865 results about "Collation" patented technology

The invention discloses a power plant operation and maintenance knowledge intelligent query method based on a large language model and an RAG technology. The method comprises the following steps: constructing a power plant operation and maintenance knowledge vector library covering structured, semi-structured and unstructured data; receiving a natural language question of a user, inputting an improved instruction to align a preprocessor, and generating a question semantic vector and an intention tag; relevant knowledge fragments are retrieved and sorted through a semantic matching retriever in combination with the intention labels; constructing a large language model cue word structure based on the retrieval result and the original question, generating candidate answers and recording a reference path; and finally, performing term specification and consistency verification according to the expert rule base, and outputting a structured and traceable final answer. According to the invention, the improved RAG technology is fused to realize intelligent query of the operation and maintenance knowledge of the power plant.

The invention discloses an intelligent report generation method and system based on multi-source heterogeneous data fusion, and particularly relates to the technical field of data fusion, and the method comprises the steps: constructing a fact anchor point containing a stable identifier in a fusion layer, and solidifying an aperture version signature and a time interval; packaging a screening condition, a sorting rule, an access plan, time window limitation and the like into a controlled context packet and freezing the controlled context packet into a unique fact source; in the range, generating sentences by using a limited template and writing sentence-level reference marks, so that each sentence can be traced back to a data slice and an evidence fragment; performing consistency verification according to a source, an aperture and time, performing conservative degradation on the attribution expression according to a consistency threshold value, and outputting a degradation strategy description; and finally, page two-way pointer and cross-sentence consistency recording and audit playback are realized through an evidence chain pointer table and a published version signature, and the problems of caliber drift, cross-window access and difficulty in evidence tracing are solved.

The invention provides a distributed storage method based on source code semantic partitioning, and particularly relates to the technical field of cloud data distributed storage. The method comprises the steps of performing semantic partitioning on a source code, and segmenting the source code into a plurality of semantic blocks according to dimensions such as functional semantics, an abstract syntax tree structure, author information and version information; generating metadata containing information such as grammar type tags, file paths, line number ranges, author identifiers, version identifiers and access popularity for each semantic block; constructing a weighted directed acyclic graph (DAG) based on the semantic chunks and the dependency relationship thereof; superposing a metadata layer in the DAG structure, and recording information such as function call dependency, inter-block reference relationship and version evolution chain; blocks with relatively high access frequency and close semantics are aggregated into super blocks, the traversal depth is reduced, and meanwhile, hot data and cold data are differentiated for hierarchical storage by adopting a cold and hot data management strategy; and evaluating a parent block aggregation degree through a BDS algorithm, determining a block sorting priority, and optimizing super block boundary division. Compared with the prior art, the method has the advantages that the semantic retrieval efficiency, the incremental updating capability and the distributed query performance of the source code storage system are improved.

In a method for providing a visual representation of search results, a natural language query may be obtained from a user. The natural language query may be processed to obtain key query terms. Based on the key query terms, a relevance-ranked search on a plurality of documents may be performed to obtain a subset of the plurality of documents relevant to the natural language query. Answers to the natural language query may be extracted from relevant documents among the subset of documents, and graph data may be obtained using document links associated with the relevant documents from which the answers were extracted. The graph data may include entities and relationships identified in the relevant documents. The graph data may be used to generate a graph that may be displayed to the user to enable the user to discover information previously unknown to the user.

A method includes applying a document ranking layer of a document selection large language model (LLM) to a document list including multiple reference documents to obtain a ranked document list. The method further includes selecting a subset of reference documents from the ranked document list and processing a user prompt and the document subset by a field LLM to generate an answer. The method further includes ranking the answer with an answer score by a ranking LLM. The method further includes ranking the document subset by the ranking LLM to obtain a ranked document subset. The method further includes calculating a loss function of a preference optimization layer of the document selection LLM based on the answer score and updating at least one training parameter of a foundation model of the document selection LLM based on the loss function of the preference optimization layer.

The invention provides a document analysis method and device, equipment and a storage medium, and relates to the technical field of computers, in particular to the technical field of deep learning, data processing and document analysis. According to the specific implementation scheme, at least one layout area divided by an article to which the document image belongs is determined according to the layout of the document image; identifying element contents of a plurality of layout elements in the document image; sorting the reading sequence of the layout elements in the same layout area; and obtaining structured document information according to the sorting result and the corresponding element content. According to the technical scheme, different article areas on the same page can be accurately identified and separated, the respective reading sequence is correctly reconstructed on the basis, and the content in the document image is converted into structured information.

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.

Systems and methods disclosed herein are enabled to dynamically generate structured documents using one or more artificial intelligence models. A computing device receives an output generation request and uses a first AI model to retrieve data chunks from source documents and applicable templates. A second AI model ranks the retrieved chunks based on one or more metrics, such as vector similarity, keyword density, and temporal relevance. A third AI model subsequently generates a response using the ranked chunks, templates, and predefined operational boundaries for each chunk. The generated response is tagged with source identifiers to enable the traceability of the response back to corresponding chunks. The system transmits, via the computing device, the response, the retrieved chunks, and / or the source identifiers.

The invention provides a household appliance knowledge question-answering method and system based on retrieval enhancement generation. The method comprises the following steps: acquiring household appliance field multi-modal data from a multi-format document library; extracting text information, table information and chart information in the multi-modal data; performing domain term injection processing on the extracted information, and constructing a packet domain enhancement index; receiving a natural language question input by a user; the natural language problem is analyzed through a query optimizer, and semantic retrieval and keyword retrieval are executed in parallel; carrying out fusion processing on the semantic retrieval result and the keyword retrieval result; selecting matched document fragments by adopting a relevancy sorting algorithm; inputting the matched document fragments into a large language model to generate candidate answers; verifying the compliance and traceability of the candidate answers through a credibility evaluation module; outputting a final answer with a reference source; and storing the high-frequency questions and the final answers into a cache library to solve the problems that the answer accuracy of a knowledge question-answering system is reduced and the response efficiency is limited.

The invention relates to the technical field of anti-counterfeiting watermarks, in particular to a digital intelligent water affair form text anti-counterfeiting watermark generation method based on an open source gap, which comprises the following steps of: acquiring field difference and repetition frequency, screening jitter fields to generate a perturbation table, indexing and marking sensitive segments in sections, establishing a weight structure and adjusting bit width, and sequencing nodes to generate a binding list. And constructing a watermark path chain, and matching track consistency to generate a drift label. According to the method, a field disturbance weight structure is established through proportional mapping, dynamic affinity sorting is performed in combination with node processing capacity and field task attributes, watermark track sequence binding is realized in a field path structure, and a track consistency detection mechanism is introduced in time sequence matching of field filling and node execution. The concealment of field embedding disturbance and the verifiability of a watermark writing path can be enhanced under the condition of not changing a form external display structure, and it is ensured that node selection in the field content embedding process has logical continuity and a track chain can be traced back.

The invention provides a method and system based on NLP file analysis, and relates to the technical field of natural language processing. According to the method, time and identifier unification and format and character set standardization are carried out on the multi-source file, layout segmentation, table structure extraction, reference analysis, term standardization and anaphora resolution are combined, semantic representation is constructed, a hierarchical index and a unique traceability identifier are generated, intention recognition, retrieval sorting, incremental updating and consistency verification are supported, and the method is suitable for large-scale popularization and application. Unification, semantization and traceability of the file analysis process are achieved, and the processing efficiency and accuracy are improved.

The invention relates to the technical field of language information processing, and provides a large language model-based structured information retrieval method, which comprises the following steps of: deploying an adapter, accessing operation and maintenance data, carrying out timestamp synchronization on an original entry and identifying a source, mapping equipment, parts and personnel based on an asset directory and recording mapping confidence, extracting structural elements from the records; injecting three types of metadata into candidate evidences, establishing corresponding nodes and edges in a graph database, analyzing query into a structured retrieval intention, expanding a candidate evidence set along three chains to form candidate sub-graphs, and calculating comprehensive scores for sorting; the candidate evidences should be verified and constrained, meanwhile, LLM is called for inference, feasibility inference confidence is output, actual measurement results, the inference confidence and source confidence are fused according to preset weights, feasibility scores of the candidate evidences are obtained, and for the candidate evidences passing feasibility verification, accurate anchor points are calibrated for each evidence along a source chain; and generating a structured answer.

The invention discloses an analyzing and editing system and device for high-frame-rate rendering of a PDF document, and the system comprises a file list uploading module which is used for uploading a pdf file and displaying a version historical record list and information; the document analysis logic module is used for reading and analyzing document information, determining a block level sequence of obtained data, deleting useless elements, and carrying out content sorting and splicing according to a layout; the pdf original document display module is used for executing pdf document title display operation, document display page cutting operation, document directory generation operation and document analysis display result operation in original document uploading, and the document directory generation operation comprises the steps of capturing multi-level digital numbers of documents, classifying number types according to matching results of capture groups in regular expressions, and storing the classified numbers in a database; and then numbering processing is carried out through a specified hierarchy mapping rule, and finally a file directory number is obtained. The problems that a traditional tool is inaccurate in analysis, low in editing efficiency and poor in professional adaptation can be effectively solved.

The invention discloses a retrieval enhancement generation method and system based on context awareness, and relates to the technical field of information retrieval, and the method comprises the following steps: carrying out block processing on a knowledge base document, distributing a unique identifier and a sorting sequence number, and establishing an association relationship between text blocks to save a document structure; constructing a structure index of the document; according to a query request of a user, performing initial retrieval on query content based on the vector similarity to obtain an initial related text block set with the highest relevancy; performing context extension retrieval on the initial related text block set to obtain an extended preamble text block set; and carrying out duplicate removal, merging and reordering processing on the preamble text block set, and adopting a smooth transition technology to obtain a natural text as a retrieval result. Through the technical scheme of the invention, not only is the correlation ensured, but also the context integrity is ensured, the semantic fragmentation problem of a traditional RAG system can be effectively solved, and the retrieval quality is remarkably improved.

The invention relates to an intelligent document information real-time retrieval method and system based on the RAG technology, and the method comprises the steps: analyzing documents of various formats, extracting a text, maintaining the content continuity through adaptive semantic partitioning processing, and building a character-level position index at the same time; after vectorizing the text blocks, generating a plurality of rewriting queries for the original query; respectively carrying out mixed retrieval (combining keywords and semantic retrieval) for each rewriting query, and fusing and reordering results to obtain candidate text blocks; after correlation filtering, inputting a large language model according to correlation to generate an answer, and if the result is negative, triggering secondary retrieval and reordering; and finally, outputting a structured answer containing position information and supporting front-end visualization. According to the method, the semantic integrity maintenance, the multi-format document processing efficiency and the key information retrieval accuracy are effectively improved.

The invention discloses a large model-based government affair knowledge retrieval method, apparatus and device, and a medium, and relates to the technical field of natural language processing, and the method comprises the steps of performing information extraction on a to-be-processed government affair work order through a preset government affair large model to obtain target structured information; based on a word processing fusion algorithm constructed by a word frequency-inverse document frequency algorithm and a graph sorting algorithm, performing word processing on the target structured information to obtain work order keywords; converting the work order keyword into a target semantic vector; performing retrieval operation on a preset government affair knowledge base by utilizing a preset retrieval condition and the target semantic vector to obtain a target government affair knowledge document; the preset retrieval condition is a retrieval condition constructed based on time, space and the work order emergency degree. Therefore, the information accuracy and the processing efficiency can be improved through word processing; and in combination with retrieval conditions constructed based on time, space and work order emergency degrees, accurate retrieval can be provided, and the processing effect on various complex government affair work orders is improved.

The invention relates to the technical field of intelligent retrieval, in particular to an engineering knowledge base construction and deep retrieval method, which comprises the following steps of: in a knowledge base construction stage, performing knowledge level identification on an engineering knowledge document to form a multi-level tree structure taking chapter titles and corresponding contents as nodes; performing analysis processing on the heterogeneous content of each node to generate knowledge fragments; and generating embedded vectors of knowledge fragments by adopting a way of combining path embedding and content embedding, and storing association relationships among knowledge, vector data and original information to complete knowledge base construction. In the deep retrieval stage, after a user question is received, a knowledge blank question list is generated through query and rewriting, a knowledge base is retrieved through traversal of the list, the retrieval process is optimized through knowledge grouping, correlation sorting and loop termination judgment, and finally reply content subjected to traceable verification is generated and output based on existing knowledge accumulated in a circulation mode. According to the method, the reuse efficiency and the retrieval accuracy of the engineering knowledge can be improved.

The invention discloses a use case testing method and device, electronic equipment and a medium, and relates to the technical field of computers. The method comprises the steps that multiple to-be-tested use cases are acquired; determining a comprehensive weight of each to-be-tested case according to the priority information, the function modification information, the historical test fault information and the execution duration corresponding to each to-be-tested case; according to the comprehensive weight corresponding to each to-be-tested case, determining a sorting sequence of the to-be-tested cases; and grouping the to-be-tested cases according to the sorting sequence, and distributing each group of to-be-tested cases to test environments with different test efficiencies for testing. According to the application, the comprehensive weight of each to-be-tested case during testing is determined according to the priority information, the function modification information, the historical test fault information and the execution duration of different to-be-tested cases, and the to-be-tested cases are distributed to test environments with different test efficiencies for testing based on the comprehensive weight, so that the test period is shortened, and the test efficiency is improved. The test efficiency is improved.

A computer-implemented method for performing searches in a document database is disclosed. The method comprises automatically detecting a line of business associated with a user, receiving a text query from the user, and generating a query embedding from the text query. The method further comprises scoring entries in a reverse index using a hybrid scoring function. The reverse index comprises titles, title embeddings, sentences, sentence embeddings, and entity tags corresponding to documents in the document database. The hybrid scoring function is used to generate a score based both on a keyword match score between the text query and the reverse index and on a cosine similarity score calculated from embeddings in the text query and in the reverse index. The method also comprises ranking scores for sentences in the document database, and displaying a sentence associated with a top score to the user.

The embodiment of the invention relates to the technical field of data processing, and particularly provides a smart library information retrieval method and system based on AI session interaction. According to the embodiment of the invention, an information retrieval session request initiated by a user through a smart library interaction interface is received; collecting session interaction data containing natural language query content and context association information; performing semantic structuring processing on the session interaction data to generate a session semantic feature set containing an entity relationship network and an intention classification vector; carrying out deep mining on the session semantic feature set by utilizing a debugged retrieval intention understanding model, identifying potential retrieval demands and demand priority ranking, and generating retrieval demand feature vectors; and on the basis of library collection information in the vector matching intelligent library resource database, generating an information retrieval result set containing resource association degree sorting, and feeding back the information retrieval result set to the interactive interface for visual display. According to the embodiment of the invention, the accuracy and comprehensiveness of information retrieval are improved, and more intelligent and efficient retrieval experience is provided for users.

The invention provides an external plug-in system based on retrieval enhancement. The external plug-in system comprises an indexing module, a retrieval module and a generation module, the index module is used for performing cleaning, segmentation, organization, semantic coding and storage tasks of text data; the index module comprises a dynamic data management sub-module, a strategy decision case library management sub-module, a Chinese character recursive segmentation sub-module, a tree index structure construction sub-module and a vectorization storage sub-module; the retrieval module is used for completing semantic matching and sorting on the basis of the structured index; the retrieval module comprises a double-tower rough arrangement sub-module, a cross fine arrangement sub-module, a real-time update sensing sub-module and a cache management sub-module; the generation module is used for carrying out integration and semantic modeling on the high-quality intelligence information provided by the retrieval module; the generation module comprises an information integration and reasoning sub-module and a credibility evaluation and feedback sub-module; the method supports fact alignment, real-time updating, logic interpretability and output evaluability.

The invention relates to the technical field of information retrieval, and provides an intelligent searching and sorting method and system for a library. Obtaining environment parameters of a search terminal and query statements and historical retrieval records input by a user; generating a dynamic environment matrix based on the environment parameters, and generating an intention vector based on a query statement and the historical retrieval record; constructing a knowledge graph based on the metadata of the library, extracting a literature feature vector from the knowledge graph, and performing linear processing on the intention vector and the literature feature vector to generate a literature association graph; generating a comprehensive weight vector based on the statistical parameters extracted from the literature association map and the dynamic environment matrix; and performing comprehensive evaluation based on the comprehensive weight vector and the score of the literature in the preset dimension, and displaying a search result on a user interface according to the retrieval score of the literature. According to the scheme, the search result is highly matched with the user requirement and the scene, and the accuracy and the search efficiency of library intelligent search are remarkably improved.

The invention provides an efficient annotation-driven hierarchical fault positioning method, which comprises the following steps of: guiding a large language model to intelligently analyze and generate semantic annotations of a text based on a positioning algorithm of large model annotations, and performing fine-grained software fault positioning from a file level to a function level and then to a position level by using the annotation-based hierarchical positioning method. And an efficient hierarchical progressive sorting and screening algorithm is used for ensuring the software fault positioning efficiency. According to the method, the defect positioning efficiency can be ensured while the positioning accuracy is ensured. The positioning algorithm based on large model annotation strategically balances the information density, provides enough context clues, and improves the model understanding ability; according to the annotation-based hierarchical positioning method, positioning is divided into file / function / position hierarchies, and annotations are added by using a large model cue word project in sequence, so that the positioning accuracy is improved; and an efficient hierarchical progressive sorting and screening algorithm is provided, so that the optimal balance between the performance and the cost is realized.

The invention relates to the technical field of building information, and provides a knowledge base construction and retrieval method and system based on a multi-source text in the building field, and the method comprises the following steps: a knowledge base construction stage: constructing a multi-dimensional metadata feature vector for a multivariate text based on a standard classification index table; the method comprises the following steps of: converting and segmenting a document, splicing an end clause with all superior title texts by utilizing a context inheritance algorithm to form a text unit with complete semantics, and performing dynamic filtering based on an analyzed query intention and a metadata vector at a user retrieval stage; then, in the screening set, performing fusion calculation on semantic vector similarity, keyword matching degree and authority offset weight based on effectiveness attribute and implementation time, and performing mixed retrieval and reordering on the text units; and finally selecting a text unit according to a sorting result and inputting the text unit into the large language model to generate answers. According to the method, high-precision and high-compliance intelligent retrieval and question answering of building domain knowledge are realized.

A computer-implemented method includes: in response to receiving a query, retrieving a plurality of documents; generating a first graph representation which graphically represents the plurality of documents utilizing a plurality of nodes indicating a concept and a plurality of edges indicating a relationship between at least two nodes among the plurality of nodes; generating a second graph representation with respect to the plurality of documents, based on connection information associated with the first graph representation; ranking the plurality of documents, based on the second graph representation; and applying one or more machine-learned models to generate a response to the query based on the plurality of documents ranked based on the second graph representation.

The embodiment of the invention discloses a multi-dimensional intention recognition method and device, computer equipment and a readable storage medium. The method comprises the steps of obtaining domain knowledge data, RAG data of user history retrieval and user portrait data, obtaining an original data set, converting the original data set into structured fields, and constructing a domain knowledge base based on all the structured fields; receiving a current question, preprocessing the current question to obtain a standard text, and performing semantic deep analysis on the standard text through a preset large language model to obtain a semantic analysis result; multi-dimensional scoring is carried out based on the semantic analysis result, weighted summation is carried out based on the multi-dimensional scoring result, and a plurality of candidate intentions and corresponding confidence coefficients are obtained; and performing descending sorting according to the confidence, and selecting the candidate intention with the highest confidence as an intention recognition result. According to the method, the recognition accuracy of the core appeal in the user question is improved.

The invention relates to the technical field of information, in particular to an implementation method and system of a specialized agent based on knowledge retrieval enhancement, accurate knowledge retrieval is achieved by using a BGE-ES-Reranker model, and the BGE-ES-Reranker model is used for reordering retrieved documents in an RAG system; the method has the beneficial effects that the knowledge is divided into the knowledge bases of different levels, and different weights are configured for each knowledge base, so that the retrieval strategy can be automatically adjusted according to the query content, and relevant information can be quickly and accurately recalled in mass data.

The invention discloses a bank intelligent operation knowledge base implementation method and system based on a large language model and RAG, and the method comprises the steps: firstly extracting business terms from structured, semi-structured and unstructured multi-source heterogeneous multi-modal data of a bank, and then constructing a business dictionary containing term similarity data and an ontology tree; quantitatively calculating the release mechanism weight, the timeliness coefficient and the punishment association degree of the supervision clauses to obtain authority values, and sorting the authority values; then using a double-constraint loss function to finely adjust the pre-trained large language model as a vector representation model, vectorizing service data, and storing the vectorized service data into a vector database; and finally, aiming at user query, retrieving in a vector database, rearranging in combination with a supervision clause sorting result, and generating a business response after compliance verification. According to the method, the bank unstructured knowledge processing capability and retrieval precision can be improved, and efficient intelligent operation is realized.

The invention relates to a method for realizing file handling service question answering based on a large language model. The method comprises the following steps: S1, preparing data; s2, inputting a query question by a user, and judging whether the user question is related to the file handling service or not through a knowledge evaluation module; s3, performing retrieval in the file handling service knowledge base by using a knowledge base retrieval module; s4, performing reordering evaluation on the retrieval content in the previous step through a retrieval evaluation module; s5, the retrieval content is added into the cue word template to conduct reasoning generation on the user question; s6, querying a knowledge base document relation graph according to the retrieval content, finding out an associated document, combining the associated document with a user question, and delivering the associated document and the user question to a large language model to predict the next question of the user; and S7, performing question verification on the new question of the user. According to the overall structure provided by the embodiment of the invention, the user query is enabled to generate a plurality of queries in the retrieval enhancement generation process, and the diversity and coverage of retrieval can be improved; and the advantages of the dense retrieval algorithm and the sparse retrieval algorithm can be reserved.

The invention discloses a book data processing and intelligent service system based on artificial intelligence, and relates to the technical field of artificial intelligence and digital library crossing, and the technical scheme is characterized in that a multi-format document intelligent conversion and regular cleaning mechanism is adopted, a multi-level metadata extraction algorithm is combined, and a structured knowledge base is accurately constructed; vector semantic retrieval, BM25 keyword retrieval and a cross encoder reordering model are creatively fused, and high-precision context retrieval is achieved through dynamic weight configuration and locality sensitive hash de-duplication. A parallel data processing architecture supporting GPU acceleration is constructed based on a Chroma vector database, the knowledge response capability of a local large model is enhanced in combination with an RAG technology, and verifiable standardized content is generated. The system is compatible with a Linux / Windows platform and containerized deployment, has hundred million-level data throughput efficiency, can be widely applied to the fields of intelligent libraries, knowledge questions and answers and personalized recommendation, and remarkably improves the retrieval accuracy of book data and the intellectualization level of knowledge services.