18 results about "Query analysis" patented technology

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.

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.

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.

This invention discloses a two-stage hybrid retrieval method and system based on keyword guidance and semantic ranking. The method performs query analysis and keyword extraction on the user's query text to form a query guidance set; it then uses an inverted index for keyword coarse screening to obtain a coarse screening result set of candidate document fragments; adaptive context expansion is performed on the candidate document fragments; semantic vectorization and similarity calculation are performed to obtain semantic similarity scores; dynamic weight fusion is used to re-rank the coarse screening result set; and the ranked candidate document fragments are optimized to output the final retrieval result list. The system includes a keyword extraction module, an inverted index and coarse screening module, a context intelligent expansion module, a semantic ranking module, and a result optimization module. These modules are sequentially connected and work collaboratively to complete the hybrid retrieval process from query input to result output. This method and system improve retrieval accuracy and recall, while also achieving high retrieval efficiency and low resource consumption.

The application belongs to the technical field of databases, and specifically discloses a multi-mode time series data processing method and system. The method comprises the following steps: establishing a row storage table and a column storage table according to a table creation request, and establishing an associated mapping relationship between the row storage table and the column storage table; receiving time series data to be inserted and extracting label data and index data, determining a target data node based on label data hash calculation, writing the label data into the row storage table, and writing the index data into a target time series data block of the column storage table; performing partition pruning and scanning on the stored time series data based on a screening condition, and generating a query result by parallel execution of split query tasks and merging of results based on cross-mode query requirements; determining the affected time series data block based on an operation request, and updating the label data and the index data or deleting invalid data according to the type of the operation request. The application can realize low-redundancy storage, high-concurrency read-write and fast query analysis of time series data.

This application provides a method and system for joint query analysis of multimodal data supporting natural language interaction, relating to the technical field of data query analysis. This application collects historical operation data, audio data, and text data from an intelligent search platform supporting natural language interaction; then models the historical operation data to obtain a user preference vector; processes the audio data through speech recognition and text standardization to obtain standard query data, and integrates it with the text data to form contextual information; then uses an attention-based algorithm for semantic understanding and intent recognition to obtain an intent feature vector, and fuses it with the user preference vector to obtain a classification result; finally, based on this result, a collaborative filtering algorithm is used to query a pre-set multimodal database to obtain joint query results. This enables personalized and accurate querying of multimodal data under natural language interaction, ensuring that the results match user intent and long-term preferences.

Disclosed herein are systems and methods for aggregating knowledge from a plurality specialized knowledge models (KMs). In one aspect, an exemplary method includes: receiving a query; analyzing the query to determine that the query is directed to different subject matter areas; selecting a plurality of specialized KMs each having respective expertise in one of the different subject matter areas of the query; generating a first sub-query directed to the first subject matter of the query and a second sub-query directed to the second subject matter of the query; transmitting the first sub-query for processing by the selected first specialized KM; obtaining a first response from the first specialized KM based on the first sub-query; transmitting the second sub-query and the first response from the first specialized KM for processing by the selected second specialized KM; and generating an aggregated response from the second specialized KM.

The application relates to an IFC model cognitive agent based on an MCP protocol and a working method thereof. The IFC model cognitive agent obtains and analyzes a processing requirement of an IFC model file proposed by a user in natural language through an intelligent hub, calls a filesystem-Server module to obtain a target IFC model file from a file system according to the user requirement, calls an IFC-MCP-Server module to query and analyze the target IFC model file, modify data and / or automatically process, completes automatic processing of the target IFC model file, and finally feeds back the processed target IFC model file to the user in a required format. In the overall operation process of the IFC model cognitive agent, an IFC-Viewer module is used to load and display the target IFC model file, a GLTF model file and a GLB model file, so that the user can check and review.

This invention relates to the field of in-memory computing query and analysis technology for fiscal data, specifically a method for real-time query and analysis of fiscal big data based on in-memory computing. This invention constructs a dynamic in-memory index using the analysis results of historical fiscal data to generate initial in-memory index weights. Based on the time-series characteristics of historical fiscal data, it dynamically adjusts the in-memory index weights through feature classification using a time-series analysis model. Furthermore, it updates the in-memory index weights by incorporating historical write volume data. Through these in-memory index weights, it performs memory mapping and data analysis on real-time fiscal data. This method enables quantitative analysis of data characteristics, data size, and future analysis trends for data in different scenarios. It can summarize and organize historical analysis data, while reducing the need for re-analysis of existing data, thus lowering the amount of in-memory computation. It also allows for the analysis of fiscal data without disrupting the connection between time-series data, ensuring the accuracy of fiscal data analysis.

The application belongs to the field of intelligent diagnosis of industrial equipment, and particularly discloses a numerical control machine tool fault diagnosis method and system based on a knowledge graph and a large language model, which comprises: using a large language model for query analysis LLM-R, logical reasoning LLM-I and answer generation verification LLM-E respectively, and combining the structured knowledge retrieval and verification capability of the knowledge graph, a chain question and answer method is formed. Specifically, LLM-R is used to analyze user queries, and candidate entities and context subgraphs are obtained by combining the knowledge graph; LLM-I is used to construct a few-shot prompt guide model to carry out chain reasoning to generate a reasoning chain; finally, LLM-E generates a natural language answer and carries out consistency verification through the knowledge graph to output the final result. The application can deeply mine the hidden association between the numerical control machine tool alarm phenomenon and the fault cause, greatly improve the completeness and reasoning accuracy of the knowledge base, and improve the fault diagnosis effect under complex working conditions.

This application relates to the field of computer technology, and particularly to a graph-based data query method, device, medium, and product. The method includes: acquiring stored graph data, which represents the business data of various business objects within an enterprise and the relationships between these objects; responding to a user's dialogue message as a business query, analyzing and processing the message to obtain query analysis results, including the target business object and its corresponding data query conditions; acquiring the user's data query permission information and generating a query instruction based on the query analysis results and permission information; querying the target business object's business data in the graph data according to the query instruction to obtain query results; and generating a feedback message based on the query results and returning it to the user. This method solves the problem of low efficiency in traditional query methods.

The application provides an entity classification method and system based on a knowledge graph, a terminal and a storage medium. The method comprises the following steps: establishing a DNS knowledge graph based on the hierarchical relationship and query analysis relationship of DNS, wherein the DNS knowledge graph comprises a preset label corresponding to at least one domain name; splitting the DNS knowledge graph into entities and relationships, and fusing the entities and relationships according to an entity attribute alignment mode to obtain a fused DNS knowledge graph; vectorizing the entities and relationships of the fused DNS knowledge graph to obtain a DNS knowledge graph vector, wherein the DNS knowledge graph vector comprises a knowledge graph vector corresponding to each domain name; training a neural network model by taking the knowledge graph vector corresponding to the domain name as an input quantity and taking the preset label corresponding to the domain name as an output quantity to obtain an entity classification model; and classifying and detecting the domain name according to the entity classification model. The application can improve the accuracy and speed of domain name classification and detection.

The application provides a kind of power equipment multi-source sensing data quality evaluation and adaptive repair method and system, belongs to power equipment state monitoring and intelligent operation technology field. Including: real-time acquisition multi-source heterogeneous sensing data and carry out semantic alignment processing, mapping is standardized time series data;Standardized time series data are input into graph-time sequence basic model, obtain multi-parameter predicted value;Calculate data health degree score, assess real-time data quality and identify abnormal data interval;Combining physical consistency constraint and space-time correlation constraint, construct the optimization objective function of fusion multi-prior information, optimize the data in abnormal data interval and obtain the repaired data;Calculate each period abnormality, and according to the index structure of data storage system is dynamically optimized according to abnormality.The application realizes the unified management of multi-source data, physical consistency repair and abnormality-driven storage index optimization, improves data management efficiency and query analysis performance.

A computer-implemented method includes receiving a query at a media streaming platform for media items that are to be provisioned by the media streaming platform. The method also includes analyzing the query to determine a level of breadth associated with the query, where the level of breadth indicates a degree of specificity associated with the query. The method further includes identifying media items that match the query then presenting the identified media items in an interactive user interface. The identified media items are visually presented in a manner that corresponds to the determined level of breadth associated with the query. Various other methods, systems, and computer-readable media are also disclosed.

This invention relates to a compressed-aware streaming SQL query method, system, device, and medium for heterogeneous hybrid computing architectures. The method includes: dynamically selecting the optimal compression encoding scheme for the input raw streaming data using an adaptive compression selection strategy, and processing the data in real time to obtain compressed data in the corresponding compressed encoding format; redesigning the basic SQL operators involved in the query analysis operation to obtain compressed-aware operators; decomposing the received SQL query statement into a series of compressed-aware operators, and dynamically making the optimal query execution decision based on the computational characteristics of each compressed-aware operator and the heterogeneous hardware status; and directly applying the compressed-aware operators to the compressed data to obtain the query results. This invention can be widely applied in the fields of database systems and data processing technology.

Disclosed herein are systems and methods for aggregating knowledge from a plurality specialized knowledge models (KMs). In one aspect, an exemplary method includes: receiving a query; analyzing the query to determine that the query is directed to different subject matter areas; selecting a plurality of specialized KMs each having respective expertise in one of the different subject matter areas of the query; generating a first sub-query directed to the first subject matter of the query and a second sub-query directed to the second subject matter of the query; transmitting the first sub-query for processing by the selected first specialized KM; obtaining a first response from the first specialized KM based on the first sub-query; transmitting the second sub-query and the first response from the first specialized KM for processing by the selected second specialized KM; and generating an aggregated response from the second specialized KM.