2526results about "Text processing" patented technology

The invention relates to the technical field of machine vision, and discloses a machine vision-oriented cross-modal image-text analysis method, which comprises the following steps of: partitioning an input image to generate an image block sequence; inputting the image block sequence into a visual converter for multi-scale feature extraction, and generating target visual features; encoding the input text to generate a target text feature; inputting the target visual features and the target text features into a deep reconstruction bottleneck network for compression alignment, and generating a cross-modal compression vector; and inputting the cross-modal compression vector into a large language model to generate cross-modal decoding information, so that cross-modal redundant information can be effectively filtered, compact shared semantic representation can be learned, the information integrity of the compression process is ensured through bidirectional reconstruction verification, cross-modal semantic alignment is realized, and the method has the advantages of high efficiency and high reliability. Omnibearing cross-modal content generation from the whole to details is achieved, and the requirements of different application scenes are met.

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.

The invention relates to the field of computer vision, and particularly provides a table recognition reconstruction method and system, a terminal and a medium, and the method comprises the steps: firstly decomposing a large-size table image into a plurality of overlapped sub-images, and carrying out the table structure detection and OCR character recognition of each sub-image through parallel recognition; then, sub-graph recognition results are integrated through a coordinate mapping and confidence coefficient weighted fusion algorithm, and boundary errors are eliminated; then, automatically distinguishing common cells based on an area clustering algorithm, merging the cells and a header region, and reconstructing a complete table logic structure; further understanding header semantics through a natural language model and repairing identification errors; and finally, realizing intelligent splicing and standardized output of the cross-page table. According to the method, the memory limitation of the traditional OCR technology is broken through, an oversized table can be processed, the recognition accuracy of a complex structure is improved, and the digitization efficiency of professional documents such as financial statements and engineering drawings is improved.

The invention relates to the technical field of large model intention understanding, and discloses an APP dialogue type service reaching method and system based on large model intention understanding. The method comprises the steps of receiving a natural language text input by a user on an APP dialogue interface and performing large model semantic analysis to obtain service semantic data; inputting the business semantic data into an intention recognition model for business intention understanding to obtain business intention data; performing multi-agent cooperative process planning to obtain execution process data; carrying out interactive confirmation on the service parameters to obtain service execution parameters; transmitting the service execution parameters to corresponding service tool interfaces for calling execution to obtain a service tool calling result, and performing intelligent analysis and card rendering on the service tool calling result to obtain display card data. According to the method, the real business intention of the user is accurately understood, and the problems of execution efficiency and stability of a traditional single interface calling mode in a complex business scene are solved.

The invention discloses a method for converting a natural language into a low code based on multi-modal reinforcement learning, which comprises the following steps of: performing word segmentation, embedding and multi-layer feature extraction on a natural language instruction input by a user, combining a self-attention mechanism and a graph attention mechanism, extracting and optimizing an original semantic feature vector, and automatically identifying a business field; semantic relationship triples in the domain knowledge graph are fused, and a multi-modal semantic alignment and context enhancement strategy is adopted, so that the accuracy and stability of semantic representation are remarkably improved; when semantic drift or ambiguity is detected, a multi-candidate correction mechanism is triggered to obtain a better analysis result, and the robustness of semantic understanding is enhanced; and finally, mapping an analysis result into an instruction which can be identified by a low-code platform, automatically generating a code structure, continuously optimizing a semantic model and a knowledge graph based on user feedback, realizing adaptive learning, and improving the conversion efficiency and quality from a natural language to codes.

The invention relates to the technical field of automatic data analysis, in particular to an intelligent data report generation method and system based on an MCP protocol. The method comprises the following steps: constructing a semantic intention vector through a context semantic structure according to an obtained natural language instruction of a user; constructing a semantic task path graph based on the semantic intention vector by utilizing a knowledge graph; according to the constructed semantic task path diagram, performing intention path and tool capability mapping based on an MCP protocol; constructing a task flow based on an MCP protocol and performing task scheduling optimization; carrying out data source unified modeling based on the constructed task flow, and generating a stable and controllable calculation task flow; and visual expression generation from data to graphs is realized through an intelligent recommendation and structural modeling mechanism. According to the method, a semantic-driven MCP plug-in tool chain and a chart capability component index are constructed, so that a full-automatic closed loop from a natural language intention to data analysis and chart generation is realized, and meanwhile, the use threshold of non-technical users is reduced.

A document preparation method involves creating a hierarchical representation of an input document without summarizing or omitting any content. The method uses a generative language model to generate the hierarchical representation and stores it in a repository for later use by a client generative language model. This allows for more accurate and complete generation of text, enabling the use of retrieval units to enhance the output of the client generative language model while efficiently exploiting its limited context window.

The invention relates to an RAG-based voucher classification method, a medium and equipment, and the method comprises the steps: receiving digital image data of a to-be-classified voucher, carrying out the multi-modal optical character recognition processing to generate a structured OCR result, extracting a text semantic feature vector and a visual layout feature vector based on the structured OCR result, carrying out the fusion of the text semantic feature vector and the visual layout feature vector to generate a multi-modal query vector, and carrying out the classification of the to-be-classified voucher. Similar samples and semantic similarity scores and category metadata thereof are obtained through approximate nearest neighbor retrieval, after an initial candidate category list is generated, key field values are extracted for each candidate category, evidence credibility scores are calculated, comprehensive confidence scores are generated by fusing the semantic similarity scores and the evidence credibility scores, reordering is conducted, and a candidate category list is obtained. And finally, selecting a classification decision path according to the score distribution, and outputting a classification result and an interpretability report. The accuracy and robustness of voucher classification are effectively improved, and complex voucher scenes with changeable formats and fuzzy semantics can be processed; and the interpretability and reliability of the classification decision are enhanced.

A computing system including one or more processing devices configured to receive prompt generation instructions that specify an initial prompt and a prompt evaluation criterion. In each of a plurality of iterations of a prompt generation loop, the one or more processing devices are further configured to generate candidate prompts at least in part at a machine learning model. The candidate prompts are generated based on a current-iteration prompt that is initialized as the initial prompt in a first iteration. As specified by the prompt evaluation criterion, the one or more processing devices are further configured to compute respective evaluation scores associated with the candidate prompts. Based on the evaluation scores, the one or more processing devices are further configured to replace the current-iteration prompt. The one or more processing devices are further configured to output a final prompt generated in a final iteration.

The invention belongs to the technical field of text processing, and particularly relates to a method and system for retrieving DOCX document content based on keywords, which comprises the following steps: analyzing an Office Open XML structure of a DOCX document, combining with multi-dimensional features such as style names, and utilizing a title classification score model to accurately distinguish a title and a text, so that a semantic hierarchical structure of the document is effectively reserved; and secondly, a multi-level semantic extension mechanism is introduced, and a Sension-BERT, a HowNet knowledge base and a Word2Vec model are fused, so that intelligent extension of synonyms and synonyms of keywords is realized, and the recall rate and semantic understanding ability of retrieval are remarkably improved. And in addition, a BM25 model is combined with paragraph length normalization and structure position weight to calculate a correlation score, so that retrieval results are sorted more accurately and reasonably. The construction of the reverse index is combined with the position coding and compression optimization strategy, and the retrieval efficiency and the storage performance are both considered.

The invention discloses an automatic feasibility research report generation method fusing a knowledge graph and a large model, and relates to the technical field of leather inlaying, and the method comprises the following steps: extracting and cleaning information from multi-source heterogeneous data, and carrying out standardization processing on terminologies; constructing a knowledge graph, and converting the knowledge into embedded vectors which can be processed by a computer; according to user information, related sub-graph information is accurately retrieved from the knowledge graph, and cue words containing professional knowledge are dynamically combined; the large model generates a first draft according to cue words, a system automatically checks, and when conflicts are found, the model is required to be regenerated; and integrating the content which is verified to be correct with related standard terms, automatically adding a data reference source, and generating a document which is standard in format and clear in risk point. According to the method, real-time and automatic professional knowledge conformity checking and self-correction can be carried out on technical parameters and logic assertions output by the model in the generation process.

The invention relates to the technical field of natural language processing, in particular to a context-based Prompt generative model optimization system, which comprises a context analysis module, a cue word generation module, a context optimization module, a semantic check module and a structure reconstruction module. According to the method, the context path and the semantic hierarchy information of the semantic unit are introduced, the fine degree of semantic matching degree recognition is improved, semantic guide deviation caused by statement template solidification is avoided, the cue words are recombined in combination with the semantic coherence weight and the logic dependency relationship, and the recognition accuracy is improved. The consistency and expression accuracy of the prompt content in the context are enhanced, the prompt word insertion sequence and connection mode are dynamically adjusted through a semantic conflict detection and structure rechecking mechanism, coherence and stability of a semantic structure and controllable generation of the prompt content are kept, semantic conflicts and expression chaos caused by static matching are effectively avoided in the generation process, and the generation efficiency is improved. And dynamic adaptation of prompt configuration and smooth optimization of language output are integrally realized.

The invention discloses an intelligent data query system and method based on natural language processing, and relates to the technical field of natural language processing and database query. According to the method, natural language input and database mode information are received, a historical query log is combined to construct mode knowledge representation, a query skeleton is generated on the basis, a fine-tuned large language model is called to generate candidate SQL statements, error detection and ambiguity recognition are carried out on the candidate statements, and the query result is obtained. And if necessary, triggering interaction clarification and updating a query result according to user feedback. And meanwhile, the wrong clauses are locally repaired through a gating mechanism, and the clauses are returned and regenerated when multiple times of repair fails, so that the correctness of the query statement is ensured. And finally, after the SQL is executed in the database, a result is fed back to the user. Besides, the system records generation and repair tracks in the operation process, and continuously optimizes the model based on reward shaping, comparative learning and self-game training, so as to improve the generalization ability in different business scenes.

The invention provides a data analysis mapping method and system based on context awareness, and the method comprises the steps: carrying out the directional crawling of a target website through a crawler instance, so as to obtain multi-format webpage source data, then extracting the structural features, semantic features and source features of the multi-format webpage source data, and constructing a source data context feature set; dynamically matching an adaptive analysis strategy based on the source data context feature set to accurately extract target field data, performing semantic similarity matching on the target field data and a preset service scene standard field library, and performing data format unified conversion in combination with a context format rule to obtain standardized field data; and finally, performing multi-dimensional verification on the standardized field data, and generating an SQL statement after the verification is passed, so as to solve the problems that an existing data analysis mapping scheme depends on a fixed template and cannot dynamically adapt to page structure change of a target website, manual auxiliary field mapping consumes time, and errors are easy to introduce.

The invention relates to a BOM table generation method based on full life cycle management, computer equipment and a computer readable storage medium, and the method comprises the following steps: S1, generating a client matrix according to a product design document and an engineering material, and obtaining information in the client matrix; the customer matrix is presented in the form of a two-dimensional table, column information in the two-dimensional table represents serial numbers of product parts and / or components of different assembly levels, and row information at least comprises names, numbers, specifications and models of the product parts and / or components and parent component identifiers; s2, generating an in-plant BOM matrix according to the material key information, the in-plant material number, the in-plant inventory status and the supply chain constraint condition, inputting the in-plant BOM matrix into a preset management system to generate an electronic drawing and document database, and establishing a structured D-BOM; s3, comparing the client matrix information obtained in the step S1 with the D-BOM established in the step S2; s4, carrying out adjustment and optimization on the D-BOM; and S5, importing the optimized D-BOM into a client matrix, and generating a final BOM.

Methods and systems for retrieving relevant information in response to an input question. The method includes obtaining text content related to the input question and partitioning the content into one or more paragraphs based on predefined rules. The method further involves extracting one or more evidence spans that are relevant to the input question by inputting the text content and the question into a trained language model. A semantic search is then performed on both the paragraphs and the extracted evidence spans, ranking the candidate passages based on their relevance to the input question. Each candidate passage may comprise either a paragraph or an evidence span that addresses the question. The disclosed methods and systems improve the quality and relevance of retrieved information by combining heuristic-based content partitioning with machine learning-based evidence extraction.

The invention relates to the technical field of computer application, in particular to an industry document generation method based on a large language model and retrieval enhancement, which comprises the following steps: acquiring a first user demand of a building engineering project to be input, performing data preprocessing on the first user demand, and acquiring the preprocessed first user demand; performing semantic alignment on the preprocessed first user demand by adopting a cross-modal embedding model to generate a multi-modal fusion vector; according to a dynamic semantic slicing strategy, performing slice segmentation on the multi-modal fusion vector, and obtaining a plurality of slice units containing complete semantics as segmented multi-modal fusion vectors; inputting the segmented multi-modal fusion vector into a pre-trained special retrieval enhancement large model for the building industry, and generating an industry document which conforms to a building engineering specification and a first user demand; according to the method, the problems that the generated building related document content in the building industry is low in professionality and accuracy and cannot meet the industry requirements are solved.

The invention provides a multi-mode PDF (Portable Document Format) document analysis method, device and equipment and a medium, and the method comprises the following steps: loading a PDF document and carrying out preprocessing, including page splitting and content cleaning, to generate standardized document data; dynamically extracting contents in the standardized document data, wherein the contents comprise paragraphs, pictures and table elements; processing the dynamically extracted pictures, including shielding meaningless pictures based on a preset rule, and analyzing picture contents by using a multi-modal model to generate readable picture information; processing the dynamically extracted table, including optimizing and merging the table structure into a single element format to generate structured table data; combining paragraphs, readable picture information and structured table data, converting the paragraphs, the readable picture information and the structured table data into a complete structured document format, and inserting in key positions to enhance coherence context description; and outputting the complete structured document format as a final analysis result. According to the method, the analysis precision and the knowledge extraction efficiency of the complex PDF document can be remarkably improved.

The invention discloses a micro-report generation method and device and a storage medium, and relates to the technical field of data processing, and the micro-report generation method comprises the steps that a natural language question input by a user is received, the natural language question is matched with a preset question example library, and a target question example is determined; obtaining a business index in the natural language question, and determining a data model corresponding to the business index; based on the target problem example, the business index and the data model, generating a calculation semantic configuration; generating a target SQL query by combining the corresponding model information, the data source information and the authority control rule based on the calculation semantic configuration; and generating the micro-report by executing the target SQL query. According to the method, the target question example is determined by matching the natural language question with the preset question library, and the semantic configuration is dynamically generated and calculated in combination with the specific business index and the data model, so that the target SQL query is generated, and the effect of immediately responding to the dynamic query according to the natural language question is achieved.

The invention relates to the technical field of data import, in particular to an intelligent Excel data import method and system based on dynamic mapping, and the method comprises the steps that the system traverses all Excel fields for each database field, calculates the feature similarity and field matching degree of the Excel fields, and generates a field mapping template set according to all field matching results; reading the Excel content according to rows, cleaning each row of content into a standard value, performing quality scoring, and outputting a target format data structure set; screening a structure alignment data row and a field structure exception record set which conform to a threshold value; the system reads the structure alignment data rows line by line, processes the extraction value of each field and outputs a legal data set; and extracting a field sequence set, searching whether fields marked as primary keys or unique keys exist in the field sequence set, and combining the fields into a uniqueness judgment set to judge whether the currently written Excel data exists in the target database table.

Technology is disclosed herein for the integration of spreadsheet environments with LLM services. In an implementation, an application service receives a natural language input from a user associated with a spreadsheet hosted by a spreadsheet application. The application service generates a prompt based on the natural language input which includes asking a large language model (LLM) service to classify a statement in the input as referring to one of multiple capabilities of the spreadsheet application. The application service inputs the prompt to the LLM service and receives an output from the LLM service which identifies a determined one of the multiple capabilities. The application service generates a revised prompt based on the input and the determined one of the multiple capabilities and inputs the revised prompt to the LLM service.

The invention belongs to the technical field of semantic understanding systems, and particularly relates to a semantic understanding system based on a large language model.The semantic understanding system is characterized in that firstly, a data preprocessing module is used for conducting cleaning, denoising and cross-modal conversion on input multi-modal data such as texts and images, and standardized data is generated; a semantic feature extraction module extracts general semantic features by using a pre-training model, adapts to field requirements through dynamic learning rate fine adjustment, and outputs scenarized semantic vectors; then, a dynamic semantic-knowledge bidirectional fusion module adjusts token weight according to a dynamic semantic weight algorithm, realizes real-time alignment of semantics and a knowledge graph by means of a knowledge entity association strength algorithm, and a knowledge enhancement fusion module further optimizes knowledge weight and dynamically updates association; then, the semantic reasoning module performs multi-round reasoning based on fusion information, and evaluates the result reliability in combination with a confidence coefficient algorithm; and finally, the output and optimization module generates a structured result, and iteratively optimizes parameters of each module according to feedback data to complete a semantic understanding processing flow.

The invention discloses a data security risk assessment method and system based on a large model, and relates to the field of artificial intelligence large models. The method comprises the following steps: firstly, preprocessing a benchmarking and checking material, and then encoding the preprocessed benchmarking and checking material and a standard specification into a unified semantic space by utilizing a pre-training language model to form a benchmarking and checking vector database; then, on the basis of standard specifications, a data security risk assessment vertical large model is utilized to construct a compliance and security risk analysis assessment item prompt, and a structured assessment problem library is formed after manual verification; and then respectively calling each evaluation item prompt in the structured evaluation problem library, carrying out semantic retrieval in the benchmarking check vector database to generate an enhanced prompt, and then carrying out multi-stage reasoning to obtain a data security risk evaluation result. Through the pre-training language model and the data security risk assessment vertical large model, automatic and intelligent data security risk assessment is realized.

The invention relates to a big language model dialogue type intelligent report generation method based on LoRA fine tuning, and belongs to the technical field of intelligent report generation. The method comprises the following steps: constructing a field data set by constructing a tetrad training sample and conditional enhancement, finely adjusting and optimizing SQL generation, chart classification and text summarization tasks by adopting multi-target LoRA, and introducing a strategy network to improve query understanding; dialogue state tracking and dynamic modulation are realized through a super network, and precise SQL analysis and correction are realized in combination with a service semantic mapping library; and finally, based on data feature analysis and multi-model fusion recommendation, generating an optimal visual chart and a multi-modal summary, and completing end-to-end intelligent report generation. According to the method, the end-to-end conversion from a natural language to an accurate SQL, a chart and a summary is realized through conditional enhancement of tetrad data and multi-target LoRA fine tuning in combination with dynamic dialogue modulation and business semantic mapping, and the analysis accuracy and the interaction experience are remarkably improved.

A method for automated control code generation and verification includes: receiving a natural language command, the natural language command being configured to instruct the large language model to output a code text that meets control requirements corresponding to the natural language command; performing matching retrieval on a vector database according to the natural language command to obtain a sample code snippet; obtaining API structured information corresponding to the sample code snippet from a knowledge graph database; generating an initial control code according to the sample code snippet and the API structured information; and performing a multi-level virtual operation verification on the initial control code in a software motion control system, and generating a target control code according to multi-level verification results confirmed multiple times by the user and the initial control code.

The invention relates to the field of clinical terms and the technical field of digital coding, and discloses a data processing method for ICD coding recommendation of outpatient medical records, and the method comprises the following steps: carrying out the preprocessing of outpatient medical record texts, including cleaning, segmentation, coding standardization and punctuation standardization; carrying out medical entity identification and assertion identification on the preprocessed medical record text by adopting a medical entity identification model and an assertion identification model, generating structured information containing evidence fragment offset, and analyzing unstructured and spoken contents by carrying out preprocessing and medical entity identification and assertion identification on the medical record text, so as to obtain a pre-processed medical record text; key information of negative expression and time sequence dependence is captured, structured and standardized processing of medical entity information is guaranteed, the code error and missing risks in code recommendation are reduced, and the accuracy of medical quality control and medical insurance settlement is improved.

The invention relates to the technical field of data processing, and discloses a research report text extraction optimization method and device, equipment and a medium, which can be applied to the financial field, and the method comprises the following steps: obtaining research report text data, and processing to obtain a segmented text fragment set; establishing a fragment vector index database by utilizing the segmented text fragment set; receiving a query request based on the fragment vector index database and processing the query request to obtain candidate fragment sequence data; and processing the candidate fragment sequence data to obtain context data, injecting a cue word template to generate a target cue word, inputting the target cue word into a language model for reasoning, and outputting a text optimization result. In the invention, aiming at the problem that the output of the existing research and report text is lack of standardization, the target cue word can be generated by utilizing the context data obtained by calculation, and is input into the language model for reasoning, and finally the text optimization result is output, so that a multi-level abstract and key information summary can be quickly generated, the manual reading cost is reduced, and the efficiency is improved. The working efficiency of personnel is improved.

The invention provides an enterprise knowledge graph automatic construction and intelligent retrieval method, which comprises the following steps: collecting multi-source data from a heterogeneous enterprise information system, and carrying out data cleaning and standardization processing; on the basis of a comprehensive scoring mechanism of field similarity and behavior semantic vectors, entities from different systems are merged, and a standard entity set with a unique identifier is generated; based on the standard entity set, in combination with a scoring mechanism of a task-type relationship and a collaborative relationship, extracting a semantic relationship from a behavior record, and constructing an enterprise knowledge graph structure; extracting representative semantic paths from the knowledge graph structure, screening high-quality paths through a path scoring model, and organizing the high-quality paths into a structured path index set; and receiving a natural language query statement, encoding the natural language query statement into a semantic vector, matching the semantic vector with the path index set, executing query in the atlas in combination with an authority control mechanism, and returning a result.

The invention provides a test case library construction method and system based on a large model, and relates to the technical field of intelligent automobile tests.The method comprises the steps that semantic analysis of a tested function document is separated from test scene recognition, and an initial test case is generated in combination with a rule-guided large language model; systematization, intellectualization and standardization of a test case library construction process are realized; formats and content specifications output by the large model are effectively restrained through preset professional rules, and the professionality and structural consistency of the generated use cases are guaranteed; meanwhile, problems are found and fed back in time through a multi-dimensional verification and quality evaluation mechanism, self-adaptive optimization of a construction strategy is driven, and the quality and generation efficiency of the test cases are improved; finally, qualified cases are integrated into standardized records, a high-quality test case library with both manageability and technicality is formed, and efficient and automatic conversion from unstructured documents to reusable and maintainable test assets is achieved on the whole.