529results about "Database querying" patented technology

Techniques for generating AI-powered responses tailored to a specific entity's communication style. The techniques involve selecting a particular AI agent associated with an entity, receiving a user query, and generating an embedding from it. This embedding is used to retrieve relevant content from the entity's knowledge database. The entity's communication type is then determined. A large language model (LLM) prompt is created, combining the retrieved content and instructions to apply the entity's communication style. This prompt is submitted to an LLM service, which generates an output. A response based on this output is returned to the user. The techniques enable the creation of AI-generated responses that are both informative and aligned with the entity's preferred communication style, enhancing the consistency and effectiveness of AI-powered customer interactions or information dissemination for the entity.

Systems, methods, and devices that relate to intelligent query decomposition and parallel routing for specialized model processing are disclosed. In one example aspect, the system receives a query from a user comprising a request relating to a particular domain. The system determines, using a decomposition model, a set of sub-queries based on semantic boundaries, syntactics, tasks, relationships, and rules relating to particular domains. The system inputs the set of sub-queries into a routing model to determine a set of specialized models. For each sub-query, the system routes the sub-query to a respective specialized model, generates an output, and assigns a confidence score. The system detects conflicts among outputs using a conflict detection model configured to identify discrepancies. The system generates an aggregated output by combining outputs according to a weighted aggregation algorithm prioritizing higher confidence scores and conflict resolution rules, then displays the aggregated output.

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.

This invention provides a method and system for automatic generation and multi-dimensional review of standard documents based on a large model, relating to the field of artificial intelligence technology. The method includes: Step 1, establishing a distributed database of multi-source documents and analyzing heterogeneous texts through natural language processing to obtain a standardized knowledge network; Step 2, extracting indicator elements based on the standardized knowledge network and forming a structured parameter library through verification and validation; Step 3, constructing a template library based on the structured parameter library, analyzing user needs by combining semantic matching, and automatically generating a standard document outline. This invention improves document generation efficiency and quality, reduces manual review costs, and enhances the comprehensiveness and accuracy of review.

A system and method for generating a database query based on a natural language query is presented. The method includes receiving an unstructured natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a group of database queries from a plurality of preexisting database queries based on a similarity to the unstructured natural language query; generating a context for processing by a language model, the context including the selected group of database queries, an identified technology, and a schema of the computing environment; processing a prompt and the generated context utilizing the language model to generate a second database query; and executing the second database query on the security database.

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 a multi-modal information analysis and scheme reminding method, device, equipment and medium, which comprises the steps of receiving input data and converting the input data into multi-modal data, executing optical character recognition and image classification recognition to generate a recognition result, and sending the recognition result to a server; a natural language processing model is used for analyzing fuzzy description to generate an analysis result, a knowledge base is inquired, a knowledge graph is combined to generate an association result, the analysis result, the association result and user feature data are fused to generate an execution scheme, the execution scheme is compared with an abnormal list, supervision confirmation is triggered, and a compliance instruction is generated. And personalized reminding contents are generated. The information analysis integrity is improved through multi-modal recognition, natural language processing and the knowledge graph, supervision confirmation and personalized reminding are introduced, and intelligent, compliant and reliable reminding management is achieved.

Retrieval of structured tabular data in a retrieval-augmented generation (RAG) platform is described. An example method includes receiving a tabular file including structured data, extracting short and long metadata from the file, and generating a dataset agent object using the extracted metadata. A human-readable description is associated with the structured data, and the dataset agent object and metadata are stored in a metadata index. A natural language query is received through a query agent object rendered within a canvas including interconnected agent objects. A dataset agent object is identified based at least in part on metadata filtering using the natural language query and short metadata. A structured query conforming to structured query language syntax is generated using a large language model (LLM) and executed on the structured data. A response including the result set and dataset agent identifier is generated and transmitted to a downstream agent object for further processing.

The invention provides a shield tunneling parameter intelligent optimization method and system based on an AI big language model, and relates to the technical field of tunnel construction optimization. Comprising the following steps: training an open source large model to obtain a preliminary shield tunnel construction industry large model; an upstream task and a downstream task which are relatively independent are set, and the upstream task comprises a tunneling speed prediction module, a tool wear prediction module, a ground surface settlement prediction module and a tunneling attitude prediction module; a downstream task combines an RAG technology and an NSGA-III multi-objective optimization framework, four prediction modules and an energy consumption calculation formula serve as objective functions, the value range of constraint conditions is determined, a tunneling parameter optimization module is established, a professional shield tunnel construction industry large model is formed, and scientific and intelligent decision making of tunneling parameters is achieved. According to the method, information in multiple aspects can be comprehensively considered during decision making, the reasoning process is output, and scientificity, comprehensiveness and reliability of decision making are ensured.

The invention provides a vector retrieval method of a database and a computer program product. The vector retrieval method of the database comprises the following steps: obtaining a query vector point and a hierarchical navigation small-world graph index of the database; starting a search process, and configuring the search process to access vector points on the hierarchical navigation small-world graph index from the topmost layer to the bottommost layer of the hierarchical navigation small-world graph index so as to search the most adjacent vector points of the query vector points; under the condition that the search process searches the bottommost layer, determining a vector point currently accessed by the search process as a current access vector point, and determining a neighbor vector point adjacent to the current access vector point; evaluating the consistency degree between two directions in which the current access vector point respectively points to the neighbor vector point and the query vector point; the neighbor vector points with the consistency degree larger than or equal to a set threshold value are included into a candidate set of the most adjacent vector points. According to the vector retrieval method of the database, the retrieval efficiency and the working performance of the database can be effectively improved.

The invention relates to a method for constructing and visualizing a knowledge graph of a secondary virtual loop of an intelligent substation, which solves the problems of incomplete visualization, incomplete relevance, poor readability and the like in the prior art, and adopts the technical scheme that the method comprises the following steps of: acquiring an SCD configuration file and an optical fiber link table of the intelligent substation; reading and analyzing the SCD configuration file, and obtaining virtual loop configuration information of the whole station; matching the virtual loop with the soft pressing plate based on the configuration information of the virtual loop of the whole station; matching the physical loop with the virtual loop based on the total station virtual loop configuration information and the optical fiber link table; constructing a knowledge graph based on the total station virtual loop configuration information; and constructing a total station secondary equipment virtual loop configuration database based on the knowledge graph. The method has the advantages that the configuration information of the virtual loop of the whole station can be completely presented in a graphic form, the visualization degree of the secondary virtual loop of the intelligent station is effectively improved, and the working efficiency of on-site technical improvement, defect elimination, verification and the like is improved.

Exemplary system and methods for processing a natural language query in an edge computing system are disclosed. A processor of the computing system receives a natural language textual input as a query from a user interface and receives one or more containers of documentation over a communication channel. The processor generates a query embedding vector from the textual input. The processor extracts text from the received container and generates text chunks of specified length from the extracted data. Text embeddings are generated from the text chunks and stored in memory for a specified period. The query embeddings are compared with the text embeddings to determine relevant context information. The processor passes the relevant context information and the query through a trained neural network to generate a response. The response generated by the trained neural network is formatted and output to a user interface.

The invention provides a minority language threat intelligence clue expansion method, system and device and a medium, and belongs to the technical field of network security. The method comprises the steps of obtaining minority language threat intelligence, performing data cleaning, format conversion and semantic index establishment on intelligence information, and constructing a vector knowledge base by utilizing a vectorization model based on the processed intelligence information to serve as an initial threat intelligence base; acquiring tactics, technologies and associated information related to the attack from the initial threat intelligence library by using a preset agent, and outputting semi-structured attack process description information; and based on the attack process description information, obtaining extension information and context promotion information, obtaining depth information related to an attack technology through analysis and retrieval, and generating a structured or reported threat intelligence clue extension result. According to the method, the multi-agent with domain knowledge and scene memory is utilized, and analysis and understanding of cross-language threat intelligence, intelligence key point extraction and semi-structured representation output are achieved.

Systems, methods, and apparatuses are disclosed for detection and prevention of adversarial attacks against large language models. Techniques may include receiving an input associated with a target large language model, analyzing the input with a pre-trained classification algorithm to determine a first deconstruction process to be applied to the input, and modifying the input with a first deconstruction model using the determined first deconstruction process. Techniques may also include determining a score of a likelihood of the input being adversarial based on an output of the first deconstruction model and by applying a classification model and updating at least one of the first deconstruction model or the classification model based on the score.

The invention discloses a header field intelligent benchmarking method and system based on semantic index segmentation, relates to the technical field of data management, and aims to solve the problems that in the prior art, header field standardization depends on manpower, efficiency is low, and semantic fuzzy and non-Chinese fields are difficult to process. An intelligent benchmarking scheme fusing semantic comprehension and vector matching is provided. According to the method, header fields are decomposed through a large language model to generate an initial word bank, and a historical benchmarking database is constructed to realize rapid matching; semantic segmentation is carried out by adopting a fine tuning BERT model, and precise benchmarking is completed in combination with vector library retrieval and Top-K recommendation; code set recognition is achieved by applying regular rules and semantic logic for non-Chinese fields. The system comprises a lexicon construction module, a history matching module, a semantic segmentation module, a vector matching module and the like. The device comprises hardware units such as a processor and a memory. According to the invention, automatic standardized processing of header fields is realized, and the data governance efficiency is improved.

The invention relates to the technical field of data retrieval and analysis, and discloses an intelligent knowledge retrieval method based on an RAG framework, which comprises the following steps: acquiring a question text input by a user, judging the specialty and concrete of a question, judging the professional degree of the user in the field to which the question searched by the user belongs, and according to the conditions of the user and the question, determining whether the question belongs to the field or not; according to the intelligent knowledge retrieval method based on the RAG framework, the situation that the user outputs the answer with the high professional degree to the user under the condition that the professional degree is not high is avoided, the user cannot understand the answer easily, the situation that the user outputs the answer with the low professional degree to the user under the condition that the professional degree is high is also avoided, and the user experience is improved. Therefore, the user cannot use the searched data, the method is more friendly to the user, and the use experience of the user is improved.

The invention provides an AD accelerometer acquisition module loop zero offset compensation method based on a table look-up method, and the method comprises the following steps: building a zero offset database, and storing the zero offset data of an AD accelerometer at different temperature points in the database; acquiring parameters of the AD acceleration acquisition module at different temperature points: acquiring the working temperature of the AD acceleration acquisition module in the actual working process of the AD acceleration acquisition module; performing zero offset compensation by using a zero offset data performing matching search on the acquired working temperature and the pre-established zero offset database to obtain a zero offset compensation value of the AD acceleration acquisition module so as to obtain an actual measurement value of the AD acceleration acquisition module; the measurement accuracy of the AD acceleration acquisition module is effectively improved, and the influence of zero offset on the system performance is reduced.

The invention discloses a retrieval method and related equipment, and relates to the technical field of data processing, and the retrieval method comprises the steps of obtaining a retrieval word in response to a retrieval request, searching N groups of initial data related to the retrieval word from a preset retrieval database based on a preset retrieval strategy and the retrieval word, and performing deep semantic correlation evaluation on the N groups of initial data by using a preset semantic correlation evaluation model to obtain a semantic correlation value corresponding to each group of initial data, and sorting the N groups of initial data according to a sequence of the semantic correlation values from large to small to obtain a retrieval result. According to the method, the preliminary retrieval result is deeply mined based on the preset semantic correlation evaluation model, the semantic correlation value between each retrieval result and the retrieval word is determined, and the target retrieval result with higher accuracy is obtained based on the semantic correlation values, so that the retrieval accuracy is improved.

The invention discloses a chip test retrieval method and system based on a large model, and the method comprises the following steps: collecting chip test documents, and constructing a chip test vector knowledge base; receiving user query, and executing search in the knowledge base by using the word vector model structure; richer context information is generated based on a retrieval result and user query, and a test case or a test analysis report is generated through a fine-tuned large model. According to the method, the retrieval capability and the generation capability are effectively integrated, efficient retrieval of test instructions and knowledge and automatic generation of test cases / reports are achieved, and the chip test efficiency and quality are remarkably improved. Meanwhile, the retrieval generation system adopts a Django framework development platform, performs real-time streaming output and real-time feedback of retrieval results by means of the MyWebsocket technology, and writes question and answer data into MySQL. The system comprises a knowledge base construction module, a retrieval module, a generation module, a streaming interaction module and a data storage module. Compared with the prior art, the method has the advantages that the test instruction retrieval accuracy is improved by more than 18%, the test case generation time is shortened by 60%, and the test knowledge is shared and reused.

Techniques for handling natural language query processing are described. In some examples, semantic meanings of words are determined during the natural language query processing. These semantic meanings are generated from metadata associated with the query and are to be used by an entity linker to help the linker link candidates to columns.

A computer-implemented method is provided of generating a system prompt for a large language model to convert a natural language query (NLQ) to a structured query language(SQL). 5 The method includes generating (1606) the system prompt for the NLQ based on information extracted from a first database. The information identified from a knowledge graph includes a structured view of data including telecommunications domain knowledge and relations within the data filtered by a chain of thought prompt that represents a logical relation between a plurality of nodes in the knowledge graph. The method further includes passing (1614) the system prompt to 0 the LLM to convert the NLQ to the SQL.

The invention discloses a high-concurrency database page consistency reading method and system connected with massive power distribution terminals, and relates to the technical field of database management, and the method comprises the steps: checking whether a visible tuple offset list of a current page is empty or not when an MVCC database storage engine is updated in situ to trigger a sequential scanning operation; if the original data page is empty, reading the original data page from the buffer area; copying the original data page to generate a page copy, performing unsubmitted transaction rollback and submitted transaction rollback on the page copy, and restoring to obtain an MVCC visible page; all tuple pointers in the page are traversed, the tuple pointers are screened, tuple entities are positioned, tuples marked as delayed deletion are filtered, and visible tuples are reserved; and pre-allocating a memory space in the sequential scanning descriptor, covering the copy of the storage page, recording the offset of the visible tuple to a visible tuple offset list, positioning the visible tuple according to the page and the offset, and returning the visible tuple. According to the invention, the memory allocation and rollback section access overhead can be reduced, and the sequential scanning performance is improved.

The invention discloses a vehicle software upgrading information management method and device, equipment and a program product, and relates to the technical field of information management. The method comprises the following steps: acquiring upgrading information of various types of vehicle software, the system comprises a software upgrading management system file, a software upgrading project process recording file, software upgrading configuration information and software upgrading compliance information. Vehicle type authentication information and first information sharing activity data generated inside a vehicle management party and second information sharing activity data generated between the vehicle management party and an external software upgrade related party in a vehicle software upgrade life cycle; carrying out storage management on the various types of vehicle software upgrading information; and in response to the consulting request for the at least one type of vehicle software upgrading information, generating a consulting record for the at least one type of vehicle software upgrading information. The management comprehensiveness of the vehicle software upgrading information can be improved.

The embodiment of the invention provides a data query method, a database system, equipment, a storage medium and a program product. The method comprises the following steps: when a query request is received, determining a first column table storage mode of a to-be-queried table according to a table name of the to-be-queried table carried in the query request; obtaining a column type storage file of the to-be-queried table; determining a second column storage table mode used when the column storage file is generated according to mode version information correspondingly recorded by the column storage file; obtaining first column data of the to-be-queried column from the column type storage file according to the second column table storage mode and the column name of the to-be-queried column carried in the query request; according to the first column table storage mode and the second column table storage mode, converting the first column data into second column data adaptive to the first column table storage mode; and determining a query result of the query request according to the second column of data. According to the technical scheme provided by the embodiment of the invention, the influence of database mode change on database service can be reduced.

The invention relates to the technical field of data processing, in particular to a power generation field technical supervision and evaluation report automatic generation method based on a large model, and the method comprises the steps: carrying out the preprocessing of multi-source data in the power generation field, obtaining a standardized database, and constructing a vector knowledge base; obtaining prompt information input by a user, and retrieving the vector knowledge base through a mixed retrieval mechanism and a knowledge quality verification mechanism to obtain an original retrieval result; performing structured expansion on the original retrieval result to obtain a retrieval enhanced knowledge set; and according to the prompt information, retrieving the enhanced knowledge set and the prompt word engineering system, and driving each agent to generate a technical supervision examination and evaluation report through a preset multi-agent collaboration mechanism. According to the embodiment of the invention, full-process automation of the technical supervision and evaluation report from data processing, knowledge base construction and retrieval enhancement generation to multi-agent cooperation generation of the report is realized, and the generation efficiency and the content quality of production of the technical supervision and evaluation report are remarkably improved.

Embodiments of the present disclosure provide a data query method and apparatus, an electronic device, and a storage medium. A data query statement inputted by a user is displayed. The data query statement includes at least one expression field, and the expression field includes a first object field and a second object field that are arranged in sequence, the first object field representing a data object, and the second object field representing a data attribute of the data object. The expression field is used to define a data query dimension of a target business process, and the query statement is used to represent a target query dimension formed by at least one data query dimension. Target data is obtained by responding to the data query statement. The target data is a result of querying business node data of the target business process based on the target query dimension.

The present disclosure recites methods and systems for context augmentation. The method includes generating, in accordance with a policy of a RAG system, a first set of queries for a chunk of a plurality of chunks, the plurality of chunks represents a context of the RAG system. Further, the method includes determining, based upon a set of quality indicators, a quality class of each query of the first set of queries. Thereafter, the method includes, updating, the chunk to an updated chunk to further include query data of one or more queries of the first set of queries based upon the quality class of the each query of the first set of queries. Further, the method includes storing, the updated chunk in a database as a vector for retrieval from the RAG system upon receiving a prompt.

The invention relates to an assembly type bridge modeling method based on a large model and an RAG technology. The method comprises the steps that a bridge standard component library is established based on an assembly type bridge standard image set; calling a large language model, and generating standardized knowledge statements based on the bridge standard component library; a word embedding method is adopted to convert the normalized knowledge statement into vectors, and the vectors are stored in a vector database; converting an input natural statement query into a vector by adopting a word embedding method, and retrieving in a vector database to obtain a normalized knowledge statement related to the query; performing parameter analysis and optimization on the retrieved knowledge statements based on a large language model by utilizing an RAG technology so as to generate component parameters; and calling a component modeling algorithm based on each component parameter to generate a component three-dimensional model, and assembling the component three-dimensional model to obtain a fabricated bridge model. According to the method, component parameter sorting, knowledge statement intelligent combination, efficient semantic retrieval, parameter optimization, three-dimensional model automatic generation and the like are organically integrated, and the modeling efficiency and quality are improved.

Database instances can be clustered using various attributes, such as attributes reflecting resources assigned to a database instance, attributes reflecting configuration or software version information, or attributes that reflect users or usage of a database instance. As it can be resource intensive to capture database workloads, one or more database instances can be selected from one or more of the clusters and workloads can be captured from such instances. In a similar manner, various attributes can be used to cluster database instances, and one or more instances can be selected from one or more of the clusters, and a captured workload can be replayed on such instances.

The invention relates to an automatic verification method and system for a power distribution station project file, and the method comprises the following steps: deep analysis of multi-modal information: receiving design assets of a power project of a power distribution station, carrying out the multi-modal structured analysis, and generating structured design data; performing rigidity specification verification: inputting the structured design data into a rule engine, executing rigidity specification verification in combination with rule terms, and outputting a first type of verification result; prediction-reasoning evaluation: performing risk prediction and optimization reasoning on the structured design assets based on a large language model to obtain a second type of evaluation result; and intelligent report generation: fusing the first type of verification result and the second type of evaluation result, and automatically generating a visual review report. Compared with the prior art, the system has the advantages of intelligent examination and the like.

The invention belongs to the field of supply management, and discloses a supplier full-life-cycle management system and method based on supply chain management. Comprising the steps of performing comprehensive data acquisition by deploying a data acquisition component, comparing comprehensive data of adjacent time steps to extract change data, generating metamorphic data through metabolic purification, constructing a three-dimensional supply tree ecological neuromorphic network, performing association by adopting a layered spherical structure and using diversity edges of multi-style expression among nodes, and performing data analysis on the three-dimensional supply tree ecological neuromorphic network. Clustering to form a polymer through an aggregation evolution mechanism, adding multi-level attributes, weights and tags, and performing distributed storage; updating the supply ecological neuromorphic network by using the metamorphic data on each time step, updating the network by adopting an edge-sub-sphere-global three-level processing architecture, and constructing a multi-level index; based on the updated supply tree ecological neuromorphic network, a user performs management through an interactive interface; and the full-life-cycle management of suppliers is realized.