801results about "Database querying" patented technology

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a multi-source knowledge processing and querying method, which comprises the following steps: data acquisition, cleaning and standardization processing, and standardized database construction; extracting core concepts and association relationships, and generating knowledge elements; constructing a multi-dimensional knowledge graph based on knowledge elements, and establishing a semantic index to realize data semantic annotation and bidirectional mapping; and analyzing the query intention, extracting a query constraint condition, and executing association reasoning based on the multi-dimensional knowledge graph to generate a query result. According to the method, the standardized database of the multi-source heterogeneous data is constructed, so that the data consistency is improved; through multi-dimensional knowledge graph construction and semantic index establishment, the relevance and structural expression of data are enhanced, so that the relationship between knowledge elements is clear and traceable; and through association reasoning based on query constraint conditions, the query accuracy and efficiency are improved.

The invention provides a standard document automatic generation and multi-dimensional auditing method and system based on a large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: 1, building a distributed database of a multi-source document, and analyzing a heterogeneous text through natural language processing to obtain a standardized knowledge network; step 2, extracting index elements based on the standardized knowledge network, and forming a structured parameter library through verification and verification; and step 3, based on the structured parameter library, constructing a template library, analyzing user demands in combination with semantic matching, and automatically generating a standard document outline. The document generation efficiency and quality are improved, the manual auditing cost is reduced, and the auditing comprehensiveness and accuracy are enhanced.

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.

An exemplary machine-learning-based method for generating a response to a natural-language query from an entity associated with an organization comprises: obtaining a sub-query from the natural-language query; determining if a cache associated with the organization stores a matching historical sub-query: if the cache stores the matching historical sub-query, retrieving a historical sub-response associated with the historical sub-query from the cache as a sub-query response; if the cache does not store any matching historical sub-query: inputting the sub-query into a cache-based enhancement machine-learning model to enhance the sub-query, wherein the cache-based enhancement machine-learning model is trained based on historical query data stored in the cache associated with the organization; and inputting the enhanced sub-query into a language model to generate the sub-query response; and generating the response to the natural-language query based on the sub-query response.

Techniques for handling natural language query processing are described. In some examples, entities are recognized during an entity recognition phase and then relations between those entities are determined. Those relations are fed to an entity linker to help the linker link candidate to columns and / or a intent representation generator to help parse multiple values and column pairs of a natural language query.

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 data processing method, an electronic device, and a storage medium. The method comprises: receiving a query request, wherein the query request comprises a target data table participating in a join operation; on the basis of the query request, executing a logical plan, wherein the execution of the logical plan comprises: constructing a data filtering condition corresponding to column statistical information, the column statistical information being information obtained by means of compiling statistics in advance on a column value range of columns included in the target data table; and executing a physical plan corresponding to the logical plan, wherein the execution of the physical plan comprises: performing partition pruning and / or dynamic filtering on the target data table on the basis of the data filtering condition, so as to generate a processed target data table, and executing the join operation on the processed target data table, so as to obtain an execution result.

Aspects of the invention provide a method, system, and computer program product for retrieval augmented generation. In one aspect, the method includes receiving a query. The method further includes classifying the query to a first domain within a plurality of domains. The method additionally includes determining an ambiguity associated with classifying the query. The method also includes retrieving an index of domain-specific vector embeddings corresponding to the domains when the ambiguity does not exceed a threshold for ambiguity. The method further includes prompting a large language model with the query and the domain-specific vector embeddings. The method also includes receiving a query response from the large language model as grounded with the most relevant index results. The method further includes forwarding the query response.

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 embodiment of the invention provides a data retrieval method, device and system, and relates to the technical field of data processing.The method comprises the steps that an original retrieval statement and an associated retrieval statement are obtained, and a target retrieval statement is obtained; and performing keyword extraction on the target retrieval statement to obtain a retrieval keyword. Retrieving in a knowledge graph database based on the retrieval keyword to obtain a first retrieval result including the first data file; performing retrieval in a basic database based on the retrieval keyword and the target retrieval statement to obtain a second retrieval result including a second data file; performing retrieval in the vectorization database based on the statement feature vector of the target retrieval statement to obtain a third retrieval result including a third data file; and determining a target retrieval result comprising the target data file from the first retrieval result, the second retrieval result and the third retrieval result based on the interaction information of each data file, so as to improve the accuracy of data retrieval and improve the user experience.

A system for an automated real-time management of a directed acyclic graph (DAG) based on predictive analytics of DAG input-related data including a processor of a graph compute manager (GCM) node configured to host a machine learning (ML) module and connected to at least one DAG source entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire the DAG input-related data from the at least one DAG source entity node; parse the DAG input-related data to derive a plurality of key features; query a local DAGs' database to retrieve local historical DAGs'-related data associated with previous DAG parameters based on the plurality of key features; generate at least one feature vector based on the plurality of key features and the local historical DAGs'-related data; and provide the at least one feature vector to the ML module for generating a predictive model configured to produce at least one DAG update parameter for updating the DAG at the at least one DAG source entity.

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 relates to the technical field of video detection, in particular to a video anomaly detection method based on a large language model, and the method comprises the steps: constructing a searchable knowledge base through normal videos; after video clips are extracted from a to-be-detected video, a description text is obtained through the large language model; encoding the description text into a vector, and retrieving in a knowledge base to obtain a plurality of most similar behavior patterns; and inputting the most similar behavior patterns and the description text into the large language model, and reasoning whether the video to be detected is abnormal or not through guidance of a third prompt word. By introducing the large language model, the problem that a traditional video anomaly detection method is lack of interpretation is solved, a detailed natural language reasoning process can be output while anomaly detection is carried out, the background, behavior logic and reasoning basis of abnormal event occurrence are clearly described, and the understandability and decision reference value of a detection result are greatly improved.

The invention provides a method and system for dynamically exporting Excel based on asynchronous streaming block data, and belongs to the technical field of large data volume export. Comprising the following steps: receiving a user export request, and generating a unique request ID; an export task is generated based on the export request, a task ID is returned, and an asynchronous response is made; submitting the task to a thread pool queue, and obtaining block data from a database in batches through streaming query; wherein the core thread count and the maximum thread count of the thread pool are adjusted based on a dynamic resource allocation method, and load change is smoothed in combination with a sliding window algorithm; writing the block data into an Excel file batch by batch by adopting a streaming writing technology; and after the task is completed, pushing a notification to the user side. According to the method, the rapidity, the availability and the safety during data export can be ensured when large data volume export is performed.

The invention relates to the technical field of information retrieval, and provides a privacy information retrieval system and method based on a block chain and function secret sharing, and the method comprises the steps: carrying out the encryption and keyword extraction of a data document; generating a Bloom filter index table between the keyword and the document based on the keyword trap door; uploading the encrypted file and the Bloom filter index table to a DSSP end, and transmitting the hash value of the encrypted file and the Bloom filter index table to a block chain for on-chain storage; storing the encrypted file in a distributed storage node under the chain according to the divided share; and the block chain calculates data of each column of the Bloom filter index table through consensus to obtain a message verification code, and the message verification code is stored on the block chain. According to the method, the index is constructed through symmetric encryption and the Bloom filter, and block chain evidence storage and FSS distributed storage are combined, so that data privacy protection and index credibility are realized. Through verification and tampering prevention, the problem that the existing FSS lacks verification and auditing mechanisms is solved.

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.

The invention provides a data processing method, electronic equipment and a storage medium, and the method comprises the steps: receiving a query request which comprises a target data table participating in a connection operation; executing a logic plan according to the query request, wherein the execution of the logic plan comprises the following steps: constructing a data filtering condition corresponding to the column statistical information; wherein the column statistical information is information obtained by performing statistics on a column value range of columns contained in the target data table in advance; in the execution process of a physical plan corresponding to the logic plan, partition cutting and / or dynamic filtering are / is conducted on the target data table according to the data filtering condition, a processed target data table is generated, connection operation is conducted on the processed target data table, and an execution result is obtained. According to the method, the data processing efficiency can be improved, and the query performance is improved.

A set of values can be selected from a plurality of fields of a table in a database. At least one adverb or adjective can be selected for the set of values. Join paths for values in the set of values can be determined. A structured query language pattern can be determined based, at least in part, on at least one value in the set of values, the at least one adverb or adjective for the set of values, and the join paths for the set of values. The structured query language pattern can be stored to first training data configured, at least in part, for use in machine learning to train a text-to-SQL model, the text-to-SQL model comprising a first artificial neural network and configured to convert first natural language text to a first structured query language query.

The invention discloses an optical system design method and system based on a knowledge base retrieval enhancement large model, and belongs to the technical field of optical design and artificial intelligence. The method comprises the following steps: constructing an optical system knowledge base containing vectorized coded optical lens design data; a data pair is formed based on an optical system design requirement and a corresponding parameter list of each surface of the optical system, a training data set is constructed, the training data set is utilized and a low-rank adaptive technology is adopted to train a large language model, and then the trained large language model is combined with an optical system knowledge base through a knowledge base retrieval enhancement module to obtain an optical system knowledge base. Obtaining a knowledge base retrieval enhancement large model; and inputting the design requirement of the to-be-designed optical system into the knowledge base to retrieve the enhanced large model to obtain optical system structure parameters meeting the design requirement of the to-be-designed optical system, namely obtaining a parameter list of each surface of the optical system. The method can effectively improve the optical design efficiency, and is suitable for the design of various optical systems.

The present application discloses a tag information processing method, an electronic device, and a storage medium. The method comprises: obtaining original link tracing information corresponding to a request message, wherein the original link tracing information is used for distinguishing the request message and tagging a calling feature of the request message; on the basis of a preset hierarchical tagging mode, sorting a plurality of tags contained in the original link tracing information to obtain a sorting result, wherein the preset hierarchical tagging mode is used for determining a tagging classification level to which each tag among the plurality of tags belongs, and different tagging classification levels respectively correspond to different sorting priorities; and screening the sorting result on the basis of a preset screening strategy to obtain target link tracing information, wherein the preset screening strategy is used for determining a tag elimination rule matched with the preset hierarchical tagging mode. The present application solves the technical problems of bloated requests, large system burden, and high risks caused by using free tagging during link tracing.

The present disclosure involves systems, software, and computer implemented methods for managing database query plans with dynamic conditions. One example method includes receiving a request to process a query based on at least one dynamic condition. A serialized abstract query plan is identified, deserialized, and converted into a query optimizer tree. A condition provider procedure is invoked to generate a first filter string for each view in the query optimizer tree. Comparisons are made to determine matches between first filter strings and corresponding second filter string stored in the query optimizer tree. In response to determining that a first filter string does not match the corresponding second filter string, query plan compilation is triggered. In response to determining that each filter string matches each corresponding second filter string, a query plan is generated based on the converted query optimizer tree and the query is executed using the query plan.

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.

A database query processing service is provided for efficiently processing query workloads with natural language statements and native database commands. The database query processing service may receive some database queries that do not contain a natural language marker and process these database queries without using large language models to generate replacement query content. The database query processing service may also receive other database queries that do contain the natural language marker and process the other database queries using large language model(s) to generate replacement query content or leverage replacement query content already generated by the large language model(s). The replacement query content is checked to ensure the content is natively valid for the content to retrieve data from database structures referenced in the content. The database query processing service may use the natively valid replacement query content to cause execution of operations responsive to the other database queries.

The invention provides a retrieval enhancement generation large language model system and method, and relates to the technical field of artificial intelligence. The system comprises a host deployed with a question and answer service module and a big language model, host side network card equipment, a big data cluster comprising a plurality of computing power nodes and a knowledge database, and cluster side network card equipment, the host obtains a question through the question and answer service module, sends the question to the host side network card device, and then transmits the question to the cluster side network card device; the cluster side network card equipment calls at least one computing power node to convert the question into a query vector, and accesses a knowledge database according to the query vector to obtain corresponding retrieval knowledge; the cluster side network card equipment returns the retrieval knowledge to the host side network card equipment and then returns the retrieval knowledge to the host; and the host generates answers based on the retrieval knowledge through the large language model. According to the system, the requirement of the retrieval enhancement generation service for the graphics processor resources of the host is reduced.

The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.