing a semantic embedding vector and a document structure fingerprint. The efficiency bottleneck and accuracy problems of a traditional archiving mode are solved, and the efficiency, accuracy and compliance of contract archiving are remarkably 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 intelligent document processing method, device, equipment and medium, comprising: receiving a document processing request and generating a task identifier, creating and sending a document processing task, constructing an intermediate document structure and generating a plurality of subtasks, and processing the sub-tasks, writing states and page contents into a cache, regularly querying task states, updating accessible identifiers, responding to an access request, loading the page contents, and integrating pages to generate a complete document after all the sub-tasks are completed. According to the method, the document generation task is divided into a plurality of sub-tasks which can be independently processed, and page-level asynchronous loading is realized in combination with a state cache and a regular query mechanism, so that the response speed and the system processing efficiency are remarkably improved while the generation accuracy is ensured; the problems that in the prior art, document generation delay is high, loading is slow, and user waiting time is long are effectively solved.

The embodiment of the invention provides a document-based intelligent question and answer method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: performing document analysis on a target electronic document to obtain document attributes and document contents; based on the document attribute and the document content, document segmentation is conducted on the target electronic document, document segmentation units and unit position information of the document segmentation units are obtained, and the document segmentation units comprise segmentation unit identifiers; based on the document attribute, performing network construction on the target electronic document to obtain a document structure tree network; on the basis of the unit position information, the document segmentation units are arranged in sequence, and a document linear list is obtained; performing document query on the document structure tree network or the document linear list based on the document problem information and the segmentation unit identifier to obtain document query information; and based on the document query information, performing question reply on the document question information. According to the embodiment of the invention, the intelligent question and answer accuracy based on the document can be improved.

Systems and methods are disclosed for training an electronic document extraction model, including the generation of the training data to train the model based on sampling a pool of electronic documents based on a rareness metric of the documents. Each electronic document has a document-level rareness metric generated, with the document-level rareness metric being based on one or more of a structural rareness metric or a content rareness metric of the document. The structural rareness metric measures the rareness of the document structure, which may be irrespective of the text content of the document. The content rareness metric measures the rareness of the document content, which may be irrespective of the document structure. The electronic documents are sampled based on the document-level rareness metrics to increase the number of rare documents in the training data without unduly biasing the sampling to optimize the training data for training the extraction model.

The invention discloses an LLM-based document structuring automatic processing method and system, and relates to the technical field of document processing, and the method comprises the following steps: S1, document input and preprocessing: removing special characters in a disordered text, unifying a coding format, and adjusting the semantic density; s2, redundancy removal and keyword extraction: removing repeated information in the disordered text, and extracting a first keyword; s3, semantic extraction and structuring: parsing the disordered text sentence by sentence according to the first keyword, and extracting main meaning and related semantic information corresponding to each sentence to obtain an extraction result which comprises a second keyword and sentence meaning of the same sentence; s4, constructing and updating a semantic stack: constructing and updating the semantic stack according to an extraction result, and storing a semantic unit; one semantic unit corresponds to the same extraction result; and S5, structured output: outputting the data in the semantic stack in a preset format. The method is suitable for structured processing of complex disordered documents in multiple fields and multiple types.

The invention provides an enhanced retrieval generation method and device based on a document structure knowledge graph. The method comprises the following steps: analyzing a document to obtain document analysis content; according to the document analysis content, constructing document entities and text fragment entities, and establishing association relationships between the document entities and the text fragment entities and between the text fragment entities; constructing a knowledge graph of the document structure; the knowledge graph is stored in a graph database, text content embedding processing is conducted on text fragment entities in the knowledge graph, and vector representation of the text content is generated; in response to a query request of a user, retrieving a text fragment entity related to the query request from the knowledge graph, and generating a corresponding query result according to the text content of the retrieved text fragment entity; and processing the query result by using a preset language model to generate an answer corresponding to the query request. The reasoning ability can be improved, and the traceability of the generated result and the accuracy of the answer can be improved.

The invention discloses an intelligent document generation system and method for bidding documents, and the system comprises a preprocessing module which is used for obtaining bidding document files in various formats, and converting the obtained bidding document files in various formats into structured bidding document files; the analysis module is used for analyzing the converted bidding document file based on the large model cue word to obtain a structured bidding document file analysis result; the directory generation module is used for generating a directory of the bidding document file according to the analysis result; the retrieval module is used for generating a corresponding knowledge content set according to the bidding document file directory; and the content generation module is used for generating the text content of the bidding document file based on the knowledge content set and the structure information of the bidding document file directory. And the content assembly module is used for performing multi-dimensional feature extraction and classification on the generated bidding document file text content, and completing content assembly according to a preset document structure tree to obtain a standard document meeting bidding document requirements.

The invention provides a large model retrieval question and answer method and device based on a document structure tree, and the method is characterized in that the method comprises the following steps: S1, respectively analyzing each document, and respectively obtaining a corresponding business tree; s2, for each service tree, converting each complete path from a root node to a leaf node in the service tree into a corresponding path vector; s3, obtaining a candidate node set according to the user problem and all the path vectors in combination with a vector model; s4, judging whether an answer is generated according to the candidate node set or not, if yes, generating the answer according to the large language model and the candidate node set, and if not, executing the step S5; s5, performing hierarchical matching on all the service trees through a large language model according to the user question to obtain a hierarchical matching result; and S6, generating an answer through a large language model in combination with a hierarchical matching result. In short, the method can realize accurate, efficient and low-cost question reply.

The invention discloses a multi-modal document structured processing method and device, equipment and a medium. For other different data types extracted from the to-be-processed document, the data of the other data types can be subjected to structured processing according to the preset structured processing steps corresponding to the other data types, so that the information carried by the data of the other data types is deeply mined, and the processing efficiency of the to-be-processed document is improved. And the information carried by the data of other data types in the to-be-processed document is presented in a unified and structured form through effective integration and utilization of the multi-modal data. For the image data, the character image type and the non-character image type are distinguished, and different processing modes are adopted, so that the information carried in the image data is extracted more accurately. For the table data, the table data is converted into an information set with rich semantics from a simple numerical matrix by obtaining a header name and a line name and generating a descriptive text for each data item.

A method includes obtaining a document structure from a data repository. The document structure includes multiple structured sections. A table is detected in a first structured section. A table representation of the table is processed by a general large language model (LLM) to generate a natural language description of the table. An image is detected in the first structured section. The image is processed by an image-processing LLM to generate a natural language description of the image. A form is detected in the first structured section. The form is processed by the general LLM to generate a natural language description of the form. The natural language descriptions of the table, image and form are inserted into the first structured section to obtain a modified first structured section. A modified document structure including the modified first structured section is outputted.

The invention discloses a PDF (Portable Document Format) document structured extraction system based on a multi-modal language model, belongs to the technical field of document processing and optical character recognition, and aims at solving the technical problem of how to improve the existing OCR (Optical Character Recognition) technology to improve the analysis capability of a complex document structure and improve the recognition precision of handwritten forms and other non-standard fonts. According to the technical scheme, the system adopts a layered decoupling architecture and comprises an input layer, a preprocessing layer, a reasoning layer, an output layer and a monitoring and fault-tolerant module; wherein the output layer is used for multi-source data access and path management to realize a local file system or S3 cloud storage; the preprocessing layer is used for invalid document filtering and visual feature extraction; the reasoning layer is used for multi-modal model interaction and content processing; the output layer is used for outputting a content aggregation result; and the monitoring and fault-tolerant module is used for realizing real-time state monitoring, resource consumption analysis and exception handling.

The invention provides a PDF (Portable Document Format) document structured loading method based on image recognition, which relates to the technical field of image data processing, and comprises the following steps: setting a predetermined extraction scale of a document image based on margin information of a PDF document; obtaining an initial image of the PDF document with a predetermined coarse scale in predetermined extraction scales; introducing a preprocessing strategy to process the initial image to obtain a target image; a target feature parameter set of the target image is collected in a multi-dimensional mode, the loading engine classifier is activated to analyze the target feature parameter set, and a target engine category is determined; and carrying out structured loading on the initial image through the target engine category. The technical problem that in the prior art, due to the fact that a loading engine cannot be adaptively selected according to semantic features and layout forms of images in a PDF document, the image type PDF structure restoration effect is poor is solved, and the technical effect of improving complex PDF document structure reconstruction accuracy and data loading quality is achieved.

The invention provides a permission configuration method and device. The method comprises the following steps: acquiring a source code and relative path information of a target document edited in a current editing environment; wherein the relative path information is used for representing position information of the target document relative to a root directory of a working area of the current editing environment; analyzing the source code to obtain a document structure object of the target document; performing node traversal on the document structure object to identify a target node corresponding to the interaction component from each node of the document structure object; according to the permission attribute corresponding to the target node and the relative path information, generating an attribute value of the permission attribute in the source code; wherein the attribute value is used for performing permission configuration of the interaction component, so that the accuracy and consistency of permission configuration are improved, the workload of manual permission configuration of developers is reduced, and the development efficiency is improved.

The invention discloses a document content generation method based on a title structuring demand and related equipment, and relates to the technical field of artificial intelligence, the method comprises the following steps: performing structured analysis on a target electronic document to extract at least one title element in the target electronic document; receiving personalized requirements input by a user for the title elements, and performing data association on the personalized requirements and the corresponding title elements; the personalized requirements are used for guiding content generation; the title elements and the personalized requirements corresponding to the data association are combined into a generation request, and the generation request is sent to an artificial intelligence model; using an artificial intelligence model to obtain generation content according to the generation request; and inserting the generated content into a position related to the corresponding title element in the target electronic document. According to the method, the document structure can be deeply understood, a user is allowed to set independent generation requirements for different title elements, a document processing scheme for batch generation and selective optimization can be realized, and the flexibility of document processing is greatly improved.

The invention discloses a document compliance detection method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the document compliance detection method comprises the steps: obtaining a product demand document, converting the product demand document into a to-be-recognized text in a markup language format, and storing the to-be-recognized text in the markup language format; the to-be-recognized text and the compliance rule are input into a pre-trained recognition model, the recognition model outputs a compliance recognition result of the product requirement document based on a content recognition result of the to-be-recognized text and the compliance rule, the compliance rule is used for indicating a corresponding relation between the content recognition result and the compliance recognition result, and the content recognition result is used for recognizing the product requirement document. The compliance identification result comprises compliance and a non-compliance reason under a non-compliance condition; the method not only improves the detection efficiency of the document compliance, but also improves the detection accuracy when processing complex document structures and contents.

Certain aspects of the disclosure provide for information retrieval that exploits context derived from document structure. Source documents can be preprocessed to identify fields and determine context attributes related to each field based on the structural layout of a source document. Resource documents can also be preprocessed to segment a resource document into passages and determine context related to the passages based on structural layout. Queries pertaining to a field can be enhanced by adding context metadata associated with the field. A query embedding can be generated and compared with previously generated passage embeddings to locate candidate matches based on similarity. A machine learning model can be provided with the top-ranked passages and tasked with re-ranking the passages based on relevancy to the original query. The highest re-ranked passage or set of passages can be output in response to the query.

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 provides an enterprise-level unstructured knowledge governance-oriented method and a storage medium. The method comprises the steps that an initial document is input into a visual language model for layout analysis, and document structure information of a target document is obtained; segmenting the document structure information to generate text slices to be vectorized; metadata extraction is carried out based on the text slices, and structured knowledge content vectors are generated; and performing index configuration according to the knowledge content vectors to form enterprise-level knowledge governance standards. According to the method, the document structure is analyzed through the visual language model, the text slices are generated through the intelligent segmentation algorithm, the knowledge vector is constructed through metadata extraction, and the mixed index configuration is performed, so that the problems of inaccurate layout analysis, logic structure damage and low retrieval efficiency in the traditional technology are effectively solved, and the method has the advantage of improving the knowledge management precision and availability.

The invention relates to the cross technical field of computer vision and natural language processing, and particularly provides a zero sample template inference and document structured recognition method and device, and the method comprises the following steps: S1, document image collection and preprocessing; s2, performing layout sensing partitioning and position coding; s3, priori or example information is constructed and injected; s4, performing cross-modal fusion and expression construction; s5, performing automatic format analysis and field slot filling; s6, generating a cue word-free extraction instruction; s7, performing field area parallel character recognition; s8, performing semantic verification and result standardization; and S9, outputting the structured field-value data. Compared with the prior art, the method has the advantages that the dependence of a traditional method on template making, cue word writing and large-scale sample training can be avoided, the flexibility, accuracy and online speed of document structured recognition are remarkably improved, and the method has good intelligence and rapid adaptation capacity and is suitable for diversified document recognition scenes.

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.

Systems, methods, and software are disclosed herein for automating the editing of documents based on ink gestures via an LLM integration in various implementations. In an implementation, a computing device receives an ink gesture made by a user with respect to a document displayed in the user interface of an application. The ink gesture includes ink strokes made on a portion of the document. The computing device generates a prompt for an LLM to obtain suggested revisions to the document, wherein the prompt includes an image of the ink strokes made on the portion of the document. The prompt also includes a document tree indicative of a structure of the document. The computing device surfaces a suggested revision generated by the LLM in response to the prompt in the user interface.

The invention particularly relates to a Word document format conversion method based on Java. The Word document format conversion method based on Java comprises the following steps: analyzing the content of an original. Doc file, and extracting text paragraphs, tables, pictures, style information and document metadata; the extracted content is divided into different categories, and the XML node type corresponding to each category of elements in the target. Docx document is established; the method comprises the following steps of: constructing a pattern mapping rule base, constructing a new. Docx document structure by using an XWPF Document object model according to an Office Open XML (Extensible Markup Language) specification, sequentially inserting paragraphs, tables and pictures, and applying corresponding pattern configuration; and outputting and generating a. Docx file, detecting and processing abnormal conditions, and recording a conversion log at the same time. The Word document format conversion method based on Java is efficient and accurate, has good compatibility, expandability and cross-platform capability, is suitable for enterprise-level document management systems, cloud services and batch document processing scenes, and remarkably improves document compatibility and processing efficiency of office automation systems.

The invention belongs to the technical field of bidding document auditing, and provides a multimodal bidding document intention anchoring and rule pluggable auditing method and system in order to solve an auditing blind area caused by an information island. The intention anchoring and rule pluggable auditing method for the multi-modal bidding document comprises the following steps: carrying out analysis and element splitting on an original bidding document file; generating a document structure tree; performing cross-modal information alignment and consistency verification on the document structure tree; analyzing the auditing intention into a structured instruction packet containing a context and guiding subsequent operation, traversing the checked document structure tree to generate a hierarchical outline with a type label, injecting the hierarchical outline and the structured instruction packet together as the context into a cue word, and anchoring the auditing intention based on cue word guidance and closed-loop verification; and calling a rule engine and starting the rule engine to execute auditing operation according to the anchored auditing intention. The bidding document auditing system can improve auditing quality and quickly adapt to changeable business rules and bidding document auditing requirements of different industries and different types.

The embodiment of the invention relates to the technical field of information, and discloses an interface verification and test method and device based on a large model, a medium and a product, and the method comprises the steps: obtaining a to-be-tested interface document, analyzing the interface document through a first large model, constructing a test request parameter based on a request parameter definition, acquiring a real-time response message by calling a real interface; performing semantic comparison on a response example in the document structure information and the real-time response message by using the second large model to obtain a corrected interface document; comparing the parameter constraint condition in the corrected interface document with the verification logic in the business logic code snippet by using a third model to generate a logic verification passing result; and on the basis of the corrected interface document and a logic verification passing result, a Mock rule and an automatic test case script are generated by utilizing the fourth model, so that the maintenance cost and the false alarm rate of the automatic test script are reduced, and the automatic test efficiency is improved.

The invention discloses a product image-text and document association identifier generation method fusing a PLM coding rule, relates to the technical field of product data management, and is used for solving the problem that an image-text and document association identifier is not clear. According to the method, document information vectors are constructed by fusing PLM coding rules and collecting document metadata of design drawings, process documents, quality management and bill-of-material systems, an image-text document structure mapping chain is generated through a structure mapping judgment model, a unique association identifier of the image-text document is generated in combination with a preset template, and the unique association identifier of the image-text document is obtained. And real-time updating is carried out during structure replacement, hierarchical reconstruction or configuration change, cross-system serial number semantic alignment is realized through a heterogeneous coding analysis rule base, heterogeneous naming conflicts are solved, identifiers are written into a product data management platform, an index relationship is established, hierarchical retrieval, version switching and historical backtracking are supported, and the product data management efficiency is improved. And the consistency and cooperation efficiency of product research and development and quality control are improved.