144 results about "Query analysis" patented technology

The invention discloses a retrieval enhancement generated document screening system and method fusing a verification mechanism, and relates to the technical field of document screening, the system comprises a user input and query analysis module for extracting key information through natural language processing, and converting the key information into a high-dimensional semantic vector, a meta-tag and a keyword set; the multi-source document retrieval module is used for obtaining documents from multiple data sources through mixed retrieval and generating a candidate set through preliminary screening and sorting; the credibility evaluation and security verification module is used for generating scores and labels after multi-dimensional evaluation and screening qualified documents; the document consistency detection module is used for detecting document conflicts, processing and sequencing, and ensuring logic consistency; the document acquisition and generation module is used for inputting qualified documents into a generation model and generating answers with references; and the result output and tracing module is used for outputting answers and recording whole-process data to ensure traceability. The invention aims to ensure the accuracy and credibility of the generated content through a multi-dimensional verification mechanism.

The invention relates to the technical field of natural language processing, in particular to an intelligent search engine system and method based on NLP and vector hybrid retrieval, and the system comprises a query analysis module which is used for receiving a query statement input by a user and obtaining structured query information based on the query statement; the symbiotic index module comprises a sparse extension index unit which is used for constructing a sparse inverted index table based on keywords in the structured query information; the vectorization hypergraph index unit is used for forming a hypergraph index based on the business data; the recall arrangement module is used for acquiring a candidate object set according to the structured query information; the constraint rearrangement module is used for calculating a joint priority score between query and candidate objects based on the candidate object set, and obtaining a sorting result under constraint conditions of meeting a supplier proportion, a category proportion and a price interval; and the evidence generation module is used for generating evidence information based on the sorting result and outputting the evidence information to the user interface.

The invention provides a knowledge question and answer library agent construction method and system. Efficient knowledge management and question and answer are achieved through cooperation of multiple agents. According to the system, firstly, a multi-modal information extraction agent is constructed, and heterogeneous data such as texts, images and tables are converted into structured vectors and stored; meanwhile, the knowledge graph is dynamically constructed and continuously optimized by the self-adaptive knowledge graph construction agent, and a new relationship is derived through combination of symbolic logic and a graph neural network, so that an evolvable knowledge network is formed. In the question and answer stage, a query analysis agent deeply analyzes the intention of a user and generates sub-queries; retrieving the vector library and the knowledge graph in parallel by the retrieval enhancement generation agent; and the reasoning and synthesizing agent integrates multi-source information and generates an accurate answer with a complete source label through a large language model. Dynamic knowledge management, precise semantic analysis and system self-evolution are achieved, and the method is particularly suitable for professional field scenes needing high-reliability questions and answers.

The invention provides a user interface generation method, electronic equipment, a vehicle and a storage medium, and relates to the field of artificial intelligence and large language model application, and the method comprises the following steps: constructing a cue word according to a natural language description input by a user, inputting the cue word into a preset large language model, controlling the large language model to perform knowledge base query analysis based on the cue word; querying the target knowledge base based on a query decision output by the large language model, and returning a knowledge base query result to the large language model, so that the large language model generates a user interface description for the cue word based on the knowledge base query result; a user interface is generated in the electronic device in accordance with the user interface description. A large language model and knowledge base collaborative interface generation mechanism is introduced, by means of real-time knowledge base query, it is avoided that the model generates a non-compliant interface due to illusion, it is ensured that the generated interface only contains functions and components actually supported by electronic equipment, and the intelligent level of man-machine interaction is improved.

The invention provides a multi-modal database query engine and method based on unified semantic representation, the engine associates and stores a vector library of unified semantic vectors of entities, and the core comprises five modules: a query analysis and semantic coding module for processing single-modal and multi-modal queries; analyzing the semantic structure and generating a corresponding semantic vector or a fused semantic vector; the query execution plan generation module generates an optimal plan including a retrieval strategy, weight allocation and resource scheduling according to semantic features, vector library features and the like; a multi-modal query execution module queries a vector library according to a plan to obtain candidate results; the query result fusion module performs weighted fusion on the candidate results to generate weighted scores of the candidate results; and the output module outputs a result according to weighted score sorting. The engine realizes efficient processing of multi-modal query and accurate result return.

The invention discloses a bacterial selenoprotein online resource platform, an application method, a terminal and a medium, and relates to the technical field of biological medicine, the online resource platform is deployed on a server, the cloud server is a Ubuntu cloud server, and Nginx, Waitpress and Flask are configured; the server side is in butt joint with a background resource, and the background resource is in butt joint with the constructed bacterial selenoprotein database; the server is in butt joint with a user interface of the front end, the user interface designs a corresponding interface framework and an interaction function based on static resources hosted by Nginx, and the interaction function is used for realizing query, analysis and use of relevant information of the bacterial selenoprotein. According to the method, a convenient online access channel of an integrated database and a real-time data analysis tool are provided for users in related fields, and important support is provided for accurate annotation of selenoprotein genes in a bacterial genome plan.

The invention provides a multi-modal data joint query analysis method and system supporting natural language interaction, and relates to the technical field of data query analysis. Historical operation data, audio data and text data of a user on an intelligent search platform supporting natural language interaction are collected; modeling the historical operation data to obtain a user preference vector, performing voice recognition and text standardization processing on the audio data to obtain standard query data, and integrating the standard query data and the text data into context information; an attention mechanism-based algorithm is used for semantic understanding and intention recognition to obtain an intention feature vector, and the intention feature vector is fused with a user preference vector to obtain a classification result; and finally, based on the result, querying in a preset multi-modal database through a collaborative filtering algorithm to obtain a joint query result, so that personalized accurate query of the multi-modal data under natural language interaction can be realized, and the result fits the intention and long-term preference of the user.

The embodiment of the invention provides a large-model-based natural language query analysis and database field matching method, which comprises the following steps of: performing intention analysis and entity recognition on natural language query of a user based on a large language model (LLM), and extracting a potential keyword set; for each extracted keyword, binding an entity type through an STAM mechanism, and constructing an entity-type mapping relationship; a column description vector library is constructed, efficient retrieval is achieved in combination with an approximate nearest neighbor ANN algorithm, and results of semantic vector matching, editing distance matching and traceability enhancement matching are combined through a multi-strategy fusion matching mechanism to form a final candidate column set; a database metadata interface is connected, information is analyzed, a basic structure is constructed, semantic enhancement field description is generated, and output is organized in a tetrad structure; and reversely deducing the affiliated table through column matching, judging the table structure value by combining the information density in the table and the connectivity with other tables, eliminating redundant tables, and optimizing database representation.

The invention relates to the field of internet search service, and discloses a big data-based investment attraction clue analysis system, which comprises a query analysis unit, a query analysis unit, a query analysis unit, a query analysis unit and a query analysis unit, wherein the query analysis unit calls an event sequence template for defining a meta event type and a standard relative time window constraint interval; the distributed retrieval and aggregation unit is used for retrieving and aggregating meta event types to entity objects; the sequential logic verification unit is used for counting the global document frequency of the meta event, reversely mapping the global document frequency to generate a zoom coefficient, dynamically correcting a time window, and judging that only an entity object in a dynamic verification interval after correction is passed; the object sorting unit generates sorting scores according to template matching integrity, and by constructing a density and window reverse coupling mechanism, the sequential logic boundary is adaptively compressed in a high background noise environment, and the retrieval signal-to-noise ratio and the logic confidence coefficient are maintained.

The invention relates to the technical field of electric power intelligent interaction, and discloses an AI large language model intelligent interaction system oriented to the electric power industry. The system comprises a multi-source data acquisition module for acquiring power industry multi-dimensional data from a plurality of heterogeneous data sources and integrating the power industry multi-dimensional data into a data warehouse; the semantic feature extraction module performs semantic analysis and feature extraction on the integrated data warehouse to obtain a semantic feature matrix; the industry knowledge graph construction module is used for constructing a power industry knowledge graph based on the semantic feature matrix; the user query analysis module is used for analyzing user query and extracting query feature vectors; the core semantic screening module is used for screening a core semantic sequence based on the semantic feature matrix and the query feature vector; and the intelligent response generation module generates an intelligent response based on the core semantic sequence and the power industry knowledge graph. The system can deeply understand user demands, generates professional and accurate responses, and is suitable for diversified intelligent interaction scenes in the power industry.

The invention relates to the field of information retrieval, in particular to a verifiable retrieval enhancement generation method based on collaborative multi-agent, which mainly comprises the following steps: transmitting a user query to a query analysis agent, performing intention analysis and optimization on the user query by the query analysis agent, and generating an optimized query variant; distributing the optimized query variant to a plurality of retrieval intelligent agents to execute retrieval in parallel, and outputting an original information set; inputting the original information set into an information verification and evaluation agent, wherein the information verification and evaluation agent performs dynamic verification processing on the original information set; and capturing and recording the data flow of each agent in real time through the auditing agent, and generating auditing tracking data. According to the method, the dynamic verification layer and the auditing agent are introduced, a nonlinear process is constructed, and the verifiability of the RAG system is enhanced.

The invention discloses a query analysis method, device and equipment fusing multi-modal spatio-temporal data. The method comprises the steps of receiving a query request; based on a large language model, space-time elements in the query request are extracted, and the space-time elements are converted into corresponding query task sequences; executing the query task sequence, and obtaining a multi-modal query result based on the dynamic spatio-temporal knowledge graph and the original database; and returning a multi-modal query result to the large language model, and generating a multi-modal response report. By means of the mode, non-technical users can conduct deep exploration on complex multi-source heterogeneous data through daily languages by introducing the natural language understanding and analysis capacity of the large language model, and the user query threshold is greatly lowered; by constructing the dynamic space-time knowledge graph, barriers among different modal data are broken, so that linkage fusion analysis can be carried out among the different modal data, and a basis is provided for space-time concept analysis of a large language model.

The invention provides a medical data intelligent query analysis method, system and device and a medium, and belongs to the technical field of medical data query. The method comprises the steps that a query request of a medical worker is responded, and a first analysis instruction is generated in combination with the role of the medical worker, the emergency degree of a diagnosis and treatment scene, historical behaviors and patient real-time data; and calling a rendering engine to generate an adaptive visual interface according to the terminal type and the like. And then receiving a second analysis instruction with a larger analysis amount, evaluating the data demand complexity of the second analysis instruction, and after identifying a data gap with the first instruction, calling the knowledge base and the interface supplementary data to generate second medical query information. And then identifying the difference between the first analysis instruction and the second analysis instruction, generating a recommendation card based on historical interaction, performing split-screen comparison or progressive replacement display, recording, feeding back and updating the user preference model, further adjusting a subsequent first analysis instruction generation strategy, and circularly responding to a new request. The whole medical query requirement can be accurately met, the information display suitability and the interaction efficiency are improved, the user experience is optimized, and efficient diagnosis and treatment are assisted.

The invention discloses a data analysis system and method based on agent collaboration. The system comprises a front end, a context maintenance module, an agent scheduling module and a result synthesis module. The front end is used for receiving a natural language query request of a user; the context maintenance module is used for task management; the agent scheduling module is used for coordinating a plurality of agents to collaboratively analyze and query tasks through a preset state machine decision mechanism to obtain a processing result of each agent; the state machine decision-making mechanism comprises the following steps: determining a subsequently scheduled agent in combination with a current agent processing result and a current task state; and the result synthesis module is used for performing information fusion on the processing results of all the agents and the natural language query request, and outputting an analysis report to a front end. According to the method, the semantic understanding accuracy, the query analysis reliability, the execution efficiency and the practicability can be improved.

The application provides a low-altitude target monitoring method and system based on a civilian platform, which comprises the following steps: acquiring the current latitude and longitude data information, the alignment azimuth data information, the pitch angle data information and the time information of a plurality of positions of a civilian collection terminal; distinguishing the batch and type of a plurality of low-altitude target flying objects; storing the distinguished data in the form of original data; dividing according to the time dimension; and performing aggregated calculation on the distinguished data in each divided window. A target trajectory is generated, and positioning and fusion processing are performed. Point trail data is obtained from the target trajectory to perform track processing. The results of the positioning and fusion processing and the track processing are visually displayed and monitored. In combination with the spatial and temporal characteristics of the track data, the monitoring data is indexed according to time slices and spatial regions, and the monitoring data is queried and analyzed through hierarchical positioning and sequential traversal. The application reduces the complexity of detection equipment, improves the detection and target tracking capability, and improves the identification accuracy of low-altitude target flying objects.

The invention provides a database slow query statement intelligent analysis and optimization system based on artificial intelligence, which belongs to the technical field of data processing, and specifically comprises a slow query detection module, a slow query analysis module, a slow query analysis module and a slow query analysis module, the standardization processing module is responsible for formatting a slow query statement by adopting a structured query language analysis technology to obtain a standardized statement, and the execution plan analysis module is used for extracting a performance index and a structured execution plan analysis table; the table structure acquisition module is responsible for automatically acquiring table structure information of a data definition language of a related table of the table name, and the performance analysis module is responsible for determining a recognition optimization scheme of the slow query statement by utilizing optimization suggestions, so that the recognition processing accuracy of the slow query statement is further improved.

The application discloses a kind of test acceptance wiring scheme generation method, device, electronic equipment and storage medium.The method includes obtaining the description file of the secondary system to be accepted, including primary wiring diagram and secondary equipment description diagram;First type representation vector is obtained by graph information analysis to primary wiring diagram, and second type representation vector is obtained by graph information analysis to secondary equipment description diagram;With first type representation vector as the feature vector of simulation query, and with second type representation vector as the feature vector of simulation key, the query analysis based on local neighborhood optimization is carried out to simulation query feature vector and simulation key feature vector to capture the deployment relationship of primary equipment and secondary equipment and obtain wiring scheme semantic representation;Test acceptance wiring scheme diagram of the secondary system to be accepted is generated based on wiring scheme semantic representation.The technical scheme of the application ensures that the generated wiring scheme has comprehensiveness and reliability.

The application discloses a kind of AI coordination edge computing's distributed power grid monitoring system, it is related to the technical field of distributed power grid monitoring based on AI model;Including distributed data acquisition module, acquisition multiple operating data, support multi-protocol access;Edge node preprocessing module, local processing original data, classified storage;AI collaborative analysis module, edge and cloud model cooperation, iteration updates model;Abnormal intelligent identification module, constructs rule base and identifies multiple abnormalities, generates alarm;Hierarchical data transmission module, hierarchical transmission data, with link redundancy;Visual management module, show power grid state, support query analysis;Security protection module, encrypted data, hierarchical authorization, detect attack, double backup.The application eliminates power grid monitoring blind area, improves data processing real-time and transmission efficiency;AI collaborative analysis improves the accuracy of abnormal identification, hierarchical disposal improves fault efficiency;Adapt to multiple scenarios, reduce operation and maintenance cost, help power grid safe operation.

The invention discloses a data warehouse model design method, and belongs to the technical field of data warehouse modeling. According to the method, a dimension modeling method is adopted to construct and analyze a mart model, data is organized into a non-standardized relation model, and the relation between tables is defined through keywords and foreign keys. And designing a dimension table system comprising a product dimension, a calendar day dimension, an institution dimension, an insurance policy attribute dimension and a customer dimension, and a fact table system comprising an insurance claim settlement analysis fact table, a payment analysis fact table, a claim settlement efficiency analysis fact table and a corresponding intermediate fact table. The analysis subject domain data is formatted into a star model or snowflake model, where the analysis fact table is a lightly summarized transactional fact table, and the intermediate fact table is a versioned historical table. According to the method, repeated data construction is avoided through a unified modeling method, and the cost is reduced; an efficient query analysis function is provided by optimizing a model structure; and the data traceability is ensured through versioning management.

A platform for automatically detecting and analyzing defects in infrastructure assets uses aerial imagery, machine learning models and an interactive geographic information system (GIS) interface. Drone or aircraft images are processed by deep neural networks to identify and quantify surface and subsurface defects. Defects are mapped onto 3D models with precise geospatial coordinates. A web platform allows engineers to visualize defects from individual or multiple time periods, filter and query based on attributes, analyze density heatmaps, highlight changes, and perform side-by-side comparative analysis. The unique interface enables rapid condition assessments to support proactive maintenance.

Methods and apparatus for enhancing Large Language Models for IT Operations (Ops) are provided. Logs, alerts, infrastructure configuration, traces, and other telemetry data from a variety of data sources including cloud based IT platforms are selected and dynamically schematized into a database in response to a query by IT personnel. The database is leveraged by a structured LLM to generate insights and responses to queries, enhancing diagnostic and troubleshooting capabilities. An IT administrator query may be deconstructed into subqueries by the LLM and LLM generated responses to the subqueries lead to generation of a response to the IT administrator query.

The invention discloses a real-time broadcast program emergency processing system and method, and the system comprises a hardware layer which completes signal physical access, computing power support and data storage; the data acquisition and preprocessing layer is used for uniformly acquiring and processing multi-source heterogeneous monitoring signals, mainly realizing demodulation, descrambling and demultiplexing of the signals, code stream trimming and IP multicast, and realizing radio frequency monitoring, TS stream monitoring, environment monitoring and EFD intrusion alarm; the core algorithm layer is used for completing feature modeling, multi-channel signal comparison, abnormal classification positioning and continuous model iteration; the application service layer is used for realizing business logic processing, fault warning and automatic handling, and data query and analysis; and the user interaction layer realizes man-machine interaction, state display, model management and compliance auditing. In the aspect of broadcast program degradation identification, the accuracy and the response speed can be improved, so that the aims of reducing the error rate and the accident level of safe broadcast and improving the operation and management level of the safe broadcast are fulfilled.

This invention relates to the field of data analysis technology, and in particular to a query analysis method, apparatus, device, and storage medium based on a large language model. The method provides comprehensive and effective data support for enterprise decision-making. It achieves semantic fusion of multi-source heterogeneous data through a unified knowledge graph, breaking the limitations of traditional data silos and providing a comprehensive and accurate business knowledge foundation for the large language model, reducing model illusions from the source. It achieves differentiated analysis processing through task type classification, automatically scheduling different capability modules of the large language model according to user needs, balancing the efficiency of basic queries with the accuracy of in-depth analysis. It obtains the full business context through entity links, limiting the reasoning process of the large language model to the enterprise's real business scenarios, significantly improving the business relevance of the analysis results. It achieves in-depth diagnostic analysis through causal attribution, fully leveraging the logical reasoning capabilities of the large language model and overcoming the limitations of descriptive analysis.

The invention relates to the technical field of medical services, in particular to a statistical database-based disease cause formation intelligent query analysis system, which comprises a case processing module, a case analysis module and a disease cause query module. The case processing module is used for collecting historical case information and extracting a plurality of classification topics according to the historical case information, each classification topic comprises but is not limited to disease causes and influence factors, a plurality of analysis nodes are divided according to the acquired classification topics and the corresponding disease causes and influence factors, and the analysis nodes are used for analyzing the disease causes and influence factors; determining a corresponding relation of the historical case information among the analysis nodes, and constructing the historical case information into an analysis network according to pathogenesis and influence factors; the case analysis module is used for analyzing the historical case information according to the obtained analysis network, obtaining behavior information corresponding to the patient, setting behavior nodes corresponding to the behavior information, and setting a plurality of behavior tags according to different corresponding behavior nodes of patient behaviors; and the diagnosis efficiency and accuracy are improved.

An apparatus comprises a memory communicatively coupled to a processor. The processor is configured to receive peripheral information from a network device, determine an annotation for the peripheral information, and generate a request to generate communication information based on the peripheral information. Further, the processor is configured to determine a service interface of the plurality of service interfaces associated with the modification, determine, via the service interface, whether the modification is within the tolerated change referenced by the annotation, generate the communication information based on the peripheral information in response to determining that the modification is within the tolerated change referenced by the annotation, and transmit the communication information to the network device. The communication information preserves an amount of information within an accuracy tolerance that matches the peripheral information after the communication information is generated based on the peripheral information.

The invention belongs to the technical field of databases, and particularly discloses a multi-mode time series data processing method and system.The method comprises the steps that a row storage table and a column storage table are established according to a table establishing request, and an association mapping relation between the row storage table and the column storage table is established; receiving time sequence data to be inserted, extracting tag data and index data, determining a target data node based on tag data hash calculation, writing the tag data into the row storage table, and writing the index data into a target time sequence data block of the column storage table; performing partition cutting and scanning on the stored time series data based on the screening condition, and generating a query result by splitting query tasks and performing parallel execution and merging results based on a cross-mode query requirement; and determining the influenced time sequence data block based on the operation request, and updating the label data and the index data according to the type of the operation request, or deleting the marked invalid data. According to the method and the device, low-redundancy storage, high-concurrency read-write and quick query analysis of the time series data can be realized.

In a computer system with multiple physical computers at different physical locations, a transformer module receives an original query from a client-side computer, analyzes the query statements and annotates the query. The transformer module forwards the annotated query to server-computers. This approach allows a data-analyst 190-ALPHA to use a query that is relatively simple, wherein a data-analyst 190-BETA who does not benefit from the transformer module would have to write a more complex query.

An object of a system according to an embodiment is to accurately and efficiently respond to various inquiries.SOLUTION: A system includes an inquiry analysis part, an answer generation part, and an information acquisition part. The query analyzer includes a generation AI. The answer generation unit generates an answer on the basis of the inquiry content analyzed by the inquiry analysis unit. The information acquisition unit acquires information necessary for the answer generated by the answer generation unit from the cloud.SELECTED DRAWING: Figure 1

The invention relates to the technical field of test report generation and analysis, in particular to an electrical test report generation and analysis system which comprises an image acquisition and OCR (optical character recognition) unit, a data processing and intelligent analysis unit, a regulation query analysis unit, a data transcription unit and a user interaction unit. The image acquisition and OCR recognition unit comprises an image acquisition module, an image preprocessing module, an OCR recognition module and a data cleaning and verification module; the image acquisition module is used for acquiring a high-definition image of the standardized test record paper form by utilizing acquisition equipment and carrying out page turning and conveying on the test record paper form; the image preprocessing module is used for carrying out automatic cutting, rotation correction, denoising and brightness equalization processing on the uploaded image so as to optimize the recognition effect; according to the method, errors of manual input and calculation of test record paper form contents are reduced, the accuracy of test reports is improved, the management efficiency of the test reports is improved, and accurate data support is provided for making of equipment maintenance plans.

The embodiment of the specification provides a natural language query analysis and database field matching method based on a large model, which comprises the following steps: performing intent analysis and entity recognition on a user natural language query based on a large language model (LLM) to extract a potential keyword set; for each extracted keyword, binding an entity type through an STAM mechanism and constructing an entity-type mapping relationship; constructing a column description vector library, combining an approximate nearest neighbor (ANN) algorithm to realize efficient retrieval, and combining a multi-strategy fusion matching mechanism to combine the results of semantic vector matching, edit distance matching and traceability enhanced matching to form a final candidate column set; connecting a database metadata interface, analyzing information and constructing a basic structure to generate an enhanced semantic field description, and outputting in a four-tuple structure; reversing the corresponding table through column matching, judging the table structure value in combination with the information density in the table and the connectivity with other tables, eliminating redundant tables, and optimizing the database representation.