291 results about "Query analysis" patented technology

The emergency decision support method based on cross-domain knowledge fusion comprises the steps of building an emergency semantic model, building an emergency response graph, building an emergency decision auxiliary model based on the graph and the like, a full-cycle emergency disposal knowledge graph oriented to multiple disaster types is provided, and various emergency disposal knowledge can be effectively integrated and stored. Meanwhile, an emergency decision-making auxiliary model is constructed on the downstream based on the atlas, disaster grade division, multi-hop path query analysis and decision-making recommendation can be carried out according to real-time multi-dimensional disaster data, and the cross-domain text retrieval efficiency is greatly improved; meanwhile, a plan chain recommendation model based on an event chain is provided, disaster chain prediction can be carried out based on rules, refined plan pushing can be given, and a vulnerable community is helped to make emergency risk avoiding and planning in advance in a complex scene.

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 discloses a retrieval generation method and system based on multi-agent collaboration, a terminal and a medium, and relates to the field of artificial intelligence. Performing semantic analysis on the input word embedding converted by the natural language query instruction through a query analysis agent, and determining a semantic intention vector; performing reinforcement learning and meta learning on the semantic intention vector through a strategy construction agent, and determining a retrieval strategy; performing semantic enhancement on the semantic intention vector according to knowledge graph node embedding to obtain a semantic enhancement vector; determining a data channel according to the semantic enhancement vector, a retrieval strategy and a real-time system load, and calling the data channel for retrieval to obtain candidate documents; and generating a target answer according to each candidate document based on an adaptive reflection feedback mechanism in combination with an auto-encoder and a generative adversarial network. The problems that the prior art depends on a fixed retrieval strategy, has limitation when facing complex query, multi-round interaction and cross-modal data fusion, is easily interfered by noise and is not accurate enough in semantic matching are effectively solved.

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.

A system (100) for AI-assisted query optimization and self-optimizing data pipelines, comprising: a query analysis and classification module configured to monitor, log, and categorize incoming queries based on execution complexity, type, and frequency; an AI-based query execution optimizer that uses machine learning models to analyze historical query performance and recommend dynamically optimized execution plans; a self-optimizing data pipeline module configured to monitor and adapt ETL workflows by modifying transformation logic, workload balancing, and execution sequences; an adaptive indexing and storage management module configured to dynamically restructure indexing strategies, including predictive indexing and partitioning, to improve query performance; an intelligent caching and pre-fetching module configured to anticipate frequently used queries and pre-load relevant data to reduce redundant computations; a workload balancing and resource optimization module configured to dynamically distribute query loads among computing resources based on the constraints of the real-time system (100); an anomaly detection and performance monitoring module configured to continuously track query execution metrics, identify inefficiencies, and mitigate performance bottlenecks using AI-based predictive analytics; a reinforcement learning-based query optimization module configured to iteratively refine query execution strategies based on real-time feedback and evolving data properties; a multi-cloud and cross-platform optimization engine configured to ensure seamless operation across heterogeneous database management systems and cloud platforms.

The invention relates to the technical field of soil fertility query analysis, and discloses a citrus orchard soil fertility query method and system based on artificial intelligence. The method comprises the following steps: collecting surface and deep soil samples in a citrus orchard for physical and chemical detection, and filling missing values with space-time dual interpolation to obtain a data set; extracting physicochemical and biological activity features, and forming feature vectors by adopting a fusion strategy; inputting a cascade forest model to carry out five-level classification; classifying the sampling points to generate a distribution diagram; intelligent diagnosis is carried out, the fertilization amount is calculated, and a differentiated fertilization recommendation map is generated. According to the application, soil fertility accurate evaluation, spatial distribution visualization and differentiated fertilization recommendation based on multi-source data are realized, so that the fertilizer utilization rate is improved, the environmental risk is reduced, and the citrus yield and quality are improved.

The invention relates to the technical field of data processing, and discloses an employee performance assessment-oriented dynamic data desensitization system, which comprises a field analysis module, a query analysis module, a desensitization strategy module and a desensitization execution module, wherein the field analysis module is used for identifying a risk field combination between fields of object data; the query analysis module is used for calculating the sensitivity of each query party to each field in the risk field combination; a desensitization strategy module synchronously generates a joint desensitization strategy of each query party for the risk field combination based on the sensitivity and desensitization constraint condition of each query party for each field in the risk field combination; and the desensitization execution module is used for executing the joint desensitization strategy and performing joint desensitization of the risk field combination for each query party. According to the method, the desensitization strategy is optimized, and the efficiency and safety of employee performance assessment data management are improved.

Systems and methods for training and using large language models to respond to queries and cascading to new models when their performance falls below a threshold during the response generation cycle are described. The methods involve receiving and analyzing a query using heuristics to determine the query's categories and a level of granularity at which its response is to be evaluated. Selecting a language model based on the query analysis and during the generation of the response, evaluating the model's performance for its ability to predict the next segment in a response. The methods score the evaluation and use the score to determine whether the language model's performance exceeds a confidence threshold. If it does not, then during the response is being generated, e.g., at a point before the response generation is completed, cascading from the model to a different model that can perform at a higher confidence level.

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 relates to an operation and maintenance decision-making system and method based on a large language model and a graph database, the system comprises an intelligent operation and maintenance platform, a large model layer and an operation and maintenance database, the intelligent operation and maintenance platform is connected with the large model layer and the operation and maintenance database, and the intelligent operation and maintenance platform is used for receiving a user query demand and sending the user query demand to the large model layer. After a task instruction is automatically generated, an operation and maintenance tool is called to execute query analysis operation, and an operation and maintenance processing result is correspondingly returned; the large model layer is used for providing various large model bases so as to correspondingly process task instructions output by the intelligent operation and maintenance platform; and the operation and maintenance database is used for storing the operation and maintenance topological relation data, the operation and maintenance historical event data and the operation and maintenance knowledge data so as to provide a data source for the intelligent operation and maintenance platform. Compared with the prior art, the method has the advantages that the query result can be quickly and accurately obtained, the component fault influence can be determined, the historical operation and maintenance event can be queried and matched, and the operation and maintenance decision efficiency and accuracy are improved.

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.

A system with graph-based Natural Language Processing (NLP) for querying, analyzing, and visualizing complex data structures is described. Such a system executes a generalized AI language model; defines and migrates a training dataset into a graph database by exposing data sources to an executing AI language model that self-defines a structure and self-writes an executable script to query the original data sources and self-writes code to load the extracted data into a graph database in the form of new nodes and new relationships with directionality between the nodes. The system further includes means for loading the extracted data into the graph database; condensing the information stored within the graph database into a condensed data structure representing the full architecture of the data in a natural language format; and responding to human language inquiries with responsive text, speech, and visualizations using the data loaded into the graph database.

The invention relates to the technical field of meteorological visualization, in particular to a meteorological visualization engine based on a space-time database, which comprises a meteorological data storage and processing module, a data processing algorithm cluster module, a data query analysis module and a WebGL-based visualization rendering module, and is characterized in that the meteorological data storage and processing module comprises the space-time database and a cloud storage; the data processing algorithm cluster module is used for analyzing, processing and processing various kinds of meteorological data, and has the following beneficial effects that the problems of performance bottleneck and expansibility when large-scale meteorological data is processed in a traditional storage mode can be solved, and the data processing algorithm cluster module can perform real-time or batch processing on the meteorological data; according to the technical scheme, the meteorological data are analyzed and processed to meet different scenes and requirements, and accurate and high-quality data support can be provided for subsequent data query, analysis and visualization by analyzing and processing the meteorological data.

Systems, apparatuses, methods, and computer program products are disclosed for dynamic query optimization. An example method includes receiving, by communications hardware, a first query and determining, by a query analysis engine, whether the first query corresponds to a stored query of a historical query repository. The method also includes determining, in response to determining that the first query does not correspond to the stored query, determining, by the query analysis engine, a parameter set indicating a current state associated with a system involved in an execution of the first query, processing, by a reinforcement learning engine, the first query and the parameter set in accordance with a plurality of policies, selecting, by the reinforcement learning engine and based on the processing, a first policy from the plurality of policies, and generating, by the reinforcement learning engine and based on the first policy, a query execution plan for the first query.

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 an Oracle removing method based on Clickhouse, which comprises the steps of data model design, real-time and off-line data complete storage and Clickhouse complete monitoring, and is characterized by comprising the following steps of: 1, designing a data model based on a materialized view of the Clickhouse; 2, developing offline and real-time tasks based on Spark and Flink, and respectively writing results into Hive and Topic; step 3, completely synchronizing the off-line and real-time data bins to the Clickhouse; and step 4, on the basis of the Grafana and the Prometheus, the Clickhome is monitored, and the Clickhome is monitored on the basis of the Grafana and the Prometheus. And a set of online query analysis model construction method for replacing Oracle from a data model to data storage, data analysis and Clickhome monitoring is formed.

Aspects of the disclosure relate to using machine learning methods for chatbot selection. A computing platform may train a plurality of machine learning models, each corresponding to a chatbot. The computing platform may train an additional machine learning model to route queries to the plurality of machine learning models based on contents of the queries. The computing platform may receive a query, and may analyze the query using the additional machine learning model. The computing platform may route, based on the query analysis, the query to the plurality of machine learning models. The computing platform may generate, using the plurality of machine learning models, a response to the query. The computing platform may send the response to the query and one or more commands directing a client device to display the response to the query, which may cause the client device to display the response to the query.

The invention discloses a database query plan optimization method and system, and belongs to the technical field of databases and artificial intelligence. AI model training: training the model by using the preprocessed historical query data according to a specific model architecture, a training mode and resource and time arrangement, and dynamically adjusting the model according to the change of the database; query analysis: evaluating the complexity of the query and the possibly involved data volume, and allocating a complexity level and a data access volume range for the query; query plan generation: generating and evaluating a plurality of candidate query plans based on prediction and query analysis results of the AI model, and selecting an optimal query plan as a final execution scheme; and performing query execution and monitoring. According to the method, the query execution time can be greatly shortened, and the database response speed is increased; system resources are utilized more effectively; dynamic changes of the database can be adapted, and the query plan is automatically updated according to structure and data distribution adjustment.

The invention provides a highway pavement intelligent maintenance scientific decision-making system and a decision-making method thereof. A data collection and preprocessing module collects multi-source data in a public network and a highway management intranet, and performs cleaning, integration and dimensionality reduction by using a specific algorithm. And building an automatic road detection system based on a Python Django framework, determining an index weight by using a rough set theory, calculating PQI and MQI values, and generating a road technical condition evaluation report. And the pavement performance prediction module integrates multi-source data and predicts pavement performance by adopting a composite kernel function and a Gaussian process regression model. And the maintenance decision module determines the maintenance priority through ANP, generates a maintenance demand list in combination with a hidden Markov model, and generates a fund distribution scheme by using a cuckoo search algorithm. And the maintenance benefit evaluation module evaluates the maintenance benefit by adopting a gray ideal solution and a super-efficiency DEA model. And finally, utilizing a WebGL-based 3D visualization technology and an Echarts-based interactive query analysis function to realize road condition information display and auxiliary decision making.

The invention discloses a data search method and system based on artificial intelligence, and relates to the technical field of information retrieval, and the method comprises the steps: receiving a user query, carrying out query analysis, and calculating the similarity between the query analysis and a data point feature vector; constructing a distance matrix, constructing a simple form, and calculating topological characteristics of the simple form through a boundary operator; and converting the topological features into topological feature vectors, fusing the topological feature vectors with the data point feature vectors, constructing a deep neural network DNN, and generating a search result. The method comprises the following steps: receiving a user inquiry, carrying out inquiry analysis, and calculating the similarity with a data point feature vector; constructing a distance matrix, constructing a simple form, and calculating topological characteristics of the simple form through a boundary operator; the topological features are converted into topological feature vectors to be fused with the data point feature vectors, a deep neural network DNN is constructed, and a search result is generated; the discrimination capability of similarity difference is obviously improved, the efficiency of data processing and query response is enhanced, and the accuracy and correlation of search results are improved.

Systems and methods are disclosed for selection operations for improving quality of Artificial Intelligence responses. The operations include accessing an application that employs AI functionality, receiving from a user, via the application, a query for which a response is sought from an AI agent, analyzing the query for determining a context, based on the context, selecting a particular AI agent from a pool of a plurality of AI agents, to which the query should be sent for response, and directing the query to the selected AI agent.

The invention provides a financial question and answer method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring original financial data; constructing a financial database based on the original financial data, and establishing a data index corresponding to the original financial data in the financial database; receiving a financial question input by a user, and matching a target data index corresponding to the financial question in the financial database; querying reference data of the financial problem in the financial database based on the target data index, and generating prompt information based on the reference data; and inputting the prompt information into a large language model, generating a target answer corresponding to the financial question, and returning the target answer to the user. According to the method, the query analysis and information integration capabilities of the large language model in the financial field are improved, and the accuracy, correlation, integrity and security of financial answers are ensured.

The invention relates to a precast beam appearance quality intelligent detection method and system, and the method comprises the steps: building an improved YOLOv8 network, training a target detection model through a data set, and obtaining a precast beam appearance quality detection model; the method can effectively replace a traditional detection mode that appearance quality defects of precast beams are detected through manual observation, the problems that manual detection of the appearance defects is high in subjectivity, poor in accuracy, small in detection coverage rate and the like are solved, time and manpower are saved, timely dynamic monitoring can be carried out, and wrong detection, false detection and missing detection caused by fatigue of recorders and the like are avoided; precast beam images can be automatically collected for detection and analysis, storage, query and analysis of appearance quality defect detection data and automatic generation of an evaluation report are achieved, and control over the appearance quality of the precast beam in the later period of a project is facilitated.

The invention provides a coal mine equipment network operation and maintenance management system and method based on 3DMineGlS, and particularly relates to the technical field of coal mine network operation and maintenance management, and the system comprises a data access module, a network topology visualization module, an equipment state monitoring module, a flow monitoring and analysis module, and an alarm and fault recording module. According to the system, basic information of coal mine equipment is collected through the data access module, and three-dimensional visual display is achieved through the network topology visualization module. The equipment state monitoring module monitors the running state of equipment in real time, and the flow monitoring and analyzing module monitors and analyzes flow data. And the alarm and fault recording module triggers alarm when the equipment fails or the flow is abnormal, and records related information for subsequent query and analysis. The technical problems of original equipment access mode, inaccurate fault positioning and insufficient visualization degree in the operation and maintenance management platform of the coal mine network system are solved, the operation and maintenance management efficiency of the coal mine network system is improved, and the operation and maintenance cost and difficulty are reduced.

The invention relates to the technical field of power system automation, in particular to a gateway machine double-confirmation closed-loop test system and method, and the system comprises an intelligent equipment simulation module which is used for simulating secondary equipment in an intelligent substation, achieving the transmission of remote signaling and telemetering data, and responding to remote control and remote regulation commands; the multi-source database interface module is used for being connected with a database of the tested gateway machine and querying, analyzing and monitoring data of the database; the master station protocol simulation module is used for simulating a master station protocol and communicating with the tested gateway machine; the intelligent analysis module compares and analyzes signal data from different modules through double-layer verification to ensure the consistency and accuracy of the data; the intelligent equipment simulation module, the multi-source database interface module and the master station protocol simulation module are respectively connected with the tested gateway machine and the intelligent analysis module. According to the method, the whole-process data reliability and the system function integrity are ensured.

The invention relates to the technical field of databases, and discloses a time sequence database processing method and system based on artificial intelligence. A data acquisition module is used for acquiring multi-source heterogeneous time sequence data, and a data processing module adopts a skip list structure and an out-of-order optimization technology; the data storage module is used for optimizing fragmentation storage and compression through hardware acceleration, the query analysis module is used for multi-dimensional query of a user-defined time window, and the anomaly detection module is used for detecting an abnormal mode in time series data through a machine learning algorithm. And the data indexing module is used for generating an index based on a timestamp and a multi-dimensional condition. According to the method, real-time processing and storage of the time series data can be achieved under the conditions of high concurrency and large data volume, and by using a skip list structure and an out-of-order optimization technology, the system can still keep efficient data writing efficiency under the out-of-order and cut-off environments; and the effect of the requirement for real-time access and storage of mass data in multiple scenes is achieved.

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 provides a natural language-based chart generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining a data query text input in a natural language manner, and analyzing the data query text to obtain a basic dimension and a basic index corresponding to the data query text; extracting an expansion dimension associated with the basic dimension through a pre-constructed domain knowledge graph, and generating a corresponding derivative index for the expansion dimension; generating a database query statement based on the basic dimension, the basic index, the expansion dimension and the derivative index, and performing data query based on the database query statement to obtain target data; and based on the target data, rendering a data chart corresponding to the data query text. According to the method and the device, the domain knowledge graph is utilized to carry out analysis dimension expansion and derivative index generation, so that a complete dimension and index system meeting user data query analysis is constructed, and the generated data graph can accurately meet deep analysis requirements of a user.